Skip to main content

Northern hemisphere tropical cyclones during the quasi-El Niño of late 2014

Abstract

During the second half of 2014, the tropical Pacific was in a state marginally consistent with El Niño. While oceanic indicators were indicative of a weak El Niño event, a number of atmospheric indicators were not, and a number of forecast centers did not declare an El Niño. Nonetheless, the most active tropical cyclone basins of the northern hemisphere—those of the North Atlantic and Pacific—showed tropical cyclone statistics that in some respects were consistent with El Niño. In particular, the numbers of relatively intense storms in the four basins considered—major hurricanes in the Eastern North Pacific and North Atlantic, super typhoons in the Western North Pacific, and hurricanes in the Central North Pacific—formed a pattern strongly consistent with El Niño.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. The US Weather Service Web site for ENSO is http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/index.shtml, the Australian Bureau of Meteorology Web site for ENSO is http://www.bom.gov.au/climate/enso; the World Meteorological Organization ENSO website is http://www.wmo.int/pages/themes/climate/el_nino_la_nina.php.

  2. The Japanese Meteorological Agency website for ENSO is http://ds.data.jma.go.jp/tcc/tcc/products/elnino/.

References

  • Ashok K, Behera SK, Rao SA, Weng H, Yamagata T (2007) El Niño modoki and its possible teleconnection. J Geophys Res 112:C11007. doi:10.1029/2006JC003798

    Article  Google Scholar 

  • Balaguru K, Ruby L, Yoon JH (2013) Oceanic control of Northeast Pacific hurricane activity at interannual timescales. Environ Res Lett 8:044009. doi:10.1088/1748-9326/8/4/044009

    Article  Google Scholar 

  • Barnston AG, Li S, Mason SJ, deWitt DG, Goddard L, Gong X (2010) Verification of the first 11 years of IRI’s seasonal climate forecasts. J Appl Meteorol Climatol 49:493–520. doi:10.1175/2009JAMC2325.1

    Article  Google Scholar 

  • Barnston AG, Tippett MK, L’Heureux ML, Li S, DeWitt DG (2012) Skill of real-time seasonal ENSO model predictions during 2002–2011. Is our capability increasing? Bull Am Meteorol Soc 93:631–651. doi:10.1175/BAMS-D-11-00111.1

    Article  Google Scholar 

  • Bell GD, Halpert MS, Schnell RC, Higgins RW, Lawrimore J, Kousky VE, Tinker R, Thiaw W, Chelliah M, Artusa A (2000) Climate assessment for 1999. Bull Am Meteorol Soc 81:S1–S50

    Article  Google Scholar 

  • Bell GD, Blake ES, Landsea CW, Goldenberg SB, Kimberlain TB, Pasch RJ, Schemm J (2015) Atlantic basin. In: State of the climate in 2014. Bull Am Meteorol Soc 96(7):S101–S107

  • Blake ES, Gibney EJ, Brown DP, Mainelli M, Franklin JL, Kimberlain TB, Hammer GR (2009). Tropical cyclones of the eastern North Pacific basin, 1949–2006. In: Historical climatology series 6-5. National Climate Data Center, Ashville, NC

  • Bove MC, Elsner JB, Landsea CW, Niu X, O’Brien J (1998) Effect of El Niño on US landfalling hurricanes, revisited. Bull Am Meteorol Soc 79:2477–2482

    Article  Google Scholar 

  • Camargo SJ (2015) Western North Pacific basin. In: State of the climate in 2014. Bull Am Meteorol Soc 96 (7):S112–S115

  • Camargo SJ, Barnston AG (2009) Experimental seasonal dynamical forecasts of tropical cyclone activity at IRI. Weather Forecast 24:472–491

    Article  Google Scholar 

  • Camargo SJ, Sobel AH (2005) Western North Pacific tropical cyclone intensity and ENSO. J Clim 18:2996–3006

    Article  Google Scholar 

  • Camargo SJ, Robertson AW, Gaffney SJ, Smyth P, Ghil M (2007) Cluster analysis of typhoon tracks. Part II: large-scale circulation and ENSO. J Clim 20:3654–3676

