Climate Dynamics

, Volume 47, Issue 3–4, pp 817–829 | Cite as

Regional climate model projections of the South Pacific Convergence Zone

  • J. P. Evans
  • K. Bormann
  • J. Katzfey
  • S. Dean
  • R. Arritt


This study presents results from regional climate model (RCM) projections for the south-west Pacific Ocean. The regional models used bias corrected sea surface temperatures. Six global climate models (GCMs) were used to drive a global variable resolution model on a quasi-uniform 60 km grid. One of these simulations was used to drive three limited area regional models. Thus a four member ensemble was produced by different RCMs downscaling the same GCM (GFDL2.1), and a six member ensemble was produced by the same RCM (Conformal Cubic Atmospheric Model—CCAM) downscaling six different GCMs. Comparison of the model results with precipitation observations shows the differences to be dominated by the choice of RCM, with all the CCAM simulations performing similarly and generally having lower error than the other RCMs. However, evaluating aspects of the model representation of the South Pacific Convergence Zone (SPCZ) does not show CCAM to perform better in this regard. In terms of the future projections of the SPCZ for the December–January–February season, the ensemble showed no consensus change in most characteristics though a majority of the ensemble members project a decrease in the SPCZ strength. Thus, similar to GCM based studies, there is large uncertainty concerning future changes in the SPCZ and there is no evidence to suggest that future changes will be outside the natural variability. These RCM simulations do not support an increase in the frequency of zonal SPCZ events.


Regional climate model South Pacific Convergence Zone Precipitation Tropical pacific 



The research discussed in this paper was conducted with the support of the Pacific Climate Change Science Program, a program supported by AusAID, in collaboration with the Department of Climate Change and Energy Efficiency, and delivered by the Bureau of Meteorology and the Commonwealth Scientific and Industrial Research Organisation (CSIRO). The research also was supported in part by the U.S. Department of Agriculture National Institute of Food and Agriculture (NIFA). We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM), for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. More details on model documentation are available at the PCMDI Web site (


