An artificial intelligence (AI) model that can calculate the distribution of ice thickness of all the glaciers on Earth has been developed by an international team of researchers in a world first.

The model, which has been published in the journal Geoscientific Model Development, is expected to become a reference tool for scientists studying future glacier melt scenarios.

Accurate knowledge of glacier volumes is essential for projecting future sea level rise, managing water resources, and assessing societal impacts linked to glacier retreat. However, estimating their absolute volume remains a major scientific challenge.

Over the years, more than 4 million in situ measurements of glacier thickness have been collected, thanks in large part to NASA’s Operation IceBridge. Despite the extensive dataset, current modelling approaches have not yet exploited its potential.

Power of data

Direct measurements of glacier thickness cover less than 1% of the planet’s glaciers, highlighting the need for models capable of providing global-scale estimates of ice thickness and volume. This newly published study is the first to leverage such observational data in conjunction with the power of machine-learning algorithms.

The researchers were led by Niccolò Maffezzoli, a Marie Curie fellow at Ca’ Foscari University of Venice and the University of California, Irvine, and an associate member of the Institute of Polar Sciences of the National Research Council of Italy.

He said, “Our model combines two decision tree algorithms, trained on thickness measurements and 39 features including ice velocity, mass balance, temperature fields, and geometric and geodetic variables. The trained model shows errors that are up to 30-40% lower than current traditional global models, particularly at polar latitudes and along the peripheries of the ice sheets, where the majority of the planet’s ice is located.”

Projecting sea level rise

In polar regions and in the margins of Greenland and Antarctica, having accurate ice thickness estimates is particularly important. These estimates serve as initial conditions for numerical models that simulate ice flow and its interactions with the ocean—interactions that are key to projecting sea level rise under future climate scenarios.

The model demonstrates strong generalisation capabilities in these regions and, the researchers believe, may help to refine current maps of subglacial topography in specific areas of the ice sheets, such as the Geikie Plateau or the Antarctic Peninsula.

This work represents an initial step towards producing updated estimates of global glacier volumes that will be useful to modellers, the IPCC, and policymakers. Maffezzoli shares visualisations of the data on the web-app ICEBOOST.

“We aim to release two datasets totalling half-a-million ice thickness maps by the end of 2025,” Maffezzoli explains. “There is still a long way to go, but this work shows that AI and machine learning approaches are opening up new and exciting possibilities for ice modelling.”

The significance of glaciers
At present, glaciers contribute approximately 25-30% of observed global sea level rise, and their melting is accelerating. This is particularly significant in arid regions such as the Andes or the major mountain ranges of the Himalaya and Karakoram, where glacial headwaters support the livelihoods of billions. It is also critical for understanding the stability of the polar ice sheets in Greenland and Antarctica, where peripheral interactions with the ocean influence ice sheet dynamics.