1137
13
This chapter should be cited as:
Church, J.A., P.U. Clark, A. Cazenave, J.M. Gregory, S. Jevrejeva, A. Levermann, M.A. Merrifield, G.A. Milne, R.S.
Nerem, P.D. Nunn, A.J. Payne, W.T. Pfeffer, D. Stammer and A.S. Unnikrishnan, 2013: Sea Level Change. In: Climate
Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung,
A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA.
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 (Denmark/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 (Australia/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), Ricarda 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)
Sea Level Change
1138
13
Table of Contents
Executive Summary ................................................................... 1139
13.1 Components and Models of Sea Level Change .... 1142
13.1.1 Introduction and Chapter Overview ........................ 1142
13.1.2 Fundamental Definitions and Concepts .................. 1142
13.1.3 Processes Affecting Sea Level.................................. 1143
13.1.4 Models Used to Interpret Past and Project Future
Changes in Sea Level .............................................. 1144
13.2 Past Sea Level Change ................................................. 1145
13.2.1 The Geological Record ............................................ 1145
13.2.2 The Instrumental Record (~1700–2012) ................. 1146
13.3 Contributions to Global Mean Sea Level Rise
During the Instrumental Period ................................ 1150
13.3.1 Thermal Expansion Contribution ............................. 1150
13.3.2 Glaciers ................................................................... 1151
13.3.3 Greenland and Antarctic Ice Sheets ........................ 1153
13.3.4 Contributions from Water Storage on Land ............. 1155
13.3.5 Ocean Mass Observations from the Gravity
Recovery and Climate Experiment .......................... 1156
13.3.6 Budget of Global Mean Sea Level Rise.................... 1156
Box 13.1: The Global Energy Budget ..................................... 1159
13.4 Projected Contributions to Global Mean
Sea Level .......................................................................... 1161
13.4.1 Ocean Heat Uptake and Thermal Expansion............ 1161
13.4.2 Glaciers ................................................................... 1163
13.4.3 Greenland Ice Sheet ................................................ 1165
13.4.4 Antarctic Ice Sheet .................................................. 1170
Box 13.2: History of the Marine Ice-Sheet Instability
Hypothesis ................................................................................ 1175
13.4.5 Anthropogenic Intervention in Water Storage
on Land ................................................................... 1176
13.5 Projections of Global Mean Sea Level Rise ........... 1179
13.5.1 Process-Based Projections for the 21st Century ...... 1179
13.5.2 Semi-Empirical Projections for the 21st Century ..... 1182
13.5.3 Confidence in Likely Ranges and Bounds ................ 1184
13.5.4 Long-term Scenarios ............................................... 1186
13.6 Regional Sea Level Changes ...................................... 1191
13.6.1 Regional Sea Level Changes, Climate Modes and
Forced Sea Level Response...................................... 1191
13.6.2 Coupled Model Intercomparison Project Phase 5
General Circulation Model Projections on Decadal
to Centennial Time Scales ....................................... 1192
13.6.3 Response to Atmospheric Pressure Changes ........... 1193
13.6.4 Response to Freshwater Forcing .............................. 1193
13.6.5 Regional Relative Sea Level Changes ...................... 1194
13.6.6 Uncertainties and Sensitivity to Ocean/Climate
Model Formulations and Parameterizations ............ 1197
13.7 Projections of 21st Century Sea Level
Extremes and Waves..................................................... 1200
13.7.1 Observed Changes in Sea Level Extremes ............... 1200
13.7.2 Projections of Sea Level Extremes ........................... 1200
13.7.3 Projections of Ocean Waves .................................... 1202
13.8 Synthesis and Key Uncertainties .............................. 1204
References ................................................................................ 1206
Frequently Asked Questions
FAQ 13.1 Why Does Local Sea Level Change Differ
from the Global Average? ................................... 1148
FAQ 13.2 Will the Greenland and Antarctic Ice Sheets
Contribute to Sea Level Change over the
Rest of the Century? ............................................ 1177
Supplementary Material
Supplementary Material is available in online versions of the report.
1139
Sea Level Change Chapter 13
13
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 evi-
dence 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).
Executive Summary
This chapter considers changes in global mean sea level, regional sea
level, sea level extremes, and waves. Confidence in projections of global
mean sea level rise has increased since the Fourth Assessment Report
(AR4) because of the improved physical understanding of the compo-
nents of sea level, the improved agreement of process-based models
with observations, and the inclusion of ice-sheet dynamical changes.
Past Sea Level Change
Paleo sea level records from warm periods during the last 3
million years indicate that global mean sea level has exceeded
5 m above present (very high confidence)
1
when global mean
temperature was up to 2°C warmer than pre-industrial (medium
confidence). There is very high confidence that maximum global
mean sea level during the last interglacial period (~129 to 116 ka)
was, for several thousand years, at least 5 m higher than present and
high confidence that it did not exceed 10 m above present, implying
substantial contributions from the Greenland and Antarctic ice sheets.
This change in sea level occurred in the context of different orbital forc-
ing and with high latitude surface temperature, averaged over several
thousand years, at least 2°C warmer than present (high confidence)
{5.3.4, 5.6.1, 5.6.2, 13.2.1}
Proxy and instrumental sea level data indicate a transition in
the late 19th century to the early 20th century from relative-
ly low mean rates of rise over the previous two millennia to
higher rates of rise (high confidence). It is likely
2
that the rate
of global mean sea level rise has continued to increase since
the early 20th century, with estimates that range from 0.000
[–0.002 to 0.002] mm yr
–2
to 0.013 [0.007 to 0.019] mm yr
–2
. It
is very likely that the global 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 [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
; similarly high rates likely occurred between 1920 and
1950. {3.7.2, 3.7.4, 5.6.3, 13.2.1, 13.2.2, Figure 13.3}
Understanding of Sea Level Change
Ocean thermal expansion and glacier melting have been the
dominant contributors to 20th century global mean sea level
rise. Observations since 1971 indicate that thermal expansion and gla-
ciers (excluding Antarctic glaciers peripheral to the ice sheet) explain
75% of the observed rise (high confidence). The contribution of the
Greenland and Antarctic ice sheets has increased since the early 1990s,
partly from increased outflow induced by warming of the immediate-
ly adjacent ocean. Natural and human-induced land water storage
changes have made only a small contribution; the rate of ground-
water depletion has increased and now exceeds the rate of reservoir
impoundment. Since 1993, when observations of all sea level com-
ponents are available, the sum of contributions equals the observed
global mean sea level rise within uncertainties (high confidence).
{Chapters 3, 4, 13.3.6, Figure 13.4, Table 13.1}
There is high confidence in projections of thermal expansion
and Greenland surface mass balance, and medium confidence
in projections of glacier mass loss and Antarctic surface mass
balance. There has been substantial progress in ice-sheet modelling,
particularly for Greenland. Process-based model calculations of contri-
butions to past sea level change from ocean thermal expansion, gla-
cier mass loss and Greenland ice-sheet surface mass balance are con-
sistent with available observational estimates of these contributions
over recent decades. Ice-sheet flowline modelling is able to reproduce
the observed acceleration of the main outlet glaciers in the Green-
land ice sheet, thus allowing estimates of the 21st century dynamical
response (medium confidence). Significant challenges remain in the
process-based projections of the dynamical response of marine-termi-
nating glaciers and marine-based sectors of the Antarctic ice sheet.
Alternative means of projection of the Antarctic ice-sheet contribution
(extrapolation within a statistical framework and informed judgement)
provide medium confidence in a likely range. There is currently low
confidence in projecting the onset of large-scale grounding line insta-
bility in the marine-based sectors of the Antarctic ice sheet. {13.3.1 to
13.3.3, 13.4.3, 13.4.4}
The sum of thermal expansion simulated by Coupled Model
Intercomparison Project phase 5 (CMIP5) Atmosphere–Ocean
General Circulation Models (AOGCMs), glacier mass loss com-
puted by global glacier models using CMIP5 climate change
simulations, and estimates of land water storage explain 65%
of the observed global mean sea level rise for 1901–1990 and
90% for 1971–2010 and 1993–2010 (high confidence). When
observed climate parameters are used, the glacier models indicate a
larger Greenland peripheral glacier contribution in the first half of the
20th century such that the sum of thermal expansion, glacier mass
loss and changes in land water storage and a small ongoing Antarctic
ice-sheet contribution are within 20% of the observations throughout
the 20th century. Model-based estimates of ocean thermal expansion
and glacier contributions indicate that the greater rate of global mean
sea level rise since 1993 is a response to radiative forcing (RF, both
anthropogenic and natural) and increased loss of ice-sheet mass and
not part of a natural oscillation (medium confidence). {13.3.6, Figures
13.4, 13.7, Table 13.1}
1140
Chapter 13 Sea Level Change
13
The Earth’s Energy Budget
Independent estimates of effective RF of the climate system,
the observed heat storage, and surface warming combine to
give an energy budget for the Earth that is closed within uncer-
tainties (high confidence), and is consistent with the likely range
of climate sensitivity. The largest increase in the storage of heat in
the climate system over recent decades has been in the oceans; this
is a powerful observation for the detection and attribution of climate
change. {Boxes 3.1, 13.1}
Global Mean Sea Level Rise Projections
It is very likely that the rate of global mean sea level rise during
the 21st century will exceed the rate observed during 1971–
2010 for all Representative Concentration Pathway (RCP) sce-
narios due to increases in ocean warming and loss of mass from
glaciers and ice sheets. Projections of sea level rise are larger
than in the AR4, primarily because of improved modeling of
land-ice contributions. For the period 2081–2100, compared to
1986–2005, global mean sea level rise is likely (medium confi-
dence) to be in the 5 to 95% range of projections from process-
based models, which give 0.26 to 0.55 m for RCP2.6, 0.32 to
0.63 m for RCP4.5, 0.33 to 0.63 m for RCP6.0, and 0.45 to 0.82 m
for RCP8.5. For RCP8.5, the rise by 2100 is 0.52 to 0.98 m with a
rate during 2081–2100 of 8 to 16 mm yr
–1
. We have considered the
evidence for higher projections and have concluded that there is cur-
rently insufficient evidence to evaluate the probability of specific levels
above the assessed likely range. Based on current understanding, only
the collapse of marine-based sectors of the Antarctic ice sheet, if initi-
ated, could cause global mean sea level to rise substantially above the
likely range during the 21st century. This potential additional contribu-
tion cannot be precisely quantified but there is medium confidence
that it would not exceed several tenths of a meter of sea level rise
during the 21st century. {13.5.1, Table 13.5, Figures 13.10, 13.11}
Some semi-empirical models project a range that overlaps the
process-based likely range while others project a median and
95th percentile that are about twice as large as the process-
based models. In nearly every case, the semi-empirical model
95th percentile is higher than the process-based likely range.
Despite the successful calibration and evaluation of semi-empirical
models against the observed 20th century sea level record, there is
no consensus in the scientific community about their reliability, and
consequently low confidence in projections based on them. {13.5.2,
13.5.3, Figure 13.12}
It is virtually certain that global mean sea level rise will con-
tinue beyond 2100, with sea level rise due to thermal expan-
sion to continue for many centuries. The amount of longer term
sea level rise depends on future emissions. The few available
process-based models that go beyond 2100 indicate global mean sea
level rise above the pre-industrial level to be less than 1 m by 2300
for greenhouse gas concentrations that peak and decline and remain
below 500 ppm CO
2
-eq, as in scenario RCP2.6. For a radiative forcing
that corresponds to above 700 ppm CO
2
-eq but below 1500 ppm, as
in the scenario RCP8.5, the projected rise is 1 m to more than 3 m
(medium confidence). This assessment is based on medium confidence
in the modelled contribution from thermal expansion and low con-
fidence in the modelled contribution from ice sheets. The amount of
ocean thermal expansion increases with global warming (0.2 to 0.6
m °C
–1
) but the rate of the glacier contribution decreases over time
as their volume (currently 0.41 m sea level equivalent) decreases. Sea
level rise of several meters could result from long-term mass loss by
ice sheets (consistent with paleo data observations of higher sea levels
during periods of warmer temperatures), but there is low confidence in
these projections. Sea level rise of 1 to 3 m per degree of warming is
projected if the warming is sustained for several millennia (low confi-
dence). {13.5.4, Figures 13.4.3, 13.4.4}
The available evidence indicates that sustained global warming
greater than a certain threshold above pre-industrial would lead
to the near-complete loss of the Greenland ice sheet over a mil-
lennium or more, causing a global mean sea level rise of about
7 m. Studies with fixed ice-sheet topography indicate the threshold
is greater than 2°C but less than 4°C (medium confidence) of global
mean surface temperature rise with respect to pre-industrial. The one
study with a dynamical ice sheet suggests the threshold is greater
than about 1°C (low confidence) global mean warming with respect to
pre-industrial. We are unable to quantify a likely range. Whether or not
a decrease in the Greenland ice sheet mass loss is irreversible depends
on the duration and degree of exceedance of the threshold. Abrupt and
irreversible ice loss from a potential instability of marine-based sectors
of the Antarctic ice sheet in response to climate forcing is possible, but
current evidence and understanding is insufficient to make a quantita-
tive assessment. {5.8, 13.3, 13.4 }
Regional Sea Level Change Projections
It is very likely that in the 21st century and beyond, sea level
change will have a strong regional pattern, with some places
experiencing significant deviations of local and regional sea
level change from the global mean change. Over decadal periods,
the rates of regional sea level change as a result of climate variability
can differ from the global average rate by more than 100% of the
global average rate. By the end of the 21st century, it is very likely
that over about 95% of the world ocean, regional sea level rise will
be positive, and most regions that will experience a sea level fall are
located near current and former glaciers and ice sheets. About 70% of
the global coastlines are projected to experience a relative sea level
change within 20% of the global mean sea level change. {13.6.5, Fig-
ures 13.18 to 13.22}
Projections of 21st Century Sea Level Extremes and
Surface Waves
It is very likely that there will be a significant increase in the
occurrence of future sea level extremes in some regions by 2100,
with a likely increase in the early 21st century. This increase will
primarily be the result of an increase in mean sea level (high confi-
dence), with the frequency of a particular sea level extreme increasing
by an order of magnitude or more in some regions by the end of the
21st century. There is low confidence in region-specific projections of
storminess and associated storm surges. {13.7.2, Figure 13.25}
1141
Sea Level Change Chapter 13
13
It is likely (medium confidence) that annual mean significant
wave heights will increase in the Southern Ocean as a result of
enhanced wind speeds. Southern Ocean generated swells are likely
to affect heights, periods, and directions of waves in adjacent basins.
It is very likely that wave heights and the duration of the wave season
will increase in the Arctic Ocean as a result of reduced sea-ice extent.
In general, there is low confidence in region-specific projections due to
the low confidence in tropical and extratropical storm projections, and
to the challenge of downscaling future wind fields from coarse-resolu-
tion climate models. {13.7.3; Figure 13.26}
1142
Chapter 13 Sea Level Change
13
13.1 Components and Models of Sea
Level Change
13.1.1 Introduction and Chapter Overview
Changes in sea level occur over a broad range of temporal and spatial
scales, with the many contributing factors making it an integral meas-
ure of climate change (Milne et al., 2009; Church et al., 2010). The pri-
mary contributors to contemporary sea level change are the expansion
of the ocean as it warms and the transfer of water currently stored on
land to the ocean, particularly from land ice (glaciers and ice sheets)
(Church et al., 2011a). Observations indicate the largest increase in the
storage of heat in the climate system over recent decades has been in
the oceans (Section 3.2) and thus sea level rise from ocean warming
is a central part of the Earth’s response to increasing greenhouse gas
(GHG) concentrations.
The First IPCC Assessment Report (FAR) laid the groundwork for much
of our current understanding of sea level change (Warrick and Oer-
lemans, 1990). This included the recognition that sea level had risen
during the 20th century, that the rate of rise had increased compared
to the 19th century, that ocean thermal expansion and the mass loss
from glaciers were the main contributors to the 20th century rise, that
during the 21st century the rate of rise was projected to be faster than
during the 20th century, that sea level will not rise uniformly around
the world, and that sea level would continue to rise well after GHG
emissions are reduced. They also concluded that no major dynamic
response of the ice sheets was expected during the 21st century, leav-
ing ocean thermal expansion and the melting of glaciers as the main
contributors to the 21st century rise. The Second Assessment Report
(SAR) came to very similar conclusions (Warrick et al., 1996).
By the time of the Third Assessment Report (TAR), coupled Atmos-
phere–Ocean General Circulation Models (AOGCMs) and ice-sheet
models largely replaced energy balance climate models as the primary
techniques supporting the interpretation of observations and the pro-
jections of sea level (Church et al., 2001). This approach allowed con-
sideration of the regional distribution of sea level change in addition to
the global average change. By the time of the Fourth Assessment Report
(AR4), there were more robust observations of the variations in the rate
of global average sea level rise for the 20th century, some understand-
ing of the variability in the rate of rise, and the satellite altimeter record
was long enough to reveal the complexity of the time-variable spatial
distribution of sea level (Bindoff et al., 2007). Nevertheless, three cen-
tral issues remained. First, the observed sea level rise over decades
was larger than the sum of the individual contributions estimated from
observations or with models (Rahmstorf et al., 2007, 2012a), although
in general the uncertainties were large enough that there was no sig-
nificant contradiction. Second, it was not possible to make confident
projections of the regional distribution of sea level rise. Third, there was
insufficient understanding of the potential contributions from the ice
sheets. In particular, the AR4 recognized that existing ice-sheet models
were unable to simulate the recent observations of ice-sheet acceler-
ations and that understanding of ice-sheet dynamics was too limited
to assess the likelihood of continued acceleration or to provide a best
estimate or an upper bound for their future contributions.
Despite changes in the scenarios between the four Assessments, the
sea level projections for 2100 (compared to 1990) for the full range
of scenarios were remarkably similar, with the reduction in the upper
end in more recent reports reflecting the smaller increase in radiative
forcing (RF) in recent scenarios due to smaller GHG emissions and the
inclusion of aerosols, and a reduction in uncertainty in projecting 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 in AR4 (not including a possible
additional allowance for a dynamic ice-sheet response).
Results since the AR4 show that for recent decades, sea level has contin-
ued to rise (Section 3.7). Improved and new observations of the ocean
(Section 3.7) and the cryosphere (Chapter 4) and their representation
in models have resulted in better understanding of 20th century sea
level rise and its components (this chapter). Records of past sea level
changes constrain long-term land-ice response to warmer climates as
well as extend the observational record to provide a longer context for
current sea level rise (Section 5.6).
This chapter provides a synthesis of past and contemporary sea level
change at global and regional scales. Drawing on the published ref-
ereed literature, including as summarized in earlier chapters of this
Assessment, we explain the reasons for contemporary change and
assess confidence in and provide global and regional projections of
likely sea level change for the 21st century and beyond. We discuss
the primary factors that cause regional sea level to differ from the
global average and how these may change in the future. In addition,
we address projected changes in surface waves and the consequences
of sea level and climate change for extreme sea level events.
13.1.2 Fundamental Definitions and Concepts
The height of the ocean surface at any given location, or sea level, is
measured either with respect to the surface of the solid Earth (relative
sea level (RSL)) or a geocentric reference such as the reference ellipsoid
(geocentric sea level). RSL is the more relevant quantity when consider-
ing the coastal impacts of sea level change, and it has been measured
using tide gauges during the past few centuries (Sections 13.2.2 and
3.7) and estimated for longer time spans from geological records (Sec-
tions 13.2.1 and 5.6). Geocentric sea level has been measured over the
past two decades using satellite altimetry (Sections 13.2.2 and 3.7).
A temporal average for a given location, known as Mean Sea Level
(MSL; see Glossary), is applied to remove shorter period variability.
Apart from Section 13.7, which considers high-frequency changes in
ocean surface height, the use of ‘sea level’ elsewhere in this chapter
refers to MSL. It is common to average MSL spatially to define global
mean sea level (GMSL; see Glossary). In principle, integrating RSL
change over the ocean area gives the change in ocean water volume,
which is directly related to the processes that dominate sea level
change (changes in ocean temperature and land-ice volume). In con-
trast, a small correction (–0.15 to –0.5 mm yr
–1
) needs to be subtracted
from altimetry observations to estimate ocean water volume change
(Tamisiea, 2011). Local RSL change can differ significantly from GMSL
because of spatial variability in changes of the sea surface and ocean
floor height (see FAQ 13.1 and Section 13.6).
1143
Sea Level Change Chapter 13
13
13.1.3 Processes Affecting Sea Level
This chapter focusses on processes within the ocean, atmosphere, land
ice, and hydrological cycle that are climate sensitive and are expected
to contribute to sea level change at regional to global scales in the
coming decades to centuries (Figure 13.1). Figure 13.2 is a navigation
aid for the different sections of this chapter and sections of other chap-
ters that are relevant to sea level change.
Changes in ocean currents, ocean density and sea level are all tightly
coupled such that changes at one location impact local sea level and
sea level far from the location of the initial change, including changes
in sea level at the coast in response to changes in open-ocean tem-
perature (Landerer et al., 2007; Yin et al., 2010). Although both tem-
perature and salinity changes can contribute significantly to region-
al sea level change (Church et al., 2010), only temperature change
produces a significant contribution to global average ocean volume
change due to thermal expansion or contraction (Gregory and Lowe,
2000). Regional atmospheric pressure anomalies also cause sea level
to vary through atmospheric loading (Wunsch and Stammer, 1997). All
of these climate-sensitive processes cause sea level to vary on a broad
range of space and time scales from relatively short-lived events, such
as waves and storm surges, to sustained changes over several decades
or centuries that are associated with atmospheric and ocean modes of
climate variability (White et al., 2005; Miller and Douglas, 2007; Zhang
and Church, 2012).
Water and ice mass exchange between the land and the oceans leads
to a change in GMSL. A signal of added mass to the ocean propagates
rapidly around the globe such that all regions experience a sea level
change within days of the mass being added (Lorbacher et al., 2012).
In addition, an influx of freshwater changes ocean temperature and
salinity and thus changes ocean currents and local sea level (Stammer,
2008; Yin et al., 2009), with signals taking decades to propagate around
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 com-
ponents 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.
the global ocean. The coupled atmosphere–ocean system can also
adjust to temperature anomalies associated with surface freshwater
anomalies through air–sea feedbacks, resulting in dynamical adjust-
ments of sea level (Okumura et al., 2009; Stammer et al., 2011). Water
mass exchange between land and the ocean also results in patterns
of sea level change called ‘sea level fingerprints’ (Clark and Lingle,
1977; Conrad and Hager, 1997; Mitrovica et al., 2001) due to change in
the gravity field and vertical movement of the ocean floor associated
with visco-elastic Earth deformation (Farrell and Clark, 1976). These
changes in mass distribution also affect the Earth’s inertia tensor and
therefore rotation, which produces an additional sea level response
(Milne and Mitrovica, 1998).
There are other processes that affect sea level but are not associated
with contemporary climate change. Some of these result in changes
that are large enough to influence the interpretation of observational
records and sea level projections at regional and global scales. In par-
ticular, surface mass transfer from land ice to oceans during the last
deglaciation contributes significantly to present-day sea level change
due to the ongoing visco-elastic deformation of the Earth and the cor-
responding changes of the ocean floor height and gravity (referred to
as glacial isostatic adjustment (GIA)) (Lambeck and Nakiboglu, 1984;
Peltier and Tushingham, 1991). Ice sheets also have long response
times and so continue to respond to past climate change (Section
13.1.5).
Anthropogenic processes that influence the amount of water stored in
the ground or on its surface in lakes and reservoirs, or cause changes in
land surface characteristics that influence runoff or evapotranspiration
rates, will perturb the hydrological cycle and cause sea level change
(Sahagian, 2000; Wada et al., 2010). Such processes include water
impoundment (dams, reservoirs), irrigation schemes, and groundwater
depletion (Section 13.4.5).
1144
Chapter 13 Sea Level Change
13
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Figure 13.2 | Schematic representation of key linkages between processes and com-
ponents 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 Assessment 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.
Sea level changes due to tectonic and coastal processes are beyond
the scope of this chapter. With the exception of earthquakes, which
can cause rapid local changes and tsunamis (Broerse et al., 2011) and
secular RSL changes due to post-seismic deformation (Watson et al.,
2010), tectonic processes cause, on average, relatively low rates of sea
level change (order 0.1 mm yr
–1
or less; Moucha et al., 2008). Sediment
transfer and compaction (including from ground water depletion) in
the coastal zone are particularly important in deltaic regions (Blum and
Roberts, 2009; Syvitski et al., 2009). Although they can dominate sea
level change in these localized areas, they are less important as a source
of sea level change at regional and global scales and so are not consid-
ered further in this chapter (see discussion in Working Group II, Chapter
5). Estimates of sediment delivery to the oceans (Syvitski and Kettner,
2011) suggest a contribution to GMSL rise of order 0.01 mm yr
–1
.
13.1.4 Models Used to Interpret Past and Project Future
Changes in Sea Level
AOGCMs have components representing the ocean, atmosphere, land,
and cryosphere, and simulate sea surface height changes relative to
the geoid resulting from the natural forcings of volcanic eruptions and
changes in solar irradiance, and from anthropogenic increases in GHGs
and aerosols (Chapter 9). AOGCMs also exhibit internally generated
climate variability, including such modes as the El Niño-Southern Oscil-
lation (ENSO), the Pacific Decadal Oscillation (PDO), the North Atlantic
Oscillation (NAO) and others that affect sea level (White et al., 2005;
Zhang and Church, 2012). Critical components for global and regional
changes in sea level are changes in surface wind stress and air–sea
heat and freshwater fluxes (Lowe and Gregory, 2006; Timmermann et
al., 2010; Suzuki and Ishii, 2011) and the resultant changes in ocean
density and circulation, for instance in the strength of the Atlantic
Meridional Overturning Circulation (AMOC) (Yin et al., 2009; Lorbacher
et al., 2010; Pardaens et al., 2011a). As in the real world, ocean density,
circulation and sea level are dynamically connected in AOGCMs and
evolve together. Offline models are required for simulating glacier and
ice-sheet changes (Section 13.1.4.1).
Geodynamic surface-loading models are used to simulate the RSL
response to past and contemporary changes in surface water and land-
ice mass redistribution and contemporary atmospheric pressure chang-
es. The sea surface height component of the calculation is based solely
on water mass conservation and perturbations to gravity, with no con-
siderations of ocean dynamic effects. Application of these models has
focussed on annual and interannual variability driven by contemporary
changes in the hydrological cycle and atmospheric loading (Clarke et
al., 2005; Tamisiea et al., 2010), and on secular trends associated with
past and contemporary changes in land ice and hydrology (Lambeck et
al., 1998; Mitrovica et al., 2001; Peltier, 2004; Riva et al., 2010).
Semi-empirical models (SEMs) project sea level based on statistical
relationships between observed GMSL and global mean temperature
(Rahmstorf, 2007a; Vermeer and Rahmstorf, 2009; Grinsted et al.,
2010) or total RF (Jevrejeva et al., 2009, 2010). The form of this rela-
tionship is motivated by physical considerations, and the parameters
are determined from observational data—hence the term ‘semi-em-
pirical’ (Rahmstorf et al., 2012b). SEMs do not explicitly simulate the
underlying processes, and they use a characteristic response time that
could be considerably longer than the time scale of interest (Rahm-
storf, 2007a) or one that is explicitly determined by the model (Grin-
sted et al., 2010).
Storm-surge and wave-projection models are used to assess how
changes in storminess and MSL impact sea level extremes and wave
climates. The two main approaches involve dynamical (Lowe et al.,
2010) and statistical models (Wang et al., 2010). The dynamical
models are forced by near-surface wind and mean sea level pressure
fields derived from regional or global climate models (Lowe et al.,
2010).
In this chapter, we use the term ‘process-based models’ (see Glossary)
to refer to sea level and land-ice models (Section 13.1.4.1) that aim
to simulate the underlying processes and interactions, in contrast to
1145
Sea Level Change Chapter 13
13
‘semi-empirical models’ which do not. Although these two approaches
are distinct, semi-empirical methods are often employed in compo-
nents of the process-based models (e.g., glacier models in which sur-
face mass balance is determined by a degree-day method (Braithwaite
and Olesen, 1989)).
13.1.4.1 Models Used to Project Changes in Ice Sheets
and Glaciers
The representation of glaciers and ice sheets within AOGCMs is not
yet at a stage where projections of their changing mass are routinely
available. Additional process-based models use output from AOGCMs
to evaluate the consequences of projected climate change on these
ice masses.
The overall contribution of an ice mass to sea level involves changes
to either its surface mass balance (SMB) or changes in the dynamics
of ice flow that affect outflow (i.e., solid ice discharge) to the ocean.
SMB is primarily the difference between snow accumulation and the
melt and sublimation of snow and ice (ablation). An assessment of
observations related to this mass budget can be found in Section 4.4.2.
Although some ice-sheet models used in projections incorporate both
effects, most studies have focussed on either SMB or flow dynamics.
It is assumed that the overall contribution can be found by summing
the contributions calculated independently for these two sources,
which is valid if they do not interact significantly. Although this can be
addressed using a correction term to SMB in ice-sheet projections over
the next century, such interactions become more important on longer
time scales when, for example, changes in ice-sheet topography may
significantly affect SMB or dynamics.
Projecting the sea level contribution of land ice requires comparing the
model results with a base state that assumes no significant sea level
contribution. This base state is taken to be either the pre-industrial
period or, because of our scant knowledge of the ice sheets before
the advent of satellites, the late 20th century. In reality, even at these
times, the ice sheets may have been contributing to sea level change
(Huybrechts et al., 2011; Box and Colgan, 2013) and this contribution,
although difficult to quantify, should be included in the observed sea
level budget (Gregory et al., 2013b).
Regional Climate Models (RCMs), which incorporate or are coupled
to sophisticated representations of the mass and energy budgets
associated with snow and ice surfaces, are now the primary source
of ice-sheet SMB projections. A major source of uncertainty lies in the
ability of these schemes to adequately represent the process of inter-
nal refreezing of melt water within the snowpack (Bougamont et al.,
2007; Fausto et al., 2009). These models require information on the
state of the atmosphere and ocean at their lateral boundaries, which
are derived from reanalysis data sets or AOGCMs for past climate, or
from AOGCM projections of future climate.
Models of ice dynamics require a fairly complete representation of
stresses within an ice mass in order to represent the response of ice
flow to changes at the marine boundary and the governing longitudinal
stresses (Schoof, 2007a). For Antarctica, there is also a need to employ
high spatial resolution (<1 km) to capture the dynamics of grounding
line migration robustly so that results do not depend to an unreason-
able extent on model resolution (Durand et al., 2009; Goldberg et al.,
2009; Morlighem et al., 2010; Cornford et al., 2013; Pattyn et al., 2013).
One-dimensional flowline models have been developed to the stage
that modelled iceberg calving is comparable with observations (Nick et
al., 2009). The success of this modelling approach relies on the ability
of the model’s computational grid to evolve to continuously track the
migrating calving front. Although this is relatively straightforward in a
one-dimensional model, this technique is difficult to incorporate into
three-dimensional ice-sheet models that typically use a computational
grid that is fixed in space.
The main challenge faced by models attempting to assess sea level
change from glaciers is the small number of glaciers for which mass
budget observations are available (about 380) (Cogley, 2009a) (see
Sections 4.3.1 and 4.3.4) as compared to the total number (the Ran-
dolph Glacier Inventory contains more than 170,000) (Arendt et al.,
2012). Statistical techniques are used to derive relations between
observed SMB and climate variables for the small sample of surveyed
glaciers, and then these relations are used to upscale to regions of the
world. These techniques often include volume–area scaling to estimate
glacier volume from their more readily observable areas. Although
tidewater glaciers may also be affected by changes in outflow related
to calving, the complexity of the associated processes means that most
studies limit themselves to assessing the effects of SMB changes.
13.2 Past Sea Level Change
13.2.1 The Geological Record
Records of past sea level change provide insight into the sensitivity
of sea level to past climate change as well as context for understand-
ing current changes and evaluating projected changes. Since the AR4,
important progress has been made in understanding the amplitude
and variability of sea level during past intervals when climate was
warmer than pre-industrial, largely through better accounting of the
effects of proxy uncertainties and GIA on coastal sequences (Kopp
et al., 2009, 2013; Raymo et al., 2011; Dutton and Lambeck, 2012;
Lambeck et al., 2012; Raymo and Mitrovica, 2012) (Chapter 5). Here
we summarize the constraints provided by the record of past sea level
variations during times when global temperature was similar to or
warmer than today.
13.2.1.1 The Middle Pliocene
There is medium confidence that during the warm intervals of the
middle Pliocene (3.3 to 3.0 Ma), global mean surface temperatures
were 2°C to 3.5°C warmer than for pre-industrial climate (Section
5.3.1). There are multiple lines of evidence that GMSL during these
middle Pliocene warm periods was higher than today, but low agree-
ment on how high it reached (Section 5.6.1). The most robust lines of
evidence come from proximal sedimentary records that suggest peri-
odic deglaciation of the West Antarctic ice sheet (WAIS) and parts of
the East Antarctic ice sheet (EAIS) (Naish et al., 2009; Passchier, 2011)
and from ice-sheet models that suggest near-complete deglaciation
of the Greenland ice sheet, WAIS and partial deglaciation of the EAIS
1146
Chapter 13 Sea Level Change
13
(Pollard and DeConto, 2009; Hill et al., 2010; Dolan et al., 2011). The
assessment by Chapter 5 suggests that GMSL was above present, but
that it did not exceed 20 m above present, during the middle Pliocene
warm periods (high confidence).
13.2.1.2 Marine Isotope Stage 11
During marine isotope stage 11 (MIS 11; 401 to 411 ka), Antarctic ice
core and tropical Pacific paleo temperature estimates suggest that
global temperature was 1.5°C to 2.0°C warmer than pre-industrial
(low confidence) (Masson-Delmotte et al., 2010). Studies of the mag-
nitude of sea level highstands from raised shorelines attributed to MIS
11 have generated highly divergent estimates. Since the AR4, stud-
ies have accounted for GIA effects (Raymo and Mitrovica, 2012) or
reported elevations from sites where the GIA effects are estimated to
be small (Muhs et al., 2012; Roberts et al., 2012). From this evidence,
our assessment is that MIS 11 GMSL reached 6 to 15 m higher than
present (medium confidence), requiring a loss of most or all of the
present Greenland ice sheet and WAIS plus a reduction in the EAIS of
up to 5 m equivalent sea level if sea level rise was at the higher end
of the range.
