Editing IPCC:AR6/WGI/Chapter-9 (section)

== 9.6 Sea Level Change ==

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=== 9.6.1 Global and Regional Sea Level Change in the Instrumental Era ===

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==== 9.6.1.1 Global Mean Sea Level Change Budget in the Pre-satellite Era ====

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The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) discussed the development and application of new statistical methodologies for reconstructing global mean sea level (GMSL) from tide gauge data over the 20th century (Box 9.1). Based on an ensemble of tide gauge reconstructions, SROCC assessed an average rate of GMSL rise of 1.38 [0.81 to 1.95, ''very likely'' range] mm yr <sup>–1</sup> for the period 1901–1990. Since SROCC, two new GMSL reconstructions have been published ( [[#Dangendorf--2019|Dangendorf et al., 2019]] ; [[#Frederikse--2020b|Frederikse et al., 2020b]] ) and are included in an updated ensemble estimate of GMSL change ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ; [[#Palmer--2021|Palmer et al., 2021]] ). Based on these updated data and methods, the GMSL change over the (pre-satellite) period 1901–1990 is assessed to be 0.12 [0.07 to 0.17, ''very likely'' range] m with an average rate of 1.35 [0.78 to 1.92, ''very likely'' range] mm yr <sup>–1</sup> ( ''high confidence'' ) (Table 9.5; [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ) in agreement with SROCC assessment. Both this assessment and SROCC have substantially larger uncertainties than the AR5 assessment, which was based on a single tide gauge reconstruction and did not account for structural uncertainty (see [[#Palmer--2021|Palmer et al., 2021]] for a discussion).

The SROCC found that four of the five available tide gauge reconstructions that extend back to at least 1902 showed a robust acceleration ( ''high confidence'' ) of GMSL rise over the 20th century, with estimates for the period 1902–2010 (–0.002 to +0.019 mm yr <sup>–2</sup> ) that were consistent with AR5. New tide gauge reconstructions published since SROCC ( [[#Dangendorf--2019|Dangendorf et al., 2019]] ; [[#Frederikse--2020b|Frederikse et al., 2020b]] ) support this assessment and suggest that increased ocean heat uptake related to changes in Southern Hemisphere winds and increased mass loss from Greenland are the primary physical mechanisms for the acceleration ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ). Therefore, the SROCC assessment on the acceleration of GMSL rise over the 20th century is maintained.

The evaluation of the sea level budget presented here, and in [[#9.6.1.2|Section 9.6.1.2]] , draws on assessments of the individual components (Sections 2.3.3.1 and 9.2.4.1 for global-mean thermosteric and Sections 9.5.1.1, 9.4.1.1 and 9.4.2.1 for ice mass loss contributions to GMSL change from glaciers and ice sheets). Following SROCC approach, the mass loss from ice sheet peripheral glaciers is included in the ice-sheet contributions to GMSL change (glacier mass loss from regions 5 and 19 of the Randolph Glacier Inventory 6.0 ( [[#RGI%20Consortium--2017|RGI Consortium, 2017]] ) are added to ice-sheet mass loss where applicable, with uncertainties added in quadrature). The total change in GMSL for each component, and their sum, is summarized in Table 9.5 (uncertainties added in quadrature). For consistency across the report, and to simplify the treatment of uncertainties, all budget calculations are based on the difference between the first and last year in each period ( [[#Palmer--2021|Palmer et al., 2021]] ), rather than a linear fit to the underlying time series as used in SROCC and AR5.

The sea level budget in SROCC included the anthropogenic contribution of land-water storage (LWS; Box 9.1) change from a single estimate ( [[#Wada--2016|Wada, 2016]] ). Since SROCC, two studies have combined estimates of natural LWS change with anthropogenic LWS changes from reservoir impoundment and groundwater depletion ( [[#Cáceres--2020|Cáceres et al., 2020]] ; [[#Frederikse--2020b|Frederikse et al., 2020b]] ). For [[#Cáceres--2020|Cáceres et al. (2020)]] , zero change is assumed for the period 1901–1948, since their LWS change estimates are not available before 1948. Given the large year-to-year changes associated with hydrological variability, the assessed changes in LWS (Table 9.5) are based on linear trends for each period, following [[#Palmer--2021|Palmer et al. (2021)]] . Structural uncertainty is estimated from the standard deviation of the trends across the two studies, and parametric uncertainty is estimated based on the Monte Carlo simulations of [[#Frederikse--2020b|Frederikse et al. (2020b)]] . These two sources of uncertainty are combined in quadrature, and the assessed central estimate is taken as the average of the ensemble mean trends. Compared to SROCC-assessed LWS trend of -0.12 mm yr <sup>–1</sup> for the period 1901–1990, the updated assessment leads to a more negative trend of –0.16 [–0.35 to 0.04] mm yr <sup>–1</sup> , although the two are consistent within the estimated uncertainties. Previous studies and SROCC have highlighted the large uncertainty in estimates of LWS change over the 20th century ( [[#Gregory--2013|Gregory et al., 2013]] ), and therefore SROCC assessment of ''low confidence'' in the estimated LWS contribution to GMSL change is maintained.

Since SROCC, a new ocean heat content reconstruction ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] ; [[#Zanna--2019|Zanna et al., 2019]] ) has allowed global thermosteric sea level change to be estimated over the 20th century. As a result, the sea level budget for the 20th century can now be assessed for the first time. For the periods 1901–1990 and 1901–2018, the assessed ''very likely'' range for the sum of components is found to be consistent with the assessed ''very likely'' range of observed GMSL change ( ''medium confidence'' ), in agreement with Frederikse et al. (2020b; Table 9.5). This represents a major step forward in the understanding of observed GMSL change over the 20th century, which is dominated by glacier (52%) and Greenland Ice Sheet mass loss (29%) and the effect of ocean thermal expansion (32%), with a negative contribution from the LWS change (–14%). While the combined mass loss for Greenland and glaciers is consistent with SROCC, updates in the underlying datasets lead to differences in partitioning of the mass loss.

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<span id="global-mean-sea-level-change-budget-in-the-satellite-era"></span>
==== 9.6.1.2 Global Mean Sea Level Change Budget in the Satellite Era ====

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The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) concluded that GMSL increased at a rate of 3.16 [2.79 to 3.53, ''very likely'' range] mm yr <sup>–1</sup> in the period 1993–2015 (the satellite altimetry era), and a rate of 3.58 [3.10 to 4.06, ''very likely'' range] mm yr <sup>–1</sup> in the period 2006–2015 – the Gravity Recovery and Climate Experiment (GRACE)/Argo data era ( ''high confidence'' ). An updated assessment for the periods 1993–2018 and 2006–2018 yields values of 3.25 [2.88 to 3.61] and 3.69 [3.21 to 4.17] mm yr <sup>–1</sup> ( ''high confidence'' ) (Table 9.5), with the slightly larger central estimates consistent with the observed acceleration in GMSL rise since the late 1960s ( [[#Dangendorf--2019|Dangendorf et al., 2019]] ), given the longer assessment periods. Based on the GMSL assessed time series presented in [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] , GMSL acceleration is estimated as 0.075 [0.066 to 0.080] mm yr <sup>–2</sup> for 1971–2018 and 0.094 [0.082–0.115] mm yr <sup>–2</sup> for 1993–2018 ( ''high confidence'' ). For the common period of 1993–2010, the assessed rate of GMSL rise based on tide gauge reconstructions (3.19 [1.18 to 5.20] mm yr <sup>–1</sup> ) is consistent with the assessment based on satellite altimetry (2.77 [2.26 to 3.28] mm yr <sup>–1</sup> ), within the estimated uncertainties.

Since SROCC, two new estimates of the LWS contribution have been published ( [[#9.6.1.1|Section 9.6.1.1]] ; [[#Cáceres--2020|Cáceres et al., 2020]] ; [[#Frederikse--2020b|Frederikse et al., 2020b]] ). For the early 21st century (the periods 1993–2018 and 2006–2018) both publications find a positive LWS contribution (Table 9.5), based on the most recent GRACE-derived estimates. This contrasts with the negative LWS contribution presented for the same periods in SROCC based on World Climate Research Programme (WCRP) Global Sea Level Budget Group (2018), and reinforces the ''low confidence'' assessment of the LWS contribution.

For both periods in the satellite era – that is, 1993–2018 and 2006–2018 – the sum of contributions is consistent with the total observed GMSL change ( ''high confidence'' ) (Table 9.5). However, the latter period, which is characterized by improved data quality and coverage associated with satellite and Argo observations, shows much closer agreement in the central estimates. The marginal sea level budget closure for the period 1993–2018 may indicate underestimated uncertainty, which may be structural as well as parametric. The sea level budget assessments across the various periods in Table 9.5 demonstrate that the acceleration in GMSL rise ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ) since the late 1960s is mostly the result of increased ice-sheet mass loss. However, all contributions to GMSL rise show their largest rate during 2006–2018, with the ice sheets accounting for 27% of the total change during this period. Because of the increased ice-sheet mass loss, the total loss of land ice (glaciers and ice sheets) was the largest contributor to GMSL rise over the period 2006–2018 ( ''high confidence'' ).

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'''Table''' '''9.5 |''' '''Observed contributions to global mean sea level (GMSL) change for five different periods.''' Values are expressed as the total change (Δ) in the annual mean or year mid-point value over each period (mm) along with the equivalent rate (mm yr <sup>–1</sup> ). The ''very likely'' ranges appear in brackets based on the various section assessments as indicated. Uncertainties for the sum of contributions are added in quadrature, assuming independence. Percentages are based on central estimate contributions compared to the central estimate of the sum of contributions.

{| class="wikitable"
|-
| '''Observed contribution to GMSL change'''

|
| '''1901–1990'''

{9.6.1.1}

| '''1971–2018'''

{CCBox 9.1}

| '''1993–2''' '''018'''

{9.6.1.2}

| '''2006–2018'''

{9.6.1.2}

| '''1901–2018'''

{9.6.1.1}

|-
| rowspan="2"| Thermal expansion

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.1|Section 2.3.3.1]] ; Table 2.7)

| Δ (mm)

| 31.6

[14.7 to 48.5]

(31.9%)

| 47.5

[34.3 to 60.7]

(50.4%)

| 32.7

[23.8 to 41.6]

(45.9%)

| 16.7

[8.9 to 24.6]

(38.6%)

| 63.2

[47.0 to 79.4]

(38.4%)

|-
| mm yr <sup>–1</sup>

| 0.36

[0.17 to 0.54]

| 1.01

[0.73 to 1.29]

| 1.31

[0.95 to 1.66]

| 1.39

[0.74 to 2.05]

| 0.54

[0.40 to 0.68]

|-
| rowspan="2"| Glaciers (excluding peripheral glaciers)

(Sections 2.3.2.3, 9.5.1.1)

| Δ (mm)

| 51.8

[30.4 to 73.2]

(52.3%)

| 20.9

[10.0 to 31.7]

(22.2%)

| 13.8

[10.0 to 17.6]

(19.4%)

| 7.5

[6.8 to 8.2]

(17.3%)

| 67.2

[41.8 to 92.6]

(40.8%)

|-
| mm yr <sup>–1</sup>

| 0.58

[0.34 to 0.82]

| 0.44

[0.21 to 0.67]

| 0.55

[0.40 to 0.70]

| 0.62

[0.57 to 0.68]

| 0.57

[0.36 to 0.79]

|-
| rowspan="2"| Greenland Ice Sheet (including peripheral glaciers)

(Sections 2.3.2.4.1, 9.4.1.1)

| Δ (mm)

| 29.0

[16.3 to 41.7]

(29.3%)

| 11.9

[7.7 to 16.1]

(12.6%)

| 10.8

[8.9 to 12.7]

(15.2%)

| 7.5

[6.2 to 8.9]

(17.3%)

| 40.4

[27.2 to 53.5]

(24.5%)

|-
| mm yr <sup>–1</sup>

| 0.33

[0.18 to 0.47]

| 0.25

[0.16 to 0.34]

| 0.43

[0.36 to 0.51]

| 0.63

[0.51 to 0.74]

| 0.35

[0.23 to 0.46]

|-
| rowspan="2"| Antarctic Ice Sheet (including peripheral glaciers)

(Sections 2.3.2.4.2, 9.4.2.1)

| Δ (mm)

| 0.4

[–8.8 to 9.6]

(0.4%)

| 6.7

[–4.0 to 17.3]

(7.1%)

| 6.1

[4.0 to 8.3]

(8.6%)

| 4.4

[2.9 to 6.0]

(10.2%)

| 6.7

[–4.0 to 17.4]

(4.1%)

|-
| mm yr <sup>–1</sup>

| 0.00

[–0.10 to 0.11]

| 0.14

[–0.09 to 0.37]

| 0.25

[0.16 to 0.33]

| 0.37

[0.24 to 0.50]

| 0.06

[–0.03 to 0.15]

|-
| rowspan="2"| Land-water storage <sup>a</sup>

( [[#9.6.1.1|Section 9.6.1.1]] )

| Δ (mm)

| –13.8

[–31.4 to 3.8]

(-13.9%)

| 7.3

[–2.4 to 16.9]

(7.7%)

| 7.8

[3.3 to 12.2]

(10.9%)

| 7.2

[3.8 to 10.6]

(16.6%)

| –12.9

[–45.8 to 20.0]

(–7.8%)

|-
| mm yr <sup>–1</sup>

| –0.15

[–0.35 to 0.04]

| 0.15

[–0.05 to 0.36]

| 0.31

[0.13 to 0.49]

| 0.60

[0.32 to 0.88]

| –0.11

[–0.39 to 0.17]

|-
|
|-
| rowspan="2"| '''Sum of observed contributions'''

| Δ (mm)

| '''99.0'''

[63.0 to 135.1]

| '''94.2'''

[71.5 to 117.0]

| '''71.2'''

[60.2 to 82.3]

| '''43.4'''

[34.5 to 52.2]

| '''164.6'''

[116.9 to 212.4]

|-
| mm yr <sup>–1</sup>

| '''1.11'''

[0.71 to 1.52]

| '''2.00'''

[1.52 to 2.49]

| '''2.85'''

[2.41 to 3.29]

| '''3.61'''

[2.88 to 4.35]

| '''1.41'''

[1.00 to 1.82]

|-
| rowspan="2"| '''Observed GMSL change'''

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] )

| Δ (mm)

| '''120.1''' <sup>T</sup>

[69.3 to 170.8]

| '''109.6''' <sup>T&amp;A</sup>

[72.8 to 146.4]

| '''81.2''' <sup>A</sup>

[72.1 to 90.2]

| '''44.3''' <sup>A</sup>

[38.6 to 50.0]

| '''201.9''' <sup>T&amp;A</sup>

[150.3 to 253.5]

|-
| mm yr <sup>–1</sup>

| '''1.35''' <sup>T</sup>

[0.78 to 1.92]

| '''2.33''' <sup>T&amp;A</sup>

[1.55 to 3.12]

| '''3.25''' <sup>A</sup>

[2.88 to 3.61]

| '''3.69''' <sup>A</sup>

[3.21 to 4.17]

| '''1.73''' <sup>T&amp;A</sup>

[1.28 to 2.17]

|}

<sup>T, A</sup> and <sup>T&amp;A</sup> indicate assessments based on tide gauge reconstructions (T), satellite altimetry (A), or a combination of both (T&amp;A). The assessment uses tide gauge reconstructions before 1993 and satellite altimetry after 1993.

<sup>a</sup> For the periods 1971–2018, 1993–2018, 2006–2018 and 1901–2018 the [[#Cáceres--2020|Cáceres et al. (2020)]] linear trends are based on the period up to 2016.

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<span id="regional-sea-level-change-in-the-satellite-era"></span>
==== 9.6.1.3 Regional Sea Level Change in the Satellite Era ====

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Regional sea level changes are resolved by both tide gauge and satellite altimetry observations ( [[#Hamlington--2020a|Hamlington et al., 2020a]] ). Altimeters have the advantage of quasi-global coverage but are limited to a period (1993–present) in which the forced trend response is just emerging on regional scales ( [[#9.6.1.4|Section 9.6.1.4]] ). An analysis of the local altimetry error budget to estimate 90% confidence intervals on regional sea level trends and accelerations reports that 98% of the ocean surface has experienced significant sea level rise over the satellite era ( [[#Prandi--2021|Prandi et al., 2021]] ). The same study finds that sea level accelerations display a less uniform pattern, with an east–west dipole in the Pacific, a north–south dipole in the Southern Ocean and in the North Atlantic, and 85% of the ocean surface experiencing significant sea level acceleration or deceleration, above instrumental and post-processing noise. Longer records are available from tide gauges, albeit with variable coverage by basin. Regional departures from GMSL rise are primarily driven by ocean transport divergences that result from wind stress anomalies and spatial variability in atmospheric heat and freshwater fluxes ( [[#9.2.4|Section 9.2.4]] ).

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) noted the occurrence of large multiannual sea level variations in the Pacific, associated with the Pacific Decadal Oscillation (PDO) in particular, and involving the El Niño Southern Oscillation (ENSO), North Pacific Gyre Oscillation (NPGO) and Indian Ocean Dipole (IOD; Annex IV; [[#Royston--2018|Royston et al., 2018]] ; [[#Hamlington--2020b|Hamlington et al., 2020b]] ). There was intensified sea level rise during the 1990s and 2000s, with 10-year trends exceeding 20 mm yr <sup>–1</sup> in the western tropical Pacific Ocean, while sea level trends were negative on the North American west coast. During the 2010s, the situation reversed, with western Pacific sea level falling at more than 10 mm yr <sup>–1</sup> ( [[#Hamlington--2020b|Hamlington et al., 2020b]] ). For the Atlantic Ocean, SROCC described regional sea level variability as being driven primarily by wind and heat flux variations associated with the North Atlantic Oscillation (NAO) and heat transport changes associated with Atlantic Meridional Overturning Circulation (AMOC) variability ''.'' During periods of subpolar North Atlantic warming, winds along the European coast are predominantly from the south and may communicate steric anomalies onto the continental shelf, driving regional sea level rise, with the reverse during periods of cooling ( [[#Chafik--2019|Chafik et al., 2019]] ). High rates of sea level rise in the North Indian Ocean are accompanied by a weakening summer South Asian monsoon circulation ( [[#Swapna--2017|Swapna et al., 2017]] ).

The Arctic ocean is typically excluded from global sea level studies, owing to the uncertainties associated with resolving sea level in ice-covered regions, strong variations in gravitational, rotational, and deformational (GRD) effects, and uncertain glacial isostatic adjustment (GIA) estimates (Box 9.1). Spanning 1991–2018, a ''very likely'' sea level rise of 1.16–1.81 mm yr <sup>–1</sup> is observed ( [[#Rose--2019|Rose et al., 2019]] ). Since SROCC, the forced response in regional sea level varies in time with the relative influence of different forcing agents ( [[#Fasullo--2020|Fasullo et al., 2020]] ).

The SROCC estimated regional sea level changes from combinations of the various contributions to sea level change from CMIP5 climate model outputs, allowing comparison with satellite altimeter and tide gauge observations. Closure of the regional sea level budget is complicated by the fact that regional sea level variability is larger than GMSL variability. Also, there are more processes that need to be considered, such as vertical land movement and ocean dynamical changes (Box 9.1). A number of observation-based studies have focused on specific areas, such as the Mediterranean ( [[#García--2006|García et al., 2006]] ), the South China Sea ( [[#Feng--2012|Feng et al., 2012]] ), the east coast of the USA ( [[#Frederikse--2017|Frederikse et al., 2017]] ; [[#Piecuch--2018|Piecuch et al., 2018]] ), the North Atlantic basin ( [[#Kleinherenbrink--2016|Kleinherenbrink et al., 2016]] ) and the north-western European continental shelf seas ( [[#Frederikse--2016|Frederikse et al., 2016]] ). Studies using tide gauge data and observation-based estimates of the contributions find that, while local agreement is not yet possible, the observational sea level budget can be closed on a basin scale ( [[#Slangen--2014b|Slangen et al., 2014b]] ; [[#Frederikse--2016|Frederikse et al., 2016]] , 2018, 2020b). A budget analysis for the GRACE era found that the budget closes in some, but not all, coastal regions: substantial parts of the sea level change signal in the North Atlantic could not be explained by steric or barystatic changes ( [[#Rietbroek--2016|Rietbroek et al., 2016]] ). This is in agreement with other work comparing climate model estimates to 20th-century tide gauge observations ( [[#Meyssignac--2017|Meyssignac et al., 2017]] ), where the majority of local spatial variability is determined by the ocean dynamic component. Vertical land movement is another major cause of local spatial variability in sea level change and, for instance, relevant for oceanic islands ( [[#Forbes--2013|Forbes et al., 2013]] ; [[#Martínez-Asensio--2019|Martínez-Asensio et al., 2019]] ). In summary, the regional sea level budget, using either observations or models, can currently only be closed on basin scales ( ''medium confidence'' ), with large uncertainties remaining on smaller scales ''.''

