HR: 14:40h
AN: GC53A-05    [Abstracts]
TI: Evaluating Significance of Uncertainties in Climate Model Development Against Observational Uncertainty
AU: * Jackson, C S
EM: charles@ig.utexas.edu
AF: Institute for Geophysics, University of Texas at Austin, J.J. Pickle Research Campus, Bldg 196 10100 Burnet Rd., Austin, TX 78758, United States
AU: Tobis, M
EM: tobis@ig.utexas.edu
AF: Institute for Geophysics, University of Texas at Austin, J.J. Pickle Research Campus, Bldg 196 10100 Burnet Rd., Austin, TX 78758, United States
AU: Huerta, G
EM: ghuerta@stat.unm.edu
AF: Department of Mathematics and Statistics, University of New Mexico, 441 Humanities Building, Albuquerque, NM 87131, United States
AB: A statistical approach is presented to select members of an ensemble of climate models so that the ensemble is representative of observational uncertainty. Observational constraints on the choice of different models or versions of a single model can be imposed through quantification of a model's skill to reproduce observations. Whatever measure is used, one may calculate the effects of observational uncertainty on this skill score in order to define a range of acceptability. Estimates of this range will be presented based on the effect of differences in the NCEP and ECMWF reanalysis data products on a variety of measures of model skill. We then use these ranges to evaluate the significance of the improvements that have occurred through several generations of the NCAR atmospheric climate model and the spread in predictions from models that participated in the 2007 IPCC Fourth Assessment Report.
DE: 1616 Climate variability (1635, 3305, 3309, 4215, 4513)
DE: 1626 Global climate models (3337, 4928)
DE: 3275 Uncertainty quantification (1873)
SC: Global Environmental Change [GC]
MN: 2007 Fall Meeting