Abstract

The eastern side of the Antarctic Peninsula (AP) mountain range and the adjacent ice shelves are frequently affected by föhn winds originating from upwind of the mountains. Six automatic weather stations (AWSs) and archived model output from 5 km resolution Antarctic Mesoscale Prediction System (AMPS) forecasts have been combined to identify the occurrence of föhn conditions, and their spatial distribution over the Larsen C Ice Shelf (LCIS) from 2009 to 2012.Algorithms for semi‐automatic detection of föhn conditions have been developed for both AWS and AMPS data. The frequency of föhn varies by location, being most frequent at the foot of the AP and in the north of the ice shelf. They are most common in spring, when they can prevail for 50% of the time. The results of this study have important implications for further research, investigating the impact of föhn on surface melting, and the surface energy budget of the ice shelf. This is of particular interest due to the collapse of Larsen A and B ice shelves in 1995 and 2002 respectively, and the potential instability issues following a large calving event on Larsen C in 2017.

Highlights

  • The Antarctic Peninsula (AP) was the fastest warming region on Earth during the late 20th century, with air temperatures increasing at twice the global average (Vaughan et al, 2003)

  • Antarctic Mesoscale Prediction System (AMPS) does not always represent the near-surface conditions accurately over the ice (King et al, 2015), it does provide some measure of the spatial extent and variability in föhn conditions which is hard to obtain with the limited number of automatic weather stations (AWSs) observations

  • Warm, föhn conditions have been observed over the whole of Larsen C Ice Shelf (LCIS), stretching 260 km in a north–south direction and up to 130 km east of the AP

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Summary

INTRODUCTION

The Antarctic Peninsula (AP) was the fastest warming region on Earth during the late 20th century, with air temperatures increasing at twice the global average (Vaughan et al, 2003). Once the barrier is overcome, the drier air parcel descends on the leeside of the obstacle, and warms at the higher dry adiabatic lapse rate, leading to warmer and drier conditions in the lee. In this case, low-level blocking on the windward side leads to high-altitude air interacting with the mountain. The present study combines observations from five sites on the LCIS and one on the remnants of Larsen B, with modelling output at 5 km horizontal resolution to provide a detailed analysis of the frequency of föhn conditions, and of the spatial distribution of föhn winds over four years. Without understanding the frequency and temporal distribution of föhn conditions, analyses of their climatological contribution to surface melt remain uncertain

Study area
Near-surface data
AWS föhn identification algorithm
AMPS föhn identification algorithm
Algorithm combination
Temporal variability
Spatial distribution
Near-surface characteristics
DISCUSSION
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