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Climate Change 2021: The Physical Science Basis. Contribution of Working Group14 I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Chapter Three; Human influence on the climate system

January 2021

Conference: The Intergovernmental Panel on Climate Change AR6

Authors:

Veronika Eyring

German Aerospace Center (DLR), Oberpfaffenhofen, Gemany

Krishna Achutarao

Indian Institute of Technology Delhi

Rondrotiana Barimalala

Georgia Institute of Technology

Show all 13 authorsHide

The evidence for human influence on recent climate change strengthened from the IPCC Second Assessment Report to the IPCC Fifth Assessment Report, and is now even stronger in this assessment. The IPCC Second Assessment Report (1995) concluded ‘the balance of evidence suggests that there is a discernible human influence on global climate’. In subsequent assessments (TAR, 2001; AR4, 2007 and AR5, 2013), the evidence for human influence on the climate system was found to have progressively strengthened. AR5 concluded that human influence on the climate system is clear, evident from increasing greenhouse gas concentrations in the atmosphere, positive radiative forcing, observed warming, and physical understanding of the climate system. This chapter updates the assessment of human influence on the climate system for large-scale indicators of climate change, synthesizing information from paleo records, observations and climate models. It also provides the primary evaluation of large-scale indicators of climate change in this report, that is complemented by fitness-for-purpose evaluation in subsequent chapters.

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423

Human Inﬂuence on

theClimate System

Coordinating Lead Authors:

Veronika Eyring (Germany) and Nathan P. Gillett (Canada)

Lead Authors:

Krishna M. Achuta Rao (India), Rondrotiana Barimalala (South Africa/Madagascar), MarceloBarreiro

Parrillo (Uruguay), Nicolas Bellouin (United Kingdom/France), Christophe Cassou (France),

PaulJ.Durack (United States of America/Australia), Yu Kosaka (Japan), ShayneMcGregor(Australia),

Seung-Ki Min (Republic of Korea), Olaf Morgenstern (NewZealand/Germany), YingSun (China)

Contributing Authors:

Lisa Bock (Germany), Elizaveta Malinina (Canada/Russian Federation), Guðﬁnna Aðalgeirsdóttir

(Iceland), Jonathan L. Bamber (United Kingdom), Chris Brierley (United Kingdom), LeedeMora

(United Kingdom), John P. Dunne (United States of America), John C. Fyfe (Canada), PeterJ.Gleckler

(United States of America), Peter Greve (Austria/Germany), Lukas Gudmundsson (Switzerland/

Germany, Iceland), Karsten Haustein (United Kingdom, Germany/Germany), Ed Hawkins

(UnitedKingdom), Benjamin J. Henley (Australia), Marika M. Holland (United States of America),

ChrisHuntingford (United Kingdom), Colin Jones (United Kingdom), Masa Kageyama (France),

Rémi Kazeroni (Germany/France), Yeon-Hee Kim (Republic of Korea), Charles Koven (UnitedStates

of America), Gerhard Krinner (France/Germany, France), Eunice Lo (United Kingdom/China),

DanielJ. Lunt (United Kingdom), Sophie Nowicki (United States of America/France, UnitedStates

of America), Adam S. Phillips (United States of America), Valeriu Predoi (United Kingdom),

Krishnan Raghavan (India), Malcolm J. Roberts (United Kingdom), Jon Robson (United Kingdom),

Lucas Ruiz (Argentina), Jean-Baptiste Sallée (France), Benjamin D. Santer (United States

of America), Andrew P. Schurer (United Kingdom), Jessica Tierney (United States of America),

Blair Trewin (Australia), Katja Weigel (Germany), Xuebin Zhang (Canada), Anni Zhao

(UnitedKingdom/China), Tianjun Zhou (China)

Review Editors:

Tomas Halenka (Czech Republic), Jose A. Marengo Orsini (Brazil/Peru), Daniel Mitchell

(United Kingdom)

Chapter Scientists:

Lisa Bock (Germany), Elizaveta Malinina (Canada/Russian Federation)

This chapter should be cited as:

Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka,

S.McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Inﬂuence on the Climate System. In Climate Change 2021: The

Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on

Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L.Goldfarb, M.I.

Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou

(eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552,

doi:10.1017/9781009157896.005.

424

Chapter 3 Human Inﬂuence on the Climate System

Table of Contents

Executive Summary �� 425

3.1 Scope and Overview �� 428

3.2 Methods �� 429

3.3 Human Inﬂuence

on the Atmosphere andSurface �� 430

3.3.1 Temperature �� 430

Cross-Chapter Box3.1 |

Global Surface Warming

Over the Early 21st Century �� 445

3.3.2 Precipitation, Humidity and Streamﬂow �� 449

Cross-Chapter Box3.2 |

Human Inﬂuence on Large-scale Changes

in Temperature andPrecipitation Extremes �� 457

3.3.3 Atmospheric Circulation �� 459

3.4 Human Inﬂuence on the Cryosphere �� 466

3.4.1 Sea Ice �� 466

3.4.2 Snow Cover �� 470

3.4.3 Glaciers and Ice Sheets �� 471

3.5 Human Inﬂuence on the Ocean �� 473

3.5.1 Ocean Temperature �� 473

3.5.2 Ocean Salinity �� 478

3.5.3 Sea Level �� 481

3.5.4 Ocean Circulation �� 483

3.6 Human Inﬂuence on the Biosphere �� 485

3.6.1 Terrestrial Carbon Cycle �� 485

3.6.2 Ocean Biogeochemical Variables �� 488

3.7 Human Inﬂuence on Modes

of Climate Variability �� 489

3.7.1 North Atlantic Oscillation and Northern

AnnularMode �� 489

3.7.2 Southern Annular Mode �� 493

3.7.3 ElNiño–Southern Oscillation (ENSO) �� 495

3.7.4 Indian Ocean Basin and Dipole Modes �� 499

3.7.5 Atlantic Meridional and Zonal Modes �� 501

3.7.6 Paciﬁc Decadal Variability �� 502

3.7.7 Atlantic Multi-decadal Variability �� 504

3.8 Synthesis Across Earth SystemComponents �� 506

3.8.1 Multivariate Attribution of Climate Change �� 506

3.8.2 Multivariate Model Evaluation �� 508

Acknowledgements �� 514

Frequently Asked Questions

FAQ 3.1 |

How Do We Know Humans

Are Responsible for Climate Change? �� 515

FAQ 3.2 |

What is Natural Variability and How Has it

Inﬂuenced Recent Climate Changes? �� 517

FAQ 3.3 |

Are Climate Models Improving? �� 519

References �� 521

425

Human Inﬂuence on the Climate System Chapter 3

Executive Summary

The evidence for human inﬂuence on recent climate change

strengthened from the IPCC Second Assessment Report to the IPCC Fifth

Assessment Report, and is now even stronger in this assessment. The

IPCC Second Assessment Report (SAR, 1995) concluded ‘the balance

of evidence suggests that there is a discernible human inﬂuence on

global climate’. In subsequent assessments (TAR, 2001; AR4, 2007; and

AR5, 2013), the evidence for human inﬂuence on the climate system

was found to have progressively strengthened. The AR5 concluded

that human inﬂuence on the climate system is clear, evident from

increasing greenhouse gas concentrations in the atmosphere, positive

radiative forcing, observed warming, and physical understanding of

the climate system. This chapter updates the assessment of human

inﬂuence on the climate system for large-scale indicators of climate

change, synthesizing information from paleo records, observations and

climate models. It also provides the primary evaluation of large-scale

indicators of climate change in this Report, complemented by ﬁtness-

for-purpose evaluation in subsequent chapters.

Synthesis Across the Climate System

It is unequivocal that human inﬂuence has warmed the

atmosphere, ocean and land since pre-industrial times. Combining

the evidence from across the climate system increases the level of

conﬁdence in the attribution of observed climate change to human

inﬂuence and reduces the uncertainties associated with assessments

based on single variables. Large-scale indicators of climate change in

the atmosphere, ocean, cryosphere and at the land surface show clear

responses to human inﬂuence consistent with those expected based

on model simulations and physical understanding. {3.8.1}

For most large-scale indicators of climate change, the

simulated recent mean climate from the latest generation

Coupled Model Intercomparison Project Phase 6 (CMIP6)

climate models underpinning this assessment has improved

compared to the Coupled Model Intercomparison Project

Phase 5 (CMIP5) models assessed in AR5 (high conﬁdence).

Some differences from observations remain, for example in regional

precipitation patterns. High-resolution models exhibit reduced

biases in some but not all aspects of surface and ocean climate

(medium conﬁdence), and most Earth system models, which include

biogeochemical feedbacks, perform as well as their lower-complexity

counterparts (medium conﬁdence). The multi-model mean captures

most aspects of observed climate change well (high conﬁdence). The

multi-model mean captures the proxy-reconstructed global-mean

surface air temperature (GSAT) change during past high- and low-CO2

climates (high conﬁdence) and the correct sign of temperature and

precipitation change in most assessed regions in the mid-Holocene

(medium conﬁdence). The simulation of paleoclimates on continental

scales has improved compared to AR5 (medium conﬁdence), but

models often underestimate large temperature and precipitation

differences relative to the present day (high conﬁdence). {3.8.2}

1 In this chapter, ‘greenhouse gases’ refers to well-mixed greenhouse gases.

2 In this chapter, ‘main driver’ means responsible for more than 50% of the change.

Human Inﬂuence on the Atmosphere and Surface

The likely range of human-induced warming in global-mean

surface air temperature (GSAT) in 2010–2019 relative to

1850–1900 is 0.8°C–1.3°C, encompassing the observed warming

of 0.9°C–1.2°C, while the change attributable to natural forcings

is only −0.1°C to +0.1°C. The best estimate of human-induced

warming is 1.07°C. Warming can now be attributed since 1850–1900,

instead of since 1951 as done in AR5, thanks to abetter understanding

of uncertainties and because observed warming is larger. The likely

ranges for human-induced GSAT and global mean surface temperature

(GMST) warming are equal (medium conﬁdence). Attributing

observed warming to speciﬁc anthropogenic forcings remains more

uncertain. Over the same period, forcing from greenhouse gases1

likely increased GSAT by 1.0°C–2.0°C, while other anthropogenic

forcings including aerosols likely decreased GSAT by 0.0°C–0.8°C. Itis

very likely that human-induced greenhouse gas increases were the

main driver2 of tropospheric warming since comprehensive satellite

observations started in 1979, and extremely likely that human-induced

stratospheric ozone depletion was the main driver of cooling in the

lower stratosphere between 1979 and the mid-1990s. {3.3.1}

The CMIP6 model ensemble reproduces the observed

historical global surface temperature trend and variability

with biases small enough to support detection and attribution

of human-induced warming (very high conﬁdence). The CMIP6

historical simulations assessed in this report have an ensemble mean

global surface temperature change within 0.2°C of the observations

over most of the historical period, and observed warming is within the

5–95% range of the CMIP6 ensemble. However, some CMIP6 models

simulate a warming that is either above or below the assessed 5–95%

range of observed warming. CMIP6 models broadly reproduce surface

temperature variations over the past millennium, including the cooling

that follows periods of intense volcanism (medium conﬁdence). For

upper air temperature, there is medium conﬁdence that most CMIP5

and CMIP6 models overestimate observed warming in the upper

tropical troposphere by at least 0.1°C per decade over the period

1979 to 2014. The latest updates to satellite-derived estimates of

stratospheric temperature have resulted in decreased differences

between simulated and observed changes of global mean temperature

through the depth of the stratosphere (medium conﬁdence). {3.3.1}

The slower rate of GMST increase observed over 1998–2012

compared to 1951–2012 was a temporary event followed

by a strong GMST increase (very high conﬁdence). Improved

observational datasets since AR5 show a larger GMST trend over

1998–2012 than earlier estimates. All the observed estimates of

the 1998–2012 GMST trend lie within the 10th–90th percentile

range of CMIP6 simulated trends (high conﬁdence). Internal

variability, particularly Paciﬁc Decadal Variability, and variations in

solar and volcanic forcings partly offset the anthropogenic surface

warming trend over the 1998–2012 period (high conﬁdence). Global

ocean heat content continued to increase throughout this period,

indicating continuous warming of the entire climate system (very

426

Chapter 3 Human Inﬂuence on the Climate System

high conﬁdence). Since 2012, GMST has warmed strongly, with the

past ﬁve years (2016–2020) being the warmest ﬁve-year period

in the instrumental record since at least 1850 (high conﬁdence).

{Cross-Chapter Box3.1, 3.3.1; 3.5.1}

It is likely that human inﬂuence has contributed to3 moistening

in the upper troposphere since 1979. Also, there is medium

conﬁdence that human inﬂuence contributed to a global increase

in annual surface speciﬁc humidity, and medium conﬁdence that it

contributed to a decrease in surface relative humidity over mid-latitude

Northern Hemisphere continents during summertime. {3.3.2}

It is likely that human inﬂuence has contributed to observed

large-scale precipitation changes since the mid-20th century.

New attribution studies strengthen previous ﬁndings of a detectable

increase in Northern Hemisphere mid- to high-latitude land

precipitation (high conﬁdence). Human inﬂuence has contributed to

strengthening the zonal mean precipitation contrast between the wet

tropics and dry subtropics (medium conﬁdence). Yet, anthropogenic

aerosols contributed to decreasing global land summer monsoon

precipitation from the 1950s to 1980s (medium conﬁdence). There

is also medium conﬁdence that human inﬂuence has contributed

to high-latitude increases and mid-latitude decreases in Southern

Hemisphere summertime precipitation since 1979 associated with

the trend of the Southern Annular Mode toward its positive phase.

Despite improvements, models still have deﬁciencies in simulating

precipitation patterns, particularly over the tropical ocean (high

conﬁdence). {3.3.2, 3.3.3, 3.5.2}

Human-induced greenhouse gas forcing is the main driver of

the observed changes in hot and cold extremes on the global

scale (virtually certain) and on most continents (very likely).

It is likely that human inﬂuence, in particular due to greenhouse

gas forcing, is the main driver of the observed intensiﬁcation of

heavy precipitation in global land regions during recent decades.

There is high conﬁdence in the ability of models to capture the

large-scale spatial distribution of precipitation extremes over land.

The magnitude and frequency of extreme precipitation simulated

byCMIP6 models are similar to those simulated by CMIP5 models

(high conﬁdence). {Cross-Chapter Box3.2}

It is likely that human inﬂuence has contributed to thepoleward

expansion of the zonal mean Hadley cell in the Southern

Hemisphere since the 1980s. There is medium conﬁdence that

the observed poleward expansion of the zonal mean Hadley cell in

the Northern Hemisphere is within the range of internal variability.

The causes of the observed strengthening of the Paciﬁc Walker

circulation since the 1980s are not well understood, and the observed

strengthening trend is outside the range of trends simulated in the

coupled models (medium conﬁdence). While CMIP6 models capture

the general characteristics of the tropospheric large-scale circulation

(high conﬁdence), systematic biases exist in the mean frequency of

atmospheric blocking events, especially in the Euro-Atlantic sector,

some of which reduce with increasing model resolution (medium

conﬁdence). {3.3.3}

3 In this chapter the phrase ‘human inﬂuence has contributed to’ an observed change means that the response to human inﬂuence is non-zero and consistent in sign with the observed change.

Human Inﬂuence on the Cryosphere

It is very likely that anthropogenic forcing, mainly due to

greenhouse gas increases, was the main driver of Arctic sea

ice loss since the late 1970s. There is new evidence that increases

in anthropogenic aerosols have offset part of the greenhouse

gas-induced Arctic sea ice loss since the 1950s (medium conﬁdence).

In the Arctic, despite large differences in the mean sea ice state, loss

of sea ice extent and thickness during recent decades is reproduced

in all CMIP5 and CMIP6 models (high conﬁdence). By contrast, global

climate models do not generally capture the small observed increase

in Antarctic sea ice extent during the satellite era, and there is low

conﬁdence in attributing the causes of this change. {3.4.1}

It is very likely that human inﬂuence contributed to the

observed reductions in Northern Hemisphere spring snow cover

since 1950. The seasonal cycle in Northern Hemisphere snow cover is

better reproduced by CMIP6 than by CMIP5 models (high conﬁdence).

Human inﬂuence was very likely the main driver of the recent global,

near-universal retreat of glaciers. It is very likely that human inﬂuence

has contributed to the observed surface melting of the Greenland

Ice Sheet over the past two decades, and there is medium conﬁdence

in an anthropogenic contribution to recent overall mass loss from the

Greenland Ice Sheet. However, there is only limited evidence, with

medium agreement, of human inﬂuence on Antarctic Ice Sheet mass

balance through changes in ice discharge. {3.4.2, 3.4.3}

Human Inﬂuence on the Ocean

It is extremely likely that human inﬂuence was the main driver

of the ocean heat content increase observed since the 1970s,

which extends into the deeper ocean (very high conﬁdence).

Since AR5, there is improved consistency between recent observed

estimates and model simulations of changes in upper (<700 m) ocean

heat content, when accounting for both natural and anthropogenic

forcings. Updated observations and model simulations show

that warming extends throughout the entire water column (high

conﬁdence), with CMIP6 models simulating 58% of industrial-era

heat uptake (1850–2014) in the upper layer (0–700 m), 21% in

the intermediate layer (700–2000 m) and 22% in the deep layer

(>2000 m). The structure and magnitude of multi-model mean ocean

temperature biases have not changed substantially between CMIP5

and CMIP6 (medium conﬁdence). {3.5.1}

It is extremely likely that human inﬂuence has contributed

to observed near-surface and subsurface ocean salinity

changes since the mid-20th century. The associated pattern of

change corresponds to fresh regions becoming fresher and salty

regions becoming saltier (high conﬁdence). Changes to the coincident

atmospheric water cycle and ocean-atmosphere ﬂuxes (evaporation

and precipitation) are the primary drivers of the observed basin-scale

salinity changes (high conﬁdence). The observed depth-integrated

basin-scale salinity changes have been attributed to human inﬂuence,

with CMIP5 and CMIP6 models able to reproduce these patterns

427

Human Inﬂuence on the Climate System Chapter 3

only in simulations that include greenhouse gas increases (medium

conﬁdence). The basin-scale changes are consistent across models and

intensify through the historical period (high conﬁdence). The structure

of the biases in the multi-model mean has not changed substantially

between CMIP5 and CMIP6 (medium conﬁdence). {3.5.2}

Combining the attributable contributions from glaciers,

ice-sheet surface mass balance and thermal expansion, it is

very likely that human inﬂuence was the main driver of the

observed global mean sea level rise since at least 1971. Since

AR5, studies have shown that simulations that exclude anthropogenic

greenhouse gases are unable to capture the sea level rise due to

thermal expansion (thermosteric) during the historical period

andthat model simulations that include all forcings (anthropogenic

and natural) most closely match observed estimates. It is very likely

that human inﬂuence was the main driver of the observed global

mean thermosteric sea level increase since 1970. {3.5.3, 3.5.1, 3.4.3}

While observations show that the Atlantic Meridional

Overturning Circulation (AMOC) has weakened from the mid-

2000s to the mid-2010s (high conﬁdence) and the Southern

Ocean upper overturning cell has strengthened since the

1990s (low conﬁdence), observational records are too short

to determine the relative contributions of internal variability,

natural forcing, and anthropogenic forcing to these changes

(high conﬁdence). No changes in Antarctic Circumpolar Current

transport or meridional position have been observed. The mean zonal

and overturning circulations of the Southern Ocean and the mean

overturning circulation of the North Atlantic (the Atlantic Meridional

Overturning Circulation, AMOC) are broadly reproduced by CMIP5

and CMIP6 models. However, biases are apparent in the modelled

circulation strengths (high conﬁdence) and their variability (medium

conﬁdence). {3.5.4}

Human Inﬂuence on the Biosphere

The main driver of the observed increase in the amplitude of

the seasonal cycle of atmospheric CO2 is enhanced fertilization

of plant growth by the increasing concentration of atmospheric

CO2 (medium conﬁdence). However, there is only low conﬁdence

that this CO2 fertilization has also been the main driver of observed

greening because land management is the dominating factor in

some regions. Earth system models simulate globally averaged

land carbon sinks within the range of observation-based estimates

(high conﬁdence), but global-scale agreement masks large regional

disagreements. {3.6.1}

It is virtually certain that the uptake of anthropogenic CO2

was the main driver of the observed acidiﬁcation of the global

surface open ocean. The observed increase in CO2 concentration

in the subtropical and equatorial North Atlantic since 2000 is likely

associated in part with an increase in ocean temperature, a response

that is consistent with the expected weakening of the ocean carbon

sink with warming. Consistent with AR5 there is medium conﬁdence

that deoxygenation in the upper ocean is due in part to human

inﬂuence. There is high conﬁdence that Earth system models simulate

a realistic time evolution of the global mean ocean carbon sink. {3.6.2}

Human Inﬂuence on Modes of Climate Variability

It is very likely that human inﬂuence has contributed to the

observed trend towards the positive phase of the Southern

Annular Mode (SAM) since the 1970s and to the associated

strengthening and southward shift of the Southern

Hemispheric extratropical jet in austral summer. The inﬂuence of

ozone forcing on the SAM trend has been small since the early 2000s

compared to earlier decades, contributing to a weaker SAM trend

observed over 2000–2019 (medium conﬁdence). Climate models

reproduce the summertime SAM trend well, with CMIP6 models

outperforming CMIP5 models (medium conﬁdence). By contrast,

the cause of the Northern Annular Mode (NAM) trend towards its

positive phase since the 1960s and associated northward shifts of

the Northern Hemispheric extratropical jet and storm track in boreal

winter is not well understood. Models reproduce the observed

spatial features and variance of the SAM and NAM very well (high

conﬁdence). {3.3.3, 3.7.1, 3.7.2}

Human inﬂuence has not affected the principal tropical modes

of interannual climate variability or their associated regional

teleconnections beyond the range of internal variability (high

conﬁdence). Further assessment since AR5 conﬁrms that climate and

Earth system models are able to reproduce most aspects of the spatial

structure and variance of the ElNiño–Southern Oscillation and Indian

Ocean Basin and Dipole modes (medium conﬁdence). However, despite

a slight improvement in CMIP6, some underlying processes are still

poorly represented. In the Tropical Atlantic basin, which contains the

Atlantic Zonal and Meridional modes, major biases in modelled mean

state and variability remain. {3.7.3 to 3.7.5}

There is medium conﬁdence that anthropogenic and volcanic

aerosols contributed to observed changes in the Atlantic

Multi-decadal Variability (AMV) index and associated regional

teleconnections since the 1960s, but there is low conﬁdence

in the magnitude of this inﬂuence. There is high conﬁdence that

internal variability is the main driver of Paciﬁc Decadal Variability

(PDV) observed since pre-industrial times, despite some modelling

evidence for potential human inﬂuence. Uncertainties remain in

quantiﬁcation of the human inﬂuence on AMV and PDV due to

brevity of the observational records, limited model performance in

reproducing related sea surface temperature (SST) anomalies despite

improvements from CMIP5 to CMIP6 (medium conﬁdence), and

limited process understanding of their key drivers. {3.7.6, 3.7.7}

428

Chapter 3 Human Inﬂuence on the Climate System

3.1 Scope and Overview

This chapter assesses the extent to which the climate system has

been affected by human inﬂuence and to what extent climate models

are able to simulate observed mean climate, changes and variability.

This assessment is the basis for understanding what impacts of

anthropogenic climate change may already be occurring and informs

our conﬁdence in climate projections. Moreover, an understanding

of the amount of human-induced global warming to date is key to

assessing our status with respect to the Paris Agreement goals of

holding the increase in global average temperature to well below

2°C above pre-industrial levels and pursuing efforts to limit the

temperature increase to 1.5°C (UNFCCC, 2016).

The evidence of human inﬂuence on the climate system has

strengthened progressively over the course of the previous ﬁve IPCC

assessments, from the Second Assessment Report that concluded ‘the

balance of evidence suggests a discernible human inﬂuence on climate’

through to the Fifth Assessment Report (AR5) which concluded that ‘it

is extremely likely that human inﬂuence caused more than half of the

observed increase in global mean surface temperature (GMST) from

1951 to 2010’ (see also Sections 1.3.4 and 3.3.1.1). The AR5 concluded

that climate models had been developed and improved since the

Fourth Assessment Report (AR4) and were able to reproduce many

features of observed climate. Nonetheless, several systematic biases

were identiﬁed (Flato et al., 2013). This chapter additionally builds on

the assessment of attribution of global temperatures contained in the

IPCC Special Report on Global Warming of 1.5°C (SR1.5; IPCC, 2018),

assessments of attribution of changes in the ocean and cryosphere in

the IPCC Special Report on the Ocean and Cryosphere in a Changing

Climate (SROCC; IPCC, 2019b), and assessments of attribution of

changes in the terrestrial carbon cycle in the IPCC Special Report on

Climate Change and Land (SRCCL, IPCC, 2019a).

This chapter assesses the evidence for human inﬂuence on

observed large-scale indicators of climate change that are described

in Cross-Chapter Box 2.2 and assessed in Chapter 2. It takes

advantage of the longer period of record now available in many

observational datasets. The assessment of the human-induced

contribution to observed climate change requires an estimate of the

expectedresponse to human inﬂuence, as well as an estimate of the

expected climate evolution due to natural forcings and an estimate of

variability internal to the climate system (internal climate variability).

For this we need high quality models, primarily climate and Earth

system models. Since AR5, a new set of coordinated model results

from the World Climate Research Programme (WCRP) Coupled Model

Intercomparison Project Phase 6 (CMIP6; Eyring et al., 2016a) has

become available. Together with updated observations of large-scale

indicators of climate change (Chapter 2), CMIP simulations are

akey resource for assessing human inﬂuence on the climate system.

Pre-industrial control and historical simulations are of most relevance

for model evaluation and assessment of internal variability, and these

simulations are evaluated to assess ﬁtness-for-purpose for attribution,

which is the focus of this chapter (see also Section 1.5.4). This chapter

provides the primary evaluation of large-scale indicators of climate

change in this Report, and is complemented by other ﬁtness-for-

purpose evaluations in subsequent chapters. CMIP6 also includes an

extensive set of idealized and single forcing experiments for attribution

Chapter 3: Human influence on the climate system Chapter 3: Quick guide

Cross-chapter boxes

Section 3.1

Scope and overview

Section 3.2

Methods

Carbon cycle

3.6.1 | 3.6.2 | 3.8.2

El Niño–Southern Oscillation (ENSO)

3.7.3

Multivariate evaluation and attribution of climate change

3.8.1 | 3.8.2

Ocean temperature and heat content

3.5.1

Precipitation, humidity and streamflow

3.3.2 | 3.8.2 | CC Box 3.2

Surface temperature

3.3.1.1 | 3.8.2 | CC Box 3.1 | CC Box 3.2

Sea ice

3.4.1 | 3.8.2

Sea level

3.5.3

Section 3.8

Synthesis

CC Box 3.1

Early 21st century warming

CC Box 3.2

Extremes

Chapter 3 assesses human influence on the climate system and evaluates

climate models on large scales.

Key topics and corresponding sub-sections

Human

influence

on the...

Section 3.3

...atmosphere

and surface

Section 3.4

...cryosphere

Section 3.7

...modes

of variability

Section 3.5

...ocean

Section 3.6

...biosphere

FAQs

Figure3.1 | Visual guide to Chapter 3.

429

Human Inﬂuence on the Climate System Chapter 3

(Eyring et al., 2016a; Gillett et al., 2016). In addition to the assessment

of model performance and human inﬂuence on the climate system

during the instrumental era up to the present-day, this chapter also

includes evidence from paleo-observations and simulations over past

millennia (Kageyama et al., 2018).

Whereas in previous IPCC assessment reports the comparison of

simulated and observed climate change was done separately in a

model evaluation chapter and a chapter on detection and attribution,

in AR6 these comparisons are integrated together. This has the

advantage of allowing a single discussion of the full set of explanations

for any inconsistency in simulated and observed climate change,

including missing forcings, errors in the simulated response to forcings,

and observational errors, as well as an assessment of the application

of detection and attribution techniques to model evaluation. Where

simulated and observed changes are consistent, this can be interpreted

both as supporting attribution statements, and as giving conﬁdence in

simulated future change in the variable concerned (see also Box4.1).

However, if a model’s simulation of historical climate change has

been tuned to agree with observations, or if the models used in an

attribution study have been selected or weighted on the basis of the

realism of their simulated climate response, this information would

need to be considered in the assessment and any attribution results

correspondingly tempered. An integrated discussion of evaluation and

attribution supports such a robust and transparent assessment.

This chapter starts with a brief description of methods for detection

and attribution of observed changes in Section 3.2, which builds on

the more general introduction to attribution approaches in the Cross-

Working Group Box on Attribution in Chapter1. In this chapter we

assess the detection of anthropogenic inﬂuence on climate on large

spatial scales and long temporal scales, a concept related to, but

distinct from, that of the emergence of anthropogenically-induced

climate change from the range of internal variability on local scales

and shorter time scales (Section 1.4.2.2). The following sections

address the climate system component by component, in each

case assessing human inﬂuence and evaluating climate models’

simulations of the relevant aspects of climate and climate

change. This chapter assesses the evaluation and attribution of global,

hemispheric, continental and ocean basin-scale indicators of climate

change in the atmosphere and at the Earth’s surface (Section 3.3),

cryosphere (Section 3.4), ocean (Section 3.5), and biosphere (Section

3.6), and the evaluation and attribution of modes of variability

(Section 3.7), the period of slower warming in the early 21st century

(Cross-Chapter Box3.1) and large-scale changes in extremes (Cross-

Chapter Box3.2). Model evaluation and attribution on sub-continental

scales are not covered here, since these are assessed in the Atlas and

in Chapter10, and extreme event attribution is not covered since it is

assessed in Chapter11. Section3.8 assesses multivariate attribution

and integrative measures of model performance based on multiple

variables, as well as process representation in different classes of

models. The chapter structure is summarized in Figure3.1.

3.2 Methods

New methods for model evaluation that are used in this chapter

are described in Section 1.5.4. These include new techniques for

process-based evaluation of climate and Earth system models against

observations that have rapidly advanced since the publication of AR5

(Eyring et al., 2019) as well as newly developed CMIP evaluation

tools that allow a more rapid and comprehensive evaluation of the

models with observations (Eyring et al., 2016a, b).

In this chapter, we use the Earth System Model Evaluation Tool

(ESMValTool, Eyring et al., 2020; Lauer et al., 2020; Righi et al., 2020)

and the NCAR Climate Variability Diagnostic Package (CVDP, Phillips

et al., 2014) that is included in the ESMValTool to produce most of

the ﬁgures. This ensures traceability of the results and provides an

additional level of quality control. The ESMValTool code to produce

the ﬁgures in this chapter was released as open source software at

the time of the publication of this Report (see details in the Chapter

Data Table, Table SM.3.1). Figures in this chapter are produced either

using one ensemble member from each model, or using all available

ensemble members and weighting each simulation by 1/(NMi), where

N is the number of models and Mi is the ensemble size of the ith

model, prior to calculating means and percentiles. Both approaches

ensure that each model used is given equal weight in the ﬁgures, and

details on which approach is used are provided in the ﬁgure captions.

An introduction to recent developments in detection and attribution

methods in the context of this Report is provided in the Cross-

Working Group Box on Attribution in Chapter1. Here we discuss new

methods and improvements applicable to the attribution of changes in

large-scale indicators of climate change which are used in this chapter.

3.2.1 Methods Based on Regression

Regression-based methods, also known as ﬁngerprinting methods,

have been widely used for detection of climate change and attribution

of the change to different external drivers. Initially, these methods were

applied to detect changes in global surface temperature, and were

then extended to other climate variables at different time andspatial

scales (e.g., Hegerl et al., 1996; Hasselmann, 1997; Allen and Tett,

1999; Gillett et al., 2003b; Zhang et al., 2007; Min et al., 2008a, 2011).

These approaches are based on multivariate linear regression and

assume that the observed change consists of a linear combination of

externally forced signals plus internal variability, which generally holds

for large-scale variables (Hegerl and Zwiers, 2011). The regressors

are the expected space–time response patterns to different climate

forcings (ﬁngerprints), and the residuals represent internal variability.

Fingerprints are usually estimated from climate model simulations

following spatial and temporal averaging. A regression coefﬁcient

which is signiﬁcantly greater than zero implies that a detectable

change is identiﬁed in the observations. When the conﬁdence interval

of the regression coefﬁcient includes unity and is inconsistent with

zero, the magnitude of the model simulated ﬁngerprints is assessed

to be consistent with the observations, implying that the observed

changes can be attributed in part to a particular forcing. Variants

of linear regression have been used to address uncertainty in the

ﬁngerprints due to internal variability (Allen and Stott, 2003) as well

as structural model uncertainty (Huntingford et al., 2006).

In order to improve the signal-to-noise ratio, observations and

model-simulated responses are usually normalized by an estimate

of internal variability derived from climate model simulations. This

430

Chapter 3 Human Inﬂuence on the Climate System

procedure requires an estimate of the inverse covariance matrix of

the internal variability, and some approaches have been proposed

for more reliable estimation of this (Ribes et al., 2009). A signal can

be spuriously detected due to too-small noise, and hence simulated

internal variability needs to be evaluated with care. Model-simulated

variability is typically checked through comparing modelled variance

from unforced simulations with the observed residual varianceusing

a standard residual consistency test (Allen and Tett, 1999), or an

improved one (Ribes and Terray, 2013). Imbers et al. (2014) tested

the sensitivity of detection and attribution results to different

representations of internal variability associated with short-memory

and long-memory processes. Their results supported the robustness

of previous detection and attribution statements for the global mean

temperature change but they also recommended the use of a wider

variety of robustness tests.

Some recent studies focused on the improved estimation of the

scaling factor (regression coefﬁcient) and its conﬁdence interval.

Hannart et al. (2014) described an inference procedure for scaling

factors which avoids making the assumption that model error and

internal variability have the same covariance structure. An integrated

approach to optimal ﬁngerprinting was further suggested in which all

uncertainty sources (i.e., observational error, model error, and internal

variability) are treated in one statistical model without apreliminary

dimension reduction step (Hannart, 2016). Katzfuss et al. (2017)

introduced a similar integrated approach based on a Bayesian

model averaging. On the other hand, DelSole et al. (2019) suggested

a bootstrap method to better estimate the conﬁdence intervals of

scaling factors even in a weak-signal regime. It is notable that some

studies do not optimize ﬁngerprints, as uncertainty in the covariance

introduces a further layer of complexity, but results in only a limited

improvement in detection (Polson and Hegerl, 2017).

Another ﬁngerprinting approach uses pattern similarity between

observations and ﬁngerprints, in which the leading empirical

orthogonal function obtained from the time-evolving multi-model

forced simulation is usually deﬁned as a ﬁngerprint (e.g., Santer

et al., 2013; Marvel et al., 2019; Bonﬁls et al., 2020). Observations and

model simulations are then projected onto the ﬁngerprint to measure

the degree of spatial pattern similarity with the expected physical

response to a given forcing. This projection provides the signal time

series, which is in turn tested against internal variability, as estimated

from long control simulations. As a way to extend this pattern-based

approach to a high-dimensional detection variable at daily time

scales, Sippel et al. (2019, 2020) proposed using the relationship

pattern with a global climate change metric as a ﬁngerprint. To solve

the high-dimensional regression problem which makes regression

coefﬁcients not well constrained, they incorporated a statistical

learning technique based on a regularized linear regression, which

optimizes a global warming signal by giving lower weight to regions

with large internal variability.

3.2.2 Other Probabilistic Approaches

Considering the difﬁculty in accounting for climate modelling

uncertainties in the regression-based approaches, Ribes et al. (2017)

introduced a new statistical inference framework based on an

additivity assumption and likelihood maximization, which estimates

climate model uncertainty based on an ensemble of opportunity and

tests whether observations are inconsistent with internal variability

and consistent with the expected response from climate models. The

method was further developed by Ribes et al. (2021), who applied

it to narrow the uncertainty range in the estimated human-induced

warming. Hannart and Naveau (2018), on the other hand, extended

the application of standard causal theory (Pearl, 2009) to the context

of detection and attribution by converting a time series into an event,

and calculating the probability of causation, an approach which

maximizes the causal evidence associated with the forcing. On the

other hand, Schurer et al. (2018) employed a Bayesian framework

to explicitly consider climate modelling uncertainty in the optimal

regression method. Application of these approaches to attribution of

large-scale temperature changes supports a dominant anthropogenic

contribution to the observed global warming.

Climate change signals can vary with time and discriminant analysis

has been used to obtain more accurate estimates of time-varying

signals, and has been applied to different variables such as seasonal

temperatures (Jia and DelSole, 2012) and the South Asian monsoon

(Srivastava and DelSole, 2014). The same approach was applied

to separate aerosol forcing responses from other forcings (X. Yan

et al., 2016) and results using climate model output indicated

that detectability of the aerosol response is maximized by using

a combination of temperature and precipitation data. Paeth et al.

(2017) introduced a detection and attribution method applicable for

multiple variables based on a discriminant analysis and a Bayesian

classiﬁcation method. Finally, a systematic approach has been

proposed to translating quantitative analysis into a description of

conﬁdence in the detection and attribution of a climate response

toanthropogenic drivers (Stone and Hansen, 2016).

Overall, these new ﬁngerprinting and other probabilistic methods for

detection and attribution as well as efforts to better incorporate the

associated uncertainties have addressed a number of shortcomings in

previously applied detection and attribution techniques. They further

strengthen the conﬁdence in attribution of observed large-scale

changes to a combination of external forcings as assessed in the

following sections.

3.3 Human Inﬂuence on the Atmosphere

andSurface

3.3.1 Temperature

3.3.1.1 Surface Temperature

Surface temperature change is the aspect of climate in which the

climate research community has had most conﬁdence over past IPCC

assessment reports. This conﬁdence comes from the availability of longer

observational records compared to other indicators, a large response to

anthropogenic forcing compared to variability in the global mean, and

a strong theoretical understanding of the key thermodynamics driving

its changes (Collins et al., 2010; Shepherd, 2014). The AR5 assessed

that it was extremely likely that human activities had caused more than

431

Human Inﬂuence on the Climate System Chapter 3

half of the observed increase in global mean surface temperature from

1951 to 2010, and virtually certain that internal variability alone could

not account for the observed global warming since 1951 (Bindoff et al.,

2013). The AR5 also assessed with very high conﬁdence that climate

models reproduce the general features of the global-scale annual

mean surface temperature increase over 1850–2011 and with high

conﬁdence that models reproduce global and Northern Hemisphere

temperature variability on a wide range of time scales (Flato et al.,

2013). This section assesses the performance of the new generation

CMIP6 models (see Table AII.5) in simulating the patterns, trends, and

variability of surface temperature, and the evidence from detection

and attribution studies of human inﬂuence on large-scale changes in

surface temperature.

3.3.1.1.1 Model evaluation

To be ﬁt for detecting and attributing human inﬂuence on globally-

averaged surface temperatures, climate models need to represent, based

on physical principles, both the response of surface temperatureto

external forcings and the internal variability in surface temperature

over various time scales. This section assesses the performance of

those aspects in the latest generation CMIP6 climate models. See

Section 3.8 for evaluation at continental scales, Chapter10 for model

evaluation in the context of regional climate information, and the Atlas

for region-by-region assessments of model performance.

Reconstructions of past temperature from paleoclimate proxies

(Section 2.3.1.1 and Cross-Chapter Box 2.1) have been used to

evaluate modelled past climate temperature change patterns. The

AR5 found that CMIP5 (Taylor et al., 2012) models were able to

reproduce the large-scale patterns of temperature during the Last

Glacial Maximum (LGM) (Flato et al., 2013) and simulated a polar

ampliﬁcation broadly consistent with reconstructions for warm

(Pliocene and Eocene) and cold (LGM) periods (Masson-Delmotte

et al., 2013). Since AR5, a better understanding of temperature

proxies and their uncertainties and in some cases the forcing applied

to model simulations has led to better agreement between models

and reconstructions over a wide range of past climates. For the

Pliocene and Eocene warm periods, understanding of uncertainties in

temperature proxies (Hollis et al., 2019; McClymont et al., 2020) and

the boundary conditions used in climate simulations (Haywood et al.,

2016; Lunt et al., 2017) has improved, and some models now agree

better with temperature proxies for these time periods compared

to models assessed in AR5 (Sections 7.4.4.1.2, 7.4.4.2.2 and Cross-

Chapter Box2.4; Zhu et al., 2019; Haywood et al., 2020; Luntet al.,

2021). For the Last Interglacial (LIG), improved temporal resolution of

temperature proxies (Capron et al., 2017) and better appreciation of

the importance of freshwater forcing (Stone et al., 2016) have clariﬁed

the reasons behind apparent model-data inconsistencies. Regional

LIG temperature responses simulated by CMIP6 are within the

uncertainty ranges of reconstructed temperature responses, except

in regions where unresolved changes in regional ocean circulation,

meltwater, or vegetation changes may cause model mismatches

(Otto-Bliesner et al., 2021). For the LGM, the CMIP5 and CMIP6

ensembles compare similarly to new sea surface temperature (SST)

and surface air temperature (SAT) proxy reconstructions (Figure3.2a;

Cleator et al., 2020; Tierney et al., 2020b). The very cold CMIP6

LGM simulation by the Community Earth System Model Version 2.1

(CESM2.1) is an exception related to the high equilibrium climate

sensitivity (ECS) of that model (Section 7.5.6; Kageyama et al., 2021a;

Zhu et al., 2021). Figure3.2a illustrates the wide range of simulated

global LGM temperature responses in both ensembles. CMIP6 models

tend to underestimate the cooling over land, but agree better with

oceanic reconstructions. For the mid-Holocene, the regional biases

found in CMIP5 simulations are similar to those in pre-industrial and

historical simulations (Harrison et al., 2015; Ackerley et al., 2017),

suggesting common causes. CMIP5 models underestimate Arctic

warming in the mid-Holocene (Yoshimori and Suzuki, 2019). CMIP6

models simulate a mid-latitude, subtropical, and tropical cooling

compared to the pre-industrial period, whereas temperature proxies

indicate a warming (see Section 2.3.1.1.2; Brierley et al., 2020;

Kaufman et al., 2020), although accounting for seasonal effects in the

proxies may reduce the discrepancy (Bova et al., 2021). Over the past

millennium, reconstructed and simulated temperature anomalies,

internal variability, and forced response agree well over Northern

Hemisphere continents, but those statistics disagree strongly in

the Southern Hemisphere, where models seem to overestimate

the response (PAGES 2k-PMIP3 group, 2015). That disagreement is

partly explained by the lower quality of the reconstructions in the

Southern Hemisphere, but model and/or forcing errors may also

contribute (Neukom et al., 2018). Figure3.2b shows that land/sea

warming contrast behaves coherently in model simulations across

multiple periods, with a slight non-linearity in land warming due to

a smaller contribution of snow cover to temperature response in

warmer climates. A multivariate assessment of paleoclimate model

simulations is carried out in Section 3.8.2.

For the historical period, AR5 assessed with very high conﬁdence

that CMIP5 models reproduced observed large-scale mean surface

temperature patterns, although errors of several degrees appear in

elevated regions, like the Himalayas and Antarctica, near the edge

of the sea ice in the North Atlantic, and in upwelling regions. This

assessment is updated here for the CMIP6 simulations. Figure 3.3

shows the annual mean surface air temperature at 2 m for the

CMIP5 and CMIP6 multi-model means, both compared to the ﬁfth

generation European Centre for Medium-Range Weather Forecasts

(ECMWF) atmospheric reanalysis (ERA5; Section 1.5.2) for the

period 1995–2014. The distribution of biases is similar in CMIP5 and

CMIP6 models, as already noted by several studies (Crueger et al.,

2018; Găinuşă-Bogdan et al., 2018; Kuhlbrodt et al., 2018; Lauer

et al., 2018). Arctic temperature biases seem more widespread in

both ensembles than assessed at the time of AR5. The fundamental

causes of temperature biases remain elusive, with errors in clouds

(Lauer et al., 2018), ocean circulation (Kuhlbrodt et al., 2018), winds

(Lauer et al., 2018), and surface energy budget (Hourdin et al., 2015;

Séférian et al., 2016; Găinuşă-Bogdan et al., 2018) being frequently

cited candidates. Increasing horizontal resolution shows promise for

decreasing long-standing biases in surface temperature over large

regions (Bock et al., 2020). Panels e and f of Figure3.3 show that

biases in the mean High-Resolution Model Intercomparison Project

(HighResMIP, Haarsma et al., 2016) models (see also Table AII.6) are

smaller than those in the mean of the corresponding lower-resolution

versions of the same models simulating the same period (see also

Section 3.8.2.2). However, the bias reduction is modest (Palmer and

432

Chapter 3 Human Inﬂuence on the Climate System

3(c) Volcanic forcing and reconstructed and modelled GMST over the past millennium

Temperature anomaly over land (°C)

Last Glacial Maximum

mid-Holocene

Last Interglacial

mid-Piacenzian Warm Period

Early Eocene Climatic Optimum

1pctCO2

abrupt4xCO2

Time periods:

Instrumental

Reconstruction

Observations:

CMI P 6

CMI P5

non-CMI P

Multi-model means:

CMIP6

CMIP5

non-CMIP

Individual models:

Fit to data:

Temperature anomaly over oceans (°C)

Temperature anomaly over oceans on reconstruction data pts (°C)

Temperature anomaly over land on reconstruction data pts (°C)

-10 0 10 20

(b) Global temperature anomaly over

land and ocean for a range of climates

(a) Last Glacial Maximum reconstructed and modelled

land and ocean tropical temperature anomaly

Figure3.2 | Changes in surface temperature for different paleoclimates. (a) Comparison of reconstructed and modelled surface temperature anomalies for the Last

Glacial Maximum over land and ocean in the Tropics (30°N–30°S). Land-based reconstructions are from Cleator et al. (2020). Ocean-based reconstructions are from Tierney

et al. (2020b). Model anomalies are calculated as the difference between Last Glacial Maximum and pre-industrial control simulations of the PMIP3 and PMIP4 ensembles,

sampled at the reconstruction data points. (b) Land–sea contrast in global mean surface temperature change for different paleoclimates. Small symbols show individual model

simulations from the CMIP5 and CMIP6 ensembles. Large symbols show ensemble means and assessed values. (c) Upper panel shows time series of volcanic radiative forcing,

in W m−2, as used in the CMIP5 (Gaoet al., 2008; Crowley and Unterman, 2013; see also Schmidt et al., 2011) and CMIP6 (850 CE to 1900 CE from Toohey and Sigl (2017),

1850–2015 from Luo (2018)). The forcing was calculated from the stratospheric aerosol optical depth at 550nm shown in Figure2.2. Lower panel shows time series of global

mean surface temperature anomalies, in °C, with respect to 1850–1900 for the CMIP5 and CMIP6 past1000simulations and their historical continuation simulations. Simulations

are coloured according to the volcanic radiative forcing dataset they used. The median reconstruction of temperature from PAGES 2k Consortium (2019) is shown in black, the

5–95% conﬁdence interval is shown by grey lines and the grey envelopes show the 1st, 5th, 15th, 25th, 35th, 45th, 55th, 65th, 75th, 85th, 95th, and 99th percentiles. All data

in both panels are band-passed ﬁltered, where frequencies longer than 20 years have been retained. Further details on data sources and processing are available in the chapter

data table (Table3.SM.1).

433

Human Inﬂuence on the Climate System Chapter 3

Stevens, 2019). In addition, the biases of the limited number of models

participating in HighResMIP are not entirely representative ofoverall

CMIP6 biases, especially in the Southern Ocean, as indicated by

comparing panels b and f of Figure3.3.

The AR5 assessed with very high conﬁdence that models reproduce

the general history of the increase in global-scale annual mean

surface temperature since the year 1850, although AR5 also reported

that an observed reduction in the rate of warming over the period

1998–2012 was not reproduced by the models (Cross-Chapter

Box3.1; Flato et al., 2013). Figure3.2c and Figure3.4 show time series

of anomalies in annually and globally averaged surface temperature

simulated by CMIP5 and CMIP6 models for the past millennium

and the period 1850 to 2020, respectively, with the baseline set

to 1850–1900 (see Section 1.4.1). As also indicated by Figure 3.4,

the spread in simulated absolute temperatures is large (Palmer and

Stevens, 2019). However, the discussion is based on temperature

anomaly time series instead of absolute temperatures because our

focus is on evaluation of the simulation of climate change in these

models, and also because anomalies are more uniformly distributed

and are more easily deseasonalized to isolate long-term trends (see

Section 1.4.1). CMIP6 models broadly reproduce surface temperature

variations over the past millennium, including the cooling that follows

periods of intense volcanism (medium conﬁdence) (Figure 3.2c).

Simulated GMST anomalies are well within the uncertainty range

of temperature reconstructions (medium conﬁdence) since about

the year 1300, except for some short periods immediately following

large volcanic eruptions, for which simulations driven by different

forcing datasets disagree (Figure3.2c). Before the year 1300, larger

disagreements between models and temperature reconstructions

are expected because forcing and temperature reconstructions are

increasingly uncertain further back in time, but speciﬁc causes have

not been identiﬁed conclusively (Ljungqvist et al., 2019; PAGES 2k

Consortium, 2019) (medium conﬁdence). For the historical period,

results for CMIP6 shown in Figure3.4 suggest that the qualitative

history of surface temperature increase is well reproduced, including

the increase in warming rates beginning in the 1960s and the

temporary cooling that follows large volcanic eruptions.

Although virtually all CMIP6 modelling groups report improvements

in their model’s ability to simulate current climate compared to the

CMIP5 version (Gettelman et al., 2019; Golaz et al., 2019; Mauritsen

et al., 2019; Swart et al., 2019; Voldoire et al., 2019b; T. Wu et al.,

2019b; Bock et al., 2020; Boucher et al., 2020; Dunne et al., 2020), it

does not necessarily follow that the simulation of temperature trends

is also improved (Bock et al., 2020; Fasullo et al., 2020). TheCMIP6

multi-model ensemble encompasses observed warming and the

multi-model mean tracks those observations within 0.2°C over most

of the historical period. Figure3.4 conﬁrms the ﬁndings of Papalexiou

et al. (2020), who highlighted based on 29 CMIP6 models that most

models replicate the period of slow warming between 1942 and

1975 and the late twentieth century warming (1975–2014). The

CMIP6 multi-model mean is cooler over the period 1980–2000 than

both observations and CMIP5 (Figure3.4; Bock et al., 2020; Flynn

and Mauritsen, 2020; Gillett et al., 2021). Biases of several tenths

of a degree in some CMIP6 models over that period may be due

to an overestimate in aerosol radiative forcing (Sections 6.3.5 and

7.3.3, and Figure6.8; Andrews et al., 2020; Dittus et al., 2020; Flynn

and Mauritsen, 2020). Papalexiou et al. (2020), Tokarska et al. (2020)

and Stolpe et al. (2021) all report that CMIP6 models on average

overestimate warming from the 1970s or 1980s to the 2010s,

although quantitative conclusions depend on which observational

dataset is compared against (see also Table2.4). However, Figure3.4,

which includes a larger number of models than available to those

studies, indicates that the CMIP6 multi-model mean tracks observed

warming better than the CMIP5 multi-model mean after the year

2000. The CMIP6 multi-model mean GSAT warming between 1850–

1900 and 2010–2019 and associated 5–95% range is 1.09 [0.66

to 1.64] °C. Cross-Chapter Box2.3 assessed GSAT warming over

the same period at 1.06 [0.88 to 1.21] °C. So some CMIP6 models

simulate awarming that is smaller than the assessed observed range,

and other CMIP6 models simulate a warming that is larger. That

overestimated warming may be an early symptom of overestimated

ECS in some CMIP6 models (Section7.5.6; Meehl et al., 2020; Schlund

et al., 2020), and has implications for projections of GSAT changes

(Chapter4; Liang et al., 2020; Nijsse et al., 2020; Tokarska et al., 2020;

Ribes et al., 2021). In some models, a large ECS and a strong aerosol

forcing lead to too large a mid-20th century cooling followed by

overestimated warming rates in the late 20th century when aerosol

emissions decrease (Golaz et al., 2019; Flynn and Mauritsen, 2020).

Temperature biases are driven by both model physics and prescribed

forcing, which is achallenge for model development.

Chylek et al. (2020) argue that CMIP5 models overestimate the

temperature response to volcanic eruptions. Lehner et al. (2016),

Rypdal (2018) and Stolpe et al. (2021) point instead to missed

compensating effects on surface temperature change associated

with internal variability in the ElNiño–Southern Oscillation (ENSO)

or the Atlantic Multi-decadal Oscillation (AMO). An alternative view

sees those ENSO and AMO responses as expressions of changes in

climate feedbacks driven by the geographical pattern of SST changes

(Andrews et al., 2018). At least one model is able to reproduce such

pattern effects (Gregoryand Andrews, 2016). Errors in the volcanic

forcing prescribed in simulations, including for CMIP6 (Rieger et al.,

2020), also introduce differences with the observed temperature

response, independently of the quality of the model physics. In

addition, comparisons of the modelled temperature response

to large eruptions over the past millennium to temperature

reconstructions based on tree rings show a much better agreement

(Lücke et al., 2019; F. Zhu et al., 2020) than comparisons to the annual,

multi-temperature proxy reconstructions shown in Figure3.2c. These

considerations, and Figures 3.2c and 3.4, suggest that CMIP6 models

do not systematically overestimate the cooling that follows large

volcanic eruptions (see also Cross-ChapterBox4.1).

When interpreting model simulations of historical temperature change,

it is important to keep in mind that some models are tuned towards

representing the observed trend in global mean surface temperature

over the historical period (Hourdin et al., 2017). In Figure 3.4 the

CMIP6 models that are documented to have been tuned to reproduce

observed warming, typically by tuning aerosol forcing or factors

that inﬂuence the model’s ECS, are marked with an asterisk. Such

tuning of a model can strongly impact its temperature projections

(Mauritsen and Roeckner, 2020). However, Bock et al. (2020) reported

434

Chapter 3 Human Inﬂuence on the Climate System

No robust biasRobust bias Conflicting signals

Colour No robust model improvement

Figure3.3 | Annual mean near-surface (2 m) air temperature (°C) for the period 1995–2014. (a) Multi-model (ensemble) mean constructed with one realization of

the CMIP6 historical experiment from each model. (b) Multi-model mean bias, deﬁned as the difference between the CMIP6 multi-model mean and the climatology of the ﬁfth

generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5). (c) Multi-model mean of the root mean square

error calculated over all months separately and averaged, with respect to the climatology from ERA5. (d) Multi-model mean bias deﬁned as the difference between the CMIP6

multi-model mean and the climatology from ERA5. The difference between the multi-model mean of (e) high-resolution and (f)low-resolution simulations of four HighResMIP

models and the climatology from ERA5 is also shown. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of

models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines indicate regions with no change or no robust signal,

where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conﬂicting signal, where ≥66% of models show change greater

than the variability threshold and <80% of all models agree on sign of change. For more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1.

Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least three out of four models than in the corresponding

low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

435

Human Inﬂuence on the Climate System Chapter 3

that there is no statistically signiﬁcant difference in multi-model

mean GSAT between the models that had been tuned based on

observed warming compared to those which had not. Moreover,

only two of thirteen models used for the Detection and Attribution

Model Intercomparison Project (DAMIP) simulations on which CMIP6

attribution studies are based were tuned towards historical warming

(Bock et al., 2020; Gillett et al., 2021). Further, tuning is done on

globally averaged quantities, so does not substantially change the

spatio-temporal pattern of response on which many regression-

based attribution studies are based (Bock et al., 2020). Therefore, we

assess with high conﬁdence that the tuning of a small number of

CMIP6 models to observed warming has not substantially inﬂuenced

attribution results assessed in thischapter.

