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
A chain of processes illustrated in Figure 3 control the contribution from Antarctica to SLR. The stability of the ice shelves is central, and this is controlled by surface melt, bottom melt, calving and hydrofracturing. The relative importance of these factors changes because of regional climate change as estimated by global climate models. The uncertainty in the regional climate in the southern hemisphere is generally larger than in the northern hemisphere, increasing uncertainties in the Antarctic component (Heuzé et al., 2013; Russell et al., 2018). Once the ice shelves are broken up, the dynamics of the ice sheet, including the MISI and MICI mechanisms, control how much ice is lost. All studies for a 5°C warming at the end of the century indicate a multi-meter contribution to GMSL from Antarctica on longer than a century time scale. Major ice shelves will disintegrate eventually under that magnitude of warming. The timing of the disintegration is uncertain, but unlikely to have a large effect on high-end SLR already during the 21st century. For this reason, we consider the upper range of Bulthuis et al. (2019), Golledge et al. (2019, 2015), and Levermann et al. (2020), to estimate the high-end contribution of the Antarctic Ice Sheet in 2100 to be 0.39 m for a +2°C scenario (Levermann et al., 2020) and 0.59 m for a +5°C scenario, which is close to the results by Edwards et al. (2021). We do this as no formal probability distributions are available for the likelihood of ice shelf collapse and cliff instability. The study by DeConto and Pollard (2016) is not included for our estimates for 2100, because of a potential overestimation of surface melt rates which initiates shelf disintegration too early. For 2300, only a limited number of ice dynamical studies exist, but they all agree that several meters of SLR from Antarctica is possible because of ice shelf collapse, and limited constraints on instability mechanisms and ice dynamics. Based on Bulthuis et al. (2019), DeConto et al. (2021), and Golledge et al. (2015), we estimate a high-end contribution to be 1.35 m for a +2°C scenario and 6 m for a +8°C–10°C scenario in 2300. A more recent study by DeConto et al. (2021) including improved estimates for surface melt rates is included for the 2300 estimates. So, despite the different physics of all those studies, we believe that we can combine those studies for a high-end estimate because they agree on the onset of shelf disintegration around 2100 and far ahead of 2300. For the +8°C–10°C scenario, we take the average of the three dynamical studies, while realizing that constraints on the rates of mass loss are highly uncertain and vary strongly among the models.
Figure 3: Causal relation between processes leading to a high-end contribution of Antarctica to sea level rise (SLR). The Antarctic climate response affects Surface Melt and Bottom Melt, which together with Calving and Hydrofracturing determine the stability of the ice shelves. If the ice shelves break up, the dynamics encompassing instability mechanisms like Marine Ice Sheet Instability (MISI) and Marine Ice Cliff Instability (MICI) and basal sliding control the final contribution of the Antarctic ice sheet to high-end SLR.
Table 1 illustrates the critical processes for a high-end estimate for the Antarctic contribution.
In summary, it is not only the poor understanding of the dynamics of ice flow, but also the limited understanding of the processes controlling the break-up of the major ice shelves that determines the uncertainty in the timing and magnitude of the Antarctic contribution to sea level. When combined, this leads to the Antarctic component having the largest uncertainties in the sea level projections.
Lines of Evidence for High-End Scenarios
In Sections 4–6, 4–6, 4–6, we discussed the contribution of cryospheric components to SLR, which largely follow from CMIP climate model outputs applied as offline-forcing for ice sheet model simulations. The critical processes for the different components are summarized in Table 1.
In this section, we integrate these components into a total high-end SLR estimate focusing on the time slices 2100 and 2300 and the two temperature scenarios because there is a reasonable sample of studies available. The multiple lines of evidence enable us to go beyond single studies or even single multimodel experiments and provide a more complete synthesis of the plausible physical response, thereby creating estimates that are more salient to practitioners. Such an approach has been used for other seemingly intractable problems such as narrowing the range of Equilibrium Climate Sensitivity (Sherwood et al., 2020) as used in AR6.
For Greenland and Antarctica, the lines of evidence include an assessment of the physical processes. While we cannot define a precise percentile for the total high-end SLR, our interpretation of the multiple lines of evidence as outlined in the Greenland and Antarctic Sections above, is that it lies in the tail and comprises an unlikely outcome. Circulation changes may be important for high-end estimates but only under high forcing for Greenland, instability mechanisms and basal processes and uncertainty in timing of ice shelf collapse result in the high-estimate for Antarctica under a high forcing. For low forcing the SMB changes control the high-estimate for Greenland and the basal melt rate changes control the high-estimate for Antarctica.
Since for longer time scales and higher temperature scenarios, the Antarctic ice sheet contribution dominates the uncertainty in SLR, we can essentially obtain an estimate of high-end SLR by combining the cryospheric components and adding known contributions from thermal expansion and land water changes. Here, the thermal expansion component of SLR and its contribution to the high-end follows directly from the thermal expansion of sea water assessed by Fox-Kemper et al. (2021) as the resulting mean plus twice the standard deviation. The LWSC results mainly from groundwater changes and is partly induced by socio-economic changes and partly due to climate change. In a review by Bierkens and Wada (2019), the upper end of the socio-economic contribution is estimated to be 0.9 mm/yr, and the climate driven component is estimated to be 40 mm in 2100, independent of the scenario (Karabil et al., 2021). This is partly offset by the projections for more dams being built in the early 22nd century (Hawley et al., 2020; Zarfl et al., 2015). Recent papers argue for possible changes in precipitation (Wada et al., 2012), endorheic basin storage changes (Reager et al., 2016; Wang et al., 2018) and increased droughts (Pokhrel et al., 2021), all affecting SLR in a positive or a negative sense. As the LWSC components remains small in all cases and it is not critical for a high-end estimate, here we simply follow (Fox-Kemper et al., 2021).


For Greenland and Antarctica, the lines of evidence include an assessment of the physical processes. While we cannot define a precise percentile for the total high-end SLR, our interpretation of the multiple lines of evidence as outlined in the Greenland and Antarctic Sections above, is that it lies in the tail and comprises an unlikely outcome. Circulation changes may be important for high-end estimates but only under high forcing for Greenland, instability mechanisms and basal processes and uncertainty in timing of ice shelf collapse result in the high-estimate for Antarctica under a high forcing. For low forcing the SMB changes control the high-estimate for Greenland and the basal melt rate changes control the high-estimate for Antarctica.
Since for longer time scales and higher temperature scenarios, the Antarctic ice sheet contribution dominates the uncertainty in SLR, we can essentially obtain an estimate of high-end SLR by combining the cryospheric components and adding known contributions from thermal expansion and land water changes. Here, the thermal expansion component of SLR and its contribution to the high-end follows directly from the thermal expansion of sea water assessed by Fox-Kemper et al. (2021) as the resulting mean plus twice the standard deviation. The LWSC results mainly from groundwater changes and is partly induced by socio-economic changes and partly due to climate change. In a review by Bierkens and Wada (2019), the upper end of the socio-economic contribution is estimated to be 0.9 mm/yr, and the climate driven component is estimated to be 40 mm in 2100, independent of the scenario (Karabil et al., 2021). This is partly offset by the projections for more dams being built in the early 22nd century (Hawley et al., 2020; Zarfl et al., 2015). Recent papers argue for possible changes in precipitation (Wada et al., 2012), endorheic basin storage changes (Reager et al., 2016; Wang et al., 2018) and increased droughts (Pokhrel et al., 2021), all affecting SLR in a positive or a negative sense. As the LWSC components remains small in all cases and it is not critical for a high-end estimate, here we simply follow (Fox-Kemper et al., 2021).
A summary overview of the different components to SLR is shown in Table 2. Assuming perfect correlation between all contributions, the total global high-end SLR estimate in 2100 amounts to 0.86 and 1.55 m for +2°C and +5°C, respectively. Focusing on 2300, these numbers increase considerably to 2.5 and 10.4 m, for +2°C and +8°C–10°C, respectively. Alternatively, assuming total independence of contributions, the high-end rise is 0.72 and 1.27 m for 2100 and 2.2 and 8.6 m in 2300, for +2°C and +8°C–10°C, respectively. Hence, the assumption of independence significantly lowers the estimates; for a high scenario, the difference is around 0.3 m in 2100 and nearly 2 m in 2300.
Table 2: The High-End Estimates for the Different Sea Level Components, and Their Sum
2100    2100    2300    2300
+2°C    +5°C    +2°C    +8°C–10°C
Glaciers        0.15a   0.27    0.28    0.32
Greenland       0.10    0.29    0.39    2.5
Antarctica      0.39    0.59    1.35    6
Thermal expansion       0.18    0.36    0.35    1.51
LWSC    0.04    0.04    0.10    0.10
Total high-end estimateb        Upper end of the range  0.9     1.6     2.5     10
Lower end of the range  0.7     1.3     2.2     9
^a Values are presented relative to 1995–2014 in meters. To compare to a baseline of 1986–2005 as used in AR5 and SROCC add 0.03 m for total sea level and 0.01 m for individual components.
^b The high-end of the range follows from the assumption of perfect correlation (all covariances between the components equal to one), the low-end of the range follows from the assumption of fully uncorrelated (all covariances between the components equal to zero).
Simply summing all high-end components implies a perfect dependency between all the components which is unlikely, as explained above. It would for instance imply that enhanced basal melting in Antarctica is perfectly correlated to specific atmospheric conditions surrounding the Greenland ice sheet. Alternatively, less risk-averse users could assume that all components are independent of each other, which is also not very likely. The high-end estimates should be considered in the context of the mean and likely ranges reported by the IPCC assessments. This also implies that users who are less risk-averse, or have the ability, to iteratively build resilience, can decide to consider the mean values for all components from an IPCC assessment and add the high-end contribution from Antarctica and Greenland to develop a tailored, but still transparent high-end estimate. In this way, the high-end components and how best to sum them encourage discussion between sea level scientists and practitioners and co-production of the most appropriate SLR scenarios for the respective needs, including the development of storylines (T. G. Shepherd & Lloyd, 2021). For a more easily accessible approach, and because both perfect correlation and full independence of all components seem unlikely based on today’s understanding, practitioners might simply average the high end estimate projections in this paper between the two to derive a single, high end projection for use in planning, if that is more useful than a range.
