Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales



In this chapter we present advances in forecasting Atlantic tropical cyclone (TC) landfall statistics at both seasonal and multi-annual timescales using coupled global climate models. First, we demonstrate potential for forecasting TC landfall frequency on seasonal timescales using the Met Office seasonal forecast system, GloSea5, in some regions: statistically significant skill is found in the Caribbean and moderate skill is found for Florida. In contrast, low skill is found along the US Coast as a whole. We show that the skill over the Caribbean is likely due to a good model response to El Niño–Southern Oscillation (ENSO) forcing. Lack of skill along the US Coast may be due to a weaker influence from ENSO compounded by a low bias in model storm tracks crossing the US coastline. Secondly, we demonstrate that it is possible to construct reliable 4-year mean forecasts of landfalling hurricane numbers in the Atlantic using initialised global climate models to predict an index that relies on subpolar gyre temperature and subtropical sea level pressure, two quantities with links to hurricane activity. Furthermore, we give evidence that the forecast system anticipates large changes in at least one of the two components of this index, which suggests that the technique could be used to forecast shifts between active and inactive regimes of hurricane activity in the Atlantic.


Tropical storms Hurricanes Landfall Seasonal forecasting Decadal forecasting Ensembles United States Caribbean El Niño Atlantic variability Atlantic multi-decadal oscillation Accumulated cyclone energy 



We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We would also like to thank the Earth System Research Laboratory (NOAA) and the Japan Meteorological Agency for making their data available and Katherine Barrett for proofreading this manuscript. JC acknowledges financial support from the UK Public Weather Service, NSF of China (NSFC) grants (40805028) and China Meteorological Special Project (GYHY201506013). LPC acknowledges financial support from the Ministerio de Economía y Competitividad (MINECO; project CGL2014-55764-R), Risk Prediction Initiative at BIOS (grant number RPI2.0-2013-CARON) and from the EU-funded SPECS project (grant number 308378). Both authors wish to thank Dr Philip Klotzbach and one anonymous reviewer for their valuable comments for improving the manuscript.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Met Office Hadley CentreExeterUK
  2. 2.Earth Science DepartmentBarcelona Supercomputing Center, Nexus II buildingBarcelonaSpain

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