Climate Dynamics

, Volume 21, Issue 5–6, pp 371–390 | Cite as

High-resolution simulations of global climate, part 1: present climate

  • P. B. Duffy
  • B. Govindasamy
  • J. P. Iorio
  • J. Milovich
  • K. R. Sperber
  • K. E. Taylor
  • M. F. Wehner
  • S. L. Thompson
Article

Abstract

We examine simulations of today's climate performed with a global atmospheric general circulation model run at spectral truncations of T42, T170, and T239, corresponding to grid cell sizes of roughly 310 km, 75 km, and 55 km, respectively. The simulations were forced with observed sea-surface temperatures and sea-ice concentrations. The T42 simulations and initial simulations at T170 and T239 were performed using a model version that was carefully "tuned" to optimize results at T42; subsequent simulations at T170 and T239 used a model version that was partly re-tuned to improve results at T170. On the scales of a T42 grid cell and larger, nearly all quantities we examined in all the T170 and T239 simulations agree better with observations, at least in terms of spatial patterns, than in the T42 simulations. In some cases the improvements are very substantial. Improvements are seen in all-season, global domain results, and in results pertaining to most seasons and latitude bands. Increasing the model resolution from T42 introduces biases (errors in the mean) into some simulated quantities; the worst of these were removed by the partial retuning we performed at T170. This retuning has little effect on the spatial patterns of results, except in Northern Hemisphere winter at T170, where it tends to bring improvements. We discuss aspects of simulated regional climates, and their dependence on model resolution.

