Editing IPCC:AR6/SROCC/Chapter-4 (section)

== 4.2 Physical Basis for Sea Level Change and Associated Hazards ==

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As a consequence of natural and anthropogenic changes in the climate system, sea level changes are occurring on temporal and spatial scales that threaten coastal communities, cities, and low-lying islands. Sea level in this context means the time average height of the sea surface, thus eliminating short duration fluctuations like waves, surges and tides. GMSL rise refers to an increase in the volume of ocean water caused by warmer water having a lower density, and by the increase in mass caused by loss of land ice or a net loss in terrestrial water reservoirs. Spatial variations in volume changes are related to spatial changes in the climate. In addition, mass changes due to the redistribution of water on the Earth’s surface and deformation of the lithosphere leads to a change in the Earth’s rotation and gravitational field, producing distinct spatial patterns in regional sea level change. In addition to the regional changes associated with contemporary ice and water redistribution, the solid Earth may cause sea level changes due to tectonics, mantle dynamics or glacial isostatic adjustment (see Section 4.2.1.5). These processes cause vertical land motion (VLM) and sea surface height changes at coastlines. Hence, RSL change is defined as the change in the difference in elevation between the land and the sea surface at a specific time and location (Farrell and Clark, 1976 <sup>[[#fn:r3|3]]</sup> ). Here, regional sea level refers to spatial scales of around 100 km, while local sea level refers to spatial scales smaller than 10 km.

In most places around the world, current annual mean rates of RSL changes are typically on the order of a few mm yr <sup>–1</sup> (see Figure 4.6). Risk associated with changing sea level also is related to individual events that have a limited duration, superimposed on the background of these gradual changes. As a result, the gradual changes in time and space have to be assessed together with processes that lead to flooding and erosion events. These processes include storm surges, waves and tides or a combination of these processes and lead to ESL events (see Figure 4.4). In this section, newly emerging understanding of these different episodic and gradual aspects of sea level change are assessed, within a context of sea level changes measured directly over the last century, and those inferred for longer geological time scales. This longer-term perspective is important for contextualising future projections of sea level and providing guidance for process-based models of the individual components of SLR, in particular the ice sheets. In addition, anthropogenic subsidence may affect local sea level substantially in many locations but this process is not taken into account in values reported here for projected SLR unless specifically noted.

<span id="processes-of-sea-level-change"></span>
=== 4.2.1 Processes of Sea Level Change ===

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Sea level changes have been discussed throughout the various IPCC assessment reports as SLR is a key feature of climate change. Complex interactions between the oceans and ice sheets only recently have been recognised as important drivers of processes that can lead to rapid dynamical changes in the ice sheets. Understanding of basal melt below the ice shelves, ice calving processes and glacial hydrological processes was also limited. Projections of future sea level in the IPCC 4th Assessment Report (AR4; Lemke et al., 2007 <sup>[[#fn:r4|4]]</sup> ) were presented with the caveat that dynamical ice sheet processes were not accounted for, as our physical understanding of these processes was too rudimentary and no literature could be assessed (Bindoff et al., 2007 <sup>[[#fn:r5|5]]</sup> ). In AR5 (Church et al., 2013 <sup>[[#fn:r6|6]]</sup> ), a first attempt was made to quantify the dynamic contribution of the ice sheets, although still with modeling based on limited physcis, relying mainly on an extrapolation of existing observations (Little et al., 2013 <sup>[[#fn:r7|7]]</sup> ) and a single process based case study (Bindschadler et al., 2013 <sup>[[#fn:r8|8]]</sup> ). Here the focus is on sea level changes around coastlines and low-lying islands, updating the GMSL rise by including a new estimate of the dynamic contribution of Antarctica. The mechanism driving past and contemporary sea level changes and episodic extremes of sea level is explained, and confidence in regional projections of future sea level over the 21st century and beyond is assessed.

<div id="section-4-2-1-1ice-sheets-and-ice-shelves"></div>

<span id="ice-sheets-and-ice-shelves"></span>
==== 4.2.1.1 Ice Sheets and Ice Shelves ====

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The ice sheets on Greenland and Antarctica contain most of the fresh water on the Earth’s surface. As a consequence, they have the greatest potential to cause changes in sea level. Figure 4.4 illustrates the size of land ice reservoirs and the most important processes that drive mass changes of ice sheets.

Ice sheets change sea level through the loss or gain of ice above flotation, defined as the ice thickness in exceedance of the smallest thickness that would remain in contact with the sea floor at hydrostatic equilibrium. The GIS is currently losing mass at roughly twice the pace of the AIS (Table 4.1). However, Antarctica contains eight times more ice above flotation than Greenland. Furthermore, a substantial fraction of the AIS rests on bedrock below sea level, making the ice sheet responsive to changes in ocean-driven melt and possibly vulnerable to marine ice sheet instabilities (Cross-Chapter Box 8 in Chapter 3) that can drive rapid mass loss.

Ice sheets gain or lose mass through changes in surface mass balance (SMB), the sum of accumulation and ablation controlled by atmospheric processes, the loss of ice to the ocean though melting of ice shelves, and by calving (breaking off of ice bergs) at marine-terminating ice fronts (see Chapter 3). Ice shelves, the floating extensions of grounded ice flowing into the ocean (Figure 4.4) do not directly contribute to sea level, but they play an important role in ice sheet dynamics by providing resistance to the seaward flow of the grounded ice upstream (Fürst et al., 2016 <sup>[[#fn:r9|9]]</sup> ; Reese et al., 2018b <sup>[[#fn:r10|10]]</sup> ). Ice shelves gain mass through the inflow of ice from the ice sheet, precipitation, and accretion at the ice-ocean interface. They lose mass through a combination of calving and by melting from below, especially where basal ice is in contact with warm water (Paolo et al., 2015, Khazendar et al., 2016). Calving rates at the terminus of marine terminating ice fronts are governed by complex ice-mechanical processes, the internal strength of the ice, and interaction with ocean waves and tides (Benn et al., 2007 <sup>[[#fn:r11|11]]</sup> ; Bassis, 2011 <sup>[[#fn:r12|12]]</sup> ; Massom et al., 2018 <sup>[[#fn:r13|13]]</sup> ). Sub-ice shelf melts rates are controlled by ice-ocean interactions involving the large-scale circulation, more localised heat and fresh water fluxes, and micro (mm)-scale processes in the ice-ocean boundary layer (Gayen et al., 2015 <sup>[[#fn:r14|14]]</sup> ; Dinniman et al., 2016 <sup>[[#fn:r15|15]]</sup> ; Schodlok et al., 2016 <sup>[[#fn:r16|16]]</sup> ). Ice shelves are also impacted by surface processes. Where surface melt rates are high, ice shelves not only lose mass, they can collapse (hydrofracture) from flexural stresses caused by the movement of the meltwater and the deepening of water-filled crevasses (Banwell et al., 2013 <sup>[[#fn:r17|17]]</sup> ; Macayeal and Sergienko, 2013 <sup>[[#fn:r18|18]]</sup> ; Kuipers Munneke et al., 2014 <sup>[[#fn:r19|19]]</sup> ). These complex ice-ocean interactions, calving and hydrofracture processes remain difficult to model, particularly at the scale of ice sheets.Our understanding of ice sheets has progressed substantially since AR5, although deep uncertainty (Cross-Chapter Box 5 in Chapter 1) remains with regard to their potential contribution to future SLR on time scales longer than a century under any given emissions scenario. This is particularly true for Antarctica.

<div id="section-4-2-1-2glaciers"></div>

<span id="glaciers"></span>
==== 4.2.1.2 Glaciers ====

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Glaciers outside of the GIS and AIS are important contributors to sea level change (Figure 4.4). Because of their specific accumulation and ablation rates, which are often high compared to those of the ice sheets, they are sensitive indicators of climate change and respond quickly to changes in  climate. Over the past century, glaciers have added more mass to the ocean than the GIS and AIS combined (Gregory et al., 2013 <sup>[[#fn:r20|20]]</sup> ). However, the mass of glaciers is small by comparison, equivalent to only 0.32 ± 0.08 m mean SLR if only the fraction of ice above sea level is considered (Farinotti et al., 2019 <sup>[[#fn:r21|21]]</sup> ). Sections 2.2.3, 3.3.2 and Cross-Chapter Box 6 in Chapter 2 provide a detailed discussion of glacier response to climate change.

<div id="section-4-2-1-3ocean-processes"></div>

<span id="ocean-processes"></span>
==== 4.2.1.3 Ocean Processes ====

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In general, increasing temperatures lead to a lower density (‘thermal expansion’) and therefore the larger its volume per unit of mass. Thus, warming leads to a higher sea level even when the ocean mass remains constant. Over at least the last 1500 years changes in sea level were related to global mean temperatures (Kopp et al., 2016 <sup>[[#fn:r22|22]]</sup> ), partly because of ice mass loss, and partly because of thermal expansion. Models and observations indicate that over recent decades, more than 90% of the increase in energy in the climate system has been stored in the ocean. Hence, thermal expansion provides insight into climate sensitivity (Church et al., 2013 <sup>[[#fn:r23|23]]</sup> ). Findings from sea level studies and the energy budget are consistent (Otto et al., 2013 <sup>[[#fn:r24|24]]</sup> ). As thermal expansion per degree is dependent on the temperature itself, heat uptake by a warm region has a larger impact on SLR than heat uptake by a cold region. This contributes to regional changes in sea level, which are also caused by the water temperature and salinity variations (e.g., Lowe and Gregory, 2006; Suzuki and Ishii, 2011 <sup>[[#fn:r25|25]]</sup> ; Bouttes et al., 2014 <sup>[[#fn:r26|26]]</sup> ; Saenko et al., 2015 <sup>[[#fn:r27|27]]</sup> ). Regional patterns in sea level change are also modified from the global average by oceanic and atmospheric (fluid) dynamics (Griffies and Greatbatch, 2012 <sup>[[#fn:r28|28]]</sup> ), including trends in ocean currents, redistribution of temperature and salinity (sea water density), buoyancy, and atmospheric pressure. An analysis of these trends in Coupled Model Intercomparison Project Phase 5 (CMIP5) General Circulation Models (GCMs; Yin, 2012) demonstrates the potential for &gt;15 cm of SLR by 2100 and &gt;30 cm by 2300 (RCP8.5) along the east coast of the USA and Canada from fluid dynamical processes alone. However, Coupled Model Intercomparison Project Phase 6 (CMIP6) GCM simulations are not yet available for an updated analysis of these processes in SROCC.

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==== 4.2.1.4 Terrestrial Reservoirs ====

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Global sea level changes are also affected by changes in terrestrial reservoirs of liquid water. Withdrawal of groundwater and storage of fresh water through dam construction (Chao et al., 2008 <sup>[[#fn:r29|29]]</sup> ; Fiedler and Conrad, 2010 <sup>[[#fn:r30|30]]</sup> ) in the earlier parts of the 20th century dominated, leading to sea level fall, but in recent decades, land water depletion due to domestic, agricultural and industrial usage has begun to contribute to sea level change (Wada et al., 2017 <sup>[[#fn:r31|31]]</sup> ). Changes in terrestrial reservoirs may also be related to climate variability: in particular, the El Niño Southern Oscillation (ENSO) has a strong impact on precipitation distribution and temporary storage of water on continents (Boening et al., 2012 <sup>[[#fn:r32|32]]</sup> ; Cazenave et al., 2012 <sup>[[#fn:r33|33]]</sup> ; Fasullo et al., 2013 <sup>[[#fn:r34|34]]</sup> ).

<div id="section-4-2-1-5geodynamic-processes"></div>

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==== 4.2.1.5 Geodynamic Processes ====

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Changing distributions of water mass between land, ice and ocean reservoirs cause nearly instantaneous changes in the Earth’s gravity field and rotation, and elastic deformation of the solid Earth. These processes combine to produce spatially varying patterns of sea level change (Mitrovica et al., 2001 <sup>[[#fn:r35|35]]</sup> ; Mitrovica et al., 2011 <sup>[[#fn:r36|36]]</sup> ). For example, adjacent to an ice sheet losing mass, reduced gravitational attraction between the ice and nearby ocean causes RSL to fall, despite the rise in GMSL from the input of melt water to the ocean. The opposite effect is found far from the ice sheet, where RSL rise can be enhanced as much as 30% relative to the global average.

On time scales longer than the elastic Earth response, redistributions of water and ice cause time-dependent, visco-elastic deformation. This is observed in regions previously covered by ice during the Last Glacial Maximum (LGM), including much of Scandinavia and parts of North America (Lambeck et al., 1998 <sup>[[#fn:r37|37]]</sup> ; Peltier, 2004 <sup>[[#fn:r38|38]]</sup> ), where glacio-isostatic adjustment (GIA) is causing uplift and a lowering of RSL that continues today. In other locations proximal to the previous ice load, and where a glacial forebulge once existed, the relaxing forebulge can contribute to a relative SLR, as currently being experienced along the coastline of the northeast United States. Water being syphoned to high latitudes as the peripheral bulges collapse leads to a widespread RSL fall in equatorial regions, while the overall loading of ocean crust by melt water can cause uplift of land areas near continental margins, far from the location of previous ice loading (Mitrovica and Milne, 2003 <sup>[[#fn:r39|39]]</sup> ; Milne and Mitrovica, 2008 <sup>[[#fn:r40|40]]</sup> ). Rates of modern VLM associated with these post-glacial processes are generally on the order of a few mm yr <sup>–1</sup> or less, but can exceed 1 cm yr <sup>–1</sup> in some places. Because these gravity, rotation, and deformation (GRD) processes control spatial patterns of SLR from melting land ice, they need to be accounted for in regional-to-local sea level assessments. GRD processes are also important for marine-based ice sheets themselves, because they locally reduce RSL at retreating grounding lines which can slow and reduce retreat (Gomez et al., 2015 <sup>[[#fn:r41|41]]</sup> ; see 4.3.3.1.2 and Cross-chapter Box 8 in Chapter 3; Larour et al., 2019 <sup>[[#fn:r42|42]]</sup> ).

VLM from tectonics and dynamic topography associated with viscous mantle processes also affect spatial patterns of relative sea level change. These geological processes are important for reconstructing ancient sea levels based on geological indicators (Austermann and Mitrovica, 2015 <sup>[[#fn:r43|43]]</sup> ; see SM4.1). Along with other natural and anthropogenic processes including volcanism, compaction, and anthropogenic subsidence from ground water extraction (Section 4.2.2.4) these geodynamic processes can be locally important, producing rates of VLM comparable to or greater than recent climate-driven rates of GMSL change (Wöppelmann and Marcos, 2016 <sup>[[#fn:r44|44]]</sup> ). In this chapter, GIA and anthropogenic subsidence are used, and other components of VLM are ignored unless explicitly stated.Changing distributions of water mass between land, ice and ocean reservoirs cause nearly instantaneous changes in the Earth’s gravity field and rotation, and elastic deformation of the solid Earth. These processes combine to produce spatially varying patterns of sea level change (Mitrovica et al., 2001; Mitrovica et al., 2011). For example, adjacent to an ice sheet losing mass, reduced gravitational attraction between the ice and nearby ocean causes RSL to fall, despite the rise in GMSL from the input of melt water to the ocean. The opposite effect is found far from the ice sheet, where RSL rise can be enhanced as much as 30% relative to the global average.

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<span id="extreme-sea-level-events"></span>
==== 4.2.1.6 Extreme Sea Level Events ====

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Superimposed on gradual changes in RSL, as described in the previous sections, tides, storm surges, waves and other high-frequency processes (Figure 4.4) can be important. Understanding the localised impact of such processes requires detailed knowledge of bathymetry, erosion and sedimentation, as well as a good description of the temporal variability of the wind fields generating waves and storm surges. The potential for compounding effects, like storm surge and high SLR, are of particular concern as they can contribute significantly to flooding risks and extreme events (Little et al., 2015a <sup>[[#fn:r45|45]]</sup> ) . These processes can be captured by hydrodynamical models (see Section 4.2.3.4).

<span id="observed-changes-in-sea-level-past-and-present"></span>
=== 4.2.2 Observed Changes in Sea Level (Past and Present) ===

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Sea level changes in the distant geologic past provide information on the size of the ice sheets in climate states different from today. Past intervals with temperatures comparable to or warmer than today are of particular interest, and since AR5 (Masson-Delmotte et al., 2013 <sup>[[#fn:r46|46]]</sup> ) they have been increasingly used to test and calibrate process-based ice sheet models used in future projections (DeConto and Pollard, 2016 <sup>[[#fn:r47|47]]</sup> ; Edwards et al., 2019 <sup>[[#fn:r48|48]]</sup> ; Golledge et al., 2019 <sup>[[#fn:r49|49]]</sup> ) . These intervals include the mPWP around 3.3–3.0 Ma, when atmospheric CO <sub>2</sub> concentrations were similar to today (~300–450 ppmv; Badger et al., 2013 <sup>[[#fn:r50|50]]</sup> ; Martínez-Botí et al., 2015 <sup>[[#fn:r51|51]]</sup> ; Stap et al., 2016 <sup>[[#fn:r52|52]]</sup> ) and global mean temperature was 2ºC–4ºC warmer than pre-industrial (Dutton et al., 2015a <sup>[[#fn:r53|53]]</sup> ; Haywood et al., 2016 <sup>[[#fn:r54|54]]</sup> ) and the LIG around 129–116 ka, when global mean temperature was 0.5ºC–1.0ºC warmer (Capron et al., 2014 <sup>[[#fn:r55|55]]</sup> ; Dutton et al., 2015a <sup>[[#fn:r56|56]]</sup> ; Fischer et al., 2018 <sup>[[#fn:r57|57]]</sup> ) and sea surface temperatures were similar to today (Hoffman et al., 2017 <sup>[[#fn:r58|58]]</sup> ) . Updated reconstructions of GMSL (Dutton et al., 2015a <sup>[[#fn:r59|59]]</sup> ) based on ancient shoreline elevations corrected to account for geodynamic processes (4.2.1.5), and geochemical records extracted from marine sediment cores, indicate sea levels were &gt;5 m higher than today during these past warm periods ( ''medium confidence'' ).

Most estimates of peak GMSL during the mPWP range between 6 and 30 m higher than today (Miller et al., 2012 <sup>[[#fn:r60|60]]</sup> ; Rovere et al., 2014 <sup>[[#fn:r61|61]]</sup> ; Dutton et al., 2015a <sup>[[#fn:r62|62]]</sup> ) but with deep uncertainty (Cross-Chapter Box 5 in Chapter 1) and few constraints on the high end of the range. The large uncertainty is contributed by uncertain GIA corrections applied to palaeo shoreline indicators (Raymo et al., 2011 <sup>[[#fn:r63|63]]</sup> ; Rovere et al., 2014 <sup>[[#fn:r64|64]]</sup> ) , dynamic topography, the vertical land surface motion associated with Earth’s mantle flow (Rowley et al., 2013 <sup>[[#fn:r65|65]]</sup> ) , and possible biases in geochemical records of ice volume derived from marine sediments (Raymo et al., 2018 <sup>[[#fn:r66|66]]</sup> ) . Estimates of GMSL &gt;10 m higher than today require a meltwater contribution from the East Antarctic Ice Sheet in addition to the GIS and West Antarctic Ice Sheets (WAIS; Miller et al., 2012 <sup>[[#fn:r67|67]]</sup> ; Dutton et al., 2015a <sup>[[#fn:r68|68]]</sup> ) . Pliocene modelling studies appearing since AR5 (Masson-Delmotte et al., 2013 <sup>[[#fn:r69|69]]</sup> ) demonstrate the potential for substantial retreat of East Antarctic ice into deep submarine basins (Austermann and Mitrovica, 2015 <sup>[[#fn:r70|70]]</sup> ; Pollard et al., 2015 <sup>[[#fn:r71|71]]</sup> ; Aitken et al., 2016 <sup>[[#fn:r72|72]]</sup> ; DeConto and Pollard, 2016 <sup>[[#fn:r73|73]]</sup> ; Gasson et al., 2016 <sup>[[#fn:r74|74]]</sup> ; Golledge et al., 2019 <sup>[[#fn:r75|75]]</sup> ) , as does emerging geological evidence from marine sediment cores recovered from the East Antarctic margin (Cook et al., 2013 <sup>[[#fn:r76|76]]</sup> ; Patterson et al., 2014 <sup>[[#fn:r77|77]]</sup> ; Bertram et al., 2018 <sup>[[#fn:r78|78]]</sup> ) . However, the range of maximum Pliocene GMSL contributions from Antarctic modelling (Austermann and Mitrovica, 2015 <sup>[[#fn:r79|79]]</sup> ; Pollard et al., 2015 <sup>[[#fn:r80|80]]</sup> ; Yamane et al., 2015 <sup>[[#fn:r81|81]]</sup> ; DeConto and Pollard, 2016 <sup>[[#fn:r82|82]]</sup> ; Gasson et al., 2016 <sup>[[#fn:r83|83]]</sup> ) remains large (5.4–17.8 m), providing little additional constraint on the geological estimates. Land surface exposure measurements on sediment sourced from East Antarctica (Shakun et al., 2018 <sup>[[#fn:r84|84]]</sup> ) suggests Pliocene ice loss was limited to marine-based ice, where the bedrock is below sea level and possibly prone to marine ice sheet instabilities (Cross-Chapter box 8 in Chapter 3). The total potential contribution to GMSL rise from marine-based ice in Antarctica is ~22.5 m (Fretwell et al., 2013 <sup>[[#fn:r85|85]]</sup> ) . Combined with the complete loss of the GIS, this could conceivably produce ~30 m of GMSL rise. However, this would require maximum retreat of GIS and AIS to be synchronous, which is not probable due to orbitally paced, inter-hemispheric asymmetries in Greenland and Antarctic climate (de Boer et al., 2017) . As such, 25 m is found to be a reasonable upper bound on GMSL during the mPWP, but with ''low confidence'' .

An updated estimate of maximum GMSL during the more geologically recent LIG ranges between 6–9 m higher than today (Dutton et al., 2015a <sup>[[#fn:r86|86]]</sup> ) . This is close to the values reported by a probabilistic analysis of globally distributed sea level indicators (Kopp et al., 2009 <sup>[[#fn:r87|87]]</sup> ) , but slightly higher than AR5’s central estimate of 6 m. Like the mid-Pliocene, the LIG estimates also suffer from uncertainties in GIA corrections and dynamic topography. Düsterhus et al. (2016) <sup>[[#fn:r88|88]]</sup> applied data assimilation techniques including GIA corrections to the same LIG dataset used by Kopp et al. (2009) <sup>[[#fn:r89|89]]</sup> and found good agreement (7.5 ± 1.1 m likely range) with Kopp et al. (2009)  and Dutton et al. (2015a) , but the upper range remains poorly constrained. Their estimates of peak LIG sea level are sensitive to the assumed ice history before and after the LIG, consistent with the results of other studies (Lambeck et al., 2012 <sup>[[#fn:r90|90]]</sup> ; Dendy et al., 2017 <sup>[[#fn:r91|91]]</sup> ) . Austermann et al. (2017) compared a compilation of LIG shoreline indicators with dynamic topography simulations. They found that vertical surface motions driven by mantle convection can produce several metres of uncertainty in LIG sea level estimates, but their mean and most probable estimates of 6.7 m and 6.4 m are broadly in line with other estimates.

Greenland and Antarctic climate change on these time scales is influenced by inter-hemispheric differences in polar amplification (Stap et al., 2018 <sup>[[#fn:r93|93]]</sup> ) , changes in Earth’s orbit, and long-term climate system processes. This complicates relationships between global mean temperature and ice sheet response. On Greenland, the magnitude of LIG summer warming and changes in ice sheet volume continue to be contested. Extreme summer warming of 6ºC or more, reconstructed from ice cores (Dahl-Jensen et al., 2013 <sup>[[#fn:r94|94]]</sup> ; Landais et al., 2016 <sup>[[#fn:r95|95]]</sup> ; Yau et al., 2016 <sup>[[#fn:r96|96]]</sup> ) and lake archives (McFarlin et al., 2018 <sup>[[#fn:r97|97]]</sup> ) is in apparent conflict with a persistent, spatially extensive GIS reconstructed from ice cores and radar imaging (Dahl-Jensen et al., 2013 <sup>[[#fn:r98|98]]</sup> ) . Maximum retreat of the GIS during the LIG varies widely among modelling studies, ranging from ~1 m to ~6 m (Helsen et al., 2013 <sup>[[#fn:r99|99]]</sup> ; Quiquet et al., 2013 <sup>[[#fn:r100|100]]</sup> ; Dutton et al., 2015a <sup>[[#fn:r101|101]]</sup> ; Goelzer et al., 2016 <sup>[[#fn:r102|102]]</sup> ; Yau et al., 2016 <sup>[[#fn:r103|103]]</sup> ) ; however, the models consistently indicate a small Greenland contribution to GMSL early in the interglacial, implying Antarctica was the dominant contributor to the early interglacial highstand of 6 ± 1.5 m, beginning around 129 ka (Dutton et al., 2015b <sup>[[#fn:r104|104]]</sup> ) . An early LIG loss of Antarctic ice is consistent with recent ice sheet modelling (DeConto and Pollard, 2016 <sup>[[#fn:r105|105]]</sup> ; Goelzer et al., 2016 <sup>[[#fn:r106|106]]</sup> ) . Due to its bedrock configuration and susceptibility to marine ice sheet instabilities (Cross-Chapter Box 8 in Chapter 3), the WAIS would have been especially vulnerable to subsurface ocean warming during the LIG (Sutter et al., 2016 <sup>[[#fn:r107|107]]</sup> ) . However, most evidence of WAIS retreat during the LIG remains indirect (Steig et al., 2015 <sup>[[#fn:r108|108]]</sup> ) and firm geological evidence has yet to be uncovered. A recent analysis of East Antarctic sediments provides evidence of some ice retreat in Wilkes subglacial basin during the LIG (Wilson and Forsyth, 2018 <sup>[[#fn:r109|109]]</sup> ) , but the volume of ice loss is not quantified.

GMSL during the LIG was at times higher than today ( ''virtually certain'' ), with a ''likely range'' between 6–9 m, and not expected to be more than 10 m ( ''medium confidence'' ). Due to ongoing uncertainties in the evolution of atmospheric and oceanic warming over and around the ice sheets, and low confidence in the relative contributions of Antarctic versus Greenland meltwater to GMSL change, the LIG is not used here to directly assess the sensitivity of the ice sheets under current or future climate conditions. There is ''low confidence'' in the utility of changes in either mPWP or LIG sea level changes to quantitatively inform near-term future rates of GMSL rise.

An expanded summary of recent advances and ongoing difficulties in reconstructing these time periods in terms of climate, sea level, and implications for the future evolution of ice sheets and sea level is provided in SM4.1.

