A gradient-boosted tree framework to model the ice thickness of the world's glaciers (IceBoost v1.1)

Knowledge of glacier ice volumes is crucial for constraining future sea level potential, evaluating freshwater resources, and assessing impacts on societies, from regional to global. Motivated by the disparity in existing ice volume estimates, we present IceBoost, a global machine learning framework trained to predict ice thickness at arbitrary coordinates, thereby enabling the generation of spatially distributed thickness maps for individual glaciers. IceBoost is an ensemble of two gradient-boosted trees trained with 3.7 million globally available ice thickness measurements and an array of 39 numerical features. The model error is similar to those of existing models outside polar regions and is up to 30 %-40 % lower at high latitudes. Providing supervision by exposing the model to available glacier thickness measurements reduces the error by a factor of up to 2 to 3. A feature-ranking analysis reveals that geodetic data are the most informative variables, while ice velocity can improve the model performance by 6 % at high latitudes. A major feature of IceBoost is a capability to generalize outside the training domain, i.e. producing meaningful ice thickness maps in all regions of the world, including on the ice sheet peripheries.