• Becker, E., H. van den Dool, and Q. Zhang, 2014: Predictability and forecast skill in NMME. J. Climate, 27, 58915906, https://doi.org/10.1175/JCLI-D-13-00597.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Befort, D. J., and et al. , 2019: Seasonal forecast skill for extratropical cyclones and windstorms. Quart. J. Roy. Meteor. Soc., 145, 92108, https://doi.org/10.1002/qj.3406.

    • Search Google Scholar
    • Export Citation
  • Blackmon, M. L., 1976: A climatological spectral study of the 500 mb geopotential height of the Northern Hemisphere. J. Atmos. Sci., 33, 16071623, https://doi.org/10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., and Y. Fu, 2002: Interdecadal variations in Northern Hemisphere winter storm track intensity. J. Climate, 15, 642658, https://doi.org/10.1175/1520-0442(2002)015<0642:IVINHW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., S. Lee, and K. L. Swanson, 2002: Storm track dynamics. J. Climate, 15, 21632183, https://doi.org/10.1175/1520-0442(2002)015<02163:STD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., Y. Guo, X. Xia, and M. Zheng, 2013: Storm-track activity in IPCC AR4/CMIP3 model simulations. J. Climate, 26, 246260, https://doi.org/10.1175/JCLI-D-11-00707.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, E. K. M., C. Zheng, P. Lanigan, A. M. W. Yau, and J. D. Neelin, 2015: Significant modulation of variability and projected change in California winter precipitation by extratropical cyclone activity. Geophys. Res. Lett., 42, 59835991, https://doi.org/10.1002/2015GL064424.

    • Crossref
    • 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, 717725, https://doi.org/10.1175/2009JCLI3090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Wang, and A. Kumar, 2013: Lagged ensembles, forecast configuration, and seasonal predictions. Mon. Wea. Rev., 141, 34773497, https://doi.org/10.1175/MWR-D-12-00184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dee, D. P., and et al. , 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, https://doi.org/10.1002/qj.828.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Deng, Y., and T. Jiang, 2011: Intraseasonal modulation of the North Pacific storm track by tropical convection in boreal winter. J. Climate, 24, 11221137, https://doi.org/10.1175/2010JCLI3676.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eichler, T., and W. Higgins, 2006: Climatology and ENSO-related variability of North American extratropical cyclone activity. J. Climate, 19, 20762093, https://doi.org/10.1175/JCLI3725.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Froude, L. S. R., 2010: TIGGE: Comparison of the prediction of Northern Hemisphere extratropical cyclones by different ensemble prediction systems. Wea. Forecasting, 25, 819836, https://doi.org/10.1175/2010WAF2222326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Froude, L. S. R., L. Bengtsson, and K. I. Hodges, 2007a: The predictability of extratropical storm tracks and the sensitivity of their prediction to the observing system. Mon. Wea. Rev., 135, 315333, https://doi.org/10.1175/MWR3274.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Froude, L. S. R., L. Bengtsson, and K. I. Hodges, 2007b: The prediction of extratropical storm tracks by the ECMWF and NCEP ensemble prediction systems. Mon. Wea. Rev., 135, 25452567, https://doi.org/10.1175/MWR3422.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Guo, Y., T. Shinoda, J. Lin, and E. K. Chang, 2017: Variations of Northern Hemisphere storm track and extratropical cyclone activity associated with the Madden–Julian oscillation. J. Climate, 30, 47994818, https://doi.org/10.1175/JCLI-D-16-0513.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagedorn, R., F. J. Doblas-Reyes, and T. N. Palmer, 2005: The rationale behind the success of multi-model ensembles in seasonal forecasting–I. Basic concept. Tellus, 57A, 219233, https://doi.org/10.3402/tellusa.v57i3.14657.

    • Search Google Scholar
    • Export Citation
  • Haynes, P. H., M. E. McIntyre, T. G. Shepherd, C. J. Marks, and K. P. Shine, 1991: On the “downward control” of extratropical diabatic circulations by eddy-induced mean zonal forces. J. Atmos. Sci., 48, 651678, https://doi.org/10.1175/1520-0469(1991)048<0651:OTCOED>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidston, J., A. A. Scaife, S. C. Hardiman, D. M. Mitchell, N. Butchart, M. P. Baldwin, and L. J. Gray, 2015: Stratospheric influence on tropospheric jet streams, storm tracks, and surface weather. Nat. Geosci., 8, 433440, https://doi.org/10.1038/ngeo2424.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klein, W. H., 1957: Principal tracks and mean frequencies of cyclones and anticyclones in the Northern Hemisphere. U.S. Weather Bureau Research Paper 40, 60 pp.

