Tuning the climate of a global model

During a development stage global climate models have their properties adjusted or tuned in various ways to best match the known state of the Earth's climate system. These desired properties are observables, such as the radiation balance at the top of the atmosphere, the global mean temperature, sea ice, clouds and wind fields. The tuning is typically performed by adjusting uncertain, or even non‐observable, parameters related to processes not explicitly represented at the model grid resolution. The practice of climate model tuning has seen an increasing level of attention because key model properties, such as climate sensitivity, have been shown to depend on frequently used tuning parameters. Here we provide insights into how climate model tuning is practically done in the case of closing the radiation balance and adjusting the global mean temperature for the Max Planck Institute Earth System Model (MPI‐ESM). We demonstrate that considerable ambiguity exists in the choice of parameters, and present and compare three alternatively tuned, yet plausible configurations of the climate model. The impacts of parameter tuning on climate sensitivity was less than anticipated.

[1]  Fuzhong Weng Cloud Liquid Water , 2014, Encyclopedia of Remote Sensing.

[2]  Pierre Friedlingstein,et al.  Carbon Dioxide and Climate: Perspectives on a Scientific Assessment , 2013 .

[3]  Alexander Loew,et al.  Evaluation of vegetation cover and land‐surface albedo in MPI‐ESM CMIP5 simulations , 2013 .

[4]  H. Douville,et al.  The CNRM-CM5.1 global climate model: description and basic evaluation , 2013, Climate Dynamics.

[5]  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.

[6]  Stephen E. Schwartz,et al.  Observing and Modeling Earth’s Energy Flows , 2012, Surveys in Geophysics.

[7]  Karl E. Taylor,et al.  An overview of CMIP5 and the experiment design , 2012 .

[8]  E. Roeckner,et al.  Impact of melt ponds on Arctic sea ice in past and future climates as simulated by MPI‐ESM , 2012 .

[9]  Robert S. Kandel,et al.  Observing and modeling earth's energy flows , 2012 .

[10]  Heikki Haario,et al.  Ensemble prediction and parameter estimation system: the concept , 2012 .

[11]  Klaus Wyser,et al.  EC-Earth V2.2: description and validation of a new seamless earth system prediction model , 2012, Climate Dynamics.

[12]  Robert Pincus,et al.  On Constraining Estimates of Climate Sensitivity with Present-Day Observations through Model Weighting , 2011 .

[13]  C. Jones,et al.  Development and evaluation of an Earth-System model - HadGEM2 , 2011 .

[14]  Young Ho Kim,et al.  El Niño–Southern Oscillation sensitivity to cumulus entrainment in a coupled general circulation model , 2011 .

[15]  E. Maloney,et al.  A Systematic Relationship between Intraseasonal Variability and Mean State Bias in AGCM Simulations , 2011 .

[16]  C. Jones,et al.  The HadGEM2 family of Met Office Unified Model climate configurations , 2011 .

[17]  Pierre Rampal,et al.  IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline , 2011 .

[18]  Jean-Christophe Golaz,et al.  Sensitivity of the Aerosol Indirect Effect to Subgrid Variability in the Cloud Parameterization of the GFDL Atmosphere General Circulation Model AM3 , 2011 .

[19]  J. Lamarque,et al.  The HadGEM2-ES implementation of CMIP5 centennial simulations , 2011 .

[20]  Makiko Sato,et al.  Earth's energy imbalance and implications , 2011, 1105.1140.

[21]  H. Hasumi,et al.  Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity , 2010, Journal of Climate.

[22]  U. Lohmann,et al.  Impact of parametric uncertainties on the present-day climate and on the anthropogenic aerosol effect , 2010 .

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

[24]  Erkki Oja,et al.  Estimation of ECHAM5 climate model closure parameters with adaptive MCMC , 2010 .

[25]  Ramaswamy,et al.  The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL global coupled model CM3 , 2011 .

[26]  C. Jakob Accelerating progress in global atmospheric model development through improved parameterizations: challenges, opportunities, and strategies , 2010 .

[27]  Ron Kwok,et al.  Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008 , 2009 .

[28]  Mikael Lüthje,et al.  A new sea ice albedo scheme including melt ponds for ECHAM5 general circulation model , 2009 .

