Abstract

Coupled global climate models are essential tools for past, current and future climate studies. The better understanding of Earth’s climate systems heavily relies on the fidelity of model representations of processes, from small-scale turbulence and clouds to large-scale planetary circulation. Nevertheless, contemporary climate models still suffer from various systematic errors in these processes that can significantly hinder models’ ability to faithfully reproduce past or current climate. In this presentation, we will focus on the errors in the surface temperature simulations in the models since accurate simulations and predictions of surface temperature evolution are important for regional and global weather forecasts. A coupled model hindcast framework to better understand and identify the causal relationship of model errors will be presented.

Presenter Profile

Dr. Hsi-Yen Ma received his Ph.D. from Department of Atmospheric and Oceanic Sciences, University of California Los Angeles (UCLA). He is currently a Research Scientist in the Cloud Processes Research and Modeling Group, Atmospheric, Earth, and Energy Division of Lawrence Livermore National Laboratory. He is the lead scientist for the Cloud-Associated Parameterizations Testbed project which applies the numerical weather prediction technique to evaluate the representation of cloud processes in global climate models. His research interests include climate modeling, model parameterizations related to cloud processes, dynamics of coupled atmosphere-ocean-land interactions, and monsoon climate.