Limited sensitivity analysis of regional climate change probabilities for the 21st century

[1] Quantifying uncertainty in regional climate change projections is important for a range of reasons. We examine the sensitivity of regional climate change probabilities to various uncertainties. We use a simple probabilistic energy balance model that samples uncertainty in greenhouse gas emissions, the climate sensitivity, the carbon cycle, ocean mixing, and aerosol forcing. We then propagate global mean temperature probabilities to General Circulation Models (GCMs) through the pattern-scaling technique. In order to combine the resulting probabilities we devised regional skill scores for each GCM, season (DJF, JJA), and climate variable (surface temperature, and precipitation) in 22 world regions, based on model performance and model convergence. A range of sensitivity experiments are carried out with different skill score schemes, climate sensitivities, and emissions scenarios. It was shown that whether skill scores as applied in this paper were used or not, makes little difference to regional climate change probabilities. However, both these approaches provide more information than simply using the multi-model ensemble average. For temperature change probabilities, emissions scenarios uncertainty tends to dominate the 95th percentile whereas climate sensitivity uncertainty plays a more important role at the 5th percentile. The sensitivity of precipitation change probabilities to the tested uncertainties are region specific, but some conclusions can be drawn. At the 95th percentile, the uncertainty that tends to dominate is emissions scenarios, closely followed by GCM weighting scheme and the climate sensitivity. At the 5th percentile, GCM weighting scheme uncertainty tends to dominate for JJA, but for DJF all uncertainties have similar proportionate influence.

[1]  Andrei P. Sokolov,et al.  Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations , 2002, Science.

[2]  P. Stott,et al.  Origins and estimates of uncertainty in predictions of twenty-first century temperature rise , 2002, Nature.

[3]  Mike Hulme,et al.  Adapting to the inevitable , 1998, Nature.

[4]  M. Hulme,et al.  Portraying climate scenario uncertainties in relation to tolerable regional climate change , 1998 .

[5]  F. Giorgi,et al.  Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the reliability ensemble averaging (REA) method , 2002 .

[6]  Masahiro Endoh,et al.  ENSO‐like interdecadal variability in the Pacific Ocean as simulated in a coupled general circulation model , 2000 .

[7]  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 .

[8]  G. Boer,et al.  CMIP1 evaluation and intercomparison of coupled climate models , 2001 .

[9]  Roger Jones,et al.  Analysing the risk of climate change using an irrigation demand model , 2000 .

[10]  Richard S. J. Tol,et al.  A Bayesian Statistical Analysis of the Enhanced Greenhouse Effect , 1998 .

[11]  T. D. Mitchell,et al.  Pattern Scaling: An Examination of the Accuracy of the Technique for Describing Future Climates , 2003 .

[12]  Ingo Kirchner,et al.  ENSO variability and atmospheric response in a global coupled atmosphere-ocean GCM , 1996 .

[13]  Frank O. Bryan,et al.  Improvements to the NCAR CSM-1 for Transient Climate Simulations , 2001 .

[14]  W. G. Strand,et al.  Parallel climate model (PCM) control and transient simulations , 2000 .

[15]  P. Jones,et al.  Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology , 1999 .

[16]  M. Hulme,et al.  Relative impacts of human-induced climate change and natural climate variability , 1999, Nature.

[17]  R. Benestad.  Tentative probabilistic temperature scenarios for northern Europe , 2004 .

[18]  Richard L. Smith,et al.  Regional probabilities of precipitation change: A Bayesian analysis of multimodel simulations , 2004 .

[19]  Tim N. Palmer,et al.  Physical Climate Processes and Feedbacks , 2001 .

[20]  I. Watterson,et al.  Non-Dimensional Measures of Climate Model Performance , 1996 .

[21]  M. Allen.  Do-it-yourself climate prediction , 1999, Nature.

[22]  Zong-ci Zhao,et al.  Climate models—evaluation , 2001 .

[23]  T. Wigley,et al.  Interpretation of High Projections for Global-Mean Warming , 2001, Science.

[24]  Reto Knutti,et al.  Constraints on radiative forcing and future climate change from observations and climate model ensembles , 2002, Nature.

[25]  A. H. Murphy,et al.  Skill Scores Based on the Mean Square Error and Their Relationships to the Correlation Coefficient , 1988 .

