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Climate Dynamics

, Volume 49, Issue 5–6, pp 1747–1764 | Cite as

Mechanisms of improved rainfall simulation over the Maritime Continent due to increased horizontal resolution in an AGCM

  • Harun A. RashidEmail author
  • Anthony C. Hirst
Article

Abstract

The General Circulation Models experience a significant challenge in realistically simulating rainfall over the tropical Maritime Continent (hereafter, MC). Here, we investigate the mechanisms of an improvement in monthly rainfall simulation over the MC region in the UK Met Office Unified Model (version Global Atmosphere 6.0), which occurs when the horizontal resolution is increased from N96 (grid spacing ~135 km) to N216 (~60 km). The increased resolution enhances the area-averaged rainfall rate over the MC, thereby reducing the dry rainfall bias seen in the model at the N96 resolution. We find that the enhanced area-averaged rainfall is mostly due to an increase in the medium rainfall rates that occurs over the MC islands in the N216 experiment. The rainfall change is predominantly associated with changes in the atmospheric convective circulation and the related horizontal moisture flux convergence. The vertical profiles of convective circulation show a strong sensitivity to the increased horizontal resolution over the MC islands, but not over the surrounding oceans. It is shown that a significant underestimation of the deep convection (as opposed to the shallow convection) in the N96 experiment is primarily responsible for the stronger dry bias in this experiment. We present evidence that the dry bias, and the associated weaker deep convection, are in part caused by the strongly smoothed orography used in the N96 experiment, which provides a weaker orographic lifting of the moist surface air (in a conditionally unstable atmosphere) than that in the N216 experiment.

Keywords

Rainfall bias Maritime Continent GCMs Tropical convection Orographic lifting 

Notes

Acknowledgements

We thank two anonymous reviewers for providing useful comments, which have helped improve this paper. We also thank Ian Watterson and Matthew Wheeler for providing useful feedbacks on an earlier version of this manuscript. Thanks are also due to Martin Dix for setting up the AMIP-style experiments analysed in this work and Faina Tseitkin for performing one of the experiments. This work has been undertaken as part of the Australian Climate Change Science Program, funded jointly by the Australian Government Department of the Environment, the Bureau of Meteorology and CSIRO. The simulations were performed at the NCI National Facility at the Australian National University. We thank the members of the Maritime Continent Process Evaluation Group (MC-PEG) for discussions during the course of this work. GPCP and CMAP Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/.

Supplementary material

382_2016_3413_MOESM1_ESM.docx (3.1 mb)
Supplementary material 1 (DOCX 3199 kb)

