Investigating the drivers of global glacier volume changes over the last two decades using machine learning

Machine learning plays an increasingly important role in modelling and better quantifying observed changes in various subcomponents of the Earth system. For instance, in the field of glaciology, machine learning methods have the potential to help unravel the ever-growing datasets on observed glacier changes, allowing for a better understanding of these changes and their driving factors. In this study, we investigate the benefit of using a non-linear machine learning framework to model the observed recent glacier changes (individual glaciers’ geodetic mass balance over the 2000-2019 period) for nearly all the land-terminating glaciers larger than 2 km^2. To this end, we build a Random Forest model driven by a set of predictors, composed of both topographic (e.g. area, slope, debris coverage) and climatic features (e.g. temperature and precipitation anomalies), which explain up to 70% of the global variance in the observational dataset. Generally, we find that the climatic features are more important, explaining alone approx. 55% of the variance, as compared to the approx. 40% obtained with the topographical ones alone. We further investigate the importance of the topographical predictors within subregions that are assumed to be climatically homogeneous, showing different behaviours across them. Our study illustrates the benefit of using non-linear models when statistically modelling multi-decadal geodetic mass balances, providing further insights into the drivers of current glacier changes. The proposed framework also has the potential to be used as a gap-filling tool to estimate the geodetic mass balance of unmeasured glaciers or those with uncertain geodetic mass balance observations and to predict future mass balance when forced with CMIP6 climate data or similar Earth System Model output.