EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

From climate variability to heavy precipitation – Learning transfer functions from data

Michał Kałczyński1,2, Krzysztof Krawiec2,3, and Zbigniew Kundzewicz1
Michał Kałczyński et al.
  • 1Institute for Agricultural and Forest Environment, Polish Academy of Sciences, Poznan, Poland
  • 2Institute of Computing Science, Poznań University of Technology, Poznan, Poland
  • 3Center for Artificial Intelligence and Machine Learning (CAMIL), Poznan, Poland

The contribution deals with spatial extremes of intense precipitation at the global scale, with the help of data-driven modelling. We ask whether the inter-annual and inter-decadal climate variability track plays a dominant role in the interpretation of the variability of heavy precipitation, globally. The study aims at discovering spatially and temporally organized links between climate oscillation indices, such as El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, Atlantic Multidecadal Oscillation and heavy precipitation. To this aim, we induce a range of machine-learning models, primarily recurrent neural networks, from multiple sources of global observations, including E-OBS data set from the UERRA project, GPCC Full Data Daily, and climate variability indices. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We expect to identify climate-oscillation drivers for spatial dependence of heavy precipitation.

How to cite: Kałczyński, M., Krawiec, K., and Kundzewicz, Z.: From climate variability to heavy precipitation – Learning transfer functions from data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18705,, 2020