Cooperative Institute for Research in Environmental Sciences

Collaborative Research: Arctic extreme temperature and precipitation - Detection and projection of their climatic change and physical causes

Collaborative Research: Arctic extreme temperature and precipitation - Detection and projection of their climatic change and physical causes

Changes in temperature and precipitation extremes could be especially large in the Arctic, which is projected to have some of the greatest anthropogenic warming. Consistency of physical causes of extreme temperature and precipitation in observations and in simulations of past and future scenarios can indicate the robustness of projected changes. Consistent simulations of extremes by several models can provide much greater sampling of extreme events, leading to more confident assessment of the changing risk of extreme events. We will investigate possible changes in extreme temperature and precipitation events in the Arctic using observational records and output from the CORDEX (regional) and CMIP5 (global) climate modeling programs. Our guiding hypothesis will be: A robust understanding, detection and attribution of changes in extreme temperature or precipitation occurs through analysis that combines extreme temperature or precipitation events with the physical processes supporting them. We will assess observed and simulated changes in extreme precipitation processes using a pattern-recognition tool, Self-Organizing Maps (SOMs), that allows construction of a coherent, multivariate view of collections of extreme events. The methods developed will also have application to studies of extremes in other regions of the planet, extending capacity for assessing changes in extremes and their underlying physical causes. The project will train a new generation of scientists equipped to perform ensemble analysis of the changes in extremes and the uncertainties in their projection.

Research Group

Resources

Participants

University of Colorado
John Cassano
Elizabeth Cassano
Matt Higgins

Iowa State University
William Gutowski

Funding Information

This research is supported by the National Science Foundation.