    Article  Google Scholar 

  • Camargo SJ, Robertson AW, Barnston AG, Ghil M (2008) Clustering of eastern North Pacific tropical cyclone tracks: ENSO and MJO effects. Geochem Geophys and Geosys 9:Q06V05. doi:10.1029/2007GC001861

  • Camargo SJ, Sobel AH, Barnston AG, Klotzbach PJ (2010) The influence of natural climate variability on tropical cyclones and seasonal forecasts of tropical cyclone activity. In: Chan JCL, Kepert JD (eds) Global perspectives on tropical cyclones, from science to mitigation, 2nd edn., Series on earth system science in Asia chap 11, pp. 325–360. World Scientific, Singapore

    Chapter  Google Scholar 

  • Chan JCL (1985) Tropical cyclone activity in the Northwest Pacific in relation to El Niño/southern oscillation phenomenon. Mon Weather Rev 113:599–606

    Article  Google Scholar 

  • Chia HH, Ropelewski CF (2002) The interannual variability in the genesis location of tropical cyclones in the Northwest Pacific. J Clim 15:2934–2944

    Article  Google Scholar 

  • Chu PS (2004) Hurricanes and typhoons, past, present and future. In: Murnane RJ, Liu K-B (eds) ENSO and tropical cyclone activity. Columbia University Press, New York, pp 297–332

    Google Scholar 

  • Chu JH, Sampson CR, Levine AS, Fukada E (2002) The joint typhoon warning center tropical cyclone best-tracks, 1945–2000. Tech Rep. NRL/MR/7540-02-16, Naval Research Laboratory

  • Davis K, Zeng X, Ritchie EA (2015) A new statistical model to predict seasonal North Atlantic hurricane activity. Weather Forecast 30:730–741. doi:10.1175/WAF-D-14-00156.1

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrea U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer A, Haimberger L, Healy S, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BP, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quart J R Meteorol Soc 137:553–597

    Article  Google Scholar 

  • Fogarty CT, Klotzbach P (2015) 2013 versus 2014 Atlantic hurricane activity - Brief comparison of two below-average seasons. In: State of the climate in 2014. Bull Am Meteorol Soc 96 (7):S104–S105

  • Frank WM, Young GS (2007) The interannual variability of tropical cyclones. Mon Weather Rev 135:3587–3598

    Article  Google Scholar 

  • Goddard L, Dilley M (2005) El Niño: catastrophe or opportunity? J Clim 18:651–665

    Article  Google Scholar 

  • Goddard L, Kumar A, Hoerling MP, Barnston AG (2006) Diagnosis of anomalous winter temperatures over the eastern United States during the 2002/03 El Niño. J Clim 19:5624–5636

    Article  Google Scholar 

  • Gray WM (1984) Atlantic seasonal hurricane frequency. Part I: El-Niño and 30-MB quasi-biennial oscillation influences. Mon Weather Rev 112:1649–1688

    Article  Google Scholar 

  • Irwin RP, Davis RE (1999) The relationship between the Southern oscillation index and tropical cyclone tracks in the eastern North Pacific. Geophys Res Lett 26:2251–2254

    Article  Google Scholar 

  • Jin EK, Kinter J, Wang B, Park CK, Kang IS, Kirtman BP, Kug JS, Kumar A, Luo JJ, Schemm J, Shukla J, Yagamata T (2008) Current status of ENSO prediction skill in coupled ocean-atmosphere models. Clim Dyn 31:647–664

    Article  Google Scholar 

  • Jin FF, Boucharel J, Lin II (2014) Eastern Pacific tropical cyclones intensified by El Niño delivery of subsurface ocean heat. Nature 516:82–85

    Article  Google Scholar 

  • Johnson NC (2013) How many ENSO flavors can we distinguish? J Clim 26:4816–4827

    Article  Google Scholar 

  • Kim H, Webster PJ, Curry JA (2009) Impact of shifting patterns of Pacific Ocean warming on North Atlantic tropical cyclones. Science 325:77–80