13.2.1.3 The Last Interglacial Period
New data syntheses and model simulations since the AR4 indicate
that during the Last Interglacial Period (LIG, ~129 to 116 ka), global
mean annual temperature was 1°C to 2
o
C warmer than pre-industrial
(medium confidence) with peak global annual sea surface tempera-
tures (SSTs) that were 0.7°C ± 0.6°C warmer (medium confidence)
(Section 5.3.4). High latitude surface temperature, averaged over sev-
eral thousand years, was at least 2°C warmer than present (high con-
fidence) (Section 5.3.4). There is robust evidence and high agreement
that under the different orbital forcing and warmer climate of the LIG,
sea level was higher than present. There have been a large number of
estimates of the magnitude of LIG GMSL rise from localities around
the globe, but they are generally from a small number of RSL recon-
structions, and do not consider GIA effects, which can be substantial
(Section 5.6.2). Since the AR4, two approaches have addressed GIA
effects in order to infer LIG sea level from RSL observations at coast-
al sites. Kopp et al. (2009, 2013) obtained a probabilistic estimate of
GMSL based on a large and geographically broadly distributed data-
base of LIG sea level indicators. Their analysis accounted for GIA effects
(and their uncertainties) as well as uncertainties in geochronology, the
interpretation of sea level indicators, and regional tectonic uplift and
subsidence. Kopp et al. (2013) concluded that GMSL was 6.4 m (95%
probability) and 7.7 m (67% probability) higher than present, and
with a 33% probability that it exceeded 8.8 m. The other approach,
taken by Dutton and Lambeck (2012), used data from far-field sites
that are tectonically stable. Their estimate of 5.5 to 9 m LIG GMSL is
consistent with the probabilistic estimates made by Kopp et al. (2009,
2013). Chapter 5 thus concluded there is very high confidence that the
maximumGMSL during the LIG was at least 5 m higher than present
and high confidenceit did not exceed 10 m. The best estimate is 6 m
higher than present. Chapter 5 also concluded from ice-sheet model
simulations and elevation changes derived from a new Greenland ice
core that the Greenland ice sheet very likely contributed between 1.4
and 4.3 m sea level equivalent. This implies with medium confidence a
contribution from the Antarctic ice sheet to the global mean sea level
during the last interglacial period, but this is not yet supported by
observational and model evidence.
There is medium confidence for a sea level fluctuation of up to 4 m
during the LIG, but regional sea level variability and uncertainties in
sea level proxies and their ages cause differences in the timing and
amplitude of the reported fluctuation (Kopp et al., 2009, 2013; Thomp-
son et al., 2011). For the time interval during the LIG in which GMSL
was above present, there is high confidence that the maximum 1000-
year average rate of GMSL rise associated with the sea level fluctua-
tion exceeded 2 m kyr
–1
but that it did not exceed 7 m kyr
–1
(Chapter 5)
(Kopp et al., 2013). Faster rates lasting less than a millennium cannot
be ruled out by these data. Therefore, there is high confidence that
there were intervals when rates of GMSL rise during the LIG exceeded
the 20th century rate of 1.7 [1.5 to 1.9] mm yr
–1
.
13.2.1.4 The Late Holocene
Since the AR4, there has been significant progress in resolving the sea
level history of the last 7000 years. RSL records indicate that from ~7
to 3 ka, GMSL likely rose 2 to 3 m to near present-day levels (Chapter
5). Based on local sea level records spanning the last 2000 years, there
is medium confidence that fluctuations in GMSL during this interval
have not exceeded ~ ±0.25 m on time scales of a few hundred years
(Section 5.6.3, Figure 13.3a). The most robust signal captured in salt
marsh records from both Northern and Southern Hemispheres sup-
ports the AR4 conclusion for a transition from relatively low rates of
change during the late Holocene (order tenths of mm yr
–1
) to modern
rates (order mm yr
–1
) (Section 5.6.3, Figure 13.3b). However, there
is variability in the magnitude and the timing (1840–1920) of this
increase in both paleo and instrumental (tide gauge) records (Section
3.7). By combining paleo sea level records with tide gauge records at
the same localities, Gehrels and Woodworth (2013) concluded that
sea level began to rise above the late Holocene background rate
between 1905 and 1945, consistent with the conclusions by Lambeck
et al. (2004).
13.2.2 The Instrumental Record (~1700–2012)
The instrumental record of sea level change is mainly comprised of
tide gauge measurements over the past two to three centuries (Figures
13.3b and 13.3c) and, since the early 1990s, of satellite-based radar
altimeter measurements (Figure 13.3d).
13.2.2.1 The Tide Gauge Record (~1700–2012)
The number of tide gauges has increased since the first gauges at
some northern European ports were installed in the 18th century;
Southern Hemisphere (SH) measurements started only in the late 19th
century. Section 3.7 assesses 20th century sea level rise estimates
from tide gauges (Douglas, 2001; Church and White, 2006, 2011;
Jevrejeva et al., 2006, 2008; Holgate, 2007; Ray and Douglas, 2011),
and concludes that even though different strategies were developed
to account for inhomogeneous tide gauge data coverage in space and
time, and to correct for vertical crustal motions (also sensed by tide
gauges, in addition to sea level change and variability), it is very likely
1147
Sea Level Change Chapter 13
13
Figure 13.3 | (a) Paleo sea level data for the 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), grey = 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 by subtracting the long-term trend (Gehrels and
Woodworth, 2013). Ordinate axis on the left corresponds to the paleo sea level data. Ordinate axis on the right corresponds to tide gauge data. 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 global mean sea level (GMSL) reconstructed from tide gauges by three different approaches. Orange from Church and White (2011),
blue from Jevrejeva et al. (2008), green from Ray and Douglas (2011) (see Section 3.7). (d) Altimetry data sets from five groups (University of Colorado (CU), National Oceanic and
Atmospheric Administration (NOAA), Goddard Space Flight Centre (GSFC), Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO), Commonwealth Scientific
and Industrial Research Organisation (CSIRO)) with mean of the five shown as bright blue line (see Section 3.7). (e) Comparison of the paleo data from salt marshes (purple
symbols, from (b)), with tide gauge and altimetry data sets (same line colours as in (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 the 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.
1148
Chapter 13 Sea Level Change
13
Frequently Asked Questions
FAQ 13.1 | Why Does Local Sea Level Change Differ from the Global Average?
Shifting surface winds, the expansion of warming ocean water, and the addition of melting ice can alter ocean cur-
rents which, in turn, lead to changes in sea level that vary from place to place. Past and present variations in the
distribution of land ice affect the shape and gravitational field of the Earth, which also cause regional fluctuations
in sea level. Additional variations in sea level are caused by the influence of more localized processes such as sedi-
ment compaction and tectonics.
Along any coast, vertical motion of either the sea or land surface can cause changes in sea level relative to the land
(known as relative sea level). For example, a local change can be caused by an increase in sea surface height, or by a
decrease in land height. Over relatively short time spans (hours to years), the influence of tides, storms and climatic
variability—such as El Niño—dominates sea level variations. Earthquakes and landslides can also have an effect by
causing changes in land height and, sometimes, tsunamis. Over longer time spans (decades to centuries), the influ-
ence of climate change—with consequent changes in volume of ocean water and land ice—is the main contributor
to sea level change in most regions. Over these longer time scales, various processes may also cause vertical motion
of the land surface, which can also result in substantial changes in relative sea level.
Since the late 20th century, satellite measurements of the height of the ocean surface relative to the center of the
Earth (known as geocentric sea level) show differing rates of geocentric sea level change around the world (see
FAQ 13.1, Figure 1). For example, in the western Pacific Ocean, rates were about three times greater than the global
mean value of about 3 mm per year from 1993 to 2012. In contrast, those in the eastern Pacific Ocean are lower
than the global mean value, with much of the west coast of the Americas experiencing a fall in sea surface height
over the same period. (continued on next page)
Pago Pago
Manila
Antofagasta
San Francisco
Charlottetown
Stockholm
Antofagasta
Manila
Pago Pago
−14
−12
−10
−8
−6
−4
−2
0
2
4
6
8
10
12
14
Sea level change (mm yr
-1
)
500
250
0
250
500
Sea level (mm)
1960 1980 2000
Year
San Francisco
1960 1980 2000
Year
Charlottetown
1960 1980 2000
Year
Stockholm
-500
-250
0
250
500
Sea level (mm)
1960 1980 2000
Year
1960 1980 2000
Year
1960 1980 2000
Year
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.
1149
Sea Level Change Chapter 13
13
FAQ 13.1 (continued)
Much of the spatial variation shown in FAQ 13.1, Figure 1 is a result of natural climate variability—such as El Niño and
the Pacific Decadal Oscillation—over time scales from about a year to several decades. These climate variations alter
surface winds, ocean currents, temperature and salinity, and hence affect sea level. The influence of these processes
will continue during the 21st century, and will be superimposed on the spatial pattern of sea level change associated
with longer term climate change, which also arises through changes in surface winds, ocean currents, temperature
and salinity, as well as ocean volume. However, in contrast to the natural variability, the longer term trends accu-
mulate over time and so are expected to dominate over the 21st century. The resulting rates of geocentric sea level
change over this longer period may therefore exhibit a very different pattern from that shown in FAQ 13.1, Figure 1.
Tide gauges measure relative sea level, and so they include changes resulting from vertical motion of both the land
and the sea surface. Over many coastal regions, vertical land motion is small, and so the long-term rate of sea level
change recorded by coastal and island tide gauges is similar to the global mean value (see records at San Francisco
and Pago Pago in FAQ 13.1, Figure 1). In some regions, vertical land motion has had an important influence. For
example, the steady fall in sea level recorded at Stockholm (FAQ 13.1, Figure 1) is caused by uplift of this region
after the melting of a large (>1 km thick) continental ice sheet at the end of the last Ice Age, between ~20,000 and
~9000 years ago. Such ongoing land deformation as a response to the melting of ancient ice sheets is a significant
contributor to regional sea level changes in North America and northwest Eurasia, which were covered by large
continental ice sheets during the peak of the last Ice Age.
In other regions, this process can also lead to land subsidence, which elevates relative sea levels, as it has at Char-
lottetown, where a relatively large increase has been observed, compared to the global mean rate (FAQ 13.1, Figure
1). Vertical land motion due to movement of the Earth’s tectonic plates can also cause departures from the global
mean sea level trend in some areas—most significantly, those located near active subduction zones, where one tec-
tonic plate slips beneath another. For the case of Antofagasta (FAQ 13.1, Figure 1) this appears to result in steady
land uplift and therefore relative sea level fall.
In addition to regional influences of vertical land motion
on relative sea level change, some processes lead to land
motion that is rapid but highly localized. For example,
the greater rate of rise relative to the global mean at
Manila (FAQ 13.1, Figure 1) is dominated by land subsid-
ence caused by intensive groundwater pumping. Land
subsidence due to natural and anthropogenic processes,
such as the extraction of groundwater or hydrocarbons,
is common in many coastal regions, particularly in large
river deltas.
It is commonly assumed that melting ice from glaciers
or the Greenland and Antarctic ice sheets would cause
globally uniform sea level rise, much like filling a bath
tub with water. In fact, such melting results in region-
al variations in sea level due to a variety of processes,
including changes in ocean currents, winds, the Earth’s
gravity field and land height. For example, computer
models that simulate these latter two processes predict a regional fall in relative sea level around the melting ice
sheets, because the gravitational attraction between ice and ocean water is reduced, and the land tends to rise
as the ice melts (FAQ 13.1, Figure 2). However, further away from the ice sheet melting, sea level rise is enhanced,
compared to the global average value.
In summary, a variety of processes drive height changes of the ocean surface and ocean floor, resulting in distinct
spatial patterns of sea level change at local to regional scales. The combination of these processes produces a
complex pattern of total sea level change, which varies through time as the relative contribution of each process
changes. The global average change is a useful single value that reflects the contribution of climatic processes (e.g.,
land-ice melting and ocean warming), and represents a good estimate of sea level change at many coastal loca-
tions. At the same time, however, where the various regional processes result in a strong signal, there can be large
departures from the global average value.
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)
−3.0 −2.0 −1.0 0.0 0.2 0.4 0.6 0.8 1.0 1.1 1.2 1.3
Sea level change (mm yr
-1
)
1150
Chapter 13 Sea Level Change
13
that the long-term trend estimate in GMSL is 1.7 [1.5 to 1.9] mm
yr
–1
between 1901 and 2010 for a total sea level rise of 0.19 [0.17
to 0.21] m (Figure 13.3c). Interannual and decadal-scale variability is
superimposed on the long-term MSL trend, and Chapter 3 noted that
discrepancies between the various published MSL records are present
at these shorter time scales.
Section 3.7 also concludes that it is likely that the rate of sea level
rise increased from the 19th century to the 20th century. Taking this
evidence in conjunction with the proxy evidence for a change of rate
(Sections 5.6.3 and 13.2.1; Figure 13.3b), there is high confidence that
the rate of sea level rise has increased during the last two centu-
ries, and it is likely that GMSL has accelerated since the early 1900’s.
Because of the presence of low-frequency variations (e.g., multi-dec-
adal variations seen in some tide gauge records; Chambers et al.
(2012)), sea level acceleration results are sensitive to the choice of
the analysis time span. When a 60-year oscillation is modelled along
with an acceleration term, the estimated acceleration in GMSL (twice
the quadratic term) computed over 1900–2010 ranges from 0.000
[–0.002 to 0.002] mm yr
–2
in the Ray and Douglas (2011) record, to
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
in the Church and White (2011)
record. For comparison, Church and White (2011) estimated the accel-
eration term to be 0.009 [0.004 to 0.014] mm yr
–2
over the 1880–2009
time span when the 60-year cycle is not considered.
13.2.2.2 The Satellite Altimeter Record (1993–2012)
The high-precision satellite altimetry record started in 1992 and pro-
vides nearly global (±66°) sea level measurements at 10-day inter-
vals. Ollivier et al. (2012) showed that Envisat, which observes to
±82° latitude, provides comparable GMSL estimates. Although there
are slight differences at interannual time scales in the altimetry-based
GMSL time series produced by different groups (Masters et al., 2012),
there is very good agreement on the 20-year long GMSL trend (Figure
13.3d). After accounting for the ~ –0.3 mm yr
–1
correction related
to the increasing size of the global ocean basins due to GIA (Peltier,
2009), a GMSL rate of 3.2 [2.8 to 3.6] mm yr
–1
over 1993–2012 is found
by the different altimetry data processing groups. The current level of
precision is derived from assessments of all source of errors affecting
the altimetric measurements (Ablain et al., 2009) and from tide gauge
comparisons (Beckley et al., 2010; Nerem et al., 2010). Chapter 3 con-
cludes that the GMSL trend since 1993 is very likely higher compared
to the mean rates over the 20th century, and that it is likely that GMSL
rose between 1920 and 1950 at a rate comparable to that observed
since 1993. This recent higher rate is also seen in tide gauge data over
the same period, but on the basis of observations alone it does not
necessarily reflect a recent acceleration, considering the previously
reported multi-decadal variations of mean sea level. The rapid increase
in GMSL since 2011 is related to the recovery from the 2011 La Niña
event (Section 13.3.5) (Boening et al., 2012).
13.3 Contributions to Global Mean Sea Level
Rise During the Instrumental Period
In order to assess our understanding of the causes of observed changes
and our confidence in projecting future changes we compare obser-
vational estimates of contributions with results derived from AOGCM
experiments, beginning in the late 19th century, forced with estimated
past time-dependent anthropogenic changes in atmospheric compo-
sition and natural forcings due to volcanic aerosols and variations in
solar irradiance (Section 10.1). This period and these simulations are
often referred to as “historical.
13.3.1 Thermal Expansion Contribution
13.3.1.1 Observed
Important progress has been realized since AR4 in quantifying the
observed thermal expansion component of global mean sea level rise.
This progress reflects (1) the detection of systematic time-dependent
depth biases affecting historical expendable bathythermograph data
(Gouretski and Koltermann, 2007) (Chapter 3), (2) the newly available
Argo Project ocean (temperature and salinity) data with almost global
coverage (not including ice-covered regions and marginal seas) of the
oceans down to 2000 m since 2004–2005, and (3) estimates of the
deep-ocean contribution using ship-based data collected during the
World Ocean Circulation Experiment and revisit cruises (Johnson and
Gruber, 2007; Johnson et al., 2007; Purkey and Johnson, 2010; Kouke-
tsu et al., 2011).
For the period 1971–2010, the rate for the 0 to 700 m depth range is
0.6 [0.4 to 0.8] mm yr
–1
(Section 3.7.2 and Table 3.1). Including the
deep-ocean contribution for the same period increases the value to
0.8 [0.5 to 1.1] mm yr
–1
(Table 13.1). Over the altimetry period (1993–
2010), the rate for the 0 to 700 m depth range is 0.8 [0.5 to 1.1] mm
yr
–1
and 1.1 [0.8 to 1.4] mm yr
–1
when accounting for the deep ocean
(Section 3.7.2, Table 3.1, Table 13.1).
13.3.1.2 Modelled
GMSL rise due to thermal expansion is approximately proportional to
the increase in ocean heat content (Section 13.4.1). Historical GMSL
rise due to thermal expansion simulated by CMIP5 models is shown
in Table 13.1 and Figure 13.4a. The model spread is due to uncertainty
in RF and modelled climate response (Sections 8.5.2, 9.4.2.2, 9.7.2.5
and 13.4.1).
In the time mean of several decades, there is a negative volcanic forc-
ing if there is more volcanic activity than is typical of the long term, and
a positive forcing if there is less. In the decades after major volcanic
eruptions, the rate of expansion is temporarily enhanced, as the ocean
recovers from the cooling caused by the volcanic forcing (Church et al.,
2005; Gregory et al., 2006) (Figure 13.4a). During 1961–1999, a period
when there were several large volcanic eruptions, the CMIP3 simula-
tions with both natural and anthropogenic forcing have substantially
smaller increasing trends in the upper 700 m than those with anthro-
pogenic forcing only (Domingues et al., 2008) because the natural vol-
canic forcing tends to cool the climate system, thus reducing ocean
1151
Sea Level Change Chapter 13
13
heat uptake (Levitus et al., 2001). The models including natural forcing
are closer to observations, though with a tendency to underestimate
the trend by about 10% (Sections 9.4.2.2 and 10.4.1).
Gregory (2010) and Gregory et al. (2013a) proposed that AOGCMs
underestimate ocean heat uptake in their historical simulations
because their control experiments usually omit volcanic forcing, so
the imposition of historical volcanic forcing on the simulated climate
system represents a time mean negative forcing relative to the con-
trol climate. The apparent long persistence of the simulated oceanic
cooling following the 1883 eruption of Krakatau (Delworth et al., 2005;
Gleckler et al., 2006a, 2006b; Gregory et al., 2006) is a consequence
of this bias, which also causes a model-dependent underestimate of
up to 0.2 mm yr
–1
of thermal expansion on average during the 20th
century (Gregory et al., 2013a, 2013b). This implies that CMIP5 results
may be similarly underestimated, depending on the details of the indi-
vidual model control runs. Church et al. (2013) proposed a correction
of 0.1 mm yr
–1
to the model mean rate, which we apply in the sea level
budget in Table 13.1 and Figure 13.7. The corrected CMIP5 model mean
rate for 1971–2010 is close to the central observational estimate; the
model mean rate for 1993–2010 exceeds the central observational
estimate but they are not statistically different given the uncertainties
(Table 13.1 and Figure 13.4a). This correction is not made to projec-
tions of thermal expansion because it is very small compared with the
projected increase in the rate (Section 13.5.1).
In view of the improvement in observational estimates of thermal
expansion, the good agreement of historical model results with obser-
vational estimates, and their consistency with understanding of the
energy budget and RF of the climate system (Box 13.1), we have high
confidence in the projections of thermal expansion using AOGCMs.
13.3.2 Glaciers
13.3.2.1 Observed
‘Glaciers’ are defined here as all land-ice masses, including those
peripheral to (but not including) the Greenland and Antarctic ice
sheets. The term ‘glaciers and ice caps’ was applied to this category
in the AR4. Changes in aggregate glacier volume have conventional-
ly been determined by various methods of repeat mapping of surface
elevation to detect elevation (and thus volume) change. Mass changes
are determined by compilation and upscaling of limited direct observa-
tions of surface mass balance (SMB). Since 2003, gravity observations
from Gravity Recovery and Climate Experiment (GRACE) satellites have
been used to detect mass change of the world’s glaciers.
The combined records indicate that a net decline of global glacier
volume began in the 19th century, before significant anthropogenic
RF had started, and was probably the result of warming associated
with the termination of the Little Ice Age (Crowley, 2000; Gregory et
al., 2006, 2013b). Global rates of glacier volume loss did not increase
significantly during much of the 20th century (Figure 4.12). In part this
may have been because of an enhanced rate of loss due to unforced
high-latitude variability early in the century, while anthropogenic
warming was still comparatively small (Section 13.3.2.2). It is likely
that anthropogenic forcing played a statistically significant role in
acceleration of global glacier losses in the latter decades of the 20th
Table 13.1 | Global mean sea level budget (mm yr
–1
) over different time intervals from observations and from model-based contributions. Uncertainties are 5 to 95%. The Atmo-
sphere–Ocean General Circulation Model (AOGCM) historical integrations end in 2005; projections for RCP4.5 are used for 2006–2010. The modelled thermal expansion and
glacier contributions are computed from the CMIP5 results, using the model of Marzeion et al. (2012a) for glaciers. The land water contribution is due to anthropogenic intervention
only, not including climate-related fluctuations.
Notes:
a
Data for all glaciers extend to 2009, not 2010.
b
This contribution is not included in the total because glaciers in Greenland are included in the observational assessment of the Greenland ice sheet.
c
Observed GMSL rise – modelled thermal expansion – modelled glaciers – observed land water storage.
Source 1901–1990 1971–2010 1993–2010
Observed contributions to global mean sea level (GMSL) rise
Thermal expansion 0.8 [0.5 to 1.1] 1.1 [0.8 to 1.4]
Glaciers except in Greenland and Antarctica
a
0.54 [0.47 to 0.61] 0.62 [0.25 to 0.99] 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]
b
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 GMSL rise 1.5 [1.3 to 1.7] 2.0 [1.7 to 2.3] 3.2 [2.8 to 3.6]
Modelled contributions to GMSL rise
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
c
0.5 [0.1 to 1.0] 0.2 [–0.4 to 0.8] 0.4 [–0.4 to 1.2]
1152
Chapter 13 Sea Level Change
13
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 Atmosphere–Ocean General Circulation Model (AOGCM) simulations are
shown in grey, the AOGCM average is black, observations in teal with the 5 to 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 in 1991. (d) The rate of change (19-year centred trends) for the terms in (a)–(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)
1153
Sea Level Change Chapter 13
13
century relative to rates in the 19th century (Section 10.5.2.2). It is also
likely that, during the 20th century, the progressive loss of glacier area
significantly restricted the rate of mass loss (Gregory et al., 2013b).
The earliest sea level assessments recognized that glaciers have been
significant contributors to GMSL rise (Meier, 1984). As assessed in
Chapter 4, observations, improved methods of analysis and a new,
globally complete inventory indicate that glaciers, including those
around the ice-sheet peripheries, very likely continue to be significant
contributors to sea level, but are also highly variable on annual to dec-
adal time scales. It is assumed that all glacier losses contribute to sea
level rise, but the potential role of terrestrial interception of runoff,
either in lakes formed following future ice retreat or in groundwater,
has yet to be evaluated. For the period 2003–2009, the sea level con-
tribution of all glaciers globally, including those glaciers surrounding
the periphery of the two ice sheets, is 0.71 [0.64 to 0.79] mm yr
–1
sea level equivalent (SLE) (Section 4.3.3, Table 4.4). Depending on the
method used, however, loss-rate measurements of the two ice sheets
can be very difficult to separate from losses from the peripheral gla-
ciers. To avoid double counting, total cryospheric losses are determined
by adding estimates of glacier losses excluding the peripheral glaciers
to losses from the ice sheets including their peripheral glaciers. The sea
level contribution of all glaciers excluding those glaciers surrounding
the periphery of the two ice sheets was 0.54 [0.47-0.61] mm yr
-1
SLE
for 1901-1990, 0.62 [0.25-0.99] mm yr
-1
SLE for 1971-2009, 0.76 [0.39-
1.13] mm yr
-1
SLE for 1993-2009, and 0.83 [0.46-1.20] mm yr
-1
SLE for
2005-2009 (Section 4.3.3.4, Table 13.1).
13.3.2.2 Modelled
Global glacier mass balance models are calibrated using data from the
few well-observed glaciers. Approximately 100 glacier mass balance
records are available in any given year over the past half-century; only
17 glaciers exist with records of 30 years or more (Dyurgerov and Meier,
2005; Kaser et al., 2006; Cogley, 2012). Confidence in these models for
projections of future change (Section 13.4.2) depends on their ability
to reproduce past observed glacier change using corresponding cli-
mate observations as the forcing (Raper and Braithwaite, 2005; Meier
et al., 2007; Bahr et al., 2009; Radić and Hock, 2011; Marzeion et al.,
2012b; 2012a; Giesen and Oerlemans, 2013). Model validation is chal-
lenging owing to the scarcity of independent observations (unused in
model calibration), but uncertainties have been evaluated by methods
such as cross validation of hindcast projections for individual glaciers
drawn from the sample of glacier observations averaged for calibration
(Marzeion et al., 2012a; Radić et al., 2013).
Confidence in the use of AOGCM climate simulations as input to
glacier projections is gained from the agreement since the mid-20th
century of glacier models forced by AOGCM simulations with gla-
cier models forced by observations (Marzeion et al., 2012a) (Figure
13.4b). In the earlier 20th century, around the 1930s, glaciers at high
northern latitudes lost mass at an enhanced rate (Oerlemans et al.,
2011; Leclercq et al., 2012); in the model, observed forcings produced
larger glacier losses than did AOGCM forcings (Marzeion et al., 2012a)
(Figure 13.4d). This is judged likely to be due to an episode of unforced,
regionally variable warming around Greenland (Box, 2002; Chylek et
al., 2004) rather than to RF of the climate system, and is consequently
not reproduced by AOGCM experiments (Section 10.2). In our analysis
of the budget of GMSL rise (Section 13.3.6), we take the difference
between the simulations using AOGCM forcing and the simulation
using observations as an estimate of the influence of unforced climate
variability on global glacier mass balance (Figure 13.4b).
There is medium confidence in the use of glacier models to make
global projections based on AOGCM results. The process-based under-
standing of glacier surface mass balance, the consistency of models
and observations of glacier changes, and the evidence that AOGCM cli-
mate simulations can provide realistic input all give confidence, which
on the other hand is limited because the set of well-observed glaciers
is a very small fraction of the total.
13.3.3 Greenland and Antarctic Ice Sheets
13.3.3.1 Observed Mass Balance
The Greenland ice sheet’s mass balance is comprised of its surface
mass balance and outflow, whereas Antarctica’s mass budget is domi-
nated by accumulation and outflow in the form of calving and ice flow
into floating (and therefore sea level neutral) ice shelves. Knowledge of
the contribution of the Greenland and Antarctic ice sheets to observed
sea level changes over the last two decades comes primarily from sat-
ellite and airborne surveys. Three main techniques are employed: the
mass budget method, repeat altimetry, and gravimetric methods that
measure temporal variations in the Earth’s gravity field (Section 4.4.2).
Observations indicate that the Greenland contribution to GMSL has
very likely increased from 0.09 [–0.02 to 0.20] mm yr
–1
for 1992–2001
to 0.59 [0.43 to 0.76] mm yr
–1
for 2002–2011 (Section 4.4.3, Figure
13.4). The average rate of the Antarctica contribution to sea level rise
likely increased from 0.08 [–0.10 to 0.27] mm yr
–1
for 1992–2001 to
0.40 [0.20 to 0.61] mm yr
–1
for 2002–2011 (Section 4.4.3). For the
budget period 1993–2010, the combined contribution of the ice sheets
is 0.60 [0.42 to 0.78] mm yr
–1
. For comparison, the AR4’s assessment
for the period 1993–2003 was 0.21 ± 0.07 mm yr
–1
for Greenland and
0.21 ± 0.35 mm yr
–1
for Antarctica.
13.3.3.2 Modelled Surface Mass Balance
Projections of changes in the SMB of the Antarctic and Greenland ice
sheets are obtained from RCM or downscaled AOGCM simulations
(Sections 13.4.3.1 and 13.4.4.1). A spatial resolution of a few tens
kilometres or finer is required in order to resolve the strong gradi-
ents in SMB across the steep slopes of the ice-sheet margins. Although
simulations of SMB at particular locations may have errors of 5 to 20%
compared with in situ observations, there is good agreement between
methods involving RCMs and observational methods of evaluating ice-
sheet mass balance (Shepherd et al., 2012). In the present climate,
for both Greenland and Antarctica, the mean SMB over the ice-sheet
area is positive, giving a negative number when expressed as sea level
equivalent (SLE).
In Greenland, the average and standard deviation of accumulation
(precipitation minus sublimation) estimates for 1961–1990 is –1.62
± 0.21 mm yr
–1
SLE from the models in Table 13.2, agreeing with
1154
Chapter 13 Sea Level Change
13
published observation-based accumulation maps, for example –1.42 ±
0.11 mm yr
–1
SLE by Bales et al. (2009) and –1.63 ± 0.23 mm yr
–1
SLE by
Burgess et al. (2010). For SMB (accumulation minus runoff, neglecting
drifting snow erosion, which is small), the models give –0.92 ± 0.26
mm yr
–1
SLE for 1961–1990 (Table 13.2).
All of these models indicate that Greenland ice sheet SMB showed no
significant trend from the 1960s to the 1980s, then started becoming
less positive (becoming less negative expressed as SLE) in the early
1990s, on average by 3% yr
–1
. This results in a statistically significant
and increasing (i.e., becoming more positive) contribution to the rate
of GMSL rise (SMB trend column of Table 13.2, Figure 13.5). The largest
trends are found in models with coupled snow and atmosphere sim-
ulations using the Regional Atmospheric Climate Model 2 (RACMO2)
and the Modèle Atmosphérique Régional (MAR). Van den Broeke et
al. (2009) concluded that the mass loss during 2000–2008 is equally
split between SMB and dynamical change. Rignot et al. (2011) indicat-
ed that SMB change accounts for about 60% of the mass loss since
1992 and Sasgen et al. (2012) showed that SMB change, simulated by
RACMO2 (Ettema et al., 2009, an earlier version of the model in Table
13.2), accounts for about 60% of the observed rate of mass loss during
2002–2010, with an observational estimate of the increase in ice out-
flow accounting for the remainder. This satisfactory consistency, within
uncertainties, in estimates for the Greenland ice-sheet mass budget
gives confidence in SMB simulations of the past, and hence also in the
similar models used for projections of SMB changes (Section 13.4.3.1).
This recent trend towards increasingly less positive SMB is caused
almost entirely by increased melting and subsequent runoff, with vari-
ability in accumulation being comparatively small (Sasgen et al., 2012;
Vernon et al., 2013). This tendency is related to pronounced regional
warming, which may be attributed to some combination of anthro-
pogenic climate change and anomalous regional variability in recent
years (Hanna et al., 2008; 2012; Fettweis et al., 2013). Greenland SMB
models forced by boundary conditions from AOGCM historical simula-
tions (Rae et al., 2012; Fettweis et al., 2013) do not show statistically
significant trends towards increasing contributions to GMSL, implying
that the dominant contribution is internally generated regional climate
variability, which is not expected to be reproduced by AOGCM histori-
cal simulations (Section 10.2). We have high confidence in projections
of future warming in Greenland because of the agreement of models
in predicting amplified warming at high northern latitudes (Sections
12.4.3.1, 14.8.2) for well-understood physical reasons, although there
remains uncertainty in the size of the amplification, and we have
high confidence in projections of increasing surface melting (Section
13.4.3.1) because of the sensitivity to warming demonstrated by SMB
models of the past.
All Greenland SMB simulations for the first half of the 20th century
depend on reconstructions of meteorological variability over the ice
sheet made using empirical relationships based on observations from
coastal stations and estimates of accumulation from ice cores. Despite
the similar input data sets in all cases, the various climate reconstruction
and SMB methods used have led to a range of results (Fettweis et al.,
2008; Wake et al., 2009; Hanna et al., 2011; Box, 2013; Box and Colgan,
2013; Box et al., 2013; Gregory et al., 2013b). For 1901–1990, Hanna et
al. (2011) have a time-mean GMSL contribution of –0.3 mm yr
–1
, while
Box and Colgan (2013) have a weakly positive contribution and the
others are about zero. In all cases, there is substantial variability associ-
ated with regional climate fluctuations, in particular the warm episode
in the 1930s, during which glaciers retreated in southeastern Greenland
(Bjork et al., 2012). Chylek et al. (2004) argued that this episode was
associated with the NAO rather than with global climate change.
In Antarctica, accumulation (precipitation minus sublimation) approx-
imates SMB because surface melting and runoff are negligible in the
present climate (Section 4.4.2.1.1). There are uncertainties in model-
and observation-based estimates of Antarctic SMB. Global climate
models do not account for snow hydrology or for drifting snow pro-
cesses which remove an estimated 7% of the accumulated snow (Len-
aerts et al., 2012), and the ice sheet’s steep coastal slopes are not well
captured by coarse-resolution models. Observation-based estimates
rely on sparse accumulation measurements with very little coverage
in high-accumulation areas. For the Antarctic ice sheet and ice shelves
Table 13.2 | Surface mass balance (SMB) and rates of change of SMB of the Greenland ice sheet, calculated from ice-sheet SMB models using meteorological observations and
reanalyses as input, expressed as sea level equivalent (SLE). A negative SLE number for SMB indicates that accumulation exceeds runoff. A positive SLE for SMB anomaly indicates
that accumulation has decreased, or runoff has increased, or both. Uncertainties are one standard deviation. Uncertainty in individual model results reflects temporal variability (1
standard deviations of annual mean values indicated); the uncertainty in the model average is 1 standard deviation of variation across models.
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:
a
The approximate spatial resolution is stated and the model type denoted by PDD = positive degree day, EBM = Energy Balance Model, RCM = Regional Climate Model.
b
Difference from the time-mean SMB of 1961–1990. This difference equals the sea level contribution from Greenland SMB changes if the ice sheet is assumed to have been near zero mass balance
during 1961–1990 (Hanna et al., 2005; Sasgen et al., 2012).
1155
Sea Level Change Chapter 13
13
together, CMIP3 AOGCMs simulate SMB for 1979–2000 of –7.1 ± 1.5
mm yr
–1
SLE (Connolley and Bracegirdle, 2007; Uotila et al., 2007), the
mean being about 10% larger in magnitude than observation-based
estimates, for instance –6.3 mm yr
–1
SLE from Vaughan et al. (1999).
For the SMB of the grounded ice sheet alone, four global reanalysis
models, with resolutions of 38 to 125 km (Bromwich et al., 2011), give
–5.2 ± 0.5 mm yr
–1
SLE for 1979–2010, which compares well with
an observational estimate of –4.9 ± 0.1 mm yr
–1
SLE for 1950–2000
(Arthern et al., 2006). Because of higher accumulation near the coast,
the regional climate model RACMO2 gives the somewhat larger value
of –5.5 ± 0.3 mm yr
–1
SLE for 1979–2000 (Lenaerts et al., 2012). This
relatively good agreement, combined with the similarity of the geo-
graphical distribution of modelled and observed SMB, give medium
confidence in the realism of the RCM SMB simulation.
Some global reanalyses have been shown to contain spurious trends in
various quantities in the SH related to changes in the observing systems,
for example, new satellite observations (Bromwich et al., 2007; 2011).
In the RCMs and in global reanalyses that are not affected by spurious
trends, no significant trend is present in accumulation since 1980 (Sec-
tion 4.4.2.3). This agrees with observation-based studies (Monaghan
et al., 2006; Anschütz et al., 2009) (Chapter 4) and implies that Ant-
arctic SMB change has not contributed significantly to recent changes
in the rate of GMSL rise. Likewise, CMIP3 historical simulations do not
exhibit any systematic trend in Antarctic precipitation during the late
20th century (Uotila et al., 2007). No observational assessments have
been made of variability in SMB for the whole ice sheet for the earlier
part of the 20th century, or of its longer term mean.
