The Recent Increase in Atlantic Hurricane Activity: Causes and Implications

Abstract Interannual and multidecadal extremes in Atlantic hurricane activity are shown to result from a coherent and interrelated set of atmospheric and oceanic conditions associated with three leading modes of climate variability in the Tropics. All three modes are related to fluctuations in tropical convection, with two representing the leading multidecadal modes of convective rainfall variability, and one representing the leading interannual mode (ENSO). The tropical multidecadal modes are shown to link known fluctuations in Atlantic hurricane activity, West African monsoon rainfall, and Atlantic sea surface temperatures, to the Tropics-wide climate variability. These modes also capture an east–west seesaw in anomalous convection between the West African monsoon region and the Amazon basin, which helps to account for the interhemispheric symmetry of the 200-hPa streamfunction anomalies across the Atlantic Ocean and Africa, the 200-hPa divergent wind anomalies, and both the structure and spatial scale of the low-level tropical wind anomalies, associated with multidecadal extremes in Atlantic hurricane activity. While there are many similarities between the 1950–69 and 1995–2004 periods of above-normal Atlantic hurricane activity, important differences in the tropical climate are also identified, which indicates that the above-normal activity since 1995 does not reflect an exact return to conditions seen during the 1950s–60s. In particular, the period 1950–69 shows a strong link to the leading tropical multidecadal mode (TMM), whereas the 1995–2002 period is associated with a sharp increase in amplitude of the second leading tropical multidecadal mode (TMM2). These differences include a very strong West African monsoon circulation and near-average sea surface temperatures across the central tropical Atlantic during 1950–69, compared with a modestly enhanced West African monsoon and exceptionally warm Atlantic sea surface temperatures during 1995–2004. It is shown that the ENSO teleconnections and impacts on Atlantic hurricane activity can be substantially masked or accentuated by the leading multidecadal modes. This leads to the important result that these modes provide a substantially more complete view of the climate control over Atlantic hurricane activity during individual seasons than is afforded by ENSO alone. This result applies to understanding differences in the “apparent” ENSO teleconnections not only between the above- and below-normal hurricane decades, but also between the two sets of above-normal hurricane decades.

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Improving Multiseason Forecasts of North Atlantic Hurricane Activity

Journal of Climate ◽

10.1175/2007jcli1731.1 ◽

2008 ◽

Vol 21 (6) ◽

pp. 1209-1219 ◽

Cited By ~ 14

Author(s):

James B. Elsner ◽

Thomas H. Jagger ◽

Michael Dickinson ◽

Dail Rowe

Keyword(s):

North Atlantic ◽

Monte Carlo Sampling ◽

Sea Surface ◽

Economic Losses ◽

Atlantic Hurricane ◽

Time Period ◽

Hurricane Season ◽

Hurricane Activity ◽

Term Management

Abstract Hurricanes cause drastic social problems as well as generate huge economic losses. A reliable forecast of the level of hurricane activity covering the next several seasons has the potential to mitigate against such losses through improvements in preparedness and insurance mechanisms. Here a statistical algorithm is developed to predict North Atlantic hurricane activity out to 5 yr. The algorithm has two components: a time series model to forecast average hurricane-season Atlantic sea surface temperature (SST), and a regression model to forecast the hurricane rate given the predicted SST value. The algorithm uses Monte Carlo sampling to generate distributions for the predicted SST and model coefficients. For a given forecast year, a predicted hurricane count is conditional on a sampled predicted value of Atlantic SST. Thus forecasts are samples of hurricane counts for each future year. Model skill is evaluated over the period 1997–2005 and compared against climatology, persistence, and other multiseasonal forecasts issued during this time period. Results indicate that the algorithm will likely improve on earlier efforts and perhaps carry enough skill to be useful in the long-term management of hurricane risk.

