For the EAIS, the experts concluded that buttressing is the dominant and primary factor for all time periods (Figure 4). It is interesting to note that for 2200H the 95th percentile estimate for EAIS discharge is larger than any other ice sheet process and hydro-fracture is considered to be increasingly important (Table S2 in Supporting Information S1) and also, but to a lesser extent, for 2100H (Figure 4). This is consistent with recent evidence that suggests that as much as 60% of Antarctic ice shelf area is vulnerable to hydrofracture from surface meltwater, including almost all of the Filchner Ronne, Ross and Amery ice shelves that buttress large drainage basins in East Antarctica (Lai et al., 2020).
Conversely, because runoff is limited over both the WAIS and EAIS at present, it is considered to play a limited role in direct mass loss (as opposed to an indirect role in accelerating ice shelf collapse) under the high temperature scenario up to 2100 (Figure 2 and Table 1) and even up to 2200 (Figure 2, Table 1). Hence, albedo changes are considered to be of limited importance over grounded ice for both Antarctic ice sheets. In this case, it is changes in moisture content and circulation that are identified as the dominant control on SMB. Thus, for example, increased accumulation of the WAIS has a 5% probability of mitigating the ice sheet contribution to SLR by at least 65 cm for 2200H. This is also reflected in ice sheet model simulations using climate model output, particularly for the EAIS (Seroussi et al., 2020). The experts conclude that changes in summer sea ice extent will have some impact on ice shelf SMB for all three ice sheets up to 2200 (Figures S4 and S5 in Supporting Information S1), with the largest contribution over the GrIS.
Finally, we asked the experts whether they considered the recent (decadal) trends in mass balance for the GrIS and WAIS, as observed from satellite data, were due predominantly to internal variability (IV) or external forcing (EF) (Figure 5). This is an important question for four reasons. First, these same observations are used to initialize numerical ice sheet models (DeConto et al., 2021; Seroussi et al., 2020). To do this, it is necessary to assign the recent trends to either IV or to EF, or some combination of the two. That is because, as for GCMs, ice sheet models are not aimed at reproducing the conditions of one particular day, a season or a year, but to model climatically forced trends. Second, this is a central question for process understanding and also for probabilistic approaches that are conditioned on the observations, as are semi empirical models (Little et al., 2013). Third, recent observations have been used to calibrate tuneable parameters in an ice sheet model (DeConto et al., 2021). This requires assigning the trend in the observations to IV or EF. Note that model calibration and initialization are not, in general, the same process. Fourth, observations are an important tool for verifying the performance of a numerical model but only if the signal(s) in the observations can be assigned to some combination of IV and EF (Randall et al., 2007). The experts concluded that the trends in Greenland are predominantly driven by EF, whereas for the WAIS there was no consensus and no certainty (Figure 5) as was also the case in a previous SEJ exercise (Bamber & Aspinall, 2013).
Figure 5: Expert judgements for whether internal variability (IV) or external forcing (EF) is the dominant driver of recent (last two decade) observational trends in mass balance for the Greenland Antarctic ice sheets (GrIS) and West Antarctic Ice Sheet (WAIS).
Conclusions
The findings just described, which are drawn from the SEJ exercise presented in B19, are generally consonant with recently reported observational evidence but are in sharp contrast to the latest ice sheet model intercomparison analyses in terms of both the dominant drivers of uncertainty and their magnitudes (Goelzer et al., 2020; Seroussi et al., 2020). An important contribution we have been able to provide through our analysis is to express these influences—on sea level projections and associated uncertainties—in probabilistic terms. We can, thus, quantify the relative role of different processes not just for their median response but also for the tails of the distributions, which are lower probability but higher impact. Where the distributions display high kurtosis (e.g., WAIS dynamics and GrIS runoff) the median and standard deviation do not capture the full uncertainty and risk associated with that process. In IPCC assessment reports prior to the AR6 (Fox-Kemper et al., 2021) this has been a major limitation in their sea level rise projections, which were limited to the likely range, equivalent to ± one standard deviation (Bamber et al., 2019).
We found that for all time periods out to 2300 CE, quantified uncertainties are dominated by WAIS dynamics and GrIS runoff. The former is influenced by the marine ice sheet instability, MISI, which in turn, is influenced by changes in ocean circulation and heat content in ways that are not well understood or, as yet, adequately modeled (Seroussi et al., 2020). Subglacial topography has an important controlling influence on the initiation of the MISI and how rapidly it evolves but is imperfectly known for many key sectors of the WAIS (Cornford et al., 2020; Rosier et al., 2021). GrIS runoff is relatively well understood as a process, but is sensitive to climate drivers that are poorly captured in GCMs and, therefore, imperfectly represented in future projections. For example, changes in cloud properties, such as optical depth, altitude and seasonality, can have a dramatic impact on melt rates but are inconsistent between GCMs and are known to be poorly modeled in general (Hofer et al., 2019). Runoff is also sensitive to albedo. Relatively small concentrations of both inorganic and organic material on the ice sheet surface can have a significant impact on albedo and, therefore, melting, but this is a factor that is yet to be included in ice sheet models (Williamson et al., 2020). The seasonality of both temperature and precipitation changes over Greenland has a strong influence on SMB trends but is also not consistently projected by GCMs. Reducing future uncertainties in ice sheet projections will require, therefore, improvements in ice sheet process understanding and modeling of those processes as well as more robust projections of the climate forcing for a given greenhouse gas emissions pathway.
An important challenge, building on this analysis, is to extend and refine our expert judgement elicitation so that we can better quantify critical parameters, variables and processes related to model projections of ice sheet contributions to sea level rise. For instance, while the uncertainties in our experts’ assessments likely include some elements that relate to processes that are not formally identified in the present exercise, an elicitation could be designed that would enable us to disaggregate these complexities, and their associated uncertainties, in more detail. This would allow us to quantify the role of additional factors in limiting process certainty. As is usual with structured elicitations of this type, such additional findings—based on informed expert judgements—will almost certainly highlight specific topics meriting further research and analysis. As discussed above, this is not limited to ice sheet processes but also to the climate projections used to force them.
Supporting Information
Ice Sheet Instabilities
For the Greenland Ice Sheet (GrIS), a potential key threshold relates to a concept termed the ‘small ice cap instability’ (Maqueda et al., 1998). This instability originates from a positive feedback between changes in surface elevation and increased runoff, i.e. it is linked to surface mass balance (SMB). As the ice sheet loses mass, the surface elevation lowers, resulting in warmer surface temperature and increased melting. When the amount of surface melting exceeds the total accumulation, via snowfall, the ice sheet is no longer sustainable in the long-term. The increase in global temperature required to pass this threshold has been estimated to lie between +0.8 to +3.2 ̊C above pre-industrial with a best estimate of +1.6 ̊C (Robinson et al., 2012). Recent evidence from satellite observations suggests that the GrIS had reached a state of persistent ice loss by about 2005 (King et al., 2020) and that it experienced its largest recorded mass loss, equivalent to 1.5 mm sea level equivalent, in 2018. About 60% of the mass loss over the last three decades is attributable to SMB and the rest to discharge (King et al., 2020; Sasgen et al., 2020). Whether the GrIS would completely disintegrate or reach a new, smaller metastable state is a topic of current debate (Gregory et al., 2020; Robinson et al., 2012).


An important challenge, building on this analysis, is to extend and refine our expert judgement elicitation so that we can better quantify critical parameters, variables and processes related to model projections of ice sheet contributions to sea level rise. For instance, while the uncertainties in our experts’ assessments likely include some elements that relate to processes that are not formally identified in the present exercise, an elicitation could be designed that would enable us to disaggregate these complexities, and their associated uncertainties, in more detail. This would allow us to quantify the role of additional factors in limiting process certainty. As is usual with structured elicitations of this type, such additional findings—based on informed expert judgements—will almost certainly highlight specific topics meriting further research and analysis. As discussed above, this is not limited to ice sheet processes but also to the climate projections used to force them.
Supporting Information
Ice Sheet Instabilities
For the Greenland Ice Sheet (GrIS), a potential key threshold relates to a concept termed the ‘small ice cap instability’ (Maqueda et al., 1998). This instability originates from a positive feedback between changes in surface elevation and increased runoff, i.e. it is linked to surface mass balance (SMB). As the ice sheet loses mass, the surface elevation lowers, resulting in warmer surface temperature and increased melting. When the amount of surface melting exceeds the total accumulation, via snowfall, the ice sheet is no longer sustainable in the long-term. The increase in global temperature required to pass this threshold has been estimated to lie between +0.8 to +3.2 ̊C above pre-industrial with a best estimate of +1.6 ̊C (Robinson et al., 2012). Recent evidence from satellite observations suggests that the GrIS had reached a state of persistent ice loss by about 2005 (King et al., 2020) and that it experienced its largest recorded mass loss, equivalent to 1.5 mm sea level equivalent, in 2018. About 60% of the mass loss over the last three decades is attributable to SMB and the rest to discharge (King et al., 2020; Sasgen et al., 2020). Whether the GrIS would completely disintegrate or reach a new, smaller metastable state is a topic of current debate (Gregory et al., 2020; Robinson et al., 2012).
The West Antarctic Ice Sheet (WAIS) is termed a marine ice sheet because most of the bedrock it rests on lies below sea level, in some places by as much as 2,500 m (Bamber et al., 2009). In addition, the ice sheet also rests, predominantly, on a retrograde bed slope: one that deepens inland. These two conditions are hypothesised to be necessary (but not sufficient) to invoke the Marine Ice Sheet Instability (MISI) whereby the grounding line is inherently unstable and can rapidly migrate inland (Schoof, 2007; Seroussi et al., 2020). Recent evidence indicates that part of the WAIS may already be experiencing irreversible grounding line retreat as a result of MISI (Joughin et al., 2014). Unlike the GrIS instability mechanism, the WAIS MISI is a dynamic response driven predominantly by ocean forcing.
