• Altman, N., and M. Krzywinski, 2018: The curse(s) of dimensionality. Nat. Methods, 15, 399400, https://doi.org/10.1038/s41592-018-0019-x.

  • Ambrizzi, T., B. Hoskins, and H. Hsu, 1995: Rossby wave propagation and teleconnection patterns in the austral winter. J. Atmos. Sci., 52, 36613672, https://doi.org/10.1175/1520-0469(1995)052<3661:RWPATP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 10831126, https://doi.org/10.1175/1520-0493(1987)115<1083:CSAPOL>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauckhage, C., and C. Thurau, 2009: Making archetypal analysis practical. Lect. Notes Comput. Sci., 5748, 272281, https://doi.org/10.1007/978-3-642-03798-6_28.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Black, A., and et al. , 2021: Australian northwest cloudbands and their relationship to atmospheric rivers and precipitation. Mon. Wea. Rev., 149, 11251139, https://doi.org/10.1175/MWR-D-20-0308.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boening, C., T. Lee, and V. Zlotnicki, 2011: A record-high ocean bottom pressure in the South Pacific observed by GRACE. Geophys. Res. Lett., 38, L04602, https://doi.org/10.1029/2010GL046013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Branstator, G., 2002: Circumglobal teleconnections, the jet stream waveguide, and the North Atlantic Oscillation. J. Climate, 15, 18931910, https://doi.org/10.1175/1520-0442(2002)015<1893:CTTJSW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Charney, J. G., and J. G. DeVore, 1979: Multiple flow equilibria in the atmosphere and blocking. J. Atmos. Sci., 36, 12051216, https://doi.org/10.1175/1520-0469(1979)036<1205:MFEITA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Christiansen, B., 2002: On the physical nature of the Arctic Oscillation. Geophys. Res. Lett., 29, 1805, https://doi.org/10.1029/2002GL015208.

  • Christiansen, B., 2007: Atmospheric circulation regimes: Can cluster analysis provide the number? J. Climate, 20, 22292250, https://doi.org/10.1175/JCLI4107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J., and K. Saito, 2002: A test for annular modes. J. Climate, 15, 25372546, https://doi.org/10.1175/1520-0442(2002)015<2537:ATFAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, J., J. Foster, M. Barlow, K. Saito, and J. Jones, 2010: Winter 2009–2010: A case study of an extreme Arctic oscillation event. Geophys. Res. Lett., 37, L17707, https://doi.org/10.1029/2010GL044256.

    • Search Google Scholar
    • Export Citation
  • Cutler, A., and L. Breiman, 1994: Archetypal analysis. Technometrics, 36, 338347, https://doi.org/10.1080/00401706.1994.10485840.

  • Cutler, A., and E. Stone, 1997: Moving archetypes. Physica D, 107, 116, https://doi.org/10.1016/S0167-2789(97)84209-1.

  • Dole, R., 1986: The life cycles of persistent anomalies and blocking over the North Pacific. Advances in Geophysics, Vol. 29, Academic Press, 31–69, https://doi.org/10.1016/S0065-2687(08)60034-5.

    • Crossref
    • Export Citation
  • Dole, R., M. Hoerling, J. Perlwitz, J. Elscheid, P. Pegion, T. Zhang, X. Quan, and D. Murray, 2011: Was there a basis for anticipating the 2010 Russian heat wave? Geophys. Res. Lett., 38, L06702, https://doi.org/10.1029/2010GL046582.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garfinkel, C., and N. Harnik, 2017: The non-Gaussianity and spatial asymmetry of temperature extremes relative to the storm track: The role of horizontal advection. J. Climate, 30, 445464, https://doi.org/10.1175/JCLI-D-15-0806.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Haines, K., 1994: Low-frequency variability in atmospheric middle latitudes. Surv. Geophys., 15, 161, https://doi.org/10.1007/BF00665686.

