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 se...

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Autores: Camp, Joanne, Caron, Louis-Philippe|||0000-0001-5221-0147
Tipo de recurso: capítulo de libro
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/101808
Acceso en línea:https://hdl.handle.net/2117/101808
https://dx.doi.org/10.1007/978-3-319-47594-3_9
Access Level:acceso abierto
Palabra clave:Forecasting--Computer simulation
Hurricanes--Caribbean Area
Cyclone forecasting
Tropical storms
Hurricanes
Seasonal forecasting
Landfall
Decadal forecasting
Ensembles
United States
Caribbean
El Niño
Atlantic variability
Atlantic multi-decadal oscillation
Accumulated cyclone energy
Huracans
Previsió del temps
Àrees temàtiques de la UPC::Energies
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spelling Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal TimescalesCamp, JoanneCaron, Louis-Philippe|||0000-0001-5221-0147Forecasting--Computer simulationHurricanes--Caribbean AreaCyclone forecastingTropical stormsHurricanesSeasonal forecastingLandfallDecadal forecastingEnsemblesUnited StatesCaribbeanEl NiñoAtlantic variabilityAtlantic multi-decadal oscillationAccumulated cyclone energyHuracansPrevisió del tempsÀrees temàtiques de la UPC::EnergiesIn 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.We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP, the U.S. 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.Peer ReviewedSpringer International Publishing20172017-02-2120172017-03-01book parthttp://purl.org/coar/resource_type/c_3248AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/bookPartapplication/pdfhttps://hdl.handle.net/2117/101808https://dx.doi.org/10.1007/978-3-319-47594-3_9reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengMinisterio de Economía y Competitividad http://doi.org/10.13039/501100003329 CGL2014-55764-R PREDICCIONES ESTACIONALES A ESCALA REGIONAL Y PREDICCIONES MULTI-ANUALES DE CICLONES TROPICALESopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1018082026-05-27T15:37:01Z
dc.title.none.fl_str_mv Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
title Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
spellingShingle Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
Camp, Joanne
Forecasting--Computer simulation
Hurricanes--Caribbean Area
Cyclone forecasting
Tropical storms
Hurricanes
Seasonal forecasting
Landfall
Decadal forecasting
Ensembles
United States
Caribbean
El Niño
Atlantic variability
Atlantic multi-decadal oscillation
Accumulated cyclone energy
Huracans
Previsió del temps
Àrees temàtiques de la UPC::Energies
title_short Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
title_full Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
title_fullStr Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
title_full_unstemmed Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
title_sort Analysis of Atlantic Tropical Cyclone Landfall Forecasts in Coupled GCMs on Seasonal and Decadal Timescales
dc.creator.none.fl_str_mv Camp, Joanne
Caron, Louis-Philippe|||0000-0001-5221-0147
author Camp, Joanne
author_facet Camp, Joanne
Caron, Louis-Philippe|||0000-0001-5221-0147
author_role author
author2 Caron, Louis-Philippe|||0000-0001-5221-0147
author2_role author
dc.subject.none.fl_str_mv Forecasting--Computer simulation
Hurricanes--Caribbean Area
Cyclone forecasting
Tropical storms
Hurricanes
Seasonal forecasting
Landfall
Decadal forecasting
Ensembles
United States
Caribbean
El Niño
Atlantic variability
Atlantic multi-decadal oscillation
Accumulated cyclone energy
Huracans
Previsió del temps
Àrees temàtiques de la UPC::Energies
topic Forecasting--Computer simulation
Hurricanes--Caribbean Area
Cyclone forecasting
Tropical storms
Hurricanes
Seasonal forecasting
Landfall
Decadal forecasting
Ensembles
United States
Caribbean
El Niño
Atlantic variability
Atlantic multi-decadal oscillation
Accumulated cyclone energy
Huracans
Previsió del temps
Àrees temàtiques de la UPC::Energies
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-02-21
2017
2017-03-01
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/101808
https://dx.doi.org/10.1007/978-3-319-47594-3_9
url https://hdl.handle.net/2117/101808
https://dx.doi.org/10.1007/978-3-319-47594-3_9
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://doi.org/10.13039/501100003329 CGL2014-55764-R PREDICCIONES ESTACIONALES A ESCALA REGIONAL Y PREDICCIONES MULTI-ANUALES DE CICLONES TROPICALES
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer International Publishing
publisher.none.fl_str_mv Springer International Publishing
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
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