HR: 1330h
AN: GC12A-0155    [PDF]
TI: A Novel Method for Analyzing and Interpreting GCM Results Using Clustered Climate Regimes
AU: * Hoffman, F M
EM: forrest@climate.ornl.gov
AF: Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6036 United States
AU: Hargrove, W W
EM: hnw@fire.esd.ornl.gov
AF: Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6036 United States
AU: Erickson, D J
EM: ericksondj@ornl.gov
AF: Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, TN 37831-6036 United States
AU: Oglesby, R J
EM: Bob.Oglesby@msfc.nasa.gov
AF: NASA Marshall Space Flight Center, National Space Science and Technology Center, Huntsville, AL 35812 United States
AB: A high-performance parallel clustering algorithm has been developed for analyzing and comparing climate model results and long time series climate measurements. Designed to identify biases and detect trends in disparate climate change data sets, this tool combines and simplifies large temporally-varying data sets from atmospheric measurements to multi-century climate model output. Clustering is a statistical procedure which provides an objective method for grouping multivariate conditions into a set of states or regimes within a given level of statistical tolerance. The groups or clusters--statistically defined across space and through time--possess centroids which represent the synoptic conditions of observations or model results contained in each state no matter when or where they occurred. The clustering technique was applied to five business-as-usual (BAU) scenarios from the Parallel Climate Model (PCM). Three fields of significance (surface temperature, precipitation, and soil moisture) were clustered from 2000 through 2098. Our 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 same analysis subsequently applied to the ensemble as a whole demonstrates the consistency and variability of trends from each ensemble member. The patterns of cluster changes can be used to show predicted variability in climate on global and continental scales. Novel three-dimensional phase space representations of these climate regimes show 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, and by incrementing time, that same spot will trace out a trajectory or orbit among these climate regimes in phase space. When a geographic region enters a state it never previously visited, a climatic change is said to have occurred.
UR: http://climate.ornl.gov/
DE: 0315 Biosphere/atmosphere interactions
DE: 0325 Evolution of the atmosphere
DE: 1610 Atmosphere (0315, 0325)
DE: 1620 Climate dynamics (3309)
DE: 1833 Hydroclimatology
SC: Global Climate Change [GC]
MN: 2003 Fall Meeting