The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica

Author:

Hofsteenge Marte G.ORCID,Cullen Nicolas J.ORCID,Reijmer Carleen H.ORCID,van den Broeke MichielORCID,Katurji MarwanORCID,Orwin John F.ORCID

Abstract

Abstract. The McMurdo Dry Valleys (MDV) are a polar desert, where glacial melt is the main source of water to streams and the ecosystem. Summer air temperatures are typically close to zero, and therefore foehn events can have a large impact on the meltwater production. A 14-month record of automatic weather station (AWS) data on Joyce Glacier is used to force a 1D surface energy balance model to study the impact of foehn events on the energy balance. AWS data and output of the Antarctic Mesoscale Prediction System (AMPS) on a 1.7 km grid are used to detect foehn events at the AWS site. Foehn events at Joyce Glacier occur under the presence of cyclones over the Ross Sea. The location of Joyce Glacier on the leeward side of the Royal Society Range during these synoptic events causes foehn warming through isentropic drawdown. This mechanism differs from the foehn warming through gap flow that was earlier found for other regions in the MDV and highlights the complex interaction of synoptic flow with local topography of the MDV. Shortwave radiation is the primary control on melt at Joyce Glacier, and melt often occurs with subzero air temperatures. During foehn events, melt rates are enhanced, contributing to 23 % of the total annual melt. Foehn winds cause a switch from a diurnal stability regime in the atmospheric surface layer to a continuous energy input from sensible heat flux throughout the day. The sensible heating during foehn, through an increase in turbulent mixing resulting from gustier and warmer wind conditions, is largely compensated for by extra heat losses through sublimation. Melt rates are enhanced through an additional energy surplus from a reduced albedo during foehn.

Funder

Antarctica New Zealand

Royal Society Te Apārangi

Publisher

Copernicus GmbH

Subject

Earth-Surface Processes,Water Science and Technology

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1. Foehn wind detection using unsupervised machine learning;2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS);2024-04-08

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