The reliance of detection and attribution studies on climate models

(see Section 3.2) requires that those models simulate realistic

statistics of internal variability on multi-decadal time scales. An

incorrect estimate of variability in models would affect conﬁdence

***

Reference period

Global mean surface air temperature

Figure3.4 | Observed and simulated time series of the anomalies in annual and global mean surface air temperature (GSAT). All anomalies are differences

from the 1850–1900 time-mean of each individual time series. The reference period 1850–1900 is indicated by grey shading. (a) Single simulations from CMIP6 models (thin

lines) and the multi-model mean (thick red line). Observational data (thick black lines) are from the Met Ofﬁce Hadley Centre/Climatic Research Unit dataset (HadCRUT5),

and are blended surface temperature (2 m air temperature over land and sea surface temperature over the ocean). All models have been subsampled using the HadCRUT5

observational data mask. Vertical lines indicate large historical volcanic eruptions. CMIP6 models which are marked with an asterisk are either tuned to reproduce observed

warming directly, or indirectly by tuning equilibrium climate sensitivity. Inset: GSAT for each model over the reference period, not masked to any observations. (b)Multi-model

means of CMIP5 (blue line) and CMIP6 (red line) ensembles and associated 5th to 95th percentile ranges (shaded regions). Observational data are HadCRUT5, Berkeley Earth,

National Oceanic and Atmospheric Administration NOAAGlobalTemp-Interim and Kadow et al. (2020). Masking was done as in (a). CMIP6 historical simulations were extended

with SSP2-4.5 simulations for the period 2015–2020 and CMIP5 simulations were extended with RCP4.5 simulations for the period 2006–2020. All available ensemble

members were used (see Section 3.2). The multi-model means and percentiles were calculated solely from simulations available for the whole time span (1850–2020). Figure

is updated from Bock et al. (2020), their Figures 1 and 2. CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and processing are available

in the chapter data table (Table3.SM.1).

436

Chapter 3 Human Inﬂuence on the Climate System

in the conclusions from detection and attribution. The AR5 found

that CMIP5 models simulate realistic variability in global-mean

surface temperature on decadal time scales, with variability on

multi-decadal time scales being more difﬁcult to evaluate because

of the short observational record (Flato et al., 2013). Since AR5, new

work has characterized the contributions of variability in different

ocean areas to SST variability, with tropical modes of variability like

ENSO dominant on time scales of ﬁve to ten years, while longer time

scales see the variance maxima move poleward to the North Atlantic,

North Paciﬁc, and Southern oceans (Monselesan et al., 2015). There

may, however, be sizeable, two-way interdependencies between

ENSO and sea surface temperature variability in different basins

(Kumar et al., 2014; Cai et al., 2019), and ENSO’s inﬂuence on global

surface temperature variability may not be conﬁned only to decadal

time scales (Triacca et al., 2014). Studies based on large ensembles

of 20th and 21st century climate change simulations conﬁrm that

internal variability has a substantial inﬂuence on global warming

trends over periods shorter than 30–40 years (Kay et al., 2015; Dai

and Bloecker, 2019). Although the equatorial Paciﬁc seems to be the

main source of internal variability on decadal time scales, Brown

et al. (2016a) linked diversity in modelled oceanic convection, sea

ice, and energy budget in high-latitude regions to overall diversity in

modelled internal variability.

Interest in internal variability since the publication of AR5 stems in

part from its importance in understanding the slower global surface

temperature warming over the early 21st century (see Cross-Chapter

Box3.1). Evidence coming mostly from paleo studies is mixed on

whether CMIP5 models underestimate decadal and multi-decadal

variability in global mean temperature. Schurer et al. (2013) found

good agreement between internal variability derived from paleo

reconstructions, estimated as the fraction of variance that is not

explained by forced responses, and modelled variability, although

the subset of CMIP5 models they used may have been associated

with larger variability than the full CMIP5 ensemble. PAGES 2k

Consortium (2019) found that the largest 51-year trends in both

reconstructions of global mean temperature and fully forced climate

simulations over the period 850 to 1850 were almost identical. Zhu

et al. (2019) showed agreement in the modelled and reconstructed

temporal spectrum of global surface temperatures on annual to

multi-millennial time scales. However, they suggest that decadal- to

centennial variability is partly forced by slow orbital changes that

predate the last millennium. This is consistent with Gebbie and

Huybers (2019), who showed that the deep ocean has been out

of equilibrium over that period. Laepple and Huybers (2014) found

good agreement between modelled and proxy-derived decadal

ocean temperature variability, but underestimates of variance by

models by at least a factor of ten at centennial time scales because

models underestimate the difference between the warm and cold

periods of the last millennium. Parsons et al. (2020) found that some

CMIP6 models exhibit much higher multi-decadal variability in GSAT

than CMIP5 models, with indications that variability in these models

Figure3.5 | The standard deviation of annually averaged zonal-mean near-surface air temperature. This is shown for four detrended observed temperature

datasets (HadCRUT5, Berkeley Earth, NOAAGlobalTemp-Interim and Kadow et al. (2020), for the years 1995-2014) and 59 CMIP6 pre-industrial control simulations (one

ensemble member per model, 65 years) (after Jones et al., 2013). For line colours see the legend of Figure3.4. Additionally, the multi-model mean (red) and standard deviation

(grey shading) are shown. Observational and model datasets were detrended by removing the least-squares quadratic trend. Further details on data sources and processing are

available in the chapter data table (Table3.SM.1).

437

Human Inﬂuence on the Climate System Chapter 3

is also higher than that from proxy reconstructions. CMIP6 models

may not share the underestimation by CMIP5 models of variability

in decadal to multi-decadal modes of variability, such as Paciﬁc

Decadal Variability (Section 3.7.6; England et al., 2014; Thompson

et al., 2014; Schurer et al., 2015) and Atlantic Multi-decadal

Variability (AMV), which may be partly forced, (see Section 3.7.7)

but this assessment is limited by the small number of available

studies. For the Southern Hemisphere, Hegerl et al. (2018) found an

instance of internal variability in the early 20th century larger than

that modelled, but indicated that could be an observational issue.

Friedman et al. (2020) found biases in interhemispheric SST contrast

in some models that may be consistent with underestimated cooling

after early-20th century eruptions or underestimated Paciﬁc Decadal

Variability, but could also be due to an imperfect separation between

internal variability and forced signal in the observations. Figure3.2c,

updated from PAGES 2k Consortium (2019), compares modelled

temperatures to reconstructions over the last millennium. It indicates

that models reproduce the observed variability well, at least for the

time scales between 20 and 50 years that paleo reconstructions

typically resolve and that the ﬁgure represents. In summary, decadal

Simulated variability of GSAT versus observed changes

GSAT anomaly (°C)

Figure3.6 | Simulated internal variability of global surface air temperature (GSAT) versus observed changes. (a) Time series of ﬁve-year running mean GSAT

anomalies in 45 CMIP6 pre-industrial control (unforced) simulations. The 10 most variable models in terms of ﬁve-year running mean GSAT are coloured according to the legend

on Figure3.4. (b) Histograms of GSAT changes in CMIP6 historical simulations (extended by using SSP2-4.5 simulations) from 1850–1900 to 2010–2019 are shown by pink

shading in (c), and GSAT changes between the average of the ﬁrst 51 years and the average of the last 20 years of 170-year overlapping segments of the pre-industrial control

simulations shown in (a) are shown by blue shading. GMST changes in observational datasets for the same period are indicated by black vertical lines. (c) Observed GMST

anomaly time series relative to the 1850–1900 average. Black lines represent the ﬁve-year running means while grey lines show unﬁltered annual time series. Further details

on data sources and processing are available in the chapter data table (Table3.SM.1).

438

Chapter 3 Human Inﬂuence on the Climate System

GMST variability simulated in CMIP6 models spans the range of

residual decadal variability in large-scale reconstructions (medium

evidence, lowagreement).

In addition, new literature suggests that anthropogenic forcing

itself may locally increase or decrease variability in surface

temperatures (Screen et al., 2014; Qian and Zhang, 2015; Brown

et al., 2017; Park et al., 2018; Santer et al., 2018; Weller et al.,

2020). These studies imply limitations in the use of pre-industrial

control simulations to quantify the role of unforced variability over

the historical period. Some recent attribution studies (Gillett et al.,

2021; Ribes et al., 2021) have estimated variability from ensembles

of forced simulations instead, which would be expected to resolve

any such changes invariability.

Figure 3.5 shows the standard deviation of zonal-mean surface

temperature in CMIP6 pre-industrial control simulations and observed

temperature datasets. Results are consistent with those based

on CMIP5 models, which showed the largest model spread where

variability is also large, in the tropics and mid- to high latitudes (Flato

et al., 2013). Modelled variability is within a factor two of observed

variability over most of the globe. The apparent overestimation of

high latitude variability in models compared to observations may

be due to interpolation and inﬁlling over data sparse high latitude

regions in the observational products shown here (Jones, 2016).

The previous paragraph took an ensemble-mean view of model

performance, but individual models disagree on unforced variability.

Figure3.6 illustrates the large differences in GSAT variability in unforced

CMIP6 pre-industrial control simulations, following the method of

Parsons et al. (2020). Surface temperatures in pre-industrial conditions

are especially variable in the ten models highlighted in Figure3.6a,

and some models substantially exceed the variability seen in CMIP5

models (Parsons et al., 2020). Figure3.6b shows that the distribution

of warming trends simulated by CMIP6 models in historical simulations

is clearly distinct from that simulated in unforced pre-industrial control

simulations. Still, the unforced variability of the ﬁve most variable

models approaches half that observed over the historical period

under anthropogenically forced conditions (Figure3.6c; Parsons et al.,

2020; Ribes et al., 2021). Forthe Centre National de la Recherche

Météorologique (CNRM) models, which are among the most variable,

the large, low-frequency variability is attributed to strong simulated

Atlantic Multi-decadal Variability (Séférianet al., 2019; Voldoire et al.,

2019b), which is difﬁcult to rule out because of the short observational

record (Section 3.7.7; Cassou et al., 2018). But, importantly, patterns

of temperature variability simulated by even the most variable models

differ from the pattern of forced temperature change (Parsons et al.,

2020). Taken together, this discussion and Figures 3.2, 3.5 and 3.6

indicate that the statistics of internal variability in models compare

well in most cases to observational estimates and temperature proxy

reconstructions, though some CMIP6 models appear to have higher

multi-decadal variability than CMIP5 models or proxy reconstructions.

When used in attribution studies, models with overestimated

variability would increase estimated uncertainties and make results

statisticallyconservative.

In summary, there is high conﬁdence that CMIP6 models reproduce

observed large-scale mean surface temperature patterns and internal

variability as well as their CMIP5 predecessors, but with little evidence

for reduced biases. CMIP6 models also reproduce historical GSAT

changes similarly to their CMIP5 counterparts (medium conﬁdence).

However, in spite of model imperfections, there is very high conﬁdence

that biases in surface temperature trends and variability simulated

by the CMIP5 and CMIP6 ensembles are small enough to support

detection and attribution of human-induced warming.

3.3.1.1.2 Detection and attribution

Looking at periods preceding the instrumental record, AR5 assessed

with high conﬁdence that the 20th century annual mean surface

temperature warming reversed a 5000-year cooling trend in Northern

Hemisphere mid- to high latitudes caused by orbital forcing, and

attributed the reversal to anthropogenic forcing with high conﬁdence

(see also Section 2.3.1.1). Since AR5, the combined response to solar,

volcanic and greenhouse gas forcing was detected in all Northern

Hemisphere continents (PAGES 2k-PMIP3 group, 2015) over the

period 864 to 1840. In contrast, the effect of those forcings was not

detectable in the Southern Hemisphere (Neukom et al., 2018). Global

and Northern Hemisphere temperature changes from reconstructions

over this period have been attributed mostly to volcanic forcing

(Schurer et al., 2014; McGregor et al., 2015; Otto-Bliesner et al., 2016;

PAGES 2k Consortium, 2019; Büntgen et al., 2020), with a smaller

role for changes in greenhouse gas forcing, and solar forcing playing

a minor role (Schurer et al., 2014; PAGES 2k Consortium, 2019).

Focusing now on warming over the historical period, AR5 assessed

that it was extremely likely that human inﬂuence was the dominant

cause of the observed warming since the mid-20th century, and

that it was virtually certain that warming over the same period

could not be explained by internal variability alone. Since AR5 many

new attribution studies of changes in global surface temperature

have focused on methodological advances (see also Section 3.2).

Those advances include better accounting for observational and

model uncertainties, and internal variability (Ribes and Terray, 2013;

Hannart, 2016; Ribes et al., 2017; Schurer et al., 2018); formulating

the attribution problem in a counterfactual framework (Hannart and

Naveau, 2018); and reducing the dependence of the attribution on

uncertainties in climate sensitivity and forcing (Otto et al., 2015;

Haustein et al., 2017, 2019). Studies now account for uncertainties

in the statistics of internal variability, either explicitly (Hannart, 2016;

Hannart and Naveau, 2018; Ribes et al., 2021) or implicitly (Ribes and

Terray, 2013; Schurer et al., 2018; Gillett et al., 2021), thus addressing

concerns about over-conﬁdent attribution conclusions. Accounting for

observational uncertainty increases the range of warming attributable

to greenhouse gases by only 10 to 30% (Jones and Kennedy, 2017;

Schurer et al., 2018). While some attribution studies estimate

attributable changes in globally-complete GSAT (Schurer et al., 2018;

Gillett et al., 2021; Ribes et al., 2021), others attribute changes in

observational GMST, but this makes little difference to attribution

conclusions (Schurer et al., 2018). Moreover, based on asynthesis of

observational and modelling evidence, Cross-Chapter Box2.3 assesses

that the current best estimate of the scaling factor between GMST and

GSAT is one, and therefore attribution studies of GMST and GSAT are

439

Human Inﬂuence on the Climate System Chapter 3

here treated together in deriving assessed warming ranges. Studies

also increasingly validate their multi-model approaches using imperfect

model tests (Schurer et al., 2018; Gillett et al., 2021; Ribes et al., 2021).

Alternative techniques, based purely on statistical or econometric

approaches, without the need for climate modelling, have also been

applied (Estrada et al., 2013; Stern and Kaufmann, 2014; Dergiades

et al., 2016) and match the results of physically-based methods. The

larger range of attribution techniques and improvements to those

techniques increase conﬁdence in the results compared to AR5.

In contrast, studies published since AR5 indicate that closely

constraining the separate contributions of greenhouse gas changes

and aerosol changes to observed temperature changes remains

challenging. Nonetheless, attribution of warming to greenhouse gas

forcing has been found as early as the end of the 19th century (Schurer

et al., 2014; Owens et al., 2017; PAGES 2k Consortium, 2019). Hegerl

et al. (2019) found that volcanism cooled global temperatures by

about 0.1°C between 1870 and 1910, then a lack of volcanic activity

warmed temperatures by about 0.1°C between 1910 and 1950,

with anthropogenic aerosols cooling temperatures throughout the

20th century, especially between 1950 and 1980 when the estimated

range of aerosol cooling was about 0.1°C to 0.5°C. Jones et al. (2016)

attributed a warming of 0.87 to 1.22°C per century over the period

1906 to 2005 to greenhouse gases, partially offset by a cooling of

−0.54°C to −0.22°C per century attributed to aerosols. But they also

found that detection of the greenhouse gas or the aerosol signal

often fails, because of uncertainties in modelled patterns of change

and internal variability. That point is illustrated by Figure3.7, which

shows two- and three-way ﬁngerprinting regression coefﬁcients for 13

CMIP6 models and the corresponding attributable warming ranges,

derived using HadCRUT4 (Gillett et al., 2021). Regression coefﬁcients

with an uncertainty range that includes zero mean that detection has

failed. Models with regression coefﬁcients signiﬁcantly less than one

signiﬁcantly overpredict the temperature response to the corresponding

forcing. Conversely, models with regression coefﬁcients signiﬁcantly

greater than one underpredict the response to these forcings. While

estimates of warming attributable to anthropogenic inﬂuence derived

using individual models are generally consistent, estimates of warming

(a) (b)

(d)(c)

Figure3.7 | Regression coefﬁcients and corresponding attributable warming estimates for individual CMIP6 models. Upper panels show regression coefﬁcients

based on a two-way regression (left) and three-way regression (right), of observed ﬁve-year mean, globally averaged, masked and blended surface temperature (HadCRUT4)

onto individual model response patterns, and a multi-model mean, labelled ‘Multi’. Anthropogenic, natural, greenhouse gas, and other anthropogenic (aerosols, ozone, land-use

change) regression coefﬁcients are shown. Regression coefﬁcients are the scaling factors by which the model responses must be multiplied to best match observations.

Regression coefﬁcients consistent with one indicate a consistent magnitude response in observations and models, and regression coefﬁcients signiﬁcantly greater than zero

indicate adetectable response to the forcing concerned. Lower panels show corresponding observationally-constrained estimates of attributable warming in globally-complete

GSAT for the period 2010–2019, relative to 1850–1900, and the horizontal black line shows an estimate of observed warming in GSAT for this period. Figure is adapted from

Gillett et al. (2021), their Extended Data Figure3. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

440

Chapter 3 Human Inﬂuence on the Climate System

attributable to greenhouse gases and aerosols separately based on

individual models are not all consistent, and detection of the aerosol

inﬂuence fails more often than that of greenhouse gases. Hence,

results of recent studies emphasize the need to use multi-model

means to better constrain estimates of GSAT changes attributable to

greenhouse gas and aerosol forcing (Schurer et al., 2018; Gillett et al.,

2021; Ribes et al., 2021).

Figure3.8 compares attributable changes in globally complete GSAT

for the period 2010–2019 relative to 1850–1900 from three detection

and attribution studies, two of which use CMIP6 multi-model means

(Gillett et al., 2021; Ribes et al., 2021), and an estimate based on

assessed effective radiative forcing and transient and equilibrium

climate sensitivity (see Section 7.3.5.3). The reference period

1850–1900 is used to assess attributable temperature changes

because this is when the earliest gridded surface temperature

records start, this is when the CMIP6 historical simulations start,

this is the earliest base period used in attribution literature, and

this is a reference period used in IPCC SR1.5 and earlier reports.

It should, however, be noted that Cross-Chapter Box 1.2 assesses

with medium conﬁdence that there was an anthropogenic warming

with a likely range of 0.0°C–0.2°C between 1750 and 1850–1900.

Figure 3.8 also shows the GSAT changes directly simulated in

response to these forcings in thirteen CMIP6 models. In spite of their

different methodologies and input datasets, the three attribution

approaches yield very similar results, with the anthropogenic

attributable warming range encompassing observed warming, and

the natural attributable warming being close to zero. The warming

driven by greenhouse gas increases is offset in part by cooling due

to other anthropogenic forcing agents, mostly aerosols, although

uncertainties in these contributions are larger than the uncertainty in

the net anthropogenic warming, as discussed above. Estimates based

on physical understanding of forcing and ECS made by Chapter7 are

close to estimates from attribution studies, despite being the products

Chapter 7

Figure3.8 | Assessed contributions to observed warming, and supporting lines of evidence. Shaded bands show assessed likely ranges of temperature change

in GSAT, 2010–2019 relative to 1850–1900, attributable to net human inﬂuence, well-mixed greenhouse gases, other human forcings (aerosols, ozone, and land-use change),

natural forcings, and internal variability, and the 5–95% range of observed warming. Bars show 5–95% ranges based on (left to right) Haustein et al. (2017), Gillett et al. (2021)

and Ribes et al. (2021), and crosses show the associated best estimates. No 5–95% ranges were provided for the Haustein et al. (2017) greenhouse gas or other human forcings

contributions. The Ribes et al. (2021) results were updated using a revised natural forcing time series, and the Haustein et al. (2017) results were updated using HadCRUT5.

TheChapter7 best estimates and ranges were derived using assessed forcing time series and a two-layer energy balance model as described in Section 7.3.5.3. Coloured

symbols show the simulated responses to the forcings concerned in each of the models indicated. Further details on data sources and processing are available in the chapter

data table (Table3.SM.1).

441

Human Inﬂuence on the Climate System Chapter 3

of a different approach. This agreement enhances conﬁdence in the

magnitude and causes of attributable surface temperature warming.

The AR5 found high conﬁdence for a major role for anthropogenic

forcing in driving warming over each of the inhabited continents,

except for Africa where they found only medium conﬁdence

because of limited data availability (Bindoff et al., 2013). At the

hemispheric scale, Friedman et al. (2020) and Bonﬁls et al. (2020)

detected an anthropogenically forced response of inter-hemispheric

contrast in surface temperature change, which has a complex time

evolution but shows the Northern Hemisphere cooling relative

to the Southern Hemisphere until around 1975 but then warming

after that. Bonﬁls et al. (2020) attribute the Northern Hemisphere

reversal to acombination of reduced aerosol forcing and greenhouse

gas induced warming of Northern Hemisphere land masses.

Friedman et al. (2020) found that CMIP5 models simulate the

Figure3.9 | Global, land, ocean and continental annual mean near-surface air temperatures anomalies in CMIP6 models and observations. Time series are

shown for CMIP6 historical anthropogenic and natural (brown), natural-only (green), greenhouse gas only (grey) and aerosol only (blue) simulations (thick lines show multi-

model means and shaded regions show the 5th to 95th percentile ranges) and for HadCRUT5 (black). All models have been subsampled using the HadCRUT5 observational data

mask. Temperature anomalies are shown relative to 1950–2010 for Antarctica and relative to 1850–1900 for other continents. CMIP6 historical simulations are extended using

the SSP2-4.5 scenario simulations. All available ensemble members were used (see Section 3.2). Regions are deﬁned by Iturbide et al. (2020). Further details on data sources

and processing are available in the chapter data table (Table3.SM.1).

442

Chapter 3 Human Inﬂuence on the Climate System

correct sign of the inter-hemispheric contrast when forced with all

forcings but underestimate its magnitude. Figure 3.9 shows global

surface temperature change in CMIP6 all-forcing and natural-only

simulations globally, averaged over continents, and separately over

land and ocean surfaces. All-forcing simulations encompass observed

temperature changes for all regions, while natural-only simulations

fail to do so in recent decades except in Antarctica, based on the

annual means shown. As stated above, warming results from

apartial offset of greenhouse gas warming by aerosol cooling. That

offset is stronger over land than ocean. Regionally, models show

alarge range of possible temperature responses to greenhouse gas

and aerosol forcing, which complicates single-forcing attribution.

A more detailed discussion of regional attribution can be found in

Section 10.4. Over global land surfaces, Chan and Wu (2015) used

CMIP5 simulations to attribute a warming trend of 0.3 (2.5%–97.5%

conﬁdence interval: 0.2–0.36) °C per decade to anthropogenic

forcing, with natural forcing only contributing 0.05 (0.02–0.06) °C

per decade. Accounting for unsampled sources of uncertainty and the

availability of only a single study, their result suggests that it is very

likely that human inﬂuence is the main driver of warming over land.

In summary, since the publication of AR5, new literature has

emerged that better accounts for methodological and climate model

uncertainties in attribution studies (Ribes et al., 2017; Hannart and

Naveau, 2018) and that concludes that anthropogenic warming

is approximately equal to observed warming over the 1951–2010

period. The IPCC SR1.5 reached the same conclusion for 2017 relative

to 1850–1900 based on anthropogenic warming and associated

uncertainties calculated using the method of Haustein et al. (2017).

Moreover, the improved understanding of the causes of the apparent

slowdown in warming over the beginning of the 21st century and

the difference in simulated and observed warming trends over this

period (Cross-Chapter Box3.1) further improve our conﬁdence in the

assessment of the dominant anthropogenic contribution to observed

warming. In deriving our assessments, these considerations are

balanced against new literature that raises questions about the ability

of some models to simulate variability in surface temperatures over

a range of time scales (Laepple and Huybers, 2014; Parsons et al.,

2017; Friedman et al., 2020), and the ﬁnding that some CMIP6 models

exhibit substantially higher multi-decadal internal variability than

that seen in CMIP5, which remains to be fully understood (Parsons

et al., 2020; Ribes et al., 2021). Further, uncertainties in simulated

aerosol-cloud interactions are still large (Section 7.3.3.2.2), resulting

in very diverse spatial responses of different climate models to

aerosol forcing, and inter-model differences in the historical global

mean temperature evolution and in diagnosed cooling attributable to

aerosols (Figure3.8). Moreover, like previous generations of coupled

model simulations, historical and single forcing CMIP6 simulations

follow a common experimental design (Eyring et al., 2016a; Gillett

et al., 2016) and are thus all driven by the same common set of

forcings, even though these forcings are uncertain. Hence, forcing

uncertainty is not directly accounted for in most of the attribution and

model evaluation studies assessed here, although this limitation can

to some extent be addressed by comparing with previous generation

multi-model ensembles or individual model studies using different

sets of forcings.

The IPCC SR1.5 best estimate and likely range of anthropogenic

attributable GMST warming was 1.0 ± 0.2°C in 2017 with respect

to the period 1850–1900. Here, the best estimate is expressed

in terms of GSAT and is calculated as the average of the three

estimates shown in Figure3.9, yielding a value of 1.07°C. Ranges

for attributable GSAT warming are derived by ﬁnding the smallest

ranges with a precision of 0.1°C which span all of the 5–95% ranges

from the attribution studies shown in Figure3.9. These ranges are

then assessed as likely rather than very likely because the studies

may underestimate the importance of the structural limitations of

climate models, which probably do not represent all possible sources

of internal variability; use too simple climate models, which may

underestimate the role of internal variability; or underestimate model

uncertainty, especially when using model ensembles of limited size

and inter-dependent models, for example through common errors in

forcings across models, as discussed above. This leads to a likely range

for anthropogenic attributable warming in 2010–2019 relative to

1850–1900 of 0.8 to 1.3°C in terms of GSAT. This range encompasses

the best estimate and very likely range of observed GSAT warming of

1.06 [0.88 to 1.21] °C over the same period (Cross-Chapter Box2.3).

There is medium conﬁdence that the best estimate and likely ranges

of attributable warming expressed in terms of GMST are equal to

those for GSAT (Cross-Chapter Box2.3). Repeating the process for

other time periods leads to the best estimates and likely ranges listed

in Table3.1. GSAT change attributable to natural forcings is −0.1 to

+0.1°C. The likely range of GSAT warming attributable to greenhouse

gases is assessed in the same way to be 1.0 to 2.0°C while the GSAT

change attributable to aerosols, ozone and land-use change is −0.8

to 0.0°C. Progress in attribution techniques allows the important

advance of attributing observed surface temperature warming since

1850–1900, instead of since 1951 as was done in AR5.

Table3.1 | Estimates of warming in GSAT attributable to human inﬂuence for different periods in °C, all relative to the 1850–1900 base period.

Uncertainty ranges are 5–95% ranges for individual studies and likely ranges for the assessment. The results shown in the table use the methods described in the three studies

indicated, but applied to additional periods and the warming trend. Ribes et al. (2021) results were updated using a corrected natural forcing time series, and Haustein et al.

(2017) results were updated to use HadCRUT5.

1986–2005 1995–2014 2006–2015 2010–2019 Warming Rate

2010–2019

Ribes et al. (2021) 0.65 (0.52 to 0.77) 0.82 (0.69 to 0.94) 0.94 (0.8 to 1.08) 1.03 (0.89 to 1.17) 0.23 (0.18 to 0.29)

Gillett et al. (2021) 0.63 (0.32 to 0.94) 0.84 (0.63 to 1.06) 0.98 (0.74 to 1.22) 1.11 (0.92 to 1.30) 0.35 (0.30 to 0.41)

Haustein et al. (2017) 0.73 (0.58 to 0.82) 0.88 (0.75 to 0.98) 0.98 (0.87 to 1.10) 1.06 (0.94 to 1.22) 0.23 (0.19 to 0.35)

Assessment 0.68 (0.3 to 1.0) 0.85 (0.6 to 1.1) 0.97 (0.7 to 1.3) 1.07 (0.8 to 1.3) 0.2 (0.1 to 0.3)

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Human Inﬂuence on the Climate System Chapter 3

The IPCC AR5 assessed the likely range of the contribution of internal

variability to GMST warming to be −0.1 to +0.1°C over the period

1951–2010. Since then, several studies have downplayed the

contribution of internal modes of variability to global temperature

variability, often by arguing for a forced component to those internal

modes (Mann et al., 2014; Folland et al., 2018; Haustein et al.,

2019; Liguori et al., 2020). Haustein et al. (2017) found a 5–95%

conﬁdence interval of −0.09°C to +0.12°C for the contribution of

internal variability to warming between 1850–1879 and 2017.

Ribes et al. (2021) imply a contribution of internal variability of

−0.02°C ± 0.16°C to warming between 2010–2019 and 1850–1900,

assuming independence between errors in the observations and in

the estimate of the forced response. Based on these studies, but

allowing for unsampled sources of error, we assess the likely range

of the contribution of internal variability to GSAT warming between

2010–2019 and 1850–1900 to be −0.2°C to +0.2°C.

The IPCC SR1.5 gave a likely range for the human-induced warming rate

of 0.1°C to 0.3°C per decade in 2017, with a best estimate of0.2°C per

decade (Allen et al., 2018). Table3.1 lists the estimates of attributable

anthropogenic warming rate over the period 2010–2019 based on the

three studies that underpin the assessment of GSAT warming (Haustein

et al., 2017; Gillett et al., 2021; Ribes et al., 2021). Estimates from

Haustein et al. (2017), based on observed warming, and Ribes et al.

(2021), based on CMIP6 simulations constrained by observed warming,

are in good agreement. The Gillett et al. (2021) estimate, also based

on CMIP6 models, corresponds to a larger anthropogenic attributable

warming rate, because of a smaller warming rate attributed to natural

forcing than in Ribes et al. (2021). This disagreement does not support

a decrease in uncertainty compared to the SR1.5 assessment. So the

range for anthropogenic attributable surface temperature warming

rate of 0.1°C to 0.3°C per decade is again assessed to be likely, with a

best estimate of 0.2°C per decade.

3.3.1.2 Upper-air Temperature

Chapter2 assessed that the troposphere has warmed since at least

the 1950s, that it is virtually certain that the stratosphere has cooled,

and that there is medium conﬁdence that the upper troposphere

in the tropics has warmed faster than the near-surface since at

least 2001 (Section 2.3.1.2). The AR5 assessed that anthropogenic

forcings, dominated by greenhouse gases, likely contributed to the

warming of the troposphere since 1961 and that anthropogenic

forcings, dominated by the depletion of the ozone layer due to

ozone-depleting substances, very likely contributed to the cooling

of the lower stratosphere since 1979. Since AR5, understanding

of observational uncertainties in the radiosonde and satellite data

has improved with more available data and longer coverage, and

differences between models and observations in the tropical

atmosphere have been investigated further.

3.3.1.2.1 Tropospheric temperature

The AR5 assessed with low conﬁdence that most, though not

all, CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012)

models overestimated the observed warming trend in the tropical

troposphere during the satellite period 1979–2012, and that a third

to a half of this difference was due to an overestimate of the SST trend

during this period (Flato et al., 2013). Since AR5, additional studies

based on CMIP5 and CMIP6 models show that this warming bias in

tropospheric temperatures remains. Recent studies have investigated

the role of observational uncertainty, the model response to external

forcings, the inﬂuence of the time period considered, and the role

ofbiases in SST trends in contributing to this bias.

Several studies since AR5 have continued to demonstrate an

inconsistency between simulated and observed temperature trends

in the tropical troposphere, with models simulating more warming

than observations (Mitchell et al., 2013, 2020; Santer et al., 2017a,b;

McKitrick and Christy, 2018; Po-Chedley et al., 2021). Santer et al.

(2017b) used updated and improved satellite retrievals to investigate

model performance in simulating the tropical mid- to upper-

troposphere trends, and removed the inﬂuence of stratospheric cooling

by regression. These factors were found to reduce the size of the

discrepancy in mid- to upper-tropospheric temperature trends between

models and observations over the satellite era, but a discrepancy

remained. Santer et al. (2017a) found that during the late 20th century,

the discrepancies between simulated and satellite-derived mid- to

upper-tropospheric temperature trends were consistent with internal

variability, while during most of the early 21st century, simulated

tropospheric warming was signiﬁcantly larger than observed, which

they related to systematic deﬁciencies in some of the external forcings

used after year 2000 in the CMIP5 models. However, in CMIP6,

differences between simulated and observed upper-tropospheric

temperature trends persist despite updated forcing estimates (Mitchell

et al., 2020). Figure3.10 shows that CMIP6 models forced by combined

anthropogenic and natural forcings overestimate temperature trends

compared to radiosonde data (Haimberger et al., 2012) throughout

the tropical troposphere (Mitchell et al., 2020). Over the 1979–2014

period, models are more consistent with observations in the lower

troposphere, and least consistent in the upper troposphere around

200 hPa, where biases exceed 0.1°C per decade. Several studies using

CMIP6 models suggest that differences in climate sensitivity may

be an important factor contributing to the discrepancy between the

simulated and observed tropospheric temperature trends (McKitrick

and Christy, 2020; Po-Chedley et al., 2021), though it is difﬁcult to

deconvolve the inﬂuence of climate sensitivity, changes in aerosol

forcing and internal variability in contributing to tropospheric warming

biases (Po-Chedley et al., 2021). Another study found that the absence

of a hypothesized negative tropical cloud feedback could explain half

of the upper troposphere warming bias in one model (Mauritsen and

Stevens, 2015).

Mitchell et al. (2013) and Mitchell et al. (2020) found a smaller

discrepancy in tropical tropospheric temperature trends in models

forced with observed SSTs (see also Figure 3.10a), and CMIP5

modelsand observations were found to be consistent below 150 hPa

when viewed in terms of the ratio of temperature trends aloft to

those at the surface (Mitchell et al., 2013). Flannaghan et al. (2014)

and Tuel (2019) showed that most of the tropospheric temperature

trend difference between CMIP5 models and the satellite-based

observations over the 1970–2018 period is due to respective

differences in SST warming trends in regions of deep convection,

and Po-Chedley et al. (2021) showed that CMIP6 models with a more

444

Chapter 3 Human Inﬂuence on the Climate System

realistic SST simulation in the central and eastern Paciﬁc show

abetter performance than other models. Though systematic biases

still remain, this indicates that the bias in tropospheric temperature

warming in models is in part linked to surface temperature warming

biases, especially in the lower troposphere.

In summary, studies continue to ﬁnd that CMIP5 and CMIP6

model simulations warm more than observations in the tropical

mid- and upper-troposphere over the 1979–2014 period (Mitchell

et al., 2013, 2020; Santer et al., 2017a, b; Suárez-Gutiérrez et al.,

2017; McKitrick and Christy, 2018), and that overestimated surface

warming is partially responsible (Mitchell et al., 2013; Po-Chedley

et al., 2021). Some studies point to forcing errors in the CMIP5

simulations in the early 21st century as a possible contributor

(Mitchell et al., 2013; Sherwood and Nishant, 2015; Santer et al.,

2017a), but CMIP6 simulations use updated forcing estimates yet

generally still warm more than observations. Although accounting

for internal variability and residual observational errors can reconcile

models with observations to some extent (Suárez-Gutiérrez et al.,

2017; Mitchell et al., 2020), some studies suggest that climate

sensitivity also plays a role (Mauritsen and Stevens, 2015; McKitrick

and Christy, 2020; Po-Chedley et al., 2021). Hence, we assess with

medium conﬁdence that CMIP5 and CMIP6 models continue to

overestimate observed warming in the upper tropical troposphere

over the 1979–2014 period by at least 0.1°C per decade, in part

because of an overestimate ofthe tropical SST trend pattern over

this period.

The AR5 assessed as likely that anthropogenic forcings, dominated

by greenhouse gases, contributed to the warming of the troposphere

since 1961 (Bindoff et al., 2013). Since then, there has been further

progress in detecting and attributing tropospheric temperature

changes. Mitchell et al. (2020) used CMIP6 models to ﬁnd that the

main driver of tropospheric temperature changes is greenhouse gases.

Previous detection of the anthropogenic inﬂuence on tropospheric

warming may have overestimated uncertainties: Pallotta and Santer

(2020) found that CMIP5 climate models overestimate the observed

natural variability in global mean tropospheric temperature on time

scales of 5–20 years. Nevertheless, Santer et al. (2019) found that

stochastic uncertainty is greater for tropospheric warming than

stratospheric cooling because of larger noise and slower recovery time

from the Mount Pinatubo eruption inthe troposphere. The detection

time of the anthropogenic signal in the tropospheric warming can be

affected by both the model climate sensitivity and the model response

to aerosol forcing. Volcanic forcing is also important, as models that

do not consider the inﬂuence of volcanic eruptions in the early 21st

century overestimate the observed tropospheric warming since 1998

(Santer et al., 2014). Changes in the amplitude of the seasonal cycle

of tropospheric temperatures have also been attributed to human

inﬂuence. Santer et al. (2018) found that satellite data and climate

Temperature trend (°C/decade) Temperature trend (°C/decade) Temperature trend (°C/decade)

Figure3.10 | Observed and simulated tropical mean temperature trends through the atmosphere. Vertical proﬁles of temperature trends in the tropics (20°S–20°N)

Composite Homogenization (RICH) 1.7 (long dashed) and Radiosonde Observation Correction using Reanalysis (RAOBCORE) 1.7 (dashed) radiosonde datasets (Haimberger

et al., 2012), and in the ERA5/5.1 reanalysis (solid). Grey envelopes are centred on the RICH 1.7 trends, but show the uncertainty based on 32 RICH-observations members of

version1.5.1 of the dataset, which used version1.7.3 of the RICH software but with the parameters of version1.5.1. ERA5 was used as reference for calculating the adjustments

between 2010 and 2019, and ERA-Interim was used for the years before that. Red lines show trends in CMIP6 historical simulations from one realization of each of 60 models.

Blue lines show trends in 46 CMIP6 models that used prescribed, rather than simulated, sea surface temperatures (SSTs). Figure is adapted from Mitchell et al. (2020), their

Figure1. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

445

Human Inﬂuence on the Climate System Chapter 3

models driven by anthropogenic forcing show consistent amplitude

increases at mid-latitudes in both hemispheres, amplitude decreases

at high latitudes in the Southern Hemisphere, and small changes in

the tropics.

In summary, these studies conﬁrm the dominant role of human activities

in tropospheric temperature trends. We therefore assess that it is very

likely that anthropogenic forcing, dominated by greenhouse gases,

was the main driver of the warming of the troposphere since 1979.

3.3.1.2.2 Stratospheric temperature

The AR5 concluded that the CMIP5 models simulated a generally

realistic evolution of lower-stratospheric temperatures (Bindoff et al.,

2013; Flato et al., 2013), which was better than that of the CMIP3

models, in part because they generally include time-varying ozone

concentrations, unlike many of the CMIP3 models. Nonetheless, it was

noted that there was a tendency for the simulations to underestimate

stratospheric cooling compared to observations. Bindoff et al.

(2013) concluded that it was very likely that anthropogenic forcing,

dominated by stratospheric ozone depletion by chemical reactions

involving trace species known as ozone-depleting substances (ODS),

had contributed to the cooling of the lower stratosphere since 1979.

Increased greenhouse gases cause near-surface warming but cooling

of stratospheric temperatures.

For the lower stratosphere, a debate has been ongoing since AR5

between studies ﬁnding that models underestimate the cooling

ofstratospheric temperature (Santer et al., 2017b), in part because of

underestimated stratospheric ozone depletion (Eyring et al., 2013;

Young et al., 2013), and studies ﬁnding that lower stratospheric

temperature trends are within the range of observed trends (Young

et al., 2013; Maycock et al., 2018). Different observational data and

different time periods explain the different conclusions. Aquila et al.

(2016) used forced chemistry-climate models with prescribed SST to

investigate the inﬂuence of different forcings on global stratospheric

temperature changes. They found that in the lower stratosphere,

the simulated cooling trend due to increasing greenhouse gases

was roughly constant over the satellite era, while changes in ODS

concentrations ampliﬁed that stratospheric cooling trend during

the era of increasing ozone depletion up until the mid-1990s, with

a ﬂattening of the temperature trend over the subsequent period

over which stratospheric ozone has stabilized (Section 2.2.5.2).

Mitchell et al. (2020) showed that while models simulate realistic

trends in tropical lower-stratospheric temperature over the whole

1979–2014 period when compared with radiosonde data, they

tend to overestimate the cooling trend over the ozone depletion era

(1979–1997) and underestimate it over the ozone stabilization era

(1998–2014; Figure3.10b,c). They speculate that those disagreements

are due to poor representations of stratospheric ozone forcing.

Upper stratospheric temperature changes were not assessed in

the context of attribution or model evaluation in AR5, but this is

an area where there has been considerable progress over recent

years (Section 2.3.1.2.1). Simulated temperature changes in

chemistry-climate models show good consistency with the reprocessed

dataset from NOAA STAR but are less consistent with the revised

UK Met Ofﬁce record (Karpechko et al., 2018). The latter still shows

stronger cooling than simulated in chemistry-climate models (Maycock

et al., 2018). Reanalyses, which assimilate AMSU and SSU datasets,

indicate an upper-stratospheric cooling from 1979 to 2009 of about

3°C at 5 hPa and 4°C at 1 hPa that agrees well with the cooling in

simulations with prescribed SST and using CMIP5 forcings (Simmons

et al., 2014). Mitchell (2016) used regularized optimal ﬁngerprinting

techniques to carry out an attribution analysis of annual mid- to upper-

stratospheric temperature in response to external forcings. They found

that anthropogenic forcing has caused a cooling of approximately

2°C–3°C in the upper stratosphere over the period of 1979–2015, with

greenhouse gases contributing two thirds of this change and ozone

depletion contributing one third. They found alarge upper-stratospheric

temperature change in response to volcanic forcing (0.4°C–0.6°C

for Mount Pinatubo) but that change is still smaller than the lower-

stratospheric signal. Aquila et al. (2016) found that the cooling of the

middle and upper stratosphere after 1979 is mainly due to changes in

greenhouse gas concentrations. Volcanic eruptions and the solar cycle

were found not to affect long-term stratospheric temperature trends

but to have short-terminﬂuences.

In summary, based on the latest updates to satellite observations of

stratospheric temperature, we assess that simulated and observed

trends in global mean temperature through the depth of the

stratosphere are more consistent than based on previous datasets,

but some differences remain (medium conﬁdence). Studies published

since AR5 increase our conﬁdence in the simulated stratospheric

temperature response to greenhouse gas and ozone changes, and

support an assessment that it is extremely likely that stratospheric

ozone depletion due to ozone-depleting substances was the main

driver of the cooling of the lower stratosphere between 1979 and

the mid-1990s, as expected from physical understanding. Similarly,

revised observations and new studies support an assessment that it

is extremely likely that anthropogenic forcing, both from increases in

greenhouse gas concentrations and depletion of stratospheric ozone

due to ozone-depleting substances, was the main driver of upper-

stratospheric cooling since 1979.

Cross-Chapter Box3.1 | Global Surface Warming Over the Early 21st Century

Contributors: Christophe Cassou (France), Yu Kosaka (Japan), John C. Fyfe (Canada), Nathan P. Gillett (Canada), Ed Hawkins

(UnitedKingdom), Blair Trewin (Australia)

The AR5 found that the rate of global mean surface temperature (GMST) increase inferred from observations over the 1998–2012

period was lower than the rate of increase over the 1951–2012 period, and lower than the ensemble mean increase in historical

simulations from CMIP5 climate models extended by Representative Concentration Pathway (RCP) scenario simulations beyond 2005

446

Chapter 3 Human Inﬂuence on the Climate System

Cross-Chapter Box 3.1 (continued)

(Flato et al., 2013). This apparent slowdown of surface global warming compared to the 62-year rate was assessed with medium

conﬁdence to have been caused in roughly equal measure by a cooling contribution from internal variability and a reduced trend in

external forcing (particularly associated with solar and volcanic forcing) in AR5 based on expert judgement (Flato et al., 2013). InAR5

it was assessed that almost all CMIP5 simulations did not reproduce the observed slower warming, and that there was medium

conﬁdence that the trend difference from the CMIP5 ensemble mean was to a substantial degree caused by internal variability

with possible contributions from forcing error and model response uncertainty. This Cross-Chapter Box assesses new ﬁndings from

observational products and statistical and physical models on trends over the 1998–2012 period considered in AR5.

Updated observational and reanalyses datasets and comparison with model simulations

Since AR5, there have been version updates and new releases of most observational GMST datasets (Cross-Chapter Box 2.3). All the

updated products now available consistently ﬁnd stronger positive trends for 1998–2012 than those assessed in AR5 (Cowtan and Way,

2014; Karl et al., 2015; Hausfather et al., 2017; Medhaug et al., 2017; Simmons et al., 2017; Risbey et al., 2018). Simmonset al.(2017)

reported that the 1998–2012 GMST trends in the updated observational and reanalysis datasets available at that time ranged from

0.06°C to 0.14°C per decade, compared with the 0.05°C per decade on average reported in AR5, while the latest data products reported

in Chapter2 Table2.4 show GMST or global mean near-surface air temperature (GSAT) trends over that period ranging from 0.12°C to

0.14°C per decade. The lowest trend in Simmons et al. (2017) is from HadCRUT4, now superseded by HadCRUT5, which shows a trend

of 0.12°C per decade. The upward revision is mainly due to improved sea surface temperature(SST) datasets and inﬁlling of surface

temperature in locations with missing records in observational products, mainly in the Arctic (seeCross-Chapter Box2.3 for details).

With these updates, all the observed trends assessed here lie within the 10th–90th percentile range of the simulated trends in the

CMIP5 and CMIP6 simulations (Cross-Chapter Box3.1, Figure1a). This result is insensitive to whether model GSAT (based on surface

air temperature) or GMST (based on a blend of surface air temperature over land and sea ice and SST over open ocean) is used, and to

whether or not masking with the observational data coverage is applied. Therefore, the observed 1998–2012 trend is consistent with

both the CMIP5 or CMIP6 multi-model ensemble of trends over the same period (high conﬁdence).

Internal variability

All the observation-based GMST and GSAT trends are lower than the multi-model mean GMST and GSAT trends of both CMIP5 and

CMIP6 for 1998–2012 (Cross-Chapter Box3.1, Figure1a). This suggests a possible cooling contribution from internal variability during

this period. This is supported by initialized decadal hindcasts, which account for the phase of the multi-decadal modes of variability

(Sections 3.7.6 and 3.7.7), and which reproduce observed global mean SST and GSAT trends better than uninitialized historical

simulations (Guemas et al., 2013; Meehl et al., 2014).

Studies since AR5 identify Paciﬁc Decadal Variability (PDV) as the leading mode of variability associated with unforced decadal GSAT

ﬂuctuations, with additional inﬂuence from Atlantic Multi-decadal Variability (Annex IV.2.6, IV.2.7; Brown et al., 2015; Dai et al., 2015;

Steinman et al., 2015; Pasini et al., 2017). PDV transitioned from positive (ElNiño-like) to negative (La Niña-like) phases during the

slow warming period (Figure3.39f and Cross-Chapter Box3.1, Figure1c). Model ensemble members that capture the observed slower

decadal warming under transient forcing, and time segments of model simulations that show decadal GSAT decreases under ﬁxed

radiative forcing, also feature negative PDV trends (Cross-Chapter Box3.1, Figure1d; Meehl et al., 2011, 2013, 2014; Maher et al.,

2014; Middlemas and Clement, 2016), suggesting the inﬂuence of PDV. This is conﬁrmed by statistical models with the PDV-GSAT

relationship estimated from observations and model simulations (Schmidt et al., 2014; Meehl et al., 2016b; Hu and Fedorov, 2017),

selected ensemble members and time segments from model simulations where PDV by chance evolves in phase with observations

over the slow warming period (Huber and Knutti, 2014; Risbey et al., 2014), and coupled model experiments in which PDV evolution is

constrained to follow the observations (Kosaka and Xie, 2013, 2016; England et al., 2014; Watanabe et al., 2014; Delworth et al., 2015).

Part of the PDV trend may have been driven by anthropogenic aerosols (Smith et al., 2016); however, this result is model-dependent,

and internally-driven PDV dominates the forced PDV signal in the CMIP6 multi-model ensemble (Section 3.7.6). It is also notable

that there is large uncertainty in the magnitude of the PDV inﬂuence on GSAT across models (Deser et al., 2017a; C.-Y. Wang et al.,

2017) and among the studies cited above. In addition to PDV, contributions to the reduced warming trend from wintertime Northern

Hemisphere atmospheric internal variability, particularly associated with a trend towards the negative phase of the Northern Annular

Mode/North Atlantic Oscillation (Annex IV.2.1; Guan et al., 2015; Safﬁoti et al., 2015; Iles and Hegerl, 2017) or the Cold Ocean–Warm

Land (COWL) pattern (Molteni et al., 2017; Yang et al., 2020) have been suggested, leading to regional continental cooling over a large

part of Eurasia and North America (Cross-Chapter Box3.1, Figure1c; C. Li et al., 2015; Deser et al., 2017a; Gan et al., 2019).

Such internally-driven variation of decadal GSAT trends is not unique to the 1998–2012 period (Section 1.4.2.1; Lovejoy, 2014; Roberts

et al., 2015; Dai and Bloecker, 2019). Due to the nature of internal variability, surface temperature changes over the 1998–2012 period

447

Human Inﬂuence on the Climate System Chapter 3

Cross-Chapter Box 3.1 (continued)

are regionally- and seasonally-varying (Cross-Chapter Box3.1, Figure1c; Trenberth et al., 2014; Zang et al., 2019). Further, there was no

slowdown in the increasing occurrence of hot extremes over land (Kamae et al., 2014; Seneviratne et al., 2014; Imada et al., 2017). Thus,

the internally-driven slowdown of GSAT increase does not correspond to slowdown of warming everywhere on the Earth’ssurface.

Updated forcing

CMIP5 historical simulations driven by observed forcing variations ended in 2005 and were extended with RCP scenario simulations

for model-observation comparisons beyond that date. Post AR5 studies based on updated external forcing show that while no

net effect of updated anthropogenic aerosols is found on GSAT trends (Murphy, 2013; Gettelman et al., 2015; Oudar et al., 2018),

natural forcing by moderate volcanic eruptions in the 21st century (Haywood et al., 2014; Ridley et al., 2014; Santer et al., 2014)

and a prolonged solar irradiance minimum around 2009 compared to the normal 11-year cycle (Lean, 2018) yield anegative

contribution to radiative forcing, which was missing in CMIP5 (Figure2.2). This explains part of the difference between observed

and CMIP5 trends, as shown based on EMIC simulations (Huber and Knutti, 2014; Ridley et al., 2014), statistical and mathematical

models (Schmidt et al., 2014; Lean, 2018), and process-based climate models (Santer et al., 2014). However, in asingle climate

model study by Thorne et al. (2015), updating most forcings (greenhouse gas concentrations, solar irradiance, and volcanic and

anthropogenic aerosols) available when the study was done made no signiﬁcant difference to the 1998–2012 GMST trend from that

obtained with original CMIP5 forcing. Potential underestimation of volcanic (negative) forcing may have played arole (Outten et al.,

2015). In the multi-model ensemble mean, the 1998–2012 GMST trends are almost equal in CMIP5 and CMIP6 (Cross-Chapter

Box3.1, Figure1a), suggesting compensation by a higher transient climate response and equilibrium climate sensitivity in CMIP6

than CMIP5 (Section 7.5.6). To summarize, while there is medium conﬁdence that natural forcing that was missing in CMIP5

contributed to the difference of observed and simulated GMST trends, conﬁdence remains low in the quantitative contribution of

net forcing updates.

Energy budget and heat redistribution

The early 21st century slower warming was observed in atmospheric temperatures, but the heat capacity of the atmosphere is very

small compared to that of the ocean. Although there is noticeable uncertainty among observational products (H. Su et al., 2017) and

observation quality changes through time, global ocean heat content continued to increase during the slower surface warming period

(very high conﬁdence), at a rate consistent with CMIP5 and CMIP6 historical simulations (Sections 2.3.3.1, 3.5.1.3 and 7.2.2.2).

There is high conﬁdence that the Earth’s energy imbalance was larger in the 2000s than in the 1985–1999 period (Section 7.2.2.1),

consistent with accelerating ocean heat uptake in the past two decades (Section 3.5.1.3). Internal decadal variability is mainly associated

with redistribution of heat within the climate system (X.H. Yan et al., 2016; Drijfhout, 2018) while associated top of the atmosphere

radiation anomalies are weak (Palmer and McNeall, 2014). Heat redistribution in the top 350 m of the Indian and Paciﬁc Oceans has

been found to be the main contributor to reduced surface warming during the slower surface warming period (Lee et al., 2015; Nieves

et al., 2015; F. Liu et al., 2016), consistent with the simulated signature of PDV (England et al., 2014; Maher et al., 2018a; Gastineau

et al., 2019). Below 700 m, enhanced heat uptake over the slower surface warming period was observed mainly in the North Atlantic and

Southern Ocean (Chen and Tung, 2014), though whether this was a response to forcing or a unique signature of the slow GMST warming

has been questioned (W. Liu et al., 2016).

Summary and implications

With updated observation-based GMST datasets and forcing, improved analysis methods, new modelling evidence and deeper

understanding of mechanisms, there is very high conﬁdence that the slower GMST and GSAT increase inferred from observations in

the 1998–2012 period was a temporary event induced by internal and naturally-forced variability that partly offset the anthropogenic

warming trend over this period. Nonetheless, the heating of the climate system continued during this period, as reﬂected in the

continued warming of the global ocean (very high conﬁdence) and in the continued rise of hot extremes over land (medium conﬁdence).

Considering all the sources of uncertainties, it is impossible to robustly identify a single cause of the early 2000s slowdown (Hedemann

et al., 2017; Power et al., 2017); rather, it should be interpreted as due to a combination of several factors (Huber and Knutti, 2014;

Schmidt et al., 2014; Medhaug et al., 2017).

A major ElNiño event in 2014–2016 led to three consecutive years of record annual GMST with unusually strong heat release from

the North-western Paciﬁc Ocean (Yin et al., 2018), which marked the end of the slower warming period (Hu and Fedorov, 2017; J. Su

et al., 2017; Cha et al., 2018). The past ﬁve-year period (2016–2020) is the hottest ﬁve-year period in the instrumental record up to

2020 (high conﬁdence). This rapid warming was accompanied by a PDV shift toward its positive phase (J. Su et al., 2017; Cha et al.,

2018). A higher rate of warming following the 1998–2012 period is consistent with the predictions in AR5 Box9.2 (Flato et al., 2013)

and with a statistical prediction system (Sévellec and Drijfhout, 2018). Initialized decadal predictions show higher GMST trends in the

early 2020s compared to uninitialized simulations (Thoma et al., 2015; Meehl et al., 2016a).

448

Chapter 3 Human Inﬂuence on the Climate System

Cross-Chapter Box 3.1 (continued)

While some recent studies ﬁnd that internal decadal GSAT variability may become weaker under GSAT warming, associated in part

with reduced amplitude PDV (Section 4.5.3.5; Brown et al., 2017), the weakening is small under a realistic range of warming. A

large volcanic eruption would temporarily cool GSAT (Cross-Chapter Box4.1). Thus, there is very high conﬁdence that reduced and

increased GMST and GSAT trends at decadal time scales will continue to occur in the 21st century (Meehl et al., 2013; Roberts et al.,

2015; Medhaug and Drange, 2016). However, such internal or volcanically forced decadal variations in GSAT trend have little effect on

centennial warming (England et al., 2015; Cross-Chapter Box4.1).

Cross-Chapter Box3.1, Figure1 | 15-year trends of global surface temperature for 1998–2012 and 2012–2026. (a, b) GSAT and GMST trends for

1998–2012 (a) and 2012–2026 (b). Histograms are based on GSAT in historical simulations of CMIP6 (red shading, extended by SSP2-4.5) and CMIP5 (grey shading;

extended by RCP4.5). Filled and open diamonds at the top represent multi-model ensemble means of GSAT and GMST trends, respectively. Diagonal lines show histograms

of HadCRUT5.0.1.0. Triangles at the top of (a) represent GMST trends from Berkeley Earth, GISTEMP, Kadow et al. (2020) and NOAAGlobalTemp-Interim, and the GSAT

trend from ERA5. Selected CMIP6 members whose 1998–2012 trends are lower than the HadCRUT5.0.1.0 mean trend are indicated by purple shading (a) and (b). In (a),

model GMST and GSAT, and ERA5 GSAT are masked to match HadCRUT data coverage. (c–d) Trend maps of annual near-surface temperature for 1998–2012 based on

HadCRUT5.0.1.0 mean (c), and composited surface air temperature trends of subsampled CMIP6 simulations (d) with GSAT trends in the purple shaded area in (a). In

(c), cross marks indicate trends that are not signiﬁcant at the 10% level based on t-tests with serial correlation taken into account. The ensemble size used for each of the

histograms and the trend composite is indicated at the top right of each of the panels (a, b, d). Model ensemble members are weighted with the inverse of the ensemble

size of the same model, so that each model is equally weighted. Further details on data sources and processing are available inthechapter data table (Table3.SM.1).