General Circulation Model (GCM) and Regional Circulation Model
(RCM) projections consistently indicate significant Antarctic warming
and concomitant increase in precipitation. We have high confidence in
expecting a relationship between these quantities on physical grounds
(Section 13.4.4.1) and from ice core evidence (Van Ommen et al., 2004;
Lemieux-Dudon et al., 2010; Stenni et al., 2011). The absence of a sig-
nificant trend in Antarctic precipitation up to the present is not incon-
sistent with the expected relationship, because observed temperature
trends over the majority of the continent are weak (Section 10.5.2.1)
and trends in Antarctic precipitation simulated for recent decades are
much smaller than interannual variability (van den Broeke et al., 2006;
Uotila et al., 2007). Taking all these considerations together, we have
medium confidence in model projections of a future Antarctic SMB
increase, implying a negative contribution to GMSL rise (see also Sec-
tions 13.4.4.1, 13.5.3 and 14.8.15).
13.3.4 Contributions from Water Storage on Land
Changes in water storage on land in response to climate change and
variability (i.e., water stored in rivers, lakes, wetlands, the vadose zone,
aquifers and snow pack at high latitudes and altitudes) and from direct
human-induced effects (i.e., storage of water in reservoirs and ground-
water pumping) have the potential to contribute to sea level change.
Based on satellite observations of the Northern Hemisphere (NH)
snowpack, Biancamaria et al. (2011) found no significant trend in the
contribution of snow to sea level. Estimates of climate-related changes
in land water storage over the past few decades rely on global hydro-
logical models because corresponding observations are inadequate
(Milly et al., 2010). In assessing the relation between terrestrial water
storage and climate, Milly et al. (2003) and Ngo-Duc et al. (2005) found
no long-term climatic trend in total water storage, but documented
interannual to decadal fluctuations, equivalent to several millimetres
of sea level. Recent studies have shown that interannual variability
in observed GMSL correlates with ENSO indices (Nerem et al., 2010)
and is inversely related to ENSO-driven changes of terrestrial water
storage, especially in the tropics (Llovel et al., 2011). During El Niño
events, sea level (and ocean mass) tends to be higher because ocean
precipitation increases and land precipitation decreases in the tropics
(Cazenave et al., 2012). The reverse happens during La Niña events, as
Figure 13.5 | Annual mean surface mass balance (accumulation minus ablation) for the Greenland ice sheet, simulated by five regional climate models for the period 1960–2010.
1156
Chapter 13 Sea Level Change
13
seen during 2010–2011, when there was a decrease in GMSL due to a
temporary increase in water storage on the land, especially in Austral-
ia, northern South America, and southeast Asia (Boening et al., 2012)
(Section 13.3.5).
Direct human interventions on land water storage also induce sea level
changes (Sahagian, 2000; Gornitz, 2001; Huntington, 2008; Lettenmai-
er and Milly, 2009). The largest contributions come from impoundment
in reservoirs and groundwater withdrawal. Over the past half-century,
storage in tens of thousands of reservoirs has offset some of the sea
level rise that would otherwise have occurred. Chao et al. (2008) esti-
mated that the nearly 30,000 reservoirs built during the 20th century
resulted in nominal reservoir storage up to 2007 equivalent to ~23
mm of sea level fall (mostly since 1940), with a stabilization in recent
years. Chao et al. further assumed that the reservoirs were 85% full,
and by including seepage into groundwater as estimated from a model,
they obtained a total of 30 mm of sea level fall (equivalent to a rate
of sea level fall of 0.55 mm yr
–1
from 1950 to 2000). Their seepage
estimate was argued to be unrealistically large, however, because it
assumes aquifers are infinite and have no interfering boundary con-
ditions (Lettenmaier and Milly, 2009; Konikow, 2013). Chao et al.
(2008) argued that sedimentation of reservoirs does not reduce their
sea level contribution, but their argument is disputed (Gregory et al.,
2013b). Lettenmaier and Milly (2009) suggested a loss of capacity due
to sedimentation at 1% yr
–1
. Given the uncertainty about them, neither
the seepage nor the effect of sedimentation is included in the budget
(Section 13.3.6). Here the (negative) GMSL contribution from reservoir
storage is estimated as 85% [70 to 100%] of the nominal capacity
(with the lower limit coming from Pokhrel et al. (2012)).
Konikow (2011) estimated that human-induced groundwater deple-
tion contributed 0.26 ± 0.07 mm yr
–1
to GMSL rise over 1971–2008
and 0.34 ± 0.07 mm yr
–1
over 1993–2008 (based mostly on obser-
vational methods), whereas Wada et al. (2012) estimated values of
0.42 ± 0.08 mm yr
–1
over 1971–2008 and 0.54 ± 0.09 mm yr
–1
over
1993–2008 (based on modelling of water fluxes). The average of
these two series with the difference as a measure of the uncertainty is
used in the sea level budget (Section 13.3.6). Pokhrel et al. (2012) esti-
mated a larger groundwater depletion, but Konikow (2013) (disputed
by Pohkrel et al. (2013)) argued that their underlying assumptions of
defining depletion as equivalent to groundwater use, and allowing
unlimited extraction to meet water demand, led to substantial over-
estimates of depletion.
In summary, climate-related changes in water and snow storage on
land do not show significant long-term trends for the recent decades.
However, direct human interventions in land water storage (reservoir
impoundment and groundwater depletion) have each contributed at
least several tenths of mm yr
–1
of sea level change (Figure 13.4, Table
13.1). Reservoir impoundment exceeded groundwater depletion for the
majority of the 20th century but groundwater depletion has increased
and now exceeds current rates of impoundment, contributing to an
increased rate of GMSL rise. The net contribution for the 20th century
is estimated by adding the average of the two groundwater depletion
estimates to the reservoir storage term (Figure 13.4c). The trends are
-0.11 [-0.16 to -0.06] mm yr
-1
for 1901-1990, 0.12 [0.03 to 0.22] mm
yr
-1
for 1971 to 2010 and 0.38 [0.26 to 0.49] mm yr
-1
for 1993 to 2010
(Table 13.1).
13.3.5 Ocean Mass Observations from Gravity Recovery
and Climate Experiment
As discussed in Chapter 3, it has been possible to directly estimate
changes in ocean mass using satellite gravity data from GRACE since
2002 (Chambers et al., 2004, 2010; Chambers, 2006; Cazenave et al.,
2009; Leuliette and Miller, 2009; Llovel et al., 2010). These measure-
ments represent the sum of total land ice plus land water components,
and thus provide an independent assessment of these contributions.
However, GRACE is also sensitive to mass redistribution associated with
GIA and requires that this effect (on the order of –0.7 to –1.3 mm yr
–1
when averaged over the ocean domain) (Paulson et al., 2007; Peltier,
2009; Chambers et al., 2010; Tamisiea, 2011) be removed before esti-
mating the ocean-mass component. Most recent estimates (Leuliette
and Willis, 2011; von Schuckmann and Le Traon, 2011) report 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 component. The associated error results
from the low signal-to-noise ratio over the ocean domain and uncer-
tainty in the model-based GIA correction (Quinn and Ponte, 2010).
Chapter 3 notes that, in terms of global averages, the sum of the con-
tribution to GMSL due to change in global ocean mass (the barystatic
contribution), measured by GRACE, and the contribution due to global
ocean thermal expansion (the thermosteric contribution), measured by
the Argo Project, agrees within uncertainties with the GMSL change
observed by satellite altimetry (Leuliette and Willis, 2011; von Schuck-
mann and Le Traon, 2011), although there is still a missing contribution
from expansion in the deep ocean below 2000 m. These data sets have
allowed an investigation of the cause of variability in sea level over
the last few years (Figure 13.6). In particular, Boening et al. (2012) con-
cluded that the decrease in GMSL over 2010–2011 followed by a rapid
increase since 2011 was related to the 2011 La Niña event, where-
by changes in land/ocean precipitation patterns caused a temporary
increase in water storage on the land (and corresponding decrease in
GMSL) during the La Niña event, especially in Australia, northern South
America and southeast Asia (Boening et al., 2012).
13.3.6 Budget of Global Mean Sea Level Rise
Drawing on Sections 13.3.1 to 13.3.5, the budget of GMSL rise (Table
13.1, Figure 13.7) is analysed using models and observations for the
periods 1901–1990 (the 20th century, excluding the period after 1990
when ice-sheet contributions to GMSL rise have increased; Sections
4.4 and 13.3.3.1), since 1971 (when significantly more ocean data
became available and systematic glacier reconstructions began), and
since 1993 (when precise satellite sea level altimetry began). The
2005–2010 period when Argo and GRACE data are available is short
and strongly affected by interannual climate variability, as discussed in
the previous subsection (Section 13.3.5 and Figure 13.6). Such varia-
bility is not externally forced and is therefore not expected to be repro-
duced in AOGCM historical experiments. For the contribution from land
water storage (Figure 13.4c) we use the estimated effect of human
intervention and neglect effects from climate-related variation, which
1157
Sea Level Change Chapter 13
13
are unimportant on multi-decadal time scales (Section 13.3.4). Contri-
butions due to runoff from thawed permafrost, change in atmospheric
moisture content, and sedimentation in the ocean are not considered
in the budget because they are negligible compared with observed
GMSL rise and the uncertainties.
For 1993–2010, allowing for uncertainties, the observed GMSL rise is
consistent with the sum of the observationally estimated contributions
(high confidence) (Table 13.1, Figure 13.7e). The two largest terms are
ocean thermal expansion (accounting for about 35% of the observed
GMSL rise) and glacier mass loss (accounting for a further 25%, not
including that from Greenland and Antarctica). Observations indicate
an increased ice-sheet 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 closure of
the observational budget since 1993, within uncertainties, represents
a significant advance since the AR4 in physical understanding of the
causes of past GMSL change, and provides an improved basis for criti-
cal evaluation of models of these contributions in order to assess their
reliability for making projections.
The observational budget cannot be rigorously assessed for 1901–1990
or 1971–2010 because there is insufficient observational information
2005 2006 2007 2008 2009 2010 2011 2012 2013
−15
−10
−5
0
5
10
15
20
25
GMSL (mm)
year
Total SSH (Altimetry)
Ocean Mass (GRACE)
Thermosteric (Argo)
GRACE + Argo
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 Gravity Recovery and Climate
Experiment (GRACE)) and thermosteric sea level changes (as measured by the Argo Project) are shown in red. The black line shows the sum of the ocean mass and thermosteric
contributions. (Updated from Boening et al., 2012)
to estimate ice-sheet contributions with high confidence before the
1990s, and ocean data sampling is too sparse to permit an estimate of
global mean thermal expansion before the 1970s. However, a closed
observational GMSL budget since the 1970s can be demonstrated
with reasonable estimates of ice-sheet contributions (Church et al.,
2011a; Moore et al., 2011) (Table 13.1, Figure 13.7). For 1971–2010,
the observed contributions from thermal expansion and mass loss from
glaciers (not including those in Antarctica) alone explain about 75% of
the observed GMSL (high confidence).
AOGCM-based estimates of thermal expansion, which agree well
with observations since 1971, observational estimates of the glacier
contribution, and the estimated change in land water storage (Figure
13.4c), which is relatively small, can all be made from the start of
the 20th century (Sections 13.3.1.2, 13.3.2.2 and 13.3.4, Table 13.1).
Model estimates of Greenland ice-sheet SMB changes give an uncer-
tain but relatively small contribution during most of the 20th century,
increasing since the early 1990s (Section 13.3.3.2). There could be a
small constant contribution from the Antarctic ice sheet (Huybrechts
et al., 2011; Gregory et al., 2013b) due to long-term adjustment to
climate change in previous millennia. Any secular rate of sea level rise
in the late Holocene was small (order of few tenths mm yr
–1
) (Section
1158
Chapter 13 Sea Level Change
13
Figure 13.7 | (a) The observed and modelled sea level for 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) The observed and modelled sea level for 1961 to 2010. (d) The observed and modelled sea level 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). The estimates of global mean sea level are from Jevrejeva et al. (2008), Church and White
(2011), and Ray and Douglas (2011), with the shading indicating the uncertainty estimates (two standard deviations). The satellite altimeter data since 1993 are shown in red.
The grey lines in panels (a)-(d) are the sums of the contributions from modelled ocean thermal expansion and glaciers (excluding glaciers peripheral to the Antarctic ice sheet; from
Marzeion et al., 2012a), plus changes in land-water storage (see Figure 13.4). The black line is the mean of the grey lines plus a correction of thermal expansion for the omission
of volcanic forcing in the AOGCM control experiments (see Section 13.3.1.2). The dashed black line (adjusted model mean) is the sum of the corrected model mean thermal expan-
sion, the change in land water storage, the Marzeion et al. (2012a) glacier estimate using observed (rather than modelled) climate (see Figure 13.4), and an illustrative long-term
ice-sheet contribution (of 0.1 mm yr
–1
). The dotted black line is the adjusted model mean but now including the observed ice-sheet contributions, which begin in 1993. Because the
observational ice-sheet estimates include the glaciers peripheral to the Greenland and Antarctic ice sheets (from Section 4.4), the contribution from glaciers to the adjusted model
mean excludes the peripheral glaciers to avoid double counting. (Figure and caption updated from Church et al., 2013).
1159
Sea Level Change Chapter 13
13
13.2.1.4), probably less than 0.2 mm yr
–1
(see discussion in Gregory
et al., (2013b). Including these ice-sheet contributions (but omitting
Antarctic SMB variations, for which no observationally based infor-
mation for the ice sheet as a whole is available for the majority of
the 20th century), GMSL rise during the 20th century can be account-
ed for within uncertainties, including the observation that the linear
trend of GMSL rise during the last 50 years is little larger than for the
20th century, despite the increasing anthropogenic forcing (Gregory
et al., 2013b). Model-based attribution of sea level change to RFs is
discussed in Section 10.4.3.
The sum of CMIP5 AOGCM thermal expansion (Section 13.3.1.2), gla-
cier model results with CMIP5 AOGCM input (not including glaciers in
Antarctica; Section 13.3.2.2; Marzeion et al., (2012a)), and anthropo-
genic intervention in land water storage (Section 13.3.4) accounts for
about 65% of the observed rate of GMSL rise for 1901–1990, and 90%
for 1971–2010 and 1993–2010 (high confidence) (Table 13.1; Figure
13.7). In all periods, the residual is small enough to be attributed to the
ice sheets (Section 13.3.3.2).
The unusually warm conditions in the Arctic during the 1930s (Chylek
et al., 2004), which are attributed to unforced climate variability (Del-
worth and Knutson, 2000) and are therefore not expected to be sim-
ulated by AOGCMs, likely produced a greater mass loss by glaciers in
high northern latitudes (Section 13.3.2.2). The difference between the
glacier mass loss calculated with the Marzeion et al. (2012a) model
when it is forced with observed climate rather than AOGCM simulated
climate (the purple and blue curves in Figure 13.4b) is an estimate of
this effect.
If the glacier model results for observational input are used (Marzeion
et al. (2012a), not including glaciers in Antarctica) and an illustrative
value of 0.1 mm yr
–1
is included for a long-term Antarctic contribution,
the model mean is within 20% of the observed GMSL rise for the 20th
century (Figure 13.7a,c, dashed line), and 10% since 1993 (Figure
13.7d, dashed line; Church et al. (2013)). When the observed ice-
sheet contributions since 1992 are included as well, the sum is almost
equivalent to the observed rise (dotted line in Figure 13.7). Both obser-
vations and models have a maximum rate of rise in the 1930–1950
period, a minimum rate in the 1960s and a maximum rate over the last
two decades (Figure 13.7b). This agreement provides evidence that the
larger rate of rise since 1990, with a significant component of ocean
thermal expansion (Figure 13.4d), results from increased RF (both nat-
ural and anthropogenic) and increased ice-sheet discharge, rather than
a natural oscillation (medium confidence) (Church et al., 2013).
In summary, the evidence now available gives a clearer account of
observed GMSL change than in previous IPCC assessments, in two
respects. First, reasonable agreement can be demonstrated throughout
the period since 1900 between GMSL rise as observed and as calcu-
lated from the sum of contributions. From 1993, all contributions can
be estimated from observations; for earlier periods, a combination of
models and observations is needed. Second, when both models and
observations are available, they are consistent within uncertainties.
These two advances give confidence in the 21st century sea level pro-
jections. The ice-sheet contributions have the potential to increase sub-
stantially due to rapid dynamical change (Sections 13.1.4.1, 13.4.3.2
and 13.4.4.2) but have been relatively small up to the present (Sections
4.4 and 13.3.3.2). Therefore, the closure of the sea level budget to date
does not test the reliability of ice-sheet models in projecting future
rapid dynamical change; we have only medium confidence in these
models, on the basis of theoretical and empirical understanding of
the relevant processes and observations of changes up to the present
(13.4.3.2, 13.4.4.2).
Box 13.1 | The Global Energy Budget
The global energy balance is a fundamental aspect of the Earth’s climate system. At the top of the atmosphere (TOA), the boundary of
the climate system, the balance involves shortwave radiation received from the Sun, and shortwave radiation reflected and longwave
radiation emitted by the Earth (Section 1.2.2). The rate of storage of energy in the Earth system must be equal to the net downward
radiative flux at the TOA.
The TOA fluxes (Section 2.3) have been measured by the Earth Radiation Budget Experiment (ERBE) satellites from 1985 to 1999 (Wong
et al., 2006) and the Cloud and the Earth’s Radiant Energy System (CERES) satellites from March 2000 to the present (Loeb et al., 2009).
The TOA radiative flux measurements are highly precise, allowing identification of changes in the Earth’s net energy budget from year
to year within the ERBE and CERES missions (Kato, 2009; Stackhouse et al., 2010; Loeb et al., 2012), but the absolute calibration of the
instruments is not sufficiently accurate to allow determination of the absolute TOA energy flux or to provide continuity across missions
(Loeb et al., 2009).
The ocean has stored more than 90% of the increase in energy in the climate system over recent decades (Box 3.1), resulting in ocean
thermal expansion and hence sea level rise (Sections 3.7, 9.4 and 13.3.1). Thus the energy and sea level budgets are linked and must
be consistent (Church et al., 2011b). This Box focusses on the Earth’s global energy budget since 1970 when better global observational
data coverage is available. The RFs (from Chapter 8), the global averaged surface temperatures (Hadley Centre/Climate Research Unit
gridded surface temperature data set 4 (HadCRUT4) (Morice et al., 2012), and the rate of energy storage are relative to the time mean
of 1860 to 1879. Otto et al. (2013) used an energy imbalance over this reference period of 0.08 ± 0.03 W m
–2
, which is subtracted from
the observed energy storage. (continued on next page)
1160
Chapter 13 Sea Level Change
13
Box 13.1 (continued)
Since 1970, the effective radiative forcing (ERF) of the climate system has been positive as a result of increased greenhouse gas (GHG)
concentrations (well-mixed and short-lived GHGs, tropospheric and stratospheric ozone, and stratospheric water vapour) and a small
increase in solar irradiance (Box 13.1, Figure 1a). This positive ERF has been partly compensated by changes in tropospheric aerosols
which predominantly reflect sunlight and modify cloud properties and structure in ways that tend to reinforce the negative ERF,
although black carbon produces positive forcing. Explosive volcanic eruptions (such as El Chichón in Mexico in 1982 and Mt. Pinatubo
in the Philippines in 1991) can inject sulphur dioxide into the stratosphere, giving rise to stratospheric aerosol, which persists for several
years. This reflects some of the incoming solar radiation, and thus gives a further negative forcing. Changes in surface albedo from
land-use change have also led to a greater reflection of shortwave radiation back to space and hence a negative forcing. Since 1970,
the net ERF of the climate system (including black carbon on snow and combined contrails and contrail-induced cirrus, not shown) has
increased (Chapter 8), resulting in a cumulative total energy inflow (Box 13.1, Figure 1a). From 1971 to 2010, the total energy inflow
(relative to the reference period 1860-1879) is estimated to be 790 [105 to 1,370] ZJ (1 ZJ = 10
21
J).
If the ERF were fixed, the climate system would eventually warm sufficiently that the radiative response would balance the ERF, and
there would be zero net heat flux into the system. As the ERF is increasing, the ocean’s large capacity to store heat means the climate
system is not in equilibrium (Hansen et al., 2005), and continues to store energy (Box 3.1 and Box 13.1, Figure 1b). This storage provides
Year Year
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, respectively). 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.
(continued on next page)
1161
Sea Level Change Chapter 13
13
Box 13.1 (continued)
strong evidence of a changing climate. The majority of this additional heat is in the upper 700 m of the ocean but there is also warming
in the deep and abyssal ocean (Box 3.1). The associated thermal expansion of the ocean has contributed about 40% of the observed
sea level rise since 1971 (Sections 13.3.1, 13.3.6; Church et al., (2011b)). A small amount of additional heat has been used to warm the
continents, warm and melt glacial and sea ice, and warm the atmosphere. The estimated increase in energy in the Earth system between
1971 and 2010 is 274 [196 to 351] ZJ (Box 3.1).
As the climate system warms, energy is lost to space through increased outgoing radiation. This radiative response by the system is pre-
dominantly due to increased thermal grey-body radiation emitted by the atmosphere and surface, but is modified by climate feedbacks,
such as changes in water vapour, surface albedo and clouds, which affect both outgoing longwave and reflected shortwave radiation.
Following Murphy et al. (2009), Box 13.1, Figure 1b relates the cumulative total energy inflow to the Earth system to the change in
energy storage and the cumulative outgoing radiation. Calculation of the latter is based on the observed globally averaged surface
temperature change ΔT relative to a reference temperature for which the Earth system would be in radiative balance. This temperature
change is multiplied by the climate feedback parameter α, which in turn is related to the equilibrium climate sensitivity. For equilibrium
climate 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 W m
–2
(Sections 8.1,
8.3), the corresponding estimates of the climate feedback parameter α are 0.82, 1.23 and 2.47 W m
–2
°C
–1
.
In addition to these forced variations in the Earth’s energy budget, there is also internal variability on decadal time scales. Observations
and models indicate that because of the comparatively small heat capacity of the atmosphere, a decade of steady or even decreasing
surface temperature can occur in a warming world (Easterling and Wehner, 2009; Palmer et al., 2011). General Circulation Model
simulations indicate that these periods are associated with a transfer of heat from the upper to the deeper ocean, of order 0.1 W m
–2
(Katsman and van Oldenborgh, 2011; Meehl et al., 2011), with a near steady (Meehl et al., 2011) or an increased radiation to space
(Katsman and van Oldenborgh, 2011), again of order 0.1 W m
–2
. Although these natural fluctuations represent a large amount of heat,
they are significantly smaller than the anthropogenic forcing of the Earth’s energy budget (Huber and Knutti, 2012), particularly when
looking at time scales of several decades or more (Santer et al., 2011).
These independent estimates of ERF, observed heat storage, and surface warming combine to give an energy budget for the Earth that
is consistent with the assessed likely range of climate sensitivity (1.5°C to 4.5°C; Box 12.2) to within estimated uncertainties (high
confidence). Quantification of the terms in the Earth’s energy budget and verification that these terms balance over recent decades
provides strong evidence for our understanding of anthropogenic climate change. Changes in the Earth’s energy storage are a powerful
observation for the detection and attribution of climate change (Section 10.3) (Gleckler et al., 2012; Huber and Knutti, 2012).
13.4 Projected Contributions to Global
Mean Sea Level
13.4.1 Ocean Heat Uptake and Thermal Expansion
More than 90% of the net energy increase of the climate system on
multiannual time scales is stored in the ocean (Box 3.1). GMSL rise due
to thermal expansion is approximately proportional to the increase in
ocean heat content. The constant of proportionality is 0.11 ± 0.01 m
per 10
24
J for the ensemble of CMIP5 models (Kuhlbrodt and Gregory,
2012); it depends on the vertical and latitudinal distribution of warm-
ing in the ocean, because the expansion of sea water per degree Cel-
sius of warming is greater at higher temperature and higher pressure
(Russell et al., 2000; Hallberg et al., 2012; Körper et al., 2013; Perrette
et al., 2013).
For the early decades of the 21st century, the upper ocean dominates
the ocean heat uptake, and ocean heat content rises roughly linearly
with global mean surface air temperature (SAT) change (Pardaens et
al., 2011b; Körper et al., 2013). On multi-decadal time scales under
scenarios of steadily increasing RF, the rate of increase of ocean heat
content is approximately proportional to the global mean SAT change
from equilibrium (Gregory, 2000; Meehl et al., 2007; Rahmstorf, 2007a;
Gregory and Forster, 2008; Katsman et al., 2008; Schwartz, 2012), with
the constant of proportionality (in W m
–2
°C
–1
) being the ocean heat
uptake efficiency k.
The ocean heat uptake efficiency quantifies the effect of ocean heat
uptake on moderating time-dependent climate change; neglecting the
small fraction of heat stored elsewhere in the climate system, the sur-
face warming can be approximated as F/(α+k), where F is the RF and
α is the climate feedback parameter (Raper et al., 2002), and hence the
rate of ocean heat uptake is approximately kF/(α+k). In CMIP3 and
CMIP5, the model spread in projections of surface warming is dominat-
ed by the spread in F and α, but the spread in k accounts for a substan-
tial part of the spread in projections of ocean heat uptake (Dufresne
and Bony, 2008; Gregory and Forster, 2008; Knutti and Tomassini, 2008;
Geoffroy et al., 2012; Sriver et al., 2012; Forster et al., 2013).
The spread in k relates to differences among models in heat-transport
processes within the ocean. The warming spreads downwards from
the surface over time, and the greatest increases in projected ocean
1162
Chapter 13 Sea Level Change
13
heat content occur where the warming penetrates most deeply, in the
Southern Ocean and the North Atlantic (Figure 12.12; Section 12.4.7.1)
(Kuhlbrodt and Gregory, 2012). Changes in convection and the large-
scale vertical circulation are particularly important to heat uptake in
the North Atlantic (Banks and Gregory, 2006; Rugenstein et al., 2013).
Heat is also transported vertically by eddies, especially in the Southern
Ocean, and by turbulent mixing. These processes are parameterized in
models when they occur at unresolved scales. Observed ocean heat
uptake has been used in conjunction with observed global SAT change
to constrain the ocean effective thermal diffusivity representing all
unresolved vertical transports in simple climate models and EMICs
(Forest et al., 2008; Knutti and Tomassini, 2008; Marčelja, 2010; Soko-
lov et al., 2010). The simulated ocean vertical temperature profile and
the depth of penetration of the warming in AOGCMs have also been
evaluated by comparison with observations, and both bear a relation-
ship to k (Hallberg et al., 2012; Kuhlbrodt and Gregory, 2012). Such
comparisons suggest that model projections might be biased towards
overestimating ocean heat uptake and thermal expansion for a given
surface warming (Sections 9.4.2.2, 10.8.3 and 13.3.1.2). The physical
causes of this tendency are unclear. Although the simulated vertical
temperature profile is affected by the model representation of vertical
heat transport processes, Brierley et al. (2010) found only a small effect
on k from variation of model parameters that influence interior heat
transport.
Because the ocean integrates the surface heat flux, thermal expan-
sion projections following different scenarios do not significantly
diverge for several decades. Scenarios assuming strong mitigation of
GHG emissions begin to show a reduced rate of thermal expansion
beyond about 2040; the amount by 2100 is about one third less than
in a non-mitigation scenario (Washington et al., 2009; Pardaens et al.,
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
2011b; Körper et al., 2013), and half as much in RCP2.6 as in RCP8.5
(Yin, 2012) (Section 13.5.1). The integrating effect means that ther-
mal expansion depends not only on the cumulative total, but also on
the pathway of CO
2
emissions; reducing emissions earlier rather than
later, for the same cumulative total, leads to a larger mitigation of sea
level rise due to thermal expansion (Zickfeld et al., 2012; Bouttes et
al., 2013). The integrating effect also means that annual time series of
global ocean thermal expansion show less interannual variability than
time series of global SAT. For the present assessment of GMSL rise,
projections of ocean heat uptake and thermal expansion up to 2100
have been derived from the CMIP5 AOGCMs (Yin, 2012). Methods are
described in Section 13.5.1 and the Supplementary Material and the
results for ocean heat uptake are shown in Figure 13.8, and for thermal
expansion in Table 13.5 and Figures 13.10 and 13.11.
Ocean heat uptake efficiency is not constant on time scales of many
decades or in scenarios of stable or decreasing RF (Rahmstorf, 2007a;
Schewe et al., 2011; Bouttes et al., 2013). A good representation of
AOGCM behaviour is obtained by distinguishing a shallow layer, which
is associated with surface temperature variations on decadal time
scales, from a deep layer, which has the majority of the heat capacity
(Hansen et al., 1985; Knutti et al., 2008; Held et al., 2010; Olivié et
al., 2012; Schwartz, 2012; Bouttes et al., 2013; Geoffroy et al., 2013).
Ocean heat uptake and thermal expansion take place not only while
atmospheric GHG concentrations are rising, but continue for many cen-
turies to millennia after stabilization of RF, at a rate which declines
on a centennial time scale (Stouffer, 2004; Meehl et al., 2005; 2007;
Solomon et al., 2009; Hansen et al., 2011; Meehl et al., 2012; Schwartz,
2012; Bouttes et al., 2013; Li et al., 2013; Zickfeld et al., 2013). This is
because the time scale for warming the deep ocean is much longer
than for the shallow ocean (Gregory, 2000; Held et al., 2010).
Figure 13.8 | Heat uptake by the climate system during the 21st century relative to 1986–2005 projected by CMIP5 Atmosphere–Ocean General Circulation Models (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 data set), 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.
1163
Sea Level Change Chapter 13
13
The rate and the stabilization time scale for thermal expansion depend
on the GHG stabilization level. For the highest scenario (RCP8.5), GMSL
rise due to thermal expansion can exceed 2 m above the pre-industrial
level by the year 2500 (Section 12.5.2, Figure 12.44, Figure 13.14a),
and is still rising at that time. Changes in ocean circulation, particularly
due to a reduction in deep water formation, can also have a large
effect on global ocean heat uptake, and may relate nonlinearly to
global surface warming (Levermann et al., 2005; Fluckiger et al., 2006;
Vellinga and Wood, 2008). As the rate of ocean heat uptake decreases,
the surface warming approaches the level determined by the equilibri-
um climate sensitivity.
On a multi-millennial time scale, the range from Earth System Models
of Intermediate Complexity suggests that thermal expansion contrib-
utes between 0.20 to 0.63 m per degree Celsius of global mean tem-
perature increase (Meehl et al., 2007; Zickfeld et al., 2013) (Section
12.5.2 and Figure 13.14a). The median of the six models of 0.42 m
°C
–1
is consistent with a thermal expansion of 0.38 m °C
–1
that would
result from a uniform increase in ocean temperature from the presently
observed temperature and salinity distribution (Levitus et al., 2009).
Uncertainty arises due to the different spatial distribution of the warm-
ing in models and the dependence of the expansion on local tempera-
ture and salinity.
13.4.2 Glaciers
The 21st century sea level contribution from glaciers presented in the
AR4 assessment ranged from 0.06 to 0.15 m SLE by 2100 across a
range of scenarios (Meehl et al., 2007). The Randolph Glacier Inventory
(RGI) (Arendt et al., 2012) has improved projections of glacier contribu-
tion to sea level rise by providing the first globally complete account-
ing of glacier location, area, and area-elevation distribution (hypsom-
etry). Several analyses of scenario-dependent SMB glacier projections
(referred to here as process-based models) have been produced using
the RGI, including Marzeion et al. (2012a), Giesen and Oerlemans
(2013), and Radić et al. (2013). The Marzieon and Radić approaches
each used different suites of CMIP5 AOGCM models to calculate SMB
terms from RCP forcings, and the model by Slangen and van de Wal
(2011) was used to calculate SMB terms from RCP forcings (Supple-
mentary Material 13.SM.1). Giesen and Oerlemans (2013) used CRU
forcing but calculated SMB from three different combinations of varia-
tions in modelled temperature, precipitation, and atmospheric trans-
missivity. Only their results for varying temperature are shown here.
Machguth et al. (2013) is also included in Table 13.3, but this projection
represents changes in Greenland peripheral glaciers only, and is not
included in the global glacier summaries. Although these details differ
among the models, all share a generally common time-evolving struc-
ture, with SMB initially determined by model-generated climate forcing
applied to a subset of global glaciers, the ensuing volume change con-
verted to area change via volume-area scaling, and this result upscaled
to a new global distribution and hypsometry to create initial conditions
for the subsequent time step. These methods are described further in
Section13.5.1 and in the Supplementary Material. Related results are
shown in Table 13.5 and Figures 13.10 and 13.11.
Although the peripheral glaciers surrounding the ice sheets are includ-
ed with the ice sheets in assessment of present-day changes (Table
13.1), future projections should ideally assess the peripheral glaciers
separately, as these are too small and dynamically responsive to be
modelled adequately with coarse-grid, non-dynamic ice-sheet SMB
models. The peripheral glaciers surrounding both the Greenland and
Antarctic ice sheets are thus included in the process-based models
described above, but for projections shown in Table 13.5, the Antarctic
peripheral glaciers are included with the Antarctic ice sheet where-
as the Greenland peripheral glaciers are included with the remaining
world’s glaciers. Projected losses from glaciers peripheral to both ice
sheets are listed separately in Table 13.3.
Several glacier loss projections derived from model types other than
process-based models have been published since 2007; their pro-
jections range from 0.08 to 0.39 m SLE by 2100 (Table 13.3). These
used methods of projecting future losses from glaciers developed in
response to the absence of a global compilation of glacier observations
after 2005 and the absence of a globally complete glacier inventory to
provide geographic boundary conditions for conventional modelling.
These methods include extrapolation from observed rates (Meier et
al., 2007), semi-empirical methods applied to sea level change compo-
nents (Jevrejeva et al., 2012b), kinematic (or ‘limit seeking’) projections
(Pfeffer et al., 2008), and power-law scaling estimates based on re-es-
tablishing equilibrium accumulation-area ratios (AARs) from initial
non-equilibrium AARs (Bahr et al., 2009). Strengths of these approach-
es include the fact that observations used to calibrate extrapolation
and semi-empirical projection partially account for future dynamically
forced losses, that semi-empirical methods use modelled future forc-
ings as guidance for projections, and that AAR equilibration has strong
physical and theoretical underpinnings and gives generalized but
robust projections. These strengths partially offset the weaknesses of
these models, which include, in the case of extrapolation and semi-em-
pirical projection, an assumption of statistical stationarity that may not
be valid, while the AAR equilibration approach gives only a final steady
state value, so that rates or cumulative losses at any intermediate time
must be estimated by area-response time scaling. However, these
alternate methods are valuable because of their construction on fun-
damental and robust principles together with their use of the limited
available information to produce projections that, although imprecise,
are transparent, and require less detailed input information or knowl-
edge of details of complex processes in comparison to process-based
models.
Published results from process-based models are shown in Table 13.3.
Glacier contributions at 2100, expressed as SLE, range between 0.04
and 0.11 m for Special Report on Emission Scenarios (SRES) A1B, 0.07
and 0.17 m for RCP2.6, between 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 RCP8.5.
