Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change

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1
Chapter 13: Sea Level Change 2
3
Coordinating Lead Authors: John A. Church (Australia), Peter U. Clark (USA) 4
5
Lead Authors: Anny Cazenave (France), Jonathan M. Gregory (UK), Svetlana Jevrejeva (UK), Anders 6
Levermann (Germany), Mark A. Merrifield (USA), Glenn A. Milne (Canada), R. Steven Nerem (USA), 7
Patrick D. Nunn (Australia), Antony J. Payne (UK), W. Tad Pfeffer (USA), Detlef Stammer (Germany), 8
Alakkat S. Unnikrishnan (India) 9
10
Contributing Authors: David Bahr (USA), Jason E. Box (USA), David H. Bromwich (USA), Mark Carson 11
(Germany), William Collins (UK), Xavier Fettweis (Belgium), Piers Forster (UK), Alex Gardner (USA), W. 12
Roland Gehrels (UK), Rianne Giesen (Netherlands), Peter J. Gleckler (USA), Peter Good (UK), Rune Grand 13
Graversen (Sweden), Ralf Greve (Japan), Stephen Griffies (USA), Edward Hanna (UK), Mark Hemer 14
(Australia), Regine Hock (USA), Simon J. Holgate (UK), John Hunter (Australia), Philippe Huybrechts 15
(Belgium), Gregory Johnson (USA), Ian Joughin (USA), Georg Kaser (Austria), Caroline Katsman 16
(Netherlands), Leonard Konikow (USA), Gerhard Krinner (France), Anne Le Brocq (UK), Jan Lenaerts 17
(Netherlands), Stefan Ligtenberg (Netherlands), Christopher M. Little (USA), Ben Marzeion (Austria), 18
Kathleen L. McInnes (Australia), Sebastian H. Mernild (USA), Didier Monselesan (Australia), Ruth 19
Mottram (Denmark), Tavi Murray (UK), Gunnar Myhre (Norway), J.P. Nicholas (USA), Faezeh Nick 20
(Norway), Mahé Perrette (Germany), David Pollard (USA),Valentina Radić (Canada), Jamie Rae (UK), 21
Markku Rummukainen (Sweden), Christian Schoof (Canada), Aimée Slangen (Netherlands), Jan H. van 22
Angelen (Netherlands), Willem Jan van de Berg (Netherlands), Michiel van den Broeke (Netherlands), 23
Miren Vizcaíno (Netherlands), Yoshihide Wada (Netherlands), Neil J. White (Australia), R.Winkelmann 24
(Germany), Jianjun Yin (USA), Masakazu Yoshimori (Japan), Kirsten Zickfeld (Canada) 25
26
Review Editors: Jean Jouzel (France), Roderik van de Wal (Netherlands), Philip L. Woodworth (UK), 27
Cunde Xiao (China) 28
29
Date of Draft: 7 June 2013 30
31
32
Table of Contents 33
34
Executive Summary ........................................................................................................................................ 3 35
13.1 Components and Models of Sea Level Change ..................................................................................... 6 36
13.1.1 Introduction and Chapter Overview .............................................................................................. 6 37
13.1.2 Fundamental Definitions and Concepts ......................................................................................... 7 38
13.1.3 Processes Affecting Sea Level ........................................................................................................ 7 39
13.1.4 Models Used to Interpret Past and Project Future Changes in Sea Level .................................... 8 40
13.2 Past Sea Level Change .......................................................................................................................... 10 41
13.2.1 The Geological Record ................................................................................................................ 10 42
13.2.2 The Instrumental Record (~1700–2012) ...................................................................................... 12 43
FAQ 13.1: Why Does Local Sea Level Change Differ from the Global Average? .................................. 13 44
13.3 Contributions to Global Mean Sea Level Rise During the Instrumental Period ............................. 14 45
13.3.1 Thermal Expansion Contribution ................................................................................................ 14 46
13.3.2 Glaciers ........................................................................................................................................ 16 47
13.3.3 Greenland and Antarctic Ice Sheets ............................................................................................ 17 48
13.3.4 Contributions from Water Storage on Land ................................................................................ 20 49
13.3.5 Ocean Mass Observations from GRACE ..................................................................................... 21 50
13.3.6 Budget of GMSL Rise ................................................................................................................... 21 51
Box 13.1: The Global Energy Budget .......................................................................................................... 23 52
13.4 Projected Contributions to Global Mean Sea Level ........................................................................... 25 53
13.4.1 Ocean Heat Uptake and Thermal Expansion .............................................................................. 25 54
13.4.2 Glaciers ........................................................................................................................................ 27 55
13.4.3 Greenland Ice Sheet ..................................................................................................................... 29 56
13.4.4 Antarctic Ice Sheet ....................................................................................................................... 36 57

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Box 13.2: History of the Marine Ice Sheet Instability Hypothesis ............................................................ 42 1
FAQ 13.2: Will the Greenland and Antarctic Ice Sheets Contribute to Sea Level Change Over the Rest 2
of the Century? ...................................................................................................................................... 43 3
13.4.5 Anthropogenic Intervention in Water Storage on Land ............................................................... 45 4
13.5 Projections of Global Mean Sea Level Rise ........................................................................................ 46 5
13.5.1 Process-Based Projections for the 21st Century ......................................................................... 46 6
13.5.2 Semi-Empirical Projections for the 21st Century ........................................................................ 48 7
13.5.3 Confidence in Likely Ranges and Bounds .................................................................................... 51 8
13.5.4 Long-Term Scenarios ................................................................................................................... 54 9
13.6 Regional Sea Level Changes ................................................................................................................. 58 10
13.6.1 Regional Sea Level Changes, Climate Modes and Forced Sea Level Response ......................... 59 11
13.6.2 CMIP5 GCM Projections on Decadal to Centennial Time Scales .............................................. 59 12
13.6.3 Response to Atmospheric Pressure Changes ............................................................................... 60 13
13.6.4 Response to Freshwater Forcing ................................................................................................. 61 14
13.6.5 Net Regional Sea Level Changes .................................................................................................. 62 15
13.6.6 Uncertainties and Sensitivity to Ocean/Climate Model Formulations and Parameterizations ... 64 16
13.7 Projections of 21st Century Sea Level Extremes and Waves ............................................................ 65 17
13.7.1 Observed Changes in Sea Level Extremes ................................................................................... 65 18
13.7.2 Projections of Sea Level Extremes ............................................................................................... 65 19
13.7.3 Projections of Ocean Waves ........................................................................................................ 67 20
13.8 Synthesis and Key Uncertainties .......................................................................................................... 69 21
References ...................................................................................................................................................... 71 22
Figures ............................................................................................................................................................ 89 23
24

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Executive Summary 1
2
This chapter considers changes in global mean sea level, regional sea level, sea level extremes and waves. 3
Confidence in projections of global mean sea level rise has increased since the AR4 because of the improved 4
physical understanding and the improved agreement of process-based models with observations, and the 5
inclusion of ice-sheet dynamical changes. 6
7
Past Sea Level Change 8
9
Paleo sea level records from warm periods during the last 3 million years indicate that global mean 10
sea level exceeded 5 m above present (very high confidence)
1
when global temperature was up to 2°C 11
warmer than pre-industrial (medium confidence). During the last interglacial period (~129–116 ka), peak 12
global warmth was not more than 2°C above pre-industrial temperatures (medium confidence) and global 13
mean sea level was 5 m higher than present (very high confidence) to 10 m higher than present (high 14
confidence), implying substantial contributions from the Greenland and Antarctic ice sheets. [5.3.4, 5.6.1, 15
5.6.2, 13.2.1] 16
17
Based on proxy and instrumental data, it is virtually certain
2
that the rate of global mean sea level rise 18
has accelerated during the last two centuries, marking the transition from relatively low rates of 19
change during the late Holocene (order tenths of mm yr
–1
) to modern rates (order mm yr
–1
). It is very 20
likely that the mean rate was 1.7 [1.5 to 1.9] mm yr
–1
between 1901 and 2010 for a total sea level rise of 0.19 21
[0.17 to 0.21] m. Between 1993 and 2010, the rate was very likely higher at 3.2 [2.8 to 3.6] mm yr
–1
; 22
similarly high rates likely occurred between 1930 and 1950. It is likely that global mean sea level has 23
accelerated since the early 1900s, with estimates ranging from 0.000 to 0.013 [–0.002 to 0.019] mm yr
–2
. 24
[3.7.2, 3.7.4, 5.6.3, 13.2.1–13.2.2, Figure 13.3] 25
26
Understanding of Sea Level Change 27
28
Ocean thermal expansion and glacier melting have been the dominant contributors to 20th century 29
global mean sea level rise. Observations since 1971 indicate that thermal expansion and glaciers (excluding 30
the glaciers in Antarctica) explain 75% of the observed rise (high confidence). The contribution of the 31
Greenland and Antarctic ice sheets has increased since the early 1990s, partly from increased outflow 32
induced by warming of the immediately adjacent ocean. Natural and human-induced land water storage 33
changes have made only a small contribution; the rate of groundwater depletion has increased and now 34
exceeds the rate of reservoir impoundment. Since 1993, when observations of all sea level components are 35
available, the sum of contributions equals the observed global mean sea level rise within uncertainties (high 36
confidence). [Chapter 3; Chapter 4; 13.3.6; Figure 13.4; Table 13.1] 37
38
There is high confidence in projections of thermal expansion, high confidence in projections of 39
Greenland surface mass balance, and medium confidence in projections of Antarctic surface mass 40
balance and glacier mass loss. There has been substantial progress in ice-sheet modelling, particularly for 41
Greenland. Process-based model calculations of contributions to past sea level change from ocean thermal 42
expansion, glacier mass loss and Greenland ice-sheet surface mass balance are consistent with available 43
observational estimates over recent decades. Ice sheet flow-line modelling is able to reproduce observed 44
acceleration of the main outlet glaciers in the Greenland Ice Sheet, thus allowing estimates of the 21st 45
century dynamic response (medium confidence). Significant challenges remain in the process-based 46
projection of the dynamics response of marine terminating glaciers and the Antarctic Ice Sheet. Alternative 47
1
In this Report, the following summary terms are used to describe the available evidence: limited, medium, or robust;
and for the degree of agreement: low, medium, or high. A level of confidence is expressed using five qualifiers: very
low, low, medium, high, and very high, and typeset in italics, e.g., medium confidence. For a given evidence and
agreement statement, different confidence levels can be assigned, but increasing levels of evidence and degrees of
agreement are correlated with increasing confidence (see Section 1.4 and Box TS.1 for more details).
2
In this Report, the following terms have been used to indicate the assessed likelihood of an outcome or a result:
Virtually certain 99–100% probability, Very likely 90–100%, Likely 66–100%, About as likely as not 33–66%,
Unlikely 0–33%, Very unlikely 0–10%, Exceptionally unlikely 0–1%. Additional terms (Extremely likely: 95–100%,
More likely than not >50–100%, and Extremely unlikely 0–5%) may also be used when appropriate. Assessed
likelihood is typeset in italics, e.g., very likely (see Section 1.4 and Box TS.1 for more details).

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means of projection (extrapolation within a statistical framework and informed judgement) provide medium 1
confidence in a likely range. There is currently low confidence in projecting the onset of large-scale 2
grounding line instability in the marine sectors of the ice sheet. [13.3.1–13.3.3, 13.4.3, 13.4.4] 3
4
The sum of thermal expansion simulated by CMIP5 AOGCMs, glacier mass loss computed by global 5
glacier models using CMIP5 climate change simulations, and estimates of land water storage explain 6
65% of the observed global mean sea level rise for 1901–1990 and 90% for 1971–2010 and 1993–2010 7
(high confidence). When observed climate parameters are used, the glacier models indicate a larger 8
Greenland glacier contribution in the first half of the 20th century such that the sum of thermal expansion, 9
glacier mass loss and changes in land water storage and a small ongoing Antarctic ice-sheet contribution are 10
within 20% of the observations throughout the 20th century. In all periods, the residual is small enough to be 11
attributed to the ice sheets. Model-based estimates of ocean thermal expansion and glacier contributions 12
indicate that the greater rate of rise since 1990 is a response to radiative forcing (both anthropogenic and 13
natural) and increased loss of ice-sheet mass and not part of a natural oscillation (medium confidence). 14
[13.3.6; Figures 13.4 and 13.7; Table 13.1] 15
16
The Earth’s Energy Budget 17
18
Independent estimates of effective radiative forcing of the climate system, the observed heat storage, 19
and surface warming combine to give an energy budget for the Earth that is very likely closed within 20
uncertainties, and is consistent with the likely range of climate sensitivity. The largest increase in the 21
storage of heat in the climate system over recent decades has been in the oceans; this is a powerful 22
observation for the detection and attribution of climate change. [Box 3.1 and Box 13.1] 23
24
Global Mean Sea Level Rise Projections 25
26
It is very likely that the rate of global mean sea level rise during the 21st century will exceed the rate 27
observed during 1971–2010 for all RCP scenarios due to increased ocean warming and loss of mass 28
from glaciers and ice sheets. For the period 2081–2100, compared to 1986–2005, global mean sea level 29
rise is likely (medium confidence) to be in the 5–95% range of process-based models, which give 0.26–30
0.54 m for RCP2.6, 0.32–0.62 m for RCP4.5, 0.33–0.62 m for RCP6.0, and 0.45–0.81 m for RCP8.5. 31
For RCP8.5, the rise by 2100 is 0.53–0.97 m with a rate during 2081–2100 of 7–15 mm yr
–1
. We have 32
considered the evidence for higher projections and have concluded that there is currently insufficient 33
evidence to evaluate the probability of specific levels above the likely range. Based on current 34
understanding, only the collapse of marine-based sectors of the Antarctic Ice Sheet, if initiated, could cause 35
global mean sea level to rise substantially above the likely range during the 21st century. This potential 36
additional contribution cannot be precisely quantified but there is medium confidence that it would not 37
exceed several tenths of a meter of sea level rise during the 21st century. [13.5.1, Table 13.5, Figures 13.10 38
and 13.11] 39
40
Some semi-empirical models project a range that overlaps the process-based likely range while others 41
project a median and 95-percentile that are about twice as large as the process-based models. In 42
nearly every case, the semi-empirical model 95-percentile is higher than the process-based likely range. 43
Despite their successful calibration and evaluation against the observed 20th century sea level record, there 44
is low agreement in their projections and no consensus in the scientific community about the reliability of 45
SEM projections, and there is low confidence in their projections [13.5.2-3; Figure 13.12]. 46
47
It is virtually certain that global mean sea level rise will continue beyond 2100, with sea level rise due to 48
thermal expansion to continue for many centuries. Longer term sea level rise depends on future 49
emissions. The few available process-based models indicate global mean sea level rise by 2300 to be less 50
than 1 m for greenhouse gas concentrations that peak and decline and do not exceed 500 ppm CO
2
-51
equivalent but 1–3 m for concentrations above 700 ppm CO
2
-equivalent (medium confidence). The amount 52
of ocean thermal expansion increases with global warming (0.2–0.6 m °C
–1
) but the glacier contribution 53
decreases over time as their volume (currently 0.43 m sea level equivalent) decreases. The contribution from 54
the ice sheets will likely continue beyond 2100, but confidence in projections is low. Sea level rise of 1–3 m 55
per degree of warming is expected if the warming is sustained for several millennia (low confidence). 56
[13.5.4, Figures 13.13, 13.14] 57

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1
The available evidence indicates that global warming greater than a certain threshold would lead to 2
the near-complete loss of the Greenland Ice Sheet over a millennium or more, causing a global mean 3
sea level rise of about 7 m. Studies with fixed ice sheet topography indicate the threshold is greater than 4
2°C but less than 4°C of global mean surface temperature rise with respect to preindustrial. The one study 5
with a dynamical ice sheet suggests the threshold could be as low as 1°C. We are unable to quantify a likely 6
range. Whether or not a decrease in the Greenland Ice Sheet mass loss is irreversible also depends on the 7
duration and degree of exceedance of the threshold. [13.4.3.3] 8
9
Regional Sea Level Change Projections 10
11
It is very likely that in the 21st century and beyond, sea level change will have a strong regional 12
pattern, with some places experiencing significant deviations of local and regional sea level change 13
from the global mean change. Over decadal periods, the rates of regional sea level change as a result of 14
climate variability can differ from the global average rate by more than 100% of the global average rate. By 15
the end of the 21st century, it is very likely that over about 95% of the world ocean, regional sea level rise 16
will be positive, while most regions experiencing a sea level fall are located near current and former glaciers 17
and ice sheets. About 70% of the global coastlines are projected to experience a sea level change within 20% 18
of the global mean sea level change. [13.6.5, Figures 13.18–13.20] 19
20
Projections of 21st
Century Sea Level Extremes and Surface Waves 21
22
It is very likely that there will be a significant increase in the occurrence of future sea level extremes by 23
2050 and 2100. This increase will primarily be the result of an increase in mean sea level (high confidence), 24
with the frequency of a particular sea level extreme increasing by an order of magnitude or more in some 25
regions by the end of the 21st century. There is low confidence in region-specific projections of storminess 26
and associated storm surges. [13.7.2, Figure 13.25] 27
28
It is likely (medium confidence) that annual mean significant wave heights will increase in the Southern 29
Ocean as a result of enhanced wind speeds. Southern Ocean generated swells are likely to affect heights, 30
periods, and directions of waves in adjacent basins. It is very likely that wave heights and the duration of the 31
wave season will increase in the Arctic Ocean as a result of reduced sea-ice extent. In general, there is low 32
confidence in region-specific projections due to the low confidence in tropical and extratropical storm 33
projections, and to the challenge of downscaling future wind fields from coarse-resolution climate models. 34
[13.7.3; Figure 13.26] 35
36

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13.1 Components and Models of Sea Level Change 1
2
13.1.1 Introduction and Chapter Overview 3
4
Changes in sea level occur over a broad range of temporal and spatial scales, with the many contributing 5
factors making it an integral measure of climate change (Milne et al., 2009; Church et al., 2010). The 6
primary contributors to contemporary sea level change are the expansion of the ocean as it warms and the 7
transfer of water currently stored on land, particularly from land ice (glaciers and ice sheets) (Church et al., 8
2011b). Observations indicate the largest increase in the storage of heat in the climate system over recent 9
decades has been in the oceans (Section 3.2) and thus sea level rise from ocean warming is a central part of 10
the Earth’s response to increasing greenhouse gas concentrations. 11
12
The First IPCC Assessment laid the groundwork for much of our current understanding of sea level change 13
(Warrick and Oerlemans, 1990). This included the recognition that sea level had risen during the 20th 14
century, that the rate of rise had increased compared to the 19th century, that ocean-thermal expansion and 15
the mass loss from glaciers were the main contributors to the 20th century rise, that during the 21st century 16
the rate of rise was projected to be faster than during the 20th century, that sea level will not rise uniformly 17
around the world, and that sea level would continue to rise well after greenhouse gas emissions were 18
reduced. They also concluded that no major dynamic response of the ice sheets was expected during the 21st 19
century, leaving ocean-thermal expansion and the melting of glaciers as the main contributors to the 21st 20
century rise. The Second Assessment Report came to very similar conclusions (Warrick et al., 1996). 21
22
By the time of the Third Assessment Report, coupled atmosphere-ocean general circulation models 23
(AOGCMs) and ice sheet models largely replaced energy-balance climate models as the primary techniques 24
supporting the interpretation of observations and the projections of sea level (Church et al., 2001). This 25
approach allowed consideration of the regional distribution of sea level change in addition to the global 26
average change. By the time of the Fourth Assessment Report (AR4), there were more robust observations of 27
the variations in the rate of global average sea level rise for the 20th century, some understanding of the 28
variability in the rate of rise, and the satellite altimeter record was long enough to reveal the complexity of 29
the time-variable spatial distribution of sea level (Bindoff et al., 2007). Nevertheless, three central issues 30
remained. First, the observed sea level rise over decades was larger than the sum of the individual 31
contributions estimated from observations or with models (Rahmstorf et al., 2007; Rahmstorf et al., 2012b), 32
although in general the uncertainties were large enough that there was no significant contradiction. Second, it 33
was not possible to make confident projections of the regional distribution of sea level rise. Third, there was 34
insufficient understanding of the potential contributions from the ice sheets. In particular, the AR4 35
recognised that existing ice-sheet models were unable to simulate the recent observations of ice-sheet 36
accelerations and that understanding of ice-sheet dynamics was too limited to assess the likelihood of 37
continued acceleration or to provide a best estimate or an upper bound for their future contributions. 38
39
Despite changes in the scenarios between the four Assessments, the sea level projections for 2100 (compared 40
to 1990) for the full range of scenarios were remarkably similar, with the reduction in the upper end in more 41
recent reports reflecting the smaller increase in radiative forcing in recent scenarios due to smaller 42
greenhouse gas (GHG) emissions and the inclusion of aerosols, and a reduction in uncertainty in projecting 43
the contributions: 31 to 110 cm in the FAR, 13 to 94 cm in the SAR, 9 to 88 cm in the TAR, and 18 to 59 cm 44
in AR4 (not including a possible additional allowance for a dynamic ice-sheet response). 45
46
Results since the AR4 show that for recent decades, sea level has continued to rise (Section 3.7). Improved 47
and new observations of the ocean (Section 3.7) and the cryosphere (Chapter 4) and their representation in 48
models have resulted in better understanding of 20th century sea level rise and its components (this chapter). 49
Records of past sea level changes constrain long-term land-ice response to warmer climates as well as extend 50
the observational record to provide a longer context for current sea level rise (Section 5.6). 51
52
This chapter provides a synthesis of past and contemporary sea level change at global and regional scales. 53
Drawing on the published refereed literature, including as summarised in earlier chapters of this Assessment, 54
we explain the reasons for contemporary change, and assess confidence in and provide global and 55
regional projections of likely sea level change for the 21st century and beyond. We discuss the primary 56
factors that cause regional sea level to differ from the global average and how these may change in the 57

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future. In addition, we address projected changes in surface waves and the consequences of sea level and 1
climate change for extreme sea level events. 2
3
13.1.2 Fundamental Definitions and Concepts 4
5
The height of the ocean surface at any given location, or sea level, is measured either with respect to the 6
surface of the solid Earth (relative sea level, RSL) or a geocentric reference such as the reference ellipsoid 7
(geocentric sea level). RSL is the more relevant quantity when considering the coastal impacts of sea level 8
change, and it has been measured using tide gauges during the past few centuries (Sections 13.2.2 and 3.7) 9
and estimated for longer time spans from geological records (Sections 13.2.1 and 5.6). Geocentric sea level 10
has been measured over the past two decades using satellite altimetry (Sections 13.2.2 and 3.7). 11
12
A temporal average for a given location, known as Mean Sea Level (MSL; see Glossary), is applied to 13
remove shorter period variability. Apart from Section 13.7, which considers high-frequency changes in 14
ocean surface height, the use of ‘sea level’ elsewhere in this chapter refers to MSL. It is common to average 15
MSL spatially to define Global Mean Sea Level (GMSL; see Glossary). In principle, integrating RSL change 16
over the ocean area gives the change in ocean water volume, which is directly related to the processes that 17
dominate sea level change (changes in ocean temperature and land-ice volume). In contrast, a small 18
correction (–0.15 to –0.5 mm yr
–1
) needs to be subtracted from altimetry observations to estimate ocean 19
water volume change (Tamisiea, 2011). Local RSL change can differ significantly from GMSL because of 20
spatial variability in changes of the sea-surface and ocean-floor height (see FAQ 13.1 and Section 13.7). 21
22
13.1.3 Processes Affecting Sea Level 23
24
This chapter focuses on processes within the ocean, atmosphere, land ice, and hydrological cycle that are 25
climate sensitive and are expected to contribute to sea level change at regional to global scales in the coming 26
decades to centuries (Figure 13.1). Figure 13.2 is a navigation aid for the different sections of this chapter 27
and sections of other chapters that are relevant to sea level change. 28
29
[INSERT FIGURE 13.1 HERE] 30
Figure 13.1: Climate sensitive processes and components that can influence global and regional sea level and are 31
considered in this chapter. Changes in any one of the components or processes shown will result in a sea level change. 32
The term ‘ocean properties’ refers to ocean temperature, salinity and density, which influence and are dependent on 33
ocean circulation. Both relative and geocentric sea level vary with position. Note that the geocenter is not shown. 34
35
[INSERT FIGURE 13.2 HERE] 36
Figure 13.2: Schematic representation of key linkages between processes and components that contribute to sea level 37
change and are considered in this report. Colouring of individual boxes indicates the types of models and approaches 38
used in projecting the contribution of each process or component to future sea level change. The diagram also serves as 39
an index to the sections in this report that are relevant to the assessment of sea level projections via the section numbers 40
given at the bottom of each box. Note gravity and solid Earth effects change the shape of the ocean floor and surface 41
and thus are required to infer changes in ocean water volume from both relative and geocentric sea level observations. 42
43
Changes in ocean currents, ocean density and sea level are all tightly coupled such that changes at one 44
location impact local sea level and sea level far from the location of the initial change, including changes in 45
sea level at the coast in response to changes in deep-ocean temperature (Landerer et al., 2007; Yin et al., 46
2010). While both temperature and salinity changes can contribute significantly to regional sea level change 47
(Church et al., 2010), only temperature change produces a significant contribution to global average ocean 48
volume change due to thermal expansion/contraction (Gregory and Lowe, 2000). Regional atmospheric 49
pressure anomalies also cause sea level to vary through atmospheric loading (Wunsch and Stammer, 1997). 50
All of these climate-sensitive processes cause sea level to vary on a broad range of space and time scales 51
from relatively short lived events, such as waves and storm surges, to sustained changes over several decades 52
or centuries that are associated with atmospheric and ocean modes of climate variability (White et al., 2005; 53
Miller and Douglas, 2007; Zhang and Church, 2012). 54
55
Water and ice mass exchange between the land and the oceans leads to a change in GMSL. A signal of added 56
mass to the ocean propagates rapidly around the globe such that all regions experience a sea level change 57
within days of the mass being added (Lorbacher et al., 2012). In addition, an influx of freshwater changes 58

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ocean temperature and salinity and thus changes ocean currents and local sea level (Stammer, 2008; Yin et 1
al., 2009), with signals taking decades to propagate around the global ocean. The coupled atmosphere-ocean 2
system can also adjust to temperature anomalies associated with surface freshwater anomalies through air-3
sea feedbacks, resulting in dynamical adjustments of sea level (Okumura et al., 2009; Stammer et al., 2011). 4
Water mass exchange between land and the ocean also results in patterns of sea level change called ‘sea 5
level fingerprints’ (Clark and Lingle, 1977; Conrad and Hager, 1997; Mitrovica et al., 2001) due to change in 6
the gravity field and vertical movement of the ocean floor associated with visco-elastic Earth deformation 7
(Farrell and Clark, 1976). These changes in mass distribution also affect the Earth’s inertia tensor and 8
therefore rotation, which produces an additional sea level response (Milne and Mitrovica, 1998). 9
10
There are other processes that affect sea level but are not associated with contemporary climate change. 11
Some of these result in changes that are large enough to influence the interpretation of observational records 12
and sea level projections at regional and global scales. In particular surface mass transfer from land ice to 13
oceans during the last deglaciation contributes significantly to present-day sea level change due to the on-14
going visco-elastic deformation of the Earth and the corresponding changes of the ocean floor height and 15
gravity (referred to as glacial isostatic adjustment, GIA) (Lambeck and Nakiboglu, 1984; Peltier and 16
Tushingham, 1991). Ice sheets also have long response times and so continue to respond to past climate 17
change (Section 13.1.5). 18
19
Anthropogenic processes that influence the amount of water stored in the ground or on its surface in lakes 20
and reservoirs, or cause changes in land-surface characteristics that influence runoff or evapotranspiration 21
rates, will perturb the hydrological cycle and cause sea level change (Sahagian, 2000; Wada et al., 2010). 22
Such processes include water impoundment (dams, reservoirs), irrigation schemes, and ground water 23
depletion (Section 13.4.5). 24
25
Sea level changes due to tectonic and coastal processes are beyond the scope of this chapter. With the 26
exception of earthquakes, which can cause rapid local changes and tsunamis (Broerse et al., 2011) and 27
secular RSL changes due to post-seismic deformation (Watson et al., 2010), tectonic processes cause, on 28
average, relatively low rates of sea level change (order 0.1 mm yr
–1
or less) (Moucha et al., 2008). Sediment 29
transfer and compaction (including from ground water depletion) in the coastal zone are particularly 30
important in deltaic regions (Blum and Roberts, 2009; Syvitski et al., 2009). While they can dominate sea 31
level change in these localised areas, they are less important as a source of sea level change at regional and 32
global scales and so are not considered further in this chapter (See they are discussed in WGII Chapter 5). 33
Estimates of sediment delivery to the oceans (Syvitski and Kettner, 2011) suggest a contribution to GMSL 34
rise of order 0.01 mm yr
–1
. 35
36
13.1.4 Models Used to Interpret Past and Project Future Changes in Sea Level 37
38
Atmosphere-ocean general circulation models (AOGCMs) have components representing the ocean, 39
atmosphere, land and cryosphere and simulate sea-surface height changes relative to the geoid resulting from 40
the natural forcings of volcanic eruptions and changes in solar irradiance, and from anthropogenic increases 41
in greenhouse gases and aerosols (Chapter 9). AOGCMs also exhibit internally generated climate variability, 42
including such modes as the El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), 43
the North Atlantic Oscillation (NAO), and others which affect sea level (White et al., 2005; Zhang and 44
Church, 2012). Critical components for global and regional changes in sea level are changes in surface wind 45
stress and air-sea heat and freshwater fluxes (Lowe and Gregory, 2006; Timmermann et al., 2010; Suzuki 46
and Ishii, 2011) and the resultant changes in ocean density and circulation, for instance in the strength of the 47
Atlantic meridional overturning circulation (AMOC) (Yin et al., 2009; Lorbacher et al., 2010; Pardaens et 48
al., 2011a). As in the real world, ocean density, circulation, and sea level are dynamically connected in 49
AOGCMs and evolve together. Offline models are required for simulating glacier and ice-sheet changes 50
(Section 13.1.4.1). 51
52
Geodynamic surface-loading models are used to simulate the RSL response to past and contemporary 53
changes in surface water and land-ice mass redistribution and contemporary atmospheric pressure changes. 54
The sea surface height component of the calculation is based solely on water mass conservation and 55
perturbations to gravity, with no considerations of ocean dynamic effects. Application of these models has 56
focused on annual and interannual variability driven by contemporary changes in the hydrological cycle and 57

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atmospheric loading (Clarke et al., 2005; Tamisiea et al., 2010), and on secular trends associated with past 1
and contemporary changes in land ice and hydrology (Lambeck et al., 1998; Mitrovica et al., 2001; Peltier, 2
2004; Riva et al., 2010). 3
4
Semi-empirical models (SEMs) project sea level based on statistical relationships between observed GMSL 5
and global mean temperature (Rahmstorf, 2007a; Vermeer and Rahmstorf, 2009; Grinsted et al., 2010) or 6
total radiative forcing (Jevrejeva et al., 2009; Jevrejeva et al., 2010). The form of this relationship is 7
motivated by physical considerations, and the parameters are determined from observational data—hence the 8
term “semi-empirical” (Rahmstorf et al., 2012a). SEMs do not explicitly simulate the underlying processes, 9
and they use a characteristic response time that could be considerably longer than the timescale of interest 10
(Rahmstorf, 2007a) or explicitly determined by the model (Grinsted et al., 2010). 11
12
Storm-surge and wave-projection models are used to assess how changes in storminess and MSL impact sea 13
level extremes and wave climates. The two main approaches involve dynamical (Lowe et al., 2010) and 14
statistical models (Wang et al., 2010). The dynamical models are forced by near-surface wind and mean sea 15
level pressure fields derived from regional or global climate models (Lowe et al., 2010). 16
17
In this chapter, we use the term “process-based models” (see Glossary) to refer to sea level and land-ice 18
models (Section 13.1.5.1) that aim to simulate the underlying processes and interactions, in contrast to 19
“semi-empirical models” which do not. While these two approaches are distinct, semi-empirical methods are 20
often employed in components of the process-based models (e.g., glacier models in which surface mass 21
balance is determined by a Degree Day method (Braithwaite and Olesen, 1989)). 22
23
13.1.4.1 Models Used to Project Changes in Ice Sheets and Glaciers 24
25
The representation of glaciers and ice sheets within AOGCMs is not yet at a stage where projections of their 26
changing mass are routinely available. Additional process-based models use output from AOGCMs to 27
evaluate the consequences of projected climate change on these ice masses. 28
29
The overall contribution of an ice mass to sea level involves changes to either its surface mass balance 30
(SMB) or changes in the dynamics of ice flow that affect outflow (i.e., solid ice discharge) to the ocean. 31
SMB is primarily the difference between snow accumulation and the melt and sublimination of snow and ice 32
(ablation). An assessment of observations related to this mass budget can be found in Section 4.4.2. 33
Although some ice-sheet models used in projections incorporate both effects, most studies have focussed on 34
either SMB or flow dynamics. It is assumed that the overall contribution can be found by summing the 35
contributions calculated independently for these two sources, which is valid if they do not interact 36
significantly. While this can be addressed using a correction term to SMB in ice-sheet projections over the 37
next century, such interactions become more important on longer time scales when, for example, changes in 38
ice-sheet topography may significantly affect SMB or dynamics. 39
40
Projecting the sea level contribution of land ice requires comparing the model results with a base state that 41
assumes no significant sea level contribution. This base state is taken to be either the preindustrial period or, 42
because of our scant knowledge of the ice sheets before the advent of satellites, the late 20th century. In 43
reality, even at these times, the ice sheets are likely to have been contributing to sea level change 44
(Huybrechts et al., 2011; Box and Colgan, 2013) and this contribution, although difficult to quantify, should 45
be included in the observed sea level budget (Gregory et al., 2013b) and the projections. 46
47
Regional climate models (RCMs), which incorporate or are coupled to sophisticated representations of the 48
mass and energy budgets associated with snow and ice surfaces, are now the primary source of ice-sheet 49
SMB projections. A major source of uncertainty lies in the ability of these schemes to adequately represent 50
the process of internal refreezing of melt water within the snowpack (Bougamont et al., 2007; Fausto et al., 51
2009). These models require information on the state of the atmosphere and ocean at their lateral boundaries, 52
which are derived from reanalysis data sets or AOGCMs for past climate, or from AOGCM projections of 53
future climate. 54
55
Models of ice dynamics require a fairly complete representation of stresses within an ice mass in order to 56
represent the response of ice flow to changes at the marine boundary and the governing longitudinal stresses 57

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(Schoof, 2007b). For Antarctica, there is also a need to employ high spatial resolution (<1 km) to capture the 1
dynamics of grounding line migration robustly so that results do not depend to an unreasonable extent on 2
model resolution (Durand et al., 2009; Goldberg et al., 2009; Morlighem et al., 2010; Cornford et al., 2013; 3
Pattyn et al., 2013). One-dimensional flowline models have been developed to the stage that modelled 4
iceberg calving is comparable with observations (Nick et al., 2009). The success of this modelling approach 5
relies on the ability of the model’s computational grid to evolve to continuously track the migrating calving 6
front. Although this is relatively straightforward in a one-dimensional model, this technique is difficult to 7
incorporate into three-dimensional ice sheet models that typically use a computational grid that is fixed in 8
time. 9
10
The main challenge faced by models attempting to assess sea level change from glaciers is the small number 11
of glaciers for which mass budget observations are available (~380) (Cogley, 2009a) (see Sections 4.3.1 and 12
4.3.4) as compared to the total number (the World Glacier Inventory contains more than 181,500) (Arendt et 13
al., 2012). Statistical techniques are used to derive relations between observed SMB and climate variables 14
for the small sample of surveyed glaciers, and then these relations are used to upscale to regions of the 15
world. These techniques often include volume-area scaling to estimate glacier volume from their more 16
readily observable areas. Although tidewater glaciers may also be affected by changes in outflow related to 17
calving, the complexity of the associated processes means that most studies limit themselves to assessing the 18
effects of SMB changes. 19
20
13.2 Past Sea Level Change 21
22
13.2.1 The Geological Record 23
24
Records of past sea level change provide insight into the sensitivity of sea level to past climate change as 25
well as context for understanding current changes and evaluating projected changes. Since the AR4, 26
important progress has been made in understanding the amplitude and variability of sea level during past 27
intervals when climate was warmer than pre-industrial, largely through better understanding of and 28
accounting for the effects of proxy uncertainties and GIA on coastal sequences (Kopp et al., 2009; Raymo et 29
al., 2011; Dutton and Lambeck, 2012; Lambeck et al., 2012; Raymo and Mitrovica, 2012; Kopp et al., 2013) 30
(Chapter 5). Here we summarize the constraints provided by the record of past sea level variations during 31
times when global temperature was similar to or warmer than today. 32
33
13.2.1.1 The Middle Pliocene 34
35
During the warm intervals of the middle Pliocene (3.3 to 3.0 Ma), there is medium confidence that global 36
mean surface temperatures were 2°C–3.5°C warmer than for pre-industrial climate (Section 5.3.1). There are 37
multiple lines of evidence that GMSL during these middle Pliocene warm periods was higher than today, but 38
low agreement on how high it reached (Section 5.6.1). The most robust lines of evidence come from 39
proximal sedimentary records that suggest periodic deglaciation of the WAIS and parts of the EAIS (Naish 40
et al., 2009; Passchier, 2011) and from ice-sheet models that suggest near-complete deglaciation of GIS, 41
WAIS and partial deglaciation of the EAIS (Pollard and DeConto, 2009; Hill et al., 2010; Dolan et al., 42
2011). The assessment by Chapter 5 suggests that GMSL was above present, but that it did not exceed 20 m 43
above present, during the middle Pliocene warm periods (high confidence). 44
45
13.2.1.2 Marine Isotope Stage 11 46
47
During marine isotope stage 11 (MIS 11; 401–411 ka ago), Antarctic ice-core and tropical Pacific paleo-48
temperature estimates suggest that the global temperature was 1.5°C–2.0°C warmer than pre-industrial (low 49
confidence) (Masson-Delmotte et al., 2010). Studies of the magnitude of sea level highstands from raised 50
shorelines attributed to MIS 11 have generated highly divergent estimates. Since the AR4, studies have 51
accounted for GIA effects (Raymo and Mitrovica, 2012) or reported elevations from sites where the GIA 52
effects are estimated to be small (Muhs et al., 2012; Roberts et al., 2012). From this evidence, our 53
assessment is that MIS 11 GMSL reached 6–15 m higher than present (medium confidence), requiring a loss 54
of most or all of the present Greenland and West Antarctic Ice Sheets plus a reduction in the EAIS of up to 5 55
m equivalent sea level if sea level rise was at the higher end of the range. 56
57

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13.2.1.3 The Last Interglacial Period 1
2
New data syntheses and model simulations since the AR4 indicate that during the Last Interglacial Period 3
(LIG, ~129–116 ka), global mean annual temperature was not more than 2
o
C above pre-industrial (medium 4
confidence). Data syntheses suggest peak global warmth was 1°C–2°C above pre-industrial temperatures, 5
with peak global annual sea surface temperatures that were 0.7°C ± 0.6°C warmer (medium confidence) 6
(Section 5.3.4). There is robust evidence and high agreement that under this warmer climate, sea level was 7
higher than present. There have been a large number of estimates of the magnitude of LIG GMSL rise from 8
localities around the globe, but they are generally from a small number of RSL reconstructions, and do not 9
consider GIA effects, which can be substantial (Section 5.6.2). Since the AR4, two approaches have 10
addressed GIA effects in order to infer LIG sea level from RSL observations at coastal sites. Kopp et al. 11
(2009, 2013) obtained a probabilistic estimate of GMSL based on a large and geographically broadly 12
distributed database of LIG sea level indicators. Their analysis accounted for GIA effects (and their 13
uncertainties) as well as uncertainties in geochronology, the interpretation of sea level indicators, and 14
regional tectonic uplift and subsidence. Kopp et al. (2013) concluded that GMSL was 6.4 m (95% 15
probability) and 7.7 m (67% probability) higher than present, and with a 33% probability that it exceeded 8.8 16
m. The other approach, taken by Dutton and Lambeck (2012), used data from far-field sites that are 17
tectonically stable. Their estimate of 5.5–9 m LIG GMSL is consistent with the probabilistic estimates made 18
by Kopp et al. (2009, 2013). Chapter 5 thus concluded there is very high confidence that the 19
maximum GMSL during the LIG was at least 5 m higher than present and high confidence it did not exceed 20
10 m. The best estimate is 6 m higher than present. Chapter 5 also concluded from ice-sheet model 21
simulations and elevation changes derived from a new Greenland ice core that the Greenland Ice Sheet very 22
likely contributed between 1.4 and 4.3 m sea level equivalent. This implies with medium confidence a 23
contribution from the Antarctic Ice Sheet to the global mean sea level during the last interglacial period, but 24
this is not yet supported by observational and model evidence. 25
26
There is medium confidence for a sea level fluctuation of up to 4 m during the LIG, but regional sea level 27
variability and uncertainties in sea level proxies and their ages cause differences in the timing and amplitude 28
of the reported fluctuation (Kopp et al., 2009; Thompson et al., 2011; Kopp et al., 2013). For the time 29
interval during the LIG in which GMSL was above present, there is high confidence that the maximum 30
1000-year average rate of GMSL rise associated with the sea level fluctuation exceeded 2 m kyr
–1
but that it 31
did not exceed 7 m kyr
–1
(Chapter 5) (Kopp et al., 2013). Faster rates lasting less than a millennium cannot 32
be ruled out by these data. Therefore, there is high confidence that there were intervals when rates of GMSL 33
rise during the LIG exceeded the 20th century rate of 1.7 [1.5 to 1.9] mm yr
–1
. 34
35
13.2.1.4 The Late Holocene 36
37
Since the AR4, there has been significant progress in resolving the sea level history of the last 7000 years. 38
From ~7 to 3 ka, RSL records indicate that GMSL likely rose 2–3 m to near present-day levels (Chapter 5). 39
Based on local sea level records spanning the last 2000 years, there is medium confidence that fluctuations in 40
GMSL during this interval have not exceeded ~ ±0.25 m on time scales of a few hundred years (Section 41
5.6.3, Figure 13.3a). The most robust signal captured in salt marsh records from both northern and southern 42
hemispheres supports the AR4 conclusion for an acceleration that marks the transition from relatively low 43
rates of change during the late Holocene (order tenths of mm yr
–1
) to modern rates (order mm yr
–1
) (Section 44
5.6.3, Figure 13.3b). However, there is variability in the magnitude and the timing (1840–1920) of this 45
acceleration in both paleo and instrumented (tide gauge) records (Section 3.7). By combining paleo sea level 46
records with tide gauge records at the same localities, Gehrels and Woodworth (2013) concluded that sea 47
level began to rise above the late Holocene background rate between 1905 and 1945 CE, consistent with the 48
conclusions by Lambeck et al. (2004). 49
50
[INSERT FIGURE 13.3 HERE] 51
Figure 13.3: (a) Paleo sea level data for last 3000 years from Northern and Southern Hemisphere sites. The effects of 52
glacial isostatic adjustment (GIA) have been removed from these records. Light green = Iceland (Gehrels et al., 2006), 53
purple = Nova Scotia (Gehrels et al., 2005), bright blue = Connecticut (Donnelly et al., 2004), blue = Nova Scotia 54
(Gehrels et al., 2005), red = United Kingdom (Gehrels et al., 2011), green = North Carolina (Kemp et al., 2011), brown 55
= New Zealand (Gehrels et al., 2008), gray = mid-Pacific Ocean (Woodroffe et al., 2012). (b) Paleo sea level data from 56
salt marshes since 1700 from Northern and Southern Hemisphere sites compared to sea level reconstruction from tide 57
gauges (blue time series with uncertainty) (Jevrejeva et al., 2008). The effects of GIA have been removed from these 58

