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

==== 4.2.3.3 Probabilistic Sea Level Projections ====

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Marzeion et al. (2012),

Radić et al. (2014)

Slangen and Van de Wal (2011)

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

Church et al. (2013)

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

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

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

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

| 0.28
(0.23–0.34)

|
| 0.86
(0.66–1.11)

| 1.06
(0.78–1.43)

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

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

| 0.71
(0.43–0.99)

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

| 0.26
(0.21–0.31)

| 0.29
(0.24–0.34)

| 0.50
(0.37–0.65)

| 0.59
(0.45–0.77)

| 0.79
(0.62–1.00)

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

| 0.26
(0.18–0.36)

| 0.31
(0.22–0.40)

| 0.56
(0.37–0.78)

| 0.91
(0.66–1.25)

| 1.46
(1.09–2.09)

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

| 0.75
(0.54–0.98)

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

| 1.84
(1.24-2.46)

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

| 0.25
(0.21–0.30)

| 0.27
(0.23–0.33)

| 0.45
(0.35–0.56)

| 0.55
(0.45–0.67)

| 0.79
(0.65–0.97)

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

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

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

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