The East Antarctic Ice Sheet (EAIS) has several marine basins, which could be vulnerable to oceanic erosion but are currently protected by regions of ice grounded above sea level or on prograde bed slopes (Bamber et al., 2009). For the two major marine basins, Aurora and Wilkes, this “safety band” is just tens of kilometers wide (Fig S1 and S2 of (Bamber et al., 2009). In addition, the two largest ice shelves in Antarctica, the Filchner Ronne and Ross, buttress large catchments in both the WAIS and EAIS. Inclusion of enhanced calving via hydrofracture and ice cliff failure (both components that contribute to marine ice cliff instability, MICI) in numerical models can lead to a significant loss of ice from the EAIS by 2100CE under RCP8.5 conditions (DeConto and Pollard, 2016). More recently, the rate of mass loss has been revised downward using updated climate forcing and calibration data (DeConto et al., 2021). Nonetheless, significant mass loss was predicted from the EAIS over the present century for RCP8.5 and a recent study suggests that as many as 60% of Antarctic ice shelves - which buttress inland ice - are vulnerable to hydrofracture if inundated by meltwater (Lai et al., 2020).
Explaining variation between ice sheets
In a given year, under a given temperature scenario, sea level rise (SLR) from the ice sheets is simply the sum of contributions from EAIS, WAIS and GrIS:
SLR(ice sheets) = EAIS + WAIS + GrIS
A natural question is how much the uncertainty of each ice sheet contribution influences the uncertainty in enumerating SLR(ice sheets). Suppose we could observe EAIS=x. Given this information our expectation for SLR is represented as E(SLR | EAIS = x). As we let x vary over its range, E(SLR | EAIS = x) will also vary. The question is, by how much? If EAIS had no effect on SLR, then the expected SLR value would not depend on x at all and would always be equal to the unconditional expectation E(SLR). On the other hand, if E(SLR | EAIS = x) varied substantially, that would mean that the value of EAIS has a big role in determining the value of SLR. We can capture that effect by comparing the variance of E(SLR | EAIS = x) as x varies, to the unconditional variance of SLR. This ratio is called (inappropriately) the ‘correlation ratio’ (CR), though is better thought of as the fraction of variance of SLR explained by variations in EAIS:
CR (SLR, EAIS) = Var(E(SLR | EAIS = x)) / Var(SLR).
When the variation of EAIS explains all the variation in SLR, then the above ratio is one.
For contributions from the three ice sheets, EAIS, WAIS and GrIS, if their variations are independent then:
Var(SLR) = Var(EAIS) + Var(WAIS) + Var(GrIS)
and the correlation ratios sum to one.
However, if the variations in individual ice sheet contributions are positively correlated then the sum of the correlation ratios is greater than one. In this case knowing, say, that EAIS = 2m tells us something about contributions from WAIS and GrIS. The following table gives the correlation ratios calculated from the expert judgements for High and Low temperature stabilization scenarios (H, L), for 2300CE and 2100CE.
Fraction of variance of SLR explained by each ice sheet
2300H
2300L
2100H
2100L
EAIS
0.75
0.41
0.49
0.30
GrIS
0.19
0.32
0.34
0.39
WAIS
0.61
0.63
0.67
0.57
sum
1.55
1.36
1.49
1.26
We observe that in all cases the correlation ratios sum to more than one, and that exceedances are greater for High temperature stabilization scenarios. This suggests the experts jointly consider ice sheet responses could be more strongly correlated under higher temperature trajectories, and possibly become even more so further ahead into the future. For instance, there is the implication that, under the High temperature scenario, variations in EAIS contributions could be the major influence on total SLR uncertainty by 2300CE (CR 0.49 → 0.75), while the related effect of GrIS variations will be much reduced (CR 0.34 → 0.19)
We do not extend this type of analysis down to the physical ice mass processes operating at the individual ice sheets: those processes are inter-dependent, sometimes with tail correlations, and each expert assessed such dependences for themself when making judgements on ice sheet contributions. While it might be possible to decompose, expert by expert, the variance of SLR into components – expressing numerically the way these processes appear to act at each individual ice sheet – more insight is gained by examining their joint appraisal of importance rankings for these drivers (Bamber et al, 2019).
Definitions of driving processes included in the rationale questionnaire
Six ice dynamic drivers and three SMB drivers were included in the expert rationale questionnaire, designed to provide an indication of the rationales for the uncertainties for each of the three ice sheet process elicited: accumulation, A, and runoff, R, (contributing to SMB) and discharge, D, across the grounding line. In the case of the rationale questionnaire, quantile values were not elicited but instead the relative rank order of each factor in driving the change in A, R or D (Bamber et al, 2019). Here, we provide brief descriptors for these drivers
Buttressing, B:


Fraction of variance of SLR explained by each ice sheet
2300H
2300L
2100H
2100L
EAIS
0.75
0.41
0.49
0.30
GrIS
0.19
0.32
0.34
0.39
WAIS
0.61
0.63
0.67
0.57
sum
1.55
1.36
1.49
1.26
We observe that in all cases the correlation ratios sum to more than one, and that exceedances are greater for High temperature stabilization scenarios. This suggests the experts jointly consider ice sheet responses could be more strongly correlated under higher temperature trajectories, and possibly become even more so further ahead into the future. For instance, there is the implication that, under the High temperature scenario, variations in EAIS contributions could be the major influence on total SLR uncertainty by 2300CE (CR 0.49 → 0.75), while the related effect of GrIS variations will be much reduced (CR 0.34 → 0.19)
We do not extend this type of analysis down to the physical ice mass processes operating at the individual ice sheets: those processes are inter-dependent, sometimes with tail correlations, and each expert assessed such dependences for themself when making judgements on ice sheet contributions. While it might be possible to decompose, expert by expert, the variance of SLR into components – expressing numerically the way these processes appear to act at each individual ice sheet – more insight is gained by examining their joint appraisal of importance rankings for these drivers (Bamber et al, 2019).
Definitions of driving processes included in the rationale questionnaire
Six ice dynamic drivers and three SMB drivers were included in the expert rationale questionnaire, designed to provide an indication of the rationales for the uncertainties for each of the three ice sheet process elicited: accumulation, A, and runoff, R, (contributing to SMB) and discharge, D, across the grounding line. In the case of the rationale questionnaire, quantile values were not elicited but instead the relative rank order of each factor in driving the change in A, R or D (Bamber et al, 2019). Here, we provide brief descriptors for these drivers
Buttressing, B:
This is the influence of back stresses on the grounded ice from floating ice shelves. Discharge is determined by the force balance acting at the grounding line. This force balance is comprised of several terms. On one side is the gravitational driving stress that results in ice flow. Opposing this are transverse stresses (TS) such as at the margins of the glacier or ice stream, basal traction (BT) and the backstress at the grounding line due to the buttressing effect of floating ice.
Basal traction, BT:
See Buttressing. BT is the resistive force between the glacier bed and the ice in contact with it. For a frozen bed, this term is not relevant but fast-moving ice at the margins of the ice sheets the bed is not frozen, water is present, and basal sliding occurs. For ice streams, as much as 90% of the ice motion can be due to basal sliding, which is controlled by BT.
Transverse stresses, TS:
See Buttressing. TS are largely determined by the large difference in ice speed between the slow-flow margins of a glacier and the fast-moving central trunk. TS act as a resistive force to ice motion and are influenced by damage characteristics of the ice, which in turn is a function of strain history and ice rheology.
Hydrofracture, HF:
HF is a process that enhances crevasse propagation on both ice shelves and grounded ice. It weakens the ice by accelerating crevasse growth via water filled crevasses. HF is a key process in the MICI as it leads to rapid ice shelf collapse, with sufficient surface melting.
Ice cliff instability, IC:
IC is linked to HF and MICI. After rapid ice shelf collapse, an ice cliff forms at the grounding line (ice above sea level). Above a critical height, this ice cliff is unstable, resulting in brittle failure (the shear stress exceeds the yield stress of ice).
Dissipation of icebergs, DI:
This is related to IC and MICI. During IC, icebergs are formed, which, depending on the geometry of the ice shelf and bathymetry beneath can accumulate in an embayment or become rafter on a sill or alternatively can be advected away from the ice edge by ocean currents.
Atmospheric moisture and circulation, AM:
In a changing climate, the predominant patterns of atmospheric circulation and strength of multi-annual oscillations such as the Pacific Decadal Oscillation or Arctic Oscillation may change affecting both the source and magnitude of precipitation regionally. Circulation changes can also influence surface ocean heat transport, which in turn can affect buttressing but here we were only concerned with its influence on SMB.
Albedo, AL:
Changes in surface AL have a large impact on the radiative energy balance of the snow or ice surface, which affects melt rates. Several factors that are currently not included in SMB models are known to influence albedo such as organic and inorganic impurities deposited or growing on the surface.
Sea ice, SI:
SI acts as a barrier to moisture and heat exchange between the atmosphere and ocean and changes in sea ice extent or concentration can, therefore, influence both of these factors locally, affecting rates of precipitation and air temperatures.