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records. Green and light green = North Carolina (Kemp et al., 2011), orange = Iceland (Gehrels et al., 2006), purple = 1
New Zealand (Gehrels et al., 2008), dark green = Tasmania (Gehrels et al., 2012), brown = Nova Scotia (Gehrels et al., 2
2005). (c) Yearly average GMSL reconstructed from tide gauges (1900–2010) by three different approaches. Orange 3
from Church and White (2011), blue from Jevrejeva et al. (2008), green from Ray and Douglas (2011) (see Section 4
13.7). (d) Altimetry data sets from five groups (CU, NOAA, GSFC, AVISO, CSIRO) with mean of the five shown as 5
bright blue line (see Section 13.7). (e) Comparison of the paleo data from salt marshes (purple symbols, from panel b), 6
with tide gauge and altimetry data sets (same line colors as in panels c and d). All paleo data were shifted by mean of 7
1700–1850 derived from the Sand Point, North Carolina data. The Jevrejeva et al. (2008) tide gauge data were shifted 8
by their mean for 1700–1850; other two tide gauge data sets were shifted by same amount. The altimeter time series has 9
been shifted vertically upwards so that their mean value over the 1993–2007 period aligns with the mean value of the 10
average of all three tide gauge time series over the same period. 11
12
13.2.2 The Instrumental Record (~1700–2012) 13
14
The instrumental record of sea level change is mainly comprised of tide gauge measurements over the past 15
two to three centuries (Figure 13.3b) and, since the early 1990s, of satellite-based radar altimeter 16
measurements (Figure 13.3c). 17
18
13.2.2.1 The Tide Gauge Record (~1700–2012) 19
20
The number of tide gauges has increased since the first sites at some northern Europe ports were installed in 21
the 18th century; Southern Hemisphere measurements only started in the late 19th century. Section 3.7 22
assesses 20th century sea level rise estimates from tide gauges (Douglas, 2001; Church and White, 2006; 23
Jevrejeva et al., 2006; Holgate, 2007; Jevrejeva et al., 2008; Church and White, 2011; Ray and Douglas, 24
2011), and concludes that even though different strategies were developed to account for inhomogeneous 25
tide gauge data coverage in space and time, and to correct for vertical crustal motions (also sensed by tide 26
gauges, in addition to sea level change and variability), it is very likely that long-term trend estimates in 27
GMSL are 1.7 [1.5 to 1.9] mm yr
–1
between 1900 and 2010 for a total sea level rise of 0.19 [0.17–0.21] m 28
(Figure 13.3c). Interannual and decadal-scale variability is superimposed on the long-term MSL trend, but 29
Chapter 3 noted that discrepancies between the various published MSL records are present at these shorter 30
time scales. 31
32
Section 3.7 also concludes that it is likely that the rate of sea level rise increased from the 19th century to the 33
20th century. Taking this evidence in conjunction with the proxy evidence for a change of rate (Sections 34
5.6.3, 13.2.1, Figure 13.3b), it is virtually certain that the rate of global mean sea level rise has accelerated 35
during the last two centuries. Because of the presence of low-frequency variations (e.g., multidecadal 36
variations seen in some tide gauge records; Chambers et al. (2012), sea level acceleration results are 37
sensitive to the choice of the analysis time span. When a 60-year oscillation is modeled along with an 38
acceleration term, the estimated acceleration in GMSL (twice the quadratic term) computed over 1900–2010 39
ranges from: 0.000 [–0.002 to 0.002] mm yr
–2
(90% confidence) in the Ray and Douglas (2011) record, 40
0.013 [0.007 to 0.019] mm yr
–2
in the Jevrejeva et al. (2008) record, and 0.012 [0.009 to 0.015] mm yr
–2
41
(90% confidence) in the Church and White (2011) record. For comparison, Church and White (2011) 42
estimated the acceleration term to be 0.009 [0.004 to 0.014] mm yr
–2
over the 1880–2009 time span when the 43
60-year cycle is not considered. 44
45
13.2.2.2 The Satellite Altimeter Record (1993–2012) 46
47
The high-precision satellite altimetry record started in 1992 and provides nearly global (±66°) sea level 48
measurements at 10-day intervals. Ollivier et al. (2012) showed that Envisat, which observes to ±82° 49
latitude, provides comparable GMSL estimates. Although there are slight differences at interannual time 50
scales in the altimetry-based GMSL time series produced by different groups (Masters et al., 2012), there is 51
very good agreement on the 20-year long GMSL trend (Figure 13.3d). After accounting for the ~ –0.3 mm 52
yr
–1
correction related to the increasing size of the global ocean basins due to GIA (Peltier, 2009), a GMSL 53
rate of 3.2 [2.8 to 3.6] mm yr
–1
over 1993–2012 is found by the different altimetry data processing groups. 54
The current level of precision is derived from assessments of all source of errors affecting the altimetric 55
measurements (Ablain et al., 2009) and from tide gauge comparisons (Beckley et al., 2010; Nerem et al., 56
2010). Chapter 3 concluded that the higher GMSL trend since 1993 compared to mean rates over the 20th 57
century is real (high confidence), and that it is likely that GMSL rose between 1920 and 1950 at a rate 58

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comparable to that observed since 1993. This higher rate is also seen in tide gauge data over the same period, 1
but on the basis of observations alone it does not necessarily reflect a recent acceleration, considering the 2
previously reported multi-decadal variations of mean sea level. The rapid increase in GMSL since 2011 is 3
related to the recovery from the 2011 La Niña event (Section 13.3.5) (Boening et al., 2012). 4
5
[START FAQ 13.1 HERE] 6
7
FAQ 13.1: Why Does Local Sea Level Change Differ from the Global Average? 8
9
Shifting surface winds, the expansion of warming ocean water, and the addition of melting ice can alter 10
ocean currents which, in turn, lead to changes in sea level that vary from place to place. Past and present 11
variations in the distribution of land ice affect the shape and gravitational field of the Earth, which also 12
cause regional fluctuations in sea level. Additional variations in sea level are caused by the influence of 13
more localised processes such as sediment compaction and tectonics. 14
15
Along any coast, vertical motion of either the sea or land surface can cause changes in sea level relative to 16
the land (known as relative sea level). For example, a local rise can be caused by an increase in sea surface 17
height, or by a decrease in land height. Over relatively short time spans (hours to years), the influence of 18
tides, storms and climatic variability—such as El Niño—dominates sea level variations. Earthquakes and 19
landslides can also have an effect by causing changes in land height and, sometimes, tsunamis. Over longer 20
time spans (decades to centuries), the influence of climate change—with consequent changes in volume of 21
ocean water and land ice—is the main contributor to sea level change in most regions. Over these longer 22
time scales, various processes cause vertical motion of the land surface, which can also result in substantial 23
changes in relative sea level. 24
25
Since the 1970s, satellites have measured the height of the ocean surface relative to the center of the Earth 26
(known as geocentric sea level). These measurements show differing rates of geocentric sea level rise around 27
the world during the past two decades (see FAQ 13.1, Figure 1). For example, in the western Pacific Ocean, 28
rates were about three times greater than the global mean value of about three millimeters a year. In contrast, 29
those in the eastern Pacific Ocean are lower than the global mean value, with much of the west coast of the 30
Americas experiencing a fall in sea surface height from 1993 to 2012. 31
32
Much of the spatial variation shown in FAQ 13.1, Figure 1 is a result of natural climate variability—such as 33
El Niño and the Pacific Decadal Oscillation—over time scales from about a year to several decades. These 34
climate variations alter surface winds, ocean currents, temperature and salinity, and hence affect sea level. 35
The influence of these processes will continue during the 21st century, and will be superimposed on the 36
spatial pattern of sea level change associated with longer term climate change, which also arises through 37
changes in surface winds, ocean currents, temperature and salinity, as well as ocean volume. However, in 38
contrast to the natural variability, the longer term trends accumulate over time and so are expected to 39
dominate over the 21st century. The resulting rates of geocentric sea level change over this longer period 40
may therefore exhibit a very different pattern to that shown in FAQ 13.1, Figure 1. 41
42
Tide gauges measure relative sea level, and so they include changes resulting from vertical motion of both 43
the land and the sea surface. Over many coastal regions, vertical land motion is small, and so the long-term 44
rate of sea level change recorded by coastal and island tide gauges is similar to the global mean value (see 45
records at San Francisco and Pago Pago in FAQ 13.1, Figure 1). In some regions, vertical land motion has 46
had an important influence. For example, the steady fall in sea level recorded at Stockholm (FAQ 13.1, 47
Figure 1) is caused by uplift of this region after the melting of a large (>1 km thick) continental ice sheet at 48
the end of the last Ice Age, between ~20,000 and ~9,000 years ago. Such on-going land deformation as a 49
response to the melting of ancient ice sheets is a significant contributor to regional sea level changes in 50
North America and northwest Eurasia, which were covered by large continental ice sheets during the peak of 51
the last Ice Age. 52
53
In other regions, this process can also lead to land subsidence, which elevates relative sea levels, as it has at 54
Charlottetown, where a relatively large increase has been observed, compared to the global mean rate (FAQ 55
13.1, Figure 1). Vertical land motion due to movement of the Earth’s tectonic plates can also cause 56
departures from the global mean sea level trend in some areas—most significantly, those located near active 57

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subduction zones, where one tectonic plate slips beneath another. For the case of Antofagasta (FAQ 13.1, 1
Figure 1) this appears to result in steady land uplift and therefore relative sea level fall. 2
3
In addition to regional influences of vertical land motion on relative sea level change, some processes lead to 4
land motion that is rapid, but highly localised. For example, the greater rate of rise relative to the global 5
mean at Manila (FAQ 13.1, Figure 1) is dominated by land subsidence caused by intensive ground water 6
pumping. Land subsidence due to natural and anthropogenic processes, such as the extraction of ground 7
water or hydrocarbons, is common in many coastal regions, in particular in large river deltas. 8
9
It is commonly assumed that melting ice from glaciers, or the Greenland and Antarctic ice sheets, would 10
cause globally uniform sea level rise, much like filling a bath tub with water. In fact, such melting results in 11
regional variations in sea level due to a variety of processes, including changes in ocean currents, winds, the 12
Earth’s gravity field, and land height. For example, computer models that simulate these latter two processes 13
predict a regional fall in relative sea level around the melting ice sheets, because the gravitational attraction 14
between ice and ocean water is reduced, and the land tends to rise as the ice melts (FAQ 13.1, Figure 2). 15
However, further away from the ice sheet melting, sea level rise is enhanced, compared to the global average 16
value. 17
18
In summary, a variety of processes drive height changes of the ocean surface and ocean floor, resulting in 19
distinct spatial patterns of sea level change at local to regional scales. The combination of these processes 20
produces a complex pattern of total sea level change, which varies through time as the relative contribution 21
of each process changes. The global average change is a useful single value which reflects the contribution 22
of climatic processes—land-ice melting and ocean warming, and represents a good estimate of sea level 23
change at many coastal locations. At the same time, however, where the various regional processes result in 24
a strong signal, there can be large departures from the global average value. 25
26
[INSERT FAQ 13.1, FIGURE 1 HERE] 27
FAQ13.1, Figure 1: Map of rates of change in sea surface height (geocentric sea level) for the period 1993–2012 from 28
satellite altimetry. Also shown are relative sea level changes (grey lines) from selected tide gauge stations for the period 29
1950–2012. For comparison, an estimate of global mean sea level change is also shown (red lines) with each tide gauge 30
time series. The relatively large, short-term oscillations in local sea level (grey lines) are due to the natural climate 31
variability described in the main text. For example, the large, regular deviations at Pago Pago are associated with the El 32
Niño-Southern Oscillation. 33
34
[INSERT FAQ 13.1, FIGURE 2 HERE] 35
FAQ13.1, Figure 2: Model output showing relative sea level change due to melting of the Greenland Ice Sheet and the 36
West Antarctic Ice Sheet at rates of 0.5 mm yr
–1
each (giving a global mean value for sea level rise of 1 mm yr
–1
). The 37
modelled sea level changes are less than the global mean value in areas near the melting ice but enhanced further afield. 38
Adapted from Milne et al. (2009). 39
40
[END FAQ 13.1 HERE] 41
42
13.3 Contributions to Global Mean Sea Level Rise During the Instrumental Period 43
44
In order to assess our understanding of the causes of observed changes and our confidence in projecting 45
future changes, we compare observational estimates of contributions with results derived from AOGCM 46
experiments, beginning in the late 19th century, forced with estimated past time-dependent anthropogenic 47
changes in atmospheric composition, and natural forcings due to volcanic aerosols and variations in solar 48
irradiance (Section 10.1). This period and these simulations are often referred to as “historical”. 49
50
13.3.1 Thermal Expansion Contribution 51
52
13.3.1.1 Observed 53
54
Important progress has been realized since AR4 in quantifying the observed thermal expansion component 55
of global mean sea level rise. This progress reflects (1) the detection of systematic depth-varying biases 56
affecting historical expendable bathythermograph data (Gouretski and Koltermann, 2007)) (Chapter 3), (2) 57
the newly available Argo ocean (temperature and salinity) data with almost global coverage (not including 58

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ice-covered regions and marginal seas) of the oceans down to 2000 m since 2004–2005, and (3) estimates of 1
the deep-ocean contribution using ship-based data collected during the World Ocean Circulation Experiment 2
and revisit cruises (Johnson and Gruber, 2007; Johnson et al., 2007; Purkey and Johnson, 2010; Kouketsu et 3
al., 2011). 4
5
For the period 1971–2010, the rate for the 0–700 m depth range is 0.6 [0.4–0.8] mm yr
–1
(Section 3.7.2 and 6
Table 3.1). Including the deep-ocean contribution for the same period increases the value to 0.8 [0.5–1.1] 7
mm yr
–1
(Figure 13.4a). Over the altimetry period (1993–2010), the rate for the 0–700 m depth range is 0.8 8
[0.5–1.1] mm yr
–1
and 1.1 [0.8–1.4] mm yr
–1
when accounting for the deep ocean (Section 3.7.2 and Table 9
3.1). 10
11
13.3.1.2 Modelled 12
13
GMSL rise due to thermal expansion is approximately proportional to the increase in ocean heat content 14
(Section 13.4.1). Historical GMSL rise due to thermal expansion simulated by CMIP5 models is shown in 15
Table 13.1 and Figure 13.4a. The model spread is due to uncertainty in radiative forcing and modelled 16
climate response (Sections 8.5.2, 9.4.2.2, 9.7.2.3, 13.4.1). 17
18
In the time-mean of several decades, there is a negative volcanic forcing if there is more volcanic activity 19
than is typical of the long term, and a positive forcing if there is less. In the decades after major volcanic 20
eruptions, the rate of expansion is temporarily enhanced, as the ocean recovers from the cooling caused by 21
the volcanic forcing (Church et al., 2005; Gregory et al., 2006) (Figure 13.4a). During 1961–1999, a period 22
when there were several large volcanic eruptions, the CMIP3 simulations with both natural and 23
anthropogenic forcing have substantially smaller increasing trends in the upper 700 m than those with 24
anthropogenic forcing only (Domingues et al., 2008) because the natural volcanic forcing tends to cool the 25
climate system, thus reducing ocean heat uptake (Levitus et al., 2001). The models including natural forcing 26
are closer to observations, though with a tendency to underestimate the trend by about 10% (Sections 9.4.2.2, 27
10.4.1). 28
29
Gregory (2010) and Gregory et al. (2013a) proposed that the AOGCMs underestimate ocean heat uptake in 30
their historical simulations because their control experiments usually omit volcanic forcing, so the 31
imposition of historical volcanic forcing on the simulated climate system represents a time-mean negative 32
forcing relative to the control climate. The apparent long persistence of the simulated oceanic cooling 33
following the 1883 eruption of Krakatau (Delworth et al., 2005; Gleckler et al., 2006a; Gleckler et al., 34
2006b; Gregory et al., 2006) is a consequence of this bias, which also causes a model-dependent 35
underestimate of up to 0.2 mm yr
–1
of thermal expansion on average during the 20th century (Gregory et al., 36
2013a; Gregory et al., 2013b). This implies that CMIP5 results may be similarly underestimated, depending 37
on the details of the individual model control runs. Church et al. (2013) proposed a correction of 0.1 mm yr
–1
38
to the model mean rate, which we apply in the sea level budget in Table 13.1 and Figure 13.7. The corrected 39
CMIP5 model-mean rate for 1971–2010 is close to the central observational estimate; the model-mean rate 40
for 1993–2010 exceeds the central observational estimate but they are not statistically different given the 41
uncertainties (Table 13.1, Figure 13.4a). This correction is not made to projections of thermal expansion 42
because it is very small compared with the projected increase in the rate (Section 13.5.1). 43
44
In view of the improvement in observational estimates of thermal expansion, the good agreement of 45
historical model results with observational estimates, and their consistency with understanding of the energy 46
budget and radiative forcing of the climate system (Box 13.1), we have high confidence in the projections of 47
thermal expansion using AOGCMs. 48
49
[INSERT FIGURE 13.4 HERE] 50
Figure 13.4: Comparison of modelled and observed components of global mean sea level change since 1900. Changes 51
in glaciers, ice sheets and land water storage are shown as positive sea level rise when mass is added to the ocean. (a) 52
Ocean thermal expansion. Individual CMIP5 AOGCM simulations are shown in grey, the AOGCM average is black, 53
observations in teal with the 5–95% uncertainties shaded. (b) Glaciers (excluding Antarctic peripheral glaciers). Model 54
simulations by Marzeion et al. (2012a) with input from individual AOGCMs are shown in grey with the average of 55
these results in bright purple. Model simulations by Marzeion et al. (2012a) forced by observed climate are shown in 56
light blue. The observational estimates by Cogley (2009b) are shown in green (dashed) and by Leclercq et al. (2011) in 57
red (dashed). (c) Changes in land water storage (yellow/orange, the sum of groundwater depletion and reservoir 58

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storage) start at zero in 1900. The Greenland Ice sheet (green), the Antarctic Ice Sheet (blue) and the sum of the ice-1
sheets (red), start at zero at the start of the record 1991. (d) The rate of change (19 year centred trends) for the terms in 2
(a) to (c), and for the ice sheets (5 year centred trends). All curves in (a) and (b) are shown with zero time-mean over 3
the period 1986-2005 and the colours in (d) are matched to earlier panels. Updated from Church et al. (2013). 4
5
6
Table 13.1: Global mean sea level budget (mm yr
–1
) over different time intervals from observations and from model-7
based contributions. Uncertainties are 5–95%. The AOGCM historical integrations end in 2005; projections for RCP4.5 8
are used for 2006–2010. The modelled thermal expansion and glacier contributions are computed from the CMIP5 9
results, using the model of Marzeion et al. (2012a) for glaciers. The land water contribution is due to anthropogenic 10
intervention only, not including climate-related fluctuations. 11
Source 1901–1990 1971–2010 1993–2010
Observed contributions to GMSLR
Thermal expansion 0.8 [0.5 to 1.1] 1.1 [0.8 to 1.4]
Glaciers except in Greenland and Antarctica 0.54 [0.48 to 0.61] 0.62 [0.25 to 1.00] 0.76 [0.39 to 1.13]
Glaciers in Greenland
a
0.15 [0.10 to 0.19] 0.06 [0.03 to 0.09] 0.10 [0.07 to 0.13]
Greenland ice sheet 0.33 [0.25 to 0.41]
Antarctic ice sheet 0.27 [0.16 to 0.38]
Land water storage –0.11 [–0.16 to –0.06] 0.12 [0.03 to 0.22] 0.38 [0.26 to 0.49]
Total of contributions
2.8 [2.3 to 3.4]
Observed GMSLR
1.5 [1.3 to 1.7] 2.0 [1.7 to 2.3] 3.2 [2.8 to 3.6]
Modelled contributions to GMSLR
Thermal expansion 0.37 [0.06 to 0.67] 0.96 [0.51 to 1.41] 1.49 [0.97 to 2.02]
Glaciers except in Greenland and Antarctica 0.63 [0.37 to 0.89] 0.62 [0.41 to 0.84] 0.78 [0.43 to 1.13]
Glaciers in Greenland 0.07 [–0.02 to 0.16] 0.10 [0.05 to 0.15] 0.14 [0.06 to 0.23]
Total including land water storage
1.0 [0.5 to 1.4] 1.8 [1.3 to 2.3] 2.8 [2.1 to 3.5]
Residual
b
0.5 [0.1 to 1.0] 0.2 [–0.4 to 0.8] 0.4 [–0.4 to 1.2]
Notes: 12
(a) This contribution is not included in the total, because glaciers in Greenland are included in the observational 13
assessment of the Greenland ice sheet. 14
(b) Observed GMSL rise – modelled thermal expansion – modelled glaciers – observed land water storage. 15
16
17
13.3.2 Glaciers 18
19
13.3.2.1 Observed 20
21
“Glaciers” are defined here as all land-ice masses, including those peripheral to (but not including) the 22
Greenland and Antarctic ice sheets. The term “glaciers and ice caps” was applied to this category in AR4. 23
Changes in aggregate glacier volume have conventionally been determined by various methods of repeat 24
mapping of surface elevation to detect elevation (and thus volume) change. Mass changes are determined by 25
compilation and upscaling of limited direct observations of surface mass balance (SMB). Since 2003, 26
GRACE space gravity observations have been used to detect mass change of the world’s glaciers. 27
28
The combined records indicate that a net decline of global glacier volume began in the19th century, before 29
significant anthropogenic radiative forcing had started, and was probably the result of warming associated 30
with the termination of the Little Ice Age (Crowley, 2000; Gregory et al., 2006; Gregory et al., 2013b). 31
Global rates of glacier volume loss did not increase significantly during much of the 20th century (Figure 32
4.12). In part this may have been because of an enhanced rate of loss of due to unforced high-latitude 33
variability early in the century, while anthropogenic warming was still comparatively small (Section 34
13.3.2.2). It is likely that anthropogenic forcing played a statistically significant role in acceleration of global 35
glacier losses in the latter decades of the 20th century relative to rates in the 19th century (Section 10.5.2.2). 36
It is also likely that, during the 20th century, the progressive loss of glacier area significantly restricted the 37
rate of mass loss (Gregory et al., 2013b). 38
39
The earliest sea level assessments recognized that glaciers have been significant contributors to GMSL rise 40
(Meier, 1984). As assessed in Chapter 4, observations, improved methods of analysis, and a new, globally 41
complete inventory, indicate that glaciers, including those around the ice-sheet peripheries, very likely 42
continue to be significant contributors to sea level, but are also highly variable on annual to decadal time 43

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scales. It is assumed that all glacier losses contribute to sea level rise, but the potential role of terrestrial 1
interception of runoff, either in lakes formed following future ice retreat or in groundwater, has yet to be 2
evaluated. For the period 2003–2009, the sea level contribution of all glaciers globally, including those 3
glaciers surrounding the periphery of the two ice sheets, is 0.71 [0.64–0.79] mm yr
–1
sea level equivalent 4
(SLE) (Section 4.3.3, Table 4.4). Depending upon the method used, however, loss-rate measurements of the 5
two ice sheets can be very difficult to separate from losses from the peripheral glaciers. To avoid double 6
counting, total cryospheric losses are determined by adding estimates of glacier losses excluding the 7
peripheral glaciers to losses from the ice sheets plus their peripheral glaciers. The sea level contribution of 8
all glaciers excluding those glaciers surrounding the periphery of the two ice sheets was 1901–1990: 0.54 9
[0.48–0.61] mm yr
–1
SLE; for 1971–2009: 0.62 [0.25–1.00] mm yr
–1
SLE; for 1993–2009: 0.76 [0.43–1.09] 10
mm yr
–1
SLE; for 2005–2009: 0.83 [0.70–0.97] mm yr
–1
SLE (Section 4.3.3.4). 11
12
13.3.2.2 Modelled 13
14
Global glacier mass balance models are calibrated using data from the few well-observed glaciers. 15
Approximately 100 glacier mass balance records are available in any given year over the past half-century; 16
only 17 glaciers exist with records of 30 years or more (Dyurgerov and Meier, 2005; Kaser et al., 2006; 17
Cogley, 2012). Confidence in these models for projections of future change (Section 13.4.2) depends on their 18
ability to reproduce past observed glacier change using corresponding climate observations as forcing (Raper 19
and Braithwaite, 2005; Meier et al., 2007; Bahr et al., 2009; Radić and Hock, 2011; Marzeion et al., 2012b; 20
2012a; Giesen and Oerlemans, 2013). Model validation is challenging due to the scarcity of independent 21
observations (unused in model calibration) but uncertainties have been evaluated by methods such as cross-22
validation of hindcast projections for individual glaciers drawn from the sample of glacier observations 23
averaged for calibration (Marzeion et al., 2012a; Radić et al., 2013). 24
25
Confidence in the use of AOGCM climate simulations as input to glacier projections is gained from the 26
agreement since the mid-20th century of glacier models forced by AOGCM simulations with glacier models 27
forced by observations (Marzeion et al., 2012a) (Figure 13.4b). In the earlier 20th century, around the 1930s, 28
glaciers at high northern latitudes lost mass at an enhanced rate (Oerlemans et al., 2011; Leclercq et al., 29
2012); in the model, observed forcings produced larger glacier losses than did AOGCM forcings (Marzeion 30
et al., 2012a) (Figure 13d). This is judged likely to be due to an episode of unforced, regionally variable 31
warming around Greenland (Box, 2002; Chylek et al., 2004) rather than to radiative forcing of the climate 32
system, and is consequently not reproduced by AOGCM experiments (Section 10.2). In our analysis of the 33
budget of GMSL rise (Section 13.3.6), we take the difference between the simulations using AOGCM 34
forcing and the simulation using observations as an estimate of the influence of unforced climate variability 35
on global glacier mass balance (Figure 13.4b). 36
37
There is medium confidence in the use of glacier models to make global projections based on AOGCM 38
results. The process-based understanding of glacier surface mass balance, the consistency of models and 39
observations of glacier changes, and the evidence that AOGCM climate simulations can provide realistic 40
input all give confidence, which on the other hand is limited because the set of well-observed glaciers is a 41
very small fraction of the total. 42
43
13.3.3 Greenland and Antarctic Ice Sheets 44
45
13.3.3.1 Observed Mass Balance 46
47
The Greenland Ice Sheet’s mass balance comprises its surface mass balance and outflow, whereas 48
Antarctica’s mass budget is dominated by accumulation and outflow in the form of calving and ice flow into 49
floating (and therefore sea level neutral) ice shelves. Knowledge of the contribution of the Greenland and 50
Antarctic ice sheets to observed sea level changes over the last two decades comes primarily from satellite 51
and airborne surveys. Three main techniques are employed: the mass balance, volumetric, and gravimetric 52
methods (Section 4.4.1). 53
54
Observations indicate that the Greenland contribution (all rates quoted here are 5–95%) to GMSL has very 55
likely increased from 0.09 [–0.02–0.20] mm yr
–1
for 1992–2001 to 0.59 [0.43–0.76] mm yr
–1
for 2002–2011 56
(Section 4.4.2.2, Figure 13.4). The average rate of the Antarctica contribution to sea level rise likely 57

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increased from 0.08 [–0.10–0.27] mm yr
–1
for 1992–2001 to 0.40 [0.20–0.61] mm yr
–1
for 2002–2011 1
(Section 4.4.2.3). For the budget period 1993–2010, the combined contribution of the ice sheets is 0.60 2
[0.42–0.78] mm yr
–1
. For comparison, the AR4’s assessment for the period 1993–2003 was 0.21 ± 0.07 mm 3
yr
–1
for Greenland and 0.21 ± 0.35 mm yr
–1
for Antarctica. 4
5
13.3.3.2 Modelled Surface Mass Balance 6
7
Projections of changes in the SMB of the Antarctic and Greenland ice sheets are obtained from regional 8
climate model (RCM) or downscaled AOGCM simulations (Sections 13.4.3.1 and 13.4.4.1). A spatial 9
resolution of a few 10s km or finer is required in order to resolve the strong gradients in SMB across the 10
steep slopes of the ice-sheet margins. While simulations of SMB at particular locations may have errors of 5-11
20% compared with in-situ observations, there is good agreement between methods involving RCMs and 12
observational methods of evaluating ice-sheet mass balance (Shepherd et al., 2012). In the present climate, 13
for both Greenland and Antarctica, the mean SMB over the ice-sheet area is positive, giving a negative 14
number when expressed as sea level equivalent (SLE). 15
16
In Greenland, the average and standard deviation of accumulation (precipitation minus sublimation) 17
estimates for 1961–1990 is –1.62 ± 0.21 mm yr
–1
SLE from the models in Table 13.2, agreeing with 18
published observation-based accumulation maps, for example –1.42 ± 0.11 mm yr
–1
SLE by Bales et al. 19
(2009) and –1.63 ± 0.23 mm yr
–1
SLE by Burgess et al. (2010). For SMB (accumulation minus runoff, 20
neglecting drifting snow erosion, which is small), the models give –0.92 ± 0.26 mm yr
–1
SLE for 1961–1990 21
(Table 13.2). 22
23
All of these models indicate that Greenland Ice Sheet SMB showed no significant trend from the 1960s to 24
the 1980s, then started becoming less positive (becoming less negative expressed as SLE) in the early 1990s, 25
on average by 3% yr
–1
. This results in a statistically significant and increasing (i.e., becoming more positive) 26
contribution to the rate of GMSL rise (SMB trend column of Table 13.2, Figure 13.5). The largest trends are 27
found in models with coupled snow and atmosphere simulations using the RACMO2 and MAR models. Van 28
den Broeke et al. (2009) concluded that the mass loss during 2000–2008 is equally split between SMB and 29
dynamical change. Rignot et al. (2011) indicated that SMB change accounts for about 60% of the mass loss 30
since 1992 and Sasgen et al. (2012) showed that SMB change, simulated by RACMO2 (Ettema et al., 2009, 31
an earlier version of the model in Table 13.2), accounts for about 60% of the observed rate of mass loss 32
during 2002–2010, with an observational estimate of the increase in ice outflow accounting for the 33
remainder. This satisfactory consistency, within uncertainties, in estimates for the Greenland Ice Sheet mass 34
budget gives confidence in SMB simulations of the past, and hence also in the similar models used for 35
projections of SMB changes (Section 13.4.3.1). 36
37
This recent trend towards increasingly less positive SMB is caused almost entirely by increased melting and 38
subsequent runoff, with variability in accumulation being comparatively small (Sasgen et al., 2012; Vernon 39
et al., 2013) (Figure 13.5). This tendency is related to pronounced regional warming, which may be 40
attributed to some combination of anthropogenic climate change and anomalous regional variability in recent 41
years (Hanna et al., 2008; 2012; Fettweis et al., 2013). Greenland SMB models forced by boundary 42
conditions from AOGCM historical simulations (Rae et al., 2012; Fettweis et al., 2013) do not show 43
statistically significant trends towards increasing contributions to GMSL, implying that the dominant 44
contribution is internally generated regional climate variability, which is not expected to be reproduced by 45
AOGCM historical simulations (Section 10.2). We have high confidence in projections of future warming in 46
Greenland because of the agreement of models in predicting amplified warming at high northern latitudes 47
(Sections 12.4.3.1, 14.7.2) for well-understood physical reasons, although there remains uncertainty in the 48
size of the amplification, and we have high confidence in projections of increasing surface melting (Section 49
13.4.3.1) because of the sensitivity to warming demonstrated by SMB models of the past. 50
51
52
Table 13.2: SMB and rates of change of SMB of the Greenland Ice Sheet, calculated from ice sheet SMB models using 53
meteorological observations and reanalyses as input, expressed as sea level equivalent (SLE). A negative SLE number 54
for SMB indicates that accumulation exceeds runoff. A positive SLE for SMB anomaly indicates that accumulation has 55
decreased, or runoff has increased, or both. Uncertainties are one standard deviation. Uncertainty in individual model 56
results reflects temporal variability, that is one standard deviation of annual values; the uncertainty in the model average 57
is one standard deviation of variation across models. 58

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Reference and Model
a
Time-Mean
SMB
1961–1990
mm yr
–1
SLE
Rate of change
of SMB
1991–2010
mm yr
–2
SLE
Time-Mean SMB Anomaly
(with Respect to
1961–1990 Time-Mean SMB)
b
mm yr
–1
SLE
1971–2010 1993–2010 2005–2010
RACMO2, Van Angelen et al.
(2012), 11 km RCM
–1.13 ± 0.30 0.04 ± 0.01 0.07 ± 0.33 0.23 ± 0.30 0.47 ± 0.24
MAR, Fettweis et al. (2011), 25 km
RCM
–1.17 ± 0.31 0.05 ± 0.01 0.12 ± 0.38 0.36 ± 0.33 0.64 ± 0.22
PMM5, Box et al. (2009), 25 km
RCM
–0.98 ± 0.18 0.02 ± 0.01 0.00 ± 0.19 0.10 ± 0.22 0.23 ± 0.21
ECMWFd, Hanna et al. (2011), 5 km
PDD
–0.77 ± 0.27 0.02 ± 0.01 0.02 ± 0.28 0.12 ± 0.27 0.24 ± 0.19
SnowModel, Mernild and Liston
(2012), 5 km EBM
–0.54 ± 0.21 0.03 ± 0.01 0.09 ± 0.25 0.19 ± 0.24 0.36 ± 0.23
Model Average –0.92 ± 0.26 0.03 ± 0.01 0.06 ± 0.05 0.20 ± 0.10 0.39 ± 0.17
Notes: 1
(a) The approximate spatial resolution is stated and the model type denoted by PDD = positive degree day, EBM = 2
energy balance model, RCM = regional climate model. 3
(b) Difference from the time-mean SMB of 1961–1990. This difference equals the sea level contribution from 4
Greenland SMB changes if the ice sheet is assumed to have been near zero mass balance during 1961–1990 (Hanna et 5
al., 2005; Sasgen et al., 2012). 6
7
8
All Greenland SMB simulations for the first half of the 20th century depend on reconstructions of 9
meteorological variability over the ice sheet made using empirical relationships based on observations from 10
coastal stations and estimates of accumulation from ice cores. Despite the similar input datasets in all cases, 11
the various climate reconstruction and SMB methods used have led to a range of results (Fettweis et al., 12
2008; Wake et al., 2009; Hanna et al., 2011; Box, 2013; Box and Colgan, 2013; Box et al., 2013; Gregory et 13
al., 2013b). For 1901–1990, Hanna et al. (2011) have a time-mean GMSL contribution of –0.3 mm yr
–1
, 14
while Box and Colgan (2013) have a weakly positive contribution and the others are about zero. In all cases, 15
there is substantial variability associated with regional climate fluctuations, in particular the warm episode in 16
the 1930s, during which glaciers retreated in southeastern Greenland (Bjork et al., 2012). Chylek et al. 17
(2004) argued that this episode was associated with the NAO rather than with global climate change. 18
19
In Antarctica, accumulation (precipitation minus sublimation) approximates SMB since surface melting and 20
runoff are negligible in the present climate (Section 4.4.2.1.1). There are uncertainties in model- and 21
observation-based estimates of Antarctic SMB. Global climate models do not account for snow hydrology or 22
for drifting snow processes which remove an estimated 7% of the accumulated snow (Lenaerts et al., 2012), 23
and the ice sheet’s steep coastal slopes are not well-captured by coarse-resolution models. Observation-based 24
estimates rely on sparse accumulation measurements with very little coverage in high-accumulation areas. 25
For the Antarctic ice sheet and ice shelves together, CMIP3 AOGCMs simulate SMB for 1979–2000 of –7.1 26
± 1.5 mm yr
–1
SLE (Connolley and Bracegirdle, 2007; Uotila et al., 2007), the mean being about 10% larger 27
in magnitude than observation-based estimates, for instance –6.3 mm yr
–1
SLE from Vaughan et al. (1999). 28
For the SMB of the grounded ice sheet alone, four global reanalysis models, with resolutions of 38–125 km 29
(Bromwich et al., 2011), give –5.2 ± 0.5 mm yr
–1
SLE for 1979–2010, which compares well with an 30
observational estimate of –4.9 ± 0.1 mm yr
–1
SLE for 1950–2000 (Arthern et al., 2006). Because of higher 31
accumulation near the coast, the regional climate model RACMO2 gives the somewhat larger value of –5.5 32
± 0.3 mm yr
–1
SLE for 1979–2000 (Lenaerts et al., 2012). This relatively good agreement, combined with the 33
similarity of the geographical distribution of modelled and observed SMB, give medium confidence in the 34
realism of the RCM SMB simulation. 35
36
Some global reanalyses have been shown to contain spurious trends in various quantities in the Southern 37
Hemisphere related to changes in the observing systems, for example new satellite observations (Bromwich 38
et al., 2007; 2011). In the RCMs and in global reanalyses that are not affected by spurious trends, no 39
significant trend is present in accumulation since 1980 (Section 4.4.2.3). This agrees with observation-based 40
studies (Monaghan et al., 2006; Anschütz et al., 2009) (Chapter 4) and implies that Antarctic SMB change 41
has not contributed significantly to recent changes in the rate of GMSL rise. Likewise, CMIP3 historical 42

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simulations do not exhibit any systematic trend in Antarctic precipitation during the late 20th century (Uotila 1
et al., 2007). No observational assessments have been made of variability in SMB for the whole ice sheet for 2
the earlier part of the 20th century, or of its longer term mean. 3
4
GCM and RCM projections consistently indicate significant Antarctic warming and concomitant increase in 5
precipitation. We have high confidence in expecting a relationship between these quantities on physical 6
grounds (Section 13.4.4.1) and from ice-core evidence (Van Ommen et al., 2004; Lemieux-Dudon et al., 7
2010; Stenni et al., 2011). The absence of a significant trend in Antarctic precipitation up to the present is 8
not inconsistent with the expected relationship, because observed temperature trends over the majority of the 9
continent are weak (Section 10.5.2.1) and trends in Antarctic precipitation simulated for recent decades are 10
much smaller than interannual variability (van den Broeke et al., 2006; Uotila et al., 2007). Taking all these 11
considerations together, we have medium confidence in model projections of future Antarctic SMB increase, 12
implying a negative contribution to GMSL rise (see also Sections 13.4.4.1, 13.5.3 and 14.7.14). 13
14
[INSERT FIGURE 13.5 HERE] 15
Figure 13.5: Annual-mean surface mass balance (accumulation minus ablation) for the Greenland Ice Sheet, simulated 16
by regional climate models for the period 1960–2010. 17
18
13.3.4 Contributions from Water Storage on Land 19
20
Changes in water storage on land in response to climate change and variability (i.e., water stored in rivers, 21
lakes, wetlands, the vadose zone, aquifers, and snow pack at high latitudes and altitudes) and from direct 22
human-induced effects (i.e., storage of water in reservoirs and groundwater pumping) have the potential to 23
contribute to sea level change. Based on satellite observations of the northern hemisphere snowpack, 24
Biancamaria et al. (2011) found no significant trend in the contribution of snow to sea level. Estimates of 25
climate-related changes in land water storage over the past few decades rely on global hydrological models 26
because corresponding observations are inadequate (Milly et al., 2010). In assessing the relation between 27
terrestrial water storage and climate, Milly et al. (2003) and Ngo-Duc et al. (2005) found no long-term 28
climatic trend in total water storage, but documented large interannual to decadal fluctuations, equivalent to 29
several millimetres of sea level. Recent studies have shown that interannual variability in observed GMSL is 30
correlated with ENSO indices (Nerem et al., 2010) and is inversely related to ENSO-driven changes of 31
terrestrial water storage, especially in the tropics (Llovel et al., 2011). During El Niño events, sea level (and 32
ocean mass) tends to be higher because ocean precipitation increases and land precipitation decreases in the 33
tropics (Cazenave et al., 2012) . The reverse happens during La Niña events, as seen during 2010–2011, 34
when there was a decrease in GMSL due to a temporary increase in water storage on the land, especially in 35
Australia, northern South America, and southeast Asia (Boening et al., 2012) (Section 13.3.5). 36
37
Direct human interventions on land water storage also induce sea level changes (Sahagian, 2000; Gornitz, 38
2001; Huntington, 2008; Lettenmaier and Milly, 2009). The largest contributions come from impoundment 39
in reservoirs and groundwater withdrawal. Over the past half-century, storage in tens of thousands of 40
reservoirs has offset some of the sea level rise that would otherwise have occurred. Chao et al. (2008) 41
estimated that the nearly 30,000 reservoirs built during the 20th century resulted in nominal reservoir storage 42
up to 2007 equivalent to ~23 mm of sea level fall (mostly since 1940), with a stabilization in recent years. 43
Chao et al. further assumed that the reservoirs were 85% full, and by including seepage into groundwater as 44
estimated from a model, they obtained a total of 30 mm of sea level fall (equivalent to a rate of sea level fall 45
of 0.55 mm yr
–1
from 1950 to 2000). Their seepage estimate was argued to be unrealistically large, however, 46
because it assumes aquifers are infinite and have no interfering boundary conditions (Lettenmaier and Milly, 47
2009; Konikow, 2013). Chao et al. (2008) argued that sedimentation of reservoirs does not reduce their sea 48
level contribution, but their argument is disputed (Gregory et al., 2013b). Lettenmaier and Milly (2009) 49
suggested a loss of capacity due to sedimentation at 1% yr
–1
. Given the uncertainty about them, neither the 50
seepage nor the effect of sedimentation is included in the budget (Section 13.3.6). Here the (negative) GMSL 51
contribution from reservoir storage is estimated as 85% [70–100%] of the nominal capacity (with the lower 52
limit coming from Pokhrel et al. (2012)). 53
54
Konikow (2011) estimated that human-induced groundwater depletion contributed 0.26 ± 0.07 mm yr
–1
to 55
GMSL rise over 1971–2008 and 0.34 ± 0.07 mm yr
–1
over 1993–2008 (based mostly on observational 56
methods), whereas Wada et al. (2012) estimated values of 0.42 ± 0.08 mm yr
–1
over 1971–2008 and 0.54 ± 57

