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Using Clustering to Establish Climate Regimes from PCM OutputA multivariate statistical clustering technique--based on the k-means algorithm of Hartigan has been used to extract patterns of climatological significance from 200 years of general circulation model (GCM) output. Originally developed and implemented on a Beowulf-style parallel computer constructed by Hoffman and Hargrove from surplus commodity desktop PCs, the high performance parallel clustering algorithm was previously applied to the derivation of ecoregions from map stacks of 9 and 25 geophysical conditions or variables for the conterminous U.S. at a resolution of 1 sq km. Now applied both across space and through time, the clustering technique yields temporally-varying climate regimes predicted by transient runs of the Parallel Climate Model (PCM). Using a business-as-usual (BAU) scenario and clustering four fields of significance to the global water cycle (surface temperature, precipitation, soil moisture, and snow depth) from 1871 through 2098, the authors' analysis shows an increase in spatial area occupied by the cluster or climate regime which typifies desert regions (i.e., an increase in desertification) and a decrease in the spatial area occupied by the climate regime typifying winter-time high latitude perma-frost regions. The patterns of cluster changes have been analyzed to understand the predicted variability in the water cycle on global and continental scales. In addition, representative climate regimes were determined by taking three 10-year averages of the fields 100 years apart for northern hemisphere winter (December, January, and February) and summer (June, July, and August). The result is global maps of typical seasonal climate regimes for 100 years in the past, for the present, and for 100 years into the future. Using three-dimensional data or phase space representations of these climate regimes (i.e., the cluster centroids), the authors demonstrate the portion of this phase space occupied by the land surface at all points in space and time. Any single spot on the globe will exist in one of these climate regimes at any single point in time. By incrementing time, that same spot will trace out a trajectory or orbit between and among these climate regimes (or atmospheric states) in phase (or state) space. When a geographic region enters a state it never previously visited, a climatic change is said to have occurred. Tracing out the entire trajectory of a single spot on the globe yields a 'manifold' in state space representing the shape of its predicted climate occupancy. This sort of analysis enables a researcher to more easily grasp the multivariate behavior of the climate system.
Document ID
20030055158
Acquisition Source
Marshall Space Flight Center
Document Type
Conference Paper
Authors
Oglesby, Robert
(NASA Marshall Space Flight Center Huntsville, AL, United States)
Arnold, James E.
(NASA Marshall Space Flight Center Huntsville, AL, United States)
Hoffman, Forrest
(Oak Ridge National Lab. TN, United States)
Hargrove, W. W.
(Oak Ridge National Lab. TN, United States)
Erickson, D.
(Oak Ridge National Lab. TN, United States)
Date Acquired
August 21, 2013
Publication Date
January 1, 2002
Subject Category
Statistics And Probability
Meeting Information
Meeting: American Geophysical Union Fall Meeting
Location: San Francisco, CA
Country: United States
Start Date: December 5, 2002
End Date: December 11, 2002
Sponsors: American Geophysical Union
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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