CMIP5 Scientific Gaps and Recommendations for CMIP6

AbstractThe Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, that is, CMIP6:How does the Earth system respond to changes in forcing?What are the origins and consequences of systematic model biases?How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios?CMIP has demonstrated the power of idealized experiments to better understand how the climat...

[1]  J. Mülmenstädt,et al.  Comment on ``Rethinking the Lower Bound on Aerosol Radiative Forcing'' , 2017 .

[2]  Dean N. Williams,et al.  A Global Repository for Planet-Sized Experiments and Observations , 2016 .

[3]  Charles Doutriaux,et al.  A More Powerful Reality Test for Climate Models , 2016 .

[4]  J. Dufresne,et al.  Air moisture control on ocean surface temperature, hidden key to the warm bias enigma , 2015 .

[5]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[6]  Simon Read,et al.  ESMValTool (v1.0) – a community diagnostic and performance metrics tool for routine evaluation of Earth system models in CMIP , 2015 .

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

[8]  Florian Rauser,et al.  Rethinking the default construction of multi-model climate ensembles , 2015 .

[9]  Reto Knutti,et al.  Addressing interdependency in a multimodel ensemble by interpolation of model properties , 2015 .

[10]  B. Stevens,et al.  The effect of atmospheric radiative heating by clouds on the Madden‐Julian Oscillation , 2015 .

[11]  B. Soden,et al.  An Assessment of Direct Radiative Forcing, Radiative Adjustments, and Radiative Feedbacks in Coupled Ocean–Atmosphere Models* , 2015 .

[12]  John P. Krasting,et al.  Dominance of the Southern Ocean in Anthropogenic Carbon and Heat Uptake in CMIP5 Models , 2015 .

[13]  G. Bellon,et al.  The double ITCZ bias in CMIP5 models: interaction between SST, large-scale circulation and precipitation , 2015, Climate Dynamics.

[14]  Duane E. Waliser,et al.  Satellite Observations for CMIP5: The Genesis of Obs4MIPs , 2014 .

[15]  Frédéric Hourdin,et al.  Role of clouds and land‐atmosphere coupling in midlatitude continental summer warm biases and climate change amplification in CMIP5 simulations , 2014 .

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

[17]  P. Cox,et al.  Emergent constraints on climate‐carbon cycle feedbacks in the CMIP5 Earth system models , 2014 .

[18]  Olivier Boucher,et al.  Adjustments in the Forcing-Feedback Framework for Understanding Climate Change , 2014 .

[19]  R. Houghton,et al.  Terminology as a key uncertainty in net land use and land cover change carbon flux estimates , 2014 .

[20]  G. Meehl,et al.  CMIP5 multi‐model hindcasts for the mid‐1970s shift and early 2000s hiatus and predictions for 2016–2035 , 2014 .

[21]  R. Moss,et al.  Climate model intercomparisons: Preparing for the next phase , 2014 .

[22]  Pierre Friedlingstein,et al.  Uncertainties in CMIP5 Climate Projections due to Carbon Cycle Feedbacks , 2014 .

[23]  S. Seneviratne,et al.  Systematic land climate and evapotranspiration biases in CMIP5 simulations , 2014, Geophysical research letters.

[24]  Francisco J. Doblas-Reyes,et al.  Real-time multi-model decadal climate predictions , 2013, Climate Dynamics.

[25]  C. Bretherton,et al.  Clouds and Aerosols , 2013 .

[26]  W. Collins,et al.  Evaluation of climate models , 2013 .

[27]  S. Ghan.  Technical Note: Estimating aerosol effects on cloud radiative forcing , 2013 .

[28]  Bjorn Stevens,et al.  What Are Climate Models Missing? , 2013, Science.

[29]  P. J. Young,et al.  Long‐term ozone changes and associated climate impacts in CMIP5 simulations , 2013 .

[30]  Kevin E. Trenberth,et al.  Distinctive climate signals in reanalysis of global ocean heat content , 2013 .

[31]  M. Kimoto,et al.  Initialized near-term regional climate change prediction , 2013, Nature Communications.

[32]  P. Cox,et al.  Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability , 2013, Nature.

