Climate Dynamics

, Volume 22, Issue 8, pp 795–814 | Cite as

Long-term simulation of Indonesian rainfall with the MPI regional model

  • E. Aldrian
  • L. Dümenil-Gates
  • D. Jacob
  • R. Podzun
  • D. Gunawan
Article

Abstract

Simulations of the Indonesian rainfall variability using the Max Planck Institute regional climate model REMO have been performed using three different lateral boundary forcings: Reanalyses from the European Centre for Medium-Range Weather Forecasts (ERA15), the National Centers for Environmental Prediction and National Center for Atmospheric Research (NRA) as well as from ECHAM4 climate model simulation. The result of those simulations are compared to station data. REMO simulations were performed at 0.5° horizontal resolution for the whole archipelago and at 1/6° for Sulawesi Island. In general the REMO model, reproduces the spatial pattern of monthly and seasonal rainfall well over land, but overestimates the rainfall over sea. Superiority of REMO performance over land is due to a high-resolution orography, while over sea, REMO suffers from erroneously low surface fluxes. REMO reproduces variability during El Niño-Southern Oscillations years well but fails to show a good (wet and dry) monsoon contrast. Despite strong influences of the lateral boundary fields, REMO shows a realistic improvement of a local phenomenon over Molucca. Significant improvement for the step from the relatively high global 1.125° to 0.5° resolution is noticeable, but not from 0.5° into 1/6°. The REMO simulation driven by ERA15 has the best quality, followed by NRA and ECHAM4 driven simulations. The quality of ERA15 is the main factor determining the quality of REMO simulations. A predictability study shows small internal variability among ensemble members. However, there are systematic intrinsic climatological errors as shown in the predictability analysis. These intrinsic errors have monthly, seasonal and regional dependencies and the one over Java is significantly large. The intrinsic error study suggests the presence of the spring predictability barrier and a high level of predictability in summer.

