A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015

Glacier surface mass balance (SMB) data are crucial to understand and quantify 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 surface 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 and remote sensing
SMB observations, meteorological reanalyses and topographical data from glacier inventories. This data science reconstruction
approach is embedded as a SMB component of the open-source ALpine Parameterized Glacier Model (ALPGM: https://zenodo.org/record/3609136). An extensive cross-validation allowed to assess the method’s validity, with an estimated average error (RMSE) of 0.49 m.w.e. a^-1, an explained variance (r²) of 79% and an average bias of +0.017 m.w.e. a^-1. We estimate an average regional area-weighted glacier-wide SMB of -0.72±0.20 m.w.e. a^-1 for the 1967-2015 period, with moderately negative mass balances in the 1970s (-0.52 m.w.e. a^-1) and 1980s (-0.12 m.w.e. a^-1), and an increasing negative trend from the 1990s onwards, up to -1.39
m.w.e. a^-1 in the 2010s. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for this period are the Chablais (-0.90 m.w.e. a^-1) and Ubaye and Champsaur (-0.91 m.w.e. a^-1 both) ranges, and the ones presenting the lowest mass losses are the Mont-Blanc and Oisans ranges (-0.74 and -0.78 m.w.e. a^-1 respectively). This dataset 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 meltwater contributions in glacierized catchments.

The SMB dataset is comprised of multiple CSV files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: GLIMS-ID_RGI-ID_SMB.csv. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide SMB time series. Glaciers with remote sensing-derived observations (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a^-1 as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;). All topographical data for the 661 glaciers can be found in the updated version of the 2003 glacier inventory included in the Supplementary material.