  1. Ackerley D, Dean S, Sood A, Mullan B (2012) Regional climate modelling in New Zealand: comparison to gridded and satellite observations. Weather Clim 32:3–23Google Scholar
  2. Adler RF, Huffman GJ, Chang A et al (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4:1147–1167CrossRefGoogle Scholar
  3. Australian Bureau of Meteorology, CSIRO (2011) Climate change in the Pacific: scientific assessment and new research. Volume 1: Regional overview. Volume 2: Country reportsGoogle Scholar
  4. Australian Bureau of Meteorology, CSIRO (2014) Climate variability, extremes and change in the western tropical Pacific: new science and updated country reports. Australian Bureau of Meteorology and Commonwealth Scientific and Industrial Research Organisation, MelbourneGoogle Scholar
  5. Au-Yeung AYM, Chan JCL (2012) Potential use of a regional climate model in seasonal tropical cyclone activity predictions in the western North Pacific. Clim Dyn 39:783–794. doi: 10.1007/s00382-011-1268-x CrossRefGoogle Scholar
  6. Brown JR, Power SB, Delage FP et al (2011) Evaluation of the South Pacific Convergence Zone in IPCC AR4 climate model simulations of the twentieth century. J Clim 24:1565–1582CrossRefGoogle Scholar
  7. Brown JR, Moise AF, Delage FP (2012) Changes in the South Pacific Convergence Zone in IPCC AR4 future climate projections. Clim Dyn 39:1–19. doi: 10.1007/s00382-011-1192-0 CrossRefGoogle Scholar
  8. Brown JN, Brown JR, Langlais C et al (2013a) Exploring qualitative regional climate projections: a case study for Nauru. Clim Res 58:165–182. doi: 10.3354/cr01190 CrossRefGoogle Scholar
  9. Brown JR, Moise AF, Colman RA (2013b) The South Pacific Convergence Zone in CMIP5 simulations of historical and future climate. Clim Dyn 41:2179–2197. doi: 10.1007/s00382-012-1591-x CrossRefGoogle Scholar
  10. Cai W, Lengaigne M, Borlace S et al (2012) More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature 488:365–369. doi: 10.1038/nature11358 CrossRefGoogle Scholar
  11. Chattopadhyay M, Katzfey J (2015) Simulating the climate of South Pacific Islands using a high resolution model. Int J Climatol 35:1157–1171. doi: 10.1002/joc.4046 CrossRefGoogle Scholar
  12. Emanuel KA (1991) A scheme for representing cumulus convection in large-scale models. J Atmos Sci 48:2313–2335CrossRefGoogle Scholar
  13. Emanuel KA, Rothman MZ (1999) Development and evaluation of a convection scheme for use in climate models. J Atmos Sci 56:1756–1782CrossRefGoogle Scholar
  14. Evans JP, McCabe MF (2010) Regional climate simulation over Australia’s Murray–Darling basin: a multitemporal assessment. J Geophys Res D Atmos 115:D14114. doi: 10.1029/2010JD013816 CrossRefGoogle Scholar
  15. Evans JP, McCabe MF (2013) Effect of model resolution on a regional climate model simulation over southeast Australia. Clim Res 56:131–145. doi: 10.3354/cr01151 CrossRefGoogle Scholar
  16. Evans JP, Westra S (2012) Investigating the mechanisms of diurnal rainfall variability using a regional climate model. J Clim 25:7232–7247. doi: 10.1175/JCLI-D-11-00616.1 CrossRefGoogle Scholar
  17. Frei C, Christensen JH, Deque M et al (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res Atmos 108:4124. doi: 10.1029/2002JD002287 CrossRefGoogle Scholar
  18. Giorgi F, Pal JS, Bi X, Sloan L, Elguindi N, Solmon F (2006) Introduction to the TAC special issue: the RegCNET network. Theor Appl Climatol 86:1–4. doi: 10.1007/s00704-005-0199-z CrossRefGoogle Scholar
  19. Gordon C, Cooper C, Senior CA et al (2000) The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Clim Dyn 16:147–168. doi: 10.1007/s003820050010 CrossRefGoogle Scholar
  20. Gregory D, Smith RNB, Cox PM (1994) Canopy, surface and soil hydrology, Version 3Google Scholar
  21. Grell GA, Dudhia J, Stauffer DR (1994) A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical Note NCAR/TN-398+STR. doi: 10.5065/D60Z716B
  22. Holtslag AAM, de Bruijn EIF, Pan HL (1990) A high resolution air mass transformation model for short-range weather forecasting. Mon Weather Rev 118:1561–1575CrossRefGoogle Scholar
  23. Hudson DA, Jones RG (2002) Regional climate model simulations of present-day and future climates of southern Africa. Hadley Centre technical note 39, Hadley Centre, UK Meteorological OfficeGoogle Scholar
  24. IPCC (2007) Summary for Policymakers. In: Climate change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds)]. Cambridge University Press, CambridgeGoogle Scholar
  25. Jones RG, Noguer M, Hassell DC, Hudson D, Wilson SS, Jenkins GJ, Mitchell JFB (2004) Generating high resolution climate change scenarios using PRECIS. Met Office, Hadley Centre, ExeterGoogle Scholar
  26. Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL (2002) NCEP–DOE AMIP-II Reanalysis (R-2). Bull Am Meteorol Soc 83:1631–1643CrossRefGoogle Scholar
  27. Lintner BR, Neelin JD (2008) Eastern margin variability of the South Pacific Convergence Zone. Geophys Res Lett 35:L16701. doi: 10.1029/2008GL034298 CrossRefGoogle Scholar
  28. McGregor JL (1996) Semi-Lagrangian advection on conformal-cubic grids. Mon Weather Rev 124:1311–1322CrossRefGoogle Scholar
  29. McGregor JL (2003) A new convection scheme using a simple closure. In: Current issues in the parameterization of convection, Bureau of meteorology, Melbourne, Australia, technical report, 93. pp 33–36Google Scholar
