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Probabilistic Climate-Model Diagnostics for Hydrologic and Water Resources Impact Studies

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  • 1 Hydrologic Research Center, San Diego, and Scripps Institution of Oceanography, University of San Diego, La Jolla, California
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Abstract

The use of information from climate model ensemble simulations for regional hydrologic and water resources impact studies necessitates the development of diagnostic measures of utility for this information on regional scales with account for uncertainty. Formulated are probabilistic measures of the effectiveness of monthly climate-model ensemble simulations of an indicator variable for discriminating high versus low regional observations of a target variable. The formulation uses the significance probability of the Kolmogorov–Smirnov test for detecting differences in the sample probability distribution of the conditioned high versus low regional observations of the target variable. Estimators that account for climate-model ensemble simulations and spatial association between the indicator and target variables are formulated. Generalizations for the cases of vector indicator and target variables are discussed. The methodology is exemplified for the case of a single climate-model indicator variable, seasonal surface precipitation, and a single regional target variable, seasonal mean areal precipitation over a U.S. climate division. Information from 10-member ensemble simulations of the German ECHAM3 atmospheric climate model for January 1950–December 1998 is used in the example, and results are presented for all the climate divisions of the conterminous United States. Monte Carlo simulation is used to establish the significance of the estimator values. The results show that the ensemble of climate-model seasonal precipitation simulations, when averaged over several model nodes, is skillful in discriminating the high from the low terciles of observed climate division seasonal precipitation in several regions of the United States and for all of the seasons. Over large regions in the southern, western, and northern United States in winter and spring, GCM simulations are likely useful for seasonal water resources studies on scales comparable to those of the climate divisions. The results for a coherent region east of the Rockies in summer and several regions of the northeastern United States and the southwest in autumn also exhibit significant potential benefits of using climate-model simulations for seasonal water resources studies.

Corresponding author address: Konstantine P. Georgakakos, Hydrologic Research Center, 12780 High Bluff Drive, Suite 250, San Diego, CA 92130. Email: kgeorgakakos@hrc-lab.org

Abstract

The use of information from climate model ensemble simulations for regional hydrologic and water resources impact studies necessitates the development of diagnostic measures of utility for this information on regional scales with account for uncertainty. Formulated are probabilistic measures of the effectiveness of monthly climate-model ensemble simulations of an indicator variable for discriminating high versus low regional observations of a target variable. The formulation uses the significance probability of the Kolmogorov–Smirnov test for detecting differences in the sample probability distribution of the conditioned high versus low regional observations of the target variable. Estimators that account for climate-model ensemble simulations and spatial association between the indicator and target variables are formulated. Generalizations for the cases of vector indicator and target variables are discussed. The methodology is exemplified for the case of a single climate-model indicator variable, seasonal surface precipitation, and a single regional target variable, seasonal mean areal precipitation over a U.S. climate division. Information from 10-member ensemble simulations of the German ECHAM3 atmospheric climate model for January 1950–December 1998 is used in the example, and results are presented for all the climate divisions of the conterminous United States. Monte Carlo simulation is used to establish the significance of the estimator values. The results show that the ensemble of climate-model seasonal precipitation simulations, when averaged over several model nodes, is skillful in discriminating the high from the low terciles of observed climate division seasonal precipitation in several regions of the United States and for all of the seasons. Over large regions in the southern, western, and northern United States in winter and spring, GCM simulations are likely useful for seasonal water resources studies on scales comparable to those of the climate divisions. The results for a coherent region east of the Rockies in summer and several regions of the northeastern United States and the southwest in autumn also exhibit significant potential benefits of using climate-model simulations for seasonal water resources studies.

Corresponding author address: Konstantine P. Georgakakos, Hydrologic Research Center, 12780 High Bluff Drive, Suite 250, San Diego, CA 92130. Email: kgeorgakakos@hrc-lab.org

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