<div id="section-4-2-2observed-changes-in-sea-level-past-and-present-block-2"></div>

<span id="figure-4.4"></span>
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'''Figure 4.4'''

<span id="figure-4.4-a-schematic-illustration-of-the-climate-and-non-climate-driven-processes-that-can-influence-global-regional-green-colours-relative-and-extreme-sea-level-esl-events-red-colours-along-coasts.-major-ice-processes-are-shown-in-purple-and-general-terms-in-black.-sle-stands-for-sea-level-equivalent-and-reflects-the-increase-in-gmsl"></span>
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'''Figure 4.4 | A schematic illustration of the climate and non-climate driven processes that can influence global, regional (green colours), relative and extreme sea level (ESL) events (red colours) along coasts. Major ice processes are shown in purple and general terms in black. SLE stands for Sea Level Equivalent and reflects the increase in GMSL […]'''
<!-- IMG FILE -->
[[File:293bed953e086b90d833ac1cad5362b3 IPCC-SROCC-CH_4_4-3000x1491.jpg]]

Figure 4.4 | A schematic illustration of the climate and non-climate driven processes that can influence global, regional (green colours), relative and extreme sea level (ESL) events (red colours) along coasts. Major ice processes are shown in purple and general terms in black. SLE stands for Sea Level Equivalent and reflects the increase in GMSL if the mentioned ice mass is melted completely and added to the ocean.

<!-- END IMG -->
<div id="section-4-2-2-1global-mean-sea-level-changes-during-the-instrumental-period"></div>

<span id="global-mean-sea-level-changes-during-the-instrumental-period"></span>
==== 4.2.2.1 Global Mean Sea Level Changes During the Instrumental Period ====

<div id="section-4-2-2-1global-mean-sea-level-changes-during-the-instrumental-period-block-1"></div>

Observational estimates of the sea level variations over past millennia rely essentially on proxy-based regional relative sea level reconstructions corrected for GIA. Since AR5, the increasing availability of regional proxy-based reconstructions enables the estimation of GMSL change over the last ∼ 3 kyr. The first statistical integration of the available reconstructions shows that the GMSL experienced variations of ±9 [±7 to ±11] cm (5–95% uncertainty range; Kopp et al., 2016 <sup>[[#fn:r110|110]]</sup> ) over the 2400 years preceding the 20th century ( ''medium confidence'' ). This is more tightly bound than the AR5 assessment which indicated a variability in GMSL that was &lt;±25 cm over the same period. This progress since AR5 confirms that it is ''virtually certain'' that the mean rate of GMSL has increased during the last two centuries from relatively low rates of change during the late Holocene (order tenths of mm yr <sup>–1</sup> ) to modern rates (order mm yr <sup>–1</sup> ; Woodruff et al., 2013 <sup>[[#fn:r111|111]]</sup> ) .

Over the last two centuries, sea level observations have mostly relied on tide gauge measurements. These records, beginning around 1700 in some locations (Holgate et al., 2012 <sup>[[#fn:r112|112]]</sup> ; PSMSL, 2019 <sup>[[#fn:r113|113]]</sup> ) , provide insight into historic sea level trends. Since 1992, the emergence of precise satellite altimetry has advanced our knowledge on GMSL and regional sea level changes considerably through a combination of near global ocean coverage and high spatial resolution. It has also enabled more detailed monitoring of land ice loss. Since 2002, high precision gravity measurements provided by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions show the loss of land ice in Greenland and Antarctica, and confirm independent assessments of ice sheet mass changes based on satellite altimetry (Shepherd et al., 2012 <sup>[[#fn:r114|114]]</sup> ; The Imbie team, 2018) and InSAR measurements combined with ice sheet SMB estimates (Noël et al., 2018 <sup>[[#fn:r115|115]]</sup> ; Rignot et al., 2019 <sup>[[#fn:r116|116]]</sup> ) . Since 2006, when the array of Argo profiling floats reached near-global coverage, it has been possible to get an accurate estimate of the ocean thermal expansion (down to 2000 m depth) and test the closure of the sea level budget. T he combined analysis of the different observing systems that are available has improved significantly the understanding of the magnitude and relative contributions of the different processes causing sea level change. In particular, important progress has been achieved since AR5 on estimating and understanding the increasing contribution of the ice sheets to SLR.

<div id="section-4-2-2-1global-mean-sea-level-changes-during-the-instrumental-period-block-2"></div>

<span id="tide-gauge-records"></span>
===== 4.2.2.1.1 Tide gauge records =====

The number of tide gauges has increased over time from only a few in northern Europe in the 18th century to more than 2000 today along the world’s coastlines. Because of their location and limited number, tide gauges sample the ocean sparsely and non-uniformly with a bias towards the Northern Hemisphere. Most tide gauge records are short and have significant gaps. In addition, tide gauges are anchored on land and are affected by the vertical motion of Earth’s crust caused by both natural processes (e.g., GIA, tectonics and sediment compaction; Wöppelmann and Marcos, 2016 <sup>[[#fn:r117|117]]</sup> ; Pfeffer et al., 2017 <sup>[[#fn:r118|118]]</sup> ) and anthropogenic activities (e.g., groundwater depletion, dam building or settling of landfill in urban areas; Raucoules et al., 2013 <sup>[[#fn:r119|119]]</sup> ; Pfeffer et al., 2017 <sup>[[#fn:r120|120]]</sup> ) . When estimating the GMSL due to the ocean thermal expansion and land ice melt, tide gauges must be corrected for this VLM, where VLM = GIA + anthropogenic subsidence + (tectonics, natural subsidence). This is possible with stations of the Global Positioning System (GPS) network when they are co-located with tide gauges (Santamaría-Gómez et al., 2017 <sup>[[#fn:r121|121]]</sup> ; Kleinherenbrink et al., 2018 <sup>[[#fn:r122|122]]</sup> ) . However, this approach provides information on the VLM over the past two to three decades and has limited value over longer time scales for places where the VLM has varied significantly through the last century (Riva et al., 2017 <sup>[[#fn:r123|123]]</sup> ) .

AR5 assessed the different strategies to estimate the 20th century GMSL changes. These strategies only accounted for the inhomogeneous space and time coverage of tide gauge data and for the VLM induced by GIA (Figure 4.5). Since AR5 two new approaches have been developed. The first one uses a Kalman smoother which combines tide gauge records with the spatial patterns associated with ocean dynamic change, change in land ice and GIA. It enables accounting for the inhomogeneous distribution of tide gauges and the VLM associated with both GIA and current land ice loss (Hay et al., 2015 <sup>[[#fn:r124|124]]</sup> ; Figure 4.5). The second approach uses ad hoc corrections to tide gauge records with an additional spatial pattern associated with changes in terrestrial water storage to account for the inhomogeneous distribution in tide gauges. It also accounts for the total VLM (Dangendorf et al., 2017 <sup>[[#fn:r125|125]]</sup> ; Figure 4.5). Both methods yield significantly lower GMSL changes over the period 1950–1970 than previous estimates, leading to long-term trends since 1900 that are smaller than previous estimates by 0.4 mm yr <sup>–1</sup> (Figure 4.5). Different arguments including biases in the tide gauge datasets (Hamlington and Thompson, 2015 <sup>[[#fn:r126|126]]</sup> ) , biases in the averaging technique and absence of VLM correction (Dangendorf et al., 2017 <sup>[[#fn:r127|127]]</sup> ) , or in the spatial patterns associated with the sea level contributions (Hamlington et al., 2018 <sup>[[#fn:r128|128]]</sup> ) have been proposed to explain these smaller GMSL rates. There is no agreement yet on which is the primary reason for the differences and it is not clear whether all the reasons invoked can actually explain all the differences across reconstructions. As there is no clear evidence to discard any reconstruction, this assessment considers the ensemble of AR5 sea level reconstructions augmented by the two recent reconstructions from Hay et al. (2015) <sup>[[#fn:r129|129]]</sup> and Dangendorf et al. (2017) <sup>[[#fn:r130|130]]</sup> to evaluate the GMSL changes over the 20th century. On this basis, it is estimated that it is ''very likely'' that the long-term trend in GMSL estimated from tide gauge records is 1.5 (1.1–1.9) mm yr <sup>–1</sup> between 1902 and 2010 for a total SLR of 0.16 (0.12–0.21) m (see also Table 4.1). This estimate is consistent with the AR5 assessment (but with an increased uncertainty range) and confirms that it is ''virtually certain'' that GMSL rates over the 20th century are several times as large as GMSL rates during the late Holocene (see 4.2.2.1). Over the 20th century the GMSL record also shows an acceleration ( ''high confidence'' ) as now four out of five reconstructions extending back to at least 1902 show a robust acceleration (Jevrejeva et al., 2008 <sup>[[#fn:r131|131]]</sup> ; Church and White, 2011 <sup>[[#fn:r132|132]]</sup> ; Ray and Douglas, 2011 <sup>[[#fn:r133|133]]</sup> ; Haigh et al., 2014b <sup>[[#fn:r134|134]]</sup> ; Hay et al., 2015 <sup>[[#fn:r135|135]]</sup> ; Watson, 2016 <sup>[[#fn:r136|136]]</sup> ; Dangendorf et al., 2017 <sup>[[#fn:r137|137]]</sup> ) . The estimates of the acceleration ranges between -0.002–0.019 mm yr <sup>–1</sup> over 1902–2010 are consistent with AR5.

<div id="section-4-2-2-1global-mean-sea-level-changes-during-the-instrumental-period-block-3"></div>

<span id="satellite-altimetry"></span>
===== 4.2.2.1.2 Satellite altimetry =====

High precision satellite altimetry started in October 1992 with the launch of the TOPEX/Poseidon and Jason series of spacecraft. Since then, 11 satellite altimeters have been launched providing nearly global sea level measurements (up to ±82° latitude) over more than 25 years. Six groups (AVISO/CNES, SL_cci/ESA, University of Colorado, CSIRO, NASA/GSFC, NOAA; Nerem et al., 2010; <sup>[[#fn:r138|138]]</sup> Henry et al., 2014 <sup>[[#fn:r139|139]]</sup> ; Leuliette, 2015 <sup>[[#fn:r140|140]]</sup> ; Watson et al., 2015 <sup>[[#fn:r141|141]]</sup> ; Beckley et al., 2017 <sup>[[#fn:r142|142]]</sup> ; Legeais et al., 2018 <sup>[[#fn:r143|143]]</sup> ) provide altimetry-based GMSL time series. Since AR5, several studies using two independent approaches based on tide gauge records (Watson et al., 2015 <sup>[[#fn:r144|144]]</sup> ) and the sea level budget closure (Chen et al., 2017 <sup>[[#fn:r145|145]]</sup> ; Dieng et al., 2017 <sup>[[#fn:r146|146]]</sup> ) identified a drift of 1.5 (0.4–3.4) mm yr <sup>–1</sup> in TOPEX A from January 1993 to February 1999. Accounting for this drift leads to a revised GMSL rate from satellite altimetry of 3.16 (2.79–3.53) for 1993–2015 (WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r147|147]]</sup> ; see Table 4.1) compared to 3.3 mm yr <sup>–1</sup> (2.7–3.9) for 1993–2010 in AR5. Compared to AR5, the revised satellite altimetry GMSL estimates now show with ''high confidence'' an acceleration of 0.084 (0.059–0.090) mm yr <sup>–1</sup> over 1993–2015 (5–95% uncertainty range; Watson et al., 2015 <sup>[[#fn:r148|148]]</sup> ; Nerem et al., 2018 <sup>[[#fn:r149|149]]</sup> ) . This acceleration is due to an increase in Greenland mass loss since the 2000s (Chen et al., 2017 <sup>[[#fn:r150|150]]</sup> ; Dieng et al., 2017 <sup>[[#fn:r151|151]]</sup> ) and a slight increase in all other contributions probably partly due to the recovery from the Pinatubo volcanic eruption in 1991 (Fasullo et al., 2016 <sup>[[#fn:r152|152]]</sup> ) and partly due to increased GHG concentrations e.g., (Slangen et al., 2016 <sup>[[#fn:r153|153]]</sup> ; ''high confidence'' ). The current sea level rise is 3.6 ± 0.3 mm yr <sup>–1</sup> over 2006–2015 (90% confidence level). This is the highest rate measured by satellite altimetry (Ablain et al., 2019 <sup>[[#fn:r154|154]]</sup> ; ''medium confidence'' ). Before the satellite altimetry era, the highest rate of sea level rise recorded was reached during the period 1935–1944. It amounted 2.5 ± 0.7 mm yr <sup>–1</sup> (estimate at the 90% confidence level from sea level reconstructions; Church and White, 2011 <sup>[[#fn:r155|155]]</sup> ; Ray and Douglas, 2011 <sup>[[#fn:r156|156]]</sup> ; Jevrejeva et al., 2008 <sup>[[#fn:r157|157]]</sup> ; Hay et al., 2015 <sup>[[#fn:r158|158]]</sup> ; Dangendorf et al., 2017 <sup>[[#fn:r159|159]]</sup> ). This is expected to be smaller than the current rate of sea level rise, making the current sea level rise the highest on instrumental record ( ''medium confidence'' ).

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period"></div>

<span id="contributions-to-global-mean-sea-level-change-during-the-instrumental-period"></span>
==== 4.2.2.2 Contributions to Global Mean Sea Level Change During the Instrumental Period ====

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-1"></div>

The different contributions to the GMSL rise are independently observed over various time scales. They are compared with simulated estimates from climate model experiments of CMIP5 (Taylor et al., 2012 <sup>[[#fn:r160|160]]</sup> ) when available (see Table 4.1). The observations are compared with experiments beginning in the mid-19th century, forced with past time-dependent anthropogenic changes in atmospheric composition, natural forcings due to volcanic aerosols and variations in solar irradiance (Taylor et al., 2012 <sup>[[#fn:r161|161]]</sup> ). The objective is first, to assess understanding of the causes of observed sea level changes and second, to evaluate the ability of coupled climate models to simulate these causes. It enables the evaluation of the confidence level there is in current coupled climate models that form the basis of future sea level projections.

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-2"></div>

<span id="thermal-expansion-contribution"></span>
===== 4.2.2.2.1 Thermal expansion contribution =====

The ocean thermal expansion is caused by excess heat being absorbed by the ocean, as the climate warms. Thermal expansion is estimated from ''in situ'' ocean observations and ocean heat content reanalyses that rely on assimilation of data into numerical models (Storto et al., 2017 <sup>[[#fn:r162|162]]</sup> ; Sections 1.8.1.1 and 1.8.1.4; WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r163|163]]</sup> ) . Full-depth, high-quality and unbiased ocean temperature profile data with adequate metadata and spatio-temporal coverage are required to estimate thermal expansion and to understand drivers of variability and long-term change (Pfeffer et al., 2018 <sup>[[#fn:r164|164]]</sup> ; Section 5.2.2.2.2) .

Historically, however, observational gaps exist and some ocean regions remain under-sampled to date (Sections 1.8.1.1 and 5.2.2.2.2; Figure 1.3; Appendix 1.A, Figure 1.1). Other factors also introduce uncertainty in estimates of thermal expansion like changes in instrumentation, systematic instrumental errors, changes in the quality control of the data and the mapping method used to produce regular grids (Section 5.2.2.2.2; Palmer et al., 2010 <sup>[[#fn:r165|165]]</sup> ) . In the upper 700 m, the largest sources of uncertainty for estimates of global mean thermal expansion from 1970 to 2004 are the choice of mapping methods (Boyer et al., 2016 <sup>[[#fn:r166|166]]</sup> ) , followed by the choice of bias correction for the bathythermographic observations (Cheng et al., 2016 <sup>[[#fn:r167|167]]</sup> ; Section 5.2.2.2.2). From 2006 onwards, the uncertainty is considerably reduced (Roemmich et al., 2015 <sup>[[#fn:r168|168]]</sup> ; von Schuckmann et al., 2016 <sup>[[#fn:r169|169]]</sup> ; Wijffels et al., 2016 <sup>[[#fn:r170|170]]</sup> ) , because the Argo array reached its targeted near-global ( up to ±60° latitude) coverage for the upper 2000 m in November 2007 (Riser et al., 2016 <sup>[[#fn:r171|171]]</sup> ; Section 5.2.2.2.2) .

Since AR5, in a community effort, the (WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r172|172]]</sup> ) revisited the global mean thermal expansion estimates based on observations only. On the basis of a full-depth 13-member ensemble of global mean thermal expansion time series developed with the latest data and corrections available, they estimated that the global thermal expansion was 1.40 (1.08 – 1.72) mm yr <sup>–1</sup> for 2006–2015, 1.36 (0.96 – 1.76) mm yr <sup>–1</sup> for 1993–2015 (see Table 4.1). While the relative contribution of the upper 300 m did not change (~70%) between 2006–2015 and 1993–2015, the 700–2000 m contribution increased around 10% over the Argo decade (2006–2015), when observations for that depth interval soared ( Figure 1.3; Appendix 1.A, Figure 1.1) . This suggests that observed changes for 700–2000 m may have been underestimated for 1993 – 2005. Before 1993, estimates are based on a smaller ensemble of 4 datasets in which no thermal expansion is assumed below 2000 m because of lack of data (see Section 5.2.2.2.2 for more details). This ensemble shows a thermal expansion linear rate of 0.89 (0.84 – 0.94) mm yr <sup>–1</sup> for 1970–2015 (see Table 4.1).

Coupled climate models simulate the historical thermal expansion (see Table 4.1). However, for models that omit the volcanic forcing in their control experiment, the imposition of the historical volcanic forcing during the 20th century results in a spurious time mean negative forcing and a spurious persistent ocean cooling related to the control climate (Gregory, 2010 <sup>[[#fn:r173|173]]</sup> ; Gregory et al., 2013 <sup>[[#fn:r174|174]]</sup> ) . Since AR5, the magnitude of this effect has been estimated from historical simulations forced by only natural radiative forcing. Then it has been used to correct the historical simulations forced with the full 20th century forcing (Slangen et al., 2016 <sup>[[#fn:r187|187]]</sup> ; Slangen et al., 2017b <sup>[[#fn:r188|188]]</sup> ) . The resulting ensemble mean of simulated thermal expansion provides a good fit to the observations within the uncertainty ranges of both models and observations (Slangen et al., 2017b <sup>[[#fn:r189|189]]</sup> ; Cheng et al., 2019 <sup>[[#fn:r190|190]]</sup> ; Table 4.1) . The spread, which is essentially due to uncertainty in radiative forcing and uncertainty in the modelled climate sensitivity and ocean heat uptake efficiency (Melet and Meyssignac, 2015 <sup>[[#fn:r191|191]]</sup> ) , is still larger than the observational uncertainties (Gleckler et al., 2016 <sup>[[#fn:r192|192]]</sup> ; Cheng et al., 2017 <sup>[[#fn:r193|193]]</sup> ; Table 4.1) . Compared to AR5, the availability of improved observed and modelled estimates of thermal expansion and the good agreement between both confirm the ''high confidence'' level in the simulated thermal expansion using climate models and the ''high confidence'' level in their ability to project future thermal expansion.

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-3"></div>

<span id="table-4.1"></span>
<!-- START IMG -->
<!-- TABLE IMG -->
<!-- IMG TITLE -->
'''Table 4.1'''
<!-- IMG CAPTION -->
Global mean sea level (GMSL) budget over different periods from observations and from climate model base contributions. All values are in mm yr–1. Values in brackets in 4.2 are uncertainties ranging from 5–95%. The climate model historical simulations end in 2005; projections for Representative Concentration Pathway (RCP)8.5 are used for 2006–2015. The modelled thermal expansion, glacier and ice sheet surface mass balance (SMB) contributions are computed from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models as in Slangen et al. (2017b). For the model contributions, uncertainties are estimated from the spread of the ensemble of model simulations following Slangen et al. (2017b), see the footnotes for the details on the uncertainty propagation. GIS is Greenland Ice Sheet.
<!-- IMG FILE -->
[[File:bede59e395134bf8a28829a10c2702b3 table4.1.png]]

Notes:

# (a)  The number is built from WCRP Global Sea Level Budget Group (2018) estimate of the 0–700 m depth thermal expansion, assuming no trend below 2000 m depth before 1992 and the mean value from Purkey and Johnson (2010), and Desbruyères et al. (2017) afterwards.
# (b)  The number is calculated as the mean between the estimate from a reconstruction of glacier mass balance based on glacier length (update of Leclercq et al. (2011)) and the estimate from a mass balance model forced with atmospheric observations (Marzeion et al., 2015). The uncertainty is assumed to be a gaussian with a standard deviation of half the difference between the two estimates.
# (c)  The number is calculated as the sum of the Greenland Ice Sheet (GIS) contribution from Kjeldsen et al. (2015) and the peripheral glaciers’ contribution. The peripheral glaciers’ contribution and the associated uncertainty are computed from a mass balance model forced with atmospheric observations (Marzeion et al., 2015). The total uncertainty is computed assuming that both uncertainties from the GIS contribution and from the peripheral glaciers’ contribution are independent.
# (d)  Numbers from Bamber et al. (2018). See Section 3.3.1 for more details.
# (e)  These numbers are the weighted average of the numbers from Bamber et al. (2018) and from The Imbie team (2018). The weights in the average are based on the uncertainty associated to each estimate. See Section 3.3.1 for more details.
# (f)  Only direct anthropogenic contribution, from Wada et al. (2016).
# (g)  Land water storage estimated from Gravity Recovery and Climate Experiment (GRACE) excluding glaciers, from WCRP Global Sea Level Budget Group (2018).
# (h)  Direct estimate of ocean mass from GRACE from WCRP Global Sea Level Budget Group (2018).
# (i)  Sum of the thermal expansion and the contributions from glaciers, GIS, Antarctica Ice Sheet (AIS) and land water storage. Uncertainties in the different contributions are assumed as independent.
# (j)  Sea level reconstructions that end before 2015 have been extended to 2015 with the satellite altimetry record from Legeais et al. (2018). The uncertainty is derived from the uncertainty of individual sea level reconstructions over the longest period available that start in 1970. The uncertainty from different sea level reconstructions are assumed as independent.
# (k)  The mean estimate is from the satellite altimetry estimate in WCRP Global Sea Level Budget Group (2018) corrected for GIA and for the elastic response of the ocean crust to present day mass redistribution (Frederikse et al., 2017; Lickley et al., 2018). The uncertainty is computed using the updated error budget of Ablain et al. (2015).
# (l)  Land water storage is estimated from Wada et al. (2016) and ice discharge is deduced from Shepherd et al. (2012). The ice discharge contribution is assumed to be zero before 1992. The uncertainties in the different contributions from coupled climate models are assumed independent.
# (m)  The uncertainties in the observed GMSL and the coupled climate models’ estimate of GMSL are assumed independent for the computation of the uncertainties in the residuals.
# (n)  Numbers taken from Appendix 2.A.
# (o)  Numbers taken from Zemp et al. (2019), see Sections 2.2.3 and 3.3.2 for more details.
# (p)  The Number is calculated as the mean of the estimates of Zemp et al. (2019) and Bamber et al. (2018). The uncertainties of the two estimates are assumed to be independent of each other to obtain the uncertainty estimate of the mean.

<!-- END IMG -->
<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-4"></div>

<span id="ocean-mass-observations-from-grace-and-grace-follow-on"></span>
===== 4.2.2.2.2 Ocean mass observations from GRACE and GRACE Follow-On =====

The ocean mass changes correspond to the sum of land ice and terrestrial water storage changes. Since 2002, the GRACE and GRACE follow-on missions provide direct estimates of the ocean mass changes and thus they provide an independent estimate of the sum of land ice and terrestrial water storage contributions to sea level. Since AR5, GRACE-based estimates of the ocean mass rates are increasingly consistent (WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r194|194]]</sup> ) because of the extended length of GRACE missions’ observations (over 15 years), the improved understanding of data and methods for addressing GRACE limitations (e.g., noise filtering, leakage correction and low-degree spherical harmonics estimates), and the improved knowledge of geophysical corrections applied to GRACE data (e.g., GIA). The most recent estimates (Dieng et al., 2015b <sup>[[#fn:r195|195]]</sup> ; Reager et al., 2016 <sup>[[#fn:r196|196]]</sup> ; Rietbroek et al., 2016 <sup>[[#fn:r197|197]]</sup> ; Chambers et al., 2017 <sup>[[#fn:r198|198]]</sup> ; Blazquez et al., 2018 <sup>[[#fn:r199|199]]</sup> ; Uebbing et al., 2019 <sup>[[#fn:r200|200]]</sup> ) report a global ocean mass increase of 1.7 (1.4 – 2.0) mm yr <sup>–1</sup> over 2003–2015 (see also Table 4.1). The uncertainty arises essentially from differences in the inversion method to compute the ocean mass (Chen et al., 2013 <sup>[[#fn:r201|201]]</sup> ; Jensen et al., 2013 <sup>[[#fn:r202|202]]</sup> ; Johnson and Chambers, 2013 <sup>[[#fn:r204|204]]</sup> ; Rietbroek et al., 2016 <sup>[[#fn:r205|205]]</sup> ) , uncertainties in the geocentre motion and uncertainty in the GIA correction (Blazquez et al., 2018 <sup>[[#fn:r205|205]]</sup> ; Uebbing et al., 2019 <sup>[[#fn:r206|206]]</sup> ) . The consistency between estimates of the global mean ocean mass on a monthly time scale has also increased since AR5.

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-5"></div>

<span id="glaciers-1"></span>
===== 4.2.2.2.3 Glaciers =====

To assess the mass contribution of glaciers to sea level change, global estimates are required. Recent updates and temporal extensions of estimates obtained by different methods continue to provide ''very high confidence'' in continuing glacier mass loss on the global scale during the past decade (Bamber et al., 2018 <sup>[[#fn:r207|207]]</sup> ; Wouters et al., 2019; Zemp et al., 2019 <sup>[[#fn:r208|208]]</sup> ), see Section 2.2.3 and Appendix 2.A for a detailed discussion also on regional scales). Updates of the reconstructions of Cogley (2009) <sup>[[#fn:r210|210]]</sup> , Leclercq et al. (2011) <sup>[[#fn:r211|211]]</sup> and Marzeion et al. (2012) <sup>[[#fn:r212|212]]</sup> , presented and compared in Marzeion et al. (2015) <sup>[[#fn:r212|212]]</sup> , show increased agreement on rates of mass loss during the entire 20th century (Marzeion et al., 2015 <sup>[[#fn:r213|213]]</sup> ), compared to earlier estimates reported by AR5. The contribution of glaciers that may be missing in inventories or have already melted during the 20th century is hard to constrain (Parkes and Marzeion, 2018 <sup>[[#fn:r215|215]]</sup> ), and there is ''low confidence'' in their estimated contribution. These glaciers are thus neglected in the assessment of the sea level budget (Table 4.1).

While the agreement between the observational estimates of glacier mass changes and the modelled estimates from glacier models forced with climate model simulations has increased since AR5 (Slangen et al., 2017b <sup>[[#fn:r216|216]]</sup> ), there is only ''medium'' ''confidence'' in the use of glacier models to reconstruct sea level change because of the limited number of well-observed glaciers available to evaluate models on long time scales, and because of the small number of model-based global glacier reconstructions.

