EE380 Computer Systems Colloquium: Combining Physical and Statistical Models in Order to Narrow Uncertainty in Projected of Global Warming

Topic: 
Combining Physical and Statistical Models in Order to Narrow Uncertainty in Projected of Global Warming
Wednesday, January 17, 2018 - 4:30pm
Venue: 
Gates B03
Speaker: 
Patrick Brown (Carnegie Institution for Science at Stanford)
Abstract / Description: 

A key question in climate science is How much global warming should we expect for a given increase in the atmospheric concentration of greenhouse gasses like carbon dioxide? One strategy for addressing this question is to run physical models of the global climate system but these models vary in their estimates of future warming by about a factor of two. Our research has attempted to narrow this range of uncertainty around model-projected future warming and to assess whether the upper or lower end of the model range is more likely. We showed that there are strong statistical relationships between how models simulate fundamental features of the Earth's energy budget over the recent past, and how much warming models simulate in the future. Importantly, we find that models that match observations the best over the recent past, tend to simulate more warming in the future than the average model. Thus, statistically combining information from physical models and observations tells us that we should expect more warming (with smaller uncertainty ranges) than we would expect if we were just looking at physical models in isolation and ignoring observations.

Bio:

Patrick is a postdoctoral research scientist at the Carnegie Institution for Science at Stanford. He holds a Bachelor's degree in Atmospheric and Oceanic Sciences from the University of Wisconsin - Madison and a Ph.D. in Earth and Ocean Sciences from Duke University.He has interests in climate modeling, Earth's energy budget, emergent properties of complex systems, chaos, statistics, climate-society interaction and quantifying difficult-to-quantify things.