References

  1. Aldrian E, Susanto RD (2003) Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. Int J Climatol 23: 1435–1452CrossRefGoogle Scholar
  2. Aldrian E, Dümenil-Gates L, Widodo FH (2003) Variability of Indonesian rainfall and the influence of ENSO and resolution in ECHAM4 simulations and in the reanalyses. MPI Rep 346 available from Max Planck-Institut für Meteorologie, Bundesstr. 55, D-20146, Hamburg, Germany, pp 30Google Scholar
  3. Balmaseda MA, Davey MK, Anderson DLT (1995) Decadal and seasonal dependence of ENSO prediction skill. J Clim 8: 2705–2715CrossRefGoogle Scholar
  4. Bhaskaran B, Jones RG, Murphy JM, Noguer M (1996) Simulations of the Indian summer monsoon using a nested regional climate model: domain size experiments. Clim Dyn 12: 573–587CrossRefGoogle Scholar
  5. Bhowmik SKR, Prasad K (2001) Some characteristics of limited-area model-prescription forecast of Indian monsoon and evaluation of associated flow features. Meteorol Atmos Phys 76: 223–236CrossRefGoogle Scholar
  6. Blumenthal MB (1991) Predictability of a coupled ocean-atmosphere model. J Clim 4: 766–784CrossRefGoogle Scholar
  7. Bougeault P (1983) A non-reflective upper boundary condition for limited-height hydrostatic models. Mon Weather Rev 111: 420–429CrossRefGoogle Scholar
  8. Chen WY, van den Dool HM (1997) Atmospheric predictability of seasonal, annual and decadal climate means and the role of the ENSO cycle: a model study. J Clim 10: 1236–1254CrossRefGoogle Scholar
  9. Davey MK, Anderson DLT, Lawrence S (1996) A simulation of variability in ENSO forecast skill. J Clim 9: 240–246CrossRefGoogle Scholar
  10. Davies HC (1976) A lateral boundary formulation for multi-level prediction models. Q J R Meteorol Soc 102: 405–418CrossRefGoogle Scholar
  11. Deutscher-Wetterdienst (1995) Dokumentation des EM/DM Systems with contributions from Edelmann W, Majewski D, Schättler U, Prohl P, Heise E, Doms G, Ritter B, Link A, Gertz M, Hanisch T, Fischer E. Zentralamt, Abteilung Forschung, Postfach 10 04 65, 63004 Offenbach am Main, GermanyGoogle Scholar
  12. Dümenil L, Todini E (1992) A rainfall-runoff scheme for use in the Hamburg climate model. In: O’Kane JP (ed) Advance of theoretical hydrology, vol 1 (Ed.), European Geophysical Society. Series on Hydrological Sciences vol 1, Elsevier Science, Amsterdam, pp 129–157Google Scholar
  13. Errico RM, Baumhefner DP (1987) Predictability experiments using a high resolution limited area model. Mon Weather Rev 115: 488–504CrossRefGoogle Scholar
  14. Flügel M, Chang P (1998) Does the predictability of ENSO depend on the seasonal cycle? J Atmos Sci 55: 3230–3243CrossRefGoogle Scholar
  15. Fouquart Y, Bonnel B (1980) Computations of solar heating of the Earth’s atmosphere: A new parametrization. Beitr. Phys Atmos 53: 35–62Google Scholar
  16. Gates WL (1992) AMIP: the Atmospheric Model Intercomparison Project. Bull Am Meteorol Soc 73: 1962–1970CrossRefGoogle Scholar
  17. Gates WL, Boyle JS, Covey C, Dease CG, Doutriaux CM, Drach RS, Fiorino M, Gleckler PJ, Hnilo JJ, Marlais SM, Phillips TJ, Potter GL, Santer BD, Sperber KR, Taylor KE, Williams DN (1999) An overview of the results of the Atmospheric Model Intercomparison Project (AMIP I). Bull Am Meteorol Soc 80: 29–56CrossRefGoogle Scholar
  18. Gibson JK, Kallberg P, Uppala S, Hernandez A, Nomura A, Serrano E (1997) The ECMWF Re-Analysis (ERA) 1. ERA description., ECMWF Reanalysis Project Report Series 1 ECMWF, (available from the European Centre for Medium-range Weather Forecasts, Reading, UK), pp 71Google Scholar
  19. Giorgi F (1991) Sensitivity of simulated summertime precipitation over the western United States to different physics parametrizations. Mon Weather Rev 119: 2870–2888CrossRefGoogle Scholar
  20. Goddard L, Mason SJ, Zebiak SE, Ropelowski CF, Basher R, Cane MA (2000) Current approaches to seasonal to interannual climate predictions., (IRI) Tech Rep 00-01 International Research Institute pp 62Google Scholar
  21. Goswami B, Shukla J (2000) Predictability of a coupled-atmosphere model. J Clim 4: 3–22CrossRefGoogle Scholar
  22. Hagemann S, Botzet M, Dümenil L, Machenhauer B (1999) Derivation of global GCM boundary conditions from 1 km land use satellite data. MPI Report 289, Max Planck-Institut für Meteorologie, Bundesstrasse 55, 20146, Hamburg, Germany pp 34Google Scholar