    Article  Google Scholar 

  • Kimberlain TB, Blake ES, Cangialosi JP (2016) Hurricane patricia. Tropical Cyclone Report, National Hurricane Center, Miami, FL. http://www.nhc.noaa.gov/data/tcr/EP202015_Patricia

  • Klotzbach PJ, Gray WM (2009) Twenty-five years of Atlantic basin seasonal hurricane forecasts (1984–2008). Geophys Res Lett 36:L09711

    Article  Google Scholar 

  • Kruk MC, Schreck CJ, Evans T (2015) Eastern North Pacific and Central North Pacific basins. In: State of the climate in 2014. Bull Am Meteorol Soc 96 (7):S107–S112

  • Landsea CW (2000) El Niño: impacts of multiscale variability on natural ecosystems and society. In: Díaz HF, Markgraf V (eds) El Niño-southern oscillation and the seasonal predictability of tropical cyclones. Cambridge University Press, Cambridge, pp 149–181

    Google Scholar 

  • Landsea CW, Franklin JL (2013) Atlantic hurricane database uncertainty and presentation of a new database format. Mon Weather Rev 141:3576–3592

    Article  Google Scholar 

  • Landsea CW, Knaff JA (2000) How much skill was there in forecasting the very strong 1997–1998 El Niño? Bull Am Meteorol Soc 81:2107–2119

    Article  Google Scholar 

  • Levitus S, Antonov JI, Boyer TP, Barnova OK, Garcia HE, Locarnini A, Mishonov AV, Reagan JR, Seidov D, Yarosh ES, Zweng MM (2012) World ocean heat content and thermosteric sea level change (0–2000 m), 1955–201. Geophys Res Lett 39:L10603. doi:10.1029/2012GL051106

    Article  Google Scholar 

  • L’Heureux M, Halpert M, Bell GD (2015) ENSO and the tropical Pacific. In: State of the climate in 2014. Bull Am Meteorol Soc 96 (7):S91–S93

  • Liebmann B, Smith CA (1996) Description of a complete (interpolated) outgoing longwave radiation dataset. Bull Am Meteorol Soc 77:1275–1277

    Google Scholar 

  • Lin II, Black P, Price F, Yang CY, Chen SS, Lien CC, Harr P, Chi NH, Wu CC, D’Asaro EA (2013) An ocean coupling potential intensity index for tropical cyclone. Geophys Res Lett 40:1878–1882. doi:10.1002/grl.50091

    Article  Google Scholar 

  • Livezey RE, Timofeyeva MM (2008) The first decade of long-lead U.S. seasonal forecasts. Bull Am Meteorol Soc 89:843–854. doi:10.1175/2008BAMS2488.1

    Article  Google Scholar 

  • Maue RN (2009) Northern hemisphere tropical cyclone activity. Geophys Res Lett 36:L05805

    Article  Google Scholar 

  • Maue RN (2011) Recent historically low global tropical cyclone activity. Geophys Res Lett 38:L14803

    Article  Google Scholar 

  • McPhaden MJ (2015) Playing hide and seek with el Niño. Nat Clim Change 5:791–795. doi:10.1038/nclimate2775

    Article  Google Scholar 

  • McPhaden MJ, Timmermman A, Widlansky MJ, Balsameda MA, Stockdale TN (2015) The curious case of the El Niño that never happened: a perspective from 40 years of progess in climate research and forecasting. Bull Am Meteorol Soc 96:1647–1665. doi:10.1175/BAMS-D-14-00089.1

    Article  Google Scholar 

  • Murakami H, Vecchi GA, Delworth T, Paffendorf K, Gudgel R, Jia L, Zheng F (2015) Investigating the influence of anthropogenic forcing and natural variability on the 2014 Hawaiian hurricane season. In: Explaining extremes of 2014 from a climate perspective. Bull Am Meteorol Soc 96(12):S115–S119

  • Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Clim 15:1609–1625

    Article  Google Scholar 

  • Smith TM, Reynolds RW (2004) Improved extended reconstruction of SST (1854–1997). J Clim 17:2466–2477

    Article  Google Scholar 

  • Tippett MK, Sobel AH, Camargo SJ (2012) Association of monthly U.S. tornado occurrence with large-scale atmospheric parameters. Geophys Res Lett 39:L02801. doi:10.1175/2011GL050368