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Statistical–Dynamical Predictions of Seasonal North Atlantic Hurricane Activity

Monthly Weather Review ◽

10.1175/2010mwr3499.1 ◽

2011 ◽

Vol 139 (4) ◽

pp. 1070-1082 ◽

Cited By ~ 97

Author(s):

Gabriel A. Vecchi ◽

Ming Zhao ◽

Hui Wang ◽

Gabriele Villarini ◽

Anthony Rosati ◽

...

Keyword(s):

High Resolution ◽

North Atlantic ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Atlantic Hurricane ◽

Uncertainty Estimates ◽

Hurricane Season ◽

Hurricane Activity ◽

Hurricane Forecasting

Abstract Skillfully predicting North Atlantic hurricane activity months in advance is of potential societal significance and a useful test of our understanding of the factors controlling hurricane activity. In this paper, a statistical–dynamical hurricane forecasting system, based on a statistical hurricane model, with explicit uncertainty estimates, and built from a suite of high-resolution global atmospheric dynamical model integrations spanning a broad range of climate states is described. The statistical model uses two climate predictors: the sea surface temperature (SST) in the tropical North Atlantic and SST averaged over the global tropics. The choice of predictors is motivated by physical considerations, as well as the results of high-resolution hurricane modeling and statistical modeling of the observed record. The statistical hurricane model is applied to a suite of initialized dynamical global climate model forecasts of SST to predict North Atlantic hurricane frequency, which peaks during the August–October season, from different starting dates. Retrospective forecasts of the 1982–2009 period indicate that skillful predictions can be made from as early as November of the previous year; that is, skillful forecasts for the coming North Atlantic hurricane season could be made as the current one is closing. Based on forecasts initialized between November 2009 and March 2010, the model system predicts that the upcoming 2010 North Atlantic hurricane season will likely be more active than the 1982–2009 climatology, with the forecasts initialized in March 2010 predicting an expected hurricane count of eight and a 50% probability of counts between six (the 1966–2009 median) and nine.

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Multi-year prediction skill of Atlantic hurricane activity in CMIP5 decadal hindcasts

Climate Dynamics ◽

10.1007/s00382-013-1773-1 ◽

2013 ◽

Vol 42 (9-10) ◽

pp. 2675-2690 ◽

Cited By ~ 17

Author(s):

Louis-Philippe Caron ◽

Colin G. Jones ◽

Francisco Doblas-Reyes

Keyword(s):

Prediction Skill ◽

Atlantic Hurricane ◽

Hurricane Activity

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Robust Predictors for Seasonal Atlantic Hurricane Activity Identified with Causal Effect Networks

10.5194/wcd-2020-7 ◽

2020 ◽

Author(s):

Peter Pfleiderer ◽

Carl-Friedrich Schleussner ◽

Tobias Geiger ◽

Marlene Kretschmer

Keyword(s):

Risk Reduction ◽

Disaster Preparedness ◽

Lead Time ◽

Causal Effect ◽

Atlantic Hurricane ◽

Operational Forecasts ◽

Forecast Models ◽

Hurricane Activity ◽

Hurricane Forecasting ◽

Risk Reduction Measures

Abstract. Atlantic hurricane activity varies substantially from year to year and so do the associated damages. Longer-term forecasting of hurricane risks is a key element to reduce damages and societal vulnerabilities by enabling targeted disaster preparedness and risk reduction measures. While the immediate synoptic drivers of tropical cyclone formation and intensification are increasingly well understood, precursors of hurricane activity on longer time-horizons are still not well established. Here we use a causal network-based algorithm to identify physically motivated late-spring precursors of seasonal Atlantic hurricane activity. Based on these precursors we construct seasonal forecast models with competitive skill compared to operational forecasts. We present a skillful model to forecast July to October cyclone activity at the beginning of April. Earlier seasonal hurricane forecasting provides a multi-month lead time to implement more effective disaster risk reduction measures. Our approach also highlights the potential of applying causal effects network analysis in seasonal forecasting.

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