Abstract
ABSTRACTIn this study we analyse a 15-year long time series of surface mass-balance (SMB) measurements performed between 2001 and 2016 in the ablation zone of the Morteratsch glacier complex (Engadine, Switzerland). For a better understanding of the SMB variability and its causes, multiple linear regressions analyses are performed with temperature and precipitation series from nearby meteorological stations. Up to 85% of the observed SMB variance can be explained by the mean May–June–July temperature and the total precipitation from October to March. A new method is presented where the contribution of each month's individual temperature and precipitation to the SMB can be examined in a total sample of 224 (16.8 million) combinations. More than 90% of the observed SMB can be explained with particular combinations, in which the May–June–July temperature is the most recurrent, followed by October temperature. The role of precipitation is less pronounced, but autumn, winter and spring precipitation are always more important than summer precipitation. Our results indicate that the length of the ice ablation season is of larger importance than its intensity to explain year-to-year variations. The widely used June–July–August temperature index may not always be the best option to describe SMB variability through statistical correlation.
Highlights
Mountain glaciers worldwide have retreated significantly in the past decades as a consequence of an increase in global temperature (Vaughan and others, 2013)
Zekollari and Huybrechts: Statistical modelling of the surface mass-balance variability of the Morteratsch glacier, Switzerland consists of adjacent clusters of months, we introduce a new method in which all possible monthly combinations are considered
The fact that almost the entirety of the surface mass-balance (SMB) variance can be explained by a simple multiple linear regression analysis (MLRA) with one temperature and one precipitation variable makes it redundant to add additional predictor variables to the analysis
Summary
Mountain glaciers worldwide have retreated significantly in the past decades as a consequence of an increase in global temperature (Vaughan and others, 2013). Many studies have highlighted these changes for glaciers in the Swiss Alps through a wide range of approaches, based on for instance geodetic methods, LIDAR measurements, Unmanned Aerial Vehicle (UAV) surveys and three-dimensional glacier evolution modelling (Jouvet and others, 2009; Gabbud and others, 2015, 2016; Zekollari and Huybrechts, 2015; Fischer and others, 2016; Sold and others, 2016; Gindraux and others, 2017; Rossini and others, 2018) These changes are largely driven by a strongly negative surface mass-balance (SMB) trend (Huss and others, 2015; Zemp and others, 2015; Vincent and others, 2017), which has been modelled at a variety of horizontal scales and through models of varying complexity for glaciers in the European Alps Our main objective is to make use of this new method to describe the measured SMB variations through a statistical analysis that is as simple as possible, i.e. relying on a minimal number of predictor variables to describe as much of the SMB variability as possible
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