The projections derived from alternative models are also shown in Table
13.3; the mean and range of these models listed here is 0.24 [0.08 to
0.39] m SLE, consistent with the process-based models. Results from
the process-based models, plotted as time series and grouped by forc-
ing scenario, are shown in Figure 13.9. See Table 13.3 for specific start/
end dates for each projection.
Unresolved uncertainties in the projection of glacier contributions to
sea level rise include the potential for near-term dynamic response
1164
Chapter 13 Sea Level Change
13
Table 13.3 | Twenty-first century sea level rise projections for global glaciers, from process-based surface mass balance models, and from alternate model strategies. Dates for
beginning and end of model period are as shown; mean and 5% to 95% confidence sea level equivalents are shown in metres. Process-based models all use variations on Atmo-
sphere–Ocean General Circulation Model (AOGCM) mass balance forcing applied to inventoried glacier hypsometries on a subset of global glaciers and upscaling by power-law
techniques to the global total. Calving and rapid dynamic response are not included in any of the models except for Jevrejeva et al. (2012b), where calving losses are present to a
limited degree in input data, and NRC (2012), where calving is explicitly included in future losses. Other model details are discussed in the text.
Contribution to Global
Mean Sea Level Rise (SLR)
Peripheral Glacier
(PG) Contribution
Reference Model
Starting
Date
End
Date
Projected SLR (m) from Gla-
ciers except Antarctic PGs
Greenland Ice
Sheet PG (m)
Antarctic Ice
Sheet PG (m)
Process-based Surface Mass Balance (SMB) Models Mean
[5 to 95%]
confidence
5 to 95%
confidence
5 to 95%
confidence
Scenario RCP2.6
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. (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] 0.004–0.021 0.01–0.04
Scenario A1B and RCP4.5
Machguth et al. (2013)
a
2000 2098 0.006–0.011
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
Notes
a
This projection represents changes in Greenland peripheral glaciers only, and is not included in the global glacier summaries.
from marine-terminating glaciers and interception of terrestrial
runoff. Of the about 734,000 km
2
of global glacier area exclusive of
that peripheral to the Greenland and Antarctic ice sheets, 280,500
km
2
(38%) drains through marine-terminating outlets (Gardner et
al., 2013). Although the long-term potential for dynamic discharge
from glaciers (as opposed to ice sheets) is limited by their small total
volume, dynamic losses may be an important component of total sea
level rise on the decade-to-century scale. In Alaska, Columbia Glacier
lost 7.65 Gt yr
–1
between 1996 and 2007, with 94% of that loss coming
from rapid tidewater retreat (Rasmussen et al., 2011); the loss from this
single 1000 km
2
glacier is 1.3% of the global cryospheric component
of sea level rise during 1993–2010 (Table 13.1) and 0.7% of total sea
level rise. The observations required to estimate the potential for sim-
ilar dynamic response from other glacier regions do not exist at this
time, but the dynamic contribution could be large on the century time
scale. If the basin-wide thinning rate observed at Columbia Glacier
1165
Sea Level Change Chapter 13
13
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. Colours 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 Atmosphere–Ocean General Circulation Model (AOGCM) 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 to 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
59
108
170
136
46
145
191
141
66
93
214
239
183
50
50
63
63
1
50
150
11
0
110
65
65
80
80
17
0
170
1
36
136
46
46
1
4
5
145
19
1
191
141
141
66
66
93
93
2
14
214
239
239
183
183
Years Years
GMSLR (mm)GMSLR (mm)
over the past 25 years (about 5 m yr
–1
) were to occur over the area of
global glaciers draining through marine outlets (280,500 km
2
) during
the next 89 years (2011–2100), the sea level contribution would be
approximately 30 cm SLE, compatible with Jeverajeva et al’s (2012b)
projected loss of 26 cm SLE from glaciers. Although this is a rough
calculation and an upper bound, because drainage through marine
outlets does not guarantee tidewater instability, it indicates that the
potential for a significant sea level response to dynamic retreat of gla-
ciers cannot be rejected a priori.
Completion of the global glacier inventory has allowed large improve-
ments in assessment and modelling, but further uncertainties related
to the inventory remain to be resolved, including those arising from the
size cutoff decided for the inventory (Bahr and Radić, 2012). Another
source of uncertainty is interception of glacier runoff by land hydrolo-
gy. Despite rapidly growing knowledge of changes in terrestrial water
storage, especially through increased reliability of GRACE observations,
glacier mass loss is still generally assumed to flow directly to the ocean,
with no delay or interception by surface or aquifer storage. Although
this probably will not apply to discharge from glaciers located near
coasts (e.g., Canadian Arctic, Patagonia, Alaska, ice-sheet peripheries),
runoff from interior regions (e.g., Alps, High Mountain Asia) may be
significantly intercepted before reaching the ocean. Whether terrestrial
interception has any significant effect on the net glacier contribution
to sea level rise is undetermined at this time.
13.4.3 Greenland Ice Sheet
13.4.3.1 Surface Mass Balance Change
Greenland SMB is positive in the present climate but shows a decreas-
ing trend (Section 13.3.3.2), which implies an increasing contribution
to GMSL rise. Like the AR4, all recent studies have indicated that the
future sea level contribution from Greenland SMB change will be
increasingly positive because the increase in ablation (mostly runoff)
outweighs that in accumulation (mostly snowfall), and that scenarios
1166
Chapter 13 Sea Level Change
13
of greater RF lead to a larger sea level contribution. Precipitation is pro-
jected to increase at about 5% per °C of annual-mean warming over
Greenland, but the increase in snowfall is smaller because the fraction
of rain increases as temperature rises (Gregory and Huybrechts, 2006;
Fettweis et al., 2013).
We compare post-AR4 studies of Greenland SMB change using
time-dependent simulations of the 21st century by CMIP3 AOGCMs
for scenario SRES A1B and CMIP5 AOGCMs for scenario RCP4.5 (Table
13.4). The time-integral of the Greenland SMB anomaly with respect to
a reference period is interpreted as a contribution to GMSL rise, on the
assumption that the ice sheet was in approximate mass balance during
the reference period (see discussion in Sections 13.1.4.1 and 13.3.3.2);
this assumption can be avoided only if ice-sheet outflow is also mod-
elled. Making this assumption, the Greenland SMB contribution lies in
the range 0.00 to 0.13 m for these two scenarios.
The spread in the magnitude and patterns of Greenland climate change
projected by the AOGCMs causes a large part of the spread in the
projected contribution to GMSL rise (Table 13.4). Yoshimori and Abe-
Ouchi (2012) found that the inter-model spread in global mean SAT
change accounts for about 60% of the spread in the change of project-
ed Greenland ablation. Two important contributions to the remaining
spread are the weakening of the AMOC, which affects the magnitude
of warming over Greenland, and the SAT of Greenland in the model
control climate, which affects the sensitivity of melting to warming
(Yoshimori and Abe-Ouchi, 2012; Fettweis et al., 2013).
Ablation is computed using either an EBM, which may be stand-alone
or part of a regional climate model, or from surface air temperature
using an empirical temperature index method, mostly commonly the
positive-degree-day (PDD) method, in which melting is proportional to
the time-integral of temperature above the freezing point. Meltwater
production increases faster than linearly with temperature increase
because of reduced albedo due to refreezing of meltwater in the snow-
pack and expansion of the area of bare ice (van Angelen et al., 2012;
Fettweis et al., 2013; Franco et al., 2013). The simulation of this posi-
tive albedo feedback on mass loss depends sensitively on the model
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
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) 12 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
12 km TI 1980–1999 2090–2099 0.00–0.11 0.0–1.8
Notes:
a
The spatial resolution is stated and the surface mass balance (SMB) method denoted by TI = temperature index, PDD = positive degree day, EBM = Energy Balance Model.
b
The amount of sea level rise is the time-integral of the SMB anomaly from the period or date labelled ‘starting from’ to the one labelled ‘up to’. Unless otherwise indicated, the SMB anomaly is
calculated relative to the mean SMB for the ‘starting from’ period, and the rate of sea level rise is the SMB anomaly in the ‘up to’ period.
c
These results are estimated from global mean surface air temperature (SAT) change, using formulae fitted to results from a Greenland SMB model.
d
The SMB anomaly is relative to the late 19th century.
e
This experiment used time-slices, with boundary conditions from the European Centre for Medium range Weather Forecasts (ECMWF) and Hamburg 5 (ECHAM5) GCM, rather than a simulation
of the complete century; thus, results are not available for the amount.
f
Fettweis et al. (2008) and Franco et al. (2011) used a hybrid approach: they derived a regression relationship from simulations of the recent past using a Regional Climate Model (RCM),
incorporating an EBM, between annual anomalies in Greenland climate and in Greenland SMB, then applied this relationship to project future SMB from projected future climate anomalies. The
method assumes that a relationship derived from past variability will also hold for future forced climate change.
g
Range including uncertainty in choice of emission scenario (B1, A1B or A2), SMB modelling and ice-sheet dynamical modelling, as well as uncertainty in climate modelling.
h
Results are given for the Hadley Centre Regional Model 3P (HadRM3P), High-Resolution Hamburg climate model 5 (HIRHAM5) and the Modèle Atmosphérique Régional (MAR) RCMs driven with
the same boundary conditions from the ECHAM5/MPI-OM AOGCM.
i
Results are given for two ice sheet models (Elmer/Ice, SImulation COde for POLythermal Ice Sheets (SICOPOLIS)) using the same AOGCM climate boundary conditions. The resolution given is for
SICOPOLIS; Elmer/Ice has variable resolution.
j
Results calculated from CMIP5 AOGCMs by the same method as used in the paper.
k
These results were obtained from the model of Van Angelen et al. (2012) using boundary conditions from the HadGEM2-ES AOGCM and are shown by Fettweis et al. (2013).
l
With respect to 1992–2011 as a reference period, during which there is a significant simulated trend in SMB (Section 13.3.3.2), the amount is 0.07 m and the rate 1.4 mm yr
–1
.
Table 13.4 | Contribution to sea level rise from change in the surface mass balance of the Greenland ice sheet during the 21st century. Where given, ranges are 5 to 95% esti-
mated from the published results and indicate the uncertainty due to the climate change modelling by Atmosphere–Ocean General Circulation Model (AOGCMs), except where
noted otherwise.
1167
Sea Level Change Chapter 13
13
snow-albedo parameterization (Rae et al., 2012). Goelzer et al. (2013)
projected 14 to 31% more runoff during the 21st century when using
an EBM than when using a PDD method, mainly because of the omis-
sion of the albedo feedback in the latter. However, other studies using
temperature index methods (Graversen et al., 2011; Yoshimori and
Abe-Ouchi, 2012) have ranges extending to higher values than those
from EBMs, indicating that this is not the only difference between
these classes of methods (Table 13.4).
SMB simulations are also particularly sensitive to the treatment of
meltwater refreezing (Bougamont et al., 2007; Rae et al., 2012). The
pore space in the present-day percolation zone could accommodate
1 to 5 mm SLE of refrozen meltwater over the next several decades
(Harper et al., 2012), and the importance of meltwater refreezing will
become greater as melting becomes prevalent in areas where it has
previously been rare. On the other hand, refreezing will be restricted,
and runoff consequently increased, by the expansion of the area of
bare ice (Fettweis et al., 2013).
Another source of model spread is the representation of topography,
which is lower when represented at coarser resolution. This allows
precipitation to spread further inland because of reduced topograph-
ic barriers (Bengtsson et al., 2011), and enhances ablation because
there is more area at lower, warmer altitudes (Bengtsson et al., 2011;
Seddik et al., 2012). Most of the models in Table 13.4 use a fixed
Greenland topography, and thus cannot simulate the positive feedback
on ablation that can be expected as the ice-sheet surface becomes
lower. Dynamical models are required to simulate this effect (Section
13.4.3.2).
For the present assessment of GMSL rise, changes in Greenland ice
sheet SMB up to 2100 have been computed from global mean SAT
change projections derived from the CMIP5 AOGCMs, following meth-
ods described in Section 13.5.1 and the Supplementary Material. The
distribution of results, shown in Table 13.5 and Figures 13.10 and
13.11, covers the ranges obtained using the methods of Fettweis et al.
(2013), Gregory and Huybrechts (2006), and Yoshimori and Abe-Ouchi
(2012).
On multi-centennial to millennial time scales, feedbacks between
regional climate and the ice sheet become increasingly relevant, espe-
cially under strong climate change scenarios, thus requiring coupled
climate ice-sheet models to capture potential feedbacks beyond the
year 2100. These models apply a reduced spatial resolution in order to
be computationally efficient enough to evaluate longer time scales and
to combine the different climatic components. Consistent with regional
climate models for the 21st century, they project an increasingly nega-
tive mass balance for the Greenland ice sheet for all warming scenarios
which is mainly due to a decreasing SMB (Ridley et al., 2005; Wing-
uth et al., 2005; Driesschaert et al., 2007; Mikolajewicz et al., 2007a;
Swingedouw et al., 2008; Vizcaíno et al., 2008, 2010; Huybrechts et
al., 2011; Goelzer et al., 2013). The main feedbacks between climate
and the ice sheet arise from changes in ice elevation, atmospheric and
ocean circulation, and sea-ice distribution.
Comparing the different feedbacks, high confidence can be assigned
to the models’ ability to capture the feedback between SMB and sur-
face elevation. As a consequence, a nonlinear increase in ice loss from
Greenland with increasing regional RF is found across different scenar-
ios (Driesschaert et al., 2007). This nonlinearity arises from the increase
in both the length of the ablation season and the daily amount of
melting as the ice-sheet surface lowers. This SMB-surface elevation
feedback is also the main reason for the threshold behaviour of the
Greenland ice sheet on multi-millennial time scales (Section 13.4.3.3).
Medium-to-low confidence is assigned to the models’ representation
of the atmospheric and ocean circulation and sea-ice changes. On mul-
ti-centennial time scales, Swingedouw et al. (2008) found enhanced ice
loss from Greenland in a coupled simulation (compared to the uncou-
pled version) in which ice topography and meltwater flux influence the
ocean and atmospheric circulation as well as sea-ice distribution. Viz-
caíno et al. (2010) found reduced ice loss due to the coupling, mainly
caused by the effect of topographic changes on the surface tempera-
ture, but less pronounced in amplitude compared with Swingedouw
et al. (2008). Both the atmospheric circulation and the ocean currents,
especially in coastal areas, are poorly resolved by these models. It is
therefore likely that the time scales associated with ocean transport
processes are distorted and there is low confidence that these feed-
backs, although existent, can be quantified accurately by the applied
models.
The AMOC exerts a strong influence on regional climate around the
Greenland ice sheet and consequently its SMB. Most CMIP5 models
show a reduction of the AMOC under future warming during the 21st
century and beyond (Section 12.4.7.2). Although coupled climate–ice
sheet models show some influence of meltwater from Greenland on
the AMOC, the uncertainty between models with respect to the AMOC
response to warming is significantly larger than the difference between
simulations with or without this feedback within one model.
In the coupled climate–ice sheet model applied by Mikolajewicz et al.
(2007a) and Vizcaíno et al. (2008), the AMOC shows a strongly non-
linear response to global warming. A weak AMOC reduction is found
for 1%-per-year-CO
2
-increase scenarios up to 560 and 840 ppm, and
a near-complete cessation of the AMOC for 1120 ppm. As a conse-
quence, after 600 years of integration, the sea level contribution for
the 1120 ppm scenario is similar to that of the 560 ppm scenario, but
doubles for the medium scenario, which stabilizes at 840 ppm. In the
most recent model version (Vizcaíno et al., 2010), the AMOC shows a
strong weakening of the AMOC in all scenarios (~60% reduction in
560 ppm scenario; ~80% for 1120 ppm). The total sea level contribu-
tion from Greenland, including the effect of the AMOC weakening, is
~1 m (corresponding to an 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
.
Even though the AMOC weakening in the model by Huybrechts et al.
(2011) is less pronounced (10 to 25%), the ice loss through melting is
significantly weaker in this model. During the first 1000 years of inte-
gration, the Greenland ice sheet contributes 0.36 m (corresponding to
an average rate of 0.36 mm yr
–1
) for 560 ppm 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 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. This relatively weak
warming response to GHG forcing compared to CMIP5 models and
1168
Chapter 13 Sea Level Change
13
the climate model used in Vizcaíno et al. (2010) explains the relatively
small sea level response.
Using the same model as Huybrechts et al. (2011), albeit with a slightly
higher polar warming, Goelzer et al. (2012) computed temperatures
and sea level under the SRES scenarios B1, A1B and A2, with subse-
quent GHG stabilization after the year 2100. As in Huybrechts et al.
(2011), the ice-sheet evolution is dominated by the SMB. They find sea
level contributions of 1.4, 2.6 and 4.2 m in the year 3000 for the sce-
narios B1, 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
, respectively.
In summary, coupled climate-ice sheet models consistently show an
increasingly negative mass balance of the Greenland ice sheet due
mainly to a decreasing SMB under warming scenarios on centennial
time scales beyond 2100. On multi-millennial time scales, these models
show a threshold temperature beyond which the melting of the Green-
land ice sheet self-amplifies and the ice volume is reduced to less than
30% of its present volume (Section 13.4.3.3).
13.4.3.2 Dynamical Change
Observations suggest three main mechanisms by which climate change
can affect the dynamics of ice flow in Greenland (Sections 4.4.3 and
4.4.4): by directly affecting ice loss (outflow) through the calving of
icebergs and marine melt from marine-terminating outlet glaciers; by
altering basal sliding through the interaction of surface melt water with
the glacier bed; and indirectly through the interaction between SMB
and ice flow. We assess the consequences of each of these processes.
Section 4.4.3.2 presents the observational basis on which concerns
about increased ice loss by calving and marine melt are based. In par-
ticular, recent increases in loss are thought to be linked to the migration
of subtropical water masses around the coast of Greenland (Holland
et al., 2008) and its occupation of coastal fjords (Straneo et al., 2010;
Christoffersen et al., 2011). Output from 19 AOGCMs under scenario
A1B showed warming of 1.7°C to 2.0°C around Greenland over the
course of the 21st century (Yin et al., 2011), suggesting that the trend
towards increased outflow triggered by warming coastal waters will
continue.
Although projections of outflow are at a fairly early stage, literature
now exists to make an assessment. Flowline modelling has successfully
simulated the observed retreat and associated acceleration of the main
outlet glaciers of the Greenland ice sheet (Helheim and Petermann
Glaciers (Nick et al., 2009, 2012); Jakobshavn Isbræ (Vieli and Nick,
2011)). The same model has been used to project mass loss from these
glaciers (Nick et al., 2013), as well as Kangerdlugssuaq Glacier, using
ocean and atmosphere forcing based on scenarios A1B and RCP8.5.
At 2100, total projected SLR spans 8 to 13 mm for A1B and 11 to 17
mm for RCP8.5. These figures generalize to 40 to 63 mm and 57 to 85
mm, respectively, for the whole ice sheet based on a simple scaling
between modelled and total ice-sheet area (a factor of ~5). Price et
al. (2011) modelled the century-scale response of the ice sheet to the
observed recent retreat of three outlet glaciers (Jakobshavn Isbræ, and
Helheim and Kangerdlugssuaq Glaciers). At 2100, the projected SLR
associated with the three modelled outlet glaciers is 0.6 to 1.4 mm,
which equates to SLR of 4 to 8 mm after scaling (by a factor of ~6)
to all outlet glaciers based on observed mass loss (van den Broeke et
al., 2009). Total projected SLR then varies between 10 and 45 mm at
2100 if successive retreats are specified with a notional repeat interval
between 50 and 10 years.
Goelzer et al. (2013) implemented the Nick et al. (2013) retreat chro-
nology within a 5-km resolution ice-sheet model along with their own
generalization for including unsampled outlet glaciers. Associated SLR
at 2100 is projected to vary between 8 and 18 mm. Graversen et al.
(2011) attempted to capture the effect of increased outflow by enhanc-
ing basal sliding and generated SLR of 9 to 24 mm at 2100.
Two estimates of the effect of dynamical change on Greenland’s con-
tribution to SLR by 2100 have been made on the basis of physical
intuition. Pfeffer et al. (2008) developed a low scenario by assuming
a first-decade doubling of outlet glacier velocity throughout the ice
sheet that equates to 93 mm SLR, while a high scenario that assumes
an order of magnitude increase on the same time scale contributes 467
mm. Katsman et al. (2011) used a similar methodology to obtain an
estimate of 100 mm SLR.
Based primarily on Nick et al. (2013), we assess the upper limit of the
likely range of this dynamical effect to be 85 mm for RCP8.5 and 63
mm for all other RCP scenarios for the year 2100. We have medium
confidence in this as an upper limit because it is compatible with Kats-
man et al. (2011), the low scenario of Pfeffer et al. (2008), and Price et
al. (2011) in the probable event of a sub-decadal recurrence interval.
Although the likely upper limit is less than the high scenario of Pfef-
fer et al. (2008), process modelling gives no support to the order of
magnitude increase in flow on which this scenario is based. It is higher
than the contributions found by Goelzer et al. (2013) and Graversen
et al. (2011) for which there are two potential explanations. First, the
generalization used to extrapolate from the modelled sample to all
outlet glaciers differs. Nick et al. (2013) used a scaling similar to the
independently derived value of Price et al. (2011), while the implied
scaling used by Goelzer et al. (2013) is substantially lower. Second,
Goelzer et al. (2013) suggested that surface ice melt and calving each
remove marginal ice (see below), implying that by not including sur-
face melt, overall mass loss by dynamics may be over predicted by the
flowline model of Nick et al. (2013). At present, these studies cannot be
reconciled and we therefore use the more inclusive range.
The lower limit of the likely range is assessed as 20 mm for RCP8.5 and
14 mm for all other RCP scenarios. This reflects the individual outlet
glacier projections of Nick et al. (2013) but uses a lower generalization
more similar to that found by Goelzer et al. (2013). This assessment of
the lower limit is compatible with Price et al. (2011) and Graversen et
al. (2011).
Section 4.4.3.2 assesses understanding of the link between abundant
summer meltwater, lubrication of the ice-sheet base, and enhanced
ice flow. Although this mechanism appears important in modulating
present-day ice flow, it is not supported as the cause of recent mass
loss. Goelzer et al. (2013) incorporated a parameterization of this effect
based on field observations, which results in less than a millimetre SLR
by 2100 in their projections. Bindschadler et al. (2013) reported a suite
1169
Sea Level Change Chapter 13
13
of experiments assessing this effect in an eight-model ensemble, but
their parameterization appears overly simplistic and may well exag-
gerate the importance of the effect. These projections do not incor-
porate the effect on ice flow of the latent heat released by increased
future quantities of melt water within the ice sheet (Phillips et al.,
2010; 2013), for which no projections are currently available. Basal
lubrication is therefore assessed as making an insignificant contribu-
tion to the likely range of SLR over the next century and is omitted in
the remainder of the assessment. We have medium confidence in pro-
jections of this effect primarily because recent improvements in pro-
cess-based understanding show that it has little contribution to mass
loss (Section 4.4.3.2); the potential of latent-heat effects in the future
limits a higher level of confidence.
Finally, we assess the level of interaction between SMB change and ice
flow. In AR4, this effect is assessed as 0 ± 10% (likely range) of SMB,
based on Huybrechts and de Wolde (1999) and Gregory and Huybrechts
(2006). This assessment included both the positive feedback between
SMB and the height of the ice sheet, and a countering negative feed-
back involving ice flow and depletion effects. The latter effect is partly
included in our assessment of the direct impacts of climate change
on ice flow, and we therefore limit our assessment to the SMB-height
feedback. Few studies explicitly determine this effect, but Goelzer et al.
(2013) reported that it amounts to 5 to 15% of SMB over the course of
the 21st century, which we extend slightly (0 to 15%) to reflect possi-
ble interaction with mass loss by calving (Goelzer et al., 2013).
Goelzer et al. (2013) and Gillet-Chaulet et al. (2012) suggested that
SMB and ice dynamics cannot be assessed separately because of the
strong interaction between ice loss and climate due to, for instance,
calving and SMB. The current assessment has by necessity separated
these effects because the type of coupled ice sheet-climate models
needed to make a full assessment do not yet exist. These interactions
may well combine to reduce SLR in comparison to the assessed range
because of the mass-depletion effect of retreating outlet glaciers.
Another source of uncertainty is the bedrock topography of Greenland,
although recent improvements in data coverage (Bamber et al., 2013)
suggest that the majority of the ice sheet rests on bedrock above sea
level and the number of deep bedrock troughs penetrating into the
interior of Greenland are limited, thus limiting the potential for marine
ice-sheet instability (see Box 13.2).
Although not strictly comparable because they contain a different
balance of ice-dynamical effects, the assessment is consistent with
Bindschadler et al. (2013), who reported an extensive model inter-com-
parison exercise in which standardized experiments are combined to
represent the impact of climate change under RCP8.5 on the Green-
land ice sheet. The resultant projection included contributions from
lubrication, marine melt and SMB-coupling and generated a mean SLR
at 2100 of 162 mm over five models, or 53 mm if an outlier with anom-
alously high response is removed (including SMB results in SLR at 2100
of 223 and 114 mm for five- and four-model means, respectively). This
comparison provides further weight to our confidence.
In summary, dynamical change within the Greenland ice sheet is likely
(medium confidence) to lead to SLR during the next century with a
range of 20 to 85 mm for RCP8.5, and 14 to 63 mm for all other sce-
narios by year 2100. The latter are assumed to have uniform SLR in the
absence of literature allowing these scenarios to be assessed individu-
ally, although dependency on scenario is expected to exist. In addition,
mass loss associated with SMB-height feedback is likely to contribute
a further 0 to 15% of SMB (in itself scenario dependent). This equates
to, for example, 0 to 14 mm by 2100 based on the central estimate of
RCP8.5. The peripheral glaciers of Greenland are not included here but
are in the assessment of global glaciers’ contribution to SLR (Section
13.4.2). All the available literature suggests that this dynamical contri-
bution to sea level rise will continue well beyond 2100.
13.4.3.3 Possible Irreversibility of Greenland Ice Loss and
Associated Temperature Threshold
A number of model results agree in showing that the Greenland ice
sheet, like other climatic subsystems (Lenton et al., 2008; Levermann et
al., 2012) (see Section 12.5.5), exhibits a strongly nonlinear and poten-
tially irreversible response to surface warming. The mechanism of this
threshold behaviour is the SMB-height feedback (Section 13.4.3.2);
that is, as the surface is lowered due to ice loss, the associated warm-
ing of the near surface increases ablation, leading to further ice loss.
This feedback is small but not negligible in the 21st century (Section
13.4.3.2) and becomes important for projections for the 22nd centu-
ry (Goelzer et al. 2013) and beyond. This nonlinear behaviour may be
accelerated by a reduced surface albedo caused by surface melting
which tends to further decrease the surface mass balance (Box et al.,
2012) (Section 13.4.3.1).
Although the mean SMB of the Greenland ice sheet is positive, in a
steady state it must be balanced by ice outflow, so the ice sheet must
extend to the coast. In a warmer climate, the mean SMB is reduced
(Section 13.4.3.1) and the steady-state ice sheet will have a lower sur-
face and volume. Models show a threshold in surface warming beyond
which self-amplifying feedbacks result in a partial or near-complete
ice loss on Greenland (Greve, 2000; Driesschaert et al., 2007; Charbit
et al., 2008; Ridley et al., 2010; Robinson et al., 2012). If a temperature
above this threshold is maintained over a multi-millennial time period,
the majority of the Greenland ice sheet will be lost by changes in SMB
on a millennial to multi-millennial time scale (equivalent to a sea level
rise of about 7 m; Table 4.1). During the Middle Pliocene warm inter-
vals, when global mean temperature was 2°C to 3.5°C higher than
pre-industrial, ice-sheet models suggest near-complete deglaciation of
Greenland (Hill et al., 2010).
A simplifying assumption is that the threshold is the warming required
with the current ice-sheet topography to reduce the mean SMB to zero,
on the argument that the ice sheet margin must then retreat from the
coast. Using this criterion, Gregory and Huybrechts (2006) estimat-
ed that the SMB threshold occurs for a GMST increase of 3.1 [1.9 to
4.6] °C (4.5 [3.0 to 6.0] °C for Greenland surface temperature) above
pre-industrial (assumed to be a steady state). More recent studies have
found thresholds below or in the lower part of this range. In a coupled
ice sheet–climate model of intermediate complexity, Huybrechts et al.
(2011) found this threshold at 2.5°C for annual average Greenland
SAT. Comparing three regional climate models, Rae et al. (2012) found
a strong dependence of the threshold on the model formulation of the
SMB. Based on the model’s performance against observations and the
1170
Chapter 13 Sea Level Change
13
physical detail of its surface scheme, MAR is considered the most real-
istic model, and yields a threshold value 2.8 [2.1 to 3.4] °C for changes
in Greenland annual average temperature compared to pre-industrial.
Using MAR driven with output from various CMIP5 AOGCMs, Fettweis
et al. (2013) evaluated the threshold as ~3°C in GMST above 1980–
1999 (hence about 3.5°C relative to pre-industrial), and found that it
is not exceeded in the 21st century under the RCP4.5 scenario but is
reached around 2070 under the RCP8.5 scenario.
Some of the uncertainty in the threshold results from the value assumed
for the steady state ice-sheet SMB (see Table 13.2), and whether this
is assumed to be pre-industrial or a more recent period. For 400 Gt yr
–1
(Fettweis et al., 2013), the parametrization of Greenland ice sheet SMB
used for present assessment of 21st century changes (Section 13.4.3.1,
Supplementary Material) gives a global warming threshold of 3.0 [2.1
to 4.1] °C with respect to 18601879 (the reference period used in Box
13.1); for 225 Gt yr
–1
(Gregory and Huybrechts, 2006, following Church
et al., 2001), the threshold is 2.1 [1.5 to 3.0] °C.
Although a negative SMB is a sufficient condition for passing the
threshold, it will overestimate the value of the threshold quantita-
tively, because the SMB–height feedback (even without passing the
threshold) means that the steady-state SMB is reduced by more than
is calculated assuming fixed topography. The actual SMB change will
depend on the dynamical response of the ice sheet that determines its
topography. Constraining simulations with a dynamic ice-sheet model
to changes during the last interglacial, Robinson et al. (2012) estimat-
ed the threshold as 1.6 [0.9 to 2.8] °C global averaged temperature
above pre-industrial. In these simulations, they find that the thresh-
old is passed when southeastern Greenland has a negative SMB. The
near-complete ice loss then occurs through ice flow and SMB.
The complete loss of the ice sheet is not inevitable because it has a
long time scale (tens of millennia near the threshold and a millennium
or more for temperatures a few degrees above the threshold). If the
surrounding temperatures decline before the ice sheet is eliminated,
the ice sheet might regrow. In the context of future GHG emissions, the
time scale of ice loss is competing with the time scale of temperature
decline after a reduction of GHG emissions (Allen et al., 2009; Solomon
et al., 2009; Zickfeld et al., 2009). The outcome therefore depends on
both the CO
2
concentration and on how long it is sustained. Charbit
et al. (2008) found that loss of the ice sheet is inevitable for cumula-
tive emissions above about 3000 GtC, but a partial loss followed by
regrowth occurs for cumulative emissions less than 2500 GtC. Ridley
et al. (2010) identified three steady states of the ice sheet. If the CO
2
concentration is returned to pre-industrial when more than 20 to 40%
of the ice sheet has been lost, it will regrow only to 80% of its original
volume due to a local climate feedback in one region; if 50% or more,
it regrows to 20 to 40% of the original. Similar states with ice volume
around 20%, 60 to 80% and 100% of the initial ice volume are also
found in other models (Langen et al., 2012; Robinson et al., 2012). If
all the ice is lost, temperatures must decline to below a critical thresh-
old for regrowth of the ice sheet (Robinson et al., 2012; Solgaard and
Langen, 2012).
On the evidence of paleo data and modelling (Section 5.6.2.3, 13.2.1),
it is likely that during the LIG, when global mean temperature never
exceeded 2
o
C pre-industrial, the Greenland ice sheet contributed no
more than ~4 m to GMSL. This could indicate that the threshold for
near-complete deglaciation had not been passed, or that it was not
greatly exceeded so that the rate of mass loss was low; however, the
forcing responsible for the LIG warming was orbital rather than from
CO
2
(van de Berg et al., 2011), so it is not a direct analogue and the
applicable threshold may be different. Studies with fixed-topography
ice sheets indicate a threshold of 2°C or above of global warming with
respect to pre-industrial for near-complete loss of the Greenland ice
sheet, while the one study (and therefore low confidence) presently
available with a dynamical ice sheet suggests that the threshold could
be as low as about 1°C (Robinson et al. 2012). Recent studies with
fixed-topography ice sheets indicate that the threshold is less than
about 4°C (medium confidence because of multiple studies). With cur-
rently available information, we do not have sufficient confidence to
assign a likely range for the threshold. If the threshold is exceeded
temporarily, an irreversible loss of part or most of the Greenland ice
sheet could result, depending on the duration and amount that the
threshold is exceeded.
13.4.4 Antarctic Ice Sheet
13.4.4.1 Surface Mass Balance Change
Because the ice loss from Antarctica due to surface melt and runoff is
about 1% of the total mass gain from snowfall, most ice loss occurs
through solid ice discharge into the ocean. In the 21st century, ablation
is projected to remain small on the Antarctic ice sheet because low
surface temperatures inhibit surface melting, except near the coast and
on the Antarctic Peninsula, and meltwater and rain continue to freeze
in the snowpack (Ligtenberg et al., 2013). Projections of Antarctic SMB
changes over the 21st century thus indicate a negative contribution
to sea level because of the projected widespread increase in snowfall
associated with warming air temperatures (Krinner et al., 2007; Uotila
et al., 2007; Bracegirdle et al., 2008). Several studies (Krinner et al.,
2007; Uotila et al., 2007; Bengtsson et al., 2011) have shown that the
precipitation increase is directly linked to atmospheric warming via the
increased moisture holding capacity of warmer air, and is therefore
larger for scenarios of greater warming. The relationship is exponential,
resulting in an increase of SMB as a function of Antarctic SAT change
evaluated in various recent studies with high-resolution (~60 km)
models as 3.7% °C
–1
(Bengtsson et al., 2011), 4.8% °C
–1
(Ligtenberg
et al., 2013) and ~7% °C
–1
(Krinner et al., 2007). These agree well with
the sensitivity of 5.1 ± 1.5% °C
–1
(one standard deviation) of CMIP3
AOGCMs (Gregory and Huybrechts, 2006).
The effect of atmospheric circulation changes on continental-mean
SMB is an order of magnitude smaller than the effect of warming, but
circulation changes can have a large influence on regional changes
in accumulation, particularly near the ice-sheet margins (Uotila et al.,
2007) where increased accumulation may induce additional ice flow
across the grounding line (Huybrechts and De Wolde, 1999; Gregory
and Huybrechts, 2006; Winkelmann et al., 2012). Simulated SMB is
strongly and nonlinearly influenced by ocean surface temperature and
sea-ice conditions (Swingedouw et al., 2008). This dependence means
that the biases in the model-control climate may distort the SMB sen-
sitivity to climate change, suggesting that more accurate predictions
1171
Sea Level Change Chapter 13
13
may be obtained from regional models by using boundary conditions
constructed by combining observed present-day climate with projected
climate change (Krinner et al., 2008). There is a tendency for higher
resolution models to simulate a stronger future precipitation increase
because of better representation of coastal and orographic precipita-
tion processes (Genthon et al., 2009).
