We present projections of West Antarctic surface mass balance (SMB) and surface melt to 2080–2100 under the RCP8.5 scenario and based on a regional model at 10 km resolution. Our projections are built by adding a CMIP5 (Coupled Model Intercomparison Project Phase 5) multi-model-mean seasonal climate-change anomaly to the present-day model boundary conditions. Using an anomaly has the advantage to reduce CMIP5 model biases, and a perfect-model test reveals that our approach captures most characteristics of future changes despite a 16 %–17 % underestimation of projected SMB and melt rates.
SMB over the grounded ice sheet in the sector between Getz and Abbot increases from 336
Ice-shelf surface melt rates increase by an order of magnitude in the 21st century mostly due to higher downward radiation from increased humidity and to reduced albedo in the presence of melting. There is a net production of surface liquid water over eastern ice shelves (Abbot, Cosgrove, and Pine Island) but not over western ice shelves (Thwaites, Crosson, Dotson, and Getz). This is explained by the evolution of the melt-to-snowfall ratio: below a threshold of 0.60 to 0.85 in our simulations, firn air is not entirely depleted by melt water, while entire depletion and net production of surface liquid water occur for higher ratios. This suggests that western ice shelves might remain unaffected by hydrofracturing for more than a century under RCP8.5, while eastern ice shelves have a high potential for hydrofracturing before the end of this century.
In a perfectly stable climate, the Antarctic ice sheet would have a constant mass, and the surface mass balance (SMB, the sum of rainfall and snowfall minus sublimation, runoff, and eroded snow) over the grounded ice sheet, i.e. 2000 to 2100
Recent SMB trends (1979–2000) reconstructed from firn cores are slightly negative, with SMB decreasing at a rate of
Runoff into the ocean is a negative contribution to SMB. It is produced if surface melt and/or rain rates are high enough to (i) bring the temperature of underlying snow and firn layers to the freezing point and (ii) percolate and saturate the pore space in the snow and firn layers, which is sometimes referred to as firn air depletion
Surface melt only occurs to a significant extent over the peninsula
In a warmer climate, surface melt increases exponentially with surface air temperature
Computing projections of future SMB and surface melt rates remains challenging because of the strong natural variability at regional scales
We focus on the Amundsen Sea sector, where potential future melt-induced hydrofracturing and associated loss of ice-shelf buttressing could have strong effects on the stability of the West Antarctic Ice Sheet and therefore on sea level rise
Our projections of the West Antarctic surface climate for the end of the 21st century are based on version 3.9.3 of the MAR regional atmospheric model
The radiative scheme and cloud properties are the same as in
Surface albedo depends on the evolving snow properties and on the solar zenithal angle
The simulation representative of the present climate is the one described in
For the future, we calculate the climate-change absolute anomaly from a CMIP5 multi-model mean (MMM), and we add it to the 6-hourly ERA-Interim variables used to drive MAR, i.e. sea surface temperature (SST), sea-ice concentration (SIC), and 3-dimensional wind velocity, air temperature, and specific humidity. Considering all these anomalies together allows us to keep the consistency of linear relationships, such as the geostrophic and thermal wind balances, although it does not necessarily conserve non-linear relationships. This type of method was previously referred to as “anomaly nesting”
Adding an anomaly is relatively simple but requires a specific calculation for two variables. First, specific humidity is set to zero in rare cases when applying the CMIP5 anomaly would produce unphysical negative values. Second, sea-ice concentration (SIC) anomalies are applied through an iterative process, which is needed because some locations have non-zero SIC on some days and zero SIC on other days. As negative SIC values are unphysical, applying a negative climatological SIC anomaly to all days (but keeping days with zero SIC unchanged) does not conserve the applied CMIP5 anomaly. To circumvent this issue, we apply the anomaly through 20 iterations: we start applying the CMIP5-MMM anomaly to the days and locations with SIC greater than zero (for negative anomaly) and smaller than 100 % (for positive anomaly), and after each iteration, we calculate the residual SIC that would be needed to reach the original CMIP5-MMM SIC anomaly, and we add it to the applied climatological anomaly. The effect of this sea-ice anomaly correction is briefly described in Sect.
As discussed by
All the simulation years are run in parallel with a 12-year spin-up for each simulated year, which is sufficient to obtain a steady net production of surface liquid water in the future simulation over all ice shelves except Abbot (see Sect.
We now briefly describe the CMIP5-MMM anomalies applied to ERA-Interim. The troposphere is warmed relatively uniformly from the surface to
Mean seasonal cycle of sea-ice concentration over the oceanic part of the MAR domain (solid) and southward of 70
In this section, we present SMB and surface melt projections derived from ERA-Interim and the CMIP5-MMM-RCP8.5 anomaly. We simply refer to the corresponding simulations as “present” and “future” in the following. We also investigate the causes for these changes, and we discuss consequences for potential ice-shelf hydrofracturing and sea level rise.
The future SMB is increased by 30 % to 40 %, keeping a very similar pattern to the present day (Fig.
We now briefly analyse possible causes for increased SMB in a warmer climate. In the following, the relative increase
The saturation water vapour pressure increases with air temperature at a rate of
To further understand the mechanism for increased snowfall, we now consider projections for the four seasons separately (Fig.
Changes in mean seasonal SMB (future minus present). Black narrow hatching indicates areas where the difference is not statistically significant (
Changes in mean seasonal 10 m winds (future minus present). Vectors are not displayed at locations where the change in at least one of the wind components is not statistically significant (
We have shown that runoff plays no significant role in the simulated SMB over the grounded ice sheet and therefore on sea level projections. However, surface melt, rainfall, and subsequent net production of surface liquid water may lead to ponding over ice shelves and trigger hydrofracturing. In this section, we therefore focus on liquid water budget projections over the seven major ice shelves from Getz to Abbot. In this paper, we do not investigate supra-glacial hydrology and hydrofracture mechanics in detail; we simply consider the presence of the net production of surface liquid water as an indicator of potential ice-shelf collapse (i.e. a necessary but not sufficient condition).
Surface melt rates averaged over the major individual ice shelves from Getz to Abbot are projected to increase by 1 order of magnitude, and melt occurrence is projected to increase from typically a week per year to 1–2 months per year (Table
Changes in seasonal mean melt rates (future minus present). The colour-bar labels in panel
Rainfall is also projected to increase (Table
The contrast between western (Getz to Thwaites) and eastern (Pine Island to Abbot) ice shelves can be explained by variations in the melt-to-snowfall ratio, which we now explain from simple considerations. As rainfall remains significantly weaker than melt rates, we neglect it in the following discussion, but more details on the theoretical role of rainfall are provided in Appendix
Going back to our simulations, we note the importance of the melt-to-snowfall ratio for the net production of surface liquid water simulated by MAR over the ice shelves, with episodic production for annual melt-to-snowfall ratios as low as 0.25 and a highly probable production for annual melt-to-snowfall ratios greater than
Net production of surface liquid water vs. melt-to-snowfall ratio in the future simulation (calculated from climatological means). Each circle represents the climatological annual mean at a grid point within the seven glacial drainage basins. The solid curve is a Gaussian kernel density estimate with a standard deviation of 0.1 for the melt-to-snowfall ratio. The vertical dashed lines indicate the limit above which more than 10 % and 50 % of the points experience a net production greater than 1
The existence of such a threshold explains the variations in liquid water production across the ice shelves (Table
We now briefly analyse the causes for increased melting in a warmer climate. All along the future melting season, less energy is lost by the ice-shelf surface through net longwave radiation (Fig.
We first discuss the possibility to extrapolate our results to other climate perturbations. Then, we discuss some limitations of our modelling and methodological approaches and their impacts on our projections.
While CMIP5-MMM-RCP8.5 at the end of the 21st century is meaningful, it is also interesting to estimate the likelihood of net production of surface liquid water over the ice shelves further in the future or following alternative emission scenarios. To do so, we evaluate the melt-to-snowfall ratio for a given additional warming or cooling, assuming that snowfall (SNF) and melt rates (MLTs) evolve following simple relationships with temperature. Such relationships can be obtained from the literature. The snowfall dependency to temperature can be obtained by the Magnus empirical fit of the Clausius–Clapeyron relationship
While the expressions in Eq. (
The extrapolations corresponding to Eqs. (
Extrapolated melt-to-snowfall ratio as a function of warming with respect to the present day (solid lines correspond to Eq.