References

  1. Boer GJ, Lazare M (1988) Some results concerning the effect of horizontal resolution and gravity wave drag on simulated climate. J Clim 1: 789–806Google Scholar
  2. Bonan G (1998) The land surface climatology of the NCAR Land Surface Model coupled to the NCAR Community Climate Model. J Clim 11: 1307–1326Google Scholar
  3. Boyle J (1993) Sensitivity of dynamical quantities to horizontal resolution for a climate simulation using the ECMWF (cycle 33) model. J Clim 6: 796–815Google Scholar
  4. Boville B (1991) Sensitivity of simulated climate to model resolution. J Clim 4: 469–485Google Scholar
  5. Gleckler PJ, Taylor KE (1993) The effect of horizontal resolution on ocean surface heat fluxes in the ECMWF model. Clim Dyn 9: 17–32Google Scholar
  6. Hendon HH, Zhang C, Glick JD (1999) Interannual variation of the Madden-Julian oscillation during Austral summer. J Clim 12: 2538–2550Google Scholar
  7. Kiehl JT, Williamson DL (1991) Dependence of cloud amount on horizontal resolution in the national center for atmospheric research community climate model. J Geophys Res 96: 10,955–10,980Google Scholar
  8. Kiehl JT, Hack JJ, Bonan BG, Boville BA, Williamson DL, Rasch P (1998a) The National Center for Atmospheric Research Community Climate Model: CCM3. J Clim 11: 1131–1149Google Scholar
  9. Kiehl JT, Hack JJ, Hurrell J (1998b) The energy budget of the NCAR Community Climate Model: CCM3. J Clim 11: 1151–1178Google Scholar
  10. Kittel TGF, Rosenbloom NA, Painter TH, Schimel DS, Fisher HH, Grimsdell A, VEMAP Participants, Daly C, Hunt ER Jr (1996) The VEMAP Phase I database: an integrated input dataset for ecosystem and vegetation modeling for the conterminous United States. CDROM and World Wide Web (URL=http://www.cgd.ucar.edu/vemap/)Google Scholar
  11. Lal N, Cubasch U, Perlwitz J, Waszkewitz J (1997) Simulation of the Indian monsoon in ECHAM3 climate model: sensitivity to horizontal resolution. Int J Climatol 17: 847–858Google Scholar
  12. Madden RA, Julian PR (1971) Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J Atmos Sci 28: 702–708Google Scholar
  13. Madden RA, Julian PR (1972) Description of global-scale circulation cells in the tropics with a 40–50 day period. J Atmos Sci 29: 1109–1123Google Scholar
  14. Madden RA, Julian PR (1994) Observations of the 40–50 day tropical oscillation – A review. Mon Weather Rev 122: 814–837Google Scholar
  15. Maloney ED, Hartmann DL (2001) The sensitivity of intraseasonal variability in the NCAR CCM3 to changes in convective parametrization. J Clim 14: 2015–2034Google Scholar
  16. Manabe S, Smagorinsky J, Holloway JL, Stone HM (1970) Simulated climatology of a general circulation model with a hydrological cycle. Mon Weather Rev 98: 175–213Google Scholar
  17. Mearns L, Giogi F, McDaniel L, Shields C (1995) Analysis of daily variability of precipitation in a nested regional climate model: comparison with observations and doubled CO2 results. Global Planet Change 10: 55–78Google Scholar
  18. Phillips TJ, Corsetti LC, Grotch SL (1995) The impact of horizontal resolution on moist processes in the ECMWF model. Clim Dyn 11: 85–102Google Scholar
  19. Rind D (1988) Dependence of warm and cold climate depiction on climate model resolution. J Clim 1: 965–997Google Scholar
  20. Simmons AJ, Stuefing R (1981) An energy and angular-momentum conserving finite-difference scheme, hybrid coordinates and medium-range weather prediction. ECMWF Technical Report 28, pp 68Google Scholar
  21. Slingo JM, Sperber K, Boyle JS, Ceron J-P, Dix M, Dugas B, Ebisuzaki W, Fyfe J, Gregory D, Gueremy J-F, Hack J, Harzallah A, Inness P, Kitoh A, Lau WK-M, McAvaney B, Madden R, Matthews A, Palmer TN, Park C-K, Randall D, Renno N (1996) Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject. Clim Dyn 12: 325–357Google Scholar
  22. Slingo JM, Rowell DP, Sperber KR, Nortley F (1999) On the predictability of the interannual behaviour of the Madden-Julian Oscillation and its relationship with El Niño. Q J R Meteorol Soc 125: 583–609Google Scholar
  23. Sperber KR (2003) Propagation and the vertical structure of the Madden-Julian Oscillation. Mon Weather Rev (in press)Google Scholar
  24. Sperber KR, Hameed S, Potter GL, Boyle JS (1994) Simulation of the northern summer monsoon in the ECMWF model: sensitivity to horizontal resolution. Mon Weather Rev 122: 2461–2481Google Scholar
  25. Sperber KR, Slingo JM, Inness PM, Lau WK-M (1997) On the maintenance and initiation of the intraseasonal oscillation in the NCEP/NCAR reanalysis and in the GLA and UKMO AMIP simulations. Clim Dyn 13: 769–795Google Scholar
  26. Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res 106: 7183–7192Google Scholar
  27. Tibaldi T, Palmer N, Brankovic C, Cabasch U (1990) Extended-range predictions with ECMWF models: influence of horizontal resolution on systematic error and forecast skill. Q J R Meteorol Soc 115: 835–866Google Scholar
  28. Wellck RE, Kasahara A, Washington WM, de Santo G (1971) Effect of horizontal resolution in a finite difference model of the general circulation. Mon Weather Rev 99: 673–683Google Scholar
  29. Williamson DL, Kiehl JT, Hack JJ (1995) Climate sensitivity of the NCAR Community Climate model (CCM2) to horizontal resolution. Clim Dyn 11: 377–397Google Scholar

Copyright information

© Springer-Verlag 2003

Authors and Affiliations

  • P. B. Duffy
    • 1
  • B. Govindasamy
    • 1
  • J. P. Iorio
    • 1
  • J. Milovich
    • 2
  • K. R. Sperber
    • 3
  • K. E. Taylor
    • 3
  • M. F. Wehner
    • 3
    • 4
  • S. L. Thompson
    • 1
  1. 1.Climate and Carbon Cycle Modeling Group, Lawrence Livermore National Laboratory (LLNL), PO Box 808, Livermore, CA 94550, USA
  2. 2.Center for Applied Scientific Computing, LLNL, USA
  3. 3.Program for Climate Model Diagnosis and Intercomparison, LLNL, USA
  4. 4.National Energy Research Supercomputer Center, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, MS-50F, Berkeley, CA 94720, USA

Personalised recommendations