Letter | Published:

Increasing frequency of extremely severe cyclonic storms over the Arabian Sea

Abstract

In 2014 and 2015, post-monsoon extremely severe cyclonic storms (ESCS)—defined by the WMO as tropical storms with lifetime maximum winds greater than 46 m s1—were first observed over the Arabian Sea (ARB), causing widespread damage. However, it is unknown to what extent this abrupt increase in post-monsoon ESCSs can be linked to anthropogenic warming, natural variability, or stochastic behaviour. Here, using a suite of high-resolution global coupled model experiments that accurately simulate the climatological distribution of ESCSs, we show that anthropogenic forcing has likely increased the probability of late-season ECSCs occurring in the ARB since the preindustrial era. However, the specific timing of observed late-season ESCSs in 2014 and 2015 was likely due to stochastic processes. It is further shown that natural variability played a minimal role in the observed increase of ESCSs. Thus, continued anthropogenic forcing will further amplify the risk of cyclones in the ARB, with corresponding socio-economic implications.

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References

  1. 1.

    Tropical Cyclone Operational Plan for the Bay of Bengal and Arabian Sea WMO/TD-84 (WMO, 2015); www.wmo.int/pages/prog/www/tcp/documents/TCP-21Edition2015_final.pdf

  2. 2.

    Kruk, M. C. Tropical cyclones, North Indian Ocean. Bull. Amer. Meteorol. Soc. 97(8) (Suppl.), 114–115 (2016).

  3. 3.

    Evan, A. T., Kossin, J. P., Chung, C. E. & Ramanathan, V. Arabian Sea tropical cyclones intensified by emissions of black carbon and other aerosols. Nature 479, 94–97 (2011).

  4. 4.

    Wang, B., Xu, S. & Wu, L. Intensified Arabian Sea tropical storms. Nature 489, E1–E2 (2012).

  5. 5.

    Evan, A. T. & Camargo, S. J. A climatology of Arabian Sea cyclonic storms. J. Clim. 24, 140–158 (2011).

  6. 6.

    Kossin, J. P., Olander, T. L. & Knapp, K. R. Trend analysis with a new global record of tropical cyclone intensity. J. Clim. 26, 9960–9976 (2013).

  7. 7.

    Knutson, T. et al. Tropical cyclones and climate change. Nat. Geosci. 3, 157–163 (2010).

  8. 8.

    Murakami, H. et al. Simulation and prediction of Category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model. J. Clim. 28, 9058–9079 (2015).

  9. 9.

    LaRow, T. E., Lim, Y.-K., Shin, D. W., Chassignet, E. P. & Cocke, S. Atlantic basin seasonal hurricane simulations. J. Clim. 21, 3191–3206 (2008).

  10. 10.

    Zhao, M., Held, I. M., Lin, S.-J. & Vecchi, G. A. Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50km resolution GCM. J. Clim. 22, 333–363 (2009).

  11. 11.

    Manganello, J. V. et al. Tropical cyclone climatology in a 10-km global atmospheric GCM: toward weather-resolving climate modeling. J. Clim. 24, 3867–3893 (2012).

  12. 12.

    Murakami, H., Sugi, M. & Kitoh, A. Future changes in tropical cyclone activity in the North Indian Ocean projected by high-resolution MRI-AGCMs. Clim. Dyn. 40, 1949–1968 (2013).

  13. 13.

    Murakami, H. et al. Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM. J. Clim. 25, 3237–3260 (2012).

  14. 14.

    IPCC Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) (Cambridge Univ. Press, Cambridge, 2007).

  15. 15.

    Murakami, H. et al. Seasonal forecasts of major hurricanes and landfalling tropical cyclones using a high-resolution GFDL coupled climate model. J. Clim. 29, 7977–7989 (2016).

  16. 16.

    Murakami, H. et al. Investigating the influence of anthropogenic forcing and natural variability on the 2014 Hawaiian hurricane season. Bull. Amer. Meteorol. Soc. 97(12) (Suppl.), 115–119 (2016).

  17. 17.