For the present assessment of GMSL rise, changes in Antarctic ice-sheet
SMB up to 2100 have been computed from global mean SAT change
projections derived from the CMIP5 AOGCMs, using the range of sensi-
tivities of precipitation increase to atmospheric warming summarized
above, and the ratio of Antarctic to global warming evaluated from
CMIP3 AOGCMs by Gregory and Huybrechts (2006) (see also Section
13.5.1 and Supplementary Material). The results are shown in Table
13.5 and Figures 13.10 and 13.11. The projected change in ice outflow
is affected by the SMB because of the influence of topography on ice
dynamics (Section 13.4.4.2 and Supplementary Material). Ozone recov-
ery, through its influence on atmospheric circulation at high southern
latitudes (Section 10.3.3.3), may offset some effects of GHG increase
in the 21st century, but Antarctic precipitation is nonetheless projected
to increase (Polvani et al., 2011). Bintanja et al. (2013) suggested that
Antarctic warming and precipitation increase may be suppressed in
the future by expansion of Antarctic sea ice, promoted by freshening
of the surface ocean, caused by basal melting of ice shelves, and they
conducted an AOGCM sensitivity test of this hypothesis. We consider
these possibilities in Section 13.5.3.
Beyond the year 2100, regional climate simulations run at high spatial
resolution (5 to 55 km) but without climate-ice sheet feedbacks includ-
ed show a net ice gain until the year 2200 (Ligtenberg et al., 2013).
During 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 scenario and 0.46 mm yr
–1
for the E1 scenario.
For multi-centennial to multi-millennial projections, feedbacks between
the ice sheet and regional climate need to be accounted for. This is cur-
rently done using ice-sheet models coupled to climate models of inter-
mediate complexity, which have a significantly lower spatial resolution
in the atmospheric component than regional climate models used to
assess future SMB within the 21st century. These coarser resolution
models capture the increase in snowfall under future warming, but
the regional distribution is represented less accurately. Accordingly,
there is low confidence in their ability to model spatial melting and
accumulation patterns accurately. In contrast, medium confidence can
be assigned to the models’ projection of total accumulation on Ant-
arctica, as it is controlled by the large-scale moisture transport toward
the continent.
In idealized scenarios of 1% increase of CO
2
yr
–1
up to 560 ppm with
subsequent stabilization, Vizcaíno et al. (2010) and Huybrechts et al.
(2011) found an initial increase of ice volume due to additional snow-
fall during the first 600 years of integration. In both models, the chang-
es in SMB dominate the mass changes during and beyond the first
100 years. After 600 years of integration, Vizcaíno et al. (2010) found a
mass gain corresponding to a sea level fall of 0.15 m (–0.25 mm yr
–1
on
average). For the same experiment and the same period, Huybrechts et
al. (2011) found a sea level fall of 0.08 m (–0.13 mm yr
–1
on average).
In a similar experiment but allowing GHG concentrations to reach
1120 ppm CO
2
before being stabilized, both models show a net posi-
tive sea level contribution after 600 years of integration. Huybrechts et
al. (2011) found a weak sea level contribution during the first 500 years
of integration followed by a stronger and relatively constant long-term
average rate of ~2 mm yr
–1
after 1000 years of integration up to a total
contribution of ~4 m SLE after 3000 years of integration. Although
they found some grounding line retreat due to basal ice-shelf melt, the
multi-centennial evolution of the ice sheet is dominated by changes in
SMB whereas the solid-ice discharge after an initial increase shows a
significant decrease during the scenario.
For the same scenario, Vizcaíno et al. (2010) found that the initial mass
gain is followed by a weak mass loss. After 250 years of integration,
the contribution is stronger and relatively constant at a rate of about 3
mm yr
–1
, corresponding to a net contribution of 1.2 m to global mean
sea level rise after 600 years.
The same model as in Huybrechts et al. (2011), although with a slightly
stronger polar amplification, was applied to the three SRES scenarios
used in the AR4 (B1, A1B, A2) with stabilization in the year 2100 (Goel-
zer et al., 2012). For the lowest scenario (B1), they found practically
no net Antarctic contribution to sea level in the year 3000. Under the
medium scenario (A1B), the ice sheet contributes 0.26 m, and under
the highest scenario (A2), it contributes 0.94 m SLE in the year 3000.
These simulations include a negative feedback on the regional climate
by ice-sheet melt through which summer temperatures can be signif-
icantly reduced over Antarctica (Swingedouw et al., 2008). However,
due to the coarse resolution and the high polar amplification, there
is low confidence in the model’s representation of oceanic circulation
changes around Antarctica.
In both models (Vizcaíno et al., 2010; Huybrechts et al., 2011), the ice
sheets are not equilibrated with the surrounding climate after the inte-
gration period under the 1120 ppm CO
2
forcing. Though GHG concen-
trations were stabilized after 120 years of integration, the Antarctic ice
sheet continues to contribute to sea level rise at a relatively constant
rate for another 480 years in Vizcaíno et al. (2010) and 2880 years in
Huybrechts et al. (2011). This is consistent with a positive sea level
contribution from Antarctica during past warmer climates (Sections
13.2.1 and 13.5.4).
In summary, both coupled ice sheet-climate models consistently show
that for high-emission scenarios, the surface melt increases and leads
to an ice loss on multi-centennial time scales. The long time period
over which the Antarctic ice sheet continues to lose mass indicates
a potential role of the feedback between climate and ice sheet. Con-
sistent with regional climate models for the 21st and 22nd centuries,
both coarse-resolution coupled models show a positive SMB change
for most of the first 100 years of climate change. Due to the inertia in
the climate system, regional temperatures continue to rise after that.
Together with enhanced solid ice discharge, this results in mass loss of
the ice sheet. The corresponding decline in surface elevation increases
the surface temperature and leads to additional ice loss.
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Chapter 13 Sea Level Change
13
13.4.4.2 Dynamical Change
The Antarctic ice sheet represents the largest potential source of future
SLR; the West Antarctic ice sheet alone has the potential to raise sea
level by ~4.3 m (Fretwell et al., 2013). The rate at which this reser-
voir will be depleted and cause sea level to rise, however, is not easily
quantifiable. In this section, we focus on dynamical changes (i.e., those
related to the flow of the ice sheet) that affect SLR by altering the flux
of ice across the grounding line (or outflow) that separates ice resting
on bedrock (some of which does not currently displace ocean water)
from floating ice shelves (which already displace ocean water and have
only a negligible effect on sea level) (Jenkins and Holland, 2007).
Issues associated with the inability of models to reproduce recently
observed changes in the dynamics of the Antarctic ice sheet prevented
the AR4 from quantifying the effect of these changes on future sea
level. Since the AR4, progress has been made in understanding the
observations (Sections 4.4.3 and 4.4.4), and projections are becoming
available. It must be stressed, however, that this field has yet to reach
the same level of development that exists for modelling many other
components of the Earth system. There is an underlying concern that
observations presage the onset of large-scale grounding line retreat in
what is termed the Marine Ice Sheet Instability (MISI; Box 13.2), and
much of the research assessed here attempts to understand the appli-
cability of this theoretical concept to projected SLR from Antarctica.
There are three distinct processes that could link climate change to
dynamical change of the Antarctic ice sheet and potentially trigger
increased outflow. These may operate directly through the increased
potential for melt ponds to form on the upper surface of ice shelves,
which may destabilize them, or by increases in submarine melt expe-
rienced by ice shelves as a consequence of oceanic warming, which
leads to their thinning, as well as indirectly by coupling between SMB
and ice flow. Section 4.4.3.2 presents the observational basis on which
understanding of these processes is based, while their potential future
importance is assessed here. Literature on the two mechanisms directly
linked to climate change will be assessed first, followed by their rela-
tion to outflow change and lastly SMB coupling.
There is strong evidence that regional warming and increased melt
water ponding in the Antarctic Peninsula led to the collapse of ice
shelves along the length of peninsula (Cook and Vaughan, 2010), most
notably the Larsen B ice shelf (MacAyeal et al., 2003). Substantial local
warming (~5 to 7 °C) would, however, be required before the main
Antarctic ice shelves (the Ross and Filchner-Ronne ice shelves) would
become threatened (Joughin and Alley, 2011). An assessment of the
AR4 AOGCM ensemble under scenario A1B yielded an Antarctic con-
tinental-average warming rate of 0.034 ± 0.01°C yr
–1
(Bracegirdle et
al., 2008), suggesting that the required level of warming may not be
approached by the end of the 21st century. Using an intermediate com-
plexity model with scenario A2, Fyke et al. (2010) found that melt starts
to reach significant levels over these ice shelves around 2100 to 2300.
Barrand et al. (2013) made a process-based assessment of the effect
of ice-shelf collapse on outflow from the Antarctic Peninsula, which
yields a range of SLR at 2100 between 10 and 20 mm, with a bounding
maximum of 40 mm.
There is good evidence linking the focus of current Antarctic mass loss
in the Amundsen Sea sector of the WAIS (containing Pine Island and
Thwaites Glaciers) (Shepherd and Wingham, 2007; Rignot et al., 2008;
Pritchard et al., 2009) to the presence of relatively warm Circumpolar
Deep Water on the continental shelf (Thoma et al., 2008; Jenkins et al.,
2010). However, it is not possible to determine whether this upwelling
was related directly or indirectly to a rise in global mean temperature.
Yin et al. (2011) assessed output from 19 AOGCMs under scenario A1B
to determine how subsurface temperatures are projected to evolve
around the West and East Antarctic ice sheets. They showed decad-
al-mean warming of 0.4°C to 0.7°C and 0.4°C to 0.9°C around West
and East Antarctica, respectively (25th to 75th percentiles of ensemble)
by the end of the 21st century. More detailed regional modelling using
scenario A1B illustrates the potential for warm water to invade the
ocean cavity underlying the Filchner-Ronne ice shelf in the second half
of the 21st century, with an associated 20-fold increase in melt (Hellmer
et al., 2012). Based on the limited literature, there is medium confi-
dence that oceanic processes may potentially trigger further dynamical
change particularly in the latter part of the 21st century, while there
is also medium confidence that atmospheric change will not affect
dynamics outside of the Antarctic Peninsula during the 21st century.
Several process-based projections of the future evolution of Pine Island
Glacier have now been made, and some of the issues that this mod-
elling faced (such as the need for sub-kilometre resolution to ensure
consistent results; Cornford et al. (2013), Durand et al. (2009)) are
being resolved (Pattyn et al., 2013). In experiments using an idealized
increase in marine melt, Joughin et al. (2010) demonstrated only lim-
ited (~25 km) retreat of the grounding line before a new equilibrium
position was established. Gladstone et al. (2012) used a flowline model
forced with ocean-model output (Hellmer et al., 2012) to identify two
modes of retreat: one similar to that identified by Joughin et al. (2010),
and a second characterized by complete collapse from 2150 onwards.
More sophisticated ice-flow modelling (albeit with idealized forcing)
suggests grounding line retreat of ~100 km in 50 years (Cornford et al.,
2013). These studies support the theoretical finding of Gudmundsson
et al. (2012) that grounding line retreat, if triggered, does not inev-
itably lead to MISI but may halt if local buttressing from ice rises or
channel sidewalls is sufficient. Parizek et al. (2013) used a flowline
model to study Thwaites Glacier and found that grounding line retreat
is possible only under extreme ocean forcing. It is also thought that
considerably less back pressure is exerted by Thwaites’ ice shelf in
comparison to Pine Island’s (Rignot, 2001; 2008), which may make it
less sensitive to forcing by submarine melt (Schoof, 2007a; Goldberg
et al., 2012). Based on this literature, there is high confidence that
the retreat of Pine Island Glacier (if it occurs) can be characterized by
a SLR measured in centimetres by 2100, although there is low con-
fidence in the models’ ability to determine the probability or timing
of any such retreat. There is also medium confidence (in the light of
the limited literature) that Thwaites Glacier is probably less prone to
undergo ocean-driven grounding line retreat than its neighbour in the
21st century. No process-based modelling is available on which to be
base projections of EAIS glaciers currently losing mass, such as Totten
and Cook Glaciers.
Bindschadler et al. (2013) reported a model inter-comparison exercise
on the impact of climate change under RCP8.5 on the Antarctic ice
1173
Sea Level Change Chapter 13
13
sheet. The resultant projection includes contributions from increased
marine melt in the Amundsen Sea and Amery sectors, and generat-
ed a mean SLR at 2100 of ~100 mm over four models (with overall
SLR of 81 mm when SMB change was included). There is, however,
low confidence in the projection because of the unproven ability of
many of the contributing models to simulate grounding line motion.
Bindschadler et al. (2013) also reported idealized experiments in which
ice-shelf melt is increased by 2, 20 and 200 m yr
–1
. The resulting five-
model mean SLR of 69, 693 and 3477 mm by 2100, respectively, can be
considered only as a general indication because of the shortcomings
of the contributing models (e.g., two do not include ice shelves) which
may be offset by the use of a multi-model mean. Although grounding
line migration in the 20 m yr
–1
experiment is extensive in some models
and consistent with what might be expected under MISI (Bindschadler
et al., 2013), the 200 m yr
–1
experiment is unrealistic, even if used as a
proxy for the improbable atmosphere-driven collapse of the major ice
shelves, and is not considered further.
We now assess two alternatives to process-based modelling that
exist in the literature: the development of plausible high-end projec-
tions based on physical intuition (Pfeffer et al., 2008; Katsman et al.,
2011) and the use of a probabilistic framework for extrapolating cur-
rent observations of the ice sheet’s mass budget (Little et al., 2013a;
2013b). Pfeffer et al. (2008) postulated a possible but extreme scenario
of 615 mm SLR based on vastly accelerated outflow in the Amund-
sen Sea sector and East Antarctica, whereas a more plausible scenario
involving reduced acceleration in the Amundsen Sea sector yields 136
mm. Katsman et al. (2011) used similar assumptions in a ‘modest’ sce-
nario that generates SLR of 70 to 150 mm, and a ‘severe’ scenario
that attempts to capture the consequences of the collapse of the WAIS
through the MISI and has a SLR contribution of 490 mm. The NRC
(2012) extrapolated mass-budget observations of the ice sheet to gen-
erate a projection of 157 to 323 mm (including future SMB change),
with an additional 77 to 462 mm accounting for 21st-century increases
in outflow (summing as 234 to 785 mm).
Little et al. (2013a) applied a range of linear growth rates to pres-
ent-day SMB and outflow observations of Antarctic sectors (Rignot et
al., 2008; Shuman et al., 2011; Zwally and Giovinetto, 2011), which
are then weighted using a continental-scale observational synthesis
(Shepherd et al., 2012) (consistent with the assessment of Chapter 4).
In the case of Pine Island Glacier, growth rates are based on the pro-
cess-based modelling of Joughin et al. (2010). Within this framework,
SLR at 2100 has a 5 to 95% range of –20 to 185 mm for dynamical
change only, and –86 to 133 mm when SMB change is included (based
on Uotila et al. (2007)). Projections for the Antarctic Peninsula are
consistent with the process-based modelling of Barrand et al. (2013).
Further, Little et al. (2013a) found that the upper (95%) limit of the
projected range can only approach 400 mm under scenarios expected
for MISI (such as the immediate collapse of Pine Island and Thwaites
Glaciers or all marine-based sectors experiencing the same rates of
mass loss as Pine Island Glacier).
Our assessment of the likely range of SLR is based on the weighted
5-95% range (-20 to 185 mm) of Little et al. (2013), which is consist-
ent with the lower scenarios of Katsman et al. (2011) (70 to 150 mm)
and Pfeffer et al. (2008) (136 mm), and with the RCP8.5 projection
and low-melt experiment of Bindschadler et al. (2013) (~100 and 69
mm, respectively). The base projection of the NRC (2012) (157 to 323
mm including future SMB change), however, is less compatible. This
moderate level of consistency across a range of techniques suggests
medium confidence in this assessment. We assess this as the likely (as
opposed to very likely) range because it is based primarily on pertur-
bations of the ice sheet’s present-day state of mass imbalance and
does not include the potentially large increases in outflow that may be
associated with the MISI discussed below.
The probability of extensive grounding line retreat being both triggered
and continuing to the extent that it contributes to significant SLR in the
21st century is very poorly constrained, as the results of a recent expert
elicitation indicate (Bamber and Aspinall, 2013). We have medium con-
fidence, however, that this probability lies outside of the likely range of
SLR. Five arguments support this assessment. First, the partial loss of
Pine Island Glacier is included by Little et al. (2013a) in their range and
the full loss of the ice stream (if it were to occur) is thought to raise
sea level by centimetres only (consistent with the use of the Little et
al. (2013a) 5 to 95% range as the assessed likely range). Second, the
current grounding line position of the neighbouring Thwaites Glacier
appears to be more stable than that of Pine Island Glacier (Parizek
et al., 2013) so that its potentially large contribution to SLR by 2100
is assessed to have a significantly lower probability. Third, there is a
low probability that atmospheric warming in the 21st century will lead
to extensive grounding line retreat outside of the Antarctic Peninsula
because summer air temperatures will not rise to the level where sig-
nificant surface melt and ponding occur. Fourth, although this retreat
may be triggered by oceanic warming during the 21st century (in par-
ticular, under the Filchner-Ronne ice shelf), current literature suggests
that this may occur late in the century (Hellmer et al., 2012), reducing
the time over which enhanced outflow could affect end-of-century SLR.
Finally, there are theoretical grounds to believe that grounding line
retreat may stabilize (Gudmundsson et al., 2012) so that MISI (and
associated high SLR) is not inevitable.
We next assess the magnitude of potential SLR at 2100 in the event that
MISI affects the Antarctic ice sheet. Bindschadler et al. (2013), Katsman
et al. (2011), the NRC (2012), and Pfeffer et al. (2008) presented con-
trasting approaches that can be used to make this assessment, which
are upper-end estimates of 693, 490, 785 and 615 mm, respectively.
Together this literature suggests with medium confidence that this con-
tribution would be several tenths of a metre. The literature does not
offer a means of assessing the probability of this contribution, however,
other than our assessment (above) that it lies above the likely range.
Literature investigating the relation between the SLR generated by
dynamical change and emission scenario does not currently exist. There
is also a lack of literature on the relation between emission scenario
and the intrusions of warm water into ice-shelf cavities thought to be
important in triggering observed mass loss (Jacobs et al., 2011) and
potentially important in the future (Hellmer et al., 2012). It is therefore
premature to attach a scenario dependence to projections of dynami-
cal change, even though such a dependency is expected to exist.
Likely increases in snowfall over the next century (Section 13.4.4.1)
will affect the amount of ice lost by outflow across the grounding
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Chapter 13 Sea Level Change
13
line because of local changes in ice thickness and stress regime (Huy-
brechts and De Wolde, 1999). This effect was incorporated in AR4 pro-
jections for 2100 as a compensatory mass loss amounting to 0 to 10%
of the SMB mass gain (Gregory and Huybrechts, 2006). Winkelmann
et al. (2012) re-evaluated the effect and reported a range of 15 to
35% for the next century (30 to 65% after 500 years). The two studies
are difficult to compare because of differences in model physics and
experimental design so that the use of their joint range (0 to 35%) is
an appropriate assessment of the likely range of this effect. This range
is supported by Barrand et al. (2013), who quantified the effect for the
Antarctic Peninsula as ~15% of SMB. Moreover, because this contribu-
tion relies on similar physics to the grounding line migration discussed
above, it is appropriate to assume that their uncertainties are corre-
lated. Winkelmann et al. (2012) showed that the fractional size of this
compensatory effect is independent of scenario. Accounting for this
effect equates to SLR of 0 to 32 mm by 2100 based on the SMB range
over all emission-scenario projections in Section 13.5.1.1.
Beyond the 21st century, only projections with coarse-resolution ice
sheet–climate models are available (Vizcaíno et al., 2010; Huybrechts
et al., 2011). Confidence in the ability of these two models to cap-
ture both change in the oceanic circulation around Antarctica and
the response of the ice sheet to these changes, especially a poten-
tial grounding line retreat, is low. The model applied by Vizcaíno et al.
(2010) lacks a dynamic representation of ice shelves. Because dynam-
ic ice discharge from Antarctica occurs predominately through ice
shelves, it is likely that the projections using this model considerably
underestimate the Antarctic contribution.
In summary, it is likely that dynamical change within the Antarctic ice
sheet will lead to SLR during the next century with a range of –20
to 185 mm. SLR beyond the likely range is poorly constrained and
considerably larger increases are possible (the underlying probability
distribution is asymmetric towards larger rise), which will probably be
associated with the MISI (Box 13.2). Although the likelihood of such
SLR cannot yet be assessed more precisely than falling above the likely
range, literature suggests (with medium confidence) that its potential
magnitude is several tenths of a metre. We are unable to assess SLR
as a function of emission scenario, although a dependency of SLR on
scenario is expected to exist. In addition, coupling between SMB and
dynamical change is likely to make a further contribution to SLR of 0 to
35% of the SMB. All the available literature suggests that this dynami-
cal contribution to sea level rise will continue well beyond 2100.
13.4.4.3 Possible Irreversibility of Ice Loss from West Antarctica
Due to relatively weak snowfall on Antarctica and the slow ice motion
in its interior, it can be expected that the WAIS would take at least sev-
eral thousand years to regrow if it was eliminated by dynamic ice dis-
charge. Consequently any significant ice loss from West Antarctic that
occurs within the next century will be irreversible on a multi-centen-
nial to millennial time scale. We discuss here the possibility of abrupt
dynamic ice loss from West Antarctica (see Section 12.5.5 for definition
of abrupt).
Information on the ice and bed topography of WAIS suggests that it
has about 3.3 m of equivalent global sea level grounded on areas with
downward sloping bedrock (Bamber et al., 2009). As detailed in Box
13.2, large areas of the WAIS may therefore be subject to potential
ice loss via the MISI. As it is the case for other potential instabilities
within the climate system (Section 12.5.5), there are four lines of evi-
dence to assess the likelihood of a potential occurrence: theoretical
understanding, present-day observations, numerical simulations, and
paleo records.
The MISI is based on a number of studies that indicated the theoreti-
cal existence of the instability (Weertman, 1961; Schoof, 2007a) (see
also Box 13.2). The most fundamental derivation, that is, starting from
a first-principle ice equation, states that in one-dimensional ice flow
the grounding line between grounded ice sheet and floating ice shelf
cannot be stable on a landward sloping bed. The limitation of the
one-dimensional case disregards possible stabilizing effects of the ice
shelves (Dupont and Alley, 2005). Although it is clear that ice shelves
that are laterally constrained by embayments inhibit ice flow into the
ocean, the effect has not been quantified against the MISI. The same
is true for other potentially stabilizing effects such as sedimentation
near the grounding line (Alley et al., 2007) or the influence of large-
scale bedrock roughness (i.e., topographic pinning points) on ice flow.
Although these stabilizing effects need to be further investigated and
quantified against the destabilizing effect of the MISI, no studies are
available that would allow dismissing the MISI on theoretical grounds.
Although direct observations of ice dynamics are available, they are
neither detailed enough nor cover a sufficiently long period to allow
the monitoring of the temporal evolution of an MISI. Most Antarctic
ice loss that has been detected during the satellite period has come
from the WAIS (Rignot et al., 2008; Pritchard et al., 2012). Some studies
have found an acceleration of ice loss (Rignot et al., 2011) as well as
enhanced basal ice-shelf melting (Pritchard et al., 2012), but the short
period of observations does not allow one to either dismiss or confirm
that these changes are associated with destabilization of WAIS.
Paleo records suggest that WAIS may have deglaciated several times
during warm periods of the last 5 million years, but they contain no
information on rates (Naish et al., 2009). Although coarse-resolution
models are in principle capable of modelling the MISI, there is medium
confidence in their ability to simulate the correct response time to
external perturbations on decadal to centennial time scales (Pattyn et
al., 2013). One of these models (Pollard and DeConto, 2009) repro-
duced paleo records of deglaciation with a forced ice-sheet model at
40 km resolution and parameterized ice flow across the grounding line
according to Schoof (2007a). These simulations showed a sea level rise
of about 7 m over time spans of 1000 to 7000 years with approxi-
mately equal contributions from West and East Antarctica. However, no
available model results or paleo records have indicated the possibility
of self-accelerated ice discharge from these regions.
In summary, ice-dynamics theory, numerical simulations, and paleo
records indicate that the existence of a marine-ice sheet instability asso-
ciated with abrupt and irreversible ice loss from the Antarctic ice sheet
is possible in response to climate forcing. However, theoretical consid-
erations, current observations, numerical models, and paleo records cur-
rently do not allow a quantification of the timing of the onset of such an
instability or of the magnitude of its multi-century contribution.
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Sea Level Change Chapter 13
13
Box 13.2 | History of the Marine Ice-Sheet Instability Hypothesis
Marine ice sheets rest on bedrock that is submerged below sea level (often by 2 to 3 km). The most well-researched marine ice sheet
is the West Antarctic ice sheet (WAIS) where approximately 75% of the ice sheet’s area currently rests on bedrock below sea level. The
East Antarctic ice sheet (EAIS), however, also has appreciable areas grounded below sea level (~35%), in particular around the Totten
and Cook Glaciers.
These ice sheets are fringed by floating ice shelves, which are fed by flow from grounded ice across a grounding line (GL). The GL is
free to migrate both seawards and landwards as a consequence of the local balance between the weight of ice and displaced ocean
water. Depending on a number of factors, which include ice-shelf extent and geometry, ice outflow to the ocean generally (but not
always) increases with ice thickness at the GL. Accordingly, when the ice sheet rests on a bed that deepens towards the ice-sheet
interior (see Box 13.2, Figure 1a), the ice outflow to the ocean will generally increase as the GL retreats. It is this feature that gives
rise to the Marine Ice-Sheet Instability (MISI), which states that a GL cannot remain stable on a landward-deepening slope. Even if
snow accumulation and outflow were initially in balance (Box 13.2, Figure 1b), natural fluctuations in climate cause the GL to fluctuate
slightly (Box 13.2, Figure 1c). In the case of a retreat, the new GL position is then associated with deeper bedrock and thicker ice, so
that outflow increases (Box 13.2, Figure 1d). This increased outflow leads to further, self-sustaining retreat until a region of shallower,
seaward-sloping bedrock is reached. Stable configurations can therefore exist only where the GL rests on slopes that deepen towards
the ocean. A change in climate can therefore potentially force a large-scale retreat of the GL from one bedrock ridge to another further
inland. (continued on next page)
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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Chapter 13 Sea Level Change
13
13.4.5 Anthropogenic Intervention in Water Storage
on Land
The potential future effects that human activities have on changing
water storage on land, thus affecting sea level, have been little stud-
ied in the published peer-reviewed scientific literature. For depletion of
groundwater arising from extraction (for agriculture and other uses),
we consider two possibilities. The first assumes that this contribution to
GMSL rise continues throughout the 21st century at the rate of 0.40 ±
0.11 mm yr
–1
(mean ± SD) assessed for 2001–2008 by Konikow (2011),
amounting to 38 [21 to 55] mm by 2081–2100 relative to 1986–2005.
The second uses results from land surface hydrology models (Wada et
al., 2012) with input from climate and socioeconomic projections for
SRES scenarios, yielding 70 [51 to 90] mm for the same time interval.
Because of the improved treatment of groundwater recharge by Wada
et al. (2012), this is less than Rahmstorf et al. (2012b) obtained by
Box 13.2 (continued)
The MISI has a long history based on theoretical discussions that were started by Weertman (1974) and Mercer (1978), and has seen
many refinements over the subsequent years. The advent of satellite-based observations has given fresh impetus to this debate, in
particular work on the GL retreat and associated thinning of Pine Island (PIG), Thwaites (TG) and Smith Glaciers (all part of the WAIS),
which are collectively responsible for most of Antarctica’s present mass loss (Rignot et al., 2008). These observations highlighted the
need to develop a better understanding of the MISI to make more accurate projections of the ice sheet’s future contribution to sea
level rise.
Early studies of the MISI were not based on a formal derivation from the basic laws of mechanics thought to control ice-sheet flow
and the robustness of their results was therefore difficult to assess. An open question was the expected impact of changes at the GL
on the ice-sheet flow (Hindmarsh, 1993). Recently, however, a more complete analysis from first principles has been developed that
suggests that the fundamental relation between thickness and flux at the GL exists and has a power of ~5 (i.e., that a 10% increase
in thickness leads to a 60% increase in flux) (Schoof, 2007b, 2011). This analysis, however, does not include ice shelves that occupy
laterally constrained embayments, which is often the case (for instance at PIG). In such situations, drag from ice-shelf sidewalls may
suppress the positive feedback between increasing ice thickness and ice flux at the GL (Dupont and Alley, 2005; Goldberg et al., 2009;
Gudmundsson et al., 2012). Other factors that could suppress the instability include a sea level fall adjacent to the GL resulting from
the isostatic and gravitational effects of ice loss (Gomez et al., 2010b).
Two processes that could trigger GL retreat are particularly relevant to contemporary polar climate change. The first is the presence of
warmer ocean water under ice shelves, which leads to enhanced submarine ice-shelf melt (Jacobs et al., 2011). The second is the pres-
ence of melt water ponds on the surface of the ice shelf, which can cause stress concentrations allowing fractures to penetrate the full
ice-shelf thickness. This process appears to have been a primary factor in the collapse of the Larsen B Ice Shelf (LBIS) over the course
of two months in 2002 (MacAyeal et al., 2003). The collapse of the LBIS provided a natural demonstration of the linkage between the
structural integrity of an ice shelf and the flow of grounded ice draining into it. Following the breakup of LBIS, the speeds of the glaciers
feeding the collapsed portion of the shelf increased two- to eightfold, while the flow of glaciers draining into a surviving sector was
unaltered (Rignot et al., 2004; Scambos et al., 2004; Rott et al., 2011). This indicates that a mechanical link does indeed exist between
shelf and sheet, and has important implications for the future evolution of the far more significant PIG and TG systems of the WAIS.
The recent strides made in placing MISI on a sound analytical footing are, however, limited to the analysis of steady states. Numerical
modelling is needed to simulate the GL retreat rates that are required to make accurate SLR projections. There are major challenges in
designing models whose results are not controlled by the details of their numerical design. Problems arise at the GL because, in addi-
tion to flotation, basal traction is dramatically reduced as the ice loses contact with the underlying bedrock (Pattyn et al., 2006). This
is a topic of active research, and a combination of more complete modelling of the GL stress regime (Favier et al., 2012) and the use
of high-resolution (subkilometre) models (Durand et al., 2009; Cornford et al., 2013) shows promise towards resolving these problems.
Much progress has also been made by using model inter-comparison as a means of understanding these effects (Pattyn et al., 2013).
assuming that the groundwater extraction estimates of Wada et al.
(2010) can be scaled up in the future with global population. These two
possibilities indicate a range of about 20 to 90 mm for the contribution
of groundwater depletion to GMSL rise.
For the rate of impoundment of water in reservoirs, we evaluate two
possibilities. The first assumes it will continue throughout the 21st cen-
tury (e.g., Lempérière, 2006) at the average rate of –0.2 ± 0.05 mm yr
–1
SLE (mean ± SD) estimated for 1971–2010 using data updated from
Chao et al. (2008), giving a negative contribution to GMSL rise of –19
[–11 to –27] mm by 2081–2100 relative to 1986–2005. The second
assumes it will be zero after 2010 (i.e., no further net impoundment),
as shown for the 1990s and 2000s by Lettenmaier and Milly (2009)
(see Section 13.3.4 for discussion). A zero contribution implies a bal-
ance between further construction of reservoir capacity and reduction
of storage volume by sedimentation, each of which could plausibly
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Sea Level Change Chapter 13
13
Frequently Asked Questions
FAQ 13.2: Will the Greenland and Antarctic Ice Sheets Contribute to Sea Level Change over
the Rest of the Century?
The Greenland, West and East Antarctic ice sheets are the largest reservoirs of freshwater on the planet. As such,
they have contributed to sea level change over geological and recent times. They gain mass through accumulation
(snowfall) and lose it by surface ablation (mostly ice melt) and outflow at their marine boundaries, either to a float-
ing ice shelf, or directly to the ocean through iceberg calving. Increases in accumulation cause global mean sea level
to fall, while increases in surface ablation and outflow cause it to rise. Fluctuations in these mass fluxes depend
on a range of processes, both within the ice sheet and without, in the atmosphere and oceans. Over the course of
this century, however, sources of mass loss appear set to exceed sources of mass gain, so that a continuing positive
contribution to global sea level can be expected. This FAQ summarizes current research on the topic and provides
indicative magnitudes for the various end-of-century (2081-2100 with respect to 1986-2005) sea level contributions
from the full assessment, which are reported as the two-in-three probability level across all emission scenarios.
Over millennia, the slow horizontal flow of an ice sheet carries mass from areas of net accumulation (generally, in
the high-elevation interior) to areas of net loss (generally, the low-elevation periphery and the coastal perimeter).
At present, Greenland loses roughly half of its accumulated ice by surface ablation, and half by calving. Antarctica,
on the other hand, loses virtually all its accumulation by calving and submarine melt from its fringing ice shelves. Ice
shelves are floating, so their loss has only a negligible direct effect on sea level, although they can affect sea level
indirectly by altering the mass budget of their parent ice sheet (see below).
In East Antarctica, some studies using satellite radar altimetry suggest that snowfall has increased, but recent
atmospheric modelling and satellite measurements of changes in gravity find no significant increase. This apparent
disagreement may be because relatively small long-term trends are masked by the strong interannual variability
of snowfall. Projections suggest a substantial increase in 21st century Antarctic snowfall, mainly because a warmer
atmosphere would be able to carry more moisture into polar regions. Regional changes in atmospheric circulation
probably play a secondary role. For the whole of the Antarctic ice sheet, this process is projected to contribute
between 0 and 70 mm to sea level fall.
Currently, air temperatures around Antarctica are too cold for substantial surface ablation. Field and satellite-based
observations, however, indicate enhanced outflow—manifested as ice-surface lowering—in a few localized coastal
regions. These areas (Pine Island and Thwaites Glaciers in West Antarctica, and Totten and Cook Glaciers in East
Antarctica) all lie within kilometre-deep bedrock troughs towards the edge of Antarctica’s continental shelf. The
increase in outflow is thought to have been triggered by regional changes in ocean circulation, bringing warmer
water in contact with floating ice shelves.
On the more northerly Antarctic Peninsula, there is a well-documented record of ice-shelf collapse, which appears
to be related to the increased surface melting caused by atmospheric warming over recent decades. The subsequent
thinning of glaciers draining into these ice shelves has had a positive—but minor—effect on sea level, as will any
further such events on the Peninsula. Regional projections of 21st century atmospheric temperature change suggest
that this process will probably not affect the stability of the large ice shelves of both the West and East Antarctica,
although these ice shelves may be threatened by future oceanic change (see below).