449

Human Inﬂuence on the Climate System Chapter 3

3.3.2 Precipitation, Humidity and Streamﬂow

3.3.2.1 Paleoclimate Context

A fact hindering detection and attribution studies in precipitation

and other hydrological variables is the large internal variability of

these ﬁelds relative to the anthropogenic signal. This low signal-to-

noise ratio hinders the emergence of the anthropogenic signal from

natural variability. Moreover, the sign of the change depends on

location and time of the year. Paleoclimate records provide valuable

context for observed trends in the 20th and 21st century and assist

with the attribution of these trends to human inﬂuence (see also

Section 2.3.1.3.1). By nature, hydrological proxy data represent

regional conditions, but taken together can represent large-scale

patterns. Asan example of how paleorecords have helped assessing

the origin of changes, we consider some, mainly subtropical, regions

which have experienced systematic drying in recent decades (see also

Section 8.3.1.3). Paleoclimate simulations of monsoons are assessed

in Section 3.3.3.2.

Records of tree ring width have provided evidence that recent

prolonged dry spells in the Levant and Chile are unprecedented in the

last millennium (high conﬁdence) (Cook et al., 2016a; Garreaud et al.,

2017). East Africa has also been drying in recent decades (Rowell et al.,

2015; Hoell et al., 2017), a trend that is unusual in the context of the

sedimentary paleorecord spanning the last millennium (Tierney et al.,

2015). This may be a signature of anthropogenic forcing but cannot

yet be distinguished from natural variability (Hoell et al., 2017; Philip

et al., 2018). Likewise, tree rings indicate that the 2012–2014 drought

in the south-western United States was exceptionally severe in the

context of natural variability over the last millennium, and may have

been exacerbated by the contribution of anthropogenic temperature

rise (medium conﬁdence) (Grifﬁn and Anchukaitis, 2014; Williams

et al., 2015). Furthermore, Williams et al. (2020) used a combination

of hydrological modelling and tree-ring reconstructions to show that

the period from 2000 to 2018 was the driest 19-year span in south-

western North America since the late 1500s. Nonetheless, tree rings

also indicate the presence of prolonged megadroughts in western

North America throughout the last millennium that were more severe

than 20th and 21st century events (high conﬁdence) (Cook et al., 2004,

2010, 2015). These were associated with internal variability (Coats

et al., 2016; Cook et al., 2016b) and indicate that large-magnitude

changes in the water cycle may occur irrespective of anthropogenic

inﬂuence (see also McKitrick and Christy, 2019).

Paleoclimate records also allow for model evaluation under

conditions different from present-day. The AR5 concluded that

models can successfully reproduce to ﬁrst-order patterns of past

precipitationchanges during the Last Glacial Maximum (LGM) and

mid-Holocene, though simulated precipitation changes during the

Northern Europe W. & C. Europe Mediterranean Sahara/Sahel West Africa

400

200

400

600

800

mm yr 1

Precipitation change in the Mid-Holocene

PMIP3 models

AWI-ESM-1-1-LR

CESM2

EC-Earth3-LR

FGOALS-f3-L

FGOALS-g3

GISS-E2-1-G

HadGEM3-GC31-LL

INM-CM4-8

IPSL-CM6A-LR

MIROC-ES2L

MPI-ESM1-2-LR

MRI-ESM2-0

NESM3

NorESM1-F

NorESM2-LM

UofT-CCSM-4

Reconstructions

Figure3.11 | Comparison between simulated annual precipitation changes and pollen-based reconstructions in the mid-Holocene (6000 years ago). The

area-averaged changes relative to the pre-industrial control simulations over ﬁve regions (Iturbide et al., 2020) as simulated by CMIP6 models (individually identiﬁable, one

ensemble member per model) and CMIP5 models (blue) are shown, stretching from the tropics to high-latitudes. All regions contain multiple quantitative reconstructions of

changes relative to present day; their interquartile range are shown by boxes and with whiskers for their full range excluding outliers. Figure is adapted from Brierley et al. (2020).

Further details on data sources and processing are available inthe chapter data table (Table3.SM.1).

450

Chapter 3 Human Inﬂuence on the Climate System

mid-Holocene tended to be underestimated (Flato et al., 2013).

Further analysis of CMIP5 models conﬁrmed these results but

has also revealed systematic offsets from the paleoclimate record

(DiNezio and Tierney, 2013; Hargreaves and Annan, 2014; Harrison

et al., 2014, 2015; Bartlein et al., 2017; Scheff et al., 2017; Tierney

et al., 2017). Harrison et al. (2014) concluded that CMIP5 models

do not perform better in simulating rainfall during the LGM and

mid-Holocene than earlier model versions despite higher resolution

and complexity. However, prescribing changes in vegetation and dust

was found to improve the match to the paleoclimate record (Pausata

et al., 2016; Tierney et al., 2017) suggesting that vegetation feedbacks

in the CMIP5 models may be too weak (low conﬁdence) (Hopcroft

et al., 2017). Brierley et al. (2020) compared the latitudinal gradient

of annual precipitation changes in the European–African sector

simulated by CMIP6 models for the mid-Holocene with pollen-based

reconstructions and showed that models generally reproduce the

direction of changes seen in the reconstructions (Figure3.11). They

do not show a robust signal in area averaged rainfall over most

European regions where quantitative reconstructions exist, which

is not incompatible with reconstructions. Over the Sahara/Sahel and

West Africa regions, where reconstructions suggest positive anomalies

during the mid-Holocene, both CMIP5 and CMIP6 models also

simulate a rainfall increase, but it is much weaker (see also Section

3.3.3.2). Overall, however, large discrepancies remain between

simulations andreconstructions.

Liu et al. (2018) evaluated the soil moisture changes that occurred

during the LGM and concluded that the multi-model median from

CMIP5 is consistent with available paleo-records in some regions,

but not in others. CMIP5 models accurately reproduce an increase

in moisture in the western United States, related to an intensiﬁed

winter storm track and decreased evaporative demand (Oster et al.,

2015; Ibarra et al., 2018; Lora, 2018). On the other hand, CMIP5

models show a wide variety of responses in the tropical Indo-Paciﬁc

region, with only a few matching the pattern of change inferred from

the paleoclimate record (DiNezio and Tierney, 2013; DiNezio et al.,

2018). The variable response across models is related to the effect

of the exposure of the tropical shelves during glacial times, which

CMIP5

CMIP6

RSS

ERA5

Figure3.12 | Total column water vapour trends (% per decade) for the period 1988–2019 averaged over the near-global oceans (50°S–50°N). Theﬁgure

shows satellite data (RSS) and ERA5.1 reanalysis, as well as CMIP5 (blue) and CMIP6 (red) historical simulations. All available ensemble members were used (see Section 3.2).

Fits to the model trend probability distributions were performed with kernel density estimation. Figure is updated from Santer et al. (2007). Further details on data sources and

processing are available in the chapter data table (Table3.SM.1).

451

Human Inﬂuence on the Climate System Chapter 3

variously intensiﬁes or weakens convection in the rising branch of

the Walker cell, depending on model parameterization (DiNezio

et al., 2011). For the Last Interglacial, CMIP6 models reproduce

theproxy-based increased precipitation relative to pre-industrial in the

North African, South Asian and North American regions, but not in

Australia (Scussolini et al., 2019).

In summary, there is medium conﬁdence that CMIP5 and CMIP6

models can reproduce broad aspects of precipitation changes during

paleo reference periods, but large discrepancies remain. Further

assessment of model performance and comparison between CMIP5

and CMIP6 during past climates can be found in Section 3.8.2.1.

3.3.2.2 Atmospheric Water Vapour

The AR5 concluded that an anthropogenic contribution to increases

in speciﬁc humidity is found with medium conﬁdence at and nearthe

surface. A levelling off of atmospheric water vapour over land in

thelast two decades that needed better understanding, and remaining

observational uncertainties, precluded a more conﬁdent assessment

(Bindoff et al., 2013). Sections 4.5.1.3 and 8.3.1.4 show that there

have been signiﬁcant advances in the understanding of the processes

controlling land surface humidity. In particular, there has been afocus

on the role of oceanic moisture transport and land-atmosphere

feedbacks in explaining the observed trends in relative humidity.

Water vapour is the most important natural greenhouse gas and its

amount is expected to increase in a global warming context leading

to further warming. Particularly important are changes in the upper

troposphere because there water vapour regulates the strength of

the water-vapour feedback (Section 7.4.2.2). CMIP5 models have

been shown to have a wet bias in the tropical upper troposphere

and a dry bias in the lower troposphere, with the former bias and

model spread being larger than the latter (Jiang et al., 2012; Tian

et al., 2013). Tian et al. (2013) also showed that in comparison to the

AIRS speciﬁc humidity, CMIP5 models have the well-known double

Inter-tropical Convergence Zone (ITCZ) bias in the troposphere from

1000 hPa to 300 hPa, especially in the tropical Paciﬁc. Water vapour

biases in models are dominated by errors in relative humidity

throughout the troposphere, which are in turn closely related to

errors in large scale circulation; temperature errors dominate near

the tropopause (Takahashi et al., 2016). Section 7.4.2.2 discusses

this topic in more detail for CMIP6 models. However, Schröder et al.

(2019) show that the majority of well-established water vapour

records are affected by inhomogeneity issues and thus should

be used with caution (see also Section 2.3.1.3.3). A comparison

of trends in column water vapour path for 1998–2019 in satellite

data, a reanalysis, CMIP5 and CMIP6 simulations averaged over

the near-global ocean reveals that while on average model trends

are higher than those in observations and a reanalysis, the latter lie

within the multi-model range (Figure3.12).

The detection and attribution of tropospheric water vapour changes

can be traced back to Santer et al. (2007), who used estimates of

atmospheric water vapour from the satellite-based Special Sensor

Microwave Imager (SSM/I) and from CMIP3 historical climate

simulations. They provided evidence of human-induced moistening

of the troposphere, and found that the simulated human ﬁngerprint

pattern was detectable at the 5% level by 2002 in water vapour

satellite data (from 1988 to 2006). The observed changes matched the

historical simulations forced by greenhouse gas changes and other

anthropogenic forcings, and not those due to natural variability alone.

Then, Santer et al. (2009) repeated this study with CMIP5 models,

and found that the detection and attribution conclusions were not

sensitive to model quality. These results demonstrate that the human

ﬁngerprint is governed by robust and basic physical processes, such

as the water vapour feedback. Finally, Chung et al. (2014) extended

this line of research by focusing on the global-mean water vapour

content in the upper troposphere. Using satellite-based observations

and sets of CMIP5 climate simulations run under various climate-

forcing options, they showed that the observed moistening trend of the

upper troposphere over the 1979–2005 period could not be explained

by internal variability alone, but is attributable to a combination of

anthropogenic and natural forcings. This increase in water vapour is

accompanied by a reduction in mid-tropospheric relative humidity

and clouds in the subtropics and mid-latitude in both models and

observations related to changes in the Hadley cell (Section3.3.3.1.1;

Lau and Kim, 2015).

Dunn et al. (2017) conﬁrmed earlier ﬁndings that global mean surface

relative humidity increased between 1973 and 2000, followed by

a steep decline (also reported in Willett et al., 2014) until 2013, and

speciﬁc humidity correspondingly increased and then remained

approximately constant (see also Section 2.3.1.3.2), with none of the

CMIP5 models capturing this behaviour. They noted biases in the mean

state of the CMIP5 models’ surface relative humidity (and ascribed

the failure to the representation of land surface processes and their

response to CO2 forcing), concluding that these biases preclude any

detection and attribution assessment. On the other hand, Byrne and

O’Gorman (2018) showed that the positive trend in speciﬁc humidity

continued in recent years and can be detected over land and ocean

from 1979 to 2016. Moreover, they provided a theory suggesting that

the increase in annual surface temperature and speciﬁc humidity as well

as the decrease in relative humidity observed over land are linked to

warming over the neighbouring ocean. They also pointed out that the

negative trend in relative humidity over land regions is quite uncertain

and requires further investigation. A recent study has also identiﬁed

an anthropogenically-driven decrease in relative humidity over the

Northern Hemisphere mid-latitude continents in summer between 1979

and 2014, which was underestimated by CMIP5 models (Douville and

Plazzotta, 2017). Furthermore, in a modelling study Douville et al. (2020)

showed that this decrease in boreal summer relative humidity over mid-

latitudes is related not only to global ocean warming, but also to the

physiological effect of CO2 on plants in the land surface model.

In summary, we assess that it is likely that human inﬂuence has

contributed to moistening in the upper troposphere since 1979.

Also, there is medium conﬁdence that human inﬂuence contributed

to a global increase in annual surface speciﬁc humidity, and medium

conﬁdence that it contributed to a decrease in surface relative humidity

over mid-latitude Northern Hemisphere continents during summertime.

452

Chapter 3 Human Inﬂuence on the Climate System

3.3.2.3 Precipitation

AR5 concluded that there was medium conﬁdence that human

inﬂuence had contributed to large-scale precipitation changes over

land since 1950, including an increase in the Northern Hemisphere

mid- to high latitudes. Moreover, AR5 concluded that observational

uncertainties and challenges in precipitation modelling precluded

amore conﬁdent assessment (Bindoff et al., 2013). Overall, it found

that large-scale features of mean precipitation in CMIP5 models were

in modest agreement with observations, but there were systematic

errors in the tropics (Flato et al., 2013).

Since AR5, X. Li et al. (2016b) found that CMIP5 models simulate the

large scale patterns of annual mean land precipitation and seasonality

well, as well as reproducing qualitatively the observed zonal mean

land precipitation trends for the period 1948–2005: models capture

the drying trends in the tropics and at 45°S and the wetting trend in

the Northern Hemisphere mid- to high latitudes, but the amplitudes

of the changes are much smaller than observed. Land precipitation

was found to show enhanced seasonality in observations (Chou

et al., 2013), qualitatively consistent with the simulated response to

anthropogenic forcing (Dwyer et al., 2014). However, models do not

appear to reproduce the zonal mean trends in the magnitude of the

seasonal cycle over the period 1948–2005, nor the two-dimensional

distributions of trends of annual precipitation and seasonality over

land, but differences may be explainable by internal variability

(X.Liet al., 2016b). However, observed trends in seasonality depend

on data set used (X.Li et al., 2016b; Marvel et al., 2017), and Marvel

et al. (2017) found that observed changes in the annual cycle phase

are consistent with model estimates of forced changes. These

phase changes are mainly characterized by earlier onset of the wet

season on the equatorward ﬂanks of the extratropical storm tracks,

particularly in the Southern Hemisphere. Box8.2 assesses regional

changes in water cycle seasonality.

The CMIP5 models have also been shown to adequately simulate

the mean and interannual variability of the global monsoon

(Section3.3.3.2), but maintain the double ITCZ bias in the equatorial

Paciﬁc (Lee and Wang, 2014; Tian, 2015; Ni and Hsu, 2018). Despite

the ITCZ bias, CMIP5 models have been used to detect in reanalysis

asouthward shift in the ITCZ prior to 1975, followed by a northward

shift in the ITCZ after 1975, in response to forced changes in

inter-hemispheric temperature contrast (Sections 3.3.1.1 and 8.3.2.1,

and Figure8.11; Bonﬁls et al., 2020; Friedman et al., 2020). CMIP5

models perform better than CMIP3 models, in particular regarding the

global monsoon domain and intensity (Lee and Wang, 2014).

In observations at time scales less than a day intermittent rainfall

ﬂuctuations dominate variability, but CMIP5 models systematically

underestimate them (Covey et al., 2018). Moreover, as noted in

previous generation models, CMIP5 models produce rainfall too early

in the day (Covey et al., 2016). Also, models overpredict precipitation

frequency but have weaker intensity, although comparison with

observed datasets is complex as there are large differences in intensity

among them (Herold et al., 2016; Pendergrass and Deser, 2017;

Trenberth et al., 2017). Regarding trends in precipitation intensity,

models have also been shown to reproduce the compensation

between increasing heavy precipitation and decreasing light to

moderate rainfall (Thackeray et al., 2018b), acharacteristic found

in the observational record (Gu and Adler, 2018). Regional model

performance is further assessed in Chapter8 and the Atlas, while

precipitation extremes are considered in Chapter11.

The simulation of annual mean rainfall patterns in the CMIP6 models

reveals minor improvements compared to those in CMIP5

models(Figure3.13). The persistent biases include the double ITCZ in

the tropical Paciﬁc (seen as bands of excessive rainfall on both sides

of theequatorial Paciﬁc in Figure3.13b,d) and the southward-shifted

ITCZin the equatorial Atlantic, which have been linked to the meridional

pattern of SST bias (S. Zhou et al., 2020) and the reduced sensitivity of

precipitation to local SST (Good et al., 2021). Tian and Dong (2020) also

found that all three generations of CMIP models share similar systematic

annual mean precipitation errors in the tropics, but that the double

ITCZ bias is slightly reduced in CMIP6 models in comparison to CMIP3

and CMIP5 models. They also found some improvement in the overly

intense Indian ocean ITCZ and the too dry South American continent

except over the Andes. Fiedler et al. (2020) identiﬁed improvements in

the tropical mean spatial correlations and root mean square error of

the climatology as well as in the day-to-day variability, but found little

change across CMIP phases in the double ITCZ bias and diurnal cycle.

The CMIP6 models reproduce better the domain and intensity of the

global monsoon (see Section 3.3.3.2). Moreover, CMIP6 models better

represent the storm tracks (Priestley et al., 2020; also Section 3.3.3.3),

thereby reducing the precipitation biases in the North Atlantic and

mid-latitudes of the Southern Hemisphere (Figure3.13b,d). Asaresult,

pattern correlations between simulated and observed annual mean

precipitation range between 0.80 and 0.92 for CMIP6 models, compared

to a range of 0.79 to 0.88 for CMIP5 (Bock et al., 2020). This relative

improvement may be related to increased model resolution, as found

when comparing biases in the mean of the HighResMIP models with

the mean of the corresponding lower-resolution versions of the same

models (see Figure3.13e,f), particularly in the tropics and extratropical

storm tracks. Consistent with this, a recent study using several coupled

models showed that increasing the atmospheric resolution leads to

astrong decrease in the precipitation bias in the tropical Atlantic ITCZ

(see further discussion in Section 3.8.2.2; Vannière et al., 2019). Based

on these results we assess that despite some improvements, CMIP6

models still have deﬁciencies in simulating precipitation patterns,

particularly over the tropical ocean (high conﬁdence).

Recent studies comparing observations and CMIP5 simulations have

shown that tropical volcanic eruptions induce a signiﬁcant reduction

in global precipitation, particularly over the wet tropics, including the

global monsoon regions (Iles and Hegerl, 2014; Paik and Min, 2017;

Paik et al., 2020a). Reconstructions and modelling studies also suggest

a distinct remote inﬂuence of volcanic forcing such that large volcanoes

erupting in one hemisphere can enhance monsoon precipitation in the

other hemisphere (F. Liu et al., 2016; Zuo et al., 2019). The climatic effect

of volcanic eruptions is further assessed in Cross-Chapter Box4.1.

An intensiﬁcation of the wet–dry zonal mean patterns, consisting of

the wet tropical and mid-latitude bands becoming wetter, and the dry

subtopics becoming drier is expected in response to greenhouse gas

and ozone changes (Section 8.2.2.2). However, detecting these changes

453

Human Inﬂuence on the Climate System Chapter 3

No robust biasRobust bias Conflicting signals

Colour No robust model improvement

Figure3.13 | Annual-mean precipitation rate (mm day–1) for the period 1995–2014. (a) Multi-model (ensemble) mean constructed with one realization of the CMIP6

historical experiment from each model. (b) Multi-model mean bias, deﬁned as the difference between the CMIP6 multi-model mean and the precipitation analysis from the

Global Precipitation Climatology Project (GPCP) version2.3 (Adler et al., 2003). (c) Multi-model mean of the root mean square error calculated over all months separately and

averaged with respect to the precipitation analysis from GPCP version2.3. (d) Multi-model mean bias, calculated as the difference between the CMIP6 multi-model mean and

the precipitation analysis from GPCP version2.3. Also shown is the multi-model mean bias as the difference between the multi-model mean of (e)high resolution and (f) low-

resolution simulations of four HighResMIP models and the precipitation analyses from GPCP version2.3. Uncertainty is represented using the advanced approach. No overlay

indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on sign of change; diagonal lines

indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed lines indicate regions with conﬂicting

signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on the sign of change. For more information on the advanced

approach, please refer to the Cross-Chapter Box Atlas.1. Dots in panel (e) mark areas where the bias in high resolution versions of the HighResMIP models is not lower in at least

three out of four models than in the corresponding low-resolution versions. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

454

Chapter 3 Human Inﬂuence on the Climate System

is complicated by model errors in locating the main features of rainfall

patterns. To deal with this issue, Marvel and Bonﬁls (2013) identiﬁed

in each CMIP5 historical simulation the latitudinal peaks and troughs

of the rainfall latitudinal patterns, measured the ampliﬁcation and shift

of these patterns in a pattern-based ﬁngerprinting study, and found

that the simultaneous ampliﬁcation and shift in zonal precipitation

patterns are detectable in Global Precipitation Climatology Project

(GPCP) observations over the 1979–2012 period. Similarly, Bonﬁls

et al. (2020) found that the intensiﬁcation of wet–dry zonal patterns

identiﬁed in CMIP5 historical simulations is detectable in reanalyses

over the 1950–2014 period (see also Figure8.11).

Based on long-term island precipitation records, Polson et al. (2016)

identiﬁed signiﬁcant increases in precipitation in the tropics and

decreases in the subtropics, which are consistent with those simulated

by the CMIP5 models. Moreover, results from Polson and Hegerl (2017)

give support to an intensiﬁcation of the water cycle according to the

wet-gets-wetter, dry-gets-drier paradigm over tropical land areas as

well. Other studies suggest that this paradigm does not necessarily hold

over dry regions where moisture is limited (see also Section8.2.2.1;

Greve et al., 2014; Kumar et al., 2015). Polson and Hegerl (2017)

explained this discrepancy by taking into account the seasonal and

interannual movement of the regions (Allan, 2014). A follow-up study

using CMIP6 models also found that the observed strengthening

contrast of precipitation over wet and dry regions was detectable,

although the increase was signiﬁcantly larger in observations than in

the multi-model mean. The change was attributed to a combination

of anthropogenic and natural forcings, with anthropogenic forcings

detectable in multi-signal analyses (Figure3.14; Schurer et al., 2020).

Global land precipitation has likely increased since the middle of the

20th century (medium conﬁdence), while there is low conﬁdence

in trends in land data prior to 1950 and over the ocean during the

satellite era due to disagreement between datasets (Section2.3.1.3.4).

Figure3.14 | Wet (a) and dry (b) region tropical mean (30°S–30°N) annual precipitation anomalies. Observed data are shown with black lines (GPCP), ERA5

reanalysis is shown in grey, single model simulations are shown with light blue/red lines (CMIP6), and multi-model mean results are shown with dark blue/red lines (CMIP6). Wet

and dry region annual anomalies are calculated as the running mean over 12 months relative to a 1988–2020 base period. The regions are deﬁned as the wettest third and driest

third of the surface area, calculated for the observations and for each model separately for each season (following Polson and Hegerl, 2017). Scaling factors (c, d) are calculated

for the combination of the wet and dry region mean, where the observations, reanalysis and all the model simulations are ﬁrst standardized using the mean standard deviation of

the pre-industrial control simulations. Two total least squares regression methods are used: noise in variables (following Polson and Hegerl, 2017) which estimates abest estimate

and a 5–95% conﬁdence interval using the pre-industrial controls (circle and thick green line) and the pre-industrial controls with double the variance (thin green line); and

abootstrap method (DelSole et al., 2019) (5–95% conﬁdence interval shown with a purple line and best estimate with a circle). Panel (c) shows results for GPCP and panel (d)

for ERA5. Figure is adapted from Schurer et al. (2020). Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

455

Human Inﬂuence on the Climate System Chapter 3

Figure 3.15a shows the time evolution of the global mean land

precipitation since 1950, as well as the trend during the period. Adler

et al. (2017) found no signiﬁcant trend in the global mean precipitation

during the satellite era, consistent with model simulations (Wu

et al., 2013) and physical understanding of the energy budget (Section

8.2.1). This has been suggested to be due to the negative effect of

anthropogenic sulphate aerosol that opposed the positive inﬂuence of

rising global mean temperatures due to greenhouse gases (Salzmann,

2016; Richardson et al., 2018). The precipitation change expected from

ocean warming is also partly offset by the fast atmospheric adjustment

to increasing greenhouse gases (Section 8.2.1). Over the ocean, the

negligible trend may be due to the cancelling effects of CO2 and

aerosols (Richardson et al.,2018).

A gridpoint based analysis of annual precipitation trends over

land regions since 1901 (Knutson and Zeng, 2018) comparing

observed and simulated trends found that detectable anthropogenic

increasing trends have occurred prominently over many mid- to

high-latitude regions of the Northern Hemisphere and subtropics

of the Southern Hemisphere. The observed trends in many cases are

signiﬁcantly stronger than modelled in the CMIP5 historical runs for

the 1901–2010 period (though not for 1951–2010), which may be

due to disagreement between observed datasets (Section 2.3.1.3.4),

and/or suggest possible deﬁciencies in models.

The observed precipitation increase in the Northern Hemisphere high

latitudes over the period 1966–2005 was attributed to anthropogenic

forcing by a study using CMIP5 models (Wan et al., 2015) supporting

the AR5 assessment. Initial results from CMIP6 also support the role

of anthropogenic forcing in the precipitation increase observed in

Northern Hemisphere high latitudes (see Figure3.15c): the observed

positive trend detected for the band 60°N–90°N can only be

° ° ° °

° °° °

GHCN Climatology 1950-2014

Figure3.15 | Observed and simulated time series of anomalies in zonal average annual mean precipitation. (a), (c–f) Evolution of global and zonal average annual

mean precipitation (mm day–1) over areas of land where there are observations, expressed relative to the base period of 1961–1990, simulated by CMIP6 models (one ensemble

member per model) forced with both anthropogenic and natural forcings (brown) and natural forcings only (green). Multi-model means are shown in thick solid lines and shading

shows the 5–95% conﬁdence interval of the individual model simulations. The data is smoothed using a low pass ﬁlter. Observations from three different datasets are included:

gridded values derived from Global Historical Climatology Network (GHCN version2) station data, updated from Zhang et al. (2007), data from the Global Precipitation Climatology

Product (GPCP L3 version2.3, Adler et al. (2003)) and from the Climate Research Unit (CRU TS4.02, Harris et al. (2014)). Also plotted are boxplots showing interquartile and 5–95%

ranges of simulated trends over the period for simulations forced with both anthropogenic and natural forcings (brown) and natural forcings only (blue). Observed trends for each

observational product are shown as horizontal lines. Panel (b) shows annual mean precipitation rate (mm day–1) of GHCN version2 for the years 1950–2014 over land areas used

to compute the plots. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

456

Chapter 3 Human Inﬂuence on the Climate System

reproduced when anthropogenic forcing is included, although models

tend to simulate overall a larger positive trend. A similar positive trend,

but less signiﬁcant, is also detected between 30°N–60°N, while in the

southern mid-latitudes no trend is simulated (see Figure3.15d, f).

For the Southern Hemisphere extratropics, Solman and Orlanski (2016)

found that the observed summertime rainfall increase over high

latitudes and decrease over mid-latitudes over the period 1979–2010

are quasi-zonally symmetric and related to changes in eddy activity. The

latter were in turn found to be associated with the poleward shift of

the westerlies due mostly to ozone depletion. Positive rainfall trends

in the subtropics, particularly over south-eastern South America (see

alsoSection 10.4.2.2) and northern and central Australia, have been also

attributed to stratospheric ozone depletion (Kang et al., 2011; Gonzalez

et al., 2014) and greenhouse gases (Vera and Díaz, 2015; Saurral et al.,

2019). During austral winter, wetting at high latitudes and drying at

mid-latitudes are not zonally homogenous, due to both changes in eddy

activity and increased lower troposphere humidity. Solman and Orlanski

(2016) associated these climate changes with increases in greenhouse

gas concentration levels. Recently, Blazquez and Solman (2017) have

shown that CMIP5 models represent very well the dynamical forcing

and the frequency of frontal precipitation in the Southern Hemisphere

winter extratropics, but the amount of precipitation due to fronts is

overestimated. Chapters 10 and 11 validate in more detail the simulation

of fronts in climate models (Sections 10.3.3.4.4 and 11.7.2.3).

Over the ocean, observations show coherent large-scale patterns of

fresh ocean regions becoming fresher and salty ocean regions saltier

across the globe, which has been related through modelling studies

to changes in precipitation minus evaporation and is consistent with

the wet-gets-wetter, dry-gets-drier paradigm (see Sections 3.5.2.2

and 8.2.2.1; Durack et al., 2012, 2013; Skliris et al., 2014; Durack,

2015; Hegerl et al., 2015; Levang and Schmitt, 2015; Zika et al., 2015;

Grist et al., 2016; Cheng et al., 2020).

Overall, studies published since AR5 provide further evidence of an

anthropogenic inﬂuence on precipitation, and therefore we now assess

that it is likely that human inﬂuence has contributed to large-scale

precipitation changes observed since the mid-20th century. New

attribution studies strengthen previous ﬁndings of a detectable increase

in mid to high latitude land precipitation over the Northern Hemisphere

(high conﬁdence). There is medium conﬁdence that human inﬂuence

has contributed to a strengthening of the zonal mean wet tropics-dry

subtropics contrast, and that tropical rainfall changes follow the wet-

gets-wetter, dry-gets-drier paradigm. There is also medium conﬁdence

that ozone depletion has increased precipitation over the southern high

latitudes and decreased it over southern mid-latitudes during austral

summer. Owing to observational uncertainties and inconsistent results

between studies, we conclude that there is low conﬁdence in the

attribution of changes in the seasonality of precipitation.

3.3.2.4 Streamﬂow

Streamﬂow is to-date the only variable of the terrestrial water

cycle with enough in-situ observations to allow for detection and

attribution analysis at continental to global scales. Based on evidence

from a few formal detection and attribution studies, particularly on

the timing of peak streamﬂow, and the qualitative evaluation of

studies reporting on observed and simulated trends, AR5 concluded

that there is medium conﬁdence that anthropogenic inﬂuence on

climate has affected streamﬂow in some middle and high latitude

regions (Bindoff et al., 2013). The AR5 also noted that observational

uncertainties are large and that often only a limited number of

models were considered.

Section 2.3.1.3.6 assesses that there have not been signiﬁcant trends

in global average streamﬂow over the last century, though regional

trends have been observed, driven in part by internal variability. Only

a limited number of studies have systematically compared observed

streamﬂow trends at continental to global scales with changes

simulated by global circulation models in a detection and attribution

setting. H. Yang et al. (2017) did not ﬁnd a signiﬁcant correlation

between observed runoff changes and changes simulated in CMIP5

models in most grid cells, consistent with the assessment that observed

changes are dominated by internal variability. Ina pan-European

assessment, Gudmundsson et al. (2017) attributed the spatio-

temporal pattern of decreasing streamﬂow in southern Europe and

increasing streamﬂow in northern Europe to anthropogenic climate

change, but also concluded that additional effects of human water

withdrawals could not be excluded. Focussing on continental runoff

between 1958 and 2004, Alkama et al. (2013) found a signiﬁcant

change only when using reconstructed data over all rivers, and a

large uncertainty in the estimate of the global streamﬂow trend due

to opposing changes over different continents. Gedney et al. (2014)

detected the inﬂuence of aerosols on streamﬂow in North America

and Europe, with aerosols having driven an increase in streamﬂow

due to reduced evaporation (see Section 8.3.1.5 for details on

processes). There is also evidence for a detectable anthropogenic

contribution toward earlier winter-spring streamﬂows in the north-

central US (Kam et al., 2018) and in western Canada (Najaﬁ et al.,

2017). From a model evaluation perspective, Shefﬁeld et al. (2013)

reported that CMIP5 models reproduce spatial variations in runoff in

North America well, though they tend to underestimate it.

Recently, Gudmundsson et al. (2021) performed a global detection

and attribution study on streamﬂow and found that some regions are

drying and others are wetting. Moreover, the simulated streamﬂow

trends are consistent with observations only if externally forced

climate change is considered, and the simulated effects of water and

land management cannot reproduce the observed trends. The effects

of volcanic eruptions in driving reduced streamﬂow have also been

detected in the wet tropics (Iles and Hegerl, 2015; Zuo et al., 2019).

In summary, there is medium conﬁdence that anthropogenic climate

change has altered local and regional streamﬂow in various parts

of the world and that the associated global-scale trend pattern is

inconsistent with internal variability. Moreover, human interventions

and water withdrawals, while affecting streamﬂow, cannot explain

the observed spatio-temporal trends (medium conﬁdence).

457

Human Inﬂuence on the Climate System Chapter 3

Cross-Chapter Box3.2 | Human Inﬂuence on Large-scale Changes in Temperature

andPrecipitation Extremes

Contributors: Nathan P. Gillett (Canada), Seung-Ki Min (Republic of Korea), Krishnan Raghavan (India), Ying Sun (China),

XuebinZhang (Canada)

Understanding how temperature and precipitation extremes have changed at large scales and the causes of these changes is an

important part of our overall assessment of human inﬂuence on the climate system. Chapter11 assesses changes in extremes and

their causes, while this Cross-Chapter Box summarizes relevant assessments and supporting evidence in Chapters 8 and 11 and relates

changes in extremes to mean changes on global and continental scales.

Attribution of temperature extremes

One important aspect of various indicators of temperature extremes is their connection to mean temperature at local, regional and

global scales. For example, the highest daily temperature in a summer is often highly correlated with the summer mean temperature.

Model projections show that changes in temperature extremes are often closely related to shifts in mean temperature (Seneviratne

et al., 2016; Kharin et al., 2018). It is thus no surprise that changes in temperature extremes are consistent with warming mean

temperature, with warming leading to more hot extremes and fewer cold extremes. Given the attribution of mean warming to

human inﬂuence (Section 3.3.1), and the connection between changes in mean and extreme temperatures, it is to be expected that

anthropogenic forcing has also inﬂuenced temperature extremes.

Chapter11 assesses that there is high conﬁdence that climate models can reproduce the mean state and overall warming of temperature

extremes observed globally and in most regions, although the magnitude of the trends may differ, and the ability of models to capture

observed trends in temperature-related extremes depends on the metric evaluated, the way indices are calculated, and the time

periods and spatial scales considered (Section 11.3.3). There has been widespread evidence of human inﬂuence on various aspects of

temperature extremes, at global, continental, and regional scales. This includes attribution to human inﬂuence of observed changes

in intensity, frequency, and duration and other relevant characteristics at global and continental scales (Section 11.3.4). The left-hand

panel of Cross-Chapter Box3.2, Figure1 clearly shows that long-term changes in the global mean annual maximum daily maximum

temperature can be reproduced by both CMIP5 and CMIP6 models forced with the combined effect of natural and anthropogenic

forcings, but cannot be reproduced by simulations under natural forcing alone. Consistent with the assessment for global mean

temperature (Section 3.3.1), aerosol changes are found to have offset part of the greenhouse gas induced increase in hot extremes

globally and over most continents over the 1951–2015 period (Hu et al., 2020; Seong et al., 2021), though greenhouse gas and aerosol

inﬂuences are less clearly separable in observed changes in cold extremes.

Chapter11 assesses that it is virtually certain that human-induced greenhouse gas forcing is the main contributor to the observed

increase in the likelihood and severity of hot extremes and the observed decrease in the likelihood and severity of cold extremes on

global scales, and very likely that this applies on most continents.

Attribution of precipitation extremes

An important piece of evidence supporting the SREX and AR5 assessment that there is medium conﬁdence that anthropogenic forcing

has contributed to a global scale intensiﬁcation of heavy precipitation during the second half of the 20th century is the evidence

for anthropogenic inﬂuence on other aspects of the global hydrological cycle. The most signiﬁcant aspect of that is the increase in

atmospheric moisture content associated with warming which should, in general, lead to enhanced extreme precipitation, particularly

associated with enhanced convergence in tropical and extratropical cyclones (Sections 8.2.3.2 and 11.4.1). Such a connection is

supported by the fact that annual maximum one-day precipitation increases with global mean temperature at a rate similar to the

increase in the moisture holding capacity in response to warming, both in observations and in model simulations. Additionally, models

project an increase in extreme precipitation across global land regions even in areas in which total annual or seasonal precipitation

is projected to decrease.

The overall performance of CMIP6 models in simulating extreme precipitation intensity and frequency is similar to that of CMIP5 models

(high conﬁdence), and there is high conﬁdence in the ability of models to capture the large-scale spatial distribution of precipitation

extremes over land (Section 11.4.3). Evidence of human inﬂuence on extreme precipitation has become stronger since AR5. Considering

changes in precipitation intensity averaged over all wet days, there is high conﬁdence that daily mean precipitation intensities have

increased since the mid-20th century in a majority of land regions, including Europe, North America and Asia, and it is likely that

such an increase is mainly due to anthropogenic emissions of greenhouse gases (Sections 8.3.1.3 and 11.4.4). Section11.4.4 also

ﬁnds a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-breaking one-day precipitation

than expected by chance, which can only be explained when anthropogenic greenhouse gas forcing is considered. The right-hand

458

Chapter 3 Human Inﬂuence on the Climate System

Cross-Chapter Box 3.2 (continued)

panel of Cross-Chapter Box3.2, Figure1 demonstrates the consistency between changes in global average annual maximum daily

precipitation in the observations and model simulations under combined anthropogenic and natural forcing, and inconsistency with

simulations under natural forcing alone. While there is more evidence in the literature to quantify the net anthropogenic inﬂuence on

extreme precipitation than the inﬂuence of individual forcing components, a dominant contribution of greenhouse gas forcing to the

long-term intensiﬁcation of extreme precipitation on global and continental scales has recently been quantiﬁed separately from the

inﬂuence ofanthropogenic aerosol and natural forcings (Dong et al., 2020; Paik et al., 2020b).

Chapter11 assesses that it is likely that human inﬂuence, in particular due to greenhouse gas forcing, is the main driver of the

observed intensiﬁcation of heavy precipitation in global land regions during recent decades (Section 11.4.4).

Cross-Chapter Box3.2, Figure1 | Comparison of observed and simulated changes in global mean temperature and precipitation extremes. Time

series of globally averaged ﬁve-year mean anomalies of the annual maximum daily maximum temperature (TXx in °C) and annual maximum 1-day precipitation

(Rx1day as standardized probability index in %) between 1953 and 2017 from the HadEX3 observations and the CMIP5 and CMIP6 multi-model ensembles with

natural and human forcing (top) and natural forcing only (bottom). For CMIP5, historical simulations for 1953–2005 are combined with corresponding RCP4.5

scenario runs for 2006–2017. For CMIP6, historical simulations for 1953–2014 are combined with SSP2-4.5 scenario simulations for 2015–2017. Numbers in

brackets represent the number of models used. The time-ﬁxed observational mask has been applied to model data throughout the whole period. Grid cells with more

than 70% of data available between 1953 and 2017 plus data for at least three years between 2013 and 2017 are used. Coloured lines indicate multi-model means,

while shading represents 5th–95th percentile ranges, based on all available ensemble members with equal weight given to each model (Section 3.2). Anomalies are

relative to 1961–1990 means. Figure is updated from Seong et al. (2021), their Figure3 and Paik et al. (2020b), their Figure3. Further details on data sources and

processing are available in the chapter data table (Table3.SM.1).

Climate extremes indices

459

Human Inﬂuence on the Climate System Chapter 3

3.3.3 Atmospheric Circulation

3.3.3.1 The Hadley and Walker Circulations

The tropical tropospheric circulation features meridional and zonal

overturning circulations, called Hadley and Walker circulations. Inthe

zonal mean, the downwelling branch of the Hadley circulation cell

is located in the subtropics and is often used as an indicator of the

meridional extent of the tropics. In the equatorial zonal-vertical

section, the major rising branch of the Walker circulation is located

over the Maritime continent with secondary ascending regions over

northern South America and Africa. The zonal component of the

surface trade winds over most of the equatorial Paciﬁc and Atlantic

is associated with the Walker circulation. This section assesses the

zonal-mean Hadley cell extent and the Paciﬁc Walker circulation

strength. Regional and water cycle aspects of these circulations are

assessed in more detail in Section 8.3.2.

AR5 found medium conﬁdence that the depletion of stratospheric

ozone had contributed to Hadley cell widening in the Southern

Hemisphere in austral summer (Bindoff et al., 2013). It also noted

that in contrast to a simulated weakening in response to greenhouse

gas forcing, the Walker circulation had actually strengthened since

the early 1990s, precluding any detection of human inﬂuence.

3.3.3.1.1 Hadley cell extent

Grise et al. (2019) found that a metric based on surface zonal winds,

which are well constrained by surface observations, best compares

reanalyses with CMIP5 models. With this method and new reanalysis

products, the CMIP5 historical simulations exhibit comparable mean

states and variability of the subtropical edge latitude of the Hadley

cells to those observed (Grise et al., 2019).

Chapter2 assesses that there has very likely been a widening of the

Hadley circulation since the 1980s (Section 2.3.1.4.1). The CMIP5

(Davis and Birner, 2017; Grise et al., 2018) and CMIP6 (Grise and

Davis, 2020) historical simulation ensembles span the observed

trends of the zonal-mean Hadley cell edges since the 1980s

(Figure3.16a–c). Studies based on CMIP5 models ﬁnd a contribution

from human inﬂuence to the observed widening trend, especially in

the Southern Hemisphere (Gerber and Son, 2014; Staten et al., 2018,

2020; Grise et al., 2019; Jebri et al., 2020), which is conﬁrmed based

on CMIP6 (Figure3.16b,c; Grise and Davis, 2020).

In the annual mean, internal variability, including Paciﬁc Decadal

Variability (PDV; Annex IV.2.6), contributed to the observed

zonal-mean Hadley cell expansion since 1980 comparably with

human inﬂuence (Allen et al., 2014; Allen and Kovilakam, 2017;

Mantsis et al., 2017; Amaya et al., 2018; Grise et al., 2018). Indeed,

the ensemble-mean expansion in historical simulations is signiﬁcantly

weaker than in most of the reanalyses shown in Figure3.16a–c,

while the Atmospheric Model Intercomparison Project (AMIP)

simulations forced by observed SSTs (Figure3.16a–c) show stronger

trends than historical coupled simulations on average (Nguyen et al.,

2015; Davis and Birner, 2017; Grise et al., 2018). The human-induced

change has not yet clearly emerged out of the internal variability

range in the Northern Hemisphere (Quan et al., 2018; Grise et al.,

2019), whereas the trend in the annual-mean Southern Hemisphere

edge is outside the 5th–95th percentile range of internal variability

in CMIP6 in three out of the four reanalyses (Figure3.16b). For the

Southern Hemisphere summer when the simulated human inﬂuence

is strongest, the 1981–2000 trend in three out of the four reanalyses

falls outside the 5th–95th percentile range of internal variability

(Figure 3.16c; L.Tao et al., 2016; Grise et al., 2018, 2019).

In CMIP5 simulations, greenhouse gas increases and, in austral summer,

stratospheric ozone depletion, contribute to the Southern Hemisphere

expansion (Gerber and Son, 2014; Nguyen et al., 2015; L. Tao et al.,

2016; Y.H. Kim et al., 2017), but the ozone inﬂuence is not signiﬁcant

in available CMIP6 simulations (Figure3.16b–c). Since the 2000s, the

stabilization or slight recovery of stratospheric ozone (Section 2.2.5.2)

is consistent with the smaller observed trends (Banerjee et al., 2020).

While many CMIP5 models under-represent the magnitude of the PDV,

implying potential overconﬁdence on the detection of human inﬂuence

on the Hadley cell expansion, this is less the case for the CMIP6 models

(Section 3.7.6). However, the mechanism underlying the Hadley cell

expansion remains unclear (Staten et al., 2018, 2020), precluding

aprocess-based validation of the simulated human inﬂuence.

3.3.3.1.2 Walker circulation strength

CMIP5 models reproduce the mean state of the Walker circulation

with reasonable ﬁdelity, evidenced by the spatial pattern correlations

of equatorial zonal mass stream function between models and

observations being larger than 0.88 (Ma and Zhou, 2016). CMIP5

historical simulations on average simulate a signiﬁcant weakening

of the Paciﬁc Walker circulation over the 20th century (DiNezio et al.,

2013; Sandeep et al., 2014; Kociuba and Power, 2015), which is

also seen in CMIP6 (Figure3.16d). This weakening is accompanied

by a reduction of convective activity over the Maritime Continent

and an enhancement over the central equatorial Paciﬁc (DiNezio

et al., 2013; Sandeep et al., 2014; Kociuba and Power, 2015).

In the CMIP6 simulations, greenhouse gas forcing induces this

weakening (Figure3.16d), which is consistent with theories based

on radiative-convective equilibrium (Vecchi et al., 2006; Vecchi and

Soden, 2007) and thermodynamic air-sea coupling (Xie et al., 2010),

but inconsistent with a theory highlighting the ocean dynamical

effect which suggests a strengthening in response to greenhouse

gas increases (Clement et al., 1996; Seager et al., 2019; see also

Section 7.4.4.2.1). Seager et al. (2019) attributed this inconsistency

to equatorial Paciﬁc SST biases in the models (Section3.5.1.2.1).

However, observational and reanalysis datasets disagree on the sign

of trends in the Walker Circulation strength over the 1901–2010

period (Figure3.16d), and Section 2.3.1.4.1 assesses low conﬁdence

in observed long-term Walker Circulation trends. The observational

uncertainty remains high in the trends since the 1950s (Tokinaga

et al., 2012; L’Heureux et al., 2013), though both CMIP5 and CMIP6

historical simulations span trends of all but one observational data

set (Figure 3.16e). For this period, external inﬂuence simulated

in CMIP6 is insigniﬁcant due to a partial compensation of forced

responses to greenhouse gases and aerosols and large internal

decadal variability (Figure 3.16e). It is notable that while AMIP

simulations on average show strengthening over both the periods,

460

Chapter 3 Human Inﬂuence on the Climate System

those simulations are forced by one reconstruction of SST, which

itself is subject to uncertainty before the 1970s (Deser et al., 2010;

Tokinaga et al., 2012).

Observational SST products indicate that the equatorial zonal SST

gradient from the western to the eastern equatorial Paciﬁc has

strengthened since 1870 (Section 7.4.4.2.1). While CMIP5 historical

simulations on average simulate a weakening, large ensemble

simulations span the observed strengthening since the 1950s

(Watanabe et al., 2021) suggesting an important contribution from

internal variability. Coats and Karnauskas (2017) also ﬁnd that the

anthropogenic inﬂuence on the SST gradient is yet to emerge out of

internal variability even on centennial time scales.

Trends since the 1980s in in-situ and satellite observations and

reanalyses exhibit strengthening of the Paciﬁc Walker circulation

and SST gradient (Section 2.3.1.4.1 and Figure3.16f; L’Heureux et al.,

2013; Boisséson et al., 2014; England et al., 2014; Kociuba and

Power, 2015; Ma and Zhou, 2016). AMIP simulations reproduce this

strengthening (Figure3.16d; Boisséson et al., 2014; Ma and Zhou, 2016),

indicating adominant role of SST changes. However, all reanalysis

trends lie outside the 5–95% range of simulated CMIP6 historical

Walker circulation trends over this period (Figure3.16f), consistent with

CMIP5 results (England et al., 2014; Kociuba and Power, 2015). This

may be in part caused by the underestimation of the PDV magnitude

especially in CMIP5 models (Section 3.7.6; Kociuba and Power, 2015;

Chung et al., 2019), but also suggests a potential error in simulating the

Figure3.16 | Model evaluation and attribution of changes in Hadley cell extent and Walker circulation strength. (a–c) Trends in subtropical edge latitude of the

December–January–February means. Positive values indicate northward shifts. (d–f) Trends in the Paciﬁc Walker circulation strength for (d) 1901–2010, (e)1951–2010 and

(f) 1980–2014. Positive values indicate strengthening. Based on CMIP5 historical (extended with RCP4.5), CMIP6 historical, AMIP, pre-industrial control, and single forcing

simulations along with HadSLP2 and reanalyses. Pre-industrial control simulations are divided into non-overlapping segments of the same length as the other simulations. White

boxes and whiskers represent means, interquartile ranges and 5th and 95th percentiles, calculated after weighting individual members with the inverse of the ensemble size of

the same model, so that individual models are equally weighted (Section 3.2). The ﬁlled boxes represent the 5–95% conﬁdence interval on the multi-model mean trends of the

models with at least three ensemble members, with dots indicating the ensemble means of individual models. The edge latitude of the Hadley cell is where the surface zonal

wind velocity changes sign from negative to positive, as described in the Appendix of Grise et al. (2018). The Paciﬁc Walker circulation strength is evaluated as the annual mean

difference of sea level pressure between 5°S–5°N, 160°W–80°W and 5°S–5°N, 80°E–160°E. Further details on data sources and processing are available in the chapter data

table (Table3.SM.1).

461

Human Inﬂuence on the Climate System Chapter 3

forced changes of the Walker circulation. Speciﬁcally, anthropogenic

and volcanic aerosol changes over this period may have driven

astrengthening (DiNezio et al., 2013; Takahashi and Watanabe, 2016;

Hua et al., 2018). This aerosol inﬂuence may be indirect via Atlantic

Multi-decadal Variability (AMV; Annex IV.2.7) through inter-basin

teleconnections (McGregor et al., 2014; Chikamoto et al., 2016;

Kucharski et al., 2016; X. Li et al., 2016a; Ruprich-Robert et al., 2017),

which may be underestimated in models due to SST biases in the

equatorial Atlantic (Section 3.5.1.2.2; McGregor et al., 2018). Note

also the large uncertainty in aerosol inﬂuence on the Walker circulation

(Kuntz and Schrag, 2016; Hua et al., 2018; Oudar et al., 2018), which is

also seen in CMIP6 (Figure3.16f).

Paleoclimate data from the Pliocene epoch suggest that there

was a reduction in the zonal SST gradient in the tropical Paciﬁc

under a similar CO2 concentration as today (Section 7.4.4.2.2 and

Cross-Chapter Box2.4). Tierney et al. (2019) found that this weaker

gradient compared to pre-industrial, which suggests a weaker Walker

circulation, is captured by climate models under Pliocene CO2 levels,

in agreement with the CMIP6 response to greenhouse gas forcing

(Figure3.16d), though the magnitude of this effect varies strongly

between models (Corvec and Fletcher, 2017).

3.3.3.1.3 Summary

It is likely that human inﬂuence has contributed to the poleward

expansion of the zonal mean Hadley cell in the Southern Hemisphere

since the 1980s. This assessment is supported by studies since AR5,

which consistently ﬁnd human inﬂuence from greenhouse gas

increases on the expansion, with additional inﬂuence from ozone

depletion in austral summer. For the strong ozone depletion period

of 1981–2000, human inﬂuence is detectable in the summertime

poleward expansion in the Southern Hemisphere (medium

conﬁdence). By contrast, there is medium conﬁdence that the

expansion of the zonal mean Hadley cell in the Northern Hemisphere

is within the range of internal variability, with contributions from

PDV and other internal variability. The causes of the observed

strengthening of the Paciﬁc Walker circulation over the 1980–2014

period are not well understood, since the observed strengthening

trend is outside the range of variability simulated in the coupled

models (medium conﬁdence). Large observational uncertainty, lack

of understanding of the mechanism underlying the poleward Hadley

cell expansion, and contradicting theories on the greenhouse gas

inﬂuence and uncertainty in the aerosol inﬂuence on the Walker

circulation strength, limit conﬁdence in these assessments.

3.3.3.2 Global Monsoon

Monsoons are seasonal transitions of regimes in atmospheric

circulation and precipitation with the annual cycle of solar insolation,

in association with redistribution of moist static energy (Wang and

Ding, 2008; P.X. Wang et al., 2014; Biasutti et al., 2018). The global

monsoon can be deﬁned to encompasses all monsoon systems based

on precipitation contrast in the solstice seasons (Wang and Ding,

2008; Figure3.17). All regional monsoons are intimately connected

to the global tropical atmospheric overturning by mass (Trenberth

et al., 2000), momentum and energy budgets (Biasutti et al., 2018;

Geen et al., 2020). Assessments of regional monsoon changes are

made in Sections 8.3.2.4, 10.4.2.1 and 10.6.3.

AR5 assessed that CMIP5 models simulated monsoons better than

CMIP3 models but that biases remained in domains and intensity

(high conﬁdence) (Flato et al., 2013). There were no detection and

attribution assessment statements on the decreasing trend of global

monsoon precipitation over land from the 1950s to the 1980s or

the increasing trend of global monsoon precipitation afterwards.

Inthepaleoclimate context, it was determined with high conﬁdence

that orbital forcing produces strong interhemispheric rainfall variability

evident in multiple types of proxies (Masson-Delmotte et al., 2013).

Paleoclimate proxy evidence shows that the global monsoon has

varied with orbital forcing and greenhouse gases (Section 2.3.1.4.2;

Mohtadi et al., 2016; Seth et al., 2019). These large-magnitude

intensiﬁcations and weakenings in the global monsoon involved

in some cases orders-of-magnitude changes in precipitation locally

(Harrison et al., 2014; Tierney et al., 2017). Paleoclimate modelling

and limited data from past climate states with high CO2 suggest

that precipitation intensiﬁes in the monsoon domain under elevated

greenhouse gases, providing context for present and future trends

(Passey et al., 2009; Haywood et al., 2013; Zhang et al., 2013b). In

model simulations of the mid-Pliocene, when globally averaged

temperature was higher than present day, precipitation was larger

in West African, South Asian and East Asian monsoons than under

pre-industrial conditions, consistent with proxy evidence (Zhang

et al., 2015; Sun et al., 2016, 2018; Corvec and Fletcher, 2017;

X.Liet al., 2018). Prescott et al. (2019) and R. Zhang et al. (2019)

ﬁnd an important role for orbital forcing and CO2 in the mid-Pliocene

monsoon expansion and intensiﬁcation. Models are also able to

capture interhemispherically contrasting monsoon changes in the

Last Interglacial in response to orbital forcing and greenhouse gases,

with wetter West African and Asian monsoons and a drier South

American monsoon as seen in proxies (Govin et al., 2014; Gierz

et al., 2017; Pedersen et al., 2017). In overall agreement with proxy

evidence, a model with transient forcing simulates wetting and drying

respectively of the Southern and Northern Hemisphere monsoons

during the last deglaciation, with an important contribution from

Atlantic Meridional Overturning Circulation (AMOC) slowdown

(Otto-Bliesner et al., 2014; Mohtadi et al., 2016).

During the mid-Holocene, global monsoons were stronger especially

in the Northern Hemisphere with an expansion of the West African

monsoon domain in response to orbital forcing (Biasutti et al., 2018;

Section 2.3.1.4.2). Simulations of the mid-Holocene with CMIP5

and CMIP6 models qualitatively capture the stronger Northern

Hemisphere monsoon (Jiang et al., 2015; Brierley et al., 2020), mainly

driven by atmospheric circulation changes (D’Agostino et al., 2019).

