Does An ENSO-Conditional Skill Mask Improve Seasonal Predictions?

Kathy Pegion Cooperative Institute for Research in Environmental Sciences, University of Colorado, and Physical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, Colorado

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Arun Kumar Climate Prediction Center, National Oceanic and Atmospheric Administration/National Centers for Environmental Prediction, College Park, Maryland

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Abstract

The National Centers for Environmental Prediction Climate Prediction Center uses statistical tools together with the Climate Forecast System (CFS) to produce forecasts for seasonal outlooks of U.S. temperature and precipitation. They are combined using an optimal weighting procedure that depends on a skill mask consisting of the average historical forecast skill of each tool. However, it is likely that skill during El Niño–Southern Oscillation events is higher and the use of this information in developing forecasts could lead to improved seasonal predictions. This study explores the potential to improve the skill of seasonal predictions by developing an ENSO-conditional skill mask. The conditional masks are developed in a perfect-model framework using the CFS version 2 hindcasts and two indices of ENSO. The skill of the indices in forecasting variations in conditional skill is evaluated. The ENSO-conditional skill masks provide improvements in correlation skill over the unconditional mask when averaged over the globe. The masks are applied to tercile forecasts of seasonal temperature and precipitation during the spring and forecasts are verified in a perfect-model context. Application of the conditional masks to tercile forecasts results in modified Heidke skill scores of more than 10% less than using the average mask for temperature and little difference in skill for precipitation. This is attributed to the larger number of equal chances forecasts when using the conditional masks, particularly for temperature. For precipitation, the skill predicted by the average and conditional masks is frequently below 0.3, leading to low skill regardless of which mask is used.

Corresponding author address: Kathy Pegion, Cooperative Institute for Research in Environmental Sciences, University of Colorado, and Physical Sciences Division, NOAA/Earth System Research Laboratory, 325 Broadway R/PSD1, Boulder, CO 80305. E-mail: kathy.pegion@noaa.gov

Abstract

The National Centers for Environmental Prediction Climate Prediction Center uses statistical tools together with the Climate Forecast System (CFS) to produce forecasts for seasonal outlooks of U.S. temperature and precipitation. They are combined using an optimal weighting procedure that depends on a skill mask consisting of the average historical forecast skill of each tool. However, it is likely that skill during El Niño–Southern Oscillation events is higher and the use of this information in developing forecasts could lead to improved seasonal predictions. This study explores the potential to improve the skill of seasonal predictions by developing an ENSO-conditional skill mask. The conditional masks are developed in a perfect-model framework using the CFS version 2 hindcasts and two indices of ENSO. The skill of the indices in forecasting variations in conditional skill is evaluated. The ENSO-conditional skill masks provide improvements in correlation skill over the unconditional mask when averaged over the globe. The masks are applied to tercile forecasts of seasonal temperature and precipitation during the spring and forecasts are verified in a perfect-model context. Application of the conditional masks to tercile forecasts results in modified Heidke skill scores of more than 10% less than using the average mask for temperature and little difference in skill for precipitation. This is attributed to the larger number of equal chances forecasts when using the conditional masks, particularly for temperature. For precipitation, the skill predicted by the average and conditional masks is frequently below 0.3, leading to low skill regardless of which mask is used.

Corresponding author address: Kathy Pegion, Cooperative Institute for Research in Environmental Sciences, University of Colorado, and Physical Sciences Division, NOAA/Earth System Research Laboratory, 325 Broadway R/PSD1, Boulder, CO 80305. E-mail: kathy.pegion@noaa.gov
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  • Ashok, K., S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, 2007: El Niño–Modoki and its possible teleconnection. J. Geophys. Res., 112, C11007, doi:10.1029/2006JC003798.

    • Search Google Scholar
    • Export Citation
  • Barnett, T. P., and R. 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
  • Chen, M., P. Xie, and J. E. Janowiak, 2002: Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeor., 3, 249266.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., and H. van den Dool, 2008: A global monthly land surface air temperature analysis for 1948-present. J. Geophys. Res., 113, D01103, doi:10.1029/2007JD008470.

    • Search Google Scholar
    • Export Citation
  • Higgins, R. W., A. Leetma, Y. Xue, and A. Barnston, 2000: Dominant factors influencing the seasonal predictability of U.S. precipitation and surface air temperature. J. Climate, 13, 39944017.

