Five Year Prediction of the Number of Hurricanes that make United States Landfall

Jewson, Stephen; Bellone, Enrica; Laepple, Thomas; Nzerem, Kechi; Khare, Shree; Lonfat, Manuel; O'Shay, Adam; Penzer, Jeremy; Coughlin, K.

doi:10.1007/978-0-387-09410-6_5

Cited by 12 publications

(14 citation statements)

References 48 publications

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“…The diversity of available predictions exceeds the range of observed landfall behaviour. Consider, for example, Jewson et al (2009) which presents a suite of 20 different models that lead to predictions of 2007 -2012 landfall activity to be from more than 8 per cent below the 1900 -2006 mean to 43 per cent above that mean, with 18 values falling in between. Over the next five years it is virtually certain that one or more of these models will have provided a prediction that will be more accurate than the long-term historical baseline (i.e.…”

Section: Sea Surface Temperatures Climate Oscillations Solar Cyclesmentioning

confidence: 99%

“…Risk Management Solutions (RMS) Ltd, a leading catastrophe modelling firm, has used a range of models coupled with expert elicitation to develop five-year forecasts of US hurricane landfall activity that it utilizes in its models used widely in the insurance and reinsurance industries (Lonfat et al, 2007;Jewson et al, 2009). 6 The RMS methodology resulted in an estimated 2.1 landfalling hurricanes and 0.9 landfalling intense hurricanes each year from 2006 to 2010.…”

Section: Sea Surface Temperatures Climate Oscillations Solar Cyclesmentioning

confidence: 99%

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United States hurricane landfalls and damages: Can one- to five-year predictions beat climatology?

Pielke

2009

Environmental Hazards

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This paper asks whether one-to five-year predictions of United States hurricane landfalls and damages improve upon a baseline expectation derived from the climatological record. The paper argues that the large diversity of available predictions means that some predictions will improve upon climatology, but for decades if not longer it will be impossible to know whether these improvements were due to chance or actual skill. A review of efforts to predict hurricane landfalls and damage on timescales of one to five years does not lend much optimism to such efforts in any case. For decision makers, the recommendation is to use climatology as a baseline expectation and to clearly identify hedges away from this baseline, in order to clearly distinguish empirical from non-empirical justifications for judgements of risk.

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Section: Sea Surface Temperatures Climate Oscillations Solar Cyclesmentioning

confidence: 99%

Section: Sea Surface Temperatures Climate Oscillations Solar Cyclesmentioning

confidence: 99%

United States hurricane landfalls and damages: Can one- to five-year predictions beat climatology?

Pielke

2009

Environmental Hazards

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“…The continuous improvement of seasonal forecasts during the past decades (Doblas-Reyes et al, 2013a,b) has benefited different socioeconomic sectors, such as insurance companies (Jewson et al, 2008;Emanuel et al, 2012) wind and hydro-electric energy companies (De Felice et al, 2015;Garcia-Morales and Dubus, 2007), the agriculture sector (DoblasReyes et al, 2006;Crane et al, 2010;Soret et al, 2016) and climate-driven disease prevention (Tompkins and di Giuseppe, 2015). In addition, recent studies have shown that seasonal forecasts could be successful in forecasting high impact events, such as heatwaves or tropical cyclones, a few months in advance (e.g.…”

Section: Introductionmentioning

confidence: 99%

Origin of the warm eastern tropical Atlantic SST bias in a climate model

et al. 2017

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The substantial warm sea surface temperature bias in the eastern Tropical Atlantic reported in most CMIP5 climate simulations with various models, in particular along the coast of Namibia and Angola, remains an issue in more recent and CMIP6-ready versions of climate models such as EC-Earth. A complete and original set of experiments with EC-Earth3.1 is performed to investigate the causes and mechanisms responsible for the emergence and persistence of this bias. The fully-developed bias is studied in a historical experiment that has reached quasi-equilibrium, while retrospective prediction experiments are used to highlight the development/growth from an observed initial state. Prediction experiments are performed at both low and high resolution to assess the possible dependence of the bias on horizontal resolution. Standalone experiments with the ocean and the atmosphere components of EC-Earth are also analyzed to separate the respective contributions of the ocean and atmosphere to the development of the bias. EC-Earth3.1 exhibits a bias similar to that reported in most climate models that took part in CMIP5. The magnitude of this bias, however, is weaker than most CMIP5 models by few degrees. Increased horizontal resolution only leads to a minor reduction of the bias in EC-Earth. The warm SST bias is found to be the result of an excessive solar absorption in the ocean mixed layer, which can be linked to the excessive solar insolation due to unrealistically low cloud cover, and the absence of spatial and temporal variability of the biological productivity in the ocean component. The warm SST bias is further linked to deficient turbulent vertical mixing of cold water to the mixed layer. Our study points at a need for better representation of clouds in the vicinity of eastern boundaries in atmosphere models, and better representation of solar penetration and turbulent mixing in the ocean models in order to eliminate the Tropical Atlantic biases.

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“…Similar to the relative sea surface temperature hybrid method, a local increase in SST over the MDR leads to an increase in Atlantic hurricane numbers, while an increase in SST over the tropical Pacific leads to a decrease in hurricane activity. The other two component models are averages of the past hurricane counts in the Atlantic basin in either active or inactive conditions-the activity state being determined using a changepoint detection technique (Jewson et al 2009). One model includes the probability of shifting from an active to inactive state or vice versa, while the other model does not.…”

Section: Combined Statistical Techniquesmentioning

confidence: 99%

“…However, in comparison to seasonal hurricane forecasts, which originated in the mid-1980s (Gray 1984), the field of multiannual forecasting is very much in its infancy. Until recently, this type of long-term forecast was exclusively produced using statistical models, wherein hurricane activity is first derived by forecasting a subset of the climate conditions deemed to control hurricane activity (e.g., sea surface temperature over certain key regions) and then combining that forecast with a statistical model linking past climate conditions and past hurricane activity to produce a prediction of upcoming hurricane activity (Jewson et al 2009). Statistical approaches of varying complexity have been adopted by the risk modeling industry In final form 14 July 2017 ©2018 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy.…”

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confidence: 99%

How Skillful are the Multiannual Forecasts of Atlantic Hurricane Activity?

Caron

Hermanson

Dobbin

et al. 2018

Bulletin of the American Meteorological Society

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The recent emergence of near-term climate prediction, wherein climate models are initialized with the contemporaneous state of the Earth system and integrated up to 10 years into the future, has prompted the development of three different multiannual forecasting techniques of North Atlantic hurricane frequency. Descriptions of these three different approaches, as well as their respective skill, are available in the peer-reviewed literature, but because these various studies are sufficiently different in their details (e.g., period covered, metric used to compute the skill, measure of hurricane activity), it is nearly impossible to compare them. Using the latest decadal reforecasts currently available, we present a direct comparison of these three multiannual forecasting techniques with a combination of simple statistical models, with the hope of offering a perspective on the current state-of-the-art research in this field and the skill level currently reached by these forecasts. Using both deterministic and probabilistic approaches, we show that these forecast systems have a significant level of skill and can improve on simple alternatives, such as climatological and persistence forecasts.

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Five Year Prediction of the Number of Hurricanes that make United States Landfall

Cited by 12 publications

References 48 publications

United States hurricane landfalls and damages: Can one- to five-year predictions beat climatology?

United States hurricane landfalls and damages: Can one- to five-year predictions beat climatology?

Origin of the warm eastern tropical Atlantic SST bias in a climate model

How Skillful are the Multiannual Forecasts of Atlantic Hurricane Activity?

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