AI in climate research: Detecting climate extremes

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Climate extremes such as heat waves, heavy rainfall or droughts have far-reaching impacts on ecosystems, agriculture, water resources and human health. In order to understand recent extremes and to be able to assess the resulting climate risks, they must be examined in a historical context.

How do recent extremes compare to past events and are there regional long-term trends? Data analysts at DKRZ have developed a method that can effectively reconstruct incomplete observation data on climate extremes by using methods of artificial intelligence (AI). The results of the study are published in Nature Communications.

The analysis of past climate extremes is complicated by the fact that existing datasets of observed extremes generally exhibit spatial gaps and inaccuracies due to inadequate spatial extrapolation. This problem arises from traditional statistical methods used to account for the lack of measurements, particularly prevalent before the mid-20th century.

The study demonstrates how AI can effectively reconstruct sparse observational data of European climate extremes (warm and cold days and nights) and reveal spatial trends across the time span from 1901 to 2018 that is not covered by most reanalysis datasets. The analysis shows that the AI method surpasses established statistical methods such as Kriging.

The reconstruction is based on transfer learning with Earth System Model data, e.g., large data amounts from the Coupled Model Intercomparison Project CMIP6. The computations used the GPU part of DKRZ's HPC system Levante.

The AI reconstructed dataset reveals quantitative evidence for hot and cold extremes in the early 20th century and sheds a new light on the evolution of these extremes. The dataset is provided to the climate community for a better characterization of climate extremes and to improve risk management and policy development.

More information: Étienne Plésiat et al, Artificial intelligence reveals past climate extremes by reconstructing historical records, Nature Communications (2024). DOI: 10.1038/s41467-024-53464-2

Journal information: Nature Communications

Provided by Deutsches Klimarechenzentrum