However, the models underestimate the monsoon expansion found

in proxy reconstructions (Perez-Sanz et al., 2014; Harrison et al.,

2015; Tierney et al., 2017), which may be linked to mean biases in

the monsoon domain (Brierley et al., 2020) and may be improved

by imposing vegetation and dust changes (Pausata et al., 2016). The

models simulate the weaker Southern Hemisphere monsoon during

462

Chapter 3 Human Inﬂuence on the Climate System

the mid-Holocene (D’Agostino et al., 2020), consistent with proxy

evidence (Section 2.3.1.4.2). These studies indicate that models

can qualitatively reproduce past global monsoon changes seen in

proxies, though issues remain in quantitatively reproducing proxy

observations. Studies of last millennium simulations show that

simulated global monsoon precipitation increases with global mean

temperature, while changes in monsoon circulation and hemispheric

monsoon precipitation depend on forcing sources (Liu et al., 2012;

Chai et al., 2018). Compared to greenhouse gas and solar variations,

volcanic forcing is more effective in changing the global monsoon

precipitation over the last millennium (Chai et al., 2018).

Reproducing monsoons in terms of domain, precipitation amount,

and timings of onset and retreat over the historical period also

remains difﬁcult. While CMIP5 historical simulations broadly capture

global monsoon domains and intensity based on summer and winter

precipitation differences, they underestimate the extent and intensity

of East Asian and North American monsoons while overestimating

them over the tropical western North Paciﬁc (Lee and Wang, 2014;

M. Yan et al., 2016). B. Wang et al. (2020) reported that CMIP6

models simulate the global monsoon domain and precipitation better

(Figure3.17a,b), albeit with biases in annual mean precipitation and

the timings of onset and withdrawal of the Southern Hemisphere

monsoon. Notable inter-model differences were identiﬁed in

CMIP5, with the multi-model ensemble mean outperforming

individual models (Lee and Wang, 2014). Common biases were

identiﬁed across CMIP5 models in moist static energy and upper-

tropospheric temperature associated with the South Asian summer

monsoon, which may arise from overly smoothed model topography

(Boos and Hurley, 2012). However, in atmospheric models with

increasing resolution approaching 20 km, improvements in monsoon

precipitation are not universal across regions and models, and overall

improvements are unclear (Johnson et al., 2016; Ogata et al., 2017;

L. Zhang et al., 2018b).

In instrumental records, global summer monsoon precipitation

intensity (measured by summer precipitation averaged over the

monsoon domain) decreased from the 1950s to 1980s, followed

by an increase (Section 2.3.1.4.2 and Figure3.17c), arising mainly

from variations in Northern Hemispheric land monsoons. A CMIP5

multi-model study by Y. Zhang et al. (2018) found that observed

1951–2004 trends of the global and Northern Hemisphere summer

land monsoon precipitation intensity are well captured by historical

simulations, and CMIP6 models show similar results for global land

Figure3.17 | Model evaluation of global monsoon domain, intensity, and circulation. (a, b) Climatological summer-winter range of precipitation rate, scaled by

annual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations

for 1979–2014. The region enclosed by red lines is the monsoon domain based on the deﬁnition by Wang and Ding (2008). (c, d) Five-year running mean anomalies of (c) global

land monsoon precipitation index deﬁned as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average

for 1979–2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index deﬁned as the vertical shear of zonal winds between 850 and 200

hPa levels averaged over 0°–20°N, from 120°W eastward to 120°E in Northern Hemisphere summer (Wang et al., 2013; m s–1) in CMIP5 historical and RCP4.5 simulations,

and CMIP6 historical and AMIP simulations. Summer and winter are deﬁned for individual hemispheres: May to September is deﬁned as Northern Hemisphere summer and

Southern Hemisphere winter, and November to March is deﬁned as Northern Hemisphere winter and Summer Hemisphere summer. The numbers of models and simulations

are given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual ensemble members with the inverse of the ensemble size of

the same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data

table (Table3.SM.1).

463

Human Inﬂuence on the Climate System Chapter 3

summer monsoon precipitation (Figure3.17c). However, the 1960s

peak in the Northern Hemisphere summer monsoon circulation

is outside the 5th–95th percentile range of CMIP5 and CMIP6

historical simulations for two out of three reanalyses (Figure3.17d).

Modelling studies show that greenhouse gas increases act to

enhance Northern Hemisphere summer monsoon precipitation

intensity (Liu et al., 2012; Polson et al., 2014; Chai et al., 2018; L.

Zhang et al., 2018b). Since the mid-20th century, however, modelling

studies show that this effect was overwhelmed by the inﬂuence of

anthropogenic aerosols in CMIP5 (Polson et al., 2014; Guo et al.,

2015; Y. Zhang et al., 2018; Giannini and Kaplan, 2019) and in

CMIP6 (T. Zhou et al., 2020). Weakening of the monsoon circulation

and reduction of moisture availability are important in this aerosol

inﬂuence (T. Zhou et al., 2020). Besides these human inﬂuences,

the global monsoon is sensitive to internal variability and natural

forcing including ENSO and volcanic aerosols on interannual

time scales and PDV and AMV on decadal to multi-decadal time

scales (Wang et al., 2013, 2018; F. Liu et al., 2016; Jiang and Zhou,

2019; Zuo et al., 2019); though AMV in the 20th century may

have been partly driven by aerosols, see Section 3.7.7. Indeed,

AMIP simulations better reproduce the observed multi-decadal

variations of the global monsoon precipitation and circulation

(Figure3.17c,d). Y. Zhang et al. (2018) ﬁnd that the multi-model

ensemble mean trend of global land monsoon precipitation in

historical simulations, dominated by anthropogenic aerosol forcing

contributions, emerges out of the 90% range of internally-driven

trends in pre-industrial control simulations. However, it should

be noted that CMIP5 models tend to under-represent the PDV

magnitude (Section 3.7.6), suggesting potential overconﬁdence

in the detection of the forced signal. An observed enhancement

in global summer monsoon precipitation since the 1980s is

accompanied by an intensiﬁcation of the Northern Hemisphere

summer monsoon circulation (Figure 3.17c,d). These trends

appear to be at the extreme of the range of the CMIP6 historical

simulation ensemble but are well captured by AMIP simulations

(Figure3.17c,d). While the precipitation increase is consistent with

greenhouse gas forcing, the circulation intensiﬁcation is opposite

to the simulated response to greenhouse gas forcing, and these

enhancements have been attributed to PDV and AMV (Wang et al.,

2013; Kamae et al.,2017).

In summary, while greenhouse gas increases acted to enhance the global

land monsoon precipitation over the 20th century (medium conﬁdence),

consistent with projected future enhancement (Section4.5.1.5), this

tendency was overwhelmed by anthropogenic aerosols from the 1950s

to the 1980s, which contributed to weakening of global land summer

monsoon precipitation intensity for this period (medium conﬁdence).

There is medium conﬁdence that the intensiﬁcation of global monsoon

precipitation and Northern Hemisphere summer monsoon circulation

since the 1980s is dominated by internal variability. These assessments

are supported respectively by multi-model detection and attribution

studies which ﬁnd an important role for anthropogenic aerosols

in the weakening trend, and studies that identify a role for AMV

and PDV in inducing the Northern Hemisphere summer monsoon

circulation enhancement since the 1980s. Supported by multi-model

simulations that are qualitatively consistent with proxy evidence,

there is high conﬁdence that orbital forcing contributed to higher

Northern Hemisphere monsoon precipitation in the mid-Pliocene and

mid-Holocene than pre-industrial. While CMIP5 models can capture

the domain and precipitation intensity of the global monsoon, biases

remain in their regional representations, and they are unsuccessful

in quantitatively reproducing changes in paleo reconstructions (high

conﬁdence). CMIP6 models reproduce the domain and precipitation

intensity of the global monsoon observed over the instrumental

period better than CMIP5 models (medium conﬁdence). However,

CMIP5 and CMIP6 models fail to fully capture the variations of the

Northern Hemisphere summer monsoon circulation (Figure3.17d), but

there is low conﬁdence in this assessment due to a lack of evidence in

theliterature.

3.3.3.3 Extratropical Jets, Storm Tracks and Blocking

Extratropical jets are wind maxima in the upper troposphere which

are often associated with storms, blocking, and weather extremes.

Blocking refers to long-lived, stationary high-pressure systems that

are often associated with a poleward displacement of the jet, causing

cold spells in winter and heatwaves in summer (e.g., Sousa et al.,

2018). Sections 2.3.1.4.3, 8.3.2.7, and 11.7.2 discuss these features

in more detail.

AR5 concluded that models were able to capture the general

characteristics of extratropical cyclones and storm tracks, although

it also noted that most models underestimated cyclone intensity,

that biases in cyclone frequency were linked to biases in sea surface

temperatures, and that resolution can play a signiﬁcant role in the

quality of the simulation of storms (Flato et al., 2013). Similarly, AR5

found with high conﬁdence that simulation of blocking was improved

with increases in resolution. The AR5 did not speciﬁcally assess changes

in Southern Hemisphere storm track characteristics or blocking.

Since AR5, new research using CMIP5 and CMIP6 models has conﬁrmed

that increasing the model resolution improves the simulation of

cyclones and blocking in all seasons albeit with some exceptions and

caveats (Zappa et al., 2013; Davini et al., 2017; Schiemann et al., 2017,

2020; Davini and D’Andrea, 2020; Priestley et al., 2020). New research

also ﬁnds that model performance with respect to the simulation

of cyclones and that of blocking events are correlated (Zappa et al.,

2014), suggesting biases in both are aspects of the same underlying

problems in models (Figure 3.18). In the North Paciﬁc basin the

annual mean blocking frequency is now well simulated compared to

earlier evaluations, but substantial errors in the blocking frequency

remain in the Euro-Atlantic sector (Figure3.18; Dunn-Sigouin and

Son, 2013; Davini and D’Andrea, 2016, 2020; Mitchell et al., 2017;

Woollings et al., 2018b). While there is a resolution dependence

in the size of this bias, even at very high resolution blocking in the

Euro-Atlantic sector remains underestimated (Schiemann et al., 2017),

and there is evidence of a compensation of errors as the resolution is

increased (Davini et al., 2017). Davini and D’Andrea (2020) show that

while the simulation of blocking improves with increasing resolution

in CMIP3, CMIP5, and CMIP6 models, other factors contribute to

biases, particularly to the underestimation of Euro-Atlantic blocking

(Schiemann et al., 2020). The persistence of blocking events, typically

464

Chapter 3 Human Inﬂuence on the Climate System

underestimated, has not improved from CMIP5 to CMIP6 (Schiemann

et al., 2020). Section 10.3.3.3 discusses the implications of the biases

discussed here for regional climate.

For the North Paciﬁc storm track CMIP6 simulations exhibit large

remaining underestimations of cyclone frequencies during summer

(June to August), which for the low-resolution models have essentially

remained unchanged versus CMIP5, and there is only a small resolution

dependence of this bias (Priestley et al., 2020). During winter (December

to February), both CMIP5 and CMIP6 models tend to place the North

Paciﬁc storm track too far equatorward (M. Yang et al., 2018; Priestley

et al., 2020), leading to an overestimation of cyclones between 30°N

and 40°N in the Paciﬁc and an underestimation to the north of

this. Both low- and high-resolution models show this pattern, but

low-resolution models generally simulate fewer cyclones throughout

the North Paciﬁc (Priestley et al., 2020).

In winter, the North Atlantic storm track remains displaced to the

south and east in many models (Harvey et al., 2020), leading to

underestimation of cyclone frequencies near the North American coast

and overestimation in the eastern North Atlantic. Higher-resolution

CMIP6 models perform slightly better in this regard than low-

resolution models. In summer (June to August), cyclone frequencies

throughout the extratropical North Atlantic, which were substantially

underestimated in CMIP5, have improved in CMIP6 high-resolution

models. In low-resolution CMIP6 models, the problem is essentially

unchanged (Priestley et al., 2020); this is associated with generally

underestimated variability of sea level pressure in CMIP models

(Harvey et al., 2020).

For the Southern Hemisphere (not considered in AR5), Priestley

et al. (2020) ﬁnd considerable improvement in the placement of

the Southern Ocean storm track during summer (December to

February) in CMIP6 models versus CMIP5, consistent with a more

realistic annual mean surface wind maximum latitude in CMIP6

than in CMIP5 (Goyal et al., 2021). Relative to CMIP5, both low- and

high-resolution CMIP6 models have increased track densities south

of about 55°S and decreased track densities between about 40°S

and 55°S, in better agreement with observations than CMIP5 models

(Parsons et al., 2016; Patterson et al., 2019). CMIP5 models and high-

resolution CMIP6 models simulate a storm track that is positioned

too far equatorward, although the bias is smaller in the high-

resolution models. By contrast, the low-resolution CMIP6 models

simulate astorm track that is slightly too far poleward on average

(Priestley et al., 2020). In winter (June to August), the biases found in

CMIP5 are only slightly improved in CMIP6, with models continuing

to underestimate the broad maximum cyclone track density in the

south-eastern Indian Ocean and overestimate the minimum density

in thesouth-western South Paciﬁc (Priestley et al., 2020).

There is only one contiguous blocking region in the Southern

Hemisphere, with the blocking frequency maximizing in the South

Paciﬁc and minimizing in the southern Indian Ocean regions (Parsons

et al., 2016; Patterson et al., 2019). CMIP5 simulations agree relatively

well with ERA-Interim in this region regarding the distribution of

blocking events (Parsons et al., 2016). Individual models exhibit

considerable biases in the blocking frequency; however only in austral

summer do Patterson et al. (2019) ﬁnd a systematic, multi-model

underestimation of the blocking frequency in and around the Tasman

Sea. The blocking frequency is anticorrelated with the amplitude

of the SAM. Ozone depletion, through stratosphere-troposphere

coupling, may have caused an increase in the blocking frequency in

the South Atlantic sector (Dennison et al., 2016); this ﬁnding requires

conﬁrmation using a multi-model approach.

In addition to inadequate resolution, blocking and storm track biases

in both hemispheres also result from mean state biases, in particular,

biases related to the parameterization of orographic effects and to

the misrepresentation of the Gulf Stream SST front (Anstey et al.,

2013; Berckmans et al., 2013; Davini and D’Andrea, 2016; O’Reilly

et al., 2016a; Pithan et al., 2016; Schiemann et al., 2017). Nonetheless

overall SST biases have been suggested to have only a weak relevance

to blocking (Davini and D’Andrea, 2016).

Section 2.3.1.4.3 assesses that the total number of extratropical

cyclones has likely increased since the 1980s in the Northern

Hemisphere (low conﬁdence), but with fewer deep cyclones

particularly in summer. This observed reduction in cyclone activity

by about 4% per decade in the Northern Hemisphere in summer

(Chang et al., 2016; Section 2.3.1.4.3) may be associated with

human-induced warming. CMIP5 historical simulations generally

reproduce a reduction but underestimate its magnitude (Chang

et al., 2016). Furthermore, feedback mechanisms associated with

clouds may be responsible for substantial inter-model spread (Chang

et al., 2016; Voigt and Shaw, 2016). In boreal winter, recent studies

have suggested a potential inﬂuence of the rapid Arctic warming on

observed intensiﬁcation of Northern Hemisphere storm track activity

in the past few decades, while other studies question this possibility

(Cross-Chapter Box10.1).

Section 2.3.1.4.3 assesses that the extratropical jets and cyclone

tracks have likely shifted poleward in both hemispheres since the

1980s with marked seasonality in trends (medium conﬁdence). For

the Southern Hemisphere, studies using CMIP5 and other models

imply that both ozone depletion and increasing greenhouse gases

have caused substantial atmospheric circulation change since

the 1960s when concentrations of ozone-depleting substances

started to increase (Eyring et al., 2013; Iglesias-Suarez et al.,

2016; Karpechko et al., 2018; Son et al., 2018). In particular, ozone

depletion, during austral summer, has been linked to a poleward

shift of the westerly jet and Southern Hemisphere circulation zones

and a southward expansion of the tropics (Kang et al., 2011), which

is associated with a strengthening trend of the Southern Annular

Mode (SAM; Section 3.7.2). This has been well reproduced by

climate models with prescribed historical ozone concentration or

interactive ozone chemistry (Gerber and Son, 2014; Son et al., 2018;

Figure3.19).

In summary, there is low conﬁdence that an observed decrease in

the frequency of Northern Hemisphere summertime extratropical

cyclones is linked to anthropogenic inﬂuence. In the Southern

Hemisphere, there is high conﬁdence that human inﬂuence, in the

form of ozone depletion, has contributed to the observed poleward

shift of the jet in austral summer, while conﬁdence is low for

465

Human Inﬂuence on the Climate System Chapter 3

Figure3.19 | Long-term mean (thin black contours) and linear trend (colour) of zonal mean December–January–February zonal winds from 1985 to 2014

in the Southern Hemisphere. The ﬁgure shows (a)ERA5 and (b)the CMIP6 multi-model mean (58 CMIP6 models). The solid contours show positive (westerly) and zero

long-term mean zonal wind, and the dashed contours show negative (easterly) long-term mean zonal wind. Only one ensemble member per model is included. Figure is modiﬁed

from Eyring et al. (2013), their Figure12. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

Figure 3.18 | Instantaneous Northern-Hemisphere blocking frequency (% of days) in the extended northern winter season (December–January–February–

March – DJFM) for the years 1979–2000. Results are shown for the ERA5 reanalysis (black), CMIP5 (blue) and CMIP6 (red) models. Coloured lines show multi-model means and

shaded ranges show corresponding 5–95% ranges constructed with one realization from each model. Figure is adapted from Davini and D’Andrea (2020), their Figure12 and

following the D’Andrea et al. (1998) deﬁnition of blocking. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

466

Chapter 3 Human Inﬂuence on the Climate System

human inﬂuence on historical blocking activity. The low conﬁdence

statements are due to the limited number of studies available. The

shift of the Southern Hemisphere jet is correlated with modulations

of the SAM (Section 3.7.2). There is medium conﬁdence in model

performance regarding the simulation of the extratropical jets, storm

track and blocking activity, with increased resolution sometimes

corresponding to better performance, but important shortcomings

remain, particularly for the Euro-Atlantic sector of the Northern

Hemisphere. Nonetheless, synthesizing across Sections 3.3.3.1–

3.3.3.3, there is high conﬁdence that CMIP6 models capture the

general characteristics of the tropospheric large-scalecirculation.

3.3.3.4 Sudden Stratospheric Warming Activity

Sudden stratospheric warmings (SSWs) are stratospheric weather

events associated with anomalously high temperatures at high

latitudes persisting from days to weeks. Section 2.3.1.4.5 discusses

the deﬁnition and observational aspects of SSWs. SSWs are often

associated with anomalous weather conditions, for example, winter

cold spells, in the lower atmosphere (e.g., Butler et al., 2015; Baldwin

et al., 2021).

Seviour et al. (2016) found that stratosphere-resolving CMIP5

models, on average, reproduce the observed frequency of vortex

splits (one form of SSWs) but with a wide range of model-speciﬁc

biases. Models that produce a better mean state of the polar vortex

also tend to produce a more realistic SSW frequency (Seviour

et al., 2016). The mean sea level pressure anomalies occurring in

CMIP5 model simulations when an SSW is underway, however,

differ substantially from those in reanalyses (Seviour et al.,

2016). Unlike stratosphere-resolving models, models with limited

stratospheric resolution, which make up more than half of the

CMIP5 ensemble, underestimate the frequency of SSWs (Osprey

et al., 2013; J. Kim et al., 2017). Taguchi (2017) found a general

underestimation in CMIP5 models of the frequency of ‘major’ SSWs

(which are associated with a break-up of the polar vortex), an

aspect of an under-representation in those models of dynamical

variability in the stratosphere. Wu and Reichler (2020) found that

ﬁner vertical resolution in the stratosphere and a model top above

the stratopause tend to be associated with a more realistic SSW

frequency in CMIP5 and CMIP6 models.

Some studies ﬁnd an increase in the frequency of SSWs under

increasing greenhouse gases (e.g., Schimanke et al., 2013; Young

et al., 2013; J. Kim et al., 2017). However, this behaviour is not

robust across ensembles of chemistry-climate models (Mitchell et al.,

2012; Ayarzagüena et al., 2018; Rao and Garﬁnkel, 2021). There is

an absence of studies speciﬁcally focusing on simulated trends in

SSWs during recent decades, and the short record and substantial

decadal variability yields low conﬁdence in any observed trends in

the occurrence of SSW events in the Northern Hemisphere winter

(Section 2.3.1.4.5). Such an absence of a trend and large variability

would also be consistent with a recent reconstruction of SSWs

extending back to 1850, based on sea level pressure observations

(Domeisen, 2019), although this time series has limitations as it is not

based on direct observations of SSWs.

In summary, an anthropogenic inﬂuence on the frequency or other

aspects of SSWs has not yet been robustly detected. There is low

conﬁdence in the ability of models to simulate any such trends over

the historical period because of large natural interannual variability

and also due to substantial common biases in the simulated mean

state affecting the simulated frequency of SSWs.

3.4 Human Inﬂuence on the Cryosphere

3.4.1 Sea Ice

3.4.1.1 Arctic Sea Ice

The AR5 concluded that ‘anthropogenic forcings are very likely to have

contributed to Arctic sea ice loss since 1979’ (Bindoff et al., 2013), based

on studies showing that models can reproduce the observed decline only

when including anthropogenic forcings, and formal attribution studies.

Since the beginning of the modern satellite era in 1979, Northern

Hemisphere sea ice extent has exhibited signiﬁcant declines in all

months with the largest reduction in September (see Section 2.3.2.1.1,

and Figures 3.20 and 3.21 for more details on observed changes). The

recent Arctic sea ice loss during summer is unprecedented since 1850

(high conﬁdence), but as in AR5 and SROCC there remains only medium

conﬁdence that the recent reduction is unique during at least the past

1000 years due to sparse observations (Sections 2.3.2.1.1 and 9.3.1.1).

CMIP5 models also simulate Northern Hemisphere sea ice loss over the

satellite era but with large differences among models (e.g.,Massonnet

et al., 2012; Stroeve et al., 2012). The envelope of simulated ice loss

across model simulations encompasses the observed change, although

observations fall near the low end of the CMIP5 and CMIP6 distributions

of trends (Figure3.20). CMIP6 models on average better capture the

observed Arctic sea ice decline, albeit with large inter-model spread. Notz

et al. (2020) found that CMIP6 models better reproduce the sensitivity

of Arctic sea ice area to CO2 emissions and global warming than earlier

CMIP models although the models’ underestimation of this sensitivity

remains. Davy and Outten (2020) also found that CMIP6 models can

simulate the seasonal cycle of Arctic sea ice extent and volume better

than CMIP5 models. For the assessment of physical processes associated

with changes in Arctic sea ice, see Section 9.3.1.1.

Since AR5, there have been several new detection and attribution

studies on Arctic sea ice. While the attribution literature has mostly used

sea ice extent (SIE), it is closely proportional to sea ice area (SIA; Notz,

2014), which is assessed in Chapters 2 and 9 and shown in Figures 3.20

and 3.21. Kirchmeier-Young et al. (2017) compared the observed time

series of the September SIE over the period 1979–2012 with those from

different large ensemble simulations which provide a robust sampling

of internal climate variability (CanESM2, CESM1, and CMIP5) using an

optimal ﬁngerprinting technique. They detected anthropogenic signals

which were separable from the response to natural forcing due to solar

irradiance variations and volcanic aerosol, supporting previous ﬁndings

(Figure3.21; Min et al., 2008b; Kay et al., 2011; Notz and Marotzke,

2012; Notz and Stroeve, 2016). Using selected CMIP5 models and

three independently derived sets of observations, Mueller et al. (2018)

detected ﬁngerprints from greenhouse gases, natural, and other

anthropogenic forcings simultaneously in the September Arctic SIE over

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Human Inﬂuence on the Climate System Chapter 3

the period 1953–2012. They further showed that about a quarter of the

greenhouse gas induced decrease in SIE has been offset by an increase

due to other anthropogenic forcing (mainly aerosols). Similarly, Gagné

et al. (2017b) suggested that the observed increase in Arctic sea

ice concentration over the 1950–1975 period was primarily due to

the cooling contribution of anthropogenic aerosol forcing based on

single model simulations. Gagné et al. (2017a) identiﬁed adetectable

increase in Arctic SIE in response to volcanic eruptions using CMIP5

models and four observational datasets. Polvani et al. (2020) suggested

that ozone depleting substances played a substantial role in the Arctic

sea ice loss over the 1955–2005 period.

Differences in sea ice loss among the models (Figure 3.20) have

been attributed to a number of factors (see also Section 9.3.1.1).

These factors include the late 20th century simulated sea ice state

(Massonnet et al., 2012), the magnitude of changing ocean heat

transport (Mahlstein and Knutti, 2011), and the rate of global warming

(e.g., Gregory et al., 2002; Mahlstein and Knutti, 2012; Rosenblum

and Eisenman, 2017). Sea ice thermodynamic considerations

indicate that the magnitude of sea ice variability and loss depends

on ice thickness (Bitz, 2008; Massonnet et al., 2018) and hence

the climatology simulated by different models may inﬂuence their

simulated sea ice trends (medium conﬁdence), as indicated by the

regression lines in Figure3.20.

An important consideration in comparing Arctic sea ice loss in

models and observations is the role of internal variability (medium

conﬁdence). Using ensemble simulations from a single model,

Kay et al. (2011) suggested that internal variability could account

for about half of the observed September ice loss. More recently,

large ensemble simulations have been performed with many more

ensemble members (Kay et al., 2015). These enable a more robust

characterization of internal variability in the presence of forced

anthropogenic change. Using such large ensembles, some studies

discussed the inﬂuence of internal variability on Arctic sea ice trends

(Swart et al., 2015). Song et al. (2016) also compared the trends in the

Figure3.20 | Mean (x-axis) and trend (y-axis) of Arctic sea ice area (SIA) in September (left) and Antarctic SIA in February (right) for 1979–2017 from

CMIP5 (upper) and CMIP6 (lower) models. All individual models (ensemble means) and the multi-model mean values are compared with the observations (OSISAF, NASA

Team, and Bootstrap; see Figure 9.13). Solid line indicates a linear regression slope with corresponding correlation coefﬁcient (r) and p-value provided. Note the different scales

used on the y-axis for Arctic and Antarctic SIA. Results remain essentially the same when using sea ice extent (SIE; not shown). Further details on data sources and processing

are available in the chapter data table (Table3.SM.1).

468

Chapter 3 Human Inﬂuence on the Climate System

forced and unforced simulations using multiple climate models and

found that internal variability explains about 40% of the observed

September sea ice melting trend, supporting previous studies (Stroeve

et al., 2012). Based on the large ensembles of CESM1 and CanESM2,

the September Arctic sea ice extent variance ﬁrst increases and then

decreases as SIE declines from its pre-industrial value (Kirchmeier-

Young et al., 2017; Mueller et al., 2018) consistent with previous work

(Goosse et al., 2009), but neither study found a strong sensitivity of

detection and attribution results to the change in variability. Further

work has indicated that internally-driven summer atmospheric

circulation trends with enhanced atmospheric ridges over Greenland

and the Arctic Ocean, which project on the negative phase of the

North Atlantic Oscillation (Section 3.7.1), play an important role

in the observed Arctic sea ice loss (Hanna et al., 2015; Ding et al.,

2017). A ﬁngerprint analysis using the CESM large ensemble suggests

that this internal variability accounts for 40–50% of the observed

September Arctic sea ice decline (Ding et al., 2019; England et al.,

2019). Internally-generated decadal tropical variability and associated

atmospheric teleconnections were suggested to have contributed to

the changing atmospheric circulation in the Arctic and the associated

rapid sea ice decline from 2000 to 2014 (Meehl et al.,2018).

Some recent studies evaluated the human contribution to recent

record minimum SIE events in the Arctic. Analysing CMIP5 simulations,

Zhang and Knutson (2013) found that the observed 2012 record low

in September Arctic SIE is inconsistent with internal climate variability

alone. Based on several large ensembles, Kirchmeier-Young et al.

(2017) concluded that the observed 2012 SIE minimum cannot be

reproduced in a simulation excluding human inﬂuence. Fučkar et al.

(2016) showed that climate change contributed to the record low

March Arctic SIE in 2015, which was accompanied by the record

minimum SIE in the Sea of Okhotsk (Paik et al., 2017).

Based on the new attribution studies since AR5, we conclude that it

is very likely that anthropogenic forcing mainly due to greenhouse

gas increases was the main driver of Arctic sea ice loss since

1979. Increases in anthropogenic aerosols have offset part of the

greenhouse gas induced Arctic sea ice loss since the 1950s (medium

conﬁdence). Despite large differences in the mean sea ice state in

the Arctic, Arctic sea ice loss is captured by all CMIP5 and CMIP6

models. Nonetheless, large inter-model differences in the Arctic sea

ice decline remain, limiting our ability to quantify forced changes and

internal variability contributions.

3.4.1.2 Antarctic Sea Ice

AR5 concluded that ‘there is low conﬁdence in the attribution of the

observed increase in Antarctic SIE since 1979’ (Bindoff et al., 2013) due

to the limited understanding of the external forcing contribution as well

as the role of internal variability. Based on a difference between the

ﬁrst and last decades, Antarctic sea ice cover exhibited a small increase

in summer and winter over the 1979–2017 period (Section2.3.2.1.2,

and Figures 3.20 and 3.21). However, these changes are not statistically

signiﬁcant and starting in late 2016, anomalously low sea ice area

has been observed (Section 2.3.2.1.2). The mean hemispheric sea ice

changes result from much larger, but partially compensating, regional

changes with increases in the western Ross Sea and Weddell Sea

and declines in the Bellingshausen and Amundsen Seas (Hobbs et al.,

2016). Observed regional trends have been particularly large in austral

autumn (see Section 2.3.2.1.2, and also Section 9.3.2.1 for more details

of regional changes and related physical processes). Starting in austral

spring of 2016, the ice extent decreased strongly (Turner et al., 2017) and

has since remained anomalously low (Figure3.21 and Figure 2.20). This

decrease has been associated with anomalous atmospheric conditions

associated with teleconnections from warming in the eastern Indian

Ocean and a negative Southern Annular Mode (Chenoli et al., 2017;

Stuecker et al., 2017; Schlosser et al., 2018; Meehl et al., 2019; Purich

and England, 2019; G. Wang et al., 2019). A decadal-scale warming of

the near-surface ocean that resulted from strengthened westerlies may

also have contributed to and helped to sustain the sea ice loss (Meehl

et al., 2019). Before satellites and on even longer time scales, very

limited observational data and proxy coverage leads to low conﬁdence

in all aspects of Antarctic sea ice (Sections 2.3.2.1.2 and 9.3.2.1).

CMIP5 climate models generally simulate Antarctic sea ice loss over

the satellite era since 1979 (Mahlstein et al., 2013; Turner et al., 2013)

in contrast to the observed change, and CMIP6 models also simulate

Antarctic ice loss (Roach et al., 2020; Figure3.20 and 3.21). A number

of studies have suggested that this discrepancy may be in part due

to the role of internal variability in the observed change (Mahlstein

et al., 2013; Polvani and Smith, 2013; Zunz et al., 2013; Meehl et al.,

2016c; Turner et al., 2016), including teleconnections associated with

tropical Paciﬁc variability (Meehl et al., 2016c) and changing surface

conditions resulting from multi-decadal ocean circulation variations

(Singh et al., 2019). However, when the spatial pattern is considered,

trends in the summer and autumn (from 1979–2005) appear outside

the range of internal variability (Hobbs et al., 2015). This suggests

that the models may exhibit an unrealistic simulation of the Antarctic

sea ice forced response or the internal variability of the system.

Discrepancies among the models in simulated sea ice variability

(Zunz et al., 2013), the sea ice climatological state (Roach et al.,

2018), upper ocean temperature trends (Schneider and Deser, 2018),

Southern Hemisphere westerly wind trends (Purich et al., 2016), or

the sea ice response to Southern Annular Mode variations (Ferreira

et al., 2014; Holland et al., 2017; Kostov et al., 2017; Landrum et al.,

2017) may all play some role in explaining these differences with the

observed trends. Increased fresh water ﬂuxes caused by mass loss of

the Antarctic Ice Sheet (either by melting at the front of ice shelves

or via iceberg calving) have been suggested as a possible mechanism

driving the multi-decadal Antarctic sea ice expansion (Bintanja et al.,

2015; Pauling et al., 2016) but there is a lack of consensus on this

mechanism’s impacts (Pauling et al., 2017). A recent study based on

a decadal prediction system suggests that initializing the state of

the Antarctic Bottom Water cell allows the system to reproduce the

observed Antarctic sea ice increase (Zhang et al., 2017), consistent

with the suggestion that multi-decadal variability associated with

variations in deep convection has contributed to the observed

increase in Antarctic sea ice since 1979 (Latif et al., 2013; Zhang

et al., 2017; L. Zhang et al., 2019) (see also Section 9.3.2.1).

There have been several studies that aimed to identify causes of the

observed Antarctic SIE changes. Gagné et al. (2015) assessed

theconsistency of observed and simulated changes in Antarctic SIE for

an extended period using recovered satellite-based estimates, and found

469

Human Inﬂuence on the Climate System Chapter 3

Figure3.21 | Seasonal evolution of observed and simulated Arctic (left) and Antarctic (right) sea ice area (SIA) over 1979–2017. SIA anomalies relative to the

1979–2000 means from observations (OBS from OSISAF, NASA Team, and Bootstrap, top) and historical (ALL, middle) and natural only (NAT, bottom) simulations

from CMIP5 and CMIP6 models. These anomalies are obtained by computing non-overlapping three-year mean SIA anomalies for March (February for Antarctic SIA), June,

September, and December separately. CMIP5 historical simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 historical simulations are

extended by using SSP2-4.5 scenario simulations after 2014. CMIP5 NAT simulations end in 2012. Numbers in brackets represent the number of models used. The multi-model

mean is obtained by taking the ensemble mean for each model ﬁrst and then averaging over models. Grey dots indicate multi-model mean anomalies stronger than inter-

model spread (beyond ± 1 standard deviation). Results remain very similar when based on sea ice extent (SIE – not shown). Units: 106 km2. Further details on data sources and

processing are available in the chapter data table (Table3.SM.1) and in the caption to Figure 9.13.

470

Chapter 3 Human Inﬂuence on the Climate System

that the observed trends since the mid-1960s are not inconsistent

with model simulated trends. Studies based on the satellite period

also indicate that the observed trends are largely within the range of

simulated internal variability (Hobbs et al., 2016). A few distinct factors

that led to the weak signal-to-noise ratio in Antarctic SIE trends have

been further identiﬁed, which include large multi-decadal variability

(Monselesan et al., 2015), the short observational record (e.g., Abram

et al., 2013), and the limited model performance at representing

the complex Antarctic climate system as discussed above (Bintanja

et al., 2013; Uotila et al., 2014). The short period of comprehensive

satellite observations, beginning in 1979, makes it challenging to set

the observed increase between 1979 and 2015, or the subsequent

decrease, in a long-term context, and to assess whether the difference

in trend between observations and models, which mostly simulate

long-term decreases, is systematic or a rare expression of internal

variability on decadal to multi-decadal time scales.

In conclusion, the observed small increase in Antarctic sea ice extent

during the satellite era is not generally captured by global climate

models, and there is low conﬁdence in attributing the causes of

thechange.

3.4.2 Snow Cover

Seasonal snow cover is a deﬁning climate feature of the northern

continents. It is therefore of considerable interest that climate

models correctly simulate this feature. It is discussed in more detail in

Section9.5.3, and observational aspects of snow cover are assessed

in Section 2.3.2.2.

The AR5 noted the strong linear correlation between Northern

Hemisphere snow cover extent (SCE) and annual-mean surface air

temperature in CMIP5 models. It was assessed as likely that there

had been an anthropogenic contribution to observed reductions in

Northern Hemisphere snow cover since 1970 (Bindoff et al., 2013).

The AR5 assessed that CMIP5 models reproduced key features of

observed snow cover well, including the seasonal cycle of snow cover

over northern regions of Eurasia and North America, but had more

difﬁculties in more southern regions with intermittent snow cover.

The AR5 also found that CMIP5 models underestimated the observed

reduction in spring snow cover over this period (Figure3.22; see

also Brutel-Vuilmet et al., 2013; Thackeray et al., 2016; Santolaria-

Otín and Zolina, 2020). This behaviour has been linked to how

the snow-albedo feedback is represented in models (Thackeray

et al., 2018a). The CMIP5 multi-model ensemble has been shown to

represent the snow-albedo feedback more realistically than CMIP3,

although models from some individual modelling centres have not

improved or have even got worse (Thackeray et al., 2018a). There is

still asystematic overestimation of the albedo of boreal forest covered

by snow (Thackeray et al., 2015; Y. Li et al., 2016). Consequently, the

snow albedo feedback might have been overestimated by CMIP5

models (Section 9.5.3; Xiao et al., 2017).

CMIP6 models improve on CMIP5 models in producing slightly

increased SCE versus CMIP5, correcting the low bias in CMIP5

(Mudryk et al., 2020). The linear relationship noted above between

GSAT and SCE also exists in CMIP6 (Mudryk et al., 2020). Like CMIP5,

the CMIP6 models capture the negative trend in spring snow cover

that has occurred in recent decades (Figure 3.22). However, the

median CMIP6 model now produces slightly stronger post-1981

declines in the March to April mean SCE than the CMIP5 median

(Mudryk et al., 2020). Until about 1980, the models produce a

generally stable March to April SCE, but after that a substantial

decline, reaching a loss of about 2 × 106 km2 in 2012–2017 relative

to the 1971–2000 average. Compared to earlier studies which found

that models underestimate observed trends for the 1979–2005

period (Brutel-Vuilmet et al., 2013), both CMIP5 and CMIP6 models

5th-95th percentile

range

5th-95th percentile

range

Figure3.22 | Time series of Northern Hemisphere March–April mean snow

cover extent (SCE) from observations, CMIP5 and CMIP6 simulations. The

observations (grey lines) are updated Brown-NOAA (Brown and Robinson, 2011),

Mudryk et al. (2020), and GLDAS2. CMIP5 (top) and CMIP6 (bottom) simulations

of the response to natural plus anthropogenic forcing are shown in brown, natural

forcing only in green, and the pre-industrial control simulation range is presented in

blue. Five-year mean anomalies are shown for the 1923–2017 period with the x-axis

representing the centre years of each ﬁve-year mean. CMIP5 all forcing simulations

are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 all forcing

simulations are extended by using SSP2-4.5 scenario simulations after 2014. Shading

indicates 5th–95th percentile ranges for CMIP5 and CMIP6 all and natural forcings

simulations, and solid lines are ensemble means, based on all available ensemble

members with equal weight given to each model (Section 3.2). The blue vertical bar

indicates the mean 5th–95th percentile range of pre-industrial control simulation

anomalies, based on non-overlapping segments. The numbers in brackets indicate the

number of models used. Anomalies are relative to the average over 1971–2000. For

models, SCE is restricted to grid cells with land fraction ≥50%. Greenland is excluded

from the total area summation. Figure is modiﬁed from Paik and Min (2020), their

Figure1. Further details on data sources and processing are available in the chapter

data table (Table3.SM.1).

471

Human Inﬂuence on the Climate System Chapter 3

show improved agreement with the observations over the period to

2017 (Figure3.22). One remaining concern is a failure of most CMIP6

models to correctly represent the relationship between snow cover

extent and snow mass, reﬂecting too slow seasonal increases and

decreases of SCE in the models (Mudryk et al., 2020).

Several CMIP5 and CMIP6 based studies have consistently

attributed the observed Northern Hemisphere spring SCE changes

(Section2.3.2.2) to anthropogenic inﬂuences (Rupp et al., 2013; Najaﬁ

et al., 2016; Paik and Min, 2020), with the observed changes being

found to be inconsistent with natural variability alone. Similarly, spring

snow thickness (Snow Water Equivalent) changes on the scale of the

Northern Hemisphere have been attributed to greenhouse gas forcing

(Jeong et al., 2017). Using individual forcing simulations from multiple

CMIP6 models, Paik and Min (2020) detected greenhouse gas inﬂuence

in the observed decrease of early spring SCE between 1925 and 2019,

which was found to be separable from the responses to other forcings.

In summary, it is very likely that anthropogenic inﬂuence contributed

to the observed reductions in Northern Hemisphere springtime snow

cover since 1950. CMIP6 models better represent the seasonality

and geographical distribution of snow cover than CMIP5 simulations

(high conﬁdence). Both CMIP5 and CMIP6 models simulate strong

declines in spring SCE during recent years, in general agreement

with observations, causing the multi-model mean decreasing trend

in spring SCE to now better agree with observations than in earlier

evaluations. Evidence has yet to emerge that interactions between

vegetation and snow, found problematic in CMIP5, have improved in

CMIP6 models (Section 9.5.3). Such deﬁciencies in the representation

of snow in climate models mean there is medium conﬁdence in the

simulation of snow cover over the northern continents in CMIP6 model

simulations. The models consistently link snow extent to surface air

temperature (Figure9.24). With warming of near-surface air linked to

anthropogenic inﬂuence, and particularly to greenhouse gas increases

(Section 3.3.1.1), this provides additional evidence that reductions in

snow cover are also caused by humanactivity.

3.4.3 Glaciers and Ice Sheets

While Chapter 9 (Sections 9.4 and 9.5) discusses process

understanding for glaciers and ice sheets, as well as evaluation of

global and regional-scale glacier and ice-sheet models, our focus

here is on the attribution of large-scale changes in glaciers and ice

sheets. Land ice in the form of glaciers has been included in CMIP

climate and Earth system models as components of the land surface

models for many years. However, their representation is simpliﬁed

and is omitted altogether in the less complex modelling systems. In

CMIP3 (Meehl et al., 2007) and CMIP5 (Taylor et al., 2012) land ice

area fraction, a component of land surface models, was deﬁned as

a time-independent quantity, and in most model conﬁgurations was

preset at the simulation initialization as a permanent land feature.

In CMIP6 considerable progress has been made in improving and

evaluating the representation of modelled land ice. For glaciers,

an example is the expansion of the Joint UK Land Environment

Simulator (JULES) land surface model to enable elevated tiles, and

hence more accurately simulate the altitudinal atmospheric effects on

glaciers (Shannon et al., 2019). Moreover, standalone glacier models

have now been systematically compared in GlacierMIP (Hock et al.,

2019a; Marzeion et al., 2020). The Antarctic and Greenland Ice Sheets

were absent in global climate models that pre-date CMIP6 (Eyring

et al., 2016a), however some preliminary analyses that used results

from CMIP5 to drive standalone ice-sheet models were included

in AR5 (Church et al., 2013a). For the ﬁrst time in CMIP, the latest

CMIP6 phase includes a coordinated effort to simulate temporally

evolving ice sheets within the Ice Sheet Model Intercomparison

Project (ISMIP6; Box9.3; Nowicki et al., 2016). Our understanding

of aspects of the global water storage contained in glaciers and ice

sheets, and their contribution to sea-level rise, has improved since

AR5 and SROCC (Hock et al., 2019b; Meredith et al., 2019) both in

models andobservations (see assessment of observations and model

evaluation for the Greenland Ice Sheet in Sections 2.3.2.4.1 and 9.4.1;

Antarctica in Sections 2.3.2.4.2 and 9.4.2; and glaciers in Sections

2.3.2.3 and9.5.1).

3.4.3.1 Glaciers

Glaciers are deﬁned as perennial surface land ice masses

independent of the Antarctic and Greenland Ice Sheets (Sections 9.5

and 2.3.2.3). The AR5 assessed that anthropogenic inﬂuence had

likely contributed to the retreat of glaciers observed since the 1960s

(Bindoff et al., 2013), based on a high level of scientiﬁc understanding

and robust estimates of observed mass loss, internal variability, and

glacier response to climatic drivers. The SROCC (Hock et al., 2019b)

concluded that atmospheric warming was very likely the primary

driver of glacier recession.

Simulations of glacier mass changes under climate change rely on

glacier models driven by climate model output, often in collaborative

research efforts such as GlacierMIP (Hock et al., 2019a; Marzeion

et al., 2020). The GlacierMIP project is a systematic coordinated

modelling effort designed to further understanding of glacier loss

using global models. While the low resolution and remaining biases

of climate model-derived boundary forcing data is a limitation, the

release of the Randolph Glacier Inventory (Pfeffer et al., 2014; RGI

Consortium, 2017) has supported more sophisticated, systematic and

comprehensive modelling of glaciers worldwide (Hock et al., 2019a).

A regional study considering 85 Northern Hemisphere glacier systems

concluded that there is a discernible human inﬂuence on glacier mass

balance, with glacier model simulations driven by CMIP5 historical

and greenhouse gas-only simulations showing a glacier mass loss,

whereas those driven by natural-only forced simulations showed anet

glacier growth (Hirabayashi et al., 2016). In addition, a study of the

role of climate change in glacier retreat using a simple mass-balance

model for 37 glaciers worldwide, concluded that observed length

changes would not have occurred without anthropogenic climate

change, with observed length variations exceeding those associated

with internal variability by several standard deviations in many

cases (Roe et al., 2017). Roe et al. (2021) used the same model to

estimate that at least 85% of cumulative glacier mass loss since

1850 is attributable to anthropogenic inﬂuence. While Marzeion

et al. (2014) found that anthropogenic inﬂuence contributed only

25 ± 35% of glacier mass loss for the period 1851–2010, their

472

Chapter 3 Human Inﬂuence on the Climate System

naturally-forced simulations exhibited a substantial negative mass

balance, which Roe et al. (2021) argued is unrealistic. Moreover,

Marzeion et al. (2014) estimated that anthropogenic inﬂuence

contributed 69 ± 24% of glacier mass loss for the period 1991 to

2010, consistent with a progressively increasing fraction of mass loss

attributable toanthropogenic inﬂuence found by Roe et al. (2021).

In summary, considering together the SROCC assessment that

atmospheric warming was very likely the primary driver of glacier

recession, the results of Roe et al. (2017, 2021) and our assessment of

the dominant role of anthropogenic inﬂuence in driving atmospheric

warming (Section 3.3.1), we conclude that human inﬂuence is very

likely the main driver of the near-universal retreat of glaciers globally

since the 1990s.

3.4.3.2 Ice Sheets

3.4.3.2.1 Greenland Ice Sheet

The AR5 assessed that it is likely that anthropogenic forcing

contributed to the surface melting of the Greenland Ice Sheet since

1993 (Bindoff et al., 2013). The SROCC did not directly assess the

attribution of Greenland Ice Sheet change to anthropogenic forcing,

but it did assess with medium conﬁdence that summer melting of the

Greenland Ice Sheet has increased to a level unprecedented over at

least the last 350 years, which is two-to-ﬁvefold the pre-industrial

level (see also Trusel et al., 2018).

Section 2.3.2.4.1 assesses that Greenland Ice Sheet mass loss began

in the latter half of the 19th century and that the rate of loss has

increased substantially since the turn of the 21st century (high

conﬁdence), and also notes that integration of proxy evidence and

modelling indicates that the last time the rate of mass loss was similar

to the 20th century rate was during the early Holocene. Models of

Greenland Ice Sheet evolution are evaluated in detail in Section

9.4.1.2, which assesses that there is overall medium conﬁdence in

these models. Model evaluation of surface mass balance changes

over the Greenland Ice Sheet, including regional aspects, is also

assessed in Atlas.11.2.3.

Detection and attribution studies of change in the Greenland Ice

Sheet remain challenging (Kjeldsen et al., 2015; Bamber et al.,

2019). This is in part due to the short observational record (Shepherd

et al., 2012, 2018, 2020; Bamber et al., 2018; Cazenave et al., 2018;

Mouginot et al., 2019; Rignot et al., 2019) and the challenges this

poses to the evaluation of modelling efforts (Section 9.4.1.2). The

latter require not only dynamic ice-sheet models, but also appropriate

atmospheric and oceanic conditions to use as a boundary forcing

to drive the models (Nowicki and Seroussi, 2018; Barthel et al.,

2020). Nonetheless, new literature since AR5 ﬁnds that ice-sheet

mass balance calculations using reanalysis-driven regional model

simulations of surface mass balance are found to agree well with

the observed decrease in ice-sheet mass over the past twenty years

(Fettweis et al., 2020; Sasgen et al., 2020; Tedesco and Fettweis,

2020), consistent with earlier studies (Flato et al., 2013). These

studies also show that the exceptional melt events observed in 2012

and 2019 were associated with exceptional atmospheric conditions

(Sasgen et al., 2020; Tedesco and Fettweis, 2020). These results

support the ﬁnding that increased surface melting is associated with

warming, although atmospheric circulation anomalies, including

the summer North Atlantic Oscillation (NAO) and variations in

snowfall play an important role in driving interannual variations

(Section 9.4.1.1; Sasgen et al., 2020; Tedesco and Fettweis, 2020).

Further, a coupled ice-sheet-climate model study found emergence

of decreased surface mass balance prior to the present day in coastal

locations in Greenland, which dominate the integrated surface mass

balance (Fyke et al., 2014), suggesting that observed variations

in surface mass balance in these regions might be expected to be

distinguishable from internal variability. A CMIP6 simulation of the

historical period showed stable Greenland surface mass balance up

to the 1990s, after which it declined due to increased melt and runoff,

consistent with a downscaled reanalysis (van Kampenhout et al.,

2020). Further, all experts surveyed in astructured expert judgement

exercise examining the causes of the increase in mass loss from

the Greenland Ice Sheet over the last two decades (Bamber et al.,

2019) concluded that external forcing was responsible for at least

50% of the mass loss. A comparison of Greenland Ice Sheet mass

loss trends from observations and AR5 model projections for the

period 2007–2017 found that the magnitude of the observed surface

mass balance trends was at the top of the AR5 assessed range,

while mass loss due to changing ice dynamics was near the centre

of the AR5 range (Slater et al., 2020), providing further evidence of

consistent anthropogenically-forced mass loss trends in models and

observations.

Drawing together the evidence from the continued and strengthened

observed mass loss, the agreement between anthropogenically

forced climate simulations and observations, and historical and paleo

evidence for the unusualness of the observed rate of surface melting

and mass loss, we assess that it is very likely that human inﬂuence has

contributed to the observed surface melting of the Greenland Ice Sheet

over the past two decades, and that there is medium conﬁdence in an

anthropogenic contribution to recent overall mass loss fromGreenland.

3.4.3.2.2 Antarctic Ice Sheet

AR5 assessed that there was low conﬁdence in attributing the causes

of the observed mass loss from the Antarctic Ice Sheet since 1993

(Bindoff et al., 2013). The SROCC assessed that there is medium

agreement but limited evidence of anthropogenic forcing of Antarctic

mass balance through both surface mass balance and glacier

dynamics. It further assessed that Antarctic ice loss is dominated by

acceleration, retreat and rapid thinning of the major West Antarctic

Ice Sheet outlet glaciers (very high conﬁdence), driven by melting

of ice shelves by warm ocean waters (high conﬁdence). Based on

updated observations, Chapter 2 assesses that there is very high

conﬁdence that the Antarctic Ice Sheet lost mass between 1992 and

2017, and that there is medium conﬁdence that this mass loss has

accelerated. Models of Antarctic Ice Sheet evolution are evaluated

in detail in Section 9.4.2.2, which assesses that there is medium

conﬁdence in many ice-sheet processes in Antarctic Ice Sheet models,

but low conﬁdence in the ocean forcing affecting basal melt rates.

CMIP5 and CMIP6 models perform similarly in their simulation of

Antarctic surface mass balance (Section 9.4.2.2, Gorte et al., 2020).

473

Human Inﬂuence on the Climate System Chapter 3

Model evaluation of surface mass balance over the Antarctic Ice

Sheet, including regional aspects, is also assessed in Atlas.11.1.3.

Ice discharge around the West Antarctic Ice Sheet is strongly

inﬂuenced by variability in basal melt (Jenkins et al., 2018; Hoffman

et al., 2019), in particular at decadal and longer time scales (Snow

et al., 2017). Basal melt rate variability can be induced by wind-driven

ocean current changes, which may partly be of anthropogenic origin

via greenhouse gas forcing (Holland et al., 2019). Moreover, ice

discharge losses from the Antarctic Ice Sheet over the 2007–2017

period are close to the centre of the model-based range projected

in AR5 (Slater et al., 2020). However, expert opinion differs as to

whether recent Antarctic ice loss from the West Antarctic Ice Sheet

has been driven primarily by external forcing or by internal variability,

and there is no consensus (Bamber et al., 2019). Anthropogenic

inﬂuence on the Antarctic surface mass balance, which is expected

to partially compensate for ice discharge losses through increases in

snowfall, is currently masked by strong natural variability (Previdi and

Polvani, 2016; Bodart and Bingham, 2019), and observations suggest

that it has been close to zero over recent years (see further discussion

in Section 9.4.2.1; Slater et al., 2020).

Overall, there is medium agreement but limited evidence of

anthropogenic inﬂuence on Antarctic mass balance through changes

in ice discharge.

3.5 Human Inﬂuence on the Ocean

The global ocean plays an important role in the climate system, asit

is responsible for transporting and storing large amounts of heat

(Sections 3.5.1 and 9.2.2.1), freshwater (Sections 3.5.2 and 9.2.2.2)

and carbon (Sections 3.6.2 and 5.2.1.3) that are exchanged with the

atmosphere. Therefore, accurate ocean simulation in climate models

is essential for the realistic representation of the climatic response

to anthropogenic warming, including rates of warming, sea level

rise and carbon uptake, and the representation of coupled modes of

climate variability.

Ocean model development has advanced considerably since AR5

(Section 1.5.3.1). Ongoing model developments since AR5 have

focused on improving the realism of the simulated ocean in coupled

models, with horizontal resolutions increasing to 10–100 km (from

about 200 km in CMIP5), and increased vertical resolutions in

many modelling systems of 0–1 m for near-surface levels (from the

highest resolution of 10 m in CMIP5). These developments are aimed

at improving the representation of the diurnal cycle and coupling

to the atmosphere (e.g., Bernie et al., 2005, 2007, 2008). General

improvements to simulated ocean ﬁdelity in response to increasing

resolution are expected (Hewitt et al., 2017), and the effects of model

resolution on the ﬁdelity of ocean models are discussed in more

detail in Sections 9.2.2 and 9.2.4.

In this section we assess the global and basin-scale properties of

the simulated ocean, with a focus on evaluation of the realism

ofsimulated ocean properties, and the detection and attribution of

human-induced changes in the ocean over the period ofobservational

coverage. Observed changes to ocean temperature (Section2.3.3.1),

salinity (Section 2.3.3.2), sea level (Section 2.3.3.3) and ocean

circulation (Section 2.3.3.4) are reported in Chapter 2. A more

process-based assessment of ocean changes, alongside the

assessment of variability and changes in ocean properties with

spatial scales smaller than ocean basins, is presented in Chapter9.

3.5.1 Ocean Temperature

Ocean temperature and ocean heat content are key physical variables

considered for climate model evaluation and are primary indicators

of a changing ocean climate. This section assesses the performance

of climate models in representing the mean state ocean temperature

and heat content (Section 3.5.1.1), with a particular focus on the

tropical oceans given the importance of air-sea coupling in these

areas (Section 3.5.1.2). This is followed by an assessment of detection

and attribution studies of changes in ocean temperature and heat

content (Section 3.5.1.3). Changes in global surface temperature are

assessed in Section 3.3.1.1.

3.5.1.1 Sea Surface and Zonal Mean Ocean

TemperatureEvaluation

In CMIP3 and CMIP5 models, large SST biases were found in the

mid- and high latitudes (Flato et al., 2013). In CMIP6, the Northern

Hemisphere mid-latitude surface temperature biases appear to be

marginally improved in the multi-model mean when contrasted to

CMIP5 despite large biases remaining in a few models (Figures 3.23a

and 3.24). There is a decreased spread of the zonal mean SST error

between 50°N and 30°S, relative to CMIP5 (Figure 3.24a). On the

other hand, the Southern Ocean’s warm surface temperature bias

remains (Figure3.23a; Beadling et al., 2020), and is on average larger

in CMIP6 than in CMIP5 models (Figures 3.23a and 3.24). This warm

bias is often associated with persistent overlying atmospheric cloud

biases (Hyder et al., 2018). Several other large biases also appear

to remain largely unresolved in CMIP6, particularly warm biases in

excess of 1°C along the equatorial eastern continental boundaries of

the tropical Atlantic and Paciﬁc Oceans (Figure3.23a).

