Institute for Advanced Study (IAS)

Causality in Climate Science



In this workshop we bring together experts from climate science, seasonal forecasting and complex systems research to trigger a lively debate on how data-driven methods and physical models can best be merged to gain new insights into extreme weather and atmospheric teleconnections.


Welcome and Intro Peter Sloot, UvA
Combining causal inference and climate models Dim Coumou, VU
Improving sub-seasonal forecasts Maurice Schmeits, KNMI
Modes of variability in climate Frank Selten, KNMI
Applying causality in RGCPD tools Marlene Kretschmer, PIK
General concepts behind causality tools Rick Quax, UvA
Emphasizing limitations and pitfalls Joris Mooij, UvA
Brainstorm: applying causality in climate -
Finding causal precursors of extreme events Sem Vijverberg, VU
Merging data-driven tools with climate models Fei Luo, VU
Machine learning for improving sub-seasonal forecasts Chiem van Straaten, KNMI
Brainstorm on possible collaboration -
Wrap-up -


Published by  UvA Institute for Advanced Study