Climate extreme are the source of most of the climate related risks that can affect society and ecosystem. The machine learning techniques could help the more traditional dynamical and statistical methods to detect them, attribute the extremes to global warming and to have a more accurate future projection.
The ability to correctly communicate how global warming is connected with changes in extremes and the role of machine learning in this is of crucial importance for the society for developing and planning mitigation and adaptation capacity.
- Artificial Intelligence for Detection and Attribution of Climate Extremes ( Download)
- A direct approach to detection and attribution [...] | AI & Climate Change | Eniko Székely ( Download)
- Dr. William Collins | Machine Learning for Detection of Climate Extremes ( Download)
- Detection and attribution of biodiversity change: a role for AI | AI for Biodiversity ( Download)
- 5 Dr. Jana Sillmann - Lecture on Identification and attribution of climate extremes and indices ( Download)
- Webinar #4 - The AIDE Toolbox: Artificial Intelligence for Disentangling Extreme Events ( Download)
- CLINT | Andrea Castelletti - CLINT: climate science and artificial intelligence ( Download)
- 4 Climate Models Detection and Attribution ( Download)
- Artificial Intelligence for Climate Impacts ( Download)
- CLINT | Claudia Bertini, Dorien Lugt - The Rhine delta hotspot ( Download)
- OneNOAA Climate Science Special Report webinar series: Detection and Attribution ( Download)
- AI-model-data-integration for understanding climate impacts in the Earth System | Discovery ( Download)
- Explainable AI for identifying regional climate change patterns (Zack Labe, Princeton) ( Download)
- Explainable AI for Climate Science: Detection, Prediction and Discovery with Elizabeth Barnes ( Download)
- XAIDA - WP4 by Jakob Runge (TU Dresden) ( Download)
