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Uncertainty Quantification in Climate Prediction

Uncertainty Quantification in Climate Prediction. Charles Jackson (1) Mrinal Sen (1) Gabriel Huerta (2) Yi Deng (1) Ken Bowman (3) Institute for Geophysics, The University of Texas at Austin (2) Department of Mathematics and Statistics, University of New Mexico

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Uncertainty Quantification in Climate Prediction

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  1. Uncertainty Quantification in Climate Prediction Charles Jackson (1) Mrinal Sen (1) Gabriel Huerta (2) Yi Deng (1) Ken Bowman (3) Institute for Geophysics, The University of Texas at Austin (2) Department of Mathematics and Statistics, University of New Mexico (3) Department of Atmospheric Science, Texas A&M University

  2. (IPCC 2001)

  3. Surface air temperature clouds (AchutaRao et al., 2004)

  4. Where can clouds go wrong?

  5. Are current approaches to climate model development convergent? Address question using: • Bayesian inference • Stochastic sampling • Simulated annealing to focus sampling • Multiple search attempts for uncertainties

  6. Posterior probability density for 3 parameters: MVFSA Grid Search Metropolis Metropolis MVFSA

  7. Target: Match observed climate 1990-2001One 11-year climate model integration takes 11 hours over 64 processors of an Intel-based compute cluster.

  8. Results • Analysis of top six performing model configurations • T42 CAM3.1, forced by observed SST March 1990 to February 2001. • ~400 experiments completed (so far).

  9. Histogram of configurations with Improved skill

  10. Convergence in predictions of global means does not imply predictions are correct.

  11. Much improved simulation of rain intensities over tropics.

  12. climateprediction.net 27,000 experiments completed in past year on 10,000 personal computers

  13. (Stainforth et al., Nature 2005)

  14. Conclusions • Stochastic optimization of CAM3.1 suggests the model may provide convergent results of global mean predictions. • Assumes parameters tested are key sources of uncertainty. • Hadley Center model supports inference. • Unanticipated gains in model skill. • Important differences at regional scales remain.

  15. Each parameter affects many aspects of the model

  16. There are multiple ways to combine parameter values to yield better model skill.

  17. Definition of model-observational data mismatch

  18. Villagran-Hernandez et al. (in prep)

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