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0.09 mm yr
–1
over 1993–2008 (based on modelling of water fluxes). The average of these two series with the 1
difference as a measure of the uncertainty is used in the sea level budget (Section 13.3.6). Pokhrel et al. 2
(2012) estimated a larger groundwater depletion, but Konikow (2013) (disputed by Pohkrel et al. (2013)) 3
argued that their underlying assumptions of defining depletion as equivalent to groundwater use, and 4
allowing unlimited extraction to meet water demand, lead to substantial overestimates of depletion. 5
6
In summary, climate-related changes in water and snow storage on land do not show significant long-term 7
trends for the recent decades. However, direct human interventions in land water storage (reservoir 8
impoundment and groundwater depletion) have each contributed at least several tenths of mm yr
–1
of sea 9
level change (Figure 13.4, Table 13.1). Reservoir impoundment exceeded groundwater depletion for the 10
majority of the 20th century but groundwater depletion has increased and now exceeds current rates of 11
impoundment, contributing to an increased rate of GMSL rise. The net contribution for the 20th century is 12
estimated to be small. 13
14
13.3.5 Ocean Mass Observations from GRACE 15
16
As discussed in Chapter 3, it has been possible to directly estimate changes in ocean mass using satellite 17
gravity data from GRACE since 2002 (Chambers et al., 2004; Chambers, 2006; Cazenave et al., 2009; 18
Leuliette and Miller, 2009; Chambers et al., 2010; Llovel et al., 2010). These measurements represent the 19
sum of total land-ice plus land-water components, and thus provide an independent assessment of these 20
contributions. However, GRACE is also sensitive to mass redistribution associated with GIA and requires 21
that this effect (on the order of –0.7 to –1.3 mm yr
–1
when averaged over the ocean domain) (Paulson et al., 22
2007; Peltier, 2009; Chambers et al., 2010; Tamisiea, 2011) be removed before estimating the ocean-mass 23
component. Most recent estimates (Leuliette and Willis, 2011; von Schuckmann and Le Traon, 2011) report 24
a global mean ocean-mass increase of 1.8 [1.4 to 2.2] mm yr
–1
over 2003-2012 after correcting for the GIA 25
component. The associated error results from the low signal-to-noise ratio over the ocean domain and 26
uncertainty in the model-based GIA correction (Quinn and Ponte, 2010). 27
28
Chapter 3 notes that, in terms of global averages, the sum of the contribution to GMSL due to change in 29
global ocean mass (the barystatic contribution), measured by GRACE, and the contribution due to global 30
ocean thermal expansion (the thermosteric contribution), measured by Argo, agrees within uncertainties with 31
the GMSL change observed by satellite altimetry (Leuliette and Willis, 2011; von Schuckmann and Le 32
Traon, 2011), although there is still a missing contribution from expansion in the deep ocean below 2000 m. 33
These datasets have allowed an investigation of the cause of variability in sea level over the last few years 34
(Figure 13.6). In particular, Boening et al. (2012) concluded that the decrease in GMSL over 2010–2011 35
followed by a rapid increase since 2011 was related to the 2011 La Niña event, whereby changes in 36
land/ocean precipitation patterns caused a temporary increase in water storage on the land (and 37
corresponding decrease in GMSL) during the La Niña event, especially in Australia, northern South 38
America, and southeast Asia (Boening et al., 2012). 39
40
[INSERT FIGURE 13.6 HERE] 41
Figure 13.6: Global mean sea level from altimetry from 2005 to 2012 (blue line). Ocean mass changes are shown in 42
green (as measured by GRACE) and thermosteric sea level changes (as measured by Argo) are shown in red. The black 43
line shows the sum of the ocean mass and thermosteric contributions (updated from Boening et al. (2012)). 44
45
13.3.6 Budget of GMSL Rise 46
47
Drawing on Sections 13.3.1–13.3.5, the budget of GMSL rise (Table 13.1, Figure 13.7) is analysed using 48
models and observations for the periods 1901–1990 (the 20th century, excluding the period after 1990 when 49
ice-sheet contributions to GMSL rise have increased; Sections 4.4 and 13.3.3.1), since 1971 (when 50
significantly more ocean data became available and systematic glacier reconstructions began), and since 51
1993 (when precise satellite sea level altimetry began). The 2005–2010 period when Argo and GRACE data 52
are available is short and strongly affected by interannual climate variability, as discussed in the previous 53
subsection (Section 13.3.5, Figure 13.6). Such variability is not externally forced and is therefore not 54
expected to be reproduced in AOGCM historical experiments. For the contribution from land water storage 55
(Figure 13.4c) we use the estimated effect of human intervention, neglecting climate-related variation, whose 56
effect is unimportant on multidecadal timescales (Section 13.3.4). Contributions due to runoff from thawed 57

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permafrost, change in atmospheric moisture content, and sedimentation in the ocean are not considered in the 1
budget because they are negligible compared with observed GMSL rise and the uncertainties. 2
3
For 1993–2010, allowing for uncertainties, the observed GMSL rise is consistent with the sum of the 4
observationally estimated contributions (Table 13.1, Figure 13.7e). The two largest terms are ocean thermal 5
expansion (accounting for about 35% of the observed GMSL rise) and glacier mass loss (accounting for a 6
further 25%, not including those in Greenland and Antarctica). Observations indicate an increased ice-sheet 7
contribution over the last two decades (Sections 4.4.2.2, 4.4.2.3 and 13.3.3.1) (Shepherd et al., 2012). The 8
closure of the observational budget since 1993, within uncertainties, represents a significant advance since 9
the AR4 in physical understanding of the causes of past GMSL change, and provides an improved basis for 10
critical evaluation of models of these contributions in order to assess their reliability for making projections. 11
12
The observational budget cannot be rigorously assessed for 1901–1990 or 1971–2010 because there is 13
insufficient observational information to estimate ice-sheet contributions with high confidence before the 14
1990s, and ocean data sampling is too sparse to permit an estimate of global mean thermal expansion before 15
the 1970s. However, a closed observational GMSL budget since the 1970s can be demonstrated with 16
reasonable estimates of ice-sheet contributions (Church et al., 2011a; Moore et al., 2011) (Table 13.1, Figure 17
13.7). For 1971–2010, the observed contributions from thermal expansion and mass loss from glaciers (not 18
including those in Antarctica) alone explain about 75% of the observed GMSL. 19
20
AOGCM-based estimates of the thermal expansion, which agree well with observations since 1971, 21
observational estimates of the glacier contribution, and the estimated change in land water storage (Figure 22
13.4c), which is relatively small, can all be made from the start of the 20th century (Sections 13.3.1.2, 23
13.3.2.2 and 13.3.4, Table 13.1). Model estimates of Greenland Ice Sheet SMB changes give an uncertain 24
but relatively small contribution during most of the 20th century, increasing since the early 1990s (Section 25
13.3.2.2). There could be a small constant contribution from the Antarctic Ice Sheet (Huybrechts et al., 2011; 26
Gregory et al., 2013b) due to long-term adjustment to climate change in previous millennia. Any secular rate 27
of sea level rise in the late Holocene was small (order of few tenths mm yr
–1
) (Section 13.2.1.4), probably 28
less than 0.2 mm yr
–1
(see discussion in Gregory et al. (2013b)). Including these ice-sheet contributions (but 29
omitting Antarctic SMB variations, for which no observationally based information for the ice sheet as a 30
whole is available for the majority of the 20th century), GMSL rise during the 20th century can be accounted 31
for within uncertainties, including the observation that the linear trend of GMSL rise during the last 50 years 32
is little larger than for the 20th century, despite the increasing anthropogenic forcing (Gregory et al., 2013b). 33
Model-based attribution of sea level change to radiative forcings is discussed in Section 10.4.3. 34
35
The sum of CMIP5 AOGCM thermal expansion (Section 13.3.1.2), glacier model results with CMIP5 36
AOGCM input (not including glaciers in Antarctica; Section 13.3.2.2; Marzeion et al. (2012a)) and 37
anthropogenic intervention in land water storage (Section 13.3.4) accounts for about 65% of the observed 38
rate of GMSL rise for 1901–1990, and 90% for 1971–2010 and 1993–2010 (Table 13.1; Figure 13.7). In all 39
periods, the residual is small enough to be attributed to the ice sheets (Section 13.3.2.2). 40
41
The unusually warm conditions in the Arctic during the 1930s (Chylek et al., 2004), which are attributed to 42
unforced climate variability (Delworth and Knutson, 2000) and are not therefore expected to be simulated by 43
AOGCMs, likely produced a greater mass loss by glaciers in high northern latitudes (Section 13.3.2.2). The 44
difference between the glacier mass loss calculated with the Marzeion et al. (2012a) model when it is forced 45
with observed climate rather than AOGCM simulated climate (the purple and blue curves in Figure 13.4b) is 46
an estimate of this effect. 47
48
If the glacier model results for observational input are used (Marzeion et al. (2012a), not including glaciers 49
in Antarctica) and an illustrative value of 0.1 mm yr
–1
is included for a long-term Antarctic contribution, the 50
model mean is within 20% of the observed GMSL rise (Figure 13.7a,c, dashed line), and 10% since 1993 51
(Figure 13.7d, dashed line; Church et al. (2013)). When the observed ice-sheet contributions since 1992 are 52
included as well, the sum is almost equivalent to the observed rise (dotted line in Figure 13.7). Both 53
observations and models have a maximum rate of rise in the 1930–1950 period, a minimum rate in the 54
1960s, and a maximum rate over the last two decades (Figure 13.7b). This agreement provides evidence that 55
the larger rate of rise since 1990, with a significant component of ocean thermal expansion (Figure 13.4d), 56

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results from increased radiative forcing (both natural and anthropogenic) and increased ice-sheet discharge, 1
rather than a natural oscillation (Church et al., 2013). 2
3
In summary, the evidence now available gives a clearer account of observed GMSL change than in previous 4
IPCC assessments, in two respects. First, reasonable agreement can be demonstrated throughout the period 5
since 1900 between GMSL rise as observed and as calculated from the sum of contributions. From 1993, all 6
contributions can be estimated from observations; for earlier periods, a combination of models and 7
observations is needed. Second, when both models and observations are available, they are consistent within 8
uncertainties. These two advances give confidence in the 21st century sea level projections. The ice-sheet 9
contributions have the potential to increase substantially due to rapid dynamical change (Sections 13.1.5.1, 10
13.4.3.2 and 13.4.4.2) but have been relatively small up to the present (Sections 4.4 and 13.3.2.2). Therefore, 11
the closure of the sea level budget to date does not test the reliability of ice-sheet models in projecting future 12
rapid dynamical change; we have only medium confidence in these models, on the basis of theoretical and 13
empirical understanding of the relevant processes and observations of changes up to the present (13.4.3.2, 14
13.4.4.2). 15
16
[INSERT FIGURE 13.7 HERE] 17
Figure 13.7: Comparison of the observed global mean sea level rise with the sum of the modelled contributions from 18
ocean thermal expansion, glaciers (excluding glaciers peripheral to the Antarctic ice sheet; from Marzeion et al. 19
(2012a)) and changes in land-water storage. In panels (a)-(d): the grey lines are results for individual CMIP5 AOGCMs; 20
the black line is the model mean plus a correction for the omission of volcanic forcing in the AOGCM control 21
experiments (see Section 13.3.1.2); the adjusted model mean (dashed black line) is the sum of the corrected model 22
mean thermal expansion, the change in land-water storage, the Marzeion et al. (2012a) glacier estimate using observed 23
(rather than modelled) climate, and an illustrative long-term ice-sheet contribution (of 0.1 mm yr
–1
); the adjusted model 24
mean including the observed ice sheet contribution (dotted black line) begins in 1993 and includes the ice-sheet 25
contributions (from Section 4.4), but excludes the glaciers peripheral to the both the Greenland Ice Sheet and the 26
Antarctic Ice Sheet, to avoid double counting because the observational ice-sheet estimates include the peripheral 27
glaciers; the estimates of global mean sea level are from Church and White (2011), Ray and Douglas (2011) and 28
Jevrejeva et al. (2008), with the shading indicating the uncertainty estimates (two standard deviations); the satellite 29
altimeter data since 1993 are shown in red. The modelled sea level is given (a) for the period 1900 to 2010, (b) the rates 30
of sea level change for the same period, with the satellite altimeter data shown as a red dot for the rate, (c) for 1961 to 31
2010 and (d) for 1990 to 2010. Panel (e) compares the sum of the observed contributions (orange) and the observed sea 32
level from the satellite altimeter data (red). (Figure and caption updated from Church et al. (2013)) 33
34
[START BOX 13.1 HERE] 35
36
Box 13.1: The Global Energy Budget 37
38
The global energy balance is a fundamental aspect of the Earth’s climate system. At the top of the 39
atmosphere (TOA), the boundary of the climate system, the balance involves shortwave radiation received 40
from the sun, shortwave radiation reflected and long-wave radiation emitted by the Earth (Section 1.2.2). 41
The rate of storage of energy in the Earth system must be equal to the net downward radiative flux at the 42
TOA. 43
44
The TOA fluxes (Section 2.3) have been measured by the Earth Radiation Budget Experiment (ERBE) 45
satellites from 1985 to 1999 (Wong et al., 2006) and the Cloud and the Earth’s Radiant Energy System 46
(CERES) satellites from March 2000 to the present (Loeb et al., 2009). The TOA radiative flux 47
measurements are highly precise, allowing identification of changes in the Earth’s net energy budget from 48
year to year within the ERBE and CERES missions (Kato, 2009; Stackhouse et al., 2010; Loeb et al., 2012), 49
but the absolute calibration of the instruments is not sufficiently accurate to allow determination of the 50
absolute TOA energy flux or to provide continuity across missions (Loeb et al., 2009). 51
52
The ocean has stored over 90% of the increase in energy in the climate system over recent decades (Box 53
3.1), resulting in ocean thermal expansion and hence sea level rise (Sections 3.7, 9.4, 13.4). Thus the energy 54
and sea level budgets are linked and must be consistent (Church et al., 2011b). This Box focuses on the 55
Earth’s global energy budget since 1970 when better global observational data coverage is available. The 56
radiative forcings (from Chapter 8), the global averaged surface temperatures (HadCRUT4) (Morice et al., 57
2012) and the rate of energy storage are relative to the time mean of 1860 to 1879. Otto et al. (2013) used an 58

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energy imbalance over this reference period of 0.08 ± 0.03 W m
–2
, which is subtracted from the observed 1
energy storage. 2
3
Since 1970, the effective radiative forcing (ERF) of the climate system has been positive as a result of 4
increased greenhouse gas concentrations (well-mixed and short-lived greenhouse gases, tropospheric and 5
stratospheric ozone and stratospheric water vapour) and a small increase in solar irradiance (Box 13.1, 6
Figure 1a). This positive ERF has been partly compensated by changes in tropospheric aerosols which 7
predominantly reflect sunlight and modify cloud properties and structure in ways that tend to reinforce the 8
negative ERF, although black carbon produces positive forcing. Explosive volcanic eruptions (such as El 9
Chichón in Mexico in 1982 and Mount Pinatubo in the Philippines in 1991) can inject sulphur dioxide into 10
the stratosphere, giving rise to stratospheric aerosol, which persists for several years. This reflects some of 11
the incoming solar radiation, and thus gives a further negative forcing. Changes in surface albedo from land-12
use change have also led to a greater reflection of shortwave radiation back to space and hence a negative 13
forcing. Since 1970, the net ERF of the climate system (including black carbon on snow and combined 14
contrails and contrail induced cirrus, not shown) has increased (Chapter 8) resulting in a cumulative total 15
energy inflow over this period (Box 13.1, Figure 1a). 16
17
[INSERT BOX 13.1, FIGURE 1 HERE] 18
Box 13.1, Figure 1: The Earth’s energy budget from 1970 through 2011. (a) The cumulative energy flux into the Earth 19
system from changes in well-mixed and short-lived greenhouse gases, solar forcing, changes in tropospheric aerosol 20
forcing, volcanic forcing and surface albedo, (relative to 1860–1879) are shown by the coloured lines and these are 21
added to give the cumulative energy inflow (black; including black carbon on snow and combined contrails and contrail 22
induced cirrus, not shown separately). (b) The cumulative total energy inflow from (a, black) is balanced by the sum of 23
the warming of the Earth system (blue; energy absorbed in warming the ocean, the atmosphere and the land and in the 24
melting of ice) and an increase in outgoing radiation inferred from changes in the global averaged surface temperature. 25
The sum of these two terms is given for a climate feedback parameter α of 0.82, 1.23 and 2.47 W m
–2
°C
–1
, 26
(corresponding to an equilibrium climate sensitivity of 4.5, 3.0 and 1.5ºC). The energy budget would be closed for a 27
particular value of α if that line coincided with the total energy inflow. For clarity, all uncertainties (shading) shown are 28
for a likely range. 29
30
If the ERF were fixed, the climate system would eventually warm sufficiently that the radiative response 31
would balance the ERF, and there would be zero net heat flux into the system. As the ERF is increasing, the 32
ocean’s large capacity to store heat means the climate system is not in equilibrium (Hansen et al., 2005), and 33
continues to store energy (Box 3.1; Box 13.1, Figure 1b). This storage provides strong evidence of a 34
changing climate. The majority of this additional heat is in the upper 700 m of the ocean but there is also 35
warming in the deep and abyssal ocean (Box 3.1). The associated thermal expansion of the ocean has 36
contributed about 40% of the observed sea level rise since 1970 (Section 13.4.2.2.6; Church et al., (2011b)). 37
A small amount of additional heat has been used to warm the continents, warm and melt glacial and sea ice, 38
and warm the atmosphere (Box 3.1). 39
40
As the climate system warms, energy is lost to space through increased outgoing radiation. This radiative 41
response by the system is predominantly due to increased thermal grey-body radiation emitted by the 42
atmosphere and surface, but is modified by climate feedbacks, such as changes in water vapour, surface 43
albedo and clouds, which affect both outgoing longwave and reflected shortwave radiation. Following 44
Murphy et al. (2009), Box 13.1, Figure 1b relates the cumulative total energy inflow to the Earth system to 45
the change in energy storage and the cumulative outgoing radiation. Calculation of the latter is based on the 46
observed globally averaged surface temperature change ΔΤ relative to a reference temperature for which the 47
Earth system would be in radiative balance. This temperature change is multiplied by the climate feedback 48
parameter α, which in turn is related to the equilibrium climate sensitivity. For equilibrium climate 49
sensitivities of 4.5°C, 3.0°C to 1.5°C (Box 12.2) and an ERF for a doubled CO
2
concentration of 3.7 ± 0.74 50
W m
–2
(Sections 8.1, 8.3), the corresponding estimates of the climate feedback parameter α are 0.82, 1.23 51
and 2.47 W m
–2
°C
–1
. 52
53
In addition to these forced variations in the Earth’s energy budget, there is also internal variability on 54
decadal time scales. Observations and models indicate that because of the comparatively small heat capacity 55
of the atmosphere, a decade of steady or even decreasing surface temperature can occur in a warming world 56
(Easterling and Wehner, 2009; Palmer et al., 2011). GCM simulations indicate that these periods are 57
associated with a transfer of heat from the upper to the deeper ocean, of order 0.1 W m
–2
(Katsman and van 58

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Oldenborgh, 2011; Meehl et al., 2011), with a near steady (Meehl et al., 2011) or an increased radiation to 1
space (Katsman and van Oldenborgh, 2011), again of order 0.1 W m
–2
. While these natural fluctuations 2
represent a large amount of heat, they are significantly smaller than the anthropogenic forcing of the Earth’s 3
energy budget (Huber and Knutti, 2012), particularly when looking at time scales of several decades or more 4
(Santer et al., 2011). 5
6
These independent estimates of ERF, observed heat storage and surface warming combine to give an energy 7
budget for the Earth that is consistent with the assessed range of climate sensitivity (2°C to 4.5°C; Box 12.2) 8
to within estimated uncertainties. Quantification of the terms in the Earth’s energy budget and verification 9
that these terms balance over recent decades provides strong evidence for our understanding of 10
anthropogenic climate change. Changes in the Earth’s energy storage are a powerful observation for the 11
detection and attribution of climate change (Section 10.3) (Gleckler et al., 2012; Huber and Knutti, 2012). 12
13
[END BOX 13.1 HERE] 14
15
13.4 Projected Contributions to Global Mean Sea Level 16
17
13.4.1 Ocean Heat Uptake and Thermal Expansion 18
19
Over 90% of the net energy increase of the climate system on multiannual timescales is stored in the ocean 20
(Box 3.1). GMSL rise due to thermal expansion is approximately proportional to the increase in ocean heat 21
content. The constant of proportionality is 0.11 ± 0.01 m per 10
24
J in CMIP5 models (Kuhlbrodt and 22
Gregory, 2012); it depends on the vertical and latitudinal distribution of warming in the ocean, because the 23
expansion of sea water per degree of warming is greater at higher temperature and higher pressure (Russell 24
et al., 2000; Hallberg et al., 2012; Körper et al., 2013; Perrette et al., 2013). 25
26
For the early decades of the 21st century, the upper ocean dominates the ocean heat uptake, and ocean heat 27
content rises roughly linearly with global mean surface air temperature (SAT) change (Pardaens et al., 28
2011b; Körper et al., 2013). On multidecadal timescales under scenarios of steadily increasing radiative 29
forcing, the rate of increase of ocean heat content is approximately proportional to the global mean SAT 30
change from equilibrium (Gregory, 2000; Meehl et al., 2007; Rahmstorf, 2007a; Gregory and Forster, 2008; 31
Katsman et al., 2008; Schwartz, 2012), with the constant of proportionality (in W m
–2
K
–1
) being the ocean 32
heat uptake efficiency . 33
34
The ocean heat uptake efficiency quantifies the effect of ocean heat uptake on moderating time-dependent 35
climate change; neglecting the small fraction of heat stored elsewhere in the climate system, the surface 36
warming can be approximated as F/(+), where F is the radiative forcing and  is the climate feedback 37
parameter (Raper et al., 2002), and hence the rate of ocean heat uptake is approximately F/(+). In 38
CMIP3 and CMIP5, the model spread in projections of surface warming is dominated by the spread in F and 39
, but the spread in  accounts for a substantial part of the spread in projections of ocean heat uptake 40
(Dufresne and Bony, 2008; Gregory and Forster, 2008; Knutti and Tomassini, 2008; Geoffroy et al., 2012; 41
Sriver et al., 2012; Forster et al., 2013). 42
43
The spread in  relates to differences among models in heat-transport processes within the ocean. The 44
warming spreads downwards from the surface over time, and the greatest increases in projected ocean heat 45
content occur where the warming penetrates most deeply, in the Southern Ocean and the North Atlantic 46
(Figure 12.12; Section 12.4.7.1) (Kuhlbrodt and Gregory, 2012). Changes in convection and the large-scale 47
vertical circulation are particularly important to heat uptake in the North Atlantic (Banks and Gregory, 2006; 48
Rugenstein et al., 2013). Heat is also transported vertically by eddies, especially in the Southern Ocean, and 49
by turbulent mixing. These processes are parameterised in models when they occur at unresolved scales. 50
Observed ocean heat uptake has been used in conjunction with observed global SAT change to constrain the 51
ocean effective thermal diffusivity representing all unresolved vertical transports in simple climate models 52
and EMICs (Forest et al., 2008; Knutti and Tomassini, 2008; Marčelja, 2010; Sokolov et al., 2010). The 53
simulated ocean vertical temperature profile and the depth of penetration of the warming in AOGCMs have 54
also been evaluated by comparison with observations, and both bear a relationship to  (Hallberg et al., 55
2012; Kuhlbrodt and Gregory, 2012). Such comparisons suggest that model projections might be biased 56
towards overestimating ocean heat uptake and thermal expansion for a given surface warming (Sections 57

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9.4.2.2, 10.8.3, and 13.3.1.2). The physical causes of this tendency are unclear. Although the simulated 1
vertical temperature profile is affected by the model representation of vertical heat transport processes, 2
Brierley et al. (2010) found only a small effect on  from variation of model parameters that influence 3
interior heat transport. 4
5
Because the ocean integrates the surface heat flux, thermal expansion projections following different 6
scenarios do not significantly diverge for several decades. Scenarios assuming strong mitigation of 7
greenhouse gas emissions begin to show a reduced rate of thermal expansion beyond about 2040; the amount 8
by 2100 is about one-third less than in a non-mitigation scenario (Washington et al., 2009; Pardaens et al., 9
2011b; Körper et al., 2013), and half as much in RCP2.6 as in RCP8.5 (Yin, 2012a); Section 13.5.1). The 10
integrating effect means that thermal expansion depends on the pathway of CO
2
emissions, not only on the 11
cumulative total; reducing emissions earlier rather than later, for the same cumulative total, leads to a larger 12
mitigation of sea level rise due to thermal expansion (Zickfeld et al., 2012; Bouttes et al., 2013). The 13
integrating effect also means that annual time series of global ocean thermal expansion show less interannual 14
variability than time series of global SAT. For the present assessment of GMSL rise, projections of ocean 15
heat uptake and thermal expansion up to 2100 have been derived from the CMIP5 AOGCMs (Yin, 2012a). 16
Methods are described in Section 13.5.1 and the Supplementary Material and the results for ocean heat 17
uptake are shown in Figure 13.8, and for thermal expansion in Table 13.5 and Figures 13.10 and 13.11. 18
19
[INSERT FIGURE 13.8 HERE] 20
Figure 13.8: Heat uptake by the climate system during the 21st century relative to 1986–2005 projected by CMIP5 21
AOGCMs under RCP scenarios (1 YJ = 10
24
J). The heat uptake is diagnosed by two different methods. The thick solid 22
lines, and the coloured ranges for RCP2.6 and RCP8.5, are the time- and global integral of the net downward radiative 23
flux perturbation at the top of the atmosphere, from the 21 AOGCMs used to make the global mean sea level 24
projections (in some cases estimated from other scenarios, as described in the Supplementary Material). The broken 25
solid lines, and the thin solid lines delimiting ranges for RCP2.6 and RCP8.5, are the global volume integral of ocean 26
temperature change, in a smaller and different set of AOGCMs for each scenario. The difference between the two 27
diagnoses is due partly to the different sets of models (which is a consequence of diagnostics available in the CMIP5 28
dataset), and partly to heat uptake in other parts of the simulated climate system than the ocean water. In both methods, 29
climate drift in the pre-industrial control run has been subtracted. 30
31
Ocean heat uptake efficiency is not constant on timescales of many decades or in scenarios of stable or 32
decreasing radiative forcing (Rahmstorf, 2007a; Schewe et al., 2011; Bouttes et al., 2013). A good 33
representation of AOGCM behaviour is obtained by distinguishing a shallow layer, which is associated with 34
surface temperature variations on decadal timescales, from a deep layer, which has the majority of the heat 35
capacity (Hansen et al., 1985; Knutti et al., 2008; Held et al., 2010; Olivié et al., 2012; Schwartz, 2012; 36
Bouttes et al., 2013; Geoffroy et al., 2013). Ocean heat uptake and thermal expansion take place not only 37
while atmospheric GHG concentrations are rising, but continue for many centuries to millennia after 38
stabilization of radiative forcing, at a rate which declines on a centennial time scale (Stouffer, 2004; Meehl et 39
al., 2005; 2007; Solomon et al., 2009; Hansen et al., 2011; Meehl et al., 2012; Schwartz, 2012; Bouttes et al., 40
2013; Li et al., 2013; Zickfeld et al., 2013). This is because the timescale for warming the deep ocean is 41
much longer than for the shallow ocean (Gregory, 2000; Held et al., 2010). 42
43
The rate and the stabilization timescale for thermal expansion depend on the GHG stabilization level. For the 44
highest scenario (RCP8.5), GMSL rise due to thermal expansion can reach up to 2 m above the pre-industrial 45
level by the year 2500 (Section 12.5.2, Figure 12.44, Figure 13.14a), and is still rising at that time. Changes 46
in ocean circulation, particularly due to a reduction in deep water formation, can also have a large effect on 47
global ocean heat uptake, and may relate non-linearly to global surface warming (Levermann et al., 2005; 48
Fluckiger et al., 2006; Vellinga and Wood, 2008). As the rate of ocean heat uptake decreases, the surface 49
warming approaches the level determined by the equilibrium climate sensitivity. 50
51
On a multi-millennial time scale, the range from Earth System Models of Intermediate complexity suggests 52
that thermal expansion contributes between 0.20–0.63 m per °C of global mean temperature increase (Meehl 53
et al., 2007; Zickfeld et al., 2013) (Section 12.5.2, Figure 13.14a). The median of the six models of 0.38 m 54
°C
–1
is consistent with a uniform increase in ocean temperature from the presently observed temperature and 55
salinity distribution (Levitus et al., 2009). Uncertainty arises due to the different spatial distribution of the 56
warming in models and the dependence of the expansion on local temperature and salinity. 57
58

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13.4.2 Glaciers 1
2
The 21st century sea level contribution from glaciers presented in the AR4 assessment ranged from 0.06 to 3
0.15 m SLE by 2100 across a range of scenarios (Meehl et al., 2007). The Randolph Glacier Inventory (RGI) 4
(Arendt et al., 2012) has improved projections of glacier contribution to sea level rise by providing the first 5
globally complete accounting of glacier location, area, and area-elevation distribution (hypsometry). Several 6
analyses of scenario-dependent SMB glacier projections (referred to here as “process-based” models) have 7
been produced using the RGI, including Marzeion et al. (2012a), Giesen and Oerlemans (2013) and Radić et 8
al. (2013). The Marzieon, and Radić approaches each used different suites of CMIP5 AOGCM models to 9
calculate SMB terms from RCP forcings, and the model by Slangen and van de Wal (2011) was used to 10
calculate SMB terms from RCP forcings (Supplementary Material 13.SM.1). Giesen and Oerlemans (2013) 11
used CRU forcing but calculated SMB from three different combinations of variations in modelled 12
temperature, precipitation, and atmospheric transmissivity. Only their results for varying temperature are 13
shown here. Machguth et al. (2013) is also included in Table 13.3, but this projection represents changes in 14
Greenland peripheral glaciers only, and is not included in the global glacier summaries. While these details 15
differ among the models, all share a generally common time-evolving structure, with SMB initially 16
determined by model-generated climate forcing applied to a subset of global glaciers, the ensuing volume 17
change converted to area change via volume-area scaling, and this result upscaled to a new global 18
distribution and hypsometry to create initial conditions for the subsequent time step. These methods are 19
described further in Section13.5.1 and in the Supplementary Material. Related results are shown in Table 20
13.5 and Figures 13.10 and 13.11. 21
22
While the peripheral glaciers surrounding the ice sheets are included with the ice sheets in assessment of 23
present-day losses, future projections should ideally assess the peripheral glaciers separately, since these are 24
too small and dynamically responsive to be modeled adequately with coarse-grid, non-dynamic ice-sheet 25
SMB models. The peripheral glaciers surrounding both Greenland and Antarctica are thus included in the 26
process-based models described above, but for summation with the ice sheet projections, the Antarctic 27
peripheral glaciers are excluded, and included instead with the Antarctic ice sheet projection models. 28
Projected losses from both ice-sheet peripheral glaciers are listed separately in Table 13.3 29
30
Several glacier loss projections derived from model types other than process-based models have been 31
published since 2007; their projections range from 0.08 to 0.39 m SLE by 2100 (Table 13.3). These used 32
methods of projecting future losses from glaciers developed in response to the absence of a global 33
compilation of glacier observations after 2005 and the absence of a globally complete glacier inventory to 34
provide geographic boundary conditions for conventional modeling. These methods include extrapolation 35
from observed rates (Meier et al., 2007), semi-empirical methods applied to sea level change components 36
(Jevrejeva et al., 2012b), kinematic (or “limit seeking”) projections (Pfeffer et al., 2008), and power-law 37
scaling estimates based on re-establishing equilibrium accumulation-area ratios (AARs) from initial non-38
equilibrium AARs (Bahr et al., 2009). Strengths of these approaches include the fact that observations used 39
to calibrate extrapolation and semi-empirical projection partially account for future dynamically forced 40
losses, that semi-empirical methods use modelled future forcings as guidance for projections, and that AAR 41
equilibration has strong physical and theoretical underpinnings and gives generalized but robust projections. 42
These strengths partially offset the weaknesses of these models, which include, in the case of extrapolation 43
and semi-empirical projection, an assumption of statistical stationarity that may not be valid, while the AAR 44
equilibration approach gives only a final steady state value, so that rates or cumulative losses at any 45
intermediate time must be estimated by area-response time scaling. However, these alternate methods are 46
valuable because of their construction on fundamental and robust principles together with their use of the 47
limited available information to produce projections that, while imprecise, are transparent, and require less 48
detailed input information or knowledge of details of complex processes in comparison to conventional 49
“process-based” models. 50
51
Published results from process-based models are shown in Table 13.3. Glacier contributions at 2100, 52
expressed as SLE, range between 0.04 and 0.11 m for SRES A1B, 0.07 and 0.17 m for RCP2.6, between 53
0.07 and 0.20 m for RCP4.5, between 0.07 and 0.20 m for RCP6.0, and between 0.12 and 0.26 m for 54
RCP8.5. 55
56

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The projections derived from alternative models are also shown in Table 13.3; the mean and range of these 1
models listed here is 0.24 [0.08–0.39] m SLE, consistent with the process-based models. Results from the 2
process-based models, plotted as time series and grouped by forcing scenario, are shown in Figure 13.9. See 3
Table 13.3 for specific start/end dates for each projection. 4
5
6
Table 13.3: 21st century sea level rise projections for global glaciers, from process-based surface mass balance models, 7
and from alternate model strategies. Dates for beginning and end of model period are as shown; mean and 5%/95% 8
confidence sea level equivalents are shown in meters. Process-based models all use variations on AOGCM mass 9
balance forcing applied to inventoried glacier hypsometries on a subset of global glaciers and upscaling by power-law 10
techniques to the global total. Calving and rapid dynamic response is not included in any models except for Jevrejeva et 11
al. (2012b), where calving losses are present to a limited degree in input data, and NRC (2012), where calving is 12
explicitly included in future losses. Other model details are discussed in the text. 13
Contribution to Global
Mean Sea Level Rise
Peripheral Glacier
Contribution
Reference Model Starting Date End Date Projected SLR (m) from
Glaciers except Ant PGs
Gr IS PG (m) Ant IS (m)
Processed-based SMB Models Mean [5–95%] 5–95% 5–95%
Scenario RCP2.6
confidence confidence confidence
Marzeion et al. (2012a) 1986–2005
mean
2099 0.12 [0.07–0.17] 0.007–0.02 0.02–0.04
Slangen and van de Wal
(2011)
2000 2099 0.10 [0.07–0.13] 0.004–0.007 0.02–0.03
Scenario RCP4.5
Marzeion et al. (2012a) 1986–2005
mean
2099 0.14 [0.08–0.20] 0.009–0.022 0.02–0.04
Radic et al. (2013) 2006 2099 0.13 [0.07–0.20] 0.0–0.024 0.02
Slangen and van de Wal
(2011)
2000 2099 0.12 [0.07–0.17] 0.005–0.01 0.03–0.04
Scenario RCP6.0
Marzeion et al. (2012a) 1986–2005
mean
2099 0.15 [0.09–0.20] 0.01–0.022 0.02–0.04
Slangen and van de Wal
(2011)
2000 2099 0.14 [0.07–0.20] 0.006–0.01 0.04
Scenario RCP8.5
Marzeion et al. (Marzeion et
al., 2012a)
1986–2005
mean
2099 0.18 [0.12–0.25] 0.015–0.025 0.02–0.05
Radic et al. (2013) 2006 2099 0.19 [0.12–0.26] 0.009–0.031 0.02–0.03
Slangen and van de Wal
(2011)
2000 2099 0.18 [0.12–0.24] 0.008–0.015 0.04–0.06
Scenario A1B
Giesen and Oerlemans (2013) 2012 2099 0.08 [0.04–0.11] 0.04–0.21 0.01–0.04
Scenario A1B and RCP4.5
Machguth et al. (2013) 2000 2098 0.06–0.11
Alternate Models
Meier et al. (2007) Extrapolation with fixed rate 2006 2100 0.3 [0.08–0.13]
Extrapolation with fixed
acceleration
2006 2100 0.24 [0.11–0.37]
Pfeffer et al. (2008) Low range projection 2007 2100 0.17
High range projection 2007 2100 0.24
Bahr et al. (2009) AAR fixed at present values Find equilibrium value 0.18 [0.15–0.27]
AAR declines at current rate Find equilibrium value 0.38 [0.35–0.39]
National Research
Council (2012)
Generalized linear model
extrapolation, variable rate
2010 2100 0.14 [0.13–0.16]
Jeverjeva et al.
(2012b)
Semi-empirical projection of
components of sea level rise,
forced by radiation
2009 2100 0.26

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1
2
[INSERT FIGURE 13.9 HERE] 3
Figure 13.9: Time series plots for process-based model projections of sea level contributions from global glaciers (in 4
mm), including peripheral glaciers surrounding the Greenland Ice Sheet but excluding the glaciers surrounding the 5
Antarctic Ice Sheet. Projections are grouped by forcing scenario as indicated on the plots. Results are plotted for a 6
common time interval of 2011 to 2099. Colors correspond to particular model analyses: red = Marzeion et al. (2012a); 7
blue = Slangen and van de Wal (2011); green = Radić et al. (2013); black = Giesen and Oerlemans (2013). Individual 8
AOGCM model projections are plotted for each analysis, so the ranges of the curves at 2099 are different than those 9
listed in Table 13.3, where 5–95% confidence limits are shown. In the panel showing results for RCP6.0 and A1B 10
forcings, only Geisen and Oerlemans (black lines) use the A1B forcing. 11
12
Unresolved uncertainties in the projection of glacier contributions to sea level rise include the potential for 13
near-term dynamic response from marine-terminating glaciers and interception of terrestrial runoff. Of the 14
734,400 km
2
of global glacier area exclusive of the Greenland and Antarctic ice sheets, 287,300 km
2
(38%) 15
is now known to drain through marine-terminating outlets(Gardner et al., 2013). While the long-term 16
potential for dynamic discharge from glaciers (as opposed to ice sheets) is limited by their small total 17
volume, dynamic losses may be an important component of total sea level rise on the decade-to-century 18
scale. In Alaska, Columbia Glacier lost 7.65 Gt yr
–1
between 1996 and 2007, with 94% of that loss coming 19
from rapid tidewater retreat (Rasmussen et al., 2011); the loss from this single 1000 km
2
glacier is 1.3% of 20
the global cryospheric component of sea level rise during 1993-2010 (Table 13.1) and 0.7% of total sea level 21
rise. The observations required to estimate the potential for similar dynamic response from other glacier 22
regions are unknown at this time, but the dynamic contribution could be large on the century time scale. If 23
the basin-wide thinning rate observed at Columbia Glacier over the past 25 years (ca. 5 m yr
–1
) were to occur 24
over the area of global glaciers draining through marine outlets (280,500 km
2
) during the next 89 years 25
(2011–2100), the sea level contribution would be approximately 30 cm SLE, compatible with Jeverajeva et 26
al’s (2012b) projected loss of 26 cm SLE from glaciers. While this is a rough calculation and an upper 27
bound, since drainage through marine outlets does not guarantee tidewater instability, it indicates that the 28
potential for a significant sea level response to dynamic retreat of glaciers cannot be rejected a priori. 29
30
Completion of the global inventory has allowed large improvements in assessment and modelling, but 31
further uncertainties related to the inventory remain to be resolved, including those arising from the size 32
cutoff decided for the inventory (Bahr and Radić, 2012). Another source of uncertainty is interception of 33
glacier runoff by land hydrology. Despite rapidly growing knowledge of changes in terrestrial water storage, 34
especially through increased reliability of GRACE observations, glacier mass loss rates are still generally 35
assumed to flow directly to the ocean, with no delay or interception by surface or aquifer storage. While this 36
is very unlikely to apply to discharge from glaciers located near coasts (e.g., Canadian Arctic, Patagonia, 37
Alaska, ice-sheet peripheries), runoff from regions within interiors (e.g., Alps, High Mountain Asia) may be 38
significantly depleted before reaching the ocean. Whether terrestrial interception has any significant effect 39
on the net glacier contribution to sea level rise is undetermined at this time. 40
41
13.4.3 Greenland Ice Sheet 42
43
13.4.3.1 Surface Mass Balance Change 44
45
Greenland SMB is positive in the present climate but shows a decreasing trend (Section 13.3.3.2), which 46
implies an increasing contribution to GMSL rise. Like the AR4, all recent studies have indicated that the 47
future sea level contribution from Greenland SMB change will be increasingly positive because the increase 48
in ablation (mostly runoff) outweighs that in accumulation (mostly snowfall), and that scenarios of greater 49
radiative forcing lead to a larger sea level contribution. Precipitation is projected to increase at about 5% per 50
°C of annual-mean warming over Greenland, but the increase in snowfall is smaller because the fraction of 51
rain increases as temperature rises (Gregory and Huybrechts, 2006; Fettweis et al., 2013). 52
53
We compare post-AR4 studies of Greenland SMB change using time-dependent simulations of the 21st 54
century by CMIP3 AOGCMs for scenario SRES A1B and CMIP5 AOGCMs for scenario RCP4.5 (Table 55
13.4). The time-integral of the Greenland SMB anomaly with respect to a reference period is interpreted as a 56
contribution to GMSL rise, on the assumption that the ice sheet was in approximate mass balance during the 57
reference period (see discussion in Section 13.1.5.1 and 13.3.3.2); this assumption can be avoided only if 58

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ice-sheet outflow is also modelled. Making this assumption, the Greenland SMB contribution lies in the 1
range 0.00–0.13 m for these two scenarios. 2
3
The spread in the magnitude and patterns of Greenland climate change projected by the AOGCMs causes a 4
large part of the spread in the projected contribution to GMSL rise (Table 13.4). Yoshimori and Abe-Ouchi 5
(2012) found that the inter-model spread in global mean SAT change accounts for about 60% of the spread 6
in the change of projected Greenland ablation. Two important contributions to the remaining spread are the 7
weakening of the AMOC, which affects the magnitude of warming over Greenland, and the SAT of 8
Greenland in the model control climate, which affects the sensitivity of melting to warming (Yoshimori and 9
Abe-Ouchi, 2012; Fettweis et al., 2013). 10
11
Ablation is computed using either a surface energy balance model (EBM), which may be stand-alone or part 12
of a regional climate model, or from surface air temperature using an empirical temperature-index method, 13
mostly commonly the positive-degree-day (PDD) method, in which melting is proportional to the time-14
integral of temperature above freezing point. Meltwater production increases faster than linearly with 15
temperature increase because of reduced albedo due to refreezing of meltwater in the snowpack and 16
expansion of the area of bare ice (van Angelen et al., 2012; Fettweis et al., 2013; Franco et al., 2013). The 17
simulation of this positive albedo feedback on mass loss depends sensitively on the model snow-albedo 18
parameterisation (Rae et al., 2012). Goelzer et al. (2013) projected 14–31% more runoff during the 21st 19
century when using an EBM than when using a PDD method, mainly because of the omission of the albedo 20
feedback in the latter. However, other studies using temperature-index methods (Graversen et al., 2011; 21
Yoshimori and Abe-Ouchi, 2012) have ranges extending to higher values than those from EBMs, indicating 22
that this is not the only difference between these classes of methods (Table 13.4). 23
24
SMB simulations are also particularly sensitive to the treatment of meltwater refreezing (Bougamont et al., 25
2007; Rae et al., 2012). The pore space in the present-day percolation zone could accommodate 1–5 mm 26
SLE of refrozen meltwater over the next several decades (Harper et al., 2012), and the importance of 27
meltwater refreezing will become greater as melting becomes prevalent in areas where it has previously been 28
rare. On the other hand, refreezing will be restricted, and runoff consequently increased, by the expansion of 29
the area of bare ice (Fettweis et al., 2013). 30
31
Another source of model spread is the representation of topography, which is lower when represented at 32
coarser resolution. This allows precipitation to spread further inland because of reduced topographic barriers 33
(Bengtsson et al., 2011), and enhances ablation because there is more area at lower, warmer altitudes 34
(Bengtsson et al., 2011; Seddik et al., 2012). Most of the models in Table 13.4 use a fixed Greenland 35
topography, and thus cannot simulate the positive feedback on ablation that can be expected as the ice-sheet 36
surface becomes lower. Dynamical models are required to simulate this effect (Section 13.4.3.2). 37
38
For the present assessment of GMSL rise, changes in Greenland ice sheet SMB up to 2100 have been 39
computed from global mean SAT change projections derived from the CMIP5 AOGCMs, following methods 40
described in Section 13.5.1 and the Supplementary Material. The distribution of results, shown in Table 13.5 41
and Figures 13.10 and 13.11, covers the ranges obtained using the methods of Fettweis et al. (2013), Gregory 42
and Huybrechts (2006) and Yoshimori and Abe-Ouchi (2012). 43
44
45
Table 13.4: Contribution to sea level rise from change in the surface mass balance of the Greenland ice sheet during the 46
21st century. Where given, ranges are 5–95% estimated from the published results and indicate the uncertainty due to 47
the climate change modelling by AOGCMs, except where noted otherwise. 48
Reference Model
a
Contribution to Global Mean Sea Level Rise
starting from up to amount (m)
b
rate (mm yr
–1
)
b
Scenario SRES A1B, CMIP3 AOGCMs
AR4 (Meehl et al., 2007)
c
20 km PDD 1990 2090–2099 0.01–0.08
d
0.3–1.9
d
Bengtsson et al. (2011)
e
60 km (T213)
EBM
1959–1989 2069–2099 — 1.4
Fettweis et al. (2008)
f
TI from 25
km EBM
1970–1999 2090–2099 0.03–0.05 0.3–1.0

