David Battisti is The Tamaki Endowed Chair of Atmospheric Sciences at the University of Washington. His research is focused on understanding the natural variability of the climate system. He is especially interested in understanding how the interactions between the ocean, atmosphere, land and sea ice lead to variability in climate on time scales from seasonal to decades. His previous research includes coastal oceanography, the physics of the El Nino/Southern Osciallation (ENSO) phenomenon, abrupt climate change during the last glacial period, and variability in the coupled atmosphere/sea ice system in the Arctic. Battisti is presently working to improve the El Nino models and their forecast skill, to better understand variability in the midlatitude atmosphere/ocean system, and to better understand the monsoons. He is also working on the impacts of climate variability and climate change on global food production.
Battisti has served on numerous international science panels, and on Committees of the National Research Council. He has published over 120 papers in peer-review journals in atmospheric sciences and oceanography, and twice been awarded distinguished teaching awards. He has received many awards both for research and teaching, most recently the Sustainability Science Award from the Ecological Society of America. Additionally, he is a Fellow at the Food Security Institute at Stanford University and a Fellow of the American Meteorological Society and the American Geophysical Union. He served as Professor II at the Universitetet i Bergen, and the Carnegie Centennial Professor of Scotland.
The uncertainty in projections of climate change at the end of the 21st Century due to burning of fossil fuels is roughly equally due to uncertainty in greenhouse gas emissions and to the response of the climate to those emissions. Concerning the latter, it is well known that the climate models share a similar global pattern of warming. Here, I will show that the differences in the projections of warming across the models also share a common (albeit different) pattern of variability. The physical significance of the common difference pattern is explored. Once the common difference pattern is accounted for, there is little uncertainty left in the projections. Implications for numerical downscaling will be discussed.
Arranged date for the seminar talk: Nov 01, 2018
Place: BCCR lecture room 4020, Jahnebakken 5