Bayesian Design and Analysis for Superensemble-Based Climate Forecasting

@article{citeulike:2966599, abstract = {The authors develop statistical data models to combine ensembles from multiple climate models in a fashion that accounts for uncertainty. This formulation enables treatment of model specific means, biases, and covariance matrices of the ensembles. In addition, the authors model the uncertainty in using computer model results to estimate true states of nature. Based on these models and principles of decision making in the presence of uncertainty, this paper poses the problem of superensemble experimental design in a quantitative fashion. Simple examples of the resulting optimal designs are presented. The authors also provide a Bayesian climate modeling and forecasting analysis. The climate variables of interest are Northern and Southern Hemispheric monthly averaged surface temperatures. A Bayesian hierarchical model for these quantities is constructed, including time-varying parameters that are modeled as random variables with distributions depending in part on atmospheric CO2 levels. This allows the authors to do Bayesian forecasting of temperatures under different Special Report on Emissions Scenarios (SRES). These forecasts are based on Bayesian posterior distributions of the unknowns conditional on observational data for 1882\&\#8211;2001 and climate system model output for 2002\&\#8211;97. The latter dataset is a small superensemble from the Parallel Climate Model (PCM) and the Community Climate System Model (CCSM). After summarizing the results, the paper concludes with discussion of potential generalizations of the authors\&\#8217; strategies.}, author = {Berliner, Mark L. and Kim, Yongku }, citeulike-article-id = {2966599}, doi = {10.1175/2007JCLI1619.1}, journal = {Journal of Climate}, keywords = {bayesian, bma, climate, ensemble, forecasting, intermodel, model, statistics}, month = {May}, number = {9}, pages = {1891--1910}, posted-at = {2008-07-05 20:57:50}, priority = {2}, title = {Bayesian Design and Analysis for Superensemble-Based Climate Forecasting}, url = {http://dx.doi.org/10.1175/2007JCLI1619.1}, volume = {21}, year = {2008} }

TY - JOUR ID - citeulike:2966599 L3 - citeulike-article-id:2966599 TI - Bayesian Design and Analysis for Superensemble-Based Climate Forecasting JF - Journal of Climate VL - 21 IS - 9 SP - 1891 EP - 1910 N2 - The authors develop statistical data models to combine ensembles from multiple climate models in a fashion that accounts for uncertainty. This formulation enables treatment of model specific means, biases, and covariance matrices of the ensembles. In addition, the authors model the uncertainty in using computer model results to estimate true states of nature. Based on these models and principles of decision making in the presence of uncertainty, this paper poses the problem of superensemble experimental design in a quantitative fashion. Simple examples of the resulting optimal designs are presented. The authors also provide a Bayesian climate modeling and forecasting analysis. The climate variables of interest are Northern and Southern Hemispheric monthly averaged surface temperatures. A Bayesian hierarchical model for these quantities is constructed, including time-varying parameters that are modeled as random variables with distributions depending in part on atmospheric CO2 levels. This allows the authors to do Bayesian forecasting of temperatures under different Special Report on Emissions Scenarios (SRES). These forecasts are based on Bayesian posterior distributions of the unknowns conditional on observational data for 1882–2001 and climate system model output for 2002–97. The latter dataset is a small superensemble from the Parallel Climate Model (PCM) and the Community Climate System Model (CCSM). After summarizing the results, the paper concludes with discussion of potential generalizations of the authors’ strategies. KW - bayesian KW - bma KW - climate KW - ensemble KW - forecasting KW - intermodel KW - model KW - statistics AU - Berliner, Mark AU - Kim, Yongku PY - 2008/05/01/ UR - http://dx.doi.org/10.1175/2007JCLI1619.1 ER -