Importance of gravitational, rotational and deformation effects
Recent observations and developments in numerical modeling have suggested that gravitational, rotational and solid Earth deformation (GRD) effects on regional sea level and isostatic bedrock elevation caused by changes in ice mass loading could have a stabilising effect on, in particular, grounding line migration associated with MISI. Fig S7 shows the results of the experts’ judgement on the importance of GRD for the stability of the three ice sheets where D implies decreasing stability, I is increasing stability and N is no impact. The GrIS has limited sectors that satisfy the MISI criteria: a retrograde bed slope that is below sea level close to, or at, the present- day grounding line. Consequently, GRD effects are considered of negligible significance here. The WAIS is the ice sheet that is most susceptible to the MISI and is thus the ice sheet where GRD may act as a negative, stabilising feedback. However, only 50% of the experts consider this to be the case and recent modeling suggest the effect is, however, small (Larour et al., 2019). For the EAIS, about a third of the experts consider GRD to be of relevance and, as for the WAIS, any reduction in grounding line migration due to GRD effects is likely to be small (Larour et al., 2019). Consequently, we do not discuss GRD effects further and consider them to be of second- order importance.
Supporting Figures
Figure S1: The two temperature scenarios prescribed: L (+2° C) and H (+5° C)
Figure S2: Indicative probability distribution plots for SLR contributions by 2050CE from the three ice sheets and for three physical processes, identified on the x-axis (runoff from EAIS is omitted as this is presumed zero under either temperature rise scenario). Results are derived from expert elicitation for the 2050L (low +2°C) global temperature trajectory (left hand curves) and for the 2050H (high +5°C) global temperature trajectory (right hand curves); probability density curves are approximate and extend from values corresponding to a 99% chance of SLR being exceeded to a 1% chance of SLR being exceeded. Median values are shown by the black rectangle and the total SLR contribution from the ice sheets is shown in orange.
Figure S3: As for figure S2 but for 2300.
Figure S4: Expert judgements on the relative role of the three drivers for changes in SMB: atmospheric moisture and circulation (AM), albedo (AL) and sea ice extent (SI) for both grounded and floating ice for the Low temperature scenario.
Figure S5: Expert judgements on the relative role of the three drivers for changes in SMB: atmospheric moisture and circulation (AM), albedo (AL) and sea ice extent (SI) for both grounded and floating ice for the Low temperature scenario.
Figure S6: Expert judgements on the relative role of the two ocean processes: circumpolar deep water (CDW) and the Atlantic Meridional Overturning Circulation (AMOC) for the High temperature scenario.
Figure S7: Relative importance of GRD effects for decreasing ice stability (D), increasing it (I) or having no effect (N).
Supporting Tables
Table S1: SLR for different probabilities from 1-99.9% and at ten year increments from 2010- 2300. All values are relative to the year 2000 baseline for the High temperature scenario. Values are in cms.
Table S2: Change in process significance between Low and High temperature scenarios for ice dynamic processes.
Table S3: As for Table S1 but for SMB processes.


van de Wal et al. (2022)
Title:
A High-End Estimate of Sea Level Rise for Practitioners
Key Points:
A high-end estimate of sea level rise in 2100 and 2300
Decisionmaker/practitioner perspective on high-end
Timing of collapse of ice shelves critical
Corresponding author:
Roderik S. W. van de Wal
Citation:
van de Wal, R. S. W., Nicholls, R. J., Behar, D., McInnes, K., Stammer, D., Lowe, J. A., et al. (2022). A High‐End Estimate of Sea Level Rise for Practitioners. Earth’s Future, 10(11), e2022EF002751. doi:10.1029/2022ef002751
URL:
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF002751
Abstract
Sea level rise (SLR) is a long-lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate for the deep ocean and ice sheets. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process-based models. However, risk-averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientists and practitioners builds on a framework of discussing physical evidence to quantify high-end global SLR for practitioners. The approach is complementary to the IPCC AR6 report and provides further physically plausible high-end scenarios. High-end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2°C in 2100 (RCP2.6/SSP1-2.6) relative to pre-industrial values our high-end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for a (RCP8.5/SSP5-8.5), we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long-term benefits of mitigation. However, even a modest 2°C warming may cause multi-meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high-end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica. This is highly uncertain due to low understanding of the driving processes. Hence both process understanding and emission scenario control high-end SLR.
Plain Language Summary
Taking a co-production approach between scientists and practioners, we provide high-end sea level rise (SLR) estimates for practitioner application based on an expert evaluation of physical evidence and approaches currently used in policy environments to understand high end risk. We do this for two global warming scenarios, a modest and a strong one, for two time slices 2100 and 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long-term benefits of mitigation. However, even a modest warming may cause multi-meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high-end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica as well as how practitioners use high end projections to frame risk. We stress that both emission scenario and limited physical understanding control the outcome.
Introduction
Sea level rise (SLR) is a key aspect of climate change, with important consequences for coastal societies and low-lying areas, especially small islands, deltas, and coastal cities (Oppenheimer et al., 2019). Human interference in the climate system leads to a continuing gradual warming and expansion of ocean water (i.e., the steric effect), mass loss from glaciers and polar ice sheets. Most of these effects continue long after emissions have slowed or stopped. Climate models simulating physical processes are used to reconstruct historical sea level change (excluding the ice sheet contribution), and consequently provide a method to project SLR given specific future anthropogenic CO2 emissions and associated warming of the Earth system. Such a process-based approach provides robust estimates of changes in the central part of the SLR distribution for projections and published studies using this method are in general agreement. However, estimating the tails of the distribution, which includes the ice sheet contribution remains contentious as not all the relevant processes are sufficiently understood or represented in the models, leading to variations between projections and multiple views of how the upper tail of the SLR distribution will evolve in future.
High-end SLR projections provide information about the upper tail of the probability distribution of SLR, and are especially important for decisionmakers and practitioners (collectively referred to as practitioners) assessing long-term risks and adaptation responses. High-end projections, though by definition unlikely to occur, can provide information for adaptation planning, that is, defining a plausible “worst case” SLR to consider in an adaptation plan (Hinkel et al., 2015; Nicholls, Hanson, et al., 2021; Vogel et al., 2016). In addition, high-end estimates provide insight on potential adaptation limits, tipping points and thresholds, and the level of climate mitigation required to keep SLR adaptation manageable in the future. In this context, it is also important to consider the long-term commitment of SLR, requiring high-end projections for time horizons well beyond 2100.
We emphasize that high-end SLR information does not replace the quantification of the more likely central parts of the SLR distribution, but rather supplements these estimates. For example, a default adaptation plan may follow the median projection, with high-end estimates used to inform the development of contingency options that can be applied in the case that high-end SLR manifests. Such a planning approach is known as “adaptive planning” or “dynamic adaptive planning” in the literature (Haasnoot et al., 2013; Ranger et al., 2013). This is particularly the case when there are long lead times for action (i.e., the time to plan, design, finance, obtain support and implement the work) and long operational lives, such as for storm surge barriers or nuclear power stations, or where there is significant path-dependency for decisions (e.g., when decisions have a long legacy that may preclude future options such as choosing between protection and retreat). Therefore, a “likely” range as used by Oppenheimer et al. (2019) as the central 66% of the probability distribution is not always sufficient (Hinkel et al., 2015).
Obtaining estimates of high-end SLR can be approached in a statistical sense with probabilistic projections, as provided by Kopp et al. (2017, 2019) and Le Bars et al. (2017), but this approach may not capture possible contributions from processes not yet understood or included in climate models. To overcome this some studies define every percentile of conditional probability distributions based on an underlying assumption, such as including the Antarctic contribution from a single study (e.g., Goodwin et al., 2017). This suggests a higher confidence in the outcomes than is warranted by current physical understanding and is potentially misleading to practitioners since it does not reflect or communicate limits in our physical understanding of these processes. An alternative approach that provides estimates to address these difficulties are structured expert elicitation studies which have also been applied to provide estimates of high-end SLR (Bamber et al., 2019). They attempt to capture the uncertainty due to the lack of knowledge (Lempert et al., 2003; Oppenheimer et al., 2019) that exists in model projections without relying on models, and which is impossible to constrain using a deterministic modeling approach. This approach combines the ad hoc judgment of a group of experts. However, the considerations regarding which processes are included, and which are not, is not made explicit and the interpretation of these estimates by experts is not necessarily the same as those of uninformed practitioners because they do not know the considerations of the experts. For this reason, in this paper, we prefer to use expert judgment based on physical reasoning to arrive at estimates which cannot be constrained by deterministic modeling. This is outlined in the Greenland and Antarctic sections and provides a transparent attribution of cause and effect.


Obtaining estimates of high-end SLR can be approached in a statistical sense with probabilistic projections, as provided by Kopp et al. (2017, 2019) and Le Bars et al. (2017), but this approach may not capture possible contributions from processes not yet understood or included in climate models. To overcome this some studies define every percentile of conditional probability distributions based on an underlying assumption, such as including the Antarctic contribution from a single study (e.g., Goodwin et al., 2017). This suggests a higher confidence in the outcomes than is warranted by current physical understanding and is potentially misleading to practitioners since it does not reflect or communicate limits in our physical understanding of these processes. An alternative approach that provides estimates to address these difficulties are structured expert elicitation studies which have also been applied to provide estimates of high-end SLR (Bamber et al., 2019). They attempt to capture the uncertainty due to the lack of knowledge (Lempert et al., 2003; Oppenheimer et al., 2019) that exists in model projections without relying on models, and which is impossible to constrain using a deterministic modeling approach. This approach combines the ad hoc judgment of a group of experts. However, the considerations regarding which processes are included, and which are not, is not made explicit and the interpretation of these estimates by experts is not necessarily the same as those of uninformed practitioners because they do not know the considerations of the experts. For this reason, in this paper, we prefer to use expert judgment based on physical reasoning to arrive at estimates which cannot be constrained by deterministic modeling. This is outlined in the Greenland and Antarctic sections and provides a transparent attribution of cause and effect.
