Abstract
We consider the problem of forecasting future regional climate. Our method is based on blending different members of an ensemble of regional climate model (RCM) simulations while accounting for the discrepancies between these simulations, under present day conditions, and observational records for the recent past. To this end, we develop Bayesian space-time models that assess the discrepancies between climate model simulations and observational records. Those discrepancies are then propagated into the future to obtain blended forecasts of 21st century climate. The model allows for location-dependent spatial heterogeneities, providing local comparisons between the different simulations. Additionally, we estimate the different modes of spatial variability, and use the climate model-specific coefficients of the spatial factors for comparisons. We focus on regional climate model simulations performed in the context of the North American Regional Climate Change Assessment Program (NARCCAP). We consider, in particular, simulations from RegCM3 using three different forcings: NCEP, GFDL and CGCM3. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the SRES A2 emissions scenario, covering 2041 to 2070. We investigate yearly mean summer temperature for a domain in the South West of the United States. The results indicated the RCM simulations underestimate the mean summer temperature increase for most of the domain compared to our model.
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References
Banerjee, S., Carlin, B., and Gelfand, A. (2004), Hierarchical Modeling and Analysis of Spatial Data, New York: Chapman and Hall.
Carlin, B. P., Polson, N. G., and Stoffer, D. (1992), “A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modelling,” Journal of the American Statistical Association, 87, 493–500.
Carter, C. K., and Kohn, R. (1994), “On Gibbs Sampling for State Space Models,” Biometrika, 81, 541–553.
Finley, A., Banerjee, S., Waldman, P., and Ericsson, T. (2009a), “Hierarchical Spatial Modeling of Additive and Dominance Genetic Variance for Large Spatial Trial Datasets,” Biometrics, 65, 441–451.
Finley, A. O., Sang, H., Banerjee, S., and Gelfand, A. E. (2009b), “Improving the Performance of Predictive Process Modeling for Large Datasets,” Computational Statistics & Data Analysis, 53, 2873–2884.
Frühwirth-Schnatter, S. (1994), “Data Augmentation and Dynamic Linear Models,” Journal of Time Series Analysis, 15 (2), 183–202.
Fuentes, M., and Raftery, A. (2005), “Model Evaluation and Spatial Interpolation by Bayesian Combination of Observations With Outputs From Numerical Models,” Biometrics, 66, 36–45.
Gamerman, D., and Lopes, H. F. (2006), Markov Chain Monte Carlo—Stochastic Simulation for Bayesian Inference (2nd ed.), London: Chapman and Hall.
Giorgi, F., and Mearns, L. O. (1999), “Introduction to Special Section: Regional Climate Modeling Revisited,” Journal of Geophysical Research, 104 (D6), 6335–6352.
Gneiting, T., and Raftery, A. (2007), “Strictly Proper Scoring Rules, Prediction, and Estimation,” Journal of the American Statistical Association, 102 (477), 359–378.
Gneiting, T., Balabdaoui, F., and Raftery, A. (2007), “Probabilistic Forecasts, Calibration and Sharpness,” Journal of the Royal Statistical Society, B, 69 (2), 243–268.
Gneiting, T., Stanberry, L., Grimit, E., Held, L., and Johnson, N. (2008), “Assessing Probabilistic Forecasts of Multivariate Quantities, With an Application to Ensemble Predictions of Surface Winds,” Test, 17 (2), 211–235.
Gschlößl, S., and Czado, C. (2007), “Spatial Modelling of Claim Frequency and Claim Size in Non-life Insurance,” Scandinavian Actuarial Journal, 107, 202–225.
IPCC (2007), Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the IPCC, Cambridge: Cambridge University Press. 996 pp.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Leetmaa, A., Reynolds, R., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Jenne, R., and Joseph, D. (1996), “The NCEP/NCAR 40-Year Reanalysis Project,” Bulletin of the American Meteorological Society, 77, 437–471.
Kaufman, C., and Sain, S. (2010), “Bayesian Functional ANOVA Modeling Using Gaussian Prior Distributions,” Bayesian Analysis, 5, 123–150.
Kennedy, M., and O’Hagan, A. (2001), “Bayesian Calibration of Computer Models” (with discussion), Journal of the Royal Statistical Society, B, 63, 425–464.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. (2010), “Challenges in Combining Projections From Multiple Climate Models,” Journal of Climate, 23, 2739–2758.
Lemos, R., and Sansó, B. (2011), “Conditionally Linear Models for Non-homogeneous Spatial Random Fields,” Statistical Methodology (to appear). doi:10.1016/j.stamet.2011.02.001.
Mearns, L., Gutowski, W., Jones, R., Leung, L., McGinnis, S., Nunes, A., and Qian, Y. (2009), “A Regional Climate Change Assessment Program for North America,” EOS, 90, 311–312.
Meehl, G., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J., Stouffer, R., and Taylor, K. (2007), “The WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research,” Bulletin of the American Meteorological Society, 88, 1383–1394.
Pal, J. S., Giorgi, F., Bi, X. Q., Elguindi, N., Solmon, F., Gao, X. J., Rauscher, S. A., Francisco, R., Zakey, A., Winter, J., Ashfaq, M., Syed, F. S., Bell, J. L., Diffenbaugh, N. S., Karmacharya, J., Konare, A., Martinez, D., da Rocha, R. P., Sloan, L. C., and Steiner, A. L. (2007), “Regional Climate Modeling for the Developing World—The ICTP RegCM3 and RegCNET,” Bulletin of the American Meteorological Society, 88, 1395–1409.
Rougier, J. (2007), “Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations,” Climatic Change, 81, 247–264.
Rougier, J., Goldstein, M., and House, L. (2010), “Assessing Climate Uncertainty Using Evaluations of Several Different Climate Simulators,” Tech. rep., Dept. of Mathematics, University of Bristol.
Sansó, B., and Guenni, L. (2004), “A Bayesian Approach to Compare Observed Rainfall Data to Deterministic Simulations,” Environmetrics, 15, 597–612.
Smith, R., Tebaldi, C., Nychka, D., and Mearns, L. (2009), “Bayesian Modeling of Uncertainty in Ensembles of Climate Models,” Journal of the American Statistical Association, 97–116.
Stein, M. L., and Fang, D. (1997), “Ozone Exposure and Population Density in Harris County, Texas: Comment,” Journal of the American Statistical Association, 92 (438), 408–411.
Tebaldi, C., and Sansó, B. (2008), “Joint Projections of Temperature and Precipitation Change From Multiple Climate Models: A Hierarchical Bayes Approach,” Journal of the Royal Statistical Society, A, 172, 83–106.
Whittle, P. (1954), “On Stationary Processes in the Plane,” Biometrika, 41, 434–449.
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Salazar, E., Sansó, B., Finley, A.O. et al. Comparing and Blending Regional Climate Model Predictions for the American Southwest. JABES 16, 586–605 (2011). https://doi.org/10.1007/s13253-011-0074-6
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DOI: https://doi.org/10.1007/s13253-011-0074-6