The Optimal Multimodel Ensemble of Bias-Corrected CMIP5 Climate Models over China

AbstractA multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multi...

[1]  Earl J. Kirkland,et al.  Advanced Computing in Electron Microscopy , 1998 .

[2]  G. Meehl,et al.  The Coupled Model Intercomparison Project (CMIP) , 2000 .

[3]  L. Hay,et al.  A COMPARISON OF DELTA CHANGE AND DOWNSCALED GCM SCENARIOS FOR THREE MOUNTAINOUS BASINS IN THE UNITED STATES 1 , 2000 .

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

[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]  Arun Kumar,et al.  Long‐range experimental hydrologic forecasting for the eastern United States , 2002 .

[7]  F. Giorgi,et al.  Guidelines for Use of Climate Scenarios Developed from Regional Climate Model Experiments , 2003 .

[8]  H. Su,et al.  The Scatter in Tropical Average Precipitation Anomalies , 2003 .

[9]  K. Taylor,et al.  An overview of results from the Coupled Model Intercomparison Project , 2003 .

[10]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[11]  P. Whetton,et al.  Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods , 2004 .

[12]  R. Balling,et al.  Trends in extreme daily precipitation indices in India , 2004 .

[13]  Yuqing Wang,et al.  Correction to “Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large‐scale circulation” , 2005 .

[14]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[15]  G. Meehl,et al.  OVERVIEW OF THE COUPLED MODEL INTERCOMPARISON PROJECT , 2005 .

[16]  Xuebin Zhang,et al.  Trends in Total Precipitation and Frequency of Daily Precipitation Extremes over China , 2005 .

[17]  P. Stott,et al.  Uncertainty in continental‐scale temperature predictions , 2006 .

[18]  Martin Ehrendorfer,et al.  Bayesian Approach to Decision Making Using Ensemble Weather Forecasts , 2006 .

[19]  T. Oki,et al.  Impact of Climate Change on River Discharge Projected by Multimodel Ensemble , 2006 .

[20]  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.

[21]  H. Fowler,et al.  Modelling the impacts of projected future climate change on water resources in north-west England , 2007 .

[22]  Charles Doutriaux,et al.  Performance metrics for climate models , 2008 .

[23]  G. Stephens,et al.  Controls of Global-Mean Precipitation Increases in Global Warming GCM Experiments , 2008 .

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

[25]  E. Guilyardi,et al.  UNDERSTANDING EL NINO IN OCEAN-ATMOSPHERE GENERAL CIRCULATION MODELS : Progress and Challenges , 2008 .

[26]  R. Toumi,et al.  Climate projections: Past performance no guarantee of future skill? , 2009 .

[27]  B. Santer,et al.  Selecting global climate models for regional climate change studies , 2009, Proceedings of the National Academy of Sciences.

[28]  E. Wood,et al.  Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching , 2010 .

[29]  C. Piani,et al.  Statistical bias correction for daily precipitation in regional climate models over Europe , 2010 .

[30]  John F. B. Mitchell,et al.  The next generation of scenarios for climate change research and assessment , 2010, Nature.

[31]  D. Maraun,et al.  Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user , 2010 .

[32]  F. Giorgi,et al.  Upgrades to the reliability ensemble averaging method for producing probabilistic climate-change projections , 2010 .

[33]  Sei‐Woong Choi,et al.  Altitudinal distribution of moths (Lepidoptera) in Mt. Jirisan National Park, South Korea , 2010 .

[34]  Reto Knutti,et al.  Challenges in Combining Projections from Multiple Climate Models , 2010 .

[35]  Qiang Liu,et al.  Precipitation variability in Northeast China from 1961 to 2008 , 2011 .

[36]  G. Ren,et al.  Change in extreme temperature event frequency over mainland China, 1961-2008 , 2011 .

[37]  Zhongbo Yu,et al.  Changes of climate extremes in a typical arid zone: Observations and multimodel ensemble projections , 2011 .

[38]  Philip B. Duffy,et al.  Guidelines for Constructing Climate Scenarios , 2011 .

[39]  Laurent Z. X. Li,et al.  Probabilistic Projections of Climate Change over China under the SRES A1B Scenario Using 28 AOGCMs , 2011 .

[40]  D. Jiang,et al.  Latest update of the climatology and changes in the seasonal distribution of precipitation over China , 2013, Theoretical and Applied Climatology.

[41]  Bin Wang,et al.  The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century , 2013, Climate Dynamics.

[42]  P. Webster,et al.  Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts , 2012 .

[43]  Lukas H. Meyer,et al.  Summary for Policymakers , 2022, The Ocean and Cryosphere in a Changing Climate.

[44]  A. Weerts,et al.  Extracting information from an ensemble of GCMs to reliably assess future global runoff change , 2012 .

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

[46]  J. Seibert,et al.  Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? , 2012 .

[47]  J. Seibert,et al.  Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods , 2012 .

[48]  C. Piani,et al.  Two dimensional bias correction of temperature and precipitation copulas in climate models , 2012 .

[49]  Ashish Sharma,et al.  An error estimation method for precipitation and temperature projections for future climates , 2012 .

[50]  Chong Xu,et al.  Preliminary Assessment of Simulations of Climate Changes over China by CMIP5 Multi-Models , 2012 .

[51]  Bin Wang,et al.  Future change of global monsoon in the CMIP5 , 2012, Climate Dynamics.

[52]  P. Webster,et al.  Evaluation of short-term climate change prediction in multi-model CMIP 5 decadal hindcasts , 2012 .

[53]  Tereza Cavazos,et al.  Downscaled Climate Change Scenarios for Baja California and the North American Monsoon during the Twenty-First Century , 2012 .