    • Search Google Scholar
    • Export Citation
  • 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
  • Hoerling, M. P., and A. Kumar, 2002: Atmospheric response patterns associated with tropical forcing. J. Climate, 15, 21842203.

  • Kousky, V. E., and R. W. Higgins, 2007: An alert classification system for monitoring and assessing the ENSO cycle. Wea. Forecasting, 22, 353371.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., 2007: On the interpretation and utility of skill information for seasonal climate predictions. Mon. Wea. Rev., 135, 19741084.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., 2009: Finite samples and uncertainty estimates for skill measures of seasonal prediction. Mon. Wea. Rev., 137, 26222628.

  • Kumar, A., and M. P. Hoerling, 1997: Interpretation and implications of the observed inter-El Niño variability. J. Climate, 10, 8391.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., and M. P. Hoerling, 1998: Annual cycle of Pacific–North American seasonal predictability associated with different phases of ENSO. J. Climate, 11, 32953308.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., and M. P. Hoerling, 2000: Analysis of a conceptual model of seasonal climate variability and implications for seasonal prediction. Bull. Amer. Meteor. Soc., 81, 255264.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., A. G. Barnston, P. Peng, M. P. Hoerling, and L. Goddard, 2000: Changes in the spread of variability of the seasonal mean atmospheric states associated with ENSO. J. Climate, 13, 31303151.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., Q. Zhang, P. Peng, and B. Jha, 2005: SST-forced atmospheric variability in an atmospheric general circulation model. J. Climate, 18, 39533967.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., B. Jha, and M. L'Heureux, 2010: Are tropical SST trends changing the global teleconnection duiing La Niña? Geophys. Res. Lett., 37, L12702, doi:10.1029/2010/GL043394.

    • Search Google Scholar
    • Export Citation
  • Larkin, N. K., and D. E. Harrison, 2005: Global seasonal temperature and precipitation anomalies during El Niño autumn and winter. Geophys. Res. Lett., 32, L16705, doi:10.1029/2005GL022860.

  • Lim, E.-P., H. H. Hendon, D. Hudson, G. Wang, and O. Alves, 2009: Dynamical forecast of inter–El Niño variations of tropical SST and Australian spring rainfall. Mon. Wea. Rev., 137, 37963810.

    • Search Google Scholar
    • Export Citation
  • Livezey, R. E., and T. M. Smith, 1999: Covariability of aspects of North American climate with global sea surface temperatures on interannual to interdecadal timescales. J. Climate, 12, 289302.

    • Search Google Scholar
    • Export Citation
  • 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, 843854.

    • Search Google Scholar
    • Export Citation
  • O'Lenic, E. A., D. A. Unger, M. S. Halpert, and K. S. Pelman, 2008: Developments in operational long-range climate prediction at CPC. Wea. Forecasting, 23, 496515.

    • Search Google Scholar
    • Export Citation
  • Peng, P., and A. Kumar, 2005: A large ensemble analysis of the influence of tropical SSTs on seasonal atmospheric variability. J. Climate, 18, 10681085.

    • 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
  • Quan, X., M. Hoerling, J. Whitaker, G. Bates, and T. Xu, 2006: Diagnosing sources of U.S. seasonal forecast skill. J. Climate, 19, 32793293.

    • Search Google Scholar
    • Export Citation
  • Rasmussen, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354384.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1986: North American precipitation and temperature patterns associated with the El Niño/Southern Oscillation (ENSO). Mon. Wea. Rev., 114, 23522362.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., and M. S. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev., 115, 16061626.

    • Search Google Scholar
    • Export Citation
  • Sardeshmukh, P. D., G. P. Compo, and C. Penland, 2000: Changes of probability associated with El Niño. J. Climate, 13, 42684286.

  • Trenberth, K. E., 1997: The definition of El Niño. Bull. Amer. Meteor. Soc., 78, 27712777.

  • Unger, D. A., H. van den Dool, E. O'Lenic, and D. Collins, 2009: Ensemble regression. Mon. Wea. Rev., 137, 23652379.

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

  • Wang, G., and H. H. Hendon, 2007: Sensitivity of Australian rainfall to Inter–El Niño variation. J. Climate, 20, 42114226.

  • Wang, H., A. Kumar, W. Wang, and B. Jha, 2012: U.S. summer precipitation and temperature patterns following the peak phase of El Niño. J. Climate, 25, 72047215.

    • Search Google Scholar
    • Export Citation
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