Emulating Climate Models
Tuesday, June 29, 2021 1:55 PM to 2:15 PM · 20 min. (Africa/Abidjan)
Stream#2
Information
Contributors:
Abstract:
Two of the most pressing climate questions we currently face are that of the effect of anthropogenic aerosol on the climate system, and the feedbacks which clouds exert on the changing climate. The large uncertainties in both effects hinder our efforts to attribute historical trends and accurately predict future climate warming. Efforts to address these uncertainties have both faltered, in part, due to the inability of general circulation models (GCMs) to accurately parameterize key microphysical processes.
Here I will describe work exploiting recent advances in machine learning (ML) to aid in climate model emulation for the reduction of this uncertainty in GCMs. I will introduce a general climate model emulation framework and outline some of the opportunities available for extending this approach in the future to help improve our understanding of the changing climate.
Abstract:
Two of the most pressing climate questions we currently face are that of the effect of anthropogenic aerosol on the climate system, and the feedbacks which clouds exert on the changing climate. The large uncertainties in both effects hinder our efforts to attribute historical trends and accurately predict future climate warming. Efforts to address these uncertainties have both faltered, in part, due to the inability of general circulation models (GCMs) to accurately parameterize key microphysical processes.
Here I will describe work exploiting recent advances in machine learning (ML) to aid in climate model emulation for the reduction of this uncertainty in GCMs. I will introduce a general climate model emulation framework and outline some of the opportunities available for extending this approach in the future to help improve our understanding of the changing climate.
