Atlantic Hurricane Activity Research Articles

Can Expert Prediction Markets Forecast Climate-Related Risks?

Abstract Prediction markets use a betting-like mechanism to aggregate information from disparate sources to produce probability forecasts that represent the collective view of participants. They have similarities with financial markets but are designed specifically to discover information rather than transfer assets or risks. Contracts that pay out a fixed amount if a well-defined event occurs are traded in these markets and, under certain conditions, the price at which these event-contracts trade can be interpreted as a probability estimate that the event will occur. This article examines the performance of 24 prediction markets for climate-related variables that have been run over the past 5 years. The predicted variables included the monthly temperature and rainfall for the United Kingdom, an index of El Niño–Southern Oscillation, Atlantic hurricane activity, and U.K. wheat yield. The markets had horizons of 2–12 months. Invitations to participate in the markets were extended to individuals and teams with relevant expertise. Participants did not have to pay to take part but did receive cash rewards based on their performance. Trades were made through an automated market maker overcoming the problems of low activity that have affected previous prediction markets for specialized topics. The predictions of the markets were consistent with good reliability, given the resolving power afforded by the sample size. Whether this level of reliability would persist for longer, multiyear, horizons relevant to climate change cannot be known without running markets on such time scales, something that we strongly advocate.

Tropical Atlantic multidecadal variability is dominated by external forcing.

The tropical Atlantic climate is characterized by prominent and correlated multidecadal variability in Atlantic sea surface temperatures (SSTs), Sahel rainfall and hurricane activity1-4. Owing to uncertainties in both the models and the observations, the origin of the physical relationships among these systems has remained controversial3-7. Here we show that the cross-equatorial gradient in tropical Atlantic SSTs-largely driven by radiative perturbations associated with anthropogenic emissions and volcanic aerosols since 19503,7-is a key determinant of Atlantic hurricane formation and Sahel rainfall. The relationship is obscured in a large ensemble of CMIP6 Earth system models, because the models overestimate long-term trends for warming in the Northern Hemisphere relative to the Southern Hemisphere from around 1950 as well as associated changes in atmospheric circulation and rainfall. When the overestimated trends are removed, correlations between SSTs and Atlantic hurricane formation and Sahel rainfall emerge as a response to radiative forcing, especially since 1950 when anthropogenic aerosol forcing has been high. Our findings establish that the tropical Atlantic SST gradient is a stronger determinant of tropical impacts than SSTs across the entire North Atlantic, because the gradient is more physically connected to tropical impacts via local atmospheric circulations8. Our findings highlight that Atlantic hurricane activity and Sahel rainfall variations can be predicted from radiative forcing driven by anthropogenic emissions and volcanism, but firmer predictions are limited by the signal-to-noise paradox9-11 and uncertainty in future climate forcings.

A Decadal Climate Service for Insurance: Skillful Multiyear Predictions of North Atlantic Hurricane Activity and U.S. Hurricane Damage

Abstract North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful. Significance Statement The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.

Increase in Cape Verde hurricanes during Atlantic Niño

At seasonal-to-interannual timescales, Atlantic hurricane activity is greatly modulated by El Niño–Southern Oscillation and the Atlantic Meridional Mode. However, those climate modes develop predominantly in boreal winter or spring and are weaker during the Atlantic hurricane season (June–November). The leading mode of tropical Atlantic sea surface temperature (SST) variability during the Atlantic hurricane season is Atlantic Niño/Niña, which is characterized by warm/cold SST anomalies in the eastern equatorial Atlantic. However, the linkage between Atlantic Niño/Niña and hurricane activity has not been examined. Here, we use observations to show that Atlantic Niño, by strengthening the Atlantic inter-tropical convergence zone rainband, enhances African easterly wave activity and low-level cyclonic vorticity across the deep tropical eastern North Atlantic. We show that such conditions increase the likelihood of powerful hurricanes developing in the deep tropics near the Cape Verde islands, elevating the risk of major hurricanes impacting the Caribbean islands and the U.S.

