• Bakalian, F., , H. Ritchie, , K. Thompson, , and W. Merryfield, 2010: Exploring atmosphere–ocean coupling using principal component and redundancy analysis. J. Climate, 23, 49264943.

    • Search Google Scholar
    • Export Citation
  • Barnett, T. P., , and R. W. Preisendorfer, 1987: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon. Wea. Rev., 115, 18251850.

    • Search Google Scholar
    • Export Citation
  • Barnett, T. P., , M. Latif, , N. Graham, , M. Flügel, , S. Pazan, , and W. White, 1993: ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperatures with a hybrid coupled ocean–atmosphere model. J. Climate, 6, 15451566.

    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., 1994: Linear statistical short-term climate predictive skill in the Northern Hemisphere. J. Climate, 7, 15131564.

  • Barnston, A. G., , and S. J. Mason, 2011: Evaluation of IRI’s seasonal climate forecasts for the extreme 15% tails. Wea. Forecasting, 26, 545554.

    • Search Google Scholar
    • Export Citation
  • Barnston, A. G., , S. Li, , S. J. Mason, , D. G. DeWitt, , L. Goddard, , and X. Gong, 2010: Verification of the first 11 years of IRI’s seasonal climate forecasts. J. Appl. Meteor. Climatol., 49, 493520.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C. S., , C. Smith, , and J. M. Wallace, 1992: An intercomparison of methods for finding coupled patterns in climate data. J. Climate, 5, 541560.

    • Search Google Scholar
    • Export Citation
  • Briggs, W. M., , and D. S. Wilks, 1996: Estimating monthly and seasonal distributions of temperature and precipitation using the new CPC long-range forecasts. J. Climate, 9, 818826.

    • Search Google Scholar
    • Export Citation
  • Draper, N. R., , and H. Smith, 1981: Applied Regression Analysis. 2nd ed. John Wiley and Sons, 709 pp.

  • Epstein, E. S., 1969: A scoring system for probability forecasts of ranked categories. J. Appl. Meteor., 8, 985987.

  • Glahn, H. R., 1968: Canonical correlation and its relationship to discriminant analysis and multiple regression. J. Atmos. Sci., 25, 2331.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., , A. E. Raftery, , A. H. Westveld, , and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 10981118.

    • Search Google Scholar
    • Export Citation
  • Goddard, L., , and N. E. Graham, 1999: The importance of the Indian Ocean for simulating rainfall anomalies over eastern and southern Africa. J. Geophys. Res., 104, 10 42310 436.

    • Search Google Scholar
    • Export Citation
  • Goddard, L., , A. G. Barnston, , and S. J. Mason, 2003: Evaluation of the IRI’s “Net Assessment” seasonal climate forecasts. Bull. Amer. Meteor. Soc., 84, 17611781.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 2001: Interpretation of rank histograms for verifying ensemble forecasts. Mon. Wea. Rev., 129, 550560.

  • Higgins, R. W., , H.-K. Kim, , and D. Unger, 2004: Long-lead seasonal temperature and precipitation prediction using tropical Pacific SST consolidation forecasts. J. Climate, 17, 33983414.

    • Search Google Scholar
    • Export Citation
  • Hsieh, H. H., 2009: Machine Learning in the Environmental Sciences. Cambridge University Press, 349 pp.

  • Johnson, R. A., , and D. W. Wichern, 2002: Applied Multivariate Statistical Analysis. 5th ed. Prentice Hall, 767 pp.

  • Kauker, F., , C. Köberle, , R. Gerdes, , and M. Karcher, 2008: Modeling the 20th century Arctic Ocean/sea ice system: Reconstruction of surface forcing. J. Geophys. Res., 113, C09027, doi:10.1029/2006JC004023.

    • Search Google Scholar
    • Export Citation
  • Krakauer, N. Y., , M. D. Grossberg, , I. Gladkova, , and H. Aizenman, 2013: Information content of seasonal forecasts in a changing climate. Adv. Meteor., 2013, 480210, doi:10.1155/2013/480210.

    • Search Google Scholar
    • Export Citation
  • Krzysztofowicz, R., 1983: Why should a forecaster and a decision maker use Bayes’ theorem? Water Resour. Res., 19, 327336.

