Testing Vertical Wind Shear and Nonlinear MJO–ENSO Interactions as Predictors for Subseasonal Atlantic Tropical Cyclone Forecasts

Kurt A. Hansen aDepartment of Atmospheric Sciences, Rosenstiel School of Marine and Atmospheric Science, Miami, Florida

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Sharanya J. Majumdar aDepartment of Atmospheric Sciences, Rosenstiel School of Marine and Atmospheric Science, Miami, Florida

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Ben P. Kirtman aDepartment of Atmospheric Sciences, Rosenstiel School of Marine and Atmospheric Science, Miami, Florida

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Matthew A. Janiga bMarine Meteorology Division, Naval Research Laboratory, Monterey, California

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Abstract

Hansen et al. found patterns of vertical wind shear, relative humidity (RH), and nonlinear interactions between the Madden–Julian oscillation and El Niño–Southern Oscillation that impact subseasonal Atlantic TC activity. We test whether these patterns can be used to improve subseasonal predictions. To do this we build a statistical–dynamical hybrid model using Navy-ESPC reforecasts as a part of the SUBX project. By adding and removing Navy-ESPC reforecasted values of predictors from a logistic regression model, we assess the contribution of skill from each predictor. We find that Atlantic SSTs and the MJO are the most important factors governing subseasonal Atlantic TC activity. RH contributes little to subseasonal TC predictions; however, shear predictors improve forecast skill at 5–10-day lead times, before forecast shear errors become too large. Nonlinear MJO–ENSO interactions did not improve skill compared to separate linear considerations of these factors but did improve the reliability of predictions for high-probability active TC periods. Both nonlinear MJO–ENSO interactions and the subseasonal shear signal appear linked to PV streamer activity. This study suggests that correcting model shear biases and improving representation of Rossby wave breaking is the most efficient way to improve subseasonal Atlantic TC forecasts.

© 2022 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: Kurt A. Hansen, kurt.hansen@rsmas.miami.edu

Abstract

Hansen et al. found patterns of vertical wind shear, relative humidity (RH), and nonlinear interactions between the Madden–Julian oscillation and El Niño–Southern Oscillation that impact subseasonal Atlantic TC activity. We test whether these patterns can be used to improve subseasonal predictions. To do this we build a statistical–dynamical hybrid model using Navy-ESPC reforecasts as a part of the SUBX project. By adding and removing Navy-ESPC reforecasted values of predictors from a logistic regression model, we assess the contribution of skill from each predictor. We find that Atlantic SSTs and the MJO are the most important factors governing subseasonal Atlantic TC activity. RH contributes little to subseasonal TC predictions; however, shear predictors improve forecast skill at 5–10-day lead times, before forecast shear errors become too large. Nonlinear MJO–ENSO interactions did not improve skill compared to separate linear considerations of these factors but did improve the reliability of predictions for high-probability active TC periods. Both nonlinear MJO–ENSO interactions and the subseasonal shear signal appear linked to PV streamer activity. This study suggests that correcting model shear biases and improving representation of Rossby wave breaking is the most efficient way to improve subseasonal Atlantic TC forecasts.

© 2022 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: Kurt A. Hansen, kurt.hansen@rsmas.miami.edu
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  • Barton, N., and Coauthors, 2021: The Navy’s Earth System Prediction Capability: A new global coupled atmosphere-ocean-sea ice prediction system designed for daily to subseasonal forecasting. Earth Space Sci., 8, e2020EA001199, https://doi.org/10.1029/2020EA001199.

    • Crossref
    • Export Citation
  • Belanger, J. I., J. A. Curry, and P. J. Webster, 2010: Predictability of North Atlantic tropical cyclone activity on intraseasonal time scales. Mon. Wea. Rev., 138, 43624374, https://doi.org/10.1175/2010MWR3460.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., 2013: Global and regional aspects of tropical cyclone activity in the CMIP5 models. J. Climate, 26, 98809902, https://doi.org/10.1175/JCLI-D-12-00549.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., K. A. Emanuel, and A. H. Sobel, 2007: Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J. Climate, 20, 48194834, https://doi.org/10.1175/JCLI4282.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., M. C. Wheeler, and A. H. Sobel, 2009: Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. J. Atmos. Sci., 66, 30613074, https://doi.org/10.1175/2009JAS3101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camp, J., and Coauthors, 2018: Skilful multiweek tropical cyclone prediction in ACCESS-S1 and the role of the MJO. Quart. J. Roy. Meteor. Soc., 144, 13371351, https://doi.org/10.1002/qj.3260.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carman, J. C., and Coauthors, 2017: The National Earth System Prediction Capability: Coordinating the giant. Bull. Amer. Meteor. Soc., 98, 239–252, https://doi.org/10.1175/BAMS-D-16-0002.1.

    • Crossref
    • Export Citation
  • de Boyer Montégut, C., G. Madec, A. S. Fischer, A. Lazar, and D. Iudicone, 2004: Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology. J. Geophys. Res., 109, C12003, https://doi.org/10.1029/2004JC002378.

