Abstract

Studying glacier mass changes at regional scale provides critical insights into the impact of climate change on glacierized regions, but is impractical using in situ estimates alone due to logistical and human constraints. We present annual mass-balance time series for 239 glaciers in the European Alps, using optical satellite images for the period 2000-2016. Our approach, called the SLA-method, is based on the estimation of the glacier snowline altitude (SLA) for each year combined with the geodetic mass balance over the study period to derive the annual mass balance. In situ annual mass-balances from 23 glaciers were used to validate our approach and underline its robustness to generate annual mass-balance time series. Such temporally resolved observations provide a unique potential to investigate the behavior of glaciers in regions where few or no data are available. At the European Alps scale, our geodetic estimate was performed for 361 glaciers (75% of the glacierized area) and indicates a mean annual mass loss of -0.74±0.20 m w.e. yr-1 from 2000 to 2016. The spatial variability in the average glacier mass loss is significantly correlated to three morpho-topographic variables (mean glacier slope, median and maximum altitudes) altogether explaining 36% of the observed variance. Comparing the mass losses from in situ and SLA-method estimates and taking into account the glacier slope and maximum elevation, we show that steeper glaciers and glaciers with higher maximum elevation experienced less mass loss. Because steeper glaciers (>20°) are poorly represented by in situ estimates, we suggest that region-wide extrapolation of field measurements could be improved by including a morpho-topographic dependency. The analysis of the annual mass changes with regard to a global atmospheric dataset (ERA5) showed that: i) extreme climate events are registered by all glaciers across the European Alps and we identified opposite weather regimes favorable or detrimental to the mass change; ii) the interannual variability of glacier mass balances in the “central European Alps” is lower; and iii) current strong imbalance of glaciers in the European Alps is likely mainly the consequence of the multi-decadal increasing trend in atmospheric temperature, clearly documented from ERA5 data.

Highlights

  • Beyond their role as an iconic symbol of climate change (Mackintosh et al, 2017), mountain glaciers are considered as a natural “climate-meter” (Vaughan et al, 2013) because of their high sensitivity to climate change

  • We propose an automation and a regional application of the SLA-method based on the estimation of the end-of-summer snowline altitude (SLA), a known proxy of the equilibrium-line altitude (ELA) for glacier where superimposed ice is negligible (Lliboutry, 1965), and whose position is a predictor of annual mass change (e.g., Braithwaite, 1984; Rabatel et al, 2005, 2017; Pelto, 2011; Chandrasekharan et al, 2018)

  • The European Alps being one of the most monitored glacierized regions worldwide, we were able to validate our estimates by comparing to 23 glacier-wide annual mass balance time series estimated with the glaciological method

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Summary

INTRODUCTION

Beyond their role as an iconic symbol of climate change (Mackintosh et al, 2017), mountain glaciers are considered as a natural “climate-meter” (Vaughan et al, 2013) because of their high sensitivity to climate change. The recent development of algorithms to automate the processing of satellite images (e.g., Sirguey, 2009; Noh and Howat, 2015; Shean et al, 2016; Beyer et al, 2018), the increasing storage and computing capacities, and the amount of free optical satellite data (e.g., Landsat, Sentinel-2, ASTER) suitable to monitor glacier surface elevation changes or snowline altitude have encouraged the development of automatic methods enabling to overcome the above mentioned limitations (e.g., Brun et al, 2017; Racoviteanu et al, 2019; Rastner et al, 2019) Despite their minor potential impact on the future expected sea-level rise (Farinotti et al, 2019) and the relatively low ice extent (2,092 km, Randolph Glacier Inventory RGI 6.0, RGI Consortium, 2017), we applied the SLA-method on the European Alps for the period 2000–2016 because of the availability of a high number of in situ monitored glaciers with annual mass balance data (23 in this study), among which nine are considered as reference glaciers by the World Glacier Monitoring Service (WGMS, 2019, see Supplementary Table S3 for detailed list), and manually delineated snowlines from 34 glaciers in our dataset (Rabatel et al, 2013, 2016) suitable to validate our estimates. Balance, we performed principal component analysis (Wilks, 2011) on these variables

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