  • Hannachi, A., and N. Trendafilov, 2017: Archetypal analysis: Mining weather and climate extremes. J. Climate, 30, 69276944, https://doi.org/10.1175/JCLI-D-16-0798.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hannachi, A., I. Jolliffe, and D. Stephenson, 2007: Empirical orthogonal functions and related techniques in atmospheric science: A review. Int. J. Climatol., 27, 11191152, https://doi.org/10.1002/joc.1499.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hassanzadeh, P., and Z. Kuang, 2016: The linear response function of an idealized atmosphere. Part II: Implications for the practical use of the fluctuation-dissipation theorem and the role of operator’s nonnormality. J. Atmos. Sci., 73, 34413452, https://doi.org/10.1175/JAS-D-16-0099.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hasselmann, K., 1988: PIPs and POPs: The reduction of complex dynamical systems using principal interaction and oscillation patterns. J. Geophys. Res., 93, 11 01511 021, https://doi.org/10.1029/JD093iD09p11015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hendon, H., E.-P. Lim, J. Arblaster, and D. Anderson, 2014: Causes and predictability of the record wet east Australian spring 2010. Climate Dyn., 42, 11551174, https://doi.org/10.1007/s00382-013-1700-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B., and T. Ambrizzi, 1993: Rossby wave propagation on a realistic longitudinally varying flow. J. Atmos. Sci., 50, 16611671, https://doi.org/10.1175/1520-0469(1993)050<1661:RWPOAR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jolliffe, I., 1993: Principal component analysis: A beginner’s guide—II: Pitfalls, myths, and extensions. Weather, 48, 246253, https://doi.org/10.1002/j.1477-8696.1993.tb05899.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jolliffe, I., and J. Cadima, 2016: Principal component analysis: A review and recent developments. Philos. Trans. Roy. Soc. London, 374A, 116, https://doi.org/10.1098/rsta.2015.0202.

    • Search Google Scholar
    • Export Citation
  • Knighton, J., G. Pleiss, E. Carter, S. Lyon, M. Walter, and S. Steinschneider, 2019: Potential predictability of regional precipitation and discharge extremes using synoptic-scale climate information via machine learning: An evaluation for the eastern continental United States. J. Hydrometeor., 20, 883900, https://doi.org/10.1175/JHM-D-18-0196.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kobayashi, S., and et al. , 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 548, https://doi.org/10.2151/jmsj.2015-001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, K., P. Sheu, and I. Kang, 1994: Multiscale low-frequency circulation modes in the global atmosphere. J. Atmos. Sci., 51, 11691193, https://doi.org/10.1175/1520-0469(1994)051<1169:MLFCMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, W. K., and K.-M. Kim, 2012: The 2010 Pakistan flood and Russian heat wave: Teleconnection of hydrometeorological extremes. J. Hydrometeor., 13, 392403, https://doi.org/10.1175/JHM-D-11-016.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lim, E.-P., and H. Hendon, 2015: Understanding and predicting the strong Southern Annular Mode and its impact on the record wet Australian spring 2010. Climate Dyn., 44, 28072824, https://doi.org/10.1007/s00382-014-2400-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lim, E.-P., H. Hendon, G. Boschat, D. Hudson, D. Thompson, A. Dowdy, and J. Arblaster, 2019: Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex. Nat. Geosci., 12, 896901, https://doi.org/10.1038/s41561-019-0456-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E., 1951: Seasonal and irregular variations of the Northern Hemisphere sea-level pressure profile. J. Meteor., 8, 5259, https://doi.org/10.1175/1520-0469(1951)008<0052:SAIVOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21, 289307, https://doi.org/10.3402/tellusa.v21i3.10086.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lorenz, E., 2004: The Essence of Chaos. University of Washington Press, 240 pp.

  • Matthewman, N., and G. Magnusdottir, 2012: Clarifying ambiguity in intraseasonal Southern Hemisphere climate modes during austral winter. J. Geophys. Res., 117, D03105, https://doi.org/10.1029/2011JD016707.