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-6"></div>

<span id="greenland-and-antarctic-ice-sheets"></span>
===== 4.2.2.2.4 Greenland and Antarctic ice sheets =====

Frequent observations of ice sheet mass changes have only been available since the advent of space observations (see Section 3.3.1). In the pre-satellite era, mass balance was geodetically reconstructed only for the GIS (Kjeldsen et al., 2015 <sup>[[#fn:r217|217]]</sup> ) . These geodetic reconstructions empirically constrain the contribution of the GIS to SLR between 1900 and 1983 to 17.2 (10.7 – 23.2; Kjeldsen et al., 2015 <sup>[[#fn:r218|218]]</sup> ) . During the satellite era, three approaches have been developed to estimate ice sheet mass balance: 1) Mass loss is estimated by direct measurements of ice sheet height changes with satellite laser or radar altimetry in combination with climatological/glaciological models for firn density and compaction, 2) the input–output method combines measurements of ice flow velocities estimated from satellite (synthetic aperture radar or optical imagery) across key outlets with estimates of net surface balance derived from ice thickness data, 3) space gravimetry data yields direct estimate of the mass changes by inversion of the anomalies in the gravity field (see Section 3.3.1 for more details). AR5 concluded that the three space-based methods give consistent results. They agree in showing that the rate of SLR due to the GIS and AIS’ contributions has increased since the early 1990s. Since AR5, up-to-date observations confirm this statement with increased confidence for both ice sheets (Rignot et al., 2019 <sup>[[#fn:r219|219]]</sup> ; see Section 3.3.1) . The assessment of the literature since AR5 made in Section 3.3.1 shows that the contribution from Greenland to SLR over 2012–2016 (0.68 (0.64 – 0.72) mm yr <sup>–1</sup> ) was similar to the contribution over 2002–2011 (0.73 (0.67 – 0.79) mm yr <sup>–1</sup> ) and ''extremely likely'' greater than over 1992–2001 (0.02 (0.21 – 0.25) mm yr <sup>–1</sup> ). The contribution from Antarctica over 2012–2016 (0.55 (0.48 – 0.62) mm yr <sup>–1</sup> ) was ''extremely likely'' greater than over the 2002–2011 period (0.23 (0.16 – 0.30) mm yr <sup>–1</sup> ) and ''likely'' greater than over the period 1992–2001 (0.14 (0.12 – 0.16); see Section 3.3.1 for more details).

Here, the approach of Section 3.3.1 is followed, using the two multi-method assessments from Bamber et al. (2018) <sup>[[#fn:r220|220]]</sup> and the IMBIE team (2018) to evaluate the contribution of ice sheet mass loss to SLR over 1993–2015 and 2006–2015 (see Table 4.1). These two studies agree with results from the WCRP Global Sea Level Budget Group (2018) . For the estimation of the AIS contribution, Bamber et al. (2018) <sup>[[#fn:r221|221]]</sup> and the The IMBIE team (2018) use similar but not identical data sources and processing. Both studies find consistent results within uncertainties over both periods. In Table 4.1, the results of these two studies were averaged, and weighted the average on the basis of their uncertainties, because there is no apparent reason to discount either study. For the estimation of the GIS contribution only the Bamber et al. (2018) <sup>[[#fn:r222|222]]</sup> estimate is used, as there is no other multi-method assessment available.

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-7"></div>

<span id="contributions-from-water-storage-on-land"></span>
===== 4.2.2.2.5 Contributions from water storage on land =====

Water is stored on land not only in the form of ice but snow, surface water, soil moisture and groundwater. Temporal changes in land water storage, defined as all forms of water stored on land excluding land ice, contribute to observed changes in ocean mass and thus sea level on annual to centennial time scales (Döll et al., 2016 <sup>[[#fn:r225|225]]</sup> ; Reager et al., 2016 <sup>[[#fn:r226|226]]</sup> ; Hamlington et al., 2017 <sup>[[#fn:r227|227]]</sup> ; Wada et al., 2017 <sup>[[#fn:r228|228]]</sup> ) . They are caused by both climate variability and direct human interventions, at the multi-decadal to centennial time scales. Over the past century, the main cause for land water storage changes are the groundwater depletion and impoundment of water behind dams in reservoirs (Döll et al., 2016 <sup>[[#fn:r229|229]]</sup> ; Wada et al., 2016 <sup>[[#fn:r230|230]]</sup> ) . While the rate of groundwater depletion and thus its contribution to SLR increased during the 20th century and up to today (Wada et al., 2016 <sup>[[#fn:r231|231]]</sup> ) , its effect on sea level was more than balanced by the increase in land water storage due to dam construction between 1950 and 2000 (Wada et al., 2016 <sup>[[#fn:r232|232]]</sup> ) . Since about 2000, based on hydrological models, the combined effect of both processes is a positive contribution to SLR (Wada et al., 2016 <sup>[[#fn:r233|233]]</sup> ) . Decreased water storage in lakes, wetlands and soils due to human activities are less important for ocean mass changes (Wada et al., 2016 <sup>[[#fn:r234|234]]</sup> ) . Overall, the integrated effects of the direct human intervention on land hydrology have reduced land water storage during the last decade, increasing the rate of SLR by 0.15–0.24 mm yr <sup>–</sup> <sup>1</sup> (Wada et al., 2016 <sup>[[#fn:r235|235]]</sup> ; Wada et al., 2017 <sup>[[#fn:r236|236]]</sup> ; Scanlon et al., 2018 <sup>[[#fn:r237|237]]</sup> ; WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r238|238]]</sup> ) . Over periods of a few decades, land water storage was affected significantly by climate variability (Dieng et al., 2015a <sup>[[#fn:r239|239]]</sup> ; Reager et al., 2016 <sup>[[#fn:r240|240]]</sup> ; Dieng et al., 2017 <sup>[[#fn:r241|241]]</sup> ) . Net land water storage change driven by both climate and direct human interventions can be determined based on GRACE observations and global hydrological modelling. They indicate different estimates of the rate of SLR. Over the period 2002–2014 GRACE-based estimates of the net land water storage (i.e., not including glaciers) show a negative contribution to sea level (e.g., Scanlon et al., 2018) resulting in the negative value after 2006 in Table 1while hydrological models determined a slightly positive one. The reasons for this difference between estimates are not elucidated. There is scientific consensus that uncertainties of both net land water storage contribution to sea level and its individual contributions remain high (WCRP Global Sea Level Budget Group, 2018 <sup>[[#fn:r242|242]]</sup> ) . The differences in estimates and the lack of multiple consistent studies give ''low confidence'' in the net land water storage contribution to current SLR.

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-8"></div>

<span id="budget-of-global-mean-sea-level-change"></span>
===== 4.2.2.2.6 Budget of global mean sea level change =====

Drawing on previous sections, the budget of GMSL rise (Table 4.1, Figure 4.5) is assessed with observations over 4 periods: 1901–1990 (which corresponds to the period in the 20th century that is prior to the increase in ice sheet contributions to GMSL rise), 1970–2015 (when ocean observations are sufficiently accurate to estimate the global ocean thermal expansion and when glacier mass balance reconstructions start), 1993–2015 (when precise satellite altimetry is available) and 2006–2015 (when GRACE data is available in addition to satellite altimetry and when the Argo network reaches a near-global coverage). The budget of GMSL rise is also assessed with sea level contributions simulated by climate models over the same periods (Table 4.1, Figure 4.5). The periods 1993–2015 and 2006–2015 are only 23 and 10 years long respectively, short enough so that they can be affected by internal climate variability. Therefore, it is not expected that observations over these periods will be precisely reproduced by climate model historical experiments. For the contribution from land water storage, the estimated effect of direct human intervention was used, neglecting climate-related variations until 2002 (Ngo ‐ Duc et al., 2005) . From 2002 to 2015, total land water storage estimated with GRACE was used. In general, historical simulations of climate models end in 2005. Historical simulations were extended here to 2015 using the RCP8.5 scenario. This choice of RCP scenario is not critical for the simulated sea level, as the different scenarios only start to diverge significantly after the year 2030 (Church et al., 2013 <sup>[[#fn:r243|243]]</sup> ) .

For 1993–2015 and 2006–2015, the observed GMSL rise is consistent within uncertainties with the sum of the estimated observed contributions (Table 4.1). Over the period 1993–2015 the two largest terms are the ocean thermal expansion (accounting for 43% of the observed GMSL rise) and the glacier mass loss (accounting for a further 20%). Compared to AR5, the extended observations corrected for the TOPEX-A drift (see Section 4.2.2.1.2) allow us now to identify an acceleration in the observed SLR over 1993–2015 and to attribute this acceleration mainly to Greenland ice loss along with an acceleration in Antarctic ice loss (Velicogna et al., 2014 <sup>[[#fn:r244|244]]</sup> ; Harig and Simons, 2015 <sup>[[#fn:r245|245]]</sup> ; Chen et al., 2017 <sup>[[#fn:r246|246]]</sup> ; Dieng et al., 2017 <sup>[[#fn:r247|247]]</sup> ; Yi et al., 2017 <sup>[[#fn:r248|248]]</sup> ; see also Sections 4.2.2.2.2, 4.2.2.3.4, 3.3.1) . Since 2006, land ice, collectively from glaciers and the ice sheets has become the most important contributor to GMSL rise over the thermal expansion with mountain glaciers contributing 20% and ice sheets 33% (see Table 4.1) . Over the periods 1993–2015, the sum of the observed sea level contributions is consistent with the total observed sea level within uncertainties at monthly-scales (not shown, e.g., Dieng et al., 2017) . This is also true for the period 2006–2015, when uncertainties are significantly smaller. This agreement at monthly time scales represents a significant advance since the AR5 in physical understanding of the causes of past GMSL change. It provides an improved basis for the evaluation of models. Given these elements there is ''high confidence'' that the current observing system is capable of resolving decadal to multidecadal changes in GMSL and its components (with an uncertainty of &lt;0.7 mm yr <sup>–1</sup> at decadal and longer time scales, see Table 4.1 and for example, WCRP Global Sea Level Budget Group, 2018) . However, despite this advance since AR5 there are still no comprehensive observations of ocean thermal expansion below 2000 m, in regions covered by sea ice and in marginal seas. The understanding of glacier mass loss can be improved at regional scale and the understanding of the land water storage contribution is still limited. Thus, for smaller changes in sea level of the order of a few tenths of a mm yr <sup>–1</sup> at decadal time scales and shorter time scales there is ''medium confidence'' in the capability of the current observing system to resolve them (e.g., WCRP Global Sea Level Budget Group, 2018) .

Before 1992, observations are not sufficient to confidently estimate the ice sheet mass balance and before 1970, the space and time sampling of ocean observations are not sufficient to estimate the global ocean thermal expansion. For these reasons, it is difficult to assess the closure of the GMSL rise budget over 1901–1990 and 1970–2015 (Church et al., 2013 <sup>[[#fn:r249|249]]</sup> ; Gregory et al., 2013 <sup>[[#fn:r250|250]]</sup> ; Jevrejeva et al., 2017 <sup>[[#fn:r251|251]]</sup> ; Meyssignac et al., 2017c <sup>[[#fn:r252|252]]</sup> ; Slangen et al., 2017b <sup>[[#fn:r253|253]]</sup> ; Parkes and Marzeion, 2018 <sup>[[#fn:r254|254]]</sup> ) . For the period 1970–2015, the thermal expansion of the ocean represents 43% of the observed GMSL rise while the glaciers’ contribution represents 22% (see Table 4.1). This result indicates a slightly smaller contribution from glaciers than reported by AR5. If the GIS contribution and the Antarctic SMB is added, then the sum of the contributors to sea level is in agreement with the low end observed SLR estimates over 1970–2015 (Frederikse et al., 2018 <sup>[[#fn:r255|255]]</sup> ) . This result suggests that the contribution of Antarctica ice sheet dynamics to SLR has been small, if any, before the 1990s.

Since AR5, extended simulations along with recent findings in observations and improved model estimates allow for a new more robust, consistent and comprehensive comparison between sea level estimates based on observations and climate model simulations (e.g., Meyssignac et al., 2017c; Slangen et al., 2017b <sup>[[#fn:r256|256]]</sup> ; Parkes and Marzeion, 2018 <sup>[[#fn:r257|257]]</sup> ) . Compared to AR5, the simulated thermal expansion from climate models has improved with a new correction for the volcanic activity (see Section 4.2.2.2.1). The glacier contribution from glacier models forced with inputs from climate models is updated with a new glacier inventory and improvements to the glacier mass balance model (Marzeion et al., 2015 <sup>[[#fn:r258|258]]</sup> ) . The simulated Greenland SMB is estimated with a new regional SMB-component downscaling technique, which accounts for the regional variations in components of the Greenland SMB (Noël et al., 2015 <sup>[[#fn:r259|259]]</sup> ; Meyssignac et al., 2017a) <sup>[[#fn:r260|260]]</sup> . In addition, an updated groundwater extraction contribution from Döll et al. (2014) <sup>[[#fn:r261|261]]</sup> is now used for the land water storage contribution.

For the periods 1970–2015, 1993–2015 and 2006–2015 the simulated contributions from thermal expansion, glaciers mass loss and Greenland SMB explain respectively 84%, 81% and 77% of the observed GMSL (see Table 4.1). For all these periods the residual is consistent within uncertainty with the sum of the contribution from land water storage and ice discharge from Greenland and Antarctica. For each period the consistency is improved compared to AR5 (see Table 4.1) although the uncertainty on the residual is slightly larger because of a larger uncertainty in simulated Glaciers and Greenland SMB contributions.

For the period 1901–1990 the simulated contributions from thermal expansion, glaciers mass loss and Greenland SMB explain only 60% of the observed GMSL and the residual is too large to be explained by the sum of the contribution from land water storage and ice discharge from Greenland and Antarctica. The gap can be explained by a bias in the simulated Greenland SMB and glacier ice loss around Greenland in the early 20th century (Slangen et al., 2017b <sup>[[#fn:r262|262]]</sup> ) . When the glacier model and the Greenland SMB downscaling technique are forced with observed climate from atmospheric reanalyses, rather than the simulated climate from coupled climate models, simulated SLR becomes consistent with the observed SLR (see the dashed blue line on Figure 4.5). This is because atmospheric reanalyses show an increase in air temperatures in and around Greenland over the period 1900–1940, which lead to increased melt in Greenland (Bjørk et al., 2012 <sup>[[#fn:r267|267]]</sup> ; Fettweis et al., 2017 <sup>[[#fn:r268|268]]</sup> ) and surrounding glaciers in the first half of the 20th century. This increase in air temperature over 1900–1940 is not reproduced by climate models (Slangen et al., 2017b <sup>[[#fn:r269|269]]</sup> ) . It may be because this increase in air temperature was due to internal climate variability on temporal and spatial scales that cannot be precisely reproduced by climate models. It may also be due to a bias in atmospheric circulation in climate models (Fettweis et al., 2017 <sup>[[#fn:r270|270]]</sup> ) , or an issue with the spatial pattern of the historical aerosol forcing.

In summary, the agreement between climate model simulations and observations of the global thermal expansion, glacier mass loss and Greenland SMB has improved compared to AR5 for periods starting after 1970. However, for periods prior to 1970, significant discrepancies between climate models and observations arise from the inability of climate models to reproduce some observed regional changes in glacier and GIS SMB around the southern tip of Greenland. It is not clear whether this bias in climate models is due to the internal variability of the climate system or deficiencies in climate models. For this reason, there is still ''medium confidence'' in the ability of climate models to simulate past and future changes in glaciers mass loss and Greenland SMB.

<div id="section-4-2-2-2contributions-to-global-mean-sea-level-change-during-the-instrumental-period-block-9"></div>

<span id="figure-4.5"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 4.5'''

<span id="figure-4.5-comparison-of-simulated-by-coupled-climate-models-as-in-section-4.4.2.6-and-observed-global-mean-sea-level-change-gmsl-since-1901-a-and-since-1993-b.-the-average-estimate-of-12-coupled-model-intercomparison-project-phase-5-cmip5-climate-model-simulations-is-shown-in-blue-with-the-595-uncertainty-range-shaded-in"></span>
<!-- IMG CAPTION -->
'''Figure 4.5 | Comparison of simulated (by coupled climate models as in Section 4.4.2.6) and observed global mean sea level change (GMSL) since 1901 (a) and since 1993 (b). The average estimate of 12 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model simulations is shown in blue with the 5–95% uncertainty range shaded in […]'''
<!-- IMG FILE -->
[[File:a361928b5f4ba7db98666b4e46217e56 IPCC-SROCC-CH_4_5-3000x2599.jpg]]

Figure 4.5 | Comparison of simulated (by coupled climate models as in Section 4.4.2.6) and observed global mean sea level change (GMSL) since 1901 (a) and since 1993 (b). The average estimate of 12 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model simulations is shown in blue with the 5–95% uncertainty range shaded in blue and calculated according to the procedures in Church et al. (2013) <sup>[[#fn:r263|263]]</sup> . The average of the 12 model estimates corrected for the bias in glaciers mass loss and Greenland surface mass balance (SMB) over 1900–1940 (see Section 4.2.2.2.6) is shown in dashed blue. The estimates from tide gauge reconstructions is shown in other colours in panel a), with the 5–95% uncertainty range shaded in grey. The satellite altimetry observations from Legeais et al. (2018) <sup>[[#fn:r264|264]]</sup> is shown in black in panel b). GMSL from altimetry corrected for the TOPEX-A drift (Watson et al., 2015 <sup>[[#fn:r265|265]]</sup> ) in orange as well as the tide gauge reconstruction. The 5–95% uncertainty range is shaded in orange (Ablain et al., 2015 <sup>[[#fn:r266|266]]</sup> ). All curves in (a) represent anomalies in sea level with respect to the period 1986–2005 (i.e., with zero time-mean over the period 1986–2005) in order to be consistent with sea level projections in Section 4.2.3. Vertical lines indicate the occurrence of major volcanic eruptions, which cause temporary drops in GMSL. Updated from Slangen et al. (2017b).

<!-- END IMG -->
<div id="section-4-2-2-3regional-sea-level-changes-during-the-instrumental-period"></div>

<span id="regional-sea-level-changes-during-the-instrumental-period"></span>
==== 4.2.2.3 Regional Sea Level Changes During the Instrumental Period ====

<div id="section-4-2-2-3regional-sea-level-changes-during-the-instrumental-period-block-1"></div>

Sea level does not rise uniformly. Observations from tide gauges and satellite altimetry (Figure 4.6) indicate that sea level shows substantial regional variability at decadal to multi-decadal time scales (e.g., Carson et al., 2017; Hamlington et al., 2018 <sup>[[#fn:r271|271]]</sup> ). These regional changes are essentially due to changing winds, air-sea heat and freshwater fluxes, atmospheric pressure loading and the addition of melting ice into the ocean, which alters the ocean circulation (Stammer et al., 2013 <sup>[[#fn:r272|272]]</sup> ; Forget and Ponte, 2015 <sup>[[#fn:r273|273]]</sup> ; Meyssignac et al., 2017b <sup>[[#fn:r274|274]]</sup> ). The addition of water into the ocean also change the geoid, alter the rotation of the Earth and deform the ocean floor which in turn change sea level (e.g., Tamisiea, 2011; Stammer et al., 2013 <sup>[[#fn:r275|275]]</sup> ).

Sea level is rising in all ocean basins ( ''virtually certain'' ; Legeais et al. 2018 <sup>[[#fn:r276|276]]</sup> ). Part of this regional sea level rise is due to global sea level rise of which a majority is attributable to anthropogenic greenhouse gas emissions ( ''high confidence'' ; Slangen et al. 2016 <sup>[[#fn:r277|277]]</sup> ). The remaining part of the regional sea-level rise in ocean basins is a combination of the response to anthropogenic GHG emissions and internal variability (e.g., Stammer et al. 2013; ''medium confidence'' ).

In the open ocean, the spatial variability and trends in sea level observed during the recent altimetry era or reconstructed over the previous decades are dominated by the thermal expansion of the ocean. In shallow shelf seas and at high latitudes (&gt;60°N and &lt;55°S), the effect of dynamic mass redistribution becomes important. At local scale, salinity changes can also generate sizeable changes in the ocean density similar to thermal expansion and lead to significant variability in sea level (Forget and Ponte, 2015 <sup>[[#fn:r278|278]]</sup> ; Meyssignac et al., 2017b <sup>[[#fn:r279|279]]</sup> ). On global average, the heat and freshwater fluxes from the atmosphere into the ocean are responsible for the total heat that enters the ocean and for the associated GMSL rise. At regional scale and local scale, both the ocean transport divergences caused by wind stress anomalies and the spatial variability in atmospheric heat fluxes are responsible for the spatial variability in thermal expansion and thus for most of the regional sea level departures around the GMSL rise (e.g., Stammer et al., 2013; Forget and Ponte, 2015 <sup>[[#fn:r280|280]]</sup> ).

Over the Pacific, the surface wind anomalies responsible for the sea level spatio-temporal variability are associated with the ENSO, Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation modes (Hamlington et al., 2013 <sup>[[#fn:r281|281]]</sup> ; Moon et al., 2013 <sup>[[#fn:r282|282]]</sup> ; Palanisamy et al., 2015 <sup>[[#fn:r283|283]]</sup> ; Han et al., 2017 <sup>[[#fn:r284|284]]</sup> ). In the Indian Ocean they are associated with the ENSO and Indian Ocean Dipole (IOD) modes (Nidheesh et al., 2013 <sup>[[#fn:r285|285]]</sup> ; Han et al., 2014 <sup>[[#fn:r286|286]]</sup> ; Thompson et al., 2016 <sup>[[#fn:r287|287]]</sup> ; Han et al., 2017 <sup>[[#fn:r288|288]]</sup> ). In particular, the PDO is responsible for most of the intensified SLR that has been observed in the western tropical Pacific Ocean since the 1990s (Moon et al., 2013 <sup>[[#fn:r289|289]]</sup> ; Han et al., 2014 <sup>[[#fn:r290|290]]</sup> ; Thompson and Mitchum, 2014 <sup>[[#fn:r291|291]]</sup> ). Several studies suggested that in addition to the PDO signal, warming of the tropical Indian and Atlantic Oceans enhanced surface easterly trade winds and thus also contributes to the intensified SLR in the western tropical Pacific (England et al., 2014 <sup>[[#fn:r292|292]]</sup> ; Hamlington et al., 2014 <sup>[[#fn:r293|293]]</sup> ; McGregor et al., 2014 <sup>[[#fn:r294|294]]</sup> ).

Over the Atlantic, the regional sea level variability at interannual to multi-decadal time scales, is generated by surface wind anomalies and heat fluxes associated with the North Atlantic Oscillation (NAO; Han et al., 2017 <sup>[[#fn:r295|295]]</sup> ) and also by ocean heat transport due to changes in the Atlantic Meridional Overturning Circulation (AMOC; McCarthy et al., 2015 <sup>[[#fn:r296|296]]</sup> ). Both mechanisms are not independent as heat fluxes and wind stress anomalies associated with NAO can induce changes in the AMOC (Schloesser et al., 2014 <sup>[[#fn:r297|297]]</sup> ; Yeager and Danabasoglu, 2014 <sup>[[#fn:r298|298]]</sup> ). In the Southern Ocean, the sea level variability is dominated by the SAM influence in particular in the Indian and Pacific sectors. The Southern Annular Mode (SAM) influence becomes weaker equator-wards in these sectors while the influence of PDO, ENSO and IOD increases (Frankcombe et al., 2015 <sup>[[#fn:r299|299]]</sup> ). In the southern ocean, the zonal asymmetry in westerly winds associated to the SAM, generates convergent and divergent transport in the Antarctic Circumpolar Current which may have contributed to the regional asymmetry of decadal sea level variations during most of the twentieth century (Thompson and Mitchum, 2014 <sup>[[#fn:r300|300]]</sup> ).

As for GMSL, net regional sea level changes can be estimated from a combination of the various contributions to sea level change. The contributions from dynamic sea level, atmospheric loading, glacier mass changes and ice sheet SMB can be derived from CMIP5 climate model outputs either directly or through downscaling techniques (Perrette et al., 2013 <sup>[[#fn:r301|301]]</sup> ; Kopp et al., 2014 <sup>[[#fn:r302|302]]</sup> ; Slangen et al., 2014a <sup>[[#fn:r303|303]]</sup> ; Bilbao et al., 2015 <sup>[[#fn:r304|304]]</sup> ; Carson et al., 2016 <sup>[[#fn:r305|305]]</sup> ; Meyssignac et al., 2017a <sup>[[#fn:r306|306]]</sup> ). The contributions from groundwater depletion, reservoir storage and dynamic ice sheet mass changes are not simulated by coupled climate models over the 20th century and have to be estimated from observations. The sum of all contributions, including the GIA contribution, provides a modelled estimate of the 20th century net regional sea level changes that can be compared with observations from satellite altimetry and tide-gauge records (see Figure 4.6).

In terms of interannual to multi-decadal variability, there is a general agreement between the simulated regional sea level and tide gauge records, over the period 1900–2015 (see inset figures in Figure 4.6). The relatively large, short-term oscillations in observed sea level (black lines in insets in Figure 4.6), which are due to the natural internal climate variability, are included in general within the modelled internal variability of the climate system represented by the blue shaded area (5–95% uncertainty). But, as for GMSL, climate models tend to systematically underestimate the observed sea level trends from tide gauge records, particularly in the first half of the 20th century. This underestimation is explained by a bias identified in modelled Greenland SMB, and glacier ice loss around Greenland in the early 20th century (see Section 4.2.2.2.6; Slangen et al., 2017b <sup>[[#fn:r307|307]]</sup> ). The correction of this bias improves the agreement between the spatial variability in sea level trends from observations and from climate models (see Figure 4.6). Climate models indicate that the spatial variability in sea level trends observed by tide-gauge records over the 20th century is dominated by the GIA contribution and the thermal expansion contribution over 1900–2015. Locally all contributions to sea level changes are important as any contribution can cause significant local deviations. Around India for example, groundwater depletion is responsible for the low 20th century SLR (because the removal of groundwater mass generated a local decrease in geoid that made local SLR slower; Meyssignac et al., 2017c <sup>[[#fn:r308|308]]</sup> )

These results show the ability of models to reproduce the major 20th century regional sea level changes due to GIA, thermal expansion, glacier mass loss and ice sheet SMB. This is tangible progress since AR5. But some doubts remain regarding the ability of climate models to reproduce local variations such as the glaciers and the Greenland SMB contributions to sea level in the region around the southern tip of Greenland (Slangen et al., 2017b <sup>[[#fn:r309|309]]</sup> ) or such as the thermal expansion in some eddy active regions (Sérazin et al., 2016 <sup>[[#fn:r310|310]]</sup> ). Because of these doubts there is still ''medium confidence'' in climate models to project future regional sea level changes associated with thermal expansion, glacier mass loss and ice sheet SMB. Coupled climate models have not simulated the other contributions to 20th century sea level, including the growing ice sheet dynamical contribution and land water storage changes.

