Transferring climate change knowledge

• URL: http://arxiv.org/abs/2309.14780v4
• Date: Wed, 19 Jun 2024 08:50:50 GMT
• Title: Transferring climate change knowledge
• Authors: Francesco Immorlano, Veronika Eyring, Thomas le Monnier de Gouville, Gabriele Accarino, Donatello Elia, Giovanni Aloisio, Pierre Gentine,
• Abstract summary: We show that Machine Learning can be used to optimally leverage and merge the knowledge gained from Earth system models simulations and historical observations. We reach an uncertainty reduction of more than 50% with respect to state-of-the-art approaches.
• Score: 0.15742383563959128
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate and precise climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been developed to reduce the spread of climate projections and feedbacks, yet those methods cannot capture the non-linear complexity inherent in the climate system. Using a Transfer Learning approach, we show that Machine Learning can be used to optimally leverage and merge the knowledge gained from Earth system models simulations and historical observations to more accurately project global surface air temperature fields in the 21st century. We reach an uncertainty reduction of more than 50% with respect to state-of-the-art approaches. We give evidence that our novel method provides narrower projection uncertainty together with more accurate mean climate projections, urgently required for climate adaptation.

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