    • Search Google Scholar
    • Export Citation
  • Mo, K., and R. Livezey, 1986: Tropical-extratropical geopotential height teleconnections during the Northern Hemisphere winter. Mon. Wea. Rev., 114, 24882515, https://doi.org/10.1175/1520-0493(1986)114<2488:TEGHTD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K., and M. Ghil, 1987: Statistics and dynamics of persistent anomalies. J. Atmos. Sci., 44, 877902, https://doi.org/10.1175/1520-0469(1987)044<0877:SADOPA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K., and R. Higgins, 1998: The Pacific–South American modes and tropical convection during the Southern Hemisphere winter. Mon. Wea. Rev., 126, 15811596, https://doi.org/10.1175/1520-0493(1998)126<1581:TPSAMA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mo, K., and J. Paegle, 2001: The Pacific–South American modes and their downstream effects. Int. J. Climatol., 21, 12111229, https://doi.org/10.1002/joc.685.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monahan, A., J. Fyfe, M. Ambaum, D. Stephenson, and G. North, 2009: Empirical orthogonal functions: The medium is the message. J. Climate, 22, 65016514, https://doi.org/10.1175/2009JCLI3062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mørup, M., and L. Hansen, 2012: Archetypal analysis for machine learning and data mining. Neurocomputing, 80, 5463, https://doi.org/10.1016/j.neucom.2011.06.033.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Müller, G., and T. Ambrizzi, 2007: Teleconnection patterns and Rossby wave propagation associated with generalized frosts over southern South America. Climate Dyn., 29, 633645, https://doi.org/10.1007/s00382-007-0253-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • North, G., 1984: Empirical orthogonal functions and normal modes. J. Atmos. Sci., 41, 879887, https://doi.org/10.1175/1520-0469(1984)041<0879:EOFANM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • North, G., T. Bell, and R. Cahalan, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699706, https://doi.org/10.1175/1520-0493(1982)110<0699:SEITEO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • O’Kane, T., D. Monselesan, and J. Risbey, 2017: A multiscale reexamination of the Pacific–South American pattern. Mon. Wea. Rev., 145, 379402, https://doi.org/10.1175/MWR-D-16-0291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pelly, J., and B. Hoskins, 2003: A new perspective on blocking. J. Atmos. Sci., 60, 743755, https://doi.org/10.1175/1520-0469(2003)060<0743:ANPOB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pook, M., and T. Gibson, 1999: Atmospheric blocking and storm tracks during SOP-1 of the FROST project. Aust. Meteor. Mag., 48, 5160.

    • Search Google Scholar
    • Export Citation
  • Renwick, J., 2005: Persistent positive anomalies in the Southern Hemisphere circulation. Mon. Wea. Rev., 133, 977988, https://doi.org/10.1175/MWR2900.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richardson, D., A. Black, D. Monselesan, T. Moore, J. Risbey, D. Squire, and C. Tozer, 2021: Identifying periods of forecast model confidence for improved subseasonal prediction of precipitation. J. Hydrometeor., 22, 371385, https://doi.org/10.1175/JHM-D-20-0054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Risbey, J., T. O’Kane, D. Monselesan, C. Franzke, and I. Horenko, 2015: Metastability of Northern Hemisphere teleconnection modes. J. Atmos. Sci., 72, 3554, https://doi.org/10.1175/JAS-D-14-0020.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Risbey, J., D. Monselesan, T. O’Kane, C. Tozer, M. Pook, and P. Hayman, 2019: Synoptic and large-scale determinants of extreme austral frost events. J. Appl. Meteor. Climatol., 58, 11031124, https://doi.org/10.1175/JAMC-D-18-0141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Röthlisberger, M., L. Frossard, L. Bosart, D. Keyser, and O. Martius, 2019: Recurrent synoptic-scale Rossby wave patterns and their effect on the persistence of cold and hot spells. J. Climate, 32, 32073226, https://doi.org/10.1175/JCLI-D-18-0664.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneidereit, A., S. Schubert, P. Vargin, F. Lunkeit, X. Zhu, D. Peters, and K. Fraedrich, 2012: Large-scale flow and the long-lasting blocking high over Russia: Summer 2010. Mon. Wea. Rev., 140, 29672981, https://doi.org/10.1175/MWR-D-11-00249.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sheshadri, A., and R. A. Plumb, 2017: Propagating annular modes: Empirical orthogonal functions, principal oscillation patterns, and time scales. J. Atmos. Sci., 74, 13451361, https://doi.org/10.1175/JAS-D-16-0291.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spensberger, C., M. Reeder, T. Spengler, and M. Patterson, 2020: The connection between the southern annular mode and a feature-based perspective on Southern Hemisphere midlatitude winter variability. J. Climate, 33, 115129, https://doi.org/10.1175/JCLI-D-19-0224.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Steinschneider, S., and U. Lall, 2015: Daily precipitation and tropical moisture exports across the eastern United States: An application of archetypal analysis to identify spatiotemporal structure. J. Climate, 28, 85858602, https://doi.org/10.1175/JCLI-D-15-0340.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Teng, H., and G. Branstator, 2017: Causes of extreme ridges that induce California droughts. J. Climate, 30, 14771492, https://doi.org/10.1175/JCLI-D-16-0524.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D., and J. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 10001016, https://doi.org/10.1175/1520-0442(2000)013<1000:AMITEC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D., and S. Solomon, 2002: Interpretation of recent Southern Hemisphere climate change. Science, 296, 895899, https://doi.org/10.1126/science.1069270.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, D., J. Wallace, and G. Hegerl, 2000: Annular modes in the extratropical circulation. Part II: Trends. J. Climate, 13, 10181036, https://doi.org/10.1175/1520-0442(2000)013<1018:AMITEC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tibaldi, S., and F. Molteni, 2002: On the operational predictability of blocking. Tellus, 42, 343365, https://doi.org/10.3402/tellusa.v42i3.11882.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tozer, C., J. Risbey, T. O’Kane, D. Monselesan, and M. Pook, 2018: The relationship between wave trains in the Southern Hemisphere storm track and rainfall extremes over Tasmania. Mon. Wea. Rev., 146, 42014230, https://doi.org/10.1175/MWR-D-18-0135.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K., and K. Mo, 1985: Blocking in the Southern Hemisphere. Mon. Wea. Rev., 113, 321, https://doi.org/10.1175/1520-0493(1985)113<0003:BITSH>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Trenberth, K., G. Branstator, and P. Arkin, 1988: Origins of the 1988 North American drought. Science, 242, 16401645, https://doi.org/10.1126/science.242.4886.1640.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Den Dool, H., 1994: Searching for analogues, how long must we wait? Tellus, 46, 314324, https://doi.org/10.3402/tellusa.v46i3.15481.

