• Akihiko, T., Y. Morioka, and S. K. Behera, 2015: Role of climate variability in the heatstroke death rates of Kanto region in Japan. Sci. Rep., 4, 5655, https://doi.org/10.1038/srep05655.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, H. E., N. E. Zimmermann, T. R. McVicar, N. Vergopolan, A. Berg, and E. F. Wood, 2018: Present and future Köppen-Geiger climatic classification maps at 1-km resolution. Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doi, T., S. K. Behera, and T. Yamagata, 2016: Improved seasonal prediction using the SINTEX-F2 coupled model. J. Adv. Model. Earth Syst., 8, 18471867, https://doi.org/10.1002/2016MS000744.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Doi, T., A. Storto, S. K. Behera, A. Navarra, and T. Yamagata, 2017: Improved prediction of the Indian Ocean dipole mode by use of subsurface ocean observations. J. Climate, 30, 79537970, https://doi.org/10.1175/JCLI-D-16-0915.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Efron, B., and R. J. Tibshirani, 1994: An Introduction to the Bootstrap. Chapman and Hall, 456 pp.

    • Crossref
    • 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, https://doi.org/10.1029/2007JD008470.

    • Search Google Scholar
    • Export Citation
  • Feudale, L., and A. M. Tompkins, 2011: A simple bias correction technique for modeled monsoon precipitation applied to West Africa. Geophys. Res. Lett., 38, L03803, https://doi.org/10.1029/2010GL045909.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jolliffe, I. T., and D. B. Stephenson, 2012: Forecast Verification: A Practioner’s Guide in Atmospheric Sciences. 2nd ed. Wiley, 292 pp.

    • Crossref
    • Export Citation
  • Kalnay, E., and et al. , 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Luo, J. J., S. Masson, S. Behera, S. Shingu, and T. Yamagata, 2005: Seasonal climate predictability in a coupled OAGCM using a different approach for ensemble forecasts. J. Climate, 18, 44744497, https://doi.org/10.1175/JCLI3526.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madec, G., 2008: NEMO ocean engine, version 3.0. Note du Pôle de modélisation de l’Institut Pierre?Simon Laplace 27, 209 pp.

  • Mason, S. J., 2008: Understanding forecast verification statistics. Meteor. Appl., 15, 3140, https://doi.org/10.1002/met.51.

  • Mason, S. J., and N. E. Graham, 1999: Conditional probabilities, relative operating characteristics, and relative operating levels. Wea. Forecasting, 14, 713725, https://doi.org/10.1175/1520-0434(1999)014<0713:CPROCA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Masson, S., P. Terray, G. Madec, J. J. Luo, T. Yamagata, and K. Takahashi, 2012: Impact of intra-daily SST variability on ENSO characteristics in a coupled model. Climate Dyn., 39, 681707, https://doi.org/10.1007/s00382-011-1247-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Miyakoda, K., J. Sirutis, and J. Ploshay, 1986: One-month forecast experiments—Without anomaly boundary forcings. Mon. Wea. Rev., 114, 23632401, https://doi.org/10.1175/1520-0493(1986)114<2363:OMFEAB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nishimura, M., and M. Yamaguchi, 2015: Selective ensemble mean technique for tropical cyclone track predictions using multi-model ensemble. Trop. Cyclone Res. Rev., 4, 7178, https://doi.org/10.6057/2015TCRR02.03.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., and D. L. T. Anderson, 1994: The prospects for seasonal forecasting—A review paper. Quart. J. Roy. Meteor. Soc., 120, 755793, https://doi.org/10.1002/QJ.49712051802.

    • Search Google Scholar
    • Export Citation
  • Peel, M. C., B. L. Finlayson, and T. A. Mcmahon, 2007: Updated world map of Köppen–Geiger climatic classification. Hydrol. Earth Syst. Sci., 11, 16331644, https://doi.org/10.5194/hess-11-1633-2007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Qi, L., H. Yu, and P. Chen, 2014: Selective ensemble-mean technique for tropical cyclone track forecast by using ensemble prediction systems. Quart. J. Roy. Meteor. Soc., 140, 805813, https://doi.org/10.1002/qj.2196.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ratnam, J. V., T. Doi, and S. K. Behera, 2019a: Improving austral summer precipitation forecasts of SINTEX-F2 coupled ocean-atmosphere general circulation model over southern Africa by simple bias correction technique. Atmos. Sci. Lett., 20, 3885, https://doi.org/10.1002/asl.885.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ratnam, J. V., H. A. Dijkstra, T. Doi, Y. Morioka, M. Nonaka, and S. K. Behera, 2019b: Improving seasonal forecasts of air temperature using a genetic algorithm. Sci. Rep., 9, 12781, https://doi.org/10.1038/s41598-019-49281-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Roeckner, E., and et al. , 2003: The atmospheric general circulation model ECHAM5. Part I: Model description. Max-Planck-Institut fur Meteorologie Rep. 349, 127 pp., http://www.mpimet.mpg.de/fileadmin/models/echam/mpi_report_349.pdf.