  23. Hamada JI, Yamanaka MD, Matsumoto J, Fukao S, Winarso PA, Sribimawati T (2002) Spatial and temporal variations of the rainy season over Indonesia and their link to ENSO. J Meteorol Soc Japan 80: 285–310Google Scholar
  24. Haylock M, McBride J (2001) Spatial coherence and predictability of Indonesian wet season rainfall. J Clim 14: 3882–3887CrossRefGoogle Scholar
  25. Hendon HH (2003) Indonesian rainfall variability: impacts of ENSO and local air-sea interaction. J Clim 16: 1775–1790CrossRefGoogle Scholar
  26. Huffman GJ, Adler RF, Arkin P, Chang A, Ferraro R, Gruber A, Janowiak J, McNab A, Rudolf B, Schneider U (1997) The Global Precipitation Climatology Project (GPCP) combined precipitation data set. Bull Am Meteorol Soc 78: 5–20CrossRefGoogle Scholar
  27. Jacob D (2001) A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin. Meteorol Atmos Phys 77: 61–73CrossRefGoogle Scholar
  28. Jacob D, Podzun R (1997) Sensitivity studies with the regional climate model REMO. Meteorol Atmos Phys 63: 119–129Google Scholar
  29. Jacob D, den Hurk BJJMV, AndræG, Elgered G, Fortelius C, Graham LP, Jackson SD, Karstens U, Köpken C, Lindau R, Podzun R, Roeckel B, Rubel F, Sass BH, Smith RNB, Yang X (2001) A comprehensive model inter-comparison study investigation the water budget during the BALTEX-PIDCAP period. Meteorol Atmos Phys 77: 19–43Google Scholar
  30. Jha B, Khrisnamurti TN, Christides Z (2000) A note on horizontal resolution dependence for monsoon rainfall simulations. Meteorol Atmos Phys 74: 11–17CrossRefGoogle Scholar
  31. Ji Y, Vernekar AD (1997) Simulation of the Asian summer monsoons of 1987 and 1988 with a regional model nested in a global GCM. J Clim 10: 1965–1979CrossRefGoogle Scholar
  32. Jones RG, Murphy JM, Noguer M (1995) Simulation of climate change over Europe using a nested regional climate model. I: assesment of control climate, including sensitivity to location of lateral boundaries. Q J R Meteorol Soc 121: 1413–1449CrossRefGoogle Scholar
  33. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuza W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77: 437–471CrossRefGoogle Scholar
  34. Kesler E (1969) On the distribution and continuity of water substance in atmospheric circulations. Meteorolological Monograph 32, pp 84Google Scholar
  35. Klemp JB, Durran DR (1983) An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models. Mon Weather Rev 111: 430–444CrossRefGoogle Scholar
  36. Laprise R, Varma MR, Denis B, Caya D, Zawadzki I (2000) Predictability of a nested limited area model. Mon Weather Rev 128: 4149–4154CrossRefGoogle Scholar
  37. Latif M, Flügel M (1991) An investigation of hot-range climate predictability in the tropical Pacific. J Geophys Res 96: 2661–2673Google Scholar
  38. Latif M, Graham NE (1992) How much predictive skill is contained in the thermal structure of an OGCM? J Phys Ocean 22: 951–962CrossRefGoogle Scholar
  39. Latif M, Anderson DLT, Barnett TP, Cane MA, Kleeman R, Leetma A, O’Brien J, Rosati A, Schneider E (1998) A review of the predictability and prediction of ENSO. J Geophys Res 103: 14,375–14,393CrossRefGoogle Scholar
  40. Lorenz EN (1969) The predictability of a flow which possesses many scales of motion. Tellus 21: 289–307Google Scholar
  41. Mc Gregor JL (1997) Regional climate modeling. Meteorol Atmos Phys 63: 105–117Google Scholar
  42. Mc Gregor JL, Walsh K (1994) Climate change simulations of Tasmanian precipitation using multiple nesting. J Geophys Res 99: 20,889–20,905CrossRefGoogle Scholar
  43. Menendez CG, Saulo AC, Li Z (2001) Simulation of South American wintertime climate with a nesting system. Clim Dyn 17: 219–231CrossRefGoogle Scholar
  44. Mesinger F (1997) Dynamics of Limited-area models: formulation and numerical methods. Meteorol Atmos Phys 63: 3–14Google Scholar
  45. Moore AM, Kleeman R (1996) The dynamics of error growth and predictability in a coupled model of ENSO. Q J R Meteorol Soc 122: 1405–1446CrossRefGoogle Scholar
  46. Morcrette JJ, Fouquart Y (1986) Pressure and temperature dependence of the absorption in longwave radiation parametrizations Beitr. Phys Atmos 59: 455–469Google Scholar
  47. Nobre P, Moura AD, Sun L (2001) Dynamical downscaling of seasonal climate prediction over Nordeste Brazil with ECHAM3 and NCEP’s regional sprectral model at IRI Bull Am Meteorol Soc 82: 2787–2796Google Scholar
  48. Nordeng TE (1994) Extended versions of the convective parametrization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics., ECMWF Tech Mem 206, European Centre for Medium-range Weather Forecasts, Reading, UKGoogle Scholar
  49. Nouger M, Jones RG, Murphy JM (1998) Sources of systematic errors in the climatology of a regional climate model over Europe. Clim Dyn 14: 691–712CrossRefGoogle Scholar
  50. Paegle J, Yang Q, Wang M (1997) Predictability in limited area and global models. Meteorol Atmos Phys 63: 53–69Google Scholar
  51. Peterson TC, Vose R, Schmoyer R, Razuvaev V (1998) Global Historical Climatology Network (GHCN) quality control of monthly temperature data. Int J Clim 18: 1169–1179CrossRefGoogle Scholar
  52. Podzun R, Cress A, Majewski A, Renner V (1995) Simulation of European Climate with a limited area model. Part II. AGCM boundary conditions. Contrib Atmos Phys 68: 205–225Google Scholar
  53. Rayner NA, Horton EB, Parker DE, Folland CK, Hackett RB (1996) Version 2.2 of the global sea-ice and sea surface temperature data set, 1903–1994. Tech Note CRTN74 Climate Research, pp 35Google Scholar
  54. Roeckner E, Arpe K, Bengston L, Christoph M, Claussen M, Dumenil L, Esch M, Giorgetta M, Schlese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM-4: model description and simulation of present-day climate. MPI Report 218, Max Planck-Institut für Meteorologie, Bundesstr. 55, D-20146, Hamburg, Germany pp 90Google Scholar
  55. Simmons AJ, Burridge DM (1981) An energy and angular-momentum conserving vertical finite-difference scheme and hybrid vertical coordinates. Mon Weather Rev 109: 758–766CrossRefGoogle Scholar
  56. Staniforth A (1997) Regional modeling: a theoretical discussion. Meteorol Atmos Phys 63: 15–29Google Scholar
  57. Stendel M, Roeckner E (1998) Impacts of horizontal resolution on simulated climate statistics in ECHAM4. MPI Rep 253, Max Planck-Institut für Meteorologie, Bundesstr. 55, D-20146, Hamburg, Germany pp 57Google Scholar
  58. Sundqvist H (1978) A parametrization scheme for non-convective condensation including prediction of cloud water content. Q J R Meteorol Soc 104: 677–690CrossRefGoogle Scholar
  59. Thompson CJ, Battisti DS (2001) A linear stochastic dynamical model of ENSO. Part II: analysis. J Clim 14: 445–466CrossRefGoogle Scholar
  60. Tiedtke M (1989) A comprehensive mass flux scheme for cumulus parametrization in large scale models. Mon Weather Rev 117: 1779–1800CrossRefGoogle Scholar
  61. Vose RS, Schmoyer RL, Steurer PM, Peterson TC, Heim R, Karl TR, Eischeid JK (1992) The Global Historical Climatology Network: long-term monthly temperature, precipitation, sea level pressure, and station pressure data. ORNL/CDIAC-53 NDP-041 pp 325Google Scholar
  62. Vukicevic T, Errico RM (1990) The influence of artificial and physical factors upon predictability estimates using a complex limited area model. Mon Weather Rev 118: 1460–1482CrossRefGoogle Scholar
  63. Warner TT, Peterson RA, Treadon RE (1997) A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull Am Meteorol Soc 78: 2599–2617CrossRefGoogle Scholar
  64. Webster PJ, Yang S (1992) Monsoon and ENSO: selectively interactive systems. Q J R Meteorol Soc 118: 877–925CrossRefGoogle Scholar
  65. Weiss JP, Weiss JB (1999) Quantifying persistence in ENSO. J Atmos Sci 56: 2737–2760CrossRefGoogle Scholar
  66. Yu ZP, Chu PS, Schroeder T (1997) Predictive skills of seasonal to annual rainfall variations in the US affiliated Pacific Islands: canonical correlation analysis and multivariate principal component regression approaches. J Clim 10: 2586–2599CrossRefGoogle Scholar

Copyright information

© Springer-Verlag  2004

Authors and Affiliations

  • E. Aldrian
    • 1
    • 2
  • L. Dümenil-Gates
    • 1
    • 3
  • D. Jacob
    • 1
  • R. Podzun
    • 1
  • D. Gunawan
    • 4
  1. 1.Max Planck Institut für MeteorologieHamburgGermany
  2. 2.The Agency for the Assessment and Application of TechnologyBPPTJakartaIndonesia
  3. 3.National Science FoundationArlingtonUSA
  4. 4.Institute of Bioclimatology Georg–August–UniversitätGöttingenGermany

Personalised recommendations