Table 2 also indicates that the high-end estimate for GMSL in 2100 for a significant warming of +5°C does differ from the conclusions drawn by Fox-Kemper et al. (2021) and Oppenheimer et al. (2019), who argue that a GMSL of 2 m cannot be excluded, as supported by results from an expert elicitation process (Bamber et al., 2019). Table 3 shows the detailed differences between this study and (Fox-Kemper et al., 2021) for Greenland and Antarctica showing lower values in this study for Greenland in 2100 for both scenarios and for Greenland and Antarctic for the 2°C scenario in 2300. A reason might be that the expert elicitation used by Fox-Kemper et al. (2021) was influenced by DeConto and Pollard (2016) which is not used here. However, the closed nature of the expert elicitation method does not allow a firm conclusion.
Table 3: A Comparison Between This Paper and the IPCC AR6 Values
References^a    Approach/processes      This paper      AR6 (Table 9.8 and Table 9.11)  Remarks
2100 +2°C
Thermal expansion       Fox-Kemper et al. (2021)        AR6 assessment  0.18b   0.18
Glaciers        Marzeion et al. (2020)  Temperature change, ensemble 10 climate models, 10 glacier models       0.15    0.11
Greenland       Fox-Kemper et al. (2021)        AR6 assessment, medium confidence       0.10    0.30    <<cAR6
Antarctica      Levermann et al. (2020) Basal melt for 16 ice sheet models      0.39    0.25    >>AR6
Land water storage change       Fox-Kemper et al. (2021)        AR6 assessment  0.04    0.04
Total           Range depending on correlation (Section 3)      0.72–0.86       0.79
2100 +5°C
Thermal expansion       Fox-Kemper et al. (2021)        AR6 assessment  0.36    0.36
Glaciers        Marzeion et al. (2020)  Temperature change, ensemble 10 climate models, 10 glacier models       0.27    0.20
Greenland       Delhasse et al. (2018, 2020) and Goelzer et al. (2020)  ISMIP6 assessment including circulation changes and missing feedbacks leading to deep uncertainty       0.29    0.59    <<AR6
Antarctica      Bulthuis et al. (2019), DeConto et al. (2021), and Golledge et al. (2015)       Mixture basal melt and ice dynamical studies    0.59    0.56
Land water storage change       Fox-Kemper et al. (2021)        AR6 assessment  0.04    0.04
Total           Range depending on correlation (Section 3)      1.27–1.55       1.60
2300 +2°C


References^a    Approach/processes      This paper      AR6 (Table 9.8 and Table 9.11)  Remarks
2100 +2°C
Thermal expansion       Fox-Kemper et al. (2021)        AR6 assessment  0.18b   0.18
Glaciers        Marzeion et al. (2020)  Temperature change, ensemble 10 climate models, 10 glacier models       0.15    0.11
Greenland       Fox-Kemper et al. (2021)        AR6 assessment, medium confidence       0.10    0.30    <<cAR6
Antarctica      Levermann et al. (2020) Basal melt for 16 ice sheet models      0.39    0.25    >>AR6
Land water storage change       Fox-Kemper et al. (2021)        AR6 assessment  0.04    0.04
Total           Range depending on correlation (Section 3)      0.72–0.86       0.79
2100 +5°C
Thermal expansion       Fox-Kemper et al. (2021)        AR6 assessment  0.36    0.36
Glaciers        Marzeion et al. (2020)  Temperature change, ensemble 10 climate models, 10 glacier models       0.27    0.20
Greenland       Delhasse et al. (2018, 2020) and Goelzer et al. (2020)  ISMIP6 assessment including circulation changes and missing feedbacks leading to deep uncertainty       0.29    0.59    <<AR6
Antarctica      Bulthuis et al. (2019), DeConto et al. (2021), and Golledge et al. (2015)       Mixture basal melt and ice dynamical studies    0.59    0.56
Land water storage change       Fox-Kemper et al. (2021)        AR6 assessment  0.04    0.04
Total           Range depending on correlation (Section 3)      1.27–1.55       1.60
2300 +2°C
Thermal expansion       Fox-Kemper et al. (2021)        AR6 assessment  0.35    0.35
Glaciers        Goelzer et al. (2012) and Marzeion et al. (2012)        Temperature change, single parameterized glacier models 0.28    0.29
Greenland       Fox-Kemper et al. (2021)        AR6 assessment  0.39    1.28    <<AR6
Antarctica      Bulthuis et al. (2019), DeConto et al. (2021), and Golledge et al. (2015)       Four ice dynamical studies with a range of physical processes simulated 1.35    1.56
Land water storage change       Fox-Kemper et al. (2021)        AR6 assessment  0.1     0.1
Total           Range depending on correlation (Section 3)      2.19–2.47       3.1
2300 +8°C–10°C
Thermal expansion       Fox-Kemper et al. (2021)        AR6 assessment  1.51    1.51
Glaciers        Farinotti et al. (2019) Temperature change, all glaciers melted 0.32    0.32
Greenland       Church et al. (2013), Delhasse et al. (2018, 2020)      SMB changes including deep uncertainty  2.5     2.23
Antarctica      Bulthuis et al. (2019), DeConto et al. (2021), and Golledge et al. (2015)       Four ice dynamical studies with a range of physical processes simulated 6       13.54   <<AR6
Land water storage change       Fox-Kemper et al. (2021)        AR6 assessment  0.10    0.10
Total           Range depending on correlation (Section 3)      8.59–10.43      16.2
^a Reference used to compile the values in this study.
^b Values are in meters relative to a baseline period of 1995–2014.
^c >>/<< indicates more than 20% difference between this study and AR6. We used from AR6 the highest 83rd percentile projections across all probability distributions considered, including low confidence processes.
In 2300, the contribution of the Antarctic ice sheet is poorly constrained, so the high-end estimate is considerably higher than most previous estimates (Church et al., 2013; Oppenheimer et al., 2019), but not as high as (Fox-Kemper et al., 2021). This points to the large uncertainties in projecting sea levels over multiple centuries which arises from: (a) the poorly constrained timing of the collapse of major ice shelves around Antarctica, and (b) the limited understanding of ice-dynamical and subglacial processes. For 2100, the difference for Greenland seems to arise from the difference in structured expert judgment and our physical assessment of the literature.
All the high-end scenarios imply a major adaptation challenge due to SLR, especially beyond 2100 (Haasnoot et al., 2020). What we present builds on a combination of model results and an assessment of different studies leading to lines of evidence per component, thereby providing practical and flexible guidance to practitioners. Further discussions between sea level scientists and practitioners facilitate the application of this knowledge most effectively. We recommend that these storylines should be updated at regular intervals (consistent to the IPCC process), reflecting the evolution of the body of knowledge. This provides a more robust update process than a whiplash response due to single new papers, which may contain high-profile results but lack community consensus or understanding.
Table 2 indicates that the projected temperature has a large effect on the projected high-end SLR during the 21st century and beyond. It also shows that the long timescales associated with slow processes in the ocean and ice sheets provide a strong incentive for mitigation. An SLR of 10 m by 2300 would be extremely challenging and costly, suggesting the need for a near-universal retreat from the present coastline including the most developed and valuable areas, or alternatively, protection/advance on a scale that is hard to envisage, even where artificial protection is the norm today. For a 2°C temperature rise, a high-end 2.5 m rise by 2300 would still present significant challenges, but with rates of SLR that are much slower, offering a wider range of adaptation options and choices. Current experience of rapidly subsiding cities (Nicholls & Tol, 2006) demonstrates that protection for such a magnitude of SLR is feasible if desired and it can be financed. Hence, both from an adaptation and mitigation perspective, smaller temperature increases are preferred.
Considering 2050, there is little difference between low and high-temperature scenarios, as the tails of the distribution are more constrained on decadal time scales. This reflects that the major source of uncertainty—the break-up of major ice shelves in Antarctica—is not foreseen over these time scales.
Addressing 2150 as a time horizon is desirable as many decisions extend over a century (i.e., beyond 2100), but difficult scientifically because of the uncertainty in the timing of a possible break-up of the major Antarctica ice shelves. A first attempt is offered by Fox-Kemper et al. (2021). We argue that there is no evidence for an early break-up of major ice shelves combined with a major loss of grounded Antarctic ice mass influencing the high-end estimate during the 21st century. At the same time, DeConto et al. (2021) indicate a break up of major ice shelves around 2100 or soon after for the high-forcing scenario. The rate of mass loss which might then occur either by enhanced basal sliding or marine ice cliff and shelf instability is poorly constrained, making it extremely difficult to provide a high-end SLR for 2150. It illustrates the high uncertainty in the acceleration of Antarctic ice mass loss. This uncertainty affects the high-end estimate for 2300 much less than for 2150 under the high forcing scenario, as by then the major ice shelves are assumed to have broken up, and sufficient time has passed to allow for accelerated Antarctic ice mass loss. Hence, the precise timing is for this reason less critical at this time scale. For low +2°C forcing scenarios, the prevailing view (DeConto et al., 2021) is that ice shelf break up will occur in fewer regions and therefore the high-end contribution of Antarctica will be considerably lower irrespective of the time scale.


Considering 2050, there is little difference between low and high-temperature scenarios, as the tails of the distribution are more constrained on decadal time scales. This reflects that the major source of uncertainty—the break-up of major ice shelves in Antarctica—is not foreseen over these time scales.
Addressing 2150 as a time horizon is desirable as many decisions extend over a century (i.e., beyond 2100), but difficult scientifically because of the uncertainty in the timing of a possible break-up of the major Antarctica ice shelves. A first attempt is offered by Fox-Kemper et al. (2021). We argue that there is no evidence for an early break-up of major ice shelves combined with a major loss of grounded Antarctic ice mass influencing the high-end estimate during the 21st century. At the same time, DeConto et al. (2021) indicate a break up of major ice shelves around 2100 or soon after for the high-forcing scenario. The rate of mass loss which might then occur either by enhanced basal sliding or marine ice cliff and shelf instability is poorly constrained, making it extremely difficult to provide a high-end SLR for 2150. It illustrates the high uncertainty in the acceleration of Antarctic ice mass loss. This uncertainty affects the high-end estimate for 2300 much less than for 2150 under the high forcing scenario, as by then the major ice shelves are assumed to have broken up, and sufficient time has passed to allow for accelerated Antarctic ice mass loss. Hence, the precise timing is for this reason less critical at this time scale. For low +2°C forcing scenarios, the prevailing view (DeConto et al., 2021) is that ice shelf break up will occur in fewer regions and therefore the high-end contribution of Antarctica will be considerably lower irrespective of the time scale.
These new high-end estimates provide practitioners with a range of plausible, transparent, and salient high-end sea level estimates that reflect our current physical understanding and reflect the author’s views that it is not possible with the current level of understanding to match these to precise likelihoods. Further, it encourages practitioners to consider their vulnerability and adaptation options without misleading them about the level of understanding. In this way sea level scientists and practitioners can learn together about the application and co-develop appropriate bespoke solutions. How practioners decide to use these numbers, including the low/high ranges should in our view depend on their risk-averseness, among other factors, which they have to evaluate themselves.
