Climate Dynamics

, Volume 28, Issue 7–8, pp 829–848 | Cite as

Intercomparison of the northern hemisphere winter mid-latitude atmospheric variability of the IPCC models

  • Valerio Lucarini
  • Sandro Calmanti
  • Alessandro Dell’Aquila
  • Paolo M. Ruti
  • Antonio Speranza
Article

Abstract

We compare, for the overlapping time frame 1962–2000, the estimate of the northern hemisphere mid-latitude winter atmospheric variability within the available 20th century simulations of 19 global climate models included in the Intergovernmental Panel on Climate Change—4th Assessment Report with the NCEP-NCAR and ECMWF reanalyses. We compute the Hayashi spectra of the 500 hPa geopotential height fields and introduce an ad hoc integral measure of the variability observed in the Northern Hemisphere on different spectral sub-domains. The total wave variability is taken as a global scalar metric describing the overall performance of each model, while the total variability pertaining to the eastward propagating baroclinic waves and to the planetary waves are taken as scalar metrics describing the performance of each model phenomenologically in connection with the corresponding specific physical process. Only two very high-resolution global climate models have a good agreement with reanalyses for both the global and the process-oriented metrics. Large biases, in several cases larger than 20%, are found in all the considered metrics between the wave climatologies of most IPCC models and the reanalyses, while the span of the climatologies of the various models is, in all cases, around 50%. In particular, the travelling baroclinic waves are typically overestimated by the climate models, while the planetary waves are usually underestimated, in agreement with what found is past analyses performed on global weather forecasting models. When comparing the results of similar models, it is apparent that in some cases the vertical resolution of the model atmosphere, the adopted ocean model, and the advection schemes seem to be critical in the bulk of the atmospheric variability. The models ensemble obtained by arithmetic averaging of the results of all models is biased with respect to the reanalyses but is comparable to the best five models. Nevertheless, the models results do not cluster around their ensemble mean. This study suggests caveats with respect to the ability of most of the presently available climate models in representing the statistical properties of the global scale atmospheric dynamics of the present climate and, a fortiori, in the perspective of modeling climate change.

