EGU22-3701, updated on 27 Mar 2022
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Snow accumulation over the world's glaciers (1981-2021) inferred from climate reanalyses and machine learning

Matteo Guidicelli1, Marco Gabella2, Matthias Huss1,3,4, and Nadine Salzmann5,6
Matteo Guidicelli et al.
  • 1Department of Geosciences, University of Fribourg, Fribourg, Switzerland (
  • 2Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, Switzerland
  • 3Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
  • 4Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 5WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 6Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos, Switzerland

The scarcity and limited accuracy of snow and precipitation observation and estimation in high-mountain regions reduce our understanding of climatic-cryospheric processes. Thus, we compared the snow water equivalent (SWE) from winter mass balance observations of 95 glaciers distributed over the Alps, Canada, Central Asia and Scandinavia, with the cumulative gridded precipitation data from the ERA-5 and the MERRA-2 reanalysis products. We propose a machine learning model to downscale the gridded precipitation from the reanalyses to the altitude of the glaciers. The machine learning model is a gradient boosting regressor (GBR), which combines several meteorological variables from the reanalyses (air temperature and relative humidity are also downscaled to the altitude of the glaciers) and topographical parameters. Among the most important variables selected by the GBR model, are the downscaled relative humidity and the downscaled air temperature. These GBR-derived estimates are evaluated against the winter mass balance observations by means of a leave-one-glacier-out cross-validation (site-independent GBR) and a leave-one-season-out cross-validation (season-independent GBR). The estimates downscaled by the GBR show lower biases and higher correlations with the winter mass balance observations than downscaled estimates derived with a lapse-rate-based approach. Finally, the GBR estimates are used to derive SWE trends between 1981 and 2021 at high-altitudes. The trends obtained from the GBRs are more enhanced than those obtained from the gridded precipitation of the reanalyses. When the data is regrouped regionwide, significant trends are only observed for the Alps (positive) and for Scandinavia (negative), while significant positive or negative trends are observed in all the regions when looking locally at single glaciers and specific elevations. Positive (negative) SWE trends are typically observed at higher (lower) elevations, where the impact of rising temperatures is less (more) dominating.

How to cite: Guidicelli, M., Gabella, M., Huss, M., and Salzmann, N.: Snow accumulation over the world's glaciers (1981-2021) inferred from climate reanalyses and machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3701,, 2022.