Working Group I Contribution To The Climate Change 2013-Free PDF

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WORKING GROUP I TWELFTH SESSION,Stockholm 23 26 September 2013. WG I 12 Doc 2b CH09,12 VIII 2013,Agenda Item 5,ENGLISH ONLY. WORKING GROUP I CONTRIBUTION TO THE IPCC FIFTH ASSESSMENT. REPORT AR5 CLIMATE CHANGE 2013 THE PHYSICAL SCIENCE BASIS. Chapter 9 Evaluation of Climate Models Final Draft Underlying Scientific Technical. Assessment,Submitted by the Co Chairs of Working Group I. Confidential This document is being made available in preparation of. WGI 12 only and should not be cited quoted or distributed. The Final Draft Underlying Scientific Technical Assessment is submitted to the Twelfth Session of Working. Group I for acceptance The IPCC at its Thirty sixth Session Stockholm 26 September 2013 will be informed. of the actions of the Twelfth Session of Working Group I in this regard. IPCC Secretariat, c o WMO 7bis Avenue de la Paix C P 2300 1211 Geneva 2 Switzerland. telephone 41 0 22 730 8208 54 84 fax 41 0 22 730 8025 13 email IPCC Sec wmo int www ipcc ch. Final Draft 7 June 2013 Chapter 9 IPCC WGI Fifth Assessment Report. Chapter 9 Evaluation of Climate Models, Coordinating Lead Authors Gregory Flato Canada Jochem Marotzke Germany.
Lead Authors Babatunde Abiodun South Africa Pascale Braconnot France Sin Chan Chou Brazil. William Collins USA Peter Cox UK Fatima Driouech Morocco Seita Emori Japan Veronika Eyring. Germany Chris Forest USA Peter Gleckler USA Eric Guilyardi France Christian Jakob Australia. Vladimir Kattsov Russia Chris Reason South Africa Markku Rummukainen Sweden. Contributing Authors Krishna AchutaRao USA Alessandro Anav UK Timothy Andrews UK. Johanna Baehr Germany Nathan Bindoff Australia Alejandro Bodas Salcedo UK Jennifer Catto. Australia Don Chambers USA Ping Chang USA Aiguo Dai USA Clara Deser USA Francisco. Doblas Reyes Spain Paul Durack USA Michael Eby Canada Ramon de Elia Canada Thierry. Fichefet Belgium Piers Forster UK David Frame UK New Zealand John Fyfe Canada Emiola. Gbobaniyi Sweden Nigerian Nathan Gillett Canada Fidel Gonzales Rouco Spain Clare Goodess. UK Stephen Griffies USA Alex Hall USA Sandy Harrison Australia Elizabeth Hunke USA. Tatiana Ilyina Germany Detelina Ivanova USA Gregory Johnson USA Masa Kageyama France. Viatcheslav Kharin Canada Stephen A Klein USA Jeff Knight UK Reto Knutti Switzerland Felix. Landerer USA Tong Lee USA Hongmei Li Germany China Natalie Mahowald USA Carl Mears. USA Gerald Meehl USA Colin Morice UK Rym Msadek USA Gunnar Myhre Norway J David. Neelin USA Jeff Painter USA Tatiana Pavlova Russia Judith Perlwitz USA Jean Yves Peterschmitt. France Florian Rauser Germany Jouni R is nen Finland Jeffrey Reid USA Mark Rodwell UK. Benjamin Santer USA Adam A Scaife UK John Scinocca Canada David Sexton UK Drew Shindell. USA Hideo Shiogama Japan Jana Sillmann Canada Adrian Simmons UK Kenneth Sperber USA. David Stephenson UK Bjorn Stevens Germany Peter Stott UK Rowan Sutton UK Peter Thorne. USA UK Geert Jan van Oldenborgh Netherlands Gabriel Vecchi USA Mark Webb UK Keith. Williams UK Tim Woollings UK Shang Ping Xie USA Jianglong Zhang USA. Review Editors Isaac Held USA Andy Pitman Australia Serge Planton France Zong Ci Zhao. Date of Draft 7 June 2013,Table of Contents,Executive Summary 3. 9 1 Climate Models and their Characteristics 7,9 1 1 Scope and Overview of this Chapter 7. 9 1 2 Overview of Model Types to be Evaluated 7,9 1 3 Model Improvements 9. Box 9 1 Climate Model Development and Tuning 9,9 2 Techniques for Assessing Model Performance 15. 9 2 1 New Developments in Model Evaluation Approaches 15. 9 2 2 Ensemble Approaches for Model Evaluation 17, 9 2 3 The Model Evaluation Approach used in this Chapter and its Limitations 18.
