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

  • Barnston, A. G., He Y. , and Unger D. A. , 2000: A forecast product that maximizes utility for state-of-the-art seasonal climate predictions. Bull. Amer. Meteor. Soc., 81, 12711279.

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

    • Search Google Scholar
    • Export Citation
  • Cai, M., Shin C. S. , Van den Dool H. M. , Wang W. Q. , Saha S. , and Kumar A. , 2009: The role of long-term trends in seasonal predictions: Implication of global warming in the NCEP CFS. Wea. Forecasting, 24, 965973.

    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., Hagedorn R. , Palmer T. N. , and Morcrette J. J. , 2006: Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts. Geophys. Res. Lett., 33, L07708, doi:10.1029/2005GL025061.

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

  • Goddard, L., Barnston A. G. , and Mason S. J. , 2003: Evaluation of the IRI’s “net assessment” seasonal climate forecasts. Bull. Amer. Meteor. Soc., 84, 17611781.

    • Search Google Scholar
    • Export Citation
  • Goddard, L., Kumar A. , Barnston A. G. , and Hoerling M. P. , 2006: Diagnosis of anomalous winter temperatures over the eastern United States during the 2002/03 El Niño. J. Climate, 19, 56245636.

    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., 1999: Hypothesis tests for evaluating numerical precipitation forecasts. Wea. Forecasting, 14, 155167.

  • Hoerling, M. P., Whitaker J. S. , Kumar A. , and Wang W. Q. , 2001: The midlatitude warming during 1998–2000. Geophys. Res. Lett., 28, 755758.

    • Search Google Scholar
    • Export Citation
  • Huang, J., Van den Dool H. M. , and Barnston A. G. , 1996: Long-lead seasonal temperature prediction using optimal climate normals. J. Climate, 9, 809817.

    • Search Google Scholar
    • Export Citation
  • Ji, M., Kumar A. , and Leetmaa A. , 1994: A multiseason climate forecast system at the National Meteorological Center. Bull. Amer. Meteor. Soc., 75, 569577.

    • Search Google Scholar
    • Export Citation
  • Kanamitsu, M. W., and Coauthors, 2002a: NCEP dynamical seasonal forecast system 2000. Bull. Amer. Meteor. Soc., 83, 10191037.

  • Kanamitsu, M. W., and Coauthors, 2002b: NCEP–DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 16311643.

  • Kumar, A., and Hoerling M. P. , 2003: The nature and causes for the delayed atmospheric response to El Niño. J. Climate, 16, 13911403.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., Barnston A. G. , and Hoerling M. P. , 2001a: Seasonal predictions, probabilistic verifications, and ensemble size. J. Climate, 14, 16711676.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., Wang W. Q. , Hoerling M. P. , Leetmaa A. , and Ji M. , 2001b: The sustained North American warming of 1997 and 1998. J. Climate, 14, 345353.

    • Search Google Scholar
    • Export Citation
  • Livezey, R. E., and Chen W. Y. , 1983: Field significance and its determination by Monte Carlo techniques. Mon. Wea. Rev., 111, 4659.

  • Livezey, R. E., and Timofeyeva M. M. , 2008: The first decade of long-lead U.S. seasonal forecasts. Bull. Amer. Meteor. Soc., 89, 843854.

    • Search Google Scholar
    • Export Citation
  • Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291303.

  • Mason, S. J., and Graham N. E. , 2002: Areas beneath the relative operating characteristics (ROC) and levels (ROL) curves: Statistical significance and interpretation. Quart. J. Roy. Meteor. Soc., 128, 21452166.

    • Search Google Scholar
    • Export Citation
  • Mo, K. C., 2003: Ensemble canonical correlation prediction of surface temperature over the United States. J. Climate, 16, 16651683.

  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12, 595600.

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

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

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., Janowiak J. E. , and Halpert M. S. , 1985: The analysis and display of real-time surface climate data. Mon. Wea. Rev., 113, 11011106.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 34833517.

  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057.

  • Unger, D. A., 1995: Long lead climate prediction using screening multiple linear regression. Proc. 20th Annual Climate Diagnostics Workshop, Seattle, WA, NOAA/Climate Prediction Center, 425–428.