    Murakami, H. et al. Dominant role of subtropical Pacific warming in extreme eastern Pacific hurricane seasons: 2015 and the future. J. Clim. 30, 243–264 (2017).

  18. 18.

    Vecchi, G. A. & Soden, B. J. Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature 450, 1066–1071 (2007).

  19. 19.

    Vecchi, G. A. & Soden, B. J. Increased tropical Atlantic wind shear in model projections of global warming. Geophys. Res. Lett. 34, L08702 (2007).

  20. 20.

    Sugi, M., Murakami, H. & Yoshimura, J. A reduction in global tropical cyclone frequency due to global warming. SOLA 5, 164–167 (2009).

  21. 21.

    Murakami, H., Mizuta, R. & Shindo, E. Future changes in tropical cyclone activity projected by multi-physics and multi-SST ensemble experiments using the 60-km-mesh MRI-AGCM. Clim. Dyn. 39, 2569–2584 (2012).

  22. 22.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Amer. Meteorol. Soc. 93, 485–498 (2012).

  23. 23.

    IPCC Climate Change 2013. The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, Cambridge, 2013).

  24. 24.

    Murakami, H., Wang, B., Li, T. & Kitoh, A. Projected increase in tropical cyclones near Hawaii. Nat. Clim. Change 3, 749–754 (2013).

  25. 25.

    Chu, J.-H., C. R. Sampson, Levin, A. S. & Fukada, E. The Joint Typhoon Warning Center Tropical Cyclone Best Tracks 1945–2000 NRL; https://www.gfdl.noaa.gov/cm2-5-and-flor/

  26. 26.

    Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J. & Neuman, C. J. The international best track archive for climate stewardship (IBTrACS): unifying tropical cyclone best track data. Bull. Amer. Meteorol. Soc. 91, 363–376 (2010).

  27. 27.

    Unisys Weather Hurricane/Tropical Data (UNISYS, 2017); http://weather.unisys.com/hurricane/

  28. 28.

    Rayner, N. A. et al. Global analysis of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003).

  29. 29.

    Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn 93, 5–48 (2015).

  30. 30.

    Jaeger, C. C., Krause, J., Haas, A., Klein, R. & Hasselmann, K. A method for computing the fraction of attributable risk related to climate damages. Risk Anal. 28, 815–823 (2008).

  31. 31.

    Chiang, J. C. H. & Vimont, D. J. Analogous Pacific and Atlantic meridional modes of tropical atmosphere–ocean variability. J. Clim. 17, 4143–4158 (2004).

  32. 32.

    Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M. & Francis, R. C. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteorol. Soc. 78, 1069–1079 (1997).

  33. 33.

    Saji, N. H., Goswami, B. N., Vinayachandran, P. N. & Yamagata, T. A dipole mode in the tropical Indian Ocean. Nature 401, 360–363 (1999).

  34. 34.

    Wang, B. & Fan, Z. Choice of south Asian summer monsoon indices. Bull. Amer. Meteorol. Soc. 80, 629–638 (1999).

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Acknowledgements

The authors thank T. L. Delworth and L. Krishnamurthy for their suggestions and comments. H.M. appreciates P.-C. Hsu for her editorial service support. This report was prepared by H.M. under award NA14OAR4830101 from the National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce. The statements, findings, conclusions and recommendations are those of the authors and do not necessarily reflect the views of the NOAA or the US Department of Commerce.

Author information

H.M. designed the study, carried out the experiments and analysed the results. G.A.V. and S.W. carried out the experiments and made comments on the manuscript.

Correspondence to Hiroyuki Murakami.

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The authors declare no competing financial interests.

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Supplementary Information Supplementary Table 1, Supplementary Figures 1–12, Supplementary Reference

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Further reading

Fig. 1: Observed ESCSs.
Fig. 2: Projected changes in the seasonal mean density of ESCSs.
Fig. 3: Projected probability of exceedance of ESCSs over the ARB during October–December for each experiment.
Fig. 4: Projected changes in seasonal mean SST and V s.