TY - JOUR

T1 - Extreme temperature events on Greenland in observations and the MAR regional climate model

AU - Leeson, Amber Alexandra

AU - Eastoe, Emma Frances

AU - Fettweis, Xavier

PY - 2018/3/26

Y1 - 2018/3/26

N2 - 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 which are known to be robust at the ice-sheet scale, but occasionally miss regional and local scale climate variability. To date, the fidelity of these models in the context of short period variability in time has not been 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 GC-Net, to assess the ability of the MAR RCM to reproduce observed extreme 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, although this bias is much smaller than that exhibited by coarse-scale Era-Interim reanalysis data. As a corollary, 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 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 addressing shortcomings in this area should be a priority for model development.

AB - 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 which are known to be robust at the ice-sheet scale, but occasionally miss regional and local scale climate variability. To date, the fidelity of these models in the context of short period variability in time has not been 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 GC-Net, to assess the ability of the MAR RCM to reproduce observed extreme 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, although this bias is much smaller than that exhibited by coarse-scale Era-Interim reanalysis data. As a corollary, 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 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 addressing shortcomings in this area should be a priority for model development.

U2 - 10.5194/tc-12-1091-2018

DO - 10.5194/tc-12-1091-2018

M3 - Journal article

VL - 12

JO - Cryosphere

JF - Cryosphere

SN - 1994-0416

ER -