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<span id="attribution-and-time-of-emergence-of-regional-sea-level-change"></span>

==== 9.6.1.4 Attribution and Time of Emergence of Regional Sea Level Change ====

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The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) attributed anthropogenic forcing to be the dominant cause of GMSL rise since 1970 (see also [[IPCC:Wg1:Chapter:Chapter-3#3.5.3.2|Section 3.5.3.2]] ), but detection and attribution (Cross-Working Group Box: Attribution in Chapter 1) of 20th century externally forced regional sea level changes is more challenging, as regional variability is larger ( [[#9.6.1.3|Section 9.6.1.3]] ), and therefore the signal-to-noise ratio is smaller ( [[#Richter--2014|Richter and Marzeion, 2014]] ; [[#Monselesan--2015|Monselesan et al., 2015]] ; [[#Palanisamy--2015|Palanisamy et al., 2015]] ). Whereas SROCC assessed with ''high confidence'' that GMSL rise is attributable to anthropogenic greenhouse gas emissions, they assessed with ''medium confidence'' that the regional anomalies in ocean basins are a combination of the response to anthropogenic greenhouse gas emissions and internal variability.

The simulated ocean dynamic and thermosteric response to external forcings during 1861–2005 is only larger than simulated internal variability in the Southern Ocean and North Pacific on a 1° grid ( [[#Slangen--2015|Slangen et al., 2015]] ). However, on spatial scales exceeding 2000 km, a detectable signal is revealed in the last 45 years in 63% of the global ocean area ( [[#Richter--2017|Richter et al., 2017]] ). The thermosteric change in the upper 700 m in the period 1970–2005 shows similar observed and simulated forced geographical patterns, and anthropogenic forcing accounts for part (North Atlantic, 65%) or all (tropical Pacific, Southern Ocean) of the observed regional mean ( [[#Marcos--2014|Marcos and Amores, 2014]] ). The influences of greenhouse gases and anthropogenic aerosols can be partially distinguished by considering geographical or vertical ocean temperature variations ( [[#Slangen--2015|Slangen et al., 2015]] ; [[#Bilbao--2019|Bilbao et al., 2019]] ; [[#Fasullo--2020|Fasullo et al., 2020]] ). Zonal-mean forced ocean dynamic sea level change alone is not detectable but, using spatial correlation, the global geographical pattern during the altimeter period is detectable in sea level trends (Fasullo and Nerem, 2018). This patternmay already or will soon be detectable in individual years, based on an analysis of CMIP5 climate model simulations ( [[#Bilbao--2015|Bilbao et al., 2015]] ). Anthropogenic forcing, dominated by greenhouse gases, has strengthened the meridional sea level gradient in the Southern Ocean since the 1960s ( [[#Slangen--2015|Slangen et al., 2015]] ; [[#Bilbao--2019|Bilbao et al., 2019]] ; [[#Fasullo--2020|Fasullo et al., 2020]] ). New evidence finds that observed zonal-mean total sea level trends during 1993–2018 in all basins are inconsistent with unforced variability alone, but are consistent with the modelled response to external forcing ( [[#Richter--2020|Richter et al., 2020]] ).

A region that has been studied intensely in the context of sea level detection and attribution is the tropical Pacific. Observed sea level trends in the tropical Pacific show a PDO-like (Annex IV) east–west dipole (with a greater rate of rise in the west, see [[#9.6.1.3|Section 9.6.1.3]] ). This dipole does not occur in CMIP5 simulations with the magnitude and duration that was observed in the 1990s and 2000s, neither in response to historical forcing, nor as internal variability after removing the variability associated with the PDO ( [[#Bilbao--2015|Bilbao et al., 2015]] ). [[#Hamlington--2014|Hamlington et al. (2014)]] did obtain a residual trend pattern for 1993–2010 in the tropical Pacific that may link to anthropogenic warming of the tropical Indian Ocean. Allowing for PDO and ENSO variations, ( [[#Royston--2018|Royston et al., 2018]] ) describe patches of the Pacific Ocean where the sea level trend for 1993–2015 is distinguishable from temporally correlated noise. The acceleration in eastern Pacific sea level rise is largely accounted for by variations resembling PDO and ENSO ( [[#Hamlington--2020a|Hamlington et al., 2020a]] ).

In the future, the anthropogenic signal in regional sea level change from ocean density and dynamics is projected to emerge first in regions with relatively small internal variability, such as the tropical Atlantic Ocean and the tropical Indian Ocean ( [[#Jordà--2014|Jordà, 2014]] ; Lyuet al., 2014; [[#Richter--2014|Richter and Marzeion, 2014]] ; [[#Bilbao--2015|Bilbao et al., 2015]] ). The signal is projected to emerge over 50% of the ocean area by the 2040s ( [[#Lyu--2014|Lyu et al., 2014]] ), but in regions where variability is large and projected changes are small, such as the Southern Ocean, the signal will not emerge before late in the century. Adding the projected sea level change from land ice mass loss and groundwater extraction strengthens and modifies the forced signal, leading to times of emergence 10 to 20 years earlier in most parts of the ocean, except in regions close to sources of mass loss, with emergence over 50% of the ocean area by 2020, and nearly everywhere by 2100 ( ''medium confidence'' ) ( [[#Lyu--2014|Lyu et al., 2014]] ; [[#Richter--2017|Richter et al., 2017]] ).

In summary, detection of forced regional changes for some ocean areas in recent decades is possible ( ''medium confidence'' ), but attribution of regional sea level change to forcings over longer periods (20th century) and for all ocean basins is not yet possible.

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'''Cross-Chapter Box 9.1 | Global Energy Inventory and Sea Level Budget'''

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'''Coordinators:''' Matthew D. Palmer (United Kingdom), Aimée B.A. Slangen (The Netherlands)

'''Contributors:''' Guðfinna Aðalgeirsdóttir (Iceland), Fábio Boeira Dias (Finland/Brazil), Catia M. Domingues (Australia, United Kingdom/Brazil), Gerhard Krinner (France/Germany, France), Johannes Quaas (Germany), Lucas Ruiz (Argentina)

Increased atmospheric greenhouse gas emissions since the 19th century have led to a net positive radiative forcing of Earth’s climate (Sections [[IPCC:Wg1:Chapter:Chapter-2#2.2|2.2]] and [[IPCC:Wg1:Chapter:Chapter-7#7.3|7.3]] ) and a corresponding accumulation of energy in the Earth system. Quantification of this energy gain is essential to our understanding of observed climate change, and for estimates of climate sensitivity ( [[IPCC:Wg1:Chapter:Chapter-7#7.5|Section 7.5]] ). The global energy inventory is closely linked to our understanding of observed global sea level change, through the energy associated with loss of land-based ice and the effect of thermal expansion associated with ocean warming (Box 9.1, Sections 2.3.3.1 and 9.6.1; Table 9.5).

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[[File:847e2fce28810a2743bd4ce868abd493 IPCC_AR6_WGI_CCBox_9_1_Figure_1.png]]

'''Cross-Chapter 9.1,'''

'''Figure 1 |'''

'''Global Energy Inventory and Sea Level Budget. (a)''' Observed changes in the global energy inventory for 1971–2018 (shaded time series) with component contributions as indicated in the figure legend. Earth System Heating for the whole period and associated uncertainty is indicated to the right of the plot (red bar = central estimate; shading = ''very likely'' range); '''(b)''' Observed changes in components of global mean sea level for 1971–2018 (shaded time series) as indicated in the figure legend. Observed global mean sea level change from tide gauge reconstructions (1971–1993) and satellite altimeter measurements (1993–2018) is shown for comparison (dashed line) as a three-year running mean to reduce sampling noise. Closure of the global sea level budget for the whole period is indicated to the right of the plot (red bar = component sum central estimate; red shading = ''very likely'' range; black bar = total sea level central estimate; grey shading = ''very likely'' range). Full details of the datasets and methods used are available in Annex I. Further details on energy and sea level components are reported in Table 7.1 and Table 9.5.

The Earth system gained substantial energy over the period 1971–2018 ( ''high confidence'' ), with an assessed ''very likely'' range of 325–546 ZJ or 0.43–0.72 W m <sup>–2</sup> expressed per unit area of the Earth’s surface (Cross-Chapter Box 9.1, Figure 1a; [[IPCC:Wg1:Chapter:Chapter-7#7.2|Section 7.2]] , Box 7.2). Ocean warming dominates the energy inventory change ( ''high confidence'' ), accounting for 91% of the observed energy increase for the period 1971–2018, with upper-ocean warming (0–700 m) accounting for 56% ( [[IPCC:Wg1:Chapter:Chapter-7#7.2|Section 7.2]] ). Much smaller amounts went into melting of ice (3%) and heating of the land (5%) and atmosphere (1%). Overall, the percentage contributions are similar to those reported in IPCC’s Fifth Assessment Report (AR5) for the period 1971–2010 ( [[#Rhein--2013|Rhein et al., 2013]] ).

The observed global mean sea level (GMSL) budget is assessed through comparison of the sum of individual components of GMSL change with independent observations of total GMSL change from tide gauge and satellite altimeter observations (Cross-Chapter Box 9.1, Figure 1b; Sections 2.3.3 and 9.6.1 and Table 9.5). The assessed sum of the observed components indicates that GMSL ''very likely'' increased by 72 mm to 117 mm over the period 1971–2018 (Table 9.5), with the largest contributions from ocean thermal expansion (50%) and melting of ice sheets and glaciers (42%). The assessed total GMSL change ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3|Section 2.3.3]] ) for the period 1971–2018 has a ''very likely'' range of 73–146 mm and, as a result, the sea level budget is closed for this period (Cross-Chapter Box 9.1, Figure 1b; [[#9.6.1|Section 9.6.1]] , Table 9.5).

The sea level budget closure demonstrates improved quantification of the processes of observed GMSL change for this period relative to previous IPCC assessments ( [[#Church--2013b|Church et al., 2013b]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). A related assessment presented in [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] demonstrates closure of the global energy budget ( ''high confidence'' ) (Box 7.2) and strengthens the confidence in scientific understanding of both of these key aspects of climate change.

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<span id="paleo-context-of-global-and-regional-sea-level-change"></span>
=== 9.6.2 Paleo Context of Global and Regional Sea Level Change ===

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As SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) noted, paleo sea level records provide information on past ice-sheet changes, and process-based ice-sheet models of past warm periods inform equilibrium responses. However, given uncertainties in paleo sea level and polar paleoclimate, and limited temporal resolution of paleo sea level records, there is ''low confidence'' in the utility of paleo sea level records for quantitatively informing near-term GMSL change. Nonetheless, the paleorecord does contextualize sea level and can test projection models (see also FAQ 1.3). Proxy constraints on GMSL and global ice volume are assessed in Sections 2.3.2.4. and 2.3.3.3 (see also FAQ 9.1). This section updates prior assessments of drivers of past GMSL changes and climatically coherent areas of relative sea level (RSL) variability. GMSL changes are framed in terms of global mean surface temperature (GMST) but noting that amplified high-latitude warming is a robust equilibrium response to elevated CO <sub>2</sub> ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ): polar air temperatures during past warm periods were up to twice the GMST changes shown in Table 9.6. The SROCC assessment that past multi-metre sea level changes have resulted from significant ice-sheet changes beyond those presently observed is confirmed ( ''very high confidence'' ).

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'''Table 9.6''' '''|''' '''Reference ranges of age, global mean surface temperature, atmospheric carbon dioxide (CO''' <sub>2</sub> ''') concentration, and global mean sea level (GMSL) for the paleo periods discussed in this chapter.'''

{| class="wikitable"
|-
| '''Paleo Period'''

| '''Years'''

'''Cross-Chapter Box 2.1'''

| '''GMST relative to 1850–1900'''

'''Section 2.3.1.1'''

| '''CO''' <sub>2</sub>

'''Sections 2.2.3.1 and 2.2.3.2'''

| '''Global Mean Sea Level (GMSL)'''

'''Section 2.3.3.3'''

|-
| '''Early Eocene Climatic Optimum (EECO)'''

| 53–49 Ma

| +10°C to +18°C

| 1150 to 2500 ppm

| +70 to +76 m

|-
| '''Mid-Pliocene Warm Period (MPWP)'''

| 3.3–3.0 Ma

| +2.5°C to +4°C

| 360 to 420 ppm

| +5 to +25 m

|-
| '''Marine Isotope Stage (MIS) 11'''

| about 424–395 ka

| 0.5°C ± 1.6°C <sup>a</sup>

| 265 to 286 ppm

| +6 to +13 m

|-
| '''Last Interglacial (LIG)'''

| about 129–116 ka

| +0.5°C to +1.5°C

| 266 to 282 ppm

| +5 to +10 m

|-
| '''Last Glacial Maximum (LGM)'''

| 21–19 ka

| –5°C to –7°C

| 188 to 194 ppm

| –125 to –134 m

|-
| '''Last Deglacial Transition'''

| 18–11 ka

| n/a

| 193 to 271 ppm

| –120 to –50 m

|-
| '''Early Holocene'''

| 11.65–6.5 ka

| n/a

| 250 to 268 ppm

| –50 to –3.5 m

|-
| '''Mid-Holocene'''

| 6.5–5.5 ka

| +0.2°C to +1.0°C

| 260 to 268 ppm

| –3.5 to +0.5 m

|-
| '''Last Millennium'''

| 850–1850 CE

| –0.14°C to +0.24°C

| 278 to 285 ppm

| –0.05 to +0.03 m

|}

<sup>a</sup> Based on one study ( [[#Irvalı--2020|Irvalı et al., 2020]] ) relative to SST values around year 2000.

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<span id="mid-pliocene-warm-period"></span>
==== 9.6.2.1 Mid-Pliocene Warm Period ====

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During the mid-Pliocene Warm Period (MPWP), GMST was 2.5°C–4°C warmer than 1850–1900 ( ''medium confidence'' ) and GMSL was between 5 and 25 m higher than today ( ''medium confidence'' ) (Table 9.6 and [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ). The AR5 ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ) concluded that ice-sheet models consistently produce near-complete deglaciation of the Greenland and West Antarctic ice sheets, and multi-meter loss of the East Antarctic Ice Sheet (EAIS) in response to MPWP climate conditions. Studies since AR5 have yielded a consistent but broader range, due in part to larger ensembles exploring more parameters ( [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#Yan--2016|Yan et al., 2016]] ; [[#DeConto--2021|DeConto et al., 2021]] ). Partly on the basis of these studies, SROCC proposed a ‘plausible’ upper bound on GMSL of 25 m ( ''low confidence'' ) with evidence suggesting an Antarctic contribution of anywhere between 5.4 and 17.8 m.

The MPWP climate had substantial polar amplification, up to 8°C above pre-industrial levels in Arctic Russia ( [[IPCC:Wg1:Chapter:Chapter-7#7.4.4.1|Section 7.4.4.1]] ; [[#Fischer--2018|Fischer et al., 2018]] ). Ice-sheet model simulations indicate that Northern Hemisphere glaciation was limited to high-elevation regions in eastern and southern Greenland ( ''medium confidence'' ) (Figure 9.17; [[#De%20Schepper--2014|De Schepper et al., 2014]] ; [[#Yan--2014|Yan et al., 2014]] ; [[#Koenig--2015|Koenig et al., 2015]] ; [[#Dowsett--2016|Dowsett et al., 2016]] ; [[#Berends--2019|Berends et al., 2019]] ) with Northern Hemisphere glaciation only becoming more widespread from the (cooler) late Pliocene ( [[#Bachem--2017|Bachem et al., 2017]] ; [[#Blake-Mizen--2019|Blake-Mizen et al., 2019]] ; [[#Knutz--2019|Knutz et al., 2019]] ; [[#Sánchez-Montes--2020|Sánchez-Montes et al., 2020]] ). Southern Hemisphere glaciation was characterized by an Antarctic Ice Sheet (AIS) reduced in volume from the present ( ''medium confidence'' ) (Figure 9.18; [[#Dowsett--2016|Dowsett et al., 2016]] ; [[#Berends--2019|Berends et al., 2019]] ; [[#Grant--2019|Grant et al., 2019]] ; [[#Miller--2020|Miller et al., 2020]] ) with mountain ice fields in the Andes of South America ( [[#De%20Schepper--2014|De Schepper et al., 2014]] ). Ice-sheet models are inconsistent in the magnitude of the sea level contribution from Antarctica ( [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#Yan--2016|Yan et al., 2016]] ; [[#Golledge--2017b|Golledge et al., 2017b]] ; [[#Berends--2019|Berends et al., 2019]] ; [[#DeConto--2021|DeConto et al., 2021]] ) but near-field sedimentological reconstructions support precessionally modulated and eccentricity-paced multi-metre sea level contributions from the Wilkes Subglacial Basin over 3–5 kyr ( [[#Patterson--2014|Patterson et al., 2014]] ; [[#Bertram--2018|Bertram et al., 2018]] ). Insummary, under a past warming level of around 2.5°C–4°C, ice sheets in both hemispheres were reduced in extent compared to present ( ''high confidence'' ). Proxy-based evidence ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ) combined with numerical modelling indicates that, on millennial time scales, the GMSL contribution arising from ice sheets was &gt;5 m ( ''high confidence'' ) or &gt;10 m ( ''medium confidence'' ) (Figures 9.17 and 9.18; [[#Moucha--2017|Moucha and Ruetenik, 2017]] ; [[#Berends--2019|Berends et al., 2019]] ; [[#Dumitru--2019|Dumitru et al., 2019]] ).

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<span id="marine-isotope-stage-11"></span>
==== 9.6.2.2 Marine Isotope Stage 11 ====

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The SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) noted that Greenland may have been ice-free for extensive periods during Pleistocene interglaciations, implying a high sensitivity of the Greenland Ice Sheet to warming levels close to present day. The AR5 ( [[#Church--2013b|Church et al., 2013b]] ) assigned ''medium confidence'' to a Marine Isotope Stage 11 (MIS 11) GMSL of 6–15 m above present, requiring a loss of much of the Greenland and West Antarctic ice sheets, and a possible contribution from East Antarctica. High-resolution multi-proxy sea surface temperature reconstructions and climate model simulations concur that MIS 11 was an extremely long interglacial that exhibited positive annual at 0.5°C ± 1.6 °C ( [[#Irvalı--2020|Irvalı et al., 2020]] ) and summer at 2.1°C–3.4 °C ( [[#Robinson--2017|Robinson et al., 2017]] ) temperature anomalies ( [[#de%20Wet--2016|de Wet et al., 2016]] ). The GMSL was 6–13 m above present ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ). The Greenland Ice Sheet lost 4.5–6 m ( [[#Reyes--2014|Reyes et al., 2014]] ) or about 6.1 m (3.9–7 m, 95% confidence) sea level equivalent (SLE) by about 7 kyr after peak summer warmth ( [[#Robinson--2017|Robinson et al., 2017]] ), with marine-based ice from AIS ( [[#Blackburn--2020|Blackburn et al., 2020]] ) contributing 6.4–8.8 m SLE at this time ( [[#Mas%20e%20Braga--2021|Mas e Braga et al., 2021]] ). Agreement between GMSL and ice-sheet reconstructions gives ''high confidence'' in identifying a high sensitivity of both ice sheets to the protracted duration of thermal forcing, even at low warming levels ( [[#Reyes--2014|Reyes et al., 2014]] ; [[#Robinson--2017|Robinson et al., 2017]] ; [[#Irvalı--2020|Irvalı et al., 2020]] ; [[#Mas%20e%20Braga--2021|Mas e Braga et al., 2021]] ). Modelled mean mass loss rates for the Greenland Ice Sheet of 0.4 m kyr <sup>–1</sup> during MIS 11 ( [[#Robinson--2017|Robinson et al., 2017]] ) are indistinguishable from recent mass loss rates averaged over 1992–2018 ( [[#9.4.1.1|Section 9.4.1.1]] ). In summary, geological reconstructions and numerical simulations consistently show that past warming levels of &lt;2°C (GMST) are sufficient to trigger multi-metre mass loss from both the Greenland and Antarctic ice sheets if maintained for millennia ( ''high confidence'' ), in agreement with SROCC findings for comparable warming levels during MIS 5e, the Last Interglacial.

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<span id="last-interglacial"></span>
==== 9.6.2.3 Last Interglacial ====

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The AR5 found that the Last Interglacial (LIG) GMSL was &gt;5 m ( ''very high confidence'' ) but &lt;10 m ( ''high confidence'' ). Their best estimate of 6 m was based on two studies ( [[#Kopp--2009|Kopp et al., 2009]] ; [[#Dutton--2012|Dutton and Lambeck, 2012]] ). The SROCC concluded that, during the LIG, Greenland’s contribution to the GMSL highstand (the highest sea levels during the LIG) of 6–9 m increased gradually, whereas the Antarctic contribution occurred early, from about 129 ka. Due to widely varying reconstructions from model studies (Greenland) and the paucity of direct evidence of ice-sheet change (Antarctic), the magnitude of sea level contributions from both ice sheets was assigned ''low confidence.''

Since AR5, information has improved about the LIG, when GMST was about 0.5°C–1.5°C above 1850–1900 ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1|Section 2.3.1.1]] ). The LIG had higher summer insolation than present and polar amplified sea surface and surface air temperatures that reached &gt;1°C–4°C and &gt;3°C–11 °C in the Arctic respectively ( [[#Landais--2016|Landais et al., 2016]] ; [[#Capron--2017|Capron et al., 2017]] ; [[#Fischer--2018|Fischer et al., 2018]] ). Mean annual and maximum summer ocean temperatures peaked early (129–125 ka) in the interglacial period, reaching 1.1 ± 0.3 °C above the modern global mean ( [[#Shackleton--2020|Shackleton et al., 2020]] ) with summer anomalies of 2.5°C–3.5 °C in the Southern Ocean ( [[#Bianchi--2002|Bianchi and Gersonde, 2002]] ) and spatially variable timing ( [[#Chadwick--2020|Chadwick et al., 2020]] ). It is ''virtually certain'' that GMSL was higher than today, ''likely'' by 5–10 m ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ). Global mean thermal expansion peaked at about 0.9 ± 0.3 m early in the LIG (about 129 ka), declining to modern levels by about 127 ka ( [[#Shackleton--2020|Shackleton et al., 2020]] ). With no more than 0.3 ± 0.1 m of GMSL rise from glaciers ( [[#9.5.1|Section 9.5.1]] ), at most 1.0 ± 0.3 m of the GMSL rise originated from sources other than the polar ice sheets.

Recent LIG ice-sheet simulations agree that peak loss from the Greenland Ice Sheet occurred late (125–120 ka; [[#Goelzer--2016|Goelzer et al., 2016]] ; [[#Tabone--2018|Tabone et al., 2018]] ; [[#Plach--2019|Plach et al., 2019]] ) when Northern Hemisphere insolation was greater than at present ( ''medium confidence'' ) ( [[#Capron--2017|Capron et al., 2017]] ), consistent with inferences from marine sediment records ( [[#Hatfield--2016|Hatfield et al., 2016]] ; [[#Irvalı--2020|Irvalı et al., 2020]] ) and far-field GMSL indicators ( [[#Rohling--2019|Rohling et al., 2019]] ). Best estimates of the GMSL contribution from Greenland (Figure 9.17) differ between models: ≤1 m ( [[#Albrecht--2020|Albrecht et al., 2020]] ; [[#Clark--2020|Clark et al., 2020]] ), 1–2 m ( [[#Calov--2015|Calov et al., 2015]] ; [[#Goelzer--2016|Goelzer et al., 2016]] ; [[#Bradley--2018|Bradley et al., 2018]] ), up to 3 m ( [[#Tabone--2018|Tabone et al., 2018]] ; [[#Plach--2019|Plach et al., 2019]] ), and &gt;5 m ( [[#Yau--2016|Yau et al., 2016]] ). There is ''high confidence'' that the response time of the Greenland Ice Sheet to LIG warming was multi-millennial, and ''high confidence'' that it contributed to LIG GMSL change, but ''low agreement'' in the contribution magnitude.

Far-field GMSL records suggest that the AIS contributed to LIG sea level from 129.5–125 ka (Figure 9.18) but direct evidence is sparse. Thinning of part of the WAIS is interpreted from a 130–80 ka hiatus in the Patriot Hills horizontal ice core record ( [[#Turney--2020|Turney et al., 2020]] ). Marine sediment records suggest a dynamic response of the Wilkes Subglacial Basin (WSB) of the EAIS during this period, indicating a response time scale of 1000–2500 yr ( [[#Wilson--2018|Wilson et al., 2018]] ), consistent with modelling studies ( [[#Mengel--2014|Mengel and Levermann, 2014]] ; [[#Golledge--2017b|Golledge et al., 2017b]] ; [[#Sutter--2020|Sutter et al., 2020]] ). Isotopic changes in the Talos Dome ice core are inconsistent with local surface lowering, limiting retreat to 0.4–0.8 m SLE from this sector ( [[#Sutter--2020|Sutter et al., 2020]] ). Ice-sheet models forced with unmodified atmosphere–ocean models ( [[#Goelzer--2016|Goelzer et al., 2016]] ; [[#Clark--2020|Clark et al., 2020]] ) simulate 3–4.4 m SLE mass loss, primarily from the WAIS, with no retreat in WSB (e.g., Figure 9.18). Models forced with proxy-based or ad hoc LIG ocean temperature anomalies ( [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#Sutter--2016|Sutter et al., 2016]] ) indicate collapse of West Antarctica under 2°C–3°C ocean forcing yielding 3–7.5 m sea level contribution, but modest or no retreat in the WSB. Based on ''limited evidence'' and ''limited agreement'' between models, there is ''low confidence'' in both the magnitude and timing of LIG mass loss from the AIS.

In summary, paleo-environmental and modelling studies indicate that, under past warming of the level achieved during the LIG (ca. 0.5°C–1.5°C), it is ''likely'' that both the Greenland and Antarctic ice sheets responded dynamically over multiple millennia ( ''high confidence'' ).

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<span id="last-glacial-maximum"></span>
==== 9.6.2.4 Last Glacial Maximum ====

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At the Last Glacial Maximum (LGM) geological proxies and GIA models indicate that GMSL was 125–134 m below present ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] and Figures 9.17 and 9.18). New studies have not changed AR5’s conclusions regarding the size or timing of the LGM and last glacial termination, but have further examined the LGM sea level budget. Based on a synthesis of multiple prior studies, ( [[#Simms--2019|Simms et al., 2019]] ) estimated central 67% probability contributions to the LGM lowstand (i.e., lowest levels during the LGM) of 76 ± 7 m from the North American Laurentide Ice Sheet, 18 ± 5 m from the Eurasian Ice Sheet, 10 ± 2 m from Antarctica, 4 ± 1 m from Greenland, 5.5 ± 0.5 m from glaciers, and 2.4 ± 0.3 m due to an increase in ocean density. Of the residual, up to about 1.4 m may be ascribed to groundwater, leaving a shortfall of 16 ± 10 m yet to be allocated among land ice reservoirs or lakes.

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<span id="last-deglacial-transition-meltwater-pulse-1a"></span>
==== 9.6.2.5 Last Deglacial Transition: Meltwater pulse 1A ====

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During Meltwater pulse 1A (MWP-1A), GMSL ''very likely'' ( ''medium confidence'' ) rose by 8–15 m ( [[#Liu--2016|Liu et al., 2016]] ). Consistent with AR5, the drivers of this rapid rise remain ambiguous. The spatial patterns of RSL change over this interval are inadequately observed to constrain the relative contributions of the North American and Antarctic ice sheets ( [[#Liu--2016|Liu et al., 2016]] ). Modelling studies of the North American Ice Sheet permit a 3–6 m ( [[#Gregoire--2016|Gregoire et al., 2016]] ) or 6–9 m contribution over the duration of MWP-1A ( [[#Tarasov--2012|Tarasov et al., 2012]] ). Sedimentological evidence ( [[#Weber--2014|Weber et al., 2014]] ; [[#Bart--2018|Bart et al., 2018]] ) provides near-field evidence for an Antarctic contribution, consistent with modelling studies ( [[#Golledge--2014|Golledge et al., 2014]] ; [[#Stuhne--2015|Stuhne and Peltier, 2015]] ), but does not constrain the magnitude of the contribution. A recent statistical analysis of Norwegian Sea and Arctic Ocean sediments suggests a 3–7 m contribution from the Eurasian Ice Sheet ( [[#Brendryen--2020|Brendryen et al., 2020]] ), a possibility not considered in AR5 or the meta-analysis of [[#Liu--2016|Liu et al. (2016)]] . In summary, MWP-1A appears to have been driven by a combination of melt in North America ( ''high confidence'' ), Eurasia ( ''low confidence'' ), and Antarctica ( ''low confidence'' ), but the budget is not closed.

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<span id="holocene"></span>
==== 9.6.2.6 Holocene ====

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Around half (50–60 m) of the GMSL rise since the LGM occurred during the early Holocene at a sustained rate of about 15 m kyr <sup>–1</sup> from around 11.4–8.2 ka ( [[#Lambeck--2014|Lambeck et al., 2014]] ), possibly punctuated by abrupt meltwater pulses ( [[#Smith--2011|Smith et al., 2011]] ; [[#Carlson--2012|Carlson and Clark, 2012]] ; [[#Törnqvist--2012|Törnqvist and Hijma, 2012]] ; [[#Harrison--2019|Harrison et al., 2019]] ). An abrupt (about 1.1 m) sea level rise around 8.2 ka was associated with drainage of the pro-glacial Agassiz and Ojibway lakes, attributed to accelerated melt from collapsing Laurentide Ice Sheet ice saddles ( [[#Matero--2017|Matero et al., 2017]] ). The Laurentide Ice Sheet provided the greatest contribution (27 m) to early Holocene GMSL ( [[#Peltier--2015|Peltier et al., 2015]] ; [[#Roy--2017|Roy and Peltier, 2017]] ), the Scandinavian Ice Sheet contributed about 2 m from the beginning of the Holocene until its demise by around 10.5 ka, ( [[#Cuzzone--2016|Cuzzone et al., 2016]] ), while the Barents Sea Ice Sheet contributed a small but unknown amount ( [[#Patton--2015|Patton et al., 2015]] , 2017; [[#Auriac--2016|Auriac et al., 2016]] ). The Greenland Ice Sheet contributed about 4 m, consistent with ice thinning rates inferred from the Camp Century ice core ( [[#Lecavalier--2017|Lecavalier et al., 2017]] ; [[#McFarlin--2018|McFarlin et al., 2018]] ). Recent estimates of Antarctic contributions during the early Holocene vary considerably from about 1.2 m to 8.5 m ( [[#Whitehouse--2012|Whitehouse et al., 2012]] ; [[#Ivins--2013|Ivins et al., 2013]] ; [[#Argus--2014|Argus et al., 2014]] ; [[#Briggs--2014|Briggs et al., 2014]] ; [[#Golledge--2014|Golledge et al., 2014]] ; [[#Pollard--2016|Pollard et al., 2016]] ; [[#Roy--2017|Roy and Peltier, 2017]] ; [[#Albrecht--2020|Albrecht et al., 2020]] ). In summary, the early Holocene was characterized by steadily rising GMSL as global ice sheets continued to retreat from their LGM extents. This steady rise was punctuated by abrupt pulses during episodes of rapid meltwater discharge.

In the middle Holocene, GMST peaked at 0.2°C–1.0°C higher than 1850–1900 temperature between 7 and 6 ka ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.1.2|Section 2.3.1.1.2]] ). GMSL rise slowed coincidently with final melting of the Laurentide ice sheet by 6.7 ± 0.4 ka ( [[#Ullman--2016|Ullman et al., 2016]] ), after which only Greenland and Antarctic ice sheets could have contributed significantly. At 6 ka, GMSL was –3.5 to +0.5 m ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ). Simulations of the Holocene Thermal Maximum give a Greenland Ice Sheet broadly consistent with geological reconstructions so, despite uncertainties regarding the timing of minimum ice-sheet volume and extent, there is ''medium confidence'' that minima were reached at different times in different areas during the period 8–3 ka BP ( [[#Larsen--2015|Larsen et al., 2015]] ; [[#Young--2015|Young and Briner, 2015]] ; [[#Briner--2016|Briner et al., 2016]] ). Geochronological and numerical modelling studies indicate that it is ''likely'' ( ''medium confidence'' ) that the period of smaller-than-present ice extent in all sectors of Greenland persisted for at least 2000 to 3000 years ( [[#Larsen--2015|Larsen et al., 2015]] ; [[#Young--2015|Young and Briner, 2015]] ; [[#Briner--2016|Briner et al., 2016]] ; [[#Nielsen--2018|Nielsen et al., 2018]] ). Based on ice-sheet modelling and carbon-14 ( <sup>14</sup> C) dating ( [[#Kingslake--2018|Kingslake et al., 2018]] ) suggested that West Antarctic grounding lines retreated prior to around 10 ka BP, followed by a readvance. Other studies from the same region conclude that retreat was fastest from 9–8 ka BP ( [[#Spector--2017|Spector et al., 2017]] ), or from 7.5–4.8 ka BP ( [[#Venturelli--2020|Venturelli et al., 2020]] ). Marine geological evidence indicates open marine conditions east of Ross Island by 8.6 ± 0.2 ka BP ( [[#McKay--2016|McKay et al., 2016]] ). In the western Weddell Sea, [[#Johnson--2019|Johnson et al. (2019)]] reported rapid glacier thinning from 7.5–6 ka BP. [[#Hein--2016|Hein et al. (2016)]] concluded that the fastest thinning further south took place from 6.5–3.5 ka BP, potentially contributing 1.4–2 m to GMSL. Geophysical data indicate stabilization or readvance in this area around 6 ± 2 ka BP ( [[#Wearing--2019|Wearing and Kingslake, 2019]] ). In coastal Dronning Maud Land (East Antarctica) rapid thinning occurred 9–5 ka BP ( [[#Kawamata--2020|Kawamata et al., 2020]] ), whereas glaciers in the Northern Antarctic Peninsula receded during the period 11–8 ka BP and readvanced to their maximal extents by 7–4 ka BP ( [[#Kaplan--2020|Kaplan et al., 2020]] ). In summary, higher-than-pre-industrial GMST during the mid-Holocene coincided with recession of the Greenland Ice Sheet to a smaller-than-present extent ( ''high confidence'' ). Multiple lines of evidence give ''high confidence'' that thinning or retreat in parts of Antarctica during the Holocene took place at different times in different places. However, limited data means there is only ''low confidence'' in whether or not the ice sheet as a whole was smaller than present during the mid-Holocene.

In summary, both proxies and model simulations indicate that GMSL changes during the early to mid-Holocene were the result of episodic pulses, due to drainage of meltwater lakes, superimposed on a trend of steady rise due to continued ice-sheet retreat ( ''high confidence'' ).

The combination of tide gauge observations and geological reconstructions indicates that a sustained increase of GMSL began between 1820–1860 and led to a 20th-century GMSL rise that was ''very likely'' ( ''high confidence'' ) faster than in any preceding century in the last 3000 years ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ). At a regional level, tide gauge and geological data from the North Atlantic and Australasia show inflections in RSL trends between 1895–1935, with an increase of 0.8 to 2.5 mm yr <sup>–1</sup> across the inflection ( [[#Gehrels--2013|Gehrels and Woodworth, 2013]] ). A statistical meta-analysis of globally distributed geological and tide gauge data ( [[#Kopp--2016|Kopp et al., 2016]] ) found that, in all 20 examined regions with geological records stretching back at least 2000 years, the rate of RSL rise in the 20th century was greater than the local average over 0–1700 CE. In four of the 20 regions, all in the North Atlantic (Connecticut, New Jersey, North Carolina, and Iceland), the 19th century rate was also greater than the 0–1700 CE average (90% confidence interval). In summary, rates of RSL rise exceeding the pre-industrial background rate of rise are apparent in parts of the North Atlantic in the 19th century ( ''medium confidence'' ), and in most of the world in the 20th century ( ''high confidence'' ).

<div id="9.6.3" class="h2-container"></div>

<span id="future-sea-level-changes"></span>
=== 9.6.3 Future Sea Level Changes ===

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This section first assesses sea level projections since AR5 ( [[#Church--2013b|Church et al., 2013b]] ) and including SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) based on Representative Concentration Pathways (RCPs; [[#9.6.3.1|Section 9.6.3.1]] ). Process-level assessments in sections 9.2.4, 9.4.1.3, 9.4.1.4, 9.4.2.5, 9.4.2.6 and 9.5.1.3 are synthesized ( [[#9.6.3.2|Section 9.6.3.2]] ) to produce new global mean and regional sea level projections based on the Shared Socio-economic Pathways up to 2150 ( [[#9.6.3.3|Section 9.6.3.3]] ) and on global warming levels up to 2100 ( [[#9.6.3.4|Section 9.6.3.4]] ). Long-term global mean sea level (GMSL) projections, both at 2300 and on multimillennial time scales, are also assessed ( [[#9.6.3.5|Section 9.6.3.5]] ).

Sections 9.6.3.3 and 9.6.3.4 present ''likely'' ranges of the new global mean sea levels, incorporating only processes in whose projections there is at least ''medium confidence'' , consistent with headline projections in AR5 and SROCC. As emphasized by SROCC, there is a substantial likelihood that sea level rise will be outside the ''likely'' range. As described in Box 1.1, since the definition of ‘ ''likely'' ’ refers to at least 66% probability, there may be as much as a 34% probability that the processes in which there is at least ''medium confidence'' will generate outcomes outside the ''likely'' range. Furthermore, additional processes in which there is ''low confidence'' [[#9.4.2.4|Section 9.4.2.4]] ; Box 9.4) may also contribute to sea level change. The presentation of ''likely'' sea level change (Tables 9.8–9.9 and in Figures 9.27, 9.29) is therefore accompanied by a ''low confidence'' range intended to reflect potential contributions from additional processes under high-emissions scenarios. The ''low confidence'' range incorporates ice-sheet projections based on Structured Expert Judgement (SEJ) – that is, a formal, calibrated method of combining quantified expert assessments that incorporates all potential processes – and projections from an AIS model that includes the marine ice cliff instability (a specific uncertain process not generally included in ice-sheet models; [[#9.4.2.4|Section 9.4.2.4]] ).

<div id="9.6.3.1" class="h3-container"></div>

<span id="global-mean-sea-level-projections-based-on-the-representative-concentration-pathways"></span>
==== 9.6.3.1 Global Mean Sea Level Projections Based on the Representative Concentration Pathways ====

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The AR5 ( [[#Church--2013b|Church et al., 2013b]] ) generated GMSL projections for the RCPs by combining information from CMIP5 climate models with glacier and ice-sheet surface mass balance (SMB) models and assessments of projected ice-sheet dynamic and land-water storage contributions ( [[#9.6.3.2|Section 9.6.3.2]] ). The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) updated AR5 projections based on a revised assessment of the AIS contribution to GMSL rise. The AR5 and SROCC employ a baseline period of 1986 to 2005, which is updated in this Report to a baseline period of 1995 to 2014 ( [[IPCC:Wg1:Chapter:Chapter-1#1.4.1|Section 1.4.1]] ). Between these two periods, GMSL rose by 3 cm, and this correction is applied to projections from previous reports to allow comparison (Table 9.8). Accounting for this shift, SROCC concludes that, with ''medium confidence,'' GMSL will rise between 0.40 (0.26–0.56, ''likely'' range) m (RCP2.6) and 0.81 (0.58–1.07 m, ''likely'' range) m (RCP8.5) by 2100 relative to 1995–2014. The AR5 and SROCC GMSL projections for the 2007–2018 period have been shown to be consistent with observed trends in GMSL and regional weighted mean tide gauges (J. [[#Wang--2021|]] [[#Wang--2021|Wang et al., 2021]] ).

Since AR5, a number of projections of GMSL rise have been published based on the RCPs ( [[#Kopp--2014|Kopp et al., 2014]] , 2017; [[#Slangen--2014b|Slangen et al., 2014b]] ; Grinsted et al., 2015; [[#Jackson--2016|Jackson et al., 2016]] ; [[#Mengel--2016|Mengel et al., 2016]] ; [[#Bakker--2017|Bakker et al., 2017]] ; [[#Bittermann--2017|Bittermann et al., 2017]] ; [[#Le%20Bars--2017|Le Bars et al., 2017]] ; [[#Nauels--2017|Nauels et al., 2017]] ; [[#Wong--2017|Wong et al., 2017]] ; [[#Goodwin--2018|Goodwin et al., 2018]] ; [[#Nicholls--2018|Nicholls et al., 2018]] ; [[#Le%20Cozannet--2019|Le Cozannet et al., 2019]] ; [[#Palmer--2020|Palmer et al., 2020]] ). See [[#Garner--2018|Garner et al. (2018)]] or a database (Tables 9.SM.5, 9.SM.6). Some studies also produced associated global sets of regional projections ( [[#Kopp--2014|Kopp et al., 2014]] , 2017; [[#Slangen--2014b|Slangen et al., 2014b]] ; [[#Le%20Cozannet--2019|Le Cozannet et al., 2019]] ; [[#Palmer--2020|Palmer et al., 2020]] ). Since SROCC ( [[#Le%20Cozannet--2019|Le Cozannet et al., 2019]] ) focused on the low end of the probability distribution of GMSL rise, [[#Palmer--2020|Palmer et al. (2020)]] extended projections beyond 2100 using a climate model emulator (Cross-Chapter Box 7.1), and [[#Horton--2020|Horton et al. (2020)]] conducted a survey of 106 sea level experts, providing additional context for interpreting sea level rise projections for 2100 and 2300.

As noted by SROCC, the largest differences between projections of GMSL in 2100 are due to the ice-sheet projection method, which generally fall into one of three categories: (i) projections from ice-sheet models that represent processes where there is at least ''medium confidence'' (Sections 9.4.1.2 and 9.4.2.2); (ii) projections from an Antarctic ice-sheet model that incorporates the marine ice cliff instability (MICI; [[#9.4.2.4|Section 9.4.2.4]] ; [[#DeConto--2016|DeConto and Pollard, 2016]] ); or (iii) projections based on SEJ (Sections 9.4.1.3, 9.4.1.4, 9.4.2.5 and 9.4.2.6; [[#Bamber--2013|Bamber and Aspinall, 2013]] ; [[#Bamber--2019|Bamber et al., 2019]] ). ''Low confidence'' is ascribed to projections incorporating MICI because there is ''low confidence'' in the current ability to quantify MICI ( [[#9.4.2.4|Section 9.4.2.4]] ). ''Low confidence'' is also ascribed to projections based on SEJ, because individual experts participating in the SEJ study may have incorporated processes in whose quantification there is ''low confidence'' , and the experts’ reasoning has not been examined in detail. In general, the range of GMSL projections based on ice-sheet models not incorporating MICI overlaps with, but is lower than, projections incorporating MICI or employing SEJ (Figure 9.25).

