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Award Abstract #1243102

Collaborative Research: EaSM 2 Advancing extreme value analysis of high impact climate and weather events

NSF Org: DMS
Division of Mathematical Sciences
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Initial Amendment Date: June 20, 2013
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Latest Amendment Date: June 20, 2013
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Award Number: 1243102
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Award Instrument: Standard Grant
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Program Manager: Michael H. Steuerwalt
DMS Division of Mathematical Sciences
MPS Directorate for Mathematical & Physical Sciences
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Start Date: July 1, 2013
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End Date: June 30, 2018 (Estimated)
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Awarded Amount to Date: $857,275.00
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Investigator(s): Daniel Cooley Cooleyd@stat.colostate.edu (Principal Investigator)
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Sponsor: Colorado State University
601 S Howes St
Fort Collins, CO 80523-2002 (970)491-6355
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NSF Program(s): CR, Earth System Models
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Program Reference Code(s): 8012
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Program Element Code(s): 8012

ABSTRACT

1243102

Dan Cooley, Colorado State University

1243333

J. Keith Gilless, University of California-Berkeley

1242957

Richard Smith, University of North Carolina at Chapel Hill

One of the most important questions in climate science is how extreme weather changes as a result of natural and anthropogenic forcings. At this project's core is a goal to address three areas that are critical to understanding extreme events, but whose methods are not yet developed enough to answer impact-relevant questions. First, the investigators advance and develop multivariate statistical methods that can describe and model extreme events that arise from a combination of meteorological factors that may or may not individually be extreme. Second, the investigators advance spatial downscaling methods to be applicable to studying extreme phenomena that occur at spatial scales not resolved by climate models. Third, the investigators put the study of detection of changes in extremes and attribution of extreme events on a solid statistical foundation, and apply the spatial and multivariate techniques to this area. The participants collaborate with social scientists in incorporating the improved methodology into models that analyze the impact of extreme weather events on agricultural production and the forestry sector for specific regions of the US. They also develop risk assessment measures that take into account possible increases in the frequency of extreme weather events.

Extreme weather can have profound consequences for the well-being of individuals, societies, and natural systems. This project studies several aspects of weather extremes and how the nature of extreme events is likely to change under an altered climate. The project develops probability-based measures of risk that take account of the fact that impacts of extreme weather events cannot be predicted with certainty. Another feature of the project is that high-impact events often arise from a combination of extreme weather conditions. For example, wildfires may arise from a combination of heat, wind, and lack of moisture; we therefore need methods of analysis that take into account multiple meteorological variables. Most of the methods being developed rely on large computer programs known as climate models to project future climatic events. However, climate models often work on time and space scales that are much larger than the high-impact events we are interested in; therefore, data from climate models must be translated to the scales of interest. The specific focus on extreme events is different from most current research using climate models for the detection and attribution of climate change. Detecting changes in extreme behavior is essential for risk assessment, as risk-related quantities such as the 100-year flood, fire, burning, and heat indexes are estimated based on data that may no longer be relevant if extreme behavior has changed. The determination of the impact of such changes at a broad range of socio-economic levels is needed to determine appropriate societal responses to extreme weather events associated with climate change. In addition to university-based investigators, the project includes Michael Wehner and collaborators at Lawrence Berkeley National Laboratory. who are an intrinsic part of the team. University-based investigators are funded by NSF while the Department of Energy supports LBNL investigators.

 

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