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

==== 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]] ).

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<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).

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===== 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.

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

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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).

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

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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.

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===== 9.6.3.2.5 Glaciers =====

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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.

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

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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.

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'''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>

|}

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

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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.

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[[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).

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===== 9.6.3.2.8 Gravitational, rotational and deformational effects =====

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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.

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===== 9.6.3.2.9 Glacial isostatic adjustment and other drivers of vertical land motion =====

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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.

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