• Balmaseda, M. A., A. Vidard, and D. L. T. Anderson, 2008: The ECMWF Ocean Analysis System: ORA-S3. Mon. Wea. Rev., 136, 30183034, https://doi.org/10.1175/2008MWR2433.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Balmaseda, M. A., K. Mogensen, and A. T. Weaver, 2013: Evaluation of the ECMWF ocean reanalysis system ORAS4. Quart. J. Roy. Meteor. Soc., 139, 11321161, https://doi.org/10.1002/qj.2063.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Behringer, D. W., 2005: The Global Ocean Data Assimilation System (GODAS) as NCEP. 11th Symp. on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, San Antonio, TX, Amer. Meteor. Soc., 3.3, https://ams.confex.com/ams/87ANNUAL/webprogram/Paper119541.html.

  • Black, J., N. C. Johnson, S. Baxter, S. B. Feldstein, D. S. Harnos, and M. L. L’Heureux, 2017: The predictors and forecast skill of Northern Hemisphere teleconnection patterns for lead times of 3–4 weeks. Mon. Wea. Rev., 145, 28552877, https://doi.org/10.1175/MWR-D-16-0394.1.

    • 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
  • DelSole, T., L. Trenary, M. K. Tippett, and K. Pegion, 2017: Predictability of weeks 3–4 average temperature and precipitation over the contiguous United States. J. Climate, 30, 34993512, https://doi.org/10.1175/JCLI-D-16-0567.1.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffies, S. M., M. J. Harrison, R. C. Pacanowski, and A. Rosati, 2004: Technical guide to MOM4. GFDL Ocean Group Tech. Rep. 5, 371 pp., http://www.gfdl.noaa.gov/fms.

  • Hoskins, B., 2013: The potential for skill across the range of the seamless weather-climate prediction problem: A stimulus for our science. Quart. J. Roy. Meteor. Soc., 139, 573584, https://doi.org/10.1002/qj.1991.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and et al. , 2015: Climate drift of AMOC, North Atlantic salinity and Arctic sea ice in CFSv2 decadal predictions. Climate Dyn., 44, 559583, https://doi.org/10.1007/s00382-014-2395-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hudson, D., O. Alves, H. H. Hendon, and A. G. Marshall, 2011: Bridging the gap between weather and seasonal forecasting: Intraseasonal forecasting for Australia. Quart. J. Roy. Meteor. Soc., 137, 673689, https://doi.org/10.1002/qj.769.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koster, R. D., S. P. P. Mahanama, T. J. Yamada, G. Balsamo, A. A. Berg, M. Boisserie, and Z. Guo, 2011: The second phase of the global land–atmosphere coupling experiment: Soil moisture contributions to subseasonal forecast skill. J. Hydrometeor., 12, 805822, https://doi.org/10.1175/2011JHM1365.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, S., and A. W. Robertson, 2015: Evaluation of submonthly precipitation forecast skill from global ensemble prediction systems. Mon. Wea. Rev., 143, 28712889, https://doi.org/10.1175/MWR-D-14-00277.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mariotti, A., and et al. , 2020: Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond. Bull. Amer. Meteor. Soc., 101, E608E625, https://doi.org/10.1175/BAMS-D-18-0326.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merryfield, W. J., and et al. , 2020: Current and emerging developments in subseasonal to decadal prediction. Bull. Amer. Meteor. Soc., 101, E869E896, https://doi.org/10.1175/BAMS-D-19-0037.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • National Research Council, 2010: Assessment of Intraseasonal to Interannual Climate Prediction and Predictability. National Academic Press, 192 pp., https://doi.org/10.17226/12878.

    • Crossref
    • Export Citation
  • Pegion, K., and P. D. Sardeshmukh, 2011: Prospects for improving subseasonal predictions. Mon. Wea. Rev., 139, 36483666, https://doi.org/10.1175/MWR-D-11-00004.1.

    • Crossref
    • 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
  • Robertson, A. W., A. Kumar, M. Peña, and F. Vitart, 2015: Improving and promoting subseasonal to seasonal prediction. Bull. Amer. Meteor. Soc., 96, ES49ES53, https://doi.org/10.1175/BAMS-D-14-00139.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saha, S., and et al. , 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 10151057, https://doi.org/10.1175/2010BAMS3001.1.

