Climate change and risks

Artificial intelligence helps scientists to better predict the evolution of glaciers under climate change

Glaciers are rapidly losing mass as a consequence of human-induced climate change. It is of paramount importance to properly understand the physical processes behind these regional and global changes, in order to anticipate the different possible future glacier scenarios and their impacts on sea level rise, water resources and ecosystems. For scientists to investigate these matters, numerical models are used to simulate glacier evolution in a simplified manner for whole regions or even the entire world, for both past and future periods of time.

Published on 23 January 2022

illustration Artificial intelligence helps scientists to better predict the evolution of glaciers under climate change

In a new study published in the scientific journal Nature Communications, an interdisciplinary team of glaciologists, climatologists and mathematicians from Université Grenoble Alpes, INRAE, Utrecht University, Météo-France, Université Libre de Bruxelles and TU Delft, has used for the first time deep learning - a type of artificial intelligence - to simulate the future evolution of glaciers at a regional scale. As for most physical processes in nature, the evolution of glaciers and climate is nonlinear, meaning that they do not evolve in a constant manner through time. The ability to capture these nonlinear effects is precisely one of the main advantages of deep learning compared to the classic models currently used to simulate glacier evolution at regional-to-global scale. This marks a new generation of scientific models better equipped to predict the fate of glaciers under climate change.

These results have important consequences for our understanding of the future evolution of glaciers and sea level rise. Glaciers in the Arctic and Patagonia host the largest ice reserves in the world besides the Antarctic and Greenland ice sheets, and according to this study they will be the most affected regions by this nonlinear response to global warming. This calls for a revision of current predictions, adjusting glacier models to correctly take into account these complex processes. Artificial intelligence, combined with knowledge on the physics of glaciers and climate, will play a very important role in future discoveries. 

Image: Artist impression of the artificial intelligence model, based on a deep neural network, used to model the future evolution of glaciers (in this picture: Aoraki/Mount Cook, New Zealand). The different nodes represent the artificial neurons, with the numbers representing the input data used to train the model. Attribution: photo: Tom Bernardo, artist impression: Jordi Bolibar.  

Jordi Bolibar, Antoine Rabatel, Isabelle Gouttevin, Harry Zekollari, Clovis Galiez Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning. Nature Communications, 13, 409 (20 January 2022).


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Climate change and risks

Patrick Taillandier, a model modeler

From the moment he started studying informatics, Patrick Taillandier's interest in research has grown unceasingly. He has worked in such diverse places as Australia, Vietnam, Lyon, Rouen, and Toulouse, cultivating an expertise in modelling complex systems, which comes in handy for managing risks such as floods, fires, and epidemics.

19 December 2019

Climate change and risks

Dealing with rising sea levels

On 24 October 2019, the French alliance for research on the environment (AllEnvi) published the results of a foresight study, which had been steered in part by three members of the soon-to-be-established INRAE. This study covered the economic, social and environmental consequences of a rise in sea levels by 2100, along with possible ways to anticipate it. Using eight worldwide scenarios and three territorial focuses, the report provides the keys to anticipating and limiting this phenomenon worldwide – one of the direct effects of climate change – and adapting to it at the territorial scale.

11 December 2019

Climate change and risks

With climate change, avalanches are migrating upslope

PRESS RELEASE - Mountain areas are particularly affected by global warming, but how it impacts snow avalanches remains poorly known. Researchers from INRAE, Météo France, the CNRS* and the Universities of Grenoble Alpes, Genève and Haute-Alsace have together evaluated changes in avalanche activity over nearly two-and-a-half centuries in the Vosges Mountains, combining historical analysis with statistical modelling and climate research. Their results, published on 25 October in the PNAS, show that avalanches now occur at higher altitudes than previously, with avalanche prone areas now restricted to the range’s highest elevations. This upslope migration has resulted in a sevenfold reduction of the number of avalanches, a shortening of the avalanche season, and a shrinkage in their size by comparison with the last phase of the Little Ice Age. The results also show that low to medium elevation mountain ranges such as the Vosges Mountains may serve as sentinels for the impacts of climate warming.

26 October 2021