We also purposely choose to define high-end estimates for low/+2°C and high/+5°C in 2100 and +8°C–10°C in 2300 temperature increase, with respect to the pre-industrial levels. We cannot provide a likelihood for either of these emissions-driven warming scenarios, and moreover it is also not possible at present to define a high-end for an intermediate emissions or temperature rise scenario (e.g., RCP4.5). While it is obvious that this will be intermediate to the values in Table 2, more detailed specification is not possible due to limited understanding of the time scales and strengths of the feedbacks of the ice components for an intermediate scenario. Essentially, we are convinced that the ice shelves will break-up under high scenarios, but whether they will largely remain intact under lower scenarios is highly uncertain thereby making a distinction between RCP4.5 and RCP2.6/SSP1-2.6 impossible with present levels of knowledge. In addition, there are fewer studies available for a robust high-end estimate for RCP4.5. Irrespective of the scenario (Fox-Kemper et al., 2021) estimate the sea level commitment associated with historical estimates to be 0.7–1.1 m up to 2300, which could probably be considered as the lower end of SLR to consider for practitioners.
Discussion
In this paper, we have attempted to provide physically based high-end estimates of global SLR to 2100 and 2300 by providing specific high-end numbers for SLR under the assumption of a +2°C and +5°C global mean temperature increase (in 2100). In particular, we aimed to provide practitioners with salient well-supported information on low likelihood, high-consequence cases that complement those provided by Fox-Kemper et al. (2021). These high-end estimates can be debated and tailored to individual risk-averse decisions in adaptation planning and implementation, supporting more sound risk management, while adhering to a reasonable standard of practice to ensure appropriate resource allocation. In this way, planners have information available allowing them to frame high-end risk using a standard that balances risk management objectives with finite resources, while avoiding large opportunity costs where possible.
This approach is different than that taken by Fox-Kemper et al. (2021), in particular for projected sea level contributions from Greenland and Antarctica, and we highlight that our approach does not replace that of Fox-Kemper et al. (2021), but instead complements it. Details of the difference are given in Table 3.
We present a range for the high-end estimates, which is defined by the assumptions of how the different components are correlated. The choice of where in this range a user chooses to focus will depend on aspects such as their level of risk aversion and ideally will arise for any particular application through a detailed dialogue between the practitioners and sea level experts.
Hence, as an expert sea level community group we have attempted to quantify the processes controlling the sea level contribution from the different components based largely on the same evidence as used by Fox-Kemper et al. (2021). The independent assessment of the literature presented here results in a different outcome. A key difference in the methods is that here we emphasize that the Antarctic contribution is likely to be controlled by the timing of the loss of major ice shelves around Antarctica. We attempted to follow lines of physical evidence which represent a snapshot of the current knowledge, and this will evolve as knowledge improves. As new physical insights emerge, so individual components of the analysis could be repeated by sub-groups of experts (e.g., for Antarctica), resulting in an update of Table 3. In this way, the approach is modular and comparatively easy to update.
In this respect, the improved use of climate models including a dynamical ice sheet component will fill knowledge gaps with respect to the quantification of feedbacks which are not yet included in the modeling frameworks, and an improved understanding of correlations between different components of the climate system that contribute to global SLR. In addition, growing observational time series will also constrain the physics of the slow processes controlling ice shelf and ice sheet evolution. A strong focus on the timing of thinning and breakup of the Antarctic ice shelves is a critical aspect. At the same time, we also acknowledge that most studies fail to convincingly address the paleo sea level record and this requires further investigation, which may affect future high-end sea level estimates.
This work was originally inspired by questions focusing on “what is a credible high-end SLR for different timeframes?”, to aid climate risk assessment and adaptation planning. In addition, it demonstrates the large benefits of greenhouse gas mitigation for SLR over many centuries, which have only been explored in DeConto et al. (2021). Practitioners can use the high-end estimates to “stress-test” decisions for high-end SLR and develop robust adaptive plans that acknowledge uncertainties about SLR and identify short-term actions and long-term options to adapt as necessary. While our results suggest a plausible high-end, there are still aspects of sea level that are not well understood or which we cannot yet quantify and which might impact a future estimate of high-end SLR, especially on timescales beyond 2100. These include processes associated with the Antarctic ice sheet that are not well understood but which have the potential to cause rapid SLR: better understanding might impact future estimates of the high-end. Qualitatively this is consistent with the rapid expansion of high-end SLR uncertainty identified by Fox-Kemper et al. (2021) from 2100 to 2150, which is over a timescale of high interest to risk-adverse practitioners. Future research on high-end estimates in 2150 would be especially valuable, including under intermediate forcing scenarios (e.g., SSP3).
First, among these uncertainties is the rate of ice loss caused by MICI in Antarctica. The only continental-scale model attempting to quantify the contribution of MICI to future SLR, uses constraints based on observations of calving at the termini of large marine-terminating glaciers in Greenland. However, the geometry of some Antarctic outlet glaciers is very different to the relatively narrow, mélange-filled fjordal settings in Greenland. For example, Thwaites Glacier in West Antarctica is about 10 times wider than Jakobshavn and drains a deep basin in the heart of West Antarctica >2 km deep in places. While MICI has not commenced at Thwaites, the ongoing loss of shelf ice and the retreat of the grounding line onto deeper bedrock could eventually produce a much taller and wider calving front than anything observed on Earth today. Hence models that include MICI in Antarctica, but limit calving rates to those observed on Greenland could be too conservative (e.g., DeConto et al., 2021) and should not be considered an upper bound on the possible SLR contribution from Antarctica. Similar uncertainties also exist for basal processes controlling the rate of mass loss once buttressing ice shelves are lost, with a large simulated range in SLR from Antarctica in response to strong imposed forcing (Sun et al., 2020).


First, among these uncertainties is the rate of ice loss caused by MICI in Antarctica. The only continental-scale model attempting to quantify the contribution of MICI to future SLR, uses constraints based on observations of calving at the termini of large marine-terminating glaciers in Greenland. However, the geometry of some Antarctic outlet glaciers is very different to the relatively narrow, mélange-filled fjordal settings in Greenland. For example, Thwaites Glacier in West Antarctica is about 10 times wider than Jakobshavn and drains a deep basin in the heart of West Antarctica >2 km deep in places. While MICI has not commenced at Thwaites, the ongoing loss of shelf ice and the retreat of the grounding line onto deeper bedrock could eventually produce a much taller and wider calving front than anything observed on Earth today. Hence models that include MICI in Antarctica, but limit calving rates to those observed on Greenland could be too conservative (e.g., DeConto et al., 2021) and should not be considered an upper bound on the possible SLR contribution from Antarctica. Similar uncertainties also exist for basal processes controlling the rate of mass loss once buttressing ice shelves are lost, with a large simulated range in SLR from Antarctica in response to strong imposed forcing (Sun et al., 2020).
Second, the timing when Antarctic ice shelves might be lost remains a key unknown. Shelf collapse may be caused by hydrofracturing, but this process is poorly understood. Some models assume hydrofracturing occurs if surface melt exceeds a threshold, but due to limited observations, the threshold is poorly constrained, as is the role of interannual variability in the melt, accumulation, and the detailed physics of the firn layer. For the break-up of the Larsen B Ice Shelf in 2002, this variability was probably important, but there is insufficient data for a robust calibration. In addition, break-up of ice shelves has been observed in response to processes triggered by ocean warming, processes which are not yet well quantified and that are omitted from all major existing models.
Third, most models are unable to capture the magnitude of SLR in previous warm periods in Earth history, suggesting that there are either processes missing or that the importance of the processes that are included are underestimated. Antarctica lost ice during these warm periods, but we do not know understand why, even not, if we use the lower estimates of Last Interglacial highstands as recently published (Dyer et al., 2021).
Because of these “Unknown Unknowns”, a flexible approach to risk and adaptation assessment is advisable recognizing the uncertainties of future SLR and realizing that major mitigation will prevent locking in a catastrophic commitment to SLR over multiple centuries. The fact that multiple lines of evidence are needed to build a salient and credible high-end estimate also implies that the publication of a single new study should not change the approach—overreaction and a whiplash approach needs to be prevented. However, it also implies that the evidence leading to the high-end values need to be periodically revisited at regular timescales to IPCC assessments.


Slangen et al. (2022)
Title:
The evolution of 21st century sea-level projections from IPCC AR5 to AR6 and beyond
Keywords:
Sea-level changes, Numerical modelling, Climate change, Coastal change
Corresponding author:
Aimée B.A. Slangen
Citation:
Slangen, A. B. A., Palmer, M. D., Camargo, C. M. L., Church, J. A., Edwards, T. L., Hermans, T. H. J., et al. (2022). The evolution of 21st century sea-level projections from IPCC AR5 to AR6 and beyond. Cambridge Prisms: Coastal Futures, 1. https://doi.org/10.1017/cft.2022.8
URL:
https://www.cambridge.org/core/journals/cambridge-prisms-coastal-futures/article/evolution-of-21st-century-sealevel-projections-from-ipcc-ar5-to-ar6-and-beyond/BECA28410452901A67B01B68F9B358E0
Abstract
Sea-level science has seen many recent developments in observations and modelling of the different contributions and the total mean sea-level change. In this overview, we discuss (1) the evolution of the Intergovernmental Panel on Climate Change (IPCC) projections, (2) how the projections compare to observations and (3) the outlook for further improving projections. We start by discussing how the model projections of 21st century sea-level change have changed from the IPCC AR5 report (2013) to SROCC (2019) and AR6 (2021), highlighting similarities and differences in the methodologies and comparing the global mean and regional projections. This shows that there is good agreement in the median values, but also highlights some differences. In addition, we discuss how the different reports included high-end projections. We then show how the AR5 projections (from 2007 onwards) compare against the observations and find that they are highly consistent with each other. Finally, we discuss how to further improve sea-level projections using high-resolution ocean modelling and recent vertical land motion estimates.
Impact statement
Sea-level rise is an important aspect of climate change, with potentially large consequences for coastal communities around the world. Sea-level change is therefore an active area of research that has seen many developments in the past decades. Based on the available research, the Intergovernmental Panel on Climate Change (IPCC) provides regular updates on sea-level projections which are used by policymakers and for adaptation planning. In this review, we compare the sea-level projections from different IPCC reports in the past 10 years and explain what has changed in the methods used and in the numbers presented. We also compare observed changes from the 2021 IPCC report to projected changes from the 2013 IPCC report, for the overlapping period 2007–2018, and find that they are highly consistent. Finally, we share some potential future research directions on improving sea-level projections.