The approach builds on Stammer et al. (2019), where they quantify high-end SLR by synthesizing all the available physical evidence across observations, model sensitivity studies and modeled SLR scenario studies, and then assess and synthesize this information. Importantly, this approach aims to meet practitioner needs, which depend less on precise estimates of likelihood and more on evidence that is sufficiently credible, salient, and legitimate to support adaptation planning, including financing (Cash et al., 2003, 2002). “Salient” is used here in the context of relevance to practical needs. Within this framework, projections supported by multiple lines of evidence and eliciting broader confidence from the scientific community are of greater value as compared to projections further along the tail that feature fewer lines of evidence, and hence have lower confidence. This is an expansion of the approach based on building blocks (Stammer et al., 2019), in which the building blocks represent the amount of SLR beyond the likely range that practitioners will consider according to their risk-averseness, emission scenarios, and how these evolve over time. It is key that the main processes are considered explicitly. The work is based on a WCRP grand challenge workshop on this topic where a wide variety of people were invited (∼25 scientists and ∼10 practitioners) including experts on all relevant sea level components and experts on the application of SLR information. The estimates for the specific components are made by a subset of authors as outlined in the acknowledgment statement.
Because the level of understanding of each sea level component differs, we employ different methods to assess each of them separately. For example, the understanding of the thermal expansion of the ocean and the glacier-melt component is sufficient to use distributions derived from climate models directly. For those components, we assume that all necessary knowledge of the high-end is captured in the distribution. However, for the Greenland and Antarctic ice sheet components the uncertainty is much larger, as understanding of physical processes is more limited, and hence a robust and reliable probability density function does not exist. We, therefore, choose to apply a process-based expert judgment to the available lines of evidence to estimate a high-end ice sheet contribution. By following this approach we deviate from (Fox-Kemper et al., 2021), which provides a high-end scenario with and without a specific Antarctic instability mechanism and includes structured expert elicitation. Hence, we take a complementary approach where we explicitly and transparently assess the physical processes leading to a high-end estimate for Greenland and Antarctica.
The aim of this paper is to develop high-end projections that are most strongly supported by physical evidence and yet are also salient for the decision and practitioner environment. We derive new high-end estimates based on present physical understanding and demonstrate a methodological approach that may be regularly updated as the science evolves and improves, especially knowledge on ice sheets. Table S1 lists the author’s contribution by section. Throughout this paper, we follow the definition of technical terms as defined in the glossary of the IPCC AR6 report (Matthews et al., 2021).
Practitioner Perspectives on High-End Sea Level Projections
This paper explicitly considers practitioner perspectives in addition to SLR science to promote developing salient projections (e.g., Hinkel et al., 2019). Risk-averse practitioners need to consider low likelihood, high consequence SLR futures that poses challenges to adaptation, in addition to median outcomes (Fox-Kemper et al., 2021; Garner et al., 2018; Haasnoot et al., 2020; Hall et al., 2019; Hinkel et al., 2015; Nicholls, Hanson, et al., 2021). While median SLR projections have been relatively stable over time, several high-end projections have emerged, especially in recent years (e.g., DeConto & Pollard, 2016). However, these high-end projections have not been reviewed systematically from a user perspective, and most adaptation practitioners find them challenging to use, if they use them at all. Those practitioners that have applied them have had to develop their own understanding and guidance, including expertise on sea level science. This constitutes a high overhead to application when adaptation is often poorly funded.
An influential approach linking scientific exploration and decision requirements advises that scientific influence on decisions depends on the “salience, credibility, and legitimacy” of the information presented from the decision perspective (Cash et al., 2003, 2002). Of particular importance for high-end SLR projections is salience, defined as “the relevance of information for an actor’s decision choices, or for the choices that affect a given stakeholder.” In our view, salience for high-end SLR projections derives from two factors.
First, scientific information used for decision-making must consider all the major uncertainties and ambiguities across experts and models (Gold, 1993; Jones et al., 2014; Simpson et al., 2016). This requirement may be at odds with the physics-based design of SLR projections. For example, the SLR scenarios provided by IPCC AR4 did not assign values outside the central likely range as information was absent (Meehl et al., 2007). In AR5, the possibility of several tenths of a meter above the likely range was considered as a high-end possibility, reflecting rapid melting of the Antarctica and Greenland ice sheets: these processes, however were poorly understood and not captured directly in the physics-based design (Church et al., 2013). While this exclusion is explicitly stated and makes sense from a physical science perspective, practitioners may misuse the results, as they will expect/assume that IPCC SLR scenarios cover all major uncertainties. AR6 moved to an emulator approach and covered a wider range of probabilities than earlier assessments reflecting the increased understanding of key physical processes that was unavailable for earlier assessments: the central range of estimates to 2100 is similar to earlier estimates, but also addresses high-impact/low-probability outcomes (Section 5), and provides a range of values from the literature. This evolution of the IPCC reports reflect increased understanding and provides improved treatment of the risk management context for adaptation planning, but alternative interpretations as presented here are possible, thereby increasing the understanding of high-end estimates.
Second, salience requires a differentiation between scientific endeavors in general and what is sometimes called “actionable science,” which in the climate field is intended to support risk assessment and adaptation planning/investment (Bamzai et al., 2021; Beier et al., 2017; Moss et al., 2013; Vogel et al., 2016). New studies that challenge prior lines of evidence should be carefully reviewed, assessed, and debated before any application or incorporation into guidance (Nicholls, Hanson, et al., 2021). This avoids the “whiplash effect” wherein planners and all their efforts are undermined each time a new study questions their adopted projections. In this respect, we advocate this work to be used alongside (Fox-Kemper et al., 2021) rather than replacing it.


First, scientific information used for decision-making must consider all the major uncertainties and ambiguities across experts and models (Gold, 1993; Jones et al., 2014; Simpson et al., 2016). This requirement may be at odds with the physics-based design of SLR projections. For example, the SLR scenarios provided by IPCC AR4 did not assign values outside the central likely range as information was absent (Meehl et al., 2007). In AR5, the possibility of several tenths of a meter above the likely range was considered as a high-end possibility, reflecting rapid melting of the Antarctica and Greenland ice sheets: these processes, however were poorly understood and not captured directly in the physics-based design (Church et al., 2013). While this exclusion is explicitly stated and makes sense from a physical science perspective, practitioners may misuse the results, as they will expect/assume that IPCC SLR scenarios cover all major uncertainties. AR6 moved to an emulator approach and covered a wider range of probabilities than earlier assessments reflecting the increased understanding of key physical processes that was unavailable for earlier assessments: the central range of estimates to 2100 is similar to earlier estimates, but also addresses high-impact/low-probability outcomes (Section 5), and provides a range of values from the literature. This evolution of the IPCC reports reflect increased understanding and provides improved treatment of the risk management context for adaptation planning, but alternative interpretations as presented here are possible, thereby increasing the understanding of high-end estimates.
Second, salience requires a differentiation between scientific endeavors in general and what is sometimes called “actionable science,” which in the climate field is intended to support risk assessment and adaptation planning/investment (Bamzai et al., 2021; Beier et al., 2017; Moss et al., 2013; Vogel et al., 2016). New studies that challenge prior lines of evidence should be carefully reviewed, assessed, and debated before any application or incorporation into guidance (Nicholls, Hanson, et al., 2021). This avoids the “whiplash effect” wherein planners and all their efforts are undermined each time a new study questions their adopted projections. In this respect, we advocate this work to be used alongside (Fox-Kemper et al., 2021) rather than replacing it.
Relevant examples of high-end scenarios in planning exist in other fields. These support sound risk management, while adhering to a reasonable standard of practice to ensure appropriate resource allocation to the level of risk aversion. Accordingly, planners have found it advisable to frame high-end risk with a standard that balances risk management objectives with finite resources, avoiding large opportunity costs where possible. For example, the UK National Risk Register defines a “reasonable worst-case scenario” (RWCS) for use in planning. This is defined as “the worst plausible manifestation of that particular risk (once highly unlikely variations have been discounted) to enable relevant bodies to undertake proportionate planning” (HM Government, 2020). The RWCS “is designed to exclude theoretically possible scenarios which have so little probability of occurring that planning for them would be likely to lead to disproportionate use of resources” (Memorandum Submitted by the Government Office for Science and the Cabinet Office, 2011). The US Army Corps of Engineers selected a “maximum probable flood” for design purposes after the Great Mississippi River Flood of 1927. This is the “greatest flood having a reasonable probability of occurrence” and was preferred over a larger “maximum possible flood”, reflecting a meteorological sequence that, though reflective of historic events, was deemed highly implausible (Jadwin, 1928). This reasonableness standard has stood the test of time, including periodic review, and may be modified in the future to reflect changes to climate, land use, or other factors as appropriate.
For SLR, an example of a salient approach is The Thames Estuary Plan (TE2100), which addresses management of future coastal flood risk for London, UK. It was one of the first long-term adaptation plans to address deep uncertainty (sometimes popularized as the unknown unknowns) with consideration of both more likely and high-end SLR (Ranger et al., 2013). The term “H++” was created by TE2100 to describe a highly unlikely but possible high-end range of SLR. While most attention is focused on the definite upper bound, the high-end represents a range of values. H++ was designed to support a “dynamic robustness” planning approach that allows for consideration of a wide range of adaptation options as SLR observations and science develop over time (Ranger et al., 2013). This approach examines which extreme adaptation options should be kept open, whilst actively planning for smaller more likely SLR estimates and regularly reviewing the observed rates of SLR and the robustness of SLR projections. In TE2100, an upper-end SLR exceeding 4.2 m in 2100 was initially adopted for planning. This includes a strom surge component which is not expected to change greatly in future. In 2009, after consideration of emerging science and observations, especially Greenland and West Antarctica, the 2100 upper-end SLR projection was revised downwards to 2.7 m, of which 2 m is the time-mean SLR (Lowe et al., 2009). This revised value is still used in practice today (Environmental Agency Guidance, 2021; Palmer et al., 2018). Hence, TE2100 demonstrates an adaptive process of science evaluation and revision of a salient high-end scenario for adaptation planning. This inspires the estimates in this paper.
