Climate Dynamics

, Volume 40, Issue 3–4, pp 677–707 | Cite as

Origins of differences in climate sensitivity, forcing and feedback in climate models

  • Mark J. Webb
  • F. Hugo Lambert
  • Jonathan M. Gregory
Article

Abstract

We diagnose climate feedback parameters and CO2 forcing including rapid adjustment in twelve atmosphere/mixed-layer-ocean (“slab”) climate models from the CMIP3/CFMIP-1 project (the AR4 ensemble) and fifteen parameter-perturbed versions of the HadSM3 slab model (the PPE). In both ensembles, differences in climate feedbacks can account for approximately twice as much of the range in climate sensitivity as differences in CO2 forcing. In the AR4 ensemble, cloud effects can explain the full range of climate sensitivities, and cloud feedback components contribute four times as much as cloud components of CO2 forcing to the range. Non-cloud feedbacks are required to fully account for the high sensitivities of some models however. The largest contribution to the high sensitivity of HadGEM1 is from a high latitude clear-sky shortwave feedback, and clear-sky longwave feedbacks contribute substantially to the highest sensitivity members of the PPE. Differences in low latitude ocean regions (30°N/S) contribute more to the range than those in mid-latitude oceans (30–55°N/S), low/mid latitude land (55°N/S) or high latitude ocean/land (55–90°N/S), but contributions from these other regions are required to account fully for the higher model sensitivities, for example from land areas in IPSL CM4. Net cloud feedback components over the low latitude oceans sorted into percentile ranges of lower tropospheric stability (LTS) show largest differences among models in stable regions, mainly due to their shortwave components, most of which are positive in spite of increasing LTS. Differences in the mid-stability range are smaller, but cover a larger area, contributing a comparable amount to the range in climate sensitivity. These are strongly anti-correlated with changes in subsidence. Cloud components of CO2 forcing also show the largest differences in stable regions, and are strongly anticorrelated with changes in estimated inversion strength (EIS). This is qualitatively consistent with what would be expected from observed relationships between EIS and low-level cloud fraction. We identify a number of cases where individual models show unusually strong forcings and feedbacks compared to other members of the ensemble. We encourage modelling groups to investigate unusual model behaviours further with sensitivity experiments. Most of the models fail to correctly reproduce the observed relationships between stability and cloud radiative effect in the subtropics, indicating that there remains considerable room for model improvements in the future.

Keywords

Cloud Climate models Climate sensitivity Feedback Effective forcing Rapid adjustment Carbon dioxide CO2 

Notes

Acknowledgments

We would like to acknowledge Rob Wood for providing code to calculate the EIS, and Tim Andrews, Alejandro Bodas-Salcedo, Ben Booth, Chris Bretherton, Philip Brohan, Leo Donner, William Ingram, Manoj Joshi, Adrian Lock, Tomoo Ogura, Mark Ringer, David Sexton, Yoko Tsushima, Keith Williams, Tokuta Yokohata and the anonymous reviewers for their helpful comments and suggestions. We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 and CFMIP multi-model datasets. Support of these datasets is provided by the Office of Science, US Department of Energy. This work was supported by the Joint DECC/Defra Met Office Hadley Centre Climate Programme (GA01101).