Introduction
Present-day sea-level change (SLC) is primarily a consequence of human-induced climate change, which will impact people and communities all over the world. From a decision-making perspective, knowing how much the sea level will rise, and when, can help to decide which protective measures need to be taken at which point in time. Therefore, sea-level projections are among the most anticipated outcomes of the Intergovernmental Panel on Climate Change (IPCC) assessment reports (ARs). While sea-level extremes are also an important consideration for future coastal hazards, in this review we focus our attention on projections of mean sea level.


Impact statement
Sea-level rise is an important aspect of climate change, with potentially large consequences for coastal communities around the world. Sea-level change is therefore an active area of research that has seen many developments in the past decades. Based on the available research, the Intergovernmental Panel on Climate Change (IPCC) provides regular updates on sea-level projections which are used by policymakers and for adaptation planning. In this review, we compare the sea-level projections from different IPCC reports in the past 10 years and explain what has changed in the methods used and in the numbers presented. We also compare observed changes from the 2021 IPCC report to projected changes from the 2013 IPCC report, for the overlapping period 2007–2018, and find that they are highly consistent. Finally, we share some potential future research directions on improving sea-level projections.
Introduction
Present-day sea-level change (SLC) is primarily a consequence of human-induced climate change, which will impact people and communities all over the world. From a decision-making perspective, knowing how much the sea level will rise, and when, can help to decide which protective measures need to be taken at which point in time. Therefore, sea-level projections are among the most anticipated outcomes of the Intergovernmental Panel on Climate Change (IPCC) assessment reports (ARs). While sea-level extremes are also an important consideration for future coastal hazards, in this review we focus our attention on projections of mean sea level.
In the past 15 years, process-based sea-level projections (i.e., projections which use models to simulate the physical processes and interactions contributing to sea-level change) in the IPCC reports have developed from global-mean only (AR4, Meehl et al., Reference Meehl, Stocker, Collins and Zhao2007) to regional projections (AR5, Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a). More recent reports focused on the Antarctic contribution (Special Report on Oceans and Cryosphere in a Changing Climate SROCC, Oppenheimer et al., Reference Oppenheimer, Glavovic, Hinkel, van de Wal, Magnan, Abd-Elgawad, Cai, Cifuentes-Jara, DeConto, Ghosh, Hay, Isla, Marzeion, Meyssignac and Sebesvari2019), and provided projections consistent with the assessed Equilibrium Climate Sensitivity (ECS) (AR6, Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a). The research community has dedicated significant research effort and published many papers on improving the understanding and modelling of the different contributions to SLC, such as ice sheets, glaciers and sterodynamic changes (e.g., Gregory et al., Reference Gregory, Bouttes, Griffies, Haak, Hurlin, Jungclaus, Kelley, Lee, Marshall, Romanou, Saenko, Stammer and Winton2016; Nowicki et al., Reference Nowicki, Payne, Larour, Seroussi, Goelzer, Lipscomb, Gregory, Abe-Ouchi and Shepherd2016; The IMBIE Team, 2018, 2019; Hock et al., Reference Hock, Bliss, Marzeion, Giesen, Hirabayashi, Huss, Radic and Slangen2019). Since AR5, new global mean and regional projections have been published, using various methods: for instance based on fully coupled climate models (Slangen et al., Reference Slangen, Katsman, van de Wal, Vermeersen and Riva2012; Kopp et al., Reference Kopp, Horton, Little, Mitrovica, Oppenheimer, Rasmussen, Strauss and Tebaldi2014; Slangen et al., Reference Slangen, Carson, Katsman, van de Wal, Köhl, Vermeersen and Stammer2014a; Carson et al., Reference Carson, Köhl, Stammer, Slangen, Katsman, van de Wal, Church and White2015; Jackson and Jevrejeva, Reference Jackson and Jevrejeva2016; Buchanan et al., Reference Buchanan, Oppenheimer and Kopp2017; Palmer et al., Reference Palmer, Gregory, Bagge, Calvert, Hagedoorn, Howard, Klemann, Lowe, Roberts, Slangen and Spada2020), reduced-complexity models (Perrette et al., Reference Perrette, Landerer, Riva, Frieler and Meinshausen2013; Schleussner et al., Reference Schleussner, Lissner, Fischer, Wohland, Perrette, Golly, Rogelj, Childers, Schewe, Frieler, Mengel, Hare and Schaeffer2016; Nauels et al., Reference Nauels, Meinshausen, Mengel, Lorbacher and Wigley2017), semi-empirical models (Kopp et al., Reference Kopp, Kemp, Bittermann, Horton, Donnelly, Gehrels, Hay, Mitrovica, Morrow and Rahmstorf2016; Mengel et al., Reference Mengel, Levermann, Frieler, Robinson, Marzeion and Winkelmann2016; Bakker et al., Reference Bakker, Wong, Ruckert and Keller2017; Bittermann et al., Reference Bittermann, Rahmstorf, Kopp and Kemp2017; Goodwin et al., Reference Goodwin, Haigh, Rohling and Slangen2017; Wong et al., Reference Wong, Bakker, Ruckert, Applegate, Slangen and Keller2017; Jackson et al., Reference Jackson, Grinsted and Jevrejeva2018; Jevrejeva et al., Reference Jevrejeva, Jackson, Grinsted, Lincke and Marzeion2018), structured expert judgement (SEJ) (Bamber et al., Reference Bamber, Oppenheimer, Kopp, Aspinall and Cooke2019), or a mixture of methods (Grinsted et al., Reference Grinsted, Jevrejeva, Riva and Dahl-Jensen2015; Kopp et al., Reference Kopp, DeConto, Bader, Hay, Horton, Kulp, Oppenheimer, Pollard and Strauss2017; Le Bars et al., Reference Le Bars, Drijfhout and De Vries2017; Le Cozannet et al., Reference Le Cozannet, Manceau and Rohmer2017a). There have also been a number of reviews, including a database of sea-level projections (Garner et al., Reference Garner, Weiss, Parris, Kopp RE, Horton RM, Overpeck and Arbor2018), reviews on developments following AR5 (Clark et al., Reference Clark, Church, Gregory and Payne2015; Slangen et al., Reference Slangen, Adloff, Jevrejeva, Leclercq, Marzeion, Wada and Winkelmann2017a), overviews of processes and timescales (Horton et al., Reference Horton, Kopp, Garner, Hay, Khan, Roy and Shaw2018a; Hamlington et al., Reference Hamlington, Gardner, Ivins, Lenaerts, Reager, Trossman, Zaron, Adhikari, Arendt, Aschwanden, Beckley, Bekaert, Blewitt, Caron, Chambers, Chandanpurkar, Christianson, Csatho, Cullather, DeConto, Fasullo, Frederikse, Freymueller, Gilford, Girotto, Hammond, Hock, Holschuh, Kopp, Landerer, Larour, Menemenlis, Merrifield, Mitrovica, Nerem, Nias, Nieves, Nowicki, Pangaluru, Piecuch, Ray, Rounce, Schlegel, Seroussi, Shirzaei, Sweet, Velicogna, Vinogradova, Wahl, Wiese and Willis2020), reviews on coastal sea-level change (e.g., Van de Wal et al., Reference Van de Wal, Zhang, Minobe, Jevrejeva, Riva, Little, Richter and Palmer2019) and reviews integrating risk and adaptation assessments (e.g., Nicholls et al., Reference Nicholls, Hanson, Lowe, Slangen, Wahl, Hinkel and Long2021).


One thing that all sea-level projections have in common, despite the different approaches and methodologies, is an uncertainty that grows substantially through time. The uncertainties in regional sea-level projections over the coming years to decades result primarily from internal climate variability (see e.g., Palmer et al., Reference Palmer, Gregory, Bagge, Calvert, Hagedoorn, Howard, Klemann, Lowe, Roberts, Slangen and Spada2020, their Figure 11). On decadal to centennial timescales, uncertainties depend on the future forcings (such as greenhouse gas emissions) and the response of the climate system; and on the modelling uncertainty associated with simulating the different contributions to SLC. The forcing uncertainty can be assessed using different emissions or radiative forcing scenarios, varying from scenarios with net-zero CO2 emissions by 2050 to scenarios with a tripling of the present-day CO2 emissions by 2100. The modelling uncertainty can be relatively well quantified for some contributions, such as global mean thermal expansion. For other contributions, such as (multi)-century timescale ice mass loss of the Antarctic Ice Sheet, the uncertainty is characterised as ‘deep uncertainty’, which means that experts do not know or cannot agree on appropriate conceptual models or the probability distributions used (Lempert et al., Reference Lempert, Popper and Bankes2003; Kopp et al., Reference Kopp, Oppenheimer, O’Reilly, Drijfhout, Edwards, Fox-Kemper, Garner, Golledge, Hermans, Hewitt, Horton, Krinner, Notz, Nowicki, Palmer, Slangen and Xiao2022). These contributions are therefore a topic of much research and debate (e.g., Oppenheimer et al., Reference Oppenheimer, Glavovic, Hinkel, van de Wal, Magnan, Abd-Elgawad, Cai, Cifuentes-Jara, DeConto, Ghosh, Hay, Isla, Marzeion, Meyssignac and Sebesvari2019).
