• Abbas, O. A., 2008: Comparisons between data clustering algorithms. Int. Arab J. Inf. Technol., 5, 320325.

  • Adler, R. F., and Coauthors, 2003: The version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeor., 4, 11471167, https://doi.org/10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Allen, T., and B. E. Mapes, 2017: The late spring Caribbean rain-belt: Climatology and dynamics. Int. J. Climatol., 37, 49814993, https://doi.org/10.1002/joc.5136.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Banzon, V., T. M. Smith, T. M. Chin, C. Liu, and W. Hankins, 2016: A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data, 8, 165176, https://doi.org/10.5194/essd-8-165-2016.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, https://doi.org/10.1175/2009JCLI3002.1.

    • Crossref
    • 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, 46954703, https://doi.org/10.1002/2014GL060551.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brown, J. N., and Coauthors, 2013: Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific. Climatic Change, 119, 147161, https://doi.org/10.1007/s10584-012-0603-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Capotondi, A., and Coauthors, 2015: Understanding ENSO diversity. Bull. Amer. Meteor. Soc., 96, 921938, https://doi.org/10.1175/BAMS-D-13-00117.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, H., and F. Jin, 2020: Fundamental behavior of ENSO phase-locking. J. Climate, 33, 19531968, https://doi.org/10.1175/JCLI-D-19-0264.1.

  • Choi, K.-Y., G. A. Vecchi, and A. T. Wittenberg, 2015: Nonlinear zonal wind response to ENSO in the CMIP5 models: Roles of the zonal and meridional shift of the ITCZ/SPCZ and the simulated climatological precipitation. J. Climate, 28, 85568573, https://doi.org/10.1175/JCLI-D-15-0211.1.

    • Crossref
    • 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, https://doi.org/10.1029/2001GL014201.

    • Crossref
    • 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, https://doi.org/10.1002/jgrd.50838.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hubert, L. J., P. Arabie, and J. J. Meulman, 2002: Linear unidimensional scaling in the L2-norm: Basic optimization methods using MATLAB. J. Classif., 19, 303328, https://doi.org/10.1007/s00357-001-0047-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., and K. M. Weickmann, 1992: Extratropical forcing of tropical Pacific convection during northern winter. Mon. Wea. Rev., 120, 19241938, https://doi.org/10.1175/1520-0493(1992)120<1924:EFOTPC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., H. von Storch, and H. van Loon, 1989: Origin of the South Pacific convergence zone. J. Climate, 2, 11851195, https://doi.org/10.1175/1520-0442(1989)002<1185:OOTSPC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., J. Dias, K. H. Straub, M. C. Wheeler, S. N. Tulich, K. Kikuchi, K. M. Weickmann, and M. J. Ventrice, 2014: A comparison of OLR and circulation based indices for tracking the MJO. Mon. Wea. Rev., 142, 16971715, https://doi.org/10.1175/MWR-D-13-00301.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kodinariya, T. M., and P. R. Makwana, 2013: Review on determining number of cluster in K-means clustering. Int. J. Adv. Res. Comput. Sci. Manage. Stud., 1, 9095, http://www.ijarcsms.com/docs/paper/volume1/issue6/V1I6-0015.pdf.

    • 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, https://doi.org/10.1175/JCLI4272.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Linsley, B. K., P. Zhang, A. Kaplan, S. S. Howe, and G. M. Wellington, 2008: Interdecadal–decadal climate variability from multicoral oxygen isotope records in the South Pacific convergence zone region since 1650 A.D. Paleoceanography, 23, PA2219, https://doi.org/10.1029/2007PA001539.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lintner, B. R., and J. D. Neelin, 2008: Eastern margin variability of the South Pacific convergence zone. Geophys. Res. Lett., 35, L16701, https://doi.org/10.1029/2008GL034298.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lintner, B. R., and W. R. Boos, 2019: Using atmospheric energy transport to quantitatively constrain South Pacific convergence zone shifts during ENSO. J. Climate, 32, 18391855, https://doi.org/10.1175/JCLI-D-18-0151.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorrey, A. M., and N. C. Fauchereau, 2017: Southwest Pacific atmospheric weather regimes: Linkages to ENSO and extra-tropical teleconnections. Int. J. Climatol., 38, 18931909, https://doi.org/10.1002/joc.5304.

    • Crossref
    • 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, https://doi.org/10.1002/qj.1870.

