4 papers
Probabilistic Deep Learning for Drought Forecasting: Role of Internal Climate Variability
Henri Funk, Cornelia Gruber, Göran Kauermann +2
Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning. Yet drought forecasts remain uncertain b…
Capturing Aleatoric Uncertainty in Climate Models
Cornelia Gruber, Henri Funk, Magdalena Mittermeier +2
Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk-based decision-making…
Omega-blocks with spatially compounding extremes over Europe are highly sensitive to remote atmospheric drivers
Magdalena Mittermeier, Christian M. Grams, Urs Beyerle +5
Omega-blocks can trigger spatially compounding heat-precipitation extremes with severe societal impacts, as seen in September 2023 when a heatwave over France coincided with devast…
Identifying the atmospheric drivers of drought and heat using a smoothed deep learning approach
Magdalena Mittermeier, Maximilian Weigert, David Rügamer
Europe was hit by several, disastrous heat and drought events in recent summers. Besides thermodynamic influences, such hot and dry extremes are driven by certain atmospheric situa…