In addition to studies on future sea-level projections, much research has been focused on understanding past observations. A lot of progress has been made in the closing of the sea-level budget for the 20th century, which compares the sum of the observed contributions to the total observed changes, on global (e.g., Gregory et al., Reference Gregory, White, Church, Bierkens, Box, Van Den Broeke, Cogley JG. Fettweis, Hanna, Huybrechts, Konikow, Leclercq, Marzeion, Oerlemans, Tamisiea, Wada, Wake and Van De Wal2013; Chambers et al., Reference Chambers, Cazenave, Champollion, Dieng, Llovel, Forsberg, von Schuckmann and Wada2016; Cazenave et al., Reference Cazenave2018; Frederikse et al., Reference Frederikse, Landerer, Caron, Adhikari, Parkes, Humphrey, Dangendorf, Hogarth, Zanna, Cheng and Wu2020) and basin scales (e.g., Slangen et al., Reference Slangen, Van De Wal, Wada and Vermeersen2014b; Frederikse et al., Reference Frederikse, Riva, Kleinherenbrink, Wada, van den Broeke and Marzeion2016; Rietbroek et al., Reference Rietbroek, Brunnabend, Kusche, Schröter and Dahle2016; Frederikse et al., Reference Frederikse, Jevrejeva, Riva and Dangendorf2018; Wang et al., Reference Wang, Church, Zhang, Gregory, Zanna and Chen2021b). These budget studies have led to important advances in the understanding of sea-level change and its contributing processes on global and regional scales. In addition, the observations can be compared with model simulations (Church et al., Reference Church, Monselesan, Gregory and Marzeion2013c; Meyssignac et al., Reference Meyssignac, Slangen, Melet, Church, Fettweis, Marzeion, Agosta, Ligtenberg, Spada, Richter, Palmer, Roberts and Champollion2017; Slangen et al., Reference Slangen, Meyssignac, Agosta, Champollion, Church, Fettweis, Ligtenberg, Marzeion, Melet, Palmer, Richter, Roberts and Spada2017b; Oppenheimer et al., Reference Oppenheimer, Glavovic, Hinkel, van de Wal, Magnan, Abd-Elgawad, Cai, Cifuentes-Jara, DeConto, Ghosh, Hay, Isla, Marzeion, Meyssignac and Sebesvari2019) to test, understand and improve the model representation of the different processes. This has turned out to be challenging, especially for the earlier part of the 20th century: SROCC stated that only 51% of the 1901–1990 observed global mean sea-level (GMSL) change could be explained by models, due to ‘the inability of climate models to reproduce some observed regional changes’, in particular before 1970. The agreement between models and observations increased to 91% for 1971–2015 and 99% for 2006–2015 (Oppenheimer et al., Reference Oppenheimer, Glavovic, Hinkel, van de Wal, Magnan, Abd-Elgawad, Cai, Cifuentes-Jara, DeConto, Ghosh, Hay, Isla, Marzeion, Meyssignac and Sebesvari2019).
It is also possible to evaluate past projections against observations that have been made since. For instance, for total SLC, Wang et al. (Reference Wang, Church, Zhang and Chen2021a) found an almost identical GMSL trend in the observations and AR5 projections for the period 2007–2018. Lyu et al. (Reference Lyu, Zhang and Church2021) compared observations and climate model output of ocean warming for the purpose of constraining projections. They found a high correlation for the Argo period (2005–2019) and concluded that the observational record over this period is currently the most useful constraint for projections of ocean warming. Such evaluations against the already realised SLC are important to provide further insights and build confidence in sea-level projections.
Here, we will first discuss ‘how we got here’: recent methodological developments in process-based sea-level projections for the 21st century, with a brief recap of the IPCC sea-level projection methods up to IPCC AR5, followed by a discussion of the key differences between AR5, SROCC and AR6 projections (section ‘Key advances in sea-level projections up to IPCC AR6’). Next, we discuss ‘where we are’, by evaluating AR5 projections of SLC (which start from 2007 onwards) against observational time series (up to 2018), both for total GMSL and for individual contributions (section ‘Comparison of the AR5 model simulations with observations’). Finally, we discuss ‘where we’re going’: how can sea-level projections be better tailored for coastal information (section ‘Moving towards local information’). Throughout this review, we adopt the sea-level terminology defined by Gregory et al. (Reference Gregory, Griffies, Hughes, Lowe, Church, Fukimori, Gomez, Kopp, Landerer, Le Cozannet, Ponte, Stammer, Tamisiea and van de Wal2019) and we refer to Box 9.1 of IPCC AR6 (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a) for a summary of the key drivers of SLC.
Key advances in sea-level projections up to IPCC AR6


Here, we will first discuss ‘how we got here’: recent methodological developments in process-based sea-level projections for the 21st century, with a brief recap of the IPCC sea-level projection methods up to IPCC AR5, followed by a discussion of the key differences between AR5, SROCC and AR6 projections (section ‘Key advances in sea-level projections up to IPCC AR6’). Next, we discuss ‘where we are’, by evaluating AR5 projections of SLC (which start from 2007 onwards) against observational time series (up to 2018), both for total GMSL and for individual contributions (section ‘Comparison of the AR5 model simulations with observations’). Finally, we discuss ‘where we’re going’: how can sea-level projections be better tailored for coastal information (section ‘Moving towards local information’). Throughout this review, we adopt the sea-level terminology defined by Gregory et al. (Reference Gregory, Griffies, Hughes, Lowe, Church, Fukimori, Gomez, Kopp, Landerer, Le Cozannet, Ponte, Stammer, Tamisiea and van de Wal2019) and we refer to Box 9.1 of IPCC AR6 (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a) for a summary of the key drivers of SLC.
Key advances in sea-level projections up to IPCC AR6
There have been substantial methodological and scientific advances in sea-level projections since the publication of the IPCC First Assessment Report in 1990 (Warrick and Oerlemans, Reference Warrick and Oerlemans1990). The use of global climate models (GCMs) in IPCC sea-level projections dates back to the IPCC Third Assessment Report (Church et al., Reference Church, Gregory, Huybrechts, Kuhn, Lambeck, Nhuan, Qin and Woodworth2001). In IPCC AR4, climate models from the third phase of the Climate Model Intercomparison Project (CMIP3) were used as the ‘backbone’ of the process-based GMSL projections (Meehl et al., Reference Meehl, Stocker, Collins and Zhao2007), with a similar approach adopted for AR5 using the CMIP5 generation of climate models (Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a). A major change for the AR5 was the inclusion of regional projections (following Slangen et al., Reference Slangen, Katsman, van de Wal, Vermeersen and Riva2012). The IPCC Special Report on Global Warming of 1.5° for the first time assessed GMSL based on warming levels (Hoegh-Guldberg et al., Reference Hoegh-Guldberg, Jacob, Taylor, Bindi, Brown, Camilloni, Diedhiou, Djalante, Ebi, Engelbrecht, Guiot, Hijioka, Mehrotra, Payne, Seneviratne, Thomas, Warren, Zhou, Masson-Delmotte, Zhai, Pörtner and Waterfield2018). The SROCC added new information on the dynamical ice sheet contribution to the AR5 projections (Oppenheimer et al., Reference Oppenheimer, Glavovic, Hinkel, van de Wal, Magnan, Abd-Elgawad, Cai, Cifuentes-Jara, DeConto, Ghosh, Hay, Isla, Marzeion, Meyssignac and Sebesvari2019). The main advance in AR6 was the use of physics-based emulators to ensure consistency of the sea-level projections with the AR6-assessed ECS and global surface air temperature (GSAT).
We will now discuss some of the key differences of the global mean and regional sea-level projections in IPCC AR6 relative to AR5 and SROCC, by explaining what has been done differently, why these changes were made, and what the effects are on the projections. We do not include the SR1.5 projections in this discussion (Hoegh-Guldberg et al., Reference Hoegh-Guldberg, Jacob, Taylor, Bindi, Brown, Camilloni, Diedhiou, Djalante, Ebi, Engelbrecht, Guiot, Hijioka, Mehrotra, Payne, Seneviratne, Thomas, Warren, Zhou, Masson-Delmotte, Zhai, Pörtner and Waterfield2018), as the SR1.5 report made a literature-based assessment of GMSL changes for 1.5° and 2°, but did not produce new projections.
Before we discuss the projections, we note that the interpretation and communication of the uncertainties in sea-level projections has varied across the different IPCC assessment reports (Kopp et al., Reference Kopp, Oppenheimer, O’Reilly, Drijfhout, Edwards, Fox-Kemper, Garner, Golledge, Hermans, Hewitt, Horton, Krinner, Notz, Nowicki, Palmer, Slangen and Xiao2022). IPCC reports use calibrated uncertainty language, in which the confidence level is a qualitative reflection of the evidence and agreement, whereas the likelihood metric is a quantitative measure of uncertainty, expressed probabilistically (Box 1.1, Chen et al., Reference Chen, Rojas, Samset, Cobb, Diongue Niang, Edwards, mori, Faria, Hawkins, Hope, Huybrechts, Meinshausen, Mustafa, Plattner, Tréguier, Masson-Delmotte, Zhai, Pirani and Zhou2021). For the medium confidence projections in AR5, the 5–95th percentile range of the model ensemble was interpreted as the likely range (the central range with about two-thirds probability, 17–83%), with the uncertainty range of all contributions inflated relative to the model spread to account for structural uncertainties arising from the CMIP5 model ensemble. For the sea-level projections in AR6 (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a, Section 9.6), the likely range was redefined as the central range with at least two-thirds probability, encompassing the outer 17th to 83rd percentiles of the probability distributions considered in a p-box (e.g., Le Cozannet et al., Reference Le Cozannet, Manceau and Rohmer2017a). That is, the definition of likely range in AR5, SROCC and AR6 is comparable but not exactly the same, and the way of determining the range from the available information is different. The AR6 medium confidence projections include estimated distributions for each emissions scenario with two different methodological choices for the Antarctic ice sheet (see Table 1). AR6 also presented a set of lowconfidence projections, which include additional contributions from ice sheet processes and estimates for which there is less agreement and/or less evidence (see Table 1).
Table 1: High-level summary of the methods used in the AR5, SROCC and AR6 reports to project global mean and regional SLC (1° × 1° resolution) to 2100. Note: This is an adapted version of Table 9.7 in Fox-Kemper et al. (Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a).


Table 1: High-level summary of the methods used in the AR5, SROCC and AR6 reports to project global mean and regional SLC (1° × 1° resolution) to 2100. Note: This is an adapted version of Table 9.7 in Fox-Kemper et al. (Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a).
The methodologies of the projections in AR5, SROCC and AR6 are briefly summarised in Table 1; for more details, we refer to Chapter 13 of AR5 (Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a), Chapter 4 of SROCC (Oppenheimer et al., Reference Oppenheimer, Glavovic, Hinkel, van de Wal, Magnan, Abd-Elgawad, Cai, Cifuentes-Jara, DeConto, Ghosh, Hay, Isla, Marzeion, Meyssignac and Sebesvari2019) and Chapter 9 of AR6 (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a). We focus on three major elements of the projections that have changed: (1) the use of CMIP5 versus CMIP6 model output and consistency with the assessed ECS (section ‘Updated climate model information and the use of emulators’); (2) differences in the approaches to project the contributions to SLC (section ‘Differences in the projected contributions to SLC’); (3) the way the reports addressed potential outcomes outside the likely range (section ‘Sea-level projections outside the likely range’).