<div id="section-4-2-2-3regional-sea-level-changes-during-the-instrumental-period-block-2"></div>

<span id="figure-4.6"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 4.6'''

<span id="figure-4.6-20th-century-simulated-regional-sea-level-changes-by-coupled-climate-models-and-comparison-with-a-selection-of-local-tide-gauge-time-series.-in-the-upper-left-corner-map-of-changes-in-simulated-relative-sea-level-rsl-for-the-period-19011920-to-19962015-estimated-from-climate-model-outputs.-insets-observed-rsl-changes-black"></span>
<!-- IMG CAPTION -->
'''Figure 4.6 | 20th century simulated regional sea level changes by coupled climate models and comparison with a selection of local tide gauge time series. In the upper left corner: map of changes in simulated relative sea level (RSL) for the period 1901–1920 to 1996–2015 estimated from climate model outputs. Insets: Observed RSL changes (black […]'''
<!-- IMG FILE -->
[[File:ce92a975877f501626ca4d1003740770 IPCC-SROCC-CH_4_6-3000x1993.jpg]]

Figure 4.6 | 20th century simulated regional sea level changes by coupled climate models and comparison with a selection of local tide gauge time series. In the upper left corner: map of changes in simulated relative sea level (RSL) for the period 1901–1920 to 1996–2015 estimated from climate model outputs. Insets: Observed RSL changes (black lines) from selected tide gauge stations for the period 1900–2015. For comparison, the estimate of the simulated RSL change at the tide gauge station is also shown (blue plain line for the model estimates and blue dashed line for the model estimates corrected for the bias in glaciers mass loss and Greenland surface mass balance (SMB) over 1900–1940, see Section 4.2.2.2.6). The relatively large, short-term oscillations in observed local sea level (black lines) are due to the natural internal climate variability. For Mediterranean tide gauges, that is, Venice and Alexandria, the local simulated sea level has been computed with the simulated sea level in the Atlantic ocean at the entrance of the strait of Gibraltar following (Adloff et al., 2018). Tide gauge records have been corrected for vertical land motion (VLM) not associated with GIA where available, that is, for New York, Balboa and Lusi. Updated from Meyssignac et al. (2017b) to mimic RSL as good as possible.

<!-- END IMG -->
<div id="section-4-2-2-4local-coastal-sea-level"></div>

<span id="local-coastal-sea-level"></span>
==== 4.2.2.4 Local Coastal Sea Level ====

<div id="section-4-2-2-4local-coastal-sea-level-block-1"></div>

Since the local coastal sea level (scale ~10 km) is affected by global, regional (scale ~100 km) and coastal scale features and processes like anthropogenic subsidence, it may differ substantially from the regional sea level. At the coast, the sea level change is additionally affected by wave run up, tidal level, wind forcing, sea level pressure (SLP), the dominant modes of climate variability, seasonal climatic periodicities, mesoscale eddies, changes in river flow, as well as anthropogenic subsidence (see also Box 4.1). These local contributions, combined with sea level events generated by storm surges and tides result in anomalous conditions (ESL) which last for a short time in contrast to the gradual increase over time from for instance ice mass loss. Flood risk due to ESL is exacerbated due to its interaction with RSL and hence physical vulnerability assessments combine uncertainties around ESL and RSL, both in terms of contemporary assessments and future projections (Little et al., 2015b <sup>[[#fn:r312|312]]</sup> ; Vousdoukas, 2016 <sup>[[#fn:r313|313]]</sup> ; Vousdoukas et al., 2016 <sup>[[#fn:r314|314]]</sup> ; Wahl et al., 2017 <sup>[[#fn:r315|315]]</sup> ). Changes in mean sea level have been dealt with in previous sections (e.g., Section 4.2.2.2.6). Here the focus is on some of the components of ESL that have been assessed in combination with changes in RSL. Church et al. (2013) concluded that change in sea level extremes is ''very'' ''likely'' to be caused by a RSL increase, and that storminess and surges will contribute towards these extremes; however, it was noted that there was ''low confidence'' in region-specific projections as there was only a limited number of studies with a poor geographical coverage available.

Recent advances in statistical and dynamical modelling of wave effects at the coast, storm surges and inundation risk have reduced the uncertainties around the inundation risks at the coast (Vousdoukas et al., 2016 <sup>[[#fn:r316|316]]</sup> ; Vitousek et al., 2017 <sup>[[#fn:r317|317]]</sup> ; Melet et al., 2018 <sup>[[#fn:r318|318]]</sup> ; Vousdoukas et al., 2018c <sup>[[#fn:r319|319]]</sup> ) and assessments of the resulting highly resolved coastal sea levels are now emerging (Cid et al., 2017 <sup>[[#fn:r320|320]]</sup> ; Muis et al., 2017 <sup>[[#fn:r321|321]]</sup> ; Wahl et al., 2017 <sup>[[#fn:r322|322]]</sup> ). This progress was facilitated due to the availability of, for example, the Global Extreme Sea Level Analysis (GESLA-2; Woodworth et al., 2016 <sup>[[#fn:r323|323]]</sup> ) high-frequency (hourly) datasets, advances in the Coordinated Ocean Wave Climate Project (COWCLIP; Hemer et al., 2013 <sup>[[#fn:r324|324]]</sup> ), coastal altimetry datasets (Cipollini et al., 2017 <sup>[[#fn:r325|325]]</sup> ), and the Global Tide and Surge Reanalysis (GTSR; Muis et al., 2016 <sup>[[#fn:r326|326]]</sup> ), while new analyses of datasets that have been available since before the publication of AR5 have continued (e.g., PSML; Holgate et al., 2012 <sup>[[#fn:r327|327]]</sup> ).

Although ESL is experienced episodically by definition, Marcos et al. (2015) <sup>[[#fn:r328|328]]</sup> examined the long-term behaviour of storm surge models and detected decadal and multidecadal variations in storm surge that are not related to changes in RSL. They found that, although 82% of their observed time series showed synchronous patterns at regional scales, the pattern tended to be non-linear, implying that it would be difficult to infer future behaviour unless the physical basis for the responses was understood. An analysis of the relative contributions of SLR and ESL due to storminess showed that in the US Pacific northwest since the early 1980s, increases in wave height and period have had a larger effect on coastal flooding and erosion than RSL (Ruggiero, 2012 <sup>[[#fn:r329|329]]</sup> ) since the early 1980s. This is also true in other regions distributed over the entire globe (Melet et al., 2016 <sup>[[#fn:r330|330]]</sup> ; Melet et al., 2018 <sup>[[#fn:r331|331]]</sup> ). Changes since 1990 in the sea level harmonics and seasonal phases and amplitudes of the wave period and significant wave height were found for the Gulf of Mexico coast and along the US east coast (Wahl et al., 2014 <sup>[[#fn:r332|332]]</sup> ; Wahl and Plant, 2015 <sup>[[#fn:r333|333]]</sup> ). These authors found that high waters have increased twice as much as one would expect from long-term SLR alone, because of additional changes in the seasonal cycle, yielding a 30% increase in risk of flooding. Such effects are ''likely'' to be highly dependent on the local conditions. For example, using WAVEWATCH III, TOPEX/Poseidon altimetry tide model data and atmospheric forcing physically downscaled using Delft3D-WAVE and Delft3D-FLOW in what they call the Coastal Storm Modeling System (CoSMoS), Vitousek et al. (2017) were able to detect local inundation hazards (at a scale of hundreds of metres) across regions along the Californian coast. Similarly, Castrucci and Tahvildari (2018) <sup>[[#fn:r334|334]]</sup> simulated the impact of SLR along the Mid-Atlantic region in the USA. A study for the Maldives shows that the contribution of wave setup is essential to estimate flood risks (Wadey et al., 2017 <sup>[[#fn:r335|335]]</sup> ).

In deltas, the local sea level can be dominated by anthropogenic subsidence more than by the processes outlined above. It is often a primary driver of elevated local SLR and increased flood hazards in those regions. This is particularly true for deltaic systems, where fertile soils, low-relief topography, freshwater access, and strategic ports have encouraged the development of many of the world’s most densely populated coastlines and urban centres. For example, globally, one in fourteen humans resides in mid-to-low latitude deltas (Day et al., 2016 <sup>[[#fn:r336|336]]</sup> ). Although in these areas RSL is dominated by anthropogenic subsidence, climate effects need to be included for estimating risks associated with RSL (Syvitski et al., 2009 <sup>[[#fn:r337|337]]</sup> ).

Deltas are formed by the accumulation of unconsolidated river born sediments and porous organic material, both of which are particularly prone to compaction. It is the compaction which causes a drop in land elevation that increases the rate of local SLR above what would be observed along a static coastline or one where only climatological forced processes control the RSL. Under stable deltaic conditions, the accumulation of fluvially-sourced surficial sediment and organic matter offsets this natural subsidence (Syvitski and Saito, 2007 <sup>[[#fn:r338|338]]</sup> ); however, in many cases this natural process of delta construction has been disturbed by reductions in fluvial sediment supply via upstream dams and fluvial channelisation (Vörösmarty et al., 2003 <sup>[[#fn:r339|339]]</sup> ; Syvitski and Saito, 2007 <sup>[[#fn:r340|340]]</sup> ; Syvitski et al., 2009 <sup>[[#fn:r341|341]]</sup> ; Luo et al., 2017 <sup>[[#fn:r342|342]]</sup> ). Further, the extraction of fluids and gas that fill the pore space of deltaic sediments and provide support for overlying material has significantly increased the rate of compaction and resultant anthropogenic subsidence along many populated deltas (Higgins, 2016 <sup>[[#fn:r343|343]]</sup> ). In addition, Nicholls (2011) pointed to anthropogenic subsidence by the weight of buildings in megacities in South-East Asia.

Average natural and anthropogenic subsidence rates of 6–9 mm yr <sup>–1</sup> are reported for the highly populated areas of Ganges-Brahmaputra-Meghna delta in the urban centres of Kolkata and Dhaka (Brown and Nicholls, 2015 <sup>[[#fn:r344|344]]</sup> ). A fraction of these subsidence rates might be caused by long-term processes of increased sediment loading during the Holocene resulting from changes in the monsoon system (Karpytchev et al., 2018 <sup>[[#fn:r345|345]]</sup> ). Subsidence rates are expected to decrease in the Ganges-Brahmaputra-Meghna delta in the near future due to planned dam projects and an estimated 21% drop in resulting sediment supply (Tessler et al., 2018 <sup>[[#fn:r346|346]]</sup> ). Observations of enhanced natural and anthropogenic subsidence on the Ganges-Brahmaputra-Meghna are common to most heavily populated deltaic systems. Coastal mega-cities that have been particularly prone to human-enhanced subsidence include Bangkok, Ho Chi Minh city (Vachaud et al., 2018 <sup>[[#fn:r347|347]]</sup> ), Jakarta, Manila, New Orleans, West Netherlands and Shanghai (Yin et al., 2013 <sup>[[#fn:r348|348]]</sup> ; Cheng et al., 2018 <sup>[[#fn:r349|349]]</sup> ). On a global scale, observed rates of modern deltaic anthropogenic subsidence range from 6–100 mm yr <sup>–1</sup> (Bucx et al., 2015 <sup>[[#fn:r350|350]]</sup> ; Higgins, 2016 <sup>[[#fn:r351|351]]</sup> ). Rates of recent deltaic subsidence over the last few decades have been at least twice the 3 mm yr <sup>–1</sup> rate of GMSL rise observed over this same interval (Higgins, 2016 <sup>[[#fn:r352|352]]</sup> ; Tessler et al., 2018 <sup>[[#fn:r353|353]]</sup> ). Numerical models that have reproduced these observed rates of anthropogenic deltaic subsidence by considering human-induced compaction and reduced sediment supply, support anthropogenic causes for elevated rates of subsidence (Tessler et al., 2018 <sup>[[#fn:r354|354]]</sup> ).

In summary, ESL interacts with RSL rise including anthropogenic subsidence in many vulnerable areas (see Box 4.1). Therefore, it is concluded with ''high confidence'' that the inclusion of local processes (wave effects, storm surges, tides, erosion, sedimentation and compaction) is essential to estimate local, relative and changes in ESL events. Although the effect of anthropogenic subsidence may be very large locally, it is not accounted for in the projection sections of this chapter as no global data sets are available which are consistent with RCP scenarios, and because the scale at which these processes take place is often smaller than the spatial scale used in climate models.

<div id="section-4-2-2-5attribution-of-sea-level-change-to-anthropogenic-forcing"></div>

<span id="attribution-of-sea-level-change-to-anthropogenic-forcing"></span>
==== 4.2.2.5 4.2.2.5 Attribution of Sea Level Change to Anthropogenic Forcing ====

<div id="section-4-2-2-5attribution-of-sea-level-change-to-anthropogenic-forcing-block-1"></div>

Bindoff et al. (2013) concluded that it is ''very likely'' that there has been a substantial contribution to ocean heat content from anthropogenic forcing (i.e., anthropogenic greenhouse gases, anthropogenic aerosols and land use change) since the 1970s, that it is ''likely'' that loss of land ice is partly caused by anthropogenic forcing, and that as a result, it is ''very likely'' that there is an anthropogenic contribution to the observed trend in GMSL rise since 1970. However, these conclusions were based on the understanding of the responsible physical processes, since formal attribution studies dedicated to quantifying the effect of individual external forcings were not available for GMSLR. Since AR5, such formal studies have attributed changes in individual components of sea level change (i.e., thermosteric sea level change and glacier mass loss), and in the total GMSL, to anthropogenic forcing.

<div id="section-4-2-2-5attribution-of-sea-level-change-to-anthropogenic-forcing-block-2"></div>

<span id="attribution-of-individual-components-of-sea-level-change-to-anthropogenic-forcing"></span>
===== 4.2.2.5.1 Attribution of individual components of sea level change to anthropogenic forcing =====

Marcos and Amores (2014) found that during the period 1970–2005, 87% (95% confidence interval: 72–100%) of the observed thermosteric SLR in the upper 700 m of the ocean was anthropogenic. Slangen et al. (2014b)  included the full ocean depth in their analysis. They concluded that a combination of anthropogenic and natural forcing is necessary to explain the temporal evolution of observed global mean thermosteric sea level change during the period 1957–2005. Anthropogenic forcing was responsible for the amplitude of observed thermosteric sea level change, while natural forcing caused the forced variability of observations. Observations could best be reproduced by scaling the patterns from ‘natural-only’ forcing experiments by using a factor of 0.70 ± 0.30 (2 standard deviations of the CMIP5 ensemble subset used), indicating a potential overestimation of forced variability in the CMIP5 ensemble. Patterns from the ‘anthropogenic-only’ forcing experiments needed to be scaled by a factor of 1.08 ± 0.13 (2 standard deviations of the CMIP5 ensemble subset used), indicating a realistic response of the CMIP5 ensemble to anthropogenic forcing.

For the glacier contribution to GMSL, Marzeion et al. (2014) concluded that while natural climate forcing and long-term adjustment of the glaciers to the end of the preceding Little Ice Age lead to continuous glacier mass loss throughout the simulation period of 1851–2010, the observed rates of glacier mass loss since 1990 can only be explained by including anthropogenic forcing. During the period 1851–2010, only 25 ± 35% of global glacier mass loss can be attributed to anthropogenic forcing, but 69 ± 24% during the period 1991–2010 (see Section 2.2.3 for a more detailed discussion of attribution of glacier mass change on regional scales).

There is ''medium confidence'' in evidence linking GIS mass loss to anthropogenic climate change, and ''low confidence'' in the evidence that AIS mass balance can be attributed to anthropogenic forcing (see Section 3.3.1.6 for a detailed discussion). The effects of groundwater depletion and reservoir impoundment on sea level change are anthropogenic by definition (e.g., Wada et al., 2012) .

<div id="section-4-2-2-5attribution-of-sea-level-change-to-anthropogenic-forcing-block-3"></div>

<span id="attribution-of-global-mean-sea-level-change-to-anthropogenic-forcing"></span>
===== 4.2.2.5.2 Attribution of global mean sea level change to anthropogenic forcing =====

By estimating a probabilistic upper range of long-term persistent natural sea level variability, Dangendorf et al. (2015) <sup>[[#fn:r356|356]]</sup> detected a fraction of observed sea level change that is unexplained by natural variability and concluded by inference that it is ''virtually certain'' that at least 45% of the observed increase in GMSL since 1900 is attributable to anthropogenic forcing. Similarly, Becker et al. (2014) provided statistical evidence that the observed sea level trend, both in the global mean and at selected tide gauge locations, is not consistent with unforced, internal variability. They inferred that more than half of the observed GMSL trend during the 20th century is attributable to anthropogenic forcing.

Slangen et al. (2016) <sup>[[#fn:r357|357]]</sup> reconstructed GMSL from 1900 to 2005 based on CMIP5 model simulations separating individual components of radiative climate forcing and combining the contributions of thermosteric sea level change with glacier and ice sheet mass loss. They found that the naturally caused sea level change, including the long-term adjustment of sea level to climate change preceding 1900, caused 67 ± 23% of observed change from 1900 to 1950, but only 9 ± 18% between 1970 and 2005. Anthropogenic forcing was found to have caused 15 ± 55% of observed sea level change during 1900–1950, but 69 ± 31% during 1970–2005. The sum of all contributions explains only 74 ± 22% of observed GMSL change during the period 1900–2005 considering the mean of the reconstructions of Church and White (2011) <sup>[[#fn:r358|358]]</sup> , Ray and Douglas (2011) , Jevrejeva et al. (2014b) and Hay et al. (2015) . However, the budget could be closed taking into contribution of glaciers that are missing from the global glacier inventory or have already melted (Parkes and Marzeion, 2018 <sup>[[#fn:r361|361]]</sup> ) which were not considered in Slangen et al. (2016) <sup>[[#fn:r362|362]]</sup> .

Based on these multiple lines of evidence, there is ''high confidence'' that anthropogenic forcing ''very likely'' is the dominant cause of observed GMSL rise since 1970.

<span id="projections-of-sea-level-change"></span>
=== 4.2.3 Projections of Sea Level Change ===

<div id="section-4-2-3projections-of-sea-level-change-block-1"></div>

As a consequence of climate change, the global and regional mean sea level will change. Coupled climate models are used to make projections of the climate changes and the associated SLR. Results from the CMIP5 model archive used for AR5 provide information on expected changes in the oceans and on the evolution of climate, glaciers and ice sheets. New estimates from CMIP6 are not yet available and will be discussed in the IPCC 6th Assessment Report (AR6), hence only a partly updated projection can be presented here.

Coupled climate models can be applied on century time scales, to provide estimates of the steric (temperature and salinity effects on sea water density) and ocean dynamical (ocean circulation) components of sea level change, both globally and regionally. However, the glacier and ice sheet component are calculated off-line based on temperature and precipitation changes. In the AR5 report, changes in the SMB of glaciers and ice sheet were calculated from the global surface air temperature. In addition, GCMs also resolve climate variability related to changes in precipitation and evaporation. These changes are used to calculate short duration sea level changes (Cazenave and Cozannet, 2014 <sup>[[#fn:r363|363]]</sup> ; Hamlington et al., 2017 <sup>[[#fn:r364|364]]</sup> ). With various degrees of success those models capture ENSO, PDO and other modes of variability (e.g., Yin et al., 2009; Zhang and Church, 2012 <sup>[[#fn:r365|365]]</sup> ), which affect sea level through redistributions of energy and salt in the ocean on slightly longer time scales. Off-line temperature and precipitation fields can be dynamically or statistically downscaled to match the high spatial resolution required for ice sheets and glaciers, but serious limitations remain. This deficiency limits adequate representation of potentially important feedbacks between changes in ice sheet geometry and climate, for example through fresh water and iceberg production that impact on ocean circulation and sea ice, which can have global consequences (Lenaerts et al., 2016 <sup>[[#fn:r366|366]]</sup> ; Donat-Magnin et al., 2017 <sup>[[#fn:r367|367]]</sup> ). Another limitation is the lack of coupling with the solid Earth which controls the ice sheet evolution (Whitehouse et al., 2019 <sup>[[#fn:r368|368]]</sup> ). Dynamics of the interaction of ice streams with bedrock and till at the ice base remain difficult to model due to lack of direct observations. Nevertheless, several new ice sheet models have been generated over the last few years, particularly for Antarctica (Section 4.2.3.1) focusing on the dynamic contribution of the ice sheet to sea level change, which remains the key uncertainty in future projections (Church et al., 2013), particularly beyond 2050 (Kopp et al., 2014 <sup>[[#fn:r370|370]]</sup> ; Nauels et al., 2017b <sup>[[#fn:r371|371]]</sup> ; Slangen et al., 2017a <sup>[[#fn:r372|372]]</sup> ; Horton et al., 2018 <sup>[[#fn:r373|373]]</sup> ).

Information beyond that provided by climate models is needed to describe local and RSL changes. Geodynamic models are used to calculate RSL changes due to changes in ice mass in the past and future. This includes solid Earth deformation, gravitational and rotational changes, as ice and water are redistributed around the globe. Input for those models is provided by the mass changes following from the off-line land ice models, time series of terrestrial water mass changes which typically require climate input, and reconstruction of past ice sheet changes over the last glacial cycle provided by coupled ice-Earth models (de Boer et al., 2017). Combining these different models leads to projections of RSL (Section 4.2.3.2).

At the local spatial scales of specific cities, islands and stretches of coastlines, hydrodynamical models (Section 4.2.3.3) and knowledge about anthropogenic subsidence are necessary to analyse the impacts of highly variable processes leading to ESL, such as tropical cyclone-driven storm surges. These hydrodynamical models are capable of providing statistics on the variability or the change in variability of the water level required for flood risk calculations at specific locations and at spatial scales of less than 1 km. The models also rely on input from climate models, like temperature, precipitation, wind regime, and storm tracks (Colbert et al., 2013 <sup>[[#fn:r374|374]]</sup> ; Garner et al., 2017 <sup>[[#fn:r375|375]]</sup> ).

In summary, climate models play an important role at the various stages of projections in providing, together with emission scenarios, geodynamic, ice-dynamic and hydrodynamic models, the required information for hazard estimation for coasts and low-lying islands. This report relies on results of the CMIP5 model runs.

<div id="section-4-2-3-1contribution-of-ice-sheets-to-gmsl"></div>

<span id="contribution-of-ice-sheets-to-gmsl"></span>
==== 4.2.3.1 Contribution of Ice Sheets to GMSL ====

<div id="section-4-2-3-1contribution-of-ice-sheets-to-gmsl-block-1"></div>

<span id="greenland"></span>
===== 4.2.3.1.1 Greenland =====

The GIS is currently losing mass at roughly twice the pace of the AIS (see Chapter 3 and Table 4.1). About 60% of the mass loss between 1991 and 2015 has been attributed to increasingly negative SMB from surface melt and runoff on the lower elevations of the ice sheet margin. Ice dynamical changes and increased discharge of marine-terminating glaciers account for the remaining 40% of mass loss (Csatho et al., 2014 <sup>[[#fn:r376|376]]</sup> ; Enderlin, 2014 <sup>[[#fn:r377|377]]</sup> ; van den Broeke et al., 2016 <sup>[[#fn:r378|378]]</sup> ). The ability of firn on Greenland to retain meltwater until it refreezes has diminished markedly since the late 1990s, especially in lower elevations and on peripheral ice caps (Noël et al., 2017 <sup>[[#fn:r379|379]]</sup> ). Patterns of surface melt on Greenland are highly dependent on regional atmospheric patterns (Bevis et al., 2019 <sup>[[#fn:r380|380]]</sup> ), adding uncertainty to future projections of SMB. Melt-albedo feedbacks associated with darkening of the ice surface from ponded water, changes in snow and firn properties, and accumulation of impurities are also important, because they can strongly enhance surface melt (Tedesco et al., 2016 <sup>[[#fn:r381|381]]</sup> ; Ryan et al., 2018 <sup>[[#fn:r382|382]]</sup> ; Trusel et al., 2018 <sup>[[#fn:r383|383]]</sup> ; Ryan et al., 2019 <sup>[[#fn:r384|384]]</sup> ). These processes are not fully captured by most Greenland-scale models which is an important deficiency, because surface processes tend to dominate uncertainty in future GIS model projections (e.g., Edwards et al., 2014; Aschwanden et al., 2019 <sup>[[#fn:r385|385]]</sup> ). Increases in meltwater and changes in the basal hydrologic regime, once thought to have a possible destabilising effect on the ice sheet (Zwally et al., 2002 <sup>[[#fn:r386|386]]</sup> ), have been linked with recent reductions in ice velocity in western Greenland. On decadal time scales the effect of meltwater on ice dynamics are now assessed to be small (van de Wal et al., 2015 <sup>[[#fn:r387|387]]</sup> ; Flowers, 2018 <sup>[[#fn:r388|388]]</sup> ), which is supported by ice sheet model experiments (Shannon et al., 2013 <sup>[[#fn:r389|389]]</sup> ). In sum, uncertain climate projections (Edwards et al., 2014 <sup>[[#fn:r390|390]]</sup> ), albedo evolution, uncertainties around meltwater buffering by firn, complex processes linking surface, englacial and basal hydrology with ice dynamics (Goelzer et al., 2013 <sup>[[#fn:r391|391]]</sup> ; Stevens et al., 2016 <sup>[[#fn:r392|392]]</sup> ; Noël et al., 2017 <sup>[[#fn:r393|393]]</sup> ; Hempelmann et al., 2018 <sup>[[#fn:r394|394]]</sup> ) and meltwater induced melting at marine-terminating ice fronts (Chauché et al., 2014 <sup>[[#fn:r395|395]]</sup> ), and coarse spatial model resolution (Pattyn et al., 2018 <sup>[[#fn:r396|396]]</sup> ), all continue to provide substantial challenges for ice sheet and SMB models.

Greenland-scale ice sheet modelling since AR5 (Edwards et al., 2014 <sup>[[#fn:r397|397]]</sup> ; Fürst et al., 2015 <sup>[[#fn:r398|398]]</sup> ; Vizcaino et al., 2015 <sup>[[#fn:r399|399]]</sup> ; Calov et al., 2018 <sup>[[#fn:r400|400]]</sup> ; Golledge et al., 2019 <sup>[[#fn:r401|401]]</sup> ; Aschwanden et al., 2019 <sup>[[#fn:r402|402]]</sup> ) has built upon earlier work by coupling the ice models with regional climate models and using multiple climate and ice sheet models within single studies (Edwards et al., 2014 <sup>[[#fn:r403|403]]</sup> ). Recent modelling studies use higher-order representations of ice flow (Fürst et al., 2015 <sup>[[#fn:r404|404]]</sup> ), include more explicit representations of ice sheet processes including subglacial hydrology (Calov et al., 2018 <sup>[[#fn:r405|405]]</sup> ), run the models at higher resolution and with updated boundary conditions (Aschwanden et al., 2019 <sup>[[#fn:r406|406]]</sup> ), and account for two-way coupling between the ice sheet and the global ocean (Vizcaino et al., 2015 <sup>[[#fn:r407|407]]</sup> ; Golledge et al., 2019 <sup>[[#fn:r408|408]]</sup> ). Among these studies, Fürst et al. (2015), Vizcaino et al. (2015) <sup>[[#fn:r409|409]]</sup> , and Aschwanden et al. (2019) provide projections following RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. Calov et al. (2018) <sup>[[#fn:r412|412]]</sup> and Golledge et al. (2019) <sup>[[#fn:r413|413]]</sup> did not consider RCP2.6. Edwards et al. (2014) <sup>[[#fn:r414|414]]</sup> used the Special Report on Emissions Scenarios (SRES) A1B scenario which isn’t directly comparable to the other studies assessed here, but they do provide a rigorous analysis of uncertainty contributed by different climate forcings, varying simplifications of ice flow equations and height-SMB feedbacks.