Estimates of the contribution of the Antarctic ice sheets to sea level over the last few decades vary widely, but
great strides have recently been made in reconciling the observations. There are strong indications that enhanced
outflow (primarily in West Antarctica) currently outweighs any increase in snow accumulation (mainly in East Ant-
arctica), implying a tendency towards sea level rise. Before reliable projections of outflow over the 21st century can
be made with greater confidence, models that simulate ice flow need to be improved, especially of any changes in
the grounding line that separates floating ice from that resting on bedrock and of interactions between ice shelves
and the ocean. The concept of ‘marine ice-sheet instability’ is based on the idea that the outflow from an ice sheet
resting on bedrock below sea level increases if ice at the grounding line is thicker and, therefore, faster flowing.
On bedrock that slopes downward towards the ice-sheet interior, this creates a vicious cycle of increased outflow,
causing ice at the grounding line to thin and go afloat. The grounding line then retreats down slope into thicker
ice that, in turn, drives further increases in outflow. This feedback could potentially result in the rapid loss of parts
of the ice sheet, as grounding lines retreat along troughs and basins that deepen towards the ice sheet’s interior.
1178
Chapter 13 Sea Level Change
13
FAQ 13.2 (continued)
Future climate forcing could trigger such an unstable collapse, which may then continue independently of climate.
This potential collapse might unfold over centuries for individual bedrock troughs in West Antarctica and sectors of
East Antarctica. Much research is focussed on understanding how important this theoretical concept is for those ice
sheets. Sea level could rise if the effects of marine instability become important, but there is not enough evidence
at present to unambiguously identify the precursor of such an unstable retreat. Change in outflow is projected
to contribute between –20 (i.e., fall) and 185 mm to sea level rise by year 2100, although the uncertain impact of
marine ice-sheet instability could increase this figure by several tenths of a metre. Overall, increased snowfall seems
set to only partially offset sea level rise caused by increased outflow.
In Greenland, mass loss through more surface ablation and outflow dominates a possible recent trend towards
increased accumulation in the interior. Estimated mass loss due to surface ablation has doubled since the early
1990s. This trend is expected to continue over the next century as more of the ice sheet experiences surface abla-
tion for longer periods. Indeed, projections for the 21st century suggest that increasing mass loss will dominate
over weakly increasing accumulation. The refreezing of melt water within the snow pack high up on the ice sheet
offers an important (though perhaps temporary) dampening effect on the relation between atmospheric warming
and mass loss.
Although the observed response of outlet glaciers is both complex and highly variable, iceberg calving from many
of Greenland’s major outlet glaciers has increased substantially over the last decade, and constitutes an appreciable
additional mass loss. This seems to be related to the intrusion of warm water into the coastal seas around Green-
land, but it is not clear whether this phenomenon is related to inter-decadal variability, such as the North Atlantic
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 the start and end of the 21st century using the RACMO2 regional atmospheric climate model under future warming
scenarios A1B (Antarctic) and RCP4.5 (Greenland). For Greenland, average equilibrium line locations during both these time periods are shown in purple and green,
respectively. Ice-sheet margins and grounding lines are shown as black lines, as are ice-sheet sectors. For Greenland, results of flowline modelling for four major outlet
glaciers are shown as inserts, while for Antarctica the coloured rings reflect projected change in outflow based on a probabilistic extrapolation of observed trends. The
outer and inner radius of each ring indicate the upper and lower bounds of the two-thirds probability range of the contribution, respectively (scale in upper right); red
refers to mass loss (sea level rise) while blue refers to mass gain (sea level fall). Finally, the 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 two-in-three probability range across all scenarios.
40°W
60°80°W
80°N
70°N
N
60°N
60°N
40°E20°E20°W
140°E140°W 16E160°W 180°
60°S
60°S
0 250 500125
km
0500 1000250
km
0<-15
0>
150
0
<-1000
>1000
(mm yr
-1
w.e.)
(mm yr
-1
w.e.)
-40
17th 83rd
0
(mm SLR)
(percentile)
-20206040
8
8
8
0
0
0
°
W
2
0
°
°
°
W
W
2
2
2
0
0
0
°
°
°
W
W
W
0
°
°
8
0
°
N
8
0
°
N
7
7
7
0
°
N
0
°
N
N
N
N
N
0
<
-
1
5
0
>
1
5
0
(mm yr
m
m
m
-1
0
w.e.)
)
)
6
0
°
S
S
Sea level contribution
Sea level contribution
(continued on next page)
1179
Sea Level Change Chapter 13
13
have a rate of about 1% yr
–1
of existing capacity (Lempérière, 2006;
Lettenmaier and Milly, 2009). These two possibilities together indicate
a range of about 0 to 30 mm of GMSL fall for the contribution of res-
ervoir impoundment.
Our assessment thus leads to a range of –10 to +90 mm for the net con-
tribution to GMSL rise from anthropogenic intervention in land water
storage by 2081–2100 relative to 1986–2005. This range includes the
range of 0 to 40 mm assumed by Katsman et al. (2008). Because of the
limited information available, we do not have sufficient confidence to
give ranges for individual RCP scenarios.
13.5 Projections of Global Mean Sea Level Rise
Process-based projections for GMSL rise during the 21st century, given
in Section 13.5.1, are the sum of contributions derived from models
that were evaluated by comparison with observations in Section 13.3
and used to project the contributions in Section 13.4. Projections
of GMSL rise by semi-empirical models (SEMs) are given in Section
13.5.2. We compare these two and other approaches in Section 13.5.3
and assess the level of confidence that we can place in each approach.
Longer term projections are discussed in Section 13.5.4.
13.5.1 Process-Based Projections for the 21st Century
The process-based projections of GMSL rise for each RCP scenario are
based on results from 21 CMIP5 AOGCMs from which projections of
SAT change and thermal expansion are available (see Section 13.4.1).
Where CMIP5 results were not available for a particular AOGCM
and scenario, they were estimated (Good et al., 2011; 2013) (Section
12.4.1.2; Supplementary Material). The projections of thermal expan-
sion do not include an adjustment for the omission of volcanic forcing
in AOGCM spin-up (Section 13.3.4.2), as this is uncertain and relatively
small (about 10 mm during the 21st century). Changes in glacier and
ice-sheet SMB are calculated from the global mean SAT projections
FAQ 13.2 (continued)
Oscillation, or a longer term trend associated with greenhouse gas–induced warming. Projecting its effect on 21st
century outflow is therefore difficult, but it does highlight the apparent sensitivity of outflow to ocean warming.
The effects of more surface melt water on the lubrication of the ice sheet’s bed, and the ability of warmer ice to
deform more easily, may lead to greater rates of flow, but the link to recent increases in outflow is unclear. Change
in the net difference between surface ablation and accumulation is projected to contribute between 10 and 160
mm to sea level rise in 2081-2100 (relative to 1986-2005), while increased outflow is projected to contribute a fur-
ther 10 to 70 mm (Table 13.5).
The Greenland ice sheet has contributed to a rise in global mean sea level over the last few decades, and this trend
is expected to increase during this century. Unlike Antarctica, Greenland has no known large-scale instabilities
that might generate an abrupt increase in sea level rise over the 21st century. A threshold may exist, however, so
that continued shrinkage might become irreversible over multi-centennial time scales, even if the climate were to
return to a pre-industrial state over centennial time scales. Although mass loss through the calving of icebergs may
increase in future decades, this process will eventually end when the ice margin retreats onto bedrock above sea
level where the bulk of the ice sheet resides.
using parameterizations derived from the results of process-based
models of these components (note that glaciers on Antarctica are cov-
ered by the Antarctic ice-sheet SMB projection, and are therefore not
included in the glacier projections) (Sections 13.4.2, 13.4.3.1, 13.4.4.1
and Supplementary Material). According to the assessment in Section
12.4.1.2, global mean SAT change is likely to lie within the 5 to 95%
range of the projections of CMIP5 models. Following this assessment,
the 5 to 95% range of model results for each of the GMSL rise contri-
butions that is projected on the basis of CMIP5 results is interpreted
as the likely range.
Possible ice-sheet dynamical changes by 2100 are assessed from the
published literature (Sections 13.4.3.2 and 13.4.4.2), which as yet pro-
vides only a partial basis for making projections related to particular
scenarios. They are thus treated as independent of scenario, except
that a higher rate of change is used for Greenland ice sheet outflow
under RCP8.5. Projections of changes in land water storage due to
human intervention are also treated as independent of emissions sce-
nario, because we do not have sufficient information to give ranges
for individual scenarios. The scenario-independent 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. For each of these con-
tributions, our assessment of the literature provides a 5-95% range
for the late 21st century (2100 for Greenland and Antarctic ice-sheet
dynamics, 2081-2100 for land water storage). For consistency with the
treatment of the CMIP5-derived results, we interpret this range as the
likely range. We assume that each of these contributions begins from
its present-day rate and that the rate increases linearly in time, in order
to interpolate from the present day to the late 21st century (see Sup-
plementary Material for details).
The likely range of GMSL rise given for each RCP combines the uncer-
tainty in global climate change, represented by the CMIP5 ensemble
(Section 12.4.1.2), with the uncertainties in modelling the contributions
to GMSL. The part of the uncertainty related to the magnitude of global
1180
Chapter 13 Sea Level Change
13
climate change is correlated among all the scenario-dependent contri-
butions, while the methodological uncertainties are treated as inde-
pendent (see also Supplementary Material).
The sum of the projected contributions gives the likely range for future
GMSL rise. The median projections for GMSL in all scenarios lie within a
range of 0.05 m until the middle of the century (Figure 13.11), because
the divergence of the climate projections has a delayed effect owing to
the time-integrating characteristic of sea level. By the late 21st century
(over an interval of 95 years, between the 20-year mean of 2081–2100
and the 20-year mean of 1986–2005), they have a spread of about
0.25 m, with RCP2.6 giving the least amount of rise (0.40 [0.26 to
0.55] m) (likely range) and RCP8.5 giving the most (0.63 [0.45 to 0.82]
m). RCP4.5 and RCP6.0 are very similar at the end of the century (0.47
[0.32 to 0.63] m and 0.48 [0.33 to 0.63]] m respectively), but RCP4.5
has a greater rate of rise earlier in the century than RCP6.0 (Figure
13.10 and Table 13.5). At 2100, the likely ranges are 0.44 [0.28–0.61]
m (RCP2.6), 0.53 [0.36–0.71] m (RCP4.5), 0.55 [0.38–0.73] m (RCP6.0),
and 0.74 [0.52–0.98] m (RCP8.5).
In all scenarios, the rate of rise at the start of the RCP projections
(2007–2013) is about 3.7 mm yr
–1
, slightly above the observational
range of 3.2 [2.8 to 3.6] mm yr
–1
for 1993–2010, because the modelled
contributions for recent years, although consistent with observations
for 1993–2010 (Section 13.3), are all in the upper part of the observa-
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
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 dynamical change and anthropogenic land water storage 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 Sec-
tions 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 global mean sea
level (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.
tional ranges, perhaps related to the simulated rate of climatic warm-
ing being greater than has been observed (Box 9.2). In the projections,
the rate of rise initially increases. In RCP2.6 it becomes roughly con-
stant (central projection 4.5 mm yr
–1
) before the middle of the century,
and subsequently declines slightly. The rate of rise becomes roughly
constant in RCP4.5 and RCP6.0 by the end of the century, whereas
acceleration continues throughout the century in RCP8.5, reaching 11
[8 to 16] mm yr
–1
in 2081–2100.
In all scenarios, thermal expansion is the largest contribution, account-
ing for about 30 to 55% of the projections. Glaciers are the next largest,
accounting for 15-35% of the projections. By 2100, 15 to 55% of the
present volume of glaciers outside Antarctica is projected to be elim-
inated under RCP2.6, and 35 to 85% under RCP8.5 (Table 13.SM.2).
SMB change on the Greenland ice sheet makes a positive contribu-
tion, whereas SMB change in Antarctica gives a negative contribution
(Sections 13.4.3.1 and 13.4.4.1). The positive contribution due to rapid
dynamical changes that result in increased ice outflow from both ice
sheets together has a likely range of 0.03 to 0.20 m in RCP8.5 and 0.03
to 0.19 m in the other RCPs. There is a relatively small positive contri-
bution from human intervention in land water storage, predominantly
due to increasing extraction of groundwater.
1181
Sea Level Change Chapter 13
13
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
Figure 13.11 | Projections from process-based models of (a) global mean sea level (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 of 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 metre of sea level rise.
(a)
(b)
1182
Chapter 13 Sea Level Change
13
Table 13.5 | Median values and likely ranges for projections of global mean sea level (GMSL) rise and its contributions in metres in 2081–2100 relative to 1986–2005 for the four
RCP scenarios and SRES A1B, GMSL rise in 2046–2065 and 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.
Because some of the uncertainties in modelling the contributions are treated as uncorrelated, the sum of the lower bound of contributions does not equal the lower bound of the
sum, and similarly for the upper bound (see Supplementary Material). Because of imprecision from rounding, the sum of the medians of contributions may not exactly equal the
median of the sum. The net contribution (surface mass balance (SMB) + dynamics) for each ice sheet, and the contribution from rapid dynamical change in both ice sheets together,
are shown as additional lines below the sum; they are not contributions in addition to those given above the sum. The contributions from ice-sheet rapid dynamical change and
anthropogenic land water storage are treated as having uniform probability distributions, uncorrelated with the magnitude of global climate change (except for the interaction
between Antarctic ice sheet SMB and outflow), 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 assess-
ment of the dependence. Regional sea level change is expected in general to differ from the global mean (see Section 13.6).
13.5.2 Semi-Empirical Projections for the 21st Century
The development of semi-empirical models (SEMs) was motivated
by two problems. First, process-based modelling was incomplete in
the AR4 because of the unavailability of ice-sheet dynamical models
which could be used to simulate the observed recent accelerations in
ice flow and make projections with confidence (Meehl et al., 2007)
(Sections 13.1.4.1, 13.4.3.2 and 13.4.4.2). Second, in all previous IPCC
assessments, observed GMSL rise during the 20th century could not be
completely accounted for by the contributions to GMSL from thermal
expansion, glaciers and ice sheets. For example, the AR4 assessed the
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 yr
–1
(Bindoff et al., 2007; Hegerl
et al., 2007). With the central estimates, only about 60% of observed
sea level rise was thus explained, and the potential implication was
that projections using process-based models which reproduce only
those known contributions would underestimate future sea level rise
(Rahmstorf, 2007a; Jevrejeva et al., 2009; Grinsted et al., 2010). SEMs
do not aim to solve the two problems that motivated their develop-
ment, but instead provide an alternative approach for projecting GMSL.
Notes:
a
Excluding glaciers on Antarctica but including glaciers peripheral to the Greenland ice sheet.
b
Including the height–SMB feedback.
c
Including the interaction between SMB change and outflow.
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 0.09] 0.04 [–0.01 to 0.09] 0.04 [–0.01 to 0.09] 0.04 [–0.01 to 0.09] 0.04 [–0.01 to 0.09]
Global mean sea level
rise in 2081–2100
0.52 [0.37 to 0.69] 0.40 [0.26 to 0.55] 0.47 [0.32 to 0.63] 0.48 [0.33 to 0.63] 0.63 [0.45 to 0.82]
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 global mean
sea level rise
8.1 [5.1 to 11.4] 4.4 [2.0 to 6.8] 6.1 [3.5 to 8.8] 7.4 [4.7 to 10.3] 11.2 [7.5 to 15.7]
Global mean sea level
rise in 2046–2065
0.27 [0.19 to 0.34] 0.24 [0.17 to 0.32] 0.26 [0.19 to 0.33] 0.25 [0.18 to 0.32] 0.30 [0.22 to 0.38]
Global mean sea
level rise in 2100
0.60 [0.42 to 0.80] 0.44 [0.28 to 0.61] 0.53 [0.36 to 0.71] 0.55 [0.38 to 0.73] 0.74 [0.52 to 0.98]
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.
The semi-empirical approach regards a change in sea level as an
integrated response of the entire climate system, reflecting changes
in the dynamics and thermodynamics of the atmosphere, ocean and
cryosphere; it does not explicitly attribute sea level rise to its individual
physical components. SEMs use simple physically motivated relation-
ships, with various analytical formulations and parameters determined
from observational time series, to predict GMSL for the 21st century
(Figure 13.12 and Table 13.6) and beyond, from either global mean SAT
(Rahmstorf, 2007a; Horton et al., 2008; Vermeer and Rahmstorf, 2009;
Grinsted et al., 2010; Rahmstorf et al., 2012b) or RF (Jevrejeva et al.,
2009; 2010, 2012a).
SEMs are designed to reproduce the observed sea level record over
their period of calibration, as this provides them with model param-
eters needed to make projections (Rahmstorf, 2007a; Jevrejeva et al.,
2009; Vermeer and Rahmstorf, 2009; Grinsted et al., 2010). A test of the
predictive skill of the models requires simulating a part of the observed
record that has not been used for calibration. For instance, Rahmstorf
(2007b) calibrated for 1880–1940 and predicted 1940–2000, obtaining
results within 0.02 m of observed. Jevrejeva et al. (2012b) calibrated
1183
Sea Level Change Chapter 13
13
up to 1950 and predicted 0.03 m (about 25%) less than observed for
1950–2009, and 3.8 mm yr
–1
for 1993–2010, which is about 20% more
than observed.
The GMSL estimates used for calibrating the SEMs are based on the
existing sparse network of long tide-gauge records, and are thus uncer-
tain, especially before the late 19th century; these uncertainties are
reflected in the observational estimates of the rate of GMSL rise (Sec-
tions 3.7 and 13.2.2). Consequently, the projections may be sensitive
to the statistical treatment of the temporal variability in the instrumen-
tal record of sea level change (Holgate et al., 2007; Rahmstorf, 2007b;
Schmith et al., 2007). Rahmstorf et al. (2012b) reported that GMSL
projections for the RCP4.5 scenario for 2100 (Table 13.6) varied by
±0.04 m when the embedding dimension used for temporal smoothing
during the calibration was varied within a range of 0 to 25 years.
Furthermore, there is some sensitivity to the choice of data sets used
for calibration. For instance, when calibrated up to 1960 and used
Figure 13.12 | Median and range (5 to 95%) for projections of global mean sea level
rise (metres) 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 (RF). 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 projec-
tions) or reconstruction of RF (for studies using RCP RF). (1) Rahmstorf et al. (2012b),
with Kemp et al. (2011); (2) Schaeffer et al. (2012); (3) Rahmstorf et al. (2012b), with
Church and White (2006); (4) Rahmstorf et al. (2012b), with Church and White (2011);
(5) Rahmstorf et al. (2012b), 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 grey 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 9
0
0.5
1.0
1.5
b)
1 2 3 4 5 6 7 8 9
0
0.5
1.0
1.5
Global mean sea level rise (m)
c)
1 2 3 4 5 6 7 8 9
0
0.5
1.0
1.5
d)
1 2 3 4 5 6 7 8 9
0
0.5
1.0
1.5
to predict 1961–2003, the model of Bittermann et al. (2013) over-
estimates the GMSL data set of Jevrejeva et al. (2008) by 75%, but
makes an accurate estimate for the Church and White (2011) data set,
although these two data sets have similar rates of sea level rise in the
predicted period. The central projections of Rahmstorf et al. (2012b)
for 2100 under RCP4.5 (Table 13.6) for calibration with the GMSL data
set of Church and White (2006) are about 0.2 m more than for cali-
bration with the Church and White (2011) data set, although the two
Church and White (2006, 2011) data sets differ at all times by less
than one standard deviation. The ranges of the projections by Grinsted
et al. (2010) and Jevrejeva et al. (2010, 2012a, 2012b) allow for the
uncertainty in the GMSL reconstructions through the use of an uncer-
tainty covariance matrix in determining the model parameters. Grin-
sted et al. (2010) also investigated the sensitivity to the temperature
data set used as predictor, and Jevrejeva et al. (2010) investigated the
sensitivity to RF as predictor (Table 13.6). In the latter case, three data
sets gave median projections under RCP4.5 for 2100 within a range of
about ±0.20 m.
SEM projections will be biased unless contributions to past GMSL rise
which correlate with but are not physically related to contemporary
changes in the predictor variable (either global mean SAT change or RF)
are subtracted from the observational sea level record before the cali-
bration (Vermeer and Rahmstorf, 2009; Jevrejeva et al., 2012b; Rahm-
storf et al., 2012b; Orlić and Pasarić, 2013). These include groundwater
depletion due to anthropogenic intervention and storage of water by
dams (Section 13.3.4), ongoing adjustment of the Greenland and Ant-
arctic ice sheets to climate change in previous centuries and millen-
nia (Section 13.3.6), and the effects of internally generated regional
climate variability on glaciers (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).
For instance, Jevrejeva et al. (2012b) found that their median projec-
tions for 2100 were reduced by 0.02 to 0.10 m by excluding some such
contributions.
Making projections with a SEM assumes that sea level change in the
future will have the same relationship as it has had in the past to RF or
global mean temperature change. The appropriate choice for the for-
mulation of the SEM may depend on the nature of the climate forcing
and the time scale, and potentially nonlinear physical processes may
not scale in the future in ways which can be calibrated from the past
(von Storch et al., 2008; Vermeer and Rahmstorf, 2009; Rahmstorf et
al., 2012b; Orlić and Pasarić, 2013). Two such effects that could lead
to overestimated or underestimated projections by SEMs have been
discussed in the literature.
First, AOGCMs indicate that the ocean heat uptake efficiency tends
to decline as warming continues and heat penetrates more deeply
(Gregory and Forster, 2008). A linear scaling of the rate of global ocean
heat uptake with global SAT determined from the past, as proposed by
Rahmstorf (2007a), will thus overestimate future time-integrated heat
content change and the consequent global ocean thermal expansion
on a century time scale (Orlić and Pasarić, 2013). Rahmstorf (2007a)
found that the linear scaling overestimated by 0.12 m (about 30%) the
thermal expansion simulated by a climate model with a 3D ocean from
1990 to 2100 under scenario SRES A1FI. Furthermore, the Rahmstorf
(2007a) model is inadequate for simulating sea level variations of the
1184
Chapter 13 Sea Level Change
13
last millennium (von Storch et al., 2008), which arise predominantly
from episodic volcanic forcing, rather than the sustained forcing on
multi-decadal time scales for which it was intended. In both applica-
tions, the AOGCM behaviour is more accurately reproduced by taking
into account the vertical profile of warming, at least by distinguishing
the upper (mixed layer) and lower (thermocline) layers (Vermeer and
Rahmstorf, 2009; Held et al., 2010) (Section 13.4.1), or by introducing a
relaxation time scale for sea level rise (Jevrejeva et al., 2012b).
Second, the sensitivity of glaciers to warming will tend to decrease as
the area most prone to ablation and the remaining volume decrease,
partly counteracted by lowering of the surface due to thinning (Huss et
al., 2012) Section 13.4.2). On the other hand, glaciers at high latitudes
that currently have negligible surface melting will begin to ablate as
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 0.42 0.60 0.80
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
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 0.63
Grinsted et al. (2010) calibrated with
Moberg et al. (2005) temperature
1986–2005 2081–2100 0.63 0.88 1.14
Rahmstorf et al. (2012b) calibrated with
Church and White (2006) GMSL
1986–2005 2081–2100 0.79 0.86 0.93
Rahmstorf et al. (2012b) calibrated with
Church and White (2011) GMSL
1986–2005 2081–2100 0.57 0.63 0.68
Rahmstorf et al. (2012b) calibrated with
Jevrejeva et al. (2008) GMSL
1986–2005 2081–2100 0.82 0.97 1.12
Rahmstorf et al. (2012b) 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 Crow-
ley 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:
a
Extrapolated to 2100 using the projected rates of sea level rise for 2090–2099 in Table 10.7 of Meehl et al. (2007).
b
Including scaled-up ice-sheet discharge given in Table 10.7 of Meehl et al. (2007) and extrapolated to 2100 as an illustration of the possible magnitude of this effect.
c
Uncertainty range not given.
d
The mean value and the range are shown for semi-empirical model projections based on results from 11 GCMs.
Table 13.6 | Global mean sea level (GMSL) rise (metres) projected by semi-empirical models and compared with the IPCC AR4 and AR5 projections. In each case the results have
a probability distribution whose 5th, 50th and 95th percentiles are shown in the columns as indicated. The AR5 5 to 95% process-based model range is interpreted as a likely range
(medium confidence) (Section 13.5.1).
the climate becomes warmer, tending to give an increase in sensitiv-
ity (Rahmstorf et al., 2012b) (Section 13.4.2). Estimating the balance
of these two effects will require detailed modelling of glacier SMB.
The absence of a multidecadal acceleration in the rate of glacier mass
loss in observations of the 20th and simulations of the 21st centuries
(Section 4.3.3) (Radic and Hock, 2010; Marzeion et al., 2012a), despite
rising global temperatures, suggests that the reduction in sensitivity
may dominate (Gregory et al., 2013b).
13.5.3 Confidence in Likely Ranges and Bounds
The AR4 (Meehl et al., 2007) presented process-model-based pro-
jections of GMSL rise for the end of the 21st century, but did not
provide a best estimate or likely range principally because scientific
1185
Sea Level Change Chapter 13
13
understanding at the time was not sufficient to allow an assessment of
the possibility of future rapid changes in ice-sheet dynamics (on time
scales of a few decades, Section 4.4.5). Future rapid changes in ice-
sheet outflow were consequently not included in the ranges given by
the AR4. For the SRES A1B scenario, the AR4 range was 0.21 to 0.48 m,
and for the highest emissions scenario, A1FI, it was 0.26 to 0.59 m. The
AR4 also noted that if ice-sheet outflow increased linearly with global
mean surface air temperature, the AR4 maximum projections would be
raised by 0.1 to 0.2 m. The AR4 was unable to exclude larger values or
to assess their likelihood.
Since the publication of the AR4, upper bounds of up to 2.4 m for
GMSL rise by 2100 have been estimated by other approaches, namely
SEMs (Section 13.5.2), evidence from past climates (Section 13.2.1)
and physical constraints on ice-sheet dynamics (Sections 13.4.3.2 and
13.4.4.2). The broad range of values reflects the different methodolo-
gies for obtaining the upper bound, involving different constraining fac-
tors and sources of evidence. In particular, the upper bound is strongly
affected by the choice of probability level, which in some approaches is
unknown because the probability of the underlying assumptions is not
quantified (Little et al., 2013b).
The confidence that can be placed in projections of GMSL rise and its
upper bound by the various approaches must be considered. Confidence
arises from the nature, quantity, quality and consistency of the evidence.
The first approach is based on process-based projections, which use
the results from several models for each contribution (Sections 13.4
and 13.5.1; Table 13.5). There is medium evidence in support of this
approach, arising from our understanding of the modelled physical
processes, the consistency of the models with wider physical under-
standing of those processes as elements of the climate system (e.g.,
Box 13.1), the consistency of modelled and observed contributions
(Sections 13.3.1 to 13.3.5), the consistency of observed and modelled
GMSL (Section 13.3.6), and the consistency of process-based projec-
tions based on the CMIP5 ensemble of AOGCMs, which have a range
of 50 to 60% of the ensemble mean under a given scenario (Table
13.5). Considering this evidence, we have medium confidence in the
process-based projections.
The second approach uses SEMs (Section 13.5.2, Table 13.6), which
make projections by calibrating a physically motivated relationship
between GMSL and some other parameter of the climate system in
the past and applying it to the future, without quantifying the con-
tributory physical processes. If we had no physical understanding of
the causes of sea level rise, the semi-empirical approach to projections
would be the only possible one, but extrapolation beyond the range
of calibration implies uncertainty that is difficult to quantify, owing to
the assumption that sea level change in the future will have the same
relationship as it has had in the past to RF or global mean tempera-
ture change (Section 13.5.2). As a result, there is low agreement and
no consensus in the scientific community about the reliability of SEM
projections, despite their successful calibration and evaluation against
the observed 20th century sea level record.
For a given RCP, some SEMs project a range that overlaps the process-
based likely range while others project a median and 95-percentile
that are about twice as large as the process-based models. In nearly
every case, the SEM 95-percentile is above the process-based likely
range (Figure 13.12). Two physical explanations have been suggest-
ed for the higher projections. First, the contribution from accelerated
calving of tidewater glaciers may be substantial and included in SEMs
but not process-based models (Jevrejeva et al., 2012b); however, this
could account for only 0.1 to 0.2 m of additional GMSL rise. Second,
SEMs may allow for rapid ice-sheet dynamical change (Section 4.4.4)
in response to future climate change (Grinsted et al., 2010; Little et
al., 2013a). In order for large ice-sheet dynamical changes to be pre-
dictable by SEMs, two conditions must be met. First, these changes
must have contributed substantially to sea level rise during the period
of calibration. This is very unlikely to be the case, because it is very
likely that dynamical changes have contributed only a small part of
the observed sea level rise during the 20th century, rising to about
15% during 1993–2010 (Section 13.3.6). Second, the changes must
have a link to global surface temperature or RF. Current understanding
of recent dynamical changes in Greenland and Antarctica is that they
have been triggered by local changes in ocean temperature (Holland
et al., 2008; Thoma et al., 2008; Jacobs et al., 2011), but a link has
not been demonstrated between these changes and global climate
change or its drivers. Consequently there is great uncertainty regard-
ing whether recent ice-sheet dynamical changes indicate a long-term
trend or instead arise from internal variability (Bamber and Aspinall,
2013). Hence there is no evidence that ice-sheet dynamical change is
the explanation for the higher GMSL rise projections of SEMs, implying
that either there is some other contribution which is presently uniden-
tified or underestimated by process-based models, or that the projec-
tions of SEMs are overestimates (cf. Section 13.5.2). Because of the
limited or medium evidence supporting SEMs, and the low agreement
about their reliability, we have low confidence in their projections.
The third approach uses paleo records of sea level change that show
that rapid GMSL rise has occurred during glacial terminations, at
rates that averaged about 10 mm yr
–1
over centuries, with at least
one instance (Meltwater Pulse 1A) that exceeded 40 mm yr
–1
(Section
5.6.3), but this rise was primarily from much larger ice-sheet sourc-
es that no longer exist. Contributions from these vanished ice sheets
could have continued even after sea level and climate had reached
interglacial states, if the Greenland and Antarctic ice sheets contracted
during the termination to smaller sizes than at present. During past
interglacial periods, only the Greenland and Antarctic ice sheets were
present. For the time interval during the LIG in which GMSL was above
present, there is high confidence that the maximum 1000-year average
rate of GMSL rise during these periods exceeded 2 m kyr
–1
but did
not exceed 7 m kyr
–1
(Kopp et al., 2013) (Sections 5.6.2 and 13.2.1.3).
Because climate variations during interglacial periods had different
forcings from anthropogenic climate change, they give only a limited
basis for predictions of the future, and we do not consider that they
provide upper bounds for GMSL rise during the 21st century.
The fourth approach is concerned particularly with the contribution
from ice-sheet dynamical change, for which it considers kinematic
limits. Pfeffer et al. (2008) argued that scenarios of GMSL rise exceed-
ing 2 m by 2100 are physically untenable, ruling out, for example, the
heuristic argument of Hansen et al. (2007) giving 5 m by 2100. Pfeffer
et al. (2008) constructed scenarios of 0.8 m and 2.0 m, and Katsman
1186
Chapter 13 Sea Level Change
13
et al. (2011) of 1.15 m, for GMSL rise by 2100, including ice-sheet
rapid dynamical acceleration. Although these authors considered their
scenarios to be physically possible, they are unable to quantify their
likelihood, because the probability of the assumptions on which they
depend cannot be estimated from observations of the response of the
Greenland and Antarctic ice sheets to climate change or variability on
century time scales. These scenarios involve contributions of ~0.5 m
from Antarctica. This is much greater than any process-based projec-
tions of dynamical ice-sheet change (Section 13.4.4.2), and would
require either a sustained high increase in outflow in all marine-based
sectors or the localized collapse of the ice sheet in the Amundsen Sea
sector (Little et al., 2013a).
In summary, we have greater confidence in the process-based projec-
tions than in the other approaches, and our assessment is that GMSL
rise during the 21st century for each RCP scenario is likely (medium
confidence) to lie within the 5 to 95% range given by the process-
based projections (Section 13.5.1 and Table 13.5; see Section 13.5.4
for following centuries), which are consistent with the likely ranges
projected for global mean surface air temperature change (Section
12.4.1.2). We are not able to assess a very likely range on the same
basis, because there is no assessment available of the very likely range
for global mean SAT change, and because we cannot robustly quantify
the probability of ice-sheet dynamical changes which would give rise
to greater values.
Under the RCP8.5 scenario, which has the highest RF, the likely range
reaches 0.98 m by 2100 relative to 1986–2005. Observations do not
show an increase in Antarctic precipitation, which is projected by
models and makes a negative contribution to the projected GMSL rise
(Table 13.5). The recovery of Antarctic stratospheric ozone concentra-
tion and increased basal melting of Antarctic ice shelves have both
been suggested as giving rise to mechanisms whereby the Antarctic
warming and precipitation increase might be suppressed with respect
to CMIP5 projections (Section 13.4.4.1). If the Antarctic precipitation
increase is omitted from the process-based projections, the likely range
for RCP8.5 at 2100 reaches 1.03 m (assuming uncorrelated errors).
Higher values for 2100 are given in the scientific literature on the basis
of various approaches: 1.15 m (Katsman et al., 2011), 1.21 m (Schaef-
fer et al., 2012) (for RCP4.5), 1.40 m (National Research Council, 2012),
1.65 m (Jevrejeva et al., 2012b) (for RCP8.5), 1.79 m (Vermeer and
Rahmstorf, 2009) (for SRES A1FI), 1.90 m (Rahmstorf et al., 2012b)
(with proxy calibration, for RCP8.5), 2.0 m (Pfeffer et al., 2008), 2.25
m (Sriver et al., 2012), and 2.4 m (Nicholls et al., 2011). Considering
this inconsistent evidence, we conclude that the probability of specific
levels above the likely range cannot be reliably evaluated.
Only the collapse of marine-based sectors of the Antarctic ice sheet
could cause GMSL rise substantially above the likely range during the
21st century. Expert estimates of contributions from this source have
a wide spread (Bamber and Aspinall, 2013), indicating a lack of con-
sensus on the probability for such a collapse. The potential additional
contribution to GMSL rise also cannot be precisely quantified, but
there is medium confidence that, if a collapse were initiated, it would
not exceed several tenths of a metre during the 21st century (Section
13.4.4.2).
The time mean rate of GMSL rise during the 21st century is very likely to
exceed the rate of 2.0 [1.7 to 2.3] mm yr
–1
observed during 1971–2010,
because the process-based GMSL projections indicate a significantly
greater rate even under the RCP2.6 scenario, which has the lowest
RF. It has been asserted that the acceleration of GMSL rise implied by
the IPCC AR4 projections is inconsistent with the observed magnitude
of acceleration during the 20th century (Boretti, 2011, 2012b, 2012a,
2012c, 2013a, 2013b. 2013c; Boretti and Watson, 2012; Parker, 2013a,
2013b, 2013c). Refuting this argument, Hunter and Brown (2013) show
that the acceleration projected in the AR4 is consistent with observa-
tions since 1990s. Present understanding of the contributions to GMSL
rise (Section 13.3) gives an explanation of the rate of 20th century
GMSL rise and confidence in the process-based projections, which indi-
cate a greater rate of rise in the 21st century because of increasing
forcing.
