Uncertainty Quantification in Climate Prediction Charles Jackson (1) Mrinal Sen (1) Gabriel Huerta (2) Yi Deng (1) Ken Bowman (3) (1)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

(IPCC 2001)

Sources of uncertainty: Parameterization of unresolved processes Insufficient data constraints Model development process

~60km resolution

Disappointing Performance Super-high resolution models cost 100x but only give modest performance gains. “Superparameterization” versions also cost 100x, and also only give modest performance gains. (source: recent experiments by Philip Duffy of Lawrence Livermore National Laboratory)

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

Where can clouds go wrong?

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

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

Villagran-Hernandez et al. (in prep)

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

Definition of cost function

Inverse Data Covariance Matrix C -1 non-trivial challenges: Scale mismatch between observations and model predictions. Singularities Uncertainties among different fields are not independent Correct normalization is not fixed, dependent on size of systematic errors.

Why treatment of C -1 matters:

List of Constrained Fields Model QuantityDescription CLDLOW, MED, …Cloud cover at different levels in the atmosphere FLNTNet longwave radiation at the top of the atmosphere FSNTNet shortwave radiation at the top of the atmosphere FSDSDownwelling shortwave radiation at the surface LHFLXSurface latent heat flux PRECTTotal precipitation PSLSea level pressure RELHUMRelative humidity (zonal means at all levels) SHFLXSurface sensible heat flux TTemperature (zonal means at all levels) TREFHTAir temperature at 2 meter reference height UZonal winds (zonal means at all levels)

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

Calculation of climate prediction uncertainty due to clouds: Search for optimal values for the 6 parameters important to clouds and convection Repeat process in an independent manner Experiment is designed to measure scatter among predictions of multiple model development groups who all use the same –model –set of observational constraints –skill criteria (Jackson et al., submitted)

Histogram of configurations with Improved skill

Default Config 4 changes

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

Much improved simulation of rain intensities over tropics.

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

(Stainforth et al., Nature 2005)

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.

Each parameter affects many aspects of the model

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

Definition of model-observational data mismatch

Villagran-Hernandez et al. (in prep)