  • Lau, N. C., 1978: On the three-dimensional structure of the observed transient eddy statistics of the Northern Hemisphere wintertime circulation. J. Atmos. Sci., 35, 19001923, https://doi.org/10.1175/1520-0469(1978)035<1900:OTTDSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, Y. Y., and G. H. Lim, 2012: Dependency of the North Pacific winter storm tracks on the zonal distribution of MJO convection. J. Geophys. Res., 117, D14101, https://doi.org/10.1029/2011JD016417.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lukens, K. E., and E. H. Berbery, 2019: Winter storm tracks and related weather in the NCEP climate forecast system weeks 3–4 reforecasts for North America. Wea. Forecasting, 34, 751772, https://doi.org/10.1175/WAF-D-18-0113.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ma, C., and E. K. Chang, 2017: Impacts of storm-track variations on wintertime extreme weather events over the Continental United States. J. Climate, 30, 46014624, https://doi.org/10.1175/JCLI-D-16-0560.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monhart, S., C. Spirig, J. Bhend, K. Bogner, C. Schär, and M. A. Liniger, 2018: Skill of subseasonal forecasts in Europe: Effect of bias correction and downscaling using surface observations. J. Geophys. Res. Atmos., 123, 79998016, https://doi.org/10.1029/2017JD027923.

    • Search Google Scholar
    • Export Citation
  • Pegion, K., and et al. , 2019: The Subseasonal Experiment (SubX): A multimodel subseasonal prediction experiment. Bull. Amer. Meteor. Soc., 100, 20432060, https://doi.org/10.1175/BAMS-D-18-0270.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Riddle, E. E., A. H. Butler, J. C. Furtado, J. L. Cohen, and A. Kumar, 2013: CFSv2 ensemble prediction of the wintertime Arctic Oscillation. Climate Dyn., 41, 10991116, https://doi.org/10.1007/s00382-013-1850-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and et al. , 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scaife, A. A., and et al. , 2012: Climate change projections and stratosphere–troposphere interaction. Climate Dyn., 38, 20892097, https://doi.org/10.1007/s00382-011-1080-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shaw, T. A., and et al. , 2016: Storm track processes and the opposing influences of climate change. Nat. Geosci., 9, 656664, https://doi.org/10.1038/ngeo2783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, D. M., and et al. , 2013: Real-time multi-model decadal climate predictions. Climate Dyn., 41, 28752888, https://doi.org/10.1007/s00382-012-1600-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stockdale, T. N., and et al. , 2010: Understanding and predicting seasonal-to-interannual climate variability—The producer perspective. Proc. Environ. Sci., 1, 5580, https://doi.org/10.1016/j.proenv.2010.09.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Straus, D. M., and J. Shukla, 1997: Variations of midlatitude transient dynamics associated with ENSO. J. Atmos. Sci., 54, 777790, https://doi.org/10.1175/1520-0469(1997)054<0777:VOMTDA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and et al. , 2017: The Subseasonal to Seasonal (S2S) prediction project database. Bull. Amer. Meteor. Soc., 98, 163173, https://doi.org/10.1175/BAMS-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallace, J. M., G. H. Lim, and M. L. Blackmon, 1988: Relationship between cyclone tracks, anticyclone tracks, and baroclinic waveguides. J. Atmos. Sci., 45, 439462, https://doi.org/10.1175/1520-0469(1988)045<0439:RBCTAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walter, K., and H. F. Graf, 2005: The North Atlantic variability structure, storm tracks, and precipitation depending on the polar vortex strength. Atmos. Chem. Phys., 5, 239248, https://doi.org/10.5194/acp-5-239-2005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., H. M. Kim, and E. K. Chang, 2018a: Interannual modulation of Northern Hemisphere winter storm tracks by the QBO. Geophys. Res. Lett., 45, 27862794, https://doi.org/10.1002/2017GL076929.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, J., H. M. Kim, E. K. Chang, and S. W. Son, 2018b: Modulation of the MJO and North Pacific storm track relationship by the QBO. J. Geophys. Res. Atmos., 123, 39763992, https://doi.org/10.1029/2017JD027977.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, X., and et al. , 2015: Seasonal predictability of extratropical storm tracks in GFDL’s high-resolution climate prediction model. J. Climate, 28, 35923611, https://doi.org/10.1175/JCLI-D-14-00517.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Y., and I. M. Held, 1999: A linear stochastic model of a GCM’s midlatitude storm tracks. J. Atmos. Sci., 56, 34163435, https://doi.org/10.1175/1520-0469(1999)056<3416:ALSMOA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, C., E. K. M. Chang, H. Kim, M. Zhang, and W. Wang, 2018: Impacts of the Madden–Julian Oscillation on storm-track activity, surface air temperature, and precipitation over North America. J. Climate, 31, 61136134, https://doi.org/10.1175/JCLI-D-17-0534.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zheng, C., E. K. M. Chang, H. Kim, M. Zhang, and W. Wang, 2019: Subseasonal to seasonal prediction of wintertime Northern Hemisphere extratropical cyclone activity by S2S and NMME models. J. Geophys. Res. Atmos., 124, 12 05712 077, https://doi.org/10.1029/2019JD031252.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, J., B. Huang, M. A. Balmaseda, J. L. Kinter III, P. Peng, Z.-Z. Hu, and L. Marx, 2013: Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization. Climate Dyn., 41, 27852795, https://doi.org/10.1007/s00382-013-1965-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Subseasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by SubX and S2S Models