How We Develop a High-End Estimate
To avoid overreliance on single studies, for example, as illustrated in the (Griggs, 2017) approach, we consider SLR-related processes that are ideally supported by multiple lines of independent evidence. Our approach to construct high-end SLR estimates uses information on SLR components that meet the following three requirements: (a) there is sufficient physical understanding of the relevant processes involved; (b) this understanding can be linked to a quantitative estimate of the associated SLR; (c) there is evidence to explain why the estimates we produce are expected to be in the upper tail of the range of responses. For SLR components where robust distributions are available, two times the standard deviation is warranted in view of the need to sample in the tail. For some components there is sufficient quantitative understanding to use the tail of a probability density function derived from physical models, but not for all components. In particular, the mean and variance of the ice sheet components are poorly constrained, and they cannot be derived directly from climate models. This continues to complicate development of a high-end estimate.
Additionally, the covariance between sea level components is largely unknown because only the ocean component of SLR is directly derived from a large ensemble of climate models in which the relevant processes are coupled. The other sea level components are calculated off-line from climate and land-ice models, and hence require ad-hoc assumptions about the co-variance between components (Lambert et al., 2021), similar to what has been done in Fox-Kemper et al. (2021) or via a covariance controlled by temperature changes (Palmer et al., 2020). To address this problem, we provide a range of high-end values based on the assumption that the different components (glaciers, Greenland, Antarctica, steric expansion, land water storage change [LWSC]) are fully dependent (covariances all equal to 1, maximizing the uncertainty, and hence the upper end of the range) or fully independent (covariance all equal to 0, minimizing the uncertainty, providing a lower end of the range). At present, this is the only fully transparent way to consider the co-variance between for instance the Greenland and Antarctic component. Additionally, it spans the full range of possible outcomes. However, it is unlikely that the complexity of processes involved, and the climate change patterns themselves are fully correlated or fully independent. To illustrate this one can think of the importance of atmospheric circulation changes and basal melt to high end. The first process is important in Greenland and the second in Antarctica. To what end both will change in a similar way is not known, hence full dependency is unlikely. At the same time global warming plays a role in both processes, hence fully independency is also unlikely.
For this reason, practitioners can decide whether to treat the uncertainties as fully independent, fully dependent, or in between depending on their level of risk-averseness. For the independent case (all co-variances zero), we take the median values of AR6 for the different components and define the high-end to be characterized by two standard deviations above the median value. For the dependent case we can simply add the estimates of the different components.
The problem of estimating high-end values for SLR is therefore not only about constraining the uncertainty in the component with the largest uncertainty, but also about understanding how the uncertainty in the SLR components are correlated with each other. The first problem is due to insufficient process understanding of the dynamics of the Antarctic ice sheet. The second problem is due to the surface mass balance (SMB) of the Greenland ice sheet, which requires Earth system models with fully coupled interactive ice sheets models to solve.


For this reason, practitioners can decide whether to treat the uncertainties as fully independent, fully dependent, or in between depending on their level of risk-averseness. For the independent case (all co-variances zero), we take the median values of AR6 for the different components and define the high-end to be characterized by two standard deviations above the median value. For the dependent case we can simply add the estimates of the different components.
The problem of estimating high-end values for SLR is therefore not only about constraining the uncertainty in the component with the largest uncertainty, but also about understanding how the uncertainty in the SLR components are correlated with each other. The first problem is due to insufficient process understanding of the dynamics of the Antarctic ice sheet. The second problem is due to the surface mass balance (SMB) of the Greenland ice sheet, which requires Earth system models with fully coupled interactive ice sheets models to solve.
Here, we restrict ourselves to two time slices (2100 and 2300) and two climate scenarios (RCP2.6/SSP1-2.6 and RCP8.5/SSP5-8.5) which we call for simplicity the low and high scenario. The detailed physical reasoning behind the estimates of the individual cryospheric components is discussed in detail in Section 4 (Glaciers), Section 5 (Greenland), and Section 6 (Antarctica). Section 7 combines the storylines for the different SLR components in an estimate of the high-end global mean SLR for the four scenarios being 2100 and 2300 low and high-temperature change. We focus on the year 2100 because there is significantly more information available for this time horizon than for any other date in time. Moreover, the physical understanding decreases significantly after this time horizon. We focus on 2300 to highlight the long time-scales involved for SLR, the necessity for adaptation and the benefits of mitigation. The scenarios rely strongly on the well-known representative concentration pathways of RCP2.6/SSP1-2.6, which has a median response at 2100 of just under 2°C, and RCP8.5/SSP5-8.5 which has a median around 5°C in 2100 and 8°C–10°C in 2300. These correspond loosely to the core goal of the Paris Agreement and unmitigated emissions, respectively, and provide a significant range in future conditions. We limit our analyses to these scenarios because current understanding of the Antarctic response is not precise enough to distinguish intermediate scenarios between RCP2.6/SSP1-2.6 and RCP8.5/SSP5-8.5, as discussed in Section 7 in more detail. For each of the four scenarios, we provide a range in the high-end estimate of SLR constraint by the dependent or independent addition of the different components.
The method provides estimates of the high-end of projected global sea level change, and does not include the wide range of processes that contribute to regional sea level variations, nor does it consider regional and local vertical land motion, needed to determine the relative sea level changes at a particular coastal location, and that lead to changes in the frequency and magnitude of extreme sea level events at all time scales. Additionally, practitioners need to consider for example, bathymetric effects, possible changes in tides or surges and other near coastal processes. All these local effects and the possible changes therein need to be assessed separately, in particular human-induced subsidence (Nicholls, Lincke, et al., 2021). We in effect assume that the global terms contribute significantly to the uncertainty in local SLR at most locations, but the local terms in the uncertainty budget vary in importance with location. Hence we focus on what is common to all locations. A simple additional step that practitioners could take is to realize that a large Antarctic contribution will influence regional sea level with higher values far from Antarctica due to gravitational effects. Operational tools to include this effect and all the other local to regional processes already exist and are applicable to any global scenario.
Glaciers
In this section, we detail the physical reasoning behind the estimates of the individual cryospheric components starting with glaciers (Section 4, Greenland Section 5 and Antarctica Section 6), as they do not immediately follow from the IPCC model ensemble results. Sections 4–6, 4–6, 4–6 have a similar structure starting with the processes which are relevant and ending with an evaluation of the high-end contribution of the specific component. They each have a figure illustrating how the relevant processes contribute to high-end SLR. The critical processes are eventually per cryospheric component summarized in Table 1 for each scenario.
Table 1: Overview of Ciritical Processes for High-End Estimate of the Cryospheric Components of Sea Level Rise Per Time Scale and Scenario
2100-low
2100-high
2300-low
2300-high
Glaciers
Temperature increase
Temperature increase
Temperature increase, glacier
mass equilibrium
Temperature increase, amount of glacier ice
Greenland
Temperature increase,
outlet glacier
acceleration
Temperature increase,
albedo feedbacks,
atmospheric
circulation changes
Temperature increase
Temperature increase, albedo feedbacks,
atmospheric circulation changes, tipping points
Antarctica
SMB, BMB, switch in
flow below shelves
SMB, shelf collapse,
BMB, calving,
hydrofracturing
SMB, shelf collapse, BMB,
calving, hydrofracturing
MISI, MICI, basal sliding
The Glacier Model Intercomparison Project Phase 2 (GlacierMIP2; Marzeion et al., 2020), is a community effort based on CMIP5 model runs estimating the mass loss of global glaciers. It includes 11 different glacier models, of which seven include all the glaciers outside of Greenland and Antarctica, and four are regional. The glacier models are forced by up to 10 General Circulation Models (GCMs) per RCP scenario, such that a total of 288 ensemble members form the basis of this most recent estimate of glacier mass change projections for the 21st century. Compared to this, projections that include the 23rd century are sparse and based on individual models (e.g., Goelzer et al., 2012; Marzeion et al., 2012). Some information about long-term glacier mass change can be obtained from equilibrium experiments (e.g., Levermann et al., 2013; Marzeion et al., 2018).
Processes for Glaciers Relevant for High-End SLR Scenarios
Temperature changes are critical to calculate glacier volume changes. Through the spatial distribution of glaciers on the land surface and a strong bias to Arctic latitudes, glaciers experience roughly twice the temperature anomalies of the global mean (Marzeion et al., 2020). Biases of projected spatial patterns of temperature increase, particularly concerning Arctic Amplification (stronger temperature change at high latitude), thus have the potential to impact projected glacier mass loss. However, we assume that the GCM ensemble size of GlacierMIP2 is large enough to adequately represent this uncertainty.
Other processes which may play a role are related to debris cover and ice-ocean interaction. Only one of the glacier models taking part in GlacierMIP2 includes a parameterization of frontal ablation/calving (Huss & Hock, 2015), such that there is potential for underestimation of mass loss in the GlacierMIP2 ensemble as important ice-ocean interaction processes are not represented. However, frontal ablation and calving will most strongly affect mass loss of ice currently below mean sea level (Farinotti et al., 2019), and hence they will contribute relatively little to SLR since that constitutes only 15% of the total glacier mass. Additionally, the mass loss projected in GlacierMIP2 for 2100 under RCP2.6/SSP1-2.6 indicates that the number of tidewater glaciers will be greatly reduced even under low emissions and will retreat from contact with the ocean. Thus, ice-ocean interaction may have strong effects on the timing of mass loss within the 21st century, but this is unlikely to play a large role at the end of the 21st century or later, and for greater temperature increases.
