Colloquium Series: Angeline Pendergrass and Upmanu Lall


Monday, November 23, 2020, 12:00pm to 1:00pm



Speaker: Angeline Pendergrass, Cornell University

"Greenhouse gases alter the flow of energy through the climate system, driving changes in surface temperature, the amount of moisture in the atmosphere, and in atmospheric circulation. The hydrologic cycle transfers water from the surface to the atmosphere, so precipitation is also affected by greenhouse gas forcing - in the mean, in its intensity, and in the location of precipitating systems. In this talk I will highlight some of the key aspects of the response of the distribution of precipitation in space, time, and intensity to global warming and some of the mechanisms that drive them. "

Speaker: Upmanu Lall, Columbia University

"Climate change has emerged as a primary risk factor for water systems given the dire projections for changes in the frequency of floods and droughts around the world. Consequently, there is significant interest in how societies can adapt to changing climate risk, given that the traditional approach to flood and drought risk is contingent on estimates of probabilities of the risks of events of concern, whether the solution is infrastructure design and operation or insurance. Claims that multiple 100 year events have occurred in a span of recent years in the same location offer both support for a change in the climate , and a concern as to how to respond. The thesis of this talk is that the substantial natural climate variability, especially the regime like behavior that is well noted at inter-annual to decadal time scales, cannot be ignored in adaptation discussions. Climate risk should be thought of as a multiscale dynamic risk, whose prediction in time and space requires renewed attention, to facilitate investments needed to mitigate the changing risk given the large uncertainty associated with the distant future, the poorly characterized uncertainty associated with the near future, and finite financial resources that can be applied at discrete times over the next 30 or so years in any given place. I provide some examples of analyses to make the case that machine learning methods used in conjunction with climate models can indeed allow us to make progress in this direction, supported by physical understanding of the low frequency dynamics of interest"

Lectures will be followed by a discussion moderated by faculty host.

Please contact Katrina Blanch for Zoom link for lecture.