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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2019-240
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2019-240
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  17 Dec 2019

17 Dec 2019

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This preprint is currently under review for the journal TC.

Scoring Antarctic surface mass balance in climate models to refine future projections

Tessa Gorte1, Jan T. M. Lenaerts1, and Brooke Medley2 Tessa Gorte et al.
  • 1Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, USA
  • 2Cryospheric Sciences Laboratory, National Aeronautics and Space Administration’s Goddard Space Flight Center, USA

Abstract. An increase of Antarctic Ice Sheet (AIS) surface mass balance (SMB) has the potential to mitigate future sea level rise that is driven by enhanced solid ice discharge from the ice sheet. For climate models, AIS SMB provides a difficult challenge, as it is highly susceptible to spatial, seasonal and interannual variability.

Here we use a reconstructed data set of AIS snow accumulation as "true" observational data, to evaluate the ability of the CMIP5 and CMIP6 suites of models in capturing the mean, trends, temporal variability and spatial variability in SMB over the historical period (1850–2000). This gives insight into which models are most reliable for predicting SMB into the future. We found that the best scoring models included the National Aeronautics and Space Administration's GISS models and the Max Planck Institute far Meteorologie's MPI models.

Using a scoring system based on SMB magnitude, trend, and temporal variability across the AIS, as well as spatial SMB variability, we selected a subset of the top 10th percentile of models to refine 21st century (2000–2100) AIS-integrated SMB projections to 2295 ± 1222 Gt per year 2382 ± 1316 Gt per year, and 2648 ± 1530 Gt per year for Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, respectively. We also reduced the spread in AIS-integrated mean SMB by 78 %, 75 %, and 78 % in RCPs 2.6, 4.5, and 8.5, respectively.

Tessa Gorte et al.

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Tessa Gorte et al.

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