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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
Article

Abstract

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.

Keywords

Regional climate model South Pacific Convergence Zone Precipitation Tropical pacific 

Notes

Acknowledgments

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 (http://www-pcmdi.llnl.gov).

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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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