References

  1. Benzi R, Speranza A (1989) Statistical properties of low frequency variability in the Northern Hemisphere. J Clim 2:367–379CrossRefGoogle Scholar
  2. Benzi R, Malguzzi P, Speranza A, Sutera A (1986) The statistical properties of general atmospheric circulation: observational evidence and a minimal theory of bimodality. Q J R Meteorol Soc112:661–674CrossRefGoogle Scholar
  3. Betts AK (1986) A new convective adjustment scheme. Part I. Observational and theoretical basis. Q J R Met Soc 112:677–691Google Scholar
  4. Blackmon ML (1976) A climatological spectral study of the 500 mb geopotential height of the Northern Hemisphere. J Atmos Sci 33:1607–1623CrossRefGoogle Scholar
  5. Bony S, Emanuel KA (2001) A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data. J Atmos Sci 58:3158–3183CrossRefGoogle Scholar
  6. Bougeault P (1985) A simple parameterization of the large-scale effects of cumulus convection. Monsoon Weather Rev 113:2108–2121CrossRefGoogle Scholar
  7. Branstator G (1987) A striking example of the atmospheres leading traveling pattern. J Atmos Sci 44:2310–2323CrossRefGoogle Scholar
  8. Branstator G, Held IM (1995) Westward propagating normal modes in the presence of stationary background waves. J Atmos Sci 52:247–262CrossRefGoogle Scholar
  9. Buzzi A, Trevisan A, Speranza A (1984) Instabilities of a baroclinic flow related to topographic forcing. J Atmos Sci 41:637–650CrossRefGoogle Scholar
  10. Charney JG, DeVore JG (1979) Multiple flow equilibria in the atmosphere and blocking. J Atmos Sci 36:1205–1216CrossRefGoogle Scholar
  11. Charney JG, Straus DM (1980) Form-drag instability, multiple equilibria and propagating planetary waves in the baroclinic, orographically forced, planetary wave system. J Atmos Sci 37:1157–1176CrossRefGoogle Scholar
  12. Dell’Aquila A, Lucarini V, Ruti PM, Calmanti S (2005) Hayashi spectra of the northern hemisphere mid-latitude atmospheric variability in the NCEP-NCAR and ECMWF reanalyses. Clim Dyn 25:639–652, DOI:10.1007/s00382-005-0048-xGoogle Scholar
  13. Dell’Aquila A, Calmanti S, Ruti PM, Lucarini V (2006) Southern hemisphere mid-latitude atmospheric variability of the NCEP-NCAR and ECMWF reanalyses. J Geophys Res (in press)Google Scholar
  14. Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Cooke WF, Dixon KW, Dunne J, Dunne KA, Durachta JW, Findell KL, Ginoux P, Gnanade- Gnanadesikan A, Gordon CT, Gri_es SM, Gudgel R, Harrison MJ, Held IM, Hemler Rsikan S, Horowitz LW, Klein SA, Knutson TR, Kushner PJ, Langenhorst AR, Lee HC, Lin SJ, Lu J, Malyshev SL, Milly PCD, Ramaswamy V, Russell J, Schwarzkopf MD, Shevliakova E, Sirutis JJ, Spelman MJ, Stern WF, Winton M, Wittenberg AT, Wyman B, Zeng F, Zhang R (2005) GFDL’s CM2 global coupled climate models – Part 1: formulation and simulation characteristics. J Clim 19:643–674CrossRefGoogle Scholar
  15. Fraedrich K, Bottger H (1978) A wavenumber frequency analysis of the 500 mb geopotential at 50°N. J Atmos Sci 35:745–750CrossRefGoogle Scholar
  16. Furevik T, Bentsen M, Drange H, Kindem IKT, Kvamsto NG, Sorteberg A, (2003) Description and evaluation of the Bergen climate model: ARPEGE coupled with MICOM. Clim Dyn 21:27–51CrossRefGoogle Scholar
  17. Gleckler PJ (Ed) (2004) Proceedings of the WCRP/WGNE Workshop on the Second Phase of the Atmospheric Model Intercomparison Project (AMIP2), Meteo-France, Toulouse, pp 11–14 November 2002, August 16, 2004, UCRL-PROC-209115)Google Scholar
  18. 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 (Electronic publication). Aspendale: CSIRO Atmospheric Research. (CSIRO Atmospheric Research technical paper; no. 60). 130 pp. (http://www.dar.csiro.au/publications/gordon_2002a.pdf)
  19. Grandpeix J-Y, Phillips V, Tailleux R (2004) Improved mixing representation in Emanuel’s convection scheme. Q J R Meteorol Soc 130:3207–3222CrossRefGoogle Scholar
  20. Gregory D, Rowntree PR (1990) A mass flux convection scheme with representation of cloud ensemble characteristics and stability dependent closure. Monsoon Weather Rev 118:1483–1506CrossRefGoogle Scholar
  21. Gualdi S., Scoccimarro E, Navarra A (2006) Changes in Tropical Cyclone Activity due to Global Warming: Results from a High-Resolution Coupled General Circulation Model, submitted to J. ClimateGoogle Scholar
  22. Hack JJ (1994) Parameterization of moist convection in the NCAR Community Climate Model (CCM2). J Geophys Res 99:5551-5568Google Scholar
  23. Hansen AR, Sutera A (1986) On the probability density distribution of planetary-scale atmospheric wave amplitude. J Atmos Sci 43:3250–3265CrossRefGoogle Scholar
