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EGU Award Ceremony (Credit: EGU/Foto Pfluegl)

Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Awards 2021 Giorgia Di Capua

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European Geosciences Union

Giorgia Di Capua

Giorgia Di Capua
Giorgia Di Capua

NP Nonlinear Processes in Geosciences

The 2021 Virtual Outstanding Student and PhD candidate Presentation (vOSPP) Award is awarded to Giorgia Di Capua for the poster/PICO entitled:

Causal maps versus correlation maps: visual analysis of tropical-extratropical atmospheric teleconnections using causal discovery (Di Capua, G.; Donner, R. V.)

Click here to download the poster/PICO file.

Giorgia Di Capua is a PhD researcher in the Earth System Analysis department at the Potsdam Institute for Climate Impact Research (PIK) in Germany and the Water and Climate Risk department at the Institute for Environmental Studies (IVM) at the VU University of Amsterdam.
Her research mainly focuses on the application of advanced causal discovery tools to tropical – extratropical teleconnections between the Indian summer monsoon (ISM) and the mid-latitude circulation in boreal summer. This technique has proved useful to quantify the magnitude of tropical – extratropical links at intraseasonal timescales, to study the effect of El Niño – southern Oscillation on such teleconnections and provide statistical seasonal forecasts of the ISM rainfall a few months ahead of the monsoon onset.

The work presented in EGU2021’s online PICO session focuses on the application of causal discovery on a 2D map. “Causal maps” are introduced and show how causal discovery can identify and remove spurious links on a 2-dimentional map, thus improving the concept of correlation maps. Here, causal maps are applied to study the effect of the first mode of maximum covariance analysis (MCA) between tropical convective activity and mid-latitude circulation on surface and tropospheric circulation in the Norther Hemisphere during boreal summer. The two-way causal link between ISM convective activity and a mid-latitude wave pattern is analysed in its regional spatial characteristics. Finally, causal maps obtained from seasonal forecast data by ECMWF show quantitatively consistent results.