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Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

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Abstract

North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical–dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982–2015. The skill of these statistical–dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.

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References

  • Bell GD, Chelliah M (2006) Leading tropical modes associated with interannual and multidecadal fluctuations in North Atlantic hurricane activity. J Clim 19:590–612

    Article  Google Scholar 

  • Bister M, Emanuel KA (1998) Dissipative heating and hurricane intensity. Meteorol Atmos Phys 65:233–240

    Article  Google Scholar 

  • Blake ES, Landsea C, Gibney EJ (2007) The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2006 (and other frequently requested hurricane facts). NOAA/National Weather Service, National Centers for Environmental Prediction, National Hurricane Center, Miami

    Google Scholar 

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

    Article  Google Scholar 

  • Camargo SJ, Barnston AG, Klotzbach P, Landsea CW (2007) Seasonal tropical cyclone forecasts. WMO Bull 56:297–309

    Google Scholar 

  • Camp J et al (2015) Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. Q J R Meteorol Soc 141:2206–2219

    Article  Google Scholar 

  • Changnon SA (2008) Assessment of flood losses in the United States. J Contemp Water Res Educ 138:38–44

    Article  Google Scholar 

  • Elsner JB, Jagger TH (2006) Prediction Models for Annual U.S. Hurricane Counts. J Clim 19:2935–2952

    Article  Google Scholar 

  • Elsner JB, Kara AB (1999) Hurricanes of the North Atlantic: climate and society. Oxford University Press, Oxford

    Google Scholar 

  • Elsner JB, Schmertmann C (1993) Improving extended-range seasonal predictions of intense Atlantic hurricane activity. Weather Forecast 8:345–351

    Article  Google Scholar 

  • Elsner JB, Niu X, Jagger TH (2004) Detecting shifts in hurricane rates using a Markov chain Monte Carlo approach. J Clim 17:2652–2666

    Article  Google Scholar 

  • Emanuel K (2005) Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436:686–688

    Article  Google Scholar 

  • Emanuel K (2007) Environmental factors affecting tropical cyclone power dissipation. J Clim 20:5497–5509

    Article  Google Scholar 

  • Gray WM (1984a) Atlantic seasonal hurricane frequency. Part II: forecasting its variability. Mon Weather Rev 112:1669–1683

    Article  Google Scholar 

  • Gray WM (1984b) Atlantic seasonal hurricane frequency. Part I: El Niño and 30 mb quasi-biennial oscillation influences. Mon Weather Rev 112:1649–1668

    Article  Google Scholar 

  • Hagedorn R, Doblas-Reyes FJ, Palmer TN (2005) The rationale behind the success of multi-model ensembles in seasonal forecasting–I. Basic concept. Tellus A 57:219–233

    Google Scholar 

  • Hagen AB, Landsea CW (2012) On the classification of extreme Atlantic hurricanes utilizing mid-twentieth-century monitoring capabilities. J Clim 25:4461–4475

    Article  Google Scholar 

  • Hoerling MP, Kumar A, Zhong M (1997) El Niño, La Niña, and the nonlinearity of their teleconnections. J Clim 10:1769–1786

    Article  Google Scholar 

  • Kirtman BP et al (2013) The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull Am Meteorol Soc 95:585–601

    Article  Google Scholar 

  • Klotzbach PJ (2014) Prediction of seasonal Atlantic basin accumulated cyclone energy from 1 July. Weather Forecast 29:115–121

    Article  Google Scholar 

  • Laio F, Tamea S (2007) Verification tools for probabilistic forecasts of continuous hydrological variables. Hydrol Earth Syst Sci 11:1267–1277

    Article  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, Vecchi GA, Bengtsson L, Knutson TR (2010) Impact of duration thresholds on Atlantic tropical cyclone counts. J Clim 23:2508–2519

    Article  Google Scholar 

  • Latif M, Keenlyside N, Bader J (2007) Tropical sea surface temperature, vertical wind shear, and hurricane development. Geophys Res Lett 34:L01710

    Article  Google Scholar 

  • Lin N, Smith JA, Villarini G, Marchok TP, Baeck ML (2010) Modeling extreme rainfall, winds, and surge from Hurricane Isabel (2003). Weather Forecast 25:1342–1361

    Article  Google Scholar 

  • Mann ME, Emanuel KA (2006) Atlantic hurricane trends linked to climate change. Eos Trans Am Geophys Union. doi:10.1029/2006EO240001

