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Combine Physical and Statistical Models to narrow Uncertainty in Global Warming

At the end of 2017 with one year of data to feed into this model, 2018 likely to be colder than 2017, record high possible in 2019. 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.

Speaker: Patrick Brown, Stanford

Wednesday, 06/20/18


Website: Click to Visit



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2161 North First St.
San Jose, CA 95131