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Graversen et al. (2011) 10 km PDD 2000 2100 0.02–0.08
0.00–0.17
g
0.0–2.1
g
Mernild et al. (2010) 25 km EBM 1980–1999 2070–2079 0.02 0.5
Rae et al. (2012)
h
25 km EBM 1980–1999 2090–2099 0.01, 0.04, 0.06 0.3,1.2,1.5
Seddik et al., (2012)
i
10 km
e
PDD 2004 2104 0.02, 0.04 —
Yoshimori and Abe-Ouchi (2012)
12 km TI
1980–1999 2090–2099 0.02–0.13 0.2–2.0
Scenario RCP4.5, CMIP5 AOGCMs
Fettweis et al.(2013)
c
25 km RCM 1980–1999 2100 0.02–0.11 0.1–1.2 in 2080–2099
Gregory and Huybrechts (2006)
c,j
20 km PDD 1980–1999 2100 0.00–0.06 0.0–0.8 in 2080–2099
Van Angelen et al. (2012)
k
11 km RCM 1960–1990 2100 0.11
l
1.7
l
in 2079–2098
Yoshimori and Abe-Ouchi (2012)
j
12 km TI
1980–1999 2090–2099 0.00–0.11 0.0–1.8
Notes: 1
(a) The spatial resolution is stated and the SMB method denoted by TI = temperature index, PDD = positive degree day, 2
EBM = energy balance model. 3
(b) The amount of sea level rise is the time-integral of the SMB anomaly from the period or date labelled ‘starting 4
from’ to the one labelled ‘up to’. Unless otherwise indicated, the SMB anomaly is calculated relative to the mean SMB 5
for the ‘starting from’ period, and the rate of sea level rise is the SMB anomaly in the ‘up to’ period. 6
(c) These results are estimated from global mean SAT change, using formulae fitted to results from a Greenland SMB 7
model. 8
(d) The SMB anomaly is relative to the late 19th century. 9
(e) This experiment used time-slices, with boundary conditions from the ECHAM5 GCM, rather than a simulation of 10
the complete century; thus, results are not available for the amount. 11
(f) Fettweis et al. (2008) and Franco et al. (2011) use a hybrid approach: they derive a regression relationship from 12
simulations of the recent past using an RCM, incorporating an EBM, between annual anomalies in Greenland climate 13
and in Greenland SMB, then apply this relationship to project future SMB from projected future climate anomalies. The 14
method assumes that a relationship derived from past variability will also hold for future forced climate change. 15
(g) Range including uncertainty in choice of emission scenario (B1, A1B or A2), SMB modelling and ice sheet 16
dynamical modelling, as well as uncertainty in climate modelling. 17
(h) Results are given for the HadRM3P, HIRHAM5 and MAR RCMs driven with the same boundary conditions from 18
the ECHAM5/MPI-OM AOGCM. 19
(i) Results are given for two ice sheet models (Elmer/Ice, SICOPOLIS) using the same AOGCM climate boundary 20
conditions. The resolution given is for SICPOLIS; Elmer/Ice has variable resolution. 21
(j) Results calculated from CMIP5 AOGCMs by the same method as used in the paper. 22
(k) These results were obtained from the model of Van Angelen et al. (2012) using boundary conditions from the 23
HadGEM2-ES AOGCM and are shown by Fettweis et al. (2013). 24
(l) With respect to 1992–2011 as a reference period, during which there is a significant simulated trend in SMB 25
(Section 13.3.3.2), the amount is 0.07 m and the rate 1.4 mm yr
–1
. 26
27
28
On multi-centennial to millennial time scales, feedbacks between regional climate and the ice sheet become 29
increasingly relevant, especially under strong climate change scenarios, thus requiring coupled climate ice-30
sheet models to capture potential feedbacks beyond the year 2100. These models apply a reduced spatial 31
resolution in order to be computationally efficient enough to evaluate longer time scales and to combine the 32
different climatic components. Consistent with regional climate models for the 21st century, they project an 33
increasingly negative mass balance for the Greenland Ice Sheet for all warming scenarios which is mainly 34
due to a decreasing SMB (Ridley et al., 2005; Winguth et al., 2005; Driesschaert et al., 2007; Mikolajewicz 35
et al., 2007a; Swingedouw et al., 2008; Vizcaíno et al., 2008; Vizcaíno et al., 2010; Huybrechts et al., 2011; 36
Goelzer et al., 2013). The main feedbacks between climate and the ice sheet arise from changes in ice 37
elevation, atmospheric and ocean circulation, and sea-ice distribution. 38
39
Comparing the different feedbacks, high confidence can be assigned to the models’ ability to capture the 40
feedback between SMB and surface elevation. As a consequence, a nonlinear increase in ice loss from 41
Greenland with increasing regional radiative forcing is found across different scenarios (Driesschaert et al., 42
2007). This nonlinearity arises from the increase in both the length of the ablation season and the daily 43
amount of melting. This feedback is also the main reason for the threshold behaviour of the Greenland Ice 44
Sheet on multi-millennial time scales (Section 13.4.3.3). 45
46

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Medium-to-low confidence is assigned to the models’ representation of the atmospheric and ocean circulation 1
and sea-ice changes. On multi-centennial time scales, Swingedouw et al. (2008) find enhanced ice loss from 2
Greenland in a coupled simulation (compared to the uncoupled version) in which ice topography and melt-3
water flux influence the ocean and atmospheric circulation as well as sea-ice distribution. Vizcaíno et al. 4
(2010) found reduced ice loss due to the coupling, mainly caused by the effect of topographic changes on the 5
surface temperature, but less pronounced in amplitude compared with Swingedouw et al. (2008). Both the 6
atmospheric circulation and the ocean currents, especially in coastal areas, are poorly resolved by these 7
models. It is therefore likely that the time scales associated with ocean transport processes are distorted and 8
there is low confidence that these feedbacks, while existent, can be quantified accurately by the applied 9
models. 10
11
The AMOC exerts a strong influence on regional climate around the Greenland Ice Sheet and consequently 12
its SMB. Most CMIP5 models show a reduction of the AMOC under future warming during the 21st century 13
and beyond (Section 12.4.7.2). While coupled climate-ice sheet models show some influence of melt water 14
from Greenland on the AMOC, the uncertainty between models with respect to the AMOC response to 15
warming is significantly larger than the difference between simulations with or without this feedback within 16
one model. 17
18
In the coupled climate-ice sheet model applied by Mikolajewicz et al. (2007a) and Vizcaíno et al. (2008), the 19
AMOC shows a strongly non-linear response to global warming. A weak AMOC reduction is found for 1%-20
per-year-CO
2
-increase scenarios up to 560 and 840 ppm, and a near-complete cessation of the AMOC for 21
1120 ppm. As a consequence, after 600 years of integration, the sea level contribution for the 1120 ppm 22
scenario is similar to that of the 560 ppm scenario, but doubles for the medium scenario which stabilizes at 23
840 ppm. In the most recent model version (Vizcaíno et al., 2010), the AMOC shows a strong weakening of 24
the AMOC in all scenarios (~60% reduction in 560 ppm scenario; ~80% for 1120 ppm). The total sea level 25
contribution from Greenland, including the effect of the AMOC weakening, is ~1 m (corresponding to an 26
average rate of 1.7 mm yr
–1
) for 560 ppm CO
2
and ~3 m (5 mm yr
–1
) for 1120 ppm CO
2
. 27
28
Even though the AMOC weakening in the model by (Huybrechts et al., 2011) is less pronounced (10–25%), 29
the ice loss through melting is significantly weaker in this model. During the first 1000 years of integration, 30
the Greenland Ice Sheet contributes 0.36 m (corresponding to an average rate of 0.36 mm yr
–1
) for 560 ppm 31
CO
2
and 2.59 m (2.59 mm yr
–1
) for 1120 ppm CO
2
. In (Huybrechts et al., 2011), the respective increases in 32
global mean SAT are 2.4°C (2  CO
2
) and 6.3°C (4  CO
2
) after 1000 years with respect to pre-industrial. 33
This relatively weak warming response to GHG forcing compared to CMIP5 models and the climate model 34
used in Vizcaíno et al. (2010) explains the relatively low sea level response. 35
36
Using the same model as Huybrechts et al. (2011), albeit with a slightly higher polar warming, Goelzer et al. 37
(2012) computed temperatures and sea level under the SRES scenarios B1, A1B and A2, with subsequent 38
GHG stabilization after the year 2100. As in Huybrechts et al. (2011), the ice-sheet evolution is dominated 39
by the SMB. They find sea level contributions of 1.4, 2.6 and 4.2 m in the year 3000 for the scenarios B1, 40
A1B and B2, which correspond to mean rates of sea level rise of 1.4 mm yr
–1
, 2.6 mm yr
–1
, and 4.2 mm yr
–1
, 41
respectively. 42
43
In summary, coupled climate-ice sheet models consistently show an increasingly negative mass balance of 44
the Greenland Ice Sheet mainly due to a decreasing SMB under warming scenarios on centennial time scales 45
beyond 2100. On multi-millennial time scales, these models show a threshold temperature beyond which the 46
melting of the Greenland Ice Sheet self-amplifies and the ice volume is reduced to less than 30% of its 47
present volume (Section 13.4.3.3). 48
49
13.4.3.2 Dynamical Change 50
51
Observations suggest three main mechanisms by which climate change can affect the dynamics of ice flow 52
in Greenland (Sections 4.4.3 and 4.4.4): by directly affecting ice loss (outflow) through the calving of ice 53
bergs and marine melt from marine-terminating outlet glaciers; by altering basal sliding through the 54
interaction of surface melt water with the glacier bed; and indirectly through the interaction between SMB 55
and ice flow. We assess the consequences of each of the processes. 56
57

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Section 4.4.3.2 presents the observational basis on which concerns about increased ice loss by calving and 1
marine melt are based. In particular, recent increases in loss are thought to be linked to the migration of 2
subtropical water masses around the coast of Greenland (Holland et al., 2008) and its occupation of coastal 3
fjords (Straneo et al., 2010; Christoffersen et al., 2011). Output from 19 AOGCMs under scenario A1B 4
showed warming of 1.7°C to 2.0°C around Greenland over the course of the 21st century (Yin et al., 2011), 5
suggesting that the trend towards increased outflow triggered by warming coastal waters will continue. 6
7
Although projections of outflow are at a fairly early stage, literature now exists to make an assessment. 8
Flow-line modelling has successfully simulated the observed retreat and associated acceleration of the main 9
outlet glaciers of the Greenland ice sheet (Helheim and Petermann Glaciers (Nick et al., 2009; Nick et al., 10
2012); Jakobshavn Isbræ (Vieli and Nick, 2011)). The same model has been used to project mass loss from 11
these glaciers (Nick et al., 2013), as well as Kangerdlugssuaq Glacier, using ocean and atmosphere forcing 12
based on scenarios A1B and RCP8.5. At 2100, total projected SLR spans 8–13 mm for A1B and 11–17 mm 13
for RCP8.5. These figures generalise to 40–63 mm and 57–85 mm, respectively, for the whole ice sheet 14
based on a simple scaling between modelled and total ice-sheet area (a factor ~5). Price et al. (2011) 15
modelled the century-scale response of the ice sheet to the observed recent retreat of three outlet glaciers 16
(Jakobshavn Isbræ, and Helheim and Kangerdlugssuaq Glaciers). At 2100, the projected SLR associated 17
with the three modelled outlet glaciers is 0.6–1.4 mm, which equates to SLR of 4–8 mm after scaling (by a 18
factor of ~6) to all outlet glaciers based on observed mass loss (van den Broeke et al., 2009). Total projected 19
SLR then varies between 10 and 45 mm at 2100 if successive retreats are specified with a notional repeat 20
interval between 50 and 10 years. 21
22
Goelzer et al. (2013) implemented the Nick et al. (2013) retreat chronology within a 5-km resolution ice-23
sheet model along with their own generalization for including unsampled outlet glaciers. Associated SLR at 24
2100 is projected to vary between 8 and 18 mm. Graversen et al. (2011) attempted to capture the effect of 25
increased outflow by enhancing basal sliding and generated SLR of 9–24 mm at 2100. 26
27
Two estimates of the effect of dynamical change on Greenland’s contribution to SLR by 2100 have been 28
made on the basis of physical intuition. Pfeffer et al. (2008) developed a low scenario by assuming a first-29
decade doubling of outlet glacier velocity throughout the ice sheet that equates to 93 mm SLR, while a high 30
scenario that assumes an order of magnitude increase on the same time scale contributes 467 mm. Katsman 31
et al. (2011) used a similar methodology to obtain an estimate of 100 mm SLR. 32
33
Based primarily on Nick et al. (2013), we assess the upper limit of the likely 5–95% model range of this 34
dynamical effect to be 85 mm for RCP8.5 and 63 mm for all other RCP scenarios. We have medium 35
confidence in this as an upper limit because it is compatible with Katsman et al. (2011), the low scenario of 36
Pfeffer et al. (2008), and Price et al. (2011) in the probable event of a sub-decadal recurrence interval. 37
Although the likely upper limit is less than the high scenario of Pfeffer et al. (2008), process modelling gives 38
no support to the order of magnitude increase in flow on which this scenario is based. It is higher than the 39
contributions found by Goelzer et al. (2013) and Graversen et al. (2011) for which there are two potential 40
explanations. First, the generalization used to extrapolate from the modelled sample to all outlet glaciers 41
differs. Nick et al. (2013) used a scaling similar to the independently derived value of Price et al. (2011), 42
while the implied scaling used by Goelzer et al. (2013) is substantially lower. Second, Goelzer et al. (2013) 43
suggested that surface ice melt and calving compete to remove marginal ice (see below) implying that 44
overall mass loss may be over predicted by the flow-line model of Nick et al. (2013). At present, these 45
studies cannot be reconciled and we therefore use the more inclusive range. 46
47
The lower limit of the likely 5–95% range is assessed as 20 mm for RCP8.5 and 14 mm for all other RCP 48
scenarios. This reflects the individual outlet glacier projections of Nick et al. (2013) but uses a lower 49
generalization more similar to that found by Goelzer et al. (2013). This assessment of the lower limit is 50
compatible with Price et al. (2011) and Graversen et al. (2011). 51
52
Section 4.4.3.2 assesses understanding of the link between abundant summer meltwater, lubrication of the 53
ice-sheet base and enhanced ice flow. While this mechanism appears important in modulating present-day 54
ice flow, it is not supported as the cause of recent mass loss. Goelzer et al. (2013) incorporated a 55
parameterisation of this effect based on field observations, which results in less than a millimetre SLR by 56
2100 in their projections. Bindschadler et al. (2013) and Nowicki et al. (2013) reported a suite of 57

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experiments assessing this effect in an eight-model ensemble, but their parameterization appears overly 1
simplistic and may well exaggerate the importance of the effect. These projections do not incorporate the 2
effect on ice flow of the latent heat released by increased future quantities of melt water within the ice sheet 3
(Phillips et al., 2010; 2013), for which no projections are currently available. Basal lubrication is therefore 4
assessed as making an insignificant contribution to the likely range of SLR over the next century and is 5
omitted in the remainder of the assessment. We have medium confidence in projections of this effect 6
primarily because recent improvements in process-based understanding show that it has little contribution to 7
mass loss (Section 4.4.3.2); the potential of latent-heat effects in the future limits a higher level of 8
confidence. 9
10
Finally, we assess the level of interaction between SMB change and ice flow. In AR4, this effect is assessed 11
as 0 ± 10% (likely range) of SMB, based on Huybrechts and de Wolde (1999) and Gregory and Huybrechts 12
(2006). This assessment included both the positive feedback between SMB and the height of the ice sheet, 13
and a countering negative feedback involving ice flow and depletion effects. The latter effect is partly 14
included in our assessment of the direct impacts of climate change on ice flow, and we therefore limit our 15
assessment to SMB-height feedback. Few studies explicitly determine this effect, but Goelzer et al. (2013) 16
reported that it amounts to 5–15% of SMB over the course of the 21st century, which we extend slightly (0–17
15%) to reflect possible interaction with mass loss by calving (Goelzer et al., 2013). 18
19
Goelzer et al. (2013) and Gillet-Chaulet et al. (2012) suggested that SMB and ice dynamics cannot be 20
assessed separately because of the strong interaction between ice loss and climate due to, for instance, 21
calving and SMB. The current assessment has by necessity separated these effects because the type of 22
coupled ice sheet—climate models needed to make a full assessment do not yet exist. These interactions may 23
well combine to reduce SLR in comparison to the assessed range because of the mass-depletion effect of 24
retreating outlet glaciers. Another source of uncertainty is the bedrock topography of Greenland, although 25
recent improvements in data coverage (Bamber et al., 2013) suggest that the majority of the ice sheet rests on 26
bedrock above sea level and the number of deep bedrock troughs penetrating into the interior of Greenland is 27
limited, thus limiting the potential for marine ice-sheet instability (see Box 13.2). 28
29
Although not strictly comparable because they contain a different balance of ice-dynamical effects, the 30
assessment is consistent with Bindschadler et al. (2013) and Nowicki et al. (2013) who reported an extensive 31
model inter-comparison exercise in which standardised experiments are combined to represent the impact of 32
climate change under RCP8.5 on the Greenland Ice Sheet. The resultant projection included contributions 33
from lubrication, marine melt and SMB-coupling and generated a mean SLR at 2100 of 162 mm over five 34
models, or 53 mm if an outlier with anomalously high response is removed (including SMB results in SLR at 35
2100 of 223 and 114 mm for five- and four-model means, respectively). This comparison lends further 36
weight to our confidence. 37
38
In summary, dynamic change within the Greenland Ice Sheet is likely (medium confidence) to lead to SLR 39
during the next century with a range of 20–85 mm for RCP8.5, and 14–63 mm for all other scenarios. The 40
latter are assumed to have uniform SLR in the absence of literature allowing these scenarios to be assessed 41
individually, although dependency on scenario is expected to exist. In addition, mass loss associated with 42
SMB-height feedback is likely to contribute a further 0–15% of SMB (in itself scenario dependent). This 43
equates to, for example, 0 to 11 mm based on the central estimate of RCP8.5 in Table 13.5. The peripheral 44
glaciers of Greenland are not included here but are in the assessment of global glaciers’ contribution to SLR 45
(Section 13.4.2). All the available literature suggests that this dynamical contribution to sea level rise will 46
continue well beyond 2100. 47
48
13.4.3.3 Possible Irreversibility of Greenland Ice Loss and Associated Temperature Threshold 49
50
A number of model results agree in showing that the Greenland Ice Sheet, like other climatic subsystems 51
(Lenton et al., 2008; Levermann et al., 2012) (see Section 12.5.5), exhibits a strongly nonlinear and 52
potentially irreversible response to surface warming. The mechanism of this threshold behaviour is the SMB-53
height feedback (Section 13.4.3.2); that is, as the surface is lowered due to ice loss, the associated warming 54
of the near surface increases ablation, leading to further ice loss. This feedback is small but not negligible in 55
the 21st century (Section 13.4.3.2) and becomes important for projections for the 22nd century (Goelzer et 56

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al. 2013). This nonlinear behaviour may be accelerated by a reduced surface albedo caused by surface 1
melting which tends to further decrease the surface mass balance (Box et al., 2012) (Section 13.4.3.1). 2
3
While the ice-sheet mean SMB is positive, in a steady state it must be balanced by ice outflow, so the ice 4
sheet must extend to the coast. In a warmer climate, the mean SMB is reduced (Section 13.4.3.1) and the 5
steady-state ice sheet will have a lower surface and volume. Models show a threshold in surface warming 6
beyond which self-amplifying feedbacks result in a partial or near complete ice loss on Greenland (Greve, 7
2000; Driesschaert et al., 2007; Charbit et al., 2008; Ridley et al., 2010; Robinson et al., 2012). If a 8
temperature above this threshold is maintained over a multi-millennial time period, the majority of the 9
Greenland Ice Sheet will be lost by changes in SMB on a millennial to multi-millennial time scale 10
(equivalent to a sea level rise of about 7 m, Table 4.1). During the Middle Pliocene warm intervals, when 11
global mean temperature was 2°C–3.5°C higher than pre-industrial, ice-sheet models suggest near-complete 12
deglaciation of Greenland. 13
14
A simplifying assumption is that the threshold is the warming required with the current ice-sheet topography 15
to reduce the mean SMB to zero, on the argument that the ice sheet must then retreat from the coast. Using 16
this criterion, Gregory and Huybrechts (2006) estimated that the SMB threshold occurs for a global mean 17
surface temperature increase of 3.1 [1.9 to 4.6]°C (5–95% confidence interval, 4.5 [3.0 to 6.0]°C for 18
Greenland surface temperature) above pre-industrial (assumed to be a steady state). More recent studies have 19
found thresholds below or in the lower part of this range. In a coupled ice sheet-climate model of 20
intermediate complexity, (Huybrechts et al., 2011) found this threshold at 2.5°C of annual average 21
Greenland SAT. Comparing three regional climate models, (Rae et al., 2012) found a strong dependence of 22
the threshold on the model formulation of the SMB. Based on the model’s performance against observations 23
and the physical detail of its surface scheme, MAR is considered the most realistic model, and yields a 24
threshold value 2.8 [2.1–3.4] °C for changes in Greenland annual average temperature compared to 25
preindustrial. Using MAR driven with output from various CMIP5 AOGCMs, Fettweis et al. (2013) 26
evaluated the threshold as ~3°C in global mean surface temperature above 1980–1999 (hence about 3.5°C 27
relative to pre-industrial), and found that it is not exceeded in the 21st century under the RCP4.5 scenario but 28
is reached around 2070 under the RCP8.5 scenario. 29
30
Some of the uncertainty in the threshold results from the value assumed for the steady-state ice-sheet SMB 31
(see Table 13.2), and whether this is assumed to be pre-industrial or a more recent period. For 400 Gt yr
–1
32
(Fettweis et al., 2013), the parametrisation of Greenland ice-sheet SMB used for present assessment of 21st 33
century changes (Section 13.4.3.1, Supplementary Material) gives a global warming threshold of 3.0 34
[2.14.1]°C with respect to 18601879 (the reference period used in Box 13.1); for 225 Gt yr
–1
(Gregory and 35
Huybrechts, 2006, following Church et al., 2001), the threshold is 2.1 [1.53.0]°C. 36
37
While a negative SMB is a sufficient condition for passing the threshold, it will overestimate the value of the 38
threshold quantitatively, because the SMB-height feedback (even without passing the threshold) means that 39
the steady-state SMB is reduced by more than is calculated assuming fixed topography. The actual SMB 40
change will depend on the dynamical response of the ice sheet that determines its topography. Constraining 41
simulations with a dynamic ice sheet model to changes during the last interglacial, Robinson et al. (2012) 42
estimated the threshold as 1.6 [0.9–2.8]°C (5–95% confidence interval) global averaged temperature above 43
pre-industrial. In these simulations they find that the threshold is passed when southeastern Greenland has a 44
negative SMB. The near-complete ice loss then occurs through ice flow and SMB. 45
46
The complete loss of the ice sheet is not inevitable because it has a long timescale (tens of millennia near the 47
threshold and a millennium or more for temperatures a few degrees above the threshold). If the surrounding 48
temperatures decline before the ice sheet is eliminated, the ice sheet might regrow. In the light of future 49
GHG emissions, the time scale of ice loss is competing with the time scale of temperature decline after a 50
reduction of GHG emissions (Allen et al., 2009; Solomon et al., 2009; Zickfeld et al., 2009). The outcome 51
therefore depends on both the CO
2
concentration and on how long it is sustained. Charbit et al. (2008) found 52
that loss of the ice sheet is inevitable for cumulative emissions above about 3000 GtC, but a partial loss 53
followed by regrowth occurs for cumulative emissions less than 2500 GtC. Ridley et al. (2010) identified 54
three steady states of the ice sheet. If the CO
2
concentration is returned to pre-industrial when more than 20–55
40% of the ice sheet has been lost, it will regrow only to 80% of its original volume due to a local climate 56
feedback in one region; if 50% or more, it regrows to 20–40% of the original. Similar states with ice volume 57

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around 20, 60–80 and 100% of the initial ice volume are also found in other models (Langen et al., 2012; 1
Robinson et al., 2012). If all the ice is lost, temperatures must decline to below a critical threshold for 2
regrowth of the ice sheet (Robinson et al., 2012; Solgaard and Langen, 2012). 3
4
On the evidence of paleo data and modelling (Section 5.6.2.4, 13.2.1), it is likely that during the LIG, when 5
global mean temperature never exceeded 2
o
C pre-industrial, the Greenland Ice Sheet contributed no more 6
than 3 m to GMSL. This could indicate that the threshold for near-complete deglaciation had not been 7
passed, or that it was not greatly exceeded so that the rate of mass loss was low; however, the forcing 8
responsible for the LIG warming was orbital rather than from CO
2
(van de Berg et al., 2011), so it is not a 9
direct analogue and the applicable threshold may be different. Studies with fixed-topography ice-sheets 10
indicate a threshold of 2°C or above of global warming with respect to pre-industrial for near-complete loss 11
of the Greenland ice-sheet, while the one study presently available with a dynamical ice-sheet suggests that 12
the threshold could be as low as about 1°C (Robinson et al. 2012). Recent studies with fixed-topography ice-13
sheets indicate that the threshold is less than about 4°C. With currently available information, we do not 14
have sufficient confidence to assign a likely range for the threshold. If the threshold is exceeded temporarily, 15
an irreversible loss of part or most of the Greenland Ice Sheet could result, depending on for how long and 16
by how much the threshold is exceeded. 17
18
13.4.4 Antarctic Ice Sheet 19
20
13.4.4.1 Surface Mass Balance Change 21
22
Since the ice loss from Antarctic due to surface melt and runoff is about 1% of the of the total mass gain 23
from snowfall, most ice loss occurs through solid ice discharge into the ocean. In the 21st century, ablation is 24
projected to remain small on the Antarctic ice sheet because low surface temperatures inhibit surface 25
melting, except near the coast and on the Antarctic Peninsula, and meltwater and rain continue to freeze in 26
the snowpack (Ligtenberg et al., 2013). Projections of Antarctic SMB changes over the 21st century thus 27
indicate a negative contribution to sea level because of the projected widespread increase in snowfall 28
associated with warming air temperatures (Krinner et al., 2007; Uotila et al., 2007; Bracegirdle et al., 2008). 29
Several studies (Krinner et al., 2007; Uotila et al., 2007; Bengtsson et al., 2011) have shown that the 30
precipitation increase is directly linked to atmospheric warming via the increased moisture holding capacity 31
of warmer air, and is therefore larger for scenarios of greater warming. The relationship is exponential, 32
resulting in an increase of SMB as a function of Antarctic SAT change evaluated in various recent studies 33
with high-resolution (~60 km) models as 3.7% °C
–1
(Bengtsson et al., 2011), 4.8% °C
–
(Ligtenberg et al., 34
2013) and ~7% °C
–1
(Krinner et al., 2007). These agree well with the sensitivity of 5.1 ± 1.5% °C
–1
(one 35
standard deviation) of CMIP3 AOGCMs (Gregory and Huybrechts, 2006). 36
37
The effect of atmospheric circulation changes on continental-mean SMB is an order of magnitude smaller 38
than the effect of warming, but circulation changes can have a large influence on regional changes in 39
accumulation, particularly near the ice-sheet margins (Uotila et al., 2007) where increased accumulation may 40
induce additional ice flow across the grounding line (Huybrechts and De Wolde, 1999; Gregory and 41
Huybrechts, 2006; Winkelmann et al., 2012). Simulated SMB is strongly and non-linearly influenced by 42
ocean surface temperature and sea ice conditions (Swingedouw et al., 2008). This dependence means that the 43
biases in the model-control climate may distort the SMB sensitivity to climate change, suggesting that more 44
accurate predictions may be obtained from regional models by using boundary conditions constructed by 45
combining observed present-day climate with projected climate change (Krinner et al., 2008). There is a 46
tendency for higher resolution models to simulate a stronger future precipitation increase because of better 47
representation of coastal and orographic precipitation processes (Genthon et al., 2009). 48
49
For the present assessment of GMSL rise, changes in Antarctic ice sheet SMB up to 2100 have been 50
computed from global mean SAT change projections derived from the CMIP5 AOGCMs, using the range of 51
sensitivities of precipitation increase to atmospheric warming summarized above, and the ratio of Antarctic 52
to global warming evaluated from CMIP3 AOGCMs by Gregory and Huybrechts (2006) (see also Section 53
13.5.1 and Supplementary Material). The results are shown in Table 13.5 and Figures 13.10 and 13.11. The 54
projected change in ice outflow is affected by the SMB because of the influence of topography on ice 55
dynamics (Section 13.4.4.2 and Supplementary Material). Ozone recovery, through its influence on 56
atmospheric circulation at high southern latitudes (Section 10.3.3.5), may offset some effects of greenhouse 57

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gas increase in the 21st century, but Antarctic precipitation is nonetheless projected to increase (Polvani et 1
al., 2011). Bintanja et al. (2013) suggested that Antarctic warming and precipitation increase may be 2
suppressed in the future by expansion of Antarctic sea ice, promoted by freshening of the surface ocean, 3
caused by basal melting of ice shelves, and they conduct an AOGCM sensitivity test of this hypothesis. We 4
consider these possibilities in Section 13.5.3. 5
6
Beyond the year 2100, regional climate simulations run at high spatial resolution (5–55 km) but without 7
climate-ice sheet feedbacks included show a net ice gain until the year 2200 (Ligtenberg et al., 2013). During 8
the 22nd century, the ice gain is equivalent to an average rate of sea level fall of 1.2 mm yr
–1
for the A1B 9
scenario and 0.46 mm yr
–1
the E1 scenario. 10
11
For multi-centennial to multi-millennial projections, feedbacks between the ice sheet and regional climate 12
need to be accounted for. This is currently done using ice-sheet models coupled to climate models of 13
intermediate complexity, which have a significantly lower spatial resolution in the atmospheric component 14
than regional climate models used to assess future SMB within the 21st century. These coarser resolution 15
models capture the increase in snowfall under future warming, but the regional distribution is represented 16
less accurately. Accordingly, there is low confidence in their ability to model spatial melting and 17
accumulation patterns accurately. In contrast, medium confidence can be assigned to the models’ projection 18
of total accumulation on Antarctica, since it is controlled by the large-scale moisture transport toward the 19
continent. 20
21
In idealized scenarios of 1% increase of CO
2
yr
–1
up to 560 ppm with subsequent stabilization, Vizcaíno et al. 22
(2010) and Huybrechts et al. (2011) found an initial increase of ice volume due to additional snowfall during 23
the first 600 years of integration. In both models, the changes in SMB dominate the mass changes during and 24
beyond the first 100 years. After 600 years of integration, Vizcaíno et al. (2010) found a mass gain 25
corresponding to a sea level fall of 0.15 m (–0.25 mm yr
–1
on average). For the same experiment and the 26
same period, Huybrechts et al. (2011) found a similar sea level fall of 0.08 m (–0.13 mm yr
–1
on average). 27
28
In a similar experiment but allowing GHG concentrations to reach 1120 ppm CO
2
before being stabilized, 29
both models show a net positive sea level contribution after 600 years of integration. Huybrechts et al. 30
(2011) found a weak sea level contribution during the first 500 years of integration followed by a stronger 31
and relatively constant long-term average rate of ~2 mm yr
–1
after 1000 years of integration up to a total 32
contribution of ~4 m SLE after 3000 years of integration. While they found some grounding line retreat due 33
to basal ice-shelf melt, the multi-centennial evolution of the ice sheet is dominated by changes in SMB while 34
the solid-ice discharge after an initial increase shows a significant decrease during the scenario. 35
36
For the same scenario, Vizcaíno et al. (2010) found that the initial mass gain is followed by a weak mass 37
loss. After 250 years of integration, the contribution is stronger and relatively constant at a rate of about 3 38
mm yr
–1
, corresponding to a net contribution of 1.2 m to global mean sea level rise after 600 years. 39
40
The same model as in Huybrechts et al. (2011), although with a slightly stronger polar amplification, was 41
applied to the three SRES scenarios used in the AR4 (B1, A1B, A2) with stabilization in the year 2100 42
(Goelzer et al., 2012). For the lowest scenario (B1), they found practically no net Antarctic contribution to 43
sea level in the year 3000. Under the medium scenario (A1B), the ice sheet contributes 0.26 m, and under the 44
highest scenario (A2), it contributes 0.94 m SLE in the year 3000. 45
46
These simulations include a negative feedback on the regional climate by ice-sheet melt through which 47
summer temperatures can be significantly reduced over Antarctica (Swingedouw et al., 2008). However, due 48
to the coarse resolution and the high polar amplification, there is low confidence in the model´s 49
representation of oceanic circulation changes around Antarctica. 50
51
In both models (Vizcaíno et al., 2010; Huybrechts et al., 2011), the ice sheets are not equilibrated with the 52
surrounding climate after the integration period under the 1120 ppm CO
2
forcing. Though GHG 53
concentrations were stabilized after 120 years of integration, the Antarctic Ice Sheet continues to contribute 54
to sea level rise at a relatively constant rate for another 480 years in Vizcaíno et al. (2010) and 2880 years in 55
Huybrechts et al. (2011). This is consistent with a positive sea level contribution from Antarctica during past 56
warmer climates (Sections 13.2.1, 13.5.2). 57

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1
In summary, both coupled ice sheet-climate models consistently show that for high-emission scenarios, the 2
surface melt increases and leads to an ice loss on multi-centennial time scales. The long time period over 3
which the Antarctic Ice Sheet continues to lose mass indicates a potential role of the feedback between 4
climate and ice sheet. Consistent with regional climate models for the 21st and 22nd centuries, both coarse-5
resolution coupled models show a positive SMB change for most of the first 100 years of climate change. 6
Due to the inertia in the climate system, regional temperatures continue to rise after that. Together with 7
enhanced solid ice discharge, this results in mass loss of AIS. The corresponding decline in surface elevation 8
increases the surface temperature and leads to additional ice loss. 9
10
13.4.4.2 Dynamical Change 11
12
The Antarctic Ice Sheet represents the largest potential source of future SLR; the West Antarctic Ice Sheet 13
(WAIS) alone has the potential to raise sea level by ~4.3 m (Fretwell et al., 2013). The rate at which this 14
reservoir will be depleted and cause sea level to rise, however, is not easily quantifiable. In this section, we 15
focus on dynamical changes (i.e., those related to the flow of the ice sheet) which affect SLR by altering the 16
flux of ice across the grounding line (or outflow) that separates ice resting on bedrock (some of which does 17
not currently displace ocean water) from floating ice shelves (which already displace ocean water and have 18
only a negligible effect on sea level) (Jenkins and Holland, 2007). 19
20
Issues associated with the inability of models to reproduce recently observed changes in the dynamics of the 21
Antarctic Ice Sheet prevented the AR4 from fully quantifying the effect of these changes on future sea level. 22
Since the AR4, progress has been made in understanding the observations (Sections 4.4.3 and 4.4.4), and 23
projections are becoming available. It must be stressed, however, that this field has yet to reach the same 24
level of development that exists for modelling many other components of the Earth system. There is an 25
underlying concern that observations presage the onset of large-scale grounding line retreat in what is termed 26
the Marine Ice Sheet Instability (MISI; Box 13.2), and much of the research assessed here attempts to 27
understand the applicability of this theoretical concept to projected SLR from Antarctica. 28
29
There are three distinct processes that could link climate change to dynamical change and potentially trigger 30
increased outflow directly through the increased potential for melt ponds to form on the upper surface of ice 31
shelves, which may destabilize them, or by increases in submarine melt experienced by ice shelves as a 32
consequence of oceanic warming, which leads to their thinning, and indirectly by coupling between SMB 33
and ice flow. Section 4.4.3.2 presents the observational basis on which understanding of these processes is 34
based, while their potential future importance is assessed here. Literature on two mechansims directly linked 35
to climate change will be assessed first, followed by their relation to outflow change and lastly SMB 36
coupling. 37
38
There is strong evidence that regional warming and increased melt water ponding in the Antarctic Peninsula 39
led to the collapse of ice shelves along the length of Antarctic Peninsula (Cook and Vaughan, 2010), most 40
notably the Larsen B ice shelf (MacAyeal et al., 2003). Substantial local warming (~5-7 C) would, however, 41
be required before the main Antarctic ice shelves (the Ross and Filchner-Ronne ice shelves) would become 42
threatened (Joughin and Alley, 2011). An assessment of the AR4 AOGCM ensemble under scenario A1B 43
yielded an Antarctic continental-average warming rate of 0.034 ± 0.01C yr
–1
(Bracegirdle et al., 2008), 44
suggesting that the required level of warming may not be approached by the end of the century. Using an 45
intermediate complexity model with scenario A2, Fyke et al. (2010) found that melt starts to reach 46
significant levels over these ice shelves around 2100 to 2300. Barrand et al. (2013) made a process-based 47
assessment of the effect of ice-shelf collapse on outflow from the Antarctic Peninsula, which yields a range 48
of SLR at 2100 between 10 and 20 mm, with a bounding maximum of 40 mm. 49
50
There is good evidence linking the focus of current Antarctic mass loss in the Amundsen Sea sector of 51
WAIS (containing Pine Island and Thwaites Glaciers) (Shepherd and Wingham, 2007; Rignot et al., 2008; 52
Pritchard et al., 2009) to the presence of relatively warm Circumpolar Deep Water on the continental shelf 53
(Thoma et al., 2008; Jenkins et al., 2010). However, it is not possible to determine whether this upwelling 54
was related directly or indirectly to a rise in global mean temperature. Yin et al. (2011) assessed output from 55
19 AOGCMs under scenario A1B to determine how subsurface temperatures are projected to evolve around 56
the West and East Antarctic ice sheets. They showed decadal-mean warming of 0.4°C–0.7C and 0.4°C–57

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0.9C around West and East Antarctica, respectively (25th to 75th percentiles of ensemble) by the end of the 1
21st century. More detailed regional modelling using scenario A1B illustrates the potential for warm water 2
to invade the ocean cavity underlying the Filchner-Ronne ice shelf in the second half of the 21st century, 3
with an associated 20-fold increase in melt (Hellmer et al., 2012). Based on the limited literature, there is 4
medium confidence that oceanic processes may potentially trigger further dynamical change particularly in 5
the latter part of the 21st century, while there is also medium confidence that atmospheric change will not 6
affect dynamics outside of the Antarctic Peninsula. 7
8
Several process-based projections of the future evolution of Pine Island Glacier have now been made, and 9
some of the issues that this modelling faced (such as the need for sub-kilometre resolution to ensure 10
consistent results; Cornford et al. (2013), Durand et al. (2009)) are being resolved (Pattyn et al., 2013). In 11
experiments using an idealised increase in marine melt, Joughin et al. (2010) demonstrated only limited (~25 12
km) retreat of the grounding line before a new equilibrium position was established. Gladstone et al. (2012) 13
used a flow-line model forced with ocean-model output (Hellmer et al., 2012) to identify two modes of 14
retreat: one similar to that identified by Joughin et al. (2010), and a second characterized by complete 15
collapse from 2150 onwards. More sophisticated ice-flow modelling (albeit with idealized forcing) suggests 16
grounding line retreat of ~100 km in 50 years (Cornford et al., 2013). These studies support the theoretical 17
finding of Gudmundsson et al. (2012) that grounding line retreat, if triggered, does not inevitably lead to 18
MISI but may halt if local buttressing from ice rises or channel sidewalls is sufficient. Parizek et al. (2013) 19
used a flow-line model to study Thwaites Glacier and found that grounding line retreat is only possible under 20
extreme ocean forcing. It is also thought that considerably less back pressure is exerted by Thwaites’ ice 21
shelf in comparison to Pine Island’s (Rignot, 2001; 2008), which may make it less sensitive to forcing by 22
submarine melt (Schoof, 2007b; Goldberg et al., 2012). Based on this literature, there is high confidence that 23
the retreat of Pine Island Glacier (if it occurs) can be characterised by a SLR measured in centimetres by 24
2100, although there is low confidence in the models’ ability to determine the probability or timing of any 25
such retreat. There is also moderate confidence (in the light of the very limited literature) that Thwaites 26
Glacier is probably less prone to undergo ocean-driven grounding line retreat than its neighbour in the 21st 27
century. No process-based modelling is available on which to be base projections of EAIS glaciers currently 28
losing mass, such as Totten and Cook Glaciers. 29
30
Bindschadler et al. (2013) reported a model inter-comparison exercise on the impact of climate change under 31
RCP8.5 on the Antarctic Ice Sheet. The resultant projection includes contributions from increased marine 32
melt in the Amundsen Sea and Amery sectors, and generated a mean SLR at 2100 of ~100 mm over four 33
models (with overall SLR of 81 mm when SMB change was included). There is, however, low confidence in 34
the projection because of the unproven ability of many of the contributing models to simulate grounding line 35
motion. Bindschadler et al. (2013) also reported idealized experiments in which ice-shelf melt is increased 36
by 2, 20 and 200 m yr
–1
. The resulting five-model mean SLR of 69, 693 and 3477 mm by 2100, respectively, 37
can only be considered as a general indication because of the shortcomings of the contributing models (e.g., 38
two do not include ice shelves) which may be offset by the use of a multi-model mean. While grounding line 39
migration in the 20 m yr
–1
experiment is extensive in some models and consistent with what might be 40
expected under MISI (Nowicki et al., 2013), the 200 m yr
–1
experiment is unrealistic, even if used as a proxy 41
for the improbable atmosphere-driven collapse of the major ice shelves, and is not considered further. 42
43
We now assess two alternatives to process-based modelling that exist in the literature: the development of 44
plausible high-end projections based on physical intuition (Pfeffer et al., 2008; Katsman et al., 2011) and the 45
use of a probabilistic framework for extrapolating current observations of the ice sheet’s mass budget (Little 46
et al., 2013a; 2013b). Pfeffer et al. (2008) postulated a possible but extreme scenario of 615 mm SLR based 47
on vastly accelerated outflow in the Amundsen Sea sector and East Antarctica, whereas a more plausible 48
scenario involving reduced acceleration in the Amundsen Sea sector yields 136 mm. Katsman et al. (2011) 49
used similar assumptions in a “modest” scenario that generates SLR of 70–150 mm, and a “severe” scenario 50
that attempts to capture the consequences of the collapse of the WAIS through the MISI and has a SLR 51
contribution of 490 mm. The NRC (2012) extrapolated mass-budget observations of the ice sheet to generate 52
a projection of 157–323 mm (including future SMB change), with an additional 77–462 mm accounting for 53
21st-century increases in outflow (summing as 234 to 785 mm). 54
55
Little et al. (2013a) applied a range of linear growth rates to present-day SMB and outflow observations of 56
Antarctic sectors (Rignot et al., 2008; Shuman et al., 2011; Zwally and Giovinetto, 2011), which are then 57

