Climate Dynamics

, Volume 27, Issue 7–8, pp 787–813

The LMDZ4 general circulation model: climate performance and sensitivity to parametrized physics with emphasis on tropical convection

  • Frédéric Hourdin
  • Ionela Musat
  • Sandrine Bony
  • Pascale Braconnot
  • Francis Codron
  • Jean-Louis Dufresne
  • Laurent Fairhead
  • Marie-Angèle Filiberti
  • Pierre Friedlingstein
  • Jean-Yves Grandpeix
  • Gerhard Krinner
  • Phu LeVan
  • Zhao-Xin Li
  • François Lott
Article

Abstract

The LMDZ4 general circulation model is the atmospheric component of the IPSL–CM4 coupled model which has been used to perform climate change simulations for the 4th IPCC assessment report. The main aspects of the model climatology (forced by observed sea surface temperature) are documented here, as well as the major improvements with respect to the previous versions, which mainly come form the parametrization of tropical convection. A methodology is proposed to help analyse the sensitivity of the tropical Hadley–Walker circulation to the parametrization of cumulus convection and clouds. The tropical circulation is characterized using scalar potentials associated with the horizontal wind and horizontal transport of geopotential (the Laplacian of which is proportional to the total vertical momentum in the atmospheric column). The effect of parametrized physics is analysed in a regime sorted framework using the vertical velocity at 500 hPa as a proxy for large scale vertical motion. Compared to Tiedtke’s convection scheme, used in previous versions, the Emanuel’s scheme improves the representation of the Hadley–Walker circulation, with a relatively stronger and deeper large scale vertical ascent over tropical continents, and suppresses the marked patterns of concentrated rainfall over oceans. Thanks to the regime sorted analyses, these differences are attributed to intrinsic differences in the vertical distribution of convective heating, and to the lack of self-inhibition by precipitating downdraughts in Tiedtke’s parametrization. Both the convection and cloud schemes are shown to control the relative importance of large scale convection over land and ocean, an important point for the behaviour of the coupled model.

References

  1. EPICA community members (2004) Eight glacial cycles from an Antarctic ice core. Nature 429:623–628CrossRefGoogle Scholar
  2. Arctic Climatology Project (2000) Environmental working group arctic meteorology and climate atlas, CD-Rom. In: Fetterer F, Radionov V (eds) National Snow and Ice Data Center, BoulderGoogle Scholar
  3. Automatic Weather Stations Greenland Project (2004) Greenland aws data, digital data available on http://www.amrc.ssec.wisc.edu/greenland.html
  4. Automatic Weather Stations Project (2004) Archive aws data, digital data available on http://www.amrc.ssec.wisc.edu/aws.html
  5. Barkstrom BR (1984) The earth radiation budget experiment (ERBE). Bull Am Meteorol Soc 65:1170–1185CrossRefGoogle Scholar
  6. Blackmon ML (1976) A climatological study of the 500 mb geopotential height of the northern hemisphere. J Atmos Sci 33:1607–1623CrossRefGoogle Scholar
  7. Bony S, Dufresne J-L (2005) Marine boundary layer clouds at the heart of cloud feedback uncertainties in climate models. Geophys Res Lett 32(20), L20806, doi: 10.1029/2005GL023851Google Scholar
  8. Bony S, Emanuel J-L (2001) A parameterization of the cloudiness associated with cumulus convection; evaluation using TOGA COARE data. J Atmos Sci 58:3158–3183CrossRefGoogle Scholar
