Empirical glacier mass-balance models for South America

Abstract We investigate relationships between synoptic-scale atmospheric variability and the mass-balance of 13 Andean glaciers (located 16–55° S) using Pearson correlation coefficients (PCCs) and multiple regressions. We then train empirical glacier mass-balance models (EGMs) in a cross-validated m...

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Bibliographic Details
Published in:Journal of Glaciology
Main Authors: Mutz, Sebastian G., Aschauer, Johannes
Other Authors: Deutsche Forschungsgemeinschaft
Format: Article in Journal/Newspaper
Language:English
Published: Cambridge University Press (CUP) 2022
Subjects:
Online Access:http://dx.doi.org/10.1017/jog.2022.6
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143022000065
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Summary:Abstract We investigate relationships between synoptic-scale atmospheric variability and the mass-balance of 13 Andean glaciers (located 16–55° S) using Pearson correlation coefficients (PCCs) and multiple regressions. We then train empirical glacier mass-balance models (EGMs) in a cross-validated multiple regression procedure for each glacier. We find four distinct glaciological zones with regard to their climatic controls: (1) The mass-balance of the Outer Tropics glaciers is linked to temperature and the El Niño-Southern Oscillation (PCC ⩽ 0.6), (2) glaciers of the Desert Andes are mainly controlled by zonal wind intensity (PCC ⩽ 0.9) and the Antarctic Oscillation (PCC ⩽0.6), (3) the mass-balance of the Central Andes glaciers is primarily correlated with precipitation anomalies (PCC ⩽ 0.8), and (4) the glacier of the Fuegian Andes is controlled by winter precipitation (PCC ≈ 0.7) and summer temperature (PCC ≈ −0.9). Mass-balance data in the Lakes District and Patagonian Andes zones, where most glaciers are located, are too sparse for a robust detection of synoptic-scale climatic controls. The EGMs yield R 2 values of ~ 0.45 on average and ⩽ 0.74 for the glaciers of the Desert Andes. The EGMs presented here do not consider glacier dynamics or geometry and are therefore only suitable for short-term predictions.