Overall, the simulated and observed trends in SST patterns are

generally consistent for the historical period (Olonscheck et al.,

2020). The CMIP6 models generally represent the observed pattern of

trends better than the CMIP5 models, and observed trends fall within

the range of simulated trends over a larger area for CMIP6 models

than for CMIP5 models (Olonscheck et al., 2020).

The CMIP5 multi-model mean zonally averaged subsurface ocean

temperature showed warm biases between 200 m and 2000 m

(mid-depth) over most latitudes, with exceptions in the Southern

Ocean (>60°S, 100 –2000 m) and upper (0–400 m) Arctic Ocean. Cold

biases were simulated near the surface (0–200 m) at most latitudes

(Flato et al., 2013). CMIP6 biases are broadly consistent with those

reported in CMIP5 for the near-surface (<200 m) and mid-depth

(200–2000 m) ocean (Voldoire et al., 2019b; Beadling et al., 2020;

Y. Zhu et al., 2020). The warm bias begins between 100 and 400 m

depth in all three basins, however, it is most prominent in the Atlantic

474

Chapter 3 Human Inﬂuence on the Climate System

Ocean, with a maximum magnitude in the equatorial latitudes, as

in CMIP5 (Figure3.25). In the Paciﬁc, the large warm biases are

mostly seen in the subtropical regions (30°N–60°N and 30°S–60°S).

The cool near surface tropical bias is most prominent in the Paciﬁc

Ocean and also present in the Atlantic, with a smaller magnitude

(Figure3.25). Relative to CMIP5, the most prominent difference is an

increase to the mid-depth (300–2000 m) warm bias in CMIP6 and a

change in sign of the bias from cold to warm for the Southern Ocean

mid-depth (>60°S) from CMIP5 to CMIP6 (Figure3.25). Compared

to CMIP3 and CMIP5, there is improved agreement between most

CMIP6 models and observations in their representation of the

zonal mean temperature of the upper 100 m of the Southern Ocean

(Beadling et al., 2020).

Focusing on the deep ocean (>2000 m), the CMIP6 ensemble mean

shows a prominent and consistent warm bias (Figure3.25), in all basins

except the equatorial and northern Paciﬁc, which contrasts to a cold

bias seen in CMIP5 (Flato et al., 2013). We note that while an updated

observational temperature dataset is used in this assessment (WOA09

was used in AR5, while WOA18, 1981–2010 is used in AR6), the deep-

ocean warm bias remains and is approaching double the magnitude

(about 0.5°C) of the equivalent CMIP5 multi-model mean bias, a feature

which is particularly prominent in the Atlantic and southern Indian

No robust bias

Robust bias

Conflicting signals

Colour

Figure3.23 | Multi-model mean bias of (a) sea surface temperature and (b) near-surface salinity, deﬁned as the difference between the CMIP6 multi-model

mean and the climatology from the World Ocean Atlas 2018. The CMIP6 multi-model mean is constructed with one realization of 46 CMIP6 historical experiments

for the period 1995–2014 and the climatology from the World Ocean Atlas 2018 is an average over all available years (1955–2017). Uncertainty is represented using the

advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree

on sign of change; diagonal lines indicate regions with no change or no robust signal, where <66% of models show a change greater than the variability threshold; crossed

lines indicate regions with conﬂicting signal, where ≥66% of models show change greater than the variability threshold and <80% of all models agree on sign of change. For

more information on the advanced approach, please refer to Cross-Chapter Box Atlas.1. Further details on data sources and processing are available in the chapter data table

(Table3.SM.1).

Figure3.24 | Biases in zonal mean and equatorial sea surface temperature

(SST) in CMIP5 and CMIP6 models. CMIP6 (red), CMIP5 (blue) and HighResMIP

(green) multi-model mean (a) zonally averaged SST bias; (b) equatorial SST bias;

and (c) equatorial SST compared to observed mean SST (black line) for 1979–1999.

The inter-model 5th and 95th percentiles are depicted by the respective shaded

range. Model climatologies are derived from the 1979–1999 mean of the historical

simulations, using one simulation per model. The Hadley Centre Sea Ice and Sea Surface

Temperature version1 (HadISST) (Rayner et al., 2003) observational climatology for

1979–1999 is used as the reference for the error calculation in (a) and (b); and for

observations in (c). Further details on data sources and processing are available in the

chapter data table (Table3.SM.1).

475

Human Inﬂuence on the Climate System Chapter 3

Oceans. Increased horizontal resolution as well as the choice of the

vertical coordinate are reported to partly improve these biases in some

models (Adcroft et al., 2019; Rackow et al., 2019; Hewitt et al., 2020).

Since AR5, there has been growing evidence that the representation

of mean surface and deeper ocean temperatures in coupled climate

models can be improved by increasing the horizontal resolution both

in the ocean and the atmosphere (e.g., Small et al., 2014; Hewitt et al.,

2016; Iovino et al., 2016; Roberts et al., 2019). At an ocean resolution

of around 1°, which is typical of CMIP6 models, some processes are

parameterized rather than explicitly resolved, leading to a compromise

in their dynamical representation. An increase in the model resolution

allows for processes to be explicitly resolved, and can for example,

enhance the simulation of eddies, thus improving simulated vertical

eddy transport, and reducing temperature drifts in the deeper ocean

(Grifﬁes et al., 2015; von Storch et al., 2016). For some models, the mean

absolute error in ocean temperature below 500 m is smaller in the high

resolution version compared to the low resolution version, particularly

in eddy-active regions such as the North Atlantic (Rackow et al., 2019).

Increasing the horizontal resolution of individual climate models often

leads to an overall decrease in the surface temperature biases over

regions where they persisted through earlier CMIP generations, such

as the central and western equatorial Paciﬁc, as well as the North

and tropical Atlantic (Figure 3.3e; Roberts et al., 2019; Hewitt et al.,

2020). Despite this, as a group the four HighResMIP models included

in Figures 3.3e and 3.24 do not on average show smaller SST biases

than the CMIP6 multi-model mean, demonstrating the importance of

factors other than resolution in contributing to SST biases.

Potential temperature and salinity bias for ocean basins (1981-2010)

Potential temperature diﬀerence (°C)

CMIP6 - WOA18

Salinity diﬀerence (PSS-78)

CMIP6 - WOA18

Figure3.25 | CMIP6 potential temperature and salinity biases for the global ocean, Atlantic Ocean, Paciﬁc Ocean and Indian Ocean. Shown in colour are the

time-mean differences between the CMIP6 historical multi-model climatological mean and observations, zonally averaged for each basin (excluding marginal and regional seas).

The observed climatological values are obtained from the World Ocean Atlas 2018 (WOA18, 1981–2010; Prepared by the Ocean Climate Laboratory, National Oceanographic

Data Center, Silver Spring, MD, USA), and are shown as labelled black contours for each of the basins. The simulated annual mean climatologies for 1981 to 2010 are calculated

from available CMIP6 historical simulations, and the WOA18 climatology utilized synthesized observed data from 1981 to 2010. Output from a total of 30 available CMIP6

models is used for the temperature panels (left column) and 28 models for the salinity panels (right column). Potential temperature units are °C and salinity units are the Practical

Salinity Scale 1978 [PSS-78]. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

476

Chapter 3 Human Inﬂuence on the Climate System

In summary, there is little improvement in the multi-model mean

sea surface and zonal mean ocean temperatures from CMIP5 to

CMIP6 (medium conﬁdence). Nevertheless, the CMIP6 models show

a somewhat more realistic pattern of SST trends (low conﬁdence).

3.5.1.2 Tropical Sea Surface Temperature Evaluation

3.5.1.2.1 Tropical Paciﬁc Ocean

In CMIP5, mean state biases in the tropical Paciﬁc Ocean including the

excessive equatorial cold tongue, erroneous mean thermocline depth

and slope along the equator remained but were improved relative

to CMIP3 (Flato et al., 2013). Misrepresentation of the interaction

between the atmosphere and ocean via the Bjerknes feedback and

vertical mixing parameterizations, and a bias in winds were among

the suggested reasons for the persistent biases (Li et al., 2014; Zhu

and Zhang, 2018). Moving to CMIP6, a reduction of the cold bias in

the equatorial cold tongue in the central Paciﬁc is found on average

in the CMIP6 models (Figure 3.24b; Grose et al., 2020; Planton

et al., 2021), however, this reduced bias is not statistically signiﬁcant

when considered across the multi-model ensemble (Planton et al.,

2021). It is also noteworthy that the longitude of the 28°C isotherm

is closer to observed in CMIP6 than in CMIP5, with a coincident

reduction in the CMIP6 inter-model standard deviation (Grose et al.,

2020). The latter result implies that there is an improvement in the

representation of the tropical Paciﬁc mean state in CMIP6 models.

Comparison of biases in individual HighResMIP models with biases

in lower resolution versions of the same models indicates that there

is no consistent improvement in SST biases in most of the equatorial

Paciﬁc with resolution (Figure3.3e; Bock et al., 2020).

3.5.1.2.2 Tropical Atlantic Ocean

Fundamental features such as the mean zonal SST gradient in the

tropical Atlantic were not reproduced in CMIP5 models. Studies

have proposed that weaker than observed alongshore winds,

underestimation of stratocumulus clouds, coarse model resolution, and

insufﬁcient oceanic cooling due to a deeper thermocline depth and

weak vertical velocities at the base of the mixed layer in the eastern

basin, underpinned these tropical Atlantic SST gradient biases (Hourdin

et al., 2015; Richter, 2015). The SST gradient biases still remain in

CMIP6. On average the cold bias in the western part of the basin is

reduced while the warm bias in the eastern part has slightly increased

(Figure3.24b,c; Richter and Tokinaga, 2020). Several CMIP6 models,

however, display large reductions in biases of the zonal SST gradient,

such that the eastern equatorial Atlantic warm SST bias and associated

westerly wind biases are mostly eliminated in these models (Richter

and Tokinaga, 2020). The high resolution (HighResMIP) CMIP6 models

show a better representation of the zonal SST gradient (Figure3.24b,c),

but some lower resolution models also perform well, suggesting that

resolution is not the only factor responsible for biases in Tropical

Atlantic SST (Richter and Tokinaga, 2020).

3.5.1.2.3 Tropical Indian Ocean

The tropical Indian Ocean mean state is reasonably well simulated both

in CMIP5 and CMIP6 (Figure3.24b,c). However, CMIP5 models show

a large spread in the thermocline depth, particularly in the equatorial

part of the basin (Saji et al., 2006; Fathrio et al., 2017b), which has

been linked to the parameterization of the vertical mixing and the wind

structure, leading to a misrepresentation of the ventilation process in

some models (Schott et al., 2009; Richter, 2015; Shikha and Valsala,

2018). A common problem with the CMIP5 models is therefore a warm

bias in the subsurface, mainly at depths around the thermocline, which

is also apparent in the CMIP6 models (Figure3.25g).

In the CMIP6 multi-model mean, the western tropical Indian Ocean

shows a slightly larger warm bias compared to CMIP5 (Figure3.24b,c),

which in part could be related to excessive supply of warm water

from the Red Sea (Grose et al., 2020; Semmler et al., 2020). The

HighResMIP models show decreases in SST bias across the Indian

Ocean with increasing resolution (Figure3.3e; Bock et al., 2020),

though as a group the SST biases in the HighResMIP models are no

smaller than those of the full CMIP6 ensemble.

3.5.1.2.4 Summary

In summary, the structure and magnitude of multi-model mean ocean

temperature biases have not changed substantially between CMIP5

and CMIP6 (medium conﬁdence). Although biases remain in the latest

generation models, the broad consistency between the observed and

simulated basin-scale ocean properties suggests that CMIP5 and CMIP6

models are appropriate tools for investigating ocean temperature

and ocean heat content responses to forcing. This also provides

high conﬁdence in the utility of CMIP-class models for detection and

attribution studies, for both ocean heat content (Section3.5.1.3) and

thermosteric sea level applications (Section3.5.3.2).

3.5.1.3 Ocean Heat Content Change Attribution

The ocean plays an important role as the Earth’s primary energy store.

The AR5 and SROCC assessed that the ocean accounted for more than

90% of the Earth’s energy change since the 1970s (Rhein et al., 2013;

Bindoff et al., 2019). These assessments are consistent with recent

studies assessed in Section 7.2 and Cross-Chapter Box 9.1, which

ﬁnd that 91% of the observed change in Earth’s total energy from

1971 to 2018 was stored in the ocean (von Schuckmann et al., 2020).

The AR5 concluded that anthropogenic forcing has very likely made

a substantial contribution to ocean warming above 700 m, whereas

below 700 m, limited measurements restricted the assessment of

ocean heat content changes in AR5 and prevented a robust comparison

between observations and models (Bindoff et al., 2013).

With the recent increase in ocean sampling by Argo to 2000 m

(Roemmich et al., 2015; Riser et al., 2016; von Schuckmann et al.,

2016) and the resulting improvements in estimates of ocean heat

content (Abraham et al., 2013; Balmaseda et al., 2013; Durack et al.,

2014b; Cheng et al., 2017; von Schuckmann et al., 2020), a more

quantitative assessment of the global ocean heat content changes

that extends into the intermediate ocean (700–2000 m) over the

more recent period (from 2005 to the present; Durack et al., 2018)

can be performed. Observed ocean heat content changes are

discussed in Section 2.3.3.1, where it is reported that it is virtually

certain that the global upper ocean (0–700 m) and very likely that

477

Human Inﬂuence on the Climate System Chapter 3

the global intermediate ocean (700–2000 m) warmed substantially

from 1971 to the present. Further, ocean layer warming contributions

are reported as 61% (0–700 m), 31% (700–2000 m) and 8%

(>2000 m) for the 1971 to 2018 period (Table2.7). CMIP5 model

simulations replicate this partitioning fairly well for the industrial-era

(1865 to 2017) throughout the upper (0–700 m, 65%), intermediate

(700–2000 m, 20%) and deep (>2000 m, 15%) layers (Gleckler et al.,

2016; Durack et al., 2018). The corresponding warming percentages

for the multi-model mean of a subset of CMIP6 simulations over the

1850–2014 period are 58% for the upper, 21% for the intermediate,

and 22% for the deep-ocean layers (Figure3.26). These results are

consistent with SROCC which assessed that it is virtually certain that

both the upper and intermediate ocean warmed from 2004 to 2016,

with an increased rate of warming since 1993 (Bindoff et al., 2019).

The spatial distribution of these changes for different ocean depths

isassessed in Section 9.2.2.1.

The multi-model means of both CMIP5 and CMIP6 historical

simulations forced with time varying natural and anthropogenic

forcing shows robust increases in ocean heat content in the upper

(0–700 m) and intermediate (700–2000 m) ocean (high conﬁdence)

(Figure3.26; Cheng et al., 2016, 2019; Gleckler et al., 2016; Bilbao

et al., 2019; Tokarska et al., 2019). Temporary (<10 years) surface

and subsurface cooling during and after large volcanic eruptions

is also captured in the upper-ocean, and global mean ocean heat

content (Balmaseda et al., 2013). The ocean heat content increase is

also reﬂected in the corresponding ocean thermal expansion which is

aleading contributor to global mean sea level rise (Sections 3.5.3.2

and 9.2.4, and Box9.1).

For the period 1971–2014, the rate of ocean heat uptake for the

global ocean in the CMIP6 models is about 6.43 [2.08–8.66]

ZJyr–1, with the upper, intermediate and deeper layers respectively

accounting for 68%, 16% and 16% of the full depth global heat

uptake (Figure 3.26). Overall, the simulated ocean heat content

changes are consistent with the updated and improved observational

analyses, within the very likely uncertainty range deﬁned for each

(see also Section 2.3.3.1, Table2.7; Domingues et al., 2008; Purkey

and Johnson, 2010; Levitus et al., 2012; Good et al., 2013; Cheng

et al., 2017; Ishii et al., 2017; Zanna et al., 2019) as well as with the

ocean components of total Earth heating assessed in Section 7.2.2.2,

Table7.1. Nevertheless, large uncertainties remain, particularly in

the deeper layers due to the poor temporal and spatial sampling

coverage, particularly in the Atlantic, Southern and Indian Oceans

Ocean Heat Content (ZJ)

Global Ocean Heat Content

Figure3.26 | Global ocean heat content in CMIP6 simulations and observations. Time series of observed (black) and simulated (red) global ocean heat content

anomalies with respect to 1995–2014 for the full ocean depth (left-hand panel); upper layer: 0–700 m (top right-hand panel); intermediate layer: 700–2000m (middle

right-hand panel); and the abyssal ocean: >2000 m (bottom right-hand panel). The best estimate observations (black solid line) for the period of 1971–2018, along

with very likely ranges (black shading) are from Section 2.3.3.1. For the models (1860–2014), ensemble members from 15 CMIP6 models are used to calculate the multi-model

mean values (red solid line) after averaging across simulations for each independent model. The very likely ranges in the simulations are shown in red shading. Simulation drift

has been removed from all CMIP6 historical runs using a contemporaneous portion of the linear ﬁt to each corresponding pre-industrial control run (Gleckler et al., 2012). Units

are zettajoules (ZJ; 1021 joule). Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

478

Chapter 3 Human Inﬂuence on the Climate System

(Garry et al., 2019). The very likely ranges of the simulated trends

for the full ocean depth and below 2000 m fall within the very

likely range of observed uptake during the last two decades. In the

intermediate layer, the multi-model ensemble mean mostly stays

above the observed 5th–95th percentile range before the year

2000, and below that range after 2000. For the upper ocean, some

individual model realizations show a reduced ocean heat content

increase during the 1970s and 1980s, which is then compensated

by a greater warming than the observations from the early 1990s.

These discrepancies have been linked with a temporary increase in

the Southern Ocean deep water formation rate, as well as with the

models’ strong aerosol cooling effects and high equilibrium climate

sensitivity (see also Section 7.5.6 and Box7.2; Andrews et al., 2019,

2020; Golaz et al., 2019; Dunne et al., 2020; Winton et al., 2020).

Nevertheless, simulations show that the rate of ocean heat uptake

has doubled in the past few decades, when contrasted to the rate

over the complete 20th century (Figure3.26), with over a third of

the accumulated heat stored below 700 m (Cheng et al., 2016, 2019;

Gleckler et al., 2016; Durack et al., 2018). The Southern Ocean shows

the strongest ocean heat uptake that penetrates to deeper layers

(Section 9.2.3.2), whereas ocean heat content increases in the Paciﬁc

and Indian Oceans largely occur in the upper layers (Bilbao et al.,

2019).

Since AR5, the attribution of ocean heat content increases to

anthropogenic forcing has been further supported by more detection

and attribution studies. These studies have shown that contributions

from natural forcing alone cannot explain the observed changes in

ocean heat content in either the upper or intermediate ocean layers,

and a response to anthropogenic forcing is clearly detectable in ocean

heat content (Gleckler et al., 2016; Bilbao et al., 2019; Tokarska et al.,

2019). Moreover, a response to greenhouse gas forcing is detectable

independently of the response to other anthropogenic forcings

(Bilbao et al., 2019; Tokarska et al., 2019), which has offset part of

the greenhouse gas induced warming. Further evidence is provided

by the agreement between observed and simulated changes in

global thermal expansion associated with the ocean heat content

increase when both natural and anthropogenic forcings are included

in the simulations (Section 3.5.3.2), though internal variability plays a

larger role in driving basin-scale thermosteric sea level trends (Bilbao

et al., 2015). Over the Southern Ocean, warming is detectable over

the late 20th century and is largely attributable to greenhouse gases

(Swart et al., 2018; Hobbs et al., 2021), while other anthropogenic

forcings such as ozone depletion have been shown to mitigate the

warming in some of the CMIP5 simulations (Swart et al., 2018; Hobbs

et al., 2021). The use of the mean temperature above a ﬁxed isotherm

rather than ﬁxed depth further strengthens a robust detection of the

anthropogenic response in the upper ocean (Weller et al., 2016), and

better accounting for internal variability in the upper ocean (Rathore

et al., 2020), helps explain reported hemispheric asymmetry in ocean

heat content change (Durack et al., 2014b).

In summary, there is strong evidence for an improved understanding

of the observed global ocean heat content increase. It is extremely

likely that human inﬂuence was the main driver of the ocean heat

content increase observed since the 1970s, which extends into the

deeper ocean (very high conﬁdence). Updated observations, like

model simulations, show that warming extends throughout the

entire water column (high conﬁdence).

3.5.2 Ocean Salinity

While ocean assessments have primarily focused on temperature

changes, improved observational salinity products since the early

2000s have supported more assessment of long-term ocean salinity

change and variability from AR4 (Bindoff et al., 2007) to AR5 across

both models and observations (Flato et al., 2013; Rhein et al., 2013).

The AR5 assessed that it was very likely that anthropogenic forcings

have made a discernible contribution to surface and subsurface

ocean salinity changes since the 1960s. The SROCC augmented these

insights, noting that observed high latitude freshening and warming

have very likely made the surface ocean less dense with a stratiﬁcation

increase of between 2.18% and 2.42% from 1970 to 2017 (Bindoff

et al., 2019). A recent observational analysis has expanded on these

assessments, suggesting a very marked summertime density contrast

enhancement across the mixed layer base of 6.2–11.6% per decade,

driven by changes in temperature and salinity, which is more than six

times larger than previous estimates (Sallée et al., 2021). An idealized

ocean modelling study suggests that the enhanced stratiﬁcation can

account for a third of the salinity enhancement signal since 1990

(Zika et al., 2018). Thus, there has been an expansion of observed

global- and basin-scale salinity change assessment literature since

AR5, with many new studies reproducing the key patterns of long-

term salinity change reported in AR5 (Rhein et al., 2013), and

linking these through modelling studies to coincident changes in

evaporation–precipitation patterns at the ocean surface (Sections

2.3.1.3, 3.3.2, 8.2.2.1 and 9.2.2).

Unlike SSTs, simulated sea surface salinity (SSS) does not provide

a direct feedback to the atmosphere. However, some recent work

has identiﬁed indirect radiative feedbacks through sea-salt aerosol

interactions (Ayash et al., 2008; Amiri-Farahani et al., 2019; Z. Wang

et al., 2019) that can act to strengthen tropical cyclones, and increase

precipitation (Balaguru et al., 2012, 2016; Grodsky et al., 2012; Reul

et al., 2014; Jiang et al., 2019). The absence of a direct feedback is one

of the primary reasons why salinity simulation is difﬁcult to constrain

in ocean modelling systems, and why deviations from the observed

near-surface salinity mean state between models and observations

are often apparent (Durack et al., 2012; Shi et al., 2017).

3.5.2.1 Sea Surface and Depth-proﬁle Salinity Evaluation

When compared to the routine assessment of simulated SST,

simulated SSS has not received the same research attention at

global- to basin-scales. For CMIP3, there was reasonable agreement

between the basin-scale patterns of salinity, with a comparatively

fresher Paciﬁc when contrasted to the salty Atlantic, and basin

salinity maxima features aligning well with the corresponding

atmospheric evaporation minus precipitation ﬁeld (Durack et al.,

2012). Similar features are also reproduced in CMIP5 along with

realistic variability in the upper layers, but less variability than

observations at 300 m and deeper, especially in the poorly sampled

479

Human Inﬂuence on the Climate System Chapter 3

Antarctic region (Pierce et al., 2012). In a regional study, only

considering the Indian Ocean, CMIP5 SSS was assessed and it

was shown that model biases were primarily linked to biases in

the precipitation ﬁeld, with ocean circulation biases playing a

secondary role (Fathrio et al., 2017a). The sea surface salinity bias

in CMIP6 models is shown in Figure3.23b.

For the ﬁrst time in AR5, alongside global zonal mean temperature,

global zonal mean salinity bias with depth was assessed for

the CMIP5 models. This showed a strong upper ocean (<300 m)

negative salinity (fresh) bias of order 0.3 PSS-78, with a tendency

toward a positive salinity (salty) bias (<0.25 PSS-78) in the Northern

Hemisphere intermediate layers (200–3000 m) (Flato et al., 2013).

These biases are also present in CMIP6, albeit with slightly smaller

magnitudes (Figure3.25). Here we expand the global zonal mean

bias assessment to consider the three independent ocean basins

individually, which allows for an assessment as to which basin

biases are dominating the global zonal mean. The basin with the

most pronounced biases is the Atlantic, with a strong upper ocean

(<300m) fresh bias, of order 0.3 PSS-78 just like the global zonal

mean, and a marked subsurface salinity bias that exceeds 0.5 PSS-78

in equatorial waters between 400–1000 m.

The Paciﬁc Ocean shares the strongest similarity to the global bias,

with a similar upper ocean (<300 m) fresh bias. Lower magnitude

positive salinity biases (about 0.3 PSS-78) are also present in both

hemispheres between 200 and 3000 m, and deeper in the Southern

Hemisphere (Figure3.25). The Indian Ocean shows similar features

to the Southern Hemisphere Paciﬁc, with a marked upper ocean

(<500 m) fresh bias of order 0.3 PSS-78, and a strong near-surface

positive bias of order 0.4 PSS-78 associated with the Arabian Sea

(Figure3.25).

For the Southern Ocean in CMIP5, considerable fresh biases

exist through the water column, and are most pronounced in the

ventilated layers representing the subtropical mode and intermediate

water masses (Sallée et al., 2013). A fresh bias in upper and

intermediate layers of comparable magnitude is also seen in CMIP6

(Figure3.25). The structure of the biases in the CMIP6 multi-model

mean (which averages across many simulations with differing

subsurface geographies and differing Southern Ocean salinity

biases (Beadling et al., 2020)) is similar to that evident in the CMIP5

multi-model mean, but with slightly smaller magnitudes. The Arctic

Ocean also on average exhibits a surface-enhanced fresh bias in the

upper ocean (Figure3.25), which is much larger than its Southern

Hemispherecounterpart.

In summary, the structure of the salinity biases in the multi-model

mean has not changed substantially between CMIP5 and CMIP6

(medium conﬁdence), though there is limited evidence that the

magnitude of subsurface biases has been reduced. Biases are

sufﬁciently small to provide conﬁdence in the utility of CMIP-class

models for detection and attribution of ocean salinity.

3.5.2.2 Salinity Change Attribution

AR5 concluded that it was very likely that anthropogenic forcings

had made a discernible contribution to surface and subsurface ocean

salinity changes since the 1960s (Bindoff et al., 2013; Rhein et al.,

2013). It highlighted that the spatial patterns of salinity trends, and

the mean ﬁelds of salinity and evaporation minus precipitation are all

similar, with an enhancement to Atlantic Ocean salinity and freshening

in the Paciﬁc and Southern Oceans. Since AR5 all subsequent work

on assessing observed and modelled salinity changes has conﬁrmed

these results.

Considerable changes to observed broad- or basin-scale ocean

near-surface salinity ﬁelds have been reported (see Section2.3.3.2),

and these have been linked to changes in the evaporation minus

precipitation patterns at the ocean surface through model simulations,

typically expressing a pattern of change where climatological mean

fresh regions become fresher and corresponding salty regions

becoming saltier (Durack et al., 2012, 2013; Zika et al., 2015; Lago

et al., 2016; Skliris et al., 2016, 2018; Cheng et al., 2020), also broadly

present in the CMIP6 multi-model mean (Figure3.27). Atbasin-scales,

Observed and modelled near-surface salinity trends

Observations (1950–2019)

CMIP6 historical multi-model mean (1950–2014)

-0.05 -0.04 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.04 0.05

PSS-78 decade-1

Near-surface climatological mean salinity, PSS-78

Figure3.27 | Maps of multi-decadal salinity trends for the near-surface

ocean. Units are Practical Salinity Scale 1978 [PSS-78] per decade. (Top) The best

estimate (Section 2.3.3.2) observed trend (1950–2019, Durack and Wijffels, 2010).

(Bottom) Simulated trend from the CMIP6 historical experiment multi-model mean

(1950–2014). Black contours show the climatological mean salinity in increments of

0.5 PSS-78 (thick lines 1 PSS-78). Further details on data sources and processing are

available in the chapter data table (Table3.SM.1).

480

Chapter 3 Human Inﬂuence on the Climate System

the depth-integrated effect of mean salinity changes as captured in

halosteric sea level for the top 0 to 2000 m has also been assessed

based on observational products, and these results mirror near-surface

patterns in the CMIP5 and CMIP6 models, with most areas that are

becoming fresher at the surface exhibiting increases in halosteric sea

level, and areas becoming saltier exhibiting decreases (Durack et al.,

2014a; Figure3.28). Further investigations using observations and

models together have tied the long-term patterns of surface and

subsurface salinity changes to coincident changes to the evaporation

minus precipitation ﬁeld over the ocean (Durack et al., 2012, 2013;

Durack, 2015; Levang and Schmitt, 2015; Zika et al., 2015, 2018; Grist

et al., 2016; Lago et al., 2016; Cheng et al., 2020), however the rate of

these changes through time continues to be an active area of active

research (Skliris et al., 2014; Zika et al., 2015, 2018; Cheng et al.,

2020; Sallée et al., 2021).

Climate change detection and attribution studies have considered

salinity, with the ﬁrst of these assessed in AR5 (Bindoff et al., 2013). Since

Figure3.28 | Long-term trends in halosteric and thermosteric sea level in CMIP6 models and observations. Units are mm yr–1. In the right-hand column,

three observed maps of 0 to 2000 m halosteric sea level trends are shown: top (D&W) from Durack and Wijffels (2010), 1950–2019, updated; upper-middle (EN4) from

Good et al. (2013), 1950–2019, updated; and lower-middle (Ishii) from Ishii et al. (2017), 1955–2019, updated. Bottom-right: the CMIP6 historical multi-model mean

(1950–2014). Red and orange colours show a halosteric contraction (enhanced salinity) and blue and green a halosteric expansion (reduced salinity). In the left-hand column,

basin-integrated halosteric (top) and thermosteric (bottom) trends for the Atlantic and Paciﬁc, the two largest ocean basins, are shown, where Paciﬁc anomalies are presented

on the x-axis and Atlantic on the y-axis. Observational estimates are presented in black, CMIP6 historical (all forcings) simulations are shown in orange squares, with the multi-

model mean shown as adark orange diamond with a black bounding box. CMIP6 hist-nat (historical natural forcings only) simulations are shown in green squares with the

multi-model mean as adark green diamond with a black bounding box. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

481

Human Inﬂuence on the Climate System Chapter 3

that time, the positive detection conclusions (Stott et al., 2008; Pierce

et al., 2012; Terray et al., 2012) have been supported by a number of

more recent and independent assessments which have reproduced the

multi-decadal basin-scale patterns of change in observations and models

(Figures 3.27 and 3.28; Durack et al., 2014a; Durack, 2015; Levang and

Schmitt, 2015; Skliris et al., 2016). Observed depth-integrated basin

responses, contrasting the Paciﬁc and Atlantic basins (freshening Paciﬁc

and enhanced salinity Atlantic) were also shown to be replicated in most

historical (natural and anthropogenically forced) simulations, with

this basin contrast absent in CMIP5 and CMIP6 natural-only simulations

that exclude anthropogenic forcing (Durack et al., 2014a; Figure3.28).

While observational sparsity considerably limits quantiﬁcation of

regional changes, a recent study by Friedman et al. (2017) assessed

salinity changes in the Atlantic Ocean from 1896 to 2013 and

conﬁrmed the pattern of mid-to-low latitude enhanced salinity

andhigh latitude North Atlantic freshening over this period exists

even after accounting for the effects of the NAO and AMO.

Considering the bulk of evidence, it is extremely likely that human

inﬂuence has contributed to observed near-surface and subsurface

salinity changes across the globe since the mid-20th century. All

available multi-decadal assessments have conﬁrmed that the

associated pattern of change corresponds to fresh regions becoming

fresher and salty regions becoming saltier (high conﬁdence). CMIP5

and CMIP6 models are only able to reproduce these patterns

in simulations that include greenhouse gas increases (medium

conﬁdence). Changes to the coincident atmospheric water cycle and

ocean-atmosphere ﬂuxes (evaporation and precipitation) are the

primary drivers of the basin-scale observed salinity changes (high

conﬁdence). This result is supported by all available observational

assessments, along with a growing number of climate modelling

studies targeted at assessing ocean and water cycle changes.

Thebasin-scale changes are consistent across models and intensify

on centennial scales from the historical period through to the

projections of future climate (high conﬁdence).

3.5.3 Sea Level

In keeping with the scope of this chapter, this section addresses

global and basin-scale sea level changes, whereas regional and local

sea level changes are assessed in Section 9.6. In AR5, the observed

sea level budget was closed by considering all contributing factors

including ocean warming, mass contributions from terrestrial

storage, glaciers, and the Antarctic and Greenland ice sheets

(Church et al., 2013b). The SROCC found that the observed global

mean sea level (GMSL) rise is consistent within uncertainties with

the sum of the estimated observed contributions for 1993–2015

and 2006–2015.

3.5.3.1 Sea Level Evaluation

The current generation of climate models do not fully resolve many

of the components required to close the observed sea level budget,

such as glaciers, ice sheets and land water storage (see Section 9.6

and Box9.1). Consequently, most CMIP-based analyses of sea level

change have focused on thermosteric sea level changes (i.e., thermal

expansion due to warming) and ocean dynamic sea level change,

both of which are simulated in the CMIP5-generation of models.

The improved agreement between modelled thermal expansion and

observed estimates during the historical period led the SROCC to

assess a high conﬁdence level in the simulated thermal expansion

using climate models and high conﬁdence in their ability to project

future thermal expansion.

Since CMIP5 models do not include all necessary components of sea

level change, this gap has been bridged by using ofﬂine models (for

glacier melt and ice-sheet surface mass balance) driven by reanalyses

and model output. Some studies have used ofﬂine mass inputs to

account for dynamic ice-sheet and terrestrial contributions. Slangen

et al. (2017) and Meyssignac et al. (2017) suggested including

corrections to several contributions to sea level changes including to

the Greenland surface mass balance and glacier contributions, based

on differences between CMIP5-driven model results and reanalysis-

driven results. This helps close the gap between models and

observations for the 20th century globally, as well as providing better

agreement with tide gauge observations in terms of interannual and

multi-decadal variability at the regional scale.

In CMIP6, ice sheets (see Sections 3.4.3.2 and 9.4) are included for

the ﬁrst time in ISMIP6 (Nowicki et al., 2016). There is also scope

for new insights into terrestrial water contributions from land

surface (and sub-surface) modelling in the Land Surface, Snow and

Soil moisture Model Intercomparison Project (LS3MIP; van den Hurk

et al., 2016). In parallel, the GlacierMIP project (Hock et al., 2019a;

Marzeion et al., 2020; see Sections 3.4.3.1 and 9.5) is also underway,

and has provided more quantitative guidance and a comprehensive

assessment of the uncertainties and best estimates of the current and

future contributions of glaciers to the sea level budget.

3.5.3.2 Sea Level Change Attribution

The SROCC concluded with high conﬁdence that the dominant

cause of GMSL rise since 1970 is anthropogenic forcing. Prior to

that, AR5 had concluded that it is very likely that there has been

a substantial contribution from anthropogenic forcings to GMSL

rise since the 1970s. Since AR5, several studies have identiﬁed

ahuman contribution to observed sea level change resulting from

a warming climate as manifest in thermosteric sea level change and

the contribution from melting glaciers and ice sheets.

For the global mean thermosteric sea level change, Slangen et al.

(2014) showed the importance of anthropogenic forcings (combined

greenhouse gas and aerosol forcings) for explaining the magnitude

of the observed changes between 1957 and 2005, considering the

full depth of the ocean and natural forcings in order to capture the

variability (see also Figure3.29). Over the 1950–2005 period, Marcos

and Amores (2014) found that human inﬂuence explains 87% of

the 0–700 m global thermosteric sea level rise. Both thermosteric

and regional dynamic patterns of sea level change in individual

forcing experiments from CMIP5 were considered by Slangen et al.

(2015) who showed that responses to anthropogenic forcings are

signiﬁcantly different from both internal variability and inter-model

482

Chapter 3 Human Inﬂuence on the Climate System

differences and that although greenhouse gas and anthropogenic

aerosol forcings produce opposite GMSL responses, there are

differences in the response on regional scales. Based on these

studies, we conclude that it is very likely that anthropogenic forcing

was the main driver of the observed global mean thermosteric sea

level change since 1970.

In an attribution study of the sea-level contributions of glaciers,

Marzeion et al. (2014) found that between 1991 and 2010, the

anthropogenic fraction of global glacier mass loss was 69 ± 24% (see

also Section 3.4.3.1). Slangen et al. (2016) considered all quantiﬁable

components of the GMSL budget and showed that anthropogenically

forced changes account for 69 ± 31% of the observed sea level rise

over the period 1970 to 2005, whereas natural forcings combined

with internal variability have a much smaller effect – only contributing

9 ± 18% of the change over the same period. These studies indicate

that about 70% of the combined change in glaciers, ice-sheet surface

mass balance and thermal expansion since 1970 can be attributed

to anthropogenic forcing, and that this percentage has increased

over the course of the 20th century. Detection studies on GMSL

change in the 20th century (Becker et al., 2014; Dangendorf et al.,

2015) found that observed total GMSL change in the 20thcentury

was inconsistent with internal variability. Dangendorf et al. (2015)

determined that for 1900 to 2011 at least 45% of GMSL change is

human-induced. A study that developed a semi-empirical model to

link sea-level change to observed GMST change concluded that at

least 41% of the 20th century sea-level rise would not have happened

in the absence of the century’s increasing GMST and that there was

a 95% probability that by 1970 GMSL was higher than that which

would have occurred in the absence of increasing GMST (Kopp et al.,

2016). Richter et al. (2020) compared modelled sea level change

with the satellite altimeter observations from 1993 to 2015; aperiod

short enough that internal variability can dominate the spatial

pattern of change. They found that when GMSL is not removed,

model simulated zonally averaged sea level trends are consistent

with altimeter observations globally as well as in each ocean basin

and much larger than might be expected from internal variability.

Using spatial correlation, Fasullo and Nerem (2018) showed that the

satellite altimeter trend pattern is already detectable.

We note that current detection and attribution studies do not

yet include all processes that are important for sea-level change

(seeSection 9.6). However, based on the body of literature available,

we conclude that the main driver of the observed GMSL rise since

at least 1971 is very likely anthropogenic forcing. The assessed

period starts in 1971 for consistency with observations assessed in

Cross-Chapter Box9.1.

Figure3.29 | Simulated and observed global mean sea level change due to thermal expansion for CMIP6 models and observations relative to the

baseline period 1850–1900. Historical simulations are shown in brown, natural only in green, greenhouse gas only in grey, and aerosol only in blue (multi-model means

shown as thick lines, and shaded ranges between the 5th and 95th percentile). The best estimate observations (black solid line) for the period of 1971–2018, along with very

likely ranges (black shading) are from Section 2.3.3.1 and are shifted to match the multi-model mean of the historical simulations for the 1995–2014 period. Further details on

data sources and processing are available in the chapter data table (Table3.SM.1).

483

Human Inﬂuence on the Climate System Chapter 3

3.5.4 Ocean Circulation

Circulation of the ocean, whether it be wind or density driven, plays

a prominent role in the heat and freshwater transport of the Earth

system (Buckley and Marshall, 2016). Thus, its accurate representation

is crucial for the realistic representation of water mass properties, and

replication of observed changes driven by atmosphere-land-ocean

coupling. Here, we assess the ability of CMIP models to reproduce

the observed large-scale ocean circulation, along with assessment

of the detection and attribution of any anthropogenically-driven

changes. We also note that the process-based understanding of these

circulation changes and circulation changes occurring at smaller

scales is assessed in Section 9.2.3.

3.5.4.1 Atlantic Meridional Overturning Circulation (AMOC)

The Atlantic Meridional Overturning Circulation (AMOC) represents

a large-scale ﬂow of warm salty water northward at the surface

and a return ﬂow of colder water southward at depth. As such,

its mean state plays an important role in transporting heat in the

climate system, while its variability can act to redistribute heat (see

Sections 2.3.3.4.1 and 9.2.3.1 for more details). Paleo-climatic and

model evidence suggest that changes in AMOC strength have played

a prominent role in past transitions between warm and cool climatic

phases (e.g., Dansgaard et al., 1993; Ritz et al., 2013).

The AR5 concluded that while climate models suggested that an

AMOC slowdown would occur in response to anthropogenic forcing,

the short direct observational AMOC record precluded it from being

used to support this model ﬁnding. Chapter 2 reports with high

conﬁdence, a weakening of the AMOC was observed in the mid-2000s

to the mid-2010s, while again also noting that the observational

record was too short to determine whether this is a signiﬁcant

trend or a manifestation of decadal and multi-decadal variability

(Section 2.3.3.4.1). Indirect evidence of AMOC weakening since at

least the 1950s is also presented, but conﬁdence in this longer-term

decrease was low (Section 2.3.3.4.1).

Despite the additional six years or so of observations since AR5, the

evaluation of the AMOC in models continues to be severely hampered

by the geographically sparse and temporally short observational

record. The longest continuous observational estimates of the AMOC

are based on measurements taken at 26°N by the RAPID-MOCHA

array (Smeed et al., 2018). Basic evaluation of the AMOC at 26°N

shows that the CMIP5 and CMIP6 multi-model mean overturning

strength is comparable with RAPID (Reintges et al., 2017; Weijer et al.,

2020), but the model range is large (12–29sverdrups (Sv)) for CMIP5

(Zhang and Wang, 2013); and 10–31 Sv for CMIP6 (Weijer et al., 2020)

(Figure3.30a). It is noted that deviations of AMOC strength in CMIP5

models have been related to global-scale sea surface temperature

biases (C.Wang et al., 2014). Both coupled and ocean-only models

also underestimate the depth of the AMOC cell (Danabasoglu et al.,

2014; Weijer et al., 2020; Figure 3.30a). Paleo-climatic evidence has

also raised questions regarding theaccuracy of the representation of

the strength and depth of the modelled AMOC during past periods

(Otto-Bliesner et al., 2007; Muglia and Schmittner, 2015). Overall,

however, both the CMIP5 and CMIP6 model ensembles simulate the

general features of the AMOC mean state reasonably well, but there is

a large spread in the latitude and depth of the maximum overturning,

and the maximum AMOC strength (Figure3.30a).

The short length of the observed time-series (RAPID has measured the

AMOC since 2004), sparse observations, observational uncertainties

(Sinha et al., 2018), as well as signiﬁcant observed variability on

interannual and longer time scales, makes comparison with modelled

AMOC variability challenging. RAPID observations show that the

overturning at 26°N was 2.9 Sv weaker in the multi-year average of

2008–2012 relative to 2004–2008 and 2.5 Sv weaker in 2012–2017

relative to 2004–2008 (Smeed et al., 2014, 2018) (see also Section

2.3.3.4.1). As expected, this weakening was accompanied by

a signiﬁcant reduction in northward heat transport (Bryden et al.,

2020). CMIP5 and CMIP6 models produce a forced weakening of the

AMOC over the 2012–2017 period relative to 2004–2008, but at

26°N the multi-model mean response is substantially weaker than

the observed AMOC decline over the same period. The discrepancy

between the modelled multi-model mean (i.e., the forced response)

and the RAPID observed AMOC changes has led studies to suggest

that the observed weakening over 2004–2017 is largely due to

internal variability (Yan et al., 2018). However, comparison of

observed RAPID AMOC variability with modelled variability also

reveals that most CMIP5 models appear to underestimate the

interannual and decadal time scale AMOC variability (Roberts et al.,

2014; Yan et al., 2018), and, although the overall variance is larger

in CMIP6 than in CMIP5, similar results are found analysing the

CMIP6 models (Figure3.30b,c). It is currently unknown why most

models underestimate this AMOC variability, or whether they are

underestimating the internal or externally forced components.

This underestimation of AMOC variability may also have potential

implications for detection and attribution, the relationship between

AMOC and AMV (see Section 3.7.7), and near-term predictions.

There is also emerging evidence, based on analysis of freshwater

transports, that the AMOC in CMIP5-era models is too stable, largely

due to systematic biases in ocean salinity (W. Liu et al., 2017;

Mecking et al., 2017). Such a systematic bias may potentially be

linked with the underestimation of both simulated AMOC internal

variability through eddy-mean ﬂow interactions that are poorly

represented in standard CMIP-class model resolution (Leroux et al.,

2018), and externally forced change.

As reported in Section 2.3.3.4.1, estimates of AMOC since at least

1950, which are generated from observed surface temperatures

or sea surface height, suggest the AMOC weakened through the

20thcentury (low conﬁdence) (Ezer et al., 2013; Caesar et al., 2018).

Over the same period, the CMIP5 multi-model mean showed no

signiﬁcant net forced response in AMOC (Cheng et al., 2013). However,

asigniﬁcant forced change is simulated in the CMIP6 multi-model

mean, where a clear increase of the AMOC is seen over the 1940–1985

period (Figure3.30e; Menary et al., 2020). Although there is general

agreement that the inﬂuence of greenhouse gases acts to a weaken

the modelled AMOC (Delworth and Dixon, 2006; Caesar et al., 2018),

changes in solar, volcanic and anthropogenic aerosol emissions can

lead to temporary changes in AMOC on decadal- to multi-decadal

time scales (Delworth and Dixon, 2006; Menary et al., 2013; Menary

and Scaife, 2014; Swingedouw et al., 2017; Undorf et al., 2018b).

484

Chapter 3 Human Inﬂuence on the Climate System

Assuch, the simulated net forced response in AMOC is a balance

between the different forcing factors (Section 9.2.3.1; Delworth and

Dixon, 2006; Menary et al., 2020). The differing AMOC response of

CMIP5 and CMIP6 models during the historical period has been

associated with stronger aerosol effective radiative forcing in the

CMIP6 models (Menary et al., 2020), such that the aerosol-induced

AMOC increase during the 1940–1985 period overcomes the

greenhouse gas induced decline (Figure 3.30e). However, models

simulate a range of anthropogenic aerosol effective radiative forcing

and a range of historical AMOC trends in CMIP6 (Menary et al., 2020)

and there remains considerable uncertainty over the realism of the

CMIP6 AMOC response during the 20th century (Figure3.30d–f) due

to disagreement among the differing lines of evidence. For example,

ocean reanalysis (Jackson et al., 2019) and forced ocean model

simulations (Robson et al., 2012; Danabasoglu et al., 2016), which

show AMOC changes that are broadly consistent with the CMIP6

response, appear to disagree with observational estimates of AMOC

over the historical period (Ezer et al., 2013; Caesar et al., 2018). It is

noted, however, that the relatively short length of the forced ocean

simulations and ocean reanalysis precludes acomparable assessment

of 20th century trends. Furthermore, despite the similar AMOC

evolution seen in forced ocean model simulations and the CMIP6

models, it is unclear whether the same underlying mechanisms are

responsible for the changes.

In summary, models do not support robust assessment of the

role of anthropogenic forcing in the observed AMOC weakening

between the mid-2000s and the mid-2010s, which is assessed

Figure3.30 | Observed and CMIP6 simulated AMOC mean state, variability and long-term trends. (a) AMOC meridional stream function proﬁles at 26.5°N from

the historical CMIP5 (1860–2004) and CMIP6 (1860–2014) simulations compared with the mean maximum overturning depth (horizontal grey line) and magnitude (vertical

grey line) from the RAPID observations (2004–2018). The distributions of model ranges of AMOC maximum magnitude and depth are respectively displayed near the x- and

y-axis. (b) Distributions of overlapping eight-year AMOC trends from individual CMIP6 historical simulations (pink box plots) are plotted along with the combined distributions

of all available CMIP5 (blue boxplot) and CMIP6 (red boxplot) models. For reference, the observed eight-year trend calculated between 2004 a nd 2012 is also shown as

a horizontal grey line (following Roberts et al., 2014). (c) Distributions of interannual AMOC variability from individual CMIP6 model historical simulations, along with the

combined distributions of all available CMIP5 and CMIP6 models. Interannual variability in models and observations is estimated as annual mean (April–March) differences,

and the horizontal grey line is the observed value for 2009/2010 minus 2008/2009 (following Roberts et al., 2014). (d– f) Distributions of linear AMOC trends calculated over

various time periods (see panel titles) in CMIP6 simulations forced with: greenhouse gas forcing only (GHG), natural forcing only (NAT), anthropogenic aerosol forcing only

(AER) and all forcing combined (Historical; HIST). (a–f) Boxes indicate the 25th to 75th percentile range, whiskers indicate 1st and 99th percentiles, and dots indicate outliers,

while the horizontal black line is the multi-model mean trend. In (d–f) the multi-model mean trend is also written above each distribution. The multi-model distributions in (a– c)

were produced with one historical ensemble member per model for which the AMOC variable was available (listed), while those in (d–f) were produced with the detection and

attribution simulation datasets utilized by Menary et al. (2020). Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

485

Human Inﬂuence on the Climate System Chapter 3

to have occurred with high conﬁdence in Section 2.3.3.4.1, as

the changes are outside of the range of modelled AMOC trends

(regardless of whether they are forced or internally generated) in

most models. Thus, we have low conﬁdence that anthropogenic

forcing has inﬂuenced the observed changes in AMOC strength

in the post-2004 period. In addition, there remains considerable

uncertainty over the realism of the CMIP6 AMOC response during

the 20th century due to disagreement among the differing lines

of observational and modelled evidence (i.e., historical AMOC

estimates, ocean reanalysis, forced ocean simulations and

historical CMIP6 simulations). Thus, we have low conﬁdence that

anthropogenic forcing has had a signiﬁcant inﬂuence on changes

in AMOC strength during the 1860–2014 period.

3.5.4.2 Southern Ocean Circulation

The Southern Ocean circulation provides the principal connections

between the world’s major ocean basins through the circulation of

the Antarctic Circumpolar Current (ACC), while also largely controlling

the connection between the deep and upper layers of the global

ocean circulation, through its upper and lower overturningcells.

The assessment of observations presented in Sections 2.3.3.4.2

and 9.2.3.2 reports that there is no evidence of an ACC transport

change, and it is unlikely that the mean meridional position of the

ACC has moved southward in recent decades (Sections 2.3.3.4.2

and 9.2.3.2). This is despite observations of surface wind displaying

an intensiﬁcation and southward shift (Section 2.4.1.2). There

is low conﬁdence in an observed intensiﬁcation of upper ocean

overturning in the Southern Ocean and there is medium conﬁdence

for a slowdown of the Antarctic Bottom Water circulation and

commensurate Antarctic Bottom Water volume decrease since the

1990s (Section 9.2.3.2). Section 9.2.3.2 presents new evidence, since

SROCC, which assessed with medium conﬁdence that the lower

cell can episodically increase as a response to climatic anomalies,

temporally counteracting the forced tendency for reduced bottom

water formation.

The modelled strength of the ACC clearly improved from CMIP3, in

which the models tended to underestimate the strength of the ACC,

to CMIP5 (Meijers et al., 2012). This improvement in the realism of

ACCstrength continues from CMIP5 to CMIP6, with the modelled ACC

strength converging toward the magnitude of observed estimates of

net ﬂow through the Drake Passage (Beadling et al., 2020). There is,

however, a small number of models that still display an ACC that

ismuch weaker than that observed, while several models also display

much more pronounced ACC decadal variability than that observed

(Beadling et al., 2020). The increased realism of the ACC was at least

partly related to noted improvements in all metrics of the Southern

Ocean’s surface wind stress forcing (Beadling et al., 2020). The most

notable wind stress forcing improvements were found in the strength

and the latitudinal position of the zonally-averaged westerly wind

stress maximum (Beadling et al., 2020; Bracegirdle et al., 2020).

While the two-cell structure of the overturning circulation appears

to be well captured by CMIP5 models (Sallée et al., 2013; Russell

et al., 2018), they tend to underestimate the intensity of the lower

cell overturning, and overestimate the intensity of the upper cell

overturning (Sallée et al., 2013). As the lower overturning cell is

closely related to Antarctic Bottom Water formation and deep

convection, both ﬁelds also display substantial errors in CMIP5

models (Heuzé et al., 2013, 2015). CMIP6 climate models show

clear improvements compared to CMIP5 in their representation of

Antarctic Bottom Water, which suggests an improved representation

of the lower overturning cell (Heuzé, 2021).

Despite notable improvements of CMIP6 models compared to CMIP5

models, inherent limitations in the representation of important

processes at play in the Southern Ocean’s horizontal and vertical

circulation remain (Section 9.2.3.2). For instance, Southern Ocean

mesoscale eddies are largely parameterized in the current generation

of climate models and, despite their small spatial scales, they are

akey element for establishing the ACC and upper overturning cell, as

well as for their future evolution under changing atmospheric forcing

(Kuhlbrodt et al., 2012; Downes and Hogg, 2013; Gent, 2016; Downes

et al., 2018; Poulsen et al., 2018). The absence of ice-sheet coupling

in the CMIP6 model suite is another important limitation, as basal

meltwater and calving can inﬂuence the circulation, particularly the

lower cell of the Southern Ocean (Bronselaer et al., 2018; Golledge

et al., 2019; Lago and England, 2019; Jeong et al., 2020; Moorman

et al., 2020). We note that early development of global climate

models with interactive ice-shelf cavities has begun and is showing

potential to be developed (Jeong et al., 2020).

In summary, while there have been improvements across successive

CMIP phases (from CMIP3 to CMIP6) in the representation of

the Southern Ocean circulation, such that the mean zonal and

overturning circulations of the Southern Ocean are now broadly

reproduced, substantial observational uncertainty and climate

model challenges preclude attribution of Southern Ocean circulation

changes (highconﬁdence).

3.6 Human Inﬂuence on the Biosphere

3.6.1 Terrestrial Carbon Cycle

The AR5 did not make attribution statements on changes in global

carbon sinks. The IPCC Special Report on Climate Change and Land

(SRCCL) assessed with high conﬁdence that global vegetation

photosynthetic activity has increased over the last 2–3 decades

(Jia et al., 2019). That increase was attributed to direct land use

and management changes, as well as to CO2 fertilization, nitrogen

deposition, increased diffuse radiation and climate change (high

conﬁdence). The AR5 assessed with high conﬁdence that CMIP5

Earth System Models (ESMs) simulate the global mean land and

ocean carbon sinks within the range of observation-based estimates

(Flato et al., 2013). The IPCC SRCCL, however, noted the remaining

shortcomings of carbon cycle schemes in ESMs (Jia et al., 2019),

which for example do not properly incorporate thermal responses of

respiration and photosynthesis, and frequently omit representations

of permafrost thaw (Comyn-Platt et al., 2018), the nitrogen cycle

(R.Q. Thomas et al., 2015) and its inﬂuence on vegetation dynamics

(Jeffers et al., 2015), the phosphorus cycle (Fleischer et al., 2019), and

accurate implications of carbon store changes for a range of land use

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Chapter 3 Human Inﬂuence on the Climate System

and land management options (Erb et al., 2018; Harper et al., 2018)

(see Sections 5.2.1.4.1 and 5.4, Figure5.24 and Table5.4 for details).

This section considers three main large-scale indicators of climate

change relevant to the terrestrial carbon cycle: atmospheric

CO2 concentration, atmosphere-land CO2 ﬂuxes, and leaf area

index. These indicators were chosen because they have been

the target of attribution studies. Other indicators, like land use

and management, and wildﬁres, relate to human inﬂuence

but are discussed in Chapter 5. Chapter 7 discusses energetic

consequences of changes in the terrestrial carbon cycle in

Section 7.4.2.5.2. CMIP5 and CMIP6 ESMs are most often run

with prescribed observed historical changes in atmospheric

CO2 concentration and diagnose CO2 emissions consistent

with these. Such calculations require that the models simulate

realistic changes in the terrestrial carbon cycle over the historical

period, as changes to land carbon stores will inﬂuence the size

of CO2 emissions consistent with prescribed CO2 pathways, and

associated remaining carbon budgets (Section 5.5). Such testing of

existing models is needed while also recognising there are process

representations still requiring inclusion.