  9. Bony S, Lau K-M, Sud YC (1997) Sea surface temperature and large-scale circulation influences on tropical greenhouse effect and cloud radiative forcing. J Clim 10:2055–2077CrossRefGoogle Scholar
  10. Bony S, Dufresne J-L, Le Treut H, Morcrette J-J, Senior C (2004) On dynamic and thermodynamic components of cloud changes. Clim Dyn 22:71–86CrossRefGoogle Scholar
  11. Boucher O, Pham M (2002) History of sulfate aerosol radiative forcings. Geophys Res Lett 29:(9):Google Scholar
  12. Braconnot P (1998) Tests de sensibilité, Note technique 0076, IPSL, http://www.ipsl.jussieu.fr/poles/ Modelisation/NotesTechniques.htm
  13. Braconnot P, Hourdin F, Bony S, Dufresne J-L, Grandpeix J-Y, Marti O (2006) Impact of different convective cloud schemes on the simulation of the tropical seasonal cycle with a coupled ocean-atmosphere model. Clim Dyn (submitted)Google Scholar
  14. Chen B, Bromwich DH, Hines KM, Pan X (1995) Simulations of the 1979–1988 polar climates by global climate models. Ann Glaciol 21:83–90Google Scholar
  15. Cosme E, Hourdin F, Genthon C, Martinerie P (2005) Origin of dimethylsulfide, non-sea-salt sulfate, and methanesulfonic acid in eastern Antarctica. J Geophys Res 110, (D9), D03302, doi:10.1029/2004JD004881Google Scholar
  16. Deardorff JW (1966) The counter-gradient heat-flux in the lower atmosphere and in the laboratory. J Atmos Sci 23:503–506CrossRefGoogle Scholar
  17. Dufresne J, Grandpeix J (1996) Raccordement des modèles thermodynamiques de glace, d’océan et d’atmosphère, Note Interne 205, L.M.D., juinGoogle Scholar
  18. Dufresne J-L, Quaas J, Boucher O, Denvil F, Fairhead L (2005) Contrasts in the effects on climate of anthropogenic sulfate aerosols between the 20th and the 21st century, Geophys Res Lett 32, L21703, doi: 10.1029/2005GL023619Google Scholar
  19. Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335CrossRefGoogle Scholar
  20. Emanuel KA (1993) A cumulus representation based on the episodic mixing model: the importance of mixing and microphysics in predicting humidity. AMS Meteorol Monogr 24(46):185–192Google Scholar
  21. Emanuel KA, Neelin JD, Bretherton CS (1994) On large-scale circulations in convective atmospheres. Q J R Meteorol Soc 120:1111–1143CrossRefGoogle Scholar
  22. ERA-40 by ECMWF (2002) ERA-40, forty-year european re-analysis of the global atmosphere, 2002, http://www.ecmwf.int/products/data/archive/descriptions/e4
  23. Fichefet T, Maqueda MM (1997) Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J Geophys Res 102:12609–12646CrossRefGoogle Scholar
  24. Forget F, Hourdin F, Fournier R, Hourdin C, Talagrand O, Collins M, Lewis SR, Read PL, Huot J-P (1999) Improved general circulation models of the Martian atmosphere from the surface to above 80 km. J Geophys Res 104:24155–24176CrossRefGoogle Scholar
  25. Fouquart Y, Bonnel B (1980) Computations of solar heating of the Earth’s atmosphere: a new parametrization. Contrib Atmos Phys 53:35–62Google Scholar
  26. Gates WL (1992) AMIP: the atmospheric model intercomparison project. Bull Am Meteorol Soc 73:1962–1970CrossRefGoogle Scholar
  27. Genthon C, Krinner G, Cosme E (2002) Free and laterally-nudged Antarctic climate of an Atmospheric General Circulation Model. Mon Weather Rev 130:1601–1616CrossRefGoogle Scholar
  28. Grandpeix JY, Phillips V, Tailleux R (2004) Improved mixing representation in Emanuel’s convection scheme. Q J R Meteorol Soc 130:3207–3222CrossRefGoogle Scholar
