When can we trust climate models?

Type: 
Seminar
Date: 
Wednesday 10 May, 2017
Time: 
2.00pm
Location: 
Climate Change Research Centre Seminar Room, Level 4 Mathews Building, Kensington Campus, Sydney

Uncertainty in regional climate predictions is a critical component of understanding risks of future climate impacts. Unfortunately, while State-of-the-science Earth System Models show consistency with observations at global and hemispheric scales, they show limited skill in reproducing climate change at sub-continental and smaller scales (i.e., regional scales) despite their ability to detect and attribute climate change at global to continental scales.  Significant internal/chaotic climate variability is one reason for this lack of skill. Structural uncertainty in modelling the physical climate system is an additional issue. Uncertainty in the centennial timescale trajectory of greenhouse gases and other factors impacting long-term changes is yet another component. 

This talk will discuss components in climate models that lead to these uncertainties with a focus on basic physical processes related to both global and regional climate change.

 

Biography: 

Chris E. Forest is Associate Professor of Climate Dynamics in the Department of Meteorology and Atmospheric Science at The Pennsylvania State University.  He is also affiliated wth the Department of Geosciences, an associate in the Earth and Environmental Systems Institute, and associate director for the Network for Sustainable Climate Risk Management. He served as a lead author on the report of the Intergovernmental Panel on Climate Change for the chapter on the evaluation of climate models and on a report for the U.S. Climate Change Science Program examining the estimates of temperature trends in the atmospheric and surface climate data. He was elected to serve on the Electorate Nominating Committee for the Atmospheric and Hydrospheric Sciences Section of the American Association for the Advancement of Science. His research focuses on quantifying uncertainty in climate predictions and their implications for assessing climate risks. He has a B.S. in applied mathematics, engineering, and physics from the University of Wisconsin-Madison and a Ph.D. in meteorology from the Massachusetts Institute of Technology.