Updated climate model information and the use of emulators
The majority of the sea-level projections for the 21st century since AR5 have been based on CMIP5 climate model output (Taylor et al., Reference Taylor, Stouffer and Meehl2012), forced by Representative Concentration Pathways (RCP, Meinshausen et al., Reference Meinshausen, Smith, Calvin, Daniel, Kainuma, Lamarque, Matsumoto, Montzka, Raper, Riahi, Thomson, Velders and DPP2011), which are scenarios of future greenhouse gas concentrations and aerosol emissions. The projections in AR6 used information from CMIP6 climate models (Eyring et al., Reference Eyring, Bony, Meehl, Senior, Stevens, Stouffer and Taylor2016), which were forced by Shared Socioeconomic Pathways (SSP, O’Neill et al., Reference O’Neill, Kriegler, Riahi, Ebi, Hallegatte, Carter, Mathur and van Vuuren2014): scenarios of socio-economic development (including for instance population change, urbanisation and technological development) in combination with radiative forcing changes (GHG emissions and concentrations). These scenarios are noted as SSPx − y, where x denotes the SSP pathway (SSP1 sustainability, SSP2 middle-of-the-road, SSP3 regional rivalry, SSP4 inequality, SSP5 fossil fuel-intensive) and y the radiative concentration level in 2100 in W/m2. AR6 used five illustrative SSP scenarios: SSP1–1.9 (very low emissions), SSP1–2.6 (low emissions), SSP2–4.5 (intermediate emissions), SSP3–7.0 (high emissions) and SSP5–8.5 (very high emissions).
The ECS from the CMIP6 model ensemble has a higher average and a wider range compared to the CMIP5 model ensemble and compared to the AR6 assessment of ECS (Forster et al., Reference Forster, Storelvmo, Armour, Collins, Dufresne, Frame, Lunt, Mauritsen, Palmer, Watanabe, Wild, Zhang, Masson-Delmotte, Zhai, Pirani and Zhou2021). The consequences of this change in ECS distribution for projections of GMSL change were investigated by Hermans et al. (Reference Hermans, Gregory, Palmer, Ringer, Katsman and Slangen2021), who used CMIP6 data in combination with the AR5 methodology. They found that, while the projected change in GSAT median and range increased substantially from CMIP5 to CMIP6 (from 1.9 (1.1–2.6) K to 2.5 (1.6–3.5) K under SSP2-RCP4.5, see their Table S2 for additional scenarios), the upper end of the GMSL likely range projections at 2100 increased by only 3–7 cm across all scenarios (see their Figures 1 and 3), due to the delayed response of SLC to temperature changes. However, they also found an increase in the end-of-century GMSL rates of up to ~20%, suggesting that differences between CMIP5 and CMIP6-based GMSL projections could become substantially larger on longer time scales.
Figure 1: Comparison of 21st century projections of global mean SLC in AR5, SROCC and AR6. Total GMSL and individual contributions, between 1995 and 2014 and 2100 (m), median values and likely ranges of medium confidence projections, for (a) RCP2.6/SSP1–2.6 and (b) RCP8.5/SSP5–8.5. See also Table 9.8 in Fox-Kemper et al. (Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a) for comparative numbers of GMSL projections. AR6 low confidence projections for SSP1–2.6 and SSP5–8.5 in grey for Greenland, Antarctica and GMSL. Corrections for the differences in baseline period between AR5 (1986–2005) and AR6 (1995–2014) were done following IPCC AR6, Table 9.8.
Figure 2: Comparison of regional relative sea-level change w.r.t. the global mean sea-level change in AR5 and AR6 (2020–2100) (%), based on median values, for (a) IPCC AR5 RCP4.5, global mean of 0.46 m and (b) IPCC AR6 SSP2–4.5, global mean of 0.51 m.


Figure 1: Comparison of 21st century projections of global mean SLC in AR5, SROCC and AR6. Total GMSL and individual contributions, between 1995 and 2014 and 2100 (m), median values and likely ranges of medium confidence projections, for (a) RCP2.6/SSP1–2.6 and (b) RCP8.5/SSP5–8.5. See also Table 9.8 in Fox-Kemper et al. (Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a) for comparative numbers of GMSL projections. AR6 low confidence projections for SSP1–2.6 and SSP5–8.5 in grey for Greenland, Antarctica and GMSL. Corrections for the differences in baseline period between AR5 (1986–2005) and AR6 (1995–2014) were done following IPCC AR6, Table 9.8.
Figure 2: Comparison of regional relative sea-level change w.r.t. the global mean sea-level change in AR5 and AR6 (2020–2100) (%), based on median values, for (a) IPCC AR5 RCP4.5, global mean of 0.46 m and (b) IPCC AR6 SSP2–4.5, global mean of 0.51 m.
Figure 3: Comparison of observations (IPCC AR6, available up to 2018) and projections (IPCC AR5, available from 2007) of GMSL change. (a) Total GMSL and (b-f) individual contributions in (m) with respect to the period 1986–2005; all uncertainties recomputed to represent the likely range. Text in panels compares rates (mm/yr) of observations for 2006–2018 (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a, Table 9.5) to rates of projections for 2007–2018 (Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a); rates rounded to nearest 0.1 mm/yr; time periods used for rates differ by 1 year, allowing for traceability to the IPCC reports. Note that AR5 included the Greenland peripheral glaciers in the glacier contribution, whereas AR6 included it in the Greenland contribution; we have therefore subtracted a Greenland peripheral glacier estimate of 0.1 mm/yr from the AR6 Greenland observations in (c) and added it to the AR6 glacier observations in (d), both for the time series and the rates (Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a, Table 13.1; Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021b, Table 9.SM.2). The AR5 observed glacier change is added to (d) for reference (using the 1993–2010 linear rate from Table 13.1 of Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a).
One of the novel aspects of AR6 was the use of a physically-based emulator, which allowed for projections of 21st century GSAT and SLC that were consistent with the AR6 assessment of ECS (Forster et al., Reference Forster, Storelvmo, Armour, Collins, Dufresne, Frame, Lunt, Mauritsen, Palmer, Watanabe, Wild, Zhang, Masson-Delmotte, Zhai, Pirani and Zhou2021). The AR6 used a simple two-layer energy balance model (e.g., Geoffroy et al., Reference Geoffroy, Saint-Martin, Bellon, Voldoire, Olivié and Tytéca2013). Previous studies have used this two-layer model to successfully emulate CMIP5 model projections of GSAT and global mean thermosteric SLC to 2300 (Palmer et al., Reference Palmer, Harris and Gregory2018; Yuan and Kopp, Reference Yuan and Kopp2021). The AR6 emulator ensemble was constrained using four observational targets, including historical GSAT change and ocean heat uptake (Smith et al., Reference Smith, Nicholls, Armour, Collins, Forster, Meinshausen, Palmer, Watanabe, Masson-Delmotte, Zhai, Pirani and Zhou2021). The projected ocean heat uptake was translated to global mean thermosteric SLC using CMIP6-based estimates of expansion efficiency (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021b). The GSAT changes were also used as input for the land-ice contributions to GMSL rise, which were generated with additional emulators applied to suites of coordinated community efforts for the ice sheet (LARMIP-2, Levermann et al., Reference Levermann, Winkelmann, Albrecht, Goelzer, Golledge, Greve, Huybrechts, Jordan, Leguy, Martin, Morlighem, Pattyn, Pollard, Quiquet, Rodehacke, Seroussi, Sutter, Zhang, Van Breedam, Calov, Deconto, Dumas, Garbe, Gudmundsson, Hoffman, Humbert, Kleiner, Lipscomb, Meinshausen, Ng, Nowicki, Perego, Price, Saito, Schlegel, Sun and Van De Wal2020; ISMIP6, Nowicki et al., Reference Nowicki, Payne, Larour, Seroussi, Goelzer, Lipscomb, Gregory, Abe-Ouchi and Shepherd2016) and glacier model (GlacierMIP2, Marzeion et al., Reference Marzeion, Hock, Anderson, Bliss, Champollion, Fujita, Huss, Immerzeel, Kraaijenbrink, Malles, Maussion, Radić, Rounce, Sakai, Shannon, van de Wal and Zekollari2020) simulations carried out for AR6.
Differences in the projected contributions to SLC


Differences in the projected contributions to SLC
In AR5, the assessments of glacier and ice sheet contributions were based on a range of individual models and publications. The only difference in the SROCC projections with respect to AR5 was the reassessment of the Antarctic dynamics contribution, by replacing the AR5 Antarctic scenario-independent ice dynamic projections with scenario-dependent process-based model estimates (Levermann et al., Reference Levermann, Winkelmann, Nowicki, Fastook, Frieler, Greve, Hellmer, Martin, Meinshausen, Mengel, Payne, Pollard, Sato, Timmermann, Wang and Bindschadler2014; Golledge et al., Reference Golledge, Kowalewski, Naish, Levy, Fogwill and Gasson2015; Ritz et al., Reference Ritz, Edwards, Durand, Payne, Peyaud and Hindmarsh2015; Bulthuis et al., Reference Bulthuis, Arnst, Sun and Pattyn2019; Golledge et al., Reference Golledge, Keller, Gomez, Naughten, Bernales, Trusel and Edwards2019). This led to a decrease in 21st century GMSL change compared with AR5 for the RCP2.6 scenario, and an increase for the RCP8.5 scenario (medians and likely ranges; Figure 1a,b). However, the scenario-dependence in SROCC may have been amplified because two model estimates did not include accumulation changes (Levermann et al., Reference Levermann, Winkelmann, Nowicki, Fastook, Frieler, Greve, Hellmer, Martin, Meinshausen, Mengel, Payne, Pollard, Sato, Timmermann, Wang and Bindschadler2014; Ritz et al., Reference Ritz, Edwards, Durand, Payne, Peyaud and Hindmarsh2015), which are projected to increase with warming and partially counteract dynamic losses (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a).