  29. Gregory D, Morcrette JJ, Jakob C, Beljaars ACM, Stockdale T (2000) Revision of convection, radiation and cloud schemes in the ECMWF Integrated Forecasting System. Q J R Meteorol Soc 126:1685–1710CrossRefGoogle Scholar
  30. Gregory J, Stouffer R, Raper S, Stott P, Rayner N (2005) An observationally based estimate of the climate sensitivity. J Clim 15(22):3117–3121CrossRefGoogle Scholar
  31. Grenier H, Le Treut H, Fichefet T (2000) Ocean–atmosphere interactions and climate drift in a coupled general circulation model. Clim Dyn 16:701–717CrossRefGoogle Scholar
  32. Guichard F, Petch JC, Redelsperger J-L, Bechtold P, Chaboureau J-P, Cheinet S, Grabowski W, Grenier H, Jones CG, Köhler M, Piriou J-M, Tailleux R, Tomasini M (2004) Modelling the diurnal cycle of deep precipitating convection over land with cloud-resolving models and single-column models. Q J R Meteorol Soc 130(604C):3139–3172CrossRefGoogle Scholar
  33. Hauglustaine DA, Hourdin F, Jourdain F, Filiberti M-A, Walters S, Lamarque J-F, Holland EA (2004) Interactive chemistry in the Laboratoire de Météorologie Dynamique general circulation model: description and background tropospheric chemistry evaluation. J Geophys Res 109:4314–4357CrossRefGoogle Scholar
  34. Heymsfield AJ, Platt C (1984) A parameterization of the particle size spectrum of ice clouds in terms of the ambient temperature and the ice water content. J Atmos Sci 41:846–855CrossRefGoogle Scholar
  35. Horinouchi T, Pawson S, Shibata K, Langematz U, Manzini E, Giorgetta MA, Sassi F, Wilson RJ, Hamilton K, de Grandpré J, Scaife AA (2003) Tropical cumulus convection and upward-propagating waves in middle-atmospheric GCMs. J Atmos Sci 60:2765–2782CrossRefGoogle Scholar
  36. Hosking JRM, Wallis JR (1997) Regional frequency analysis: an approach based on L-monents. Cambridge University Press, Cambridge, 224 ppGoogle Scholar
  37. Hoskins BJ, Hsu HH, James IN, Masutani M, Sardeshmuck PD, White GH (1996) Diagnostics of the global atmospheric circulation based on ecmwf analysis 1979–1989, Technical document, WCRP/WMOGoogle Scholar
  38. Hourdin F, Armengaud A (1999) The use of finite-volume methods for atmospheric advection of trace species. Part i: test of various formulations in a general circulation model. Mon Weather Rev 127:822–837CrossRefGoogle Scholar
  39. Hourdin F, Issartel J-P (2000) Sub-surface nuclear tests monitoring through the CTBT xenon network. Geophys Res Lett 27:2245–2248CrossRefGoogle Scholar
  40. Hourdin F, Le Van P, Forget F, Talagrand O (1993) Meteorological variability and the annual surface pressure cycle on Mars. J Atmos Sci 50:3625–3640CrossRefGoogle Scholar
  41. Hourdin F, Talagrand O, Sadourny R, Régis C, Gautier D, McKay D (1995) General circulation of the atmosphere of Titan. Icarus 117:358–374CrossRefGoogle Scholar
  42. Hourdin F, Lebonnois S, Luz D, Rannou P (2004) Titan’s stratospheric composition driven by condensation and dynamics. J Geophys Res 109Google Scholar
  43. Hourdin F, Idelkadi A, Talagrand O (2005) Eulerian backtracking of atmospheric tracers: II numerical aspects, QJRMS (in press)Google Scholar
  44. Jacquart C, Choisnel E (1995) Un modèle de bilan hydrique simplifié à deux réservoirs utilisable en agrométéorologie. La Météorologie 8:4–17Google Scholar
  45. Jakob C, Siebesma AP (2003) A new subcloud model for mass-flux convection schemes: influence on triggering, updraft properties, and model climate. Mon Weather Rev 131:2765–2778CrossRefGoogle Scholar
