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Transferring Climate Change Physical Knowledge

Francesco Immorlano

Postdoctoral Researcher, Department of Computer Science, University of California, Irvine

Francesco Immorlano

Abstract: Earth system models (ESMs) are the main tools currently used to project global mean temperature rise according to several future greenhouse gases emissions scenarios. Accurate and precise climate projections are required for climate adaptation and mitigation, but these models still exhibit great uncertainties that are a major roadblock for policy makers. Several approaches have been developed to reduce the spread of climate projections, yet those methods cannot capture the non-linear complexity inherent in the climate system. Using a Transfer Learning approach, Machine Learning can leverage and combine the knowledge gained from ESMs simulations and historical observations to more accurately project global surface air temperature fields in the 21st century. This helps enhance the representation of future projections and their associated spatial patterns which are critical to climate sensitivity.

Bio: Francesco Immorlano is a Postdoctoral Researcher at the University of California, Irvine with a Ph.D. in Engineering of Complex Systems from the University of Salento. Since May 2020 he has been collaborating with the CMCC Foundation and was a visiting researcher at Columbia University in Spring 2022. His main research work is focused on deep learning and generative models with a specific application to the climate science domain.

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