Abstract
Abstract. Glacier mass balance (MB) data are crucial to understanding and quantifying the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide mass balance of all the glaciers in the French Alps for the 1967–2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network) based on direct MB observations and remote-sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method's validity was assessed previously through an extensive cross-validation against a dataset of 32 glaciers, with an estimated average error (RMSE) of 0.55 mw.e.a-1, an explained variance (r2) of 75 % and an average bias of −0.021 mw.e.a-1. We estimate an average regional area-weighted glacier-wide MB of −0.69±0.21 (1σ) mw.e.a-1 for the 1967–2015 period with negative mass balances in the 1970s (−0.44 mw.e.a-1), moderately negative in the 1980s (−0.16 mw.e.a-1) and an increasing negative trend from the 1990s onwards, up to −1.26 mw.e.a-1 in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967–2015 period are the Chablais (−0.93 mw.e.a-1), Champsaur (−0.86 mw.e.a-1), and Haute-Maurienne and Ubaye ranges (−0.84 mw.e.a-1 each), and the ones presenting the lowest mass losses are the Mont-Blanc (−0.68 mw.e.a-1), Oisans and Haute-Tarentaise ranges (−0.75 mw.e.a-1 each). This dataset – available at https://doi.org/10.5281/zenodo.3925378 (Bolibar et al., 2020a) – provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments.
Highlights
Among all the components of the Earth system, glaciers are some of the most visibly affected by climate change, with an overall worldwide shrinkage despite important differences between regions (Zemp et al, 2019)
We present a dataset of annual glacier-wide mass balance of all the glaciers in the French Alps for the 1967–2015 period
The annual glacier-wide mass balance (MB) dataset for the 661 French Alpine glaciers has been reconstructed using a deep artificial neural network (ANN), known as deep learning
Summary
Among all the components of the Earth system, glaciers are some of the most visibly affected by climate change, with an overall worldwide shrinkage despite important differences between regions (Zemp et al, 2019). Some studies are bridging the gap towards an annual temporal resolution (Rabatel et al, 2005, 2016; Rastner et al, 2019), but the coverage is still limited to glaciers without cloud cover or acquisition-related artefacts This means that these mass balance datasets are often restricted to certain glaciers and years within a region. Two studies include reconstructions in the European Alps, including the French Alps, over a substantial period of the recent past: Marzeion et al (2012, 2015) reconstructed annual MB series of all glaciers in the Randolph Glacier Inventory for the last century They used a minimal model relying only on temperature and precipitation data, based on a temperature-index method, with two parameters to calibrate the temperature sensitivity and the precipitation lapse rate. An overview of the methodology used to produce the dataset and a review of the associated uncertainties are presented in Sect. 2, followed by a dataset overview in Sect. 3, where the data structure and regional trends are described and where the dataset is compared to a previous study and observations
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