  46. James IN (1989) The Antarctic drainage flow: implications for hemispheric flow on the Southern hemisphere. Antarct Sci 1:279–290Google Scholar
  47. Kasahara A (1977) Computational aspects of numerical models for weather prediction and climate simulation. In: Chang J (ed) Methods in computational physics, vol 17, Academic, Amsterdam, pp 1–66Google Scholar
  48. King J, Connolley W (1997) Validation of the surface energy balance over the antarctic ice sheets in the UK meteorological office unified climate model. J Clim 1273–1287Google Scholar
  49. Klein SA, Jakob C (1999) Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon Weather Rev 127:2514–2531CrossRefGoogle Scholar
  50. Krinner G, Genthon C (1998) GCM simulations of the Last Glacial Maximum surface climate of Greenland and Antarctica. Clim Dyn 14:741–758CrossRefGoogle Scholar
  51. Krinner G, Genthon C (1999) Altitude dependence of the surface climate over the ice sheets. Geophys Res Lett 26:2227–2230CrossRefGoogle Scholar
  52. Krinner G, Genthon C (2003) Tropospheric transport of continental tracers towards Antarctica under varying climatic conditions. Tellus 53:54–70Google Scholar
  53. Krinner G, Genthon C, Li Z-X, Le Van P (1997) Studies of the Antarctic climate with a stretched-grid general circulation model. J Geophys Res 102:13731–13745CrossRefGoogle Scholar
  54. Krinner G, Mangerud J, Jakobsson M, Crucifix M, Ritz C, Svendsen J (2004) Enhanced ice sheet growth in Eurasia owing to adjacent ice dammed lakes. Nature 427:429–432CrossRefGoogle Scholar
  55. Krinner G, Viovy N, de Noblet-Ducoudré N, Ogée J, Polcher J, Friedlingstein P, Ciais P, Sitch S, Prentice C (2005) A dynamic global vegetation model for studies of the coupled atmosphere–biosphere system, Global Biogeochem Cycles 19, GB1015, 2005, doi:10.1029/2003GB002199Google Scholar
  56. Krinner G, Magand O, Simmonds I, Genthon C, Dufresne J (2006) Simulated antarctic precipitation and surface mass balance of the end of the 20th and 21st centuries. Clim Dyn (submitted)Google Scholar
  57. Laval K, Sadourny R, Serafini Y (1981) Land surface processes in a simplified general circulation model. Geophys Astrophys Fluid Dyn 17:129–150Google Scholar
  58. Le Treut H, Li ZX (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
  59. Le Treut H, Li Z, Forichon M (1994) Sensitivity study of the LMD GCM to greenhouse forcing associated with two different cloud water parametrizations. J Clim 7:1827–1841CrossRefGoogle Scholar
  60. Le Treut H, Forichon M, Boucher O, Li Z (1998) Sulfate aerosol indirect effect and CO 2 greenhouse forcing: equilibrium response of the LMD GCM and associated cloud feedbacks. J Climate 11:1673–1684CrossRefGoogle Scholar
  61. Levrard B, Forget F, Montmessin F, Laskar J (2004) Recent ice-rich deposits formed at high latitudes on Mars by sublimation of unstable equatorial ice during low obliquity. Nature 431:1072–1075CrossRefGoogle Scholar
  62. Li Z (1999) Ensemble atmospheric GCM simulation of climate interannual variability from 1979 to 1994. J Climate 12:986–1001CrossRefGoogle Scholar
  63. Li ZX, Conil S (2003) A 1000-year simulation with the IPSL ocean-atmosphere coupled model. Ann Geophys 46(1):39–46Google Scholar
  64. Lindzen RS (1974) Wave-CISK in the tropics. J Atmosph Sci 31:156–179CrossRefGoogle Scholar
  65. Lott F (1999) Alleviation of stationary biases in a gcm through a mountain drag parametrization scheme and a simple representation of mountain lift forces. Mon Weather Rev 127:788–801CrossRefGoogle Scholar