    • 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
  • Shukla, R. P., and J. L. Kinter, 2016: Subseasonal prediction of significant wave heights over the western Pacific and Indian Ocean region. Wea. Forecasting, 31, 17331751, https://doi.org/10.1175/WAF-D-16-0078.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, R. P., J. L. Kinter, and C.-S. Shin, 2018: Sub-seasonal prediction of significant wave heights over the western Pacific and Indian Oceans. Part II: The impact of ENSO and MJO. Ocean Modell., 123, 115, https://doi.org/10.1016/j.ocemod.2018.01.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shukla, R. P., J. L. Kinter, and C.-S. Shin, 2020: Corrigendum to “Sub-seasonal prediction of significant wave heights over the Western Pacific and Indian Oceans. Part II: The impact of ENSO and MJO.” Ocean Modell., 152, 101647, https://doi.org/10.1016/j.ocemod.2020.101647.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, S., B. W. Green, R. Bleck, and S. G. Benjamin, 2018: Subseasonal forecasting with an icosahedral, vertically quasi-Lagrangian coupled model. Part II: Probabilistic and deterministic forecast skill. Mon. Wea. Rev., 146, 16191639, https://doi.org/10.1175/MWR-D-18-0007.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tolman, H. L., 2009: User manual and system documentation of WAVEWATCH III TM version 3.14. NOAA/NWS/NCEP/MMAB Tech. Note 276, 194 pp.

  • Towns, J., and et al. , 2014: XSEDE: Accelerating scientific discovery. Comput. Sci. Eng., 16, 6274, https://doi.org/10.1109/MCSE.2014.80.

  • Vigaud, N., A. W. Robertson, and M. K. Tippett, 2017: Multimodel ensembling of subseasonal precipitation forecasts over North America. Mon. Wea. Rev., 145, 39133928, https://doi.org/10.1175/MWR-D-17-0092.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vigaud, N., M. K. Tippett, and A. W. Robertson, 2018: Probabilistic skill of subseasonal precipitation forecasts for the East Africa–West Asia sector during September–May. Wea. Forecasting, 33, 15131532, https://doi.org/10.1175/WAF-D-18-0074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., A. W. Robertson, and D. L. T. Anderson, 2012: Subseasonal to seasonal prediction project: Bridging the gap between weather and climate. WMO Bull., 61, 2328.

    • Search Google Scholar
    • Export Citation
  • Wang, L., and A. W. Robertson, 2019: Week 3–4 predictability over the United States assessed from two operational ensemble prediction systems. Climate Dyn., 52, 58615875, https://doi.org/10.1007/s00382-018-4484-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weber, N. J., and C. F. Mass, 2017: Evaluating CFSv2 subseasonal forecast skill with an emphasis on tropical convection. Mon. Wea. Rev., 145, 37953815, https://doi.org/10.1175/MWR-D-17-0109.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, C. J., and et al. , 2017: Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteor. Appl., 24, 315325, https://doi.org/10.1002/met.1654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Winton, M., 2000: A reformulated three-layer sea ice model. J. Atmos. Oceanic Technol., 17, 525531, https://doi.org/10.1175/1520-0426(2000)017<0525:ARTLSI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Relationship between Prediction Skill of Surface Winds in Average of Weeks 1–4 and Interannual Variability over the Western Pacific and Indian Ocean

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  • 1 a Center for Ocean–Land–Atmosphere Studies, George Mason University, Fairfax, Virginia
  • | 2 b Department of Atmospheric, Oceanic, and Earth Sciences, George Mason University, Fairfax, Virginia
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Abstract

