• Anderson, B. T., , B. R. Lintner, , B. Langenbruner, , J. D. Neelin, , E. Hawkins, , and J. Sykus, 2015: Sensitivity of terrestrial precipitation to the structural evolution of sea surface temperatures. Geophys. Res. Lett., doi:10.1002/2014GL062593, in press.

    • Search Google Scholar
    • Export Citation
  • Ashfaq, M., , C. B. Skinner, , and N. S. Diffenbaugh, 2011: Influence of SST biases on future climate change projections. Climate Dyn., 36, 13031319, doi:10.1007/s00382-010-0875-2.

    • Search Google Scholar
    • Export Citation
  • Australian Bureau of Meteorology and CSIRO, 2011a: Regional Overview. Vol. 1, Climate Change in the Pacific: Scientific Assessment and New Research, BOM-CSIRO, 257 pp.

  • Australian Bureau of Meteorology and CSIRO, 2011b: Regional Overview. Vol 2, Climate Change in the Pacific: Scientific Assessment and New Research, BOM-CSIRO, 273 pp.

  • Bellucci, A., , S. Gualdi, , and A. Navarra, 2010: The double-ITCZ syndrome in coupled general circulation models: The role of large-scale vertical circulation regimes. J. Climate, 23, 11271145, doi:10.1175/2009JCLI3002.1.

    • Search Google Scholar
    • Export Citation
  • Borlace, S., , A. Santoso, , W. Cai, , and M. Collins, 2014: Extreme swings of the South Pacific convergence zone and the different types of El Niño events. Geophys. Res. Lett., 41, 4695–4703, doi:10.1002/2014GL060551.

    • Search Google Scholar
    • Export Citation
  • Brown, J. R., , S. B. Power, , F. P. Delage, , R. A. Colman, , A. F. Moise, , and B. F. Murphy, 2011: Evaluation of the South Pacific convergence zone in IPCC AR4 climate model simulations of the twentieth century. J. Climate, 24, 15651582, doi:10.1175/2010JCLI3942.1.

    • Search Google Scholar
    • Export Citation
  • Brown, J. R., , A. F. Moise, , and R. A. Colman, 2013: The South Pacific convergence zone in CMIP5 simulations of historical and future climate. Climate Dyn., 41, 21792197, doi:10.1007/s00382-012-1591-x.

    • Search Google Scholar
    • Export Citation
  • Cai, W., and et al. , 2012: More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature, 488, 365369, doi:10.1038/nature11358.

    • Search Google Scholar
    • Export Citation
  • DeAngelis, A. M., , A. J. Broccoli, , and S. G. Decker, 2013: A comparison of CMIP3 simulations of precipitation over North America with observations: Daily statistics and circulation features accompanying extreme events. J. Climate, 26, 32093230, doi:10.1175/JCLI-D-12-00374.1.

    • Search Google Scholar
    • Export Citation
  • de Szoeke, S. P., , and S.-P. Xie, 2008: The tropical eastern Pacific seasonal cycle: Assessment of errors and mechanisms in IPCC AR4 coupled ocean–atmosphere general circulation models. J. Climate, 21, 25732590, doi:10.1175/2007JCLI1975.1.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., , J. A. Renwick, , M. J. Salinger, , and A. B. Mullan, 2002: Relative influences of the interdecadal Pacific oscillation and ENSO on the South Pacific convergence zone. Geophys. Res. Lett., 29, 1643, doi:10.1029/2001GL014201.

    • Search Google Scholar
    • Export Citation
  • Ganachaud, A., and et al. , 2007: Southwest Pacific Ocean Circulation and Climate Experiment (SPICE)—Part I. Scientific background. International CLIVAR Publ. Series, No. 111, NOAA/OAR Special Rep., NOAA/OAR/PMEL, 37 pp.

  • Ganachaud, A., and et al. , 2014: The Southwest Pacific Ocean Circulation and Climate Experiment (SPICE). J. Geophys. Res. Oceans, 119, 76607686, doi:10.1002/2013JC009678.

    • Search Google Scholar
    • Export Citation
  • Haffke, C., , and G. Magnusdottir, 2013: The South Pacific Convergence Zone in three decades of satellite images. J. Geophys. Res. Atmos., 118, 10 83910 849, doi:10.1002/jgrd.50838.

    • Search Google Scholar
    • Export Citation
  • Hung, M.-P., , J.-L. Lin, , W. Wang, , D. Kim, , T. Shinoda, , and S. J. Weaver, 2013: MJO and convectively coupled equatorial waves simulated by CMIP5 climate models. J. Climate, 26, 61856214, doi:10.1175/JCLI-D-12-00541.1.