  66. Lott F, Miller M (1997) A new sub-grid scale orographic drag parametrization: its formulation and testing. Q J R Meteorol Soc 123:101–128CrossRefGoogle Scholar
  67. Lott F, Fairhead L, Hourdin F, Levan P (2005) The stratospheric version of LMDz: dynamical climatologies, arctic oscillation, and impact on the surface climate. Clim Dyn 25:851–868CrossRefGoogle Scholar
  68. Louis JF (1979) A parametric model of vertical eddy fluxes in the atmosphere. Boundary Layer Meteorol 17:187–202CrossRefGoogle Scholar
  69. Madec G, Delecluse P, Imbard M, Lévy M (1998) OPA 8.1, ocean general circulation model reference manual, Notes du pôle de modélisation, n. 11, Institut Pierre-Simon Laplace (IPSL), Paris, FranceGoogle Scholar
  70. Marti O, Braconnot P, Bellier J, Benshila R, Bony S, Brockmann P, Cadule P, Caubel A, Denvil S, Dufresne JL, Fairhead L, Filiberti MA, Foujols MA, Fichefet T, Friedlingstein P, Grandpeix JY, Hourdin F, Krinner G, Lévy C, Madec G, Musat I, de Nolbet N, Polcher J, Talandier C (2005) The new IPSL climate system model: IPSL-CM4, Technical note, IPSL, available at http://www.dods.ipsl.jussieu.fr/omamce/IPSLCM4/DocIPSLCM4/
  71. Morcrette JJ, Smith L, Fouquart Y (1986) Pressure and temperature dependence of the absorption in longwave radiation parametrizations. Contrib Atmos Phys 59(4):455–469Google Scholar
  72. Myneni R, Hoffman S, Glassy J, Zhang Y, Votava P, Nemani R, Running S, Privette J (2002) Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens Environ 83:214–231CrossRefGoogle Scholar
  73. Quaas J, Boucher O, Bréon FM (2004) Aerosol indirect effects in POLDER satellite data and the Laboratoire de Météorologie Dynamique-Zoom (LMDZ) general circulation model. J Geophys Res 109Google Scholar
  74. Quadrelli R, Wallace JM (2004) A simplified linear framework for interpreting patterns of northern hemisphere wintertime climate variability. J Climate 17:3728–3744CrossRefGoogle Scholar
  75. Rannou P, Hourdin F, McKay CP (2002) A wind origin for Titan’s haze structure. Nature 418:853–856CrossRefGoogle Scholar
  76. de Rosnay P, Polcher J, Bruen M, Laval K (2002) Impact of a physically based soil water flow and soil–plant interaction representation for modeling large scale land surface processes. J Geophys Res 107, 10.1029/2001JD000634Google Scholar
  77. Rossow WB, Schiffer RA (1999) Advances in understanding clouds from isccp. Bull Am Meteorol Soc 80:2261–2287CrossRefGoogle Scholar
  78. Sadourny R (1975a) Compressible model flows on the sphere. J Atmos Sci 32:2103–2110CrossRefGoogle Scholar
  79. Sadourny R (1975b) The dynamics of finite-difference models of the shallow-water equations. J Atmos Sci 32:680–689CrossRefGoogle Scholar
  80. Sadourny R, Laval K (1984) January and July performance of the LMD general circulation model. In: Berger A, Nicolis C (eds) New perspectives in climate modeling. Elsevier, Amsterdam, pp 173–197Google Scholar
  81. Sawyer JS (1976) Observational characteristics of atmospheric fluctuations with a time scale of a month. Q J R Meteorol Soc 96:610–625Google Scholar
  82. Shuman C, Alley R, Anandakrishnan S, White J, Grootes P, Stearns C (1995) Temperature and accumulation at the Greenland Summit: comparison of high-resolution isotope profiles and satellite passive microwave brightness temperature trends. J Geophys Res 100:9165–9177CrossRefGoogle Scholar