Fürst et al. (2015) <sup>[[#fn:r415|415]]</sup> used ten different CMIP5 Atmosphere-Ocean General Circulation Model (AOGCM) simulations to provide offline SMB and ocean forcing for their Greenland-wide ice sheet model, accounting for influences of warming subsurface ocean temperatures and basal lubrication on ice dynamics. In their RCP8.5 ensemble, they found a GIS contribution to GMSL in 2100 of 10.15 cm ± 3.24 cm. Similarly, Calov et al. (2018) <sup>[[#fn:r417|417]]</sup> found a range of GMSL contributions between 4.6 – 13 cm, depending on which CMIP5 GCM is used to force their regional climate model to produce SMB forcing. The wide range of RCP8.5 results in these studies highlights the substantial climate-driven uncertainty in 21st century projections of the GIS as emphasised by Edwards et al. (2014). It was found that central estimates and ranges for RCP8.5 simulated by Fürst et al. (2015), Calov et al. (2018), and Golledge et al. (2019) are in reasonable agreement with previous multi-model results (Bindschadler et al., 2013) and the assessment of AR5 (Church et al., 2013 <sup>[[#fn:r419|419]]</sup> ), which reported a ''likely'' RCP8.5 range of Greenland’s contribution to GMSL between 7 – 21 cm by 2100 (Table 4.2.). The GIS simulations provided by Vizcaino et al. (2015) <sup>[[#fn:r420|420]]</sup> , using a relatively course-resolution ice model (10 km) with SMB forcing provided by a single GCM, estimate much less ice loss than other recent studies. Their GMSL projections (Vizcaino et al., 2015 <sup>[[#fn:r421|421]]</sup> ) also fall below the ''likely'' range of AR5 estimates. In contrast, the study by Aschwanden et al. (2019) <sup>[[#fn:r422|422]]</sup> shows a significantly higher contribution to GMSL than the other studies, especially under RCP8.5 and beyond 2100 (see 4.2.3.5). This may be due to their SMB forcing, which is based on spatially uniform warming derived from future CMIP5 GCM climatologies averaged over the entire Greenland region. As noted by earlier work (e.g., Van de Wal and Wild, 2001; Gregory and Huybrechts, 2006 <sup>[[#fn:r423|423]]</sup> ), this approach can overestimate melt rates in the ablation zone, which could account for their higher projected ice loss. It is noted that the process-based estimates of future GMSL rise from Greenland found in Aschwanden et al. (2019) <sup>[[#fn:r424|424]]</sup> are closest to those from an updated, structured judgement of glaciological and modelling experts (Bamber et al., 2019 <sup>[[#fn:r425|425]]</sup> ). Calculations from the expert elicitation (Bamber et al., 2019 <sup>[[#fn:r426|426]]</sup> ) result in higher estimates of Greenland ice loss than any of the process-based studies, with a mean and standard deviation of 33 ± 30 cm and a 17 – 83% range of 10 – 60 cm by 2100, following a climate scenario comparable to RCP8.5. The combination of the new process-based studies produces central estimates (Table 4.2) consistent with the ''likely'' ranges for Greenland’s contribution to GMSL in 2100 assessed by AR5.

<span id="table-4.2"></span>
<!-- START IMG -->
<!-- TABLE IMG -->
<!-- IMG TITLE -->
'''Table 4.2'''
<!-- IMG CAPTION -->
Estimates of the Greenland Ice Sheet (GIS) contribution to Global Mean Sea Level (GMSL; cm) in 2100 reported by process-based modelling studies including the effects of both surface mass balance (SMB) and ice dynamics published since the IPCC 5th Assessment Report (AR5). Only model results including elevation-SMB feedback are shown. All values are reported as the contribution to GMSL in 2100 relative to 2000, with the exception of Aschwanden et al. (2019) <sup>[[#fn:r428|428]]</sup> who report values relative to 2008. The median estimate for comparison with AR5 is based on the average of the three simulations in Calov et al. (2018) <sup>[[#fn:r429|429]]</sup> using different General Climate Models (GCMs), combined with the central estimates from the other studies. RMSD (Fürst et al., 2015 <sup>[[#fn:r430|430]]</sup> ) is the Root Mean Squared Deviation from their ensemble median. The range reported by Aschwanden et al. (2019) <sup>[[#fn:r431|431]]</sup> refers to the 16–84% interval of a 500 member ensemble with varying model physical parameters. RCP is Representative Concentration Pathway.
<!-- IMG FILE -->
[[File:0867c405058758b3a0714c022b257a62 table4.2.png]]

Complimentary to the ice sheet scale simulations discussed above, Nick et al. (2013) <sup>[[#fn:r427|427]]</sup> used detailed flowline models of four Greenland outlet glaciers (Petermann, Kangerdlugssuaq, Jakobshavn Isbræ, and Helheim) to estimate a dynamical contribution to sea level in an RCP8.5 scenario of 11.3–17.5 mm by 2100, and 29–49 mm, by 2200. This demonstrates the limited potential of Greenland outlet glaciers alone to drive GMSL rise. Greenland-wide modelling studies (Table 4.2) consistently find a dominant role of runoff relative to dynamic discharge of ice loss, and a long-term reduction in the rate of dynamic ice discharge to the ocean as the ice sheet margin thins and the termini of outlet glaciers retreat from the coast (Goelzer et al., 2013 <sup>[[#fn:r438|438]]</sup> ; Lipscomb et al., 2013 <sup>[[#fn:r439|439]]</sup> ). Greenland’s bedrock geography and the limited, direct access of thick interior ice to the ocean ultimately limits the potential pace of GMSL rise from the GIS. Figure 4.7 illustrates a fundamental difference between Greenland and Antarctica. In Greenland, most of the bedrock at the ice sheet margin is above sea level (land terminating), with relatively narrow (generally &lt;10 km wide) outlet glaciers reaching the ocean. In contrast, Antarctica has extensive areas with subglacial bedrock below sea level, and thick marine-terminating ice in direct contact with the open ocean. Recent subglacial mapping and mass conservation calculations since AR5 (Morlighem et al., 2014 <sup>[[#fn:r440|440]]</sup> ; Morlighem et al., 2017 <sup>[[#fn:r441|441]]</sup> ) revise earlier bathymetric maps under and around the ice sheet, and reveal deeper and more extensive valley networks extending into the GIS interior than previously known. Accurate subglacial topography is important for modelling individual Greenland outlet glaciers (Aschwanden et al., 2016 <sup>[[#fn:r442|442]]</sup> ; Morlighem et al., 2016 <sup>[[#fn:r443|443]]</sup> ); however, the importance of these revised bedrock boundary conditions for the broader ice sheet has yet to be fully tested. Based on the limited cross sectional area of subglacial valleys and outlet glaciers on Greenland (Figure 4.7) and the results of Nick et al. (2013) <sup>[[#fn:r444|444]]</sup> , the effects of uncertain bathymetric boundary conditions are assessed to be small relative to the uncertainties in future SMB forcing ( ''medium confidence'' ).

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<span id="figure-4.7"></span>
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'''Figure 4.7'''

<span id="figure-4.7-bedrock-topography-below-the-existing-ice-sheets-in-greenland-morlighem-et-al.-2017-and-antarctica-right-fretwell-et-al.-2013.-horizontal-scales-are-not-the-same-in-both-panels.-note-the-deep-subglacial-basins-in-west-antarctica-and-the-east-antarctic-margin.-the-ice-above-floatation-in-these-areas-is-equivalent-to"></span>
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'''Figure 4.7 | Bedrock topography below the existing ice sheets in Greenland (Morlighem et al., 2017) and Antarctica (right) (Fretwell et al., 2013). Horizontal scales are not the same in both panels. Note the deep subglacial basins in West Antarctica and the East Antarctic margin. The ice above floatation in these areas is equivalent to […]'''
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[[File:7c5a735653ef82fd11e92c7c390ed2d9 IPCC-SROCC-CH_4_7-3000x1355.jpg]]

Figure 4.7 | Bedrock topography below the existing ice sheets in Greenland (Morlighem et al., 2017) and Antarctica (right) (Fretwell et al., 2013). Horizontal scales are not the same in both panels. Note the deep subglacial basins in West Antarctica and the East Antarctic margin. The ice above floatation in these areas is equivalent to &gt;20 m of Global Mean Sea Level (GMSL).

In summary, new modelling since AR5 is consistent with previous studies suggesting future Greenland ice loss over the 21st century will be dominated by surface processes, rather than dynamic ice discharge to the ocean, regardless of which emissions scenario is followed ( ''high confidence'' ). Based on these modelling studies, the GIS is not expected to contribute more than 20 cm of GMSL rise by 2100 in a RCP8.5 scenario, similar to the upper end of the ''likely'' range reported by AR5 (Church et al., 2013 <sup>[[#fn:r445|445]]</sup> ). GIS simulations are most sensitive to uncertainties in the applied climate forcing, especially over this century (Edwards et al., 2014 <sup>[[#fn:r446|446]]</sup> ), but updated climate projections since AR5 are not yet available. Because of the consistency of recent modelling with the assessment of Church et al. (2013 <sup>[[#fn:r447|447]]</sup> ), Greenland’s contribution to future sea level reported in AR5 was used in our projections of GMSL.

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<div id="section-4-2-3-1contribution-of-ice-sheets-to-gmsl-block-2"></div>

<span id="antarctica"></span>
===== 4.2.3.1.2 Antarctica =====

Unlike Greenland, most of the AIS margin terminates in the ocean. The AIS also contains almost eight times more glacial ice above flotation than Greenland, and nearly half of this ice is marine-based, that is, grounded on bedrock hundreds of metres (or more) below sea level (Figure 4.7; Fretwell et al., 2013 <sup>[[#fn:r448|448]]</sup> ). In places where the subglacial bedrock slopes downward away from the coast (reverse-sloped), the marine-based glacial ice is susceptible to dynamical instabilities (Weertman, 1974 <sup>[[#fn:r449|449]]</sup> ; Schoof, 2007b <sup>[[#fn:r450|450]]</sup> ; Pollard et al., 2015 <sup>[[#fn:r451|451]]</sup> ) that can contribute rapid ice loss (Cross-Chapter Box 8 in Chapter 3). The instabilities can be triggered by the loss or thinning of ice shelves through changes in the surrounding ocean and increased sub-ice melt rates and changes in the overlying atmosphere affecting SMB and surface meltwater production. Much progress has been made since AR5 in the understanding of these processes, but their representation in continental-scale models continue to be heavily parameterised in most cases. Complex interactions between the ice sheet, ocean, atmosphere and underlying bedrock also remain difficult to simulate collectively.

In contrast to Greenland, Antarctica’s recent contribution to SLR has been dominated by ice-dynamical processes rather than changes in SMB (Mouginot et al., 2014 <sup>[[#fn:r452|452]]</sup> ; Rignot et al., 2014 <sup>[[#fn:r453|453]]</sup> ; Scheuchl et al., 2016 <sup>[[#fn:r454|454]]</sup> ; Shen et al., 2018 <sup>[[#fn:r455|455]]</sup> ; The IMBIE team, 2018). Since AR5, it has become increasingly evident that this ice loss is being driven by sub-ice oceanic melt (thinning) of ice shelves (Paolo et al., 2015 <sup>[[#fn:r456|456]]</sup> ; Wouters et al., 2015) and the resulting loss of back stress (buttressing) that impedes the seaward flow of grounded ice upstream. Elevated melt rates are generally associated with the increased presence of warm Circumpolar Deep Water (CDW) on the continental shelf (Khazendar et al., 2016 <sup>[[#fn:r457|457]]</sup> ). Dynamic ice loss driven by ocean changes have also been observed on the East Antarctic margin (Li et al., 2016 <sup>[[#fn:r458|458]]</sup> ; Shen et al., 2018 <sup>[[#fn:r459|459]]</sup> ). This is an important development, because East Antarctica contains much more ice than West Antarctica, so even minor changes there could make major contributions to sea level in the future.

Several of West Antarctica’s major outlet glaciers, including Pine Island Glacier, and Thwaites Glacier in the Amundsen Sea (Figure 4.8) have grounding lines currently retreating on retrograde bedrock (Rignot et al., 2014 <sup>[[#fn:r460|460]]</sup> ). Thwaites Glacier is particularly important (Figure 4.8), because it extends into the interior of the WAIS, where the bed is &gt;2000 m below sea level in places. By itself, the Thwaites drainage area contains the equivalent of ~0.4 m GMSL (Holt et al., 2006 <sup>[[#fn:r461|461]]</sup> ; Millan et al., 2017 <sup>[[#fn:r462|462]]</sup> ), but loss of the glacier could have a destabilising impact on the entire WAIS (Feldmann and Levermann, 2015 <sup>[[#fn:r463|463]]</sup> ). The WAIS contains enough ice to raise GMSL by ~3.4 m (Fretwell et al., 2013 <sup>[[#fn:r464|464]]</sup> ). Since AR5, a number of ice sheet modelling studies have focussed on limited fractions of Antarctica and so are not included in estimating the SROCC Antarctic contribution to GMSL (see Section 4.2.3.2). However, these studies do allow an assessment of the potential for persistent and increasing ice loss, and the role of the marine ice sheet instability (MISI, see Cross-Chapter Box 8 in Chapter 3).

Joughin et al. (2014) <sup>[[#fn:r466|466]]</sup> modelled the response of the Thwaites Glacier to a combination of elevated sub-ice melt rates and increased precipitation and found persistent future retreat, despite either the partial compensation of increased accumulation or a future reduction in melt. Sub-ice melt rates sustained at current levels were found to generate &gt;1 mm yr –1 equivalent GMSL rise within a millennium. Higher melt rates and an assumed weak ice shelf triggered rapid retreat within a few centuries. Similarly, Waibel et al. (2018) used the BISICLES ice sheet model (Cornford et al., 2015 <sup>[[#fn:r465|465]]</sup> ) to investigate the potential for self-sustained retreat of Thwaites Glacier, by incrementally increasing sub-ice melt rates until retreat is triggered, and then returning to pre-retreat melt rates. Consistent with Joughin et al. (2014) <sup>[[#fn:r466|466]]</sup> , they found self-sustained retreat of Thwaites Glacier through MISI. Most uncertainty in their future WAIS simulations arises from uncertainties in the long-term response of Thwaites Glacier (Figure 4.8). Nias et al. (2016) demonstrated model sensitivity of Thwaites Glacier to poorly resolved bedrock boundary conditions (small scale topography), pointing to the need for better geophysical information to reduce model uncertainty (Schlegel et al., 2018 <sup>[[#fn:r468|468]]</sup> ). Arthern and Williams (2017) <sup>[[#fn:r469|469]]</sup> used adaptive mesh techniques, but with a different formulation than Cornford et al., (2015) <sup>[[#fn:r470|470]]</sup> , to simulate the future response of Amundsen Sea outlet glaciers. They demonstrate a sustained, but slow future retreat when sub-ice melt is maintained at current rates, and a direct relationship between the strength of ocean forcing and the pace of MISI-driven ice loss. Yu et al. (2018) simulate future Thwaites retreat using a range of model formulations with varying approximations of ice stress balance, different ocean melt schemes, and different basal friction laws. Like Arthern and Williams (2017) they find model-specific dependencies in the rate of ice loss, but all of their simulations demonstrate sustained ice loss and a bathymetrically controlled future acceleration.

Like Thwaites, the neighbouring Pine Island Glacier (PIG) has also been thinning and retreating at an accelerating rate in recent decades, in response to incursions of warm CDW in the waters underlying the glacier’s ice shelf. These incursions of CDW are controlled in part by sea floor bathymetry and climatic variability (Dutrieux et al., 2014 <sup>[[#fn:r471|471]]</sup> ). Favier et al. (2014) used three models with differing formulations to simulate PIG’s response to elevated sub-ice melt. Consistent with modelling of Thwaites Glacier (Joughin et al., 2014 <sup>[[#fn:r473|473]]</sup> ), all three models demonstrate sustained future retreat at an increasing rate, as the glacier backs onto its retrograde bed. Only one of the three models used by Favier et al. (2014) demonstrates the possibility that the glacier can recover if sub-ice melt rates are reduced enough to allow the ice shelf to thicken and pin on bathymetric features to provide buttressing. These results highlight the long-term commitment to marine-based ice loss.

While limited to 50 year simulations, Seroussi et al. (2017) <sup>[[#fn:r474|474]]</sup> provide the first interactively coupled ice-ocean model simulations of Thwaites Glacier at a high spatial resolution. Their model demonstrates MISI-like grounding line retreat at a rate of ~1 km yr –1 , comparable to observations between 1992 and 2011 (Rignot et al., 2014). The retreat is interrupted when the main trunk of the glacier stabilises on a bathymetric ridge, ~20 km upstream of the present-day grounding line (Figure 4.8), but due to the short duration of the simulation, the long-term potential for additional retreat into the interior of the ice sheet is not captured.

Despite the use of independent model formulations, forcings, and different geographic settings, the overall agreement among these highly-resolved regional modelling studies and their ability to capture current rates of retreat, increases confidence since AR5 that observed retreat of Amundsen Sea outlet glaciers is driven by processes consistent with MISI theory ( ''medium confidence'' ), will continue ( ''medium confidence'' ), and could accelerate ( ''medium confidence'' ).

Observations of rapid bedrock uplift in the Amundsen Sea, low viscosity of the underlying mantle, and short GIA response times to glacial unloading suggest ice-Earth interactions could be important there (Barletta et al., 2018 <sup>[[#fn:r475|475]]</sup> ). Bedrock uplift and reduced gravitational attraction between the ice sheet and ocean as an ice margin loses mass reduces RSL at the grounding line, promoting stability and providing a negative feedback on retreat (Adhikari et al., 2014 <sup>[[#fn:r476|476]]</sup> ; Gomez et al., 2015 <sup>[[#fn:r477|477]]</sup> ). Using a high-resolution ice sheet-Earth model, Larour et al. (2019) showed that long-term future retreat of Amundsen Sea grounding lines are slowed by these processes, but the effect is found to be minimal until after ~2250. This agrees with other recent modelling accounting for ice-Earth interactions, including the viscoelastic Earth response to changing ice loads and self-gravitation (Gomez et al., 2015 <sup>[[#fn:r478|478]]</sup> ; Konrad et al., 2015 <sup>[[#fn:r479|479]]</sup> ; Pollard et al., 2017 <sup>[[#fn:r480|480]]</sup> ). These studies also showed a small negative feedback on future retreat over the next several centuries, particularly under strong climate forcing. However, the viscosity structure of the Earth under the AIS is not well resolved, and lateral variations in Earth structure could impact these results (Hay et al., 2017 <sup>[[#fn:r481|481]]</sup> ). Based on these consistent model results, and new observational evidence that PIG has been retreating on reverse-sloped bedrock for a half-century or more (Smith et al., 2017), ice-Earth interactions are not expected to substantially slow GMSL rise from marine-based ice in Antarctica over the 21st century ( ''medium confidence'' ). However, these processes could become important for GMSL rise on multi-century and longer time scales.

<span id="figure-4.8"></span>
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'''Figure 4.8'''

<span id="figure-4.8-processes-affecting-the-thwaites-glacier-in-the-amundsen-sea-sector-of-antarctica-adapted-from-scambos-et-al.-2017.-the-grounding-line-is-currently-retreating-on-reverse-sloped-bedrock-at-a-water-depth-of-600-m-joughin-et-al.-2014-mouginot-et-al.-2014.-the-glacier-terminus-is-120-km-wide-widens-upstream-and"></span>
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'''Figure 4.8 | Processes affecting the Thwaites Glacier in the Amundsen Sea sector of Antarctica (adapted from Scambos et al., 2017). The grounding line is currently retreating on reverse-sloped bedrock at a water depth of ~600 m (Joughin et al., 2014; Mouginot et al., 2014). The glacier terminus is ~120 km wide, widens upstream, and […]'''
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[[File:ce6dc95edf37fa50cc08aa9551b90b2d IPCC-SROCC-CH_4_8-3000x1028.jpg]]

Figure 4.8 | Processes affecting the Thwaites Glacier in the Amundsen Sea sector of Antarctica (adapted from Scambos et al., 2017). The grounding line is currently retreating on reverse-sloped bedrock at a water depth of ~600 m (Joughin et al., 2014 <sup>[[#fn:r483|483]]</sup> ; Mouginot et al., 2014 <sup>[[#fn:r484|484]]</sup> ). The glacier terminus is ~120 km wide, widens upstream, and is minimally buttressed by a laterally discontinuous ~40 km long ice shelf. The remaining shelf is thinning in response to warm, sub-shelf incursions of circumpolar deep water (CDW), with melt rates up 200 m yr–1 near the groundling line in some places (Milillo et al., 2019 <sup>[[#fn:r485|485]]</sup> ). The bathymetry upstream of the grounding zone is complex, but it generally slopes downward into a deep basin, up to 2000 m below sea level under the centre of the West Antarctic Ice Sheet (WAIS) (far left), making the glacier vulnerable to marine ice sheet instabilities (Cross-Chapter Box 8 in Chapter 3).

Atmospheric forcing is also becoming increasingly recognised to be an important factor for the future of the AIS. A sustained (15 days) melt event over the Ross Sea sector of the WAIS in 2016 illustrated both the connectivity of Antarctica to the tropics and El Niño, and the possibility that future meltwater production on ice shelf surfaces could change in the near future (Nicolas et al., 2017 <sup>[[#fn:r487|487]]</sup> ). This was highlighted by Trusel et al. (2015) <sup>[[#fn:r488|488]]</sup> , who evaluated the future expansion of surface meltwater using the snow component in the RACMO2 regional atmospheric model (Kuipers Munneke et al., 2012 <sup>[[#fn:r489|489]]</sup> ) and output from CMIP5 GCMs. Under RCP8.5, they found a substantial expansion of surface meltwater production on ice shelves late in the 21st century that exceed melt rates observed before the 2002 collapse of the Larsen B Ice Shelf. Surface meltwater is important for both ice dynamics and SMB due to its potential to reduce albedo, saturate the firn layer, deepen surface crevasses, and to cause flexural stresses that can contribute to ice shelf break-up (hydrofracturing) (Banwell et al., 2013 <sup>[[#fn:r490|490]]</sup> ; Kuipers Munneke et al., 2014 <sup>[[#fn:r491|491]]</sup> ). The presence of surface meltwater does not necessarily lead to immediate ice shelf collapse (Bell et al., 2017b <sup>[[#fn:r492|492]]</sup> ; Kingslake et al., 2017 <sup>[[#fn:r493|493]]</sup> ), although surface meltwater was a precursor on ice shelves which have collapsed (Scambos et al., 2004 <sup>[[#fn:r494|494]]</sup> ; Banwell et al., 2013 <sup>[[#fn:r495|495]]</sup> ). This dichotomy illustrates the uncertain role of meltwater and the need for additional study. When and if melt rates will be sufficiently high in future warming scenarios to trigger widespread hydrofracturing is a key question, because the loss of ice shelves is associated with the onset of marine ice sheet instabilities (Cross-chapter Box 8 in Chapter 3). Based on the single modelling study by Trusel et al. (2015) <sup>[[#fn:r496|496]]</sup> , it is not expected that widespread ice shelf loss will occur before the end of the 21st century, but due to limited observations and modelling to date, there is ''low confidence'' in this assessment.

Continental-scale ice sheet simulations are ultimately required to provide projections of future GMSL rise from Antarctica. At this spatial scale, most models rely on simplifying approximations of the equations representing three-dimensional ice flow, and in some cases they parameterise ice flow at the grounding line (Schoof, 2007b <sup>[[#fn:r497|497]]</sup> ) to improve computational efficiency. Such simplifications are necessary to allow long simulations that can be validated against geological information, in addition to modern observations (Briggs et al., 2013 <sup>[[#fn:r498|498]]</sup> ; Pollard et al., 2016), however processes related to MISI are best represented at high spatial resolution and without simplifications of the underlying physics (Pattyn et al., 2013 <sup>[[#fn:r499|499]]</sup> ; Reese et al., 2018c <sup>[[#fn:r500|500]]</sup> ).

Various ice sheet model formulations, including the choice of grounding line parameterisations and basal sliding schemes can strongly affect model response to a given forcing (Brondex et al., 2017 <sup>[[#fn:r501|501]]</sup> ; Pattyn, 2017 <sup>[[#fn:r502|502]]</sup> ), although sophisticated statistical methodologies have been increasingly used since AR5 to quantitatively gauge model uncertainty (Bulthuis et al., 2019 <sup>[[#fn:r503|503]]</sup> ; Edwards et al., 2019 <sup>[[#fn:r504|504]]</sup> ). Accurate atmospheric forcing (SMB) and sub-ice melt are also prerequisite to resolving the time-evolving dynamics of the system, with sub-ice melt rates being particularly important (Schlegel et al., 2018 <sup>[[#fn:r505|505]]</sup> ). An important ongoing deficiency is the lack of ice-ocean coupling in most continental-scale studies, which remains too computationally expensive to simulate the ocean at the spatial scales necessary to capture circulation in ice shelf cavities and time-evolving ice-ocean interactions (Donat-Magnin et al., 2017 <sup>[[#fn:r506|506]]</sup> ; Hellmer et al., 2017 <sup>[[#fn:r507|507]]</sup> ). Instead, melt rates are often parameterised as a depth dependent function of nearby ocean temperature derived from offline ocean models, but the lack of ice-ocean interaction can seriously overestimate melt rates in some settings (de Rydt et al., 2015; Seroussi et al., 2017 <sup>[[#fn:r508|508]]</sup> ). Approaches that link offline ocean temperatures with efficient box models of the circulation in ice shelf cavities have been developed (Lazeroms et al., 2018 <sup>[[#fn:r509|509]]</sup> ; Reese et al., 2018a <sup>[[#fn:r510|510]]</sup> ) and used in long-term future simulations (Bulthuis et al., 2019 <sup>[[#fn:r511|511]]</sup> ), although they still require uncoupled ocean models to provide time-evolving ocean conditions outside the cavities.

Ritz et al. (2015) <sup>[[#fn:r512|512]]</sup> used a hybrid physical-statistical modelling approach, whereby the timing of MISI onset is determined statistically rather than physically. They estimated probabilities of MISI onset in eleven different sectors around the ice sheet margin based on observations of continent-wide retreat and thinning over the last few decades, and expected future climate change following an IPCC SRES A1B emission scenario only. In places where MISI is projected to begin, the persistence and rate of grounding-line retreat is parameterised as a function of the local bedrock topography (slope), ice thickness at grounding lines following Schoof (2007b), and basal friction. This study represents a statistically rigorous approach in which model parameters are based on a synthesis of observations and projected surface and sub-shelf forcing, rather than coming directly from climate and ocean models. However, the model calibrations rely on recent observations, which may not provide adequate guidance under warmer future conditions.

