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Regional Climate Projections with Multimodel Probabilistic Scenarios

This project aims to produce regional climate outlooks for the next century, emphasizing temperature evolution and variability. By applying a Bayesian probability model to ensemble simulations, the study compares model structures and estimation techniques to enhance predictive accuracy for different regions. The findings indicate improved temperature projections for the 21st century, particularly in the annual mean, though challenges related to assumptions on stationarity persist.

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Regional Climate Projections with Multimodel Probabilistic Scenarios

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  1. Performance-based multimodel probabilistic climate change scenarios Arthur M. Greene, Lisa Goddard, Upmanu Lall International Research Institute for Climate Prediction The Earth Institute at Columbia University Palisades, NY amg@iri.columbia.edu

  2. Goal: Production of regional climate outlooks for the coming century • Initial focus: Evolution of mean regional temperatures; eventually continuing with variability, precipitation… • Methodology: Multimodel ensemble, combined in the framework of a linear Bayesian probability model • Coefficients derived by fitting to simulations of 20th-century climate (20C3M runs from IPCC AR4) • Bayesian estimation employed, in keeping with probabilistic framework • Model formulations with varying degrees of complexity explored

  3. Regional definitions as in the IPCC SAR…

  4. AOGCMs may exhibit significant regional temperature biases Obs Central American sector (CAM), raw temperature series Anomalies (relative to each model’s 1961-1990 climatology)

  5. Incoherence not limited to interannual variations

  6. Mean regional temperatures are simulated with more fidelity than are regional temperature trends. Regional annual mean T, 1961-1990 Regional annual-mean T trends, 1965-1998

  7. Probability model structures compared • A (variance of Y is uniform…) • B: (or regionally dependent…) • C: (cov(b) modeled explicitly) • The last of these represents a multilevel, or hierarchical structure: 1: Regional series of obs and simulations 2: Global structure for parent distribution of b • Priors are “diffuse” (i.e. non-informative) with the qualified exception of S, for which a scale matrix must be specified to at least an order of magnitude.

  8. Probability model C as a graph (DAG) regions k models j years i

  9. Regional input covariance exhibits varying degrees of structure…

  10. Some model comparison statistics (mean annual temperature) Dbar: Mean of the posterior deviance: mean (-2 log(p(y|q))) Dhat: Posterior deviance computed from mean q: -2 log(p|(y|qbar)) DIC: “Deviance Information Criterion,” an estimate of predictive skill. pD: Effective number of parameters in the model. Conclusion: Model B a lot better than A; C a little better than B

  11. Some fitted series… Model A Model B

  12. Fitted series, cont'd Model B Model C

  13. Model structure and estimation of bjk

  14. Prior distribution of cov(b) is “imprinted” by the data Prior correlation for b: A blank slate. Posterior correlation: (Weak) structure is present

  15. Computed with respect to decadal means “Leave-10-out”, with model fitted to remaining data. Nine values / region Cross-validation Region by region (annual mean) Comparison over all regions

  16. DJF, JJA…

  17. Coefficients are applied to the SRES scenario simulations to generate the final temperature projections

  18. Stationarity assumptions cannot be ignored…

  19. Summary • Regional temperature projections are generated for the 21st century • Based on IPCC 20C3M experiments, SRES scenario simulations • AOGCM outputs combined in the framework of a Bayesian hierarchical linear model of limited complexity • Relaxation of constraint that allows resultant to “escape the envelope” of the underlying simulations • Projections appear to be an improvement over the unweighted mean of the contributing AOGCMs. This improvement is greatest for the annual mean, decreasing but still present for DJF and JJA • There is an implicit assumption of stationarity, and with this comes the unavoidable responsibility of choosing good (or at least defensible) assumptions in model building. So what else is new?

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