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  • 1 Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York
  • | 2 School of Marine and Atmospheric Sciences, Stony Brook University, State University of New York, Stony Brook, New York
  • | 3 NOAA/Climate Prediction Center, College Park, Maryland
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Abstract

The prediction of wintertime extratropical cyclone activity (ECA) on subseasonal time scales by models participating in the Subseasonal Experiment (SubX) and the Seasonal to Subseasonal Prediction (S2S) is assessed. Consistent with a previous study that investigated the S2S models, the SubX models have skillful predictions of ECA over regions from central North Pacific across North America to western North Atlantic, as well as East Asia and northern and southern part of eastern North Atlantic at 3–4 weeks lead time. SubX provides daily mean data, while S2S provides instantaneous data at 0000 UTC each day. This leads to different variance of ECA. Different S2S and SubX models have different reforecast initialization times and reforecast time periods. These factors can all lead to differences in prediction skill. To fairly compare the prediction skill between different models, we develop a novel way to evaluate the prediction of individual model across the two ensembles by comparing every model to the Climate Forecast System, version 2 (CFSv2), as CFSv2 has 6-hourly output and forecasts initialized every day. Among the S2S and SubX models, the European Centre for Medium-Range Weather Forecasts model exhibits the best prediction skill, followed by CFSv2. Our results also suggest that while the prediction skill is sensitive to forecast lead time, including forecasts up to 4 days old into the ensemble may still be useful for weeks 3–4 predictions of ECA.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0157.s1.

© 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: Cheng Zheng, czheng@ldeo.columbia.edu

Abstract

The prediction of wintertime extratropical cyclone activity (ECA) on subseasonal time scales by models participating in the Subseasonal Experiment (SubX) and the Seasonal to Subseasonal Prediction (S2S) is assessed. Consistent with a previous study that investigated the S2S models, the SubX models have skillful predictions of ECA over regions from central North Pacific across North America to western North Atlantic, as well as East Asia and northern and southern part of eastern North Atlantic at 3–4 weeks lead time. SubX provides daily mean data, while S2S provides instantaneous data at 0000 UTC each day. This leads to different variance of ECA. Different S2S and SubX models have different reforecast initialization times and reforecast time periods. These factors can all lead to differences in prediction skill. To fairly compare the prediction skill between different models, we develop a novel way to evaluate the prediction of individual model across the two ensembles by comparing every model to the Climate Forecast System, version 2 (CFSv2), as CFSv2 has 6-hourly output and forecasts initialized every day. Among the S2S and SubX models, the European Centre for Medium-Range Weather Forecasts model exhibits the best prediction skill, followed by CFSv2. Our results also suggest that while the prediction skill is sensitive to forecast lead time, including forecasts up to 4 days old into the ensemble may still be useful for weeks 3–4 predictions of ECA.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0157.s1.

© 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: Cheng Zheng, czheng@ldeo.columbia.edu

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