9 3 Experimental Strategies in Support of Climate Model Evaluation 19. 9 3 1 The Role of Model Intercomparisons 19,9 3 2 Experimental Strategy for CMIP5 19. 9 4 Simulation of Recent and Longer Term Records in Global Models 21. 9 4 1 Atmosphere 21, Box 9 2 Climate Models and the Hiatus in Global Mean Surface Warming of the Past 15 Years 26. 9 4 2 Ocean 35,9 4 3 Sea Ice 41,9 4 4 Land Surface Fluxes and Hydrology 43. 9 4 5 Carbon Cycle 45, Do Not Cite Quote or Distribute 9 1 Total pages 205. Final Draft 7 June 2013 Chapter 9 IPCC WGI Fifth Assessment Report. 9 4 6 Aerosol Burdens and Effects on Insolation 47. 9 5 Simulation of Variability and Extremes 49, 9 5 1 Importance of Simulating Climate Variability 49.
9 5 2 Diurnal to Seasonal Variability 49,9 5 3 Interannual to Centennial Variability 52. 9 5 4 Extreme Events 57,Box 9 3 Understanding Model Performance 60. 9 6 Downscaling and Simulation of Regional Scale Climate 61. 9 6 1 Global Models 61,9 6 2 Regional Climate Downscaling 64. 9 6 3 Skill of Downscaling Methods 64,9 6 4 Value Added through RCMs 65. 9 6 5 Sources of Model Errors and Uncertainties 66. 9 6 6 Relating Downscaling Performance to Credibility of Regional Climate Information 66. 9 7 Climate Sensitivity and Climate Feedbacks 67, 9 7 1 Equilibrium Climate Sensitivity Idealised Radiative Forcing and Transient Climate Response.
in the CMIP5 Ensemble 67, 9 7 2 Understanding the Range in Model Climate Sensitivity Climate Feedbacks 69. 9 7 3 Climate Sensitivity and Model Performance 70. 9 8 Relating Model Performance to Credibility of Model Applications 71. 9 8 1 Synthesis Assessment of Model Performance 71. 9 8 2 Implications of Model Evaluation for Climate Change Detection and Attribution 73. 9 8 3 Implications of Model Evaluation for Model Projections of Future Climate 74. FAQ 9 1 Are Climate Models Getting Better and How Would We Know 75. References 78,9 A 1 Climate Models Assessed in Chapter 9 123. Tables 124,Figures 153, Do Not Cite Quote or Distribute 9 2 Total pages 205. Final Draft 7 June 2013 Chapter 9 IPCC WGI Fifth Assessment Report. Executive Summary, Climate models have continued to be developed and improved since the AR4 and many models have. been extended into Earth System models by including the representation of biogeochemical cycles. important to climate change These models allow for policy relevant calculations such as the carbon. dioxide emissions compatible with a specified climate stabilisation target In addition the range of climate. variables and processes that have been evaluated has greatly expanded and differences between models and. observations are increasingly quantified using performance metrics In this chapter model evaluation. covers simulation of the mean climate of historical climate change of variability on multiple time scales. and of regional modes of variability This evaluation is based on recent internationally coordinated model. experiments including simulations of historic and paleo climate specialized experiments designed to. provide insight into key climate processes and feedbacks and regional climate downscaling Figure 9 44. provides an overview of model capabilities as assessed in this chapter including improvements or lack. thereof relative to models assessed in the AR4 The chapter concludes with an assessment of recent work. connecting model performance to the detection and attribution of climate change as well as to future. projections 9 1 2 9 8 1 Table 9 1 Figure 9 44, The ability of climate models to simulate surface temperature has improved in many though not all.