  • Van den Dool, H. M., and Toth Z. , 1991: Why do forecasts for near-normal fail to succeed? Wea. Forecasting, 6, 7685.

  • Wang, W., Chen M. , and Kumar A. , 2010: An assessment of the CFS real-time seasonal forecasts. Wea. Forecasting, 25, 950969.

  • Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. International Geophysics Series, Vol. 59, Academic Press, 627 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 239 107 6
PDF Downloads 208 90 5

A Comparison of Skill between Two Versions of the NCEP Climate Forecast System (CFS) and CPC’s Operational Short-Lead Seasonal Outlooks

View More View Less
  • 1 NOAA/Climate Prediction Center, Washington, D.C.
  • | 2 International Research Institute for Climate and Society, Earth Institute, Columbia University, Palisades, New York
  • | 3 NOAA/Climate Prediction Center, Washington, D.C.
Restricted access

Abstract

Analyses of the relative prediction skills of NOAA’s Climate Forecast System versions 1 and 2 (CFSv1 and CFSv2, respectively), and the NOAA/Climate Prediction Center’s (CPC) operational seasonal outlook, are conducted over the 15-yr common period of 1995–2009. The analyses are applied to predictions of seasonal mean surface temperature and total precipitation over the conterminous United States for the shortest and most commonly used lead time of 0.5 months. The assessments include both categorical and probabilistic verification diagnostics—their seasonalities, spatial distributions, and probabilistic reliability. Attribution of skill to specific physical sources is attempted when possible. Motivations for the analyses are to document improvements in skill between two generations of NOAA’s dynamical seasonal prediction system and to inform the forecast producers, but more importantly the user community, of the skill of the CFS model now in use (CFSv2) to help guide the users’ decision-making processes. The CFSv2 model is found to deliver generally higher mean predictive skill than CFSv1. This result is strongest for surface temperature predictions, and may be related to the use of time-evolving CO2 concentration in CFSv2, in contrast to a fixed (and now outdated) concentration used in CFSv1. CFSv2, and especially CFSv1, exhibit more forecast “overconfidence” than the official seasonal outlooks, despite that the CFSv2 hindcasts have outperformed the outlooks more than half of the time. Results justify the greater weight given to CFSv2 in developing the final outlooks than given to previous dynamical input tools (e.g., CFSv1) and indicate that CFSv2 should be of greater interest to users.

Corresponding author address: Dr. Arun Kumar, 5830 University Research Ct., College Park, MD 20740. E-mail: arun.kumar@noaa.gov

Abstract

Analyses of the relative prediction skills of NOAA’s Climate Forecast System versions 1 and 2 (CFSv1 and CFSv2, respectively), and the NOAA/Climate Prediction Center’s (CPC) operational seasonal outlook, are conducted over the 15-yr common period of 1995–2009. The analyses are applied to predictions of seasonal mean surface temperature and total precipitation over the conterminous United States for the shortest and most commonly used lead time of 0.5 months. The assessments include both categorical and probabilistic verification diagnostics—their seasonalities, spatial distributions, and probabilistic reliability. Attribution of skill to specific physical sources is attempted when possible. Motivations for the analyses are to document improvements in skill between two generations of NOAA’s dynamical seasonal prediction system and to inform the forecast producers, but more importantly the user community, of the skill of the CFS model now in use (CFSv2) to help guide the users’ decision-making processes. The CFSv2 model is found to deliver generally higher mean predictive skill than CFSv1. This result is strongest for surface temperature predictions, and may be related to the use of time-evolving CO2 concentration in CFSv2, in contrast to a fixed (and now outdated) concentration used in CFSv1. CFSv2, and especially CFSv1, exhibit more forecast “overconfidence” than the official seasonal outlooks, despite that the CFSv2 hindcasts have outperformed the outlooks more than half of the time. Results justify the greater weight given to CFSv2 in developing the final outlooks than given to previous dynamical input tools (e.g., CFSv1) and indicate that CFSv2 should be of greater interest to users.

Corresponding author address: Dr. Arun Kumar, 5830 University Research Ct., College Park, MD 20740. E-mail: arun.kumar@noaa.gov
Save