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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.

We present a novel approach to simulate and reconstruct annual glacier-wide surface mass balance (SMB) series based on a deep artificial neural network (ANN; i.e. deep learning). This method has been included as the SMB component of an open-source regional glacier evolution model. While most glacier models tend to incorporate more and more physical processes, here we take an alternative approach by creating a parameterized model based on data science. Annual glacier-wide SMBs can be simulated from topo-climatic predictors using either deep learning or Lasso (least absolute shrinkage and selection operator; regularized multilinear regression), whereas the glacier geometry is updated using a glacier-specific parameterization. We compare and cross-validate our nonlinear deep learning SMB model against other standard linear statistical methods on a dataset of 32 French Alpine glaciers. Deep learning is found to outperform linear methods, with improved explained variance (up to +64 % in space and +108 % in time) and accuracy (up to +47 % in space and +58 % in time), resulting in an estimated r2 of 0.77 and a root-mean-square error (RMSE) of 0.51 m w.e. Substantial nonlinear structures are captured by deep learning, with around 35 % of nonlinear behaviour in the temporal dimension. For the glacier geometry evolution, the main uncertainties come from the ice thickness data used to initialize the model. These results should encourage the use of deep learning in glacier modelling as a powerful nonlinear tool, capable of capturing the nonlinearities of the climate and glacier systems, that can serve to reconstruct or simulate SMB time series for individual glaciers in a whole region for past and future climates.