Levermann et al. (2014) <sup>[[#fn:r513|513]]</sup> use simplified emulations of temperature increase in order to estimate both SMB and sub-ice melt (including a parameterised delay for ocean warming) to determine the linearised response of five ice sheet models calibrated against recent rates of retreat. Substantial uncertainty arises from the different model treatments of grounding line dynamics and ice shelves. However, they conclude that the single greatest source of uncertainty stems from the external forcing. Golledge et al. (2015) <sup>[[#fn:r514|514]]</sup> used PISM (Parallel Ice Sheet Model; Winkelmann et al., 2011 <sup>[[#fn:r515|515]]</sup> ) to simulate the future response of the AIS to RCP emission scenarios. PISM links grounded, streaming, and shelf flow, and has freely evolving grounding lines required to capture MISI. PISM’s parameterised treatment of sub-ice melt applies melt under partially grounded grid cells (Feldmann and Levermann, 2015 <sup>[[#fn:r516|516]]</sup> ), making the model sensitive to subsurface ocean warming, although the validity of this approach is contested (Arthern and Williams, 2017 <sup>[[#fn:r517|517]]</sup> ; Seroussi and Morlighem, 2018 <sup>[[#fn:r518|518]]</sup> ; Yu et al., 2018 <sup>[[#fn:r519|519]]</sup> ). While providing alternative outcomes with the two basal melt rate parameterisations, the model is not calibrated to observations and doesn’t provide a probability distribution. In a subsequent study Golledge et al. (2019) <sup>[[#fn:r520|520]]</sup> used PISM, but with updated RCP climate forcing based on CMIP5 GCMs, and with sub-ice ocean melt calibrated to observations. An offline, intermediate-complexity climate model was used to capture global ice-climate feedbacks ignored in most other studies, but the simulations only include RCP4.5 and RCP8.5 and do not extend beyond 2100. Accounting for the climatic effects of meltwater input from Greenland and Antarctica nearly doubled their estimates of Antarctic’s contribution to GMSL in 2100 from 2.4 cm to 4.6 cm in RCP4.5, and from 7.7 cm to 14 cm in RCP8.5. The increase is caused by a combination of SMB decrease over the WAIS, combined with subsurface ocean warming that increases sub-ice melt. However, the climate model used to diagnose the spatial patterns of the atmospheric and oceanic response to the meltwater input is simplistic. Bronselaer et al. (2018) <sup>[[#fn:r521|521]]</sup> tested the global climatic response to future meltwater input from Antarctica using an ensemble of GCM simulations, but without an interactive ice sheet. They simulated an RCP8.5 scenario with and without a massive input of meltwater into the Southern Ocean and demonstrate that the addition of Antarctic meltwater expands sea ice in the Southern Ocean, delays the trajectory of global warming, and moderates atmospheric warming around the Antarctic coastline. Consistent with Golledge et al. (2019) <sup>[[#fn:r522|522]]</sup> , they found meltwater-induced stratification around Antarctica warms subsurface ocean temperatures, indicating the potential for a positive meltwater feedback on ice shelf melt. These studies reinforce the need for continental-scale studies to consider two-way ice-climate coupling, but with limited published studies to draw from and no simulations run beyond 2100, firm conclusions regarding the net importance of atmospheric versus ocean melt feedbacks on the long-term future of Antarctica can not be made.

Bulthuis et al. (2019) <sup>[[#fn:r526|526]]</sup> used a different continental-scale ice sheet model (Pattyn, 2017 <sup>[[#fn:r525|525]]</sup> ) with the same simplified atmospheric and ocean forcing used by Golledge et al. (2015) <sup>[[#fn:r528|528]]</sup> to simulate RCP2.6, RCP4.5, and RCP8.5 scenarios. Simulations with varying model parameters were used to quantify uncertainties related to the atmospheric forcing, various ice-model physics, and bedrock response to changing ice loads. A key finding was that irrespective of model parametric uncertainty, the strongly mitigated RCP2.6 scenario prevents catastrophic WAIS collapse over the coming centuries. The probabilistic projections of Antarctic GMSL contributions (Bulthuis et al., 2019 <sup>[[#fn:r531|531]]</sup> ) represent a rigorous blending of physical ice sheet modelling and uncertainty quantification (UQ) techniques, albeit with a simplistic representation of future climate and using a relatively coarse-resolution ice sheet model. These results are well-supported by Schlegel et al. (2018), who blend UQ with a higher resolution ice sheet model than used by Bulthuis et al. (2019), but using an idealised climate forcing scheme not directly linked to time-evolving future climate trajectories. Their 800 simulations, run to 2100, provide not only probabilistic constraints on future GMSL-rise from Antarctica, but an assessment of key drivers of uncertainty, including uniform and regional dependencies on model physical parameters, climate forcing, and boundary conditions. Sub-ice shelf melt rates provide the greatest source of uncertainty in their projections, although the source region dominating the GMSL contribution is found to be dependent on the climate forcing applied, and different from those found by Golledge et al. (2015) <sup>[[#fn:r530|530]]</sup> .

DeConto and Pollard (2016) <sup>[[#fn:r529|529]]</sup> used an ice sheet model with a formulation similar to that used by Golledge et al. (2015) and Bulthuis et al. (2019) but they include glaciological processes not accounted for in other continental-scale models: 1) surface melt and rain water influence on hydrofracturing of ice shelves; and 2) brittle failure of thick, marine-terminating ice fronts that have lost their buttressing ice shelves. Where the ice fronts are thick enough to form tall ice cliffs above the waterline, they can produce stresses exceeding the strength of the ice, causing calving (Bassis and Walker, 2012 <sup>[[#fn:r532|532]]</sup> ). Once initiated, ice-cliff calving has been hypothesised to produce a self-sustaining Marine Ice Cliff Instability (MICI; Cross-chapter Box 8, Chapter 3). The validity of MICI remains unproven (Edwards et al., 2019 <sup>[[#fn:r533|533]]</sup> ) and is considered to be characterised by ‘deep uncertainty’, but it has the potential to raise GMSL faster than MISI. DeConto and Pollard (2016) represent hydrofracturing and ice-cliff calving with simple parameterisations, but the glaciological processes themselves are supported by more detailed modelling and observations (Scambos et al., 2009 <sup>[[#fn:r534|534]]</sup> ; Banwell et al., 2013 <sup>[[#fn:r535|535]]</sup> ; Ma et al., 2017 <sup>[[#fn:r536|536]]</sup> ; Wise et al., 2017 <sup>[[#fn:r537|537]]</sup> ; Parizek et al., 2019 <sup>[[#fn:r538|538]]</sup> ). DeConto and Pollard (2016) <sup>[[#fn:r539|539]]</sup> provide four ensembles for RCP2.6, RCP4.5, and RCP8.5 scenarios, representing two alternative ocean model treatments and two alternative palaeo sea level targets used to tune their model physical parameters. However, their ensembles do not explore the full range of model parameter space or provide a probabilistic assessment (Kopp et al., 2017 <sup>[[#fn:r540|540]]</sup> ; Edwards et al., 2019 <sup>[[#fn:r541|541]]</sup> ). Under RCP2.6, DeConto and Pollard (2016) find very little GMSL rise from Antarctica by 2100 (0.02–0.16 m), consistent with the findings of Golledge et al. (2015) <sup>[[#fn:r542|542]]</sup> and Bulthuis et al. (2019) <sup>[[#fn:r543|543]]</sup> . In contrast, their four ensemble means range between 0.26–0.58 m for RCP4.5, and 0.64–1.14 m for RCP8.5. In RCP8.5, rates of GMSL rise from Antarctica exceed 5 cm yr -1 in the 22nd century and contribute as much as 15 m of GMSL rise by 2500, largely due to the ice cliff calving process. The climate forcing used by DeConto and Pollard (2016) <sup>[[#fn:r544|544]]</sup> simulates the appearance of extensive surface meltwater several decades earlier than indicated by other CMIP5 climate simulations (Trusel et al., 2015 <sup>[[#fn:r545|545]]</sup> ). Because their model physics are sensitive to melt water through hydrofracturing, this makes the timing and magnitude of their simulated ice loss too uncertain to include in SROCC sea level projections. However, their results do demonstrate the potential for brittle ice sheet processes not considered by AR5 to exert a strong influence on future rates of GMSL rise and the possibility that GMSL beyond 2100 could be considerably higher than the ''likely'' range projected by models that do not include these processes.

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<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise"></div>

<span id="global-and-regional-projections-of-sea-level-rise"></span>
==== 4.2.3.2 Global and Regional Projections of Sea Level Rise ====

<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise-block-1"></div>

In addition to the model including MICI from DeConto and Pollard (2016) , only a subset of studies ( Levermann et al., 2014; Golledge et al., 2015; Ritz et al., 2015; Bulthuis et al., 2019; Golledge et al., 2019) , and statistical emulation of DeConto and Pollard (2016) by Edwards et al. (2019) provide continental-scale estimates of future Antarctic ice loss, under a range of GHG emissions scenarios. They all provide probabilistic information, but vary considerably, both in their physical approaches and their resulting projections of Antarctica’s future contribution to GMSL. Such variations facilitate the first quantitative uncertainty assessment of the full dynamical contribution of Antarctica, which could not be made by Church et al. (2013) in AR5. The assessment by Church et al. (2013) , based on a single statistical-physical model, reported median values (and ''likely'' ranges) of 0.05 m (-0.04–0.13) and 0.04 m (-0.06–0.12), for RCP4.5 and RCP8.5, respectively, for the total Antarctic contribution in 2081–2100 relative to 1986–2005, and added the following: ‘Based on current understanding, only the collapse of marine-based sectors of the AIS, 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 SLR during the 21st century (Church et al., 2013) . Given the above-mentioned publications after AR5, Antarctica’s contribution to sea level change was reassessed and now include the possibility of MISI allowing for a more complete assessment of the ''likely'' range of the projections for three RCP scenarios. Our assessment is based on process-based numerical models of the AIS, driven by diverse climate scenarios. Results are discussed in the context of an expert elicitation study (Bamber et al., 2019 <sup>[[#fn:r546|546]]</sup> ) , probabilistic studies (Perrette et al., 2013 <sup>[[#fn:r547|547]]</sup> ; Slangen et al., 2014a <sup>[[#fn:r548|548]]</sup> ; Grinsted et al., 2015 <sup>[[#fn:r549|549]]</sup> ; Jackson and Jevrejeva, 2016 <sup>[[#fn:r550|550]]</sup> ) and a sensitivity study (Schlegel et al., 2018 <sup>[[#fn:r551|551]]</sup> ) assessing the uncertainty in snow accumulation, ocean-induced melting, ice viscosity, basal friction, bedrock elevation and the effect of ice shelves on ice mass loss in 2100, Figure 4.4.

Ritz et al. (2015) is difficult to contextualise as they only provided estimates for the A1B scenario and not for the RCP scenarios. Despite this limitation their results, which are close to the other studies, are included as if they represent RCP8.5 and as such supports the assessment. The results by DeConto and Pollard (2016) <sup>[[#fn:r552|552]]</sup> indicate significantly higher mass loss even for RCP4.5, potentially related to their high surface melt rates on the ice shelves as contested by Trusel et al. (2015) <sup>[[#fn:r553|553]]</sup> . This early onset of high surface melt rates in DeConto and Pollard (2016) <sup>[[#fn:r554|554]]</sup> leads to extensive hydrofracturing of ice shelves before the end of the 21st century and therefore to rapid ice mass loss. For this reason, their results and probabilistic (e.g., Kopp et al., 2017; Le Bars et al., 2017) and statistical emulation estimates that build on them (Edwards et al., 2019 <sup>[[#fn:r555|555]]</sup> ) , are not used in SROCC sea level projections. Consequently, the process-based studies by Golledge et al. (2015) <sup>[[#fn:r556|556]]</sup> , Ritz et al. (2015) , Levermann et al. (2014) <sup>[[#fn:r558|558]]</sup> , Golledge et al. (2019) <sup>[[#fn:r559|559]]</sup> , and Bulthuis et al. (2019) are used to assess the Antarctic contribution for the different RCP scenarios. The study by Schlegel et al. (2018) does not provide RCP based scenarios, but is considered as an extensive sensitivity estimate providing a high-end estimate based on physical process understanding of the Antarctic contribution.

Each study expresses an uncertainty in the Antarctic contribution to GMSL rise which is, in part, dependent on a common driver, namely regional warming. The uncertainties were therefore interpreted as being dependent and propagate the total uncertainty accordingly. As a result, the total uncertainty exceeds that of the individual studies, which reflects that the individual studies only sample a fraction of the total uncertainty. The uncertainty estimates of Levermann et al. (2014) <sup>[[#fn:r561|561]]</sup> concentrate on the oceanic basal melt rates including a time delay between atmosphere and ocean temperature, but do not consider other sources of uncertainty. Ritz et al. (2015) <sup>[[#fn:r562|562]]</sup> is constrained by observations and provides an asymmetric distribution of the rate of mass loss. The ice sheet simulations by Golledge et al. (2015) and Golledge et al. (2019) only provide two alternative subgrid parameterisations for sub-ice melt, rather than a statistical estimate of the uncertainty. The more sensitive of these two parameterisations which induces more ice loss is challenged by Seroussi and Morlighem (2018) <sup>[[#fn:r565|565]]</sup> . In order to assess a realistic uncertainty for the total Antarctic contribution, it was first assumed that Golledge et al. (2015) <sup>[[#fn:r566|566]]</sup> and Golledge et al. (2019) <sup>[[#fn:r567|567]]</sup> are dependent, because they use similar parameterisations. For each study, a probabilistic distribution is used, assuming a normal distribution with a ''likely'' range bounded by the high and low estimate from those studies. Levermann et al. (2014) <sup>[[#fn:r568|568]]</sup> also provides two alternatives, one with and one without a time delay between oceanic temperatures below the Antarctic ice shelves and global mean atmospheric temperature. As it is unclear which version best matches the updated record of ice loss presented by The IMBIE team, (2018) , results are combined assuming full probabalistic dependence as for the two Golledge studies. Bulthuis et al. (2019) <sup>[[#fn:r572|572]]</sup> uses a simplified ice sheet model to study the uncertainty caused by the atmospheric forcing, ice dynamics, ice and bed rheology, calving and sub-shelf melting. Finally, the studies by Ritz et al. (2015) <sup>[[#fn:r571|571]]</sup> , Bulthuis et al. (2019) and the averages for Golledge and Levermann are combined to identify a best estimate for the Antarctic contribution under RCP8.5. This results in a median contribution of 16 cm in 2100 under RCP8.5. A Monte Carlo technique is used to combine the uncertainties in the aforementioned studies, assuming mutual dependence. The resulting 5–95 percentile range, 2–37 cm in 2100 under RCP8.5, is assessed as the ''likely'' range. This assessment is used in order to reflect ongoing limited understanding of the physics and the fact that the individual studies only reflect part of the total uncertainty. The distribution is slightly skewed to higher values, because of an underlying skewness in the studies of Levermann et al. (2014) <sup>[[#fn:r569|569]]</sup> and Ritz et al. (2015) <sup>[[#fn:r571|571]]</sup> . This skewed distribution is supported by an expert elicitation study (Bamber et al., 2009) . The expert elicitation approach (Bamber et al., 2018) , which applied elicitation to both ice sheets, suggests considerably higher values for total SLR for RCP2.6, RCP4.5 and RCP8.5 than provided in Table 4.3.

As the importance of MISI and MICI is difficult to assess on longer time scales, there remains deep uncertainty for the Antarctic contribution to GMSL after 2100 (Cross-Chapter Box 4 in Chapter 1). Results on these long-time scales are discussed in 4.2.3.5.

<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise-block-2"></div>

<span id="table-4.3"></span>
<!-- START IMG -->
<!-- TABLE IMG -->
<!-- IMG TITLE -->
'''Table 4.3:'''
<!-- IMG CAPTION -->
An overview of different studies estimating the future Antarctic contribution to sea level rise (SLR), listed here are median values. Estimates from Golledge et al. (2015) are based on the average contribution to Global Mean Sea Level (GMSL) over the full 21st century, based on two alternative ensembles using different sub-ice melt schemes. This average is not explicitly reported in the original paper where the individual values of 0.1 and 0.39 m are reported. SMB is the surface mass balance, BMB the basal melt balance, LIG is Last Interglacial, MICI is marine ice cliff instability, GCM is General Circulation Model, PDD is positive-degree day.

<!-- IMG FILE -->
[[File:4bc1e6307f36dfeee2ae0eafcf4efea3 table4.3.png]]

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<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise-block-3"></div>

There is limited evidence for major changes since AR5 in the non-Antarctic components. Recent projections of the glacier contribution are nearly identical to AR5 results used here (see Cross-Chapter Box 6 in Chapter 2). Greenland, thermal expansion and land water storage are also not updated, mainly due to a lack of updated CMIP simulations. Hence, our revised projections replace only the AR5 estimate for Antarctica by a new assessment as outlined in the previous paragraph based on post-AR5 literature and maintaining identical contributions for the non-Antarctic components. As no general dependence between the Antarctic contribution and the non-Antarctic components can be derived from the four studies, independent uncertainties are assumed, which is close to the uncertainty propagation by Church et al. (2013) <sup>[[#fn:r576|576]]</sup> .

Time series for the different RCP scenarios are shown in Figure 4.9 indicating a divergence in median and upper ''likely'' range for RCP8.5 during the second half of the century between this report and the AR5 projections (Church et al., 2013 <sup>[[#fn:r577|577]]</sup> ) . The value of the Antarctic contribution in 2081–2100 under RCP8.5 is the individual component with the largest uncertainty. As a consequence, the uncertainty in the GMSL projections is slightly increased compared to Church et al. (2013) . Nevertheless, results can also be considered to be consistent with Church et al. (2013) <sup>[[#fn:r578|578]]</sup> . In AR5, the potential additional contribution by ice dynamics, was estimated to be not more than several tenths of a metre but excluded from projections; here this value was assessed to be 16 cm (5–95 percentile; 2–37 cm) and include it in the projections. As the projections build on the CMIP5 work presented in AR5, and also given the limited exploration of uncertainty in estimates from each individual study, the results of the 5–95 percentile are interpreted to represent the ''likely'' range, that is, the 17–83 percentile, as assessed by Church et al. (2013) and as assessed in AR5 for other CMIP5-derived results.

<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise-block-4"></div>

<span id="table-4.4"></span>
<!-- START IMG -->
<!-- TABLE IMG -->
<!-- IMG TITLE -->
'''Table 4.4'''
<!-- IMG CAPTION -->
Median values and likely ranges for projections of global mean sea level (GMSL) rise in metres in 2081–2100 relative to 1986–2005 for three scenarios. In addition, values of GMSL rise are given for 2046-2065 and 2100, and the rate of GMSL rise is given for 2100. Values between parentheses reflect the likely range. SMB is surface mass balance, DYN is dynamical contribution, LWS is land water storage. Total AR5 minus Antarctica AR5 is the GMSL rise contribution in Church et al. (2013) without the Antarctic contribution of Church et al. (2013). The newly derived Antarctic contribution is added to this to arrive at the GMSL rise.

<!-- IMG FILE -->
[[File:aa63ba81f162ddaf5b939f462585d229 table4.4.png]]

Notes:

\*The uncertainty in this value is calculated as in Church et al. (2013).

<!-- END IMG -->
<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise-block-5"></div>

<span id="figure-4.9"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 4.9'''

<span id="figure-4.9-time-series-of-global-mean-sea-level-gmsl-for-representative-concentration-pathway-rcp2.6-rcp4.5-and-rcp8.5-as-used-in-this-report-and-for-reference-the-ipcc-5th-assessment-report-ar5-results-church-et-al.-2013.-results-are-based-on-ar5-results-for-all-components-except-the-antarctic-contribution.-results-for-the-antarctic"></span>
<!-- IMG CAPTION -->
'''Figure 4.9 | Time series of Global Mean Sea Level (GMSL) for Representative Concentration Pathway (RCP)2.6, RCP4.5 and RCP8.5 as used in this report and, for reference the IPCC 5th Assessment Report (AR5) results (Church et al., 2013). Results are based on AR5 results for all components except the Antarctic contribution. Results for the Antarctic […]'''
<!-- IMG FILE -->
[[File:77840ce1305f31bf8c3a27055b8fff52 IPCC-SROCC-CH_4_9-3000x895.jpg]]

Figure 4.9 | Time series of Global Mean Sea Level (GMSL) for Representative Concentration Pathway (RCP)2.6, RCP4.5 and RCP8.5 as used in this report and, for reference the IPCC 5th Assessment Report (AR5) results (Church et al., 2013). Results are based on AR5 results for all components except the Antarctic contribution. Results for the Antarctic contribution in 2081–2100 are provided in Table 4.4. The shaded region is considered to be the likely range.

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<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise-block-6"></div>

Projections as presented in Table 4.4 are used to calculate the regional RSL projections as outlined in AR5 by including gravitational and rotational patterns as shown in Figure 4.10 and subsequently used in 4.2.3.4 to calculate ESL projections. Including the updated results in terms of magnitude and uncertainty for the Antarctic component also changes the regional patterns in sea level projections. Results of the regional patterns in Figure 4.10 show an increased SLR with respect to the results presented in AR5 nearly everywhere for RCP8.5 because of the increased Antarctic contribution.

<div id="section-4-2-3-2global-and-regional-projections-of-sea-level-rise-block-7"></div>

<span id="figure-4.10"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 4.10'''

<span id="figure-4.10-regional-sea-level-change-for-rcp2.6-rcp4.5-and-rcp8.5-in-metres-as-used-in-this-report-for-extreme-sea-level-esl-events.-results-are-median-values-based-on-the-values-in-table-4.4-for-antarctica-including-gia-and-the-gravitational-and-rotational-effects-and-results-by-church-et-al.-2013-for-glaciers"></span>
<!-- IMG CAPTION -->
'''Figure 4.10 | Regional sea level change for RCP2.6, RCP4.5 and RCP8.5 in metres as used in this report for extreme sea level (ESL) events. Results are median values based on the values in Table 4.4 for Antarctica including GIA and the gravitational and rotational effects, and results by Church et al. (2013) for glaciers, […]'''
<!-- IMG FILE -->
[[File:551a1ce25a7fff5251607a55b7ae6dc3 IPCC-SROCC-CH_4_10-3000x2591.jpg]]

Figure 4.10 | Regional sea level change for RCP2.6, RCP4.5 and RCP8.5 in metres as used in this report for extreme sea level (ESL) events. Results are median values based on the values in Table 4.4 for Antarctica including GIA and the gravitational and rotational effects, and results by Church et al. (2013) for glaciers, land water storage (LWS) and Greenland. The left column is for the time slice 2046–2065 and the right column for 2081–2100.

<!-- END IMG -->
<div id="section-4-2-3-3probabilistic-sea-level-projections"></div>

<span id="probabilistic-sea-level-projections"></span>
==== 4.2.3.3 Probabilistic Sea Level Projections ====

<div id="section-4-2-3-3probabilistic-sea-level-projections-block-1"></div>

Since AR5, several studies have produced SLR projections in coherent frameworks that link together global-mean and RSL rise projections. The approaches are generally similar to those adopted by AR5 for its global-mean sea level projections: a bottom-up accounting of different contributing processes (e.g., land-ice mass loss, thermal expansion, dynamic sea level), of which many are ‘probabilistic’, in that they attempt to describe more comprehensive probability distributions of sea level change than the ''likely'' ranges presented by Church et al. (2013) <sup>[[#fn:r600|600]]</sup> . An overview of probabilistic approaches is presented in Garner et al. (2017) <sup>[[#fn:r601|601]]</sup> , indicating higher values for post AR5 studies mainly reflecting increased uncertainty based on a single contested study for the Antarctic contribution (DeConto and Pollard, 2016 <sup>[[#fn:r602|602]]</sup> ) . As such many of these probabilistic studies present full probability density function conditional not only on an RCP scenario, but with additional and equally important a priori assumptions concerning for instance the Antarctic contribution over which a consensus has yet to solidify. An example is the study by Le Bars et al. (2017) who expand the projection by Church et al. (2013) <sup>[[#fn:r603|603]]</sup> in a probabilistic way with the Antarctic projections by DeConto and Pollard (2016) <sup>[[#fn:r604|604]]</sup> to obtain a full probability density function for SLR for RCP8.5. Other probabilistic approaches are provided by Kopp et al. (2014) <sup>[[#fn:r605|605]]</sup> and Jackson and Jevrejeva (2016) <sup>[[#fn:r606|606]]</sup> using different ice sheet representations drawing on expert elicitation (Bamber and Aspinall, 2013 <sup>[[#fn:r607|607]]</sup> ) . Probabilistic estimates are useful for a quantitative risk management perspective (see Section 4.3.3). An even more general approach than the probabilistic estimates has been taken by Le Cozannet et al. (2017) who frame a ‘possibilistic’ framework of SLR including existing probabilistic estimates and combining them.

This section first briefly reviews key sources of information for probabilistic projections (Section 4.2.3.3.1), with a focus on new results since AR5, then summarises the different global and regional projections (Section 4.2.3.3.2). Eventually, bottom-up projections were distinguished which explicitly describe the different components of SLR (Section 4.2.3.3.3) from semi-empirical projections (Section 4.2.3.3.4).

<div id="section-4-2-3-3probabilistic-sea-level-projections-block-2"></div>

<span id="components-of-probabilistic-global-mean-sea-level-projections"></span>
===== 4.2.3.3.1 Components of probabilistic global mean sea level projections =====

Thermal expansion: Global mean thermal expansion projections rely on coupled climate models projections (Kopp et al., 2014 <sup>[[#fn:r608|608]]</sup> ; Slangen et al., 2014a <sup>[[#fn:r609|609]]</sup> ; Jackson and Jevrejeva, 2016 <sup>[[#fn:r610|610]]</sup> ) or simple climate model projections (Perrette et al., 2013 <sup>[[#fn:r611|611]]</sup> ; Nauels et al., 2017b <sup>[[#fn:r612|612]]</sup> ; Wong et al., 2017 <sup>[[#fn:r613|613]]</sup> ) , and are substantively unchanged since AR5. For those studies relying on the CMIP5 GCM ensemble, interpretations of the model output differ mainly with regard to how the range is understood. For example, Kopp et al. (2014) <sup>[[#fn:r614|614]]</sup> , interprets the 5–95 percentile of CMIP5 values as a ''likely'' range of thermal expansion. The differences among the studies yield discrepancies smaller than 10 cm, e.g., Slangen et al. (2014a) <sup>[[#fn:r615|615]]</sup> use 20–36 cm in 2081–2100 with respect to 1986–2005, while (Kopp et al., 2014) project a ''likely'' range of 28–46 cm in 2081–2099 with respect to 1991–2009.