  83. Slingo JM (1987) The development and verification of a cloud prediction scheme for the ecmwf model. Q J R Meteorol Soc 113(477):899–927CrossRefGoogle Scholar
  84. Smith SD (1988) Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speed and temperature. J Geophys Res 93:15467–15472CrossRefGoogle Scholar
  85. Sommeria G, Deardorff JW (1977) Subgrid-scale condensation in models of nonprecipitating clouds. J Atmos Sci 34:344–355CrossRefGoogle Scholar
  86. Suzuki T, Tanaka M, Nakajima T (1993) The microphysical feedback of cirrus cloud in climate-change. J Meteor Soc Jpn 71(6):701–714Google Scholar
  87. Taylor KE, Williamson D, Zwiers F (2000) The sea surface temperature and sea–ice concentration boundary conditions for AMIP II simulations, PCMDI Report No. 60 60 P.C.M.D.I.Google Scholar
  88. Thompson DWJ, Wallace JM (1998) The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett. 25:1297–1300CrossRefGoogle Scholar
  89. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117:1179–1800CrossRefGoogle Scholar
  90. Van Leer B (1977) Towards the ultimate conservative difference scheme : IV. A new approach to numerical convection. J Comput Phys 23:276–299CrossRefGoogle Scholar
  91. Vaughan DG, Bamber J, Giovinetto M, Russel J, Cooper A (1999) Reassessment of net surface mass balance in Antarctica. J Clim 12:933–946CrossRefGoogle Scholar
  92. 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
  93. Wigley T, Jones P, Raper S (1997) The observed global warming record: what does it tell us?. Proc Natl Acad Sci 94(16):8314–8320CrossRefGoogle Scholar
  94. Wyant MC, Bretherton CS, Bacmeister JT, Kiehl JT, Held IM, Zhao MZ, Klein SA, Soden BJ (2006) A comparison of tropical cloud properties and responses in gcms using mid-tropospheric vertical velocity. Clim Dyn (submitted)Google Scholar
  95. Xie P, Arkin PA (1997) A 17-year monthly analysis, based on gauge observations, satellite estimates, and, numerical model outputs. Bull Am Meteorol Soc 78:2539–2558CrossRefGoogle Scholar
  96. Xu K-M, Randall DA (1996) Evaluation of statistically based cloudiness parameterisations used in climate models. J Atmosph Sci 53:3103–3119CrossRefGoogle Scholar
  97. Zhang MH, Lin WY, Klein SA, Bacmeister JT, Bony S, Cderwall RT, Del Genio AD, Hack JJ, Loeb NG, Lohmann v, Minnis P, Musat I, Pincus R, Stier P, Suarez MJ, Webb MJ, Wu JB, Xie MS, Zhang JH (2005) Comparing clouds and their seasonal variations in 10 atmospheric general circulation models with satellite measurements. J Geophys Res 110, D15S02, doi:10.1029/2004JD005021Google Scholar
  98. Zhou TJ, Li ZX (2002) Simulation of the east asian summer monsoon using a variable resolution atmospheric gcm. Clim Dyn 19:167–180CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Frédéric Hourdin
    • 1
  • Ionela Musat
    • 1
  • Sandrine Bony
    • 1
  • Pascale Braconnot
    • 2
  • Francis Codron
    • 1
  • Jean-Louis Dufresne
    • 1
  • Laurent Fairhead
    • 1
  • Marie-Angèle Filiberti
    • 3
  • Pierre Friedlingstein
    • 2
  • Jean-Yves Grandpeix
    • 1
  • Gerhard Krinner
    • 4
  • Phu LeVan
    • 1
  • Zhao-Xin Li
    • 1
  • François Lott
    • 1
  1. 1.Laboratoire de Météorologie Dynamique (LMD/IPSL)CNRS/UPMCParis Cedex 05France
  2. 2.Laboratoire des Sciences du Climat et de l’Environnement (LSCE/IPSL)SaclayFrance
  3. 3.Institut Pierre Simon Laplace (IPSL)ParisFrance
  4. 4.Laboratoire de Glaciologie et Géophysique de l’EnvironnementGrenobleFrance

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