    Article  Google Scholar 

  • Mason SJ, Baddour O (2008) Statistical modelling. In: Troccoli A, Harrison M, Anderson DLT, Mason SJ (eds) Seasonal climate: forecasting and managing risk, vol 82. Springer, Amsterdam, pp 163–201

    Chapter  Google Scholar 

  • Palmer TN, Doblas-Reyes FJ, Hagedorn R, Weisheimer A (2005) Probabilistic prediction of climate using multi-model ensembles: from basics to applications. Philos Trans R Soc B Biol Sci 360:1991–1998

    Article  Google Scholar 

  • Pielke RA Jr, Landsea CN (1999) La Niña, El Niño and Atlantic hurricane damages in the United States. Bull Am Meteorol Soc 80:2027–2033

    Article  Google Scholar 

  • Ramsay HA, Sobel AH (2011) Effects of relative and absolute sea surface temperature on tropical cyclone potential intensity using a single-column model. J Clim 24:183–193

    Article  Google Scholar 

  • Rappaport EN (2000) Loss of life in the United States associated with recent Atlantic tropical cyclones. Bull Am Meteorol Soc 81:2065–2073

    Article  Google Scholar 

  • Rappaport EN (2014) Fatalities in the United States from Atlantic tropical cyclones: new data and interpretation. Bull Am Meteorol Soc 95:341–346

    Article  Google Scholar 

  • Rayner NA et al. (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res Atmos 108:1–29

    Article  Google Scholar 

  • Richardson DS (2000) Skill and relative economic value of the ECMWF ensemble prediction system. Q J R Meteorol Soc 126:649–667

    Article  Google Scholar 

  • Rigby RA, Stasinopoulos DM (2005) Generalized additive models for location, scale and shape. J R Stat Soc Ser C (Appl Stat) 54:507–554

    Article  Google Scholar 

  • Robertson AW, Lall U, Zebiak SE, Goddard L (2004) Improved combination of multiple atmospheric GCM ensembles for seasonal prediction. Mon Weather Rev 132:2732–2744

    Article  Google Scholar 

  • Shen W, Tuleya RE, Ginis I (2000) Asensitivity study of the thermodynamic environment on GFDL model hurricane intensity: implications for global warming. J Clim 13:109–121

    Article  Google Scholar 

  • Smith AB, Katz RW (2013) US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Nat Hazards 67:387–410

    Article  Google Scholar 

  • Smith DM, Eade R, Dunstone NJ, Fereday D, Murphy JM, Pohlmann H, Scaife AA (2010) Skilful multi-year predictions of Atlantic hurricane frequency. Nat Geosci 3:846–849

    Article  Google Scholar 

  • Sobel AH, Held IM, Bretherton CS (2002) The ENSO signal in tropical tropospheric temperature. J Clim 15:2702–2706

    Article  Google Scholar 

  • Swanson KL (2007) Impact of scaling behavior on tropical cyclone intensities. Geophys Res Lett 34:1–5

    Article  Google Scholar 

  • Swanson KL (2008) Nonlocality of Atlantic tropical cyclone intensities. Geochem Geophys Geosyst 9:Q04V01. doi:10.1029/2007GC001844

    Article  Google Scholar 

  • Tang BH, Neelin JD (2004) ENSO influence on Atlantic hurricanes via tropospheric warming. Geophys Res Lett 31:L24204. doi:10.1029/2004GL021072

    Article  Google Scholar 

  • Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Philos Trans R Soc Lond A Math Phys Eng Sci 365:2053–2075

    Article  Google Scholar 

  • Vecchi GA, Soden BJ (2007) Effect of remote sea surface temperature change on tropical cyclone potential intensity. Nature 450:1066–1071

    Article  Google Scholar 

  • Vecchi GA, Villarini G (2014) Next season’s hurricanes. Science 343:618–619

    Article  Google Scholar 

  • Vecchi GA, Swanson KL, Soden BJ (2008) Whither hurricane activity. Science 322:687–689

    Article  Google Scholar 

  • Vecchi GA et al (2011) Statistical-dynamical predictions of seasonal North Atlantic hurricane activity. Mon Weather Rev 139:1070–1082

    Article  Google Scholar 

  • Vecchi GA et al (2013) Multiyear predictions of North Atlantic hurricane frequency: promise and limitations. J Clim 26:5337–5357

    Article  Google Scholar 

  • Vecchi GA et al (2014) On the seasonal forecasting of regional tropical cyclone activity. J Clim 27:7994–8016