References

  1. Andrews T, Forster PM (2008) CO2 forcing induces semi-direct effects with consequences for climate feedback interpretations. Geophys Res Lett 35:L04802. doi: 10.1029/2007GL032273 CrossRefGoogle Scholar
  2. Blossey PN, Bretherton CS, Wyant MC (2009) Understanding subtropical low cloud response to a warmer climate in a superparameterized climate model. part ii: Column modeling with a cloud-resolving model. J Adv Model Earth Syst 1. doi: 10.3894/JAMES.2009.1.8
  3. Bodas-Salcedo A, Webb MJ, Brooks ME, Ringer MA, Williams KD, Milton SF, Wilson DR (2008) Evaluating cloud systems in the Met Office global forecast model using simulated Cloud Sat radar reflectivities. J Geophys Res 113:D00A13. doi: 10.1029/2007JD009620 CrossRefGoogle Scholar
  4. Bodas-Salcedo A, Webb MJ, Bony S, Chepfer H, Dufresne JL, Klein S, Zhang Y, Marchand R, Haynes JM, Pincus R, John VO (2011) COSP: satellite simulation software for model assessment. Bull Am Meteorol Soc 92(8):1023–1043. doi: 10.1175/2011BAMS2856.1 CrossRefGoogle Scholar
  5. Boer GJ, Yu B (2003) Climate sensitivity and response. Clim Dyn 20:415–429Google Scholar
  6. Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of cloud feedback uncertainties in climate models. Geophys Res Lett 32(20):L20806CrossRefGoogle Scholar
  7. Bony S, Colman R, Kattsov VM, Allan RP, Bretherton CS, Dufresne JL, Hall A, Hallegate S, Holland MM, Ingram WJ, Randall DA, Soden BJ, Tselioudis G, Webb MJ (2006) How well do we understand and evaluate climate change feedback processes? J Clim 19(15):3445–3482. doi: 10.1175/JCLI3819.1 CrossRefGoogle Scholar
  8. Bony S, Webb MJ, Bretherton CS, Klein SA, Siebesma AP, Tselioudis G and Zhang M (2011) CFMIP: Towards a better evaluation and understanding of clouds and cloud feedbacks in CMIP5 models. CLIVAR Exchanges 16(56):20–24Google Scholar
  9. Bretherton CS, Wyant MC (1997) Moisture transport, lower-tropospheric stability, and decoupling of cloud-topped boundary layers. J Atmos Sci 51(1):148–167CrossRefGoogle Scholar
  10. Brient F, Bony S (2012) Interpretation of the positive low-cloud feedback predicted by a climate model under global warming. Clim Dyn. doi: 10.1007/s00382-011-1279-7
  11. Chepfer H, Bony S, Winker D, Chiriaco M, Dufresne JL, Sèze G (2008) Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model. Geophys Res Lett 35:L15704. doi: 10.1029/2008GL034207 CrossRefGoogle Scholar
  12. Clement AC, Burgman R, Norris JR (2009) Observational and model evidence for positive low-level cloud feedback. Science 325(5939):460–464. doi: 10.1126/science.1171255 CrossRefGoogle Scholar
  13. Collins M, Booth BBB, Harris GR, Murphy JM, Sexton DMH, Webb MJ (2006a) Towards quantifying uncertainty in transient climate change. Clim Dyn 27(2–3):127147. doi: 10.1007/s00382-006-0121-0 CrossRefGoogle Scholar
  14. Collins WD, Bitz CM, Blackmon ML, Bonan GB, Bretherton CS, Carton JA, Chang P, Doney SC, Hack JJ, Henderson TB, Kiehl JT, Large WG, McKenna DS, Santer BD, Smith RD (2006b) The community climate system model version 3 (CCSM3). J Clim 19(11):2122–2143CrossRefGoogle Scholar
  15. Collins M, Booth BBB, Bhaskaran B, Harris G, Murphy JM, Sexton DMH, Webb MJ (2010) A comparison of perturbed physics and multi-model ensembles: model errors, feedbacks and forcings. Clim Dyn. doi: 10.1007/s00382-010-0808-0