References

  1. Adler RF, Huffman GJ, Chang A et al (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4:1147–1167. doi: 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2 CrossRefGoogle Scholar
  2. Bacmeister JT, Wehner MF, Neale RB et al (2014) Exploratory high-resolution climate simulations using the community atmosphere model (CAM). J Clim 27:3073–3099. doi: 10.1175/JCLI-D-13-00387.1 CrossRefGoogle Scholar
  3. Birch CE, Roberts MJ, Garcia-Carreras L et al (2015) Sea-breeze dynamics and convection initiation: the influence of convective parameterization in weather and climate model biases. J Clim 28:8093–8108. doi: 10.1175/JCLI-D-14-00850.1 CrossRefGoogle Scholar
  4. Bony S, Dufresne J-L, Le Treut H et al (2004) On dynamic and thermodynamic components of cloud changes. Clim Dyn 22:71–86. doi: 10.1007/s00382-003-0369-6 CrossRefGoogle Scholar
  5. Bony S, Bellon G, Klocke D et al (2013) Robust direct effect of carbon dioxide on tropical circulation and regional precipitation. Nat Geosci 6:447–451. doi: 10.1038/ngeo1799 CrossRefGoogle Scholar
  6. Brown AR, Beare RJ, Edwards JM et al (2008) Upgrades to the boundary-layer scheme in the Met Office numerical weather prediction model. Boundary-Layer Meteorol 128:117–132. doi: 10.1007/s10546-008-9275-0 CrossRefGoogle Scholar
  7. Cusack S, Edwards JM, Crowther JM (1999) Investigating k distribution methods for parameterizing gaseous absorption in the Hadley Centre Climate Model. J Geophys Res 104:2051–2057CrossRefGoogle Scholar
  8. Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi: 10.1002/qj.828 CrossRefGoogle Scholar
  9. Donlon CJ, Martin M, Stark J et al (2012) The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sens Environ 116:140–158. doi: 10.1016/j.rse.2010.10.017 CrossRefGoogle Scholar
  10. Edwards JM, Slingo A (1996) Studies with a flexible new radiation code. I: choosing a configuration for a large-scale model. Q J R Meteorol Soc 122:689–719. doi: 10.1002/qj.49712253107 CrossRefGoogle Scholar
  11. Gianotti RL, Zhang D, Eltahir EAB (2012) Assessment of the regional climate model version 3 over the maritime continent using different cumulus parameterization and land surface schemes. J Clim 25:638–656. doi: 10.1175/JCLI-D-11-00025.1 CrossRefGoogle Scholar
  12. Gregory D, Allen S (1991) The effect of convective scale downdrafts upon NWP and climate simulations. In: Ninth conference on numerical weather prediction. Denver, Color. Amer Met Soc, pp 122–123Google Scholar
  13. Gregory D, Rowntree PR (1990) A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure. Mon Weather Rev 118:1483–1506. doi: 10.1175/1520-0493(1990)118<1483:AMFCSW>2.0.CO;2 CrossRefGoogle Scholar
  14. Hardiman SC, Boutle IA, Bushell AC et al (2015) Processes controlling tropical tropopause temperature and stratospheric water vapor in climate models. J Clim. doi: 10.1175/JCLI-D-15-0075.1 Google Scholar
  15. Hertwig E, von Storch JS, Fast I, Krismer TR (2015) Effect of horizontal resolution on ECHAM6-AMIP performance. Clim Dyn. doi: 10.1007/s00382-014-2396-x Google Scholar
  16. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Stocker EF, Wolff DB (2007) The TRMM multi-satellite precipitation analysis: quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J Hydrometeor 8:33–55CrossRefGoogle Scholar
  17. Johnson SJ, Levine RC, Turner AG et al (2015) The resolution sensitivity of the South Asian monsoon and Indo-Pacific in a global 0.35° AGCM. Clim Dyn. doi: 10.1007/s00382-015-2614-1 Google Scholar
  18. Jourdain NC, Sen Gupta A, Taschetto AS et al (2013) The Indo-Australian monsoon and its relationship to ENSO and IOD in reanalysis data and the CMIP3/CMIP5 simulations. Clim Dyn 41:3073–3102. doi: 10.1007/s00382-013-1676-1 CrossRefGoogle Scholar
  19. Ling J, Zhang C (2013) Diabatic heating profiles in recent global reanalyses. J Clim 26:3307–3325. doi: 10.1175/JCLI-D-12-00384.1 CrossRefGoogle Scholar
  20. Lock AP (2001) The numerical representation of entrainment in parameterizations of boundary layer turbulent mixing. Mon Weather Rev 129:1148–1163CrossRefGoogle Scholar
  21. Lock A, Brown A, Bush M et al (2000) A new boundary layer mixing scheme. Part I: scheme description and single-column model tests. Mon Weather Rev 128:3187–3199CrossRefGoogle Scholar