The improved agreement of process-based models with observations
and physical understanding represents progress since the AR4, in which
there was insufficient confidence to give likely ranges for 21st century
GMSL rise, as we have done here. For scenario SRES A1B, which was
assessed in the AR4, the likely range on the basis of science assessed
in the AR5 is 0.60 [0.42 to 0.80] m by 2100 relative to 1986–2005, and
0.57 [0.40 to 0.76] m by 2090–2099 relative to 1990. Compared with
the AR4 projection of 0.21 to 0.48 m for the same scenario and period,
the largest increase is from the inclusion of rapid changes in Greenland
and Antarctic ice sheet outflow, for which the combined likely range is
0.03 to 0.21 m by 2091–2100 (assuming uncorrelated uncertainties).
These terms were omitted in the AR4 because a basis to make projec-
tions was not available in published literature at that time. The contri-
bution from thermal expansion is similar to the AR4 projection and has
smaller uncertainty. The contribution from glaciers is larger than in the
AR4 primarily because of the greater estimate of the present glacier
volume in new inventories (although the glacier area estimate is sim-
ilar, Table 4.1), and the Greenland SMB contribution is larger because
of recent improvement in models of relevant surface processes. Further
progress on modelling each of the contributions is still needed in order
to attain high confidence in GMSL projections, in particular concerning
the probability distribution of GMSL above the likely ranges.
13.5.4 Long-term Scenarios
Less information is available on climate change beyond the year 2100
than there is up to the year 2100. However, the ocean and ice sheets
will continue to respond to changes in external forcing on multi-centen-
nial to multi-millennial time scales. For the period up to the year 2500,
available physical model projections discussed in Sections 13.4.1-4 are
combined into an assessment of future sea level rise. Paleo simulations
are combined with paleo data to estimate the sea level commitment
on a multi-millennial time scale beyond 2500 for different levels of
sustained increases in global mean temperature.
The RCPs, as applied in Chapter 12 and Sections 13.4 and 13.5.1, are
defined up to the year 2100. Their extension up to the year 2300 is
used to project long-term climate change (Section 12.3.1.3) (Mein-
shausen et al., 2011), but they are not directly derived from integrated
assessment models. In simulations that are reported here up to the
year 2500, the RF has generally been kept constant at the 2300 level
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Sea Level Change Chapter 13
13
except for RCP2.6, in which the forcing continues to decline at the
2300 rate. Some model simulations of ice sheets and ocean warming
assessed here have used scenarios different from the RCP scenarios.
Because of the limited number of available simulations, sea level pro-
jections beyond the year 2100 have thus been grouped into three cat-
egories according to their GHG concentration in the 22nd century: low
scenarios in which atmospheric GHG concentrations peak and decline
and do not exceed values that are equivalent to 500 ppm CO
2
, medium
scenarios with concentrations between 500 and 700 ppm CO
2
-eq, and
high scenarios above 700 ppm. As a consequence, the model spread
shown in Figure 13.13 and Table 13.8 combines different scenarios
and is not merely due to different model physics. The low scenarios
include RCP2.6, SRES B1 and scenarios with 0.5 and 2% yr
–1
increases
in CO
2
followed by no emissions after 450 ppm has been reached, and
the commitment scenarios, CC, in Goelzer et al. (2013) which stabilize
CO
2
at present-day levels. In a number of the low scenarios, the global
mean temperature peaks during the 21st century and declines there-
after. These peak-and-decline scenarios include RCP2.6 as well as all
scenarios with no GHG emissions after a specified year. Even in these
scenarios sea level continues to rise up to the year 2500 in accordance
with the multi-millennial sea level commitment of about 2 m °C
–1
as
discussed in Section 13.5.4.2. The medium scenarios include RCP4.5
as well as scenarios with 1% yr
–1
increase in CO
2
up to 560 ppm and
SRES-B1 and SRES-A1B. The high scenarios include RCP6.0 and RCP8.5
as well as 1120 ppm scenarios and SRES A2. Also included are scenar-
ios with 0.5 and 2% increase in CO
2
and a SRES A2 scenario with zero
emissions after 1200 and 1120 ppm have been reached, respectively.
13.5.4.1 Multi-centennial Projections
The multi-centennial sea level contributions from ocean expansion
and the cryospheric components are discussed in Sections 13.4.1 to
13.4.4. A synthesis of these contributions is provided in Table 13.8 and
Figure 13.13 for the end of each century until the year 2500. Thermal
expansion contributions (dark blue bars, Figure 13.13) were obtained
from coarse-resolution coupled climate models (Vizcaíno et al., 2008;
Solomon et al., 2009; Gillett et al., 2011; Schewe et al., 2011; Zick-
feld et al., 2013). For comparison, the full model spread of the CMIP5
models which were integrated beyond 2100 is provided in Table 13.7
and as light blue bars in Figure 13.13. Even though the models used
for the long-term projections (Table 13.8) are less complex compared
to the CMIP5 models, their model spread for the different periods and
scenarios encompasses the CMIP5 spread, which provides medium
confidence in the application of the less complex models beyond 2300.
Contributions from the Greenland and Antarctic ice sheets were
obtained with climate models of comparable complexity coupled to
ice-sheet models (Vizcaíno et al., 2010; Huybrechts et al., 2011; Goelzer
et al., 2012). Glacier projections were obtained by application of the
method by Marzeion et al. (2012a) to the CMIP5 model output for sce-
narios and models that were integrated up to the year 2300. For 2400
and 2500, the same model spread as for 2300 is shown. This is proba-
bly underestimating the glacier’s sea level contribution beyond 2300.
The ranges of sea level contributions provided in Figure 13.13 and
Table 13.8 only represent the model spread and cannot be interpreted
as uncertainty ranges. An uncertainty assessment cannot be provid-
ed beyond the year 2100 because of the small number of available
simulations, the fact that different scenarios were combined within
one scenario group, and the overall low confidence in the ability of
the coarse-resolution ice-sheet models to capture the dynamic ice dis-
charge from Greenland and Antarctica, as discussed below. The range
for the total sea level change was obtained by taking the sum of con-
tributions that result in the lowest and the highest sea level rise and
thereby covers the largest possible model spread.
Except for the glacier models (Section 13.4.2), the models used here
for the period beyond 2100 are different from the models used for the
21st century (Sections 13.4.1, 13.4.3, 13.4.4, and 13.5.1). Generally,
the model spread for the total sea level contribution in 2100 is slightly
lower than the likely range provided in Section 13.5.1 (light red bars
in Figure 13.13). This is due to the ice-sheet models, particularly of
the Antarctic ice sheet, as coarse-resolution model results for thermal
expansion cover the range of the CMIP5 projections (light blue vertical
lines in Figure 13.13 and Table 13.7.) and the glacier contribution is
the same.
Projections beyond 2100 show positive contributions to sea level from
thermal expansion, glaciers and changes in Greenland ice sheet SMB.
Due to enhanced accumulation under warming, the Antarctic ice sheet
SMB change makes a negative contribution to sea level in scenarios
below 700 ppm CO
2
-eq. These results were obtained with fully cou-
pled climate–ice sheet models which need to apply a relatively low
spatial resolution. In light of the discussion in Section 13.3.3.2 and
the assessment of the 21st century changes in Section 13.4.4.1, there
is low confidence in this result. For scenarios above 700 ppm CO
2
-eq,
Antarctic SMB change is contributing positively to GMSL.
As discussed in Sections 13.4.3.2 and 13.4.4.2, there is medium con-
fidence in the ability of coupled ice sheet–climate models to project
sea level contributions from dynamic ice-sheet changes in Greenland
and Antarctica for the 21st century. In Greenland, dynamic mass loss is
limited by topographically defined outlets regions. Furthermore, solid
ice discharge induced from interaction with the ocean is self-limiting
because retreat of the ice sheet results in less contact with the ocean
and less mass loss by iceberg calving (Pfeffer et al., 2008; Graversen
et al., 2011; Price et al., 2011). By contrast, the bedrock topography of
Antarctica is such that parts of the retreating ice sheet will remain in
contact with the ocean. In particular, due to topography that is sloping
landward, especially in West Antarctica, enhanced rates of mass loss
are expected as the ice retreats.
Although the model used by Huybrechts et al. (2011) is in principle
capable of capturing grounding line motion of marine ice sheets (see
Box 13.2), low confidence is assigned to the model’s ability to cap-
ture the associated time scale and the perturbation required to ini-
tiate a retreat (Pattyn et al., 2013). The model used by Vizcaino et al.
(2010) does not represent ice-shelf dynamics and is thus lacking a
fundamental process that can trigger the instability. As stated by the
authors, low confidence is thus also assigned to the model’s ability
to project future solid ice discharge from Antarctica. It is thus likely
that the values depicted in Figure 13.13 systematically underestimate
Antarctica’s future contribution. As detailed in Section 13.5.4.2, simu-
lations of the last 5 Myr (Pollard and DeConto, 2009) indicate that on
1188
Chapter 13 Sea Level Change
13
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
-eq) in the year 2100
(upper panel: >700 ppm including RCP6.0 and RCP8.5; middle panel: 500–700 ppm including RCP4.5; lower panel: <500 ppm including RCP2.6). 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 from the CMIP5 simulations up
to 2300 (as used for the 21st century projections, section 13.5.1, light blue, with the median indicated by the horizontal bar), thermal expansion for the models considered in this
section (dark blue), glaciers (light green), Greenland ice sheet (dark green), Antarctic ice sheet (orange), and the total contribution (red). 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 show the likely range for the 21st century total
sea level projection of the corresponding scenarios from Figure 13.10 with the median as the horizontal line. In the upper panel, the left light red bar corresponds to RCP6.0 and
the right light red bar corresponds to RCP8.5.
multi-millennial time scales, the Antarctic ice sheet loses mass for ele-
vated temperatures, in contrast to the projections until the year 2500
for the low and medium scenarios.
The model spread of total sea level change in 2300 ranges from 0.41
to 0.85 m for the low scenario (Table 13.8). Using an SEM, Schaef-
fer et al. (2012) obtained a significantly larger 90% confidence range
of 1.3 to 3.3 m for the RCP2.6 scenario. The RCP4.5 scenario, for
which they obtained a range of 2.3 to 5.5 m, is categorized here as
a medium scenario, and is also significantly higher than the range
of 0.27 to 1.51 m computed by the process-based models. Using a
different semi-empirical approach, Jevrejeva et al. (2012a) obtained a
90% confidence range of 0.13 to 1.74 m for RCP2.6 in the year 2500,
which encloses the model spread of 0.50 to 1.02 m for the low scenario
from the process-based models. For the medium and high scenarios,
however, they obtained ranges of 0.72 to 4.3 m and 1.0 to 11.5 m,
respectively, which are significantly higher than the corresponding pro-
cess-based model spread of 0.18 to 2.32 m and 1.51 to 6.63 m (Table
13.8). Because projections of land water storage are not available for
years beyond 2100 these were not included here.
1189
Sea Level Change Chapter 13
13
The higher estimates from the SEMs than the process-based models
used here for the long-term projections are consistent with the relation
between the two modelling approaches for the 21st century (Figure
13.12). Section 13.5.3 concluded that the limited or medium evidence
supporting SEMs, and the low agreement about their reliability, pro-
vides low confidence in their projections for the 21st century. We note
here that the confidence in the ability of SEMs is further reduced with
the length of the extrapolation period and the deviation of the future
forcing from the forcing of the learning period (Schaeffer et al., 2012),
thus decreasing confidence over the long time frames considered here.
For increasing global mean SAT, sea level is virtually certain to contin-
ue to rise beyond the year 2500 as shown by available process-based
model simulations of thermal expansion and ice sheets that were com-
puted beyond 2500 (Rahmstorf and Ganopolski, 1999; Ridley et al.,
2005; Winguth et al., 2005; Driesschaert et al., 2007; Mikolajewicz et
al., 2007b; Swingedouw et al., 2008; Vizcaíno et al., 2008; Solomon et
al., 2009; Vizcaíno et al., 2010; Gillett et al., 2011; Goelzer et al., 2011;
Huybrechts et al., 2011; Schewe et al., 2011).
13.5.4.2 Multi-Millennial Projections
Here sea level commitment in response to a global mean tempera-
ture increase on a multi-millennial time scale is assessed. Figure 13.14
shows the sea level rise after several millennia of constant global mean
temperature increase above pre-industrial. The thermal expansion of
the ocean was taken from 1000-year integrations with six coupled cli-
mate models as used in the AR4 (models Bern2D, CGoldstein, CLIMate
and BiosphERe-2 (CLIMBER-2), Massachusetts Institute of Technology
(MIT), MoBidiC, and Loch-Vecode-Ecbilt-CLio-agIsm Model (LOVE-
CLIM) in Figure 10.34 in Meehl et al. (2007)). These yield a rate of sea
level change in the range of 0.20 to 0.63 m °C
–1
(Figure 13.14a). For
reference, a spatially uniform increase of ocean temperature yields a
global mean sea level rise of 0.38 m °C
–1
when added to observed
data (Levitus et al., 2009) (black dots in Figure 13.14a). Uncertainty
arises due to the different spatial distribution of the warming in models
and the dependence of the expansion on local temperature and salin-
ity. The contribution for glaciers was obtained with the models from
Mazeion et al. (2012a) and Radic and Hock (2011) by integration with
fixed boundary conditions corresponding to different global mean SAT
levels for 3000 years.
As detailed in Sections 13.4.3.2 and 13.4.4.2, there is low confidence in
the ability of current Antarctic ice-sheet models to capture the tempo-
ral response to changes in external forcing on a decadal to centennial
time scale. On multi-centennial to multi-millennial time scales, however,
these models can be validated against paleo sea level records. The con-
tributions from the Greenland ice sheet were computed with a dynamic
ice-sheet model coupled to an energy-moisture balance model for the
SMB (Robinson et al., 2012). The model’s parameters were constrained
by comparison with SMB estimates and topographic data for the pres-
ent day and with estimated summit-elevation changes from ice-core
records for the Last Interglacial period (LIG), in order to ensure that the
coupled model ensemble has a realistic sensitivity to climatic change.
The parameter spread leads to a spread in ice-sheet responses (dark
green lines in Figure 13.14c). The contribution to sea level commitment
from the Greenland ice sheet is relatively weak (on average 0.18 m °C
–1
up to 1°C and 0.34 m °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-industrial
(90% confidence interval in the particular model calculations reported
here) (Figure 13.14c). This represents a change from a fully ice-covered
Greenland to an essentially ice-free state, reducing the ice sheet to
around 10% of present-day volume and raising sea level by over 6 m
(Ridley et al., 2005; Ridley et al., 2010). The threshold temperature is
lower than estimates obtained from the assumption that the threshold
coincides with a negative total SMB of the Greenland ice sheet (see
Section 13.4.3.3 for a more complete discussion).
The Antarctic ice sheet contribution comes from a simulation of the
last 5 million years (Pollard and DeConto, 2009), which is in good
agreement with regional paleo records (Naish et al., 2009). The sen-
sitivity of the ice sheet was extracted from this model simulation by
correlating the ice volume with the global mean temperature which
forces the simulation. The standard deviation of the resulting scatter
is used as a measure of uncertainty (Figure 13.14d). Uncertainty arises
from uncertainty in the forcing data, the ice physics representation,
and from the time-dependent nature of the simulation. For example,
the existence of hysteresis behavior on the sub-continental scale can
lead to different contributions for the same temperature increase. The
Antarctic ice sheet shows a relatively constant commitment of 1.2 m
°C
–1
. Paleorecords indicate that a potential hysteresis behaviour of East
Antarctica requires a temperature increase above 4°C and is thereby
outside of the scope discussed here (Foster and Rohling, 2013).
In order to compare the model results with past sea level anomalies for
the temperature range up to 4°C, we focus on the three previous peri-
ods of warmer climates and higher sea levels than pre-industrial that
were 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 each case, there is reasonable
agreement between the model result of a long-term sea level response
for a given temperature with the information from the paleo record.
The ability of the physical models to reproduce paleo sea level records
on a multi-millennial time scale provides confidence in applying them
to millennial time frames. After 2000 years, the sea level contribution
will be largely independent of the exact warming path during the first
century. As can be seen from Figure 10.34 of AR4, the oceanic heat
content will be largely equilibrated after 2000 years; the same is true
for the glacier component. The situation for Antarctica is slightly more
complicated, but as can be inferred from Pollard and DeConto (2009),
much of the retreat of the West Antarctic ice sheet will have already
occurred by 2000 years, especially if the warming occurs on a decadal
to centennial time scale. The opposite and smaller trend in East Ant-
arctic ice volume due to increased snowfall in a warmer environment
will also have largely equilibrated (Uotila et al., 2007; Winkelmann et
al., 2012).
The most significant difference arises from the contribution of the
Greenland ice sheet. Consistent with previous estimates (Huybrechts
et al., 2011; Goelzer et al., 2012), the rate of the sea level contribution
from Greenland increases with temperature. The transient simulations
for an instantaneous temperature increase show a quasi-quadratic
dependence of the sea level contribution on this temperature increase
after 2000 years (Figure 13.14h) (Robinson et al. 2012). The results are
1190
Chapter 13 Sea Level Change
13
Figure 13.14 | (Left column) Multi-millennial sea level commitment per degree Celsius of warming as obtained from physical model simulations of (a) ocean warming, (b)
mountain glaciers and (c) the Greenland and (d) the Antarctic ice sheets. (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) and (e) with 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 nonlinear 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
.
°
°
0.42 m °C
-1
0.42 m °C
-1
1.2 m °C
-1
1.8 m °C
-1
2.3 m °C
-1
2.3 m °C
-1
1.2 m °C
-1
quantitatively consistent with previous estimates on a millennial time
scale (Huybrechts et al., 2011; Goelzer et al., 2012). The sea level contri-
bution of the Greenland ice sheet after 2000 years of integration at 560
ppm was plotted against the average Greenland temperature divided
by the standard polar amplification of 1.5 between global mean and
Greenland mean temperature increase (Gregory and Huybrechts, 2006,
black dot in Figure 13.14h). Taken together, these results imply that a
sea level rise of 1 to 3 m °C
–1
is expected if the warming is sustained for
several millennia (low confidence) (Figure 13.14e, 13.14j).
1191
Sea Level Change Chapter 13
13
13.6 Regional Sea Level Changes
Regional sea level changes may differ substantially from a global
average, showing complex spatial patterns which result from ocean
dynamical processes, movements of the sea floor, and changes in
gravity due to water mass redistribution (land ice and other terres-
trial water storage) in the climate system. The regional distribution is
associated with natural or anthropogenic climate modes rather than
factors causing changes in the global average value, and include such
processes as a dynamical redistribution of water masses and a change
of water mass properties caused by changes in winds and air pressure,
air–sea heat and freshwater fluxes and ocean currents. Because the
characteristic time scales of all involved processes are different, their
relative contribution to net regional sea level variability or change will
depend fundamentally on the time scale considered.
13.6.1 Regional Sea Level Changes, Climate Modes and
Forced Sea Level Response
As discussed in Chapter 3, most of the regional sea level changes
observed during the recent altimetry era or reconstructed during past
Table 13.7 | Median and model spread of the thermal expansion of CMIP5 comprehensive climate models. RCP2.6 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 ‘high scenario’. The model spread in Table 13.8 encloses the CMIP5 model spread for all scenarios. Sea level contributions
are provided in metres.
Table 13.8 | Model spread of sea level contribution and total sea level change for low, medium and high scenarios as defined in the text and shown in Figure 13.13. As detailed
in the text, there is low confidence in the ice-sheet models’ ability to project rapid dynamical change in the Antarctic ice sheet, which may result in a systematic underestimation of
the ice-sheet contributions. The unit of all sea level contributions is metres.
Notes:
a
The value is based on one simulation only.
b
Owing to lack of available simulations the same interval used as for the year 2300.
Mean 2191–2200 Mean 2291–2300
Scenario No. of Models Median Model Spread No. 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
Contribution Scenario 2100 2200 2300 2400 2500
Thermal expansion Low 0.07 to 0.31 m 0.08 to 0.41 m 0.08 to 0.47 m 0.09 to 0.52 m 0.09 to 0.57 m
Glaciers Low 0.15 to 0.18 m 0.19 to 0.23 m 0.22 to 0.26 m 0.22 to 0.26 m
b
0.22 to 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 to 0.53 m 0.35 to 0.72 m 0.41 to 0.85 m 0.46 to 0.94 m 0.50 to 1.02 m
Thermal expansion Medium 0.09 to 0.39 m 0.17 to 0.62 m 0.20 to 0.81 m 0.22 to 0.98 m 0.24 to 1.13 m
Glaciers Medium 0.15 to 0.19 m 0.21 to 0.25 m 0.25 to 0.29 m 0.25 to 0.29 m
b
0.25 to 0.29 m
b
Greenland ice sheet Medium 0.02 to 0.09 m 0.05 to 0.24 m 0.08 to 0.44 m 0.11 to 0.65 m 0.14 to 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 to 0.66 m 0.26 to 1.09 m 0.27 to 1.51 m 0.21 to 1.90 m 0.18 to 2.32 m
Thermal expansion High 0.08 to 0.55 m 0.23 to 1.20 m 0.29 to 1.81 m 0.33 to 2.32 m 0.37 to 2.77 m
Glaciers High 0.17 to 0.19 m 0.25 to 0.32 m 0.30 to 0.40 m 0.30 to 0.40 m
b
0.30 to 0.40 m
b
Greenland ice sheet High 0.02 to 0.09 m 0.13 to 0.50 m 0.31 to 1.19 m 0.51 to 1.94 m 0.73 to 2.57 m
Antarctic ice sheet High –0.07 to –0.00 m –0.04 to 0.01 m 0.02 to 0.19 m 0.06 to 0.51 m 0.11 to 0.88 m
Total High 0.21 to 0.83 m 0.58 to 2.03 m 0.92 to 3.59 m 1.20 to 5.17 m 1.51 to 6.63 m
decades from tide gauges appear to be steric (Levitus et al., 2005,
2009; Lombard et al., 2005a, 2005b; Ishii and Kimoto, 2009; Stammer
et al., 2013). Moreover, steric changes observed during the altimetry
era appear to be primarily thermosteric in nature, although haloster-
ic effects, which can reduce or enhance thermosteric changes, are
also important in some regions (e.g., Atlantic Ocean, Bay of Bengal).
Ocean models and ocean reanalysis-based results (Carton et al., 2005;
Wunsch and Heimbach, 2007; Stammer et al., 2011) as well as ocean
circulation models without data assimilation (Lombard et al., 2009)
confirm these results.
Observations and ocean reanalysis (Stammer et al., 2011; 2013) also
agree in showing that steric spatial patterns over the last half of the
20th century fluctuate in space and time as part of modes of the cou-
pled ocean–atmosphere system such as ENSO, the NAO, and the PDO
(Levitus et al., 2005; Lombard et al., 2005a; Di Lorenzo et al., 2010;
Lozier et al., 2010; Zhang and Church, 2012). In these cases, regional
sea level variability is associated with changing wind fields and result-
ing changes in the ocean circulation (Kohl and Stammer, 2008). For
example, the large rates of sea level rise in the western tropical Pacific
and of sea level fall in the eastern Pacific over the period 1993–2010
1192
Chapter 13 Sea Level Change
13
correspond to an increase in the strength of the trade winds in the
central and eastern tropical Pacific over the same period (Timmermann
et al., 2010; Merrifield and Maltrud, 2011; Nidheesh et al., 2012). The
long-term sea level trend from 1958 to 2001 in the tropical Pacific can
also be explained as the ocean’s dynamical response to variations in
the wind forcing (Qiu and Chen, 2006; Timmermann et al., 2010).
Spatial variations in trends in regional sea level may also be specific
to a particular sea or ocean basin. For example, a sea level rise of 5.4
± 0.3 mm yr
–1
in the region between Japan and Korea from 1993 to
2001 is nearly two times the GMSL trend, with more than 80% of this
rise being thermosteric (Kang et al., 2005). Han et al. (2010) found that
regional changes of sea level in the Indian Ocean that have emerged
since the 1960s are driven by changing surface winds associated with
a combined enhancement of Hadley and Walker Cells.
13.6.2 Coupled Model Intercomparison Project Phase 5
General Circulation Model Projections on Decadal
to Centennial Time Scales
CMIP5 projections of regional sea level provide information primar-
ily about dynamical sea level changes resulting from increased heat
uptake and changes in the wind forcing. On decadal time scales, the
CMIP5 model ensemble identifies strong interannual variability (up to
8 cm, root-mean square (RMS)) associated with ENSO and dynamics of
the equatorial current system in the tropical Pacific and Indian Oceans
(Figure 13.15a). Similar variability in the amplitude of sea level change
but due to other climate modes is also apparent in the North Atlantic
Current and in parts of the Southern Ocean.
60°S
30°S
30°N
60°N
a)
(mm)
0
10
20
30
40
50
60
70
60°S
30°S
30°N
60°N
b)
(mm)
10.0
−7.5
−5.0
−2.5
0.0
2.5
5.0
7.5
10.0
90°E 180° 90°W
90°E 180° 90°W
Figure 13.15 | (a) Root-mean square (RMS) interannual dynamic sea level variability
(millimetres) in a CMIP5 multi-model ensemble (21 models), built from the historically
forced experiments during the period 1951–2005. (b) Changes in the ensemble aver-
age interannual dynamic sea level variability (standard deviation (SD), in millimetres)
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.
Toward the end of the 21st century, the CMIP5 results indicate that it
is possible that the interannual to decadal variability of dynamic sea
level can weaken in some parts of the world ocean, for example, the
western low-latitude Pacific and parts of the Indian Ocean, whereas it
could be amplified in other parts, for example, the North Pacific, the
eastern tropical Pacific, the eastern subtropical Atlantic and the Arctic
(Figure 13.15b).
Longer-than-decadal-time-scale regional sea level changes can increas-
ingly be expected to result from long-term changes in the wind field,
changes in the regional and global ocean heat and freshwater content
and the associated dynamical adjustment (with associated redistribu-
tion of ocean properties), and (to a lesser extent) from atmospheric
pressure. The CMIP5 projections of steric sea level changes toward the
end of the 21st century reveal a clear regional pattern in dynamical sea
level change (Figure 13.16), in which the Southern Ocean shows a net
decline relative to the global mean, while the remaining global ocean
displays complex ridge-and-trough structures superimposed on a gen-
erally rising sea level (Yin, 2012). For example, in the North Atlantic, the
largest sea level rise is along and north of the North Atlantic Current,
but less so further to the south in the center of the warmer subtropical
gyre. A similar dipole pattern was observed in CMIP3 results there due
to a weakening of the AMOC which leads to a local steric sea level rise
east of North America, resulting in more water on the shelf and directly
impacting northeastern North America (Levermann et al., 2005; Lan-
derer et al., 2007; Yin et al., 2010). A similar pattern can be observed in
the North Pacific, but here and in other parts of the world ocean (e.g.,
Southern Ocean), regional sea level patterns are largely the result of
changes in wind forcing, associated changes in the circulation, and an
associated redistribution of heat and freshwater. Some regional chang-
es can also be expected to result from modifications in the expansion
coefficient due to changes in the ocean’s regional heat content (Kuhl-
brodt and Gregory, 2012).
The CMIP5 ensemble indicates that regions showing an enhanced sea
level toward the end of the 21st century coincide with those showing
the largest uncertainty (Figure 13.16b). Although this also appeared in
the earlier CMIP3 SRES A1B results, the CMIP5 results, by comparison,
show a general reduction in the ensemble spread, especially in high lati-
tudes. On a global average, this reduction is from 5.7 cm to 2.1 cm, RMS.
The contribution of changes of global ocean heat storage to regional
steric sea level anomalies is virtually certain to increase with time as
the climate warming signal increasingly penetrates into the deep ocean
(Pardaens et al., 2011a). For the last three decades of the 21st centu-
ry, the AR4 climate model ensemble mean shows a significant heat
storage increase (Yin et al., 2010), about half of which is stored in the
ocean below 700 m depth. Recent detection of ongoing changes in the
ocean salinity structure (Durack and Wijffels, 2010) (Section 3.3.2) may
also contribute to future regional steric sea level changes. Halosteric
effects can dominate in some regions, especially in regions of high-
latitude water mass formation where long-term heat and freshwater
changes are expected to occur (e.g., in the subpolar North Atlantic, the
Arctic, the Southern Ocean) (Yin et al., 2010; Pardaens et al., 2011a).
Because of an anticipated increase in atmospheric moisture transport
from low to high latitudes (Pardaens et al., 2003), halosteric anoma-
lies are positive in the Arctic Ocean and dominate regional sea level
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Sea Level Change Chapter 13
13
60°S
30°S
30°N
60°N
a)
(m)
0.00
0.06
0.12
0.18
0.24
0.30
60°S
30°S
30°N
60°N
b)
(m)
0.00
0.06
0.12
0.18
0.24
0.30
90°E 180° 90°W
90°E 180° 90°W
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 metres), using the RCP4.5 experi-
ment. The figure includes the globally averaged steric sea level increase of 0.18 ± 0.05
m. (b) Root-mean square (RMS) spread (deviation) of the individual model result around
the ensemble mean (metres). Note that the global mean is different from the value in
Table 13.5, by less than 0.01 m, because a slightly different set of CMIP5 models was
used (see the Supplementary Material).
anomalies there (Yin et al., 2010). It is likely that future thermosteric
changes will dominate the steric variations in the Southern Ocean, and
strong compensation between thermosteric and halosteric change will
characterize the Atlantic (Pardaens et al., 2011a).
13.6.3 Response to Atmospheric Pressure Changes
Regional sea level also adjusts to regional changes in atmospheric sea
level pressure relative to its instantaneous mean over the ocean. Over
time scales longer than a few days, the adjustment is nearly isostatic.
Sea level pressure is projected to increase over the subtropics and
mid-latitudes (depressing sea level) and decrease over high latitudes
(raising sea level), especially over the Arctic (order several millibars),
by the end of the 21st century associated with a poleward expansion
of the Hadley Circulation and a poleward shift of the storm tracks of
several degrees latitude (Section 12.4.4) (Held and Soden, 2006). These
changes may 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 for RCP4.5 and
RCP8.5, respectively (Yin et al., 2010) (Figure 13.17). In contrast, air
pressure changes oppose sea level rise in mid- and low latitudes albeit
with small amplitudes. Air pressure may also influence regional sea
level elsewhere, as demonstr ated by sea level changes in the Mediter-
ranean in the second half of the 20th century (Tsimplis et al., 2005).
13.6.4 Response to Freshwater Forcing
Enhanced freshwater fluxes derived from an increase in ice-sheet melt-
water at high latitudes results in a regional pattern of sea level rise
60°S
30°S
30°N
60°N
a)
60°S
30°S
30°N
60°N
b)
60°S
30°S
30°N
60°N
c)
60°S
30°S
30°N
60°N
d)
0.025 0.015 0.005 0.005 0.015 0.025
(m)
90°E 180° 90°W
90°E 180° 90°W
90°E 180° 90°W
90°E 180° 90°W
Figure 13.17 | Projected ensemble mean sea level change (metres) 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 0.005 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).
1194
Chapter 13 Sea Level Change
13
originating from adjustments in ocean dynamics and in the solid earth.
Neither effect is included in CMIP5 models, although the latter adjust-
ment is computed off line here.
13.6.4.1 Dynamic Ocean Response to Cryospheric Freshwater
Forcing
The addition of freshwater from glaciers and ice sheets to the ocean
leads to an instantaneous increase in global mean sea level, but
because it is communicated around the ocean basins via a dynamical
adjustment, it is not instantaneously globally uniform (Kawase, 1987;
Cane, 1989). For the addition of mass, the barotropic adjustment of
the ocean takes place in a few days (Gower, 2010; Lorbacher et al.,
2012). The addition of freshwater to the ocean from melting of the
Greenland ice sheet results in an additional basin-wide steric response
of the North Atlantic within months and is communicated to the global
ocean via boundary waves, equatorial Kelvin waves, and westward
propagating baroclinic Rossby waves on decadal time scales (Stammer,
2008). A similar response but with a different pattern can be observed
from Antarctic meltwater input. In both cases, an associated complete
baroclinic adjustment of the global ocean might take as long as sev-
eral centuries. The adjustment of the ocean to high-latitude meltwater
input also involves atmospheric teleconnections; such a response to
Greenland meltwater pulses could lead to sea level changes in the
Pacific within months (Stammer et al., 2011). On longer-than-decadal
time scales, the freshwater input to the North Atlantic raises sea level
in the Arctic Ocean and leads to an anomalous southward Bering Strait
throughflow, transporting colder, fresher water from the Arctic Ocean
into the North Pacific (Hu et al., 2010) and causing North Pacific cool-
ing (Okumura et al., 2009). Meltwater forcing in the subpolar North
Atlantic also causes changes of the AMOC (Section 12.4.7.2), which
in turn causes dynamical changes of sea level in the North Atlantic,
particularly in its northwestern region (Lorbacher et al., 2010). The
combination of this dynamic sea level rise and the global mean sea
level rise makes the northeastern North American coast vulnerable to
some of the fastest and largest sea level rises during this century (Yin
et al., 2009).
13.6.4.2 Earth and Gravitational Response to Contemporary
Surface Water Mass Redistribution
Deformational, rotational and gravitational responses to mass redis-
tribution between the cryosphere, the land and the oceans produce
distinctive regional departures from GMSL, referred to as sea level
fingerprints (Mitrovica et al., 2001, 2009; Gomez et al., 2010a; Riva
et al., 2010) (Section 13.1, FAQ 13.1). Many existing studies of these
effects have not defined a specific rate of ice-sheet mass loss (Mitro-
vica et al., 2001) or are based on end-member scenarios of ice retreat,
such as from the WAIS (Bamber et al., 2009; Mitrovica et al., 2009;
Gomez et al., 2010a) and marine-based parts of the East Antarctic ice
sheet (Gomez et al., 2010a). Bamber and Riva (2010) calculated the
sea level fingerprint of all contemporary land-ice melt and each of its
major components. Spada et al. (2013) examined the regional sea level
pattern from future ice melt based on the A1B scenario.
As can be seen from Figure 13.18, a characteristic of the sea level fin-
gerprints is that regions adjacent to the source of the mass loss are
subject to relative sea level fall of about an order of magnitude greater
than the equivalent GMSL rise from these mass contributions, whereas
in the far field the sea level rise is larger (up to about 30%) than the
global average rise (Mitrovica et al., 2001, 2009; Gomez et al., 2010a).
Gomez et al. (2010a) and Mitrovica et al. (2011) showed that differenc-
es in the maximum predicted rise (relative to the global mean) between
published results is due to the accuracy with which water expulsion
from the deglaciated marine basins is calculated. These changes are in
addition to the ongoing response to past changes (e.g., glacial isostatic
adjustment in response to the last deglaciation). Mitrovica et al. (2001)
suggested that the lower rates of sea level change inferred from tide
gauge records at European sites relative to the global average were
consistent with 20th century melting from Greenland. Similarly, Geh-
rels and Woodworth (2013) suggested that the larger magnitude of
the early 20th century sea level acceleration observed in Australia and
New Zealand, as compared with the North Atlantic, may represent a
fingerprint of the increased melt contributions of Greenland and Arctic
glaciers in the 1930s. Nevertheless, current rates of ice-sheet melting
are difficult to distinguish from dynamic variability (Kopp et al., 2010;
Hay et al., 2013), but it is likely that with further ice-sheet melting
they will begin to dominate the regional patterns of sea level change
toward the end of the 21st century, especially under climate forcing
conditions for which ice-sheet melting contributes more than 20-cm
equivalent sea level rise (Kopp et al., 2010). These changes are in addi-
tion to the ongoing response to past changes (e.g., GIA in response to
the last deglaciation; Figure 13.18a).
