HR: 17:15h
AN: B34A-06    [Abstracts]
TI: Using Tree-Ring Width Data From 1000 Sites to Predict how American Forests Will Respond to Climate Change
AU: * Williams, P
EM: williams@geog.ucsb.edu
AF: Geography Department University of California, Santa Barbara, 1832 Ellison Hall UC Santa Barbara, Santa Barbara, CA 93106, United States
AU: Still, C J
EM: still@icess.ucsb.edu
AF: Geography Department University of California, Santa Barbara, 1832 Ellison Hall UC Santa Barbara, Santa Barbara, CA 93106, United States
AU: Leavitt, S W
EM: sleavitt@ltrr.arizona.edu
AF: Laboratory of Tree-Ring Research University of Arizona, 105 W. Stadium Bldg. 51 University of Arizona, Tucson, AZ 85721, United States
AU: Fischer, D T
EM: doug.fischer@csun.edu
AF: Department of Geography California State University, Northridge, CSU Northridge 18111 Nordhoff St., Northridge, CA 91330-8241, United States
AB: Beginning in the early 1900s, tree-ring scientists began analyzing the relative widths of annual growth rings preserved in the cross-sections of trees. Over the years, many ring-width index chronologies, each representing a specific site and species, have been developed and analyzed to infer details regarding past climate, growth response to environmental fluctuation, fire activity, logging practices by past societies, and more. Of the many ring-width chronologies constructed, 1035 represent sites within the continental United States and have been published online within The International Tree-Ring Data Bank as of September 2007 (ITRDB, http://www.ncdc.noaa.gov/paleo/treering.html). Approximately 85% of these sites are located west of the Mississippi River. Here we present results from a three-step study, using this large reserve of tree-growth data to determine how various tree species in various regions have responded to climate fluctuations in the past and how they can be expected to respond to future change. In the first step, we used linear regression to compare each time series of ring-width index values to a suite of local monthly climate variables that may influence tree growth, such as rainfall, temperature, and drought severity (PDSI). We identified the range of months (of a 24- month period) during which each climate parameter most strongly affects growth by comparing Pearson correlation coefficients. In the second step, we identified all sites where at least one climate parameter, during some rage of months, correlates significantly (95% confidence) with ring-width index values. For each of these sites, we constructed a growth model that uses each significantly correlating climate parameter as a growth predictor. In the third step, we applied the growth model to predict the next 100 years of growth response to a monthly climate forecast created by the Hadley Centre for Climate Prediction and Research. This forecast (HadCM3 IS92a) assumes a business as usual scenario with no measures to reduce greenhouse-gas emissions. By comparing predictions of future growth at each site to records of past growth, and to predications of future growth at nearby sites, we identify stands of trees and regional forests where we expect significantly increased growth, decreased growth, and/or potential directional changes in species composition to occur during the next century in response to climate change.
DE: 0426 Biosphere/atmosphere interactions (0315)
DE: 0429 Climate dynamics (1620)
DE: 0439 Ecosystems, structure and dynamics (4815)
DE: 0466 Modeling
DE: 0476 Plant ecology (1851)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting