Climate Dynamics

, Volume 22, Issue 8, pp 795-814

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

  • E. AldrianAffiliated withMax Planck Institut für MeteorologieThe Agency for the Assessment and Application of Technology, BPPT Email author 
  • , L. Dümenil-GatesAffiliated withMax Planck Institut für MeteorologieNational Science Foundation
  • , D. JacobAffiliated withMax Planck Institut für Meteorologie
  • , R. PodzunAffiliated withMax Planck Institut für Meteorologie
  • , D. GunawanAffiliated withInstitute of Bioclimatology, Georg–August–Universität

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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.