None of the global models and only one of the regional models in GlacierMIP2 (Kraaijenbrink et al., 2017) includes effects of debris cover on glacier mass balance. Strong surface mass loss has the potential to cause the surface accumulation of debris layers (e.g., Kirkbride & Deline, 2013) thick enough to insulate the ice below it, thus reducing melt rates (e.g., Nicholson & Benn, 2006). At the same time, a thin debris cover layer could enhance melt rates. The lack of representation of debris cover in GlacierMIP2 is estimated to be unlikely to have a significant impact on the considered high-end range of projections.
Evaluation of the High-End Contribution for Glaciers


Other processes which may play a role are related to debris cover and ice-ocean interaction. Only one of the glacier models taking part in GlacierMIP2 includes a parameterization of frontal ablation/calving (Huss & Hock, 2015), such that there is potential for underestimation of mass loss in the GlacierMIP2 ensemble as important ice-ocean interaction processes are not represented. However, frontal ablation and calving will most strongly affect mass loss of ice currently below mean sea level (Farinotti et al., 2019), and hence they will contribute relatively little to SLR since that constitutes only 15% of the total glacier mass. Additionally, the mass loss projected in GlacierMIP2 for 2100 under RCP2.6/SSP1-2.6 indicates that the number of tidewater glaciers will be greatly reduced even under low emissions and will retreat from contact with the ocean. Thus, ice-ocean interaction may have strong effects on the timing of mass loss within the 21st century, but this is unlikely to play a large role at the end of the 21st century or later, and for greater temperature increases.
None of the global models and only one of the regional models in GlacierMIP2 (Kraaijenbrink et al., 2017) includes effects of debris cover on glacier mass balance. Strong surface mass loss has the potential to cause the surface accumulation of debris layers (e.g., Kirkbride & Deline, 2013) thick enough to insulate the ice below it, thus reducing melt rates (e.g., Nicholson & Benn, 2006). At the same time, a thin debris cover layer could enhance melt rates. The lack of representation of debris cover in GlacierMIP2 is estimated to be unlikely to have a significant impact on the considered high-end range of projections.
Evaluation of the High-End Contribution for Glaciers
Glaciers store less than 1% of the global ice mass (Farinotti et al., 2019), and contributed 0.7 mm/yr over the period 2010–2018 (Hugonnet et al., 2021). Their potential to contribute to SLR is thus limited by their total mass, and which is estimated to be 0.32 ± 0.08 m SLE (Farinotti et al., 2019). However, this limit does not affect their contribution within the 21st century: even under RCP8.5/SSP5-8.5, GlacierMIP2 projects that 64% ± 20% of the glacier mass will remain by 2100. At the same time, the GlacierMIP2 projections show that the glacier contribution strongly depends on the temperature increase itself and less on precipitation changes, both affecting the SMB (Figure 1). This temperature increase is reasonably constrained by the large set of CMIP model ensemble and shows a Gaussian distribution.
Figure 1: Causal relation between processes leading to a high-end contribution of Glaciers to sea level rise (SLR). Climate forcing leads to patterns of temperature (ΔT) and precipitation (ΔP) change over the globe (colored stripes global mean change). These local climate variables control the surface mass balance (SMB) and thereby the volume change of glaciers which determines the SLR by the glacier component. Ice dynamics are usually highly simplified in glacier models and therefore omitted here.
Hence, both climate and appropriate physical processes are captured in the GlacierMIP2 projections and therefore a high-end estimate for glaciers is based on the mean and twice the standard deviation of the GlacierMIP2 experiment as outlined in our definition of a high-end estimate in Section 3. Table 1 and Figure 1 illustrate the critical processes required for a high-end estimate of the glacier contribution. Similar tables and figures are presented in the later ice Sections to demonstrate and contrast the different processes for the different cryospheric components. Table 3 provides the references to the papers from which we derived the actual values to estimate the high-end range. Our final high-end values for the glaciers are based on the GlacierMIP2 result: 0.079 ± 0.056 m of ice volume change under RCP2.6/SSP1-2.6 and 0.159 ± 0.086 m under RCP8.5/SSP5-8.5 in 2100. We convert these to sea level equivalents by correcting for the fact that approximately 15% of the glacier volume is below sea level and arrive at a high-end estimate of 0.15 m sea level equivalents under RCP2.6/SSP1-2.6 and 0.27 m under RCP8.5/SSP5-8.5 (being the mean plus twice the standard deviation). By 2300, glaciers might approach stabilization under RCP2.6/SSP1-2.6 after having contributed 0.28 m to SLR (Cazenave et al., 2018). Their contribution would be limited by their current ice mass above flotation of 0.32 ± 0.08 m (Farinotti et al., 2019), for higher emission scenarios, which is then by definition the highest contribution possible.
Table 3: summarizes all the references used for the different high-end estimates of all the components and provides a comparison to the results of Fox-Kemper et al. (2021).
Greenland
Currently, substantial ice mass loss is observed in Greenland (Bamber et al., 2018; Cazenave et al., 2018; A. Shepherd et al., 2020) with a rate over the period 2010–2019 equivalent to 0.7 mm/yr Global Mean Sea Level Rise (GMSLR; Fox-Kemper et al., 2021). This is to a large extent driven by a change in the SMB, but also by increased dynamic loss of ice via marine-terminating outlet glaciers (Csatho et al., 2014; Enderlin et al., 2014; King et al., 2020; Van Den Broeke, 2016).
Processes
For the 21st century outlet glaciers remain important (Choi et al., 2021; Wood et al., 2021), but for longer time scales changes in SMB are expected to dominate mass loss from the Greenland ice sheet, in particular for high-emission forcing, as some marine-terminating outlet glaciers begin to retreat onto land (e.g., Fürst et al., 2015). Since the IPCC AR5 report, several new studies with projections for Greenland up to 2100 have been published that were broadly consistent with the AR5 (e.g., Calov et al., 2018; Fürst et al., 2015; Golledge et al., 2019; Vizcaino et al., 2015). More recent studies, as also reported by Fox-Kemper et al. (2021), however, have obtained significantly larger mass loss rates with values of up to 33 cm by 2100 (Aschwanden et al., 2019; Hofer et al., 2020; Payne et al., 2021). This can be explained by a larger sensitivity used for converting air temperature to melt, and averaging of the forcing over a large domain and applying a spatially constant scalar anomaly, an approach that has been disputed (Fürst et al., 2015; Gregory & Huybrechts, 2006; Van De Wal, 2001).
The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) ensemble mean results indicated a contribution of 0.096 ± 0.052 m for RCP8.5/SSP5-8.5 in 2100 for a representative range of CMIP5 GCMs (Goelzer et al., 2020), where an unaccounted contribution for committed sea level of 6 ± 2 mm is additionally added (Goelzer et al., 2020; Price et al., 2011). However, recent results with CMIP6 forcing show a larger range with one model suggesting a contribution of 256 mm (Hofer et al., 2020; Payne et al., 2021). These results were obtained with a limited number of CMIP6 models, some of which are known to exhibit a large climate sensitivity and therefore may be biased high. The ISMIP6 results based on CMIP5 therefore provide a reasonable estimate of the uncertainty caused by GCMs, but they do not include an estimate of the uncertainty due to the more detailed and accurate Regional Climate Models (RCMs), which are forced by GCMs to arrive at detailed mass balance changes. ISMIP6 results are based on only one RCM used for downscaling the GCM results to SMB changes.


The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) ensemble mean results indicated a contribution of 0.096 ± 0.052 m for RCP8.5/SSP5-8.5 in 2100 for a representative range of CMIP5 GCMs (Goelzer et al., 2020), where an unaccounted contribution for committed sea level of 6 ± 2 mm is additionally added (Goelzer et al., 2020; Price et al., 2011). However, recent results with CMIP6 forcing show a larger range with one model suggesting a contribution of 256 mm (Hofer et al., 2020; Payne et al., 2021). These results were obtained with a limited number of CMIP6 models, some of which are known to exhibit a large climate sensitivity and therefore may be biased high. The ISMIP6 results based on CMIP5 therefore provide a reasonable estimate of the uncertainty caused by GCMs, but they do not include an estimate of the uncertainty due to the more detailed and accurate Regional Climate Models (RCMs), which are forced by GCMs to arrive at detailed mass balance changes. ISMIP6 results are based on only one RCM used for downscaling the GCM results to SMB changes.
Uncertainties in modeling SMB have been further addressed using a common historical forcing (1980–2012) and comparing the output of 13 different SMB models for the Greenland ice sheet (Fettweis et al., 2020). They found that the ensemble mean produced the best estimate of SMB compared to observations, but the difference in surface melting between models was as much as a factor 3 (from 134 to 508 Gt/yr) and the trend in runoff also differed by a similar amount (from 4.0 to 13.4 Gt/yr/yr) for the common period 1980–2012. Combining the uncertainties in modeling SMB with those for the projected climate forcing indicates that the SMB component is poorly constrained and has large uncertainties, despite having dominated recent mass loss trends in Greenland (Van Den Broeke, 2016).