<div id="_idContainer065" class="Basic-Text-Frame"></div>

[[File:98d08c28f372fd0e31a76d38b0573521 IPCC_AR6_WGI_Figure_9_25.png]]

'''Figure 9.25''' '''|''' '''Literature global mean sea level (GMSL) projections (m) for 2050 (left) and 2100 (right) since 199''' '''5–2''' '''014, for RCP8.5/SSP5-8.5 (top set), RCP4.5/SSP2-4.5 (middle set), and RCP2.6/SSP1-2.6 (bottom set).''' Projections are standardized to account for minor differences in time periods. Thick bars span from the 17th–83rd percentile projections, and thin bars span the 5th–95th percentile projections. The different assessments of ice-sheet contributions are indicated by ‘MED’ (ice-sheet projections include only processes in whose quantification there is ''medium confidence'' ), ‘MICI’ (ice-sheet projections which incorporate marine ice cliff instability), and ‘SEJ’ (structured expert judgement) to assess the central range of the ice-sheet projection distributions. ‘Survey’ indicates the results of a 2020 survey of sea level experts on global mean sea level (GMSL) rise from all sources ( [[#Horton--2020|Horton et al., 2020]] ). Projection categories incorporating processes in which there is ''low confidence'' (MICI and SEJ) are lightly shaded. Dispersion among the different projections represents ''deep uncertainty'' , which arises as a result of ''low agreement'' regarding appropriate conceptual models describing ice-sheet behaviour and ''low agreement'' regarding probability distributions used to represent key uncertainties. Individual studies are shown in Tables 9.SM.5 and 9.SM.6. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

There is ''high'' ''agreement'' across published GMSL projections for 2050, and there is little sensitivity to emissions scenario (Figure 9.25, left panel). Up to 2050, projections are broadly consistent with extrapolation of the observed acceleration of GMSL rise (Sections 2.3.3.3, 9.6.1.1 and 9.6.1.2). Considering only projections incorporating ice-sheet processes in whose quantification there is at least ''medium confidence'' , the GMSL projections for 2050, across all emissions scenarios, fall between 0.1 and 0.4 m (5th–95th percentile range). Projections incorporating MICI or SEJ do not extend this range under RCP2.6 or RCP4.5 but do extend the upper part of the range to 0.6 m under RCP8.5. On the basis of these studies, we therefore have ''high confidence'' that GMSL in 2050 will be between 0.1 and 0.4 m higher than in 1995–2014 under low- and moderate-emissions scenarios, and between 0.1 and 0.6 m under high-emissions scenarios.

Conversely, there is ''low agreement'' across published GMSL projections for 2100, particularly for higher-emissions scenarios, as well as a higher degree of sensitivity to the choice of emissions scenario (Figure 9.25, right panel). Considering only projections representing processes in whose quantification there is at least ''medium'' ''confidence'' , the GMSL projections for 2100 fall between 0.2 and 1.0 m (5th–95th percentile range) under RCP2.6 and RCP4.5, and between 0.3 and 1.6 m under RCP8.5. Considering also projections incorporating MICI or SEJ ( ''low confidence'' ), the projections for 2100 fall between 0.2 and 1.0 m (5th–95th percentile range) under RCP2.6, 0.2, and 1.6 m under RCP4.5, and 0.4 and 2.4 m under RCP8.5. In summary, RCP-based projections published since AR5 show ''high agreement'' for 2050, but exhibit broad ranges and ''low agreement'' for 2100, particularly under RCP8.5.

<div id="9.6.3.2" class="h3-container"></div>

<span id="drivers-of-projected-sea-level-change"></span>
==== 9.6.3.2 Drivers of Projected Sea Level Change ====

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This section describes the choices made for the contributions to the updated global mean and regional sea level projections ( [[#9.6.3.3|Section 9.6.3.3]] ) based on assessments in this Report and compares the updated projections to AR5 ( [[#Church--2013b|Church et al., 2013b]] ) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) (Tables 9.7 and 9.8). Since there is no single model that can directly compute all of the contributions to sea level change (Box 9.1), the contributions to sea level are computed separately and then combined (Tables 9.8 and 9.9). For consistency with global surface air temperature (GSAT) projections ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.1.1|Section 4.3.1.1]] ), and assessment of equilibrium climate sensitivity (ECS) and transient climate response (TCR; [[IPCC:Wg1:Chapter:Chapter-7#7.5|Section 7.5]] ), temperature-dependent projections (thermal expansion, ice sheets, glaciers) are forced by GSAT projections from a two-layer energy budget emulator ( [[#Smith--2018|Smith et al., 2018]] ) that is calibrated to be consistent with the assessment of ECS and TCR (Box 7.1, Supplementary Material 7.SM.2). Throughout, ''likely'' ranges are assessed based on the combination of uncertainty in the GSAT distribution and uncertainty in the relationships between GSAT and changes to individual components. In general, 17th–83rd percentile results, incorporating both GSAT and sea level process uncertainty, are interpreted as ''likely'' ranges. This is distinct from the approach used by AR5, which interpreted the 5th–95th percentile range of CMIP5 projections, and therefore of GMSL projections driven by them, as ''likely'' ranges. The shift in interpretation is consistent with the use of the emulator for GSAT (Box 4.1, Cross-Chapter Box 7.1). ''Very likely'' ranges are not assessed because of the potential for processes in whose projections there is currently ''low confidence'' to substantially augment total projected GMSL change.

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'''Table''' '''9.7 |''' '''Methods used to project the drivers of global mean sea level (GMSL) and relative sea level (RSL) change in the Shared Socio-economic Pathway (SSP) and warming-level-based projections of GMSL, RSL and extreme sea level (ESL) change.''' Section numbers indicate location of primary assessment text.

{| class="wikitable"
|-
| '''Driver of Global Mean or Regional Sea Level change'''

| '''SROCC Projection Method'''

| '''AR6 Projection method'''

|-
| Thermal expansion ( [[#9.2.4.1|Section 9.2.4.1]] )

| CMIP5 ensemble drift-corrected ''zostoga'' , with surrogates derived from climate system heat content where not available

| Two-layer emulator with climate sensitivity calibrated to AR6 assessment (Supplementary Material 7.SM.2) and expansion coefficients calibrated to emulate CMIP6 models (Supplementary Material 9.SM.4.2 and 9.SM.4.3)

|-
| Greenland Ice Sheet (excluding peripheral glaciers) (Sections 9.4.1.3 and 9.4.1.4)

| ''Surface mass balance:''

scaled cubic polynomial fit to global mean surface temperature (GMST)

''Dynamics:''

Quadratic function of time, calibrated based on multi-model assessment

| ''Medium confidence'' processes up to 2100: Emulated Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) simulations (Box 9.3; [[#Edwards--2021|Edwards et al., 2021]] )

''Medium confidence'' processes after 2100: Parametric model fit to ISMIP6 simulations up to 2100, extrapolated based on either constant post-2100 rates or a quadratic interpolation to the multi-model assessed 2300 range (Supplementary Material 9.SM.4.4)

''Low confidence'' processes: Structured expert judgement ( [[#Bamber--2019|Bamber et al., 2019]] )

|-
| Antarctic Ice Sheet (excluding peripheral glaciers <sup>a</sup> ) (Sections 9.4.2.5 and 9.4.2.6)

| Multi-model assessment

| ''Medium confidence'' processes up to 2100: p-box including: (i) Emulated ISMIP6 simulations ( [[#Edwards--2021|Edwards et al., 2021]] ); and (ii) Linear Antarctic Response Model Intercomparison Project (LARMIP-2) simulations ( [[#Levermann--2020|Levermann et al., 2020]] ) augmented by AR5 surface mass balance model (Box 9.3)

''Medium confidence'' processes after 2100: p-box including: (i) AR5 parametric AIS model; and (ii) LARMIP-2 simulations augmented by AR5 surface mass balance model applied to CMIP6 models, with both methods extrapolated based on either constant post-2100 rates or a quadratic interpolation to the multi-model assessed 2300 range ( [[#9.6.3.2|Section 9.6.3.2]] )

''Low confidence'' processes: (i) Single-ice-sheet-model ensemble simulations incorporating marine ice cliff instability ( [[#DeConto--2021|DeConto et al., 2021]] ); and (ii) structured expert judgement ( [[#Bamber--2019|Bamber et al., 2019]] )

|-
| Glaciers (including peripheral glaciers) ( [[#9.5.1.3|Section 9.5.1.3]] )

| Power law function of integrated GMST fit to glacier models

| Up to 2100: Emulated GlacierMIP ( [[#Marzeion--2020|Marzeion et al., 2020]] ; [[#Edwards--2021|Edwards et al., 2021]] ) simulations (Box 9.3)

Beyond 2100: AR5 parametric model re-fit to GlacierMIP (Supplementary Material 9.SM.4.5; [[#Marzeion--2020|Marzeion et al., 2020]] )

|-
| Land-water storage ( [[#9.6.3.2|Section 9.6.3.2]] )

| ''Groundwater depletion:''

combination of: (i) continuation of early 21st-century trends; and (ii) land-surface hydrology models ( [[#Wada--2012|Wada et al., 2012]] )

''Water impoundment:''

combination of: (i) continuation of historical rate; and (ii) assumption of no net impoundment after 2010

| ''Groundwater depletion:''

Population/groundwater depletion relationship calibrated based on [[#Konikow--2011|Konikow (2011)]] and Wada et al. (2012, 2016)

''Water impoundment:''

Population/dam impoundment relationship calibrated based on [[#Chao--2008|Chao et al. (2008)]] , adjusted for new construction following [[#Hawley--2020|Hawley et al. (2020)]] for 2020 to 2040

|-
| Ocean dynamic sea level ( [[#9.2.4.2|Section 9.2.4.2]] )

| CMIP5 ensemble ''zos'' field after polynomial drift removal

| Distribution derived from CMIP6 ensemble ''zos'' field after linear drift removal (Supplementary Material 9.SM.4.2 and 9.SM.4.3)

|-
| Gravitational, rotational, and deformational effects ( [[#9.6.3.2|Section 9.6.3.2]] )

| colspan="2"| Sea level equation solver ( [[#Slangen--2014b|Slangen et al., 2014b]] ) driven by projections of ice-sheet, glacier, and land-water storage changes

|-
| Glacial isostatic adjustment and other drivers of vertical land motion [[#9.6.3.2|Section 9.6.3.2]] )

| Glacial Isostatic Adjustment model, with ice history from mean of the Australian National University (ANU) and ICE-5G reconstructions

| Spatio-temporal statistical model of tide gauge data (updated from [[#Kopp--2014|Kopp et al., 2014]] ) (Supplementary Material 9.SM.4.6)

|}

<sup>a</sup> Ice-sheet models include some of the larger islands in the Antarctic periphery, so there is some overlap in the projected glacier contribution and the projected Antarctic contribution, but the effect is estimated to be on the order of 0.5–1 cm or less ( [[#Edwards--2021|Edwards et al., 2021]] ).

<div id="9.6.3.2.1" class="h4-container"></div>

<span id="global-mean-thermosteric-sea-level-rise"></span>
===== 9.6.3.2.1 Global mean thermosteric sea level rise =====

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In AR5 and SROCC, global mean thermosteric sea level rise was derived from the 21 members of the CMIP5 ensemble that provided the required variables ( [[#9.2.4.1|Section 9.2.4.1]] ). The AR5 and SROCC removed drift estimated based on a pointwise polynomial fit to pre-industrial control simulations. They extended projections to scenarios not provided by the models by calculating the heat content of the climate system from GMST and net radiative flux, and converting this to global mean thermosteric sea level rise using each model’s diagnosed expansion efficiency coefficient. The AR5 and SROCC derived the associated uncertainties by assuming a normal distribution, with the 5th–95th percentile CMIP5 ensemble interpreted as the ''likely'' range. In this Report, global mean thermosteric sea level rise is derived from a two-layer energy budget emulator consistent with the assessment of ECS and TCR ( [[#9.2.4.1|Section 9.2.4.1]] ; Supplementary Material 9.SM.4.2 and 9.SM.4.3). Despite the change in methodology, this leads to a ''likely'' global mean thermosteric contribution (17th–83rd percentile) between 1995–2014 and 2100 that represents a minimal change from AR5 and SROCC (Table 9.8).

<div id="9.6.3.2.2" class="h4-container"></div>

<span id="greenland-ice-sheet-1"></span>
===== 9.6.3.2.2 Greenland Ice Sheet =====

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The AR5 and SROCC projected the Greenland surface-mass balance using a cubic polynomial fit to a regional climate model as a function of global mean surface temperature (with a log-normal scaling factor reflecting uncertainty in surface-mass balance models, and another scaling factor reflecting the positive feedback of ice-sheet elevation changes on mass loss), and the dynamic contribution was estimated based on a multi-model assessment interpolated as a quadratic function of time.

For processes whose projections we have at least ''medium confidence'' in, the updated projections use emulated Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) projections of the Greenland Ice Sheet ( [[#9.4.1.3|Section 9.4.1.3]] ; Figure 9.17; Tables 9.2 and 9.7; Box 9.3). Since the ISMIP6 emulator does not account for temporal correlation, a parametric fit to the ISMIP6 results is used to calculate rates of change (Supplementary Material 9.SM.4.4). For projections beyond 2100 (when the ISMIP6 simulations end), the polynomial fit is extrapolated based on two alternate approaches: (i) an assumption of constant rates of mass change after 2100; and (ii) for SSP1-2.6 and SSP5-8.5, a quadratic function of time extending to 2300 based on the multi-model assessment of contributions under RCP2.6 and RCP8.5 at 2300 ( [[#9.4.1.4|Section 9.4.1.4]] ). Differences between the two approaches are small up to 2150, and since the latter approach is not available for all scenarios, only the former (constant rates) is used for time series projections up to 2150. Both approaches are used for examining uncertainty in the timing of different levels of GMSL rise and to inform projections for the year 2300 ( [[#9.4.1.4|Section 9.4.1.4]] ). For 2100, the ISMIP6 emulator yields the ''likely'' contribution from the Greenland Ice Sheet shown in Table 9.2 and Figure 9.17, representing a slight narrowing from AR5 projections.

<div id="9.6.3.2.3" class="h4-container"></div>

<span id="antarctic-ice-sheet-1"></span>
===== 9.6.3.2.3 Antarctic Ice Sheet =====

<div id="h4-9-siblings" class="h4-siblings"></div>

For the Antarctic Ice Sheet (AIS), AR5 applied a temperature-based scaling approach for SMB and a quadratic function of time, calibrated to a multi-model assessment, for dynamic contributions. The SROCC used a new assessment based on the results of five process-based studies ( [[#9.4.2.5|Section 9.4.2.5]] ). For processes in whose projections we have at least ''medium confidence'' , the ''likely range'' projections for the AIS are based on: (i) the emulated ISMIP6 ensemble; and (ii) the LARMIP-2 ensemble, augmented with AR5 parametric Antarctic SMB model. The GMSL projections are produced with both distributions and combined in a ‘p-box’ ( [[#Kriegler--2005|Kriegler and Held, 2005]] ; [[#Le%20Cozannet--2017|Le Cozannet et al., 2017]] ), which represents the upper and lower bounds of the distribution ( [[#9.4.2.5|Section 9.4.2.5]] , Box 9.3 and Table 9.3). A ''likely'' range is then identified, spanning the lower of the two 17th percentile projections and the higher of the two 83rd percentile projections, <sup>[[#footnote-000|5]]</sup> with the median taken as the mean of the medians of the two projections. Since the ISMIP6 emulator does not account for temporal correlation, the AR5 parametric AIS model is substituted for the emulator in the p-box for rates of change. As AR5 projections are modestly lower than those from the ISMIP6 emulator, this substitution modestly broadens the ''likely'' range at the low end for projections of rate and changes beyond 2100. For projections beyond 2100 (when the ISMIP6 and LARMIP-2 simulations end), the AIS simulations are extrapolated using the same two approaches as the Greenland Ice Sheet (GrIS) projections ( [[#9.4.1.4|Section 9.4.1.4]] ). The ''likely'' ranges to 2100 are consistent with SROCC (Table 9.8).

<div id="9.6.3.2.4" class="h4-container"></div>

<span id="low-confidence-ice-sheet-projections"></span>
===== 9.6.3.2.4 Low confidence ice-sheet projections =====

<div id="h4-10-siblings" class="h4-siblings"></div>

To test the possible effect of additional ice-sheet processes for which there is ''low confidence'' (Sections 9.4.1.3, 9.4.1.4, 9.4.2.5, 9.4.2.6 and 9.6.3.1, and Box 9.4), two additional approaches are considered. For both the Greenland and Antarctic ice sheets, we produce sensitivity cases employing the SEJ projections of [[#Bamber--2019|Bamber et al. (2019)]] , mapping 2°C and 5°C stabilization scenarios to SSP1-2.6 and SSP5-8.5, respectively. For the AIS, we produce an additional sensitivity case using projections, which incorporate MICI ( [[#DeConto--2021|DeConto et al., 2021]] ), mapping projections for RCP2.6 and RCP8.5 to SSP1-2.6 and SSP5-8.5. For the Greenland Ice Sheet, the SEJ projections indicate the potential for outcomes outside the corresponding ''likely'' ranges (Table 9.8). For the AIS, there is no evidence from these studies to suggest an important role under lower-emissions scenarios for processes in whose projections we have ''low confidence'' . By contrast, for SSP5-8.5, the SEJ and MICI projections exhibit 17th–83rd percentile ranges of 0.02–0.56 m and 0.19–0.53 m by 2100, consistent with one another but considerably broader than the ''likely'' contribution for ''medium confidence'' processes of 0.03–0.34 m. This lower level of agreement for higher-emissions scenarios reflects the ''deep uncertainty'' in the AIS contribution to GMSL change under higher-emissions scenarios (Box 9.4). This ''deep uncertainty'' grows after 2100: by 2150, under SSP5-8.5, ''medium confidence'' processes ''likely'' lead to a –0.1–0.7 m AIS contribution, while SEJ- and MICI-based projections indicate 0.0–1.1 m and 1.4–3.7 m, respectively.

<div id="9.6.3.2.5" class="h4-container"></div>

<span id="glaciers-2"></span>
===== 9.6.3.2.5 Glaciers =====

<div id="h4-11-siblings" class="h4-siblings"></div>

In AR5 and SROCC, global glacier mass changes were derived from a power law of integrated global mean surface temperature change fit to results from four different glacier models. The updated projections use emulated GlacierMIP projections ( [[#9.5.1.3|Section 9.5.1.3]] ; Box 9.3). Since the GlacierMIP emulator does not account for temporal correlation and terminates, along with the GlacierMIP simulations, in 2100, we employ a parametric fit to the GlacierMIP simulations, with a functional form similar to that employed by AR5, to calculate rates of change and extrapolate changes beyond 2100 (up to a maximum potential contribution of 0.32 m; see Supplementary Material 9.SM.4.5). This approach leads to a median glacier contribution that is a minimal change (Table 9.8) from AR5 and SROCC and a modest narrowing of ''likely'' ranges ( [[#9.5.1.3|Section 9.5.1.3]] ). For RCP2.6, AR5 projected 0.10 (0.04 to 0.16, ''likely'' range) m, compared to 0.09 (0.07 to 0.11) m projected for SSP1-2.6. For RCP8.5, AR5 projected a ''likely'' contribution of 0.17 (0.09 to 0.25) m, compared to 0.18 (0.15 to 0.21) m projected here.

<div id="9.6.3.2.6" class="h4-container"></div>

<span id="land-water-storage"></span>
===== 9.6.3.2.6 Land-water storage =====

<div id="h4-12-siblings" class="h4-siblings"></div>

In AR5 and SROCC, the groundwater depletion contribution to GMSL rise was based on combining results from two approaches: one assuming a continuation of early 21st-century trends ( [[#Konikow--2011|Konikow, 2011]] ); and the other using land-surface hydrology models ( [[#Wada--2012|Wada et al., 2012]] ). Together, these yielded a range of about 0.02–0.09 m of GMSL rise by 2080–2099. The rate of water impoundment in reservoirs was likewise based on two approaches: one assuming the continuation of the average rate over 1971–2010 (and thus –0.01 to –0.03 m by 2080–2099; [[#Chao--2008|Chao et al., 2008]] ); and the other assuming no net impoundment after 2010 ( [[#Lettenmaier--2009|Lettenmaier and Milly, 2009]] ). Together, these yield a GMSL contribution from groundwater impoundment of –0.03 to 0 m. Combining groundwater depletion and water impoundment led AR5 and SROCC to infer a projected range of –0.01 to +0.11 m by 2100.