Glaciers: Projections of glacier mass change rely either on models of glacier SMB and geometry, forced by temperature and precipitation fields (Slangen and Van de Wal, 2011 <sup>[[#fn:r615|615]]</sup> ; Marzeion et al., 2012 <sup>[[#fn:r616|616]]</sup> ; Hirabayashi et al., 2013 <sup>[[#fn:r617|617]]</sup> ; Radić et al., 2014 <sup>[[#fn:r618|618]]</sup> ; Huss and Hock, 2015 <sup>[[#fn:r619|619]]</sup> ) , or simple scaling relationships with global mean temperature (Perrette et al., 2013 <sup>[[#fn:r620|620]]</sup> ; Bakker et al., 2017 <sup>[[#fn:r621|621]]</sup> ; Nauels et al., 2017a <sup>[[#fn:r622|622]]</sup> ) . Glacier mass change projections published since AR5, based on newly developed glacier models, confirm the overall assessment of AR5 (see also Section 4.2.3.2).

Land water storage: Projections of the GMSL rise contributions due to dam impoundment and groundwater withdrawal are generally either calibrated to hydrological models (e.g., Wada et al., 2012) or neglected. Recent coupled climate-hydrological modelling suggests that a significant minority of pumped groundwater remains on land, which may reduce total GMSL rise relative to studies assuming full drainage to the ocean (Wada et al., 2016 <sup>[[#fn:r623|623]]</sup> ) . Kopp et al. (2014) estimated land water storage based on population projections. However, there are no substantive updates to projections of the future land-water storage contribution to GMSL rise since AR5.

Ice sheets: GMSL projections in previous IPCC assessments were based on results from physical models of varying degree of complexity interpreted using expert judgment of the assessment authors (Meehl et al., 2007 <sup>[[#fn:r625|625]]</sup> ; Church et al., 2013 <sup>[[#fn:r626|626]]</sup> ) . AR5 (Church et al., 2013 <sup>[[#fn:r627|627]]</sup> ) used this approach and is partly based on the assessment of statistical-physical modelling of the Antarctic contribution (Little et al., 2013 <sup>[[#fn:r628|628]]</sup> ) . As an alternative to the model-based approach, several studies have applied structured expert elicitation to the GMSL contribution of ice sheets. This approach is based on a more formal expert elicitation protocol (Cooke, 1991 <sup>[[#fn:r629|629]]</sup> ; Bamber and Aspinall, 2013 <sup>[[#fn:r630|630]]</sup> ; Bamber et al., 2019 <sup>[[#fn:r631|631]]</sup> ) instead of physically based models. Combining the Antarctic contribution from the expert elicitation with the non-Antarctic components from AR5 as done for Table 4.4 leads to an estimated SLR of 0.95 m (median) for the high scenario and an upper ''likely'' range of 1.32 m (Figure 4.2), which is slightly higher than the process-based results. Results by Bamber and Aspinall (2013) <sup>[[#fn:r632|632]]</sup> were criticised because of their procedure for post-processing the expert data of individual ice sheets to a total sea level contribution from the ice sheets (de Vries and van de Wal, 2015; Bamber et al., 2016; de Vries and van de Wal, 2016) . Bamber et al. (2019) avoids this issue by eliciting expert judgments about ice sheet dependence. Alternatively, Horton et al. (2014) used a simpler elicitation protocol focusing on the total SLR rather than the ice sheet contribution alone. Finally, several probabilistic studies (e.g., Bakker et al., 2017; Kopp et al., 2017 <sup>[[#fn:r633|633]]</sup> ; Le Bars et al., 2017) used the results of a single ice sheet model study from DeConto and Pollard (2016) <sup>[[#fn:r634|634]]</sup> as the Antarctic contribution to GMSL.

Beside the total contribution of ice sheets several studies address the individual contribution of either Greenland or Antarctica (see Section 4.2.3.1.1 and 4.2.3.1.2) based on ice dynamical studies. Critical for GMSL projections is the low confidence in the dynamic contribution of the AIS beyond 2050 in previous assessments, as discussed in Section 4.2.3.1.2.

<div id="section-4-2-3-3probabilistic-sea-level-projections-block-3"></div>

<span id="from-probabilistic-global-mean-sea-level-projections-to-regional-relative-sea-level-change"></span>
===== 4.2.3.3.2 From probabilistic global mean sea level projections to regional relative sea level change =====

Differences between GMSL and RSL change are driven by three main factors: (1) changes in the ocean, for instance, the thermal expansion component and the circulation driven changes, (2) gravitational and rotational effects caused by redistribution of mass within cryosphere and hydrosphere, leading to spatial patterns, and (3) long term processes caused by GIA that lead to horizontal and VLM. Finally, the inverse barometer effect caused by changes in the atmospheric pressure, sometimes neglected in projections, can also make a small contribution, particularly on shorter time scales. For the 21st century as a whole, estimates of the latter are smaller than 5 cm at local scales (Church et al., 2013 <sup>[[#fn:r635|635]]</sup> ; Carson et al., 2016 <sup>[[#fn:r636|636]]</sup> ) .

Ocean Dynamic sea level: Projections of dynamic sea level change are necessarily derived through interpretations of coupled climate model projections. As with thermal expansion projections, interpretations of the CMIP5 ensemble differ with regard to how the model range is understood and the manner of drift correction, if any (Jackson and Jevrejeva, 2016 <sup>[[#fn:r637|637]]</sup> ) . However, relative to tide-gauge observations, coupled climate models tend to overestimate the memory in dynamic sea level; thus, they may underestimate the emergence of the externally forced signal of DSL change above scenario uncertainty (Becker et al., 2016 <sup>[[#fn:r638|638]]</sup> ) . ODSL from coupled climate models does not include the changes resulting from ice melt because ice melt is calculated off-line.

Gravitational-rotational and deformational effects (GRD; Gregory et al., 2019 <sup>[[#fn:r638|638]]</sup> ) : All projections of RSL change include spatial patterns in sea level for cryospheric changes, which however may differ in the details with which these are represented. Some studies also include a spatial pattern for land-water storage change (Slangen et al., 2014a <sup>[[#fn:r640|640]]</sup> ) , anthropogenic subsidence is not included. Recent work indicates that, for some regions with low mantle viscosity, spatial patterns cannot be treated as fixed on multi-century time scales (Hay et al., 2017 <sup>[[#fn:r641|641]]</sup> ) . This effect has not yet been incorporated into comprehensive RSL projections, but is probably only of relevance near ice sheets. For adaptation purposes, Larour et al. (2017) developed a mapping method to indicate which areas of ice mass loss are important for which major port city. There is ''high confidence'' in the patterns caused by GRD, as in AR5.

Vertical land motion (VLM): These processes can be an important driver of RSL change, particularly in the near- to intermediate-field of the large ice sheets of the LGM (e.g., North America and northern Europe). This process is incorporated either by physical modelling (Slangen et al., 2014a <sup>[[#fn:r643|643]]</sup> ) or by estimation of a long-term trend from tide-gauge data (e.g., Kopp et al., 2014) , which is then spatially extrapolated. In the former case, only the long-term GIA process is included in the projections, but it excludes other important local factors contributing to VLM (e.g., tectonic uplift/subsidence and groundwater/hydrocarbon withdrawal); by using only tide gauge measurements, projections may assume that these other processes proceed at a steady rate and thus do not allow for management changes that affect groundwater extraction.

<div id="section-4-2-3-3probabilistic-sea-level-projections-block-4"></div>

<span id="semi-empirical-projections"></span>
===== 4.2.3.3.3 Semi-empirical projections =====

Semi-empirical models provide an alternative approach to process-based models aiming to close the budget between the observed SLR and the sum of the different components contributing to SLR. In general, motivated by a mechanistic understanding, semi-empirical models use statistical correlations from time series analysis of observations to generate projections (Rahmstorf, 2007 <sup>[[#fn:r644|644]]</sup> ; Vermeer and Rahmstorf, 2009 <sup>[[#fn:r645|645]]</sup> ; Grinsted et al., 2010 <sup>[[#fn:r646|646]]</sup> ; Kemp et al., 2011 <sup>[[#fn:r647|647]]</sup> ; Kopp et al., 2016 <sup>[[#fn:r648|648]]</sup> ) . They implicitly assume that the processes driving the observations and feedback mechanisms remain similar over the past and future. In the past, differences between semi-empirical projections and process-based models were significant but for more recent studies the differences are vanishingly small. Ongoing advances in closing the sea level budget and in the process understanding of the dynamics of ice have reduced the salience of estimates from semi-empirical models. Moreover, the results from semi-empirical models (Kopp et al., 2016 <sup>[[#fn:r682|682]]</sup> ; Mengel et al., 2016 <sup>[[#fn:r683|683]]</sup> ) are in general agreement with Church et al. (2013) <sup>[[#fn:r684|684]]</sup> , except when those results reflect the combined hydrofracturing and ice cliff instability mechanism as presented by DeConto and Pollard (2016) <sup>[[#fn:r685|685]]</sup> . At the same time, semi-empirical models based on past observations capture poorly or miss altogether the recent observed changes in Antarctica. MISI may lend a very different character to ice sheet evolution in the near future than in the recent past and hydrofracturing remains impossible to quantify from observational records only. For this reason, a new generation of semi-empirical models and emulators has been developed that estimate individual components of SLR, which the former models do not (Mengel et al., 2018 <sup>[[#fn:r686|686]]</sup> ) . These newer models aim to emulate the response of more complex models providing more detailed information for different climate scenarios or probability estimates than process-based models (Bakker et al., 2017 <sup>[[#fn:r687|687]]</sup> ; Nauels et al., 2017a <sup>[[#fn:r688|688]]</sup> ; Wong et al., 2017 <sup>[[#fn:r689|689]]</sup> ; Edwards et al., 2019 <sup>[[#fn:r690|690]]</sup> ) .

<div id="section-4-2-3-3probabilistic-sea-level-projections-block-5"></div>

<span id="recent-probabilistic-and-semi-empirical-projections"></span>
===== 4.2.3.3.4 Recent probabilistic and semi-empirical projections =====

A wide range of probabilistic sea level projections exist, ranging from simple scaling relations to partly process-based components combined with scaling relations. Table 4.5 illustrates the overlap between many of the studies, a complete overview is presented by Garner et al. (2017) , and differences between different classes of models are discussed in Horton et al. (2018) <sup>[[#fn:r692|692]]</sup> . Many studies rely on CMIP simulations for an important part of their sea level components. The largest difference can be found in the treatment of the ice dynamics, particularly for Antarctica, which are usually not CMIP5 based. Instead, each derives from one of several estimates of the Antarctic contribution. These results are useful for the purposes of elucidating sensitivities of process-based studies and effects of changing components to the total projection. This report relies on the Antarctic component from Section 4.2.3.2 for calculating the ''likely'' range of RSL. Hence the values in Table 4.5 are not used for the final assessment of RSL including the SROCC specific Antarctic contribution presented in Section 4.2.3.2. Comparing the probabilistic projections (Table 4.6) is difficult because of the subtle differences between their assumptions. Nevertheless, values range much more for 2100 than for 2050.

<span id="table-4.5"></span>
<!-- START TABLE -->
'''Table 4.5'''

'''Table 4.5:''' Sources of Information Underlying Probabilistic Projections of Sea level Rise (SLR) Projections.

CMIP5 is Coupled Model Intercomparison Project Phase 5, GRD is gravitational, rotational and deformation effects, SMB is surface mass balance, AR4 is IPCC 4th Assessment Report, VLM is vertical land motion, GIA is glacio-isostatic adjustment.

<!-- TABLE -->
{| class="wikitable"
|-
| Study
| Thermal expansion
| Glaciers
| Land water storage
| Ice Sheets
| Dynamic sea level
| GRD
| VLM
|-
| Perrette et al. (2013)
| CMIP5
| Global SMB sensitivity and exponent from AR4; total glacier volume from Radić and Hock (2010)
| Not included
| Greenland’s SMB from AR4; semi-empirical model using historical observations.
| CMIP5
| Bamber et al. (2009)
| Not included
|-
| Grinsted et al. (2015)
| CMIP5
| Church et al. (2013)
| Wada et al. (2012)
| Church et al. (2013); Expert elicitation from Bamber and Aspinall (2013)
| CMIP5
| Bamber et al. (2009)
| GIA projections from Hill et al. (2010) using observations
|-
| Slangen et al. (2014a)
| CMIP5
| CMIP5; glacier area inventory Radić and Hock (2010) in a glacier mass loss model
| Wada et al. (2012)
| SMB Meehl et al. (2007), ice dynamics Meehl et al. (2007) and Katsman et al. (2011)
| CMIP5
| Slangen et al. (2014a)
| GIA resulting of ice sheet melt from glacier mass loss model
|-
| Kopp et al. (2014)
| CMIP5
| CMIP5; Marzeion et al. (2012)
| Chambers et al. (2017); Konikow (2011)
| Church et al. (2013); Expert elicitation from Bamber and Aspinall (2013)
| CMIP5
| Mitrovica et al. (2011)
| GIA, tectonics, and subsidence from Kopp et al. (2013)
|-
| Kopp et al. (2017)
| CMIP5
| CMIP5; Marzeion et al. (2012)
| Chambers et al. (2017); Konikow (2011)
| DeConto and Pollard (2016)
| CMIP5
| Mitrovica et al. (2011)
| GIA, tectonics, and subsidence from Kopp et al. (2013)
|-
| Le Bars et al. (2017)
| CMIP5
| Four glacier models:
Giesen and Oerlemans (2013)

Marzeion et al. (2012),

Radić et al. (2014)

Slangen and Van de Wal (2011)

| Wada et al. (2012)
| DeConto and Pollard (2016);
Fettweis et al. (2013)

Church et al. (2013)

| CMIP5
| –
|-
| Jackson and Jevrejeva (2016)
| CMIP5
| Marzeion et al. (2012)
| Wada et al. (2012)
| Church et al. (2013); Expert elicitation from Bamber and Aspinall (2013)
| CMIP5
| Bamber et al. (2009)
| GIA resulting of ice sheet melt from glacier mass loss model Peltier et al. (2015)
|-
| de Winter et al. (2017)
| CMIP5
| CMIP5; glacier area inventory Radić and Hock (2010) in a glacier mass loss model
| Wada et al. (2012)
| Church et al. (2013); Expert elicitation de Vries and van de Wal (2015); Ritz et al. (2015)
| CMIP5
| Mitrovica et al. (2001)
| GIA resulting of ice sheet melt from glacier mass loss model
|}
<!-- END TABLE -->

<span id="table-4.6"></span>
<!-- START TABLE -->
'''Table 4.6:'''

'''Table 4.6:''' Median and ''likely'' Global Mean Sea Level (GMSL) rise projections (m). Values between brackets are ''likely'' range, if no values are given the ''likely'' range is not available. The table shows result from the probabilistic and semi-empirical results. A is 2000 as base line year up to 2100; B is the average of 1986–2005 as base line for the projection up to 2081–2100, C 1980–1999 as baseline up to 2090–2099.

<!-- TABLE -->
{| class="wikitable"
|-
|
| colspan="3"| 2050
| colspan="3"| 2100
|-
|
| Period
| RCP2.6
| RCP4.5
| RCP8.5
| RCP2.6
| RCP4.5
| RCP8.5
|-
| Perrette et al. (2013)
| C
|
| 0.28
(0.23–0.32)

| 0.28
(0.23–0.34)

|
| 0.86
(0.66–1.11)

| 1.06
(0.78–1.43)

|-
| Grinsted et al. (2015)
| A
|
| 0.8
(0.58–1.20)

|-
| Slangen et al. (2014a) ''' '''
| B AB B
|
| 0.54
(0.35–0.73)

| 0.71
(0.43–0.99)

|-
| Kopp et al. (2014)
| A
| 0.25
(0.21–0.29)

| 0.26
(0.21–0.31)

| 0.29
(0.24–0.34)

| 0.50
(0.37–0.65)

| 0.59
(0.45–0.77)

| 0.79
(0.62–1.00)

|-
| Kopp et al. (2017)
| A
| 0.23
(0.16–0.33)

| 0.26
(0.18–0.36)

| 0.31
(0.22–0.40)

| 0.56
(0.37–0.78)

| 0.91
(0.66–1.25)

| 1.46
(1.09–2.09)

|-
| de Winter et al. (2017)
| B
|
| 0.68/0.86
|-
| Jackson and Jevrejeva (2016)
| B
|
| 0.54
(0.36–0.72)

| 0.75
(0.54–0.98)

|-
| Le Bars et al. (2017)
| B
|
| 1.06
(0.65-1.47)

| 1.84
(1.24-2.46)

|-
| Nauels et al. (2017b)
| B
| 0.24
(0.19–0.30)

| 0.25
(0.21–0.30)

| 0.27
(0.23–0.33)

| 0.45
(0.35–0.56)

| 0.55
(0.45–0.67)

| 0.79
(0.65–0.97)

|-
| Bakker et al. (2017)
| A
| 0.20
| 0.23
| 0.25
| 0.53
| 0.72
| 1.16
|-
| Wong et al. (2017)
| A
| 0.26
| 0.28
| 0.30
| 0.55
| 0.77
| 1.50
|-
| Jevrejeva et al. (2014a)
| A
|
| 0.80
(0.6-1.2)

|-
| Schaeffer et al. (2012)
| A
|
| 0.90
| 1.02
|-
| Mengel et al. (2016)
| B
| 0.18
| 0.21
| 0.39
| 0.53
| 0.85
|}
<!-- END TABLE -->

<div id="section-4-2-3-4changes-in-extreme-sea-level-events"></div>

<span id="changes-in-extreme-sea-level-events"></span>
==== 4.2.3.4 Changes in Extreme Sea Level events ====

<div id="section-4-2-3-4changes-in-extreme-sea-level-events-block-1"></div>

ESL events are water level heights that consist of contributions from mean sea level, storm surges and tides. Compound effects of surges and tides are drivers of the ESL events. Section 4.2.3.4.1 discusses the combination of mean sea level change with a characterisation of the ESL events derived from tide gauges over the historical period and the sections 4.2.3.4.2 and 4.2.3.4.3 evaluate possible changes in these characteristics caused by cyclones and waves. This section discusses the importance of ESL and different modelling strategies to improve our understanding of ESL projections.

Even a small increase in mean sea level can significantly augment the frequency and intensity of flooding. This is because SLR elevates the platform for storm surges, tides, and waves, and because there is a log-linear relationship between a flood’s height and its occurrence interval. Changes are most pronounced in shelf seas. Roughly 1.3% of the global population is exposed to a 1 in 100-year <sup>-1</sup> flood (Muis et al., 2016 <sup>[[#fn:r699|699]]</sup> ) . This exposure to ESL and resulting damage could increase significantly with SLR, potentially amounting to 10% of the global GDP by the end of the century in the absence of adaptation (Hinkel et al., 2014 <sup>[[#fn:r700|700]]</sup> ) .

The frequency and intensity of ESL events can be estimated with statistical models or hydrodynamical models constrained by observations. Hydrodynamic models simulate a series of ESL events over time, which can then be fitted by extreme value distributions to estimate the frequency and intensity (e.g. the return level of an event occurring with a period of 100 years or frequency of 0.01 yr <sup>-1</sup> , also called the 100-year event). A tide model is sometimes included and sometimes added offline to estimate the ESL events. Statistical models fit tide gauge observations to extreme value distributions to directly estimate ESL events or combine probabilistic RSL scenarios with storm surge modelling. This can be done on global scale or local scale. For example, Lin et al. (2016) and Garner et al. (2017) <sup>[[#fn:r701|701]]</sup> estimate the increase in flood frequency along the US east coast. Both of these modelling approaches can account for projections of SLR. Rasmussen et al. (2018) used a combination of a global network of tide gauges and a probabilistic localised SLR to estimate expected ESL events showing inundation reductions for different temperature stabilisation targets as shown in the SR15 report.

An advantage of the use of hydrodynamic models is that they can quantify interactions between the different components of ESL (Arns et al., 2013 <sup>[[#fn:r702|702]]</sup> ) . Hydrodynamical models can be executed over the entire ocean with flexible grids at a high resolution (up to 1/20° or ~5 km) where necessary, appropriate for local estimates (Kernkamp et al., 2011 <sup>[[#fn:r703|703]]</sup> ) . Input for these models are wind speed and direction, and atmospheric pressure. Results of those models show that the Root Mean Squared Error between modelled and observed sea level is less than 0.2 m for 80% of a data set of 472 stations covering the global coastline (Muis et al., 2016 <sup>[[#fn:r704|704]]</sup> ) at 10-minute temporal resolution over a reference period from 1980–2011. This implies that for most locations it can be used to describe the variability in ESL. However, the areas where ESL is dominated by tropical storms are problematic for hydrodynamical models. Another difficulty arises when these models are forced with climate models: they inherit the limitations (resolution, precision and accuracy) of wind and pressure in climate projections, which is often insufficient to describe the role of waves.

Statistical models have shown that the estimation of ESL is highly sensitive to the characterisation of SLR and flood frequency distributions (Buchanan et al., 2017) . This is confirmed by Wahl et al. (2017) who estimate that the 5–95 percentile uncertainty range, attained through the application of different statistical extreme value methods and record lengths, of the current 100-year event is on average 40 cm, whereas the corresponding range in projected GMSL of AR5 under RCP8.5 is 37 cm. For ESL events with a higher return period, differences will be larger. Capturing changes in the ESL return periods in the future is even more complicated because both the changing variability over time and the uncertainty in the mean projection must be combined. A statistical framework to combine RSL and ESL, based on historical tide gauge data was applied to the US coastlines (Buchanan et al., 2016 <sup>[[#fn:r705|705]]</sup> ) . Hunter (2012) <sup>[[#fn:r708|708]]</sup>  and the AR5 (Church et al., 2013 <sup>[[#fn:r709|709]]</sup> ) projected changes in flood frequency worldwide; however, these analyses used the Gumbel distribution for high water return periods, which implies that the frequency of all ESLs (e.g., whether the 1in 10-year or 1 in 500-year) will change by the same magnitude for a given RSL, an approximation that can underestimate or overestimate ESL (Buchanan et al., 2017 <sup>[[#fn:r710|710]]</sup> ) . Hence, the amplification factors of future storm return frequency in AR5 WGI Figure 13.25 may underestimate flood hazards in some areas, while overestimating them in others. By using the Gumbel distribution, Muis et al. (2016) <sup>[[#fn:r711|711]]</sup> may also inadequately estimate flood frequencies.

<div id="section-4-2-3-4changes-in-extreme-sea-level-events-block-2"></div>

<span id="relative-sea-level-and-extreme-sea-level-events-based-on-tide-gauge-records"></span>
===== 4.2.3.4.1 Relative sea level and extreme sea level events based on tide gauge records =====

Changes in ESL are presented here, based on the projections as presented in 4.2.3.2 at the tide gauge locations in the GESLA2 database (Woodworth et al., 2016 <sup>[[#fn:r712|712]]</sup> ). Results include GIA effects, but anthropogenic subsidence is not prescribed. These calculations serve as a signal to guide adaption to SLR (Stephens et al., 2018 <sup>[[#fn:r713|713]]</sup> ). Return periods are calculated as a combination of regional RSL projections and a probabilistic characterisation of the variability in sea level as derived from the GESLA2 data set which contains a quasi-global set of tide gauges. By doing so, it is assumed that the variability in the tide gauge record does not change over time. Models are not accurate enough to address whether this is correct or not.

To quantify the average return period of ESL events, a peak-over-threshold method is applied following Arns et al. (2013) <sup>[[#fn:r714|714]]</sup> and Wahl et al. (2017) <sup>[[#fn:r715|715]]</sup> . Tide gauge records are detrended by subtracting a running mean of one year. Peaks above the 99th percentile of hourly water levels are extracted and declustered by applying a minimum time between peaks of 72 hours. This threshold of 99% was recommended by Wahl et al. (2017) <sup>[[#fn:r716|716]]</sup> for global applications. Using a maximum likelihood estimator, a Generalized Pareto Distribution (GPD) is fitted to these peaks, allowing for an extrapolation to return periods beyond the available period of observations. Changes in ESL events due to regional mean SLR are quantified following Hunter (2010) <sup>[[#fn:r717|717]]</sup> . Uncertainties in the GPD parameters and projections are propagated using a Monte Carlo approach, from which a best estimate is derived (see SM4.2). Only tide gauge records of 20 years of longer, which are at least 70% complete, are used. However, as can be seen for Guam (Fig 4.9), this does not ensure a good fit of the GPD to all peaks, as rare events may have been captured in this relatively short record.

Projected changes in ESL events are shown for 12 selected tide gauges in Figure 4.11. The magnitude of these changes depends on the relation between ESL events and the associated return periods, as well as regional sea level projections, and the uncertainty therein (see inset Figure 4.11). The change in ESL events is commonly expressed in terms of the amplification factor and the allowance. The amplification factor denotes the amplification in the average occurrence frequency of a certain extreme event, often referenced to the water level with a 100-year return period during the historic period. The allowance denotes the increased height of the water level with a given return period. This allowance equals the regional projection of SLR with an additional height related to the uncertainty in the projection (Hunter, 2012 <sup>[[#fn:r718|718]]</sup> ).

Amplification factors are strongly determined by the local variability in ESL events. Locations where this variability is large due to large storm surges and astronomical tides (e.g., Cuxhaven, see Figure 4.9) will experience a relatively moderate amplification of the occurrence frequency of extremes. In comparison, locations with small variability in ESL events (e.g., Lautoka and Papeete) will experience large amplifications even for a moderate rise in mean sea level (Vitousek et al., 2017 <sup>[[#fn:r719|719]]</sup> ). Globally, this contrast between regions with large and small amplification factors becomes clear for projections by mid-century (Fig 4.11, left panels). Although regional differences in projected mean SLR are small for the coming centuries, regional contrasts in amplification factors are considerable. In particular, many coastal areas in the lower latitudes may expect amplification factors of 100 or larger by mid-century, regardless of the scenario as also shown in SR15 and Rasmussen et al. (2018). This indicates that, at these locations, water levels with return periods of 100 years during recent past will become annual or more frequent events by mid-century. By end-century and in particular under RCP8.5, such amplification factors are widespread along the global coastlines (Vousdoukas et al., 2018a <sup>[[#fn:r720|720]]</sup> ).

