TALK    [MERL Seminar Series 2021] Harnessing machine learning to build better Earth system models for climate projection

Date & Time:

Tuesday, December 14, 2021; 1:00 PM EST

Abstract:

Decision-making and adaptation to climate change requires quantitative projections of the physical climate system and an accurate understanding of the uncertainty in those projections. Earth system models (ESMs), which solve the Navier-Stokes equations on the sphere, are the only tool that climate scientists have to make projections forward into climate states that have not been observed in the historical data record. Yet, ESMs are incredibly complex and expensive codes and contain many poorly constrained physical parameters—for processes such as clouds and convection—that must be calibrated against observations. In this talk, I will describe research from my group that uses ensembles of ESM simulations to train statistical models that learn the behavior and sensitivities of the ESM. Once trained and validated the statistical models are essentially free to run, which allows climate modelling centers to make more efficient use of precious compute cycles. The aim is to improve the quality of future climate projections, by producing better calibrated ESMs, and to improve the quantification of the uncertainties, by better sampling the equifinality of climate states.

Speaker:

Prof. Chris Fletcher
University of Waterloo

Chris Fletcher is a climate scientist and contributing author to the sixth IPCC assessment report. Dr Fletcher’s research group at the University of Waterloo focuses on improving the development, calibration, and application of Earth system models, and on how satellite remote sensing is used to monitor the climate of cold regions. Currently-funded projects investigate the benefit of applying machine learning techniques in both of these aspects of climate science.

MERL Host:

Ankush Chakrabarty

Research Areas:

Dynamical Systems, Machine Learning, Multi-Physical Modeling