Supplementary material to “Fine–Resolution Climate Projections Enhance Regional Climate Change Impact Studies”
Edwin P. Maurer, Civil Engineering Department, Santa Clara University, Santa Clara, California; Levi Brekke and Tom Pruitt, Technical Service Center, U.S. Bureau of Reclamation, Denver, Colorado; Philip B. Duffy, Lawrence Livermore National Laboratory, Livermore, California
Citation:
Maurer, E. P., L. Brekke, T. Pruitt, and P. B. Duffy (2007), Fine-resolution climate projections enhance regional climate change impact studies, Eos Trans. AGU, 88(47), 504.
[Full Article (pdf)]
A new data set enhances the abilities of researchers and decision-makers to assess possible future climates, explore societal impacts, and approach policy responses from a risk-based perspective. The data set, which consists of a library of 112 fine-resolution climate projections, based on 16 climate models and three greenhouse gas emissions scenarios, is now publicly available. Monthly climate projections from 1950 to 2099 were downscaled to a spatial resolution of 1/8° (about 140 square kilometers per grid cell) covering the conterminous United States and portions of Canada and Mexico.
For the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, climate modeling groups produced hundreds of simulations of past and future climates. The colocation of these simulations in a single archive (at the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory (LLNL), established to facilitate assessment of general circulation models, or GCMs) and the conversion of all results to a common data format have made probabilistic, multimodel projections and impact assessments practical [Meehl et al., 2007]. A remaining issue is that the spatial scale of climate model output is typically too coarse for regional impact studies. Multiple downscaling approaches exist for deriving regional climate from coarse-resolution model output [Christensen et al., 2007]; these approaches are typically applied on an ad hoc basis to a particular region.
To facilitate regional climate change impact studies, the U.S. Bureau of Reclamation’s Research and Development Office, LLNL, the University of California Institute for Research on Climate Change and Its Societal Impacts, and Santa Clara University (through support from the U.S. Department of Energy’s National Energy Technology Laboratory) developed a public-access archive of downscaled projections.
A statistical technique [Wood et al., 2004] was used to generate gridded fields of precipitation and surface air temperature over the conterminous United States and portions of Canada and Mexico (e.g., Figure 1). The method involves (1) a quantile-mapping approach that corrects for GCM biases, based on observations of 1950–1999; and (2) interpolation of monthly bias-corrected GCM anomalies onto a fine-scale grid of historical climate data, producing a monthly time series at each 1/8-degree grid cell. The method has been used extensively for hydrologic impact studies (including many with ensembles of GCMs [e.g., Maurer, 2007]) and in a variety of climate change impact studies on systems as diverse as wine grape cultivation, habitat migration, and air quality.
The downscaled data are freely available for download at the Green Data Oasis, a large data store at LLNL for sharing scientific data (http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/). Users can specify particular models, emissions scenarios, time periods, geographical areas, and raw data or summary statistics. All data are archived in a standard netCDF format, a self-describing machine-independent format for sharing gridded scientific data.
References
Christensen, J. H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R. K. Kolli, W.-T. Kwon, R. Laprise, V. Magaña Rueda, L. Mearns, C. G. Menéndez, J. Räisänen, A. Rinke, A. Sarr, and P. Whetton (2007), Regional Climate Projections, in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change edited by S. Solomon, et al., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Maurer, E. P. (2007), Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California under two emissions scenarios, Climatic Change, 82(3-4, ), 309-325, doi:310.1007/s10584-10006-19180-10589.
Meehl, G. A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J. F. B. Mitchell, R. J. Stouffer, and K. E. Taylor (2007), The WCRP CMIP3 multimodel dataset: A new era in climate change research, Bull. Am. Met. Soc., 88, 1383–1394.
Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier (2004), Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Climatic Change, 62(1-3), 189-216.
Author Information
Edwin P. Maurer, Civil Engineering Department, Santa Clara University, Calif.; E-mail: emaurer@engr.scu.edu; Levi Brekke and Tom Pruitt, Technical Service Center, U.S. Bureau of Reclamation, Denver, Colo.; Philip B. Duffy, Lawrence Livermore National Laboratory, Livermore, Calif.
Fig. 1 –Median annual precipitation change between 1971–2000 and 2041–2070 (centimeters/year) based on 112 downscaled climate projections.
