What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates

• Main Authors: Mottram, Ruth, Hansen, Nicolaj, Kittel, Christoph, Wessem, Melchior, Agosta, Cécile, Amory, Charles, Boberg, Fredrik, Berg, Willem Jan, Fettweis, Xavier, Gossart, Alexandra, Lipzig, Nicole P. M., Meijgaard, Erik, Orr, Andrew, Phillips, Tony, Webster, Stuart, Simonsen, Sebastian B., Souverijns, Niels
• Format: Text
• Language: English
• Published: 2020
• Subjects: Antarctic; The Antarctic; Transantarctic Mountains; West Antarctica; Antarc*; Antarctica; Ice Sheet; Ice Shelf; Iceberg*
• Online Access: https://doi.org/10.5194/tc-2019-333; https://tc.copernicus.org/preprints/tc-2019-333/

Antarctic ice sheet mass loss is currently equivalent to around 1 mm year −1 of global mean sea level rise. Most mass is lost due to sub-ice shelf melting and calving of icebergs. Ice sheet models of the Antarctic ice sheet have thus largely concentrated on parameterising sub-shelf and calving processes. However, surface mass balance (SMB) is also of crucial importance in controlling the stability and evolution of the vast Antarctic ice sheet. In this paper we compare the performance of five different regional climate models (COSMO-CLM 2 , HIRHAM5, MAR3.10, MetUM and RACMO2.3p2) in simulating the near surface climate and SMB of Antarctica. Our results show that, when regional climate models (RCMs) are forced by the ERA-Interim reanalysis, the integrated Antarctic ice sheet ensemble mean annual SMB is 2329 ± 94 Gigatonnes (Gt) year −1 over the common 1987 to 2015 period. However, individual model estimates vary from 1961 ± 70 to 2519 ± 118 Gt year −1 . The large differences are mostly explained by different SMB estimates in West Antarctica and the peninsula as well as around the Transantarctic mountains. The calculated annual average SMB is very sensitive to the period chosen but over the climatological mean period of 1980 to 2010 the ensemble mean is 2486 Gt year −1 . The interannual variability in SMB is consistent between the models and dominated by variability in the driving ERA-Interim reanalysis. The declining trend in Antarctic SMB reported in other studies is also very sensitive to period chosen and models disagree on the sign and magnitude of the trend in Antarctic SMB over the ERA-Interim period. Evaluation of models shows that they simulate Antarctic climate well when compared with daily observed temperature (Pearson correlation of 0.85 and higher) and pressure (bias ranges from −0.39 hPa in HIRHAM5 to −6.01 hPa in MAR with a mean of −3.49 hPa over all models) and nudged models, constrained within the domain as well as at lateral boundaries, perform better than un-nudged models. We compare modelled surface mass balance with a large dataset of observations which, though biased by undersampling in some regions, indicates that many of the biases in modelled SMB are common between models. The inclusion of drifting snow schemes improves modelled SMB on ice sheet slopes between 1000 and 2000 m where strong katabatic winds form but other regions where precipitation rates are high lack observations needed for the evaluation of different SMB estimates. Different ice masks have a substantial impact on the integrated total SMB and along with model resolution is therefore factored into our analysis. The majority of the different values for continental SMB are due to differences in modelled precipitation at relatively few grid points in coastal areas. Our analysis suggests that targeting coastal areas for observational campaigns will be key to improving and refining estimates of the total surface mass balance of Antarctica.