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weighted using a continental-scale observational synthesis (Shepherd et al., 2012) (consistent with the 1
assessment of Chapter 4). In the case of Pine Island Glacier, growth rates are based on the process-based 2
modelling of Joughin et al. (2010). Within this framework, SLR at 2100 has a 5–95% range of –20 to 185 3
mm for dynamical change only, and –86 to 133 mm when SMB change is included (based on Uotila et al. 4
(2007)). Projections for the Antarctic Peninsula are consistent with the process-based modelling of Barrand 5
et al. (2013). Further, Little et al. (2013a) found that the upper (95%) limit of the projected range can only 6
approach 400 mm under scenarios expected for MISI (such as the immediate collapse of Pine Island and 7
Thwaites Glaciers or all marine-based sectors experiencing the same rates of mass loss as Pine Island 8
Glacier). 9
10
Our assessment of the likely (17 to 83%) range of SLR by 2100 is based around Little et al. (2013a). Their 11
weighted 5–95% range of –20 to 185 mm is consistent with the lower scenarios of Katsman et al. (2011) 12
(70–150 mm) and Pfeffer et al. (2008) (136 mm), and with the RCP8.5 projection and low-melt experiment 13
of Bindschadler et al. (2013) (~100–69 mm, respectively). The base projection of the NRC (2012) (157–323 14
mm including future SMB change), however, is less compatible. This moderate level of consistency across a 15
range of techniques suggests medium confidence in this assessment. We assess this as the likely (as opposed 16
to very likely) range because it is based primarily on perturbations of the ice-sheet’s present-day state of 17
mass imbalance and does not include the potentially large increases in outflow that may be associated with 18
the MISI discussed below. 19
20
The probability of extensive grounding line retreat being both triggered and continuing to the extent that it 21
contributes to significant SLR in the 21st century is very poorly constrained, as the results of a recent expert 22
elicitation indicate (Bamber and Aspinall, 2013). We have medium confidence, however, that this probability 23
lies outside of the likely range of SLR (i.e, with probability less than 17%). Five arguments support this 24
assessment. First, the partial loss of Pine Island Glacier is included by Little et al. (2013a) in their range and 25
the full loss of the ice stream (if it were to occur) is thought to raise sea level by centimetres only (consistent 26
with the use of the Little et al. (2013a) 9–95% range as the assessed likely 17–83% range). Second, the 27
current grounding line position of the neighbouring Thwaites Glacier appears to be more stable than that of 28
Pine Island Glacier (Parizek et al., 2013) so that its potentially large contribution to SLR by 2100 is assessed 29
to have a significantly lower probability. Third, there is a low probability that atmospheric warming in the 30
21st century will lead to extensive grounding line retreat outside of the Antarctic Peninsula because summer 31
air temperatures will not rise to the level where significant surface melt and ponding occur. Fourth, while 32
this retreat may be triggered by oceanic warming during the 21st century (in particular, under the Filchner-33
Ronne ice shelf), current literature suggests that this may occur late in the century (Hellmer et al., 2012), 34
reducing the time over which enhanced outflow could affect end-of-century SLR. Finally, there are 35
theoretical grounds to believe that grounding line retreat may stabilise (Gudmundsson et al., 2012) so that 36
MISI (and associated high SLR) is not inevitable. 37
38
We next assess the magnitude of potential SLR at 2100 in the event that MISI affects the Antarctic Ice Sheet. 39
Bindschadler et al. (2013), Katsman et al. (2011), the NRC (2012), and Pfeffer et al. (2008) presented 40
contrasting approaches that can be used to make this assessment, which are upper-end estimates of 693, 490, 41
785 and 615 mm, respectively. Together this literature suggests with medium confidence that this 42
contribution would be several tenths of a metre. The literature does not offer a means of assessing the 43
probability of this contribution, however, other than our assessment (above) that it lies above the likely range 44
(i.e, less than 17% probability). 45
46
Literature investigating the relation between the SLR generated by dynamical change and emission scenario 47
does not currently exist. There is also a lack of literature on the relation between emission scenario and the 48
intrusions of warm water into ice-shelf cavities thought to be important in triggering observed mass loss 49
(Jacobs et al., 2011) and potentially important in the future (Hellmer et al., 2012). It is therefore premature to 50
attach a scenario-dependence to projections of dynamical change, even though such a dependency is 51
expected to exist. 52
53
Likely increases in snowfall over the next century (Section 13.4.4.1) will affect the amount of ice lost by 54
outflow across the grounding line because of local changes in ice thickness and stress regime (Huybrechts 55
and De Wolde, 1999). This effect was incorporated in AR4 projections for 2100 as a compensatory mass loss 56
amounting to 0-10% of the SMB mass gain (Gregory and Huybrechts, 2006). Winkelmann et al. (2012) re-57

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evaluated the effect and reported a range of 15–35% for the next century (30–65% after 500 years). The two 1
studies are difficult to compare because of differences in model physics and experimental design so that the 2
use of their joint range (0-35%) is an appropriate assessment of the likely range of this effect. This range is 3
supported by Barrand et al. (2013) who quantified the effect for the Antarctic Peninsula as ~15% of SMB. 4
Moreover, because this contribution relies on similar physics to the grounding line migration discussed 5
above, it is appropriate to assume that their uncertainties are correlated. Winkelmann et al. (2012) showed 6
that the fractional size of this compensatory effect is independent of scenario. Accounting for this effect 7
equates to SLR of 0–32 mm by 2100 based on the SMB range over all emission-scenario projections in 8
Section 13.5.1.1. 9
10
Beyond the 21st century, only projections with coarse-resolution ice sheet-climate models are available 11
(Vizcaíno et al., 2010; Huybrechts et al., 2011). Confidence in the ability of these two models to capture both 12
change in the oceanic circulation around Antarctica and the response of the ice sheet to these changes, 13
especially a potential grounding line retreat, is low. The model applied by Vizcaíno et al. (2010) is lacking a 14
dynamic representation of ice shelves. Since dynamic ice discharge from Antarctica occurs predominately 15
through ice shelves, it is likely that the projections using this model considerably underestimate the Antarctic 16
contribution. 17
18
In summary, it is likely that dynamical change within the Antarctic ice sheet will lead to SLR during the next 19
century with a range of –20 to 185 mm. SLR beyond the likely range is poorly constrained and considerably 20
larger increases are possible, which will probably be associated with the MISI (Box 13.2). Although the 21
likelihood of such SLR cannot yet be assessed more precisely than falling above the likely range, literature 22
suggests (with medium confidence) that its potential magnitude is several tenths of a metre. We are unable to 23
assess SLR as a function of emission scenario, although a dependency of SLR on scenario is expected to 24
exist. In addition, coupling between SMB and dynamical change is likely to make a further contribution to 25
SLR of 0–35% of the SMB. All the available literature suggests that this dynamical contribution to sea level 26
rise will continue well beyond 2100. 27
28
13.4.4.3 Possible Irreversibility of Ice Loss from West Antarctica 29
30
Due to relatively weak snowfall on Antarctica and the slow ice motion in its interior, it can be expected that 31
the West Antarctic Ice Sheet (WAIS) would take at least several thousand years to regrow if it was 32
eliminated by dynamic ice discharge. Consequently any significant ice loss from West Antarctic that occurs 33
within the next century will be irreversible on a multi-centennial to millennial time scale. We discuss here 34
the possibility of abrupt dynamic ice loss from West Antarctica (see Section 12.5.5 for definition of abrupt). 35
36
Information on the ice- and bed-topography of WAIS suggest that it has about 3.3 m of equivalent global sea 37
level grounded on areas with downward sloping bedrock (Bamber et al., 2009). As detailed in Box 13.2, 38
large areas of WAIS may therefore be subject to potential ice loss via the marine ice-sheet instability (MISI). 39
As it is the case for other potential instabilities within the climate system (Section 12.5.5), there are four 40
lines of evidence to assess the likelihood of a potential occurrence: theoretical understanding, present-day 41
observations, numerical simulations, and paleo-records. 42
43
The MISI is based on a number of studies which indicated the theoretical existence of the instability 44
(Weertman, 1961; Schoof, 2007b) (see also Box 13.2). The most fundamental derivation, i.e. starting from a 45
first-principle ice-equation, states that in one-dimensional ice flow the grounding line between grounded ice 46
sheet and floating ice shelf cannot be stable on a landward sloping bed. The limitation of the one-47
dimensional case disregards likely stabilizing effects of the ice shelves (Dupont and Alley, 2005). While it is 48
clear that ice shelves that are laterally constrained by embayments inhibit ice flow into the ocean, the effect 49
has not been quantified against the MISI. The same is true for other potentially stabilizing effects such as 50
sedimentation near the grounding line (Alley et al., 2007) or the influence of large-scale bedrock roughness 51
(i.e. topographic pinning points) on ice flow. While these stabilizing effects need to be further investigated 52
and quantified against the destabilizing effect of the MISI, no studies are available that would allow 53
dismissing the MISI on theoretical grounds. 54
55
Although direct observations of ice dynamics are available, they are neither detailed enough nor cover a 56
sufficiently long period to allow the monitoring of the temporal evolution of an MISI. Most Antarctic ice 57

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loss that has been detected during the satellite period has come from WAIS (Rignot et al., 2008; Pritchard et 1
al., 2012). Some studies have found an acceleration of ice loss (Rignot et al., 2011) as well as enhanced basal 2
ice-shelf melting (Pritchard et al., 2012), but the short period of observations does not allow one to either 3
dismiss or confirm that these changes are associated with destabilization of WAIS. 4
5
Paleo records suggest that WAIS may have deglaciated several times during warm periods of the last 5 6
million years, but they contain no information on rates (Naish et al., 2009). While coarse-resolution models 7
are in principle capable of modelling the MISI, there is medium confidence in their ability to simulate the 8
correct response time to external perturbations on decadal to centennial time scales (Pattyn et al., 2013). One 9
of these models (Pollard and DeConto, 2009) reproduced paleo-records of deglaciation with a forced ice-10
sheet model at 40 km resolution and parameterized ice flow across the grounding line according to Schoof 11
(2007b). These simulations showed a sea level rise of about 7 m over time spans of 1000–7000 years with 12
approximately equal contributions from West and East Antarctica. However, no available model results or 13
paleo records have indicated the possibility of self-accelerated ice discharge from these regions. 14
15
In summary, based on ice-dynamics theory, numerical simulations, and paleo records, it is likely that abrupt 16
and irreversible ice loss from WAIS is possible. However, theoretical considerations, current observations, 17
numerical models, and paleo records currently do not allow a quantification of the perturbation that is 18
necessary to destabilize the ice sheet. 19
20
[START BOX 13.2 HERE] 21
22
Box 13.2: History of the Marine Ice Sheet Instability Hypothesis 23
24
Marine ice sheets rest on bedrock that is submerged below sea level (often by 2–3 km). The most well-25
researched marine ice sheet is the West Antarctic Ice Sheet (WAIS) where approximately 75% of the ice 26
sheet’s area currently rests on bedrock below sea level. The East Antarctic Ice Sheet (EAIS), however, also 27
has appreciable areas grounded below sea level (~35%), in particular around the Totten and Cook Glaciers. 28
29
These ice sheets are fringed by floating ice shelves, which are fed by flow from grounded ice across a 30
grounding line (GL). The GL is free to migrate both seawards and landwards as a consequence of the local 31
balance between the weight of ice and displaced ocean water. Depending on a number of factors, which 32
include ice-shelf extent and geometry, ice outflow to the ocean generally (but not always) increases with ice 33
thickness at the GL. Accordingly, when the ice sheet rests on a bed that deepens towards the ice-sheet 34
interior (see Box 13.2, Figure 1a), the ice outflow to the ocean will generally increase as the GL retreats. It is 35
this feature that gives rise to the marine ice sheet instability (MISI), which states that a GL cannot remain 36
stable on a landward-deepening slope. Even if snow accumulation and outflow were initially in balance (Box 37
13.2, Figure 1b), natural fluctuations in climate cause the GL to fluctuate slightly (Box 13.2, Figure 1c). In 38
the case of a retreat, the new GL position is then associated with deeper bedrock and thicker ice, so that 39
outflow increases (Box 13.2, Figure 1d). This increased outflow leads to further, self-sustaining retreat until 40
a region of shallower, seaward-sloping bedrock is reached. Stable configurations can therefore only exist 41
where the GL rests on slopes that deepen towards the ocean. A change in climate can therefore potentially 42
force a large-scale retreat of the GL from one bedrock ridge to another further inland. 43
44
The MISI has a long history based on theoretical discussions that were started by Weertman (1974) and 45
Mercer (1978), and has seen many refinements over the subsequent years. The advent of satellite-based 46
observations has given fresh impetus to this debate, in particular work on the GL retreat and associated 47
thinning of Pine Island (PIG), Thwaites (TG) and Smith Glaciers (all part of WAIS), which are collectively 48
responsible for most of Antarctica’s present mass loss (Rignot et al., 2008). These observations highlighted 49
the need to develop a better understanding of the MISI to make more accurate projections of the ice sheet’s 50
future contribution to sea level rise. 51
52
Early studies of the MISI were not based on a formal derivation from the basic laws of mechanics thought to 53
control ice-sheet flow and the robustness of their results was therefore difficult to assess. An open question 54
was the expected impact of changes at the GL on the ice-sheet flow (Hindmarsh, 1993). Recently, however, 55
a more complete analysis from first principles has been developed that suggests that the fundamental relation 56
between thickness and flux at the GL exists and has a power ~5 (i.e., that a 10% increase in thickness leads 57

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to a 60% increase in flux) (Schoof, 2007a, 2011). This analysis, however, does not include ice shelves that 1
occupy laterally constrained embayments, which is often the case (for instance at PIG). In such situations, 2
drag from ice-shelf sidewalls may suppress the positive feedback between increasing ice thickness and ice 3
flux at the GL (Dupont and Alley, 2005; Goldberg et al., 2009; Gudmundsson et al., 2012). Other factors that 4
could suppress the instability include a sea level fall adjacent to the GL resulting from the isostatic and 5
gravitational effects of ice loss (Gomez et al., 2010a). 6
7
Two processes that could trigger GL retreat are particularly relevant to contemporary polar climate change. 8
The first is the presence of warmer ocean water under ice shelves, which leads to enhanced submarine ice-9
shelf melt (Jacobs et al., 2011). The second is the presence of melt water ponds on the surface of the ice 10
shelf, which can cause stress concentrations allowing fractures to penetrate the full ice-shelf thickness. This 11
process appears to have been a primary factor in the collapse of the Larsen B Ice Shelf (LBIS) over the 12
course of two months in 2002 (MacAyeal et al., 2003). The collapse of the LBIS provided a natural 13
demonstration of the linkage between the structural integrity of an ice shelf and the flow of grounded ice 14
draining into it. Following the breakup of LBIS, the speeds of the glaciers feeding the collapsed portion of 15
the shelf increased two-to-eightfold, while the flow of glaciers draining into a surviving sector was unaltered 16
(Rignot et al., 2004; Scambos et al., 2004; Rott et al., 2011). This indicates that a mechanical link does 17
indeed exist between shelf and sheet, and has important implications for the future evolution of the far more 18
significant PIG and TG systems of the WAIS. 19
20
The recent strides made in placing MISI on a sound analytical footing are, however, limited to the analysis 21
of steady states. Numerical modelling is needed to simulate the GL retreat rates that are required to make 22
accurate SLR projections. There are major challenges in designing models whose results are not controlled 23
by the details of their numerical design. Problems arise at the GL because, in addition to flotation, basal 24
traction is dramatically reduced as the ice loses contact with the underlying bedrock (Pattyn et al., 2006). 25
This is a topic of active research, and a combination of more complete modelling of the GL stress regime 26
(Favier et al., 2012) and the use of high-resolution (sub kilometre) models (Durand et al., 2009; Cornford et 27
al., 2013) shows promise towards resolving these problems. Much progress has also been made by using 28
model inter-comparison as a means of understanding these effects (Pattyn et al., 2013). 29
30
[INSERT BOX 13.2, FIGURE 1 HERE] 31
Box 13.2, Figure 1: Schematic of the processes leading to the potentially unstable retreat of a grounding line showing 32
(a) geometry and ice fluxes of a marine ice sheet, (b) the grounding line in steady state, (c) climate change triggering 33
mass outflow from the ice sheet and the start of grounding line retreat, and (d) self-sustained retreat of the grounding 34
line. 35
36
[END BOX 13.2 HERE] 37
38
[START FAQ 13.2 HERE] 39
40
FAQ 13.2: Will the Greenland and Antarctic Ice Sheets Contribute to Sea Level Change Over the Rest 41
of the Century? 42
43
The Greenland, West and East Antarctic ice sheets are the largest reservoirs of freshwater on the planet. As 44
such, they have contributed to sea level change over geological and recent times. They gain mass through 45
accumulation (snowfall) and lose it by surface ablation (mostly ice melt) and outflow at their marine 46
boundaries, either to a floating ice shelf, or directly to the ocean through iceberg calving. Increases in 47
accumulation cause global mean sea level to fall, while increases in surface ablation and outflow cause it to 48
rise. Fluctuations in these mass fluxes depend on a range of processes, both within the ice sheet and without, 49
in the atmosphere and oceans. Over the course of this century, however, sources of mass loss appear set to 50
exceed sources of mass gain, so that a continuing positive contribution to global sea level can be expected. 51
This FAQ summarises current research on the topic and provides indicative magnitudes for the various end-52
of-century sea level contributions from the full assessment, which are reported as the 17 to 83% probability 53
range across all emission scenarios. 54
55
Over millennia, the slow horizontal flow of an ice sheet carries mass from areas of net accumulation 56
(generally, in the high-elevation interior) to areas of net loss (generally, the low-elevation periphery and the 57

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coastal perimeter). At present, Greenland loses roughly half of its accumulated ice by surface ablation, and 1
half by calving. Antarctica, on the other hand, loses virtually all its accumulation by calving and submarine 2
melt from its fringing ice shelves. Ice shelves are floating, so their loss has only a negligible direct effect on 3
sea level, although they can affect sea level indirectly by altering the mass budget of their parent ice sheet 4
(see below). 5
6
In East Antarctica, some studies using satellite radar altimetry suggest that snowfall has increased, but recent 7
atmospheric modelling and satellite measurements of changes in gravity find no significant increase. This 8
apparent disagreement may be because relatively small long-term trends are masked by the strong inter-9
annual variability of snowfall. Projections suggest a substantial increase in 21st century Antarctic snowfall, 10
mainly because a warmer atmosphere would be able to carry more moisture into polar regions. Regional 11
changes in atmospheric circulation probably play a secondary role. For the whole of the Antarctic Ice Sheet, 12
this process is projected to contribute between 0 and 70 mm to sea level fall. 13
14
Currently, air temperatures around Antarctica are too cold for substantial surface ablation. Field and satellite-15
based observations, however, indicate enhanced outflow—manifested as ice surface lowering—in a few 16
localised coastal regions. These areas (Pine Island and Thwaites Glaciers in West Antarctica, and Totten and 17
Cook Glaciers in East Antarctica) all lie within kilometre-deep bedrock troughs towards the edge of 18
Antarctica’s continental shelf. The increase in outflow is thought to have been triggered by regional changes 19
in ocean circulation, bringing warmer water in contact with floating ice shelves. 20
21
On the more northerly Antarctic Peninsula, there is a well-documented record of ice-shelf collapse, which 22
appears to be related to the increased surface melting caused by atmospheric warming over recent decades. 23
The subsequent thinning of glaciers draining into these ice shelves has had a positive—but minor—effect on 24
sea level, as will any further such events on the Peninsula. Regional projections of 21st century atmospheric 25
temperature change suggest that this process will probably not affect the stability of the large ice shelves of 26
both the West and East Antarctica, although these ice shelves may be threatened by future oceanic change 27
(see below). 28
29
Estimates of the contribution of the Antarctic ice sheets to sea level over the last few decades vary widely, 30
but great strides have recently been made in reconciling the observations. There are strong indications that 31
enhanced outflow (primarily in West Antarctica) currently outweighs any increase in snow accumulation 32
(mainly in East Antarctica), implying a tendency towards sea level rise. Before reliable projections of 33
outflow over the 21st century can be made with greater confidence, models that simulate ice flow need to be 34
improve; especially of any changes in the grounding line that separates floating ice from that resting on 35
bedrock and of interactions between ice shelves and the ocean. The concept of “marine ice-sheet instability” 36
is based on the idea that the outflow from an ice sheet resting on bedrock below sea level increases if ice at 37
the grounding line is thicker and, therefore, faster flowing. On bedrock that slopes downward towards the 38
ice-sheet interior, this creates a vicious cycle of increased outflow, causing ice at the grounding line to thin 39
and go afloat. The grounding line then retreats down slope into thicker ice that, in turn, drives further 40
increases in outflow. This feedback could potentially result in the rapid loss of parts of the ice sheet, as 41
grounding lines retreat along troughs and basins that deepen towards the ice sheet’s interior. Future climate 42
forcing could trigger such an unstable collapse, which may then continue independently of climate. This 43
potential collapse might unfold over centuries for individual bedrock troughs in West Antarctica and sectors 44
of East Antarctica. Much research is focused on understanding how important this theoretical concept is for 45
those ice sheets. Sea level could rise if the effects of marine instability become important, but there is not 46
enough evidence at present to unambiguously identify the precursor of such an unstable retreat. Change in 47
outflow is projected to contribute between –20 (i.e., fall) and 140 mm to sea level rise, although the 48
uncertain impact of marine ice-sheet instability could increase this figure by several tenths of a metre. 49
Overall, increased snowfall seems set to only partially offset sea level rise caused by increased outflow. In 50
Greenland, mass loss through more surface ablation and outflow dominates a possible recent trend towards 51
increased accumulation in the interior. Estimated mass loss due to surface ablation has doubled since the 52
early 1990s. This trend is expected to continue over the next century as more of the ice sheet experiences 53
surface ablation for longer periods. Indeed, projections for the 21st century suggest that increasing mass loss 54
will dominate over weakly increasing accumulation. The refreezing of melt water within the snow pack high 55
up on the ice sheet offers an important (though perhaps temporary) dampening effect on the relation between 56
atmospheric warming and mass loss. 57

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1
While the observed response of outlet glaciers is both complex and highly variable, iceberg calving from 2
many of Greenland’s major outlet glaciers has increased substantially over the last decade, and constitutes an 3
appreciable additional mass loss. This seems to be related to the intrusion of warm water into the coastal seas 4
around Greenland, but it is not clear whether this phenomenon is related to inter-decadal variability, such as 5
the North Atlantic Oscillation, or a longer term trend associated with greenhouse-gas induced warming. 6
Projecting its effect on 21st century outflow is therefore difficult, but it does highlight the apparent 7
sensitivity of outflow to ocean warming. The effects of more surface melt water on the lubrication of the ice 8
sheet’s bed, and the ability of warmer ice to deform more easily, may lead to greater rates of flow, but the 9
link to recent increases in outflow is unclear. Change in the net difference between surface ablation and 10
accumulation is projected to contribute between 10 and 160 mm to sea level rise, while increased outflow is 11
projected to contribute a further 10 to 70 mm. 12
13
The Greenland Ice Sheet has contributed to a rise in global mean sea level over the last few decades, and this 14
trend is expected to increase during this century. Unlike Antarctica, Greenland has no known large-scale 15
instabilities that might generate an abrupt increase in sea level rise over the 21st century. A threshold may 16
exist, however, so that continued shrinkage might become irreversible over multi-centennial timescales, even 17
if the climate were to return to a pre-industrial state over centennial time scales. While mass loss through the 18
calving of icebergs may increase in future decades, this process will eventually end when the ice margin 19
retreats onto bedrock above sea level where the bulk of the ice sheet resides. 20
21
[INSERT FAQ 13.2, FIGURE 1 HERE] 22
FAQ 13.2, Figure 1: Illustrative synthesis of projected changes in SMB and outflow by 2100 for (a) Greenland and (b) 23
Antarctic ice sheets. Colours shown on the maps refer to projected SMB change between 1990–2009 and 2079–2098 24
using the RACMO2 regional climate model under scenarios A1B (Antarctic) and RCP4.5 (Greenland) AOGCMs. For 25
Greenland, average equilibrium line altitudes during both these time periods are shown in purple and green, 26
respectively. Ice perimeter and grounding lines are shown as black lines, as are ice sheet sectors. For Greenland, results 27
of flow-line modelling for four major outlet glaciers are shown as inserts, while for Antarctica the coloured rings reflect 28
projected change in outflow on the basis of a statistical extrapolation of observed trends. The outer and inner radius of 29
each ring indicate the 17th and 83rd percentiles, respectively (scale in upper right); red refers to mass loss (sea level 30
rise) while blue refers to mass gain (sea level fall). Finally, total sea level contribution is shown for each ice sheet 31
(insert located above maps) with light grey referring to SMB (model experiment used to generate the SMB map is 32
shown as a dashed line) and dark grey to outflow. All projections refer to the 17–83% probability range across all 33
scenarios. Data provided by F. M. Nick, C. M. Little, J. H. van Angelen, S. R. M. Ligtenberg, J. T. M. Lenaerts and M. 34
R. van den Broeke. 35
36
[END FAQ 13.2 HERE] 37
38
13.4.5 Anthropogenic Intervention in Water Storage on Land 39
40
The potential future effects that human activities have on changing water storage on land, thus affecting sea 41
level, have been little studied in the published peer-reviewed scientific literature. For depletion of 42
groundwater arising from extraction (for agriculture and other uses), we consider two possibilities. The first 43
assumes that this contribution to GMSL rise continues throughout the 21st century at the rate of 0.40 ± 0.11 44
mm yr
–1
(mean ± SD) assessed for 2001–2008 by Konikow (2011), amounting to 38 [21–55, 90% 45
confidence] mm by 2081–2100 relative to 1986–2005 . The second uses results from land-surface hydrology 46
models (Wada et al., 2012) with input from climate and socioeconomic projections for SRES scenarios, 47
yielding 70 [51–90] mm for the same time interval. Because of the improved treatment of groundwater 48
recharge by Wada et al. (2012), this is less than Rahmstorf et al. (2012a) obtained by assuming that the 49
groundwater extraction estimates of Wada et al. (2010) can be scaled up in the future with global population. 50
These two possibilities indicate a range of about 20–90 mm for the contribution of groundwater depletion to 51
GMSL rise. 52
53
For the rate of impoundment of water in reservoirs, we evaluate two possibilities. The first assumes it will 54
continue throughout the 21st century (e.g., (Lempérière, 2006) at the average rate of –0.2 ± 0.05 mm yr
–1
55
SLE (mean ± SD) estimated for 1971–2010 using data updated from Chao et al. (2008), giving a negative 56
contribution to GMSL rise of –19 [–11 to –27, 90% confidence] mm by 2081–2100 relative to 1986–2005. 57
The second assumes it will be zero after 2010 (i.e., no further net impoundment), as shown for the 1990s and 58

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2000s by Lettenmaier and Milly (2009) (see Section 13.3.4 for discussion). A zero contribution implies a 1
balance between further construction of reservoir capacity and reduction of storage volume by 2
sedimentation, each of which could plausibly have a rate of about 1% yr
–1
of existing capacity (Lempérière, 3
2006; Lettenmaier and Milly, 2009). These two possibilities together indicate a range of about 0–30 mm of 4
GMSL fall for the contribution of reservoir impoundment. 5
6
Our assessment thus leads to a range of –10 to +90 mm for the net contribution to GMSL rise from 7
anthropogenic intervention in land water storage by 2081–2100 relative to 1986–2005. This range includes 8
the range of 0–40 mm assumed by Katsman et al. (2008). Because of the limited information available, we 9
do not have sufficient confidence to give ranges for individual RCP scenarios. 10
11
13.5 Projections of Global Mean Sea Level Rise 12
13
Process-based projections for GMSL rise during the 21st century, given in Section 13.5.1, are the sum of 14
contributions derived from models that were evaluated by comparison with observations in Section 13.3 and 15
used to project the contributions in Section 13.4. Projections of GMSL rise by semi-empirical models 16
(SEMs) are given in Section 13.5.2. We compare these two and other approaches in Section 13.5.3 and 17
assess the level of confidence that we can place in each approach. Longer term projections are discussed in 18
13.5.4. 19
20
13.5.1 Process-Based Projections for the 21st Century 21
22
The process-based projections of GMSL rise for each RCP scenario are based on results from 21 CMIP5 23
AOGCMs from which projections of surface air temperature (SAT) change and thermal expansion are 24
available (see Section 13.4.1). Where CMIP5 results were not available for a particular AOGCM and 25
scenario, they were estimated (Good et al., 2011; 2013) (Section 12.4.1.2; Supplementary Material). The 26
projections of thermal expansion do not include an adjustment for the omission of volcanic forcing in 27
AOGCM spin-up (Section 13.3.4.2), since this is uncertain and relatively small (about 10 mm during the 28
21st century). Changes in glacier and ice-sheet SMB are calculated from the global mean SAT projections 29
using parameterisations derived from the results of process-based models of these components (Sections 30
13.4.2, 13.4.3.1, 13.4.4.1 and Supplementary Material). According to the assessment in Section 12.4.1.2, 31
global mean SAT change is likely to lie within the 5–95% range of the projections of CMIP5 models. 32
Following this assessment, the 5–95% range of model results for each of the GMSL rise contributions that is 33
projected on the basis of CMIP5 results is interpreted as the likely range. 34
35
Possible ice-sheet dynamical changes by 2100 are assessed from the published literature (Sections 13.4.3.2 36
and 13.4.4.2), which as yet provides only a partial basis for making projections related to particular 37
scenarios; therefore they are treated as independent of scenario, except that a higher rate of change is used 38
for Greenland ice-sheet outflow under RCP8.5. Projections of changes in land-water storage due to human 39
intervention are also treated as independent of emissions scenario, because we do not have sufficient 40
information to give ranges for individual scenarios. The scenario-independent treatment does not imply that 41
the contributions concerned will not depend on the scenario followed, only that the current state of 42
knowledge does not permit a quantitative assessment of the dependence. For each of these contributions 43
(Greenland and Antarctic ice-sheet dynamics, land water storage), our assessment of the literature provides a 44
5–95% range by 2100. For consistency with the treatment of the CMIP5-derived results, we interpret this 45
range as the likely range. We assume that each of these contributions begins from its present-day rate and 46
that the rate increases linearly in time, in order to interpolate from the present day to 2100 (see 47
Supplementary Material for details). 48
49
The likely range of GMSL rise given for each RCP combines the uncertainty in global climate change, 50
represented by the CMIP5 ensemble (Section 12.4.1.2), with the uncertainties in modelling the contributions 51
to GMSL. The part of the uncertainty related to the magnitude of global climate change is correlated among 52
all the scenario-dependent contributions, while the methodological uncertainties are treated as independent 53
(see also Supplementary Material). 54
55
The sum of the projected contributions gives the likely range for future GMSL rise. The median projections 56
for GMSL in all scenarios lie within a range of 0.05 m until the middle of the century (Figure 13.11), 57

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because the divergence of the climate projections has a delayed effect owing to the time-integrating 1
characteristic of sea level. By the late 21st century (over an interval of 95 years, between the 20-year mean 2
of 2081–2100 and the 20-year mean of 1986–2005), they have a spread of about 0.2 m, with RCP2.6 giving 3
the least amount of rise (0.40 [0.26–0.54] m) (likely range) and RCP8.5 giving the most (0.62 [0.45–0.81] 4
m). RCP4.5 and RCP6.0 are very similar at the end of the century (0.47 [0.32–0.62] m and 0.47 [0.33–0.62]] 5
m respectively), but RCP4.5 has a greater rate of rise earlier in the century than RCP6.0 (Figure 13.10 and 6
Table 13.5). At 2100, the likely ranges are 0.43 [0.28–0.60] m (RCP2.6), 0.52 [0.35–0.70]] m (RCP4.5), 0.54 7
[0.37–0.72] m (RCP6.0), and 0.73 [0.53–0.97] m (RCP8.5). 8
9
In all scenarios, the rate of rise at the start of the RCP projections (2007–2013) is about 3.7 mm yr
–1
, slightly 10
above the observational range of 3.2 [2.8–3.6] mm yr
–1
for 1993–2010, because the modelled contributions 11
for recent years, although consistent with observations for 1993–2010 (Section 13.3), are all in the upper part 12
of the observational ranges, perhaps related to the simulated rate of climatic warming being greater than has 13
been observed (Box 9.2). In the projections, the rate of rise initially increases. In RCP2.6 it becomes roughly 14
constant (central projection 4.5 mm yr
–1
) before the middle of the century, and subsequently declines 15
slightly. The rate of rise becomes roughly constant in RCP4.5 and RCP6.0 by the end of the century, 16
whereas acceleration continues throughout the century in RCP8.5, reaching 11 [7–15] mm yr
–1
in 2081–17
2100. 18
19
In all scenarios, thermal expansion is the largest contribution, accounting for about 30–55% of the 20
projections. Glaciers are the next largest. By 2100, 15–55% of the present glacier volume is projected to be 21
eliminated under RCP2.6, and 35–85% under RCP8.5. SMB change on the Greenland ice sheet makes a 22
positive contribution, whereas SMB change in Antarctica gives a negative contribution (Sections 13.3.3.2 23
and 13.4.4.1). The positive contribution due to rapid dynamical changes that result in increased ice outflow 24
from both ice sheets together has a likely range of 0.03–0.20 m in RCP8.5 and 0.03–0.19 m in the other 25
RCPs. There is a relatively small positive contribution from human intervention in land-water storage, 26
predominantly due to increasing extraction of groundwater. 27
28
[INSERT FIGURE 13.10 HERE] 29
Figure 13.10: Projections from process-based models with likely ranges and median values for global-mean sea level 30
rise and its contributions in 2081–2100 relative to 1986–2005 for the four RCP scenarios and scenario SRES A1B used 31
in the AR4. The contributions from ice sheets include the contributions from ice-sheet rapid dynamical change, which 32
are also shown separately. The contributions from ice-sheet rapid dynamics and anthropogenic land water storage are 33
are treated as having uniform probability distributions, and as independent of scenario (except that a higher rate of 34
change is used for Greenland ice-sheet outflow under RCP8.5). This treatment does not imply that the contributions 35
concerned will not depend on the scenario followed, only that the current state of knowledge does not permit a 36
quantitative assessment of the dependence. See discussion in Sections 13.5.1 and 13.5.3 and Supplementary Material 37
for methods. Only the collapse of the marine-based sectors of the Antarctic Ice Sheet, if initiated, could cause GMSL to 38
rise substantially above the likely range during the 21st century. This potential additional contribution cannot be 39
precisely quantified but there is medium confidence that it would not exceed several tenths of a meter of sea level rise. 40
41
[INSERT FIGURE 13.11 HERE] 42
Figure 13.11: Projections from process-based models of (a) GMSL rise relative to 1986–2005 and (b) the rate of 43
GMSL rise and its contributions as a function of time for the four RCP scenarios and scenario SRES A1B. The lines 44
show the median projections. For GMSL rise and the thermal expansion contribution, the likely range is shown as a 45
shaded band. The contributions from ice sheets include the contributions from ice-sheet rapid dynamical change, which 46
are also shown separately. The time series for GMSL rise plotted in (a) are tabulated in Annex II (Table AII.7.7), and 47
the time series of GMSL rise and all its contributions are available in the Supplementary Material. The rates in (b) are 48
calculated as linear trends in overlapping 5-year periods. Only the collapse of the marine-based sectors of the Antarctic 49
Ice Sheet, if initiated, could cause GMSL to rise substantially above the likely range during the 21st century. This 50
potential additional contribution cannot be precisely quantified but there is medium confidence that it would not exceed 51
several tenths of a meter of sea level rise. 52
53
54
Table 13.5: Median values and likely ranges for projections of global-mean sea level rise and its contributions in 55
metres in 2081–2100 relative to 1986–2005 for the four RCP scenarios and SRES A1B, GMSL rise in 2046–2065 and 56
2100, and rates of GMSL rise in mm yr
–1
in 2081–2100. See Section 13.5.1 concerning how the likely range is defined. 57
Because some of the uncertainties in modelling the contributions are treated as uncorrelated, the sum of the lower 58
bound of contributions does not equal the lower bound of the sum, and similarly for the upper bound (see 59
Supplementary Material). Because of imprecision from rounding, the sum of the medians of contributions may not 60

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Do Not Cite, Quote or Distribute 13-48 Total pages: 121
exactly equal the median of the sum. The net contribution (SMB + dynamics) for each ice sheet, and the contribution 1
from rapid dynamical change in both ice sheets together, are shown as additional lines below the sum; they are not 2
contributions in addition to those given above the sum. The contributions from ice-sheet rapid dynamical change and 3
anthropogenic land water storage are treated as having uniform probability distributions, uncorrelated with the 4
magnitude of global climate change (except for the interaction between Antarctic ice-sheet SMB and outflow), and as 5
independent of scenario (except that a higher rate of change is used for Greenland ice-sheet outflow under RCP8.5). 6
This treatment does not imply that the contributions concerned will not depend on the scenario followed, only that the 7
current state of knowledge does not permit a quantitative assessment of the dependence. Regional sea level change is 8
expected in general to differ from the global mean (see Section 13.6). 9
SRES A1B RCP2.6 RCP4.5 RCP6.0 RCP8.5
Thermal
Expansion
0.21 [0.16 to 0.26] 0.14 [0.10 to 0.18] 0.19 [0.14 to 0.23] 0.19 [0.15 to 0.24] 0.27 [0.21 to 0.33]
Glaciers
a
0.14 [0.08 to 0.21] 0.10 [0.04 to 0.16] 0.12 [0.06 to 0.19] 0.12 [0.06 to 0.19] 0.16 [0.09 to 0.23]
Greenland Ice
Sheet SMB
b
0.05 [0.02 to 0.12] 0.03 [0.01 to 0.07] 0.04 [0.01 to 0.09] 0.04 [0.01 to 0.09] 0.07 [0.03 to 0.16]
Antarctic Ice
Sheet SMB
c
–0.03 [–0.06 to –0.01] –0.02 [–0.04 to –0.00] –0.02 [–0.05 to –0.01] –0.02 [–0.05 to –0.01] –0.04 [–0.07 to –0.01]
Greenland Ice
Sheet Rapid
Dynamics
0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.05 [0.02 to 0.07]
Antarctic Ice
Sheet Rapid
Dynamics
0.07 [–0.01 to 0.16] 0.07 [–0.01 to 0.16] 0.07 [–0.01 to 0.16] 0.07 [–0.01 to 0.16] 0.07 [–0.01 to 0.16]
Land Water
Storage
0.04 [–0.01 to 0.08] 0.04 [–0.01 to 0.08] 0.04 [–0.01 to 0.08] 0.04 [–0.01 to 0.08] 0.04 [–0.01 to 0.08]
Sea Level Rise
in 2081–2100
0.52 [0.36 to 0.69] 0.40 [0.26 to 0.54] 0.47 [0.32 to 0.62] 0.47 [0.33 to 0.62] 0.62 [0.45 to 0.81]
Greenland Ice
Sheet
0.09 [0.05 to 0.15] 0.06 [0.04 to 0.10] 0.08 [0.04 to 0.13] 0.08 [0.04 to 0.13] 0.12 [0.07 to 0.21]
Antarctic Ice
Sheet
0.04 [–0.05 to 0.13] 0.05 [–0.03 to 0.14] 0.05 [–0.04 to 0.13] 0.05 [–0.04 to 0.13] 0.04 [–0.06 to 0.12]
Ice-Sheet Rapid
Dynamics
0.10 [0.03 to 0.19] 0.10 [0.03 to 0.19] 0.10 [0.03 to 0.19] 0.10 [0.03 to 0.19] 0.12 [0.03 to 0.20]
Rate of Sea
Level Rise
8.0 [5.0 to 11.1] 4.3 [2.0 to 6.6] 6.0 [3.5 to 8.5] 7.3 [4.6 to 10.0] 11.1 [7.4 to 15.5]
Sea Level Rise
in 2046–2065 0.26 [0.19 to 0.34] 0.24 [0.17 to 0.31] 0.26 [0.19 to 0.33] 0.25 [0.18 to 0.32] 0.29 [0.22 to 0.37]
Sea Level Rise
in 2100
0.60 [0.41 to 0.79] 0.43 [0.28 to 0.60] 0.52 [0.35 to 0.70] 0.54 [0.37 to 0.72] 0.73 [0.53 to 0.97]
Only the collapse of the marine-based sectors of the Antarctic Ice Sheet, if initiated, could cause GMSL to rise substantially above the
likely range during the 21st century. This potential additional contribution cannot be precisely quantified but there is medium
confidence that it would not exceed several tenths of a meter of sea level rise.
Notes: 10
(a) Excluding glaciers on Antarctica but including glaciers peripheral to the Greenland ice sheet. 11
(b) Including the height-SMB feedback. 12
(c) Including the interaction between SMB change and outflow. 13
14
15
13.5.2 Semi-Empirical Projections for the 21st Century 16
17
The development of semi-empirical models (SEMs) was motivated by two problems. First, process-based 18
modelling was incomplete in the AR4 because of the unavailability of ice-sheet dynamical models which 19
could be used to simulate the observed recent accelerations in ice flow and make projections with confidence 20
(Meehl et al., 2007) (Sections 13.1.5.1, 13.3.3.2, 13.4.3.2, 13.4.4.2). Second, in all previous IPCC 21
assessments, observed GMSL rise during the 20th century could not be completely accounted for by the 22
contributions to GMSL from thermal expansion, glaciers and ice sheets. For example, the AR4 assessed the 23
mean observational rate for 1961–2003 as 1.8 ± 0.5 mm yr
–1
, and the sum of contributions as 1.1 ± 0.5 mm 24
yr
–1
(Bindoff et al., 2007; Hegerl et al., 2007). With the central estimates, only about 60% of observed sea 25
level rise was thus explained, and the potential implication was that projections using process-based models 26
which reproduce only those known contributions would underestimate future sea level rise (Rahmstorf, 27