    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., , and K. M. Weickmann, 1997: Horizontal structure and seasonality of large-scale circulations associated with submonthly tropical convection. Mon. Wea. Rev., 125, 19972013, doi:10.1175/1520-0493(1997)125<1997:HSASOL>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and et al. , 2000: The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteor., 39, 19651982, doi:10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, J.-L. F., , W.-L. Lee, , D. E. Waliser, , J. D. Neelin, , J. P. Stachnik, , and T. Lee, 2014: Cloud–precipitation–radiation–dynamics interaction in global climate models: A snow and radiation interaction sensitivity experiment. J. Geophys. Res. Atmos., 119, 38093824, doi:10.1002/2013JD021038.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., , and C. A. Smith, 1996: Description of a complete (interpolated) OLR dataset. Bull. Amer. Meteor. Soc., 77, 12751277.

  • Lin, J.-L., 2007: The double-ITCZ problem in IPCC AR4 coupled GCMs: Ocean–atmosphere feedback analysis. J. Climate, 20, 44974525, doi:10.1175/JCLI4272.1.

    • Search Google Scholar
    • Export Citation
  • Lin, J.-L., and et al. , 2006: Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: Convective signals. J. Climate, 19, 26652690, doi:10.1175/JCLI3735.1.

    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., 2012: A multiscale framework for the origin and variability of the South Pacific convergence zone. Quart. J. Roy. Meteor. Soc., 138, 11651178, doi:10.1002/qj.1870.

    • Search Google Scholar
    • Export Citation
  • Matthews, A. J., , B. J. Hoskins, , J. M. Slingo, , and M. Blackburn, 1996: Development of convection along the SPCZ within a Madden–Julian oscillation. Quart. J. Roy. Meteor. Soc., 122, 669688, doi:10.1002/qj.49712253106.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., , C. Covey, , T. Delworth, , M. Latif, , B. McAvaney, , J. F. B. Mitchell, , R. J. Stouffer, , and K. E. Taylor, 2007: The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc., 88, 13831394, doi:10.1175/BAMS-88-9-1383.

    • Search Google Scholar
    • Export Citation
  • Murphy, B. F., , S. B. Power, , and S. McGree, 2014: The varied impacts of El Niño–Southern Oscillation on Pacific island climates. J. Climate, 27, 40154036, doi:10.1175/JCLI-D-13-00130.1.

    • Search Google Scholar
    • Export Citation
  • Niznik, M. J., , and B. R. Lintner, 2013: Circulation, moisture, and precipitation relationships along the South Pacific convergence zone in reanalyses and CMIP5 models. J. Climate, 26, 10 17410 192, doi:10.1175/JCLI-D-13-00263.1.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and et al. , 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, doi:10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation
  • Sillmann, J., , V. V. Kharin, , F. W. Zwiers, , X. Zhang, , and D. Bronaugh, 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res. Atmos., 118, 17161733, doi:10.1002/jgrd.50203.

    • Search Google Scholar
    • Export Citation
  • Slingo, J. M., and et al. , 1996: Intraseasonal oscillations in 15 atmospheric general circulation models: Results from an AMIP diagnostic subproject. Climate Dyn., 12, 325358, doi:10.1007/BF00231106.

    • Search Google Scholar
    • Export Citation
  • Streten, N. A., 1973: Some characteristics of satellite-observed bands of persistent cloudiness over the Southern Hemisphere. Mon. Wea. Rev., 101, 486495, doi:10.1175/1520-0493(1973)101<0486:SCOSBO>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., , R. J. Stouffer, , and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, doi:10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1976: Spatial and temporal variations of the Southern Oscillation. Quart. J. Roy. Meteor. Soc., 102, 639653, doi:10.1002/qj.49710243310.

    • Search Google Scholar
    • Export Citation
  • van der Wiel, K., , A. J. Matthews, , D. P. Stevens, , and M. M. Joshi, 2015: A dynamical framework for the origin of the diagonal South Pacific and South Atlantic convergence zones. Quart. J. Roy. Meteor. Soc., doi:10.1002/qj.2508, in press.

    • Search Google Scholar
    • Export Citation
  • Vannière, B., , E. Guilyardi, , T. Toniazzo, , G. Madec, , and S. Woolnough, 2014: A systematic approach to identify the sources of tropical SST errors in coupled models using the adjustment of initialised experiments. Climate Dyn., 43, 22612282, doi:10.1007/s00382-014-2051-6.

    • Search Google Scholar
    • Export Citation
  • Vincent, D. G., 1994: The South Pacific convergence zone (SPCZ): A Review. Mon. Wea. Rev., 122, 19491970, doi:10.1175/1520-0493(1994)122<1949:TSPCZA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vincent, E. M., , M. Lengaigne, , C. E. Menkes, , N. C. Jourdain, , P. Marchesiello, , and G. Madec, 2011: Interannual variability of the South Pacific convergence zone and implications for tropical cyclone genesis. Climate Dyn., 36, 18811896, doi:10.1007/s00382-009-0716-3.

