Editorial Type:
Article
Article Type:
Research Article

Short-Term Climate Extremes: Prediction Skill and Predictability

Emily J. Becker Climate Prediction Center, NOAA/NWS/NCEP, College Park, Maryland

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Huug van den Dool Climate Prediction Center, NOAA/NWS/NCEP, College Park, Maryland

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Malaquias Peña IMSG at Environmental Modeling Center, NOAA/NWS/NCEP, College Park, Maryland

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Print Publication:
15 Jan 2013
DOI:
https://doi.org/10.1175/JCLI-D-12-00177.1
Page(s):
512–531
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Abstract

Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982–2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables.

The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space–time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.

Corresponding author address: Emily J. Becker, National Oceanic and Atmospheric Administration, Climate Prediction Center, NCWCP W/NP5 5830 University Research Court, College Park, MD 20740-3818. E-mail: emily.becker@noaa.gov

Abstract

Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982–2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables.

The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space–time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.

Corresponding author address: Emily J. Becker, National Oceanic and Atmospheric Administration, Climate Prediction Center, NCWCP W/NP5 5830 University Research Court, College Park, MD 20740-3818. E-mail: emily.becker@noaa.gov
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  • Barnston, A. G., 1994: Linear statistical short-term climate predictive skill in the Northern Hemisphere. J. Climate, 7, 1513–1564.

  • Barnston, A. G., and H. M. van den Dool, 1993: A degeneracy in cross-validated skill in regression-based forecasts. J. Climate, 6, 963–977.

    • Search Google Scholar
    • Export Citation

  • Barnston, A. G., and S. J. Mason, 2011: Evaluation of IRI’s seasonal climate forecasts for the extreme 15% tails. Wea. Forecasting, 26, 545–554.

    • Search Google Scholar
    • Export Citation

  • Chen, M., W. Wang, and A. Kumar, 2010: Prediction of monthly-mean temperature: The roles of atmospheric and land initial conditions and sea surface temperature. J. Climate, 23, 717–725.

    • Search Google Scholar
    • Export Citation

  • Compo, G. P., and P. D. Sardeshmukh, 2004: Storm track predictability on seasonal and decadal scales. J. Climate, 17, 3701–3720.

  • Easterling, D. R., J. L. Evans, P. Ya. Groisman, T. R. Karl, K. E. Kunkel, and P. Ambenje, 2000a: Observed variability and trends in extreme climate events: A brief review. Bull. Amer. Meteor. Soc., 81, 417–425.

    • Search Google Scholar
    • Export Citation

  • Easterling, D. R., G. A. Meehl, C. Parmesan, S. A. Changnon, T. R. Karl, and L. O. Means, 2000b: Climate extremes: Observations, modeling, and impacts. Science, 289, 2068–2074.

    • Search Google Scholar
    • Export Citation

  • Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarplay, 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi:10.1029/2002JD003296.

    • 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

  • Goldenberg, S. B., and L. J. Shapiro, 1996: Physical mechanisms for the association of El Niño and West African rainfall with Atlantic major hurricane activity. J. Climate, 9, 1169–1187.

    • Search Google Scholar
    • Export Citation

  • Hurrell, J. W., 1995: Decadal trends in the North Atlantic oscillation: Regional temperatures and precipitation. Science, 269, 676–679.

    • Search Google Scholar
    • Export Citation

  • Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643.

    • Search Google Scholar
    • Export Citation

  • Kumar, A., M. Chen, and W. Wang, 2010: An analysis of prediction skill of monthly mean climate variability. Climate Dyn., 37, 1119–1131, doi:10.1007/s00382-010-0901-4.

    • Search Google Scholar
    • Export Citation

  • Livezey, R. E., and M. M. Timofeyeva, 2008: The first decade of long-lead U.S. seasonal forecasts. Bull. Amer. Meteor. Soc., 89, 843–854, doi:10.1175/2008BAMS2488.1.

    • Search Google Scholar
    • Export Citation

  • Lorenz, E. N., 1982: Atmospheric predictability experiments with a large numerical model. Tellus, 34, 505–513.

  • Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in the tropics with a 40-50 day period. J. Atmos. Sci., 29, 1109–1123.

    • Search Google Scholar
    • Export Citation

  • Mitchell, K., and Coauthors, 2004: The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res., 109, D07S90, doi:10.1029/2003JD003823.

    • Search Google Scholar
    • Export Citation

  • Nicholls, N., 1995: Long-term climate monitoring and extreme events. Climatic Change, 31, 231–245.

  • NRC, 2010: Assessment of Intraseasonal to Interannual Climate Prediction and Predictability. The National Academies Press, 181 pp.

  • Rasmusson, 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, 354–384.

    • Search Google Scholar
    • Export Citation

  • Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609–1625.

    • Search Google Scholar
    • Export Citation

  • Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483–3517.

  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057.

  • Savijarvi, H., 1995: Error growth in a large numerical forecast system. Mon. Wea. Rev., 123, 212–221.

  • Sheridan, S. C., and T. J. Dolney, 2003: Heat, mortality, and level of urbanization: Measuring vulnerability across Ohio, USA. Climate Res., 24, 255–265.

    • Search Google Scholar
    • Export Citation

  • Smith, T. M., and R. E. Livezey, 1999: GCM systematic error correction and specification of the seasonal mean Pacific–North America region atmosphere from global SSTs. J. Climate, 12, 273–288.

    • Search Google Scholar
    • Export Citation

  • Stephenson, D. B., 2000: Use of the “odds ratio” for diagnosing forecast skill. Wea. Forecasting, 15, 221–232.

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

  • van den Dool, H. M., and Z. Toth, 1991: Why do forecasts for “near normal” often fail? Wea. Forecasting, 6, 76–85.

  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109, 784–812.

    • Search Google Scholar
    • Export Citation

  • Wang, W., M. Chen, and A. Kumar, 2010: An assessment of the CFS real-time seasonal forecasts. Wea. Forecasting, 25, 950–969.

  • Wilks, D., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Wolter, K., R. M. Dole, and C. A. Smith, 1999: Short-term climate extremes over the continental United States and ENSO. Part I: Seasonal temperatures. J. Climate, 12, 3255–3272.

    • Search Google Scholar
    • Export Citation

  • Wu, R., B. P. Kirtman, and H. van den Dool, 2009: An analysis of ENSO prediction skill in the CFS retrospective forecasts. J. Climate, 22, 1801–1818.

    • Search Google Scholar
    • Export Citation
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