A Multimodel Study of Parametric Uncertainty in Predictions of Climate Response to Rising Greenhouse Gas Concentrations

Abstract One tool for studying uncertainties in simulations of future climate is to consider ensembles of general circulation models where parameterizations have been sampled within their physical range of plausibility. This study is about simulations from two such ensembles: a subset of the climateprediction.net ensemble using the Met Office Hadley Centre Atmosphere Model, version 3.0 and the new “CAMcube” ensemble using the Community Atmosphere Model, version 3.5. The study determines that the distribution of climate sensitivity in the two ensembles is very different: the climateprediction.net ensemble subset range is 1.7–9.9 K, while the CAMcube ensemble range is 2.2–3.2 K. On a regional level, however, both ensembles show a similarly diverse range in their mean climatology. Model radiative flux changes suggest that the major difference between the ranges of climate sensitivity in the two ensembles lies in their clear-sky longwave responses. Large clear-sky feedbacks present only in the climatepredicti...

[1]  M. Allen,et al.  Constraints on climate change from a multi‐thousand member ensemble of simulations , 2005 .

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

[3]  Shian‐Jiann Lin A “Vertically Lagrangian” Finite-Volume Dynamical Core for Global Models , 2004 .

[4]  S. Manabe,et al.  Cloud Feedback Processes in a General Circulation Model , 1988 .

[5]  P. Rasch,et al.  Representation of Clouds and Precipitation Processes in the Community Atmosphere Model Version 3 (CAM3) , 2006 .

[6]  A. Blyth,et al.  Extension of the Stochastic Mixing Model to Cumulonimbus Clouds , 1992 .

[7]  M. Webb,et al.  Structural similarities and differences in climate responses to CO2 increase between two perturbed physics ensembles. , 2010 .

[8]  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.

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

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

[11]  Gerald L. Potter,et al.  A methodology for understanding and intercomparing atmospheric climate feedback processes in general circulation models , 1988 .

[12]  W. Ingram,et al.  Climate feedbacks determined using radiative kernels in a multi-thousand member ensemble of AOGCMs , 2010 .

[13]  Reto Knutti,et al.  Constraints on Model Response to Greenhouse Gas Forcing and the Role of Subgrid-Scale Processes , 2008 .

[14]  K. Trenberth,et al.  Global warming due to increasing absorbed solar radiation , 2009 .

[15]  V. Ramanathan,et al.  Observational determination of the greenhouse effect , 1989, Nature.

[16]  Chris G. Knight,et al.  Association of parameter, software, and hardware variation with large-scale behavior across 57,000 climate models , 2007, Proceedings of the National Academy of Sciences.

[17]  Andrew Gettelman,et al.  Observed and Simulated Upper-Tropospheric Water Vapor Feedback , 2008 .

[18]  P. Rowntree,et al.  A Mass Flux Convection Scheme with Representation of Cloud Ensemble Characteristics and Stability-Dependent Closure , 1990 .

[19]  Pierre Friedlingstein,et al.  A Review of Uncertainties in Global Temperature Projections over the Twenty-First Century , 2008 .

[20]  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.

[21]  Philip J. Rasch,et al.  Effects of Convective Momentum Transport on the Atmospheric Circulation in the Community Atmosphere Model, Version 3 , 2008 .

[22]  J. Hack Parameterization of moist convection in the National Center for Atmospheric Research community climate model (CCM2) , 1994 .

[23]  D. Stone,et al.  Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations , 2008 .

[24]  Peter E. Thornton,et al.  Improvements to the Community Land Model and their impact on the hydrological cycle , 2008 .

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

[26]  S. Bony,et al.  Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models , 2001 .

[27]  K. Taylor,et al.  The Community Climate System Model , 2001 .

[28]  Andrew J. Heymsfield,et al.  Precipitation Development in Stratiform Ice Clouds: A Microphysical and Dynamical Study , 1977 .

[29]  Xiaoqing Wu,et al.  Coupling of Convective Momentum Transport with Convective Heating in Global Climate Simulations , 2007 .

[30]  M. Webb,et al.  Stratospheric water vapour and high climate sensitivity in a version of the HadSM3 climate model , 2010 .

[31]  K. Oleson,et al.  Use of FLUXNET in the Community Land Model development , 2008 .

[32]  Peter V. Hobbs,et al.  Fall speeds and masses of solid precipitation particles , 1974 .

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

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

[35]  R. Neale,et al.  Improvements in a half degree atmosphere/land version of the CCSM , 2010 .