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- Author or Editor: Richard W. Katz x
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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.
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.
Abstract
A procedure for making statistical inferences about differences between population means from the output of general circulation model (GCM) climate experiments is presented. A parametric time series modeling approach is taken, yielding a potentially mere powerful technique for detecting climatic change than the simpler schemes used heretofore. The application of this procedure is demonstrated through the use of GCM control data to estimate the variance of winter and summer time averages of daily mean surface air temperature. The test application provides estimates of the magnitude of climatic change that the procedure should be able to detect. A related result of the analysis is that autoregressive processes of higher than first order are needed to adequately model the majority of the GCM time series considered.
Abstract
A procedure for making statistical inferences about differences between population means from the output of general circulation model (GCM) climate experiments is presented. A parametric time series modeling approach is taken, yielding a potentially mere powerful technique for detecting climatic change than the simpler schemes used heretofore. The application of this procedure is demonstrated through the use of GCM control data to estimate the variance of winter and summer time averages of daily mean surface air temperature. The test application provides estimates of the magnitude of climatic change that the procedure should be able to detect. A related result of the analysis is that autoregressive processes of higher than first order are needed to adequately model the majority of the GCM time series considered.
