Error Reduction and Convergence in Climate Prediction

Abstract Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO2 concentrations. Stochastic optimization is used to mimic how six independent climate model development efforts might use the same atmospheric general circulation model, set of observational constraints, and model skill criteria to choose different settings for parameters thought to be important sources of uncertainty related to clouds and convection. Each optimized model improved its skill with respect to observations selected as targets of model development. Of particular note were the improvements seen in reproducing observed extreme rainfall rates over the tropical Pacific, which was not specifically targeted during the optimization process. As compared to the default model sensitivity of 2.4°C, the ensemble of optimized model configurations had a larger and n...

[1]  P. Xie,et al.  Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs , 1997 .

[2]  Mrinal K. Sen,et al.  Computational methods for parameter estimation in climate models , 2008 .

[3]  P. Stoffa,et al.  A multivariate empirical‐orthogonal‐function‐based measure of climate model performance , 2004 .

[4]  James J. Hack,et al.  Cloud feedback in atmospheric general circulation models: An update , 1996 .

[5]  A. Hense,et al.  Probabilistic climate change predictions applying Bayesian model averaging , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[6]  L. Ingber.  Very fast simulated re-annealing , 1989 .

[7]  Edwin F. Harrison,et al.  Earth Radiation Budget Experiment (ERBE) archival and April 1985 results , 1989 .

[8]  W. Collins,et al.  The NCEP–NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation , 2001 .

[9]  Vincent E. Larson,et al.  Elucidating Model Inadequacies in a Cloud Parameterization by Use of an Ensemble-Based Calibration Framework , 2007 .

[10]  Matthew D. Collins,et al.  Towards quantifying uncertainty in transient climate change , 2006 .

[11]  John F. B. Mitchell,et al.  Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models , 1990 .

[12]  Heikki Haario,et al.  DRAM: Efficient adaptive MCMC , 2006, Stat. Comput..

[13]  Mrinal K. Sen,et al.  Bayesian inference, Gibbs' sampler and uncertainty estimation in geophysical inversion , 1996 .

[14]  D J Frame,et al.  Probabilistic climate forecasts and inductive problems , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[15]  B. Soden,et al.  WATER VAPOR FEEDBACK AND GLOBAL WARMING 1 , 2003 .

[16]  M. Collins,et al.  Projections of future climate change , 2002 .

[17]  Phillip A. Arkin,et al.  Analyses of Global Monthly Precipitation Using Gauge Observations, Satellite Estimates, and Numerical Model Predictions , 1996 .

[18]  R. Colman,et al.  A comparison of climate feedbacks in general circulation models , 2003 .

[19]  W. Collins,et al.  The Formulation and Atmospheric Simulation of the Community Atmosphere Model Version 3 (CAM3) , 2006 .

[20]  I. Musat,et al.  On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles , 2006 .

[21]  J D Annan,et al.  Efficient estimation and ensemble generation in climate modelling , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[22]  M. Webb,et al.  Quantification of modelling uncertainties in a large ensemble of climate change simulations , 2004, Nature.

[23]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[24]  James J. Hack,et al.  The Formulation and Atmospheric Simulation , 2004 .

[25]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[26]  K. Bowman,et al.  Differences in rain rate intensities between TRMM observations and community atmosphere model simulations , 2007 .

[27]  D A Stainforth,et al.  Confidence, uncertainty and decision-support relevance in climate predictions , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[28]  J. Murphy,et al.  A methodology for probabilistic predictions of regional climate change from perturbed physics ensembles , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[29]  Leonard A. Smith,et al.  Uncertainty in predictions of the climate response to rising levels of greenhouse gases , 2005, Nature.

[30]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[31]  Jonathan Rougier,et al.  Inference in ensemble experiments , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[32]  Faming Wang.  Investigating ENSO sensitivity to mean climate in an intermediate model using a novel statistical technique , 2007 .

[33]  J. Annan,et al.  Efficient parameter estimation for a highly chaotic system , 2004 .

[34]  G. Meehl,et al.  Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature , 2006 .

[35]  L. Donner,et al.  The Frequency of Extreme Rain Events in Satellite Rain-Rate Estimates and an Atmospheric General Circulation Model , 2007 .

[36]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[37]  Mrinal K. Sen,et al.  An Efficient Stochastic Bayesian Approach to Optimal Parameter and Uncertainty Estimation for Climate Model Predictions , 2004 .

[38]  N. McFarlane,et al.  Sensitivity of Climate Simulations to the Parameterization of Cumulus Convection in the Canadian Climate Centre General Circulation Model , 1995, Data, Models and Analysis.

[39]  James C McWilliams,et al.  Irreducible imprecision in atmospheric and oceanic simulations , 2007, Proceedings of the National Academy of Sciences.

[40]  A. Brix.  Bayesian Data Analysis, 2nd edn , 2005 .