  16. Colman R (2003) A comparison of climate feedbacks in general circulation models. Clim Dyn 20:865–873Google Scholar
  17. Colman RA, McAvaney BJ (2011) On tropospheric adjustment to forcing and climate feedbacks. Clim Dyn 36(9–10):1649–1658CrossRefGoogle Scholar
  18. Delworth TL, Rosati A, Stouffer RJ, Dixon KW, Dunne J, Findell K, Ginoux P, Gnanadesikan A, Gordon CT, Griffies SM, Gudgel R, Harrison MJ, Held IM, Hemler RS, Horowitz LW, Klein SA, Knutson TR, Lin SJ, Milly PCD, Ramaswamy V, Schwarzkopf MD, Sirutis JJ, Stern WF, Spelman MJ, Winton M, Wittenberg AT, Wyman B (2006) GFDL’s CM2 global coupled climate models—part 1: formulation and simulation characteristics. J Clim 19(5):643–674CrossRefGoogle Scholar
  19. Dong B, Gregory JM, Sutton R (2009) Understanding land-sea warming contrast in response to increasing greenhouse gases. Part I: transient adjustment. J Clim 22:3079–3097. doi: 10.1175/2009JCLI2652.1 CrossRefGoogle Scholar
  20. Doutriaux-Boucher M, Webb MJ, Gregory JM, Boucher O (2009) Carbon dioxide induced stomatal closure increases radiative forcing of climate via a rapid reduction in low cloud. Geophys Res Lett L02703. doi: 10.1029/2008GL036273
  21. Dufresne JL, Bony S (2008) An assessment of the primary sources of spread of global warming estimates from coupled atmosphere-ocean models. J Clim 21:5135–5144. doi: 10.1175/2008JCLI2239.1 CrossRefGoogle Scholar
  22. Efron B, Tibshirani RJ (1993) An introduction to the bootstrap, monographs on statistics and applied probability, vol 57. Chapman and Hall, New YorkGoogle Scholar
  23. Good P, Gregory JM, Lowe JA (2011) A step-response simple climate model to reconstruct and interpret AOGCM projections. Geophys Res Lett 38:L01703. doi: 10.1029/2010GL045208 CrossRefGoogle Scholar
  24. Good P, Gregory JM, Lowe JA, Andrews T (submitted) Predicting and understanding CMIP5 representative concentration pathway projections using the response to abrupt CO2 change. Clim DynGoogle Scholar
  25. Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168CrossRefGoogle Scholar
  26. Gordon HB, Rotstayn LD, McGregor JL, Dix MR, Kowalczyk EA, O’Farrell SP, Waterman LJ, Hirst AC, Wilson SG, Collier MA, Watterson IG, Elliott TI (2002) The CSIRO Mk3 climate system model. Technical report, CSIRO Atmospheric Research, AspendaleGoogle Scholar
  27. Gregory JM, Webb MJ (2008) Tropospheric adjustment induces a cloud component in CO2 forcing. J Clim 21:58–71CrossRefGoogle Scholar
  28. Gregory JM, Ingram WJ, Palmer MA, Jones GS, Stott PA, Thorpe RB, Lowe JA, Johns TC, Williams KD (2004) A new method for diagnosing radiative forcing and climate sensitivity. Geophys Res Lett 31:L03205. doi: 10.1029/2003gl018747 Google Scholar
  29. Hansen J, Sato M, Nazarenko L, Ruedy R, Lacis A, Koch D, Tegen I, Hall T, Shindell D, Santer B, Stone P, Novakov T, Thomason L, Wang R, Wang Y, Jacob D, Hollandsworth-Frith S, Bishop L, Logan J, Thompson A, Stolarski R, Lean J, Willson R, Levitus S, Antonov J, Rayner N, Parker D, Christy J (2002) Climate forcings in Goddard Institute for Space Studies SI2000 simulations. J Geophys Res 107. doi: 10.1029/2001JD001143