Since AR5, atmospheric inversion studies have further tested

or constrained models, while new datasets have been used to

constrain speciﬁc parts of the terrestrial carbon cycle such as plant

respiration (Huntingford et al., 2017). Figure3.31 compares historical

emissions-driven CMIP6 simulations of global mean atmospheric CO2

concentration and net ocean and land carbon ﬂuxes to the assessed

CO2 concentration and ﬂuxes from the Global Carbon Project

(Friedlingstein et al., 2019). For 2014, the CMIP6 models simulate

arange of CO2 concentrations centred around the observed value of

397 ppmv, with a range of 381 to 412 ppmv. GSAT anomalies simulated

over the historical period are very similar in models that simulate or

prescribe changes in atmospheric CO2 concentrations (Figures 3.31b

and 3.4a). Most models simulate realistic temporal evolution of the

global net ocean and land carbon ﬂuxes, although model spread is

larger over land (Figure3.31c,d; see also Sections3.6.2 and 5.4.5.2,

and Figure 5.24). Although literature published soon after AR5

highlighted the importance of representing nitrogen limitation on

plant growth (Peng and Dan, 2015; R.Q.Thomas et al., 2015), more

recent studies note that models without nitrogen limitation can still

be consistent with the latest estimates of historical carbon cycle

changes (Arora et al., 2020; Meyerholt et al., 2020). Uncertainties

in the photosynthetic response to atmospheric CO2 concentrations

at global scales, shifts in carbon allocation and turnover, land-use

change (Hoffman et al., 2014; Wieder et al., 2019), and water

limitation are also important inﬂuences on land carbonﬂuxes.

Figure3.31 | Evaluation of historical emissions-driven CMIP6 simulations for 1850–2014. Observations (black) are compared to simulations of global mean

(a) atmospheric CO2 concentration (ppmv), with observations from the National Oceanic and Atmospheric Administration Earth System Research Laboratory (NOAA ESRL;

Dlugokencky and Tans, 2020); (b) surface air temperature anomaly (°C) with respect to the 1850–1900 mean, with observations from HadCRUT4 (Morice et al., 2012);

(c)landcarbon uptake (PgC yr–1) , (d) ocean carbon uptake (PgC yr–1), both with observations from the Global Carbon Project (GCP; Friedlingstein et al., 2019) and grey shading

indicating the observational uncertainty. Land and ocean carbon uptakes are plotted using a 10-year running mean for better visibility. The ocean uptake is offset to 0 in 1850

to correct for pre-industrial riverine-induced carbon ﬂuxes. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

487

Human Inﬂuence on the Climate System Chapter 3

All models and observational estimates agree that interannual

variability in net CO2 uptake is much larger over land than over

the ocean. Studies demonstrate that regional variations in both

the trends and the yearly strength of the terrestrial carbon sink are

considerable. Land carbon uptake is dominated by the extratropical

northern latitudes (see also Section 5.4.5.3 and Figure5.25; Ciais

et al., 2019) because the tropics may have become a net source of

carbon (Baccini et al., 2017). At local to regional scales, the dominant

driver of yearly sink strength variations is water availability, but at

continental to global scales, temperature anomalies are the dominant

driver (Section 5.2.1.4.2; Jung et al., 2017). The major role of levels

of water stored in the ground in inﬂuencing land-atmosphere CO2

exchange has also been conﬁrmed through simultaneous analysis

of satellite gravimetry and atmospheric CO2 levels (Humphrey et al.,

2018). When considered globally, simulated land and ocean carbon

sinks fall within the range of observation-based estimates with high

conﬁdence. But there is also high conﬁdence that that apparent

success arises for the wrong reasons, as models underestimate the

Northern Hemisphere carbon sink, as discussed in Section 5.4.5.3.

The seasonal cycle in atmospheric CO2, which is driven by the

drawdown of carbon by photosynthesis on land during the summer

and release by respiration during the winter, has increased in

amplitude since the start of systematic monitoring (Figure3.32; see

also Section 2.3.4.1). This trend, which is larger at higher latitudes of

the Northern Hemisphere, was ﬁrst reported by Keeling et al. (1996)

and has continued. Changes in vegetation productivity have also

been observed, as well as longer growing seasons (Park et al., 2016).

However, a slow down of the increasing trend has been noted, linked to

a slow down of both vegetation greening and growing-season length

increases (Buermann et al., 2018; Z. Li et al., 2018; K. Wang et al.,

2020). Figure3.32 shows that CMIP6 terrestrial carbon cyclemodels

partially capture the increasing amplitude of the seasonal cycle of the

land carbon sink, also seen in observational reconstructions. However,

the identiﬁcation of the human inﬂuence that contributes most

strongly to these changes in the seasonal cycle is debated.

Proposed causes of the trend in the amplitude of the seasonal cycle

of CO2, and its ampliﬁcation at higher latitudes, include increases in

the summer productivity and/or increases in the magnitude of winter

respiration of northern ecosystems (Barichivich et al., 2013; Graven

et al., 2013; Forkel et al., 2016; Wenzel et al., 2016), increases in

productivity throughout the Northern Hemisphere by CO2 fertilization,

and increases in the productivity of agricultural crops in northern

mid-latitudes (Gray et al., 2014; Zeng et al., 2014). Recent studies

have attempted to quantify the different contributions by comparing

atmospheric CO2 observations with ensembles of land surface

model simulations. Piao et al. (2017) found that CO2 fertilization of

photosynthesis is the main driver of the increase in the amplitude

of the seasonal cycle of atmospheric CO2 but noted that climate

change drives the latitudinal differences in that increase. North of

40°N, Bastos et al. (2019) also found CO2 fertilization to be the most

likely driver, with warming at northern high latitudes contributing

adecrease in amplitude, in contrast to earlier conclusions (Graven

et al., 2013; Forkel et al., 2016), and agricultural and land use

changes making only a small contribution. For temperate regions

of the Northern Hemisphere, K. Wang et al. (2020) found that the

importance of CO2 fertilization is decreased by drought stress, but

also found only asmall contribution from agricultural and land use

changes. However, many global models do not include nitrogen

fertilization, changes to crop cultivars or irrigation effects, with the

latter associated with deﬁciencies in simulated terrestrial water

cycling (H. Yang et al., 2018). All these factors inﬂuence the capability

of models to simulate accurately the seasonal cycle in atmosphere-

land CO2 exchanges. Model comparisons to the atmospheric CO2

concentration record for Barrow, Alaska, suggest that models

underestimate current levels of carbon ﬁxation (Winkler et al.,

2019) and have deﬁciencies in their phenological representation of

greenness levels, particularly for autumn (Z. Li et al., 2018). Based on

these studies and noting the uncertainty in the processes ultimately

driving changes in atmospheric CO2 seasonal cycles (Section 5.2.1.4),

we assess with medium conﬁdence that fertilization by anthropogenic

increases in atmospheric CO2 concentrations is the main driver of the

increase inthe amplitude of the seasonal cycle of atmospheric CO2.

Detection and attribution methods have been applied to leaf area

index, which represents ‘greenness’ and general photosynthetic

productivity (see Section 2.3.4.3). Nitrogen deposition and land

cover change trends remain small compared to variability, so

attributing changes in leaf area index to those processes is difﬁcult.

Using three satellite products and ten land models, Zhu et al. (2016)

found increases in leaf area index (greening) over 25–50% of global

vegetated areas, and they attributed 70% of this greening to CO2

fertilization, although they found that land use change can dominate

regionally. This is consistent with the attribution study of observed

greening of Mao et al. (2016), and with Mao et al. (2013) who found

that CO2 fertilization was the dominant cause of enhanced vegetation

growth, with latitudinal changes in leaf area index explained by the

larger land surface warming in the Northern Hemisphere. These

conclusions are also consistent with those of Zhu et al. (2017),

who found a dominant role for CO2 fertilization in driving leaf area

index changes in an attribution study in which land models were

ﬁrst weighted by performance. However, Chen et al. (2019) has

challenged these results by showing that greening in India and China

was driven by land-use change.

Leaf area index increases attributed to CO2 fertilization are due

to a direct raised physiological response. However, for drylands,

CO2-induced stomatal closure may act to conserve soil moisture and

thereby indirectly drive higher photosynthesis through higher water

use efﬁciency (Lu et al., 2016). In models with nitrogen deposition,

there is evidence that this simulated effect also inﬂuences leaf area

index trends, however, because of a lack of literature based on

large-scale land simulations including both nutrient limitation and

crop intensiﬁcation, it is not yet possible to make an attribution

statement about their individual roles in leaf area index changes.

In summary, Earth system models simulate globally averaged land

carbon sinks within the range of observation-based estimates

(high conﬁdence), but global-scale agreement masks large

regional disagreements. Based on new studies that attribute

changes in atmospheric CO2 seasonal cycle to CO2 fertilization,

albeit counteracted by other factors, combined with the medium

conﬁdence that models represent the processes driving changes in

488

Chapter 3 Human Inﬂuence on the Climate System

the seasonal cycle, we assess that there is medium conﬁdence that

CO2 fertilization is the main driver of the increase in the amplitude of

the seasonal cycle of atmospheric CO2. Based on available literature,

CO2 fertilization has been the main driver of the observed greening

trend, but there is only low conﬁdence in this assessment because

of ongoing debate about the relative roles of CO2 fertilization, high

latitude warming, and land management, and the low number of

models that represent the whole suite of processes involved.

3.6.2 Ocean Biogeochemical Variables

Since CMIP5, there has been a general increase in ocean horizontal and

vertical grid resolution in ocean model components (Arora et al., 2020;

Séférian et al., 2020). The latter of these developments is particularly

signiﬁcant for projections of ocean stressors as it directly affects the

representation of stratiﬁcation. Updates in the representation of ocean

biogeochemical processes between CMIP5 and CMIP6 have typically

involved an increase in model complexity. Speciﬁc developments have

been the more widespread inclusion of micronutrients, such as iron,

variable stoichiometric ratios, more detailed representation of lower

trophic levels including bacteria and the cycling and sinking of organic

matter. CMIP6 biogeochemical model performance is generally an

improvement on that of the parent CMIP5 generation of models

(Séférian et al., 2020). The global representation of present-day air-sea

carbon ﬂuxes and surface chlorophyll concentrations show moderate

improvements between CMIP5 and CMIP6. Similar improvements are

seen in the representation of subsurface oxygen concentrations in

most ocean basins, while the representation of surface macronutrient

concentrations in CMIP6 is shown to have improved with respect

to silicic acid but declined slightly with respect to nitrate. Model

representation of the micronutrient iron has not improved substantially

since CMIP5, but many more models are capable of representing iron.

In addition, a comparison of the carbon concentration and carbon

climate feedbacks shows no signiﬁcant change between CMIP5 and

CMIP6 (Arora et al., 2020).

Since AR5, research has also focused on the detection and attribution

of regional patterns in ocean biogeochemical change relating to

interior deoxygenation, air-sea CO2 ﬂux, and ocean carbon uptake and

associated acidiﬁcation. Characterization of ﬂux variability requires

understanding of the suite of physical and biological processes

including transport, heat ﬂuxes, interior ventilation, biological

production and gas exchange which can have very different controls

on seasonal versus interannual time scales in both the North Paciﬁc

(Ayers and Lozier, 2012) and North Atlantic (Breeden and McKinley,

2016). In the Southern Ocean, models have difﬁculty reproducing

the observed seasonal cycle and interannual variability, making

attribution particularly challenging (Lovenduski et al., 2016; Mongwe

et al., 2016, 2018).

The AR5 concluded that oxygen concentrations have decreased in

the open ocean since 1960 and such decreases can be attributed

inpart to human inﬂuence with medium conﬁdence. The decrease

in ocean oxygen content in the upper 1000 m, between 1970 and

2010, is further conﬁrmed in SROCC (medium conﬁdence), with

the oxygen minimum zone expanding in volume (see also Section

5.3.3.2). Observed oxygen declines over the last several decades

(Stendardo and Gruber, 2012; Stramma et al., 2012; Schmidtko et al.,

2017) match model estimates in the surface ocean (Oschlies et al.,

2017) but are much larger than model derived estimates in the

interior (Bopp et al., 2013; Cocco et al., 2013). Some of this difference

has been interpreted as due to a lack of representation of coastal

eutrophication in these models (Breitburg et al., 2018), but much of

it remains unexplained. This disparity is particularly apparent in the

eastern Paciﬁc oxygen minimum zone, where some CMIP5 models

showed increasing trends whereas observations show a strong

decrease (Cabré et al., 2015). However, proxy reconstructions suggest

that over the last century the ocean may have in fact undergone

increases in oxygen in the most oxygen poor regions (Deutsch et al.,

2014). As discussed in Section 5.3.1, ocean oxygen went through

wide oscillations on multi-centennial time scales through the last

deglaciation, with abrupt warming resulting in loss of oxygen in

subsurface waters of the North Paciﬁc (Praetorius et al., 2015). The

global upper ocean oxygen inventory is negatively correlated with

ocean heat content with a regression coefﬁcient comparable to that

found in ocean models (Ito et al., 2017). Variability and trends in the

observed upper ocean oxygen concentration are mainly driven by

the apparent oxygen utilization component with small contributions

from oxygen solubility, suggesting that changing ocean circulation,

mixing, and/or biochemical processes, rather than thermally

induced solubility effects may be the main drivers of observed

Figure3.32 | Relative change in the amplitude of the seasonal cycle of global

land carbon uptake in the historical CMIP6 simulations from 1961–2014. Net

biosphere production estimates from 19 CMIP6 models (red), the data-led reconstruction

JMA-TRANSCOM (Maki et al., 2010; dotted) and atmospheric CO2 seasonal cycle

amplitude changes from observations (global as dashed line, Mauna Loa Observatory

(MLO) (Dlugokencky et al., 2020) in bold black). Seasonal cycle amplitude is calculated

using the curve ﬁt algorithm package from the National Oceanic and Atmospheric

Administration Earth System Research Laboratory (NOAA ESRL). Relative changes are

referenced to the 1961–1970 mean and for short time series adjusted to have the same

mean as the model ensemble in the last 10 years. Interannual variation was removed with

a nine-year Gaussian smoothing. Shaded areas show the one sigma model spread (grey)

for the CMIP6 ensemble and the one sigma standard deviation of the smoothing (red) for

the CO2 MLO observations. Inset: average seasonal cycle of ensemble mean net biosphere

production and its one sigma model spread for 1961–1970 (orange dashed line, light

orange shading) and 2005–2014 (solid green line, green shading). Further details on

data sources andprocessing are available in the chapter data table (Table3.SM.1).

489

Human Inﬂuence on the Climate System Chapter 3

deoxygenation. The spatial distribution of the ocean deoxygenation

in the interior of the ocean as well as over coastal areas is further

assessed inSection5.3.

As one of the most commonly observed surface parameters, the

partial pressure of CO2 has been the topic of considerable detection

and attribution work. In North Atlantic subtropical and equatorial

biomes, warming has been shown to be a signiﬁcant and persistent

contributor to the observed increase in the partial pressure of

CO2 since the mid-2000s with long-term warming leading to

areduction in ocean carbon uptake (Fay and McKinley, 2013), and

with both the partial pressure of CO2 and associated carbon uptake

demonstrating strong predictability as a function of interannual to

decadal climate state (H. Li et al., 2016; Li and Ilyina, 2018). In

the Southern Ocean however, detection and attribution of surface

trends in the partial pressure of CO2 has proven more elusive and

dependent on methodology, with some studies suggesting that

Southern Ocean carbon uptake slowed from about 1990 to 2006

and subsequently strengthened from 2007 to 2010 (Lovenduski

et al., 2008; Fay et al., 2014; Ritter et al., 2017). Other studies

have suggested that poor representation of the seasonal cycle in

the Southern Ocean may confound the models’ ability to represent

changes in the partial pressure of CO2 in the Southern Ocean (Nevison

et al., 2016; Mongwe et al., 2018).

Section 5.2.1.3 assesses that both observational reconstructions

based on the partial pressure of CO2 and ocean biogeochemical models

show a quasi-linear increase in the ocean sink of anthropogenic CO2

from 1.0 ± 0.3 PgC yr–1 to 2.5 ± 0.6 PgC yr –1 between 1960–1969 and

2010–2019 in response to global CO2 emissions (high conﬁdence).

During the 1990s, the global net ﬂux of CO2 into the ocean is

estimated to have weakened to 0.8±0.5 PgC yr –1 while in 2000

and thereafter, it is estimated to have strengthened considerably

to rates of 2.0 ± 0.5 PgC yr–1, associated with changes in SST, the

surface concentration of dissolved inorganic carbon and alkalinity,

and decadal variations in atmospheric forcing (Landschützer et al.,

2016, see also Section 5.2).

Ocean acidiﬁcation is one of the most detectible metrics of

environmental change and was well covered in AR5, in which it

was assessed that the uptake of anthropogenic CO2 had very likely

resulted in acidiﬁcation of surface waters (Bindoff et al., 2013).

Since then, observations and simulations of multi-decadal trends in

surface carbon chemistry have increased in robustness. The evidence

on ocean pH decline had further strengthened in SROCC with good

agreement found between CMIP5 models and observations and an

assessment that the ocean was continuing to acidify in response to

ongoing carbon uptake (Bindoff et al., 2019). An observed decrease

in global surface open ocean pH is assessed in Section 2.3.3.5 to

be virtually certain to have occurred with a rate of 0.003–0.026 per

decade for the past 40 years. The ocean acidiﬁcation has occurred not

only in the surface layer but also in the interior of the ocean (Sections

2.3.3.5 and 5.3.3). Rates have been observed to be between −0.015

and −0.020 per decade in mode and intermediate waters of the North

Atlantic through the combined effect of increased anthropogenic and

remineralized carbon (Ríos et al., 2015) and acidiﬁcation has been

observed down to 3000 m in the deep water formation regions

(Perez et al., 2018). There has also been considerable improvement in

detection and attribution of anthropogenic CO2 versus eutrophication-

based acidiﬁcation in coastal waters (Wallace et al., 2014).

The increased evidence in recent studies supports an assessment that

it is virtually certain that the uptake of anthropogenic CO2 was the

main driver of the observed acidiﬁcation of the global surface open

ocean. The observed increase in acidiﬁcation over the North Atlantic

subtropical and equatorial regions since 2000 is likely associated

in part with an increase in ocean temperature, a response which

corresponds to the expected weakening of the ocean carbon sink with

warming. Due to strong internal variability, systematic changes in

carbon uptake in response to climate warming have not been observed

in most other ocean basins at present. We further assess, consistent

with AR5 and SROCC, that deoxygenation in the upper ocean is due

in part to anthropogenic forcing, with medium conﬁdence. There is

high conﬁdence that Earth system models simulate a realistic time

evolution of the global mean ocean carbon sink.

3.7 Human Inﬂuence on Modes

of Climate Variability

This section assesses model evaluation and attribution of changes

in the modes of climate variability listed in Cross-Chapter Box2.2,

Table2. The structure of the modes is described in Annex IV, observed

changes in the modes and associated teleconnections are assessed

in Section 2.4, and the role of the modes in shaping regional climate

is assessed in Section 10.1.3.2.

3.7.1 North Atlantic Oscillation and Northern

AnnularMode

The Northern Annular Mode (NAM; also known as the Arctic

Oscillation) is an oscillation of atmospheric mass between the Arctic

and northern mid-latitudes, analogous to the Southern Annular Mode

(SAM; Section3.7.2). It is the leading mode of variability of sea-level

pressure in the northern extratropics but also has a clear ﬁngerprint

through the troposphere up to the lower stratosphere, with maximum

expression in boreal winter (Kidston et al., 2015). The North Atlantic

Oscillation (NAO) can be interpreted as the regional expression of the

NAM and captures most of the related variance in the troposphere over

abroad North Atlantic/Europe domain. Indices measuring the state of

the NAO correlate highly with those of the NAM, and teleconnection

patterns for both modes are rather similar (Feldstein and Franzke,

2006). A detailed description of the NAM and the NAO as well as

their associated teleconnection over land is given in Annex IV.2.1.

AR5 found that while models simulated correctly most of the

spatial properties of the NAM, substantial inter-model differences

remained in the details of the associated teleconnection patterns

over land (Flato et al., 2013). The AR5 reported that most models

did not reproduce the observed positive trend of the NAO/NAM

indices during the second half of the 20th century. It was unclear

to what extent this failure reﬂected model shortcomings and/or if

the observed trend could be simply related to pronounced internal

490

Chapter 3 Human Inﬂuence on the Climate System

climate variability. The AR5 accordingly did not make an attribution

assessment for the NAO/NAM.

New studies since AR5 continue to ﬁnd that CMIP5 models reproduce

the spatial structure and magnitude of the NAM reasonably well (Lee

and Black, 2013; Zuo et al., 2013; Davini and Cagnazzo, 2014; Ying

et al., 2014; Ning and Bradley, 2016; Deser et al., 2017b; Gong et al.,

2017) although the North Paciﬁc SLP anomalies remain generally too

strong (Zuo et al., 2013; Gong et al., 2017) and the subtropical North

Atlantic lobe of SLP anomalies conversely too weak (Ning and Bradley,

2016) in many models. Such overall biases noted in both CMIP3

and CMIP5 (Davini and Cagnazzo, 2014) persist in CMIP6 historical

simulations, even though the multi-model multi-member ensemble

mean spatial correlation between modelled and observed NAM is

slightly higher (Figure3.33a,d,g). Regarding the NAO, the majority

of CMIP5 models very successfully simulate its spatial structure (Lee

et al., 2019) and its associations with extratropical jet, storm track

and blocking variations over a broad North-Atlantic/Europe domain

(Davini and Cagnazzo, 2014) and over land through teleconnections

(Volpi et al., 2020). The good performance of themodels is conﬁrmed

in CMIP6 with a marginal improvement of the averaged observation-

model spatial correlation (Figure3.33b,e,h) and better skill based

on other evaluation metrics (Fasullo et al., 2020). The slight

underestimation of the SLP anomalies related to the NAO centres

of actions over the Azores and Greenland–Iceland–Norwegian Seas

remain unchanged compared to CMIP5.

CMIP5 models with a model top within the stratosphere seriously

underestimate the amplitude of the variability of the wintertime

NAM expression in the stratosphere, in contrast to CMIP5 models

which extend well above the stratopause (Lee and Black, 2015).

However, even in the latter models, the stratospheric NAM events,

and their downward inﬂuence on the troposphere, are insufﬁciently

persistent (Charlton-Perez et al., 2013; Lee and Black, 2015).

Increased vertical resolution does not show any signiﬁcant added

value in reproducing the structure and magnitude of the tropospheric

NAM (Lee and Black, 2013) nor in the NAO predictability as assessed

in a seasonal prediction context with a multi-model approach (Butler

et al., 2016). On the other hand, there is mounting evidence that

acorrect representation of the Quasi Biennal Oscillation, extratropical

stratospheric dynamics (the polar vortex and sudden stratospheric

warmings), and related troposphere-stratosphere coupling, as well as

their interplay with ENSO, are important for NAO/NAM timing (Scaife

et al., 2016; Karpechko et al., 2017; Domeisen, 2019; Domeisen et al.,

2019), in spite of underestimated troposphere–stratosphere coupling

found in models compared to observations (O’Reilly et al., 2019b).

The observed trend of the NAM and NAO indices is positive in

winter when calculated from the 1960s (Section 2.4.1.1) but it

includes large multi-decadal variability, which means that the

nature of the trend should be interpreted with caution (Gillett et al.,

2013). Themulti-model multi-member ensemble mean of the trend

estimated from historical simulations over that period is very close

to zero for both CMIP5 and CMIP6 (Figures 3.33j,k and 3.34a). Even

if one cannot rule out that 1958–2014 was an exceptional period

of variability, the observational estimates of the wintertime NAO

trend lie outside the 5th–95th percentile range of the distribution

of trends in the CMIP6 historical simulations, and the observed NAM

trends over the same period lie above the 90th percentile. There is

a tendency for the CMIP5 models to systematically underestimate

the level of multi-decadal versus interannual variability of the winter

NAO and jet stream compared to observations (X. Wang et al., 2017;

Bracegirdle et al., 2018; Simpson et al., 2018). Results from CMIP6

(Figure3.33j,k) and over the 1958–2019 period (Figure3.34a) conﬁrm

this conclusion and seriously question the ability of the models to

simulate long-term ﬂuctuations of the NAO/NAM, independently of

its forced or internal origins.

Dedicated SST-forced stand-alone atmospheric model experiments

(AMIP) suggest that ocean forcing appears to play a role in decadal

variability of the NAO and associated ﬂuctuations in the strength

of the jet (Woollings et al., 2015). In particular, Atlantic and Indian

Ocean SST anomalies (Fletcher and Cassou, 2015; Baker et al., 2019;

Douville et al., 2019; Dhame et al., 2020) may have contributed

to the long-term positive trend of the winter NAO/NAM over the

20th century, but there is only low conﬁdence in such a causal

relationship because of the limitation of the imposed SST approach

in AMIP and the uncertainties in observed SST trends among datasets

used as forcing of the atmospheric model. The representation of

the NAM and NAO spatial structure is slightly improved in AMIP

ensembles (Figure3.33g,h), which also produce slightly larger trends

than the historical simulations for the NAO, but not for the NAM.

When calculated over the most recent two decades, the wintertime

NAM/NAO trend is weakly negative since the mid-1990s (Hanna

et al., 2015). Recent studies based on observations (Gastineau and

Frankignoul, 2015) and dedicated modelling experiments (Davini

et al., 2015; Peings and Magnusdottir, 2016) suggest that the recent

dominance of negative NAM/NAO could be partly related to the latest

shift of the Atlantic Multi-decadal Variability (AMV) to a warm phase

(Sections 2.4.4 and 3.7.7). Some recent modelling studies also ﬁnd

that the Arctic sea ice decline might be partly responsible for more

recurrent negative NAM/NAO (Peings and Magnusdottir, 2013; B.M.

Kim et al., 2014; Nakamura et al., 2015), while other studies do not

robustly identify such responses in models (see also Cross-Chapter

Box10.1).

In contrast to winter, the observed trend of the NAO index over

1958–2014 is overall negative in summer and is associated with

more recurrent blocking conditions over Greenland, in particular

since the mid-1990s, thus contributing to the acceleration of melting

of the Arctic sea ice (Section 3.4.1.1) and Greenland Ice Sheet

(Section3.4.3.2; Fettweis et al., 2013; Hanna et al., 2015; Ding et al.,

2017). The origin of the negative trend of the summer NAO has

not been clearly identiﬁed, and is hypothesized to be the result of

combined inﬂuences (Lim et al., 2019), though trends in summertime

NAO should also be interpreted with caution because of the presence

of strong multi-decadal variability. The recent observed negative NAO

prevalence and related blocking over Greenland is not present in any

of the CMIP5 models (Hanna et al., 2018).

Regarding the inﬂuence of external forcings since pre-industrial

times, AR5 noted that CMIP5 models tend to show an increase in the

NAM in response to greenhouse gas increases (Bindoff et al., 2013).

491

Human Inﬂuence on the Climate System Chapter 3

Figure3.33 | Model evaluation of NAM, NAO and SAM in boreal winter. Regression of Mean Sea Level Pressure (MSLP) anomalies (in hPa) onto the normalized

principal component (PC) of the leading mode of variability obtained from empirical orthogonal decomposition of the boreal winter (December–February) MSLP poleward of

20°N for the observed Northern Annular Mode (NAM, a), over 20°N –80°N, 90°W–40°E for the North Atlantic Oscillation as shown by the black sector (NAO, b), and poleward

of 20°S for the Southern Annular Mode (SAM, c) for the JRA-55 reanalysis. Cross marks indicate regions where the anomalies are not signiﬁcant at the 10% level based on a

t-test. The period used to calculate the NAO/NAM is 1958 –2014 but 1979–2014 for the SAM. (d–f) Same but for the multi-model ensemble (MME) mean from CMIP6 historical

simulations. Models are weighted in compositing to account for differences in their respective ensemble size. Diagonal lines show regions where less than 80% of the runs

agree in sign. (g–i) Taylor diagrams summarizing the representation of the modes in models and observations following Lee et al. (2019) for CMIP5 (light blue) and CMIP6 (red)

historical simulations. The reference pattern is taken from JRA-55 (a–c). The ratio of standard deviation to that of the reference observations (radial distance), spatial correlation

(radial angle) and resulting root-mean-squared errors (solid isolines) are given for individual ensemble members (crosses) and for other observational products (ERA5 and NOAA

20CR version3, black dots). Coloured dots stand for weighted multi-model mean statistics for CMIP5 (blue) and CMIP6 (light red) as well as for AMIP simulations from CMIP6

(orange). (j–l) Histograms of the trends built from all individual ensemble members and all the models (brown bars). Vertical lines in black show all the observational estimates.

The orange, light red, and light blue lines indicate the weighted multi-model mean of CMIP6 AMIP, CMIP6 and CMIP5 historical simulations, respectively. Further details on data

sources and processing are available in the chapter data table (Table3.SM.1).

492

Chapter 3 Human Inﬂuence on the Climate System

Based on the CMIP5 historical ensemble, Gillett and Fyfe (2013)

however showed that such a trend is not signiﬁcant in all seasons.

A multi-model assessment of eight CMIP5 models found a NAM

increase in response to greenhouse gases, but no robust inﬂuence of

aerosol changes (Gillett et al., 2013). As for ozone depletion, there is

no robust detectable inﬂuence on long-term trends of the NAO/NAM

(Karpechko et al., 2018) in contrast to the SAM (Section 3.7.2), but

there are indications that extreme Arctic ozone depletion events and

their surface expression are linked to an anomalously strong NAM

episodes (Calvo et al., 2015; Ivy et al., 2017). However, the direction

of causality here is not clear.

Conclusions on external forcing inﬂuences on the NAM are supported

by CMIP6 results based on single forcing ensembles (Figure3.34a).

Positive trends are found in historical simulations over 1958–2019

in boreal winter and are mainly driven by greenhouse gas increases.

No signiﬁcant trends are simulated in response to anthropogenic

aerosols, stratospheric ozone or natural forcing. Albeit weak and

not statistically signiﬁcant, the sign of the multi-model mean forced

response due to natural forcing is consistent with the observed

reduction of solar activity since the 1980s (Section 2.2.1) whose

inﬂuence would have favoured the negative phase of wintertime

NAM/NAO based on the ﬁngerprint of the nearly periodical 11-year

solar cycle extracted from models (Scaife et al., 2013; Andrews et al.,

2015; Thiéblemont et al., 2015) or observations (Gray et al., 2016;

Lüdecke et al., 2020). But such an NAO response to solar forcing

remains highly uncertain and controversial, being contradicted by

longer proxy records over the last millennium (Sjolte et al., 2018)

and modelling evidence (Gillett and Fyfe, 2013; Chiodo et al.,

2019). For all seasons and for all individual forcings, uncertainties

remain in the estimation of the forced response in the NAM trend as

evidenced by considerable model spread (Figure3.34a) and because

the simulated forced component has small amplitude compared

tointernalvariability.

Figure3.34 | Attribution of observed seasonal trends in the annular modes to forcings. Simulated and observed trends in NAM indices over 1958–2019 (a)and

in SAM indices over 1979–2019 (b) and over 2000–2019 (c) for boreal winter (December–February average; DJF) and summer (June–August average; JJA). The indices are

based on the difference of the normalized zonally averaged monthly mean sea level pressure between 35°N and 65°N for the NAM and between 40°S and 65°S for the SAM

as deﬁned in Jianping and Wang (2003) and Gong and Wang (1999), respectively; the unit is decade–1. Ensemble mean, interquartile ranges and 5th and 95th percentiles are

represented by empty boxes and whiskers for pre-industrial control simulations and historical simulations. The number of ensemble members and models used for computing

the distribution is given in the upper-left legend. Grey lines show observed trends from the ERA5 and JRA-55 reanalyses. Multi-model multi-member ensemble means of the

forced component of the trends as well as their 5–95% conﬁdence intervals assessed from t-statistics, are represented by ﬁlled boxes, based on CMIP6 individual forcing

simulations from DAMIP ensembles; greenhouse gases in brown, aerosols in light blue, stratospheric ozone in purple and natural forcing in green. Models with at least three

ensemble members are used for the ﬁlled boxes, with black dots representing the ensemble means of individual models. Further details on data sources and processing are

available in the chapter data table (Table3.SM.1).

493

Human Inﬂuence on the Climate System Chapter 3

Despite new efforts since AR5 to reconstruct the NAO beyond the

instrumental record, it is still very challenging to assess the role

of external forcings in the apparent multi-decadal to centennial

variability present throughout the last millennium. Large uncertainties

remain in the reconstructed NAO index that are sensitive to the

types of proxies and statistical methods (Trouet et al., 2012; Ortega

et al., 2015; Anchukaitis et al., 2019; Cook et al., 2019; Hernández

et al., 2020; Michel et al., 2020) and reconstructed NAO variations

are often not reproduced using pseudo-proxy approaches in models

(Lehner et al., 2012; Landrum et al., 2013). At low frequency, it

remains challenging to evaluate if the observed or reconstructed

signal corresponds to an actual change in the NAO intraseasonal to

interannual intrinsic properties or rather to a change in the mean

background atmospheric circulation changes projecting on a speciﬁc

phase of the mode. Consequently, conﬂicting results emerge in the

attribution of reconstructed long-term variations in the NAO to solar

forcing, whose inﬂuence thus remains controversial (Gómez-Navarro

and Zorita, 2013; Moffa-Sánchez et al., 2014; Ortega et al., 2015;

AitBrahim et al., 2018; Sjolte et al., 2018; Xu et al., 2018). Inﬂuences

from major volcanic eruptions appear to be more robust (Ortega et al.,

2015; Swingedouw et al., 2017) even if some modelling experiments

question the amplitude of the response, which mostly projects on

the positive phase of the NAM/NAO (Bittner et al., 2016). The

forced response is dependent on the strength, seasonal timing and

location of the eruption but may also depend on the mean climate

background state (Zanchettin et al., 2013) and/or the phases of the

main modes of decadal variability such as the AMV (Section3.7.7;

Ménégoz et al.,2018).

Finally, there is some evidence of an apparent signal-to-noise

problem referred to as ‘paradox’ in seasonal and decadal hindcasts

of the NAO over the period 1979–2018 (Scaife and Smith, 2018),

which suggests that the NAO response to external forcing, SST or

sea ice anomalies could be too weak in models. The weakness of the

signal has been related to troposphere-stratosphere coupling which

is too intermittent (O’Reilly et al., 2019b) and to chronic model biases

in the persistence of NAO/NAM daily regimes, which is critically

underestimated in coupled models (Strommen and Palmer, 2019;

Zhang and Kirtman, 2019), and which does not exhibit signiﬁcant

improvement when model resolution is increased (Fabiano et al.,

2020). Note, however, that the apparent signal-to-noise problem may

be dependent on the period analysed over the 20th century, which

questions its interpretation as a general characteristic of coupled

models (Weisheimer et al., 2020).

In summary, CMIP5 and CMIP6 models are skilful in simulating the

spatial features and the variance of the NAM/NAO and associated

teleconnections (high conﬁdence). There is limited evidence for

asigniﬁcant role for anthropogenic forcings in driving the observed

multi-decadal variations of the NAM/NAO from the mid 20th century.

Conﬁdence in attribution is low: (i) because there is a large spread in the

modelled forced responses which is overwhelmed anyway by internal

variability; (ii) because of the apparent signal-to-noise problem; and (iii)

because of the chronic inability of models to produce a range of trends

which encompasses the observed estimates over the last 60 years.

3.7.2 Southern Annular Mode

The Southern Annular Mode (SAM) consists of a meridional

redistribution of atmospheric mass around Antarctica (Figure3.33c,f),

associated with a meridional shift of the jet and surface westerlies

over the Southern Ocean. SAM indices are variously deﬁned as the

difference in zonal-mean sea level pressure or geopotential height

between middle and high latitudes or via a principal-component

analysis (Annex IV.2.2). Observational aspects of the SAM are

assessed in Section 2.4.1.2.

AR5 assessed that CMIP5 models have medium performance in

reproducing the SAM with biases in pattern (Flato et al., 2013). It

also concluded that the trend of the SAM toward its positive phase in

austral summer since the mid-20th century is likely to be due in part

to stratospheric ozone depletion, and there was medium conﬁdence

that greenhouse gases have also played a role (Bindoff et al., 2013).

Based on proxy reconstructions, AR5 found with medium conﬁdence

that the positive SAM trend since 1950 was anomalous compared

tothe last 400 years (Masson-Delmotte et al., 2013).

Additional research has shown that CMIP5 models reproduce

the spatial structure of the SAM well, but tend to overestimate its

variability in austral summer at interannual time scales, although this

variability is within the observational uncertainty (Figure 3.33c,f,i;

Zheng et al., 2013; Schenzinger and Osprey, 2015). This is related

to the models’ tendency to simulate slightly more persistent SAM

anomalies in summer compared to reanalyses (Schenzinger and

Osprey, 2015; Bracegirdle et al., 2020). This may be due in part to

too weak a negative feedback from tropospheric planetary waves

(Simpson et al., 2013). CMIP6 models show improved performance

in reproducing the spatial structure and interannual variance of the

SAM in summer based on Lee et al. (2019) diagnostics (Figure3.33i),

with a better match of its trend with reanalyses over 1979–2014

(Figure3.33l), more realistic persistence and improved positioning of

the westerly jet, which in CMIP5 models on average is located too far

equatorward (Bracegirdle et al., 2020; Grose et al., 2020). In CMIP5, it

is also found that models which extend above the stratopause tend

to simulate stronger summertime trends in the late 20th century

than their counterparts with tops within the stratosphere (Rea

et al., 2018; Son et al., 2018), though other differences between

these sets of models, such as additional physical processes operating

in the stratosphere or interactive ozone chemistry, may have also

affected these results (Gillett et al., 2003a; Sigmond et al., 2008; Rea

et al., 2018). At the surface, Ogawa et al. (2015) demonstrate with an

atmospheric model the importance of sharp mid-latitude SST gradients

for stratospheric ozone depletion to affect the SAM in summer. These

studies imply that the well resolved stratosphere combined with ﬁner

ocean horizontal resolution has contributed to the stronger simulated

trends in CMIP6 than in CMIP5.

CMIP6 historical simulations capture the observed positive trend of

the summertime SAM when calculated from the 1970s to the 2010s

(Figure3.34b). J.L. Thomas et al. (2015) found that the chance of the

observed 1980–2004 trend occurring only due to internal variability is

less than 10% in many of the CMIP5 models, and results from CMIP6

models suggest that the chance of the 1979–2019 trend being due

494

Chapter 3 Human Inﬂuence on the Climate System

to internal variability could be even lower (Figure3.34b). Although

paleo-reconstructions of the SAM index are uncertain and vary in

terms of long-term trends (Section 2.4.1.2), new reconstructions

show that the 60-year summertime SAM trend since the mid-20th

century is outside the 5th–95th percentile range of the trends in the

pre-industrial variability, which matches the trend range of CMIP5

pre-industrial control simulations well (Dätwyler et al., 2018).

In general agreement with AR5, new research continues to indicate

that both stratospheric ozone depletion and increasing greenhouse

gases have contributed to the trend of the SAM during austral summer

toward its positive phase in recent decades (Solomon and Polvani,

2016), with the ozone depletion inﬂuence dominating (Gerber and

Son, 2014; Son et al., 2018). In CMIP6 historical simulations there are

signiﬁcant positive SAM trends over the 1979–2019 period in austral

summer, although the contribution from ozone forcing evaluated

with the four available models is not signiﬁcant (Figure3.34b). Three

of these models share the same standard prescribed ozone forcing

and produce signiﬁcantly positive SAM trends over an extended

period (1957–2019). The fourth model, MRI-ESM2-0, has the option

of interactive ozone chemistry. Its ozone-only experiment is forced

by prescribed ozone derived from its own historical simulations

and produces a negative SAM trend associated with weak ozone

depletion (Morgenstern et al., 2020). Morgenstern et al. (2014) and

Morgenstern (2021) ﬁnd an indirect inﬂuence of greenhouse gases

on the SAM via induced ozone changes in coupled chemistry-climate

simulations, which differ from the prescribed ozone simulations shown

in Figure3.34b. Since about 1997, the effective abundance of ozone-

depleting halogen has been decreasing in the stratosphere (WMO,

2018), leading to a stabilization or even a reversal of stratospheric

ozone depletion (Sections 2.2.5.2 and 6.3.2.2). The ozone stabilization

and slight recovery since about 2000 may have caused a pause in the

summertime SAM trend (Figure3.34c; Saggioro and Shepherd, 2019;

Banerjee et al., 2020), although some inﬂuence from internal variability

cannot be ruled out. While some studies ﬁnd an anthropogenic aerosol

inﬂuence on the summertime SAM (Gillett et al., 2013; Rotstayn,

2013), recent studies with larger multi-model ensembles ﬁnd that

this effect is not robust (Steptoe et al., 2016; Choi et al., 2019),

consistent with CMIP6 single forcing ensembles (Figure3.34). In the

CMIP5 simulations, volcanic stratospheric aerosol has asigniﬁcant

weakening effect on the SAM in autumn and winter (Cross-Chapter

Box4.1; Gillett and Fyfe, 2013), but there is no evidence that this

effect leads to asigniﬁcant multi-decadal trend since the late 20th

century. Beyond external forcing, Fogt et al. (2017) show a signiﬁcant

association of tropical SST variability with the summertime SAM

trend since the mid-20th century in agreement with Lim et al. (2016),

who, however, demonstrate that such ateleconnection between the

summertime SAM and ElNiño–Southern Oscillation (Annex IV.2.3),

found in observations, ismissing in many CMIP5 models.

On longer time scales, last millennium experiments from CMIP5

models fail to capture multicentennial variability evident in the

reconstructions for the pre-industrial era (Abram et al., 2014; Dätwyler

et al., 2018), which is also the case in those from available CMIP6

models (Figure 3.35). However, there is large uncertainty among

reconstructions (Section 2.4.1.2). It is therefore unclear whether this

disagreement reﬂects this observational uncertainty, whetherforcings

such as variations in the imposed insolation may be too weak, whether

models are insufﬁciently sensitive to such variations, or whether internal

variability including that associated with tropical Paciﬁc variability

is under-represented (Abram et al., 2014). The explanation could be

acombination of all these factors. However, despite the aforementioned

limitations of the reconstructions, Section 2.4.1.2 assesses that

the recent positive trend in the SAM is likely unprecedented in at

least the past millennium (medium conﬁdence). CMIP5 and CMIP6

last-millennium simulations only capture the present anomalous state

during the ﬁnal decades of the simulations which are dominated by

human inﬂuence; this state is also outside the range of simulated

variability characteristic ofpre-industrial times.

In summary, it is very likely that anthropogenic forcings have

contributed to the observed trend of the summer SAM toward its

positive phase since the 1970s. This assessment is supported by

further model studies that conﬁrm the human inﬂuence on the

summertime SAM with improved models since AR5. While ozone

depletion contributed to the trend from the 1970s to the 1990s

(medium conﬁdence), its inﬂuence has been small since 2000,

leading to a weaker summertime SAM trend over 2000–2019

(medium conﬁdence). Climate models reproduce the spatial structure

of the summertime SAM observed since the late 1970s well (high

conﬁdence). CMIP6 models reproduce the spatiotemporal features

and recent multi-decadal trend of the summertime SAM better than

CMIP5 models (medium conﬁdence). However, there is a large spread

in the intensity of the SAM response to ozone and greenhouse gas

changes in both CMIP5 and CMIP6 models (high conﬁdence), which

Figure3.35 | Southern Annular Mode (SAM) indices in the last millennium.

(a) Annual-mean SAM reconstructions by Abram et al. (2014) and Dätwyler et al. (2018).

(b) The annual-mean SAM index deﬁned by Gong and Wang (1999) in CMIP5 and

CMIP6 last millennium simulations extended by historical simulations. All indices are

normalized with respect to 1961–1990 means and standard deviations. Thin lines and

thick lines show seven-year and 70-year moving averages, respectively. Further details

on data sources and processing are available in the chapter data table (Table3.SM.1).

495

Human Inﬂuence on the Climate System Chapter 3

limits the conﬁdence in the assessment of the ozone contribution

to the observed trends. CMIP5 and CMIP6 models do not capture

multicentennial variability of the SAM found in proxy reconstructions

(low conﬁdence). This conﬁdence level reﬂects that it is unclear

whether this is due to a model or an observational shortcoming.

3.7.3 ElNiño–Southern Oscillation (ENSO)

The El Niño–Southern Oscillation (ENSO), which is generated via

seasonally modulated interactions between the tropical Paciﬁc ocean

and atmosphere, inﬂuences severe weather, rainfall, river ﬂow and

agricultural production over large parts of the world (McPhaden

et al., 2006). In fact, the remote climate inﬂuence of ENSO is so large

that knowledge of its current phase and forecasts of its future phase

largely underpin many seasonal rainfall and temperature forecasts

worldwide (Annex IV.2.3).

AR5 noted that there have been clear improvements in the simulation of

ENSO through previous generations of CMIP models (Flato et al., 2013),

such that many CMIP5 models displayed behaviour that was qualitatively

similar to that of the observed ENSO (Guilyardi et al., 2012). However,

systematic errors were identiﬁed in the models’ representation of the

tropical Paciﬁc mean state and aspects of their interannual variability

that affect quantitative comparisons. The AR5 assessment of ENSO

concluded that the considerable observed inter-decadal modulations

in ENSO amplitude and spatial pattern were largely consistent with

unforced model simulations. Thus, there was low conﬁdence in the role

of a human-induced inﬂuence in these (Bindoff et al., 2013).

Observed ENSO amplitude, which is measured by the standard

deviation of SST anomalies in a central equatorial Paciﬁc region often

referred to as the Nino 3.4 region, along with the lifecycle of events, are

both reasonably well reproduced by most CMIP5 and CMIP6 models

(Figure3.36; Bellenger et al., 2014; Planton et al., 2021). The average

CMIP5 model ENSO amplitude is slightly lower than that observed,

while the average CMIP6 model ENSO amplitude is slightly higher than

observed (Figure3.36). The ENSO amplitude of the individual models,

however, is highly variable across CMIP5 and CMIP6 models with many

displaying either more or less variability than observed (Stevenson,

2012; Grose et al., 2020; Planton et al., 2021).

ENSO events are often synchronized to the seasonal cycle in the

observations, as the associated SST anomalies tend to peak in

boreal winter (November to January) and be at their weakest in the

boreal spring (March to April) (Harrison and Larkin, 1998; Larkin

and Harrison, 2002). The majority of CMIP5 and CMIP6 models

broadly reproduce the seasonality of ENSO SST variability in the

central equatorial Paciﬁc (Taschetto et al., 2014; Abellán et al., 2017;

Grose et al., 2020; Planton et al., 2021) (Figure 3.37). However,

CMIP5 models, while displaying an improvement on CMIP3 models,

appear to under-represent the magnitude of the seasonal variance

modulation (Bellenger et al., 2014). This under-representation of

seasonal variance modulation continues in CMIP6 models, which

display no statistically signiﬁcant difference in this behaviour when

compared to CMIP5 models (Planton et al., 2021) (Figure3.37).

Observations show strong multi-decadal modulation of ENSO variance

throughout the 20th century, with the most recent period displaying

larger variability while the mid-century displayed relatively low ENSO

variability (Figure2.36; Li et al., 2013; McGregor et al., 2013; Hope

et al., 2017). As assessed in Section 2.4.2, ENSO amplitude since 1950

is higher than over the pre-industrial period from 1850 as far back

as 1400 (medium conﬁdence), but there is low conﬁdence that it is

higher than the variability over periods prior to 1400. This reported

variance increase suggests that external forcing plays a role in the

ENSO variance changes (Hope et al., 2017). However, large ensembles

of single model or multiple model simulations do not ﬁnd strong trends

in ENSO variability over the historical period, suggesting that external

forcing has not yet modulated ENSO variability with amagnitude that

exceeds the range of internal variability (Hope et al., 2017; Maher et al.,

2018b; Stevenson et al., 2019). This is consistent with the Chapter2

assessment that there is no clear evidence for a recent sustained shift

in ENSO beyond the range of variability on decadal to millennial time

scales (Section 2.4.2). CMIP5 and CMIP6 models show a decrease in

ENSO variance in the mid-Holocene (Brown et al., 2020), though not to

the extent seen in paleo-proxy records (Emile-Geay et al., 2016). This

suggests that both modelled and observed ENSO respond to changes

in external forcing, but not necessarily in the same manner.

Most CMIP5 and CMIP6 models are found to represent the general

structure of observed SST anomalies during ENSO events well (Kim

and Yu, 2012; Taschetto et al., 2014; Brown et al., 2020; Grose et al.,

2020). However, the majority of CMIP5 models display SST anomalies

that: i)extend too far to the west (Taschetto et al., 2014; Capotondi

et al., 2015); and ii) have meridional widths that are too narrow (Zhang

and Jin, 2012) compared to the observations. CMIP6 models display

astatistically signiﬁcant improvement in the longitudinal representation

of ENSO SST anomalies relative to CMIP5 models (Planton et al., 2021),

however, systematic biases in the zonal extent and meridional width

remain in CMIP6 models (Fasullo et al., 2020; Planton et al., 2021). The

ENSO phase asymmetry, where observed strong ElNiño events are larger

and have a shorter duration than strong La Niña events (Ohba and Ueda,

2009; Frauen and Dommenget, 2010), is also under-represented in both

CMIP5 and CMIP6 models (Zhang and Sun, 2014; Planton et al., 2021).

In this instance, both CMIP5 and CMIP6 models typically display ElNiño

events that have a longer duration than those observed, La Niña events

that have asimilar duration to those observed, and there is very little

asymmetry in the duration of ElNiño and La Niña phases (Figure3.36).

Roberts et al. (2018) ﬁnd an improvement in amplitude asymmetry

inaHighResMIP model, but the under-representationremains.

The continuum of El Niño events are typically stratiﬁed into two

types (often termed ‘ﬂavours’), Central Paciﬁc and East Paciﬁc, where

the name denotes the location of the events’ largest SST anomalies

(Annex IV.2.3; Capotondi et al., 2015). As discussed in Section 2.4.2,

the different types of events tend to produce distinct teleconnections

and climatic impacts (e.g., Taschetto et al., 2020). The characteristics of

ElNiño events of these two ﬂavours in CMIP5 were generally comparable

to the observations (Taschetto et al., 2014). CMIP6 models, however,

display a statistically signiﬁcant improvement in the representation of

this ENSO event-to-event SST anomaly diversity when compared with

CMIP5 models (Planton et al., 2021). In addition to this ENSO event

diversity, the short observational record also displays an increase in

496

Chapter 3 Human Inﬂuence on the Climate System

Figure3.36 | Life cycle of (left) ElNiño and (right) La Niña events in observations (black) and historical simulations from CMIP5 (blue; extended with

RCP4.5) and CMIP6 (red). An event is detected when the December ENSO index value in year zero exceeds 0.75 times its standard deviation for 1951–2010. (a,b)Composites

of the ENSO index (°C). The horizontal axis represents month relative to the reference December (the grey vertical bar), with numbers in parentheses indicating relative years.

Shading and lines represent 5th–95th percentiles and multi-model ensemble means, respectively. (c, d) Mean durations (months) of ElNiño and La Niña events deﬁned as

number of months in individual events for which the ENSO index exceeds 0.5 times its December standard deviation. Each dot represents an ensemble member from the model

indicated on the vertical axis. The boxes and whiskers represent multi-model ensemble means, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6. The

CMIP5 and CMIP6 multi-model ensemble means and observational values are indicated at the top right of each panel. The multi-model ensemble means and percentile values

are evaluated after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their

ensemble sizes. The ENSO index is deﬁned as the SST anomaly averaged over the Niño 3.4 region (5°S–5°N, 170°W–120°W). All results are based on ﬁve-month running

mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

497

Human Inﬂuence on the Climate System Chapter 3

Figure 3.37 | ENSO seasonality in observations (black) and historical simulations from CMIP5 (blue; extended with RCP4.5) and CMIP6 (red) for

1951–2010. (a) Climatological standard deviation of the monthly ENSO index (SST anomaly averaged over the Niño 3.4 region; °C). Shading and lines represent 5th–95th

percentiles and multi-model ensemble means, respectively. (b) Seasonality metric, which is deﬁned for each model and each ensemble member as the ratio of the ENSO index

climatological standard deviation in November–January (NDJ) to that in March–May (MAM). Each dot represents an ensemble member from the model indicated on the vertical

axis. The boxes and whiskers represent the multi-model ensemble means, interquartile ranges and 5th and 95th percentiles of CMIP5 and CMIP6 individually. The CMIP5 and

CMIP6 multi-model ensemble means and observational values are indicated at the top right of the panel. The multi-model ensemble means and percentile values are evaluated

after weighting individual members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of their ensemble sizes.

All results are based on ﬁve-month running mean SST anomalies with triangular-weights after linear detrending. Further details on data sources and processing are available

in the chapter data table (Table3.SM.1).

498

Chapter 3 Human Inﬂuence on the Climate System

the number of the Central Paciﬁc-type events in recent decades (Ashok

et al., 2007; McPhaden et al., 2011), which has also been identiﬁed

as unusual in the context of the last 500–800 years based on recent

paleo-climatic reconstructions (Section 2.4.2; Y. Liu et al., 2017; Freund

et al., 2019). However, the short observational record combined with

observational (L’Heureux et al., 2013) and paleo-climatic reconstruction

uncertainties preclude ﬁrm conclusions being made about the

long-term changes in the occurrence of different ElNiño event types.

Initial analysis with aselected number of CMIP3 models suggested

that there may be a forced component to this recent prominence of

ENSO teleconnections in boreal winter (Dec.-Feb.)

Figure3.38 | Model evaluation of ENSO teleconnection for near surface air temperature and precipitation in boreal winter (December–January–February).

Teleconnections are identiﬁed by linear regression with the Niño 3.4 SST index based on Extended Reconstructed Sea Surface Temperature (ERSST) version5 over the period

1958–2014. Maps show observed patterns for temperature from the Berkeley Earth dataset over land and from ERSST version5 over ocean (°C, top) and for precipitation from

GPCC over land (shading, mm day–1) and GPCP worldwide (contours, period: 1979–2014). Distributions of regression coefﬁcients (grey histograms) are provided for a subset of

AR6 reference regions deﬁned in Atlas.1.3 for temperature (top) and precipitation (bottom). All ﬁelds are linearly detrended prior to computation. Multi-model multi-member

ensemble means are indicated by thick vertical black lines. Blue vertical lines show three observational estimates of temperature, based on Berkeley Earth, GISTEMP and CRUTS

datasets, and two observational estimates of precipitation, based on GPCC and CRUTS datasets. Further details on data sources and processing are available in the chapter

data table (Table3.SM.1).

499

Human Inﬂuence on the Climate System Chapter 3

Central Paciﬁc-type events (Yeh et al., 2009), but analysis since then

suggests that this behaviour is (i)consistent with that expected from

internal variability (Newman et al., 2011); and (ii) not apparent across

the full CMIP5 ensemble of historical simulations (Taschetto et al.,

2014). Analysis of single-model large ensembles suggests that changes

to ENSO event type in response to historical radiative forcing are not

signiﬁcant (e.g.,Stevenson et al., 2019). These same results, however,

also suggest that multiple forcings can have signiﬁcant inﬂuences

on ENSO type and that the net response will depend on the accurate

representation of the balance of these forcings (Stevenson et al., 2019).

