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Machine-learned global glacier ice volumes
Niccolò Maffezzoli1,2, Eric Rignot3,4,5,6, Carlo Barbante7,8
1Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, Venezia, 30172, Italy. niccolo.maffezzoli@unive.it.
Scientific Data
|July 30, 2026
Summary
A new global glacier ice thickness dataset, IceBoost v2.0, provides accurate volume estimates consistent with previous studies. This dataset aids in modeling future sea-level rise and managing freshwater resources.
Area of Science:
- Glaciology
- Geophysics
- Climate Science
Background:
- Accurate glacier ice thickness data is crucial for estimating global ice volume and sea-level rise.
- Previous global ice thickness models have varying degrees of uncertainty and spatial coverage.
Purpose of the Study:
- To present a new global dataset of glacier ice thickness modeled using the IceBoost v2.0 scheme.
- To provide updated global ice volume and sea-level equivalent (SLE) estimates based on the Randolph Glacier Inventory (RGI) v6.0 and v7.0.
- To assess the performance and confidence of the IceBoost v2.0 model in different glaciated regions.
Main Methods:
- Developed IceBoost v2.0, a gradient-boosted decision tree model trained on extensive ice thickness measurements.
- Applied IceBoost v2.0 to glacier outlines from RGI v6.0 and v7.0, incorporating physical and geometrical predictors.
- Validated model performance by comparing root mean square error with measurements and other existing models.
Main Results:
- Generated global glacier volume estimates of (150 ± 38) × 10³ km³ (RGI v6.0) and (149 ± 38) × 10³ km³ (RGI v7.0).
- Calculated corresponding sea-level equivalents (SLE) of 323 ± 91 mm for both RGI versions.
- Demonstrated improved accuracy (20-45% lower RMSE) in the high Arctic compared to other models, with comparable performance elsewhere.
- Identified regions, such as the Geikie Plateau in East Greenland, with potentially underestimated ice volumes.
Conclusions:
- The IceBoost v2.0 dataset offers a reliable global estimate of glacier ice volume and SLE.
- Model confidence is highest in data-rich, high-latitude regions and lower in data-sparse, complex terrains.
- The dataset is a valuable resource for future glacier evolution modeling, sea-level rise projections, and water resource management.
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