Extreme temperature events on Greenland in observations and the MAR regional climate model
2017
Abstract. Meltwater from the Greenland Ice Sheet contributed 1.7–6.12 mm to global
sea level between 1993 and 2010 and is expected to contribute 20–110 mm 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 (Modele
Atmospherique Regional) 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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