  30. Hansen J, Sato M, Rudy R, Nazarenko L, Lacis A, Schmidt GA, Russel G, Aleinov I, Bauer M, Bauer S, Bell N, Cairns B, Canuto V, Chandler M, Cheng Y, Del Genio A, Faluvegi G, Fleming E, Friend A, Hall T, Jackman C, Kelley M, Kiang N, Koch D, Lean J, Lerner J, Lo K, Menon S, Miller R, Romanou A, Shindell D, Stone P, Sun S, Tausnev N, Thresher D, Wielicki B, Wong T, Yao M, Zhang S (2005) Efficacy of climate forcings. J Geophys Res 110:D18104. doi: 10.1029/2005JD005776 Google Scholar
  31. Harris GR, Sexton DMH, Booth BBB, Collins M, Murphy JM, Webb MJ (2006) Frequency distributions of transient regional climate change from perturbed physics ensembles of general circulation model simulations. Clim Dyn 27(4):357–375. doi: 10.1007/s00382-006-0142-8 CrossRefGoogle Scholar
  32. Harrison EP, Minnis P, Barkstrom BR, Ramanathan V, Cess RD, Gibson GG (1990) Seasonal variation of cloud radiative forcing derived from the earth radiation budget experiment. J Geophys Res 95:18687–18703Google Scholar
  33. Hasumi H, Emori S (2004) K-1 coupled model (miroc) description. Technical report, Center for Climate System Resarch, University of Tokyo, Tokyo, p 34. Available from http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdf
  34. Hourdin F, Musat I, Bony S, Braconnot P, Codron F, Dufresne JL, Fairhead L, Filiberti MA, Friedlingstein P, Grandpeix JY, Krinner G, LeVan P, Li ZX, Lott F (2006) The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection. Clim Dyn 27:787–813. doi: 10.1007/s00382-006-0158-0 CrossRefGoogle Scholar
  35. Johns TC, Durman CF, Banks HT, Roberts MJ, McLaren AJ, Ridley JK, Senior CA, Williams KD, Jones A, Rickard GJ, Cusack S, Ingram WJ, Crucifix M, Sexton DMH, Joshi MM, Dong BW, Spencer H, Hill RSR, Gregory JM, Keen AB, Pardaens AK, Lowe JA, Bodas-Salcedo A, Stark S, Searl Y (2006) The new Hadley Centre climate model HadGEM1: evaluation of coupled simulations. J Clim 19(7):1327–1353CrossRefGoogle Scholar
  36. Joshi MM, Gregory JM, Webb MJ, Sexton DMH, Johns TC (2008) Mechanisms for the land/sea warming contrast exhibited by simulations of climate change. Clim Dyn 30:455–465. doi: 10.1007/s00382-007-0306-1 CrossRefGoogle Scholar
  37. Joshi MM, Webb MJ, Maycock AC, Collins M (2010) Stratospheric water vapour and high climate sensitivity in a version of HadSM3 climate model. Atmos Chem Phys Discuss 10:7161–7167CrossRefGoogle Scholar
  38. Klein SA, Hartmann DL (1993) The seasonal cycle of low stratiform clouds. J Clim 6(8):1587–1606CrossRefGoogle Scholar
  39. Klein SA, Jakob C (1999) Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon Weather Rev 127(10):2514–2531CrossRefGoogle Scholar
  40. Klein SA, Hartmann DL, Norris JR (1995) On the relationships among low-cloud structure, sea-surface temperature and atmospheric circulation in the summertime northeast Pacific. J Clim 8(5):1140–1155CrossRefGoogle Scholar
  41. Klocke D, Pincus R, Quaas J (2011) On constraining estimates of climate sensitivity with present-day observations through model weighting. J Clim 24(23):6092–6099. doi: 10.1175/2011JCLI4193.1 CrossRefGoogle Scholar
  42. Lambert FH, Webb MJ, Joshi MM (2011) The relationship between land-ocean surface temperature contrast and radiative forcing. J Clim 24:3239–3256CrossRefGoogle Scholar
  43. Martin GM, Ringer MA, Pope VD, Jones A, Dearden C, Hinton TJ (2006) The physical properties of the atmosphere in the new Hadley Centre Global Environmental Model, (HadGEM1). Part I: model description and global climatology. J Clim 19:1274–1301. doi: 10.1175/JCLI3636.1 CrossRefGoogle Scholar