The climatic effects of ENSO outside the tropical Paciﬁc largely arise

through atmospheric teleconnections that are induced by ENSO-driven

changes in deep tropical atmospheric convection and heating (Yeh

et al., 2018). The teleconnections to higher latitudes are forced by

waves that propagate into the extratropics (Hoskins and Karoly,

1981) and respectively excite the Paciﬁc-North American pattern

(Horel and Wallace, 1981) and Paciﬁc-South American pattern (Karoly,

1989; Irving and Simmonds, 2016) in the Northern and Southern

Hemispheres. Given the inﬂuence of these teleconnections on climate

and extremes around the globe, it is important to understand how

well they are reproduced in CMIP models. What has also become clear

is that spatial correlations of ENSO’s teleconnections calculated over

relatively short periods (<100 years) may not be the most effective

way to assess these relationships (Langenbrunner and Neelin, 2013;

Perry et al., 2020). This is because the spatial patterns are signiﬁcantly

affected by internal atmospheric variability on relatively short time

scales (<100 years; Batehup et al., 2015; Perry et al., 2020). However,

looking at simpliﬁed metrics like the agreement in the sign of the

teleconnections (Langenbrunner and Neelin, 2013), regional average

teleconnection strength over land (Perry et al., 2020), or a combination

of both (Power and Delage, 2018) provides a more robust depiction

of the teleconnection representation. Examining sign agreement for

the teleconnection patterns, ensembles of CMIP5 AMIP simulations

display broad spatial regions with high sign agreement with the

observations, suggesting that the model ensemble is producing

useful information regarding the teleconnected precipitation signal

(Langenbrunner and Neelin, 2013). Looking at regional averages of

CMIP5 historical simulations, Power and Delage (2018) show that

the average coupled model teleconnection pattern reproduces the

sign of the observed teleconnections in the majority of the 25 regions

analysed. The sign agreement between the observed teleconnection

and the multi-model mean teleconnection remains strong in CMIP6

(18 out of 20 displayed regions; Figure3.38), and the observed DJF

(December–January–February) teleconnection strength falls within

the modelled range in all of the displayed regions for temperature

and precipitation. Note, however, that while there is broad agreement

in ENSO teleconnections between CMIP6 models and observations

during DJF (e.g., Fasullo et al., 2020), there are regions and seasons

where the modelled teleconnection strength is outside the observed

range (Chen et al., 2020).

Most CMIP5 and CMIP6 models exhibit ENSO behaviour during the

historical period that, to ﬁrst order, is qualitatively similar to that of

the observed ENSO. Many studies are now delving deeper into the

models to understand if they are accurately producing the dynamics

driving ENSO and its initiation (Jin et al., 2006; Bellenger et al., 2014;

Vijayeta and Dommenget, 2018; Bayr et al., 2019; Planton et al.,

2021). For both CMIP3 and CMIP5, diagnostics of ENSO event growth

appear to show that the models, while producing ENSO variability

that is qualitatively similar to that observed, do not represent the

balance of the underlying dynamics well. The atmospheric Bjerknes

feedback is too weak in the majority of models, while the surface heat

ﬂux feedback is also too weak in the majority of models. The former

restricts event growth, while the latter restricts event damping,

which when combined allow most models to produce variability in a

range that is consistent with the observations (Bellenger et al., 2014;

S.T.Kim et al., 2014; Vijayeta and Dommenget, 2018; Bayr et al.,

2019). Analysis of ENSO representation in a subset of CMIP6 models

by Planton et al. (2021) suggests that these issues remain.

To conclude, ENSO representation in CMIP5 models displayed

asigniﬁcant improvement from the representation of ENSO variability

in CMIP3 models, which displayed much more intermodel spread in

standard deviation, and stronger biennial periodicity (Guilyardi et al.,

2012; Flato et al., 2013). In general, there has been no large step

change in the representation of ENSO between CMIP5 and CMIP6,

however, CMIP6 models appear to better represent some key ENSO

characteristics (e.g., Brown et al., 2020; Planton et al., 2021). The

instrumental record and paleo-proxy evidence through the Holocene

all suggest that ENSO can display considerable modulations in

amplitude, pattern and period (see also Section 2.4.2). For the

period since 1850, there is no clear evidence for a sustained shift

in ENSO index beyond the range of internal variability. However,

paleo-proxy evidence indicates with medium conﬁdence that ENSO

variability since 1950 is greater than at any time between 1400 and

1850 (Section 2.4.2). Coupled models display large changes of ENSO

behaviour in the absence of external forcing changes, and little-to-no

variance sensitivity to historical anthropogenic forcing. Thus, there is

low conﬁdence that anthropogenic forcing has led to the changes of

ENSO variability inferred from paleo-proxy evidence.

Chapter2 reports low conﬁdence that the apparent change from East

Paciﬁc- to Central Paciﬁc-type El Niño events that occurred in the

last 20–30 years was representative of a long term change. While

some climate models do suggest external forcing may affect the

ElNiño event type, most climate models suggest that what has been

observed is well within the range of natural variability. Thus, there is

low conﬁdence that anthropogenic forcing has had an inﬂuence on

the observed changes in ElNiño event type.

3.7.4 Indian Ocean Basin and Dipole Modes

The Indian Ocean Basin (IOB) and Dipole (IOD) modes are the two

leading modes of interannual SST variability over the tropical Indian

Ocean, featuring basin-wide warming/cooling and an east–west

dipole of SST anomalies, respectively (Annex IV.2.4). The IOD mode

is anchored to boreal summer to autumn by the air–sea feedback,

and often develops in concert with ENSO. Driven by matured ENSO

events, the IOB mode peaks in boreal spring and often persists

into the subsequent summer. Similar patterns of Indian Ocean SST

variability also dominate its decadal and longer time scale variability

(Han et al., 2014b).

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Chapter 3 Human Inﬂuence on the Climate System

AR5 concluded that models show high and medium performance

in reproducing the IOB and IOD modes, respectively (medium

conﬁdence), with difﬁculty in reproducing the persistence of the

IOB and the pattern and magnitude of the IOD (Flato et al., 2013).

There was low conﬁdence that changes in the IOD were detectable or

attributable to human inﬂuence (Bindoff et al., 2013).

Since AR5, CMIP5 model representation of these modes has been

analysed in detail, ﬁnding that most of the models qualitatively

reproduce the spatial and seasonal features of the IOB and IOD modes

(Chu et al., 2014; Liu et al., 2014; W. Tao et al., 2016). Improvements in

simulating the IOB mode since CMIP3 have been identiﬁed in reduced

multi-model mean biases and inter-model spread (W. Tao et al., 2016).

CMIP5 models overall capture the transition from the IOD to IOB

modes during an ENSO event (W. Tao et al., 2016). The IOB mode is

forced in part through a cross-equatorial wind–evaporation–SST

feedback triggered by ENSO-forced anomalous ocean Rossby waves

that propagate to the shallow climatological thermocline dome in the

tropical south-western Indian Ocean (Du et al., 2009). Consistently,

models with a deeper climatological thermocline dome produce

aweaker and less persistent IOB mode (G. Li et al., 2015a; Zheng et al.,

2016). The deep thermocline bias remains in the ensemble mean of

CMIP5 models due to a common surface easterly wind bias over the

equatorial Indian Ocean (Lee et al., 2013) associated with weaker South

Asian summer monsoon circulation (G. Li et al., 2015b). However, the

inﬂuence of this systematic bias may be compensated by other biases,

resulting in a realistic IOB magnitude (W. Tao et al., 2016). Halder et al.

(2021) found that CMIP6 models reproduce the IOB mode reasonably

well, but did not evaluate the progress sinceCMIP5.

By contrast, the IOD magnitude is overestimated by CMIP5 models on

average, though with noticeable improvements from CMIP3 models

(Liu et al., 2014). The overestimation of the IOD magnitude remains

in most of 34 CMIP6 models examined in McKenna et al. (2020) with

worsening on average in July and August. A too steep climatological

thermocline slope along the equator due to the surface easterly wind

bias in boreal summer and autumn contributes to this IOD magnitude

bias through an excessively strong Bjerknes feedback in CMIP5 (Liu

et al., 2014; G. Li et al., 2015b; Hirons and Turner, 2018). The surface

easterly bias and associated east–west SST gradient bias are not

improved in CMIP6 (Long et al., 2020; Section 3.5.1.2.3), suggesting

that the thermocline bias also remains. McKenna et al. (2020)

additionally ﬁnd degradation in the positive-negative asymmetry

of the IOD but an improvement in IOD frequency in a subset of

CMIP6 models compared to CMIP5. In terms of teleconnections, the

equatorial surface easterly wind bias also affects the IOD-associated

moisture transport anomalies toward tropical eastern Africa

(Hirons and Turner, 2018) where the IOD is associated with strong

precipitation anomalies in boreal autumn (Annex IV.2.4). CMIP5

and CMIP6 models capture the IOD teleconnection to Southern and

Central Australian precipitation although it is weaker on average than

observed, with no clear improvements from CMIP5 to CMIP6 (Grose

et al., 2020). Strong IOD events could also inﬂuence the Northern

Hemisphere extratropical circulation in winter and in particular the

NAM (Section 3.7.1), based on interference between forced Rossby

waves emerging from the Indian Ocean and climatological stationary

waves (Fletcher and Cassou, 2015). The record positive phase of the

NAO/NAM in winter 2019–2020 assessed over the instrumental

era has been accordingly linked to the record IOD event of autumn

2019 (Hardiman et al., 2020), which has been associated with the

devastating record ﬁre season in Australia (Wang and Cai, 2020).

The observed Indian Ocean basin-average SST increase on multi-decadal

and centennial time scales is well represented by CMIP5 historical

simulations, and has been attributed to the effects of greenhouse gases

offset in part by the effects of anthropogenic aerosols mainly through

aerosol-cloud interactions (Dong and Zhou, 2014; Dong et al., 2014b).

The observed SST trend is larger in the western than eastern tropical

Indian Ocean, which leads to an apparent upward trend of the IOD

index, but this trend is statistically insigniﬁcant (Section 2.4.3). CMIP5

models capture this warming pattern, which may be associated with

Walker circulation weakening over the Indian Ocean due to greenhouse

gas forcing (Dong and Zhou, 2014). However, strong internal decadal

IOD-like variability and observational uncertainty preclude attribution

(Cai et al., 2013; Han et al., 2014b; Gopika et al., 2020). Such apositive

IOD-like change in equatorial zonal SST gradient suggests an increase

in the frequency of extreme positive events (Cai et al., 2014) and

skewness (Cowan et al., 2015) of the IOD mode. While there is some

evidence of an increase in frequency of positive IOD events during the

second half of the 20th century, the current level of IOD variability is

not unprecedented in aproxy reconstruction for the last millennium

(Section2.4.3; Abram et al., 2020). Besides, the IOD magnitude in

the late 20th century is not signiﬁcantly different between CMIP5

simulations forced by historical and natural-only forcings, though

this conclusion is based on only ﬁve selected ensemble members

that realistically reproduce statistical features of the IOD (Blau and

Ha, 2020). While selected CMIP5 models show weakening (Thielke

and Mölg, 2019) and seasonality changes (Blau and Ha, 2020)

in IOD-induced rainfall anomalies in tropical eastern Africa, no

comparison with observational records has been made. Likewise,

while a strengthening tendency of the ENSO-IOB mode correlation

and resultant intensiﬁcation of the IOB mode are found in historical or

future simulations in selected CMIP5 models (Hu et al., 2014; Tao et al.,

2015), such a change has not been detected in observationalrecords.

After linear detrending, Paciﬁc Decadal Variability (PDV; AnnexIV.2.6;

Section 3.7.6) has been suggested as a driver of decadal to

multi-decadal variations in the IOB mode (Dong et al., 2016). However,

correlation between the PDV and a decadal IOB index, deﬁned from

linearly detrended SST, changed from positive to negative during the

1980s (Han et al., 2014a). The increase in anthropogenic forcing and

recovery from the eruptions of El Chichón in 1982 and Pinatubo in

1991 may have overwhelmed the PDV inﬂuence, and explain this

change (Dong and McPhaden, 2017; L. Zhang et al., 2018a). However,

the low statistical degrees of freedom hamper clear detection of

human inﬂuence in this correlation change.

To summarize, there is medium conﬁdence that changes in the

interannual IOD variability in the late 20th century inferred from

observations and proxy records are within the range of internal

variability. There is no evidence of anthropogenic inﬂuence on the

interannual IOB. On decadal- to multi-decadal time scales, there is

low conﬁdence that human inﬂuence has caused a reversal of the

correlation between PDV and decadal variations in the IOB mode.

501

Human Inﬂuence on the Climate System Chapter 3

Thelow conﬁdence in this assessment is due to the short observational

record, limited number of models used for the attribution, lack of model

evaluation of the decadal IOB mode, and uncertainty in the contribution

from volcanic aerosols. Nevertheless, CMIP5 models have medium

overall performance in reproducing both the interannual IOB and

IOD modes, with an apparently good performance in reproducing the

IOB magnitude arising from compensation of biases in the formation

process, and overly high IOD magnitude due to the mean state bias

(high conﬁdence). There is no clear improvement in the simulation of

the IOD from CMIP5 to CMIP6 models, though there is only medium

conﬁdence in this assessment, since only a subset of CMIP6 models

have been examined. There is no evidence for performance changes

in simulating the IOB from CMIP5 to CMIP6 models.

3.7.5 Atlantic Meridional and Zonal Modes

The Atlantic Zonal Mode (AZM), often referred to as the Atlantic

Equatorial Mode or Atlantic Niño, and the Atlantic Meridional Mode

(AMM) are the two leading basin-wide patterns of interannual to

decadal variability in the tropical Atlantic. Akin to ENSO in the Paciﬁc,

the term Atlantic Niño is broadly used to refer to years when the

SSTs in the tropical eastern Atlantic basin along the cold tongue are

signiﬁcantly warmer than the climatological average. The AMM is

characterized by anomalous cross-equatorial gradients in SST. Both

modes are associated with altered strength of the Inter-tropical

Convergence Zone (ITCZ) and/or latitudinal shifts in the ITCZ,

which locally affect African and American monsoon systems and

remotely affect tropical Paciﬁc and Indian Ocean variability through

inter-basins teleconnections. A detailed description of both AZM and

AMM, as well as their associated teleconnection over land, is given

in Annex IV.2.5

AR5 mentioned the considerable difﬁculty in simulating both

Atlantic Niño and AMM despite some improvements in CMIP5 for

some models (Flato et al., 2013). Severe biases in mean state and

variance for both SST and atmospheric dynamics including rainfall

(e.g., a double ITCZ) as well as teleconnections were reported.

TheAR5highlighted the complexity of the tropical Atlantic biases,

which were explained by multiple factors both in the ocean

andatmosphere.

Since AR5, further analysis of the major persistent biases in models

has been reported (Xu et al., 2014; Jouanno et al., 2017; Y. Yang

et al., 2017; Dippe et al., 2018; Lübbecke et al., 2018; Voldoire

et al., 2019a). Errors in equatorial and basin wide trade winds,

cloud cover and ocean vertical mixing and dynamics both locally

and in remote subtropical upwelling regions, key thermodynamic

ocean–atmosphere feedbacks, and tropical land–atmosphere

interaction have been shown to be detrimental to the representation

of both the Atlantic Niño and AMM leading to poor teleconnectivity

over land (Rodríguez-Fonseca et al., 2015; Wainwright et al., 2019)

and between tropical basins (Ott et al., 2015).

Despite some improvements (Richter et al., 2014; Nnamchi et al.,

2015), biases in the mean state are so large that the mean east–west

temperature gradient at the equator along the thermocline remains

opposite to observed in two thirds of the CMIP5 models

(Section3.5.1.2.2), which clearly affects the simulation of the Atlantic

Niño and associated dynamics (Muñoz et al., 2012; Ding et al., 2015;

Deppenmeier et al., 2016). The interhemispheric SST gradient is also

systematically underestimated in models, with a too cold mean state

in the northern part of the tropical Atlantic ocean and too warm

conditions in the South Atlantic basin. The seasonality is poorly

reproduced and the wind–SST coupling is weaker than observed

so that altogether, and despite AMM-like variability in 20th century

climate simulations, AMM is not the dominant Atlantic mode in all

CMIP5 models (Liu et al., 2013; Amaya et al., 2017). These biases in

mean state translate into biases in modelling the mean ITCZ (Flato

et al., 2013). Similar biases were found in experiments using CMIP5

models but with different climate background states, such as Last

Glacial Maximum, mid-Holocene and future scenario simulations

(Brierley and Wainer, 2018). Analyses of CMIP6 show encouraging

results in the representation of Atlantic Niño and AMM modes of

variability in terms of amplitude and seasonality. Some models now

display reduced biases in the spatial structure of the modes and related

explained variance but persistent errors still remain on average in the

timing of the modes and in the coupled nature of the modes, that is,

the strength of the link between ocean (SST, mixed layer depth) and

atmospheric (wind) anomalies (Richter and Tokinaga, 2020), as well

as in the Atlantic Ocean equatorial east–west temperature gradient

(Section 3.5.1.2.2, Figure3.24).

There are some recent indications that increasing model resolution

both vertically and horizontally, in the ocean and atmospheric

component (Richter, 2015; Small et al., 2015; Harlaß et al., 2018),

could partly alleviate some tropical Atlantic biases in mean state

(Section 3.5.1.2.2), seasonality, interannual- to decadal-variability

and associated teleconnectivity over land, such as with the West

African monsoon (Steinig et al., 2018). Results from CMIP6 tend to

conﬁrm that increasing resolution is not the unique way to address

the biases in the tropical Atlantic (Richter and Tokinaga, 2020). For

instance, the inclusion of a stochastic physics scheme has a nearly

equivalent effect in the improvement of the mean number and the

strength distribution of tropical Atlantic cyclones (Vidale et al., 2021).

Section 2.4.4 assess that there is low conﬁdence in any sustained

changes to the AZM and AMM variability in instrumental

observations. Moreover, any attribution of possible human inﬂuence

on the Atlantic modes and associated teleconnections is limited

by the poor ﬁdelity of CMIP5 and CMIP6 models in reproducing

the mean tropical Atlantic climate, its seasonality and variability,

despite hints of some improvement in CMIP6, as well as other

sources of uncertainties related to limited process understanding

in the observations (Foltz et al., 2019), the response of the tropical

Atlantic climate to anthropogenic aerosol forcing (Booth et al.,

2012; Zhang et al., 2013a) and the presence of strong multi-decadal

ﬂuctuations related to AMV (Section 3.7.7) and cross-tropical basin

interactions (Martín-Rey et al., 2018; Cai et al., 2019). The fact that

most models poorly represent the climatology and variability of the

tropical Atlantic combined with the short observational record makes

it difﬁcult to place the recent observed changes in the context of

internal multi-annual variability versus anthropogenic forcing.

502

Chapter 3 Human Inﬂuence on the Climate System

In summary, based on CMIP5 and CMIP6 results, there is no robust

evidence that the observed changes in either the Atlantic Niño or

AMM modes and associated teleconnections over the second half of

the 20th century are beyond the range of internal variability or have

been inﬂuenced by natural or anthropogenic forcing. Considering

the physical processes responsible for model biases in these modes,

increasing resolution in both ocean and atmosphere components may

be an opportunity for progress in the simulation of the tropical Atlantic

changes as evidenced by some individual model studies (Roberts et al.,

2018), but this needs conﬁrmation from a multi-model perspective.

3.7.6 Paciﬁc Decadal Variability

Paciﬁc Decadal Variability (PDV) is the generic term for the modes of

variability in the Paciﬁc Ocean that vary on decadal to inter-decadal

time scales. PDV and its related teleconnections encompass the

Paciﬁc Decadal Oscillation (PDO; Mantua et al., 1997; Zhang et al.,

1997; Mantua and Hare, 2002), and an anomalous SST pattern in

the North Paciﬁc, as well as a broader structure associated with

Paciﬁc-wide SSTs termed the Inter-decadal Paciﬁc Oscillation (IPO;

Power et al., 1999; Folland et al., 2002; Henley et al., 2015). Since the

PDO and IPO indices are highly correlated, this section assesses them

together as the PDV (Annex IV.2.6).

AR5 mentioned an overall limited level of evidence for both CMIP3

and CMIP5 evaluation of the Paciﬁc modes at inter-decadal time

scales, leading to low conﬁdence statements about the models’

performance in reproducing PDV (Flato et al., 2013) and similarly

low conﬁdence in the attribution of observed PDV changes to human

inﬂuence (Bindoff et al., 2013).

The implication of PDV in the observed slowdown of the GMST

warming rate in the early 2000s (Cross-Chapter Box3.1) has triggered

considerable research on decadal climate variability and predictability

since AR5 (Meehl et al., 2013, 2016b; England et al., 2014; Dai et al.,

2015; Steinman et al., 2015; Kosaka and Xie, 2016; Cassou et al.,

2018). Many studies ﬁnd that the broad spatial characteristics of

PDV are reasonably well represented in unforced climate models

(Newman et al., 2016; Henley, 2017) and in historical simulations

in CMIP5 and CMIP6 (Figure3.39), although there is sensitivity to

the methodology used to remove the externally-forced component

of the SST (Bonﬁls and Santer, 2011; Xu and Hu, 2018). Compared

with CMIP3 models, CMIP5 models exhibit overall slightly better

performance in reproducing PDV and associated teleconnections

(Polade et al., 2013; Joshi and Kucharski, 2017), and also smaller

inter-model spread (Lyu et al., 2016). CMIP6 models on average show

slightly improved reproduction of the PDV spatial structure than

CMIP5 models (Figure3.39a–c; Fasullo et al., 2020). SST anomalies in

the subtropical South Paciﬁc lobe are, however, too weak relative to

the equatorial and North Paciﬁc lobes in CMIP5 pre-industrial control

and historical simulations (Henley et al., 2017), a bias that remains

inCMIP6 (Figure3.39b).

Biases in the PDV temporal properties and amplitude are present in

CMIP5 (Cheung et al., 2017; Henley, 2017). While model evaluation

is severely hampered by short observational records and incomplete

observational coverage before satellite measurements started, the

duration of PDV phases appears to be shorter in coupled models

than in observations, and correspondingly the ratio of decadal to

interannual variance is underestimated (Figure3.39e,f; Henley et al.,

2017). This apparent bias may be associated with overly biennial

behaviour of Paciﬁc trade wind variability and related ENSO activity,

leaving too weak variability on decadal time scales (Kociuba and

Power, 2015). ENSO inﬂuence on the extratropical North Paciﬁc

Ocean at decadal time scales is also very diverse among both CMIP3

and CMIP5 models, being controlled by multiple factors (Nidheesh

et al., 2017). In terms of amplitude, the variance of the PDV index

after decadal ﬁltering is signiﬁcantly weaker in the concatenated

CMIP5 ensemble than the three observational estimates used in

Figure3.39e (p <0.1 with an F-test). Consequently, the observed

PDV ﬂuctuations over the historical period often lie in the tails of the

model distributions (Figure3.39e,f). Even if one cannot rule out that

the observed PDV over the instrumental era represents an exceptional

period of variability, it is plausible that the tendency of the CMIP5

models to systematically underestimate the low frequency variance

is due to an incomplete representation of decadal-scale mechanisms

in these models. This situation is slightly improved in CMIP6 historical

simulations but remains a concern (Fasullo et al., 2020). The results of

McGregor et al. (2018) suggest that the under-representation of the

variability stems from Atlantic mean SST biases (Section 3.5.1.2.2)

through inter-basin coupling.

While PDV is primarily understood as an internal mode of variability

(Si and Hu, 2017), there are some indications that anthropogenically

induced SST changes project onto PDV and have contributed to its

past evolution (Bonﬁls and Santer, 2011; Dong et al., 2014a; Boo

et al., 2015; Xu and Hu, 2018). However, the level of evidence is

limited because of the difﬁculty in correctly separating internal versus

externally forced components of the observed SST variations, and

because it is unclear whether the dynamics of the PDV are operative

in this forced SST change pattern. Over the last two to three decades

which encompass the period of slower GMST increase (Cross-Chapter

Box3.1), Smith et al. (2016) found that anthropogenic aerosols have

driven part of the PDV change toward its negative phase. A similar

result is shown in Takahashi and Watanabe (2016) who found

intensiﬁcation of the Paciﬁc Walker circulation in response to aerosol

forcing (Section 3.3.3.1.2). Indeed, CMIP6 models simulate a negative

PDV trend since the 1980s (Figure3.39f), which is much weaker than

internal variability. However, aresponse to anthropogenic aerosols is

not robustly identiﬁed in a large ensemble of a model (Oudar et al.,

2018), across CMIP5 models (Hua et al., 2018), or in idealized model

simulations (Kuntz and Schrag, 2016). Alternatively, inter-basin

teleconnections associated with the warming of the North Atlantic

Ocean related to the mid-1990s phase shift of the AMV (McGregor et al.,

2014; Chikamoto et al., 2016; Kucharski et al., 2016; X. Li et al., 2016a;

Ruprich-Robert et al., 2017), and also warming in the Indian Ocean

(Luo et al., 2012; Mochizuki et al., 2016), could have favoured a PDV

transition to its negative phase in the 2000s. Considering the possible

inﬂuence of external forcing on Indian Ocean decadal variability

(Section 3.7.4) and AMV (Section 3.7.7), any such human inﬂuence

on PDV would be indirect through changes in these ocean basins,

and then imported to the Paciﬁc via inter-basin coupling. However,

this human inﬂuence on AMV, and how consistently such inter-basin

503

Human Inﬂuence on the Climate System Chapter 3

processes affect PDV phase shifts, are uncertain. Other modelling

studies ﬁnd that anthropogenic aerosols can inﬂuence the PDV (Verma

et al., 2019; Amiri-Farahani et al., 2020; Dow et al., 2020). It is however

unclear whether and how much those forcings contributed to the

observed variations of PDV. In CMIP6 models, the temporal correlation

of the multi-model ensemble mean PDV index with its observational

counterpart is insigniﬁcant and negligible (Figure3.39f), suggesting

that any externally-driven component in historical PDV variations was

weak. Lastly, the multi-model ensemble mean computed from CMIP6

historical simulations shows slightly stronger variation than the CMIP5

counterpart, suggesting a greater simulated inﬂuence from external

forcings in CMIP6. Still, the fraction of the forced signal to the total

PDV is very low (Figure3.39f), in contrast to AMV (Section 3.7.7).

Consistently, Liguori et al. (2020) estimate that the variance fraction of

the externally-driven to total PDV is up to only 15% in a multi-model

large ensemble of historical simulations. These ﬁndings support an

assessment that PDV is mostly driven by internal variability since

the pre-industrial era. The sensitivity of ensemble-mean PDV trends

to theensemble size (Oudar et al., 2018), and the dominance of the

ensemble spread over the ensemble mean in the 60-year trend of

theequatorial Paciﬁc zonal SST gradient in large ensemble simulations

(Watanabe et al., 2021), also support this statement.

In CMIP5 last millennium simulations, there is no consistency in

temporal variations of PDV across the ensemble (Fleming and

Anchukaitis, 2016). This supports the notion that PDV is internal

in nature. However, this issue remains difﬁcult to assess because

paleoclimate reconstructions of PDV have too poor a level of

Low model

agreement (<80%)

High model

agreement (≥80%)

Colour

Not significant

at the 10% level

Significant

Colour

Figure3.39 | Model evaluation of the Paciﬁc Decadal Variability (PDV). (a, b) Sea surface temperature (SST) anomalies (°C) regressed onto the Tripole Index (TPI;

Henley et al., 2015) for 1900–2014 in (a) ERSST version 5 and (b) CMIP6 multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the

inverse of the model ensemble size. A 10-year low-pass ﬁlter was applied beforehand. Cross marks in (a) represent regions where the anomalies are not signiﬁcant at the 10%

level based on a t-test. Diagonal lines in (b) indicate regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the PDV

pattern in CMIP5 (each ensemble member is shown as a cross in light blue, and the weighted multi-model mean as a dot in dark blue), CMIP6 (each ensemble member is shown

as a cross in red, and the weighted multi-model mean as a dot in orange) and observations over 40°S–60°N and 110°E–70°W. The reference pattern is taken from ERSST

version5 and black dots indicate other observational products: Hadley Centre Sea Ice and Sea Surface Temperature data set version1 (HadISST version1) and Centennial in

situ Observation-Based Estimates of Sea Surface Temperature version2 (COBE-SST2). (d) Autocorrelation of unﬁltered annual TPI at lag one year and 10-year low-pass ﬁltered

TPI at lag 10 years for observations over 1900–2014 (horizontal lines), 115-year chunks of pre-industrial control simulations (open boxes) and individual historical simulations

over 1900–2014 (ﬁlled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but showing standard deviation of the unﬁltered and ﬁltered TPI (°C). Boxes and whiskers

show weighted multi-model means, interquartile ranges and 5th and 95th percentiles. (f) Time series of the 10-year low-pass ﬁltered TPI (°C) in ERSST version 5, HadISST

version1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are the weighted multi-model mean

for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile ranges across ensemble members. The 5–95% conﬁdence

interval for the CMIP6 multi-model mean is given in thin dashed lines. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

504

Chapter 3 Human Inﬂuence on the Climate System

agreement for a rigorous model evaluation in past millennia

(Henley,2017).

To conclude, there is high conﬁdence that internal variability has been

the main driver of the PDV since pre-industrial times, despite some

modelling evidence for potential external inﬂuence. This assessment

is supported by studies based on large ensemble simulations that

found the dominance of internally-driven PDV, and the CMIP6-based

assessment (Figure 3.39). As such, PDV is an important driver of

decadal internal climate variability which limits detection of human

inﬂuence on various aspects of decadal climate change on global

to regional scales (high conﬁdence). Model evaluation of PDV is

hampered by short observational records, spatial incompleteness of

observations before the satellite observation era, and poor agreement

among paleoclimate reconstructions. Despite the limitations of these

model-observation comparisons, CMIP5 models, on average, simulate

broadly realistic spatial structures of the PDV, but with aclear bias

in the South Paciﬁc (medium conﬁdence). CMIP5 models also very

likely underestimate PDV magnitude. CMIP6 models tend to show

better overall performance in spatial structure and magnitude of

PDV, but there is low conﬁdence in this assessment due to the lack

ofliterature.

3.7.7 Atlantic Multi-decadal Variability

Atlantic Multi-decadal Variability (AMV) refers to a climate mode

representing basin-wide multi-decadal ﬂuctuations in surface

temperatures in the North Atlantic (Figure3.40a,f), with teleconnections

particularly pronounced over the adjacent continents and the Arctic.

The AMV phenomenon is usually assessed through SST anomalies

averaged over the entire North Atlantic basin, hereafter the AMV

index, but it is associated with many physical processes including

three-dimensional ocean circulation, such as AMOC ﬂuctuations

(Section 3.5.4.1), gyre adjustments, and salt and heat transport in the

entire North Atlantic and subarctic Atlantic basins. The AMV, together

with the PDV, has been shown to have modulated GSAT on multi-

decadal time scales since pre-industrial times (Cross-Chapter Box3.1;

T. Wu et al., 2019a; Li et al., 2020). A detailed description of the AMV as

well as its associated teleconnection over land is given in Annex IV.2.7.

AR5 assessed, based on climate models, that the AMV was primarily

internally-driven alongside some contribution from external forcings

(mainly anthropogenic aerosols) over the late 20th century (Bindoff et

al., 2013; Flato et al., 2013). ButAR5 also concluded that models show

medium performance in reproducing the observed AMV, with difﬁculties

in simulating the time scale, the spatial structure and the coherency

between all the physical processes involved (Flato et al., 2013).

Climate models analysed since AR5 continue to simulate AMV-like

variability as part of their internal variability. This statement is mostly

based on CMIP5 pre-industrial control and historical simulations

(Wouters et al., 2012; Schmith et al., 2014; Menary et al., 2015;

Ruprich-Robert and Cassou, 2015; Brown et al., 2016b; Chen et al.,

2016; Kim et al., 2018a) and is also true for the CMIP6 models

(Menary et al., 2018; Voldoire et al., 2019b). Models also continue

to support links to a wide array of remote climate inﬂuences through

atmospheric teleconnections (Martin et al., 2014; Ruprich-Robert

et al., 2017, 2018; Monerie et al., 2019; Qasmi et al., 2020; Ruggieri

et al., 2021). Even if debate remains (Clement et al., 2015; Cane

et al., 2017; Mann et al., 2020), there is now stronger evidence for

acrucial role of oceanic dynamics in internal AMV that is primarily

linked to the AMOC and its interplay with the NAO (Zhang et al.,

2013a; Müller et al., 2015; O’Reilly et al., 2016b, 2019a; Delworth

et al., 2017; Zhang, 2017; Sun et al., 2019; Kim et al., 2020). However,

considerable diversity in the spatio-temporal properties of the

simulated AMV is found in both pre-industrial control and historical

CMIP5 experiments (Zhang and Wang, 2013; Wills et al., 2019). Such

model diversity is presumably associated with the wide range of

coupled processes associated with AMV (Baker et al., 2017; Woollings

et al., 2018a) including large-scale atmospheric teleconnections and

regional feedbacks relating to tropical clouds, Arctic sea ice in the

subarctic basins and Saharan dust, whose relative importance and

interactions across time scales are speciﬁc to each model (Martin

et al., 2014; Brown et al., 2016b).

Additional studies since AR5 corroborate that CMIP5-era models

tend to underestimate many aspects of observed AMV and its SST

ﬁngerprint. On average, the duration of modelled AMV episodes is

too short, the magnitude of AMV is too weak and its basin-wide

SST spatial structure is limited by the poor representation of the

link between the tropical North Atlantic and the subpolar North

Atlantic/Nordic seas (Martin et al., 2014; Qasmi et al., 2017). Such

mismatches between observed and simulated AMV (Figure3.40c–e)

have been associated with intrinsic model biases in both mean state

(Menary et al., 2015; Drews and Greatbatch, 2016) and variability

in the ocean and overlying atmosphere. For instance, compared to

available observational data CMIP5 models underestimate the ratio

of decadal to interannual variability of the main drivers of AMV,

namely the AMOC, NAO and related North Atlantic jet variations

(Section 3.7.1; Bracegirdle et al., 2018; Kim et al., 2018b; Simpson

et al., 2018; Yan et al., 2018), which has strong implications for the

simulated temporal statistics of AMV, AMV-induced teleconnections

(Ault et al., 2012; Menary et al., 2015) and AMV predictability.

The increase of AMV variance in CMIP6 models (stronger magnitude

and longer duration) seems to be explained by the enhanced

variability in the subpolar North Atlantic SST (Figure3.40b,c), which

is particularly pronounced in some models, associated with greater

variability in the AMOC (Section 3.5.4.1; Voldoire et al., 2019a; Boucher

et al., 2020) and greater GMST multi-decadal variability (Section3.3.1

and Figure3.40c–f; Voldoire et al., 2019b; Parsons et al., 2020). The

decadal variance in SST in the subpolar North Atlantic seems now

to be slightly overestimated in CMIP6 compared to observational

estimates, while the AMV-related tropical SST anomalies remain

weaker in line with CMIP5 (Figure3.40b). The mechanisms producing

the tropical-extratropical relationship at decadal time scales

remain poorly understood despite stronger evidence since AR5 for

the importance of the subpolar gyre SST anomalies in generating

tropical changes through atmospheric teleconnection (Caron

et al., 2015; Ruprich-Robert et al., 2017; Kim et al., 2020). Signiﬁcant

discrepancies remain in the simulated AMV spatial pattern when

historical simulations are compared to multivariate observations (Yan

et al., 2018; Robson et al., 2020).

505

Human Inﬂuence on the Climate System Chapter 3

There is additional evidence since AR5 that external forcing has been

playing an important role in shaping the timing and intensity of

the observed AMV since pre-industrial times (Bellomo et al., 2018;

Andrews et al., 2020). The time synchronisation between observed

and multi-model mean AMV SST indices is signiﬁcant in both CMIP5

and CMIP6 historical simulations, while the explained variance of

the forced response in CMIP6 appears stronger (Figure3.40d–f). The

competition between greenhouse gas warming and anthropogenic

sulphate aerosol cooling has been proposed to be particularly

important over the latter half of the 20th century (Booth et al., 2012;

Steinman et al., 2015; Murphy et al., 2017; Undorf et al., 2018a;

Haustein et al., 2019). The latest observed AMV shift from the cold

to the warm phase in the mid-1990s at the surface ocean is well

captured in the CMIP6 forced component and may be associated with

the lagged response to increased AMOC due to strong anthropogenic

aerosol forcing over 1955–1985 (Menary et al., 2020) in combination

with the rapid response through surface ﬂux processes to declining

aerosol forcing and increasing greenhouse gas inﬂuence since then.

However, natural forcings may have also played a signiﬁcant role. For

instance, volcanic forcing has been shown to contribute in part to

the cold phases of the AMV-related SST anomalies observed in the

20thcentury (Terray, 2012; Bellucci et al., 2017; Swingedouw et al.,

2017; Birkel et al., 2018). Over the last millennium, natural forcings

including major volcanic eruptions and ﬂuctuations in solar activity

are thought to have driven a larger fraction of the multi-decadal

variations in the AMV than in the industrial era, with some interplay

with internal processes (Otterå et al., 2010; Knudsen et al., 2014;

Moffa-Sánchez et al., 2014; J. Wang et al., 2017; Malik et al., 2018;

Low model

agreement (<80%)

High model

agreement (≥80%)

Colour

Not significant

at the 10% level

Significant

Colour

Figure3.40 | Model evaluation of Atlantic Multi-decadal Variability (AMV). (a, b) Sea surface temperature (SST) anomalies (°C) regressed onto the AMV index

deﬁned as the 10-year low-pass ﬁltered North Atlantic (0°–60°N, 80°W–0°E) area-weighted SST* anomalies over 1900–2014 in (a) ERSST version 5 and (b) the CMIP6

multi-model ensemble (MME) mean composite obtained by weighting ensemble members by the inverse of each model’s ensemble size. The asterisk denotes that the global

mean SST anomaly has been removed at each time step of the computation. Cross marks in (a) represent regions where the anomalies are not signiﬁcant at the 10% level

based on a t-test. Diagonal lines in (b) show regions where less than 80% of the runs agree in sign. (c) A Taylor diagram summarizing the representation of the AMV pattern

in CMIP5 (each ensemble member is shown as a cross in light blue, and the weighted multi-model mean is shown as a dot in dark blue), CMIP6 (each ensemble member is

shown as a cross in red, and the weighted multi-model mean is shown as a dot in orange) and observations over [0°–60°N, 80°W– 0°E]. The reference pattern is taken from

ERSST version5 and black dots indicate other observational products (HadISST version 1 and COBE-SST2). (d) Autocorrelation of unﬁltered annual AMV index at lag one

year and 10-year low-pass ﬁltered AMV index at lag 10 years for observations over 1900–2014 (horizontal lines), 115-year chunks of pre-industrial control simulations (open

boxes) and individual historical simulations over 1900–2014 (ﬁlled boxes) from CMIP5 (blue) and CMIP6 (red). (e) As in (d), but showing standard deviation of the unﬁltered

and ﬁltered AMV indices (°C). Boxes and whiskers show the weighted multi-model means, interquartile ranges and 5th and 95th percentiles. (f) Time series of the AMV index

(°C) in ERSST version5, HadISST version1 and COBE-SST2 observational estimates (black) and CMIP5 and CMIP6 historical simulations. The thick red and light blue lines are

the weighted multi-model mean for the historical simulations in CMIP5 and CMIP6, respectively, and the envelopes represent the 5th–95th percentile ranges obtained from

all ensemble members. The 5– 95% conﬁdence interval for the CMIP6 multi-model mean is shown by the thin dashed line. Further details on data sources and processing are

available in the chapter data table (Table3.SM.1).

506

Chapter 3 Human Inﬂuence on the Climate System

Mann et al., 2021), but other studies question the role of natural

forcings over this period (Zanchettin et al., 2014; Lapointe et al.,

2020).

Model evaluation of the AMV phenomenon remains difﬁcult because

of short observational records (especially of detailed process-based

observations), the lack of stationarity in the variance, spatial patterns

and frequency of the AMV assessed from modelled SST (Qasmi et al.,

2017), difﬁculties in estimating the forced signals in both historical

simulations and observations (Tandon and Kushner, 2015), and

because of probable interplay between internally and externally-driven

processes (Watanabe and Tatebe, 2019). Furthermore, models

simulate a large range of historical anthropogenic aerosol forcing

(Smith et al., 2020) and questions often referred to as signal-to-noise

paradox have been raised concerning the models’ ability to correctly

simulate the magnitude of the response of AMV-related atmospheric

circulation phenomena, such as the NAO (Section 3.7.1), to both

internally and externally generated changes (Scaife and Smith, 2018).

Related methodological and epistemological uncertainties also call

into question the relevance of the traditional basin-average SST

index to assessing the AMV phenomenon (Zanchettin et al., 2014;

Frajka-Williams et al., 2017; Haustein et al., 2019; Wills et al., 2019).

To summarize, results from CMIP5 and CMIP6 models together

with new statistical techniques to evaluate the forced component

of modelled and observed AMV, provide robust evidence that

external forcings have modulated AMV over the historical period.

In particular, anthropogenic and volcanic aerosols are thought to

have played a role in the timing and intensity of the negative (cold)

phase of AMV recorded from the mid-1960s to mid-1990s and

subsequent warming (medium conﬁdence). However, there is low

conﬁdence in the estimated magnitude of the human inﬂuence.

The limited level of conﬁdence is primarily explained by difﬁculties

in accurately evaluating model performance in simulating AMV.

The evaluation is severely hampered by short instrumental records

but also, equally importantly, by the lack of detailed and coherent

long-term process-based observations (for example of the AMOC,

aerosol optical depth, surface ﬂuxes and cloud changes), which limit

our process understanding. In addition, studies often rely solely on

simplistic SST indices that may be hard to interpret (Zhang et al.,

2016) and may mask critical physical inconsistencies in simulations

of the AMV compared to observations (Zhang, 2017).

3.8 Synthesis Across Earth

SystemComponents

3.8.1 Multivariate Attribution of Climate Change

The AR5 concluded that human inﬂuence on the climate system is

clear (IPCC, 2013), based on observed increasing greenhouse gas

concentrations in the atmosphere, positive radiative forcing, observed

warming, and physical understanding of the climate system. The AR5

also assessed that it was virtually certain that internal variability alone

could not account for observed warming since 1951 (Bindoff et al.,

2013). Evidence has grown since AR5 that observed changes since

the 1950s in many parts of the climate system are attributable to

anthropogenic inﬂuence. So far, this chapter has focused on examining

individual aspects of the climate system in separate sections. The results

presented in Sections 3.3 to 3.7 substantially strengthen our assessment

of the role of human inﬂuence on climate since pre-industrial times.

In this section we look across the whole climate system to assess to

what extent a physically consistent picture of human induced change

emerges across the climate system (Figure3.41).

The observed global surface air temperature warming of 0.9°C to

1.2°C in 2010–2019 is much greater than can be explained by internal

variability (likely –0.2°C to +0.2°C) or natural forcings (likely –0.1°C

to +0.1°C) alone, but consistent with the assessed anthropogenic

warming (likely 0.8°C to 1.3°C; Section 3.3.1.1). It is very likely

that human inﬂuence is the main driver of warming over land

(Section 3.3.1.1). Moreover, the atmosphere as a whole has warmed

(Table7.1), and it is very likely that human-induced greenhouse gas

increases were the main driver of tropospheric warming since 1979

(Section 3.3.1.2). It is virtually certain that greenhouse gas forcing

was the main driver of the observed changes in hot and cold extremes

over land at the global scale (Cross-ChapterBox3.2).

As might be expected from a warming atmosphere, moisture in the

troposphere has increased and precipitation patterns have changed.

Human inﬂuence has likely contributed to the observed changes in

humidity and precipitation (Section 3.3.2). It is likely that human

inﬂuence, in particular due to greenhouse gas forcing, is the main

driver of the observed intensiﬁcation of heavy precipitation in global

land regions during recent decades (Cross-Chapter Box 3.2). The

pattern of ocean salinity changes indicate that fresh regions are

becoming fresher and that salty regions are becoming saltier as

aresult of changes in ocean-atmosphere ﬂuxes through evaporation

and precipitation (high conﬁdence) making it extremely likely

that human inﬂuence has contributed to observed near-surface

and subsurface salinity changes since the mid-20th century

(Section 3.5.2.2). Taken together, this evidence indicates a human

inﬂuence on the water cycle.

It is very likely that human inﬂuence was the main driver of Arctic

sea ice loss since the late 1970s (Section 3.4.1.1), and very likely that

it contributed to the observed reductions in Northern Hemisphere

springtime snow cover since 1950 (Section 3.4.2). Human inﬂuence

was very likely the main driver of the recent global, near-universal

retreat of glaciers and it is very likely that it contributed to the

observed surface melting of the Greenland Ice Sheet over the past

two decades (Section 3.4.3.2.1). It is extremely likely that human

inﬂuence was the main driver of the ocean heat content increase

observed since the 1970s (Section 3.5.1.3), and very likely that

human inﬂuence was the main driver of the observed GMSL rise since

atleast 1970 (Section 3.5.3.2).

Combining the evidence from across the climate system

(Sections3.3–3.7) increases the level of conﬁdence in the attribution

of observed climate change to human inﬂuence and reduces the

uncertainties associated with assessments based on a single variable.

From this combined evidence, it is unequivocal that human inﬂuence

has warmed the atmosphere, ocean and land components of the

global climate system, taken together.

507

Human Inﬂuence on the Climate System Chapter 3

Ocean heat contentNear-surface air temperature

Near-surface air temperature

over land

Global

Sea iceNear-surface air temperature

Precipitation

°°

Anthropogenic + natural

Natural

Observations

Figure3.41 | Summary ﬁgure showing simulated and observed changes in key large-scale indicators of climate change across the climate system,

for continental, ocean basin and larger scales. Black lines show observations, brown lines and shading show the multi-model mean and 5th–95th percentile ranges for

CMIP6 historical simulations including anthropogenic and natural forcing, and blue lines and shading show corresponding ensemble means and 5th–95th percentile ranges

for CMIP6 natural-only simulations. Temperature time series are as in Figure3.9, but with smoothing using a low pass ﬁlter. Precipitation time series are as in Figure3.15 and

ocean heat content as in Figure3.26. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

508

Chapter 3 Human Inﬂuence on the Climate System

3.8.2 Multivariate Model Evaluation

Similar to the assessment of multivariate attribution of climate

change in the previous section, this section covers the performance

of the models across different variables (Sections 3.8.2.1) and

different classes of models (Section 3.8.2.2). Here the focus is on

a system-wide assessment using integrative measures of model

performance that characterize model performance using multiple

diagnostic ﬁelds derived from multi-model ensembles.

3.8.2.1 Integrative Measures of Model Performance

The purpose of this section is to use multivariate analyses to address

how well models simulate present-day and historical climate. For

every diagnostic ﬁeld considered, model performance is compared

to one or multiple observational references, and the quality of the

simulation is expressed as a single number, for example a correlation

coefﬁcient or a root mean square difference versus the observational

reference. By simultaneously assessing different performance indices,

model improvements can be quantiﬁed, similarities in behaviour

between different models become apparent, and dependencies

between various indices become evident (Gleckler et al., 2008;

Waugh and Eyring, 2008).

AR5 found signiﬁcant differences between models in the simulation

of mean climate in the CMIP5 ensemble when measured against

meteorological reanalyses and observations (Flato et al., 2013), see

also Stouffer et al. (2017). The AR5 determined that for the diagnostic

ﬁelds analysed, the models usually compared similarly against two

different reference datasets, suggesting that model errors were

generally larger than observational uncertainties or other differences

between the observational references. In agreement with previous

assessments, the CMIP5 multi-model mean generally performed

better than individual models (Annan and Hargreaves, 2011; Rougier,

2016). The AR5 considered 13 atmospheric ﬁelds in its assessment

for the instrumental period but did not assess multi-variate model

performance in other climate domains (e.g., ocean, land, and sea ice).

The AR5 found only modest improvement regarding the simulation

of climate for two periods of the Earth’s history (the Last Glacial

Maximum and the mid-Holocene) between CMIP5 and previous

paleoclimate simulations. Similarly, for the modern period only

modest, incremental progress was found between CMIP3 and

CMIP5 regarding the simulation of precipitation and radiation. The

representation of clouds also showed improvement, but remained

akey challenge in climate modelling (Flato et al., 2013).

The type of multi-variate analysis of models presented in AR5 remains

critical to building conﬁdence for example in projections of climate

change. It is expanded here to the previous-generation CMIP3 and

present-generation CMIP6 models and also to more variables and

more climate domains, covering land and ocean as well as sea ice.

The multi-variate evaluation of these three generations of models

is performed relative to the observational datasets listed in Annex I,

TableAI.1. For many of these datasets, a rigorous characterization of the

observational uncertainty is not available, see discussion in Chapter2.

Here, as much as possible, multiple independent observational datasets

are used. Disagreements among them would cause differences in model

scoring, indicating that observational uncertainties may be substantial

compared to model errors. Conversely, similar scores against different

observational datasets would suggest model biases may be larger than

the observational uncertainty.

An analysis of a basket of 16 atmospheric variables (Figure 3.42a)

assessed across CMIP3, CMIP5, and CMIP6 models but excluding

high-resolution models participating in HighResMIP, reveals the

progress made between these three generations of models (Bock

et al., 2020). Progress is evidenced by the increasing prevalence of

blue colours (indicating a performance better than the median) for

the more recent model versions. Additionally, a few CMIP6 models

outperform the best-performing CMIP5 models. Progress is evident

across all 16 variables. As noted in AR5, the models typically score

similarly against both observational reference datasets, indicating

that indeed uncertainties in these reference datasets are smaller

than model biases. Several models and model families perform

better compared to observational references than the median, across

a majority of the climate variables assessed, and conversely some

other models or model families compare more poorly against these

reference datasets. Such a good correspondence across a range

of diagnostic ﬁelds probing different aspects of climate enhances

conﬁdence that the improved performances reﬂect progress in the

physical realism of these simulations. An alternative explanation,

that progress is due to acancellation of errors achieved by model

tuning, appears improbable given the large number of diagnostic

ﬁelds involved here. However, several instances of poor model

performance (red colours in Figure 3.42) still exist in the CMIP6

ensemble. Family relationships (i.e. various degrees of shared

formulations; Knutti et al., 2013) between the models are apparent,

for example, the GISS, GFDL, CESM, CNRM, and HadGEM/UKESM1/

ACCESS families score similarly across all atmospheric variables,

both for the CMIP5 and CMIP6 generations. In the cases of CESM2/

CESM2(WACCM), CNRM-CM6-1/CNRM-ESM2-1, NorCPM1/

NorESM2-LM, and HadGEM3-GC31-LL/UKESM1-0-LL, the high-

complexity model scores as well or better than its lower-complexity

counterpart, indicating that increasing complexity by adding Earth

system features, which by removing constraints could be expected to

degrade a model’s performance, does not necessarily do so. Several

high climate-sensitivity models (Section 7.5; Meehl et al., 2020), in

particular CanESM5, CESM2, CESM2-WACCM, HadGEM3-GC31-LL,

and UKESM1-0-LL, score well against the benchmarks. In accordance

with AR5 and earlier assessments, the multi-model mean, with some

notable exceptions, is better than any individual model (Annan and

Hargreaves, 2011; Rougier, 2016).

Regarding model performance for the ocean and the cryosphere

(Figure 3.42b), it is apparent that for many models there are

substantial differences between the scores for Arctic and Antarctic

sea ice concentration. This might suggest that it is not sea ice physics

directly that is driving such differences in performance but rather

other inﬂuences, such as differences in geography, the role of large

ice shelves (which are absent in the Arctic), or large-scale ocean

dynamics. As for atmospheric variables, progress is evident also

across all four ocean and ten land variables from CMIP5 to CMIP6.

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Human Inﬂuence on the Climate System Chapter 3

Figure3.42 | Relative space–time root-mean-square deviation (RMSD) calculated from the climatological seasonal cycle of the CMIP simulations (1980–1999) compared to observational datasets. (a) CMIP3,

CMIP5, and CMIP6 for 16 atmospheric variables (b) CMIP5 and CMIP6 for 10 land variables and four ocean/sea-ice variables. A relative performance measure is displayed, with blue shading indicating better and red shading indicating

worse performance than the median of all model results. A diagonal split of a grid square shows the relative error with respect to the reference data set (lower right triangle) and an additional data set (upper left triangle). Reference/

additional datasets are from top to bottom in (a): ERA5/NCEP, GPCP-SG/GHCN, CERES-EBAF, CERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55/ERA5, ESACCI-SST/HadISST, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP, ERA5/NCEP,

ERA5/NCEP, AIRS/ERA5, ERA5/NCEP and in (b): CERES-EBAF, CERES-EBAF, CERES-EBAF, CERES-EBAF, LandFlux-EVAL, Landschuetzer2016/ JMA-TRANSCOM; MTE/FLUXCOM, LAI3g, JMA-TRANSCOM, ESACCI-SOILMOISTURE, HadISST/

ATSR, HadISST, HadISST, ERA-Interim. White boxes are used when data are not available for a given model and variable. Figure is updated and expanded from Bock et al. (2020), their Figure5 CC BY 4.0 https://creativecommons.org/

licenses/by/4.0/. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

510

Chapter 3 Human Inﬂuence on the Climate System

In summary, CMIP6 models perform generally better for a basket of

variables covering mean historical climate across the atmosphere,

ocean, and land domains than previous-generation and older models

(high conﬁdence). Earth System models characterized by additional

biogeochemical feedbacks often perform at least as well as related

more constrained, lower-complexity models lacking these feedbacks

(medium conﬁdence). In many cases, the models score similarly

against both observational references, indicating that model errors

are usually larger than observational uncertainties (high conﬁdence).

Moreover, synthesizing across Sections 3.3–3.7, we assess that the

CMIP6 multi-model mean captures most aspects of observed climate

change well (high conﬁdence).

Using centred pattern correlations (quantifying pattern similarity

on a scale of –1 to 1, with 1 expressing perfect similarity and 0 no

relationship) for selected ﬁelds, AR5 documented improvements

between CMIP3 and CMIP5 in surface air temperature, outgoing

longwave radiation, and precipitation (Figure 9.6 of Flato et al.,

2013). Little further progress between CMIP3 and CMIP5 was found

for ﬁelds that were already quite well simulated in CMIP3 (such

as surface air temperature and outgoing longwave radiation). For

precipitation, the spread reduced because the worst-performing

models improved. The shortwave cloud radiative effect remained

relatively poorly simulated with signiﬁcant inter-model spread

(e.g., Calisto et al., 2014). This comparison of centred pattern

correlations is designed to help determine the quality of simulation

of different diagnostics relative to each other, and also to examine

progress between generations of models. Figure 3.43 shows the

centred pattern correlations for 16 variables for CMIP3, CMIP5 and

CMIP6 models. In the ensemble averages, CMIP6 performs better

than CMIP5 and CMIP3 for near-surface temperature, precipitation,

mean sea-level pressure, and many other variables. For the variables

shown, the uncertainties in observational datasets, in particular for

precipitation and northward wind at 850 hPa, remain substantial

relative to mean model errors (see grey dots in Figure3.43).

In addition to the multivariate assessments of simulations of the

recent historical period, simulations of selected periods of the Earth’s

more distant history can be used to benchmark climate models by

exposing them to climate forcings that are radically different from

the present and recent past (Harrison et al., 2015, 2016; Kageyama

et al., 2018; Tierney et al., 2020a). These time periods provide an out-

of-sample test of models because they are not in general used in the

process of model development. They encompass a range of climate

drivers, such as volcanic and solar forcing for the Last Millennium,

orbital forcing for the mid-Holocene and Last Interglacial, and changes

in greenhouse gases and ice sheets for the LGM, mid-Pliocene Warm

Period, and early Eocene (Sections 2.2 and 2.3). These drivers led to

climate changes, including in surface temperature (Section 2.3.1.1)

and the hydrological cycle (Section 2.3.1.3.1), which are described

by paleoclimate proxies that have been synthesized to support

evaluations of models on a global and regional scale. However, the

more sparse, indirect, and regionally incomplete climate information

available from paleo-archives motivates a different form of the

multivariate analysis of simulations covering these periods versus the

equivalent for the historical period, as described below.

AR5 found that reconstructions and simulations of past climates both

show similar responses in terms of large-scale patterns of climate

change, such as polar ampliﬁcation (Flato et al., 2013; Masson-

Delmotte et al., 2013). However, for several regional signals (e.g.,

the north–south temperature gradient in Europe and regional

precipitation changes), the magnitude of change seen in the proxies

relative to the pre-industrial period was underestimated by the models.

