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Constraining human contributions to observed warming since the pre-industrial period

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Nathan P. Gillett
Nathan P. Gillett
Nathan P. Gillett
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Megan Kirchmeier-Young
Megan Kirchmeier-Young
Megan Kirchmeier-Young
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Aurélien Ribes
Aurélien Ribes
Aurélien Ribes
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Hideo Shiogama at National Institute for Environmental Studies
Hideo Shiogama
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Abstract and Figures

Parties to the Paris Agreement agreed to holding global average temperature increases “well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. Monitoring the contributions of human-induced climate forcings to warming so far is key to understanding progress towards these goals. Here we use climate model simulations from the Detection and Attribution Model Intercomparison Project, as well as regularized optimal fingerprinting, to show that anthropogenic forcings caused 0.9 to 1.3 °C of warming in global mean near-surface air temperature in 2010–2019 relative to 1850–1900, compared with an observed warming of 1.1 °C. Greenhouse gases and aerosols contributed changes of 1.2 to 1.9 °C and −0.7 to −0.1 °C, respectively, and natural forcings contributed negligibly. These results demonstrate the substantial human influence on climate so far and the urgency of action needed to meet the Paris Agreement goals. Quantifying the temperature impacts of anthropogenic emissions helps monitor proximity to the Paris Agreement goals. Human activities warmed global mean temperature during the past decade by 0.9 to 1.3 °C above 1850–1900 values, with 1.2 to 1.9 °C from greenhouse gases and −0.7 to −0.1 °C from aerosols.
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Articles
https://doi.org/10.1038/s41558-020-00965-9
1Canadian Centre for Climate Modelling and Analysis, Environment and Climate Change Canada, Victoria, British Columbia, Canada. 2Climate Research
Division, Environment and Climate Change Canada, Toronto, Ontario, Canada. 3CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France.
4Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan. 5School of Geosciences, University of Edinburgh,
Edinburgh, UK. 6Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland. 7LOCEAN, Sorbonne Université, Institut Pierre Simon
Laplace, Paris, France. 8NOAA/OAR Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA. 9LASG, Institute of Atmospheric Physics, Beijing, China.
10NASA Goddard Institute for Space Studies, New York, NY, USA. 11NCAR, Boulder, CO, USA. 12Norwegian Meteorological Institute, Oslo, Norway. 13Beijing
Climate Center, China Meteorological Administration, Beijing, China. 14Meteorological Research Institute, Tsukuba, Japan. 15Oceans and Atmosphere,
CSIRO, Aspendale, Victoria, Australia. ✉e-mail: nathan.gillett@canada.ca
For more than 20 years, detection and attribution techniques
have been used to identify human influence on global tem-
perature changes and to quantify the contributions of indi-
vidual forcings to observed changes1–3. The commitment of parties
to the Paris Agreement4 to “holding the increase in the global aver-
age temperature to well below 2 °C above pre-industrial levels and
pursuing efforts to limit the temperature increase to 1.5 °C above
pre-industrial levels”, and the Global Stocktake process, which aims
to monitor progress towards the Paris Agreement goals, give new
relevance to efforts to quantify human climate influence so far.
While the Paris Agreement is not explicit about the meaning of
either ‘global average temperature’ or ‘pre-industrial levels’, much
of the climate impacts literature on which assessment of danger-
ous anthropogenic interference in climate is based has used glob-
ally complete global mean near-surface air temperature (GSAT)
from climate models to assess future climate impacts. Therefore, we
primarily assess human influence on GSAT in this Article. Recent
literature has demonstrated that, in climate models, this metric of
global mean temperature warms more than blended sea surface
temperatures over ocean and near-surface air temperature over
land, masked with observational coverage (global mean surface
temperature (GMST))5–7. Previous attribution studies have typically
estimated attributable trends over the past 50–60 years in GMST8,
but estimates of warming relative to pre-industrial levels are more
relevant to monitoring progress towards Paris Agreement goals.
While multiple possible periods over the Holocene could be chosen
as pre-industrial base periods9, we follow the IPCC Special Report
on Global Warming of 1.5 °C (ref. 10; SR1.5) and choose 1850–1900.