  • Sabeerali, C. T., A. Ramu Dandi, A. Dhakate, K. Salunke, S. Mahapatra, and S. A. Rao, 2013: Simulation of boreal summer intraseasonal oscillations in the latest CMIP5 coupled GCMs. J. Geophys. Res. Atmos., 118, 44014420, https://doi.org/10.1002/jgrd.50403.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sasaki, W., K. J. Richards, and J. J. Luo, 2012: Role of vertical mixing originating from small vertical scale structures above and within the equatorial thermocline in an OGCM. Ocean Modell., 57–58, 2942, https://doi.org/10.1016/j.ocemod.2012.09.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sasaki, W., K. J. Richards, and J. J. Luo, 2013: Impact of vertical mixing induced by small vertical scale structures above and within the equatorial thermocline on the tropical Pacific in a CGCM. Climate Dyn., 41, 443453, https://doi.org/10.1007/s00382-012-1593-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scher, S., and G. Messori, 2019: Selective ensemble mean technique for severe European windstorms. Quart. J. Roy. Meteor. Soc., 145, 376385, https://doi.org/10.1002/QJ.3408.

    • Search Google Scholar
    • Export Citation
  • Valcke, S., A. Caubel, R. Vogelsang, and D. Declat, 2004: OASIS3 ocean atmosphere sea ice soil user’s guide. CERFACS Tech. Rep. TR/CMGC/04/68, 70 pp.

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Improving Predictions of Surface Air Temperature Anomalies over Japan by the Selective Ensemble Mean Technique

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  • 1 Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
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Abstract

The selective ensemble mean (SEM) technique is applied to the late spring and summer months (May–August) surface air temperature anomaly predictions of the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), coupled general circulation model over Japan. Using the Köppen–Geiger climatic classification we chose four regions over Japan for applying the SEM technique. The SINTEX-F2 ensemble members for the SEM are chosen based on the anomaly correlation coefficients (ACC) of the SINTEX-F2 predicted and observed surface air temperature anomalies. The SEM technique is applied to generate the forecasts of the surface air temperature anomalies for the period 1983–2018 using the selected members. Analysis shows the ACC skill score of the SEM prediction to be higher compared to the ACC skill score of predictions obtained by averaging all the 24 members of the SINTEX-F2 (ENSMEAN). The SEM predicted surface air temperature anomalies also have higher hit rate and lower false alarm rate compared to the ENSMEAN predicted anomalies over a range of temperature anomalies. The results indicate the SEM technique to be a simple and easy to apply method to improve the SINTEX-F2 predictions of surface air temperature anomalies over Japan. The better performance of the SEM in generating the surface air temperature anomalies can be partly attributed to realistic prediction of 850-hPa geopotential height anomalies over Japan.

© 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: J. V. Ratnam, jvratnam@jamstec.go.jp

Abstract

The selective ensemble mean (SEM) technique is applied to the late spring and summer months (May–August) surface air temperature anomaly predictions of the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), coupled general circulation model over Japan. Using the Köppen–Geiger climatic classification we chose four regions over Japan for applying the SEM technique. The SINTEX-F2 ensemble members for the SEM are chosen based on the anomaly correlation coefficients (ACC) of the SINTEX-F2 predicted and observed surface air temperature anomalies. The SEM technique is applied to generate the forecasts of the surface air temperature anomalies for the period 1983–2018 using the selected members. Analysis shows the ACC skill score of the SEM prediction to be higher compared to the ACC skill score of predictions obtained by averaging all the 24 members of the SINTEX-F2 (ENSMEAN). The SEM predicted surface air temperature anomalies also have higher hit rate and lower false alarm rate compared to the ENSMEAN predicted anomalies over a range of temperature anomalies. The results indicate the SEM technique to be a simple and easy to apply method to improve the SINTEX-F2 predictions of surface air temperature anomalies over Japan. The better performance of the SEM in generating the surface air temperature anomalies can be partly attributed to realistic prediction of 850-hPa geopotential height anomalies over Japan.

© 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: J. V. Ratnam, jvratnam@jamstec.go.jp
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