  24. Hansen AR, Sutera A, Venne DE (1989) An examination of midlatitude power spectra: evidence for standing variance and the signature of El Niño. Tellus 41(A):371–384Google Scholar
  25. Hayashi Y (1971) A generalized method for resolving disturbances into progressive and retrogressive waves by space Fourier and time cross-spectral analysis. J Meteorol Soc Jap 49:125–128Google Scholar
  26. Hayashi Y (1979) A generalized method for resolving transient disturbances into standing and travelling waves by space-time spectral analysis. J Atmos Sci 36:1017–1029CrossRefGoogle Scholar
  27. Holton JR (1992) An Introduction to Dynamic Meteorology. Academic, San DiegoGoogle Scholar
  28. Jungclaus J, Botzet M, Haak H, Keenlyside N, Luo J-J, Latif M, Marotzke J, Mikolajewicz U, Roeckner E (2006) Ocean circulation and tropical variability in the AOGCM ECHAM5/MPI-OM. J Clim 19:3952–3972CrossRefGoogle Scholar
  29. K-1 model developers (2004) K-1 coupled model (MIROC) description, K-1 technical report, 1, Hasumi H, Emori S (eds.), Center for Climate System Research, University of Tokyo, 34ppGoogle Scholar
  30. Kiehl JT, Hack JJ, Bonan G, Boville BA, Williamson D, Rasch P (1998) The National Center for Atmospheric Research Community Climate Model: CCM3. J Clim 11:1131–1149CrossRefGoogle Scholar
  31. Kim S-J, Flato GM, Boer GJ, McFarlane NA (2002) A coupled climate model simulation of the Last Glacial Maximum, Part 1: transient multi-decadal response. Clim Dyn 19:515–537CrossRefGoogle Scholar
  32. Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J, Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, van den Dool H, Jenne R, Fiorino M (2001) The NCEP-NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bull Amer Meteor Soc 82:247–267CrossRefGoogle Scholar
  33. Klinker E, Capaldo M (1986) Systematic errors in the baroclinic waves of the ECMWF model. Tellus 38A:215–235Google Scholar
  34. Kushnir Y (1987)Retrograding wintertime low-frequency disturbances over the north Pacific-ocean. J Atmos Sci 44:2727–2742CrossRefGoogle Scholar
  35. Le Treut H, Li X-Z (1991) Sensitivity of an atmospheric general circulation model to prescribed SST changes: feedback effects associated with the simulation of cloud optical properties. Clim Dyn 5:175–187Google Scholar
  36. Lohmann U, Roeckner E (1996) Design and performance of a new cloud microphysics parameterization developed for the ECHAM4 general circulation model. Clim Dyn 12:557–572CrossRefGoogle Scholar
  37. Lucarini V (2002) Towards a definition of climate science. Int J Environ Pollut 18:409–414CrossRefGoogle Scholar
  38. Lucarini V, Russell GL (2002) Comparison of mean climate trends in the northern hemisphere between National Centers for Environmental Prediction and two atmosphere-ocean model forced runs. J Geophys Res 107 (D15), 10.1029/2001JD001247Google Scholar
  39. Malguzzi P, Speranza A (1981) Local Multiple Equilibria and Regional Atmospheric Blocking. J Atmos Sci 9:1939–1948CrossRefGoogle Scholar
  40. Marti O, et al (2005) The new IPSL climate system model: IPSL-CM4, Tech. rep., Institut Pierre Simon Laplace des Sciences de l’Environnement Global, IPSL, Case 101, 4 place Jussieu, Paris, FranceGoogle Scholar
  41. May W (1999) Space-time spectra of the atmospheric intraseasonal variability in the extratropics and their dependency on the El Niño/Southern Oscillation phenomenon: model versus observation. Clim Dyn (1999) 15:369–387CrossRefGoogle Scholar
  42. Min S-K, Legutke S, Hense A, Kwon W-T (2005) Internal variability in a 1000-year control simulation with the coupled climate model ECHO-G. Part I. Near-surface temperature, precipitation and mean sea level pressure. Tellus 57A:605–621CrossRefGoogle Scholar
  43. Moorthi S, Suarez MJ (1992) Relaxed Arakawa-Schubert: A parameterization of moist convection for general circulation models. Monsoon Weather Rev 120:978–1002CrossRefGoogle Scholar
  44. Nordeng TE (1994) Extended versions of the convective parameterization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. Technical Memorandum No. 206, European Centre for Medium-Range Weather Forecasts, Reading, United KingdomGoogle Scholar
  45. Pan D-M, Randall DA (1998) A Cumulus Parameterization with a Prognostic Closure. Q J R Meteorol Soc 124:949–981Google Scholar
  46. Peixoto JP, Oort AH (1992) Physics of climate, Chap. 1. American Institute of Physics, New YorkGoogle Scholar