When benchmarking CMIP5/PMIP3 models against reconstructions

of the mid-Holocene and LGM, AR5 found only a slight improvement

compared with earlier model versions across a range of variables. For

Pattern correlation with observational reference

correlation

Specific

Humidity

400 hPa

Near-Surface

Air Temperature

Precipitation TOA

Outgoing

Shortwave

Radiation

TOA

Outgoing

Longwave

Radiation

TOA SW

Cloud Rad

Effect

TOA LW

Cloud Rad

Effect

Sea Level

Pressure

Temperature

850 hPa

Temperature

200 hPa

Eastward

Wind

850 hPa

Eastward

Wind

200 hPa

Northward

Wind

850 hPa

Northward

Wind

200 hPa

Geopotential

Height

500 hPa

1.0

0.8

0.4

0.2

CMIP6

CMIP5

CMIP3

Additional observations

Surface

Temperature

Figure3.43 | Centred pattern correlations between models and observations for the annual mean climatology over the period 1980–1999. Results are

shown for individual CMIP3 (green), CMIP5 (blue) and CMIP6 (red) models (one ensemble member from each model is used) as short lines, along with the corresponding multi-model

ensemble averages (long lines). Correlations are shown between the models and the primary reference observational data set (from left to right: ERA5, GPCP-SG, CERES-EBAF,

CERES-EBAF, CERES-EBAF, CERES-EBAF, JRA-55, ESACCI-SST, ERA5, ERA5, ERA5, ERA5, ERA5, ERA5, AIRS, ERA5). In addition, the correlation between the primary reference and

additional observational datasets (from left to right: NCEP, GHCN, -, -, -, -, ERA5, HadISST, NCEP, NCEP, NCEP, NCEP, NCEP, NCEP, ERA5, NCEP) are shown (solid grey circles) if

available. To ensure a fair comparison across a range of model resolutions, the pattern correlations are computed after regridding all datasets to aresolution of 4° in longitude and

5° in latitude. Figure is updated and expanded from Bock et al. (2020), their Figure7 CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. Further details on data sources and

processing are available in the chapter data table (Table3.SM.1).

511

Human Inﬂuence on the Climate System Chapter 3

the Last Interglacial, it was noted that the magnitude of observed

annual mean warming in the Northern Hemisphere was only reached

in summer in the models. For the mid-Pliocene Warm Period, it was

noted that both proxies and models showed a polar ampliﬁcation of

temperature compared with the pre-industrial period, but a formal

model evaluation was not carried out.

Since AR5, new simulation protocols have been developed in PMIP4

(Kageyama et al., 2018), which are further described for the mid-

Holocene and the Last Interglacial by Otto-Bliesner et al. (2017), for

the LGM by Kageyama et al. (2017), for the Pliocene by Haywood

et al. (2016), and for the early Eocene by Lunt et al. (2017). These have

resulted in new model simulations for these time periods (Brierley

et al., 2020; Haywood et al., 2020; Kageyama et al., 2021a; Lunt et al.,

2021; Otto-Bliesner et al., 2021). These time periods span an assessed

temperature range of 20°C (Section 2.3.1.1), and for all periods the

PMIP4 multi-model ensemble mean is within 0.5°C of the assessed

range of GSAT (Figure3.44a). Those time periods for which the multi-

model ensemble mean is outside the assessed range of GSAT, the

mid-Holocene and the Last Interglacial, are primarily forced by orbital

changes not greenhouse gas forcing, and as a result the forcing as

well as the assessed and modelled response are relatively close to

zero in the global annual mean. During these periods, climate change

therefore is a consequence of more poorly understood Earth System

Figure3.44 | Multivariate synopsis of paleoclimate model results compared to observational references. Data-model comparisons for (a) GSAT anomalies for

ﬁve PMIP4 periods and for regional features for the (b) mid-Holocene and (c) LGM periods, for PMIP3 and PMIP4 models. The results from CMIP6 models are shown as coloured

dots. In (a) the light orange shading shows the very likely assessed ranges presented in Section 2.3.1.1. In (b) and (c), the regions and variables are deﬁned as follows: North

America (20°N–50°N, 140°W–60°W), Western Europe (35°N–70°N, 10°W–30°E) and West Africa (0°–30°N, 10°W–30°E); mean temperature of the coldest month (MTCO;

°C), mean temperature of the warmest month (MTWA; °C), mean annual precipitation (MAP; mm yr–1). In (b) and (c) the ranges shown for the reconstructions (Bartlein et al.

(2011) for mid-Holocene and Cleator et al. (2020) for LGM) are based on the standard error given at each site: the average and associated standard deviation over each area is

obtained by computing 1000 times the average of randomly drawn values from the Gaussian distributions deﬁned at each site by the reconstruction mean and standard error;

the light orange colour shows the ±1 standard deviation of these 1000 estimates. The dots on (b) and (c) show the average of the model output for grid points for which there

are reconstructions. The ranges for the model results are based on an ensemble of 1000 averages over 50 years randomly picked in the model output time series for each region

and each variable: the mean ± one standard deviation is plotted for each model. Figure is adapted from Brierley et al. (2020), their Figure S3 for the mid-Holocene; and from

Kageyama et al. (2021b), their Figure12 for the LGM. Further details on data sources and processing are available in the chapter data table (Table3.SM.1).

512

Chapter 3 Human Inﬂuence on the Climate System

feedbacks acting on the response to orbital differences versus the

present-day, affecting the seasonality of insolation.

Polar ampliﬁcation in the LGM, mid-Pliocene Warm Period, and

Early Eocene Climatic Optimum (EECO) simulations is assessed in

Section7.4.4.1.2. Here we focus on the mid-Holocene and the LGM,

which have been a part of AMIP or CMIP through several assessment

cycles, and as such serve as a reference to quantify regional model-data

agreement from one IPCC assessment to another. We compare the

results from 15 CMIP6 models using the PMIP4 protocol (CMIP6-PMIP4),

with non-CMIP6 models using the PMIP4 protocol, with PMIP3 models,

and with regional temperature and precipitation changes from proxies

for the mid-Holocene (Figure3.44b). For six out of seven variables

shown, the CMIP6 multi-model mean captures the correct sign of the

change. For ﬁve out of seven of them the CMIP6 ensemble mean is

within the reconstructed range. For the other two variables (changes in

the mean temperature of the warmest month over North America and

in the mean annual precipitation over West Africa) nearly all PMIP4

and PMIP3 models are outside the reconstructed range. CMIP6 models

show regional patterns of temperature changes similar to the PMIP3

ensemble (Brierley et al., 2020), but the slight mid-Holocene cooling in

PMIP4 compared with PMIP3, probably associated with lower imposed

mid-Holocene carbon dioxide concentrations (Otto-Bliesner et al.,

2017), improves the regional model performance for summer and

winter temperatures (Figure3.44b). However, this cooling also results

in a CMIP6 mid-Holocene GSAT that lies further from the assessed

range (Figure3.44a). All models show an expansion of the monsoon

areas from the pre-industrial to the mid-Holocene simulations in the

Northern Hemisphere, but this expansion in some cases is only large

enough to cancel out the bias in the pre-industrial control simulations

(Section 3.3.3.2; Brierley et al., 2020). There is aslight improvement in

representing the northward expansion of the West African monsoon

region in PMIP4 compared with PMIP3 (Figures 3.11 and 3.44b).

Fourteen simulations of the LGM climate have been produced

following the CMIP6-PMIP4 protocol using 11 models, five of

which are from the latest CMIP6 generation. The multi-model-mean

global cooling simulated by these models is close to that simulated

by the CMIP5-PMIP3 ensemble, but the range of results is larger.

The increase in the range is largely due to the inclusion of CESM2

which simulates a much larger cooling than the other PMIP4 models

(Figure3.44a). This is consistent with its larger climate sensitivity

(see also Section 3.3.1.1; Zhu et al., 2021). The other models on

average also simulate slightly larger cooling in PMIP4 versus PMIP3

(Kageyama et al., 2017, 2021a). The PMIP4 multi-model mean is

within the range of reconstructed regional averages for four out

of seven regional variables; this is unchanged from PMIP3 but for

different variables (Figure3.44c). For all ﬁelds, the results of many

individual models are outside the reconstructed range. For two

variables out of seven (changes in the mean temperature of the

warmest month and mean annual precipitation over Western Europe)

no model is within the range of the reconstructions. This analysis is

strengthened compared with the equivalent analysis in AR5 because

it is based on larger and improved reconstructions (Cleator et al.,

2020). Most CMIP6-PMIP4 models simulate a slightly stronger AMOC

in the LGM, but no strong deepening of the AMOC (Kageyama et al.,

2021a), while most other PMIP4 models simulate a strengthening

and strong deepening of the AMOC, as was the case for the PMIP3

models (Muglia and Schmittner, 2015; Sherriff-Tadano et al., 2018).

Only one model (CESM1.2) shows ashoaling of LGM AMOC which

is consistent with reconstructions (Marzocchi and Jansen, 2017;

Sherriff-Tadano et al., 2018).

17 PMIP4 models completed Last Interglacial simulations (Otto-Bliesner

et al., 2021). The comparison to reconstructions is generally good,

except for some discrepancies, such as for upwelling systems in

the South East Atlantic or discrepancies which may result from

local melting of remnant ice sheets absent in the Last Interglacial

simulation protocol. All models simulate a decrease in Arctic sea ice

in summer, commensurate with increased summer insolation, while

some models even simulate alarge or complete loss (Guarino et al.,

2020; Kageyama et al., 2021b). Sea ice reconstructions for the central

Arctic are, however, too uncertain to evaluate this behaviour. The

Last Interglacial simulations indicate a clear relationship between

simulated sea ice loss and model responses to increased greenhouse

gas forcing (Kageyama et al., 2021b; Otto-Bliesner et al., 2021).

Overall, the PMIP multi-model means agree very well (within 0.5°C

of the assessed range) with GSAT reconstructed from proxies

across multiple time periods, spanning a range from 6°C colder

than pre-industrial (Last Glacial Maximum) to 14°C warmer than

pre-industrial (Early Eocene Climate Optimum) (high conﬁdence).

During the orbitally-forced mid-Holocene, the CMIP6 multi-model

mean captures the sign of the regional changes in temperature

and precipitation in most regions assessed, and there have

been some regional improvements compared to AR5 (medium

confidence). The limited number of CMIP6 simulations of the

LGM hinders model evaluation of the multi-model mean, but for

both LGM and mid-Holocene, models tend to underestimate the

magnitude of large changes (high conﬁdence). Some long-standing

model-data discrepancies, such as a dry bias in North Africa in the

mid-Holocene, have not improved in CMIP6 compared with PMIP3

(highconﬁdence).

3.8.2.2 Process Representation in Different Classes of Models

Based on new scientiﬁc insights and newly available observations,

many improvements have been made to models from CMIP5 to

CMIP6, including changes in the representation of physics of the

atmosphere, ocean, sea ice, and land surface. In many cases, changes

in the detailed representation of cloud and aerosol processes have

been implemented. The new generation of CMIP6 climate models

also features increases in spatial resolution, as well as inclusion of

additional Earth system processes and new components (see further

details in Section 1.5.3.1 and in Tables AII.5 and AII.6). Such changes

to model physics and resolution are often designed to improve the

ﬁtness-for-purpose of a model such as for projecting regional aspects

of climate (Section 10.3) or to more fully represent feedbacks to make

the models more ﬁt for long-term climate projections affected for

example by carbon cycle feedbacks (see also Section 1.5.3.1).

Factors affecting model performance include resolution, the type of

dynamical core (spectral, ﬁnite difference or ﬁnite volume), physics

parameters and parameterisations, model structure, for example,

513

Human Inﬂuence on the Climate System Chapter 3

many of the coupled HighResMIP models (Haarsma et al., 2016) use

the NEMO ocean model, affecting model diversity, and the range and

degree of process realism (e.g., for aerosols, atmospheric chemistry

and other Earth System components). This section particularly

explores the inﬂuence of model resolution and of complexity on

model performance (see also Section 8.5.1).

A key advance in CMIP6 compared to CMIP5 is the presence of

high-resolution models that have participated in HighResMIP.

Resolution alone can signiﬁcantly affect a model’s performance,

with some effects propagating to the global scale. Recent studies

have shown that enhancing the horizontal resolution of models

is seen to signiﬁcantly affect aspects of large-scale circulation

as well as improve the simulation of small-scale processes and

extremes when compared to CMIP3 and CMIP5 models (see also

Section11.4.3; Haarsma et al., 2016), with some models approaching

10km resolution in the atmosphere (Kodama et al., 2021) or ocean

(Caldwell et al., 2019; Gutjahr et al., 2019; Roberts et al., 2019; Chang

et al., 2020; Semmler et al., 2020).

As discussed in Section 3.3, CMIP6 models reproduce observed

large-scale mean surface temperature patterns as well as their

CMIP5 predecessors, but biases in surface temperature in the

mean of HighResMIP models are smaller than those in the mean

of the corresponding standard resolution CMIP6 conﬁgurations of

the same models (Section 3.3.1.1 and Figure3.3). The extent and

causes of improvements due to increased horizontal resolutions

in the atmosphere and ocean domains depend on the model

(Kuhlbrodt et al., 2018; Roberts et al., 2018, 2019; Sidorenko et al.,

2019), although they typically involve better process representation

(for example of ocean currents and atmospheric storms) which

can lead to reduced biases in top of atmosphere radiation and

cloudiness. Precipitation has likewise improved in CMIP6 versus

CMIP5 models, but biases remain. The high resolution (<25 km)

class of models participating in HighResMIP compares regionally

better against observations than the standard resolution CMIP6

models (of order 100 km, Figure3.13; Section 3.3.2), partly because

of an improved representation of orographic (mountain-induced)

precipitation which constitutes amajor fraction of precipitation on

land, but other processes also play an important role (Vannière et al.,

2019). However, there are also large parts of the tropical ocean where

precipitation in high-resolution models is not improved compared to

standard resolution CMIP6 models (Vannière et al., 2019).

Additionally, the representation of surface and deeper ocean mean

temperature is improved in models with higher horizontal resolution

(Sections 3.5.1.1 and 3.5.1.2) with systematic improvements in

coupled tropical Atlantic sea surface temperature and precipitation

biases at higher resolutions (Roberts et al., 2019, single model;

Vannière et al., 2019, multi-model), the North Atlantic cold bias (Bock

et al., 2020, multi-model; Roberts et al., 2018, 2019; Caldwell et al.,

2019; all single models) as well as deep-ocean biases (Small et al.,

2014; Grifﬁes et al., 2015; Caldwell et al., 2019; Gutjahr et al., 2019;

Roberts et al., 2019; Chang et al., 2020, all single model studies).

Atlantic ocean transports (heat and volume) are also generally

improved compared to observations (Grist et al., 2018; Caldwell et al.,

2019; Docquier et al., 2019; Roberts et al., 2019, 2020c; Chang et al.,

2020), as well as some aspects of air-sea interactions (P. Wu et al.,

2019, single model; Bellucci et al., 2021, multi-model). However, warm-

biased sea surface temperatures in the Southern Ocean are worse in

comparison to standard resolution CMIP6 models (Bock et al., 2020).

The AR5 noted problems with the simulation of clouds in this region

which were later attributed to a lack of supercooled liquid clouds

(Bodas-Salcedo et al., 2016). Mesoscale ocean processes are critical

to maintaining the Southern Ocean stratiﬁcation and response to

wind forcing (Marshall and Radko, 2003; Hallberg and Gnanadesikan,

2006), and their explicit representation requires even higher ocean

resolution (Hallberg, 2013). Similarly, atmospheric convection remains

unresolved even in the highest-resolution climate models participating

in HighResMIP. However, there is also evidence of improvements

in the frequency, distribution and interannual variability of tropical

cyclones in HighResMIP (Roberts et al., 2020a, b), particularly in the

Northern Hemisphere (see further discussion in Section 11.7.1.3),

and their interaction with the ocean (Scoccimarro et al., 2017, single

model), as well as the global moisture budget (Vannière et al., 2019).

At higher resolution the track density of tropical cyclones is increased

practically everywhere where tropical cyclones occur. Simulation of

some climate extremes is shown to be improved at higher resolution

including explosively developing extra-tropical cyclones (Vries et al.,

2019; Jiaxiang et al., 2020), blocking (Section 3.3.3.3; Fabiano et al.,

2020; Schiemann et al., 2020) and European extreme precipitation

due to a better representation of the North Atlantic storm track (van

Haren et al., 2015) and orographic boundary conditions (Schiemann

et al.,2018).

In CMIP6 a number of Earth system models have increased the realism

by which key biogeochemical aspects of the coupled Earth system are

represented, affecting, for example, the carbon and nitrogen cycles,

aerosols, and atmospheric chemistry (e.g., Cao et al., 2018; Gettelman

et al., 2019; Lin et al., 2019; Mauritsen et al., 2019; Séférian et al.,

2019; Sellar et al., 2019; Sidorenko et al., 2019; Swart et al., 2019;

Dunne et al., 2020; Seland et al., 2020; Wu et al., 2020; Ziehn et al.,

2020). In addition to increased process realism, the level of coupling

between the physical climate and biogeochemical components of

the Earth system has also been enhanced in some models (Mulcahy

et al., 2020) as well as across different biogeochemical components

(see Section 5.4 for adiscussion and Table5.4 for an overview). For

example, the nitrogen cycle is now simulated in several ESMs (Zaehle

et al., 2015; Davies-Barnard et al., 2020). This advance accounts

for the fertilization effect nitrogen availability has on vegetation

and carbon uptake, reducing uncertainties in the simulations of

the carbon uptake responses to physical climate change (Section

3.6.1) and to CO2 increases (Arora et al., 2020), thus improving

conﬁdence in the simulated airborne fraction of CO2 emissions

(Jones and Friedlingstein, 2020) and better constraining remaining

carbon budgets (Section 5.5). Such advances also allow investigation

of land-based climate change mitigation options (e.g., through

changes in land management and associated terrestrial carbon

uptake (Mahowald et al., 2017; Pongratz et al., 2018)) or interactions

between different facets of the managed Earth system, such as

interactions between mitigation efforts targeting climate warming

and air quality (West et al., 2013). A number of developments also

explicitly target improved simulation of the past.

514

Chapter 3 Human Inﬂuence on the Climate System

Further such ESM developments include: (i) Apart from the nitrogen

cycle, extending terrestrial carbon cycle models to simulate

interactions between the carbon cycle and other nutrient cycles, such

as phosphorus, that are known to play an important role in limiting

future plant uptake of CO2 (Zaehle et al., 2015). (ii) Introducing

explicit coupling between interactive atmospheric chemistry and

aerosol schemes (Gettelman et al., 2019; Sellar et al., 2019), which

has been shown to affect estimates of historical aerosol radiative

forcing (Karset et al., 2018). Furthermore, interactive treatment

of atmospheric chemistry in a full ESM supports investigation of

interactions between climate and air quality mitigation efforts, such as

in AerChemMIP (Collins et al., 2017), as well as interactions between

stratospheric ozone recovery and global warming (Morgenstern

et al., 2018). (iii) Coupling between components of Earth system

models has been extended to increase their utility for studying future

interactions across the full Earth system, such as between ocean

biogeochemistry and cloud-aerosol processes (Mulcahy et al., 2020),

and vegetation and impacts on dust production (Kok et al., 2018),

production of secondary organic aerosols (SOA, Zhao et al., 2017)

and Equilibrium Climate Sensitivity (ECS), whereby enhanced CO2

fertilization of land vegetation causes changes in regional surface

albedo (Andrews et al., 2019). Increased coupling between physical

climate and biogeochemical processes in a single ESM, along with

an increased number of interactively represented processes, such as

permafrost thaw, vegetation, wildﬁres and continental ice sheets

increases our ability to investigate the potential for abrupt and

interactive changes in the Earth system (see Sections 4.7.3 and 5.4.9,

and Box5.1). Table5.4 provides an overview of recent advances in

representing the carbon cycle in ESMs.

In summary, both high-resolution and high-complexity models have

been evaluated as part of CMIP6. In comparison with standard

resolution CMIP6 models, higher resolution probed under the

HighResMIP activity (Haarsma et al., 2016) improves aspects of the

simulation of climate (particularly concerning sea surface temperature)

but discrepancies remain and there are some regions, such as parts of

the Southern Ocean, where currently attainable resolution produces

inferior performance (high conﬁdence). Such model behaviour can

indicate deﬁciencies in model physics that are not simply associated

with resolution. In several cases, high-complexity ESMs that include

additional interactions between Earth system components and thus

have potential for additional associated model errors nevertheless

perform as well as their low-complexity counterparts, illustrating that

interactively simulating these Earth System components as part of

the climate system is now well established.

Acknowledgements

Graphic developers: Bettina K. Gier (Germany), Birgit Hassler

(Germany), Souﬁane Karmouche (Germany, Morocco), SeungmokPaik

(Republic of Korea), Min-Gyu Seong (Republic of Korea)

Data providers: Leopold Haimberger (Austria), Lawrence Mudryk

(Canada), Dirk Notz (Germany)

515

Human Inﬂuence on the Climate System Chapter 3

Frequently Asked Questions

FAQ 3.1 | How Do We Know Humans Are Responsible for Climate Change?

The dominant role of humans in driving recent climate change is clear. This conclusion is based on a synthesis of

information from multiple lines of evidence, including direct observations of recent changes in Earth’s climate;

analyses of tree rings, ice cores, and other long-term records documenting how the climate has changed in the

past; and computer simulations based on the fundamental physics that governs the climate system.

Climate is inﬂuenced by a range of factors. There are two main natural drivers of variations in climate on time

scales of decades to centuries. The ﬁrst is variations in the sun’s activity, which alter the amount of incoming

energy from the sun. The second is large volcanic eruptions, which increase the number of small particles (aerosols)

in the upper atmosphere that reﬂect sunlight and cool the surface–an effect that can last for several years

(see also FAQ 3.2). The main human drivers of climate change are increases in the atmospheric concentrations

of greenhouse gases and of aerosols from burning fossil fuels, land use and other sources. The greenhouse

gases trap infrared radiation near the surface, warming the climate. Aerosols, like those produced naturally

by volcanoes, on average cool the climate by increasing the reﬂection of sunlight. Multiple lines of evidence

demonstrate that human drivers are the main cause of recent climate change.

The current rates of increase of the concentration of the major greenhouse gases (carbon dioxide, methane

and nitrous oxide) are unprecedented over at least the last 800,000 years. Several lines of evidence clearly show

that these increases are the results of human activities. The basic physics underlying the warming effect of

greenhouse gases on the climate has been understood for more than a century, and our current understanding

has been used to develop the latest generation climate models (see FAQ 3.3). Like weather forecasting models,

climate models represent the state of the atmosphere on a grid and simulate its evolution over time based on

physical principles. They include a representation of the ocean, sea ice and the main processes important in

driving climate and climate change.

Results consistently show that such climate models can only reproduce the observed warming (black line in

FAQ 3.1, Figure1) when including the effects of human activities (grey band in FAQ 3.1, Figure1), in particular

the increasing concentrations of greenhouse gases. These climate models show a dominant warming effect of

greenhouse gas increases (red band, which shows the warming effects of greenhouse gases by themselves),

which has been partly offset by the cooling effect of increases in atmospheric aerosols (blue band). By contrast,

simulations that include only natural processes, including internal variability related to ElNiño and other similar

variations, as well as variations in the activity of the sun and emissions from large volcanoes (green band in

FAQ3.1, Figure 1), are not able to reproduce the observed warming. The fact that simulations including only

natural processes show much smaller temperature increases indicates that natural processes alone cannot explain

the strong rate of warming observed. The observed rate can only be reproduced when human inﬂuence is added

to the simulations.

Moreover, the dominant effect of human activities is apparent not only in the warming of global surface

temperature, but also in the pattern of warming in the lower atmosphere and cooling in the stratosphere,

warming of the ocean, melting of sea ice, and many other observed changes. An additional line of evidence for

the role of humans in driving climate change comes from comparing the rate of warming observed over recent

decades with that which occurred prior to human inﬂuence on climate. Evidence from tree rings and other

paleoclimate records shows that the rate of increase of global surface temperature observed over the past ﬁfty

years exceeded that which occurred in any previous 50-year period over the past 2000 years (see FAQ 2.1).

Taken together, this evidence shows that humans are the dominant cause of observed global warming over

recent decades.

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Chapter 3 Human Inﬂuence on the Climate System

FAQ 3.1 (continued)

FAQ 3.1: How do we know humans are causing climate change?

Observed warming (1850-2019) is only reproduced in simulations including human influence.

Observations

Global surface temperature change since 1850

(°C)

2.5

2.0

1.5

1.0

0.5

-0.5

-1.0

-1.5

1900 1950 2000 2020

1850

Combined

(Human & natural causes)

Greenhouse gases (human)

Aerosols (Human)

Natural causes

FAQ 3.1, Figure1 | Observed warming (1850–2019) is only reproduced in simulations including human inﬂuence. Global surface temperature

changes in observations, compared to climate model simulations of the response to all human and natural forcings (grey band), greenhouse gases only

(redband), aerosols and other human drivers only (blue band) and natural forcings only (green band). Solid coloured lines show the multi-model mean,

andcoloured bands show the 5–95% range of individual simulations.

517

Human Inﬂuence on the Climate System Chapter 3

Frequently Asked Questions

FAQ 3.2 | What is Natural Variability and How Has it Inﬂuenced Recent Climate Changes?

Natural variability refers to variations in climate that are caused by processes other than human inﬂuence.

Itincludes variability that is internally generated within the climate system and variability that is driven by

natural external factors. Natural variability is a major cause of year-to-year changes in global surface climate and

can play a prominent role in trends over multiple years or even decades. But the inﬂuence of natural variability

is typically small when considering trends over periods of multiple decades or longer. When estimated over the

entire historical period (1850–2020), the contribution of natural variability to global surface warming of –0.23°C

to +0.23°C is small compared to the warming of about 1.1°C observed during the same period, which has been

almost entirely attributed to the human inﬂuence.

Paleoclimatic records (indirect measurements of climate that can extend back many thousands of years) and

climate models all show that global surface temperatures have changed signiﬁcantly over a wide range of time

scales in the past. One of these reasons is natural variability, which refers to variations in climate that are either

internally generated within the climate system or externally driven by natural changes. Internal natural variability

corresponds to a redistribution of energy within the climate system (for example via atmospheric circulation

changes similar to those that drive the daily weather) and is most clearly observed as regional, rather than

global, ﬂuctuations in surface temperature. External natural variability can result from changes in the Earth’s

orbit, small variations in energy received from the sun, or from major volcanic eruptions. Although large orbital

changes are related to global climate changes of the past, they operate on very long time scales (i.e.,thousands

of years). As such, they have displayed very little change over the past century and have had very little inﬂuence

on temperature changes observed over that period. On the other hand, volcanic eruptions can strongly cool the

Earth, but this effect is short-lived and their inﬂuence on surface temperatures typically fades within a decade

of the eruption.

To understand how much of observed recent climate change has been caused by natural variability (a process

referred to as attribution), scientists use climate model simulations. When only natural factors are used to force

climate models, the resulting simulations show variations in climate on a wide range of time scales in response to

volcanic eruptions, variations in solar activity, and internal natural variability. However, the inﬂuence of natural

climate variability typically decreases as the time period gets longer, such that it only has mild effects on multi-

decadal and longer trends (FAQ 3.2, Figure1).

Consequently, over periods of a couple of decades or less, natural climate variability can dominate the human-

induced surface warming trend – leading to periods with stronger or weaker warming, and sometimes even

cooling (FAQ 3.2, Figure1, left and centre). Over longer periods, however, the effect of natural variability is

relatively small (FAQ 3.2, Figure1, right). For instance, over the entire historical period (1850–2019), natural

variability is estimated to have caused between –0.23°C and +0.23°C of the observed surface warming of

about 1.1°C. This means that the bulk of the warming has been almost entirely attributed to human activities,

particularly emissions of greenhouse gases (FAQ 3.1).

Another way to picture natural variability and human inﬂuence is to think of a person walking a dog. The path

of the walker represents the human-induced warming, while their dog represents natural variability. Looking at

global surface temperature changes over short periods is akin to focusing on the dog. The dog sometimes moves

ahead of the owner and other times behind. This is similar to natural variability that can weaken or amplify

warming on the short term. In both cases it is difﬁcult to predict where the dog will be or how the climate will

evolve in the near future. However, if we pull back and focus on the slow steady steps of the owner, the path of

the dog is much clearer and more predictable, as it follows the path of its owner. Similarly, human inﬂuence on

the climate is much clearer over longer time periods.

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Chapter 3 Human Inﬂuence on the Climate System

FAQ 3.2 (continued)

FAQ 3.2 What is natural variability and how has it influenced recent climate changes?

Natural variability can alter global temperature over short time scales (1 year to ~2 decades) but it has a minimal

influence on longer time scales. Since 1850, natural variability ( ) has caused between -0.23°C and 0.23°C

of global temperature change, compared to the warming of about 1.1°C observed ( ) over that period.

Annual (1 year) variations Decadal (10 year) variations Multi-decadal (30 year) variations

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

-0.8

0.8

0.0

-0.8

0.8

0.0

-0.8

Dominated by natural variability Less influenced by natural variability,

but natural cooling or more intense

warming can still occur

Dominated by the human influence

Global surface temperature change (°C)

1950 1970 1990 2010 ‘20

Year

1950 1970 1990 2010 ‘20

Year

1950 1980 2010 ‘20

Year

FAQ 3.2, Figure1 | Annual (left), decadal (middle) and multi-decadal (right) variations in average global surface temperature. The thick

black line is an estimate of the human contribution to temperature changes, based on climate models, whereas the green lines show the combined effect

of natural variations and human-induced warming, different shadings of green represent different simulations, which can be viewed as showing a range of

potential pasts. The influence of natural variability is shown by the green bars, and it decreases on longer time scales. The data is sourced from the CESM1

large ensemble.

519

Human Inﬂuence on the Climate System Chapter 3

Frequently Asked Questions

FAQ 3.3 | Are Climate Models Improving?

Yes, climate models have improved and continue to do so, becoming better at capturing complex and small-

scale processes and at simulating present-day mean climate conditions. This improvement can be measured by

comparing climate simulations against historical observations. Both the current and previous generations of

models show that increases in greenhouse gases cause global warming. While past warming is well simulated by

the new generation models as a group, some individual models simulate past warming that is either below or

above what is observed. The information about how well models simulate past warming, as well as other insights

from observations and theory, are used to reﬁne this Report’s projections of global warming.

Climate models are important tools for understanding past, present and future climate change. They are

sophisticated computer programs that are based on fundamental laws of physics of the atmosphere, ocean,

ice, and land. Climate models perform their calculations on a three-dimensional grid made of small bricks

or ‘gridcells’ of about 100 km across. Processes that occur on scales smaller than the model grid cells (such

as the transformation of cloud moisture into rain) are treated in a simpliﬁed way. This simpliﬁcation is done

differently in different models. Some models include more processes and complexity than others; some represent

processes in ﬁner detail (smaller grid cells) than others. Hence the simulated climate and climate change vary

betweenmodels.

Climate modelling started in the 1950s and, over the years, models have become increasingly sophisticated as

computing power, observations and our understanding of the climate system have advanced. The models used

in the IPCC First Assessment Report published in 1990 correctly reproduced many aspects of climate (FAQ 1.1).

The actual evolution of the climate since then has conﬁrmed these early projections, when accounting for the

differences between the simulated scenarios and actual emissions. Models continue to improve and get better

and better at simulating the large variety of important processes that affect climate. For example, many models

now simulate complex interactions between different aspects of the Earth system, such as the uptake of carbon

dioxide by vegetation on land and by the ocean, and the interaction between clouds and air pollutants. While

some models are becoming more comprehensive, others are striving to represent processes at higher resolution,

for example to better represent the vortices and swirls in currents responsible for much of the transport of heat

in the ocean.

Scientists evaluate the performance of climate models by comparing historical climate model simulations to

observations. This evaluation includes comparison of large-scale averages as well as more detailed regional and

seasonal variations. There are two important aspects to consider: (i) how models perform individually and (ii)

how they perform as a group. The average of many models often compares better against observations than any

individual model, since errors in representing detailed processes tend to cancel each other out in multi-model

averages.

As an example, FAQ 3.3 Figure1 compares simulations from the three most recent generations of models (available

around 2008, 2013 and 2021) with observations of three climate variables. It shows the correlation between

simulated and observed patterns, where a value of 1 represents perfect agreement. Many individual models of

the new generation perform signiﬁcantly better, as indicated by values closer to 1. As a group, each generation

out-performs the previous generation: the multi-model average (shown by the longer lines) is progressively closer

to 1. The vertical extent of the colored bars indicates the range of model performance across each group. The

top of the bar moves up with each generation, indicating improved performance of the bestperforming models

from one generation to the next. In the case of precipitation, the performance of the worst performingmodels

is similar in the two most recent model generations, increasing the spread across models.

Developments in the latest generation of climate models, including new and better representation of physical,

chemical and biological processes, as well as higher resolution, have improved the simulation of many aspects

of the Earth system. These simulations, along with the evaluation of the ability of the models to simulate past

warming as well as the updated assessment of the temperature response to a doubling of CO2 in the atmosphere,

are used to estimate the range of future global warming (FAQ 7.3).

520

Chapter 3 Human Inﬂuence on the Climate System

FAQ 3.3 (continued)

CMIP6

CMIP5

CMIP3

1.0

0.9

0.8

0.7

0.6

FAQ 3.3: Are Climate Models Improving?

Yes, climate models have improved with increasing computer power and better understanding of climate processes.

Better model

performance

Poorer model

performance

Correlation

Multi-model

average

Individual models

Smaller

spread

across

models

Larger

spread

across

models

Near-Surface

Air Temperature

Precipitation Sea Level

Pressure

Skill of models at reproducing observations

FAQ 3.3, Figure 1 | Pattern correlations between models and observations of three different variables: surface air temperature,

precipitation and sea level pressure. Results are shown for the three most recent generations of models, from the Coupled Model Intercomparison

Project (CMIP): CMIP3 (orange), CMIP5 (blue) and CMIP6 (purple). Individual model results are shown as short lines, along with the corresponding ensemble

average (long line). For the correlations the yearly averages of the models are compared with the reference observations for the period 1980–1999, with

1 representing perfect similarity between the models and observations. CMIP3 simulations performed in 2004-2008 were assessed in the IPCC Fourth

Assessment, CMIP5 simulations performed in 2011–2013 were assessed in the IPCC Fifth Assessment, and CMIP6 simulations performed in 2018–2021

are assessed in thisReport.

521

Human Inﬂuence on the Climate System Chapter 3

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Disentangling regional climate change: assessing the contribution of global- and regional-scale anthropogenic factors to observed regional warming

Article

Full-text available

Nov 2024
ENVIRON RES LETT

Armineh Barkhordarian

While the influence of uniformly-distributed well-mixed greenhouse gas emissions on global warming is well-documented and robustly attributed through multiple lines of evidence, regional attribution remains more challenging and dependent on the performance and resolution of climate models. This study investigates the extent to which observed regional temperature trends are attributable to global-scale anthropogenic factors, mainly the direct effects of CO2, aiming to differentiate the portion of the change attributable to regional-scale drivers and local feedback mechanisms. To quantify the contribution of global- and regional-scale climate drivers to observed temperature change, I conducted regression analyses using the observed temperature change in HadCRUT4 and the global warming index derived from global radiative forcing series. Results indicate that in certain regions—specifically West Asia, East N. America, West Africa, and the Amazon— 62±7%, 61 ±13%, 58 ±7%, and 61 ±10% of the warming observed over 1991–2020 can be attributed to global anthropogenic warming, primarily the direct effects of CO2. The remaining portion, which represents 22 ±8%, 21 ±16%, 27 ±9%, 23 ±14% of the observed warming, is attributable to regional drivers. The Mediterranean showcases high sensitivity, with regional drivers contributing 31 ±13% of the observed 1.2 ∘C warming, amplifying the warming attributed to global anthropogenic drivers, estimated at 55 ± 10%. The Arctic Ocean along with the Russian-Arctic region exhibits a substantial contribution from regional drivers and local feedback mechanisms (the indirect effects of CO2) to the observed warming amplification, quantified at 43 ± 15% of the 3 ∘C warming over 1991–2020. Regional cooling drivers, however, are significant in East Asia and the Tibetan Plateau, with the latter experiencing a cooling contribution of −42±17%. By breaking down the contributions of climate drivers into global and regional-scale components, we gain valuable insights into the local climate mechanisms responsible for the observed regional warming, which is a crucial factor that affects agriculture, ecosystems and societies.

Detection, attribution and projection of changes in the extreme temperature range in the Northern Hemisphere

Article

Full-text available

Oct 2024

Opportunities and challenges of quantum computing for climate modelling

Preprint

Full-text available

Feb 2025

Adaptation to climate change requires robust climate projections, yet the uncertainty in these projections performed by ensembles of Earth system models (ESMs) remains large. This is mainly due to uncertainties in the representation of subgrid-scale processes such as turbulence or convection that are partly alleviated at higher resolution. New developments in machine learning-based hybrid ESMs demonstrate great potential for systematically reduced errors compared to traditional ESMs. Building on the work of hybrid (physics + AI) ESMs, we here discuss the additional potential of further improving and accelerating climate models with quantum computing. We discuss how quantum computers could accelerate climate models by solving the underlying differential equations faster, how quantum machine learning could better represent subgrid-scale phenomena in ESMs even with currently available noisy intermediate-scale quantum devices, how quantum algorithms aimed at solving optimisation problems could assist in tuning the many parameters in ESMs, a currently time-consuming and challenging process, and how quantum computers could aid in the analysis of climate models. We also discuss hurdles and obstacles facing current quantum computing paradigms. Strong interdisciplinary collaboration between climate scientists and quantum computing experts could help overcome these hurdles and harness the potential of quantum computing for this urgent topic.

The Sikkim flood of October 2023: Drivers, causes and impacts of a multihazard cascade

Article

Jan 2025
SCIENCE

On 3 October 2023, a multihazard cascade in the Sikkim Himalaya, India, was triggered by 14.7 million m ³ of frozen lateral moraine collapsing into South Lhonak Lake, generating an ~20 m tsunami-like impact wave, breaching the moraine, and draining ~50 million m ³ of water. The ensuing Glacial Lake Outburst Flood (GLOF) eroded ~270 million m ³ of sediment, which overwhelmed infrastructure, including hydropower installations along the Teesta River. The physical scale and human and economic impact of this event prompts urgent reflection on the role of climate change and human activities in exacerbating such disasters. Insights into multihazard evolution are pivotal for informing policy development, enhancing Early Warning Systems (EWS), and spurring paradigm shifts in GLOF risk management strategies in the Himalaya and other mountain environments.

Human-caused ocean warming has intensified recent hurricanes

Article

Full-text available

Nov 2024

Understanding how rising global air and sea surface temperatures (SSTs) influence tropical cyclone intensities is crucial for assessing current and future storm risks. Using observations, climate models, and potential intensity theory, this study introduces a novel rapid attribution framework that quantifies the impact of historically-warming North Atlantic SSTs on observed hurricane maximum wind speeds. The attribution framework employs a storyline attribution approach exploring a comprehensive set of counterfactuals scenarios—estimates characterizing historical SST shifts due to human-caused climate change—and considering atmospheric variability. These counterfactual scenarios affect the quantification and significance of attributable changes in hurricane potential and observed actual intensities since pre-industrial. A summary of attributable influences on hurricanes during five recent North Atlantic hurricane seasons (2019–2023) and a case study of Hurricane Ian (2022) reveal that human-driven SST shifts have already driven robust changes in 84% of recent observed hurricane intensities. Hurricanes during the 2019–2023 seasons were 8.3 m s⁻¹ faster, on average, than they would have been in a world without climate change. The attribution framework’s design and application, highlight the potential for this framework to support climate communication.

Abiotic and biotic-controlled nanomaterial formation pathways within the Earth’s nanomaterial cycle

Article

Full-text available

Nov 2024

Nanomaterials have unique properties and play critical roles in the budget, cycling, and chemical processing of elements on Earth. An understanding of the cycling of nanomaterials can be greatly improved if the pathways of their formation are clearly recognized and understood. Here, we show that nanomaterial formation pathways mediated by aqueous fluids can be grouped into four major categories, abiotic and biotic processes coupled and decoupled from weathering processes. These can be subdivided in 18 subcategories relevant to the critical zone, and environments such as ocean hydrothermal vents and the upper mantle. Similarly, pathways in the gas phase such as volcanic fumaroles, wildfires and particle formation in the stratosphere and troposphere can be grouped into two major groups and five subcategories. In the most fundamental sense, both aqueous-fluid and gaseous pathways provide an understanding of the formation of all minerals which are inherently based on nanoscale precursors and reactions.

Centennial‐Scale Variability of the Atlantic Meridional Overturning Circulation in CMIP6 Models Shaped by Arctic–North Atlantic Interactions and Sea Ice Biases

Article

Full-text available

Oct 2024
GEOPHYS RES LETT

Plain Language Summary Changes in ocean circulation are often proposed as drivers of natural variations of the Earth's climate on timescales of centuries. However, it is unclear how strong these natural variations of the circulation strength, called internal variability, are in the real world, because reconstructions from the past climate are sparse and climate models are expensive to run for these long timescales. Here, we compare how the latest generation of climate models simulate internal variability of the Atlantic Meridional Overturning Circulation (AMOC)—the ocean circulation that is often thought to be responsible for Europe's comparatively mild climate—on timescales of 100–250 years. We find that many models have stronger variability on these timescales than what would be expected simply from random noise. In several models, AMOC variability appears to be driven by the release of fresh water from the Arctic Ocean and amplified by intermittent sea ice cover in the North Atlantic. However, this amplification only occurs if a model simulates a too extensive sea ice cover in winter. This mechanism shows that sea ice cover—which is easily observable—could be used to constrain variability of the AMOC on timescales longer than the observational record.

Confronting Earth System Model trends with observations

Article

Mar 2025

Anthropogenically forced climate change signals are emerging from the noise of internal variability in observations, and the impacts on society are growing. For decades, Climate or Earth System Models have been predicting how these climate change signals will unfold. While challenges remain, given the growing forced trends and the lengthening observational record, the climate science community is now in a position to confront the signals, as represented by historical trends, in models with observations. This review covers the state of the science on the ability of models to represent historical trends in the climate system. It also outlines robust procedures that should be used when comparing modeled and observed trends and how to move beyond quantification into understanding. Finally, this review discusses cutting-edge methods for identifying sources of discrepancies and the importance of future confrontations.

Spatiotemporal dynamics of carbon sequestration capacity and its determinants in rubber plantation ecosystems of Hainan Island

Article

Jan 2025

An assessment of the disequilibrium of Alaskan glaciers

Preprint

Full-text available

Nov 2024

The finite response time of alpine glaciers means that glaciers will be in a state of disequilibrium in the presence of a climate trend. Using a simple model of glacier dynamics, we use metrics of glacier geometry to evaluate the present-day disequilibrium for a population of 5600 alpine glaciers in Alaska. Our results indicate that glaciers throughout the region are in a severe state of disequilibrium. We estimate that the median glacier has only undergone 27 % of the retreat necessary to achieve equilibrium with the present-day climate. In general, glaciers with smaller areas have smaller response times, and so are closer to equilibrium than large glaciers. Because much of Alaska’s glacier area is contained in a few large glaciers that are far from equilibrium, and because the rate of warming has increased in the last ~50 years, the median equilibration weighted by area is only 16 %. Our estimates are sensitive to uncertainty in response time and to the shape of the warming trend. Uncertainty is greatest for intermediate glacier response times but is small for glaciers with the smallest and largest response times. Finally, we demonstrate that accounting for the increased rate of warming in the late-20th century is important for estimating glacier disequilibrium, whereas the shape of the warming trend in the early-20th century is less relevant. Our results imply substantial future glacier retreat is already guaranteed regardless of the trajectory of future warming.

The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations

Article

Full-text available

May 2021
CLIM PAST

The Last Glacial Maximum (LGM, ∼ 21 000 years ago) has been a major focus for evaluating how well state-of-the-art climate models simulate climate changes as large as those expected in the future using paleoclimate reconstructions. A new generation of climate models has been used to generate LGM simulations as part of the Paleoclimate Modelling Intercomparison Project (PMIP) contribution to the Coupled Model Intercomparison Project (CMIP). Here, we provide a preliminary analysis and evaluation of the results of these LGM experiments (PMIP4, most of which are PMIP4-CMIP6) and compare them with the previous generation of simulations (PMIP3, most of which are PMIP3-CMIP5). We show that the global averages of the PMIP4 simulations span a larger range in terms of mean annual surface air temperature and mean annual precipitation compared to the PMIP3-CMIP5 simulations, with some PMIP4 simulations reaching a globally colder and drier state. However, the multi-model global cooling average is similar for the PMIP4 and PMIP3 ensembles, while the multi-model PMIP4 mean annual precipitation average is drier than the PMIP3 one. There are important differences in both atmospheric and oceanic circulations between the two sets of experiments, with the northern and southern jet streams being more poleward and the changes in the Atlantic Meridional Overturning Circulation being less pronounced in the PMIP4-CMIP6 simulations than in the PMIP3-CMIP5 simulations. Changes in simulated precipitation patterns are influenced by both temperature and circulation changes. Differences in simulated climate between individual models remain large. Therefore, although there are differences in the average behaviour across the two ensembles, the new simulation results are not fundamentally different from the PMIP3-CMIP5 results. Evaluation of large-scale climate features, such as land–sea contrast and polar amplification, confirms that the models capture these well and within the uncertainty of the paleoclimate reconstructions. Nevertheless, regional climate changes are less well simulated: the models underestimate extratropical cooling, particularly in winter, and precipitation changes. These results point to the utility of using paleoclimate simulations to understand the mechanisms of climate change and evaluate model performance.

Drivers and mechanisms for enhanced summer monsoon precipitation over East Asia during the mid-Pliocene in the IPSL-CM5A

Article

Full-text available

Mar 2016
CLIM DYNAM

Yong Sun

A comparative analysis of East Asian summer monsoon (EASM) precipitation is performed to reveal the drivers and mechanisms controlling the similarities of the mid-Pliocene EASM precipitation changes compared to the corresponding pre-industrial (PI) experiments derived from atmosphere-only (i.e. AGCM) and fully coupled (i.e. CGCM) simulations, as well as the large simulated differences in the mid-Pliocene EASM precipitation between the two simulations. The area-averaged precipitation over the EASM domain is enhanced in the mid-Pliocene compared to the corresponding PI experiments performed by both the AGCM (LMDZ5A) and the CGCM (IPSL-CM5A). Moisture budget analysis reveals that it is the surface warming over East Asia that drives the area-averaged EASM precipitation increase in the mid-Pliocene in both simulations. The surface warming increases the atmospheric moisture content, as revealed by an increase in the thermodynamic component of vertical moisture advection, resulting in enhanced mid-Pliocene EASM precipitation compared to PI in both simulations. Moist static energy diagnosis identifies the combined effect of enhanced zonal thermal contrast and column-integrated meridional stationary eddy velocity

\overline{{v^{*} }}

and its convergence

\frac{{\overline{{\partial v^{*} }} }}{\partial y}

as the physical mechanisms that sustain the enhancement of mid-Pliocene EASM precipitation in both simulations compared to the PI experiments. This takes place through a strengthening of the EASM circulation and moisture transport into the EASM domain associated with an increase in local moisture convergence in the mid-Pliocene in both simulations. Moisture budget analysis also reveals that the larger area-averaged mid-Pliocene EASM precipitation increase in the CGCM compared to its AGCM component is mainly caused by the dynamical component contributing more to the vertical moisture advection in the CGCM (i.e. IPSL-CM5A) compared to its AGCM (LMDZ5). The large simulated differences in the spatial pattern of the mid-Pliocene EASM precipitation between the two simulations result from the combined effect of enhanced meridional thermal contrast over the EASM domain and increased

\overline{{v^{*} }}

convergence over South China in the CGCM simulation compared to the AGCM simulation.

On the attribution of industrial-era glacier mass loss to anthropogenic climate change

Article

Full-text available

Apr 2021
TC

Around the world, small ice caps and glaciers have been losing mass and retreating since the start of the industrial era. Estimates are that this has contributed approximately 30 % of the observed sea-level rise over the same period. It is important to understand the relative importance of natural and anthropogenic components of this mass loss. One recent study concluded that the best estimate of the magnitude of the anthropogenic mass loss over the industrial era was only 25 % of the total, implying a predominantly natural cause. Here we show that the anthropogenic fraction of the total mass loss of a given glacier depends only on the magnitudes and rates of the natural and anthropogenic components of climate change and on the glacier's response time. We consider climate change over the past millennium using synthetic scenarios, palaeoclimate reconstructions, numerical climate simulations, and instrumental observations. We use these climate histories to drive a glacier model that can represent a wide range of glacier response times, and we evaluate the magnitude of the anthropogenic mass loss relative to the observed mass loss. The slow cooling over the preceding millennium followed by the rapid anthropogenic warming of the industrial era means that, over the full range of response times for small ice caps and glaciers, the central estimate of the magnitude of the anthropogenic mass loss is essentially 100 % of the observed mass loss. The anthropogenic magnitude may exceed 100 % in the event that, without anthropogenic climate forcing, glaciers would otherwise have been gaining mass. Our results bring assessments of the attribution of glacier mass loss into alignment with assessments of others aspects of climate change, such as global-mean temperature. Furthermore, these results reinforce the scientific and public understanding of centennial-scale glacier retreat as an unambiguous consequence of human activity.

Summertime increases in upper-ocean stratification and mixed-layer depth

Article

Full-text available

Mar 2021
NATURE

The surface mixed layer of the world ocean regulates global climate by controlling heat and carbon exchange between the atmosphere and the oceanic interior1,2,3. The mixed layer also shapes marine ecosystems by hosting most of the ocean’s primary production⁴ and providing the conduit for oxygenation of deep oceanic layers. Despite these important climatic and life-supporting roles, possible changes in the mixed layer during an era of global climate change remain uncertain. Here we use oceanographic observations to show that from 1970 to 2018 the density contrast across the base of the mixed layer increased and that the mixed layer itself became deeper. Using a physically based definition of upper-ocean stability that follows different dynamical regimes across the global ocean, we find that the summertime density contrast increased by 8.9 ± 2.7 per cent per decade (10⁻⁶–10⁻⁵ per second squared per decade, depending on region), more than six times greater than previous estimates. Whereas prior work has suggested that a thinner mixed layer should accompany a more stratified upper ocean5,6,7, we find instead that the summertime mixed layer deepened by 2.9 ± 0.5 per cent per decade, or several metres per decade (typically 5–10 metres per decade, depending on region). A detailed mechanistic interpretation is challenging, but the concurrent stratification and deepening of the mixed layer are related to an increase in stability associated with surface warming and high-latitude surface freshening8,9, accompanied by a wind-driven intensification of upper-ocean turbulence10,11. Our findings are based on a complex dataset with incomplete coverage of a vast area. Although our results are robust within a wide range of sensitivity analyses, important uncertainties remain, such as those related to sparse coverage in the early years of the 1970–2018 period. Nonetheless, our work calls for reconsideration of the drivers of ongoing shifts in marine primary production, and reveals stark changes in the world’s upper ocean over the past five decades.

Natural variability contributes to model–satellite differences in tropical tropospheric warming

Article

Full-text available

Mar 2021

Significance Climate models have, on average, simulated substantially more tropical tropospheric warming than satellite data, with few simulations matching observations. It has been suggested that this discrepancy arises because climate models are overly sensitive to greenhouse gas increases. Tropical tropospheric temperature trends from a large ensemble of simulations performed with a single climate model span a wide range that is solely due to natural climate variability. A subset of these simulations have warming rates in accord with satellite observations. Simulations with diminished tropical tropospheric warming due to climate variability exhibit characteristic patterns of surface warming that are similar to the observed record. Our results indicate that multidecadal variability can explain current model–observational differences in the rate of tropical tropospheric warming.

Globally observed trends in mean and extreme river flow attributed to climate change

Article

Full-text available

Mar 2021

Change of flow Anthropogenic influence on climate has changed temperatures, precipitation, atmospheric circulation, and many other related physical processes, but has it changed river flow as well? Gudmundsson et al. analyzed thousands of time series of river flows and hydrological extremes across the globe and compared them with model simulations of the terrestrial water cycle (see the Perspective by Hall and Perdigão). They found that the observed trends can only be explained if the effects of climate change are included. Their analysis shows that human influence on climate has affected the magnitude of low, mean, and high river flows on a global scale. Science , this issue p. 1159 ; see also p. 1096

The Southern Annular Mode in 6th Coupled Model Intercomparison Project Models

Article

Full-text available

Feb 2021

Olaf Morgenstern

I analyze trends in the Southern Annular Mode (SAM) in CMIP6 simulations. For the period 1957–2014, simulated linear trends are generally consistent with two observational references but seasonally in disagreement with two other representations of the SAM. Using a regression analysis applied to model simulations with interactive ozone chemistry, a strengthening of the SAM in summer is attributed nearly completely to ozone depletion because a further strengthening influence due to long‐lived greenhouse gases is almost fully counterbalanced by a weakening influence due to stratospheric ozone increases associated with these greenhouse gas increases. Ignoring such ozone feedbacks would yield comparable contributions from these two influences, an incorrect result. In winter, trends are smaller but an influence of greenhouse gas‐mediated ozone feedbacks is also identified. The regression analysis furthermore yields significant differences in the attribution of SAM changes to the two influences between models with and without interactive ozone chemistry, with ozone depletion and GHG increases playing seasonally a stronger and weaker, respectively, role in the chemistry models versus the no‐chemistry ones.

The Nonhydrostatic ICosahedral Atmospheric Model for CMIP6 HighResMIP simulations (NICAM16-S): experimental design, model description, and impacts of model updates

Article

Full-text available

Feb 2021
GMD

The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

Impact of Stochastic Physics and Model Resolution on the Simulation of Tropical Cyclones in Climate GCMs

Article

Full-text available

Feb 2021

The role of model resolution in simulating geophysical vortices with the characteristics of realistic Tropical Cyclones (TCs) is well established. The push for increasing resolution continues, with General Circulation Models (GCMs) starting to use sub-10km grid spacing. In the same context it has been suggested that the use of Stochastic Physics (SP) may act as a surrogate for high resolution, providing some of the benefits at a fraction of the cost. Either technique can reduce model uncertainty, and enhance reliability, by providing a more dynamic environment for initial synoptic disturbances to be spawned and to grow into TCs. We present results from a systematic comparison of the role of model resolution and SP in the simulation of TCs, using EC-Earth simulations from project Climate-SPHINX, in large ensemble mode, spanning five different resolutions. All tropical cyclonic systems, including TCs, were tracked explicitly. As in previous studies, the number of simulated TCs increases with the use of higher resolution, but SP further enhances TC frequencies by ≈ 30%, in a strikingly similar way. The use of SP is beneficial for removing systematic climate biases, albeit not consistently so for interannual variability; conversely, the use of SP improves the simulation of the seasonal cycle of TC frequency. An investigation of the mechanisms behind this response indicates that SP generates both higher TC (and TC seed) genesis rates, and more suitable environmental conditions, enabling a more efficient transition of TC seeds into TCs. These results were confirmed by the use of equivalent simulations with the HadGEM3-GC31 GCM.

Multidecadal climate oscillations during the past millennium driven by volcanic forcing

Article

Mar 2021

A volcanic source of variation The Atlantic Multidecadal Oscillation (AMO), a 50- to 70-year quasiperiodic variation of climate centered in the North Atlantic region, was long thought to be an internal oscillation of the climate system. Mann et al. now show that this variation is forced externally by episodes of high-amplitude explosive volcanism. They used an ensemble of climate models to evaluate the causes of the AMO, finding that volcanos are the most important influence, and that there is no evidence to show that it has been internally generated during the last millennium. Science , this issue p. 1014