In the updated projections, a statistical relationship is applied, linking historical and future SSP global population to dam impoundment and groundwater extraction ( [[#Rahmstorf--2012|Rahmstorf et al., 2012]] ; [[#Kopp--2014|Kopp et al., 2014]] ). The population/groundwater depletion relationship is calibrated based on the same studies used in AR5 ( [[#Konikow--2011|Konikow, 2011]] ; [[#Wada--2012|Wada et al., 2012]] ), reduced by about 20% to account for water retained on land ( [[#Wada--2016|Wada et al., 2016]] ). The population/dam impoundment relationship is calibrated based on [[#Chao--2008|Chao et al. (2008)]] . However, while historically dam impoundment has been declining with population, recent literature shows that planned dam construction considerably exceeds the historical trend ( [[#Zarfl--2015|Zarfl et al., 2015]] ; [[#Hawley--2020|Hawley et al., 2020]] ). Over 2020–2040, the impoundment contribution to GMSL rise based on past trends would be about –0.1 mm yr <sup>–1</sup> , compared to about –0.5 mm yr <sup>–1</sup> if all currently planned dams are built ( [[#Hawley--2020|Hawley et al., 2020]] ) and the statistical projection is therefore augmented by an additional –0.4 to 0.0 mm yr <sup>–1</sup> over 2020–2040 to account for the possible effects of planned dam construction. As in AR5 and SROCC, climatically driven changes to land-water storage (LWS) have not been included in published sea level projections, as they are not well quantified (e.g., [[#Jensen--2019|Jensen et al., 2019]] ) or are considered negligible (e.g., permafrost, [[#9.5.2|Section 9.5.2]] ). This approach yields a ''likely'' global-mean land-water storage contribution (Figure 9.27, Table 9.8) that is slightly lower and narrower than the AR5 and SROCC ''likely'' ranges. Since the projections are explicitly population driven, these projections also exhibit a weak scenario dependence, with a contribution around 0.01 m higher under SSP3 than under other scenarios.

<div id="_idContainer067" class="Basic-Text-Frame"></div>

'''Table 9.8''' '''|''' '''Global mean sea level projections between 199''' '''5–2''' '''014 and 2100 for total change and individual contributions, median values, (likely ) ranges of the process-based model ensemble''' for RCP 2.6 (from AR5 ( [[#Church--2013a|Church et al., 2013a]] ) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] )) and SSP1-2.6 (this Report), and for RCP8.5 (from AR5 ( [[#Church--2013a|Church et al., 2013a]] ) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] )) and SSP5-8.5 (this Report). Values for AR5 ( [[#Church--2013a|Church et al., 2013a]] ) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) are adjusted from the 1986–2005 baseline used in past reports. Only the Antarctic contribution changed between AR5 ( [[#Church--2013a|Church et al., 2013a]] ) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). Unshaded cells represent processes in which there is ''medium confidence'' ; shading indicates the inclusion of processes in which there is ''low confidence'' . For the MICI- and SEJ-based projections, parenthetical numbers represent the 17th–83rd percentile of the associated probability distributions, not assessed ''likely'' ranges.

{| class="wikitable"
|-
|
| colspan="2"| '''RCP2.6'''

| colspan="3"| '''SSP1-2.6'''

|-
| m relative to 1995–2014

| '''AR5'''

| '''SROCC'''

| Medium confidence '''processes'''

| '''MICI'''

| '''SEJ'''

|-
| '''Thermal expansion ( [[#9.2.4.1|Section 9.2.4.1]] )'''

| colspan="2"| 0.14 (0.10–0.19) m

| colspan="3"| 0.14 (0.11–0.18) m

|-
| '''Greenland [[#9.4.1.3|Section 9.4.1.3]] )'''

| colspan="2"| 0.07 (0.03–0.11) m

| colspan="2"| 0.06 (0.01–0.10) m

| 0.13 (0.07–0.30) m

|-
| '''Antarctica ( [[#9.4.2.5|Section 9.4.2.5]] )'''

| 0.06 (–0.04 to +0.16) m

| 0.04 (0.01–0.11) m

| 0.11 (0.03–0.27) m

| 0.08 (0.06–0.12) m

| 0.09 (–0.01 to +0.25) m

|-
| '''Glaciers ( [[#9.5.1.3|Section 9.5.1.3]] )'''

| colspan="2"| 0.10 (0.04–0.16) m

| colspan="3"| 0.09 (0.07–0.11) m

|-
| '''Land-water storage ( [[#9.6.3.2|Section 9.6.3.2]] )'''

| colspan="2"| 0.05 (–0.01 to +0.11) m

| colspan="3"| 0.03 (0.01–0.04) m

|-
|
|-
| '''Total (2100)'''

| 0.41 (0.25–0.58) m

| 0.40 (0.26–0.56) m

| 0.44 (0.33–0.62) m

| 0.41 (0.35–0.48) m

| 0.53 (0.38–0.79) m

|-
| '''Total (2150)'''

| 0.29–0.63 m

| 0.56 (0.40–0.73) m

| 0.68 (0.46–0.99) m

| 0.74 (0.62–0.91) m

| 0.84 (0.56–1.34) m

|-
|
|-
| '''GMSL rate, 2080–2100 (mm''' '''yr''' <sup>–1</sup> ''')'''

| 4.4 (2.0–6.8) mm yr <sup>–1</sup>

| 4 (2–6) mm yr <sup>–1</sup>

| 5.2 (3.2–8.0) mm yr <sup>–1</sup>

| 5.1 (4.3–6.2) mm yr <sup>–1</sup>

| 5.9 (2.8–11.0) mm yr <sup>–1</sup>

|-
|
| colspan="2"|
| colspan="3"|
|-
|
| colspan="2"| '''RCP8.5'''

| colspan="3"| '''SSP5-8.5'''

|-
| m relative to 1995–2014

| '''AR5'''

| '''SROCC'''

| Medium confidence '''processes'''

| '''MICI'''

| '''SEJ'''

|-
| '''Thermal expansion ( [[#9.2.4.1|Section 9.2.4.1]] )'''

| colspan="2"| 0.31 (0.24–0.38) m

| colspan="3"| 0.30 (0.24–0.36) m

|-
| '''Greenland [[#9.4.1.3|Section 9.4.1.3]] )'''

| colspan="2"| 0.14 (0.08–0.27) m

| colspan="2"| 0.13 (0.09–0.18) m

| 0.23 (0.10–0.59) m

|-
| '''Antarctica ( [[#9.4.2.5|Section 9.4.2.5]] )'''

| 0.04 (–0.08 to +0.14) m

| 0.12 (0.03–0.28) m

| 0.12 (0.03–0.34) m

| 0.34 (0.19–0.53) m

| 0.21 (0.02–0.56) m

|-
| '''Glaciers ( [[#9.5.1.3|Section 9.5.1.3]] )'''

| colspan="2"| 0.17 (0.09–0.25) m

| colspan="3"| 0.18 (0.15–0.20) m

|-
| '''Land-water storage ( [[#9.6.3.2|Section 9.6.3.2]] )'''

| colspan="2"| 0.05 (–0.01 to +0.11) m

| colspan="3"| 0.03 (0.01–0.04) m

|-
|
|-
| '''Total (2100)'''

| 0.71 (0.49–0.95) m

| 0.81 (0.58–1.07) m

| 0.77 (0.63–1.01) m

| 0.99 (0.82–1.19) m

| 1.00 (0.70–1.60) m

|-
| '''Total (2150)'''

| 0.34–1.35 m

| 1.27 (0.80–1.79) m

| 1.32 (0.98–1.88) m

| 3.48 (2.57–4.82) m

| 1.79 (1.22–2.94) m

|-
|
|-
| '''GMSL rate, 2080–2100 (mm''' '''yr''' <sup>–1</sup> ''')'''

| 11.2 (7.5–15.7) mm yr <sup>–1</sup>

| 15 (10–20) mm yr <sup>–1</sup>

| 12.1 (8.6–17.6) mm yr <sup>–1</sup>

| 23.1 (17.5–30.1) mm yr <sup>–1</sup>

| 16.0 (9.8–28.9) mm yr <sup>–1</sup>

|}

<div id="9.6.3.2.7" class="h4-container"></div>

<span id="ocean-dynamic-sea-level"></span>
===== 9.6.3.2.7 Ocean dynamic sea level =====

<div id="h4-13-siblings" class="h4-siblings"></div>

In AR5 and SROCC, the ocean dynamic sea level contribution to RSL projections was derived from the CMIP5 ensemble, after removing the drift estimate based on pre-industrial control simulations. This Report uses updated simulations from the CMIP6 ensemble ( [[#9.2.4.2|Section 9.2.4.2]] ; Supplementary Material 9.SM.4.2) to project the ocean dynamic sea level contribution to RSL change ( [[#9.2.4.2|Section 9.2.4.2]] ; Figure 9.26). To produce ocean dynamic sea level projections consistent with the global mean thermosteric projections from the two-layer energy budget emulator, we follow the approach of [[#Kopp--2014|Kopp et al. (2014)]] , employing a correlation between global-mean thermosteric sea level change and ocean dynamic sea level derived from the CMIP6 ensemble (Supplementary Material 9.SM.4.3). Since CMIP6 models are of fairly coarse resolution (typically about 100 km), and even the models participating in HighResMIP (near 10 km resolution) do not capture all the phenomena that contribute to coastal ocean dynamic sea level change, there is ''low confidence'' in the details of ocean dynamic sea level change along the coast ( [[#9.2.3.6|Section 9.2.3.6]] ) and in semi-enclosed basins, such as the Mediterranean, where coarse models can misrepresent key dynamic processes. Regional high-resolution models can improve projections of coastal ocean dynamic sea level change ( [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] ; [[#Hermans--2020|Hermans et al., 2020]] ), but have not been implemented at a global scale.

<div id="_idContainer069" class="Basic-Text-Frame"></div>

[[File:d23f047f4d21a35d1c98ce5e92f027cf IPCC_AR6_WGI_Figure_9_26.png]]

'''Figure 9.26''' '''|''' '''Median global mean and regional relative sea level projections (m) by contribution for the SSP1-2.6 and SSP5-8.5 scenarios. Upper time series:''' Global mean contributions to sea level change as a function of time, relative to 1995–2014. '''Lower maps:''' Regional projections of the sea level contributions in 2100 relative to 1995–2014 for SSP5-8.5 and SSP1-2.6. Vertical land motion is common to both Shared Socio-economic Pathways (SSPs). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

<div id="9.6.3.2.8" class="h4-container"></div>

<span id="gravitational-rotational-and-deformational-effects"></span>
===== 9.6.3.2.8 Gravitational, rotational and deformational effects =====

<div id="h4-14-siblings" class="h4-siblings"></div>

Gravitational, rotational, and deformational (GRD) effects (Box 9.1) lead to distinct variations in the RSL change pattern, which are similar across a range of benchmarked GRD solvers ( [[#Martinec--2018|Martinec et al., 2018]] ; [[#Palmer--2020|Palmer et al., 2020]] ). There is ''high confidence'' in the understanding of GRD processes. RSL rise associated with GRD is ''very likely'' to be largest in the Pacific, due to the combined effects of projected GrIS, AIS and glacier mass loss ( ''high confidence'' ) (e.g., [[#Kopp--2014|Kopp et al., 2014]] ; [[#Slangen--2014b|Slangen et al., 2014b]] ; [[#Larour--2017|Larour et al., 2017]] ; [[#Mitrovica--2018|Mitrovica et al., 2018]] ). The GRD effect associated with mass loss from an ice sheet is sensitive to the spatial distribution of that mass loss. For example, the GRD contribution to RSL rise in Australia will be larger for Antarctic mass loss sourced fromthe Antarctic Peninsula than for Antarctic mass loss sourced fromThwaites Glacier. In parts of north-eastern North America and north-western Europe, GRD effects associated with mass loss from southern Greenland will lead to an RSL fall, whereas mass loss from northern Greenland will lead to an RSL rise ( ''high confidence'' ) (Figure 9.26; [[#Larour--2017|Larour et al., 2017]] ; [[#Mitrovica--2018|Mitrovica et al., 2018]] ). The AR5 and SROCC computed RSL patterns using a gravitationally self-consistent GRD solver given the amounts, locations and timing of the projected barystatic sea level changes driven by glaciers, ice sheets and LWS ( [[#Church--2013b|Church et al., 2013b]] ). A similar GRD solver is used in the updated projections (following [[#Slangen--2014b|Slangen et al., 2014b]] ). The Earth model used is based on the Preliminary reference Earth model (PREM: [[#Dziewonski--1981|Dziewonski and Anderson, 1981]] ), and is elastic, compressible and radially stratified.

<div id="9.6.3.2.9" class="h4-container"></div>

<span id="glacial-isostatic-adjustment-and-other-drivers-of-vertical-land-motion"></span>
===== 9.6.3.2.9 Glacial isostatic adjustment and other drivers of vertical land motion =====

<div id="h4-15-siblings" class="h4-siblings"></div>

Glacial Isostatic Adjustment (GIA) leads to vertical land motion (VLM; see Box 9.1) and changes in sea surface height, both of which contribute to RSL change. GIA uncertainty is caused by uncertainty in the rheological structure of the solid Earth, which drives the longer-term viscous Earth deformation, as well as uncertainty in the modelled global ice history (e.g., [[#Whitehouse--2018|Whitehouse, 2018]] ). In AR5 and SROCC, GIA contributions to RSL change were calculated using a sea level equation solver with an ice-sheet history taken as the mean of the ICE5G ( [[#Peltier--2015|Peltier et al., 2015]] ) and ANU ( [[#Lambeck--2014|Lambeck et al., 2014]] ) ice-sheet models. Since AR5, new global models are emerging that more rigorously treat ice and Earth structure uncertainty ( [[#Caron--2018|Caron et al., 2018]] ). However, there is also a growing recognition that lateral variations in Earth structure limit the utility of global models that treat the solid Earth as though it were laterally uniform ( [[#Love--2016|Love et al., 2016]] ; [[#Huang--2019|Huang et al., 2019]] ; T. [[#Li--2020|]] [[#Li--2020|]] [[#Li--2020|Li et al., 2020]] ).

As noted by SROCC, VLM from sources other than GIA – including tectonics and mantle dynamic topography, volcanism, compaction, and anthropogenic subsidence – can be locally important, producing VLM rates comparable to or greater than rates of GMSL change. Complete global projections of these processes are not available because of the small spatial scales, the sensitivity of subsidence to local human activities, and the stochasticity of tectonics ( [[#Wöppelmann--2016|Wöppelmann and Marcos, 2016]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). Therefore, integrated RSL projections to date have either included only the component of VLM associated with GIA (as in AR5 and SROCC), or used a constant long-term background rate of change (including both GIA and other long-term drivers of VLM) estimated from historical tide gauge trends (e.g., [[#Kopp--2014|Kopp et al., 2014]] ). The updated projections use the second approach and extrapolate the field of long-term background rates of RSL change, including long-term VLM derived from tide gauges, to global coverage using a spatio-temporal statistical approach (Supplementary Material 9.SM.4.6; [[#Kopp--2014|Kopp et al., 2014]] ). The combined GIA and long-term VLM is assumed to be scenario independent and constant over the projected period. In areas where rapid subsidence occurs in a cluster of tide gauges (e.g., the western Gulf of Mexico), the associated rates are interpolated between the tide gauges. In areas where the available tide gauges exhibit large, tectonically driven VLM that changes considerably in rate over short distances (e.g., Alaska and the Bering Strait), a sizable uncertainty propagates into the RSL projections (Figure 9.26). Rates of RSL rise are likely to be underestimated due to subsidence in shallow strata that are not recorded by tide gauges ( [[#Keogh--2019|Keogh and Törnqvist, 2019]] ) and in some locations may therefore be minimum values, especially if anomalously high subsidence rates associated with fluid extraction are also considered (e.g., [[#Minderhoud--2017|Minderhoud et al., 2017]] ). Therefore, depending on location, there is ''low'' to ''medium confidence'' in the GIA and VLM projections employed in this Report. In many regions, higher-fidelity projections would require more detailed regional analysis.

<div id="9.6.3.3" class="h3-container"></div>

<span id="sea-level-projections-to-2150-based-on-shared-socio-economic-pathway-scenarios"></span>
==== 9.6.3.3 Sea Level Projections to 2150 Based on Shared Socio-economic Pathway Scenarios ====

<div id="h3-50-siblings" class="h3-siblings"></div>

Up to 2050, consistent with AR5 and SROCC, GMSL projections exhibit little scenario dependence ( ''high confidence'' ) (Figure 9.27 and Table 9.9) with ''likely'' ( ''medium confidence'' ) sea level rise between the baseline period (1995–2014) and 2050 of 0.19 (0.16–0.25) m under SSP1-2.6 and 0.23 (0.20–0.30) m under SSP5-8.5. These projections fall centrally within the range of published projections for RCP2.6 and RCP8.5 ( [[#9.6.3.1|Section 9.6.3.1]] ).

<div id="_idContainer070" class="Basic-Text-Frame"></div>

'''Table 9.9''' '''|''' '''Global mean sea level projections for five Shared Socio-economic Pathway (SSP) scenarios, relative to a baseline of 199''' '''5–2''' '''014, in metres.''' Individual contributions are shown for the year 2100. Median values ( ''likely'' ranges) are shown. Average rates for total sea level change are shown in mm yr <sup>–1</sup> . Unshaded cells represent processes in whose projections there is ''medium confidence'' . Shaded cells incorporate a representation of processes in which there is ''low confidence'' ; in particular, the SSP5-8.5 ''low confidence'' column shows the 17th–83rd percentile range from a p-box including SEJ- and MICI-based projections rather than an assessed ''likely'' range. Methods are described in 9.6.3.2.

{| class="wikitable"
|-
|
| '''SSP1-1.9'''

| '''SSP1-2.6'''

| '''SSP2-4.5'''

| '''SSP3-7.0'''

| '''SSP5-8.5'''

| '''SSP5-8.5''' Low Confidence

|-
| '''Thermal expansion'''

| 0.12 (0.09–0.15)

| 0.14 (0.11–0.18)

| 0.20 (0.16–0.24)

| 0.25 (0.21–0.30)

| 0.30 (0.24–0.36)

| 0.30 (0.24–0.36)

|-
| '''Greenland'''

| 0.05 (0.00–0.09)

| 0.06 (0.01–0.10)

| 0.08 (0.04–0.13)

| 0.11 (0.07–0.16)

| 0.13 (0.09–0.18)

| 0.18 (0.09–0.59)

|-
| '''Antarctica'''

| 0.10 (0.03–0.25)

| 0.11 (0.03–0.27)

| 0.11 (0.03–0.29)

| 0.11 (0.03–0.31)

| 0.12 (0.03–0.34)

| 0.19 (0.02–0.56)

|-
| '''Glaciers'''

| 0.08 (0.06–0.10)

| 0.09 (0.07–0.11)

| 0.12 (0.10–0.15)

| 0.16 (0.13–0.18)

| 0.18 (0.15–0.21)

| 0.17 (0.11–0.21)

|-
| '''Land-water Storage'''

| 0.03 (0.01–0.04)

| 0.03 (0.01–0.04)

| 0.03 (0.01–0.04)

| 0.03 (0.02–0.04)

| 0.03 (0.01–0.04)

| 0.03 (0.01–0.04)

|-
|
|-
| '''Total (2030)'''

| 0.09 (0.08–0.12)

| 0.09 (0.08–0.12)

| 0.09 (0.08–0.12)

| 0.10 (0.08–0.12)

| 0.10 (0.09–0.12)

| 0.10 (0.09–0.15)

|-
| '''Total (2050)'''

| 0.18 (0.15–0.23)

| 0.19 (0.16–0.25)

| 0.20 (0.17–0.26)

| 0.22 (0.18–0.27)

| 0.23 (0.20–0.29)

| 0.24 (0.20–0.40)

|-
| '''Total (2090)'''

| 0.35 (0.26–0.49)

| 0.39 (0.30–0.54)

| 0.48 (0.38–0.65)

| 0.56 (0.46–0.74)

| 0.63 (0.52–0.83)

| 0.71 (0.52–1.30)

|-
| '''Total (2100)'''

| 0.38 (0.28–0.55)

| 0.44 (0.32–0.62)

| 0.56 (0.44–0.76)

| 0.68 (0.55–0.90)

| 0.77 (0.63–1.01)

| 0.88 (0.63–1.60)

|-
| '''Total (2150)'''

| 0.57 (0.37–0.86)

| 0.68 (0.46–0.99)

| 0.92 (0.66–1.33)

| 1.19 (0.89–1.65)

| 1.32 (0.98–1.88)

| 1.98 (0.98–4.82)

|-
|
|-
| '''Rate (204''' '''0–2060)'''

| 4.1 (2.8–6.0)

| 4.8 (3.5–6.8)

| 5.8 (4.4–8.0)

| 6.4 (5.0–8.7)

| 7.2 (5.6–9.7)

| 7.9 (5.6–16.1)

|-
| '''Rate (2080–2100)'''

| 4.2 (2.4–6.6)

| 5.2 (3.2–8.0)

| 7.7 (5.2–11.6)

| 10.4 (7.4–14.8)

| 12.1 (8.6–17.6)

| 15.8 (8.6–30.1)

|}

Beyond 2050, the scenarios increasingly diverge. Between the baseline period (1995–2014) and 2100, processes in whose projection there is ''medium confidence'' drive ''likely'' GMSL rise of 0.44 (0.32–0.62) m and 0.77 (0.63–1.01) m under SSP1-2.6 and SSP5-8.5, respectively (Tables 9.8, 9.9). While derived using substantially updated methods, these projections are broadly consistent with SROCC, which projected ''likely'' GMSL rise of 0.41 (0.26–0.56) m and 0.81 (0.58–1.07) m under RCP2.6 and RCP8.5, respectively, over this period. They are modestly higher than those of AR5, which projected ''likely'' GMSL rise of 0.41 (0.25–0.58) m under RCP2.6 and 0.71 (0.49–0.95) m under RCP8.5 (Figure 9.25, Table 9.8). They are also broadly consistent with projections produced by driving AR5 methods with CMIP6 temperature and thermal expansion projections, which leads to 0.44 (0.27–0.61) m under SSP1-2.6 and 0.73 (0.49–1.02) m under SSP5-8.5 ( [[#Hermans--2021|Hermans et al., 2021]] ). The SSP1-2.6 and SSP5-8.5 projections are consistent with the ranges of published projections for RCP2.6 and RCP8.5 that do not incorporate MICI or SEJ ( [[#9.6.3.1|Section 9.6.3.1]] ).