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2007a; Jevrejeva et al., 2009; Grinsted et al., 2010). SEMs do not aim to solve the two problems that 1
motivated their development, but instead provide an alternative approach for projecting GMSL. 2
3
The semi-empirical approach regards a change in sea level as an integrated response of the entire climate 4
system, reflecting changes in the dynamics and thermodynamics of the atmosphere, ocean and cryosphere; it 5
does not explicitly attribute sea level rise to its individual physical components. SEMs use simple physically 6
motivated relationships, with various analytical formulations and parameters determined from observational 7
time series, to predict GMSL for the 21st century (Figure 13.12 and Table 13.6) and beyond, from either 8
global mean surface air temperature (SAT) (Rahmstorf, 2007a; Horton et al., 2008; Vermeer and Rahmstorf, 9
2009; Grinsted et al., 2010; Rahmstorf et al., 2012a) or radiative forcing (Jevrejeva et al., 2009; 2010, 10
2012a). 11
12
[INSERT FIGURE 13.12 HERE] 13
Figure 13.12: Range (5–95%) for projections of global-mean sea level rise (m) in 2081–2100 relative to 1986–2005 by 14
semi-empirical models for (a) RCP2.6, (b) RCP4.5, (c) RCP6.0, and (d) RCP8.5. Blue bars are results from the models 15
using RCP temperature projections, red bars are using RCP radiative forcing. The numbers on the horizontal axis refer 16
to the literature source of the projection and the sea level reconstruction used for calibration (for studies using RCP 17
temperature projections) or reconstruction of radiative forcing (for studies using RCP radiative forcing). (1) Rahmstorf 18
et al. (2012a), with Kemp et al. (2011); (2) Schaeffer et al. (2012); (3) Rahmstorf et al. (2012a), with Church and White 19
(2006); (4) Rahmstorf et al. (2012a), with Church and White (2011); (5) Rahmstorf et al. (2012a), with Jevrejeva et al. 20
(2008); (6) Grinsted et al. (2010), with Moberg et al. (2005); (7) Jevrejeva et al. (2012a), with Goosse et al. (2005); (8) 21
Jevrejeva et al. (2012a), with Crowley et al. (2003); (9) Jevrejeva et al. (2012a) with Tett et al. (2007). Also shown for 22
comparison is the median (thick black line) and likely range (horizontal gray bar) (as defined in Section 13.5.1) from 23
the process-based projections (Table 13.5), which are assessed as having medium confidence, in contrast to SEMs, 24
which are assessed as having low confidence (Section 13.5.3). 25
26
SEMs are designed to reproduce the observed sea level record over their period of calibration, as this 27
provides them with model parameters needed to make projections (Rahmstorf, 2007a; Jevrejeva et al., 2009; 28
Vermeer and Rahmstorf, 2009; Grinsted et al., 2010). A test of the predictive skill of the models requires 29
simulating a part of the observed record that has not been used for calibration. For instance, Rahmstorf 30
(2007b) calibrated for 1880–1940 and predicted 1940–2000, obtaining results within 0.02 m of observed. 31
Jevrejeva et al. (2012b) calibrated up to 1950 and predicted 0.03 m (about 25%) less than observed for 32
1950–2009, and 3.8 mm yr
–1
for 1993–2010, which is about 20% more than observed. 33
34
The GMSL estimates used for calibrating the SEMs are based on the existing sparse network of long tide-35
gauge records, and are thus uncertain, especially before the late 19th century; these uncertainties are 36
reflected in the observational estimates of the rate of GMSL rise (Sections 3.7 and 13.2.2). Consequently, the 37
projections may be sensitive to the statistical treatment of the temporal variability in the instrumental record 38
of sea level change (Holgate et al., 2007; Rahmstorf, 2007b; Schmith et al., 2007). Rahmstorf et al. (2012a) 39
reported that GMSL projections for the RCP4.5 scenario for 2100 (Table 13.6) varied by ±0.04 m when the 40
embedding dimension used for temporal smoothing during the calibration was varied within a range of 0–25 41
years. 42
43
Furthermore, there is some sensitivity to the choice of datasets used for calibration. For instance, when 44
calibrated up to 1960 and used to predict 1961–2003, the model of Bittermann et al. (2013) overestimates the 45
GMSL dataset of Jevrejeva et al. (2008) by 75%, but makes an accurate estimate for the Church and White 46
(2011) dataset, although these two datasets have similar rates of sea level rise in the predicted period. The 47
central projections of Rahmstorf et al. (2012a) for 2100 under RCP4.5 (Table 13.6) for calibration with the 48
GMSL dataset of Church and White (2006) are about 0.2 m more than for calibration with the Church and 49
White (2011) dataset, although the two Church and White (2006, 2011) datasets differ at all times by less 50
than one standard deviation. The ranges of the projections by Grinsted et al. (2010) and Jevrejeva et al. 51
(2010, 2012a, 2012b) allow for the uncertainty in the GMSL reconstructions through the use of an 52
uncertainty covariance matrix in determining the model parameters. Grinsted et al. (2010) also investigated 53
the sensitivity to the temperature dataset used as predictor, and Jevrejeva et al. (2010) investigated the 54
sensitivity to radiative forcing as predictor (Table 13.6). In the latter case, three datasets gave median 55
projections under RCP4.5 for 2100 within a range of about ±0.20 m. 56
57

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SEM projections will be biased unless contributions to past GMSL rise which are correlated with but not 1
physically related to contemporary changes in the predictor variable (either global mean SAT change or 2
radiative forcing) are subtracted from the observational sea level record before the calibration (Vermeer and 3
Rahmstorf, 2009; Jevrejeva et al., 2012b; Rahmstorf et al., 2012a; Orlić and Pasarić, 2013). These include 4
groundwater depletion due to anthropogenic intervention and storage of water by dams (Section 13.3.4), 5
ongoing adjustment of the Greenland and Antarctic ice sheets to climate change in previous centuries and 6
millennia (Section 13.3.6), and the effects of internally generated regional climate variability on glaciers 7
(Marzeion et al., 2012a; Church et al., 2013) (Sections 13.3.2.2 and 13.3.6) and ice sheets (Section 13.3.3.2). 8
For instance, Jevrejeva et al. (2012b) found that their median projections for 2100 were reduced by 0.02–9
0.10 m by excluding some such contributions. 10
11
Making projections with a SEM assumes that sea level change in the future will have the same relationship 12
as it has had in the past to radiative forcing or global mean temperature change. The appropriate choice for 13
the formulation of the SEM may depend on the nature of the climate forcing and the timescale, and 14
potentially non-linear physical processes may not scale in the future in ways which can be calibrated from 15
the past (von Storch et al., 2008; Vermeer and Rahmstorf, 2009; Rahmstorf et al., 2012a; Orlić and Pasarić, 16
2013). Two such effects that could lead to overestimated or underestimated projections by SEMs have been 17
discussed in the literature. 18
19
First, AOGCMs indicate that the ocean heat uptake efficiency tends to decline as warming continues and 20
heat penetrates more deeply (Gregory and Forster, 2008). A linear scaling of the rate of global ocean heat 21
uptake with global SAT determined from the past, as proposed by Rahmstorf (2007a), will thus overestimate 22
future time-integrated heat content change and the consequent global ocean thermal expansion on a century 23
timescale (Orlić and Pasarić, 2013). Rahmstorf (2007a) found that the linear scaling overestimated by 0.12 m 24
(about 30%) the thermal expansion simulated by a climate model with a 3D ocean from 1990 to 2100 under 25
scenario SRES A1FI. Furthermore, the Rahmstorf (2007a) model is inadequate for simulating sea level 26
variations of the last millennium (von Storch et al., 2008), which arise predominantly from episodic volcanic 27
forcing, rather than the sustained forcing on multidecadal timescales for which it was intended. In both 28
applications, the AOGCM behaviour is more accurately reproduced by taking into account the vertical 29
profile of warming, at least by distinguishing the upper (mixed layer) and lower (thermocline) layers 30
(Vermeer and Rahmstorf, 2009; Held et al., 2010) (Section 13.4.1), or by introducing a relaxation timescale 31
for sea level rise (Jevrejeva et al., 2012b). 32
33
Second, the sensitivity of glaciers to warming will tend to decrease as the area most prone to ablation and the 34
remaining volume decrease, partly counteracted by lowering of the surface due to thinning (Huss et al., 35
2012) (Section 13.4.2). On the other hand, glaciers at high latitudes which currently have negligible surface 36
melting will begin to ablate as the climate becomes warmer, tending to give an increase in sensitivity 37
(Rahmstorf et al., 2012a) (Section 13.4.2). Estimating the balance of these two effects will require detailed 38
modelling of glacier SMB. The absence of a multidecadal acceleration in the rate of glacier mass loss in 39
observations of the 20th and simulations of the 21st centuries (Section 4.3.3) (Radic and Hock, 2010; 40
Marzeion et al., 2012a), despite rising global temperatures, suggests that the reduction in sensitivity may 41
dominate (Gregory et al., 2013b). 42
43
44
Table 13.6: Global-mean sea level rise (m) projected by semi-empirical models and compared with the IPCC AR4 and 45
AR5 projections. In each case the results have a probability distribution, whose 5-, 50- and 95-percentiles are shown in 46
the columns as indicated. The AR5 5–95% process-based model range is interpreted as a likely range (medium 47
confidence) (Section 13.5.1). 48
From To 5% 50% 95%
Scenario SRES A1B
IPCC AR4
a
1990 2100 0.22 0.37 0.50
IPCC AR4
a,b
1990 2100 0.22 0.43 0.65
IPCC AR5 (also in Table 13.5) 1996 2100 0.40 0.58 0.78
Rahmstorf (2007a)
c
1990 2100 – 0.85 –
Horton et al. (2008)
d
2000 2100 0.62 0.74 0.88
Vermeer and Rahmstorf (2009) 1990 2100 0.98 1.24 1.56

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Grinsted et al. (2010)
with Brohan et al. (2006)
temperature for calibration
1990 2100 0.32 0.83 1.34
Grinsted et al. (2010)
with Moberg et al. (2005)
temperature for calibration
1990 2100 0.91 1.12 1.32
Jevrejeva et al. (2010)
with Crowley et al. (2003)
forcing for calibration
1990 2100 0.63 0.86 1.06
Jevrejeva et al. (2010)
with Goosse et al. (2005)
forcing for calibration
1990 2100 0.60 0.75 1.15
Jevrejeva et al. (2010)
with Tett et al. (2007)
forcing for calibration
1990 2100 0.87 1.15 1.40
Scenario RCP4.5
IPCC AR5 (also in Table 13.5) 1986–2005 2081–2100 0.32 0.47 0.62
Grinsted et al. (2010) calibrated with Moberg et al.
(2005) temperature
1986–2005 2081–2100 0.63 0.88 1.14
Rahmstorf et al. (2012a) calibrated with Church and
White (2006) GMSL
1986–2005 2081–2100 0.79 0.86 0.93
Rahmstorf et al. (2012a) calibrated with Church and
White (2011) GMSL
1986–2005 2081–2100 0.57 0.63 0.68
Rahmstorf et al. (2012a) calibrated with Jevrejeva et
al. (2008) GMSL
1986–2005 2081–2100 0.82 0.97 1.12
Rahmstorf et al. (2012a) calibrated with proxy data 1986–2005 2081–2100 0.56 0.88 1.24
Jevrejeva et al. (2012a) calibrated with Goosse et al.
(2005) radiative forcing
1986–2005 2081–2100 0.43 0.56 0.69
Jevrejeva et al. (2012a) calibrated with Crowley et al.
(2003) radiative forcing
1986–2005 2081–2100 0.48 0.65 0.80
Jevrejeva et al. (2012a) calibrated with Tett et al.
(2007) radiative forcing
1986–2005 2081–2100 0.65 0.85 1.05
Schaeffer et al. (2012) 1986–2005 2081–2100 0.58 0.80 1.05
Notes: 1
(a) Extrapolated to 2100 using the projected rates of sea level rise for 2090–2099 in Table 10.7 of Meehl et al. (2007). 2
(b) Including scaled-up ice-sheet discharge given in Table 10.7 of Meehl et al. (2007) and extrapolated to 2100 as an 3
illustration of the possible magnitude of this effect. 4
(c) Uncertainty range not given. 5
(d) The mean value and the range are shown for semi-empirical model projections based on results from 11 GCMs. 6
7
8
13.5.3 Confidence in Likely Ranges and Bounds 9
10
The AR4 (Meehl et al., 2007) presented process-model-based projections of GMSL rise for the end of the 11
21st century, but did not provide a best estimate or likely range principally because scientific understanding 12
at the time was not sufficient to allow an assessment of the possibility of future rapid changes in ice-sheet 13
dynamics (on timescales of a few decades, Section 4.4.5). Future rapid changes in ice-sheet outflow were 14
consequently not included in the ranges given by the AR4. For the SRES A1B scenario, the AR4 range was 15
0.21–0.48 m, and for the highest emissions scenario, A1FI, it was 0.26–0.59 m. The AR4 also noted that if 16
ice-sheet outflow increased linearly with global mean surface air temperature, the AR4 maximum 17
projections would be raised by 0.1–0.2 m. The AR4 was unable to exclude larger values or to assess their 18
likelihood. 19
20
Since the publication of the AR4, upper bounds of up to 2.4 m for GMSL rise by 2100 have been estimated 21
by other approaches, namely semi-empirical models (SEMs, Section 13.5.2), evidence from past climates 22
(Section 13.2.1), and physical constraints on ice-sheet dynamics (Sections 13.4.3.2 and 13.4.4.2). The broad 23
range of values reflects the different methodologies for obtaining the upper bound, involving different 24
constraining factors and sources of evidence. In particular, the upper bound is strongly affected by the choice 25
of probability level, which in some approaches is unknown because the probability of the underlying 26
assumptions is not quantified (Little et al., 2013b). 27
28

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The confidence that can be placed in projections of GMSL rise and its upper bound by the various 1
approaches must be considered. Confidence arises from the nature, quantity, quality and consistency of the 2
evidence. 3
4
The first approach is based on process-based projections, which use the results from several models for each 5
contribution (Sections 13.4 and 13.5.1, Table 13.5). There is medium evidence in support of this approach, 6
arising from our understanding of the modelled physical processes, the consistency of the models with wider 7
physical understanding of those processes as elements of the climate system (e.g., Box 13.1), the consistency 8
of modelled and observed contributions (Sections 13.3.1–13.3.5), the consistency of observed and modelled 9
GMSL (Section 13.3.6), and the consistency of process-based projections based on the CMIP5 ensemble of 10
AOGCMs, which have a range of 50-60% of the ensemble mean under a given scenario (Table 13.5). 11
Considering this evidence, we have medium confidence in the process-based projections. 12
13
The second approach uses SEMs (Section 13.5.2, Table 13.6), which make projections by calibrating a 14
physically motivated relationship between GMSL and some other parameter of the climate system in the past 15
and applying it to the future, without quantifying the contributory physical processes. If we had no physical 16
understanding of the causes of sea level rise, the semi-empirical approach to projections would be the only 17
possible one, but extrapolation beyond the range of calibration implies uncertainty that is difficult to 18
quantify, due to the assumption that sea level change in the future will have the same relationship as it has 19
had in the past to radiative forcing or global mean temperature change (Section 13.5.2). As a result, there is 20
low agreement and no consensus in the scientific community about the reliability of SEM projections, 21
despite their successful calibration and evaluation against the observed 20th century sea level record. 22
23
For a given RCP, some semi-empirical models project a range that overlaps the process-based likely range 24
while others project a median and 95-percentile that are about twice as large as the process-based models. In 25
nearly every case, the semi-empirical model 95-percentile is above the process-based likely range (Figure 26
13.12). Two physical explanations have been suggested for the higher projections. First, the contribution 27
from accelerated calving of tidewater glaciers may be substantial, and included in SEMs but not process-28
based models (Jevrejeva et al., 2012b); however, this could account for only 0.1–0.2 m of additional GMSL 29
rise. Second, SEMs may allow for rapid ice-sheet dynamical change (Section 4.4.4) in response to future 30
climate change (Grinsted et al., 2010; Little et al., 2013a). In order for large ice-sheet dynamical changes to 31
be predictable by SEMs, two conditions must be met. First, these changes must have contributed 32
substantially to sea level rise during the period of calibration. This is very unlikely to be the case, because it 33
is very likely that dynamical changes have contributed only a small part of the observed sea level rise during 34
the 20th century, rising to about 15% during 1993–2010 (Section 13.3.6). Second, the changes must have a 35
link to global surface temperature or radiative forcing. Current understanding of recent dynamical changes in 36
Greenland and Antarctica is that they have been triggered by local changes in ocean temperature (Holland et 37
al., 2008; Thoma et al., 2008; Jacobs et al., 2011), but a link has not been demonstrated between these 38
changes and global climate change or its drivers. Consequently there is great uncertainty regarding whether 39
recent ice-sheet dynamical changes indicate a long-term trend or instead arise from internal variability 40
(Bamber and Aspinall, 2013). Hence there is no evidence that ice-sheet dynamical change is the explanation 41
for the higher GMSL rise projections of SEMs, implying that either there is some other contribution which is 42
presently unidentified or underestimated by process-based models, or that the projections of SEMs are 43
overestimates (cf. Section 13.5.2). Because of the limited or medium evidence supporting SEMs, and the low 44
agreement about their reliability, we have low confidence in their projections. 45
46
The third approach uses paleo-records of sea level change that show that rapid GMSL rise has occurred 47
during glacial terminations, at rates that averaged about 10 mm yr
–1
over centuries, with at least one instance 48
(Meltwater Pulse 1A) that exceeded 40 mm yr
–1
(Section 5.6.3), but this rise was primarily from much larger 49
ice-sheet sources that no longer exist. Contributions from these vanished ice sheets could have continued 50
even after sea level and climate had reached interglacial states, if the Greenland and Antarctic ice sheets 51
contracted during the termination to smaller sizes than at present. During past interglacial periods, only the 52
Greenland and Antarctic ice sheets were present. For the time interval during the LIG in which GMSL was 53
above present, there is high confidence that the maximum 1000-year average rate of GMSL rise during these 54
periods exceeded 2 m kyr
–1
but did not exceed 7 m kyr
–1
(Kopp et al., 2013) (Sections 5.6.2, 13.2.1.3). 55
Because climate variations during interglacial periods had different forcings from anthropogenic climate 56

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change, they give only a limited basis for predictions of the future, and we do not consider that they provide 1
upper bounds for GMSL rise during the 21st century. 2
3
The fourth approach is concerned particularly with the contribution from ice-sheet dynamical change, for 4
which it considers kinematic limits. Pfeffer et al. (2008) argued that scenarios of GMSL rise exceeding 2 m 5
by 2100 are physically untenable, ruling out, for example, the heuristic argument of Hansen et al. (2007) 6
giving 5 m by 2100. Pfeffer et al. (2008) constructed scenarios of 0.8 m and 2.0 m, and Katsman et al. (2011) 7
of 1.15 m, for GMSL rise by 2100, including ice-sheet rapid dynamical acceleration. Although these authors 8
considered their scenarios to be physically possible, they are unable to quantify their likelihood, because the 9
probability of the assumptions on which they depend cannot be estimated from observations of the response 10
of the Greenland and Antarctic ice sheets to climate change or variability on century timescales. These 11
scenarios involve contributions of ~0.5 m from Antarctica. This is much greater than any process-based 12
projections of dynamical ice-sheet change (Section 13.4.4.2), and would require either a sustained high 13
increase in outflow in all marine-based sectors or the localised collapse of the ice sheet in the Amundsen Sea 14
sector (Little et al., 2013a). 15
16
In summary, we have greater confidence in the process-based projections than in the other approaches, and 17
our assessment is that GMSL rise during the 21st century for each RCP scenario is likely (medium 18
confidence) to lie within the 5–95% range given by the process-based projections (Section 13.5.1 and Table 19
13.5; see Section 13.5.4 for following centuries), which are consistent with the likely ranges projected for 20
global-mean surface air temperature change (Section 12.4.1.2). We are not able to assess a very likely range 21
on the same basis, because there is no assessment available of the very likely range for global-mean SAT 22
change, and because we cannot robustly quantify the probability of ice-sheet dynamical changes which 23
would give rise to greater values. 24
25
Under the RCP8.5 scenario, which has the highest radiative forcing, the likely range reaches 0.95 m by 2100 26
relative to 1986–2005. Observations do not show an increase in Antarctic precipitation, which is projected 27
by models and makes a negative contribution to the projected GMSL rise (Table 13.5). The recovery of 28
Antarctic stratospheric ozone concentration and increased basal melting of Antarctic ice-shelves have both 29
been suggested as giving rise to mechanisms whereby the Antarctic warming and precipitation increase 30
might be suppressed with respect to CMIP5 projections (Section 13.4.4.1). If the Antarctic precipitation 31
increase is omitted from the process-based projections, the likely range for RCP8.5 at 2100 reaches 1.03 m 32
(assuming uncorrelated errors). Higher values for 2100 are given in the scientific literature on the basis of 33
various approaches: 1.15 m (Katsman et al., 2011), 1.21 m (Schaeffer et al., 2012) (for RCP4.5), 1.40 m 34
(National Research Council, 2012), 1.65 m (Jevrejeva et al., 2012b) (for RCP8.5), 1.79 m (Vermeer and 35
Rahmstorf, 2009) (for SRES A1FI), 1.90 m (Rahmstorf et al., 2012a) (with proxy calibration, for RCP8.5), 36
2.0 m (Pfeffer et al., 2008), 2.25 m (Sriver et al., 2012), and 2.4 m (Nicholls et al., 2011). Considering this 37
inconsistent evidence, we conclude that the probability of specific levels above the likely range cannot be 38
reliably evaluated. 39
40
Only the collapse of marine-based sectors of the Antarctic Ice Sheet could cause GMSL rise substantially 41
above the likely range during the 21st century. Expert estimates of contributions from this source have a 42
wide spread (Bamber and Aspinall, 2013), indicating a lack of consensus on the probability for such a 43
collapse. The potential additional contribution to GMSL rise also cannot be precisely quantified, but there is 44
medium confidence that, if a collapse were initiated, it would not exceed several tenths of a metre during the 45
21st century (Section 13.4.4.2). 46
47
The time-mean rate of GMSL rise during the 21st century is very likely to exceed the rate of 2.0 [1.7–2.3] 48
mm yr
–1
observed during 1971–2010, because the process-based GMSL projections indicate a significantly 49
greater rate even under the RCP2.6 scenario, which has the lowest radiative forcing. It has been asserted that 50
the acceleration of GMSL rise implied by the IPCC AR4 projections is inconsistent with the observed 51
magnitude of acceleration during the 20th century (Boretti, 2011, 2012b, 2012a; Boretti and Watson, 2012; 52
Boretti, 2012c; Boretti, 2013b, 2013a; Boretti, 2013c; Parker, 2013a; Parker, 2013b, 2013c). Refuting this 53
argument, Hunter and Brown (2013) show that the acceleration projected in the AR4 is consistent with 54
observations since 1990s. Present understanding of the contributions to GMSL rise (Section 13.3) gives an 55
explanation of the rate of 20th century GMSL rise and confidence in the process-based projections, which 56
indicate a greater rate of rise in the 21st century because of increasing forcing. 57

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1
The improved agreement of process-based models with observations and physical understanding represents 2
progress since the AR4, in which there was insufficient confidence to give likely ranges for 21st century 3
GMSL rise, as we have done here. For scenario SRES A1B, which was assessed in the AR4, the likely range 4
on the basis of science assessed in the AR5 is 0.60 [0.41–0.79] m by 2100 relative to 1986–2005, and 0.57 5
[0.40–0.75] m by 2090–2099 relative to 1990. Compared with the AR4 projection of 0.21–0.48 m for the 6
same scenario and period, the largest increase is from the inclusion of rapid changes in Greenland and 7
Antarctic ice-sheet outflow, for which the combined likely range is 0.03-0.21 m by 2091–2100 (assuming 8
uncorrelated uncertainties). These terms were omitted in the AR4 because a basis to make projections was 9
not available in published literature at that time. The contribution from thermal expansion is similar to the 10
AR4 projection and has smaller uncertainty. The contribution from glaciers is larger than in the AR4 11
primarily because of the greater estimate of the present glacier volume in new inventories (although the 12
glacier area estimate is similar, Table 4.1), and the Greenland SMB contribution is larger because of recent 13
improvement in models of relevant surface processes. Further progress on modelling each of the 14
contributions is still needed in order to attain high confidence in GMSL projections, in particular concerning 15
the probability distribution of GMSL above the likely ranges. 16
17
13.5.4 Long-Term Scenarios 18
19
Less information is available on climate change beyond the year 2100 than there is up to the year 2100. 20
However, the ocean and ice sheets will continue to respond to changes in external forcing on multi-21
centennial to multi-millennial time scales. For the period up to the year 2500, available physical model 22
projections discussed in Sections 13.4.1-4 are combined into an assessment of future sea level rise. Paleo 23
simulations are combined with paleodata to estimate the sea level commitment on a multi-millennial time 24
scale beyond 2500 for different levels of sustained increases in global mean temperature. 25
26
The RCPs, as applied in Chapter 12 and Sections 13.4 and 13.5.1, are defined up to the year 2100. Their 27
extension up to the year 2300 is used to project long-term climate change (Section 12.3.1.3) (Meinshausen et 28
al., 2011), but they are not directly derived from integrated assessment models. In simulations that are 29
reported here up to the year 2500, the radiative forcing has generally been kept constant at the 2300 level 30
except for RCP2.6, in which the forcing continues to decline at the 2300 rate. Some model simulations of ice 31
sheets and ocean warming assessed here have used scenarios different from the RCP scenarios. Because of 32
the limited number of available simulations, sea level projections beyond the year 2100 have thus been 33
grouped into three categories according to their GHG concentration in the 22nd century: low scenarios in 34
which atmospheric GHG concentrations peak and decline and do not exceed values that are equivalent to 35
500 ppm CO
2
, medium scenarios with concentrations between 500 and 700 ppm CO
2
-equivalent, and high 36
scenarios above 700 ppm. As a consequence, the model spread shown in Figure 13.13 and Table 13.8 37
combines different scenarios and is not merely due to different model physics. The low scenarios include 38
RCP2.6, SRES B1 and scenarios with 0.5 and 2% yr
–1
increases in CO
2
followed by no emissions after 450 39
ppm has been reached and the commitment scenarios, CC, in Goelzer et al. 2013 which stabilizes CO
2
at 40
present-day levels. In a number of the low scenarios, the global mean temperature peaks during the 21st 41
century and declines thereafter. These peak-and-decline scenarios include RCP2.6 as well as all scenarios 42
with no GHG emissions after a specified year. Even in these scenarios sea level continues to rise up to the 43
year 2500 in accordance with the multi-millennial sea level commitment of about 2 m °C
–1
as discussed in 44
Section 13.5.4.3. The medium scenarios include RCP4.5 as well as scenarios with 1% yr
–1
increase in CO
2
45
up to 560 ppm and SRES-B1 and SRES-A1B. The high scenarios include RCP6.0 and RCP8.5 as well as 46
1120 ppm scenarios and SRES A2. Also included are scenarios with 0.5 and 2% increase in CO
2
and a SRES 47
A2 scenario with zero emissions after 1200 and 1120 ppm have been reached, respectively. 48
49
13.5.4.1 Multi-Centennial Projections 50
51
The multi-centennial sea level contributions from ocean expansion and the cryospheric components are 52
discussed in Sections 13.4.1–13.4.4. A synthesis of these contributions is provided in Table 13.8 and Figure 53
13.13 for the end of each century until the year 2500. Thermal expansion contributions (dark blue bars, 54
Figure 13.13) were obtained from coarse-resolution coupled climate models (Vizcaíno et al., 2008; Solomon 55
et al., 2009; Gillett et al., 2011; Schewe et al., 2011; Zickfeld et al., 2013). For comparison, the full model 56
spread of the CMIP5 models which were integrated beyond 2100 is provided in Table 13.7 and as light blue 57

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bars in Figure 13.13. Even though the models used for the long-term projections (Table 13.8) are less 1
complex compared to the CMIP5 models, their model spread for the different periods and scenarios 2
encompasses the CMIP5 spread, which provides medium confidence in the application of the less complex 3
models beyond 2300. 4
5
Contributions from the Greenland and Antarctic ice sheets were obtained with climate models of comparable 6
complexity coupled to ice-sheet models (Vizcaíno et al., 2010; Huybrechts et al., 2011; Goelzer et al., 2012). 7
Glacier projections were obtained by application of the method by Marzeion et al. (2012a) to the CMIP5 8
model output for scenarios and models that were integrated up to the year 2300. For 2400 and 2500, the 9
same model spread as for 2300 is shown. This is probably underestimating the glacier’s sea level 10
contribution beyond 2300. 11
12
[INSERT FIGURE 13.13 HERE] 13
Figure 13.13: Sea level projections beyond the year 2100 are grouped into three categories according to the 14
concentration of GHG concentration (in CO
2
-equivalent) in the year 2100 (upper panel: >700 ppm; middle panel: 500–15
700 ppm; lower panel: <500 ppm). Colored bars show the full model spread. Horizontal lines provide the specific 16
model simulations. The different contributions are given from left to right as thermal expansion, glaciers, Greenland Ice 17
Sheet, Antarctic Ice Sheet and the total contribution. The range provided for the total sea level change represents the 18
maximum possible spread that can be obtained from the four different contributions. Light red shaded bars exhibit the 19
likely range for the 21st century total sea level projection from Figure 13.10 with the median as the horizontal line. 20
Light blue bars provide the model spread of the thermal expansion of the CMIP5 simulations as used for the 21st 21
century projections (Section 13.5.1), with the median indicated by the horizontal bar. 22
23
The ranges of sea level contributions provided in Figure 13.13 and Table 13.8 only represent the model 24
spread and cannot be interpreted as uncertainty ranges. An uncertainty assessment cannot be provided 25
beyond the year 2100 because of the small number of available simulations, the fact that different scenarios 26
were combined within one scenario group, and the overall low confidence in the ability of the coarse-27
resolution ice-sheet models to capture the dynamic ice discharge from Greenland and Antarctica, as 28
discussed below. The range for the total sea level change was obtained by taking the sum of contributions 29
that result in the lowest and the highest sea level rise and thereby covers the largest possible model spread. 30
31
It should be noted that the models used here for the period beyond 2100 are different from the models used 32
for the 21st century (Sections 13.4.1, 13.4.3, 13.4.4, and 13.5.1) except for the glacier models (Section 33
13.4.2). Generally, the model spread for the total sea level contribution in 2100 is slightly lower than the 34
likely range provided in Section 13.5.1 (light red bars in Figure 13.13). This is due to the ice-sheets models, 35
particularly of the Antarctic Ice Sheet, since coarse-resolution model results for thermal expansion are 36
similar to the CMIP5 projections (light blue vertical lines in Figure 13.10, Table 13.7.) and the glacier 37
contribution is the same. 38
39
Projections beyond 2100 show positive contributions to sea level from thermal expansion, glaciers and 40
changes in Greenland Ice Sheet SMB. Due to enhanced accumulation under warming, the Antarctic Ice 41
Sheet SMB change makes a negative contribution to sea level in scenarios below 700 ppm CO
2
-equivalent. 42
These results were obtained with fully coupled climate-ice sheet models which need to apply a relatively low 43
spatial resolution. In light of the discussion in Section 13.3.3.2 and the assessment of the 21st century 44
changes in Section 13.4.4.1, there is low confidence in this result. For scenarios above 700 ppm CO
2
-45
equivalent, Antarctic SMB change is contributing positively to GMSL. 46
47
As discussed in Sections 13.4.3.2 and 13.4.4.2, there is medium confidence in the ability of coupled ice 48
sheet-climate models to project sea level contributions from dynamic ice-sheet changes in Greenland and 49
Antarctica for the 21st century. In Greenland, dynamic mass loss is limited by topographically defined 50
outlets regions. Furthermore, solid-ice discharge induced from interaction with the ocean is self-limiting 51
because retreat of the ice sheet results in less contact with the ocean and less mass loss by iceberg calving 52
(Pfeffer et al., 2008; Graversen et al., 2011; Price et al., 2011). By contrast, the bedrock topography of 53
Antarctica is such that parts of the retreating ice sheet will remain in contact with the ocean. In particular, 54
due to topography that is sloping landward, especially in West Antarctica, enhanced rates of mass loss are 55
expected as the ice retreats. 56
57

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While the model used by Huybrechts et al. (2011) is in principle capable of capturing grounding line motion 1
of marine ice sheets (see Box 13.2), low confidence is assigned to the model’s ability to capture the 2
associated time scale and the perturbation required to initiate a retreat (Pattyn et al., 2013). The model used 3
by Vizcaino et al. (2010) does not represent ice-shelf dynamics and is thus lacking a fundamental process 4
that can trigger the instability. As stated by the authors, low confidence is thus also assigned to the model’s 5
ability to project future solid-ice discharge from Antarctica. It is thus likely that the values depicted in Figure 6
13.13 systematically underestimate Antarctica’s future contribution. As detailed in Section 13.5.4.3, 7
simulations of the last five million years (Pollard and DeConto, 2009) indicate that on multi-millennial time 8
scales, the Antarctic Ice Sheet loses mass for elevated temperatures, in contrast to the projections until the 9
year 2500 for the low and medium scenarios. 10
11
The model spread of total sea level change in 2300 ranges from 0.41 to 0.85 m for the low scenario (Table 12
13.8). Using a SEM, Schaeffer et al. (2012) obtained a significantly larger 90% confidence range of 1.3–3.3 13
m for the RCP2.6 scenario. The RCP4.5 scenario, for which they obtained a range of 2.3–5.5 m, is 14
categorized here as a medium scenario, and is also significantly higher than the range of 0.27–1.51 m 15
computed by the process-based models. Using a different semi-empirical approach, Jevrejeva et al. (2012a) 16
obtained a 90% confidence range of 0.13–1.74 m for RCP2.6 in the year 2500, which encloses the model 17
spread of 0.50-1.02m for the low scenario from the process-based models. For the medium and high 18
scenarios, however, they obtained ranges of 0.72–4.3 m and 1.0–11.5 m, respectively, which are 19
significantly higher than the corresponding process-based model spread of 0.18–2.32 m and 1.51–6.63 m 20
(Table 13.8). Since projections of land water storage are not available beyond the year 2100 these were not 21
included here. 22
23
The higher estimates from the SEMs than the process-based models used here for the long-term projections 24
are consistent with the relation between the two modeling approaches for the 21st century (Figure 13.12). 25
Section 13.5.3 concluded that the limited or medium evidence supporting SEMs, and the low agreement 26
about their reliability, provides low confidence in their projections for the 21st century. We note here that the 27
confidence in the ability of SEMs is further reduced with the length of the extrapolation period and the 28
deviation of the future forcing from the forcing of the learning period (Schaeffer et al., 2012), thus 29
decreasing confidence over the long time frames considered here. 30
31
For increasing global mean SAT, sea level is virtually certain to continue to rise beyond the year 2500 as 32
shown by available process-based model simulations of thermal expansion and ice sheets that were 33
computed beyond 2500 (Rahmstorf and Ganopolski, 1999; Ridley et al., 2005; Winguth et al., 2005; 34
Driesschaert et al., 2007; Mikolajewicz et al., 2007b; Swingedouw et al., 2008; Vizcaíno et al., 2008; 35
Solomon et al., 2009; Vizcaíno et al., 2010; Gillett et al., 2011; Goelzer et al., 2011; Huybrechts et al., 2011; 36
Schewe et al., 2011). 37
38
13.5.4.2 Multi-Millennial Projections 39
40
Here sea level commitment in response to a global mean temperature increase on a multi-millennial time 41
scale is assessed. Figure 13.14 shows the sea level rise after several millennia of constant global mean 42
temperature increase above pre-industrial. The thermal expansion of the ocean was taken from 1000-year 43
integrations with six coupled climate models as used in the AR4 (models Bern2D, CGoldstein, CLIMBER-2, 44
MIT, MoBidiC and UCL in Figure 10.34 in Meehl et al. (2007)). These yield a rate of sea level change in the 45
range of 0.20–0.63 m °C
–1
(Figure 13.14a). For reference, a spatially uniform increase of ocean temperature 46
yields a global mean sea level rise of 0.38 m °C
–1
when added to observed data (Levitus et al., 2009) (black 47
dots in Figure 13.14a). Uncertainty arises due to the different spatial distribution of the warming in models 48
and the dependence of the expansion on local temperature and salinity. The contribution for glaciers was 49
obtained with the models from Mazeion et al. (2012a) and Radic and Hock (2011) by integration with fixed 50
boundary conditions corresponding to different global mean SAT levels for 3000 years. 51
52
[INSERT FIGURE 13.14 HERE] 53
Figure 13.14: Left column: multi-millennial sea level commitment per degree of warming as obtained from physical 54
model simulations of (a) ocean warming, (b) mountain glaciers, (c) the Greenland and (d) the Antarctic ice sheet. (e) 55
The corresponding total sea level commitment, compared to paleo-estimates from past warm periods (PI = pre-56
industrial, LIG = last interglacial period, M11 = Marine Isotope Stage 11, Plio = Mid-Pliocene). Temperatures are 57
relative to pre-industrial. Dashed lines provide linear approximations in (d) & (e) constant slopes of 1.2, 1.8 and 2.3 m 58

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°C
–1
. Shading as well as the vertical line represents the uncertainty range as detailed in the text. Right column: 2000-1
year-sea level commitment. The difference in total sea level commitment (j) compared to the fully equilibrated situation 2
(e) arises from the Greenland Ice Sheet which equilibrates on tens of thousands of years. After 2000 years one finds a 3
non-linear dependence on the temperature increase (h) consistent with coupled climate-ice-sheet simulations by 4
Huybrechts et al. (2011) (black dot). The total sea level commitment after 2000 years is quasi-linear with a slope of 2.3 5
m °C
–1
. 6
7
As detailed in Sections 13.4.3.2 and 13.4.4.2, there is low confidence in the ability of ice-sheet models to 8
capture the temporal response to changes in external forcing on a decadal to centennial time scale. On multi-9
centennial to multi-millennial time scales, however, these models can be validated against paleo-sea level 10
records. The contributions from the Greenland Ice Sheet were computed with a dynamic ice-sheet model 11
coupled to an energy-moisture balance model for the SMB (Robinson et al., 2012). The model’s parameters 12
were constrained by comparison with SMB estimates and topographic data for the present day and with 13
estimated summit-elevation changes from ice-core records for the Last Interglacial period (LIG), in order to 14
ensure that the coupled model ensemble has a realistic sensitivity to climatic change. The parameter spread 15
leads to a spread in ice-sheet responses (dark green lines in Figure 13.14c). The contribution to sea level 16
commitment from the Greenland Ice Sheet is relatively weak (on average 0.18 m °C
–1
up to 1°C and 0.34 m 17
°C
–1
between 2°C and 4°C) apart from the abrupt threshold of ice loss between 0.8°C and 2.2°C above pre-18
industrial (90% confidence interval in the particular model calculations reported here) (Figure 13.14c). This 19
represents a change from a fully ice-covered Greenland to an essentially ice-free state, reducing the ice sheet 20
to around 10% of present-day volume and raising sea level by over 6 m (Ridley et al., 2005; Ridley et al., 21
2010). The threshold temperature is lower than estimates obtained from the assumption that the threshold 22
coincides with a negative total SMB of the Greenland Ice Sheet (see Section 13.4.3.2 for a more complete 23
discussion). 24
25
The Antarctic Ice Sheet contribution comes from a simulation of the last 5 million years (Pollard and 26
DeConto, 2009), which is in good agreement with regional paleo-records (Naish et al., 2009). The sensitivity 27
of the ice sheet was extracted from this model simulation by correlating the ice volume with the global mean 28
temperature which forces the simulation. The standard deviation of the resulting scatter is used as a measure 29
of uncertainty (Figure 13.14d). Uncertainty arises from uncertainty in the forcing data, the ice-physics 30
representation, and from the time-dependent nature of the simulation. For example, the existence of 31
hysteresis behavior on the sub-continental scale can lead to different contributions for the same temperature 32
increase. The Antarctic Ice Sheet shows a relatively constant commitment of 1.2 m °C
–1
. Paleorecords 33
indicate that a potential hysteresis behaviour of East-Antarctica requires a temperature increase above 4°C 34
and is thereby out of the scope discussed here (Foster and Rohling, 2013). 35
36
In order to compare the model results with past sea level anomalies for the temperature range up to 4°C, we 37
focus on the three previous periods of warmer climates and higher sea levels than pre-industrial that were 38
assessed in Sections 5.6.1, 5.6.2, and 13.2.1: the middle Pliocene, MIS 11, and the LIG (Figure 13.14e). In 39
each case, there is reasonable agreement between the model result of a long-term sea level response for a 40
given temperature with the information from the paleo record. 41
42
The ability of the physical models to reproduce paleo-sea level records on a multi-millennial time scale 43
provides confidence in applying them to millennial time frame. After 2000 years, the sea level contribution 44
will be largely independent of the exact warming path during the first century. As can be seen from Figure 45
10.34 of AR4, the oceanic heat content will be largely equilibrated after 2000 years; the same is true for the 46
glacier component. The situation for Antarctica is slightly more complicated, but as can be inferred from 47
Pollard and DeConto (2009), much of the retreat of the West Antarctic Ice Sheet will have already occurred 48
by 2000 years, especially if the warming occurs on a decadal to centennial time scale. The opposite and 49
smaller trend in East Antarctic ice volume due to increased snowfall in a warmer environment will also have 50
largely equilibrated (Uotila et al., 2007; Winkelmann et al., 2012). 51
52
The most significant difference arises from the contribution of the Greenland Ice Sheet. Consistent with 53
previous estimates (Huybrechts et al., 2011; Goelzer et al., 2012), the rate of the sea level contribution from 54
Greenland increases with temperature. The transient simulations for an instantaneous temperature increase 55
show a quasi-quadratic dependence of the sea level contribution on this temperature increase after 2000 56
years (Figure 13.14h) (Robinson et al. 2012). The results are quantitatively consistent with previous 57
estimates on a millennial time scale (Huybrechts et al., 2011; Goelzer et al., 2012) The sea level contribution 58