This study examines the possible relationship between predictions of weekly and biweekly averages of 10-m winds at 3-week lead time and interannual variability over the western Pacific and Indian Ocean (WP-IO) using Climate Forecast System version 2 (CFSv2) reforecasts for period 1979–2008. There is a large temporal correlation between forecasts and reanalyses for zonal, meridional, and total wind magnitudes at 10 m over most of WP-IO for the average of weeks 1 and 2 (W1 and W2) in reforecasts initialized in January (JIR) and May (MIR). The model has some correlations that exceed 95% confidence in some portions of WP-IO in week 3 (W3) but no skill in week 4 (W4) over most of the region. The model depicts prediction skill in the 14-day average of weeks 3–4 (W3–4) over portions of WP-IO, similar to the level of skill in W3. The amplitude of interannual variability (IAV) for 10-m winds in W1 of JIR and MIR is close to that in reanalyses. As lead time increases, the amplitude of IAV of 10-m winds gradually decreases over WP-IO in reforecasts, in contrast to behavior in reanalyses. The amplitude of IAV of predicted 10-m winds in W3–4 over WP-IO is equivalent to that in W3 and W4 in reforecasts. In contrast, the amplitude of IAV in W3–4 in January and May of the reanalysis is much smaller than IAV of W3 and W4. Therefore, one of the possible causes for prediction skill in W3–4 over subregions of WP-IO is due to a reduction of IAV bias in W3–4 in comparison to IAV bias in W3 and W4.

SIGNIFICANCE STATEMENT

Reliable prediction at the subseasonal time scale using a coupled land–atmospheric–ocean model is useful for making management decisions in agriculture and water management. This study explores a relationship between prediction skill of weekly average surface winds at lead times of 1–4 weeks and interannual variability over the western Pacific and Indian Ocean using the Climate Forecast System version 2 reforecasts during 1979–2008. The model has prediction skill in some subregions that exceeds 95% confidence in week 3 but no skill in week 4. Taking 14-day averages of weeks 3 and 4 produces forecasts whose skill is similar to that in week 3. There is a concurrent reduction in interannual variability during weeks 3 and 4 in reforecasts. A hypothesis is put forward that the subseasonal skill is related to the diminution of variability in the model.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0181.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: Ravi P. Shukla, rshukla2@gmu.edu

Abstract

This study examines the possible relationship between predictions of weekly and biweekly averages of 10-m winds at 3-week lead time and interannual variability over the western Pacific and Indian Ocean (WP-IO) using Climate Forecast System version 2 (CFSv2) reforecasts for period 1979–2008. There is a large temporal correlation between forecasts and reanalyses for zonal, meridional, and total wind magnitudes at 10 m over most of WP-IO for the average of weeks 1 and 2 (W1 and W2) in reforecasts initialized in January (JIR) and May (MIR). The model has some correlations that exceed 95% confidence in some portions of WP-IO in week 3 (W3) but no skill in week 4 (W4) over most of the region. The model depicts prediction skill in the 14-day average of weeks 3–4 (W3–4) over portions of WP-IO, similar to the level of skill in W3. The amplitude of interannual variability (IAV) for 10-m winds in W1 of JIR and MIR is close to that in reanalyses. As lead time increases, the amplitude of IAV of 10-m winds gradually decreases over WP-IO in reforecasts, in contrast to behavior in reanalyses. The amplitude of IAV of predicted 10-m winds in W3–4 over WP-IO is equivalent to that in W3 and W4 in reforecasts. In contrast, the amplitude of IAV in W3–4 in January and May of the reanalysis is much smaller than IAV of W3 and W4. Therefore, one of the possible causes for prediction skill in W3–4 over subregions of WP-IO is due to a reduction of IAV bias in W3–4 in comparison to IAV bias in W3 and W4.

SIGNIFICANCE STATEMENT

Reliable prediction at the subseasonal time scale using a coupled land–atmospheric–ocean model is useful for making management decisions in agriculture and water management. This study explores a relationship between prediction skill of weekly average surface winds at lead times of 1–4 weeks and interannual variability over the western Pacific and Indian Ocean using the Climate Forecast System version 2 reforecasts during 1979–2008. The model has prediction skill in some subregions that exceeds 95% confidence in week 3 but no skill in week 4. Taking 14-day averages of weeks 3 and 4 produces forecasts whose skill is similar to that in week 3. There is a concurrent reduction in interannual variability during weeks 3 and 4 in reforecasts. A hypothesis is put forward that the subseasonal skill is related to the diminution of variability in the model.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/WAF-D-20-0181.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: Ravi P. Shukla, rshukla2@gmu.edu

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