<div id="_idContainer072" class="_idGenObjectStyleOverride-1"></div>

[[File:5985eebb8bd48aff38effb27264ccbea IPCC_AR6_WGI_Figure_9_27.png]]

'''Figure 9.27''' '''|''' '''Projected global mean sea level rise under different Shared Socio-economic Pathway (SSP) scenarios.''' ''Likely'' global mean sea level (GMSL) change for SSP scenarios resulting from processes in whose projection there is ''medium confidence'' . Projections and ''likely'' ranges at 2150 are shown on right. Lightly shaded ranges and thinner lightly shaded ranges on the right show the 17th–83rd and 5th–95th percentile ranges for projections including ''low confidence'' processes for SSP1-2.6 and SSP5-8.5 only, derived from a p-box including structured expert judgement and marine ice-cliff instability projections. Black lines show historical GMSL change, and thick solid and dash-dotted black lines show the mean and ''likely'' range extrapolating the 1993–2018 satellite altimeter trend and acceleration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

The ''likely'' GMSL projections for SSP3-7.0 and SSP5-8.5 are consistent with a continuation of the GMSL satellite-observed rate ( ''very likely'' 3.25 [2.88–3.61] mm yr <sup>–1</sup> ) and acceleration ( ''very likely'' 0.094 [0.082–0.115] mm yr <sup>–2</sup> ) of GMSL rise over 1993–2018 (Table 9.5 and [[IPCC:Wg1:Chapter:Chapter-2#2.3.3.3|Section 2.3.3.3]] ), which would imply a ''likely'' GMSL rise of 0.24 m (0.23–0.25 m) by 2050 and 0.73 m (0.69–0.77 m) by 2100. This extrapolation would also imply a ''likely'' rate of GMSL rise of 7.5 (7.4–7.6) mm yr <sup>–1</sup> over 2040–2060 and 11.2 (10.6–11.8) mm yr <sup>–1</sup> over 2080–2100. Over the satellite period, the observed acceleration has been driven primarily by ice-sheet contributions ( [[#9.6.1.2|Section 9.6.1.2]] and Table 9.5); in the median projections for SSP3-7.0 and SSP5-8.5, these accelerations are projected to continue at a slightly lower level, while the GMSL acceleration is augmented by an acceleration of thermal expansion and glacier loss associated with rising global temperature. Overall, these extrapolations imply that, under SSP1-1.9, SSP1-2.6, and SSP2-4.5, the GMSL acceleration is projected to decrease from its current level.

While ice-sheet processes in whose projection there is ''low confidence'' have little influence up to 2100 on projections under SSP1-1.9 and SSP1-2.6 (Table 9.9), this is not the case under higher emissions scenarios, where they could lead to GMSL rise well above the ''likely'' range. In particular, under SSP5-8.5, ''low-confidence'' processes could lead to a total GMSL rise of 0.6–1.6 m over this time period (17th–83rd percentile range of p-box, including SEJ- and MICI-based projections), with 5th–95th percentile projections extending to 0.5–2.3 m ( ''low confidence'' ). The assessed ''low confidence'' range is slightly narrower than, but broadly consistent with, the full 0.4–2.4 m range of published 5th–95th percentile projections for RCP8.5 since AR5 ( [[#9.6.3.1|Section 9.6.3.1]] ) – including those based on SEJ or incorporating MICI – and highlights the ''deep uncertainty'' in GMSL rise under the highest emissions scenarios (Box 9.4). The assessment of the potential contribution of processes in which there is ''low confidence'' to GMSL rise by 2100 is broadly consistent with the AR5’s assessment ( [[#Church--2013b|Church et al., 2013b]] ), which concluded that collapse of marine-based sectors of the AIS could cause several tenths of a metre of GMSL rise above the ''likely'' range.

While prior assessment reports, starting with the First Assessment Report ( [[#Warrick--1990|Warrick et al., 1990]] ), have focused on projecting GMSL up to the year 2100, time has progressed, and the year 2100 is now within the time frame of some long-term infrastructure decisions. For this reason, projections up to the year 2150 are also highlighted (Table 9.9). Over this time period, assuming no acceleration in ice-sheet mass fluxes after 2100, processes in which there is ''medium confidence'' lead to GMSL rise of 0.5–1.0 m under SSP1-2.6 and 1.0–1.9 m under SSP5-8.5. Processes in which there is ''low confidence'' could drive GMSL rise under SSP5-8.5 to 1.0–4.8 m (17th–83rd percentile) or even 0.9–5.4 m (5th–95th percentile).

Median projected RSL changes are shown in Figure 9.28, with driving factors highlighted in Figure 9.26. Approximately 60% (SSP1-1.9) to 70% (SSP5-8.5) of the global coastline has a projected median 21st century regional RSL rise within ±20% of the global mean increase ( ''medium confidence'' ). Consistent with AR5, loss of land ice mass will be an important contributor to spatial patterns in RSL change ( ''high confidence'' ), with ocean dynamic sea level being particularly important as a dipolar contributor in the north-west Atlantic, a positive contributor in the Arctic Ocean, and a negative contributor in the Southern Ocean south of the Antarctic Circumpolar Current ( ''medium confidence'' ) [[#9.2.4.2|Section 9.2.4.2]] ). As today, VLM will remain a major driver of RSL change ( ''high confidence'' ). Uncertainty in RSL projections is greatest in tectonically active areas in which VLM varies over short distances (e.g., Alaska) and in areas potentially subject to large ocean dynamic sea level change (e.g., the north-western Atlantic) ( ''high confidence'' ).

<div id="_idContainer074" class="Basic-Text-Frame"></div>

[[File:24f4f2d5f1718be2009d41874e62fcbd IPCC_AR6_WGI_Figure_9_28.png]]

'''Figure 9.28''' '''|''' '''Regional sea level change at 2100 for different scenarios (with respect to 199''' '''5–2''' '''014).''' Median regional relative sea level change from 1995–2014 up to 2100 for: '''(a)''' SSP1-1.9; '''(b)''' SSP1-2.6; '''(c)''' SSP2-4.5; '''(d)''' SSP3-7.0; '''(e)''' SSP5-8.5; and '''(f)''' width of the likely range for SSP3-7.0. The high uncertainty in projections around Alaska and the Aleutian Islands arises from the tectonic contribution to vertical land motion, which varies greatly over short distances in this region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

An alternative perspective on uncertainty in future sea level rise is provided by looking at uncertainty in time rather than elevation; that is, looking at the range of dates when specific thresholds of sea level rise are projected to be crossed (Figure 9.29). Considering only ''medium confidence'' processes, GMSL rise is ''likely'' to exceed 0.5 m between about 2080 and 2170 under SSP1-2.6 and between about 2070 and 2090 under SSP5-8.5. It is ''likely'' to exceed 1.0 m between about 2150 and some point after 2300 under SSP1-2.6, and between about 2100 and 2150 under SSP5-8.5. It is ''unlikely'' to exceed 2.0 m until after 2300 under SSP1-2.6, while it is ''likely'' to do so between about 2160 and 2300 under SSP5-8.5. However, processes in whose projections there is ''low confidence'' could lead to substantially earlier exceedances under higher emissions scenarios: under SSP5-8.5, 1.0 m could be exceeded by about 2080 and 2.0 m could be exceeded by about 2110 (17th percentile of p-box, incorporating projections based on SEJ and MICI), with 5th percentile projections as early as about 2070 for 1.0 m and 2090 for 2.0 m.

<div id="_idContainer076" class="Basic-Text-Frame"></div>

[[File:aac96d66d6c3036c972bc39d071d4a58 IPCC_AR6_WGI_Figure_9_29.png]]

'''Figure''' '''9.29 |''' '''Timing of when global mean sea level (GMSL) thresholds of 0.5, 1.0, 1.5 and 2.0 m are exceeded, based on four different ice-sheet projection methods informing post-2100 projections.''' Methods are labelled based on their treatment of ice sheets. ‘No acceleration’ assumes constant rates of mass change after 2100. ‘Assessed ice sheet’ models post-2100 ice-sheet losses using a parametric fit (Supplementary Material 9.SM.4) extending to 2300 based on a multi-model assessment of contributions under RCP2.6 and RCP8.5 at 2300. Structured expert judgement (SEJ) employs ice-sheet projections from [[#Bamber--2019|Bamber et al. (2019)]] . Marine ice-cliff instability (MICI) combines the parametric fit (Supplementary Material 9.SM3.4) for Greenland with Antarctic projections based on [[#DeConto--2021|DeConto et al. (2021)]] . Circles, thick bars and thin bars represent the 50th, 17th–83rd and 5th–95th percentiles of the exceedance timing for the indicated projection method. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

<div id="9.6.3.4" class="h3-container"></div>

<span id="sea-level-projections-up-to-2100-based-on-global-warming-levels"></span>
==== 9.6.3.4 Sea Level Projections up to 2100 Based on Global Warming Levels ====

<div id="h3-51-siblings" class="h3-siblings"></div>

Global warming levels represent a new dimension of integration in the AR6 cycle ( [[IPCC:Wg1:Chapter:Chapter-1#1.6.2|Section 1.6.2]] , Cross-Chapter Box 11.1). The SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) concluded that, based on an assessment of GMSL projections published for 1.5°C and 2.0°C scenarios, there is ''medium agreement'' that GMSL in 2100 would be 0.04–0.16 m higher in a 2°C warmer world, compared to a 1.5°C warmer world based on 17–84% confidence interval projections (0.00–0.24 m based on 5–95% confidence interval projections) with a central value of around 0.1 m. The SR1.5 did not attempt to standardize the definition of warming-level scenarios, or to examine additional warming levels. No new integrated GMSL projections for 1.5°C or 2.0°C scenarios have been published since SR1.5.

Most of the contributors to GMSL are more closely tied to time-integrated GSAT than instantaneous GSAT ( [[#Hermans--2021|Hermans et al., 2021]] ), which means that sea level projections by warming level can only be interpreted if the warming levels are linked to a specific time frame. Here, the warming level projections are defined based on the 2081–2100 GSAT anomaly (Supplementary Material 9.SM.4.7). Different pathways in GSAT can be followed to reach a certain temperature level, which affects the temporal evolution of the different contributors to sea level change. For instance, there will be different ice-sheet and glacier responses to a fast increase to a peak warming of 2°C in 2050, followed by a plateau or a decrease, compared to a gradual increase to the same level of warming in 2100. The sea level projections presented might include different pathways to the same warming level in 2100, which is reflected in the uncertainty ranges, and should therefore be interpreted as illustrative of sea level scenarios under a certain warming level.

Projections of ''likely'' 21st-century GMSL rise along climate trajectories leading to different increases in GSAT between 1850–1900 and 2081–2100 are shown in Table 9.10, along with the SSPs for which the temperature-level projections are most closely aligned. For example, considering only processes in which there is ''medium confidence'' , from the baseline period (1995–2014) up to 2100, GMSL in a 2°C scenario is ''likely'' to rise by 0.40–0.69, which is intermediate between the projections for SSP1-2.6 and SSP2-4.5. GMSL in a 4°C scenario is ''likely'' to rise by 0.58–0.92 m, similar to the projection for SSP3-7.0. Consistent with the discussion in [[#9.6.3.3|Section 9.6.3.3]] , there is ''deep uncertainty'' in the projections for temperature levels above 3°C, and alternative approaches to projecting ice-sheet changes may yield substantially different projections in 4°C and 5°C futures. For example, employing SEJ ice-sheet projections ( [[#Bamber--2019|Bamber et al., 2019]] ) instead of the projections for ''medium confidence'' processes only leads to a 17th–83rd percentile rise between the baseline period (1995–2014) and 2100 of 0.7–1.6 m, rather than 0.7–1.1 m in a 5°C scenario.

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'''Table 9.10''' '''|''' '''Global mean sea level (GMSL) projections and commitments for exceedance of five global warming levels, defined by sorting GSAT change in 208''' '''1–2''' '''100 with respect to 185''' '''0–1''' '''900.''' Median values and ( ''likely'' ) ranges are in metres relative to a 1995–2014 baseline. Rates are in mm yr <sup>–1</sup> . Unshaded cells represent processes in whose projections there is ''medium confidence'' . Shaded cells incorporate a representation of processes in which there is ''low confidence'' ; in particular, the SSP5-8.5 ''low confidence'' column shows the 17th–83rd percentile range from a p-box, including projections based on structured expert judgement (SEJ) and marine ice cliff instability (MICI) rather than an assessed ''likely'' range. Methods are described in 9.6.3.2.

{| class="wikitable"
|-
|
| '''1.5''' ° '''C'''

| '''2.0''' ° '''C'''

| '''3.0''' ° '''C'''

| '''4.0''' ° '''C'''

| '''5.0''' ° '''C'''

| '''SSP5-8.5''' Low Confidence

|-
| '''Closest SSPs'''

| SSP1-2.6

| SSP1-2.6/SSP2-4.5

| SSP2-4.5/SSP3-7.0

| SSP3-7.0

| SSP5-8.5

|
|-
|
|-
| '''Total (2050)'''

| 0.18 (0.16–0.24) m

| 0.20 (0.17–0.26) m

| 0.21 (0.18–0.27) m

| 0.22 (0.19–0.28) m

| 0.25 (0.22–0.31) m

| 0.24 (0.20–0.40) m

|-
| '''Total (2100)'''

| 0.44 (0.34–0.59) m

| 0.51 (0.40–0.69) m

| 0.61 (0.50–0.81) m

| 0.70 (0.58–0.92) m

| 0.81 (0.69–1.05) m

| 0.88 (0.63–1.60) m

|-
| '''Rate (2040–2060)'''

| 4.1 (2.9–5.7) mm yr <sup>–1</sup>

| 5.0 (3.7–7.0) mm yr <sup>–1</sup>

| 6.0 (4.6–8.1) mm yr <sup>–1</sup>

| 6.4 (5.0–8.6) mm yr <sup>–1</sup>

| 7.2 (5.7–9.8) mm yr <sup>–1</sup>

| 7.9 (5.6–16.1) mm yr <sup>–1</sup>

|-
| '''Rate (2080–2100)'''

| 4.3 (2.6–6.4) mm yr <sup>–1</sup>

| 5.5 (3.4–8.4) mm yr <sup>–1</sup>

| 7.8 (5.3-–11.6) mm yr <sup>–1</sup>

| 9.9 (7.1–14.3) mm yr <sup>–1</sup>

| 11.7 (8.5–17.0) mm yr <sup>–1</sup>

| 15.8 (8.6–30.1) mm yr <sup>–1</sup>

|-
|
|-
| '''2000-yr commitment'''

| 2 to 3 m

| 2 to 6 m

| 4 to 10 m

| 12 to 16 m

| 19 to 22 m

|
|-
| '''10,000-yr commitment'''

| 6 to 7 m

| 8 to 13 m

| 10 to 24 m

| 19 to 33 m

| 28 to 37 m

|
|}

<div id="9.6.3.5" class="h3-container"></div>

<span id="multi-century-and-multi-millennial-sea-level-rise"></span>
==== 9.6.3.5 Multi-century and Multi-millennial Sea Level Rise ====

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Neither AR5 nor SROCC discussed the sea level commitment associated with historical emissions. Since AR5, new evidence has suggested that historical emissions up to 2016 will lead to a ''likely'' committed sea level rise (i.e., the rise that would occur in the absence of additional emissions) of 0.7–1.1 m up to 2300, while pledged emissions through 2030 increase the committed rise to 0.8–1.4 m ( [[#Nauels--2019|Nauels et al., 2019]] ).

Between the baseline period (1995–2014) and 2300, AR5 projected a GMSL rise of 0.38–0.82 m under a non-specific low-emissions scenario and 0.9–3.6 m under a non-specific high-emissions scenario (Table 9.11). The SROCC projected 0.6–1.0 m under RCP2.6 and 2.3–5.3 m under RCP8.5 ( ''low confidence'' ). RCP-based projections for 2300 published since AR5 span a broader range, even excluding studies employing SEJ or MICI, with 17th–83rd percentile projections ranging from 0.3–2.9 m for RCP2.6 and 1.7–6.8 m for RCP8.5 (Table 9.SM.8; [[#Kopp--2014|Kopp et al., 2014]] , 2017; [[#Nauels--2017|Nauels et al., 2017]] , 2019; [[#Bamber--2019|Bamber et al., 2019]] ; [[#Palmer--2020|Palmer et al., 2020]] ). Conservatively extending the ISMIP6- and LARMIP-2-based projections beyond 2100 by assuming no subsequent change in ice-sheet mass flux rates (an approach similar to that adopted by [[#Palmer--2020|Palmer et al. (2020)]] for the Greenland Ice Sheet and for the Antarctic Ice Sheet dynamics) leads to a GMSL change up to 2300 of 0.8–2.0 m under SSP1-2.6 and 1.9–4.1 m under SSP5-8.5 (17th–83rd percentile), while incorporating the ice-sheet contributions for 2300 assessed in [[#9.4.1.4|Section 9.4.1.4]] and [[#9.4.2.6|Section 9.4.2.6]] leads to 0.6–1.5 m and 2.2–5.9 m, respectively. Incorporating Antarctic results from a model with MICI ( [[#9.4.2.4|Section 9.4.2.4]] ), using RCP forcing to inform SSP-based projections, leads to 1.4–2.1 m for SSP1-2.6 and 9.5–16.2 m for SSP5-8.5 ( [[#DeConto--2021|DeConto et al., 2021]] ). Incorporating the SEJ-based ice-sheet projections of [[#Bamber--2019|Bamber et al. (2019)]] for 2°C and 5°C stabilization scenarios yields 1.0–3.1 m for SSP1-2.6, and 2.4–6.3 m for SSP5-8.5, although because of the differences in scenarios, the SSP1-2.6 estimates may be overestimated and the SSP5-8.5 may be underestimated. The eightfold uncertainty range across projection methods under SSP5-8.5 reflects ''deep uncertainty'' in the multi-century response of ice sheets to strong climate forcing.

Taking into account all these approaches, including published projections for RCP2.6, under SSP1-2.6 GMSL will rise between 0.3 and 3.1 m by 2300 ( ''low confidence'' ). This projection range indicates that, while SROCC projections under low emissions to 2300 are consistent with no ice-sheet acceleration after 2100, there is the possibility of a much broader range of outcomes at the high end, reflected in the range of published GMSL projections. Under SSP5-8.5, GMSL will rise between 1.7 and 6.8 m by 2300 in the absence of MICI and by up to 16 m considering MICI, a wider range than AR5 or SROCC assessments, but consistent with published projections ( ''low confidence'' ).

On still longer time scales, AR5 concluded with ''low confidence'' that the multi-millennial GMSL commitment sensitivity to warming was about 1–3 m °C <sup>–1</sup> GSAT increase. Two process-model studies since AR5 ( [[#Clark--2016|Clark et al., 2016]] ; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ) indicate higher commitments (Figure 9.30). Ice sheets dominate the multi-millennial sea level commitment (Sections 9.4.1.4 and 9.4.2.6), but the two studies disagree on the relative contribution of the Greenland and Antarctic ice sheets. Notably, processes such as MICI ( [[#9.4.2.4|Section 9.4.2.4]] ) that are a major factor behind the ''deep uncertainty'' in century-scale AIS response do not appear to have a substantial effect on the multi-millennial magnitude ( [[#DeConto--2016|DeConto and Pollard, 2016]] ). Only one of the studies of multimillennial GMSL commitments includes scenarios consistent with 1.5°C of peak warming ( [[#Clark--2016|Clark et al., 2016]] ); this study suggests a 2000-year commitment at 1.5°C of about 2.3–3.1 m, with approximately an additional 1.4–2.3 m commitment between 1.5°C and 2.0°C (i.e., about 3 to 5 m °C <sup>–1</sup> ). Taken together, both studies show a 2000-year GMSL commitment of about 2–6 m for peak warming of about 2°C, 4–10 m for 3°C, 12–16 m for 4°C, and 19–22 m for 5°C ( ''medium agreement'' , ''limited evidence'' ) (Table 9.10). GMSL rise continues after 2000 years, leading to a 10,000-year commitment of about 6–7 m for 1.5°C of peak warming (based on [[#Clark--2016|Clark et al., 2016]] ), and based on both studies of about 8–13 m for 2.0°C, 10–24 m for 3.0°C, 19–33 m for 4.0°C, and 28–37 m for 5°C ( ''medium agreement'' , ''limited evidence'' ) (Table 9.10).