Natural and anthropogenic contributions to the hurricane drought of the 1970s–1980s

Atlantic hurricane activity experienced a pronounced lull during the 1970s and 1980s. The current explanation that anthropogenic aerosol radiative forcing cooled the sea surface locally fails to capture the magnitude of this large decrease in activity. To explain this hurricane drought, we propose that the radiative effects of sulfate aerosols from Europe and North-America decreased precipitation in the Sahara-Sahel region, leading to an enhancement of dust regional emissions and transport over the Atlantic. This dust in turn enhanced the local decrease of sea-surface temperature and of hurricane activity. Here, we show that dust emissions from the Sahara peaked in phase with regional sulfate aerosol optical thickness and Sahel drought conditions, and that dust optical depth variations alone can explain nearly half of the sea-surface temperature depression in the 1970s and 1980s.

What Governs the Interannual Variability of Recurving North Atlantic Tropical Cyclones?

Abstract Recurving tropical cyclones (TCs) can cause extensive damage along the U.S. East Coast and later in their life cycle over Europe as post-tropical cyclones. While the existing literature attempts to understand the drivers of basinwide and regional TC variability, less work has been undertaken looking at recurving TCs. The roles played by the interannual variabilities of TC frequency and the steering flow in governing recurving TC interannual variability are investigated in this study. Using a track-matching algorithm, we identify observed TC tracks from the NHC “best track” hurricane database, version 2 (HURDAT2) in the ERA5 and MERRA2 reanalyses. This allows for detailed analysis of the post-tropical stages of the tracks in the observational TC record, enabling robust identification and separation of TCs that recurve. We show that over 75% of the interannual variance in annual recurving TC frequency can be explained by just two predictors—the frequency of TCs forming in the subtropical Atlantic, and hurricanes (TCs with wind speeds > 33 m s−1) forming in the main development region (MDR). An index describing the seasonal mean meridional steering flow shows a weak, nonsignificant relationship with recurving TC frequency, supported by composite analysis. These results show that the interannual variability in recurving TC frequency is primarily driven by the seasonal TC activity of the MDR and the subtropical Atlantic, with seasonal anomalies in the steering flow playing a much smaller, secondary role. These results help to quantify the extent to which skillful seasonal forecasts of Atlantic hurricane activity benefit regions vulnerable to recurving TCs. Significance Statement Recurving tropical cyclones (TCs) can cause extensive damage to the U.S. East Coast, eastern Canada, and Europe. It is, therefore, crucial to understand why some years have a higher frequency of recurving TCs than other years. In this study, we show that the frequency of recurving TCs is very strongly linked to the frequency that hurricanes (TCs with wind speeds > 33 m s−1) form in the main development region, and the frequency that TCs form in the subtropical Atlantic. This result suggests that skillful seasonal prediction of hurricane activity could be used to give enhanced seasonal warning to the regions often impacted by recurving TCs.

Seasonality of Interbasin SST Contributions to Atlantic Tropical Cyclone Activity

AbstractRecent studies have demonstrated that the difference in sea surface temperature anomalies (SSTAs) between the tropical Atlantic main development region (MDR) and the tropical Pacific (Niño 3) modulates Atlantic tropical cyclone activity. This study further explores the seasonality of Pacific and Atlantic contributions to Atlantic hurricane activity. Our analysis shows that while MDR and Niño 3 SSTAs are equally important for late‐season (September–November) activity, early‐season (June‐August) activity is largely modulated by MDR SSTAs. This reflects the increased (reduced) variance of MDR (Niño 3) SSTAs in the early‐season due to their phase locking to the seasonal cycle. Further analysis yields skillful forecasts using an MDR‐Niño 3 interbasin index derived from hindcasts of the North American Multi‐Model Ensemble with May initial conditions. However, the prediction skill for MDR SSTAs is lower than that of Niño 3 SSTAs, suggesting that increasing the prediction skill for MDR SSTAs is key to improving seasonal outlooks.