  44. Medeiros B, Stevens S (2011) Revealing differences in GCM representations of low clouds. Clim Dyn 36(1–2):385–399. doi: 10.1007/s00382-009-0694-5 CrossRefGoogle Scholar
  45. Medeiros B, Stevens B, Held IM, Zhao M, Williamson DL, Olson JG, Bretherton CS (2008) Aquaplanets, climate sensitivity, and low clouds. J Clim 21(19):4974–4991. doi: 10.1175/2008JCLI1995.1 CrossRefGoogle Scholar
  46. Meinhausen M, Raper SCB, Wigley TML (2011) Emulating coupled atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6. Atmos Chem Phys 11:1417–1456CrossRefGoogle Scholar
  47. Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430:768–772CrossRefGoogle Scholar
  48. Pincus R, Batstone CP, Patrick-Hofmann RJ, Taylor KE, Gleckler PE (2008) Evaluating the present-day simulation of clouds, precipitation and radiation in climate models. J Geophys Res 133(D14209). doi: 10.1029/2007JD009334
  49. Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parametrizations in the Hadley Centre climate model—HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
  50. Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Climate models and their evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis MC, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change, pp 589–662Google Scholar
  51. Richter I, Xie SP (2008) Muted precipitation increase in global warming simulations: a surface evaporation perspective. J Geophys Res 113. doi: 10.1029/2008JD010561
  52. Rienecker MM, Suarez MJ, Gelaro R, et al (2011) MERRA—NASA’s modern-era retrospective analysis for research and applications. J Clim pp 3624–3648. doi: 10.1175/JCLI-D-11-00015.1
  53. Ringer MA, Ingram WJ (submitted) Influence of bounday-layer cloud and convection on the tropical precipitation response to global warming. Geophys Res LettGoogle Scholar
  54. Roeckner E, Bauml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A, Schlese U, Schulzweida U, Tompkins A (2003) The atmospheric general circulation model ECHAM 5. Part I: model description. Technical rep 349, Max Planck Institute for MeteorologyGoogle Scholar
  55. Rogelj J, Hare W, Lowe J, Van Vuuren DP, Riahi K, Matthews B, Hanaoka T, Jiang K, Meinshausen M (2011) Emission pathways consistent with a 2°C global temperature limit. Nat Clim Chang 1:413–418CrossRefGoogle Scholar
  56. Rougier J, Sexton DMH, Murphy JM, Stainforth DA (2009) Analyzing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. J Clim 22(13):1327–1353CrossRefGoogle Scholar
  57. Sanderson BM, Knutti R, Aina T, Christensen C, Faull N, Frame DJ, Ingram WJ, Piani C, Stainforth DA, Stone DA, Allen MR (2008a) Constraints on model response to greenhouse gas forcing and the role of subgrid-scale processes. J Clim 21:2384–2400CrossRefGoogle Scholar
  58. Sanderson BM, Piani C, Ingram WJ, Stone DA, Allen MR (2008b) Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations. Clim Dyn 30:175–190. doi: 10.1007/s00382-007-0280-7 CrossRefGoogle Scholar
  59. Schmidt GA, Ruedy R, Hansen JE, Aleinov I, Bell N, Bauer M, Baue S, Cairns B, Canut V, Cheng Y, Genio AD, Faluvegi G, Friend AD, Hall TM, Hu Y, Kelley M, Kiang NY, Koch D, Lacis AA, Lerner J, Lo KK, Miller RL, Nazarenko L, Oinas V, Perlwitz J, Perlwitz J, Rind D, Romanou A, Russell GL, Sato M, Shindell DT, Stone PH, Sun S, Tausnev N, Thresher D, Yao MS (2006) Present day atmospheric simulations using GISS Model E: Comparison to in-situ, satellite and reanalysis data. J Clim 19:153–192CrossRefGoogle Scholar