An indicative metric for the equilibrium sea level response can be provided by comparing paleo GSAT and GMSL during past multimillennial warm periods (Sections 2.3.1.1, 2.3.3.3 and 9.6.2; Figure 9.9). However, caution is needed as the present and past warm periods differ in astronomical and other forcings (Cross-chapter Box 2.1) and in terms of polar amplification. The Last Interglacial ( ''likely'' 5–10 m higher GMSL than today and 0.5°C–1.5°C warmer than 1850–1900; [[#9.6.2|Section 9.6.2]] ; Table 9.6) is consistent with the [[#Clark--2016|Clark et al. (2016)]] projections for the 10,000-year commitment associated with 1.5°C of warming. Similarly, the Mid-Pliocene Warm Period ( ''very likely'' 5–25 m higher GMSL than today and ''very likely'' 2.5°C–4°C warmer) ( [[#9.6.2|Section 9.6.2]] ; Table 9.6) is consistent with the range of 10,000-year commitments associated with 2.5–4°C of warming, but GMSL reconstructions provide only a weak, broad constraint on model-based projections. An additional paleo constraint comes from the Early Eocene Climatic Optimum, which indicates that 10–18°C of warming is associated with ice-free conditions and a ''likely'' GMSL rise of 70–76 m (Sections 2.3.3 and 9.6.2). Together with model-based projections ( [[#Clark--2016|Clark et al., 2016]] ; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ), this period suggests that commitment to ice-free conditions would occur for peak warming of about 7°C–13°C ( ''medium agreement,'' ''limited evidence'' ).

On the basis of modelling studies, paleo constraints, single-ice-sheet studies finding multimillennial nonlinear responses from both the Greenland and Antarctic ice sheets (Sections 9.4.1.4 and 9.4.2.6), and the underlying physics, we conclude that GMSL commitment is nonlinear in peak warming on time scales of both 2,000 and 10,000 years ( ''medium confidence)'' and exceeds the AR5 assessment of 1–3 m °C <sup>–1</sup> ( ''medium agreement'' , ''limited evidence'' ) (Table 9.9). Although thermosteric sea level will start to decline slowly about 2,000 years after emissions cease, the slower responses from the Greenland and Antarctic ice sheets mean that GMSL will continue to rise for 10,000 years under most scenarios ( ''medium confidence'' ).

Since AR5, a small number of modelling studies have examined the reversibility of the multimillennial sea level commitment under carbon dioxide (CO <sub>2</sub> ) removal, solar radiation modification or local ice shelf engineering. The slow response of the deep ocean to forcing leads to global-mean thermosteric sea level fall occurring long afterward, even if CO <sub>2</sub> levels are restored after a transient increase: global mean thermosteric sea level rise takes more than a millennium to reverse ( [[#Ehlert--2018|Ehlert and Zickfeld, 2018]] ). Rapid reversion to pre-industrial CO <sub>2</sub> concentrations has been found to be ineffective at fostering regrowth of the AIS ( [[#DeConto--2021|DeConto et al., 2021]] ) but may reduce the multimillennial sea level commitment ( [[#DeConto--2016|DeConto and Pollard, 2016]] ). Altering sub-ice-shelf bathymetry ( [[#Wolovick--2018|Wolovick and Moore, 2018]] ) or triggering ice shelf advance through massive snow deposition ( [[#Feldmann--2019|Feldmann et al., 2019]] ) might interrupt marine ice sheet instability ( [[#9.4.2.4|Section 9.4.2.4]] ) and thus reduce sea level commitment. A reversion to pre-industrial Greenland Ice Sheet temperatures with solar radiation modification is projected to stop mass loss in Greenland but leads to minimal regrowth ( [[#Applegate--2015|Applegate and Keller, 2015]] ). Based on ''limited evidence'' , carbon dioxide removal, solar radiation modification, and local ice-shelf engineering may be effective at reducing the yet-to-be-realized sea level commitment, but ineffective at reversing GMSL rise ( ''low confidence'' ).

<div id="_idContainer078" class="Basic-Text-Frame"></div>

'''Table 9.11''' '''|''' '''Global mean sea level (GMSL) projections between 199''' '''5–2''' '''014 and 2300 for total change and individual contributions. Low emissions projections from: AR5 ( [[#Church--2013b|Church et al., 2013b]] ); RCP2.6 from SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) and published projections (Table 9.SM.8); and SSP1-2.6 (from this Report). High emissions projections from: AR5 ( [[#Church--2013b|Church et al., 2013b]] ); RCP8.5 from SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) and published projections (Table 9.SM.8); and SSP5-8.5 (this Report).''' Values for AR5 ( [[#Church--2013b|Church et al., 2013b]] ) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) are adjusted from the 1986–2005 baseline used in past reports. Only total values are shown for published ranges. Only the Antarctic contribution changed between AR5 ( [[#Church--2013b|Church et al., 2013b]] ) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ). If a range is given, it is the 17th–83rd percentile range.

{| class="wikitable"
|-
|
| '''Low'''

| colspan="2"| '''RCP2.6'''

| colspan="4"| '''SSP1-2.6'''

|-
| m relative to 1995–2014

| '''AR5'''

| '''SROCC'''

| '''Post-AR5 Published Range'''

| '''No Ice-sheet Acceleration After 2100'''

| '''Assessed Ice-sheet Contribution'''

| '''MICI'''

| '''SEJ'''

|-
| '''Thermal expansion'''

| colspan="2"| 0.07–0.46 m

|
| colspan="4"| 0.19–0.35 m

|-
| '''Greenland'''

| colspan="2"| 0.14 m

|
| 0.22–0.39 m

| colspan="2"| 0.11–0.25 m

| 0.28–1.28 m

|-
| '''Antarctica'''

| colspan="2"| 0.21–0.25 m

|
| –0.05 to +1.14 m

| –0.14 to +0.78 m

| 0.71–1.35 m

| –0.11 to +1.56 m

|-
| '''Glaciers'''

| colspan="2"| n/a

|
| colspan="4"| 0.12–0.29 m

|-
| '''Land-water storage'''

| –0.03 m

| 0.07–0.37 m

|
| colspan="4"| 0.05–0.10 m

|-
|
|-
| '''Total (2300)'''

| 0.38–0.82 m

| 0.57–1.04 m

| 0. 3–2.9 m

| 0.8–2.0 m

| 0.6–1.5 m

| 1.4–2.1 m

| 1. 0–3.1 m

|-
|
|-
|
| '''High'''

| colspan="2"| '''RCP8.5'''

| colspan="4"| '''SSP5-8.5'''

|-
| m relative to 1995–2014

| '''AR5'''

| '''SROCC'''

| '''Post-AR5 Published Range Without (with) MICI'''

| '''No Ice-Sheet Acceleration after 2100'''

| '''Assessed Ice-sheet Contribution'''

| '''MICI'''

| '''SEJ'''

|-
| '''Thermal expansion'''

| colspan="2"| 0.28–1.80 m

|
| colspan="4"| 0.92–1.51 m

|-
| '''Greenland'''

| colspan="2"| 0.30–1.18 m

|
| 0.53–0.88 m

| colspan="2"| 0.32–1.75 m

| 0.40–2.23 m

|-
| '''Antarctica'''

| 0.02–0.19 m

| 0.60–2.89 m

|
| –0.39 to +1.55 m

| –0.28 to +3.13 m

| 6.87–13.54 m

| 0.03–3.05 m

|-
| '''Glaciers'''

| colspan="2"| 0.29–0.39 m

|
| colspan="4"| 0.32 m

|-
| '''Land-water storage'''

| colspan="2"| n/a

|
| colspan="4"| 0.05–0.10 m

|-
|
|-
| '''Total (2300)'''

| 0.89–3.56 m

| 2.25– 5.34 m

| 1.7–6.8 (up to 14.1) m

| 1.7–4.0 m

| 2.2–5.9 m

| 9.5–16.2 m

| 2.4– 6.3 m

|}

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[[File:844eaaa3c81cd3a84b1f38cbbbb50487 IPCC_AR6_WGI_Figure_9_30.png]]

'''Figure 9.30''' '''|''' '''Global mean sea level (GMSL) commitment as a function of peak global surface air temperature.''' From models ( [[#Clark--2016|Clark et al., 2016]] ; [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#Garbe--2020|Garbe et al., 2020]] ; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ) and paleo data on 2000-year '''(lower row)''' and 10,000 year '''(upper row)''' time scales. Columns indicate different contributors to GMSL rise (from left to right: total GMSL change, Antarctic Ice Sheet, Greenland Ice Sheet, global mean thermosteric sea level rise, and glaciers). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

<div id="box-9.4" class="h2-container box-container"></div>

'''Box 9.4 | High-end Storyline of 21st-century Sea Level Rise'''

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In this box, we outline a storyline (Glossary, Box 10.2; [[#Shepherd--2018|Shepherd et al., 2018]] ) for high-end sea level projections for 2100. This storyline considers processes whose quantification is highly uncertain regarding the timing of their possible onset and/or their potential to accelerate sea level rise. These processes are therefore not considered for the assessed upper bound of ''likely'' sea level rise by 2100 in section 9.6.3.3, as the ''likely'' range includes only processes that can be projected skilfully with at least ''medium confidence'' (based on ''agreement'' and ''evidence'' ).

As noted by SROCC, stakeholders with a low risk tolerance (e.g., those planning for coastal safety in cities and long-term investment in critical infrastructure) may wish to consider global-mean sea level rise above the assessed ''likely'' range by the year 2100, because ‘ ''likely'' ’ implies an assessed likelihood of up to 16% that sea level rise by 2100 will be higher (see also [[#Siegert--2020|Siegert et al., 2020]] ). Because of our limited understanding of the rate at which some of the governing processes contribute to long-term sea level rise, we cannot currently robustly quantify the likelihood with which they can cause higher sea level rise before 2100 ( [[#Stammer--2019|Stammer et al., 2019]] ).

In light of such ''deep uncertainty'' , we employ a storyline approach in examining the potential for, and early warning signals of a high-end sea level scenario unfolding within this century. In doing so, we note upfront that the main uncertainty related to high-end sea level rise is ‘when’ rather than ‘if’ it arises: the upper limit of 1.01 m of ''likely'' sea level range by 2100 for the SSP5-8.5 scenario will be exceeded in any future warming scenario on time scales of centuries to millennia ( ''high confidence'' ), but it is uncertain how quickly the long-term committed sea level will be reached ( [[#9.6.3.5|Section 9.6.3.5]] ). Hence, global mean sea level might rise well above the ''likely'' range before 2100, which is reflected by assessments of ice-sheet contributions based on structured expert judgement ( [[#Bamber--2019|Bamber et al., 2019]] ) leading to a 95th percentile of projected future sea level rise as high as 2.3 m in 2100 ( [[#9.6.3.3|Section 9.6.3.3]] ).

A plausible storyline for such high-end sea level rise in 2100 assumes a strong warming scenario ( [[IPCC:Wg1:Chapter:Chapter-4#4.8|Section 4.8]] ). The storyline considers faster-than-projected disintegration of marine ice shelves and the abrupt, widespread onset of marine ice cliff instability (MICI) and marine ice sheet instability (MISI) in Antarctica ( [[#9.4.2.4|Section 9.4.2.4]] ), and faster-than-projected changes in both the surface mass balance and dynamical ice loss in Greenland. While conceptual studies provide ''medium evidence'' of these processes, substantial uncertainties and ''low agreement'' in quantifying their future evolution arise from limited process understanding, limited availability of evaluation data, missing or crude representation in model simulations, their high sensitivity to uncertain boundary conditions and parameters, and/or uncertain atmosphere and ocean forcing (Sections 9.4.1.2; 9.4.2.2).

In Antarctica, high warming might lead to floating ice shelves starting to break up earlier than expected due to processes not yet accounted for in ice-sheet models or in current climate models used to force ice-sheet projections. Such processes include hydrofracturing driven by surface meltwater, and increase in ocean thermal forcing driven by ocean circulation changes (Sections 9.2.2.3, 9.2.3.2 and 9.4.2.3; [[#Hellmer--2012|Hellmer et al., 2012]] , 2017; [[#Silvano--2018|Silvano et al., 2018]] ; [[#Hazel--2020|Hazel and Stewart, 2020]] ). In particular, the Thwaites and Pine Island Glacier ice shelves could potentially disintegrate this century, which might trigger MICI before 2100 ( [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#DeConto--2021|DeConto et al., 2021]] ). MISI could potentially develop earlier and faster than simulated by the majority of models if fast flowing ice streams follow plastic, instead of currently assumed more viscous, sliding laws ( [[#Sun--2020|Sun et al., 2020]] ). Oceanic feedbacks could drive high-end sea level rise by changes in the meltwater-driven overturning circulation in ice cavities that cause additional melting ( [[#Jeong--2020|Jeong et al., 2020]] ); by a warming of the ocean water in contact with the ice shelves due to increased stratification and thus reduced vertical mixing (Sections 9.2.2.3 and 9.2.3.2; [[#Golledge--2019|Golledge et al., 2019]] ; [[#Moorman--2020|Moorman et al., 2020]] ; [[#Sadai--2020|Sadai et al., 2020]] ); or by an increase in sea ice cover due to increased ocean stratification ( [[#9.3.2.1|Section 9.3.2.1]] ), which could reduce the amount of warm, moist air that reaches the continent, and limit the mass gain from snowfall over the ice sheet ( [[#Sadai--2020|Sadai et al., 2020]] ).

In Greenland, stronger mass loss than currently projected might also occur ( [[#Aschwanden--2019|Aschwanden et al., 2019]] ; [[#Khan--2020|Khan et al., 2020]] ; T. [[#Slater--2020|]] [[#Slater--2020|Slater et al., 2020]] ). For example, warming-induced dynamical changes in atmospheric circulation could enhance summer blocking and produce more frequent extreme melt events over Greenland similar to the record mass loss of more than 500 Gt in summer 2019 ( [[#9.4.1.1|Section 9.4.1.1]] ; [[#Delhasse--2018|Delhasse et al., 2018]] ; [[#Sasgen--2020|Sasgen et al., 2020]] ). Cloud processes in polar areas that are not well represented in models could further enhance surface melt ( [[#Hofer--2019|Hofer et al., 2019]] ), as could feedbacks between surface melt and the increasing albedo from meltwater, detritus and pigmented algae ( [[#9.4.1.1|Section 9.4.1.1]] ; [[#Cook--2020|Cook et al., 2020]] ). The same ice dynamical processes associated with basal melt and MISI discussed for Antarctica could also occur in Greenland, as long as the ice sheet is in contact with the ocean.

The strength of all these processes is currently understood to depend strongly on global mean temperature and polar amplification, with additional linkages through feedback from global mean sea level ( [[#Gomez--2020|Gomez et al., 2020]] ). These dependencies on a joint forcing imply that processes are strongly correlated. Hence, both their uncertainties and their possible cascading contribution to high-end sea level rise are expected to combine. Therefore, high-end sea level rise can occur if one or two processes related to ice-sheet collapse in Antarctica result in an additional sea level rise at the maximum of their plausible ranges (Sections 9.4.2.5 and 9.6.3.3; Table 9.7) or if several of the processes described in this box result in individual contributions to additional sea level rise at moderate levels. In both cases, global-mean sea level rise by 2100 would be substantially higher than the assessed ''likely'' range, as indicated by the projections including ''low confidence'' processes reaching in 2100 as high as 1.6 m at the 83rd percentile and 2.3 m at the 95th percentile ( [[#9.6.3.3|Section 9.6.3.3]] ).

Identifying the potential drivers of a high-end sea level rise allows identification of sites and observables that can provide early warnings of a much faster sea level rise than the ''likely'' range of this and previous reports. One potential site for such monitoring is Thwaites Glacier, which is melting faster in some places and slower in others than models simulate. At this glacier, the effect of tides and channelling of warm water flows on the melting is evident ( [[#Milillo--2019|Milillo et al., 2019]] ), making the floating ice shelf potentially vulnerable to breakup from hydrofracturing, driven by surface meltwater, much earlier than expected. In addition, the glacier is retreating towards a zone with deeper bedrock, which at its present rate of retreat would be reached in 30 years ( [[#Yu--2019|Yu et al., 2019]] ). Thwaites Glacier is therefore a strong candidate to experience large-scale MISI and/or MICI ( [[#Golledge--2019|Golledge et al., 2019]] ; [[#DeConto--2021|DeConto et al., 2021]] ), making it the ideal site for monitoring early warning signals of accelerated sea level rise from Antarctica. Such signals could possibly be observed within the next few decades ( [[#Scambos--2017|Scambos et al., 2017]] ).

<div id="9.6.4" class="h2-container"></div>

<span id="extreme-sea-levelstides-surges-and-waves"></span>

=== 9.6.4 Extreme Sea Levels:Tides, Surges and Waves ===

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An extreme sea level (ESL) refers to an occurrence of exceptionally high or low local sea surface height (Box 9.1). This section focuses on oceanographic-driven changes in ESL (Box 9.1).

<div id="9.6.4.1" class="h3-container"></div>

<span id="past-changes"></span>
==== 9.6.4.1 Past Changes ====

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The AR5 ( [[#Church--2013b|Church et al., 2013b]] ) concluded that changes in extreme still water levels (ESWL), combining RSL, tide and surge as observed by tide gauges (Box 9.1) are ''very likely'' to be caused by observed increases in RSL, but noted ''low confidence'' in region-specific results owing to the limited number of studies considering localized contributions from storm surge, tide or wave effects. Influences from dominant modes of climate variability, particularly ENSO and NAO (Annex IV), were also noted. Climate modes affect sea level extremes in many regions, as a result of both sea level anomalies (Sections 9.2.4.2 and 9.6.1.3) and changes in storminess ( [[IPCC:Wg1:Chapter:Chapter-11#11.7|Section 11.7]] ). The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) concluded with ''high confidence'' that inclusion of local processes (wave effects, storm surges, tides plus other regional morphology changes due to erosion, sedimentation and compaction) is essential for estimation of changes in ESL events.

As in AR5 and SROCC, tide gauge observations show that RSL rise ( [[#9.6.1.3|Section 9.6.1.3]] ) is the primary driver of changes in ESWL at most locations and, across tide gauges, has led to a median 165% increase in high-tide flooding over 1995–2014 relative to those over 1960–1980 ( ''high confidence'' ) (Figure 9.31). Some locations exhibit substantial differences between long-term RSL trends and ESWL ( ''high confidence'' ), particularly given decadal to multi-decadal variations of other ESWL contributors ( [[#Rashid--2020|Rashid and Wahl, 2020]] ). Since SROCC, RSL rise has been shown to be the dominant contributor to ESWL rise at most gauge sites along the Chinese coast, but, at some locations, the surge contribution dominates ( [[#Feng--2019|Feng et al., 2019]] ). Trends in the difference between ESWL and mean RSL rise can result from changes (either positive or negative) in the surge or tidal components, and can include non-linear interactions between tide, surge, and RSL ( [[#Arns--2015|Arns et al., 2015]] ; [[#Schindelegger--2018|Schindelegger et al., 2018]] ). The positive phase of the 18.6-year nodal cycle of the astronomical tide is a further consideration, contributing to an increased flood hazard relative to the long-term average ( [[#Talke--2018|Talke et al., 2018]] ; [[#Peng--2019|Peng et al., 2019]] ; [[#Baranes--2020|Baranes et al., 2020]] ). Failing to consider the non-linear interactions between tide, surge and RSL may overestimate trends in ESWL ( ''low confidence'' ) ( [[#Arns--2020|Arns et al., 2020]] ). In some regions, changes in ESWL depend more on changes in surge or tide than on sea level trends.

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[[File:e84cc9a989047b79ec2dccfeaf4651e6 IPCC_AR6_WGI_Figure_9_31.png]]

'''Figure 9.31''' '''|''' '''Historical occurrences of minor extreme still water levels.''' Defined as the 99th percentile of daily observed water levels over 1995–2014. '''(a)''' Percent change in occurrences over 1995–2014 relative to those over 1960–1980. '''(b–g)''' Annual mean sea level (blue) and annual occurrences of extreme still water levels over the 1995–2014 99th percentile daily maximum (yellow) at six selected tide gauge locations. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

Ongoing development of the Global Extreme Sea Level Analysis (GESLA) tide gauge database ( [[#Woodworth--2016|Woodworth et al., 2016]] ) along with data archaeology ( [[#Talke--2013|Talke and Jay, 2013]] ) extends availability of tide gauge records back to the mid 19th century (or earlier). Dynamical datasets used to assess trends in ESL at global or regional scales – for example, tide and surge contributions from the Global Tide and Surge Reanalysis (GTSR; [[#Muis--2016|Muis et al., 2016]] , 2020), or wave setup/swash contributions from available wave hindcasts/reanalyses ( [[#Melet--2018|Melet et al., 2018]] ) – have model biases introduced with resolution and parametrization limitations, incomplete atmospheric data, and currently span only a few decades, so they are not yet long or accurate enough to assess long-term trends in ESLs. Therefore, there is ''medium confidence'' in observed trends in ESWL, but only ''low confidence'' in modelled ESL trends.

The AR5 indicated that the amplitude and phase of major tidal constituents have exhibited long-term change, but that their effects on ESL were not well understood. The SROCC ( [[#Bindoff--2019|Bindoff et al., 2019]] ) reported changes in tides (amplification and dampening) at some locations to be of comparable importance to changes in mean sea level for explaining changes in high water levels, with the sign of change being dependent on stability of shoreline position. RSL rise causes water depth-based alterations to the resonant characteristics of the basin, changes the bottom friction and increases the wave speed ( [[#Pickering--2012|Pickering et al., 2012]] ) and remains the primary hypothesis for observed tidal changes. Other contributing processes include strong localized anthropogenic drivers (e.g., port development, dredging, flood defences, land reclamation), changes in stratification associated with ocean warming ( [[#9.2.1.3|Section 9.2.1.3]] ), and changes in seabed roughness associated with ecological change (e.g., [[#Haigh--2019|Haigh et al., 2019]] ). Tide gauge data show that, although principal tidal components have varied in amplitude on the order of 2% to 10% per century ( [[#Jay--2009|Jay, 2009]] ; [[#Ray--2009|Ray, 2009]] ), identifying direct causality remains challenging ( [[#Haigh--2019|Haigh et al., 2019]] ). Combined, observations and models indicate RSL rise and direct anthropogenic factors are the primary drivers of observed tidal changes at tide gauge stations ( ''medium confidence'' ).

