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Abstract. Surface melting over the Antarctic Peninsula (AP) may impact thestability of ice shelves and thus the rate at which grounded ice isdischarged into the ocean. Energy and mass balance models are needed tounderstand how climatic change and atmospheric circulation variability drivecurrent and future melting. In this study, we evaluate the regional climatemodel MAR over the AP at a 10 km spatial resolution between 1999 and 2009, aperiod when active microwave data from the QuikSCAT mission is available.This model has been validated extensively over Greenland, has is applied hereto the AP at a high resolution and for a relatively long time period (fulloutputs are available to 2014). We find that melting in the northeastern AP,the focus area of this study, can be initiated both by sporadic westerlyfohn flow over the AP mountains and by northerly winds advecting warm airfrom lower latitudes. A comparison of MAR with satellite and automaticweather station (AWS) data reveals that satellite estimates show greater meltfrequency, a larger melt extent, and a quicker expansion to peak melt extentthan MAR in the centre and east of the Larsen C ice shelf. These differencesare reduced in the north and west of the ice shelf, where the comparison withsatellite data suggests that MAR is accurately capturing melt produced bywarm westerly winds. MAR shows an overall warm bias and a cool bias attemperatures above 0  ∘ C as well as fewer warm, strong westerly windsthan reported by AWS stations located on the eastern edge of the Larsen C iceshelf, suggesting that the underestimation of melt in this region may be theproduct of limited eastward flow. At higher resolutions (5 km), MAR shows afurther increase in wind biases and a decrease in meltwater production. Weconclude that non-hydrostatic models at spatial resolutions better than 5 kmare needed to better-resolve the effects of fohn winds on the easternedges of the Larsen C ice shelf.

Melting over the northeast Antarctic Peninsula (1999–2009): evaluation of a high-resolution regional climate model