For the AR6 projections, statistical emulators were applied to the ISMIP6 and GlacierMIP2 outputs, using the Gaussian process model described in Edwards et al. (Reference Edwards, Nowicki, Marzeion, Hock, Goelzer, Seroussi, Jourdain, Slater, Turner, Smith, McKenna, Simon, Abe-Ouchi, Gregory, Larour, Lipscomb, Payne, Shepherd, Agosta, Alexander, Albrecht, Anderson, Asay-Davis, Aschwanden, Barthel, Bliss, Calov, Chambers, Champollion, Choi, Cullather, Cuzzone, Dumas, Felikson, Fettweis, Fujita, Galton-Fenzi, Gladstone, Golledge, Greve, Hattermann, Hoffman, Humbert, Huss, Huybrechts, Immerzeel, Kleiner, Kraaijenbrink, Le clec’h, Lee, Leguy, Little, Lowry, Malles, Martin, Maussion, Morlighem, O’Neill, Nias, Pattyn, Pelle, Price, Quiquet, Radić, Reese, Rounce, Rückamp, Sakai, Shafer, Schlegel, Shannon, Smith, Straneo, Sun, Tarasov, Trusel, Van Breedam, van de Wal, van den Broeke, Winkelmann, Zekollari, Zhao, Zhang and Zwinger2021). For LARMIP-2, results for Antarctic ice sheet dynamics were emulated using an impulse-response function model following (Levermann et al., Reference Levermann, Winkelmann, Albrecht, Goelzer, Golledge, Greve, Huybrechts, Jordan, Leguy, Martin, Morlighem, Pattyn, Pollard, Quiquet, Rodehacke, Seroussi, Sutter, Zhang, Van Breedam, Calov, Deconto, Dumas, Garbe, Gudmundsson, Hoffman, Humbert, Kleiner, Lipscomb, Meinshausen, Ng, Nowicki, Perego, Price, Saito, Schlegel, Sun and Van De Wal2020), augmented by a parametric surface-mass balance model following AR5. There were several motivations for using these emulators: (1) to constrain the projections to the assessed ECS range, an approach that represents a marked change from previous IPCC reports; (2) to be able to make projections across all five illustrative SSP scenarios of AR6, as the ice sheet and glacier contributions were mostly based on CMIP5 RCP scenarios; and (3) to sample modelling uncertainties more thoroughly, estimating probability distributions for the contributions. The use of simple climate models and emulators is a trade-off between a more complete exploration of the uncertainties which can be done due to the computational speed of the emulators (compared to the full ice sheet and glacier models, which are limited by constraints of computing and person time), and the potential biases introduced by the necessary assumptions of a simpler model (Edwards et al., Reference Edwards, Nowicki, Marzeion, Hock, Goelzer, Seroussi, Jourdain, Slater, Turner, Smith, McKenna, Simon, Abe-Ouchi, Gregory, Larour, Lipscomb, Payne, Shepherd, Agosta, Alexander, Albrecht, Anderson, Asay-Davis, Aschwanden, Barthel, Bliss, Calov, Chambers, Champollion, Choi, Cullather, Cuzzone, Dumas, Felikson, Fettweis, Fujita, Galton-Fenzi, Gladstone, Golledge, Greve, Hattermann, Hoffman, Humbert, Huss, Huybrechts, Immerzeel, Kleiner, Kraaijenbrink, Le clec’h, Lee, Leguy, Little, Lowry, Malles, Martin, Maussion, Morlighem, O’Neill, Nias, Pattyn, Pelle, Price, Quiquet, Radić, Reese, Rounce, Rückamp, Sakai, Shafer, Schlegel, Shannon, Smith, Straneo, Sun, Tarasov, Trusel, Van Breedam, van de Wal, van den Broeke, Winkelmann, Zekollari, Zhao, Zhang and Zwinger2021). The Gaussian process emulator performed well for the cumulative change in time, but did not account for temporal correlation, so the rates could not be estimated from the emulator. As a consequence, in contexts where rates were needed, AR6 used simpler parametric emulators, based on approaches used in AR5.


A comparison of the GMSL projections to 2100 in the different reports reveals a number of differences (Figure 1a,b). In the land ice contributions, we see a narrowing of the likely ranges for glaciers (under both scenarios) and the Greenland ice sheet (under SSP5–8.5), and a widening of the Antarctic ice sheet likely range. The latter is wider as it is based on a p-box bounding distribution functions from the ISMIP6 emulator and LARMIP-2 (Table 1), where the presented likely range spans from the lowest 17th to the highest 83rd percentile of the considered methods. The ECS-constrained temperature projections in AR6 (section ‘Updated climate model information and the use of emulators’) used as input to the land ice emulators show a marked reduction in the width of the likely range at 2100 (~0.7 K for SSP1–2.6; ~1.9 K for SSP5–8.5) compared with the 21 CMIP5 models used as the basis of the AR5 sea-level projections (~1.6 K for RCP2.6; ~2.8 K for RCP8.5), which could also be one of the reasons for the reduced width of the glacier and Greenland likely ranges. The glacier range may also be slightly underestimated because each region is emulated independently, which means the projections do not account for covariances in the regional uncertainties apart from those associates with a common dependence on temperature (Marzeion et al., Reference Marzeion, Hock, Anderson, Bliss, Champollion, Fujita, Huss, Immerzeel, Kraaijenbrink, Malles, Maussion, Radić, Rounce, Sakai, Shannon, van de Wal and Zekollari2020; Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a, Section 9.5). However, the AR5 glacier and Greenland uncertainties were open-ended (≥ 66% ranges) and essentially estimated with expert judgement, at a time of far less information from – and confidence in – these process-based models, so the narrowing range is also consistent with an improving evidence base. The land-water storage contribution is reduced in AR6 compared with AR5 due to the use of a different methodology which now links land-water storage changes to global population under SSP scenarios (Kopp et al., Reference Kopp, Horton, Little, Mitrovica, Oppenheimer, Rasmussen, Strauss and Tebaldi2014), in combination with a larger negative reservoir impoundment contribution from Hawley et al. (Reference Hawley, Hay, Mitrovica and Kopp2020).
AR5 used different methodologies for estimating the uncertainties in GMSL (Figure 1a,b) and regional SLC (Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013b). In contrast, the AR6 GMSL (Figure 1a,b) and regional projected uncertainties are combined in the same way, with the different contributions all treated as conditionally independent given GSAT, which is an input for the emulator (Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021b). The total projected GMSL for SSP1–2.6 has increased in AR6 compared with RCP2.6 projections in AR5 and SROCC, with a similar likely range (Figure 1a), but with different relative contributions of each component. For SSP5–8.5, the AR6 GMSL projections are 4 cm lower than RCP8.5 in SROCC but 6 cm higher than RCP8.5 in AR5 (Figure 1b), due to differences in the model estimates included (from AR5 to SROCC) and in both models used and the methods used to combine the models (from SROCC to AR6) of the projected Antarctic contribution.
The regional projections (Figure 2) show that SLC is spatially highly variable, due to a combination of ocean dynamic changes, gravitational, rotational and deformation (GRD) effects in response to present-day mass changes, and long-term Glacial Isostatic Adjustment (GIA). There is an overall agreement in the patterns between AR5 and AR6. Some differences arise from the vertical land motion (VLM) contribution, which included only GIA in AR5 and also other VLM contributions, such as tectonics, compaction or anthropogenic subsidence, in AR6: compare for instance the larger ratios along the US East Coast ( Figure 2b) to the VLM contribution in Figure 9.26 from Fox-Kemper et al. (Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a). The increased contribution from Antarctica compared to AR5, in combination with the ocean dynamics contribution, leads to a more widespread below-average SLC in the Antarctic Circumpolar Current region.
Sea-level projections outside the likely range
One of the key uncertainties in sea-level projections is the dynamic contribution of the ice sheets (i.e., processes related to the flow of the ice). AR5 assessed the likely dynamical contribution of the Antarctic Ice Sheet by 2100 at −2 to 18.5 cm, but also noted that ‘Based on current understanding, only the collapse of marine-based sectors of the Antarctic ice sheet, if initiated, could cause global mean sea level to rise substantially above the likely range during the 21st century. There is medium confidence that this additional contribution would not exceed several tenths of a metre of sea level rise during the 21st century’. An ice sheet estimate based on SEJ was available at the time of AR5 but this could not be supported by other lines of evidence (Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a). Including the SEJ estimates would have led to an assessment that could not be transparently linked to physical evidence, as the reasoning of the experts involved in the SEJ exercise is undocumented, and it was decided not to use it for the AR5 assessment.


Sea-level projections outside the likely range
One of the key uncertainties in sea-level projections is the dynamic contribution of the ice sheets (i.e., processes related to the flow of the ice). AR5 assessed the likely dynamical contribution of the Antarctic Ice Sheet by 2100 at −2 to 18.5 cm, but also noted that ‘Based on current understanding, only the collapse of marine-based sectors of the Antarctic ice sheet, if initiated, could cause global mean sea level to rise substantially above the likely range during the 21st century. There is medium confidence that this additional contribution would not exceed several tenths of a metre of sea level rise during the 21st century’. An ice sheet estimate based on SEJ was available at the time of AR5 but this could not be supported by other lines of evidence (Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a). Including the SEJ estimates would have led to an assessment that could not be transparently linked to physical evidence, as the reasoning of the experts involved in the SEJ exercise is undocumented, and it was decided not to use it for the AR5 assessment.
After AR5, following for instance Sutton (Reference Sutton2019), low probability estimates were increasingly used in the context of risk assessment and to discuss less likely outcomes for risk-averse users (e.g., Le Cozannet et al., Reference Le Cozannet, Nicholls, Hinkel, Sweet, McInnes, Van de Wal, Slangen, Lowe and White2017b; Hinkel et al., Reference Hinkel, Church, Gregory, Gregory, Lambert, McInnes, Nicholls, Church, van der Pol and van de Wal2019; Nicholls et al., Reference Nicholls, Hanson, Lowe, Slangen, Wahl, Hinkel and Long2021). SROCC argued that stakeholders with a low risk tolerance might use the SEJ numbers (e.g., their Figure 4.2). Model results including marine ice cliff instability (MICI, Deconto and Pollard, Reference Deconto and Pollard2016) were not used in the main projections of SROCC because the too high surface melt rates led to an uncertain timing and magnitude in the simulated ice loss. In AR6, a set of low confidence projections was presented (shown in grey in Figure 1a,b) which build on the medium confidence projections. These projections include additional contributions for the ice sheets, estimated using a p-box approach (e.g., Le Cozannet et al., Reference Le Cozannet, Manceau and Rohmer2017a), considering SEJ (Bamber et al., Reference Bamber, Oppenheimer, Kopp, Aspinall and Cooke2019) together with an improved model-based estimate for Antarctica which included MICI (DeConto et al., Reference DeConto, Pollard, Alley, Velicogna, Gasson, Gomez, Sadai, Condron, Gilford, Ashe, Kopp, Li and Dutton2021). It is important to note that the low confidence ranges represent the breadth of literature estimates available at the time, but that they are not incorporated in the assessed likely ranges.