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) reported variations in storm surge not related to changes in RSL, and concluded with ''high confidence'' that consideration of localized storm surge processes was essential to monitor trends in ESL. SL events driven by storm surge are a response to tropical and extratropical cyclones. While historical trends in extra-tropical cyclones are less clear ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.2.1|Section 11.7.2.1]] ), there is mounting evidence for an increasing proportion of stronger tropical cyclones globally, with an associated poleward migration ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ). These changes are captured in the ESL record, for example, via increasing intensity and poleward shift in the location of typhoon-driven storm surges reported across 64 years (1950–2013) in the western North Pacific ( [[#Oey--2016|Oey and Chou, 2016]] ). Along the east coast of the USA, there has been an increase in frequency of ESL events due to tropical cyclone changes since 1923 that can be statistically linked to changes in global average temperature ( [[#Grinsted--2013|Grinsted et al., 2013]] ), and the signal is projected to emerge around 2030 ( [[#Lee--2017|Lee et al., 2017]] ). At century and longer time scales, geological proxies such as overwash deposits in coastal lagoons or sinkholes can be used to reconstruct past changes in storm activity (e.g., [[#Brandon--2013|Brandon et al., 2013]] ; [[#Lin--2014|Lin et al., 2014]] ) and put recent events into historical perspective (e.g., [[#Brandon--2015|Brandon et al., 2015]] ). However, there is ''low confidence'' in the current ability to quantitatively compare geological proxies with gauge data. Historical storm surge activity is being increasingly assessed with use of hydrodynamic model simulations and data-driven global reconstructions to supplement tide gauge observations to investigate historical changes at centennial to millennial time scales (e.g., [[#Ji--2020|Ji et al., 2020]] ; [[#Muis--2020|Muis et al., 2020]] ; [[#Tadesse--2020|Tadesse et al., 2020]] ). Large regional variations and limited observational data lead to ''low confidence'' in observed trends in the surge contribution to increasing ESL.

Waves contribute to ESL via wave setup, infra-gravity waves and swash processes ( [[#Dodet--2019|Dodet et al., 2019]] ), with Extreme Total Water Level (ETWL; Box 9.1) used to represent ESWL with addition of wave setup, and Extreme Coastal Water Level (ECWL; Box 9.1) also including contributions from swash. The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) reported the dependency of these processes on nearshore geomorphology and deep-water wave climate, and thus sensitivity to internal climate variability and climate change. Few long-term deployments of in situ measurements in the very dynamic surf zone means that long-term records of ETWL or ECWL are limited to a few sites; tidal gauges are typically located in sheltered locations (e.g., harbours) where wave contributions are absent ( [[#Lambert--2020|Lambert et al., 2020]] ). Consequently, trends in wave contributions to ESL are typically derived from trends in wave conditions observed offshore. On the basis of satellite altimeter observations, SROCC reported increasing extreme wave heights in the Southern and North Atlantic oceans of around 1.0 and 0.8 cm yr <sup>–1</sup> , respectively, over the period 1985–2018 ( ''medium confidence'' ). The SROCC ( [[#Collins--2019|Collins et al., 2019]] ) also identified sea ice loss in the Arctic as leading to increased wave heights over the period 1992–2014 ( ''medium confidence'' ). Since SROCC, the satellite wave record has been shown to be sensitive to alternate processing techniques, leading to important differences in reported trends ( [[#Timmermans--2020|Timmermans et al., 2020]] ). The most common observation platforms for surface waves over the past 30 years are in situ buoys. However, evolving biases associated with changing instrument type, configuration and sampling methodology introduce artificial trends (e.g., [[#Gemmrich--2011|Gemmrich et al., 2011]] ; [[#Timmermans--2020|Timmermans et al., 2020]] ). Accurate metadata is required to address these issues, and, while available locally, are only beginning to be globally coordinated ( [[#Centurioni--2019|Centurioni et al., 2019]] ). Wave reanalysis and hindcast products have also been used to investigate total water level at global scale ( [[#Melet--2018|Melet et al., 2018]] ; [[#Reguero--2019|Reguero et al., 2019]] ). Their applicability for trend analysis is limited by inhomogeneous data for assimilation ( [[#Stopa--2019|Stopa et al., 2019]] ), but they inform relationships between seasonal, interannual to inter-decadal variability of climate indices and wind-wave characteristics (A.G. [[#Marshall--2015|]] [[#Marshall--2015|Marshall et al., 2015]] , 2018; [[#Kumar--2016|Kumar et al., 2016]] ; [[#Stopa--2016|Stopa et al., 2016]] ). To summarize, satellite era trends in wave heights of order 0.5 cm yr <sup>–1</sup> have been reported, most pronounced in the Southern Ocean. However, sensitivity of processing techniques, inadequate spatial distribution of observations, and homogeneity issues in available records limit confidence in reported trends ( ''medium confidence'' ).

Only a few studies have attempted to quantify the role of anthropogenic climate change in ESL events (e.g., [[#Mori--2014|Mori et al., 2014]] ; Takayabu et al., 2015; [[#Turki--2019|Turki et al., 2019]] ). Detection and attribution of the human influence on climatic changes in surges, and waves remains a challenge ( [[#Ceres--2017|Ceres et al., 2017]] ), with ''limited evidence'' to suggest in some instances – for example, poleward migration of tropical cyclones in the Western North Pacific ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1.2|Section 11.7.1.2]] ), changes in surges and waves can be attributed to anthropogenic climate change ( ''low confidence'' ). With RSL change being considered the primary driver of observed tidal changes, there is ''medium confidence'' that these changes can be attributed to human influence. The close relationship between local ESL and long-term RSL change, combined with the robust attribution of GMSL change ( [[#9.6.1.4|Section 9.6.1.4]] ), implies that observed global changes in ESL can be attributed, at least in part, to human-caused climate change ( ''medium confidence'' ), but reconciling regional variation in these changes is not yet possible ( [[#9.6.1.4|Section 9.6.1.4]] ).

<div id="9.6.4.2" class="h3-container"></div>

<span id="future-changes"></span>
==== 9.6.4.2 Future Changes ====

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There are two distinct methods used to project future ESL changes: (i) The static, or mean sea level offset, approach employs historical distributions of tidal, surge and wave components and adjusts future ESL distributions for mean RSL rise; (ii) The dynamic approach employs hydrodynamic and/or wave models forced with atmospheric fields derived from general circulation models (GCMs) to project changes in tidal, storm surge and wave distributions, which are then combined with RSL projections to project future ESLs; and (iii) The dynamic approach is computationally expensive. Use of the dynamic approach on large spatial or global scales has only recently been successful to project 21st-century changes in ETWL ( [[#Vousdoukas--2017|Vousdoukas et al., 2017]] , 2018) and ECWL( [[#Melet--2020|Melet et al., 2020]] ). [[#Kirezci--2020|Kirezci et al. (2020)]] assume stationarity in global wave and storm surge simulations to assess projected 21st-century changes in episodic coastal ETWL-driven flooding under global sea level rise scenarios.

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) presents projections of ESL derived using a static approach. Such projections often quantify changes in ESL event frequency, expressed as ‘frequency amplification factors’ ( [[#Hunter--2010|Hunter, 2010]] , 2012). Like RSL projections, frequency amplification factors increase under higher-emissions scenarios, and differences between scenarios increase over time. The SROCC concludes that even small to moderate changes in mean RSL can lead to hundred- to thousand-fold increases in the frequencies with which certain thresholds are exceeded – for example, what is currently a 1-in-100-year ESL height (1% annual probability or 0.01 expected annual events) will be expected once or even multiple times per year in future at many locations (Figure 9.32). The SROCC showed that currently rare ESL events (e.g., with an average return period of 100 years) will occur annually or more frequently at most available locations for RCP4.5 by the end of the century ( ''high confidence'' ). Results from these assessments are sensitive to the type of ESL probability distribution assumed ( [[#Buchanan--2016|Buchanan et al., 2016]] ; [[#Wahl--2017|Wahl et al., 2017]] ), as well as the magnitude and uncertainty of projected RSL change ( [[#Slangen--2017|Slangen et al., 2017]] ; [[#Wahl--2017|Wahl et al., 2017]] ; [[#Frederikse--2020a|Frederikse et al., 2020a]] ). Frequency amplification factors tend to be largest in tropical regions due in part to higher RSL rise projections, but primarily to the relative rarity of high ESLs in areas with little historical exposure to tropical or extratropical cyclones. Alternative representation of changes in ESL, such as presenting changes in exceedances per year ( [[#Sweet--2014|Sweet and Park, 2014]] ), are subject to similar sensitivities, and lead to ''medium confidence'' in projected changes of event frequency using these methods.

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[[File:a53a0bb1c8611523ed4618421a73350f IPCC_AR6_WGI_Figure_9_32.png]]

'''Figure 9.32''' '''|''' '''Projected median frequency amplification factors for the 1% average annual probability extreme still water level in 2050 (a, c, e) and 2100 (b, d, f).''' Based on a peak-over-threshold (99.7%) method applied to the historical extreme still water levels of Global Extreme Sea Level Analysis version 2 (GESLA2) following Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and additionally fitting a Gumbel distribution between Mean Higher High Water (MHHW) and the threshold following [[#Buchanan--2016|Buchanan et al. (2016)]] , using the regional sea level projections of ( [[#9.6.3.3|Section 9.6.3.3]] for (a, b) SSP5-8.5, (c, d) SSP2-4.5 and (e, f) SSP1-2.6. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

Employing a similar static approach – fitting a Gumbel distribution between Mean Higher High Water (average of higher high water height of each tidal day) and a threshold following [[#Buchanan--2016|Buchanan et al. (2016)]] – this Report updates SROCC projections of ESL with the RSL projections from [[#9.6.3.3|Section 9.6.3.3]] (see also Supplementary Material 9.SM.4). By 2050, the median increase in frequency amplification factor at 634 tide gauge stations is 19 for SSP1-2.6, 22 for SSP2-4.5, and 30 for SSP5-8.5 (Figure 9.32). This means that, by 2050, a historical (1995–2014) 1% annual probability ESL will have increased to an 19–30% annual probability. The 1% historical annual probability event is expected to become an annual event at 19–31% of the 634 stations by 2050, consistent with SROCC. By 2100, the median frequency amplification factor is projected to be 163 for SSP1-2.6, 325 for SSP2-4.5, and 532 for SSP5-8.5, with respectively 60%, 71%, and 82% of the stations experiencing a currently 1% annual probability event at least yearly ( ''medium confidence'' ) (Figure 9.32).

In the dynamic approach, the low resolution of the forcing fields arising from GCMs limits the ability to resolve historical and future changes in tropical and extra-tropical storm frequency and intensity, and resolution of local geography and morphology limit ability to represent ECWL (Box 9.1). Not all relevant processes – such as river discharge – are included in the dynamic models, and ESL events are typically a combination of multiple contributing processes, which are often not independent ( [[#Jevrejeva--2019|Jevrejeva et al., 2019]] ). In both static and dynamical approaches, global assessment of the performance of modelled storm surge and wave contributions to ESL is limited by poor coverage of observations (limited to tide gauges for ESWL, [[#Muis--2020|Muis et al., 2020]] ), and unavailable for the wave dependent ETWL and ECWL estimates ( [[#Vitousek--2017|Vitousek et al., 2017]] ; [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Kirezci--2020|Kirezci et al., 2020]] ; [[#Lambert--2020|Lambert et al., 2020]] ; [[#Melet--2020|Melet et al., 2020]] ). In studies to date, individual models are used to simulate different contributions to ESL, non-linear interactions are not well captured, and uncertainties associated with downscaling methodology are poorly resolved, leading to ''low confidence'' in available ESL projections that include these modelled wave and surge contributions.

Assessment of dynamic ETWL changes for regions is presented in Chapter 12, following the methods of [[#Vousdoukas--2018|Vousdoukas et al. (2018)]] and [[#Kirezci--2020|Kirezci et al. (2020)]] . Consistent with studies using the static approach, [[#Vousdoukas--2018|Vousdoukas et al. (2018)]] finds that by 2050 the historical 1% average annual probability ETWL will have increased to a 2–50% average annual probability for most high latitude regions, and more often (up to multiple times a year, &gt;100% annual probability) in the tropics, under both RCP4.5 and RCP8.5. For 2100, present-day 1% average annual probability extreme sea levels will be exceeded multiple times each year almost everywhere. In summary, despite waves and surges being non-negligible contributors to projected ETWL and ECWL changes ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Melet--2020|Melet et al., 2020]] ), RSL change is expected to be the main driver in changes in future ESL return periods in most areas ( ''medium confidence'' ).

The SROCC ( [[#Bindoff--2019|Bindoff et al., 2019]] ) concluded that the majority of coastal regions will experience statistically significant changes in tidal amplitudes through the 21st century. Comprehensive high-resolution (of the order 10 km) numerical modelling studies provide evidence for spatially coherent changes in tidal amplitudes in shelf seas as a result of RSL rise ( [[#Haigh--2019|Haigh et al., 2019]] , and references therein). There is ''high confidence'' that GMSL rise will be the primary driver of global tidal amplitude increases and decreases over the next 100–200 years, changing the baseline tide that ESLs are imposed on. At local and regional scales, anthropogenic factors such as major land reclamation efforts, as in the East China Sea ( [[#Song--2013|Song et al., 2013]] ) or differing national coastal management strategies (maintaining the present coastline position or managed retreat) will locally modulate the influence of GMSL rise on tidal amplitude ( ''medium confidence'' ).

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) concluded that the intensity of severe tropical cyclones will increase in a warmer climate ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] ), but ''low confidence'' remains in the future frequency of tropical cyclones. Changes in tropical cyclone climatology will contribute to variations in frequency and magnitude of future ESL surge events, although estimates of this contribution range widely ( [[#Lin--2012|Lin et al., 2012]] ; [[#McInnes--2014|McInnes et al., 2014]] , 2016; [[#Little--2015|Little et al., 2015]] ; [[#Garner--2017|Garner et al., 2017]] ; [[#Mori--2019|Mori et al., 2019]] ; [[#Muis--2020|Muis et al., 2020]] ). In the Gulf of Mexico, changes in ESL due to tropical cyclone activity may be as important as SLR in enhancing future flood hazards ( [[#Marsooli--2019|Marsooli et al., 2019]] ). For the Korean Peninsula, a maximum change in 100-year return height associated with typhoon-induced storm surges of 10% under 4°C warming is found ( [[#Yang--2018|Yang et al., 2018]] ). The effects of projected changes in tropical cyclone intensity may be enhanced or offset in different locations by effects of changes in tracks ( [[IPCC:Wg1:Chapter:Chapter-11#11.7.1|Section 11.7.1]] ; [[#Garner--2017|Garner et al., 2017]] ). There is ''low confidence'' in projected changes in ESL driven by changes in tropical cyclone climatology.

Changes in surface wave conditions occur in response to changes in frequency; intensity and position of forcing winds and storms ( [[#Morim--2018|Morim et al., 2018]] , 2019); reduction in sea ice and associated changes in fetch conditions ( [[#Thomson--2014|Thomson and Rogers, 2014]] ; [[#Casas-Prat--2020|Casas-Prat and Wang, 2020]] ); and changes in coastal morphology associated with RSL rise ( [[#Wandres--2017|Wandres et al., 2017]] ; [[#Storlazzi--2018|Storlazzi et al., 2018]] ). A few studies considering the contribution of a non-stationary wave climate on future changes in ESL infer a small but non-negligible contribution ( [[#Vousdoukas--2018|Vousdoukas et al., 2018]] ; [[#Melet--2020|Melet et al., 2020]] ). The SROCC presented qualitative assessments of projected changes in wave conditions. Since SROCC, a quantitative assessment of a community ensemble of global wind-wave projections ( [[#Morim--2019|Morim et al., 2019]] ) found robust projected changes of around 5–10% (positive or negative, depending on region) in annual mean significant wave height, mean wave period, and/or mean wave directions along about 52% of the world’s coastline that exceed internal climate variability under RCP8.5 by 2100. Continued retreat of sea ice cover in the Arctic will lead to more energetic wind-wave conditions ( [[#Casas-Prat--2020|Casas-Prat and Wang, 2020]] ). Wave climate modelling methods introduce up to around 50% of the ensemble variance in mean wave climate projections ( [[#Morim--2019|Morim et al., 2019]] ). GCMs do not typically resolve the higher-resolution tropical and extratropical storm features required to accurately determine the contribution of extreme waves to ESLs and individual studies have sought to improve resolution to address these issues (e.g., [[#Timmermans--2017|Timmermans et al., 2017]] ). To date, projections of wave height extremes have been constrained to single wave model configurations (e.g., [[#Timmermans--2017|Timmermans et al., 2017]] ; [[#Meucci--2020|Meucci et al., 2020]] ). In summary, there is ''medium confidence'' in projections of changes in mean wave climate but ''low confidence'' in the projected changes in extreme wave conditions due to ''limited evidence'' .

Correlations between changes in sea level-forced (mean sea level and tidal) and atmospherically-forced drivers (ocean surface waves and surges) of ESLs have only been considered in a few studies, although high surge and high waves co-occur along a majority of the world’s coastlines ( [[#Marcos--2019|Marcos et al., 2019]] ). Along the east coast fo the USA, ocean dynamic sea level change and change in power dissipation index (a proxy for North Atlantic tropical cyclone activity) are correlated across CMIP5 GCMs, resulting in an increase in ESLs relative to analyses assuming independence of these changes ( [[#Little--2015|Little et al., 2015]] ). In the Irish Sea, dynamically coupled wave-tide modelling results in high water wave heights up to 20% higher than in an uncoupled analysis ( [[#Lewis--2019|Lewis et al., 2019]] ). In the German Bight, RSL rise relaxes the breaking criterion of nearshore waves (assuming no geomorphological response), allowing larger waves to propagate closer to shore, leading to increased wave runup ( [[#Arns--2017|Arns et al., 2017]] ). In south-western Australia, the influence of projected SLR was found to exceed the influence of projected changes in forcing winds on wave characteristics at the coast ( [[#Wandres--2017|Wandres et al., 2017]] ). Thus, projections of ESL that do not consider correlations between and among sea level forced and atmospherically forced drivers can differ strongly from coupled projections ( ''medium confidence'' ).

The SROCC ( [[#Collins--2019|Collins et al., 2019]] ) highlighted compound events, or coincident occurrence of multiple hazards, as an example of ''deep uncertainty'' , and noted that failing to account for multiple factors contributing to extreme events will lead to underestimation of the probabilities of occurrence ( ''high confidence'' ) ''.'' Statistical studies have shown that high rain or streamflow often co-occurs with storm surge as examples of ‘compound’ surge-rain or surge-discharge events (Sections 11.8.1 and 12.4.5.6; [[#Wahl--2015|Wahl and Chambers, 2015]] ; [[#Moftakhari--2017|Moftakhari et al., 2017]] ; Ward et al., 2018; [[#Wu--2018|Wu et al., 2018]] ; [[#Couasnon--2020|Couasnon et al., 2020]] ). Dynamical modelling studies show that co-occurrence of flood drivers raises ESLs at some locations in estuaries, such as the Rhine Delta ( [[#Zhong--2013|Zhong et al., 2013]] ), the Netherlands ( [[#van%20den%20Hurk--2015|van den Hurk et al., 2015]] ), Taiwan, China ( [[#Chen--2016|Chen and Liu, 2016]] ), and the Hudson River, USA ( [[#Orton--2020|Orton et al., 2020]] ), particularly when hydrologic catchments are steep and cause high rainfall near the coast, such as in south-west UK ( [[#Svensson--2004|Svensson and Jones, 2004]] ). The compound effect of storm surge and rainfall contributes greater projected flood risk than climate-induced amplification ( [[#Hsiao--2021|Hsiao et al., 2021]] ). However, at other locations, co-occurrence was unimportant because streamflow timing did not coincide with the coastal peak storm surge (Hudson River, [[#Orton--2012|Orton et al., 2012]] ; Rhine delta, [[#Klerk--2015|Klerk et al., 2015]] ). The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) detailed the complexity of interactions in deltaic environments. Direct increases in flooding driven by increasing RSL and storm surge, rain, or correlations between these flood-drivers (e.g., [[#Moftakhari--2017|Moftakhari et al., 2017]] ; [[#Orton--2020|Orton et al., 2020]] ) are expected to be further accompanied by increases in flooding due to subsidence (vertical land movement) and sedimentation (RSL-driven blockage of river flows). The probability of concurrent surge, wave and precipitation events has been projected to increase by more than 25% by 2100 compared to present, with high northern latitudes displaying compound flooding becoming more than 2.5 times as frequent, and weakening in the subtropics ( [[#Bevacqua--2020|Bevacqua et al., 2020]] ). However, the number of studies on compound events is still limited and so there is ''low confidence'' in understanding the extent by which compound events of surge with rain will change in response to RSL rise and climate change.

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