Preprints
https://doi.org/10.5194/tc-2022-69
https://doi.org/10.5194/tc-2022-69
 
27 Apr 2022
27 Apr 2022
Status: this preprint is currently under review for the journal TC.

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

Matteo Guidicelli1, Matthias Huss1,2,3, Marco Gabella4, and Nadine Salzmann5,6 Matteo Guidicelli et al.
  • 1Department of Geosciences, University of Fribourg, Fribourg, Switzerland
  • 2Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich, Zurich, Switzerland
  • 3Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland
  • 4Federal Office of Meteorology and Climatology MeteoSwiss, Locarno-Monti, 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

Abstract. Although reanalysis products for remote high-mountain regions provide estimates of snow precipitation, this data is inherently uncertain and assessing a potential bias is difficult due to the scarcity of observations, thus also limiting their reliability to evaluate long-term effects of climate change. Here, we compare the winter mass balance of 95 glaciers distributed over the Alps, Western Canada, Central Asia and Scandinavia, with the total precipitation from the ERA-5 and the MERRA-2 reanalysis products during the snow accumulation seasons from 1981 until today. We propose a machine learning model to adjust the precipitation of reanalysis products to the elevation of the glaciers, thus deriving snow water equivalent (SWE) estimates over glaciers uncovered by ground observations and/or filling observational gaps. We use a gradient boosting regressor (GBR), which combines several meteorological variables from the reanalyses (e.g. air temperature, relative humidity) with topographical parameters. These GBR-derived estimates are evaluated against the winter mass balance data by means of a leave-one-glacier-out cross-validation (site-independent GBR) and a leave-one-season-out cross-validation (season-independent GBR). Both site-independent and season-independent GBRs allowed reducing (increasing) the bias (correlation) between the precipitation of the original reanalyses and the winter mass balance data of the glaciers. Finally, the GBR models are used to derive SWE trends on glaciers between 1981 and 2021. The resulting trends are more pronounced than those obtained from the total precipitation of the original reanalyses. On a regional scale, significant 41-year SWE trends over glaciers are observed in the Alps (MERRA-2 season-independent GBR: +0.4 %/year) and in Western Canada (ERA-5 season-independent GBR: +0.2 %/year), while significant positive/negative trends are observed in all the regions for single glaciers or specific elevations. Negative (positive) SWE trends are typically observed at lower (higher) elevations, where the impact of rising temperatures is more (less) dominant.

Matteo Guidicelli et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-69', Anonymous Referee #1, 01 Jun 2022
    • AC1: 'Reply on RC1', Matteo Guidicelli, 25 Jul 2022
  • RC2: 'Comment on tc-2022-69', Anonymous Referee #2, 18 Jun 2022
    • AC2: 'Reply on RC2', Matteo Guidicelli, 25 Jul 2022

Matteo Guidicelli et al.

Matteo Guidicelli et al.

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Latest update: 08 Aug 2022
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Short summary
We demonstrated that data-driven methods can be powerful instruments to adjust snow precipitation estimates over glaciers. The new information provided by our study can be helpful to further evaluate the local impact of climate change on snow over glaciers in remote high-mountain regions of the world, where observations are often scarce and the spatial resolution of existing global models is too coarse to allow local impact studies and the consequent development of adaptation strategies.