Statistical Procedures for Making Inferences about Precipitation Changes Simulated by an Atmospheric General Circulation Model

Statistical Procedures for Making Inferences about Precipitation Changes Simulated by an... A statistical methodology is presented for making inferences about changes in mean daily precipitation from the results of general circulation model (GCM) climate experiments. A specialized approach is required because precipitation is inherently a discontinuous process. The proposed procedure is based upon a probabilistic model that simultaneously represents both occurrence and intensity components of the precipitation process, with the occurrence process allowed to be correlated in time and the intensifies allowed to have a non-Gaussian distribution. In addition to establishing whether the difference between experiment and control daily means is statistically significant, the procedure provides confidence intervals for the ratio of experiment to control median daily precipitation intensities and for the difference between experiment and control probabilities of daily precipitation occurrence. The technique is applied to the comparison of winter and summer precipitation data generated in a control integration of the Oregon State University atmospheric GCM. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the Atmospheric Sciences American Meteorological Society

Statistical Procedures for Making Inferences about Precipitation Changes Simulated by an Atmospheric General Circulation Model

Journal of the Atmospheric Sciences, Volume 40 (9) – Feb 7, 1983

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Publisher
American Meteorological Society
Copyright
Copyright © 1983 American Meteorological Society
ISSN
1520-0469
DOI
10.1175/1520-0469(1983)040<2193:SPFMIA>2.0.CO;2
Publisher site
See Article on Publisher Site

Abstract

A statistical methodology is presented for making inferences about changes in mean daily precipitation from the results of general circulation model (GCM) climate experiments. A specialized approach is required because precipitation is inherently a discontinuous process. The proposed procedure is based upon a probabilistic model that simultaneously represents both occurrence and intensity components of the precipitation process, with the occurrence process allowed to be correlated in time and the intensifies allowed to have a non-Gaussian distribution. In addition to establishing whether the difference between experiment and control daily means is statistically significant, the procedure provides confidence intervals for the ratio of experiment to control median daily precipitation intensities and for the difference between experiment and control probabilities of daily precipitation occurrence. The technique is applied to the comparison of winter and summer precipitation data generated in a control integration of the Oregon State University atmospheric GCM.

Journal

Journal of the Atmospheric SciencesAmerican Meteorological Society

Published: Feb 7, 1983

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