Record Display for the EPA National Library Catalog


Main Title Bayesian Methods for Model Uncertainty Analysis with Application to Future Sea Level Rise.
Author Patwardhan, A. ; Small, M. J. ;
CORP Author Carnegie-Mellon Univ., Pittsburgh, PA. Dept. of Engineering and Public Policy.;Environmental Research Lab., Athens, GA. Office of Research and Development.
Publisher c1992
Year Published 1992
Report Number EPA-R-813713; EPA/600/J-94/460;
Stock Number PB95-131124
Additional Subjects Sea level ; Monte Carlo method ; Probability theory ; Air pollution ; Environmental effects ; Mathematical models ; Forecasting ; Climatic changes ; Global warming ; Air water interactions ; Reprints ; Bayesian methods
Library Call Number Additional Info Location Last
NTIS  PB95-131124 Some EPA libraries have a fiche copy filed under the call number shown. 07/26/2022
Collation 13p
In no other area is the need for effective analysis of uncertainty more evident than in the problem of evaluating the consequences of increasing atmospheric concentrations of radiatively active gases. The major consequences of concern is global warming, with related environmental effects that include changes in local patterns of precipitation, soil moisture, forest and agricultural productivity, and a potential increase in global mean sea level. In order to identify an optimum set of responses to sea level change, a full characterization of the uncertainties associated with the predictions of future sea level rise is essential. The paper addresses the use of data for identifying and characterizing uncertainties in model parameters and predictions. The Bayesian Monte Carlo method is formally presented and elaborated, and applied to the analysis of the uncertainty in a predictive model for global mean sea level change.