[54]  Arun Kumar,et al.  SST and ENSO variability and change simulated in historical experiments of CMIP5 models , 2014, Climate Dynamics.

[55]  Zhenya Song,et al.  Water vapor transport and cross-equatorial flow over the Asian-Australia monsoon region simulated by CMIP5 climate models , 2013, Advances in Atmospheric Sciences.

[56]  E. Guilyardi,et al.  ENSO representation in climate models: from CMIP3 to CMIP5 , 2013, Climate Dynamics.

[57]  Q. Duan,et al.  Evaluation and application of Bayesian multi-model estimation in temperature simulations , 2013 .

[58]  A. Mariotti,et al.  North American Climate in CMIP5 experiments. Part I: Evaluation of historical simulations of continental and regional climatology , 2013 .

[59]  Kristopher,et al.  North American Climate in CMIP5 Experiments. Part II: Evaluation of Historical Simulations of Intraseasonal to Decadal Variability , 2013 .

[60]  F. Brissette,et al.  Performance and uncertainty evaluation of empirical downscaling methods in quantifying the climate change impacts on hydrology over two North American river basins , 2013 .

[61]  C. Fu,et al.  Statistical downscaling of summer temperature extremes in northern China , 2013, Advances in Atmospheric Sciences.

[62]  F. Brissette,et al.  Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America , 2013 .

[63]  Richard G. Jones,et al.  Selecting CMIP5 GCMs for downscaling over multiple regions , 2015, Climate Dynamics.

[64]  L. Chen,et al.  A comprehensive evaluation of precipitation simulations over China based on CMIP5 multimodel ensemble projections , 2014 .

[65]  Xuebin Zhang,et al.  Projected Changes in Temperature and Precipitation Extremes in China by the CMIP5 Multimodel Ensembles , 2014 .

[66]  L. Samaniego,et al.  Robust ensemble selection by multivariate evaluation of extreme precipitation and temperature characteristics , 2014 .

[67]  R. Wu,et al.  The Climatology and Interannual Variability of the East Asian Winter Monsoon in CMIP5 Models , 2014 .

[68]  Zhongbo Yu,et al.  Assessing CMIP5 general circulation model simulations of precipitation and temperature over China , 2015 .

[69]  Reto Knutti,et al.  A Representative Democracy to Reduce Interdependency in a Multimodel Ensemble , 2015 .

[70]  V. Singh,et al.  Hydrologic model-based Palmer indices for drought characterization in the Yellow River basin, China , 2016, Stochastic Environmental Research and Risk Assessment.

[71]  H. Moradkhani,et al.  Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis , 2017, Theoretical and Applied Climatology.

[72]  T. Zhou,et al.  Asian summer monsoon onset in simulations and CMIP5 projections using four Chinese climate models , 2015, Advances in Atmospheric Sciences.

[73]  L. Alexander,et al.  Multi‐dataset comparison of gridded observed temperature and precipitation extremes over China , 2015 .

[74]  Alex J. Cannon,et al.  Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? , 2015 .

[75]  Y. Chen,et al.  Assessment of Regional Drought Trend and Risk over China: A Drought Climate Division Perspective , 2015 .

[76]  Ying Xu,et al.  Assessment of indices of temperature extremes simulated by multiple CMIP5 models over China , 2015, Advances in Atmospheric Sciences.

[77]  Alex J. Cannon,et al.  Multivariate Bias Correction of Climate Model Output: Matching Marginal Distributions and Intervariable Dependence Structure , 2016 .

[78]  J. Sheffield,et al.  Spatial downscaling of precipitation using adaptable random forests , 2016 .

[79]  J. Fuhrmann.  Advanced Computing In Electron Microscopy , 2016 .

[80]  F. Pappenberger,et al.  Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts , 2016 .

[81]  D. Sahagian,et al.  Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble , 2016, Scientific Reports.

[82]  E. Wood,et al.  Evaluation of historical and future simulations of precipitation and temperature in central Africa from CMIP5 climate models , 2016 .

[83]  C. Schär,et al.  Does Quantile Mapping of Simulated Precipitation Correct for Biases in Transition Probabilities and Spell Lengths , 2016 .

[84]  A. Turner,et al.  Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble , 2017, Climate Dynamics.

[85]  Karsten Lehmann,et al.  Selecting a climate model subset to optimise key ensemble properties , 2017 .

[86]  J. Sheffield,et al.  Uncertainties in Future Projections of Summer Droughts and Heat Waves over the Contiguous United States , 2017 .

[87]  Gilles R. C. Essou,et al.  Impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling , 2018 .

[88]  Shamsuddin Shahid,et al.  Selection of climate models for projection of spatiotemporal changes in temperature of Iraq with uncertainties , 2018, Atmospheric Research.

[89]  Shamsuddin Shahid,et al.  Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh , 2018, Atmospheric Research.

[90]  E. Wood,et al.  Bias Correction of Historical and Future Simulations of Precipitation and Temperature for China from CMIP5 Models , 2018 .

[91]  B. Soden,et al.  Constraining Climate Model Projections of Regional Precipitation Change , 2019, Geophysical Research Letters.

[92]  Shenglian Guo,et al.  A new two-stage multivariate quantile mapping method for bias correcting climate model outputs , 2019, Climate Dynamics.

[93]  Bryan N. Lawrence,et al.  Designing and Documenting Experiments in CMIP6 , 2019 .

[94]  F. Yuan,et al.  Estimating the response of hydrological regimes to future projections of precipitation and temperature over the upper Yangtze River , 2019 .

[95]  F. Brissette,et al.  Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology , 2019, Climatic Change.