  60. Senior CA, Mitchell JFB (1993) Carbon dioxide and climate: the impact of cloud parameterization. J Clim 6:393–418CrossRefGoogle Scholar
  61. Sexton DMH, Murphy J, Collins M, Webb M (2012) Multivariate probabilistic projections using imperfect climate models. Part I: outline of methodology. Clim Dyn. doi: 10.1007/s00382-011-1208-9
  62. Shine KP, Cook J, Highwood EJ, Joshi MM (2003) An alternative to radiative forcing for estimating the relative importance of climate change mechanisms. Geophys Res Lett 30:2047. doi: 10.1029/2003GL018141 CrossRefGoogle Scholar
  63. Soden BJ, Held IM (2006) An assessment of climate feedbacks in coupled ocean-atmosphere models. J Clim 19:3354–3360CrossRefGoogle Scholar
  64. Soden BJ, Vecchi GA (2011) The vertical distribution of cloud feedback in coupled ocean-atmosphere models. Geophys Res Lett 38:12704. doi: 10.1029/2011GL047632 CrossRefGoogle Scholar
  65. Soden BJ, Broccoli AJ, Hemler RS (2004) On the use of cloud forcing to estimate cloud feedback. J Clim 17(19):3661–3665. doi: 10.1175/1520-0442(2004)017<3661:OTUOCF>2.0.CO;2. http://journals.ametsoc.org/doi/full/10.1175/1520-0442%282004%29017%3C3661:OTUOCF%3E2.0.CO%3B2 Google Scholar
  66. Stainforth DA, Aina T, Christensen C, Collins M, Frame DJ, Kettleborough JA, Knight S, Martin A, Murphy J, Piani C, Sexton D, Smith LA, Spicer RA, Thorpe AJ, Allen MR (2005) Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403–406CrossRefGoogle Scholar
  67. Stevens B, Brenguier JL (2009) Cloud controlling factors—low clouds. In: Clouds in the perturbed climate systemGoogle Scholar
  68. Taylor KE, Crucifix M, Braconnot P, Hewitt CD, Doutriaux C, Broccoli AJ, Mitchell JFB, Webb MJ (2007) Estimating shortwave radiative forcing and response in climate models. J Clim 20:2530–2543CrossRefGoogle Scholar
  69. Trenberth KE, Fasullo JT (2010) Simulation of present-day and twenty-first-century energy budgets of the southern oceans. J Clim 23(2):440–454. doi: 10.1175/2009JCLI3152.1 CrossRefGoogle Scholar
  70. Uppala SM, Kållberg PW, Simmons AJ, Andrae U, daCosta Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Hólm E, Hoskins BJ, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf JF, Morcrette JJ, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012. doi: 10.1256/qj.04.176 CrossRefGoogle Scholar
  71. Vecchi GA, Soden BJ (2007) Global warming and the weakening of the tropical circulation. J Clim 20(17):4316–4340. doi: 10.1175/JCLI4258.1 CrossRefGoogle Scholar
  72. von Salzen K, McFarlane NA, Lazare M (2005) The role of shallow convection in the water and energy cycles of the atmosphere. Clim Dyn 25:671–688CrossRefGoogle Scholar
  73. Watanabe M, Shiogama H, Yoshimori M, Ogura T, Yokohata T, Okamoto H, Emori S, Kimoto M (2011) Fast and slow timescales in the tropical low-cloud response to increasing CO2 in two climate models. Clim Dyn. doi: 10.1007/s00382-011-1178-y
  74. Webb M, Senior C, Bony S, Morcrette JJ (2001) Combining ERBE and ISCCP data to assess clouds in the Hadley Centre, ECMWF and LMD atmospheric climate models. Clim Dyn 17:905–922CrossRefGoogle Scholar