  • Livezey, R. E., , and M. M. Timofeyeva, 2008: The first decade of long-lead U.S. seasonal forecasts: Insights from a skill analysis. Bull. Amer. Meteor. Soc., 89, 842854.

    • Search Google Scholar
    • Export Citation
  • Livezey, R. E., , K. Y. Vinnikov, , M. M. Timofeyeva, , R. Tinker, , and H. M. van den Dool, 2007: Estimation and extrapolation of climate normals and climatic trends. J. Appl. Meteor. Climatol., 46, 17591776.

    • Search Google Scholar
    • Export Citation
  • Mardia, K. V., , J. T. Kent, , and J. M. Bibby, 1979: Multivariate Analysis. Academic Press, 518 pp.

  • Mason, S. J., , and G. M. Mimmack, 2002: Comparison of some statistical methods of probabilistic forecasting of ENSO. J. Climate, 15, 829.

    • Search Google Scholar
    • Export Citation
  • Matheson, J. E., , and R. L. Winkler, 1976: Scoring rules for continuous probability distributions. Manage. Sci., 22, 10871096.

  • Menne, M. J., , and C. N. Williams, 2009: Homogenization of temperature series via pairwise comparisons. J. Climate, 22, 17001717.

  • Murphy, A. H., 1977: The value of climatological, categorical, and probabilistic forecasts in the cost–loss ratio situation. Mon. Wea. Rev., 105, 803816.

    • Search Google Scholar
    • Export Citation
  • Murphy, A. H., , and R. L. Winkler, 1977: Reliability of subjective probability forecasts of precipitation and temperature. Appl. Stat., 26, 4147.

    • Search Google Scholar
    • Export Citation
  • North, G. R., , T. L. Bell, , R. F. Cahalan, , and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev., 110, 699706.

    • Search Google Scholar
    • Export Citation
  • Peng, P., , A. Kumar, , M. S. Halpert, , and A. G. Barnston, 2012: An analysis of CPC’s operational 0.5-month lead seasonal outlooks. Wea. Forecasting, 27, 898917.

    • Search Google Scholar
    • Export Citation
  • Peng, P., , A. G. Barnston, , and A. Kumar, 2013: A comparison of skill between two versions of the NCEP Climate Forecast System (CFS) and CPC’s operational short-lead seasonal outlooks. Wea. Forecasting, 28, 445462.

    • Search Google Scholar
    • Export Citation
  • Peterson, T. C., , and R. S. Vose, 1997: An overview of the Global Historical Climatology Network temperature database. Bull. Amer. Meteor. Soc., 78, 28372849.

    • Search Google Scholar
    • Export Citation
  • Pfizenmayer, A., , and H. von Storch, 2001: Anthropogenic climate change shown by local wave conditions in the North Sea. Climate Res., 19, 1523.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., , and C. K. Folland, 2002: Atlantic air–sea interaction and seasonal predictability. Quart. J. Roy. Meteor. Soc., 128, 14131443.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., , R. W. Reynolds, , T. C. Peterson, , and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006). J. Climate, 21, 22832296.

    • Search Google Scholar
    • Export Citation
  • Tang, B., , W. W. Hsieh, , A. H. Monahan, , and F. T. Tangang, 2000: Skill comparisons between neural networks and canonical correlation analysis in predicting the equatorial Pacific sea surface temperatures. J. Climate, 13, 287293.

    • Search Google Scholar
    • Export Citation
  • Thompson, J. C., 1962: Economic gains from scientific advances and operational improvements in meteorological prediction. J. Appl. Meteor., 1, 1317.

    • Search Google Scholar
    • Export Citation
  • Tippett, M. K., , T. DelSole, , S. J. Mason, , and A. G. Barnston, 2008: Regression-based methods for finding coupled patterns. J. Climate, 21, 43844398.

    • Search Google Scholar
    • Export Citation
  • Tyler, D. E., 1982: On the optimality of the simultaneous redundancy transformations. Psychometrika, 47, 7786.

  • Uvo, C. B., , C. A. Rebelli, , S. E. Zebiak, , and Y. Kushnir, 1998: Relationships between tropical Pacific and Atlantic SST and northeast Brazil monthly precipitation. J. Climate, 11, 551562.

    • Search Google Scholar
    • Export Citation
  • van den Dool, H., 2007: Empirical Methods in Short-Term Climate Prediction. Oxford University Press, 215 pp.