  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784812, https://doi.org/10.1175/1520-0493(1981)109<0784:TITGHF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wirth, V., M. Riemer, E. Chang, and O. Martius, 2018: Rossby wave packets on the midlatitude waveguide: A review. Mon. Wea. Rev., 146, 19652001, https://doi.org/10.1175/MWR-D-16-0483.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 56 56 56
Full Text Views 32 32 32
PDF Downloads 35 35 35

The Identification of Long-Lived Southern Hemisphere Flow Events Using Archetypes and Principal Components

View More View Less
  • 1 a CSIRO Oceans and Atmosphere, Hobart, Tasmania, Australia
© Get Permissions
Restricted access

Abstract

From time to time atmospheric flows become organized and form coherent long-lived structures. Such structures could be propagating, quasi-stationary, or recur in place. We investigate the ability of principal components analysis (PCA) and archetypal analysis (AA) to identify long-lived events, excluding propagating forms. Our analysis is carried out on the Southern Hemisphere midtropospheric flow represented by geopotential height at 500 hPa (Z500). The leading basis patterns of Z500 for PCA and AA are similar and describe structures representing (or similar to) the southern annular mode (SAM) and Pacific–South American (PSA) pattern. Long-lived events are identified here from sequences of 8 days or longer where the same basis pattern dominates for PCA or AA. AA identifies more long-lived events than PCA using this approach. The most commonly occurring long-lived event for both AA and PCA is the annular SAM-like pattern. The second most commonly occurring event is the PSA-like Pacific wave train for both AA and PCA. For AA the flow at any given time is approximated as weighted contributions from each basis pattern, which lends itself to metrics for discriminating among basis patterns. These show that the longest long-lived events are in general better expressed than shorter events. Case studies of long-lived events featuring a blocking structure and an annular structure show that both PCA and AA can identify and discriminate the dominant basis pattern that most closely resembles the flow event.

© 2021 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: James Risbey, james.risbey@csiro.au

Abstract

From time to time atmospheric flows become organized and form coherent long-lived structures. Such structures could be propagating, quasi-stationary, or recur in place. We investigate the ability of principal components analysis (PCA) and archetypal analysis (AA) to identify long-lived events, excluding propagating forms. Our analysis is carried out on the Southern Hemisphere midtropospheric flow represented by geopotential height at 500 hPa (Z500). The leading basis patterns of Z500 for PCA and AA are similar and describe structures representing (or similar to) the southern annular mode (SAM) and Pacific–South American (PSA) pattern. Long-lived events are identified here from sequences of 8 days or longer where the same basis pattern dominates for PCA or AA. AA identifies more long-lived events than PCA using this approach. The most commonly occurring long-lived event for both AA and PCA is the annular SAM-like pattern. The second most commonly occurring event is the PSA-like Pacific wave train for both AA and PCA. For AA the flow at any given time is approximated as weighted contributions from each basis pattern, which lends itself to metrics for discriminating among basis patterns. These show that the longest long-lived events are in general better expressed than shorter events. Case studies of long-lived events featuring a blocking structure and an annular structure show that both PCA and AA can identify and discriminate the dominant basis pattern that most closely resembles the flow event.

© 2021 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: James Risbey, james.risbey@csiro.au
Save