Further uncertainties in projections for the Greenland ice sheet related to specific processes include: (a) the importance of firn saturation which buffers meltwater prior to run off, (b) albedo lowering by darkening of the surface caused by dust or algal growth, (c) the strength of melt-albedo and height-SMB feedback mechanisms, both leading to additional mass loss, and (d) calving, all being processes that are poorly constrained and often not included in SMB models. Considering these processes has the potential to increase the contribution of Greenland and widen the uncertainty distribution. Furthermore, it is known that the current generation of GCMs do not capture recently observed atmospheric circulation changes (Delhasse et al., 2018, 2020; Fettweis et al., 2017; Hanna et al., 2018), and it is not yet clear whether these changes are forced by climate change or natural variability. Delhasse et al. (2018) estimated that Greenland atmospheric blocking, leading to persistence of enhanced warm air advection from the South and changes in cloudiness (Hofer et al., 2019), may lead to a doubling of mass loss due to SMB changes over the 21st century. This is an estimate for 2040–2050 which does not capture the positive albedo feedback arising from an expanding ablation zone, so we consider the doubling of the mass loss due to SMB changed caused on circulation changes as a lower bound of this effect. In all these studies, projections are made based by stand-alone climate models, lacking many of the feedbacks discussed above (Fyke et al., 2018).
In contrast to the Antarctic ice sheet (discussed in the next Section), only a limited contribution of the dynamics of the outlet glaciers is to be expected (Fürst et al., 2015; Goelzer et al., 2020; Nick et al., 2013), This is because they occupy only a small fraction of the ice sheet perimeter, whereas in Antarctica the majority of the perimeter is in direct contact with the ocean.
Paleo-simulations may be important for constraining near-future mass loss from the Antarctic ice sheet, but provide few constraints for the Greenland ice sheet for the future transient nature of high-end ice mass loss estimates on century time scales. They merely offer insight about sea level high stands during characteristic warm periods in the past.
Evaluation of the High-End Contribution for Greenland
Critically important for generating a high-end estimate for the Greenland ice sheet is the SMB as expressed in Figure 2. SMB and ocean changes are the driver for changes in outlet glaciers and ice sheet dynamics. While SMB and outlet glacier changes have contributed to observed SLR changes, SMB changes are expected to become more important on longer time scales and with stronger forcing. Changes in ice sheet dynamics are expected to be limited. For a high-end estimate of the Greenland ice sheet there is most likely a strong divergence between the low warming and the high warming scenario, particularly beyond 2100. A recent study (Noël et al., 2021), based on a regional climate model forced with a GCM, indicates that the SMB over the ice sheet is negative for a global warming above 2.7 K for a constant topography, ignoring elevation-change-related feedbacks. If so, no processes adding mass to the ice sheet will exist and this has been argued to be a “tipping-point” for the ice sheet. On the other hand, this is challenged by studies including dynamical changes of the topography (Gregory et al., 2020; Le clec’h et al., 2019) because the ice sheet may evolve to a smaller equilibrium state. The importance of the existence of a tipping-point is merely on the millennial time scales, but a negative SMB at least suggests a strong nonlinear response to a large climate forcing. Table 1 illustrates the critical processes to consider when estimating a high-end contribution for the Greenland ice sheet. For the 21st century, we estimate the high-end estimate for the +5°C scenario to be around 0.30 m, being twice the ISMIP6 results (Goelzer et al., 2020) where the factor two arises from the possible atmospheric circulation changes (Church et al., 2013; Delhasse et al., 2018, 2020) that are not captured in the models. This factor of two should be interpreted as the deep uncertainty around the SMB changes in a changing climate caused by a poor understanding of modeling circulation changes and surface processes affecting the albedo. At this point, our approach deviates from Fox-Kemper et al. (2021) who use expert judgment as part of their lines of evidence.
Figure 2: Causal relation between processes leading to a high-end contribution of Greenland to sea level rise (SLR). Critical processes are albedo, ocean forcing and atmospheric circulation changes. These three processes impact the surface mass balance (SMB). Outlet glaciers change by changes in SMB and ocean forcing and SMB also influences the dynamics of the main ice sheet, where the ocean affects the outlet glaciers, together controlling the SLR.
For a +2°C scenario there seem to be few processes that can be large, hence we use the upper end of the very likely range assessed by AR6 being 0.10 m as the high-end estimate (Fox-Kemper et al., 2021). The omission of feedbacks and circulation changes are judged to only be important for large perturbations, justifying excluding them for a high-end estimate. Consequently, high-end projections in 2300 for a +2°C scenario are still constrained and estimated to be 0.3 m, as the SMB is the main driving process. The few studies, based on intermediate complexity climate models (Table 13.8, Church et al., 2013) suggest a high-end contribution of 1.2 m in 2300 from the Greenland ice sheet under a high scenario. A more recent but similar result is obtained using an intermediate complexity model coupled to an ice sheet model (Van Breedam et al., 2020). Here, we suggest, following the projections in 2100, to include a factor 2 based on the possible atmospheric circulation changes above, as the deep uncertainty in the SMB, thereby arriving at a high-end estimate of 2.5 m for Greenland under a +8°C–10°C scenario in 2300. This is close to the structured expert judgment by Bamber et al. (2019), but higher than the experiment by Aschwanden where the degree-day factors are constrained by the observational period 2000–2015 (Fox-Kemper et al., 2021).
Antarctica


Figure 2: Causal relation between processes leading to a high-end contribution of Greenland to sea level rise (SLR). Critical processes are albedo, ocean forcing and atmospheric circulation changes. These three processes impact the surface mass balance (SMB). Outlet glaciers change by changes in SMB and ocean forcing and SMB also influences the dynamics of the main ice sheet, where the ocean affects the outlet glaciers, together controlling the SLR.
For a +2°C scenario there seem to be few processes that can be large, hence we use the upper end of the very likely range assessed by AR6 being 0.10 m as the high-end estimate (Fox-Kemper et al., 2021). The omission of feedbacks and circulation changes are judged to only be important for large perturbations, justifying excluding them for a high-end estimate. Consequently, high-end projections in 2300 for a +2°C scenario are still constrained and estimated to be 0.3 m, as the SMB is the main driving process. The few studies, based on intermediate complexity climate models (Table 13.8, Church et al., 2013) suggest a high-end contribution of 1.2 m in 2300 from the Greenland ice sheet under a high scenario. A more recent but similar result is obtained using an intermediate complexity model coupled to an ice sheet model (Van Breedam et al., 2020). Here, we suggest, following the projections in 2100, to include a factor 2 based on the possible atmospheric circulation changes above, as the deep uncertainty in the SMB, thereby arriving at a high-end estimate of 2.5 m for Greenland under a +8°C–10°C scenario in 2300. This is close to the structured expert judgment by Bamber et al. (2019), but higher than the experiment by Aschwanden where the degree-day factors are constrained by the observational period 2000–2015 (Fox-Kemper et al., 2021).
Antarctica
Currently significant ice mass loss is observed in West-Antarctica (Bamber et al., 2018; Cazenave et al., 2018; Rignot et al., 2019; A. Shepherd et al., 2018): over the period 2010–2019 Antarctica contributed 0.4 mm/yr to GMSL rise (Fox-Kemper et al., 2021). Most studies indicate that ice loss in West Antarctica follows from increased rates of sub-ice shelf melting caused by ocean circulation changes, in particular in the Amundsen Sea sector (Adusumilli et al., 2018; Paolo et al., 2015), but it is questioned whether this is the result of anthropogenic climate change or natural variability in the ocean as suggested by Jenkins et al. (2018) or by a combination of both processes (Holland et al., 2019). Against this background, it is important to consider which processes may lead to substantial continued or accelerated mass loss from Antarctica, and therefore its contribution to high-end sea level scenarios. In addition, it needs to be considered whether there are instabilities in the system which influence high-end estimates. We explore this in more detail than for the previous two components because of the large uncertainty and the large potential contribution to SLR from Antarctica.
Processes in Antarctica Relevant for High-End Sea Level Scenarios
A major uncertainty in future Antarctic mass losses resulting in high-end SLR is connected to the possibility of rapid and/or irreversible ice losses through instabilities in marine-based parts of the ice sheet, as hypothesized for the Marine Ice Sheet Instability (MISI) and the Marine Ice Cliff Instability (MICI), see Pattyn et al. (2018) for further explanation. MISI is a self-reinforcing mechanism within marine ice sheets that lie on a bed that slopes down towards the interior of the ice sheet. If these instabilities are activated it might be that they overshadow climate forcing scenarios. At present, floating ice shelves exert back stress on the inland ice, limiting the flow of ice off the continent and resulting in a stable ice sheet configuration. In the absence of ice-shelf buttressing caused by loss of the shelf or substantial thinning, ice sheets on a bed sloping towards the interior are, under certain circumstances, inherently unstable (Schoof, 2007; Sergienko & Wingham, 2019, 2021), and stable grounding line positions can only be reached when the bed slopes in the opposite direction (sloping bed upwards to the interior; Pattyn et al., 2012). If ice shelf buttressing remains, however, stable grounding line positions can also be reached on downward sloping beds for specific geometric configurations (Cornford et al., 2020; Gudmundsson et al., 2012; Haseloff & Sergienko, 2018; Sergienko & Wingham, 2019). Weak buttressing may not prevent grounding-line retreat, but may slow it.
Antarctic ice shelves modulate the grounded ice flow, and their thinning and weakening is crucial in the timing and magnitude of major ice mass loss or the onset of MISI. This onset of rapid MISI is controlled by the timing of ice shelf breakup or collapse, and the resulting loss of buttressing that otherwise would prevent MISI from occurring. Ice sheet models demonstrate that the permanent removal of all Antarctic ice shelves leads to MISI, West Antarctic ice sheet collapse, and 2–5 m SLR over several centuries (Sun et al., 2020).