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of the Greenland Ice Sheet after 2000 years of integration at 560 ppm was plotted against the average 1
Greenland temperature divided by the standard polar amplification of 1.5 between global mean and 2
Greenland mean temperature increase (Gregory & Huybrechts, 2006, black dot in Figure 13.14h). Taken 3
together, these results imply that a sea level rise of 1–3 m °C
–1
is expected if the warming is sustained for 4
several millennia (low confidence) (Figure 13.14e, 13.14j). 5
6
7
Table 13.7: Median and model spread of the thermal expansion of CMIP5 comprehensive climate models. RCP2.6 8
belongs to the low scenarios as shown in Figure 13.13 and Table 13.8; RCP4.5 is a ‘medium scenario’ and RCP8.5 a 9
‘high scenario’. The model spread in Table 13.8 encloses the CMIP5 model spread for all scenarios. Sea level 10
contributions are provided in meters. 11
Mean 2191–2200 Mean 2291–2300
Scenario # of Models Median Model Spread # of Models Median Model Spread
RCP2.6 3 0.19 m 0.15–0.22 m 3 0.21 m 0.15–0.25 m
RCP4.5 7 0.39 m 0.30–0.47 m 6 0.54 m 0.38–0.66 m
RCP8.5 2 0.85m 0.80–0.90 m 2 1.34 m 1.26–1.41 m
12
13
Table 13.8: Model spread of sea level contribution and total sea level change for low, medium and high scenarios as 14
defined in the text and shown in Figure 13.13. As detailed in the text confidence of the ice-sheet models’ ability to 15
project dynamic ice-discharge from Greenland and Antarctica is low which is likely to result in a systematic 16
underestimation of the ice-sheet contributions. The unit of all sea level contributions is m. 17
Contribution Scenario 2100 2200 2300 2400 2500
Thermal Expansion low 0.07–0.31 m 0.08–0.41 m 0.08–0.47 m 0.09–0.52 m 0.09–0.57 m
Glaciers low 0.15–0.18 m 0.19–0.23 m 0.22–0.26 m 0.22–0.26 m
b
0.22–0.26 m
b
Greenland Ice Sheet low 0.05 m
a
0.10 m
a
0.15 m
a
0.21 m
a
0.26 m
a
Antarctic Ice Sheet low –0.01 m
a
–0.02 m
a
–0.03 m
a
–0.05 m
a
–0.07 m
a
Total low 0.26–0.53 m 0.35–0.72 m 0.41–0.85 m 0.46–0.94 m 0.50–1.02 m
Thermal Expansion medium 0.09–0.39 m 0.17–0.62 m 0.20–0.81 m 0.22–0.98 m 0.24–1.13 m
Glaciers medium 0.15–0.19 m 0.21–0.25 m 0.25–0.29 m 0.25–0.29 m
b
0.25–0.29 m
b
Greenland Ice Sheet medium 0.02–0.09 m 0.05–0.24 m 0.08–0.44 m 0.11–0.65 m 0.14–0.91 m
Antarctic Ice Sheet medium –0.07 to –0.01 m –0.17 to –0.02 m –0.25 to –0.03 m –0.36 to –0.02 m –0.45 to –0.01 m
Total medium 0.19–0.66 m 0.26–1.09 m 0.27–1.51 m 0.21–1.90 m 0.18–2.32 m
Thermal Expansion high 0.08–0.55 m 0.23–1.20 m 0.29–1.81 m 0.33–2.32 m 0.37–2.77 m
Glaciers high 0.17–0.19 m 0.25–0.32 m 0.30–0.40 m 0.30–0.40 m
b
0.30–0.40 m
b
Greenland Ice Sheet high 0.02–0.09 m 0.13–0.50 m 0.31–1.19 m 0.51–1.94 m 0.73–2.57 m
Antarctic Ice Sheet high –0.07 to –0.00 –0.04 to 0.01 m 0.02–0.19 m 0.06–0.51 m 0.11–0.88 m
Total high 0.21–0.83 0.58–2.03 m 0.92–3.59 m 1.20–5.17 m 1.51–6.63 m
Notes: 18
(a) The value is based on one simulation only. 19
(b) Due to lack of available simulations the same interval used as for the year 2300. 20
21
22
13.6 Regional Sea Level Changes 23
24
Regional sea level changes may differ substantially from a global average, showing complex spatial patterns 25
which result from ocean dynamical processes, movements of the sea floor, and changes in gravity due to 26
water mass-redistribution (land ice and other terrestrial water storage) in the climate system. The regional 27
distribution is associated with natural or anthropogenic climate modes rather than factors causing changes in 28
the global average value, and include such processes as a dynamical redistribution of water masses and a 29
change of water-mass properties caused by changes in winds and air pressure, air-sea heat and freshwater 30
fluxes, and ocean currents. Since the characteristic time scales of all involved processes are different, their 31
relative contribution to net regional sea level variability or change will depend fundamentally on the time 32
scale considered. 33
34

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13.6.1 Regional Sea Level Changes, Climate Modes and Forced Sea Level Response 1
2
As discussed in Chapter 3, most of the regional sea level changes observed during the recent altimetry era or 3
reconstructed during past decades from tide gauges appear to be steric (Levitus et al., 2005; Lombard et al., 4
2005b; Lombard et al., 2005a; Ishii and Kimoto, 2009; Levitus et al., 2009; Stammer et al., 2013). Moreover, 5
steric changes observed during the altimetry era appear to be primarily thermosteric in nature, although 6
halosteric effects, which can reduce or enhance thermosteric changes, are also important in some regions 7
(e.g., Atlantic Ocean, Bay of Bengal). Ocean models and ocean reanalysis-based results (Carton et al., 2005; 8
Wunsch and Heimbach, 2007; Stammer et al., 2011) as well as ocean circulation models without data 9
assimilation (Lombard et al., 2009) confirm these results. 10
11
Observations and ocean reanalysis (Stammer et al., 2011; 2013) also agree in showing that steric spatial 12
patterns over the last half of the 20th century fluctuate in space and time as part of modes of the coupled 13
ocean-atmosphere system such as ENSO, the NAO, and the PDO (Levitus et al., 2005; Lombard et al., 14
2005a; Di Lorenzo et al., 2010; Lozier et al., 2010; Zhang and Church, 2012). In these cases, regional sea 15
level variability is associated with changing wind fields and resulting changes in the ocean circulation (Kohl 16
and Stammer, 2008). For example, the large rates of sea level rise in the western tropical Pacific and of sea 17
level fall in the eastern Pacific over the period 1993-2010 correspond to an increase in the strength of the 18
trade winds in the central and eastern tropical Pacific over the same period (Timmermann et al., 2010; 19
Merrifield and Maltrud, 2011; Nidheesh et al., 2012). The long-term sea level trend from 1958 to 2001 in the 20
tropical Pacific can also be explained as the ocean’s dynamical response to variations in the wind forcing 21
(Qiu and Chen, 2006; Timmermann et al., 2010). 22
23
Spatial variations in trends in regional sea level may also be specific to a particular sea or ocean basin. For 24
example, a sea level rise of 5.4 ± 0.3 mm yr
–1
in the Japan Sea from 1993 to 2001 is nearly two times the 25
GMSL trend, with more than 80% of this rise being thermosteric (Kang et al., 2005). Han et al. (2010) found 26
that regional changes of sea level in the Indian Ocean that have emerged since the 1960s are driven by 27
changing surface winds associated with a combined enhancement of Hadley and Walker cells. 28
29
13.6.2 CMIP5 GCM Projections on Decadal to Centennial Time Scales 30
31
CMIP5 projections of regional sea level provide information primarily about dynamic sea level changes 32
resulting from increased heat uptake and changes in the wind forcing. On decadal time scales, the CMIP5 33
model ensemble identifies strong interannual variability (up to 8 cm, RMS) associated with ENSO and 34
dynamics of the equatorial current system in the tropical Pacific and Indian Oceans (Figure 13.15a). Similar 35
variability in the amplitude of sea level change but due to other climate modes is also apparent in the North 36
Atlantic Current and in parts of the Southern Ocean. 37
38
[INSERT FIGURE 13.15 HERE] 39
Figure 13.15: (a) RMS interannual dynamic sea level variability (mm) in a CMIP5 multi-model ensemble (21 models), 40
built from the historically forced experiments during the period 1951–2005; (b) Changes in the ensemble average 41
interannual dynamic sea level variability (std. dev.; in mm) evaluated over the period 2081–2100 relative to the 42
reference period 1986–2005. The projection data (2081–2100) are from the CMIP5 RCP4.5 experiment. 43
44
Toward the end of the 21st century, the CMIP5 results indicate that it is possible that the interannual to 45
decadal variability of dynamic sea level can weaken in some parts of the world ocean, e.g., the western low-46
latitude Pacific and parts of the Indian Ocean, whereas it could be amplified in other parts, e.g., the North 47
Pacific, the eastern tropical Pacific, the eastern subtropical Atlantic, and the Arctic (Figure 13.15b). 48
49
Longer-than-decadal-time-scale regional sea level changes can increasingly be expected to result from long-50
term changes in the wind field, changes in the regional and global ocean heat and freshwater content and the 51
associated dynamical adjustment (with associated redistribution of ocean properties), and (to a lesser extent) 52
from atmospheric pressure. The CMIP5 projections of steric sea level changes toward the end of the 21st 53
century reveal a clear regional pattern in dynamical sea level change (Figure 13.16), in which the Southern 54
Ocean shows a net decline relative to the global mean, while the remaining global ocean displays complex 55
ridge-and-trough structures superimposed on a generally rising sea level (Yin, 2012b). For example, in the 56
North Atlantic, the largest sea level rise is along and north of the North Atlantic Current, but less so further 57

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to the south in the center of the warmer subtropical gyre. A similar dipole pattern was observed in CMIP3 1
results there due to a weakening of the AMOC which leads to a local steric sea level rise east of North 2
America, resulting in more water on the shelf and directly impacting northeastern North America 3
(Levermann et al., 2005; Landerer et al., 2007; Yin et al., 2010). A similar pattern can be observed in the 4
North Pacific, but here and in other parts of the world ocean (e.g., Southern Ocean), regional sea level 5
patterns are largely the result of changes in wind forcing, associated changes in the circulation, and an 6
associated redistribution of heat and freshwater. Some regional changes can also be expected to result from 7
modifications in the expansion coefficient due to changes in the ocean’s regional heat content (Kuhlbrodt 8
and Gregory, 2012). 9
10
The CMIP5 ensemble indicates that regions showing an enhanced sea level toward the end of the 21st 11
century coincide with those showing the largest uncertainty (Figure 13.16b). While this also appeared in the 12
earlier CMIP3 SRES A1B results, the CMIP5 results by comparison, show a general reduction in the 13
ensemble spread, especially in high latitudes. On a global average, this reduction is from 5.7 cm to 2.1 cm, 14
rms. 15
16
[INSERT FIGURE 13.16 HERE] 17
Figure 13.16: (a) Ensemble mean projection of the time-averaged dynamic and steric sea level changes for the period 18
2081–2100 relative to the reference period 1986–2005, computed from 21 CMIP5 climate models (in m), using the 19
RCP4.5 experiment. The figure includes the globally averaged steric sea level increase of 0.18 ± 0.05 m. (b) RMS 20
spread (deviation) of the individual model result around the ensemble mean (m). 21
22
The contribution of changes of global ocean heat storage to regional steric sea level anomalies is virtually 23
certain to increase with time as the climate-warming signal increasingly penetrates into the deep ocean 24
(Pardaens et al., 2011a). For the last three decades of the 21st century, the AR4 climate model ensemble 25
mean shows a significant heat storage increase (Yin et al., 2010), about half of which is stored in the ocean 26
below 700 m depth. Recent detection of ongoing changes in the ocean salinity structure (Durack and 27
Wijffels, 2010) (Section 3.3.2) may also contribute to future regional steric sea level changes. Halosteric 28
effects can dominate in some regions, especially in regions of high-latitude water mass formation where 29
long-term heat and freshwater changes are expected to occur (e.g., in the subpolar North Atlantic, the Arctic, 30
the Southern Ocean) (Yin et al., 2010; Pardaens et al., 2011a). Because of an anticipated increase in 31
atmospheric moisture transport from low to high latitudes (Pardaens et al., 2003), halosteric anomalies are 32
positive in the Arctic Ocean and dominate regional sea level anomalies there (Yin et al., 2010). It is likely 33
that future thermosteric changes will dominate the steric variations in the Southern Ocean, and strong 34
compensation between thermosteric and halosteric change will characterise the Atlantic (Pardaens et al., 35
2011a). 36
37
13.6.3 Response to Atmospheric Pressure Changes 38
39
Regional sea level also adjusts to regional changes in atmospheric sea level pressure relative to its 40
instantaneous mean over the ocean. Over time scales longer than a few days, the adjustment is nearly 41
isostatic. Sea level pressure is projected to increase over the subtropics and mid-latitudes (depressing sea 42
level) and decrease over high latitudes (raising sea level), especially over the Arctic (order several millibars 43
by the end of the 21st century associated with a poleward expansion of the Hadley Circulation and a 44
poleward shift of the storm tracks of several degrees latitude (Held and Soden, 2006). These changes may 45
therefore contribute positively to the sea level rise in the Arctic in the range of up to 1.5 cm and about 2.5 cm 46
for RCP4.5 and RCP8.5, respectively (Yin et al., 2010) (Figure 13.17). In contrast, air pressure changes 47
oppose sea level rise in mid- and low latitudes albeit with small amplitudes. Air pressure may also influence 48
regional sea level elsewhere, as demonstrated by sea level changes in the Mediterranean in the second half of 49
the 20th century (Tsimplis et al., 2005). 50
51
[INSERT FIGURE 13.17 HERE] 52
Figure 13.17: Projected ensemble mean sea level change (m) due to changes in atmospheric pressure loading over the 53
period from 1986–2005 to 2081–2100 for (a) RCP4.5 and (b) RCP8.5 (contour interval is 0.01 m). Standard deviation 54
of the model ensemble due to the atmospheric pressure loading for (c) RCP4.5 and (d) RCP8.5 (contour interval is 55
0.005 m). 56
57

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13.6.4 Response to Freshwater Forcing 1
2
Enhanced freshwater fluxes derived from an increase in ice-sheet meltwater at high latitudes results in a 3
regional pattern of sea level rise originating from adjustments in ocean dynamics and in the solid earth. 4
Neither effect is included in CMIP5 models, although the latter adjustment is computed off line here. 5
6
13.6.4.1 Dynamic Ocean Response to Cryospheric Freshwater Forcing 7
8
The addition of freshwater from glaciers and ice sheets to the ocean leads to an instantaneous increase in 9
global mean sea level, but because it is communicated around the ocean basins via a dynamical adjustment, 10
it is not instantaneously globally uniform (Kawase, 1987; Cane, 1989). For the addition of mass, the 11
barotropic adjustment of the ocean takes place in a few days (Gower, 2010; Lorbacher et al., 2012). The 12
addition of freshwater to the ocean from melting of the Greenland Ice Sheet results in an additional basin-13
wide steric response of the North Atlantic within months and is communicated to the global ocean via 14
boundary waves, equatorial Kelvin waves, and westward propagating baroclinic Rossby waves on decadal 15
time scales (Stammer, 2008). A similar response but with a different pattern can be observed from Antarctic 16
meltwater input. In both cases, an associated complete baroclinic adjustment of the global ocean might take 17
as long as several centuries. The adjustment of the ocean to high-latitude meltwater input also involves 18
atmospheric teleconnections; such a response to Greenland meltwater pulses could lead to sea level changes 19
in the Pacific within months (Stammer et al., 2011). On longer-than-decadal time scales, the freshwater input 20
to the North Atlantic raises sea level in the Arctic Ocean and leads to an anomalous southward Bering Strait 21
throughflow, transporting colder, fresher water from the Arctic Ocean into the North Pacific (Hu et al., 2010) 22
and causing North Pacific cooling (Okumura et al., 2009). Meltwater forcing in the subpolar North Atlantic 23
also causes changes of the AMOC (Section 12.4.7.2), which in turn causes dynamic changes of sea level in 24
the North Atlantic, particularly in its northwestern region (Lorbacher et al., 2010). The combination of this 25
dynamic sea level rise and the global mean sea level rise makes the northeastern North American coast 26
vulnerable to some of the fastest and largest sea level rises during this century (Yin et al., 2009). 27
28
13.6.4.2 Earth and Gravitational Response to Contemporary Surface Water Mass Redistribution 29
30
Deformational, rotational, and gravitational responses to mass redistribution between the cryosphere, the 31
land, and the oceans produce distinctive regional departures from GMSL, referred to as sea level fingerprints 32
(Mitrovica et al., 2001; Mitrovica et al., 2009; Gomez et al., 2010b; Riva et al., 2010) (Section 13.1, FAQ 33
13.1). Many existing studies of these effects have not defined a specific rate of ice-sheet mass loss 34
(Mitrovica et al., 2001) or are based on end-member scenarios of ice retreat, such as from the West Antarctic 35
Ice Sheet (Bamber et al., 2009; Mitrovica et al., 2009; Gomez et al., 2010b) and marine-based parts of the 36
East Antarctic Ice Sheet (Gomez et al., 2010b). Bamber and Riva (2010) calculated the sea level fingerprint 37
of all contemporary land-ice melt and each of its major components. Spada et al. (2013) examined the 38
regional sea level pattern from future ice melt based on the A1B scenario. 39
40
As can be seen from Figure 13.18, a characteristic of the sea level fingerprints is that regions adjacent to the 41
source of the mass loss are subject to relative sea level fall of about an order of magnitude greater than the 42
equivalent GMSL rise from these mass contributions, whereas in the far field the sea level rise is larger (up 43
to about 30%) than the global average rise (Mitrovica et al., 2001; Mitrovica et al., 2009; Gomez et al., 44
2010b). Gomez et al. (2010b) and Mitrovica et al. (2011) showed that differences in the maximum predicted 45
rise (relative to the global mean) between published results is due to the accuracy with which water 46
expulsion from the deglaciated marine basins is calculated. These changes are in addition to the ongoing 47
response to past changes (e.g., glacial isostatic adjustment in response to the last deglaciation). Mitrovica et 48
al. (2001) suggested that the lower rates of sea level change inferred from tide gauge records at European 49
sites relative to the global average were consistent with 20th century melting from Greenland. Similarly, 50
Gehrels and Woodworth (2013) suggested that the larger magnitude of the early 20th century sea level 51
acceleration observed in Australia and New Zealand, as compared with the North Atlantic, may represent a 52
fingerprint of the increased melt contributions of Greenland and Arctic glaciers in the 1930s. Nevertheless, 53
current rates of ice-sheet melting are difficult to distinguish from dynamic variability (Kopp et al., 2010; Hay 54
et al., 2013), but it is likely that with further ice-sheet melting they will begin to dominate the regional 55
patterns of sea level change toward the end of the 21st century, especially under climate forcing conditions 56
for which ice-sheet melting contributes to more than 20-cm equivalent sea level rise (Kopp et al., 2010). 57

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These changes are in addition to the ongoing response to past changes (e.g., GIA in response to the last 1
deglaciation; Figure 13.18). 2
3
Water mass redistributions associated with land hydrology changes other than those from land ice may also 4
produce spatially variable ‘fingerprints” in sea level (Fiedler and Conrad, 2010). In particular, regional 5
changes in the terrestrial storage of water can lead to a sea level response on interannual and longer time 6
scales, specifically near large river basins (Riva et al., 2010). 7
8
13.6.5 Net Regional Sea Level Changes 9
10
Net regional sea level change projections can be estimated from a combination of the various contributions 11
to sea level change described above, emerging from the ocean, atmospheric pressure loading and the solid 12
Earth. 13
14
Over the next few decades, net regional sea level changes over most parts of the world are likely to be 15
dominated by dynamical changes (mass redistribution and steric components) resulting from natural 16
variability, although exceptions are possible at sites near rapidly melting ice sheets where static effects could 17
become large. However, toward the end of the 21st century, regional patterns in sea level from all other 18
contributions will progressively emerge and eventually dominate over the natural variability. 19
20
Ensemble mean estimates of relative sea level change during the period 2081–2100 relative to 1986–2000 21
resulting from GIA and from glacier and ice-sheet melting for RCP4.5 and RCP8.5 scenarios (Figure 13.18) 22
suggest that for the 21st century, past, present and future loss of land-ice mass will very likely be an 23
important contributor to spatial patterns in sea level change, leading to rates of maximum rise at low-to-mid 24
latitudes. Hu et al. (2011) and Sallenger et al. (2012) also suggested that steric and dynamical sea level 25
changes can potentially increase the sea level near the northeastern coast of North America and in the 26
western Pacific. Considerable uncertainties remain, however, in both the sea level budget and in the regional 27
expression of sea level rise. In addition, local sea level rise can also partly be compensated by vertical land 28
movement resulting from GIA, especially in some formerly glaciated high-latitudes regions where high rates 29
of land uplift may lead to a decrease of relative sea level. For example, Johansson et al. (2013) reported a 29 30
cm sea level rise in Gulf of Finland and –27 cm fall in the Bay of Bothnia. 31
32
[INSERT FIGURE 13.18 HERE] 33
Figure 13.18: Ensemble mean regional contributions to sea level change (m) from (a) GIA, (b) glaciers and (c) ice-34
sheet SMB. Panels (b) and (c) are based on information available from scenario RCP4.5. All panels represent changes 35
between the periods 1986–2000 and 2081–2100. 36
37
The ensemble mean net regional sea level change between 1986–2005 and 2081–2100 for the RCP4.5 38
scenario (not including the dynamic ocean contribution in response to the influx of freshwater associated 39
with land-ice loss and changes in terrestrial ground water) reveals that many regions are likely to experience 40
regional sea level changes that differ substantially from the global mean (Figure 13.19). Figure 13.20 shows 41
ensemble mean net regional sea level change between 1986–2005 and 2081–2100 for RCPs 2.6, 6.0 and 8.5. 42
43
[INSERT FIGURE 13.19 HERE] 44
Figure 13.19: (a) Ensemble mean net regional sea level change (m) evaluated from 21 models of the CMIP5 scenario 45
RCP4.5, including atmospheric loading, plus land-ice, GIA and terrestrial water sources, between 1986–2005 and 46
2081–2100. Global mean is 0.50 ± 0.13 m, with a total range of –1.71 to +0.83 m. (b) Lower 90% CL uncertainty 47
bound (p = 0.05) for RCP4.5 scenario sea level rise (plus non-scenario components). (c) Upper 90% CL uncertainty 48
bound (p = 0.95) for RCP4.5 scenario sea level rise (plus non-scenario components). 49
50
[INSERT FIGURE 13.20 HERE] 51
Figure 13.20: Ensemble mean net regional sea level change (m) evaluated from 21 CMIP5 models for the RCP 52
scenarios (a) 2.6, (b) 4.5, (c) 6.0 and (d) 8.5 between 1986–2005 and 2081–2100. Each map includes effects of 53
atmospheric loading, plus land-ice, GIA and terrestrial water sources. 54
55
It is very likely that over about 95% of the world ocean, regional sea level rise will be positive, while most 56
regions experiencing a sea level fall are located near current and former glaciers and ice sheets. Figure 57
13.21b shows that over most of the oceans (except for limited regions around western Antarctica, Greenland, 58

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and high Arctic regions), estimated regional sea level changes are significant at the 90% confidence limit. 1
Local sea level changes deviate more than 10% and 25% from the global mean projection for as much as 2
30% and 9% of the ocean area, respectively, indicating that spatial variations can be very large. Regional 3
changes in sea level reach values of up to 30% above the global mean value in the Southern Ocean and 4
around North America, between 10–20% in equatorial regions, and up to 50% below the global mean in the 5
Arctic region and some regions near Antarctica (Figure 13.21a). 6
7
[INSERT FIGURE 13.21 HERE] 8
Figure 13.21: (a) Percentage of the deviation of the ensemble mean net regional sea level change between 1986–2005 9
and 2081–2100 from the global mean value. The figure was computed for RCP4.5, but to first order is representative 10
for all RCPs. (b) Total RCP4.5 sea level change (plus all other components) divided by the combined standard error of 11
all components (see Supplementary Material Section 13.SM.2). Assuming a normal distribution, or a t-distribution 12
given the number of models as an approximation of the number of degrees of freedom, a region passes the 90% 13
confidence level where the change is greater than 2 standard errors, which is most of the ocean except for limited 14
regions around western Antarctica, Greenland, and high Arctic regions. 15
16
Figure 13.22 shows that, between 1986–2005 and 2081–2100, sea level changes along the world’s coastlines 17
associated with the RCP4.5 and RCP8.5 scenarios have a substantially skewed non-Gaussian distribution, 18
with significant coastal deviations from the global mean. When the coastlines around Antarctica and 19
Greenland are excluded (Figure 13.22b), most negative changes disappear, but the general structures of the 20
global histograms remain. In general, changes along the coastlines will range from about 20 cm to 50 cm for 21
an RCP4.5 scenario, peaking near 40 cm, and from about 40 cm to more than 80 cm under a RCP8.5 22
scenario, peaking near 75 cm. About 68% and 72% of the coastlines will experience a sea level change 23
within ±20% of the GMSL change for RCP4.5 and RCP8.5, respectively. In both cases, the maximum of the 24
histogram is slightly higher than the GMSL, whereas the arithmetic mean is lower. Only some coastlines will 25
experience a sea level rise of up to 50% above the GMSL change. 26
27
[INSERT FIGURE 13.22 HERE] 28
Figure 13.22: (a) Histograms of the deviation of the ensemble mean net regional sea level change (Figure 13.20) along 29
all coastlines (represented by the closest model grit point) between 1986–2005 and 2081–2100 from the global mean 30
value. Shown are results for RCP4.5 (blue) and RCP8.5 (pink), respectively. (b) Same as in (a) but excluding Antarctic 31
and Greenland coastlines. Vertical dashed lines represent global mean sea level changes for the two RCPs. 32
33
Figure 13.23 shows the combination of the natural variability (annual mean) and the CMIP5 projected sea 34
level rise for the RCP4.5 scenario for a number of locations distributed around the world. For example, at 35
Pago Pago (14°S,195°E) in the western equatorial Pacific, the historical record indicates that annual 36
variability in mean sea level has been about 21 cm (5–95% range). Projections by individual climate models 37
indicate that it is very likely that a similar range of natural variability will continue through the 21st century 38
(Figure 13.15b). However, by 2100, the average projected sea level for the RCP4.5 scenario of 0.53 [0.34–39
0.74] m is greater than any observations of annual mean sea level in the instrumental record. Of all the 40
examples shown, the greatest sea level increase will be in New York, which is representative of the enhanced 41
sea level rise there due to ocean processes and GIA in the region (compare Figures 13.16. and 13.18). The 42
figure also reveals the large spatial inhomogeneity of interannual to decadal variability. In each case, 43
monthly variability and extreme sea levels from winds and waves associated with weather phenomena 44
(Section 13.7) need to be considered in addition to these projections of regional sea level. 45
46
[INSERT FIGURE 13.23 HERE] 47
Figure 13.23: Observed and projected relative net sea level change (compare Figure 13.20) near nine representative 48
coastal locations for which long tide-gauge measurements are available. The observed in situ relative sea level records 49
from tide gauges (since 1970) are plotted in yellow, and the satellite record (since 1993) is provided as purple lines. The 50
projected range from 21 CMIP5 RCP4.5 scenario runs (90% uncertainty) is shown by the shaded region for the period 51
2006–2100, with the bold line showing the ensemble mean. Colored lines represent three individual climate model 52
realizations drawn randomly from three different climate models used in the ensemble. Station locations of tide gauges 53
are: (a) San Francisco: 37.8°N, 122.5°W; (b) New York: 40.7°N, 74.0°W; (c) Ijmuiden: 52.5°N, 4.6°E; (d) Haldia: 54
22.0°N, 88.1°E; (e) Kanmen, China: 28.1°N, 121.3°E; (f) Brest: 48.4°N, 4.5°W; (g) Mar del Plata, Argentina: 38.0°S, 55
57.5°W; (h) Fremantle: 32.1°S, 115.7°E; (i) Pago Pago: 14.3°S, 170.7°W. Vertical bars at the right sides of each panel 56
represent the ensemble mean and ensemble spread (5–95%) of the likely (medium confidence) sea level change at each 57
respective location at the year 2100 inferred from the four RCPs 2.6 (dark blue), 4.5 (light blue), 6.0 (yellow), and 8.5 58
(red). 59

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1
13.6.6 Uncertainties and Sensitivity to Ocean/Climate Model Formulations and Parameterizations 2
3
Uncertainties of climate models are discussed in detail in Chapter 9. Sea level is a property of the ocean 4
connected to nearly all dynamical and thermodynamical processes over the full ocean column, from the 5
surface fluxes to the ocean bottom. While many of the processes are to first order correctly simulated in 6
climate models, differences between models (Figure 13.24) indicate that uncertainties in simulated and 7
projected steric sea level (globally and regionally) remain poorly understood. Moreover, the ocean heat 8
uptake efficiency differs between models leading to a spread of 50% in heat storage changes (Kuhlbrodt and 9
Gregory, 2012). In addition, some processes are not part of the CMIP5 simulations, such as the dynamical 10
response of the ocean to meltwater input or the GIA/rotational/gravitational processes associated with this 11
ice mass loss. Stammer and Hüttemann (2008) showed that coupled climate models that do not include the 12
effect of changes in atmospheric moisture content on sea level pressure will underestimate future regional 13
atmospheric pressure loading effects by up to 2 cm. Other uncertainties result from 14
GIA/rotational/gravitational effects as well as from uncertainties in air-sea fluxes. 15
16
[INSERT FIGURE 13.24 HERE] 17
Figure 13.24: Projected relative sea level change (in m) from the combined global steric plus dynamic topography and 18
glacier contributions for the RCP4.5 scenario over the period from 1986–2005 to 2081–2100 for each individual 19
climate model used in the production of Figure 13.16a. 20
21
Improvements in the skill of a sea level projection require (1) better parameterizations of unresolved physical 22
processes, (2) improved numerical algorithms for such processes as temperature and salinity advection, (3) 23
refined grid resolution to better represent such features as boundary currents and mesoscale eddies, and (4) 24
the elimination of obsolete assumptions that have a direct impact on sea level (Griffies and Greatbatch, 25
2012). Among the many limiting approximations made in ocean models, the Boussinesq approximation has 26
been found to only marginally impact regional patterns (i.e., deviations from global mean) when directly 27
compared to non-Boussinesq simulations (Losch et al., 2004), thus lending greater confidence in Boussinesq 28
models for addressing questions of regional sea level change. Furthermore, for global sea level, the now- 29
standard a posteriori adjustment (Greatbatch, 1994; Griffies and Greatbatch, 2012) accurately incorporates 30
the missing global steric effect. The representation of dense overflows can also affect sea level simulations, 31
and is particularly problematic in many ocean models used for climate studies, with direct impacts on the 32
simulated vertical patterns of ocean heat uptake (Legg et al., 2009). 33
34
Coarse-resolution ocean-climate simulations require a parameterization of mesoscale and smaller eddies, but 35
the parameterizations as well as the details of their numerical implementations can greatly impact the 36
simulation. As shown by Hallberg and Gnanadesikan (2006) and Farneti et al. (2010), coarse-resolution 37
climate models may be over-estimating the Antarctic Circumpolar Current response to wind changes. Better 38
implementations of eddy parameterizations reduce such biases (Farneti and Gent, 2011; Gent and 39
Danabasoglu, 2011), and they form the basis for some, but not all, of the CMIP5 simulations. Moreover, 40
Vinogradov and Ponte (2011) suggested that as one considers regional sea level variability and its relevant 41
dynamics and forcing, mesoscale ocean features become important factors on a sub-decadal time scale. 42
Suzuki et al. (2005) compared changes in mean dynamic sea level in 2080–2100 relative to 1980–2000 as 43
obtained from a low- and a high-resolution ocean component of a coupled model and concluded that while 44
changes are comparable between runs, the high-resolution model captures enhanced details due to resolving 45
ocean eddy dynamics. 46
47
Even with a perfect ocean model, skill in sea level projections depends on skill of the coupled climate model 48
in which errors impacting sea level may originate from non-ocean components. Furthermore, initialization is 49
fundamental to the prediction problem, particularly for simulation of low-frequency climate variability 50
modes (Meehl et al., 2010). Projections of land-ice melting and the resultant sea level rise patterns also have 51
large uncertainties, with additional uncertainties arising from GIA models such as the mantle viscosity 52
structure. Each of the many uncertainties and errors results in considerable spread in the projected patterns of 53
sea level changes (Figure 13.15) (Pardaens et al., 2011a; Slangen et al., 2012). In addition to ocean/climate 54
model formulations and parameterizations, uncertainty in predictions of sea level change may be associated 55
with specified freshwater forcing. Whether or not an ocean model is coupled with an ice-sheet model, the 56

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forcing should distinguish between runoff and iceberg flux. Martin and Adcroft (2010) reported the only 1
attempt thus far to explicitly represent iceberg drift and melting in a fully coupled climate model. 2
3
13.7 Projections of 21st Century Sea Level Extremes and Waves 4
5
Climate change will affect sea levels extremes and ocean waves in two principal ways. First, because extra-6
tropical and tropical storms are one of the key drivers of sea level extremes and waves, future changes in 7
intensity, frequency, duration, and path of these storms will impact them. Second, sea level rise adds to the 8
heights of sea level extremes, regardless of any changes in the storm-related component. MSL change may 9
also accentuate the threat of coastal inundation due to changes in wave runup. Observations of changes in 10
sea level extremes and waves are discussed in Chapter 3. Sea level extremes at the coast occur mainly in the 11
form of storm surges and tsunamis, but because the latter are not climate driven, we only assess projections 12
for sea level extremes based on estimates of future storminess and MSL change. 13
14
13.7.1 Observed Changes in Sea Level Extremes 15
16
As discussed in the AR4 (Bindoff et al., 2007) and confirmed by more recent studies (Menéndez and 17
Woodworth, 2010), statistical analyses of tide-gauge observations have shown an increase in observed sea 18
level extremes worldwide that are caused primarily by an increase in MSL (Chapter 3). Dominant modes of 19
climate variability, particularly ENSO and NAO, also have a measureable influence on sea level extremes in 20
many regions (Lowe et al., 2010; Walsh et al., 2011). These impacts are due to sea level anomalies 21
associated with climate modes, as well as mode-related changes in storminess. There has been some 22
indication that the amplitude and phase of major tidal constituents have exhibited long-term change (Jay, 23
2009; Muller et al., 2011), but their impacts on extreme sea level are not well understood. Using particle size 24
analysis of cores collected in the Mackenzie Delta in the Arctic region, Vermaire et al. (2013) inferred 25
increased storm surge activity in the region during the last ~150 years, which they related to annual mean 26
temperature anomaly in the Northern Hemisphere and a decrease in summer sea-ice extent. 27
28
13.7.2 Projections of Sea Level Extremes 29
30
13.7.2.1 Recent Assessments of Projections of Sea Level Extremes 31
32
The AR4 assessed projections of storm surges for a few regions (Europe, Australia, the Bay of Bengal) based 33
on a limited number of dynamical modeling studies (Christensen et al., 2007). Although these results 34
generally indicated higher magnitude surges in future scenarios, there was low confidence in these 35
projections because of the wide spread in underlying AOGCM and RCM projections. 36
37
Studies since the AR4 have further assessed the relative contributions of sea level rise and storminess on 38
projected sea level extremes. Lowe et al. (2010) concluded that the increases in the observed sea level 39
extremes in the 20th century occurred primarily through an increase in MSL, and that the same applies to 40
projections for the 21st century. The SREX assessment concluded that it is very likely that MSL rise will 41
contribute to an increase in future sea level extremes (Seneviratne et al., 2012). It noted that changes in 42
storminess may also affect sea level extremes but the limited geographical coverage of studies and 43
uncertainties associated with storminess changes prevent a general assessment. The global tropical cyclone 44
frequency will likely decrease or remain roughly constant, but it is more likely than not that the frequency of 45
the most intense storms will increase in some ocean basins (Chapter 14). Uncertainties in projections of 46
cyclone frequency and tracks make it difficult to project how these changes will impact particular regions. 47
Similarly, while the SREX and the current assessment (Chapter 14) find that it is likely that there has been a 48
poleward shift in the main northern and southern extra-tropical cyclone tracks during the last 50 years, and 49
that regional changes may be substantial, there is only low confidence in region-specific projections. 50
51
13.7.2.2 Projections Based on Dynamical and Statistical Approaches 52
53
Projected changes in storm surges (relative to MSL) have been assessed by applying climate-model forcing 54
to storm-surge models. Return periods of sea level extremes (see Glossary), exceeding a given threshold 55
level, referred to as return levels, are used in quantifying projected changes. Using three regionally 56
downscaled GCMs for A2, B2 and A1B scenarios, Debernard and Roed (2008) found an 8–10% increase in 57

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the 99th percentile surge heights between 1961–1990 and 2071–2100, mainly during the winter season, 1
along the coastlines of the eastern North Sea and the northwestern British Isles, and decreases south of 2
Iceland. Using a downscaled GCM under an A1B scenario, Wang et al. (2008) projected a significant 3
increase in wintertime storm surges around most of Ireland between 1961–1990 and 2031–2060. Sterl et al. 4
(2009) concatenated the output from a 17-member ensemble of A1B simulations from a GCM over the 5
periods 1950–2000 and 2050–2100 into a single longer time series to estimate 10,000-year return levels of 6
surge heights along the Dutch coastline. No statistically significant change in this value was projected for the 7
21st century because projected wind-speed changes were not associated with the maximum surge-generating 8
northerlies. Using an ensemble of three climate models under A2 simulations, Colberg and McInnes (2012) 9
found that changes in 95th percentile sea level height across the southern Australian coast in 2081–2100 10
compared to 1981–2000 were small (±0.1 m), mostly negative, and despite some inter-model differences, 11
resembled the changes in wind patterns simulated by the climate models (McInnes et al., 2011). These 12
studies demonstrate that the results are sensitive to the particular choice of GCM or RCM, therefore 13
identifying uncertainties associated with the projections. For the tropical east coast of Australia, Harper et al. 14
(2009) found that a 10% increase in tropical cyclone intensity for 2050 led to increases in the 100-year return 15
level (including tides) that at most locations were smaller than 0.1 m. 16
17
Several regional storm-surge studies have considered the relative contribution of the two main causative 18
factors on changes in future sea level extremes (e.g., McInnes et al. (2009; 2013) for the southeastern coast 19
of Australia; Brown et al. (2010) for the eastern Irish Sea; Woth et al. (2006) for the North Sea; Lowe et al. 20
(2009) for the United Kingdom coast). They concluded that sea level rise has a greater potential than 21
meteorological changes to increase sea level extremes by the end of the 21st century in these locations. 22
Unnikrishnan et al. (2011) used RCM simulations to force a storm-surge model for the Bay of Bengal and 23
found that the combined effect of MSL rise of 4 mm/yr and RCM projections for the A2 scenario (2071–24
2100) gave an increase in 100-year return levels of total sea level (including tides) between 0.40–0.67 m 25
(about 15–20%) along the northern part of the east coast of India, except around the head of the bay, 26
compared to those in the base line (1961–1990) scenario. 27
28
Using six hypothetical hurricanes producing approximate 100-year return levels, Smith et al. (2010) found 29
that in the regions of large surges on the southeastern Louisiana coast, the effect of MSL rise on simulated 30
surges was linear. Yet in the regions of moderate surges (2–3 m), particularly in wetland-fronted areas, the 31
increase in surges was larger by 1–3 m than the present values. They showed that sea level rise alters the 32
speed of propagation of surges and their amplification in different regions of the coast. For the Gulf of 33
Mexico, Mousavi et al. (2011) developed a simple relationship between hurricane-induced storm surges, sea 34
level rise and hurricane intensification through increased SSTs for three modeled major historical cyclones, 35
concluding that the dynamic interaction of surge and sea level rise lowered or amplified the surge at different 36
points within a shallow coastal bay. 37
38
Higher mean sea levels can significantly decrease the return period for exceeding given threshold levels. For 39
a network of 198 tide gauges covering much of the globe, Hunter (2012) determined the factor by which the 40
frequency of sea levels exceeding a given height would be increased for a MSL rise of 0.5 m (Figure 41
13.25a). The above calculations have been repeated here using regional RSL projections and their 42
uncertainty using the RCP4.5 scenario (Section 13.6, Figure 13.19a). This multiplication factor depends 43
exponentially on the inverse of the Gumbel scale parameter (a factor which describes the statistics of sea 44
level extremes caused by the combination of tides and storm surges) (Coles and Tawn, 1990). The scale 45
parameter is generally large where tides and/or storm surges are large, leading to a small multiplication 46
factor, and vice versa. Figure 13.25a shows that a 0.5 m MSL rise would likely result in the frequency of sea 47
level extremes increasing by an order of magnitude or more in some regions. The multiplication factors are 48
found to be slightly higher, in general, when accounting for regional MSL projections (Figure 13.25b). 49
However, in regions having higher regional projections of MSL, such as the east coast of Canada and the US 50
(where GIA results in a larger sea level rise), the multiplication factor is higher, whereas in regions having 51
lower regional projections of MSL, such as the northwest region of North America (where the land is rising 52
due to present changes in glaciers and icecaps), the multiplication factor is lower. In another study, large 53
increases in the frequency of sea level extremes for 2050 were found for a network of sites around the US 54
coastline based on semi-empirical MSL rise projections and 20th century statistics of extremes (Tebaldi et 55
al., 2012). Cayan et al. (2008) reached a similar conclusion for the California coast, using projected time 56
series of tides, MSL rise, components for sea level fluctuations from projected MSLP and wind stress fields 57