important aspects relative to the generation of models assessed in the AR4 There continues to be very. high confidence1 that models reproduce observed large scale mean surface temperature patterns pattern. correlation of 0 99 though systematic errors of several degrees are found in some regions particularly. over high topography near the ice edge in the North Atlantic and over regions of ocean upwelling near the. equator On regional scales sub continental and smaller the confidence in model capability to simulate. surface temperature is less than for the larger scales however regional biases are near zero on average with. intermodel spread of roughly 3 C There is high confidence that regional scale surface temperature is better. simulated than at the time of the AR4 Current models are also able to reproduce the large scale patterns of. temperature during the Last Glacial Maximum indicating an ability to simulate a climate state much different. from the present 9 4 1 9 6 1 Figures 9 2 9 6 9 39, There is very high confidence that models reproduce the general features of the global scale annual. mean surface temperature increase over the historical period including the more rapid warming in. the second half of the 20th century and the cooling immediately following large volcanic eruptions. Most simulations of the historical period do not reproduce the observed reduction in global mean surface. warming trend over the last 10 15 years There is medium confidence that the trend difference between. models and observations during 1998 2012 is to a substantial degree caused by internal variability with. possible contributions from forcing error and some models overestimating the response to increasing. greenhouse gas forcing Most though not all models overestimate the observed warming trend in the. tropical troposphere over the last 30 years and tend to underestimate the long term lower stratospheric. cooling trend 9 4 1 Box 9 2 Figure 9 8, The simulation of large scale patterns of precipitation has improved somewhat since the AR4. although models continue to perform less well for precipitation than for surface temperature The. spatial pattern correlation between modelled and observed annual mean precipitation has increased from 0 77. for models available at the time of the AR4 to 0 82 for current models At regional scales precipitation is. not simulated as well and the assessment remains difficult owing to observational uncertainties 9 4 1. 9 6 1 Figure 9 6, The simulation of clouds in climate models remains challenging There is very high confidence that. uncertainties in cloud processes explain much of the spread in modelled climate sensitivity However the. simulation of clouds in climate models has shown modest improvement relative to models available at the. time of the AR4 and this has been aided by new evaluation techniques and new observations for clouds. In this Report the following summary terms are used to describe the available evidence limited medium or robust. and for the degree of agreement low medium or high A level of confidence is expressed using five qualifiers very. low low medium high and very high and typeset in italics e g medium confidence For a given evidence and. agreement statement different confidence levels can be assigned but increasing levels of evidence and degrees of. agreement are correlated with increasing confidence see Section 1 4 and Box TS 1 for more details. Do Not Cite Quote or Distribute 9 3 Total pages 205. Final Draft 7 June 2013 Chapter 9 IPCC WGI Fifth Assessment Report. Nevertheless biases in cloud simulation lead to regional errors on cloud radiative effect of several tens of. watts per square meter 9 2 1 9 4 1 9 7 2 Figures 9 5 9 43. Models are able to capture the general characteristics of storm tracks and extratropical cyclones and. there is some evidence of improvement since the AR4 Storm track biases in the North Atlantic have. improved slightly but models still produce a storm track that is too zonal and underestimate cyclone. intensity 9 4 1, Many models are able to reproduce the observed changes in upper ocean heat content from 1960 to. present with the multi model mean time series falling within the range of the available observational. estimates for most of the period The ability of models to simulate ocean heat uptake including variations. imposed by large volcanic eruptions adds confidence to their use in assessing the global energy budget and. simulating the thermal component of sea level rise 9 4 2 Figure 9 17. The simulation of the tropical Pacific Ocean mean state has improved since the AR4 with a 30. reduction in the spurious westward extension of the cold tongue near the equator a pervasive bias of. coupled models The simulation of the tropical Atlantic remains deficient with many models unable to. reproduce the basic east west temperature gradient 9 4 2 Figure 9 14. Current climate models reproduce the seasonal cycle of Arctic sea ice extent with a multi model mean. error of less than about 10 for any given month There is robust evidence that the downward trend. in Arctic summer sea ice extent is better simulated than at the time of the AR4 with about one. quarter of the simulations showing a trend as strong as or stronger than in observations over the. satellite era since 1979 There is a tendency for models to slightly overestimate sea ice extent in the Arctic. by about 10 in winter and spring In the Antarctic the multi model mean seasonal cycle agrees well with. observations but inter model spread is roughly double that for the Arctic Most models simulate a small. decreasing trend in Antarctic sea ice extent albeit with large inter model spread in contrast to the small. increasing trend in observations 9 4 3 Figures 9 22 9 24. Models are able to reproduce many features of the observed global and northern hemispheric mean. temperature variance on interannual to centennial time scales high confidence and most models are. now able to reproduce the observed peak in variability associated with the El Ni o 2 to 7 year period. in the Tropical Pacific The ability to assess variability from millennial simulations is new since the AR4. REPORT AR5 CLIMATE CHANGE 2013 THE PHYSICAL SCIENCE BASIS Chapter 9 Evaluation of Climate Models Final Draft Underlying Scientific Technical Assessment Submitted by the Co Chairs of Working Group I Confidential This document is being made availabl e in preparation of WGI 12 only and should not be cited quoted or distributed NOTE The Final Draft Underlying Scientific Technical

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