A Hyperactive End to the Atlantic Hurricane Season October–November 2020

Abstract The active 2020 Atlantic hurricane season produced 30 named storms, 14 hurricanes, and 7 major hurricanes (category 3+ on the Saffir–Simpson hurricane wind scale). Though the season was active overall, the final two months (October–November) raised 2020 into the upper echelon of Atlantic hurricane activity for integrated metrics such as accumulated cyclone energy (ACE). This study focuses on October–November 2020, when 7 named storms, 6 hurricanes, and 5 major hurricanes formed and produced ACE of 74 × 104 kt2 (1 kt ≈ 0.51 m s−1). Since 1950, October–November 2020 ranks tied for third for named storms, first for hurricanes and major hurricanes, and second for ACE. Six named storms also underwent rapid intensification (≥30 kt intensification in ≤24 h) in October–November 2020—the most on record. This manuscript includes a climatological analysis of October–November tropical cyclones (TCs) and their primary formation regions. In 2020, anomalously low wind shear in the western Caribbean and Gulf of Mexico, likely driven by a moderate-intensity La Niña event and anomalously high sea surface temperatures (SSTs) in the Caribbean, provided dynamic and thermodynamic conditions that were much more conducive than normal for late-season TC formation and rapid intensification. This study also highlights October–November 2020 landfalls, including Hurricanes Delta and Zeta in Louisiana and in Mexico and Hurricanes Eta and Iota in Nicaragua. The active late season in the Caribbean would have been anticipated by a statistical model using the July–September-averaged ENSO longitude index and Atlantic warm pool SSTs as predictors.

Atlantic tropical cyclones downscaled from climate reanalyses show increasing activity over past 150 years

Historical records of Atlantic hurricane activity, extending back to 1851, show increasing activity over time, but much or all of this trend has been attributed to lack of observations in the early portion of the record. Here we use a tropical cyclone downscaling model driven by three global climate analyses that are based mostly on sea surface temperature and surface pressure data. The results support earlier statistically-based inferences that storms were undercounted in the 19th century, but in contrast to earlier work, show increasing tropical cyclone activity through the period, interrupted by a prominent hurricane drought in the 1970s and 80 s that we attribute to anthropogenic aerosols. In agreement with earlier work, we show that most of the variability of North Atlantic tropical cyclone activity over the last century was directly related to regional rather than global climate change. Most metrics of tropical cyclones downscaled over all the tropics show weak and/or insignificant trends over the last century, illustrating the special nature of North Atlantic tropical cyclone climatology.

Improved simulation of 19th- and 20th-century North Atlantic hurricane frequency after correcting historical sea surface temperatures.

Confidence in dynamical and statistical hurricane prediction is rooted in the skillful reproduction of hurricane frequency using sea surface temperature (SST) patterns, but an ensemble of high-resolution atmospheric simulation extending to the 1880s indicates model-data disagreements that exceed those expected from documented uncertainties. We apply recently developed corrections for biases in historical SSTs that lead to revisions in tropical to subtropical SST gradients by ±0.1°C. Revised atmospheric simulations have 20% adjustments in the decadal variations of hurricane frequency and become more consistent with observations. The improved simulation skill from revised SST estimates not only supports the utility of high-resolution atmospheric models for hurricane projections but also highlights the need for accurate estimates of past and future patterns of SST changes.

The response of diatom assemblages in a Jamaican coastal lagoon to hurricane and drought activity over the past millennium

Reconstructing pre-industrial hurricane activity and aridity from natural archives places modern trends within the context of long-term natural variability. The first reconstruction of Atlantic hurricane activity in Jamaica was based on a sediment record previously obtained from a coastal lagoon. Specifically, an Extended Hurricane Activity (EHA) index was developed from high-resolution geochemical data that linked fluctuations in lake-level changes to rainfall variability associated with hurricane activity. Here, we analyse the same sediment core from which the EHA index was developed to assess the response of biological indicators, namely fossil diatom assemblages and sediment chlorophyll a (chl- a) concentrations, to hydrometeorological events (tropical cyclone-induced precipitation and droughts) over the past ~1500 years. The diatom assemblages responded sensitively to changes in salinity associated with lake-level changes driven by the balance of precipitation and evaporation. Aquatic production (inferred from sediment chl- a, which includes its main diagenetic products) and salinity (inferred from ITRAXTM µXRF chlorine counts) vary inversely following ca. 1300 CE, likely due to enhanced nutrient delivery from freshwater runoff during periods of elevated precipitation. Although the temporal resolution of our biological data is less-well resolved than that of the geochemical record, it generally tracks long-term trends in rainfall variability inferred by the EHA index over the past millennium. This further demonstrates the potential of using biological proxies from coastal lagoons to track past hurricane activity and aridity.