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and a contribution for ENSO variability through projected SSTs for the 21st century, and showed that for 1
high-end scenarios of MSL rise, the frequency and magnitude of extremes increases considerably relative to 2
those experienced in the 20th century. 3
4
[INSERT FIGURE 13.25 HERE] 5
Figure 13.25: The estimated multiplication factor (shown at tide gauge locations by red circles and triangles), by which 6
the frequency of flooding events of a given height increase for (a) a MSL rise of 0.5 m (b) using regional projections of 7
MSL for the RCP4.5 scenario, shown in Figure13.19a. The Gumbel scale parameters are generally large in regions of 8
large tides and/or surges resulting in a small multiplication factor and vice versa. 9
10
13.7.3 Projections of Ocean Waves 11
12
Changes in ocean wave conditions are determined by changes in the major wind systems, especially in the 13
main areas affected by tropical and extra-tropical storms. Based on in situ and satellite altimeter observations 14
and wave-model hindcasts, it is likely that mean significant wave heights (SWH, defined as the average of 15
the highest one-third of wave heights) have increased in regions of the North Pacific and the North Atlantic 16
over the past half century, and in the Southern Ocean since the mid 1980s (Chapter 3, Seneviratne et al., 17
2012). The limited observational wave record makes it difficult to separate long-term trends from multi 18
decadal variability (Young et al., 2011). A number of studies have related changes in wind-wave 19
climatologies to modes of climate variability such as ENSO (Allan and Komar, 2006; Adams et al., 2008; 20
Menéndez et al., 2008), the NAO (Woolf et al., 2002; Izaguirre et al., 2010), and the Southern Annular Mode 21
(SAM) (Hemer et al., 2010; Izaguirre et al., 2011). Although anthropogenic influences have been considered 22
(Wang et al., 2009), it is likely that reported SWH trends over the past half-century largely reflect natural 23
variations in wind forcing. Recent reductions in summer sea-ice extent have resulted in enhanced wave 24
activity in the Arctic Ocean due to increased fetch area and longer duration of the open-water season 25
(Francis et al., 2011; Overeem et al., 2011). 26
27
In general, there is low confidence in projections of future storm conditions (Chapters 12 and 14) and hence 28
in projections of ocean waves. Nevertheless, there has been continued progress in translating climate model 29
outputs into wind-wave projections. In the AR4, projected changes in global SWHs were based on a single 30
statistical model (Wang and Swail, 2006). The projected conditions were consistent with increased wind 31
speeds associated with mid-latitude storms, but they considered only a limited five-member ensemble for a 32
single future emission scenario (SRES A2); wave parameters other than SWH were not considered. 33
34
Since the AR4, global wave-climate projections for the end of the 21st century have been made by 35
dynamically downscaling CMIP3 AOGCM results. A multi-model ensemble based on dynamical models 36
forced with various greenhouse gas emission scenarios (SRES A1B: Mori et al. (2010), Fan et al. (2013), 37
Semedo et al. (2013); SRES A2: Hemer et al. (2012a), as well as the statistical model of Wang and Swail 38
(2006) forced with emission scenarios IS92a and SRES A2 and B2, has been constructed as part of the 39
Coordinated Ocean Wave Climate Project (COWCLIP) (Hemer et al., 2013). In general, the ensemble 40
projected changes of annual mean SWH (Figure 13.26a) resemble the statistical projections of Wang and 41
Swail (2006) under an A2 scenario. The largest change is projected to be in the Southern Ocean, where mean 42
SWHs at the end of the 21st century are approximately 5–10% higher than the present-day mean. SWH 43
increase in this region reflects the projected strengthening of the westerlies over the Southern Ocean, 44
particularly during austral winter (Figure 13.26c). Another region of SWH increase in the ensembles is in the 45
tropical South Pacific associated with a projected strengthening of austral winter easterly trade winds in the 46
CMIP3 multi-model dataset (Figure 13.26c). Negligible change or a mean SWH decrease is projected for all 47
other ocean basins, with decreases identified in the trade wind region of the North Pacific, the mid-latitude 48
westerlies in all basins, and in the trade and monsoon wind regions of the Indian Ocean. Hemer et al. (2013) 49
found that variance of wave-climate projections associated with wave downscaling methodology dominated 50
other sources of variance within the projections such as climate scenario of climate model uncertainties. 51
Mori et al. (2013) reported similar findings. 52
53
Three CMIP3-based model projections (Mori et al., 2010; Hemer et al., 2012b; Fan et al., 2013) were used to 54
compare projections of wave direction and period (Hemer et al., 2013). Wave direction (Figure 13.26d) 55
exhibits clockwise rotation in the tropics, consistent with a higher contribution from northward propagating 56
swell from the Southern Ocean. Wave period (Figure 13.26e) shows an increase over the eastern Pacific, 57

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which is also attributed to enhanced wave generation in the Southern Ocean and northward swell 1
propagation. A projected decrease in wave periods in the North Atlantic and western and central North 2
Pacific is symptomatic of weaker wind forcing in these regions. 3
4
SWH projections based on CMIP5 winds for emission scenarios RCP4.5 and RCP8.5 (Dobrynin et al., 2012) 5
exhibit similar regional patterns for the end of the 21st century to the CMIP3 results presented in Figure 6
13.26A. Dobrynin et al. (2012) reported SWH increases in the Arctic Ocean, an area not considered by 7
Hemer et al. (2013), and in basins connected to the Southern Ocean, particularly for RCP8.5. The probability 8
of extreme wave heights is projected to increase in the Southern Hemisphere, the Arctic and Indian Oceans, 9
but decrease in the North and Equatorial Atlantic and in the Pacific. In addition to wind changes, the 10
projected loss of summer sea-ice extent in the Arctic Ocean is expected to increase overall wave activity 11
there (Manson and Solomon, 2007; Overeem et al., 2011). 12
13
Model intercomparisons are starting to identify common features of global wave projections yet in general 14
there is low confidence in wave-model projections because of uncertainties regarding future wind states, 15
particularly storm geography, the limited number of model simulations used in the ensemble averages, and 16
the different methodologies used to downscale climate model results to regional scales (Hemer et al., 2012a). 17
Despite these uncertainties, it appears likely (medium confidence) that enhanced westerly surface winds in 18
the Southern Hemisphere (discussed in Chapter 12) will lead to enhanced wave generation in that region by 19
the end of the 21st century. 20
21
A number of dynamical wave-projection studies have been carried out with a regional focus. For the 22
Mediterranean Sea, Lionello et al. (2008; 2010) projected a widespread shift of the wave-height distribution 23
to lower values by the mid-21st century under an SRES A1B scenario, implying a decrease in mean and 24
extreme wave heights. Caires et al. (2008) and Debernard and Røed (2008) reported a decrease (4–6% of 25
present values) in the annual 99th percentile SWH south of Iceland by the end of the 21st century, and an 26
increase (6–8%) along the North Sea east coast (SRES A2, B2, A1B scenarios). Grabemann and Weisse 27
(2008) (Grabemann and Weisse, 2008) found increases (up to 18% of present values) in annual 99th 28
percentile SWH in the North Sea by the end of the 21st century, with an increase in the frequency of extreme 29
wave events over large areas of the southern and eastern North Sea (SRES A2, B2 scenarios). Charles et al. 30
(2012) projected a general decrease in wave heights in the Bay of Biscay by the end of the 21st century 31
(SRES A2, A1B, B1 scenarios), accompanied by clockwise rotations in winter swell (attributed to a 32
projected northward shift in North Atlantic storm tracks) and summer sea and intermediate waves (attributed 33
to a projected slackening of westerly winds). Along the Portuguese coast, Andrade et al. (2007) found little 34
projected change in SWH and a tendency for a more northerly wave direction than present (SRES A2 35
scenario). 36
37
In the Pacific, multi-model projections by Graham et al. (2013) (SRES A2 scenario) indicate a decrease in 38
boreal winter upper-quantile SWHs over the mid-latitude North Pacific by the end of the 21st century 39
associated with a projected decrease in wind speeds along the southern flank of the main westerlies. There is 40
a less robust tendency for higher extreme waves at higher latitudes. On the southeastern Australian coast, 41
Hemer et al. (2012b) used multi-model projections (SRES A2 and B1 scenarios) to identify a decrease in 42
mean SWH (<0.2 m) by the end of the 21st century compared to present due to a projected decrease in 43
regional storm wave energy, and a shift to a more southerly wave direction, consistent with a projected 44
southward shift of the sub-tropical ridge in the forcing fields. 45
46
[INSERT FIGURE 13.26 HERE] 47
Figure 13.26: Projected changes in wind-wave conditions (~2075–2100 compared with ~1980–2009) derived from the 48
Coordinated Ocean Wave Climate Projection (COWCLIP) Project (Hemer et al., 2013). (a) Percentage difference in 49
annual mean significant wave height. (b) Percentage difference in January-March mean significant wave height. (c) 50
Percentage difference in July-September mean significant wave height. Hashed regions indicate projected change is 51
greater than the 5-member ensemble standard deviation. (d) As for (a), but displaying absolute changes in mean wave 52
direction, with positive values representing projected clockwise rotation relative to displayed vectors, and colours 53
shown only where ensemble members agree on sign of change. (e) As for (a), but displaying absolute changes in mean 54
wave period. The symbol ~ is used to indicate that the reference periods differ slightly for the various model studies 55
considered. 56
57

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13.8 Synthesis and Key Uncertainties 1
2
There has been significant progress in our understanding of sea level change since the AR4. Paleo data now 3
provide high confidence that sea levels were substantially higher when greenhouse gas concentrations were 4
higher or surface temperatures were warmer than pre-industrial. The combination of paleo sea level data and 5
long tide gauge records confirms that the rate of rise has increased from low rates of change during the late 6
Holocene (order tenths of mm yr
–1
) to rates of almost 2 mm yr
–1
averaged over the 20th century, with a likely 7
continuing acceleration during the 20th century (Figure 13.27). Since 1993, the sum of observed 8
contributions to sea level rise are in good agreement with the observed rise. 9
10
[INSERT FIGURE 13.27 HERE] 11
Figure 13.27: Compilation of paleo sea level data, tide gauge data, altimeter data (from Figure 13.3), and central 12
estimates and likely ranges for projections of global-mean sea level rise for RCP2.6 (blue) and RCP8.5 (red) scenarios 13
(Section 13.5.1), all relative to pre-industrial values. 14
15
Understanding of the components that contribute to total sea level rise have improved significantly. For the 16
20th century, the range from an ensemble of such process-based models encompasses the observed rise when 17
allowances are made for lack of inclusion of volcanic forcing in AOGCM control simulations, natural 18
climate variability, and a possible small long-term ice-sheet contribution. Ice-sheet contributions to the 20th 19
century sea level rise were small, however, and this agreement is thus not an evaluation of ice-sheet models. 20
Nevertheless, there has been significant improvement in accounting for important physical processes in ice-21
sheet models, particularly of the dynamic response of individual glacier systems to warmer ocean waters in 22
the immediate vicinity of the outlet glaciers. While there are as yet no complete simulations of regional 23
ocean temperature changes and of the ice-sheet response to realistic climate change forcing, the publications 24
to date have allowed an assessment of the likely range of sea level rise for the 21st century (Figure 13.27). 25
26
These observations, together with our current scientific understanding and projections of future climate and 27
sea level, imply that it is virtually certain that sea level will continue to rise during the 21st century and 28
beyond. For the first few decades of the 21st century, regional sea level change will be dominated by climate 29
variability superimposed on the climate change signal. For all scenarios, the rate of 21st century GMSL rise 30
is very likely to exceed the average rate during the 20th century. For the RCP8.5 scenario, the projected rate 31
of GMSL rise by the end of the 21st century will approach average rates experienced during the deglaciation 32
of the Earth after the Last Glacial Maximum. These rates imply a significant transfer of mass from the ice 33
sheets to the oceans and associated regional departures of sea level rise from the global average, in addition 34
to the regional patterns from changing atmosphere-ocean interactions. 35
36
Sea level rise has already led to a significant increase in the return frequency of extreme high sea levels at 37
many locations, and this will continue during the 21st century, although there is low confidence in 38
projections of changes in storminess. The first assessment of surface waves indicates a likely increase in the 39
height of waves in the Southern Ocean. 40
41
Despite this progress, significant uncertainties remain, particularly related to the magnitude and rate of the 42
ice-sheet contribution for the 21st century and beyond, the regional distribution of sea level rise, and the 43
regional changes in storm frequency and intensity. For coastal planning, sea level rise needs to be considered 44
in a risk management framework, requiring knowledge of the frequency of sea level variability (from climate 45
variability and extreme events) in future climates, projected changes in mean sea level, and the uncertainty 46
of the sea level projections (Hunter, 2010, 2012), as well as local issues such as the compaction of sediments 47
in deltaic regions and the changing supply of these sediments to maintain the height of the deltas (Syvitski et 48
al., 2009). While improved understanding has allowed the projection of a likely range of sea level rise during 49
the 21st century, it has not been possible to quantify a very likely range or give an upper bound to future rise. 50
The potential collapse of ice shelves, as observed on the Antarctic Peninsula (Rignot et al., 2004; Scambos et 51
al., 2004; Rott et al., 2011), could lead to a larger 21st century rise of up to several tenths of a meter. 52
53
Sea level will continue to rise for centuries, even if greenhouse gas concentrations are stabilised, with the 54
amount of sea level rise dependent on future greenhouse gas emissions. For higher emission scenarios and 55
warmer temperatures, surface melting of the Greenland Ice Sheet is projected to exceed accumulation, 56
leading to its long-term decay and a sea level rise of metres, consistent with paleo sea level data. 57

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1

Final Draft (7 June 2013) Chapter 13 IPCC WGI Fifth Assessment Report
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9

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Chapter 13: Sea Level Change
Coordinating Lead Authors: John A. Church (Australia), Peter U. Clark (USA)
Lead Authors: Anny Cazenave (France), Jonathan M. Gregory (UK), Svetlana Jevrejeva (UK), Anders
Levermann (Germany), Mark A. Merrifield (USA), Glenn A. Milne (Canada), R. Steven Nerem (USA),
Patrick D. Nunn (Australia), Antony J. Payne (UK), W. Tad Pfeffer (USA), Detlef Stammer (Germany),
Alakkat S. Unnikrishnan (India)
Contributing Authors: David Bahr (USA), Jason E. Box (USA), David H. Bromwich (USA), Mark Carson
(Germany), William Collins (UK), Xavier Fettweis (Belgium), Piers Forster (UK), Alex Gardner (USA), W.
Roland Gehrels (UK), Rianne Giesen (Netherlands), Peter J. Gleckler (USA), Peter Good (UK), Rune Grand
Graversen (Sweden), Ralf Greve (Japan), Stephen Griffies (USA), Edward Hanna (UK), Mark Hemer
(Australia), Regine Hock (USA), Simon J. Holgate (UK), John Hunter (Australia), Philippe Huybrechts
(Belgium), Gregory Johnson (USA), Ian Joughin (USA), Georg Kaser (Austria), Caroline Katsman
(Netherlands), Leonard Konikow (USA), Gerhard Krinner (France), Anne Le Brocq (UK), Jan Lenaerts
(Netherlands), Stefan Ligtenberg (Netherlands), Christopher M. Little (USA), Ben Marzeion (Austria),
Kathleen L. McInnes (Australia), Sebastian H. Mernild (USA), Didier Monselesan (Australia), Ruth
Mottram (Denmark), Tavi Murray (UK), Gunnar Myhre (Norway), J.P. Nicholas (USA), Faezeh Nick
(Norway), Mahé Perrette (Germany), David Pollard (USA),Valentina Radić (Canada), Jamie Rae (UK),
Markku Rummukainen (Sweden), Christian Schoof (Canada), Aimée Slangen (Netherlands), Jan H. van
Angelen (Netherlands), Willem Jan van de Berg (Netherlands), Michiel van den Broeke (Netherlands),
Miren Vizcaíno (Netherlands), Yoshihide Wada (Netherlands), Neil J. White (Australia), R.Winkelmann
(Germany),
Jianjun Yin (USA), Masakazu Yoshimori (Japan), Kirsten Zickfeld (Canada)
Review Editors: Jean Jouzel (France), Roderik van de Wal (Netherlands), Philip L. Woodworth (UK),
Cunde Xiao (China)
Date of Draft: 7 June 2013

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Figures
Figure 13.1: Climate sensitive processes and components that can influence global and regional sea level and are
considered in this chapter. Changes in any one of the components or processes shown will result in a sea level change.
The term ‘ocean properties’ refers to ocean temperature, salinity and density, which influence and are dependent on
ocean circulation. Both relative and geocentric sea level vary with position. Note that the geocenter is not shown.

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Figure 13.2: Schematic representation of key linkages between processes and components that contribute to sea level
change and are considered in this report. Colouring of individual boxes indicates the types of models and approaches
used in projecting the contribution of each process or component to future sea level change. The diagram also serves as
an index to the sections in this report that are relevant to the assessment of sea level projections via the section numbers
given at the bottom of each box. Note gravity and solid Earth effects change the shape of the ocean floor and surface
and thus are required to infer changes in ocean water volume from both relative and geocentric sea level observations.
ice-sheet surface
mass balance
ice-sheet
dynamics
thermal
expansion
waves
storm surges
density and
circulation changes
gravity and solid
Earth effects
global ocean
mass
regional sea level
sea level
extremes
global ocean
water volume
Process Model
Global Climate Model (i.e., CMIP)
Semi-Empirical Model
Paleo Analogue
Heuristic Constraint
land ice
glaciers
PO: 5.6 M: 13.6 P: 13.6
O: 3.7, 13.3 P: 13.4, 13.5
O: 3.7 PO: 5.6
P: 13.6
O: 3.7 P: 13.7
WGII: chapters 5, 29
O: 3.7
PO: 5.6 P: 13.4, 13.5
SEM: 13.5
SEM: 13.5
SEM: 13.5
O: 3.2, 3.7
M: 9.4, 13.3 P: 13.4
O: 3.4 M: 13.7 P: 13.7
O: 3.7 M: 13.7 P: 13.7
O: 3.2, 3.3, 3.6
M: 9.4, 13.6 P: 13.6
O: 4.4 PO: 5.6, 5.8
M: 13.3 P: 13.4
O: 4.4 PO: 5.6, 5.8
M: 13.3 P: 13.4
sea level impacts,
adaption &
vulnerability
O: Observations
PO: Paleo Observations & Models
M: Models
P: Projections
O: 4.3 PO: 5.5
M: 13.3 P: 13.4
groundwater
and reservoirs
O: 13.3
M: 13.3 P: 13.4

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Figure 13.3: (a) Paleo sea level data for last 3000 years from Northern and Southern Hemisphere sites. The effects of
glacial isostatic adjustment (GIA) have been removed from these records. Light green = Iceland (Gehrels et al., 2006),
purple = Nova Scotia (Gehrels et al., 2005), bright blue = Connecticut (Donnelly et al., 2004), blue = Nova Scotia
(Gehrels et al., 2005), red = United Kingdom (Gehrels et al., 2011), green = North Carolina (Kemp et al., 2011), brown
= New Zealand (Gehrels et al., 2008), gray = mid-Pacific Ocean (Woodroffe et al., 2012). (b) Paleo sea level data from
salt marshes since 1700 from Northern and Southern Hemisphere sites compared to sea level reconstruction from tide
gauges (blue time series with uncertainty) (Jevrejeva et al., 2008). The effects of GIA have been removed from these
records. Green and light green = North Carolina (Kemp et al., 2011), orange = Iceland (Gehrels et al., 2006), purple =
New Zealand (Gehrels et al., 2008), dark green = Tasmania (Gehrels et al., 2012), brown = Nova Scotia (Gehrels et al.,
2005). (c) Yearly average GMSL reconstructed from tide gauges (1900–2010) by three different approaches. Orange
from Church and White (2011), blue from Jevrejeva et al. (2008), green from Ray and Douglas (2011) (see Section
13.7). (d) Altimetry data sets from five groups (CU, NOAA, GSFC, AVISO, CSIRO) with mean of the five shown as
bright blue line (see Section 13.7). (e) Comparison of the paleo data from salt marshes (purple symbols, from panel b),
with tide gauge and altimetry data sets (same line colors as in panels c and d). All paleo data were shifted by mean of
1700–1850 derived from the Sand Point, North Carolina data. The Jevrejeva et al. (2008) tide gauge data were shifted
by their mean for 1700–1850; other two tide gauge data sets were shifted by same amount. The altimeter time series has
been shifted vertically upwards so that their mean value over the 1993–2007 period aligns with the mean value of the
average of all three tide gauge time series over the same period.

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FAQ13.1, Figure 1: Map of rates of change in sea surface height (geocentric sea level) for the period 1993–2012 from
satellite altimetry. Also shown are relative sea level changes (grey lines) from selected tide gauge stations for the period
1950–2012. For comparison, an estimate of global mean sea level change is also shown (red lines) with each tide gauge
time series. The relatively large, short-term oscillations in local sea level (grey lines) are due to the natural climate
variability described in the main text. For example, the large, regular deviations at Pago Pago are associated with the El
Niño-Southern Oscillation.

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FAQ13.1, Figure 2: Model output showing relative sea level change due to melting of the Greenland Ice Sheet and the
West Antarctic Ice Sheet at rates of 0.5 mm yr
–1
each (giving a global mean value for sea level rise of 1 mm yr
–1
). The
modelled sea level changes are less than the global mean value in areas near the melting ice but enhanced further afield.
Adapted from Milne et al. (2009).

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Figure 13.4: Comparison of modelled and observed components of global mean sea level change since 1900. Changes
in glaciers, ice sheets and land water storage are shown as positive sea level rise when mass is added to the ocean. (a)
Ocean thermal expansion. Individual CMIP5 AOGCM simulations are shown in grey, the AOGCM average is black,
observations in teal with the 5–95% uncertainties shaded. (b) Glaciers (excluding Antarctic peripheral glaciers). Model
simulations by Marzeion et al. (2012a) with input from individual AOGCMs are shown in grey with the average of
these results in bright purple. Model simulations by Marzeion et al. (2012a) forced by observed climate are shown in
light blue. The observational estimates by Cogley (2009b) are shown in green (dashed) and by Leclercq et al. (2011) in
red (dashed). (c) Changes in land water storage (yellow/orange, the sum of groundwater depletion and reservoir
storage) start at zero in 1900. The Greenland Ice sheet (green), the Antarctic Ice Sheet (blue) and the sum of the ice-
sheets (red), start at zero at the start of the record 1991. (d) The rate of change (19 year centred trends) for the terms in
(a) to (c), and for the ice sheets (5 year centred trends). All curves in (a) and (b) are shown with zero time-mean over
the period 1986-2005 and the colours in (d) are matched to earlier panels. Updated from Church et al. (2013).

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Figure 13.5: Annual-mean surface mass balance (accumulation minus ablation) for the Greenland Ice Sheet, simulated
by regional climate models for the period 1960–2010.

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Figure 13.6: Global mean sea level from altimetry from 2005 to 2012 (blue line). Ocean mass changes are shown in
green (as measured by GRACE) and thermosteric sea level changes (as measured by Argo) are shown in red. The black
line shows the sum of the ocean mass and thermosteric contributions (updated from Boening et al. (2012)).

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Figure 13.7: Comparison of the observed global mean sea level rise with the sum of the modelled contributions from
ocean thermal expansion, glaciers (excluding glaciers peripheral to the Antarctic ice sheet; from Marzeion et al.
(2012a)) and changes in land-water storage. In panels (a)-(d): the grey lines are results for individual CMIP5 AOGCMs;
the black line is the model mean plus a correction for the omission of volcanic forcing in the AOGCM control
experiments (see Section 13.3.1.2); the adjusted model mean (dashed black line) is the sum of the corrected model
mean thermal expansion, the change in land-water storage, the Marzeion et al. (2012a) glacier estimate using observed
(rather than modelled) climate, and an illustrative long-term ice-sheet contribution (of 0.1 mm yr
–1
); the adjusted model
mean including the observed ice sheet contribution (dotted black line) begins in 1993 and includes the ice-sheet
contributions (from Section 4.4), but excludes the glaciers peripheral to the both the Greenland Ice Sheet and the
Antarctic Ice Sheet, to avoid double counting because the observational ice-sheet estimates include the peripheral
glaciers; the estimates of global mean sea level are from Church and White (2011), Ray and Douglas (2011) and
Jevrejeva et al. (2008), with the shading indicating the uncertainty estimates (two standard deviations); the satellite
altimeter data since 1993 are shown in red. The modelled sea level is given (a) for the period 1900 to 2010, (b) the rates
of sea level change for the same period, with the satellite altimeter data shown as a red dot for the rate, (c) for 1961 to
2010 and (d) for 1990 to 2010. Panel (e) compares the sum of the observed contributions (orange) and the observed sea
level from the satellite altimeter data (red). (Figure and caption updated from Church et al. (2013))

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Box 13.1, Figure 1: The Earth’s energy budget from 1970 through 2011. (a) The cumulative energy flux into the Earth
system from changes in well-mixed and short-lived greenhouse gases, solar forcing, changes in tropospheric aerosol
forcing, volcanic forcing and surface albedo, (relative to 1860–1879) are shown by the coloured lines and these are
added to give the cumulative energy inflow (black; including black carbon on snow and combined contrails and contrail
induced cirrus, not shown separately). (b) The cumulative total energy inflow from (a, black) is balanced by the sum of
the warming of the Earth system (blue; energy absorbed in warming the ocean, the atmosphere and the land and in the
melting of ice) and an increase in outgoing radiation inferred from changes in the global averaged surface temperature.
The sum of these two terms is given for a climate feedback parameter α of 0.82, 1.23 and 2.47 W m
–2
°C
–1
,
(corresponding to an equilibrium climate sensitivity of 4.5, 3.0 and 1.5ºC). The energy budget would be closed for a
particular value of α if that line coincided with the total energy inflow. For clarity, all uncertainties (shading) shown are
for a likely range.

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Figure 13.8: Heat uptake by the climate system during the 21st century relative to 1986–2005 projected by CMIP5
AOGCMs under RCP scenarios (1 YJ = 10
24
J). The heat uptake is diagnosed by two different methods. The thick solid
lines, and the coloured ranges for RCP2.6 and RCP8.5, are the time- and global integral of the net downward radiative
flux perturbation at the top of the atmosphere, from the 21 AOGCMs used to make the global mean sea level
projections (in some cases estimated from other scenarios, as described in the Supplementary Material). The broken
solid lines, and the thin solid lines delimiting ranges for RCP2.6 and RCP8.5, are the global volume integral of ocean
temperature change, in a smaller and different set of AOGCMs for each scenario. The difference between the two
diagnoses is due partly to the different sets of models (which is a consequence of diagnostics available in the CMIP5
dataset), and partly to heat uptake in other parts of the simulated climate system than the ocean water. In both methods,
climate drift in the pre-industrial control run has been subtracted.
2000 2020 2040 2060 2080 2100
Year
0
1
2
3
4
Ocean heat uptake (YJ)
14
RCP2.6
20
RCP4.5
12
RCP6.0
20
RCP8.5
For individual scenarios, in their own colors:
Mean and 5-95% range from TOA radiation
Mean and 5-95% range from ocean temperature

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Figure 13.9: Time series plots for process-based model projections of sea level contributions from global glaciers (in
mm), including peripheral glaciers surrounding the Greenland Ice Sheet but excluding the glaciers surrounding the
Antarctic Ice Sheet. Projections are grouped by forcing scenario as indicated on the plots. Results are plotted for a
common time interval of 2011 to 2099. Colors correspond to particular model analyses: red = Marzeion et al. (2012a);
blue = Slangen and van de Wal (2011); green = Radić et al. (2013); black = Giesen and Oerlemans (2013). Individual
AOGCM model projections are plotted for each analysis, so the ranges of the curves at 2099 are different than those
listed in Table 13.3, where 5–95% confidence limits are shown. In the panel showing results for RCP6.0 and A1B
forcings, only Geisen and Oerlemans (black lines) use the A1B forcing.
0
50
100
150
200
250
0
50
100
150
200
250
2000 2020 2040 2060 2080 2100 2000 2020 2040 2060 2080 2100
RCP 2.6 RCP 4.5
RCP 8.5RCP 6.0 / A1B
50
63
150
110
65
80
17
45
170
136
46
63
59
145
191
141
66
108
93
214
239
183
50
50
63
63
1
5
0
150
1
1
0
110
6
5
65
80
80
1
7
0
170
136
136
46
46
6
3
63
59
59
145
145
1
9
1
191
141
141
66
66
108
108
93
93
2
1
4
214
239
239
183
183
Years Years
GMSLR (mm)GMSLR (mm)

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Box 13.2, Figure 1: Schematic of the processes leading to the potentially unstable retreat of a grounding line showing
(a) geometry and ice fluxes of a marine ice sheet, (b) the grounding line in steady state, (c) climate change triggering
mass outflow from the ice sheet and the start of grounding line retreat, and (d) self-sustained retreat of the grounding
line.

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FAQ 13.2, Figure 1: Illustrative synthesis of projected changes in SMB and outflow by 2100 for (a) Greenland and (b)
Antarctic ice sheets. Colours shown on the maps refer to projected SMB change between 1990–2009 and 2079–2098
using the RACMO2 regional climate model under scenarios A1B (Antarctic) and RCP4.5 (Greenland) AOGCMs. For
Greenland, average equilibrium line altitudes during both these time periods are shown in purple and green,
respectively. Ice perimeter and grounding lines are shown as black lines, as are ice sheet sectors. For Greenland, results
of flow-line modelling for four major outlet glaciers are shown as inserts, while for Antarctica the coloured rings reflect
projected change in outflow on the basis of a statistical extrapolation of observed trends. The outer and inner radius of
each ring indicate the 17th and 83rd percentiles, respectively (scale in upper right); red refers to mass loss (sea level
rise) while blue refers to mass gain (sea level fall). Finally, total sea level contribution is shown for each ice sheet
(insert located above maps) with light grey referring to SMB (model experiment used to generate the SMB map is
shown as a dashed line) and dark grey to outflow. All projections refer to the 17–83% probability range across all
scenarios. Data provided by F. M. Nick, C. M. Little, J. H. van Angelen, S. R. M. Ligtenberg, J. T. M. Lenaerts and M.
R. van den Broeke.

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Figure 13.10: Projections from process-based models with likely ranges and median values for global-mean sea level
rise and its contributions in 2081–2100 relative to 1986–2005 for the four RCP scenarios and scenario SRES A1B used
in the AR4. The contributions from ice sheets include the contributions from ice-sheet rapid dynamical change, which
are also shown separately. The contributions from ice-sheet rapid dynamics and anthropogenic land water storage are
are treated as having uniform probability distributions, and as independent of scenario (except that a higher rate of
change is used for Greenland ice-sheet outflow under RCP8.5). This treatment does not imply that the contributions
concerned will not depend on the scenario followed, only that the current state of knowledge does not permit a
quantitative assessment of the dependence. See discussion in Sections 13.5.1 and 13.5.3 and Supplementary Material
for methods. Only the collapse of the marine-based sectors of the Antarctic Ice Sheet, if initiated, could cause GMSL to
rise substantially above the likely range during the 21st century. This potential additional contribution cannot be
precisely quantified but there is medium confidence that it would not exceed several tenths of a meter of sea level rise.
A1B RCP2.6 RCP4.5 RCP6.0 RCP8.5
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Global mean sea level rise (m)
2081-2100 relative to 1986-2005Sum
Thermal expansion
Glaciers
Greenland ice sheet (including dynamics)
Antarctic ice sheet (including dynamics)
Land water storage
Greenland ice-sheet rapid dynamics
Antarctic ice-sheet rapid dynamics

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(a)
(b)
Figure 13.11: Projections from process-based models of (a) GMSL rise relative to 1986–2005 and (b) the rate of
GMSL rise and its contributions as a function of time for the four RCP scenarios and scenario SRES A1B. The lines
show the median projections. For GMSL rise and the thermal expansion contribution, the likely range is shown as a
shaded band. The contributions from ice sheets include the contributions from ice-sheet rapid dynamical change, which
are also shown separately. The time series for GMSL rise plotted in (a) are tabulated in Annex II (Table AII.7.7), and
the time series of GMSL rise and all its contributions are available in the Supplementary Material. The rates in (b) are
calculated as linear trends in overlapping 5-year periods. Only the collapse of the marine-based sectors of the Antarctic
Ice Sheet, if initiated, could cause GMSL to rise substantially above the likely range during the 21st century. This
potential additional contribution cannot be precisely quantified but there is medium confidence that it would not exceed
several tenths of a meter of sea level rise.
RCP2.6
0.0
0.2
0.4
0.6
0.8
1.0
Global mean sea level rise (m)
2000 2020 2040 2060 2080 2100
Year
Sum
Thermal expansion
Glaciers
Greenland ice sheet
Antarctic ice sheet
Greenland ice-sheet rapid dynamics
Antarctic ice-sheet rapid dynamics
Land water storage
RCP4.5
0.0
0.2
0.4
0.6
0.8
1.0
Global mean sea level rise (m)
2000 2020 2040 2060 2080 2100
Year
RCP6.0
0.0
0.2
0.4
0.6
0.8
1.0
Global mean sea level rise (m)
2000 2020 2040 2060 2080 2100
Year
RCP8.5
0.0
0.2
0.4
0.6
0.8
1.0
Global mean sea level rise (m)
2000 2020 2040 2060 2080 2100
Year
RCP2.6
0
5
10
15
Rate of global mean
sea level rise (mm yr
-1
)
2000 2020 2040 2060 2080 2100
Year
Sum
Thermal expansion
Glaciers
Greenland ice sheet
Antarctic ice sheet
Greenland ice-sheet rapid dynamics
Antarctic ice-sheet rapid dynamics
Land water storage
RCP4.5
0
5
10
15
Rate of global mean
sea level rise (mm yr
-1
)
2000 2020 2040 2060 2080 2100
Year
RCP6.0
0
5
10
15
Rate of global mean
sea level rise (mm yr
-1
)
2000 2020 2040 2060 2080 2100
Year
RCP8.5
0
5
10
15
Rate of global mean
sea level rise (mm yr
-1
)
2000 2020 2040 2060 2080 2100
Year

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Figure 13.12: Range (5–95%) for projections of global-mean sea level rise (m) in 2081–2100 relative to 1986–2005 by
semi-empirical models for (a) RCP2.6, (b) RCP4.5, (c) RCP6.0, and (d) RCP8.5. Blue bars are results from the models
using RCP temperature projections, red bars are using RCP radiative forcing. The numbers on the horizontal axis refer
to the literature source of the projection and the sea level reconstruction used for calibration (for studies using RCP
temperature projections) or reconstruction of radiative forcing (for studies using RCP radiative forcing). (1) Rahmstorf
et al. (2012a), with Kemp et al. (2011); (2) Schaeffer et al. (2012); (3) Rahmstorf et al. (2012a), with Church and White
(2006); (4) Rahmstorf et al. (2012a), with Church and White (2011); (5) Rahmstorf et al. (2012a), with Jevrejeva et al.
(2008); (6) Grinsted et al. (2010), with Moberg et al. (2005); (7) Jevrejeva et al. (2012a), with Goosse et al. (2005); (8)
Jevrejeva et al. (2012a), with Crowley et al. (2003); (9) Jevrejeva et al. (2012a) with Tett et al. (2007). Also shown for
comparison is the median (thick black line) and likely range (horizontal gray bar) (as defined in Section 13.5.1) from
the process-based projections (Table 13.5), which are assessed as having medium confidence, in contrast to SEMs,
which are assessed as having low confidence (Section 13.5.3).
Global mean sea level rise (m)
a)
1 2 3 4 5 6 7 8
0
0.5
1.0
1.5
b)
1 2 3 4 5 6 7 8
0
0.5
1.0
1.5
Global mean sea level rise (m)
c)
1 2 3 4 5 6 7 8
0
0.5
1.0
1.5
d)
1 2 3 4 5 6 7 8
0
0.5
1.0
1.5

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Figure 13.13: Sea level projections beyond the year 2100 are grouped into three categories according to the
concentration of GHG concentration (in CO
2
-equivalent) in the year 2100 (upper panel: >700 ppm; middle panel: 500–
700 ppm; lower panel: <500 ppm). Colored bars show the full model spread. Horizontal lines provide the specific
model simulations. The different contributions are given from left to right as thermal expansion, glaciers, Greenland Ice
Sheet, Antarctic Ice Sheet and the total contribution. The range provided for the total sea level change represents the
maximum possible spread that can be obtained from the four different contributions. Light red shaded bars exhibit the
likely range for the 21st century total sea level projection from Figure 13.10 with the median as the horizontal line.
Light blue bars provide the model spread of the thermal expansion of the CMIP5 simulations as used for the 21st
century projections (Section 13.5.1), with the median indicated by the horizontal bar.
1
3
5
7
Thermal Expansion
Glaciers
Greenland
Antarctica
Total Sea Level
High scenarios: > 700 ppm CO
2
0
1
2
Medium scenarios: 500−700 ppm CO
2
Year
2100 2200 2300 2400 2500
0
0.2
0.4
0.6
0.8
1
Year
Low scenarios: < 500 ppm CO
2
Sea Level Change (m)

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Figure 13.14: Left column: multi-millennial sea level commitment per degree of warming as obtained from physical
model simulations of (a) ocean warming, (b) mountain glaciers, (c) the Greenland and (d) the Antarctic ice sheet. (e)
The corresponding total sea level commitment, compared to paleo-estimates from past warm periods (PI = pre-
industrial, LIG = last interglacial period, M11 = Marine Isotope Stage 11, Plio = Mid-Pliocene). Temperatures are
relative to pre-industrial. Dashed lines provide linear approximations in (d) & (e) constant slopes of 1.2, 1.8 and 2.3 m
°C
–1
. Shading as well as the vertical line represents the uncertainty range as detailed in the text. Right column: 2000-
year-sea level commitment. The difference in total sea level commitment (j) compared to the fully equilibrated situation
(e) arises from the Greenland Ice Sheet which equilibrates on tens of thousands of years. After 2000 years one finds a
non-linear dependence on the temperature increase (h) consistent with coupled climate-ice-sheet simulations by
Huybrechts et al. (2011) (black dot). The total sea level commitment after 2000 years is quasi-linear with a slope of 2.3
m °C
–1
.
1
2
f.
0.42 m/°C
0.2
0.4
g.
0
1
2
3
h.
1
3
5
1.2 m/°C
i.
1 2 3 4
0
5
10
2.3 m/°C
Temperature (° C)
Sea level (m)
j.
1
2
a.
0.42 m/K
0.2
0.4
b.
2
4
6
c.
1
3
5
1.2 m/°C
d.
1 2 3 4
0
5
10
15
20
1.8 m/°C
2.3 m/°C
Temperature (° C)
Sea level (m)
e.
PI
LIG
M11
Plio

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Figure 13.15: (a) RMS interannual dynamic sea level variability (mm) in a CMIP5 multi-model ensemble (21 models),
built from the historically forced experiments during the period 1951–2005; (b) Changes in the ensemble average
interannual dynamic sea level variability (std. dev.; in mm) evaluated over the period 2081–2100 relative to the
reference period 1986–2005. The projection data (2081–2100) are from the CMIP5 RCP4.5 experiment.

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Figure 13.16: (a) Ensemble mean projection of the time-averaged dynamic and steric sea level changes for the period
2081–2100 relative to the reference period 1986–2005, computed from 21 CMIP5 climate models (in m), using the
RCP4.5 experiment. The figure includes the globally averaged steric sea level increase of 0.18 ± 0.05 m. (b) RMS
spread (deviation) of the individual model result around the ensemble mean (m).

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Figure 13.17: Projected ensemble mean sea level change (m) due to changes in atmospheric pressure loading over the
period from 1986–2005 to 2081–2100 for (a) RCP4.5 and (b) RCP8.5 (contour interval is 0.01 m). Standard deviation
of the model ensemble due to the atmospheric pressure loading for (c) RCP4.5 and (d) RCP8.5 (contour interval is
0.005 m).

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Figure 13.18: Ensemble mean regional contributions to sea level change (m) from (a) GIA, (b) glaciers and (c) ice-
sheet SMB. Panels (b) and (c) are based on information available from scenario RCP4.5. All panels represent changes
between the periods 1986–2000 and 2081–2100.

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Figure 13.19: (a) Ensemble mean net regional sea level change (m) evaluated from 21 models of the CMIP5 scenario
RCP4.5, including atmospheric loading, plus land-ice, GIA and terrestrial water sources, between 1986–2005 and
2081–2100. Global mean is 0.50 ± 0.13 m, with a total range of –1.71 to +0.83 m. (b) Lower 90% CL uncertainty
bound (p = 0.05) for RCP4.5 scenario sea level rise (plus non-scenario components). (c) Upper 90% CL uncertainty
bound (p = 0.95) for RCP4.5 scenario sea level rise (plus non-scenario components).

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Figure 13.20: Ensemble mean net regional sea level change (m) evaluated from 21 CMIP5 models for the RCP
scenarios (a) 2.6, (b) 4.5, (c) 6.0 and (d) 8.5 between 1986–2005 and 2081–2100. Each map includes effects of
atmospheric loading, plus land-ice, GIA and terrestrial water sources.

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Figure 13.21: (a) Percentage of the deviation of the ensemble mean net regional sea level change between 1986–2005
and 2081–2100 from the global mean value. The figure was computed for RCP4.5, but to first order is representative
for all RCPs. (b) Total RCP4.5 sea level change (plus all other components) divided by the combined standard error of
all components (see Supplementary Material Section 13.SM.2). Assuming a normal distribution, or a t-distribution
given the number of models as an approximation of the number of degrees of freedom, a region passes the 90%
confidence level where the change is greater than 2 standard errors, which is most of the ocean except for limited
regions around western Antarctica, Greenland, and high Arctic regions.

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Figure 13.22: (a) Histograms of the deviation of the ensemble mean net regional sea level change (Figure 13.20) along
all coastlines (represented by the closest model grit point) between 1986–2005 and 2081–2100 from the global mean
value. Shown are results for RCP4.5 (blue) and RCP8.5 (pink), respectively. (b) Same as in (a) but excluding Antarctic
and Greenland coastlines. Vertical dashed lines represent global mean sea level changes for the two RCPs.

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Figure 13.23: Observed and projected relative net sea level change (compare Figure 13.20) near nine representative
coastal locations for which long tide-gauge measurements are available. The observed in situ relative sea level records
from tide gauges (since 1970) are plotted in yellow, and the satellite record (since 1993) is provided as purple lines. The
projected range from 21 CMIP5 RCP4.5 scenario runs (90% uncertainty) is shown by the shaded region for the period
2006–2100, with the bold line showing the ensemble mean. Colored lines represent three individual climate model
realizations drawn randomly from three different climate models used in the ensemble. Station locations of tide gauges
are: (a) San Francisco: 37.8°N, 122.5°W; (b) New York: 40.7°N, 74.0°W; (c) Ijmuiden: 52.5°N, 4.6°E; (d) Haldia:
22.0°N, 88.1°E; (e) Kanmen, China: 28.1°N, 121.3°E; (f) Brest: 48.4°N, 4.5°W; (g) Mar del Plata, Argentina: 38.0°S,
57.5°W; (h) Fremantle: 32.1°S, 115.7°E; (i) Pago Pago: 14.3°S, 170.7°W. Vertical bars at the right sides of each panel
represent the ensemble mean and ensemble spread (5–95%) of the likely (medium confidence) sea level change at each
respective location at the year 2100 inferred from the four RCPs 2.6 (dark blue), 4.5 (light blue), 6.0 (yellow), and 8.5
(red).

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Figure 13.24: Projected relative sea level change (in m) from the combined global steric plus dynamic topography and
glacier contributions for the RCP4.5 scenario over the period from 1986–2005 to 2081–2100 for each individual
climate model used in the production of Figure 13.16a.

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Figure 13.25: The estimated multiplication factor (shown at tide gauge locations by red circles and triangles), by which
the frequency of flooding events of a given height increase for (a) a MSL rise of 0.5 m (b) using regional projections of
MSL for the RCP4.5 scenario, shown in Figure13.19a. The Gumbel scale parameters are generally large in regions of
large tides and/or surges resulting in a small multiplication factor and vice versa.
0 306090 150120 180E306090120180W 150
0
60S
60N
30
30
a)
10
100
1000
>1000
0 306090 150120 180E306090120180W 150
0
60S
60N
30
30
b)
10
100
1000
>1000

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Figure 13.26: Projected changes in wind-wave conditions (~2075–2100 compared with ~1980–2009) derived from the
Coordinated Ocean Wave Climate Projection (COWCLIP) Project (Hemer et al., 2013). (a) Percentage difference in
annual mean significant wave height. (b) Percentage difference in January-March mean significant wave height. (c)
Percentage difference in July-September mean significant wave height. Hashed regions indicate projected change is
greater than the 5-member ensemble standard deviation. (d) As for (a), but displaying absolute changes in mean wave
direction, with positive values representing projected clockwise rotation relative to displayed vectors, and colours
shown only where ensemble members agree on sign of change. (e) As for (a), but displaying absolute changes in mean
wave period. The symbol ~ is used to indicate that the reference periods differ slightly for the various model studies
considered.

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Figure 13.27: Compilation of paleo sea level data, tide gauge data, altimeter data (from Figure 13.3), and central
estimates and likely ranges for projections of global-mean sea level rise for RCP2.6 (blue) and RCP8.5 (red) scenarios
(Section 13.5.1), all relative to pre-industrial values.