  30. McGregor JL (2005a) Geostrophic adjustment for reversibly staggered grids. Mon Weather Rev 133:1119–1128CrossRefGoogle Scholar
  31. McGregor JL (2005b) C-CAM: geometric aspects and dynamical formulation [electronic publication]. CSIRO atmospheric research technical paper 70Google Scholar
  32. McGregor JL, Dix MR (2001) The CSIRO conformal-cubic atmospheric GCM. In: Hodnett PF (ed) IUTAM symposium on advances in mathematical modelling of atmosphere and ocean dynamics. Kluwer, Dordrecht, pp 197–202CrossRefGoogle Scholar
  33. McGregor JL, Dix MR (2008) An updated description of the conformal-cubic atmospheric model. In: Hamilton K, Ohfuchi W (eds) High resolution simulation of the atmosphere and ocean. Springer, Heidelberg, pp 51–76CrossRefGoogle Scholar
  34. Nguyen K, Katzfey J, McGregor J (2012) Global 60 km simulations with CCAM: evaluation over the tropics. Clim Dyn 39:637–654. doi: 10.1007/s00382-011-1197-8 CrossRefGoogle Scholar
  35. Niznik MJ, Lintner BR, Matthews AJ, Widlansky MJ (2015) The role of tropical–extratropical interaction and synoptic variability in maintaining the South Pacific Convergence Zone in CMIP5 models. J Clim 28:3353–3374. doi: 10.1175/JCLI-D-14-00527.1 CrossRefGoogle Scholar
  36. Pal JS, Small EE, Eltahir EAB (2000) Simulation of regional scale water and energy budgets: influence of a new moist physics scheme within RegCM. J Geophys Res 105(29):579–529 594Google Scholar
  37. Pal JS, Giorgi F, Bi X, Elguindi N, Solmon F, Rauscher SA, Gao X, Francisco R, Zakey A, Winter J, Ashfaq M, Syed FS, Sloan LC, Bell JL, Diffenbaugh NS, Karmacharya J, Konaré A, Martinez D, da Rocha RP, Steiner AL (2007) Regional climate modeling for the developing world: the ICTP RegCM3 and RegCNET. Bull Am Meteorol Soc 88:1395–1409CrossRefGoogle Scholar
  38. Pope VD, Stratton RA (2002) The processes governing horizontal resolution sensitivity in a climate model. Clim Dyn 19:211–236. doi: 10.1007/s00382-001-0222-8 CrossRefGoogle Scholar
  39. Pope VD, Gallani ML, Rowntree PR, Stratton RA (2000) The impact of new physical parametrizations in the Hadley Centre climate model: HadAM3. Clim Dyn 16:123–146CrossRefGoogle Scholar
  40. Reynolds RW (1988) A real-time global sea surface temperature analysis. J Clim 1:75–86CrossRefGoogle Scholar
  41. Salinger MJ, McGree S, Beucher F et al (2014) A new index for variations in the position of the South Pacific Convergence Zone 1910/11–2011/2012. Clim Dyn 43:881–892. doi: 10.1007/s00382-013-2035-y CrossRefGoogle Scholar
  42. Skamarock WC, Klemp JB, Dudhia J et al (2008) A Description of the advanced research WRF version 3. NCAR technical note NCAR/TN/u2013475, NCAR, Boulder, CO, USAGoogle Scholar
  43. Stratton RA (1999) A high resolution AMIP integration using the Hadley Centre model HadAM2b. Clim Dyn 15:9–28. doi: 10.1007/s003820050265 CrossRefGoogle Scholar
  44. Takahashi K, Battisti DS (2007) Processes controlling the mean tropical pacific precipitation pattern. Part II: the SPCZ and the Southeast Pacific Dry Zone. J Clim 20:5696–5706. doi: 10.1175/2007JCLI1656.1 CrossRefGoogle Scholar
  45. van der Wiel K, Matthews AJ, Joshi MM, Stevens DP (2015a) Why the South Pacific Convergence Zone is diagonal. Clim Dyn. doi: 10.1007/s00382-015-2668-0 Google Scholar
  46. van der Wiel K, Matthews AJ, Stevens DP, Joshi MM (2015b) A dynamical framework for the origin of the diagonal South Pacific and South Atlantic Convergence Zones. Q J R Meteorol Soc. doi: 10.1002/qj.2508 Google Scholar
  47. Vincent DG (1994) The South Pacific Convergence Zone (SPCZ): a review. Mon Weather Rev 122:1949–1970. doi: 10.1175/1520-0493(1994)122<1949:TSPCZA>2.0.CO;2 CrossRefGoogle Scholar
  48. Vincent EM, Lengaigne M, Menkes CE et al (2011) Interannual variability of the South Pacific Convergence Zone and implications for tropical cyclone genesis. Clim Dyn 36:1881–1896CrossRefGoogle Scholar
  49. Widlansky MJ, Webster PJ, Hoyos CD (2011) On the location and orientation of the South Pacific Convergence Zone. Clim Dyn 36:561–578. doi: 10.1007/s00382-010-0871-6 CrossRefGoogle Scholar
  50. Widlansky MJ, Timmermann A, Stein K et al (2013) Changes in South Pacific rainfall bands in a warming climate. Nat Clim Change 3:417–423. doi: 10.1038/nclimate1726 CrossRefGoogle Scholar
  51. 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 78:2539–2558CrossRefGoogle Scholar
  52. Zeng X, Zhao M, Dickinson RE (1998) Intercomparison of bulk aerodynamic algorithms for the computation of sea surface fluxes using TOGA COARE and TAO data. J Clim 11:2628–2644CrossRefGoogle Scholar
  53. Zhang Y, Dulière V, Mote PW, Salathé EP (2009) Evaluation of WRF and HadRM mesoscale climate simulations over the U.S. Pacific Northwest. J Clim 22:5511–5526. doi: 10.1175/2009JCLI2875.1 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • J. P. Evans
    • 1
  • K. Bormann
    • 2
  • J. Katzfey
    • 3
  • S. Dean
    • 4
  • R. Arritt
    • 5
  1. 1.Climate Change Research Centre, ARC Centre of Excellence for Climate System ScienceUniversity of New South WalesSydneyAustralia
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  3. 3.Centre for Australian Weather and Climate Research (A Partnership Between CSIRO and the Bureau of Meteorology)AspendaleAustralia
  4. 4.National Institute of Water and Atmospheric ResearchWellingtonNew Zealand
  5. 5.Department of AgronomyIowa State UniversityAmesUSA

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