[33]  K.,et al.  Carbon–Concentration and Carbon–Climate Feedbacks in CMIP5 Earth System Models , 2012 .

[34]  S. Xie,et al.  Origins of tropical‐wide SST biases in CMIP multi‐model ensembles , 2012 .

[35]  Brian C. O'Neill,et al.  The Need for and Use of Socio-Economic Scenarios for Climate Change Analysis , 2012 .

[36]  J. Lamarque,et al.  The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): overview and description of models, simulations and climate diagnostics , 2012 .

[37]  Grant Branstator,et al.  Potential impact of initialization on decadal predictions as assessed for CMIP5 models , 2012 .

[38]  Yan Zhao,et al.  Evaluation of climate models using palaeoclimatic data , 2012 .

[39]  K. Taylor,et al.  Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models , 2012 .

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

[41]  John M. Haynes,et al.  COSP: Satellite simulation software for model assessment , 2011 .

[42]  E. Hawkins,et al.  The potential to narrow uncertainty in projections of regional precipitation change , 2011 .

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

[44]  G. Meehl,et al.  Decadal prediction: Can it be skillful? , 2009 .

[45]  Ed Hawkins,et al.  Decadal Predictability of the Atlantic Ocean in a Coupled GCM: Forecast Skill and Optimal Perturbations Using Linear Inverse Modeling , 2009 .

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

[47]  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.

[48]  G. Meehl,et al.  A strategy for climate change stabilization experiments , 2007 .

[49]  J. Toggweiler,et al.  The Southern Hemisphere Westerlies in a Warming World: Propping Open the Door to the Deep Ocean , 2006 .

[50]  B. Soden,et al.  Robust Responses of the Hydrological Cycle to Global Warming , 2006 .

[51]  Keith W. Dixon,et al.  Intercomparison of the Southern Ocean Circulations in IPCC Coupled Model Control Simulations , 2006 .

[52]  Shaocheng Xie,et al.  Diagnosis of the summertime warm and dry bias over the U.S. Southern Great Plains in the GFDL climate model using a weather forecasting approach , 2006 .

[53]  A. Hall,et al.  Using the current seasonal cycle to constrain snow albedo feedback in future climate change , 2006 .

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

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

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

[57]  G. Meehl,et al.  Intercomparison makes for a better climate model , 1997 .

[58]  H. Treut,et al.  A methodology study of the validation of clouds in GCMs using ISCCP satellite observations , 1996 .

[59]  Tom M. L. Wigley,et al.  Towards the detection and attribution of an anthropogenic effect on climate , 1995 .

[60]  W. Gates.  AMIP: The Atmospheric Model Intercomparison Project. , 1992 .

[61]  J. Eom,et al.  The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview , 2017 .

[62]  John F. B. Mitchell,et al.  THE WCRP CMIP 3 MULTIMODEL DATASET A New Era in Climate Change Research , 2017 .

[63]  D. Shindell,et al.  Anthropogenic and Natural Radiative Forcing , 2014 .

[64]  Corinne Le Quéré,et al.  Carbon and Other Biogeochemical Cycles , 2014 .

[65]  Hisashi Nakamura,et al.  The aqua-planet experiment (APE): Response to changed meridional SST profile , 2013 .

[66]  C. Tebaldi,et al.  Long-term Climate Change: Projections, Commitments and Irreversibility , 2013 .

[67]  G. Meehl,et al.  Near-term climate change:projections and predictability , 2013 .

[68]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[69]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[70]  Thomas E. Fricker,et al.  A verification framework for interannual-to-decadal predictions experiments , 2012, Climate Dynamics.

[71]  B. Hewitson,et al.  Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections , 2010 .

[72]  S. Schiavon,et al.  Climate change 2007 : the physical science basis : contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

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

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

[75]  Lucie Svobodova,et al.  The ocean , 1996, SIGGRAPH '96.

[76]  S. Bony,et al.  PCIC SCIENCE BRIEF : SPREAD IN MODEL CLIMATE SENSITIVITY TRACED TO ATMOSPHERIC CONVECTIVE MIXING , 2022 .