Extended Abstract (744K)9.4
Using Clustered Climate Regimes for Understanding Water Cycle Variability
Forrest M. Hoffman, ORNL, Oak Ridge, TN; and W. W. Hargrove, D. J. Erickson III, and R. Oglesby
Forrest Hoffman, William Hargrove, David Erickson,
and Robert Oglesby
A 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 and resulting impacts on the global water cycle.
Supplementary URL: http://climate.esd.ornl.gov/
Session 9, Impacts Related to Global Climate Change - What do we know, and how can we best hedge our bets?
Thursday, 13 February 2003, 1:30 PM-3:30 PM
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