Research

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.