    • Search Google Scholar
    • Export Citation
  • Widlansky, M. J., , P. J. Webster, , and C. D. Hoyos, 2011: On the location and orientation of the South Pacific convergence zone. Climate Dyn., 36, 561578, doi:10.1007/s00382-010-0871-6.

    • Search Google Scholar
    • Export Citation
  • Widlansky, M. J., , A. Timmermann, , K. Stein, , S. McGregor, , N. Schneider, , M. H. England, , M. Lengaigne, , and W. Cai, 2013: Changes in South Pacific rainfall bands in a warming climate. Nat. Climate Change, 3, 417423, doi:10.1038/nclimate1726.

    • Search Google Scholar
    • Export Citation
  • Widlansky, M. J., , A. Timmermann, , S. McGregor, , M. F. Stuecker, , and W. Cai, 2014: An interhemispheric tropical sea level seesaw due to El Niño taimasa. J. Climate, 27, 10701081, doi:10.1175/JCLI-D-13-00276.1.

    • Search Google Scholar
    • Export Citation
  • Xie, P., , and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 25392558, doi:10.1175/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhang, C., 2001: Double ITCZs. J. Geophys. Res., 106, 11 78511 792, doi:10.1029/2001JD900046.

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The Role of Tropical–Extratropical Interaction and Synoptic Variability in Maintaining the South Pacific Convergence Zone in CMIP5 Models

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  • 1 Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey
  • | 2 Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences and School of Mathematics, University of East Anglia, Norwich, United Kingdom
  • | 3 International Pacific Research Center, University of Hawai‘i at Mānoa, Honolulu, Hawaii
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Abstract

The South Pacific convergence zone (SPCZ) is simulated as too zonal a feature in the current generation of climate models, including those in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This zonal bias induces errors in tropical convective heating, with subsequent effects on global circulation. The SPCZ structure, particularly in the subtropics, is governed by the tropical–extratropical interaction between transient synoptic systems and the mean background state. In this study, analysis of synoptic variability in the simulated subtropical SPCZ reveals that the basic mechanism of tropical–extratropical interaction is generally well simulated, with storms approaching the SPCZ along comparable trajectories to observations. However, there is a broad spread in mean precipitation and its variability across the CMIP5 ensemble. Intermodel spread appears to relate to a biased background state in which the synoptic waves propagate. In particular, the region of mean negative zonal stretching deformation or “storm graveyard” in the upper troposphere is displaced in CMIP5 models to the northeast of its position in reanalysis data, albeit with pronounced (≈25°) intermodel longitudinal spread. Precipitation along the eastern edge of the SPCZ shifts in accordance with a storm graveyard shift, and in general models with stronger storm graveyards show higher precipitation variability. Building on prior SPCZ research, it is suggested that SPCZs simulated by CMIP5 models are not simply too zonal; rather, in models the subtropical SPCZ manifests a diagonal tilt similar to observations while SST biases force an overly zonal tropical SPCZ, resulting in a more discontinuous SPCZ than observed.

Corresponding author address: Matthew J. Niznik, Department of Environmental Sciences, Rutgers, The State University of New Jersey, 14 College Farm Road, New Brunswick, NJ 08901-8551. E-mail: matthew.niznik@rutgers.edu

Abstract

The South Pacific convergence zone (SPCZ) is simulated as too zonal a feature in the current generation of climate models, including those in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This zonal bias induces errors in tropical convective heating, with subsequent effects on global circulation. The SPCZ structure, particularly in the subtropics, is governed by the tropical–extratropical interaction between transient synoptic systems and the mean background state. In this study, analysis of synoptic variability in the simulated subtropical SPCZ reveals that the basic mechanism of tropical–extratropical interaction is generally well simulated, with storms approaching the SPCZ along comparable trajectories to observations. However, there is a broad spread in mean precipitation and its variability across the CMIP5 ensemble. Intermodel spread appears to relate to a biased background state in which the synoptic waves propagate. In particular, the region of mean negative zonal stretching deformation or “storm graveyard” in the upper troposphere is displaced in CMIP5 models to the northeast of its position in reanalysis data, albeit with pronounced (≈25°) intermodel longitudinal spread. Precipitation along the eastern edge of the SPCZ shifts in accordance with a storm graveyard shift, and in general models with stronger storm graveyards show higher precipitation variability. Building on prior SPCZ research, it is suggested that SPCZs simulated by CMIP5 models are not simply too zonal; rather, in models the subtropical SPCZ manifests a diagonal tilt similar to observations while SST biases force an overly zonal tropical SPCZ, resulting in a more discontinuous SPCZ than observed.

Corresponding author address: Matthew J. Niznik, Department of Environmental Sciences, Rutgers, The State University of New Jersey, 14 College Farm Road, New Brunswick, NJ 08901-8551. E-mail: matthew.niznik@rutgers.edu
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