Résumé
Detecting abnormal climate events is crucial for understanding, predicting, and managing climate risks. However, most existing methods require prior knowledge about when and where to search for these events, limiting their effectiveness. In this study, we introduce ClimBurst, a new method to identify climate-related anomalies that does not require any prior information about their duration or spatial extent. We propose computing climate bursts to detect abnormal seasonal activity. The ClimBurst approach can detect anomalies at any time scale. The approach also compares anomalies at neighboring locations enabling the tracking of events across time and space. We apply our method on sea surface temperature data from the Mediterranean Sea between 1960 and 2021, where we detect particularly strong warm anomalies that can last from a few days to a few months over a few kilometers to hundreds, such as the 2015 marine heatwave. Our results reveal a noticeable increase in the frequency, magnitude and the spatial extent of these hot anomalies over time. Researchers and practitioners can use ClimBurst to detect and study climate anomalies, providing a basis for event attribution and long-term trend analysis.