  47. Randall DA, Pan D-M (1993) Implementation of the Arakawa-Schubert cumulus parameterization with a prognostic closure. In: Emanuel K, Raymond D (eds) Cumulus parameterization, a meteorological monograph. American Meteorological Society, pp 137–144Google Scholar
  48. Rasch PJ, Kristjánsson JE (1998) A comparison of the CCM3 model climate using diagnosed and predicted condensate parameterizations. J Clim 11:1587–1614CrossRefGoogle Scholar
  49. Ricard J-L, Royer J-F (1993) A statistical cloud scheme for use in a AGCM. Ann Geophysicae 11:1095–1115Google Scholar
  50. Rotstayn LD (1997) A physically based scheme for the treatment of stratiform clouds and precipitation in large-scale models. I: Description and evaluation of the microphysical processes. Q J R Meteorol Soc 123:1227–1282Google Scholar
  51. Rotstayn LD (2000) On the "tuning" of the autoconversion parameterizations in climate models. J Geophys Res D105:15495–15507CrossRefGoogle Scholar
  52. Ruti PM, Lucarini V, Dell’Aquila A, Calmanti S, Speranza A (2006), Does the subtropical jet catalyze the midlatitude atmospheric regimes? Geophys Res Lett 33, L06814, doi:10.1029/2005GL024620Google Scholar
  53. Salas-Mélia D, Chauvin F, Déqué M, Douville H, Gueremy JF, Marquet P, Planton S, Royer JF, Tyteca S (2005) Description and validation of the CNRM-CM3 global coupled model, submitted to Clim DynGoogle Scholar
  54. Schmidt GA, Ruedy R, Hansen JE, Aleinov I, Bell N, Bauer M, Bauer S, Cairns B, Canuto V, Cheng Y, DelGenio A, 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 M-S (2005) Present day atmospheric simulations using GISS Model E: Comparison to in-situ, satellite and reanalysis data. J Clim 19:153–192CrossRefGoogle Scholar
  55. Siegmund P (1995) The generation of available potential energy: a comparison of results from a general circulation model with observations. Clim Dyn 11:129–140CrossRefGoogle Scholar
  56. Simmons AJ, Gibson JK (2000) The ERA-40 Project Plan, ERA-40 Project Report Series No. 1, European Centre for Medium-Range Weather Forecasts, ReadingGoogle Scholar
  57. Speranza A (1983) Deterministic and statistical properties of the westerlies. Paleogeophysics 121:511–562Google Scholar
  58. Sumi A, Kanamitsu M (1984) A study of systematic errors in a numerical prediction model, Part I: General aspects of the systematic errors and their relation with the transient eddies. J Met Soc Japan 62:234–251Google Scholar
  59. Tibaldi S (1986) Envelope orography and maintenance of the quasi-stationary circulation in the ECMWF global models. Adv Geophys 29:339–373CrossRefGoogle Scholar
  60. Tiedke M (1993) Representation of clouds in large-scale models. Mon Wea Rev 121:3040–3061CrossRefGoogle Scholar
  61. Van Oldenburg GJ, Philip S, Collins M (2005) El Niño in a changing climate: a multi-model study. Ocean Science, Discussions 2:267-298CrossRefGoogle Scholar
  62. Volodin EM, Diansky NA (2004) El-Niño reproduction in coupled general circulation model of atmosphere and ocean. Russ Meteorol Hydrol 12:5–14Google Scholar
  63. Wallace JM, Tibaldi S, Simmons AJ (1983) Reduction of systematic forecast errors in the ECMWF model through the introduction of an envelope orography. Q J R Meterolog Soc 109:683–717CrossRefGoogle Scholar
  64. Wallace JM, Lim GH, Blackmon ML (1988) Relationship between cyclone tracks, anticyclone tracks and baroclinic waveguides. J Atmos Sci 45:439–462CrossRefGoogle Scholar
  65. Yu Y, Zhang X, Guo Y (2004) Global coupled ocean- atmosphere general circulation models in LASG/IAP. Adv Atmos Sci 21:444–455CrossRefGoogle Scholar
  66. Yukimoto S, Noda A (2002) Improvements of the Meteorological Research Institute Global Ocean–atmosphere Coupled GCM (MRI-CGCM2) and its climate sensitivity, Tech. Rep. 10, NIES, JapanGoogle Scholar
  67. Yukimoto, et al, Noda A, Kitoh A, Sugi M, Kitamura Y, Hosaka M, Shibata K, Maeda S, Uchiyama T (2001) The new Meteorological Research Institute coupled GCM (MRI-CGCM2) – Model climate and variability - Pap. Meteorol Geophys 51, 47–88. Meteor Geophys 51:47–88Google Scholar
  68. Zhang GJ, McFarlane NA (1995) Sensitivity of climate simulations to the parameterization of cumulus convection in the CCC-GCM. Atmos-Ocean 3:407–446Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Valerio Lucarini
    • 1
  • Sandro Calmanti
    • 2
  • Alessandro Dell’Aquila
    • 2
  • Paolo M. Ruti
    • 2
  • Antonio Speranza
    • 1
  1. 1.Dipartimento di Matematica ed InformaticaUniversità di CamerinoCamerino (MC)Italy
  2. 2.Progetto Speciale Clima GlobaleEnte Nazionale per le Nuove TecnologieRomeItaly

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