  75. Webb MJ, Senior CA, Sexton DMH, Ingram WJ, Williams KD, Ringer MA, McAvaney BJ, Colman R, Soden BJ, Gudgel R, Knutson T, Emori S, Ogura T, Tsushima Y, Andronova NG, Li B, Musat I, Bony S, Taylor KE (2006) On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles. Clim Dyn 27(1):17–38. doi: 10.1007/s00382-006-0111-2 CrossRefGoogle Scholar
  76. Wetherald RT, Manabe S (1988) Cloud feedback processes in a general circulation model. J Atmos Sci 45:1397–1415CrossRefGoogle Scholar
  77. Williams KD, Tselioudis G (2007) GCM intercomparison of global cloud regimes: present-day evaluation and climate change response. Clim Dyn 29:231–250. doi: 10.1007/s00382-007-0232-2 CrossRefGoogle Scholar
  78. Williams KD, Webb MJ (2009) A quantitative performance assessment of cloud regimes in climate models. Clim Dyn 33(1):141–157. doi: 10.1007/s00382-008-0443-1 CrossRefGoogle Scholar
  79. Williams KD, Ingram WJ, Gregory JM (2008) Time variation of effective climate sensitivity in GCMs. J Clim 21(19):5076–5090. doi: 10.1175/2008JCLI2371.1 CrossRefGoogle Scholar
  80. Wood R, Bretherton CS (2006) On the relationship between stratiform low cloud cover and lower tropospheric stability. J Clim 19:6425–6432CrossRefGoogle Scholar
  81. Wyant MC, Bretherton CS, Bacmeister JT, Kiehl JT, Held IM, Zhao M, Klein SA, Soden BJ (2006) A comparison of low-latitude cloud properties and their response to climate change in three agcms sorted into regimes using mid-tropospheric vertical velocity. Clim Dyn 27(2-3):261–279. doi: 10.1007/s00382-006-0138-4 CrossRefGoogle Scholar
  82. Wyant MC, Bretherton CS, Blossey PN (2009) Understanding subtropical low cloud response to a warmer climate in a superparameterized climate model. Part I: regime sorting and physical mechanisms. J Adv Model Earth Syst 1. doi: 10.3894/JAMES.2009.1.7
  83. Wyant MC, Bretherton CS, Blossey PN, Khairoutdinov M (in press) Fast cloud adjustment to increasing CO2 in a superparameterized climate model. J Adv Model Earth SystGoogle Scholar
  84. Yokohata T, Webb MJ, Collins M, Williams KD, Yoshimori M, Hargreaves JD, Annan JD (2010) Structural similarities and differences in climate responses to CO2 increase between two perturbed physics ensembles. J Clim 23(6):1392–1410. doi: 10.1175/2009JCLI2917.1 CrossRefGoogle Scholar
  85. Yukimoto S, Noda A, Kitoh A, Hosaka M, Yoshimura H, Uchiyama T, Shibata K, Arakawa O, Kusunoki S (2006) Present-day climate and climate sensitivity in the Meteorological Research Institute GCM Version 2.3 (MRICGCM2.3). J Meteorol Soc Jpn 84:333–363CrossRefGoogle Scholar
  86. Zhang M, Bretherton CS (2008) Mechanisms of low cloud climate feedback in idealized single-column simulations with the community atmospheric model (CAM3). J Clim 21(18):4859–4878. doi: 10.1175/2008JCLI2237.1 CrossRefGoogle Scholar
  87. Zhang Y, Rossow WB, Lacis AA (1995) Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP datasets 1. Method and sensitivity to input data uncertainties. J Geophys Res 100:1149–1165CrossRefGoogle Scholar

Copyright information

© Crown Copyright 2012

Authors and Affiliations

  • Mark J. Webb
    • 1
  • F. Hugo Lambert
    • 2
  • Jonathan M. Gregory
    • 1
    • 3
  1. 1.Hadley CentreMet OfficeExeterUK
  2. 2.College of Engineering, Mathematics and Physical SciencesUniversity of ExeterExeterUK
  3. 3.National Centre for Atmospheric ScienceReading UniversityReadingUK

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