The increasing proportion of liquid precipitation in a warmer climate is neglected in the above equations, although it may contribute to the production of surface liquid water. Rainfall remains significantly weaker than melt rates in our RCP8.5 projections (at most 15 % of melt rates in Table
These results are difficult to compare precisely to previous studies because different metrics and scenarios were used. Based on the CMIP3 HadCM3 model under the A1B scenario (similar global warming as CMIP5-MMM-RCP8.5 in 2100),
We now assess the ability of our projection method to capture the future climatology in a similar way to
Mean 21-year seasonal cycle of
Over the ice sheet, the near-surface projection biases are 0.6
To summarise our assessment of our projection method, it has the advantage to start from a present-day state that is not affected by present-day biases in CMIP5 models and to be applicable to a multi-model-mean projection which is expected to remove a part of the CMIP5 model biases. The counterpart of these advantages are biases in the projection itself. These biases are estimated to remain below 20 % based on our perfect-model approach. A part of these biases may be related to the imperfect method used to apply the sea-ice anomaly. Using iterative absolute anomalies typically removes half of the projection biases compared to a simple absolute anomaly, but the bias is not completely removed in summer, and more iterations or a refined method may be needed in our approach. Alternative approaches to build future sea-ice concentrations were proposed by
We now discuss the consequences of the aforementioned model and methodological biases for future surface liquid water production and potential hydrofracturing. Our projection method produces an underestimation of both snowfall and melt rates in the future by 16 % to 17 %. Adding these errors to both snowfall and melting values in Table
We now discuss another critical aspect of firn modelling, which is the spin-up duration. Our approach has consisted of running a present and a future 30-year snapshot, which means that the future firn has not experienced transient changes throughout the 21st century. Instead, we have run a 12-year spin-up under future conditions for every simulated year of the future experiment (the years are run in parallel). We now consider surface liquid water produced in DJF 1998 with climate anomalies on top, which is the summer with the highest melt rates in our projection and is preceded by a decade of relatively high melt rates
Net production of surface liquid water over individual ice shelves in DJF 1998 for various spin-up durations for Getz, Dotson, Crosson, Thwaites, Pine Island Glacier (PIG), Cosgrove, and Abbot.
In this study, we have presented future projections of SMB and surface melt at the end of the 21st century under the RCP8.5 scenario based on the MAR regional atmospheric model at 10 km resolution. The climate-change anomaly is calculated from the seasonal climatology of a CMIP5 multi-model mean, and added to the ERA-Interim reanalysis which is used for present-day boundary conditions. The use of an anomaly has the advantage to start from a present state with small biases compared to observations and is expected to reduce future biases as most CMIP5 biases were shown to be stationary. Moreover, the use of a multi-model mean is expected to cancel the biases that are not common to a majority of models. An important caveat of this method is that we assume unchanged inter-annual variability with respect to the mean. A perfect-model test indicates that our approach captures future changes in most variables despite an underestimation of SMB and melt-rate changes by 17 % on average.
Considering the drainage basins of the seven major ice shelves from Getz to Abbot, and only for the grounded parts of the ice sheet, we find that SMB increases from 336 to 455
Then, we analysed future surface melt and the liquid water budget at the surface of ice shelves because they can lead to hydrofracturing and ice-shelf collapse. At the surface of the seven major ice shelves between Getz and Abbot, future melt rates are increased by an order of magnitude compared to the present day, and the average number of melt days per year in the future exceeds 30 for most ice shelves. However, most melt water refreezes in the firn and even in the future run, as previously found by
The following ensemble of 33 CMIP5 models has been used in this study: ACCESS1-0, ACCESS1-3, BNU-ESM, CCSM4, CESM1-BGC, CESM1-CAM5, CESM1-WACCM, CMCC-CESM, CMCC-CMS, CMCC-CM, CNRM-CM5, CSIRO-Mk3-6-0, CanESM2, FGOALS-g2, FIO-ESM, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, HadGEM2-CC, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC-ESM-CHEM, MIROC-ESM, MIROC5, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, NorESM1-ME, NorESM1-M, bcc-csm1-1, and inmcm4. We take the first available ensemble member for each model (i.e. “r1i1p1” or “r2i1p1” if not available).
The following ensemble of 33 CMIP6 models has been used to calculate the global warming values corresponding to scenarios ssp126, ssp245, and ssp585 in Fig.
Here we extend the approach of
We consider a snowfall rate (SNF; in
We first consider a rainfall rate (RF; in
To keep expressions simple, we assume that rainfall and snowfall are at the freezing temperature, but accounting for their temperature has a negligible effect on the estimates below (not shown).
The snow and liquid water column reaches the close-off density
Hence, rainfall saturates the snow layer, and therefore makes water available for ponding or runoff when
We consider a surface melt-rate (MLT; in
In this case, Eq. (
Considering the effects of melt and rain together gives the following condition to saturate the snow layer:
Instructions to download the MAR code are provided on
The present-day MAR simulation is available at
MDM and NCJ initiated the study, made the plots, and wrote the first draft. MDM and MC ran the simulations. MDM, CK, CéA, ChA, and HG developed the model configuration. CK and NJ built the surface and lateral conditions for all the future experiments. GK proposed the perfect-model test. All authors took part in the resulting discussions and the manuscript preparation.
The authors declare that they have no conflict of interest.
The present work is a contribution to the TROIS-AS project, and is PROTECT contribution number 6. All the computations presented in this paper were performed using the GRICAD infrastructure (
This research has been supported by the French National Research Agency (ANR) (grant no. ANR-15-CE01-0005-01) and by the European Union's Horizon 2020 research and innovation programme (PROTECT (grant agreement no. 869304)).
This paper was edited by Carlos Martin and reviewed by Jan Lenaerts and one anonymous referee.