  22. Morcrette CJ (2012) Improvements to a prognostic cloud scheme through changes to its cloud erosion parametrization. Atmos Sci Lett 13:95–102. doi: 10.1002/asl.374 CrossRefGoogle Scholar
  23. Moron V, Robertson AW, Qian J-H, Ghil M (2015) Weather types across the Maritime Continent: from the diurnal cycle to interannual variations. Front Environ Sci 2:1–19. doi: 10.3389/fenvs.2014.00065 CrossRefGoogle Scholar
  24. Neale R, Slingo J (2003) The Maritime Continent and its role in the global climate: a GCM study. J Clim 16:834–848. doi: 10.1175/1520-0442(2003)016<0834:TMCAIR>2.0.CO;2 CrossRefGoogle Scholar
  25. Peatman SC, Matthews J, Stevens DP (2014) Propagation of the Madden–Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation. Q J R Meteorol Soc 140:814–825. doi: 10.1002/qj.2161 CrossRefGoogle Scholar
  26. Qian J-H (2008) Why precipitation is mostly concentrated over islands in the Maritime Continent. J Atmos Sci 65:1428–1441. doi: 10.1175/2007JAS2422.1 CrossRefGoogle Scholar
  27. Ramage CS (1968) Role of a tropical “Maritime Continent” in the atmospheric circulation 1. Mon Weather Rev 96:365–370. doi: 10.1175/1520-0493(1968)096<0365:ROATMC>2.0.CO;2 CrossRefGoogle Scholar
  28. Schiemann R, Demory M-E, Mizielinski MS et al (2014) The sensitivity of the tropical circulation and Maritime Continent precipitation to climate model resolution. Clim Dyn 42:2455–2468. doi: 10.1007/s00382-013-1997-0 CrossRefGoogle Scholar
  29. Trenberth KE, Fasullo JT, Mackaro J (2011) Atmospheric moisture transports from ocean to land and global energy flows in reanalyses. J Clim 24:4907–4924. doi: 10.1175/2011JCLI4171.1 CrossRefGoogle Scholar
  30. Ulate M, Dudhia J, Zhang C (2014) Sensitivity of the water cycle over the Indian Ocean and Maritime Continent to parameterized physics in a regional model. J Adv Model Earth Syst 6:1095–1120. doi: 10.1002/2014MS000313 CrossRefGoogle Scholar
  31. Veiga JAP, Rao VB, Franchito SH (2005) Heat and moisture budgets of the Walker circulation and associated rainfall anomalies during El Niño events. Int J Climatol 25:193–213. doi: 10.1002/joc.1115 CrossRefGoogle Scholar
  32. Walters DN, Best MJ, Bushell AC et al (2011) The Met Office Unified Model global atmosphere 3.0/3.1 and JULES global land 3.0/3.1 configurations. Geosci Model Dev 4:919–941. doi: 10.5194/gmd-4-919-2011 CrossRefGoogle Scholar
  33. Walters DN, Williams KD, Boutle IA, et al (2014) The Met Office Unified Model global atmosphere 4. 0 and JULES global land 4. 0 configurations. Geosci Model Dev 7:361–386. doi:  10.5194/gmd-7-361-2014 CrossRefGoogle Scholar
  34. Watterson IG (2015) Improved simulation of regional climate by global models with higher resolution: skill scores correlated with grid length. J Clim. doi: 10.1175/JCLI-D-14-00702.1 Google Scholar
  35. Wilson DR, Ballard SP (1999) A microphysically based precipitation scheme for the UK meteorological office unified model. Q J R Meteorol Soc 125:1607–1636CrossRefGoogle Scholar
  36. Wilson DR, Bushell AC, Kerr-munslow AM et al (2008a) PC2: a prognostic cloud fraction and condensation scheme. I: Scheme description. Q J R Meteorol Soc 134:2093–2107CrossRefGoogle Scholar
  37. Wilson DR, Bushell AC, Kerr-munslow AM et al (2008b) PC2: a prognostic cloud fraction and condensation scheme. II: climate model simulations. Q J R Meteorol Soc 2125:2109–2125. doi: 10.1002/qj CrossRefGoogle Scholar
  38. Wood N, Staniforth A, White A et al (2014) An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global non-hydrostatic equations. Q J R Meteorol Soc 140:1505–1520. doi: 10.1002/qj.2235 CrossRefGoogle Scholar
  39. Xie P, Arkin PA (1997) Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull Am Meteorol Soc 78:2539–2558. doi: 10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2 CrossRefGoogle Scholar
  40. Yin X, Gruber A, Arkin P (2004) Comparison of the GPCP and CMAP merged gauge-satellite monthly precipitation products for the period 1979–2001. J Hydrometeorol 5:1207–1222. doi: 10.1175/JHM-392.1 CrossRefGoogle Scholar
  41. Zwiers FW, von Storch H (1995) Taking serial correlation into account in tests of the mean. J Clim 8:336–351. doi: 10.1175/15200442(1995)008<0336:TSCIAI>2.0.CO;2 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.CSIRO Oceans and AtmosphereAspendale, MelbourneAustralia

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