Comparison of global mean temperature metrics
Annual means of global mean temperature anomalies in the fourth
Hadley Centre/Climatic Research Unit Temperature (HadCRUT4)11
dataset relative to 1850–1900 and based on an area-weighted global
mean of monthly mean anomalies are shown in Fig. 1a. These are
compared with global mean blended sea surface temperatures
over ocean and near-surface air temperatures over land and ice
masked with HadCRUT4 coverage5 (GMST, Methods) in individual
Coupled Model Intercomparison Project Phase 6 (CMIP6)12 histori-
cal simulations merged with Shared Socioeconomic Pathway 2-4.5
(SSP2-4.5)13 simulations (historical-ssp245 simulations hereafter).
The simulated warming in 2010–2019 is on average 17% (5–95%
ensemble range of 10%–24%) stronger in globally complete GSAT
than in HadCRUT4-masked GMST (Fig. 1a) (similar to previous
results based on Coupled Model Intercomparison Project Phase
5 (CMIP5) simulations14,15), demonstrating the importance of the
choice of metric for assessing attributable warming. Comparing
globally complete versions of GSAT and GMST, the simulated
warming in GSAT is only 6% stronger (5–95% range of 2–8%).
Therefore, the largest contribution to the enhanced warming in
globally complete GSAT versus HadCRUT4-masked GMST comes
from the observational masking.
Multiplying the observed 2010–2019 warming in HadCRUT4
GMST of 0.94 °C (5–95% range of 0.90–0.99 °C, Supplementary
Table 1) by the ratio of simulated warming in globally complete
GSAT to HadCRUT4-masked GMST (1.17), we infer a best esti-
mate of observed 2010–2019 warming in GSAT of 1.10 °C (5–95%
range of 1.01–1.20 °C). Similar calculations using HadCRUT5
Constraining human contributions to observed
warming since the pre-industrial period
Nathan P. Gillett 1 ✉ , Megan Kirchmeier-Young 2, Aurélien Ribes 3, Hideo Shiogama 4,
Gabriele C. Hegerl 5, Reto Knutti 6, Guillaume Gastineau 7, Jasmin G. John 8, Lijuan Li 9,
Larissa Nazarenko 10, Nan Rosenbloom 11, Øyvind Seland12, Tongwen Wu 13, Seiji Yukimoto14
and Tilo Ziehn 15
Parties to the Paris Agreement agreed to holding global average temperature increases “well below 2 °C above pre-industrial
levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels”. Monitoring the contribu-
tions of human-induced climate forcings to warming so far is key to understanding progress towards these goals. Here we use
climate model simulations from the Detection and Attribution Model Intercomparison Project, as well as regularized optimal
fingerprinting, to show that anthropogenic forcings caused 0.9 to 1.3 °C of warming in global mean near-surface air temperature
in 2010–2019 relative to 1850–1900, compared with an observed warming of 1.1 °C. Greenhouse gases and aerosols contributed
changes of 1.2 to 1.9 °C and −0.7 to −0.1 °C, respectively, and natural forcings contributed negligibly. These results demon-
strate the substantial human influence on climate so far and the urgency of action needed to meet the Paris Agreement goals.
NATURE CLIMATE CHANGE | VOL 11 | MARCH 2021 | 207–212 | www.nature.com/natureclimatechange 207
Content courtesy of Springer Nature, terms of use apply. Rights reserved
... EE approach, which addresses the limitations of prevailing approaches in the existing literature, to systematically evaluate the evidence of human influence on surface temperature at both the global and regional scales with the latest HadCRUT5 observational data (Morice et al. 2021) and CMIP6 multi-model simulations (Eyring et al. 2016). At the global scale, we re-examined the analyses of global mean temperature conducted by Gillett et al. (2021), which used ROF and a multimodel mean approach, and assessed the results in comparison to those from the EE approach. ...
... In Section 3, we detail the data utilized in our study, encompassing observational data, outputs from climate models under external forcings, and pre-industrial control runs. Our findings, in comparison with the recent study of Gillett et al. (2021), are presented in Section 4, followed by a discussion in Section 5. Additionally, for practical application, we have made the implementation of both the EE and ROF approaches accessible through the open-source R package dacc (Li et al. 2023a). ...
... While the analysis conducted by (Gillett et al. 2021) chose the global mean temperature anomalies from HadCRUT4 relative to the pre-industrial base period 1850-1900, We obtained the annual means of observed temperature anomalies from the latest HadCRUT5 datasets (Morice et al. 2021) for the period of 1951-2020. Compared to the HadCRUT4, the HadCRUT5 dataset is expected to be around 0.1 • warmer for much of the past 50 years, and up to 0.2 • in recent years due to corrections of bias in measurement techniques, which is confirmed in our later analysis of attributable warming. ...
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