Water mass redistributions associated with land hydrology changes
other than those from land ice may also produce spatially variable fin-
gerprints in sea level (Fiedler and Conrad, 2010). In particular, region-
al changes in the terrestrial storage of water can lead to a sea level
response on interannual and longer time scales, specifically near large
river basins (Riva et al., 2010).
13.6.5 Regional Relative Sea Level Changes
Regional relative sea level change projections can be estimated from a
combination of the various contributions to sea level change described
above, emerging from the ocean, atmospheric pressure loading and
the solid Earth.
Over the next few decades, regional relative sea level changes over
most parts of the world are likely to be dominated by dynamical chang-
es (mass redistribution and steric components) resulting from natu-
ral variability, although exceptions are possible at sites near rapidly
melting ice sheets where static effects could become large. However,
towards the end of the 21st century, regional patterns in sea level from
all other contributions will progressively emerge and eventually domi-
nate over the natural variability.
Ensemble mean estimates of relative sea level change during the
period 2081–2100 relative to 1986–2000 resulting from GIA and from
glacier and ice-sheet melting for RCP4.5 and RCP8.5 scenarios (Figure
13.18) suggest that for the 21st century, past, present and future loss
of land-ice mass will very likely be an important contributor to spatial
patterns in relative sea level change, leading to rates of maximum rise
at low-to-mid latitudes. Hu et al. (2011) and Sallenger et al. (2012) also
1195
Sea Level Change Chapter 13
13
60°S
30°S
30°N
60°N
a)
60°S
30°S
30°N
60°N
b)
60°S
30°S
30°N
60°N
c)
0.1 0.0 0.1 0.2 0.3
(m)
90°E 180° 90°W
90°E 180° 90°W
90°E 180° 90°W
60°S
30°S
30°N
60°N
a)
60°S
30°S
30°N
60°N
b)
60°S
30°S
30°N
60°N
c)
0.4 0.2 0.0 0.2 0.40.6 0.8
(m)
90°E 180° 90°W
90°E 180° 90°W
90°E 180° 90°W
suggested that steric and dynamical sea level changes can potentially
increase the sea level near the northeastern coast of North America
and in the western Pacific. Considerable uncertainties remain, howev-
er, in both the sea level budget and in the regional expression of sea
level rise. In addition, local sea level rise can also partly be compensat-
ed by vertical land movement resulting from GIA, especially in some
formerly glaciated high-latitude regions where high rates of land uplift
may lead to a decrease of relative sea level. For example, Johansson et
al. (2014) reported a 29 cm sea level rise in the Gulf of Finland and 27
cm fall in the Bay of Bothnia.
The ensemble mean regional relative sea level change between
1986–2005 and 2081–2100 for the RCP4.5 scenario (not including
the dynamic ocean contribution in response to the influx of freshwater
associated with land-ice loss and changes in terrestrial ground water)
reveals that many regions are likely to experience regional sea level
changes that differ substantially from the global mean (Figure 13.19).
Figure 13.18 | Ensemble mean regional contributions to sea level change (metres)
from (a) glacial isostatic adjustment (GIA), (b) glaciers and (c) ice-sheet surface mass
balance (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.
Figure 13.20 shows ensemble mean regional relative sea level change
between 1986–2005 and 2081–2100 for RCPs 2.6, 6.0 and 8.5.
It is very likely that over about 95% of the world ocean, regional rela-
tive sea level rise will be positive, while most regions that will experi-
ence a sea level fall are located near current and former glaciers and
ice sheets. Figure 13.21b shows that over most of the oceans (except
for limited regions around western Antarctica, Greenland, and high
Arctic regions), estimated regional sea level changes are significant
at the 90% confidence limit. Local sea level changes deviate more
than 10% and 25% from the global mean projection for as much as
30% and 9% of the ocean area, respectively, indicating that spatial
Figure 13.19 | (a) Ensemble mean regional relative sea level change (m) evaluated
from 21 models of the CMIP5 scenario RCP 4.5, including atmospheric loading, plus
land-ice, GIA and terrestrial water sources, between 1986–2005 and 2081–2100.
Global mean is 0.48 m, with a total range of -1.74 to +0.71 m. (b) The local, lower 90%
uncertainty bound (p=0.05) for RCP4.5 scenario sea level rise (plus non-scenario com-
ponents). (c) The local, upper 90% uncertainty bound (p=0.95) for RCP4.5 scenario sea
level rise (plus non-scenario components). Note that the global mean is different from
the value in Table 13.5, by less than 0.01 m, because a slightly different set of CMIP5
models was used (see the Supplementary Material) and that panels (b) and (c) contain
local uncertainties not present in global uncertainties.
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Chapter 13 Sea Level Change
13
60°S
30°S
30°N
60°N
a)
60°S
30°S
30°N
60°N
b)
60°S
30°S
30°N
60°N
c)
60°S
30°S
30°N
60°N
d)
0.4 0.2 0.0 0.2 0.4 0.6 0.8
(m)
90°E 180° 90°W
90°E 180° 90°W
90°E 180° 90°W
90°E 180° 90°W
60°S
30°S
30°N
60°N
a)
(%)
−50
−30
−10
10
30
50
60°S
30°S
30°N
60°N
b)
(std. err.)
−4
−2
0
2
4
90°E 180° 90°W
90°E 180° 90°W
Figure 13.20 | Ensemble mean regional relative sea level change (metres) 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, glacial isostatic adjustment (GIA) and terrestrial water sources.
Figure 13.21 | (a) Percentage of the deviation of the ensemble mean regional relative
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.
variations can be large. Regional changes in sea level reach values of
up to 30% above the global mean value in the Southern Ocean and
around North America, between 10 and 20% in equatorial regions
and up to 50% below the global mean in the Arctic region and some
regions near Antarctica (Figure 13.21a).
Figure 13.22 shows that, between 1986–2005 and 2081–2100, sea
level changes along the world’s coastlines associated with the RCP4.5
and RCP8.5 scenarios have a substantially skewed non-Gaussian dis-
tribution, with significant coastal deviations from the global mean.
When the coastlines around Antarctica and Greenland are excluded
(Figure 13.22b), many negative changes disappear, but the general
structures of the global histograms remain. In general, changes along
the coastlines will range from about 30 cm to 55 cm for an RCP 4.5
scenario, peaking near 50 cm, and from about 40 cm to more than 80
cm under a RCP 8.5 scenario, peaking near 65 cm. About 68% and
72% of the coastlines will experience a relative sea level change within
±20% of the GMSL change for RCP4.5 and RCP8.5, respectively. In
both cases, the maximum of the histogram is slightly higher than the
GMSL, whereas the arithmetic mean is lower. Only some coastlines will
experience a sea level rise of up to about 40% above GMSL change.
Figure 13.23 shows the combination of the natural variability (annual
mean) and the CMIP5 projected sea level rise for the RCP4.5 scenar-
io for a number of locations distributed around the world. For exam-
ple, at Pago Pago (14°S,195°E) in the western equatorial Pacific, the
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Sea Level Change Chapter 13
13
0.4 0.2 0.0 0.2 0.4 0.6 0.8 1.0
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Fraction of total coastline
a)
RCP4.5
RCP8.5
0.4 0.2 0.0 0.2 0.4 0.6 0.8 1.0
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Fraction of total coastline
m SSH change (2081-2100 minus 1986-2005)
b)
RCP4.5
RCP8.5
historical record indicates that annual variability in mean sea level has
been about 21 cm (5 to 95% range). Projections by individual climate
models indicate that it is very likely that a similar range of natural vari-
ability will continue through the 21st century (Figure 13.15b). However,
by 2100, the average projected sea level for the RCP4.5 scenario of
0.52 [0.32 to 0.70] m is greater than any observations of annual mean
sea level in the instrumental record. Of all the examples shown, the
greatest sea level increase will be in New York, which is representative
of the enhanced sea level rise there due to ocean processes and GIA in
the region (compare Figures 13.16 and 13.18). The figure also reveals
the large spatial inhomogeneity of interannual to decadal variability. In
each case, monthly variability and extreme sea levels from winds and
waves associated with weather phenomena (Section 13.7) need to be
considered in addition to these projections of regional sea level.
Figure 13.22 | (a) Histograms of the deviation of the ensemble mean regional rela-
tive sea level change (Figure 13.20) along all coastlines (represented by the closest
model grid 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.
13.6.6 Uncertainties and Sensitivity to Ocean/Climate
Model Formulations and Parameterizations
Uncertainties of climate models are discussed in detail in Chapter 9.
Sea level is a property of the ocean connected to nearly all dynamical
and thermodynamical processes over the full ocean column, from the
surface fluxes to the ocean bottom. Although many of the process-
es are to first order correctly simulated in climate models, differences
between models (Figure 13.24) indicate that uncertainties in simulated
and projected steric sea level (globally and regionally) remain poorly
understood. Moreover, the spread in ocean heat uptake efficiency
among models is responsible for 50% of the spread in heat uptake
(Kuhlbrodt and Gregory, 2012). In addition, some processes are not
part of the CMIP5 simulations, such as the dynamical response of the
ocean to meltwater input or the GIA/rotational/gravitational processes
associated with this ice mass loss. Stammer and Hüttemann (2008)
showed that coupled climate models that do not include the effect of
changes in atmospheric moisture content on sea level pressure will
underestimate future regional atmospheric pressure loading effects by
up to 2 cm. Other uncertainties result from GIA/rotational/gravitational
effects as well as from uncertainties in air–sea fluxes.
Improvements in the skill of a sea level projection require (1) better
parameterizations of unresolved physical processes, (2) improved
numerical algorithms for such processes as temperature and salinity
advection, (3) refined grid resolution to better represent such features
as boundary currents and mesoscale eddies, and (4) the elimination of
obsolete assumptions that have a direct impact on sea level (Griffies
and Greatbatch, 2012). Among the many limiting approximations
made in ocean models, the Boussinesq approximation has been found
to only marginally impact regional patterns (i.e., deviations from global
mean) when directly compared to non-Boussinesq simulations (Losch
et al., 2004), thus lending greater confidence in Boussinesq models
for addressing questions of regional sea level change. Furthermore,
for global sea level, the now-standard a posteriori adjustment (Great-
batch, 1994; Griffies and Greatbatch, 2012) accurately incorporates
the missing global steric effect. The representation of dense overflows
can also affect sea level simulations, and is particularly problematic in
many ocean models used for climate studies, with direct impacts on
the simulated vertical patterns of ocean heat uptake (Legg et al., 2009).
Coarse-resolution ocean–climate simulations require a parameter-
ization of mesoscale and smaller eddies, but the parameterizations
as well as the details of their numerical implementations can great-
ly impact the simulation. As shown by Hallberg and Gnanadesikan
(2006) and Farneti et al. (2010), coarse-resolution climate models may
be overestimating the Antarctic Circumpolar Current response to wind
changes. Better implementations of eddy parameterizations reduce
such biases (Farneti and Gent, 2011; Gent and Danabasoglu, 2011),
and they form the basis for some, but not all, of the CMIP5 simulations.
Moreover, Vinogradov and Ponte (2011) suggested that as one consid-
ers regional sea level variability and its relevant dynamics and forcing,
mesoscale ocean features become important factors on a sub-decadal
time scale. Suzuki et al. (2005) compared changes in mean dynam-
ic sea level in 2080–2100 relative to 1980–2000 as obtained from a
low- and a high-resolution ocean component of a coupled model and
concluded that although changes are comparable between runs, the
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Chapter 13 Sea Level Change
13
Figure 13.23 | Observed and projected relative 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.
Coloured 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 to 95%) of the likely (medium confidence) sea level change at each respective location at the year 2100 inferred from RCPs 2.6 (dark blue),
4.5 (light blue), 6.0 (yellow) and 8.5 (red).
IJmuiden
c)
New York
b)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
San Francisco
a)
(m)
Brest
f)
Kanmen, China
e)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Bay of Bengal
d)
(m)
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Pago Pago
i)
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
Fremantle
h)
1970
1980
1990
2000
2010
2020
2030
2040
2050
2060
2070
2080
2090
2100
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Mar del Plata, Argentina
g)
(m)
high-resolution model captures enhanced details owing to resolving
ocean eddy dynamics.
Even with a perfect ocean model, skill in sea level projections depends
on skill of the coupled climate model in which errors impacting sea
level may originate from non-ocean components. Furthermore, initiali-
zation is fundamental to the prediction problem, particularly for simu-
lation of low-frequency climate variability modes (Meehl et al., 2010).
Projections of land-ice melting and the resultant sea level rise patterns
also have large uncertainties, with additional uncertainties arising from
GIA models such as the mantle viscosity structure. Each of the many
uncertainties and errors results in considerable spread in the projected
patterns of sea level change (Figure 13.24) (Pardaens et al., 2011a;
Slangen et al., 2012). In addition to ocean–climate model formulations
and parameterizations, uncertainty in predictions of sea level change
may be associated with specified freshwater forcing. Whether or not
an ocean model is coupled with an ice-sheet model, the forcing should
distinguish between runoff and iceberg flux. Martin and Adcroft (2010)
reported the only attempt thus far to explicitly represent iceberg drift
and melting in a fully coupled climate model.
1199
Sea Level Change Chapter 13
13
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.
1200
Chapter 13 Sea Level Change
13
13.7 Projections of 21st Century Sea Level
Extremes and Waves
Climate change will affect sea levels extremes and ocean waves in two
principal ways. First, because extratropical and tropical storms are one
of the key drivers of sea level extremes and waves, future changes in
intensity, frequency, duration, and path of these storms will impact
them. Second, sea level rise adds to the heights of sea level extremes,
regardless of any changes in the storm-related component. MSL
change may also accentuate the threat of coastal inundation due to
changes in wave runup. Observations of changes in sea level extremes
and waves are discussed in Chapter 3. Sea level extremes at the coast
occur mainly in the form of storm surges and tsunamis, but because the
latter are not climate driven, we assess only projections for sea level
extremes based on estimates of future storminess and MSL change.
13.7.1 Observed Changes in Sea Level Extremes
As discussed in the AR4 (Bindoff et al., 2007) and confirmed by more
recent studies (Menéndez and Woodworth, 2010), statistical analyses
of tide-gauge observations have shown an increase in observed sea
level extremes worldwide that are caused primarily by an increase
in MSL (Chapter 3). Dominant modes of climate variability, particu-
larly ENSO and NAO, also have a measureable influence on sea level
extremes in many regions (Lowe et al., 2010; Walsh et al., 2011). These
impacts are due to sea level anomalies associated with climate modes,
as well as mode-related changes in storminess. There has been some
indication that the amplitude and phase of major tidal constituents
have exhibited long-term change (Jay, 2009; Muller et al., 2011), but
their impacts on extreme sea level are not well understood. Using par-
ticle size analysis of cores collected in the Mackenzie Delta in the Arctic
region, Vermaire et al. (2013) inferred increased storm surge activity in
the region during the last approximately 150 years, which they related
to the annual mean temperature anomaly in the NH and a decrease in
summer sea-ice extent.
13.7.2 Projections of Sea Level Extremes
13.7.2.1 Recent Assessments of Projections of Sea Level Extremes
The AR4 assessed projections of storm surges for a few regions (Europe,
Australia, the Bay of Bengal) based on a limited number of dynamical
modelling studies (Christensen et al., 2007). Although these results
generally indicated higher magnitude surges in future scenarios, there
was low confidence in these projections because of the wide spread in
underlying AOGCM and RCM projections.
Studies since the AR4 have further assessed the relative contributions
of sea level rise and storminess on projected sea level extremes. Lowe
et al. (2010) concluded that the increases in the observed sea level
extremes in the 20th century occurred primarily through an increase
in MSL, and that the same applies to projections for the 21st centu-
ry. The IPCC Special Report on Managing the Risks of Extreme Events
and Disasters to Advance Climate Change Adaptation (SREX) assess-
ment concluded that it is very likely that MSL rise will contribute to
an increase in future sea level extremes (Seneviratne et al., 2012). It
noted that changes in storminess may also affect sea level extremes
but the limited geographical coverage of studies and uncertainties
associated with storminess changes prevent a general assessment. The
global tropical cyclone frequency will likely decrease or remain roughly
constant, but it is more likely than not that the frequency of the most
intense storms will increase in some ocean basins (Chapter 14). Uncer-
tainties in projections of cyclone frequency and tracks make it difficult
to project how these changes will impact particular regions. Similarly,
while the SREX and the current assessment (Chapter 14) find that it is
likely that there has been a poleward shift in the main northern and
southern extra-tropical cyclone tracks during the last 50 years, and that
regional changes may be substantial, there is only low confidence in
region-specific projections.
13.7.2.2 Projections Based on Dynamical and Statistical
Approaches
Projected changes in storm surges (relative to MSL) have been
assessed by applying climate–model forcing to storm-surge models.
Return periods of sea level extremes (see Glossary) exceeding a given
threshold level, referred to as return levels, are used in quantifying
projected changes. Using three regionally downscaled GCMs for A2,
B2 and A1B scenarios, Debernard and Roed (2008) found an 8 to 10%
increase in the 99th percentile surge heights between 1961–1990 and
2071–2100, mainly during the winter season, along the coastlines of
the eastern North Sea and the northwestern British Isles, and decreas-
es south of Iceland. Using a downscaled GCM under an A1B scenario,
Wang et al. (2008) projected a significant increase in wintertime storm
surges around most of Ireland between 1961–1990 and 2031–2060.
Sterl et al. (2009) concatenated the output from a 17-member ensem-
ble of A1B simulations from a GCM over the periods 1950–2000 and
2050–2100 into a single longer time series to estimate 10,000-year
return levels of surge heights along the Dutch coastline. No statis-
tically significant change in this value was projected for the 21st
century because projected wind speed changes were not associated
with the maximum surge-generating northerlies. Using an ensemble
of three climate models under A2 simulations, Colberg and McInnes
(2012) found that changes in the 95th percentile sea level height
(with respect to mean sea level) across the southern Australian coast
in 2081–2100 compared to 1981–2000 were small (±0.1 m), mostly
negative, and despite some inter-model differences, resembled the
changes in wind patterns simulated by the climate models (McInnes
et al., 2011). These studies demonstrate that the results are sensitive
to the particular choice of GCM or RCM, therefore identifying uncer-
tainties associated with the projections. For the tropical east coast of
Australia, Harper et al. (2009) found that a 10% increase in tropical
cyclone intensity for 2050 led to increases in the 100-year return level
(including tides) that at most locations were smaller than 0.1 m with
respect to mean sea level.
Several regional storm-surge studies have considered the relative con-
tribution of the two main causative factors on changes in future sea
level extremes (e.g., McInnes et al. (2009, 2013) for the southeastern
coast of Australia; Brown et al. (2010) for the eastern Irish Sea; Woth et
al. (2006) for the North Sea; Lowe et al. (2009) for the United Kingdom
coast). They concluded that sea level rise has a greater potential than
meteorological changes to increase sea level extremes by the end of
the 21st century in these locations. Unnikrishnan et al. (2011) used
1201
Sea Level Change Chapter 13
13
RCM simulations to force a storm-surge model for the Bay of Bengal
and found that the combined effect of MSL rise of 4 mm yr
–1
and RCM
projections for the A2 scenario (2071–2100) gave an increase in 100-
year return levels of total sea level (including tides) between 0.40 to
0.67 m (about 15 to 20%) along the northern part of the east coast
of India, except around the head of the bay, compared to those in the
base line (1961–1990) scenario.
Using six hypothetical hurricanes producing approximate 100-year
return levels, Smith et al. (2010) found that in the regions of large
surges on the southeastern Louisiana coast, the effect of MSL rise
added linearly to the simulated surges. However, in the regions of
moderate surges (2–3 m), particularly in wetland-fronted areas, the
increase in surge height was 1–3 m larger than the increase in mean
sea level rise. They showed that sea level rise alters the speed of prop-
agation of surges and their amplification in different regions of the
coast. For the Gulf of Mexico, Mousavi et al. (2011) developed a simple
relationship between hurricane-induced storm surges, sea level rise
and hurricane intensification through increased SSTs for three mod-
elled major historical cyclones, concluding that the dynamic interaction
of surge and sea level rise lowered or amplified the surge at different
points within a shallow coastal bay.
Higher mean sea levels can significantly decrease the return period
for exceeding given threshold levels. For a network of 198 tide gauges
covering much of the globe, Hunter (2012) determined the factor by
which the frequency of sea levels exceeding a given height would be
increased for a MSL rise of 0.5 m (Figure 13.25a). These calculations
have been repeated here (Figure 13.25b) using regional RSL projec-
tions and their uncertainty using the RCP4.5 scenario (Section 13.6,
Figure 13.19a). This multiplication factor depends exponentially on the
inverse of the Gumbel scale parameter (a factor that describes the sta-
tistics of sea level extremes caused by the combination of tides and
storm surges) (Coles and Tawn, 1990). The scale parameter is generally
101 100 1000
60°S
30°S
30°N
60°N
60°E 60°W 120°E 120°W180°
101 100 1000
60°S
30°S
30°N
60°N
60°E 60°W 120°E 120°W180°
Figure 13.25 | The estimated multiplication factor (shown at tide gauge locations by colored dots), by which the frequency of flooding events of a given height increase for (a) a
mean sea level (MSL) rise of 0.5 m (b) using regional projections of MSL for the RCP4.5 scenario, shown in Figure13.19a.
1202
Chapter 13 Sea Level Change
13
large where tides and/or storm surges are large, leading to a small
multiplication factor, and vice versa. Figure 13.25a shows that a 0.5
m MSL rise would likely result in the frequency of sea level extremes
increasing by an order of magnitude or more in some regions. The mul-
tiplication factors are found to be similar or slightly higher, in general,
when accounting for regional MSL projections (Figure 13.25b). Specifi-
cally, in regions having higher regional projections of MSL, such as the
east coast of Canada and the USA (where GIA results in a larger sea
level rise) and/or in regions of large uncertainty (e.g. in regions near
the former Laurentide ice sheet where the GIA uncertainty is large), the
multiplication factor is higher, whereas in regions having lower region-
al projections of MSL, such as the northwest region of North America
(where the land is rising due to present changes in glaciers and ice-
caps), the multiplication factor is lower. In another study, large increas-
es in the frequency of sea level extremes for 2050 were found for a
network of sites around the USA coastline based on semi-empirical
MSL rise projections and 20th century statistics of extremes (Tebaldi et
al., 2012). Using projected time series of tides, MSL rise, components of
sea level fluctuations from projected MSLP and wind stress fields, and
a contribution for ENSO variability through projected SSTs for the 21st
century, Cayan et al. (2008) showed that for high-end scenarios of MSL
rise, the frequency and magnitude of extremes along the California
coast increases considerably relative to those experienced in the 20th
century.
In summary, dynamical and statistical methods on regional scales
show that it is very likely that there will be an increase in the occur-
rence of future sea level extremes in some regions by 2100, with a
likely increase in the early 21st century. The combined effects of MSL
rise and changes in storminess will affect future extremes. There is
high confidence that extremes will increase with MSL rise yet there is
low confidence in region-specific projections in storminess and storm
surges.
13.7.3 Projections of Ocean Waves
Changes in ocean wave conditions are determined by changes in the
major wind systems, especially in the main areas affected by tropi-
cal and extra-tropical storms. Based on in situ and satellite altimeter
observations and wave–model hindcasts, it is likely that mean signif-
icant wave heights (SWH, defined as the average of the highest one
third of wave heights) have increased in regions of the North Pacific
and the North Atlantic over the past half century, and in the South-
ern Ocean since the mid 1980s (Chapter 3, Seneviratne et al., 2012).
The limited observational wave record makes it difficult to separate
long-term trends from multi decadal variability (Young et al., 2011). A
number of studies have related changes in wind–wave climatologies
to modes of climate variability such as ENSO (Allan and Komar, 2006;
Adams et al., 2008; Menéndez et al., 2008), the NAO (Woolf et al.,
2002; Izaguirre et al., 2010), and the Southern Annular Mode (SAM)
(Hemer et al., 2010; Izaguirre et al., 2011). Although anthropogenic
influences have been considered (Wang et al., 2009), it is likely that
reported SWH trends over the past half-century largely reflect natural
variations in wind forcing. Recent reductions in summer sea ice extent
have resulted in enhanced wave activity in the Arctic Ocean due to
increased fetch area and longer duration of the open-water season
(Francis et al., 2011; Overeem et al., 2011).
In general, there is low confidence in projections of future storm
conditions (Chapters 12 and 14) and hence in projections of ocean
waves. Nevertheless, there has been continued progress in translating
climate model outputs into wind–wave projections. In the AR4, project-
ed changes in global SWHs were based on a single statistical model
(Wang and Swail, 2006). The projected conditions were consistent with
increased wind speeds associated with mid-latitude storms, but they
considered only a limited five-member ensemble for a single future
emission scenario (SRES A2); wave parameters other than SWH were
not considered.
Since the AR4, global wave–climate projections for the end of the 21st
century have been made by dynamically downscaling CMIP3 AOGCM
results. A multi-model ensemble based on dynamical models forced
with various GHG emission scenarios (SRES A1B: Mori et al. (2010),
Fan et al. (2013), Semedo et al. (2013); SRES A2: Hemer et al. (2012a),
as well as the statistical model of Wang and Swail (2006) forced with
emission scenarios IS92a and SRES A2 and B2, has been constructed
as part of the Coordinated Ocean Wave Climate Project (COWCLIP)
(Hemer et al., 2013). In general, the ensemble projected changes of
annual mean SWH (Figure 13.26a) resemble the statistical projections
of Wang and Swail (2006) under an A2 scenario. The largest change
is projected to be in the Southern Ocean, where mean SWHs at the
end of the 21st century are approximately 5 to 10% higher than the
present-day mean. SWH increase in this region reflects the projected
strengthening of the westerlies over the Southern Ocean, particu-
larly during austral winter (Figure 13.26c). Another region of SWH
increase in the ensembles is in the tropical South Pacific associated
with a projected strengthening of austral winter easterly trade winds
in the CMIP3 multi-model data set (Figure 13.26c). Negligible change
or a mean SWH decrease is projected for all other ocean basins, with
decreases identified in the trade wind region of the North Pacific, the
mid-latitude westerlies in all basins, and in the trade and monsoon
wind regions of the Indian Ocean. Hemer et al. (2013) found that var-
iance of wave–climate projections associated with wave downscaling
methodology dominated other sources of variance within the projec-
tions such as the climate scenario or climate model uncertainties. Mori
et al. (2013) reported similar findings.
Three CMIP3-based model projections (Mori et al., 2010; Hemer et al.,
2012b; Fan et al., 2013) were used to compare projections of wave
direction and period (Hemer et al., 2013). Wave direction (Figure
13.26d) exhibits clockwise rotation in the tropics, consistent with a
higher contribution from northward propagating swell from the South-
ern Ocean. Wave period (Figure 13.26e) shows an increase over the
eastern Pacific, which is also attributed to enhanced wave generation
in the Southern Ocean and northward swell propagation. A projected
decrease in wave periods in the North Atlantic and western and central
North Pacific is symptomatic of weaker wind forcing in these regions.
SWH projections based on CMIP5 winds for emission scenarios RCP4.5
and RCP8.5 (Dobrynin et al., 2012) exhibit similar regional patterns for
the end of the 21st century to the CMIP3 results presented in Figure
13.26A. Dobrynin et al. (2012) reported SWH increases in the Arctic
Ocean, an area not considered by Hemer et al. (2013), and in basins
connected to the Southern Ocean, particularly for RCP8.5. The proba-
bility of extreme wave heights is projected to increase in the SH, the
1203
Sea Level Change Chapter 13
13
60
o
E 120
o
E 180
o
W 120
o
W 60
o
W 0
o
60
o
S
30
o
S
0
o
30
o
N
60
o
N
a)
b) c)
-10 -5 0 5 10
Δ H
S
(%)
d)
-10 -5 0 5 10
°Anti−clockwise °Clockwise
e)
-0.25 0 0.25
Δ T
M
(s)
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 means of January to March significant wave height. (c)
Percentage difference in means of July to September 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.
1204
Chapter 13 Sea Level Change
13
Arctic and Indian Oceans, but decrease in the North and Equatorial
Atlantic and in the Pacific. In addition to wind changes, the project-
ed loss of summer sea ice extent in the Arctic Ocean is very likely to
increase overall wave activity there (Manson and Solomon, 2007;
Overeem et al., 2011).
Model intercomparisons are starting to identify common features of
global wave projections but in general there is low confidence in wave
model projections because of uncertainties regarding future wind
states, particularly storm geography, the limited number of model sim-
ulations used in the ensemble averages, and the different methodolo-
gies used to downscale climate model results to regional scales (Hemer
et al., 2012a). Despite these uncertainties, it appears likely (medium
confidence) that enhanced westerly surface winds in the SH (discussed
in Chapter 12) will lead to enhanced wave generation in that region by
the end of the 21st century.
A number of dynamical wave projection studies have been carried out
with a regional focus. For the Mediterranean Sea, Lionello et al. (2008;
2010) projected a widespread shift of the wave height distribution to
lower values by the mid-21st century under an SRES A1B scenario,
implying a decrease in mean and extreme wave heights. Caires et al.
(2008) and Debernard and Røed (2008) reported a decrease (4 to 6%
of present values) in the annual 99th percentile SWH south of Iceland
by the end of the 21st century, and an increase (6 to 8%) along the
North Sea east coast (SRES A2, B2, A1B scenarios). Grabemann and
Weisse (2008) found increases (up to 18% of present values) in annual
99th percentile SWH in the North Sea by the end of the 21st century,
with an increase in the frequency of extreme wave events over large
areas of the southern and eastern North Sea (SRES A2, B2 scenarios).
Charles et al. (2012) projected a general decrease in wave heights in
the Bay of Biscay by the end of the 21st century (SRES A2, A1B, B1
scenarios), accompanied by clockwise rotations in winter swell (attrib-
uted to a projected northward shift in North Atlantic storm tracks) and
summer sea and intermediate waves (attributed to a projected slack-
ening of westerly winds). Along the Portuguese coast, Andrade et al.
(2007) found little projected change in SWH and a tendency for a more
northerly wave direction than present (SRES A2 scenario).
In the Pacific, multi-model projections by Graham et al. (2013) (SRES
A2 scenario) indicate a decrease in boreal winter upper-quantile SWHs
over the mid-latitude North Pacific by the end of the 21st century asso-
ciated with a projected decrease in wind speeds along the southern
flank of the main westerlies. There is a less robust tendency for higher
extreme waves at higher latitudes. On the southeastern Australian
coast, Hemer et al. (2012b) used multi-model projections (SRES A2 and
B1 scenarios) to identify a decrease in mean SWH (<0.2 m) by the end
of the 21st century compared to present due to a projected decrease
in regional storm wave energy, and a shift to a more southerly wave
direction, consistent with a projected southward shift of the subtropi-
cal ridge in the forcing fields.
13.8 Synthesis and Key Uncertainties
There has been significant progress in our understanding of sea level
change since the AR4. Paleo data now provide high confidence that
sea levels were substantially higher when GHG concentrations were
higher or surface temperatures were warmer than pre-industrial. The
combination of paleo sea level data and long tide gauge records
confirms that the rate of rise has increased from low rates of change
during the late Holocene (order tenths of mm yr
–1
) to rates of almost
2 mm yr
–1
averaged over the 20th century, with a likely continuing
acceleration during the 20th century (Figure 13.27). Since 1993, the
sum of observed contributions to sea level rise is in good agreement
with the observed rise.
Understanding of the components that contribute to total sea level
rise has improved significantly. For the 20th century, the range from
an ensemble of such process-based models encompasses the observed
rise when allowances are made for lack of inclusion of volcanic forcing
in AOGCM control simulations, natural climate variability, and a pos-
sible small long-term ice-sheet contribution. Ice-sheet contributions to
the 20th century sea level rise were small, however, and this agreement
is thus not an evaluation of ice-sheet models. Nevertheless, there has
been significant improvement in accounting for important physical
processes in ice-sheet models, particularly of the dynamical response
of individual glacier systems to warmer ocean waters in the immediate
vicinity of the outlet glaciers. Although there are as yet no complete
simulations of regional ocean temperature changes near ice sheets
and of the ice-sheet response to realistic climate change forcing, the
publications to date have allowed an assessment of the likely range of
sea level rise for the 21st century (Figure 13.27).
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.
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Sea Level Change Chapter 13
13
These observations, together with our current scientific understanding
and projections of future climate and sea level, imply that it is virtually
certain that sea level will continue to rise during the 21st century and
beyond. For the first few decades of the 21st century, regional sea level
change will be dominated by climate variability superimposed on the
climate change signal. For all scenarios, the rate of 21st century GMSL
rise is very likely to exceed the average rate during the 20th century.
For the RCP8.5 scenario, the projected rate of GMSL rise by the end of
the 21st century will approach average rates experienced during the
deglaciation of the Earth after the Last Glacial Maximum. These rates
imply a significant transfer of mass from the ice sheets to the oceans
and associated regional departures of sea level rise from the global
average, in addition to the regional patterns from changing atmos-
phere–ocean interactions.
Sea level rise has already led to a significant increase in the return
frequency of sea level extremes at many locations, and it is very likely
that this will continue during the 21st century, although there is low
confidence in projections of changes in storminess. The first assess-
ment of surface waves indicates a likely (medium confidence) increase
in the height of waves in the Southern Ocean.
Despite this progress, significant uncertainties remain, particularly
related to the magnitude and rate of the ice-sheet contribution for
the 21st century and beyond, the regional distribution of sea level rise,
and the regional changes in storm frequency and intensity. For coastal
planning, sea level rise needs to be considered in a risk management
framework, requiring knowledge of the frequency of sea level variabili-
ty (from climate variability and extreme events) in future climates, pro-
jected changes in mean sea level, and the uncertainty of the sea level
projections (Hunter, 2010, 2012), as well as local issues such as the
compaction of sediments in deltaic regions and the changing supply
of these sediments to maintain the height of the deltas (Syvitski et al.,
2009). Although improved understanding has allowed the projection
of a likely range of sea level rise during the 21st century, it has not
been possible to quantify a very likely range or give an upper bound
to future rise. The potential collapse of ice shelves, as observed on the
Antarctic Peninsula (Rignot et al., 2004; Scambos et al., 2004; Rott
et al., 2011), could lead to a larger 21st century rise of up to several
tenths of a metre.
Sea level will continue to rise for centuries, even if GHG concentra-
tions are stabilized, with the amount of rise dependent on future GHG
emissions. For higher emission scenarios and warmer temperatures,
surface melting of the Greenland ice sheet is projected to exceed accu-
mulation, leading to its long-term decay and a sea level rise of metres,
consistent with paleo sea level data.
Acknowledgements
We thank Lea Crosswell and Louise Bell for their assistance in drafting
a number of diagrams in this chapter and Jorie Clark for assistance
with managing chapter references.
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Chapter 13 Sea Level Change
13
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