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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 3 | Copyright
The Cryosphere, 12, 1091-1102, 2018
https://doi.org/10.5194/tc-12-1091-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 26 Mar 2018

Research article | 26 Mar 2018

Extreme temperature events on Greenland in observations and the MAR regional climate model

Amber A. Leeson1, Emma Eastoe2, and Xavier Fettweis3 Amber A. Leeson et al.
  • 1Lancaster Environment Centre/Data Science Institute, Lancaster University, Lancaster, LA1 4YW, UK
  • 2Department of Mathematics and Statistics, Lancaster University, Lancaster, LA1 4YW, UK
  • 3Department of Geography, University of Liege, 4000 Liège, Belgium

Abstract. Meltwater from the Greenland Ice Sheet contributed 1.7–6.12mm to global sea level between 1993 and 2010 and is expected to contribute 20–110mm to future sea level rise by 2100. These estimates were produced by regional climate models (RCMs) which are known to be robust at the ice sheet scale but occasionally miss regional- and local-scale climate variability (e.g. Leeson et al., 2017; Medley et al., 2013). To date, the fidelity of these models in the context of short-period variability in time (i.e. intra-seasonal) has not been fully assessed, for example their ability to simulate extreme temperature events. We use an event identification algorithm commonly used in extreme value analysis, together with observations from the Greenland Climate Network (GC-Net), to assess the ability of the MAR (Modèle Atmosphérique Régional) RCM to reproduce observed extreme positive-temperature events at 14 sites around Greenland. We find that MAR is able to accurately simulate the frequency and duration of these events but underestimates their magnitude by more than half a degree Celsius/kelvin, although this bias is much smaller than that exhibited by coarse-scale Era-Interim reanalysis data. As a result, melt energy in MAR output is underestimated by between 16 and 41% depending on global forcing applied. Further work is needed to precisely determine the drivers of extreme temperature events, and why the model underperforms in this area, but our findings suggest that biases are passed into MAR from boundary forcing data. This is important because these forcings are common between RCMs and their range of predictions of past and future ice sheet melting. We propose that examining extreme events should become a routine part of global and regional climate model evaluation and that addressing shortcomings in this area should be a priority for model development.

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Future melting of the Greenland Ice Sheet is predicted using regional climate models (RCMs). Here, we assess the ability of the MAR RCM to reproduce observed extreme temperature events and the melt energy produced during these times at 14 locations. We find that MAR underestimates temperatures by >0.5 °C during extreme events, which leads to an underestimate in melt energy by up to 41 %. This is potentially an artefact of the data used to drive the MAR simulation and needs to be corrected for.
Future melting of the Greenland Ice Sheet is predicted using regional climate models (RCMs)....
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