    • Crossref
    • Export Citation
  • DeMaria, M., 1996: The effect of vertical shear on tropical cyclone intensity change. J. Atmos. Sci., 53, 2076–2088, https://doi.org/10.1175/1520-0469(1996)053<2076:TEOVSO>2.0.CO;2.

    • Crossref
    • Export Citation
  • Elsberry, R. L., M. S. Jordan, and F. Vitart, 2010: Predictability of tropical cyclone events on intraseasonal timescales with the ECMWF monthly forecast model. Asia-Pac. J. Atmos. Sci., 46, 135153, https://doi.org/10.1007/s13143-010-0013-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fitzpatrick, P. J., J. A. Knaff, C. W. Landsea, and S. V. Finley, 1995: Documentation of a systematic bias in the aviation model’s forecast of the Atlantic tropical upper-tropospheric trough: Implications for tropical cyclone forecasting. Wea. Forecasting, 10, 433–446, https://doi.org/10.1175/1520-0434(1995)010<0433:DOASBI>2.0.CO;2.

    • Crossref
    • Export Citation
  • Galarneau, T. J., R. McTaggart-Cowan, L. F. Bosart, and C. A. Davis, 2015: Development of North Atlantic tropical disturbances near upper-level potential vorticity streamers. J. Atmos. Sci., 72, 572–597, https://doi.org/10.1175/JAS-D-14-0106.1.

    • Crossref
    • Export Citation
  • Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the Tropical Oceans, B. Shaw, Ed., Royal Meteorological Society, 155–218.

  • Gray, W. M., 1984: Atlantic seasonal hurricane frequency. Part I: El Niño and 30 mb Quasi Biennial Oscillation influences. Mon. Wea. Rev., 112, 16491668, https://doi.org/10.1175/1520-0493(1984)112<1649:ASHFPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, K. A., S. J. Majumdar, and B. P. Kirtman, 2020: Identifying subseasonal variability relevant to Atlantic tropical cyclone activity. Wea. Forecasting, 35, 2001–2024, https://doi.org/10.1175/WAF-D-19-0260.1.

    • Crossref
    • Export Citation
  • Henderson, S. A., and E. D. Maloney, 2013: An intraseasonal prediction model of Atlantic and east Pacific tropical cyclone genesis. Mon. Wea. Rev., 141, 1925–1942, https://doi.org/10.1175/MWR-D-12-00268.1.

    • Crossref
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803.

    • Crossref
    • Export Citation
  • Hsu, W., and A. H. Murphy, 1986: The attributes diagram a geometrical framework for assessing the quality of probability forecasts. Int. J. Forecasting, 2, 285–293, https://doi.org/10.1016/0169-2070(86)90048-8.

    • Crossref
    • Export Citation
  • Janiga, M. A., C. J. Schreck, J. A. Ridout, M. Flatau, N. P. Barton, E. J. Metzger, and C. A. Reynolds, 2018: Subseasonal forecasts of convectively coupled equatorial waves and the MJO: Activity and predictive skill. Mon. Wea. Rev., 146, 23372360, https://doi.org/10.1175/MWR-D-17-0261.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jones, C., D. E. Waliser, K. M. Lau, and W. Stern, 2004: Global occurrences of extreme precipitation and the Madden–Julian oscillation: Observations and predictability. J. Climate, 17, 45754589, https://doi.org/10.1175/3238.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., J. Dias, K. H. Straub, M. C. Wheeler, S. N. Tulich, K. Kikuchi, K. M. Weickmann, and M. J. Ventrice, 2014: A comparison of OLR and circulation-based indices for tracking the MJO. Mon. Wea. Rev., 142, 1697–1715, https://doi.org/10.1175/MWR-D-13-00301.1.

    • Crossref
    • Export Citation
  • Kim, H., F. Vitart, and D. E. Waliser, 2018: Prediction of the Madden–Julian Oscillation: A review. J. Climate, 31, 9425–9443, https://doi.org/10.1175/JCLI-D-18-0210.1.

    • Crossref
    • Export Citation
  • Klotzbach, P. J., 2007: Revised prediction of seasonal Atlantic basin tropical cyclone activity from 1 August. Wea. Forecasting, 22, 937949, https://doi.org/10.1175/WAF1045.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., and E. C. J. Oliver, 2015: Modulation of Atlantic basin tropical cyclone activity by the Madden–Julian oscillation (MJO) from 1905 to 2011. J. Climate, 28, 204217, https://doi.org/10.1175/JCLI-D-14-00509.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann, 2010: The International Best Track Archive for Climate Stewardship (IBTrACS). Bull. Amer. Meteor. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1.

    • Crossref
    • Export Citation
  • Knutson, T. R., K. M. Weickmann, and J. E. Kutzbach, 1986: Global-scale intraseasonal oscillations of outgoing longwave radiation and 250 mb zonal wind during Northern Hemisphere summer. Mon. Wea. Rev., 114, 605–623, https://doi.org/10.1175/1520-0493(1986)114<0605:GSIOOO>2.0.CO;2.