<span id="figure-4.11"></span>
<!-- START IMG -->
<!-- IMG TITLE -->
'''Figure 4.11'''

<span id="figure-4.11-the-relation-between-expected-extreme-sea-level-esl-events-and-return-period-at-a-set-of-characteristic-tide-gauge-locations-see-upper-left-for-their-location-referenced-to-recent-past-mean-sea-level-based-on-observations-in-the-gesla-2-data-base-grey-lines-and-20812100-conditions-for-three-different-rcp-scenarios-as"></span>
<!-- IMG CAPTION -->
'''Figure 4.11 | The relation between expected extreme sea level (ESL) events and return period at a set of characteristic tide gauge locations (see upper left for their location), referenced to recent past mean sea level, based on observations in the GESLA-2 data base (grey lines) and 2081–2100 conditions for three different RCP scenarios as […]'''
<!-- IMG FILE -->
[[File:2c3ee38945aaa3d2678c0a9389d75ccb IPCC-SROCC-CH_4_11-2560x3000.jpg]]

Figure 4.11 | The relation between expected extreme sea level (ESL) events and return period at a set of characteristic tide gauge locations (see upper left for their location), referenced to recent past mean sea level, based on observations in the GESLA-2 data base (grey lines) and 2081–2100 conditions for three different RCP scenarios as presented in Section 4.2.3.2. The grey bands represent the 5–95% uncertainty range in the fit of the extreme value distribution to observations. The upper right hand panel provides an example illustrating the relationship between ESL events and return period for historical and future conditions; the blue line in this panel shows the best estimate ESL event above the 1986–2005 reference mean sea level. The coloured lines for the different locations show this expected ESL events for different RCP scenarios. The horizontal line denoting the amplification factor expresses the increase in frequency of events which historically have a return period of once every 100 years. In the example, a water level of 2.5 m above mean sea level, recurring in the recent climate approximately every 100 years in recent past climate, will occur every 2 to 3 years under future climate conditions. The allowance expresses the increase in ESL for events that historically have a return period of 100 years.

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<span id="figure-4.12"></span>
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'''Figure 4.12'''

<span id="figure-4.12-the-colours-of-the-dots-express-the-factor-by-which-the-frequency-of-extreme-sea-level-esl-events-increase-in-the-future-for-events-which-historically-have-a-return-period-of-100-years.-hence-a-value-of-50-means-that-what-is-currently-1-in-100-year-event-will-happen-every-2-years-due"></span>
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'''Figure 4.12 | The colours of the dots express the factor by which the frequency of extreme sea level (ESL) events increase in the future for events which historically have a return period of 100 years. Hence a value of 50 means that what is currently 1-in-100 year event will happen every 2 years due […]'''
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[[File:07ae878d8b95d32d616dfd462e96e0c1 IPCC-SROCC-CH_4_12-3000x2581.jpg]]

Figure 4.12 | The colours of the dots express the factor by which the frequency of extreme sea level (ESL) events increase in the future for events which historically have a return period of 100 years. Hence a value of 50 means that what is currently 1-in-100 year event will happen every 2 years due to a rise in mean sea level. Results are shown for three RCP scenarios and two future time slices as median values. Results are shown for tide gauges in the GESLA2 database. The accompanying confidence interval can be found in SM4.2 as well as a list of all locations. The data underlying the graph are identical to those presented in Figure 4.11. The amplification factor is schematically explained in the upper right panel of figure 4.11. Storm climatology is constant in these projections.

In summary, ESL events estimates as presented in this subsection, clearly show that as a consequence of SLR, events which are currently rare (e.g., with an average return period of 100 years), will occur annually or more frequently at most available locations for RCP8.5 by the end of the century ( ''high confidence'' ). For some locations, this change will occur as soon as mid-century for RCP8.5 and by 2100 for all emission scenarios. The affected locations are particularly located in low-latitude regions, away from the tropical cyclone (TC) tracks. In these locations, historical sea level variability due to tides and storm surges is small compared to projected mean SLR. Therefore, even limited changes in mean sea level will have a noticeable effect on ESLs, and for some locations, even RCP2.6 will lead to the annual occurrence of historically rare events by mid-century. Results should be treated with caution in regions where TCs are important as they are underrepresented in the observations (Haigh et al., 2014a <sup>[[#fn:r721|721]]</sup> ).

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<div id="section-4-2-3-4changes-in-extreme-sea-level-events-block-3"></div>

<span id="waves"></span>
===== 4.2.3.4.2 Waves =====

A warming climate is expected to affect wind patterns and storm characteristics, which in turn will impact wind waves that contribute to high coastal water levels. Wind-wave projections are commonly based on dynamical and statistical wave models forced by projected surface winds from GCMs, notably those participating in the CMIP. In the framework of the Coordinated Ocean Wave Climate Project (COWCLIP), an ensemble of Coupled Model Intercomparison Project Phase 3 (CMIP3)-based global wave projections (Hemer et al., 2013) was produced and the results were summarised in the AR5 (WGI Chapter 13). Casas-Prat et al. (2018) expanded the geographic domain to include the Arctic Ocean, highlighting the vulnerability of high-latitude coastlines to wave action as ice retreats. Reduced sea ice allows larger waves and stronger cyclones in the Arctic Ocean, which can further disrupt and break up sea ice (e.g., Thomson and Rogers, 2014; Day and Hodges, 2018). A review and consensus-based analysis of regional and global scale wave projections, including CMIP5-based projections, has been provided by Morim et al. (2018) as part of COWCLIP. Projections of annual and seasonal mean significant wave height changes agree on an increase in the Southern Ocean, tropical eastern Pacific and Baltic Sea; and on a decrease over the North Atlantic, northwestern Pacific and Mediterranean Sea. Projections of mean significant wave height lack consensus over the eastern North Pacific and southern Indian and Atlantic Oceans. Projections of future extreme significant wave height are consistent in projecting an increase over the Southern Ocean and a decrease over the northeastern Atlantic and Mediterranean Sea. Regional projections of wind-waves have mostly been applied to Europe so far, while highly vulnerable regions have been largely overlooked. This is the case for low-lying islands where impacts of SLR and wave-induced flooding are expected to be severe and adaptive capacity is reduced (Hoeke et al., 2013; Albert et al., 2016).

A number of studies have included waves, in addition to tides and sea level anomalies, to assess coastal vulnerability to SLR using dynamical and statistical approaches. CoSMoS (Barnard et al., 2014) includes a series of embedded wave models to estimate high resolution projections of total water levels along the Southern California coast for different extreme scenarios (O’Neill et al., 2017). Arns et al. (2017) find that an increase in sea level may reduce the depth-limitation of waves, thereby resulting in waves with greater energy approaching the coast. Including wave effects is crucial for coastal adaptation and planning (e.g., Isobe, 2013). For example Arns et al. (2017) report that coastal protection design heights need to be increased by 48–56% in the German Bight region relative to a design height based on the effect of SLR on ESL only. Combining SLR with extreme value theory applied to past observations of tides, storm surges and waves, Vitousek et al. (2017) found that a 10–20 cm SLR could result in a doubling of coastal flooding frequency in the tropics. For the southern North Sea region, Weisse et al. (2012) argue that increasing storm activity also increases hazards from ESL events. Global-scale projections of ESL event changes including wave setup indicate a very likely increase of the global average 100-year ESL of 58–172 cm under RCP8.5 (Vousdoukas et al., 2018c). Changes in storm surges and waves enhance the effects of relative SLR along the majority of northern European coasts, with contributions up to 40% in the North Sea (Vousdoukas et al., 2017).

A stationarity of the wave climate is often assumed for projections of ESL events (Vitousek et al., 2017). Yet, wave contributions to coastal sea level changes (setup and swash) depend on several factors that can vary in response to internal climate variability and climate change, including deep-water wave field, water-depth, and geomorphology. Melet et al. (2018) reported that over recent decades, wave setup and swash interannual-to-decadal changes induced by deep-water wave height and period changes alone were sizeable compared to steric and land-ice mass loss coastal sea level changes.

Comprehensive broad-scale projections of sea level at the coast including regional sea level changes, tides, waves, storm surges, interactions between these processes and accounting for changes in period and height of waves and frequency and intensity of storm surges are yet to be performed.

<div id="section-4-2-3-4changes-in-extreme-sea-level-events-block-4"></div>

<span id="effects-of-cyclones"></span>
===== 4.2.3.4.3 Effects of cyclones =====

Tropical and extratropical cyclones (TCs and ETCs) tend to determine ESL events, such as coastal storm surges, high water events, coastal floods, and their associated impacts on coastal communities around the world. The projected potential future changes in TCs and ETCs frequency, track and intensity is therefore of great importance. After AR5, it was realised that the modelled global frequency of TCs is underestimated and that the geographical pattern is poorly resolved in the case of TC tracks, very intense TCs (i.e., Category 4/5) and TC formation by using low resolution climate models (Camargo, 2013). Over recent years, multiple methods including downscaling

CMIP5 climate models (Knutson et al., 2015; Yamada et al., 2017), high-resolution simulations (Camargo, 2013; Yamada et al., 2017), TC–ocean interaction (Knutson et al., 2015; Yamada et al., 2017), statistical models (Ellingwood and Lee, 2016) and statistical-deterministic models (Emanuel et al., 2008) have been developed, and the ability to simulate TCs has been substantially improved. Most models still project a decrease or constant global frequency of TCs, but a robust increase in the lifetimes, precipitation, landfalls and ratio of intense TCs under global warming. This is consistent with IPCC AR5 and many additional studies (Emanuel et al., 2008; Holland et al., 2008; Knutson et al., 2015; Kanada et al., 2017; Nakamura et al., 2017; Scoccimarro et al., 2017; Zheng et al., 2017). It is expected that these projected increases are intensified by favourable marine environmental conditions, expansion of the tropical belt, or ocean warming in the northwest Pacific and north Atlantic, and increasing water vapour in the atmosphere (Kossin et al., 2014; Moon et al., 2015; Cai et al., 2016; Mei and Xie, 2016; Cai et al., 2017; Kossin, 2017; Scoccimarro et al., 2017; Kossin, 2018). However, it is noted that, in contrast to most models, some models do predict an increase in global TC frequency during the 21st century (Emanuel, 2013; Bhatia et al., 2018).

Previous extensive studies indicated the important role of warming oceans in the TC activity (Emanuel, 2005; Mann and Emanuel, 2006; Trenberth and Fasullo, 2007; Trenberth and Fasullo, 2008; Villarini and Vecchi, 2011; Trenberth et al., 2018) and also revealed TCs stir the ocean and mix the subsurface cold water to the surface (Shay et al., 1992; Lin et al., 2009). The resulting increased thermal stratification of the upper ocean under global warming will reduce the projected intensification of TCs (Emanuel, 2015; Huang et al., 2015; Tuleya et al., 2016). A recent study suggests a strengthening effect of ocean freshening in TC intensification, opposing the thermal effect (Balaguru et al., 2016). It is concluded that it is likely that the intensity of severe TCs will increase in a warmer climate, but there is still low confidence in the frequency change of TCs in the future.

Recent projection studies indicate that trends in regional ETCs vary from region to region, for example, a projected increase in the frequency of ETCs in the South and the northeast North Atlantic, the South Indian Ocean, and the Pacific (Colle et al., 2013; Zappa et al., 2013; Cheng et al., 2017; Michaelis et al., 2017) and a decrease in the numbers of ETCs in the North Atlantic basin and the Mediterranean (Zappa et al., 2013; Michaelis et al., 2017). Note that the projected frequency in ETCs still remains uncertain due to different definitions of cyclone, model biases or climate variability (Chang, 2014; Cheng et al., 2016). Considering these processes implies that changes in TC and ETC characteristics will vary locally and therefore there is low confidence in the regional storm changes, which is in agreement with AR5 WGI Chapter 14 (Christensen et al., 2013).

Observed damages from ETCs/TCs to coastal regions has increased over the past 30 years and will continue in the future (Ranson et al., 2014). The global population exposed to ETCs/TCs hazards is expected to continue to increase in a warming climate (Peduzzi et al., 2012; Blöschl et al., 2017; Emanuel, 2017a; Michaelis et al., 2017). The probabilities of sea level extreme events induced by TC storm surge are very likely to increase significantly over the 21st century. Risk from TCs increases in highly vulnerable coastal regions (Hallegatte et al., 2013), e.g., on coasts of China (Feng and Tsimplis, 2014), west Florida, north of Queensland, the Persian Gulf, and even in well protected area such as the Greater Tokyo area (Tebaldi et al., 2012; Lin and Emanuel, 2015; Ellingwood and Lee, 2016; Hoshino et al., 2016; Dinan, 2017; Emanuel, 2017b; Lin and Shullman, 2017). The ESL return period has greatly decreased over recent decades and is also expected to decrease greatly in the near future, for example, in NYC (by 2030–2045; Garner et al., 2017). It is very likely that the ESL return period in low-lying areas such as coastal megacities decreased over the 20th century and frequencies of still unusual ESL events are expected to increase in frequency in the future. In addition, the compound effects of SLR, storm surge and waves on ESL events and the associated flood hazard are assessed in Chapter 6 (Section 6.3.3.3 and 6.3.4).

<div id="section-4-2-3-5long-term-scenarios-beyond-2100"></div>

<span id="long-term-scenarios-beyond-2100"></span>
==== 4.2.3.5 Long-Term Scenarios, Beyond 2100 ====

<div id="section-4-2-3-5long-term-scenarios-beyond-2100-block-1"></div>

Sea level at the end of the century will be higher than present day and continuing to rise in all cases even if the Paris Agreement is followed (Nicholls, 2018 <sup>[[#fn:r794|794]]</sup> ). The reasons for this are mainly related to the slow response of glacier melt, thermal expansion and ice sheet mass loss (Solomon et al., 2009 <sup>[[#fn:r794|794]]</sup> ). These processes operate on long time scales, implying that even if the rise in global temperature slows or the trend reverses, sea level will continue to rise (SR1.5 report, AR5). A study by Levermann et al. (2013) <sup>[[#fn:r799|799]]</sup> based on palaeo-evidence and physical models formed the basis of the assessment by Church et al. (2013) <sup>[[#fn:r803|803]]</sup> indicating that committed SLR is approximately 2.3 m per degree warming for the next 2000 years with respect to pre-industrial temperatures. This rate is based on a relation between ocean warming and basal melt as used by Levermann et al. (2013), without accounting for surface melt, hydrofracturing of ice shelves and subsequent ice cliff failure, suggested to be a dominant long term mechanism for ice mass loss (DeConto and Pollard, 2016 <sup>[[#fn:r808|808]]</sup> ). Deep uncertainty (Cross-Chapter Box 5 in Chapter 1) remains on the ice dynamical contribution from Antarctica after 2100.

Beyond the 21st century, the relative importance of the long-term contributions of the various components of SLR changes markedly. For glaciers, the long-term is of limited importance, because the sea level equivalent of all glaciers is restricted to 0.32 ± 0.08 m when taking account of ice mass above present day sea level (Farinotti et al., 2019 <sup>[[#fn:r796|796]]</sup> ). Hence, there is ''high confidence'' that the contribution of glaciers to SLR expressed as a rate will decrease over the 22nd century under RCP8.5 (Marzeion et al., 2012 <sup>[[#fn:r797|797]]</sup> ). For thermal expansion the gradual rate of heat absorption in the ocean will lead to a further SLR for several centuries (Zickfeld et al., 2017 <sup>[[#fn:r798|798]]</sup> ). By far, the most important uncertainty on long time scales arises from the contribution of the major ice sheets. The time scale of response of ice sheets is thousands of years. Hence, if ice sheets contribute significantly to sea level in 2100, they will necessarily also contribute to sea level in the centuries to follow. Only for low emission scenarios, like RCP2.6, can substantial ice loss be prevented, according to ice dynamical models (Levermann et al., 2014 <sup>[[#fn:r799|799]]</sup> ; Golledge et al., 2015 <sup>[[#fn:r800|800]]</sup> ; DeConto and Pollard, 2016 <sup>[[#fn:r801|801]]</sup> ; Bulthuis et al., 2019 <sup>[[#fn:r802|802]]</sup> ). For Greenland, surface warming may lead to ablation becoming larger than accumulation, and the associated surface lowering increases ablation further (positive feedback). As a consequence, the ice sheet will significantly retreat. Church et al. (2013) <sup>[[#fn:r803|803]]</sup> concluded that the threshold for perpetual negative mass balance based on modelling studies lies between 1ºC (Robinson et al., 2012 <sup>[[#fn:r804|804]]</sup> ; ''low confidence'' ) and 4ºC ( ''medium confidence'' ) above pre-industrial temperatures. Pattyn et al. (2018) <sup>[[#fn:r805|805]]</sup> demonstrated that with more than 2.0ºC of summer warming, it becomes ''more likely than not'' that the GIS crosses a tipping point, and the ice sheet will enter a long-term state of decline with the potential loss of most or all of the ice sheet over thousands of years. If the warming is sustained, ice loss could become irreversible due to the initiation of positive feedbacks associated with elevation-SMB feedback (reinforced surface melt as the ice sheet surface lowers into warmer elevations), and albedo-melt feedback associated with darkening of the ice surface due to the presence of liquid water, loss of snow, changes in firn and biological processes (Tedesco et al., 2016 <sup>[[#fn:r806|806]]</sup> ; Ryan et al., 2018 <sup>[[#fn:r807|807]]</sup> ). The precise temperature threshold and duration of warming required to trigger such irreversible retreat remains very uncertain, and more research is still needed.

The mechanisms for decay of the AIS are related to ice shelf melt by the ocean, followed by accelerated loss of grounded ice and MISI, possibly exacerbated by hydrofracturing of the ice shelves and ice cliff failure (Cross-Chapter Box 8 in Chapter 3). The latter processes have the potential to drive faster rates of ice mass loss than the SMB processes that are ''likely'' to dominate the future loss of ice on Greenland. Furthermore, the loss of marine-based Antarctic ice represents a long-term (millennial) commitment to elevated SLR, due to the long thermal memory of the ocean. Once marine based Antarctic ice is lost, local ocean temperatures will have to cool sufficiently for buttressing ice shelves to reform, allowing retreated grounding lines to re-advance (DeConto and Pollard, 2016 <sup>[[#fn:r808|808]]</sup> ). A minimum time scale, whereby the majority of West Antarctica decays, was derived from a schematic experiment with an ice flow model by Golledge et al. (2017) <sup>[[#fn:r809|809]]</sup> , where ice shelves were removed instantaneously and prohibited from re-growing. Results of this experiment indicate that most of West Antarctica’s ice is lost in about a century.

Gradual melt of ice shelves accompanied by partial retreat of East Antarctic ice would yield greater ice melt but on a time scale of millennial or longer (Cross-Chapter Box 8 in Chapter 3). Prescribing a uniform warming of 2°C–3°C in the Southern Ocean triggers an accelerated decay of West Antarctica in a coarse resolution model with a temperature-driven basal melt formulation yielding 1–2 m SLR by the year 3000 and up to 4 m by the year 5000 (Sutter et al., 2016 <sup>[[#fn:r810|810]]</sup> ). Formulating an ice sheet model with Coulomb friction in the grounding line zone yields a SLR of 2 m after 500 year for a sub-ice shelf melt of 20 m a <sup>–1</sup> (Pattyn, 2017 <sup>[[#fn:r811|811]]</sup> ). On decadal to millennial time scales the interaction between ice and the solid Earth indicates the possibility of a negative feedback slowing retreat by viscoelastic uplift and gravitational effects that reduce the water depth at the grounding line (Gomez et al., 2010 <sup>[[#fn:r812|812]]</sup> ; de Boer et al., 2014; Gomez et al., 2015 <sup>[[#fn:r813|813]]</sup> ; Konrad et al., 2015 <sup>[[#fn:r814|814]]</sup> ; Pollard et al., 2017 <sup>[[#fn:r815|815]]</sup> ; Barletta et al., 2018 <sup>[[#fn:r816|816]]</sup> ; Section 4.2.3.1.2).

A blended statistical and physical model, calibrated by observed recent ice loss in a few basins (Ritz et al., 2015 <sup>[[#fn:r817|817]]</sup> ) projects an Antarctic contribution to sea level of 30 cm by 2100 and 72 cm by 2200, following the SRES A1B scenario, roughly comparable to RCP6.0. The projected contribution of WAIS was found to be limited to 48 cm in 2200 following the A1B scenario. The key uncertainty in these calculations comes from the dependency on the relation between the sliding velocity and the friction at the ice-bedrock interface. Several parameterisations are used to describe this process. Golledge et al. (2015) <sup>[[#fn:r820|820]]</sup> present values between 0.6–3 m by 2300 for the RCP8.5 scenario. In contrast to the previous studies, Cornford et al. (2015) used an adaptive grid model, which can describe more accurately grounding line migration (Cross-Chapter Box 8 in Chapter 3). Due to the computational complexity of their model, simulations are limited to West Antarctica. Starting from present-day observations, they find that the results are critically dependent on initial conditions, sub ice shelf melt rates, and grid resolution. The glacier with the most uncertain vulnerability is the 120 km-wide Thwaites Glacier, in the Amundsen Sea sector of West Antarctica. Thwaites Glacier is currently retreating in a reverse-sloped trough extending into the central WAIS (Figure 4.8), where the bed is up to 2 km below sea level. In addition to Thwaites, several smaller outlet glaciers and ice streams may contribute to sea level on long time scales, but in the study by Cornford et al. (2015), a full West Antarctic retreat does not occur with limited oceanic heating under the two major ice shelves (Filchner-Ronne and Ross) keeping ice streams flowing into the Ross and Weddell Seas in place. However, the representation of these processes remains simplistic at the continental ice sheet scale (Cross-Chapter Box 8 in Chapter 3).

Nonetheless, recent studies using independently developed Antarctic ice dynamical models (Golledge et al., 2015; DeConto and Pollard, 2016 <sup>[[#fn:r821|821]]</sup> ; Bulthuis et al., 2019 <sup>[[#fn:r822|822]]</sup> ) agree that low emission scenarios, are required to prevent substantial future ice loss ( ''medium confidence'' ). However, observations (Rignot et al., 2014 <sup>[[#fn:r823|823]]</sup> ) and modelling of the Thwaites Glacier in West Antarctica (Joughin et al., 2014 <sup>[[#fn:r824|824]]</sup> ), suggest grounding line retreat on the glacier’s reverse sloped bedrock is already underway and possibly capable of driving major WAIS retreat on century time scales. Whether the retreat is driven by ocean changes driven by climate change or by climate variability (Jenkins et al., 2018 <sup>[[#fn:r825|825]]</sup> ) is still under debate. Hence it is not possible to determine whether a low emission scenario would prevent substantial future ice loss ( ''medium confidence'' ). This is a further elaboration on the SR15 assertion that the chance for passing a threshold is larger for 2°C warming than for 1.5°C warming.

A study by Clark et al. (2016) addresses the evolution of the ice sheets over the next 10,000 years and concludes that given a climate model with an equilibrium climate sensitivity of 3.5°C, the estimated combined loss of Greenland and Antarctica ranges from 25–52 m of equivalent sea level, depending on the emission scenario considered, with rates of GMSL as high as 2–4 m per century. A worst-case scenario was explored with an intermediate complexity climate model coupled to a dynamical ice model (Winkelmann et al., 2015 <sup>[[#fn:r826|826]]</sup> ), in which all readily available fossil fuels are combusted at present-day rates until they are exhausted. The associated climate warming leads to the disappearance of the entire AIS with rates of SLR up to around 3 m per century. A follow up study by Clark et al. (2018) <sup>[[#fn:r827|827]]</sup> addressing the long-term commitment of SLR based on cumulative carbon dioxide emissions points to SLR as an additional measure for setting emission targets. It shows that a 2ºC scenario would result in 0.9 m in 2300 and around 7.4 m in the year 9000 CE.

Similar to the strategy for the 21st century, the long-term projections of sea level were assessed. Since no new CMIP runs are available there are no major new insights in the thermal expansion and glacier component which deviate from the AR5 assessment for the long-term contribution of these components. Some studies updated the contribution of the GIS on long time scales. Vizcaino et al. (2015) <sup>[[#fn:r828|828]]</sup> used a GCM coupled to an ice sheet model to calculate the Greenland contribution which is within the range of estimates presented by Church et al. (2013) <sup>[[#fn:r829|829]]</sup> . This is also true for the ice sheet simulations by Calov et al. (2018) <sup>[[#fn:r830|830]]</sup> which are based on off line simulations with a regional climate model forced by RCP4.5 and RCP8.5 scenarios of three different CMIP5 models. On the other hand, Aschwanden et al. (2019) <sup>[[#fn:r831|831]]</sup> used temperatures to calculate SMB which was used to force an ice sheet model to arrive at much higher values for SLR. However, they used a spatially uniform temperature forcing, which is in conflict with earlier work and overestimated temperatures in the ablation zone (e.g., Van de Wal and Wild, 2001; Gregory and Huybrechts, 2006 <sup>[[#fn:r832|832]]</sup> ). Given this limited and contrasting evidence for Greenland, the assessed values presented in Table 13.8 of Church et al. (2013) <sup>[[#fn:r836|836]]</sup> were also used, but again replacing the Antarctic component by the assessed value from the process and climate scenario-based studies published after 2013. The low scenario in Table 13.8 of Church et al. (2013) without the Antarctic contribution was combined with the RCP2.6 estimates for Antarctica simulated by Golledge et al. (2015) <sup>[[#fn:r833|833]]</sup> , the mean of the RCP2.6 simulations with and without time delay between global mean atmosphere and ocean temperature around Antarctica of Levermann et al. (2014) <sup>[[#fn:r834|834]]</sup> , and the model results of Bulthuis et al. (2019) <sup>[[#fn:r835|835]]</sup> . The medium scenario from Church et al. (2013) <sup>[[#fn:r836|836]]</sup> is combined with RCP4.5 results and the high scenario with RCP8.5. Results are shown in Figure 4.2, Section 4.1 and show a strong divergence of RSL rise over time, whereby the estimates in 2300 range from about 1–2 m under RCP2.6 up to 2–5.5 m for RCP8.5.

The specific trajectories that will be followed may depend critically on if and when certain tipping points are reached. Most critical in that respect are presumably the tipping points corresponding (1) to the threshold where the ablation in Greenland becomes larger than the accumulation, causing an irreversible and nearly full retreat of the ice sheet; and (2) the thresholds for ice shelf stability in West Antarctica, which depend on surface melt and sub-ice melt, combined with uncertainties surrounding MISI and/or MICI. There is deep uncertainty about whether and when a tipping point will be passed. For RCP8.5, the chance of passing a tipping-point are considered to be substantially higher than for RCP2.6.

In summary, there is ''high confidence'' in continued thermal expansion and the loss of ice from both the GIS and AIS sheets beyond 2100. A complete loss of Greenland ice contributing about 7 m to sea level over a millennium or more would occur for sustained GMST between 1°C ( ''low confidence'' ) and 4°C ( ''medium confidence'' ) above pre-industrial levels. Due to deep uncertainties regarding the dominant processes that could trigger a major retreat, there is ''low confidence'' in the estimates of the contribution of the AIS beyond 2100, but our estimates (2.3–5.4 m in 2300) for RCP8.5 are considerably higher than presented in AR5. High-emission scenarios or exhaustion of fossil fuels over a multi-century period lead to rates of SLR as high as several metres per century in the long term ( ''low confidence'' ). Low-emission scenarios lead to a limited contribution over multi-century time scales ( ''high confidence'' ). Discriminating between 1.5°C and 2°C scenarios in terms of long-term sea level change is not possible with the limited evidence. Hence, it is concluded that the SLR on millennial time scales is strongly dependent on the emission scenario followed. This, combined with the lack in predictability of the tipping points, indicates the importance of emissions mitigation for minimising the risk to low-lying coastlines and islands ( ''high confidence'' ).

<div id="section-4-2-3-5long-term-scenarios-beyond-2100-block-2" class="box"></div>

<span id="box-4.1-case-studies-of-coastal-hazard-and-response"></span>