    Article  Google Scholar 

  • Villarini G, Vecchi GA (2012) North Atlantic Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE): statistical modeling and sensitivity to sea surface temperature changes. J Clim 25:625–637

    Article  Google Scholar 

  • Villarini G, Vecchi GA (2013) Multiseason lead forecast of the North Atlantic Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE). J Clim 26:3631–3643

    Article  Google Scholar 

  • Villarini G, Vecchi GA, Smith JA (2010) Modeling the dependence of tropical storm counts in the North Atlantic basin on climate indices. Mon Weather Rev 138:2681–2705

    Article  Google Scholar 

  • Villarini G, Vecchi GA, Knutson TR, Smith JA (2011a) Is the recorded increase in short duration North Atlantic tropical storms spurious? J Geophys Res. doi:10.1029/2010JD015493

    Article  Google Scholar 

  • Villarini G, Smith JA, Baeck ML, Marchok T, Vecchi GA (2011b) Characterization of rainfall distribution and flooding associated with US landfalling tropical cyclones: analyses of hurricanes Frances, Ivan, and Jeanne (2004). J Geophys Res Atmos 116:1–19

    Google Scholar 

  • Villarini G, Goska R, Smith JA, Vecchi GA (2014) North Atlantic tropical cyclones and US flooding. Bull Am Meteorol Soc 95:1381–1388

    Article  Google Scholar 

  • Vitart F, Anderson J (2001) Sensitivity of Atlantic tropical storm frequency to ENSO and interdecadal variability of SSTs in an ensemble of AGCM integrations. J Clim 14:533–545

    Article  Google Scholar 

  • Wilks DS (2011) Statistical methods in the atmospheric sciences, vol 100. Academic press, London

    Google Scholar 

  • World Meteorological Organization W (2008) Report from expert meeting to evaluate skill of tropical cyclone seasonal forecasts. World Meteorological Organization Tech Doc, 1455, 27

  • Yun WT, Stefanova L, Mitra AK, Kumar TSVV, Dewar W, Krishnamurti TN (2005) A multi-model superensemble algorithm for seasonal climate prediction using DEMETER forecasts. Tellus A 57:280–289

    Article  Google Scholar 

  • Zhao M, Held IM, Lin S-J, Vecchi GA (2009) Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J Clim 22:6653–6678

    Article  Google Scholar 

  • Zhao M, Held IM, Vecchi GA (2010) Retrospective forecasts of the hurricane season using a global atmospheric model assuming persistence of SST anomalies. Mon Weather Rev 138:3858–3868

    Article  Google Scholar 

  • Zhu Y (2005) Ensemble forecast: a new approach to uncertainty and predictability. Adv Atmos Sci 22:781–788

    Article  Google Scholar 

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Acknowledgments

The authors thank the NMME program partners and acknowledge the help of NCEP, IRI and NCAR personnel in creating, updating and maintaining the NMME archive, with the support of NOAA, NSF, NASA and DOE. The first three authors acknowledge funding from the National Science Foundation under Grant No. AGS-1262099, and Award NA14OAR4830101 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. Gabriele Villarini also acknowledges financial support from the USACE Institute for Water Resources.

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Authors and Affiliations

  1. 306 C. Maxwell Stanley Hydraulics Laboratory, IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, IA, 52242, USA

    Gabriele Villarini & Beda Luitel

  2. NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA

    Gabriel A. Vecchi

  3. Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, IA, USA

    Joyee Ghosh

Authors
  1. Gabriele Villarini
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  2. Beda Luitel
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  4. Joyee Ghosh
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Correspondence to Gabriele Villarini.

Additional information

This paper is a contribution to the special collection on the North American Multi-Model Ensemble (NMME) seasonal prediction experiment. The special collection focuses on documenting the use of the NMME system database for research ranging from predictability studies, to multi-model prediction evaluation and diagnostics, to emerging applications of climate predictability for subseasonal to seasonal predictions. This special issue is coordinated by Annarita Mariotti (NOAA), Heather Archambault (NOAA), Jin Huang (NOAA), Ben Kirtman (University of Miami) and Gabriele Villarini (University of Iowa).

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Villarini, G., Luitel, B., Vecchi, G.A. et al. Multi-model ensemble forecasting of North Atlantic tropical cyclone activity. Clim Dyn 53, 7461–7477 (2019). https://doi.org/10.1007/s00382-016-3369-z

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