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

[30]  R. Neale,et al.  The Impact of Convection on ENSO: From a Delayed Oscillator to a Series of Events , 2008 .

[31]  Christopher W. O'Dell,et al.  Cloud Liquid Water Path from Satellite-Based Passive Microwave Observations: A New Climatology over the Global Oceans , 2008 .

[32]  T. Reichler,et al.  How Well Do Coupled Models Simulate Today's Climate? , 2008 .

[33]  M. Holland,et al.  Comment on “On the reliability of simulated Arctic sea ice in global climate models” by I. Eisenman, N. Untersteiner, and J. S. Wettlaufer , 2008 .

[34]  F. Bender A note on the effect of GCM tuning on climate sensitivity , 2008 .

[35]  M. Webb,et al.  Tropospheric Adjustment Induces a Cloud Component in CO2 Forcing , 2008 .

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

[37]  John F. B. Mitchell,et al.  THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research , 2007 .

[38]  M. Holland,et al.  Arctic sea ice decline: Faster than forecast , 2007 .

[39]  John S. Wettlaufer,et al.  On the reliability of simulated Arctic sea ice in global climate models , 2007 .

[40]  S. Solomon The Physical Science Basis : Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[41]  O. Edenhofer,et al.  Mitigation from a cross-sectoral perspective , 2007 .

[42]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[43]  Jouni Räisänen How reliable are climate models , 2007 .

[44]  Tim N. Palmer,et al.  Using numerical weather prediction to assess climate models , 2007 .

[45]  J. Räisänen,et al.  How reliable are climate models? , 2007 .

[46]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[47]  P. Jones,et al.  Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850 , 2006 .

[48]  W. Collins,et al.  The Community Climate System Model Version 3 (CCSM3) , 2006 .

[49]  S. Klein,et al.  Using Stochastically Generated Subcolumns to Represent Cloud Structure in a Large-Scale Model , 2005 .

[50]  Julia C. Hargreaves,et al.  Parameter estimation in an atmospheric GCM using the Ensemble , 2005 .

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

[52]  G. Danabasoglu,et al.  The Community Climate System Model Version 4 , 2011 .

[53]  Jonathan M. Gregory,et al.  A new method for diagnosing radiative forcing and climate sensitivity , 2004 .

[54]  M. Holland,et al.  Polar amplification of climate change in coupled models , 2003 .

[55]  A. Tompkins A Prognostic Parameterization for the Subgrid-Scale Variability of Water Vapor and Clouds in Large-Scale Models and Its Use to Diagnose Cloud Cover , 2002 .

[56]  B. Stevens,et al.  Observations, experiments, and large eddy simulation , 2001 .

[57]  John F. B. Mitchell,et al.  The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments , 2000 .

[58]  P. Gent,et al.  The NCAR Climate System Model, Version One* , 1998 .

[59]  Bruce A. Wielicki,et al.  Measurements, Models, and Hypotheses in the Atmospheric Sciences , 1997 .

[60]  E. Guilyardi,et al.  Performance of the OPA/ARPEGE-T21 global ocean-atmosphere coupled model , 1997 .

[61]  Ulrike Lohmann,et al.  Design and performance of a new cloud microphysics scheme developed for the ECHAM general circulation model , 1996 .

[62]  A non‐flux corrected transient CO2 experiment using the BMRC Coupled Atmosphere/Ocean GCM , 1995 .

[63]  A. P. Siebesma,et al.  Evaluation of Parametric Assumptions for Shallow Cumulus Convection , 1995 .

[64]  Robert F. Cahalan,et al.  The albedo of fractal stratocumulus clouds , 1994 .

[65]  N Oreskes,et al.  Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences , 1994, Science.

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

[67]  J. Kristjánsson,et al.  Condensation and Cloud Parameterization Studies with a Mesoscale Numerical Weather Prediction Model , 1989 .

[68]  R. Sausen,et al.  Coupled ocean-atmosphere models with flux correction , 1988 .

[69]  W. Hibler A Dynamic Thermodynamic Sea Ice Model , 1979 .

[70]  S. Arrhenius “On the Infl uence of Carbonic Acid in the Air upon the Temperature of the Ground” (1896) , 2017, The Future of Nature.