Modelling extreme rainfall and floods: sharing perspectives of extreme value theory and climate science
Christina Patricola, Iowa State University
Title: Anthropogenic Change in Extreme Precipitation from Tropical and Extratropical Cyclones: Physical Drivers and Uncertainties
Abstract: Reliable future projections of extreme precipitation are needed for decision makers to prepare for and mitigate flood hazards. The first half of this talk covers future changes in historically-impactful extreme precipitation events over the San Francisco Bay Area in convection-permitting regional climate model simulations. We found that the relationship between precipitation change and warming depends on whether the event was associated with an atmospheric river only or concurrent with an extratropical cyclone. The potential role of future changes in large-scale climate variability associated with the El Niño–Southern Oscillation (ENSO) will be discussed. The second half of this talk investigates future changes in tropical cyclone (TC) precipitation in convection-permitting regional climate model simulations and in high-resolution atmosphere-ocean global climate model simulations. The strengths and uncertainties associated with each modeling approach will be discussed, including the roles of (1) atmospheric model resolution, (2) climate model biases, and (3) coupled TC-ocean interactions.
Christina Patricola holds an Affiliate Faculty position in the Climate and Ecosystem Sciences Division at Lawrence Berkeley National Laboratory and is an Assistant Professor in the Department of Geological and Atmospheric Sciences at Iowa State University. Her research focuses on understanding mechanisms of natural climate variability and anthropogenic climate change within the coupled Earth system and improving seasonal to centennial climate prediction. She uses high-resolution numerical climate models and observations to understand connections between the large-scale climate and extreme climate events, including tropical cyclones, floods, and drought.
Mark Risser, Lawrence Berkeley National Lab, Berkeley, California
Title: Characterizing local statistics of extreme precipitation and their changes over time from in situ measurements
Abstract: The gridding of daily accumulated precipitation--especially extremes--from ground-based station observations is problematic due to the fractal nature of precipitation, and therefore estimates of long period return values and their changes based on such gridded daily data sets are generally underestimated. To address this issue, we present a method for deriving “probabilistic” high-resolution data sets specifically designed to characterize the climatological properties of extreme precipitation. Our methodology starts with in situ measurements from weather stations, provides estimates of uncertainty, and yields local information on the behavior of precipitation extremes. We furthermore extend the approach to detect both secular trends and year-to-year changes in the climatology of extreme precipitation, for which we develop a robust statistical technique to identify significant local changes. As a demonstration, we analyze measurements from the Global Historical Climatology Network over the contiguous United States, but the approach is extendable to other global land regions with sufficient weather station coverage.
Originally from Orrville, Ohio, Mark is currently a CASCADE Research Scientist at the Lawrence Berkeley National Lab in Berkeley, CA. After completing undergraduate studies at Eastern Mennonite University in Harrisonburg, VA, he received his Ph.D. in Statistics from the Ohio State University in July, 2015. Mark’s primary research is in spatial/environmental statistics and Bayesian modeling, but he also has interests in spatio-temporal statistics, computational methods, stochastic differential equations, data visualization, teaching statistics, hybrid educational methods, and meta analysis.