    • Crossref
    • 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, https://doi.org/10.1002/qj.49712253106.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., 1987: The annual cycle and interannual variability in the tropical Pacific and Indian Ocean regions. Mon. Wea. Rev., 101, 486495, https://doi.org/10.1175/1520-0493(1987)115<0027:TACAIV>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • NCAR Staff, Eds., 2007: The Climate Data Guide: Climate Forecast System Reanalysis (CFSR). Accessed 8 November 2017, https://climatedataguide.ucar.edu/climate-data/climate-forecast-system-reanalysis-cfsr.

  • Neelin, J. D., and I. M. Held, 1987: Modeling tropical convergence based on the moist static energy budget. Mon. Wea. Rev., 115, 312, https://doi.org/10.1175/1520-0493(1987)115<0003:MTCBOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Niznik, M. J., B. R. Lintner, A. J. Matthews, and M. J. Widlansky, 2015: The role of tropical–extratropical interaction and synoptic variability in maintaining the South Pacific convergence zone in CMIP5 models. J. Climate, 28, 33533374, https://doi.org/10.1175/JCLI-D-14-00527.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Power, S., 2011: Understanding the South Pacific convergence zone and its impacts. Eos, Trans. Amer. Geophys. Union, 92, 55, https://doi.org/10.1029/2011EO070006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151058, https://doi.org/10.1175/2010BAMS3001.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Salinger, M. J., J. A. Renwick, and A. B. Mullan, 2001: Interdecadal Pacific Oscillation and South Pacific climate. Int. J. Climatol., 21, 17051721, https://doi.org/10.1002/joc.691.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sampe, T., and S.-P. Xie, 2010: Large-scale dynamics of the meiyu-baiu rain band: Environmental forcing by the westerly jet. J. Climate, 23, 113134, https://doi.org/10.1175/2009JCLI3128.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takahashi, K., and D. S. Battisti, 2007: Processes controlling the mean tropical Pacific precipitation pattern. Part II: The SPCZ and the southeast Pacific dry zone. J. Climate, 20, 56965706, https://doi.org/10.1175/2007JCLI1656.1.

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

    • Crossref
    • 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., 141, 19972010, https://doi.org/10.1002/qj.2508.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • van der Wiel, K., A. J. Matthews, M. M. Joshi, and D. P. Stevens, 2016: Why the South Pacific convergence zone is diagonal. Climate Dyn., 46, 16831698, https://doi.org/10.1007/s00382-015-2668-0.

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

    • Crossref
    • 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, https://doi.org/10.1007/s00382-009-0716-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waliser, D., and Coauthors, 2009: MJO simulation diagnostics. J. Climate, 22, 30063030, https://doi.org/10.1175/2008JCLI2731.1.

  • Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • 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, https://doi.org/10.1007/s00382-010-0871-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Application of Clustering Algorithms to TRMM Precipitation over the Tropical and South Pacific Ocean

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  • 1 Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey
  • 2 Department of Environmental Sciences, Rutgers, The State University of New Jersey, and Rutgers Institute of Earth, Ocean, and Atmospheric Sciences, New Brunswick, New Jersey
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Abstract

Understanding multiscale rainfall variability in the South Pacific convergence zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) Tropical Rainfall Measuring Mission (TRMM) satellite rainfall to extract representative spatial patterns of rainfall over the SPCZ region for the period 1998–2013. For a k = 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, reflecting either warm or cool phases of El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency forcings (e.g., the Madden–Julian oscillation and synoptic-scale disturbances) that trigger deep convection where SSTs are sufficiently warm. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the “modal view” of SPCZ variability associated with the EOFs to the “state view” associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are permitted.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Maxwell Pike, mpike1@rutgers.edu

Abstract

Understanding multiscale rainfall variability in the South Pacific convergence zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) Tropical Rainfall Measuring Mission (TRMM) satellite rainfall to extract representative spatial patterns of rainfall over the SPCZ region for the period 1998–2013. For a k = 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, reflecting either warm or cool phases of El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency forcings (e.g., the Madden–Julian oscillation and synoptic-scale disturbances) that trigger deep convection where SSTs are sufficiently warm. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the “modal view” of SPCZ variability associated with the EOFs to the “state view” associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are permitted.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Maxwell Pike, mpike1@rutgers.edu
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