  • van den Dool, H., , and Z. Toth, 1991: Why do forecasts for ‘‘near normal’’ often fail? Wea. Forecasting, 6, 7685.

  • van den Wollenberg, A., 1977: Redundancy analysis as an alternative for canonical correlation analysis. Psychometrika, 42, 207219.

  • von Storch, H., , and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Wang, X. L., , and F. W. Zwiers, 2001: Using redundancy analysis to improve dynamical seasonal mean 500 hPa geopotential forecasts. Int. J. Climatol., 21, 637654.

    • Search Google Scholar
    • Export Citation
  • WASA Group, 1998: Changing waves and storms in the northeast Atlantic? Bull. Amer. Meteor. Soc., 79, 741760.

  • Wilks, D. S., 2000: Diagnostic verification of the Climate Prediction Center long-lead outlooks, 1995–98. J. Climate, 13, 23892403.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2008: Improved statistical seasonal forecasts using extended training data. Int. J. Climatol., 28, 15891598.

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Academic Press, 676 pp.

  • Wilks, D. S., 2013a: Projecting “normals” in a nonstationary climate. J. Appl. Meteor. Climatol., 52, 289302.

  • Wilks, D. S., 2013b: The calibration simplex: A generalization of the reliability diagram for three-category probability forecasts. Wea. Forecasting, 28, 12101218.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2014: Probabilistic canonical correlation analysis forecasts, with application to tropical Pacific sea-surface temperatures. Int. J. Climatol., doi:10.1002/joc.3771, in press.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., , and C. M. Godfrey, 2002: Diagnostic verification of the IRI net assessment forecasts, 1997–2000. J. Climate, 15, 13691377.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., , and R. E. Livezey, 2013: Performance of alternative “normals” for tracking climate changes, using homogenized and nonhomogenized seasonal U.S. surface temperatures. J. Appl. Meteor. Climatol., 52, 16771687.

    • Search Google Scholar
    • Export Citation
  • Yuval, , and W. W. Hsieh, 2002: The impact of time-averaging on the detectability of nonlinear empirical relations. Quart. J. Roy. Meteor. Soc., 128, 16091622.

    • Search Google Scholar
    • Export Citation
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Comparison of Probabilistic Statistical Forecast and Trend Adjustment Methods for North American Seasonal Temperatures

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  • 1 Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York
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Abstract

The three multivariate statistical methods of canonical correlation analysis, maximum covariance analysis, and redundancy analysis are compared with respect to their probabilistic accuracy for seasonal forecasts of gridded North American temperatures. Derivation of forecast error covariance matrices for the methods allows a probabilistic formulation for the forecasts, assuming Gaussian predictive distributions. The three methods perform similarly with respect to probabilistic forecast accuracy as reflected by the ranked probability score, although maximum covariance analysis may be preferred because of its slightly better forecast skill and calibration. In each case the forecast accuracy for North American seasonal temperatures compares favorably to results from previously published studies. In addition, two alternative approaches are compared for alleviating the cold biases in the forecasts that derive from ongoing climate warming. Adding lagging 15-yr means to forecast temperature anomalies improved forecast accuracy and reduced the cold bias in the forecasts, relative to using the more conventional lagging 30-yr mean.

Corresponding author address: Daniel S. Wilks, Dept. of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853. E-mail: dsw5@cornell.edu

Abstract

The three multivariate statistical methods of canonical correlation analysis, maximum covariance analysis, and redundancy analysis are compared with respect to their probabilistic accuracy for seasonal forecasts of gridded North American temperatures. Derivation of forecast error covariance matrices for the methods allows a probabilistic formulation for the forecasts, assuming Gaussian predictive distributions. The three methods perform similarly with respect to probabilistic forecast accuracy as reflected by the ranked probability score, although maximum covariance analysis may be preferred because of its slightly better forecast skill and calibration. In each case the forecast accuracy for North American seasonal temperatures compares favorably to results from previously published studies. In addition, two alternative approaches are compared for alleviating the cold biases in the forecasts that derive from ongoing climate warming. Adding lagging 15-yr means to forecast temperature anomalies improved forecast accuracy and reduced the cold bias in the forecasts, relative to using the more conventional lagging 30-yr mean.

Corresponding author address: Daniel S. Wilks, Dept. of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853. E-mail: dsw5@cornell.edu
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