The AR6 low confidence projections suggest that by 2100, under SSP1–2.6 ( Figure 1a), there is a potential Greenland contribution outside the likely range, based on SEJ. For Antarctica, the medium confidence SSP1–2.6 projections already include a wide range of values, so the impact of SEJ and MICI estimates in the lowconfidence projections is less distinct. Under SSP5–8.5 (Figure 1b), the upper values of the AR6 low confidence projections for both ice sheets are considerably larger than the corresponding medium confidence estimates. This reflects the deep uncertainty in the literature on the Antarctic contribution (see also Box 9.4 in Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a). What is needed to reduce this deep uncertainty is primarily a better understanding of the physical processes. This will lead to more physically-based model projections with larger ensembles, which will allow for a better exploration of the uncertainties.
Comparison of the AR5 model simulations with observations
In the previous section, we discussed ‘how we got here’: the developments that led to the most recent IPCC projections. However, it is also relevant to see ‘where we are’, by comparing the observed sea-level change against sea-level projections for their overlapping period. We evaluate the assessed likely ranges of the AR5 projections (from 2007 onwards, Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a) against the assessed observational time series from AR6 (up to 2018, Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a, Table 9.5), both for the total GMSL and the individual contributions (Figure 3).
For GMSL, Antarctica, Greenland and thermal expansion, the observational timeseries are close to the centre of the projections and the estimated rates of change are highly consistent (Figure 3a,b,c,e). The observed glacier timeseries in AR6 is at the lower end of the projections, even though the observed rates entirely fall within the likely range of the projected rates (Figure 3d). It is worth noting that the AR5 included glaciers peripheral to the Greenland Ice Sheet in the glacier projections (their Table 13.5), which according to the observations in their Table 13.1 adds a contribution in the order of 0.1 mm/yr. In AR6, this was included in the Greenland contribution. To facilitate the comparison, we have included the observed Greenland peripheral glacier estimate in Figure 3d (Glaciers) and subtracted it from the observations in Figure 3c (Greenland), based on linear rates presented in Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a and Fox-Kemper et al., Reference Fox-Kemper, Hewitt, Xiao, Aðalgeirsdóttir, Drijfhout, Edwards, Golledge, Hemer, Kopp, Krinner, Mix, Notz, Nowicki, Nurhati, Ruiz, Sallée, Slangen, Yu, Masson-Delmotte, Zhai, Pirani and Zhou2021a. In addition, the glacier contributions since AR5 suggest a smaller glacier contribution, both in observations and projections (for the observations: grey dashed line in Figure 3d based on Church et al., Reference Church, Clark, Cazenave, Gregory, Jevrejeva, Levermann, Merrifield, Milne, Nerem, Nunn, Payne, Pfeffer, Stammer, Unnikrishnan, Stocker, Qin, Plattner and Midgley2013a, Table 13.1 shows a higher rate than AR6, for the projections: Marzeion et al., Reference Marzeion, Leclercq, Cogley and Jarosch2015). The observed rate of land-water change is larger than the projected central value, but the observed time series, despite its interannual variability, mostly falls within the projected likely range. The observed rate of change is at the upper bound of the likely range projections (Figure 3f).
Wang et al. (Reference Wang, Church, Zhang and Chen2021a) also evaluated GMSL and regional projections from AR5 and SROCC against different tide gauge and altimetry time series for the period 2007–2018. They found that the GMSL trends for 2007–2018 from AR5 projections are almost identical to observed trends and well within the 90% confidence interval. They also showed significant local differences between observations and models, which could be improved with better VLM estimates and minimisation of the internal variability.


Wang et al. (Reference Wang, Church, Zhang and Chen2021a) also evaluated GMSL and regional projections from AR5 and SROCC against different tide gauge and altimetry time series for the period 2007–2018. They found that the GMSL trends for 2007–2018 from AR5 projections are almost identical to observed trends and well within the 90% confidence interval. They also showed significant local differences between observations and models, which could be improved with better VLM estimates and minimisation of the internal variability.
A study by Lyu et al. (Reference Lyu, Zhang and Church2021) focused on ocean warming, with the purpose of constraining projections. They compared the observations of ocean temperature by the Argo array (2005–2019) with model simulations from the CMIP5 and CMIP6 databases. They found that (1) the range of CMIP6 has shifted upwards compared with CMIP5; (2) there is a high correlation between observations and models over the Argo period; (3) the emergent constraint indicates that the larger trend of thermosteric SLC in the CMIP6 archive needs to be taken with caution. This supports the AR6 approach, where an emulator was used to constrain the thermosteric SLC of CMIP6 models with the assessed ECS range (section ‘Updated climate model information and the use of emulators’), leading to thermosteric SLC projections similar to AR5 and the constrained Lyu et al. (Reference Lyu, Zhang and Church2021) projections.
Moving towards local information
AR5 was the first IPCC assessment report to show regional sea-level projections in addition to GMSL projections, by including the effects of changes in ocean density and circulation, GIA and GRD effects (Table 1). SROCC built on AR5 but explored regional changes in sea-level extremes in more depth. In AR6 as a whole, even stronger emphasis was put on regional climate changes and on using regional information for impacts and risk assessment, in particular in Chapter 10 (Doblas-Reyes et al., Reference Doblas-Reyes, Sörensson, Almazroui, Dosio, Gutowski, Haarsma, Hamdi, Hewitson, Kwon, Lamptey, Maraun, Stephenson, Takayabu, Terray, Turner, Zuo, Masson-Delmotte, Zhai, Pirani and Zhou2021), Chapter 12 (Ranasinghe et al., Reference Ranasinghe, Ruane, Vautard, Arnell, Coppola, Cruz, Dessai, Islam, Rahimi, Ruiz, Sillmann, Sylla, Tebaldi, Wang, Zaaboul, Masson-Delmotte, Zhai, Pirani and Zhou2021) and the Interactive Atlas (Gutiérrez et al., Reference Gutiérrez, Jones, Narisma, Alves, Amjad, Gorodetskaya, Grose, Klutse, Krakovska, Li, Martínez-Castro, Mearns, Mernild, Ngo-Duc, van den Hurk, Yoon, Masson-Delmotte, Zhai, Pirani and Zhou2021). The IPCC authors and the IPCC Technical Support Unit also collaborated with NASA to develop the NASA/IPCC Sea Level Projection Tool (https://sealevel.nasa.gov/ipcc-ar6-sea-level-projection-tool) to provide easy access to global and regional projections. As the need for more detailed sea-level information is becoming increasingly evident (e.g., Le Cozannet et al., Reference Le Cozannet, Nicholls, Hinkel, Sweet, McInnes, Van de Wal, Slangen, Lowe and White2017b; Hinkel et al., Reference Hinkel, Church, Gregory, Gregory, Lambert, McInnes, Nicholls, Church, van der Pol and van de Wal2019; Nicholls et al., Reference Nicholls, Hanson, Lowe, Slangen, Wahl, Hinkel and Long2021; Durand et al., Reference Durand, van den Broeke, Le Cozannet, Edwards, Holland, Jourdain, Marzeion, Mottram, Nicholls, Pattyn, Paul, Slangen, Winkelmann, Burgard, van Calcar, Barré, Bataille and Chapuis2022), we discuss a couple of potential future research avenues which may help to further improve sea-level projections on a regional to local scale.
High-resolution ocean modelling
Ocean dynamic SLC is a major driver of spatial sea-level variability, which is typically derived from CMIP5 and CMIP6 GCM simulations. However, the extent to which GCMs can provide local information is limited because of their relatively low atmosphere and ocean grid resolutions, which are constrained by computational costs. The typical ocean grid resolution of CMIP5 models is approximately 1° by 1° (~100 km). Although the ocean components of some CMIP6 models operate at a 0.25° resolution, the resolution of most CMIP6 models has not increased much relative to CMIP5, and the CMIP5 and CMIP6 simulations of ocean dynamic SLC show similar features (Lyu et al., Reference Lyu, Zhang and Church2020). These relatively coarse resolutions may lead to misrepresentations of ocean dynamic SLC, particularly in coastal regions in which small-scale and tidal processes and bathymetric features are important. Increasing the resolution of the GCMs requires significant additional computational resources as well as more explicit modelling of high-resolution processes that are currently parameterized.
As an alternative, GCMs can be dynamically downscaled using high-resolution atmosphere or ocean models. Emerging research demonstrates the value of dynamical downscaling for SLC simulations in coastal regions such as the Northwestern European Shelf (Figure 4; Hermans et al., Reference Hermans, Tinker, Palmer, Katsman, Vermeersen and Slangen2020; Chaigneau et al., Reference Chaigneau, Reffray, Voldoire and Melet2022; Hermans et al., Reference Hermans, Katsman, Camargo, Garner, Kopp and Slangen2022), the Southern Ocean (Zhang et al., Reference Zhang, Church, Monselesan and McInnes2017), the Mediterranean Sea (Sannino et al., Reference Sannino, Carillo, Iacono, Napolitano, Palma, Pisacane and Struglia2022), the marginal seas in the Northwest Pacific Ocean (Liu et al., Reference Liu, Minobe, Sasaki and Terada2016; Kim et al., Reference Kim, Kim, Jeong, Lee, Byun and Cho2021), the marginal seas near China (Jin et al., Reference Jin, Zhang, Church and Bao2021) and the Brazilian continental shelf (Toste et al., Reference Toste, Assad and Landau2018), on both annual and sub-annual timescales. Additionally, dynamical downscaling can offer a framework in which local changes in tides, surges and waves can be resolved in conjunction with time-mean SLC and incorporated into sea-level projections (Kim et al., Reference Kim, Kim, Jeong, Lee, Byun and Cho2021; Chaigneau et al., Reference Chaigneau, Reffray, Voldoire and Melet2022), as it allows for modelling changes at higher temporal frequencies. Dynamical downscaling requires GCM output as boundary conditions, which means that the regional solutions due to the explicit modelling of higher resolution processes should always be considered in the context of the GCM model that is driving the regional model. For instance, for the South China Sea, Jin et al. (Reference Jin, Zhang, Church and Bao2021) found that ‘the downscaled results driven by ensemble mean forcings are almost identical to the ensemble average results from individually downscaled cases’. However, more extensive analysis of the uncertainties associated with dynamical downscaling remains to be done. As a result, the dynamical downscaling of ocean simulations has not yet been systematically applied in the context of regional and local sea-level projections.
Figure 4: Ocean dynamic SLC northwest of Europe, as simulated by (a) the CMIP5 GCM HadGEM2-ES and (b) dynamically downscaled using regional ocean model NEMO-AMM7, and by (c) the CMIP5 GCM MPI-ESM-LR and (d) dynamically downscaled, for the scenario RCP8.5 (2074–2099 minus 1980–2005). Figure adapted from Hermans et al. (Reference Hermans, Tinker, Palmer, Katsman, Vermeersen and Slangen2020).
Vertical land motion