The Art and Science of Climate Model Tuning

AbstractThe process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling with its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model...

[1]  Omar Bellprat,et al.  Objective calibration of regional climate models: OBJECTIVE CALIBRATION OF RCMS , 2012 .

[2]  S. Bony,et al.  On dynamic and thermodynamic components of cloud changes , 2004 .

[3]  M. Peruggia Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (2nd ed.) , 2003 .

[4]  Reto Knutti,et al.  The use of the multi-model ensemble in probabilistic climate projections , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  J. Smagorinsky,et al.  GENERAL CIRCULATION EXPERIMENTS WITH THE PRIMITIVE EQUATIONS , 1963 .

[6]  Thomas J. Santner,et al.  Design and analysis of computer experiments , 1998 .

[7]  J. Rougier,et al.  Precalibrating an intermediate complexity climate model , 2018 .

[8]  R. Fisher,et al.  On the Mathematical Foundations of Theoretical Statistics , 1922 .

[9]  P. N. Edwards A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming , 2010 .

[10]  Sonja Kuhnt,et al.  Design and analysis of computer experiments , 2010 .

[11]  Richard Neale,et al.  Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5 , 2015 .

[12]  Andrew J. Heymsfield,et al.  A scheme for parameterizing ice cloud water content in general circulation models , 1990 .

[13]  Tianjun Zhou,et al.  Parameter Tuning and Calibration of RegCM3 with MIT–Emanuel Cumulus Parameterization Scheme over CORDEX East Asia Domain , 2014 .

[14]  Norman A. Phillips,et al.  The general circulation of the atmosphere: A numerical experiment , 1956 .

[15]  William M. Putman,et al.  Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive , 2014 .

[16]  David R. Doelling,et al.  Toward Optimal Closure of the Earth's Top-of-Atmosphere Radiation Budget , 2009 .

[17]  Jean-Christophe Golaz,et al.  Cloud tuning in a coupled climate model: Impact on 20th century warming , 2013 .

[18]  Mrinal K. Sen,et al.  Error Reduction and Convergence in Climate Prediction , 2008 .

[19]  S. Bony,et al.  LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection , 2013, Climate Dynamics.

[20]  Daehyun Kim,et al.  The Tropical Subseasonal Variability Simulated in the NASA GISS General Circulation Model , 2012 .

[21]  Michael Goldstein,et al.  History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble , 2013, Climate Dynamics.

[22]  S. Manabe,et al.  Climate Calculations with a Combined Ocean-Atmosphere Model , 1969 .

[23]  Wojciech W. Grabowski,et al.  Hierarchical modelling of tropical convective systems using explicit and parametrized approaches , 2001 .

[24]  James C. McWilliams,et al.  An evaluation of neutral and convective planetary boundary-layer parameterizations relative to large eddy simulations , 1996 .

[25]  E. Fetzer,et al.  The Observed State of the Energy Budget in the Early Twenty-First Century , 2015 .

[26]  J. Hansen,et al.  Efficacy of climate forcings , 2005 .

[27]  Michael Goldstein,et al.  Fast linked analyses for scenario‐based hierarchies , 2012 .

[28]  Hui Wan,et al.  Technical Note: On the use of nudging for aerosol–climate model intercomparison studies , 2014 .

[29]  M. Webb,et al.  Multivariate probabilistic projections using imperfect climate models part I: outline of methodology , 2012, Climate Dynamics.

[30]  Amy Dahan Dalmedico,et al.  History and Epistemology of Models: Meteorology (1946–1963) as a Case Study , 2001 .

[31]  Marie-Alice Foujols,et al.  Impact of the LMDZ atmospheric grid configuration on the climate and sensitivity of the IPSL-CM5A coupled model , 2013, Climate Dynamics.

[32]  Catherine Rio,et al.  Resolved Versus Parametrized Boundary-Layer Plumes. Part I: A Parametrization-Oriented Conditional Sampling in Large-Eddy Simulations , 2010 .

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

[34]  James M. Salter,et al.  Identifying and removing structural biases in climate models with history matching , 2015, Climate Dynamics.

[35]  T. J. Mitchell,et al.  Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments , 1991 .

[36]  Hui Wan,et al.  Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models , 2014 .

[37]  David R. Anderson,et al.  Model Selection and Multimodel Inference , 2003 .

[38]  Isaac M. Held,et al.  The Gap between Simulation and Understanding in Climate Modeling , 2005 .

[39]  A. Arakawa,et al.  Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment, Part I , 1974 .

[40]  Feng Xie,et al.  An automatic and effective parameter optimization method for model tuning , 2015 .

[41]  S. Manabe,et al.  The Effects of Doubling the CO2 Concentration on the climate of a General Circulation Model , 1975 .

[42]  Jeffrey T. Kiehl,et al.  Twentieth century climate model response and climate sensitivity , 2007 .

[43]  D. Klocke,et al.  Tuning the climate of a global model , 2012 .

[44]  Peter Lynch,et al.  The origins of computer weather prediction and climate modeling , 2008, J. Comput. Phys..

[45]  Z. X. Li,et al.  Interpretation of Cloud-Climate Feedback as Produced by 14 Atmospheric General Circulation Models , 1989, Science.

[46]  James C McWilliams,et al.  Considerations for parameter optimization and sensitivity in climate models , 2010, Proceedings of the National Academy of Sciences.

[47]  Venkatramani Balaji,et al.  Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework , 2016 .

[48]  T. Andrews,et al.  Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models , 2013 .

[49]  Sally A. McFarlane,et al.  Uncertainty quantification and parameter tuning in the CAM5 Zhang‐McFarlane convection scheme and impact of improved convection on the global circulation and climate , 2012 .

[50]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[51]  Catherine Rio,et al.  Resolved Versus Parametrized Boundary-Layer Plumes. Part III: Derivation of a Statistical Scheme for Cumulus Clouds , 2013, Boundary-Layer Meteorology.

[52]  Dirk Notz,et al.  How well must climate models agree with observations? , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[53]  Richard Neale,et al.  Process-Oriented MJO Simulation Diagnostic: Moisture Sensitivity of Simulated Convection , 2014 .

[54]  Stephen J. Lord,et al.  Interaction of a Cumulus Cloud Ensemble with the Large-Scale Environment. Part III: Semi-Prognostic Test of the Arakawa-Schubert Cumulus Parameterization , 1982 .

[55]  Jonathan Rougier,et al.  Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations , 2007 .

[56]  M. Tiedtke A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models , 1989 .

[57]  Andrew Gettelman,et al.  The Evolution of Climate Sensitivity and Climate Feedbacks in the Community Atmosphere Model , 2012 .

[58]  Jean-Christophe Golaz,et al.  Evaluating cloud tuning in a climate model with satellite observations , 2013 .