Seminar: Accelerating climate science with machine learning: Earth system emulation
1:00 – 2:00 pm MST
Speaker: Duncan Watson-Parris, Scripps Institution of Oceanography and Halıcıoğlu Data Science Institute, UC San Diego
Uncertainties in estimating Earth’s future climate stem from both inaccuracies in our models and the vast array of possible choices that society will make in the intervening years. One of the most pressing uncertainties in climate modelling is that of the effect of anthropogenic aerosol, particularly through their interactions with clouds. Here I will introduce a general earth system emulation framework which leverages advances in machine learning and describe its application to the emulation of entire climate models for the reduction of this uncertainty. I will also demonstrate how such emulation can be used to better approximate the climate response to different pollutants, in their detection and attribution, and in the exploration of different future emissions pathways.
Asst. Professor, Scripps Institution of Oceanography and Halıcıoğlu Data Science Institute, UC San Diego Duncan Watson-Parris is an atmospheric physicist working at the interface of climate research and machine learning. The Climate Analytics Lab (CAL) he leads focuses on understanding the interactions between aerosols and clouds, and their representation within global climate models. These interactions are numerous and complex, involving non-linearities and feedbacks which make modelling average responses to any perturbation in aerosol extremely challenging. CAL is leading the development of a variety of machine learning tools and techniques to alleviate these difficulties and optimally combine a variety of observational datasets, including global satellite and aircraft measurements, to constrain and improve these models. Duncan is also keen to foster the application of machine learning to climate science questions more broadly and convenes the Machine Learning for Climate Science EGU session and co-convenes the “AI and Climate Science” discovery series that is part of the United Nations’ AI for Good program.
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