Peer Review10.5194/egusphere-2024-58-ac2
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Nicolas Jourdain
- 20 Jun 2024
TL;DR: A simple method to extend MAR simulations to other periods and scenarios and constrain ice sheet model ensembles. Surface mass balance and runoff are emulated using a mixed statistical-physical approach. Sea level rise contribution from surface mass balance is found to be 0.4 to 2.2 cm from 1900 to 2010 and -3.4 to -0.1 cm from 2100 to 2099 under the SSP1-2.6 scenario.
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Abstract: <strong class="journal-contentHeaderColor">Abstract.</strong> A mixed statistical-physical approach is used to emulate the spatio-temporal variability of the Antarctic ice sheet surface mass balance and runoff of a regional climate model. We demonstrate the ability of this simple method to extend existing MAR simulations to other periods, scenarios or climate models, that were not originally processed through the regional climate model. This method is useful to quickly populate ensembles of surface mass balance and runoff which are needed to constrain ice sheet model ensembles. After correcting the distribution of equilibrium climate sensitivity of 16 climate models, we find a likely contribution of surface mass balance to sea level rise of 0.4 to 2.2 cm from 1900 to 2010, and -3.4 to -0.1 cm from 2100 to 2099 under the SSP1-2.6 scenario, versus -4.4 to -1.4 cm under SSP2-4.5 and -7.8 to -4.0 cm under SSP5-8.5. Based on a more limited and uncorrected ensemble, we find a considerable uncertainty in the contribution to sea level from 2000 to 2200: between -10 and -1 cm in SSP1-2.6 and between -33 and +6 cm in SSP5-8.5. Based on a runoff criteria in our reconstructions, we identify the emergence of surface conditions prone to hydrofracturing. A majority of ice shelves could remain safe from hydrofracturing under the SSP1-2.6 scenario, but all the Antarctic ice shelves could be prone to hydrofracturing before 2130 under the SSP5-8.5 scenario.
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Figures
Table 2. Fit parameters of the reconstructed accumulation and melt rate (see Equ. 4). 
Figure 3. Evaluation of the reconstruction method from other models for SMB (upper panels) and surface melting (lower panels). The five radial lines correspond to five MAR simulations driven by different CMIP6 or CMIP5 models (thick black pentagons), which are emulated from other available MAR simulations and the corresponding CMIP model surface temperatures (colored pentagons). The thin grey pentagons indicate the SMB or melt rate iso-value. The anomalies are calculated over 2015–2100 with respect to 1995–2014 under the SSP5-8.5 or RCP8.5 scenarios. 
Figure 12. Similar to Fig. 10 but for the SSP2-4.5 scenario. The dates in italic give the likely range and the others give the very likely range. 
Figure 5. Skew-normal ECS probability (solid line) fitted to obtain its 5th, 50th and 95th percentiles at 2.0, 3.0 and 5.0°C (dashed lines). The orange triangles indicate the 5th, 50th and 95th percentiles of the ECS of the unweighted 16-CMIP model distribution, and the blue triangles show the equivalent for the weighted distribution. The skew-normal distribution was generated using the skewnorm.pdf function of the scipy.stats package (Virtanen et al., 2020), with a skewness parameter of 5.08, an offset parameter (loc) of 2.02°C, and a scale parameter of 1.52. ![Table 4. Projected sea level contributions (in cm) from the Antarctic Ice Sheet SMB from 2000 to 2099 (relative to 1995–2014, i.e. assuming that the mean SMB over that period yields no sea level rise), for the three selected SSP scenarios, shown as median (likely range, i.e., 17– 83th percentile) [very likely range, i.e., 5–95th percentile]. The IPCC-AR5/6 estimates are those presented in Tab. 9.3 of IPCC-AR6 (FoxKemper et al., 2021), i.e., recalculated for the SSP scenarios from IPCC-AR5, and originally derived from the CMIP5 global mean surface air temperature using a linear accumulation-temperature relationship (Church et al., 2013). The data of Kittel et al. (2021) are statistical reconstructions based on the air temperature averaged south of 60°S for 33 CMIP6 models, and the percentiles have been recalculated for this table.](/figures/table4-1-65mczeno7ukf.png)
Table 4. Projected sea level contributions (in cm) from the Antarctic Ice Sheet SMB from 2000 to 2099 (relative to 1995–2014, i.e. assuming that the mean SMB over that period yields no sea level rise), for the three selected SSP scenarios, shown as median (likely range, i.e., 17– 83th percentile) [very likely range, i.e., 5–95th percentile]. The IPCC-AR5/6 estimates are those presented in Tab. 9.3 of IPCC-AR6 (FoxKemper et al., 2021), i.e., recalculated for the SSP scenarios from IPCC-AR5, and originally derived from the CMIP5 global mean surface air temperature using a linear accumulation-temperature relationship (Church et al., 2013). The data of Kittel et al. (2021) are statistical reconstructions based on the air temperature averaged south of 60°S for 33 CMIP6 models, and the percentiles have been recalculated for this table. 
Figure 1. Evaluation of the extension in time from an earlier period: time series of annual SMB, surface melting and runoff anomalies over the grounded ice sheet (left) and ice shelves (right) until 2200 under the SSP5-8.5 scenario. The black lines show the original MAR–IPSLCM6A-LR simulation, while the colored lines correspond to the extended 2101–2200 period based on the MAR simulation over 2081–2100 and on the IPSL-CM6A-LR air temperatures over 2101–2200. The extensions are shown for several values of r (see eq. 4) and the biases are indicated for both 2101–2150 and 2151–2200. The anomalies are calculated with respect to the 1995–2014 mean. The time series on this plot are filtered through a 5-year running average.
![Table 4. Projected sea level contributions (in cm) from the Antarctic Ice Sheet SMB from 2000 to 2099 (relative to 1995–2014, i.e. assuming that the mean SMB over that period yields no sea level rise), for the three selected SSP scenarios, shown as median (likely range, i.e., 17– 83th percentile) [very likely range, i.e., 5–95th percentile]. The IPCC-AR5/6 estimates are those presented in Tab. 9.3 of IPCC-AR6 (FoxKemper et al., 2021), i.e., recalculated for the SSP scenarios from IPCC-AR5, and originally derived from the CMIP5 global mean surface air temperature using a linear accumulation-temperature relationship (Church et al., 2013). The data of Kittel et al. (2021) are statistical reconstructions based on the air temperature averaged south of 60°S for 33 CMIP6 models, and the percentiles have been recalculated for this table.](/figures/table4-1-65mczeno7ukf.webp)
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