    • Crossref
    • Export Citation
  • Landsea, C. W., and J. L. Franklin, 2013: Atlantic hurricane database uncertainty and presentation of a new database format. Mon. Wea. Rev., 141, 3576–3592, https://doi.org/10.1175/MWR-D-12-00254.1.

    • Crossref
    • Export Citation
  • Lee, C.-Y., S. J. Camargo, F. Vitart, A. H. Sobel, and M. K. Tippett, 2018: Subseasonal tropical cyclone genesis prediction and MJO in the S2S dataset. Wea. Forecasting, 33, 967988, https://doi.org/10.1175/WAF-D-17-0165.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, C.-Y., S. J. Camargo, F. Vitart, A. H. Sobel, J. Camp, S. Wang, M. K. Tippett, and Q. Yang, 2020a: Subseasonal predictions of tropical cyclone occurrence and ace in the S2S dataset. Wea. Forecasting, 35, 921–938, https://doi.org/10.1175/WAF-D-19-0217.1.

    • Crossref
    • Export Citation
  • Lee, J. C. K., R. W. Lee, S. J. Woolnough, and L. J. Boxall, 2020b: The links between the Madden–Julian Oscillation and European weather regimes. Theor. Appl. Climatol., 141, 567–586, https://doi.org/10.1007/s00704-020-03223-2.

    • Crossref
    • Export Citation
  • Leroy, A., and M. C. Wheeler, 2008: Statistical prediction of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 136, 36373654, https://doi.org/10.1175/2008MWR2426.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, W., Z. Wang, G. Zhang, M. S. Peng, S. G. Benjamin, and M. Zhao, 2018: Subseasonal variability of Rossby wave breaking and impacts on tropical cyclones during the North Atlantic warm season. J. Climate, 31, 9679–9695, https://doi.org/10.1175/JCLI-D-17-0880.1.

    • Crossref
    • Export Citation
  • Lin, H., and J. Derome, 2004: Nonlinearity of the extratropical response to tropical forcing. J. Climate, 17, 2597–2608, https://doi.org/10.1175/1520-0442(2004)017<2597:NOTERT>2.0.CO;2.

    • Crossref
    • Export Citation
  • Moon, Y., D. Kim, S. J. Camargo, A. A. Wing, K. A. Reed, M. F. Wehner, and M. Zhao, 2020: A new method to construct a horizontal resolution-dependent wind speed adjustment factor for tropical cyclones in global climate model simulations. Geophys. Res. Lett., 47, e2020GL087528, https://doi.org/10.1029/2020GL087528.

    • Crossref
    • Export Citation
  • Papin, P. P., L. F. Bosart, and R. D. Torn, 2020: A feature-based approach to classifying summertime potential vorticity streamers linked to Rossby wave breaking in the North Atlantic basin. J. Climate, 33, 5953–5969, https://doi.org/10.1175/JCLI-D-19-0812.1.

    • 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 Coauthors, 2019: The subseasonal experiment (SubX): A multimodel subseasonal prediction experiment. Bull. Amer. Meteor. Soc., 100, 2043–2060, https://doi.org/10.1175/BAMS-D-18-0270.1.

    • Crossref
    • Export Citation
  • Qian, Y., P.-C. Hsu, H. Murakami, B. Xiang, and L. You, 2020: A hybrid dynamical statistical model for advancing subseasonal tropical cyclone prediction over the western North Pacific. Geophys. Res. Lett., 47, e2020GL090095, https://doi.org/10.1029/2020GL090095.

    • Crossref
    • Export Citation
  • Russell, J. O., A. Aiyyer, J. D. White, and W. Hannah, 2017: Revisiting the connection between African easterly waves and Atlantic tropical cyclogenesis. Geophys. Res. Lett., 44, 587–595, https://doi.org/10.1002/2016GL071236.

    • Crossref
    • Export Citation
  • Shu, S., and L. Wu, 2009: Analysis of the influence of Saharan air layer on tropical cyclone intensity using AIRS/Aqua data. Geophys. Res. Lett., 36, L09809, https://doi.org/10.1029/2009GL037634.

    • Crossref
    • Export Citation
  • Vitart, F., A. Leroy, and M. C. Wheeler, 2010: A comparison of dynamical and statistical predictions of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 138, 36713682, https://doi.org/10.1175/2010MWR3343.1.

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

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. International Geophysics Series, Vol. 100, Academic Press, 704 pp.

  • Zavadoff, B. L., and B. P. Kirtman, 2020: Dynamic and thermodynamic modulators of European atmospheric rivers. J. Climate, 33, 4167–4185, https://doi.org/10.1175/JCLI-D-19-0601.1.

    • Crossref
    • Export Citation
  • Zhang, C., 2005: Madden–Julian Oscillation. Rev. Geophys., 43, RG2003, https://doi.org/10.1029/2004RG000158.

  • Zhang, G., Z. Wang, M. S. Peng, and G. Magnusdottir, 2017: Characteristics and impacts of extratropical Rossby wave breaking during the Atlantic hurricane season. J. Climate, 30, 2363–2379, https://doi.org/10.1175/JCLI-D-16-0425.1.

    • Crossref
    • Export Citation
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