[26]  James G. Titus,et al.  The risk of sea level rise , 1996 .

[27]  T. Palmer,et al.  A Probability and Decision-Model Analysis of a Multimodel Ensemble of Climate Change Simulations , 2001 .

[28]  J. Räisänen,et al.  A probabilistic view on recent and near future climate change in Sweden , 2003 .

[29]  Michael E. Schlesinger,et al.  Objective estimation of the probability density function for climate sensitivity , 2001 .

[30]  Raquel V. Francisco,et al.  Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM , 2000 .

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

[32]  Mike Hulme,et al.  Representing uncertainty in climate change scenarios: a Monte-Carlo approach , 2000 .

[33]  Richard L. Smith,et al.  Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles , 2005 .

[34]  Alexei G. Sankovski,et al.  Special report on emissions scenarios : a special report of Working group III of the Intergovernmental Panel on Climate Change , 2000 .

[35]  Roger Jones,et al.  Managing Uncertainty in Climate Change Projections – Issues for Impact Assessment , 2000 .

[36]  S. Raper,et al.  An Observationally Based Estimate of the Climate Sensitivity , 2002 .

[37]  F. Giorgi,et al.  Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method , 2003 .

[38]  Nebojsa Nakicenovic,et al.  Developing and applying scenarios , 2001 .

[39]  T. Nozawa.  Coupled ocean-atmosphere model experiments of future climate change based on IPCC SRES scenarios , 2000 .

[40]  G. Boer,et al.  Warming asymmetry in climate change simulations , 2001 .

[41]  H. Visser,et al.  Identifying Key Sources of Uncertainty in Climate Change Projections , 2000 .

[42]  P. Jones,et al.  Regional climate model data used within the SWURVE project – 2: addressing uncertainty in regional climate model data for five European case study areas , 2007 .

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

[44]  Christel Prudhomme,et al.  Uncertainty and climate change impact on the flood regime of small UK catchments , 2003 .

[45]  Suraje Dessai,et al.  Climatic Implications of Revised IPCC Emissions Scenarios, the Kyoto Protocol and Quantification of Uncertainties , 2001 .

[46]  S. M. Marlais,et al.  An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I) , 1999 .

[47]  G. Meehl,et al.  Intercomparsion of regional biases and doubled CO2-sensitivity of coupled atmosphere-ocean general circulation model experiments , 1997 .

[48]  J. Houghton,et al.  Climate change 1995: the science of climate change. , 1996 .

[49]  Timothy R. Carter,et al.  Future climate in world regions: an intercomparison of model-based projections for the new IPCC emissions scenarios , 2003 .

[50]  W. Adger,et al.  Successful adaptation to climate change across scales , 2005 .

[51]  M. G. Morgan,et al.  Subjective judgments by climate experts. , 1995 .

[52]  Michael E. Schlesinger,et al.  Developing climate scenarios from equilibrium GCM results , 1990 .

[53]  Dave Stainforth,et al.  Distributed computing for public-interest climate modeling research , 2002, Comput. Sci. Eng..

[54]  M. Sarofim,et al.  Uncertainty Analysis of Climate Change and Policy Response , 2003 .

[55]  Maria Antónia Valente,et al.  20th Century Portuguese climate and climate change scenarios , 2002 .

[56]  T. N. Palmer,et al.  Quantifying the risk of extreme seasonal precipitation events in a changing climate , 2002, Nature.

[57]  Andrew J. Weaver.  The Science of Climate Change , 2003 .

[58]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[59]  P. Jones,et al.  REPRESENTING TWENTIETH CENTURY SPACE-TIME CLIMATE VARIABILITY. , 1998 .

[60]  John R. Lanzante,et al.  Global mean surface air temperature and North Atlantic overturning in a suite of coupled GCM climate change experiments , 1999 .

[61]  F. D. Santos,et al.  Climate change in portugal scenarios, impacts and adaptation measures : Siam project , 2002 .

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

[63]  Anand Patwardhan,et al.  Bayesian Methods for Model Uncertainty Analysis with Application to Future Sea Level Rise , 1992 .

[64]  Raquel V. Francisco,et al.  Evaluating uncertainties in the prediction of regional climate change , 2000 .

[65]  S. O’Farrell,et al.  Transient Climate Change in the CSIRO Coupled Model with Dynamic Sea Ice , 1997 .