The MICI hypothesis of rapid, unmitigated calving of thick ice margins triggered by ice shelf collapse has been included in an ice sheet model by DeConto and Pollard (2016); DeConto et al. (2021) and Pollard et al. (2015). Including the MICI processes was partly motivated by inconsistencies with reconstructed paleo sea level proxies (Bertram et al., 2018; DeConto & Pollard, 2016), but also has a sound physical process based support (Bassis et al., 2021; Crawford et al., 2021). Like MISI, the onset of MICI is triggered by the loss of buttressing ice shelves facilitating the creation of ice cliffs which subsequently destabilize. Its onset also depends on the magnitude of ocean and atmospheric warming. A major difference is the more rapid calving of the ice cliffs at the front of the ice sheet inducing a faster retreat.
Importantly, without the disintegration of buttressing ice shelves, neither MISI nor MICI can operate and the dynamic mass loss contribution from Antarctica to SLR is limited. The current atmospheric state is too cold for a large contribution from surface melt. Further, a few degrees of Antarctic warming leads to more snow accumulation, partly offsetting the increases in oceanic melt and the resulting loss of ice by changes in the ice flow (Seroussi et al., 2020). However, the possibility of larger changes induced by ocean processes cannot be excluded. It has been argued that, in particular, the waters below the Filchner-Ronne ice-shelf could warm by more than 2°C as a result of changes in ocean circulation (Hellmer et al., 2012). Both observations (Darelius et al., 2016; Ryan et al., 2020) and models (Hazel & Stewart, 2020; Naughten et al., 2017) support this as a possibility, although a recent study (Naughten et al., 2021) suggests that such a change in circulation may be unlikely under the climate scenarios considered here for the 21st century. The LARMIP experiments (Levermann et al., 2020) provide an indication that the impact of such a change could be on the order of 0.2 m global mean SLR by 2100.
Observations of basal melt are hampered by the inaccessibility of the sub-ice-shelf cavities, and modeling of basal melt is challenging both because of the lack of observational validation and the limited resolution of the cavities that is possible in models covering continental scales. To date, most ocean model components within coupled climate models do not include the regions beneath the ice shelves. Simplified parameterizations of sub-shelf cavity circulation have been developed, such as the PICO-model (Reese et al., 2018), or the cross-sectional plume model (Lazeroms et al., 2018, 2019; Pelle et al., 2019). Alternatively (Jourdain et al., 2020), propose a parameterization of sub-shelf melt based on the use of low-resolution CMIP5 ocean models, calibrated to observed melt rates (see also Favier et al., 2019). Rather than attempting to explicitly resolve the sub-shelf circulation (Levermann et al., 2020), estimated the Antarctic contribution based on low-resolution ocean temperature change with a linear response function capturing all the uncertainties. This approach ignores dampening or self-amplifying processes and concentrates on the forced response but includes a dynamical response of the ice sheet itself.


Observations of basal melt are hampered by the inaccessibility of the sub-ice-shelf cavities, and modeling of basal melt is challenging both because of the lack of observational validation and the limited resolution of the cavities that is possible in models covering continental scales. To date, most ocean model components within coupled climate models do not include the regions beneath the ice shelves. Simplified parameterizations of sub-shelf cavity circulation have been developed, such as the PICO-model (Reese et al., 2018), or the cross-sectional plume model (Lazeroms et al., 2018, 2019; Pelle et al., 2019). Alternatively (Jourdain et al., 2020), propose a parameterization of sub-shelf melt based on the use of low-resolution CMIP5 ocean models, calibrated to observed melt rates (see also Favier et al., 2019). Rather than attempting to explicitly resolve the sub-shelf circulation (Levermann et al., 2020), estimated the Antarctic contribution based on low-resolution ocean temperature change with a linear response function capturing all the uncertainties. This approach ignores dampening or self-amplifying processes and concentrates on the forced response but includes a dynamical response of the ice sheet itself.
Ideally, sub-shelf circulation and ocean melt should be represented in three dimensions, at the high spatial resolution, and interactively coupled with the ice sheet and the ocean models (Comeau et al., 2022; Smith et al., 2021). This represents a significant ongoing modeling challenge (e.g., Van Westen & Dijkstra, 2021), together with uncertainties in the bathymetry, limiting confidence in future projections of ice shelf loss.
It is also critical to consider other processes than basal melt or circulation changes that can lead to disintegration of the major ice shelves. In particular, one needs to consider calving and surface melt that can enhance ice shelf surface crevassing and hydrofracturing. While hydrofracturing is an important process to reduce or eliminate buttressing and facilitate ice sheet instability, fracturing without surface melt also weakens the ice shelves, particularly along their margins. This is observed in the Amundsen Sea region (Lhermitte et al., 2020), but is not yet fully implemented and validated in large-scale ice sheet models, hindering an estimate of the timing of ice shelf collapse.
As the pace of future atmospheric warming and the capacity of firn to absorb melt water remain uncertain, predictions of ice shelf surface melting by 2100 and subsequent ice shelf disintegration under RCP8.5/SSP5-8.5 vary widely. Based on a regional climate model (Trusel et al., 2015), compiled melt rates under warming scenarios. Under RCP8.5/SSP5-8.5, several small ice shelves will be exposed by 2100 to melt rates exceeding the values observed at the time that the Larsen-B ice-shelf broke up in 2002. However, the major ice shelves (e.g., Filchner-Ronne, Ross Amery) remain stable over this century, but likely not over longer time scales. These melt rates contrast with the results of independent simulations using simpler climate models and a different scheme to calculate surface melt (DeConto & Pollard, 2016) that suggest a much faster disintegration of the ice shelves. An updated assessment (DeConto et al., 2021) confirms the ice shelf stability for this century, but also shows a rapid disintegration soon after under RCP8.5/SSP5-8.5. An intercomparison study showed that the increased melt is partly compensated by increased accumulation (Seroussi et al., 2020), regardless of the emissions scenario followed. It shows disintegration of some small ice shelves, but not the big shelves which constrain high-end contributions to 2100. Soon after 2100, this is likely not the case any longer under RCP8.5/SSP5-8.5. So this facilitates the construction of high-end estimates for 2100 and 2300. For 2100, we can assume that the consequence in terms of SLR is not yet visible, but for 2300 we can be sure that the ice sheet has had sufficient time to start reacting to the break-up of ice shelves under strong forcing scenarios.
What If the Major Ice Shelves Break Up?
Both MISI and MICI might be important for SLR if and when ice shelves collapse. Ice-shelf collapse, therefore, can be considered the key prerequisite for these instabilities to commence. By “instability” we imply that, once initiated, the process of retreat continues irrespective of the applied climate forcing. MISI is a dynamic response of the ice sheet to a change in the buttressing conditions, whereas MICI might lead to direct mass loss via tall collapsing cliffs, which also may be a self-sustaining process. Research on MICI has focused on the critical height at which vertical ice cliffs become unstable (Bassis & Walker, 2012; Clerc et al., 2019; Parizek et al., 2019) and plausible rates of calving and retreat (Schlemm & Levermann, 2019). Estimates of ice-cliff calving have also used observations of calving ice-fronts in Greenland as a constraint (e.g., DeConto & Pollard, 2016), although Greenland glaciers might not be representative of the behavior of wider and thicker outlet glaciers in Antarctica that have lost their ice shelves. The importance of the ice cliff calving mechanism, while likely relevant to high-end sea level scenarios if ice shelves are lost, is currently disputed in the literature (Fox-Kemper et al., 2021).
A second major uncertainty in the response of ice margins once shelves are lost is the uncertainty about the physics of the basal friction conditions near the grounding line, which could further enhance seaward ice flow (Pattyn et al., 2018; Tsai et al., 2015). As a result, the few existing ice model projections for 2300 vary considerably (Bulthuis et al., 2019; Golledge et al., 2015; Levermann et al., 2020), but should all be considered physically plausible and thereby provide independent lines of evidence for a high-end SLR (see, Table 3 for values).
The Antarctic Buttressing Model Intercomparison project (ABUMIP; Sun et al., 2020) shows that instantaneous and sustained loss of all Antarctic ice shelves leads to multi-meter SLR over several centuries (1–12 m in 500 yr from present). The participating models did not include MICI, and the variation in magnitude of ice loss was found to be related to subglacial processes, where plastic friction laws generally lead to enhanced ice loss. This experiment should be considered as an upper bound as artificially regrowth of ice shelves was prevented, and other dampening effects were ignored.
Paleo evidence of past ice loss might provide some constraints on the uncertainty in ice sheet models, but available data are mostly restricted to total ice loss and remain limited in their ability to constrain rates of ice loss (Dutton et al., 2015).
Regardless of the processes driving ice loss on the ice shelves, the retreat of ice also leads to an instantaneous and time-delayed response of the underlying bedrock and an immediate reduction in gravitational attraction between the ice sheet and the nearby ocean. The resulting reduction of relative sea level at the grounding line may stabilize its retreat, providing a negative feedback (Barletta et al., 2018; DeConto et al., 2021; Gomez et al., 2010, 2015; Larour et al., 2019; Pollard et al., 2017) showed that these effects do little to slow the pace of retreat until after the mid-twenty-third century in the Amundsen Sea region. Coulon et al. (2021) also find that the West-Antarctic ice sheet destabilizes for high-forcing regardless of the mantle viscosity. At the same time, Kachuck et al. (2020) and Pan et al. (2022) indicate that the weak viscosity in West-Antarctica might significantly reduce the West-Antarctic contribution over the next 150 yr, because the rapid bedrock uplift compensates the grounding line retreat. Altogether, this suggests that for the shorter time scales over the next centuries, it cannot be excluded that this negative feedback plays a role, but improved 3D viscosity models are needed to quantify this effect.
Evaluation of the High-End Contribution for Antarctica