    Article  Google Scholar 

  • Vecchi GA, Delworth R, Gudgel R, Kapnick S, Rosati A, Wittenberg AT, Zeng F, Anderson W, Balaji V, Dixon K, Jia L, Kim HS, Krishnamurty L, Msadek R, Stern WF, Underwood SD, Villarini G, Yang X, Zhang S (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27:7994–8016

    Article  Google Scholar 

  • Vincent EM, Emanuel KA, Lengaigne M, Vialard J, Madec G (2014) Influence of upper-ocean stratification interannual variability on tropical cyclones. J Adv Model Earth Syst 6:680–699. doi:10.1002/2014MS00032

    Article  Google Scholar 

  • Vitart F, Huddleston MR, Déqué M, Peake D, Palmer TN, Stockdale TN, Davey MK, Inenson S, Weisheimer A (2007) Dynamically-based seasonal forecasts of Atlantic tropical storm activity issued in June by EUROSIP. Geophys Res Lett 34:L16815. doi:10.1029/2007GL030740

    Article  Google Scholar 

  • Yang L, Wang X, Huang K, Wang D (2015) Anomalous tropical cyclone activity in the western North Pacific in August 2014. In: Explaining extremes of 2014 from a climate perspective. Bull Am Meteorol Soc 96(12):S120–S125

  • Zhang W, Leung Y, Fraedrich K (2015) Different El Niño types and intense typhoons in the Western North Pacific. Clim Dyn 44:2965–2977. doi:10.1007/s00382-014-2446-4

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. H. Sobel.

Additional information

This research was supported by NOAA grant NA11OAR4300193 and by an AXA Award from the AXA Research Fund.

Appendix: Logistic regression model

Appendix: Logistic regression model

Using the standard logistic regression model, the probability that a given year is an El Niño is formulated as

$$P = [1+ \exp (-b_0-b_1 x_1-b_2 x_2-b_3 x_3 -b_4 x_4-b_5 x_5)]^{-1}$$

where \(x_1, x_2, x_3, x_4\) are the number of super typhoons in the Western North Pacific, the number of hurricanes in the Central Pacific, the number of major hurricanes in the Eastern Pacific, and the number of major hurricanes in the Northern Atlantic , while \(x_5\) is an interaction term computed as \((x_1 x_2 x_3)/(x_4+1)\), with the addition of 1 in the denominator to prevent division by zero in years with no major Atlantic hurricanes. The coefficients are determined in matlab by the function “glmfit” and are

$$(b_0, b_1, b_2, b_3, b_4, b_5)=(-3.1133, 0.3593, 0.4549, -0.0912, -0.2297, 0.0275),$$

if the year 1992 is included. If 1992 is excluded, we get

$$(b_0, b_1, b_2, b_3, b_4, b_5)=-3.3137, 0.2938, 0.2206, -0.2312, -0.0827, 0.0753.$$

The signs of most of the coefficients are consistent with our expectations based on our understanding of the influence of ENSO on TCs. The exception is \(b_3\), the eastern Pacific coefficient, which is highly uncertain (essentially indistinguishable from zero); this can be understood from Fig. 3c, which shows that several neutral and La Niña years had large numbers of major hurricanes in the Eastern North Pacific. The wide confidence intervals on the estimates of the probability that 2014 was an El Niño year (see Sect. 3.4) reflect the uncertainties on the coefficients in the model. As this might be due to collinearity between the predictors, we examined alternate models using fewer predictors (but retaining the Western and Central North Pacific). These did not lead to differences large enough to change our interpretation, for either the central probability estimate (which remains above 90 % in all cases tested) or the width of the confidence interval.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sobel, A.H., Camargo, S.J., Barnston, A.G. et al. Northern hemisphere tropical cyclones during the quasi-El Niño of late 2014. Nat Hazards 83, 1717–1729 (2016). https://doi.org/10.1007/s11069-016-2389-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-016-2389-7

Keywords

  • Tropical cyclones
  • El Niño Southern oscillation
  • Climate