Optimal Climate Normals for North Atlantic Hurricane Activity

AbstractMost climatologies use 30‐year epochs that are updated at the start of each decade. They will shift from 1981–2010 to 1991–2020 in 2021. North Atlantic hurricane activity has large interdecadal variability that may lead to biases in a 30‐year climatology. A previous inactive hurricane period included 1981–1990, while 2011–2020 is a part of the ongoing active era. As a result, the 1991–2020 normals are more active than the 1981–2010 normals, with the median accumulated cyclone energy increasing by ∼40%. A 50‐year epoch would be more likely to capture a full cycle of multidecadal variability, and this study demonstrates that 50‐year climatologies have historically been better predictors of the subsequent decade's hurricane activity. This paper argues that the 1971–2020 climatology should, therefore, be the baseline for hurricane activity for the next decade with a possible adjustment for the non‐climatic increase in observed short‐lived tropical cyclones.

A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity

In this study, nine different statistical models are constructed using different combinations of predictors, including models with and without projected predictors. Multiple machine learning (ML) techniques are employed to optimize the ensemble predictions by selecting the top performing ensemble members and determining the weights for each ensemble member. The ML-Optimized Ensemble (ML-OE) forecasts are evaluated against the Simple-Averaging Ensemble (SAE) forecasts. The results show that for the response variables that are predicted with significant skill by individual ensemble members and SAE, such as Atlantic tropical cyclone counts, the performance of SAE is comparable to the best ML-OE results. However, for response variables that are poorly modeled by individual ensemble members, such as Atlantic and Gulf of Mexico major hurricane counts, ML-OE predictions often show higher skill score than individual model forecasts and the SAE predictions. However, neither SAE nor ML-OE was able to improve the forecasts of the response variables when all models show consistent bias. The results also show that increasing the number of ensemble members does not necessarily lead to better ensemble forecasts. The best ensemble forecasts are from the optimally combined subset of models.

Atlantic Hurricane Activity Prediction: A Machine Learning Approach

Long-term hurricane predictions have been of acute interest in order to protect the community from the loss of lives, and environmental damage. Such predictions help by providing an early warning guidance for any proper precaution and planning. In this paper, we present a machine learning model capable of making good preseason-prediction of Atlantic hurricane activity. The development of this model entails a judicious and non-linear fusion of various data modalities such as sea-level pressure (SLP), sea surface temperature (SST), and wind. A Convolutional Neural Network (CNN) was utilized as a feature extractor for each data modality. This is followed by a feature level fusion to achieve a proper inference. This highly non-linear model was further shown to have the potential to make skillful predictions up to 18 months in advance.

Revising evidence of hurricane strikes on Abaco Island (The Bahamas) over the last 700\xa0years

The northern Bahamas have experienced more frequent intense-hurricane impacts than almost anywhere else in the Atlantic since 1850 CE. In 2019, category 5 (Saffir-Simpson scale) Hurricane Dorian demonstrated the destructive potential of these natural hazards. Problematically, determining whether high hurricane activity levels remained constant through time is difficult given the short observational record (< 170 years). We present a 700-year long, near-annually resolved stratigraphic record of hurricane passage near Thatchpoint Blue Hole (TPBH) on Abaco Island, The Bahamas. Using longer sediment cores (888 cm) and more reliable age-control, this study revises and temporally expands a previous study from TPBH that underestimated the sedimentation rate. TPBH records at least 13 ≥ category 2 hurricanes per century between 1500 to 1670 CE, which exceeds the 9 ≥ category 2 hurricanes per century within 50 km of TPBH since 1850 CE. The eastern United States also experienced frequent hurricanes from 1500 to 1670 CE, but frequency was depressed elsewhere in the Atlantic Ocean. This suggests that spatial heterogeneity in Atlantic hurricane activity since 1850 CE could have persisted throughout the last millennium. This heterogeneity is impacted by climatic and stochastic forcing, but additional high-resolution paleo-hurricane reconstructions are required to assess the mechanisms that impact regional variability.

Robust predictors for seasonal Atlantic hurricane activity identified with causal effect networks

Abstract. Atlantic hurricane activity varies substantially from year to year and so does the associated damage. Longer-term forecasting of hurricane risks is a key element to reduce damage 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 interpretable late-spring precursors of seasonal Atlantic hurricane activity. Based on these precursors we construct statistical seasonal forecast models with competitive skill compared to operational forecasts. In particular, we present a skilful prediction model to forecast July to October tropical cyclone activity at the beginning of April. Our approach highlights the potential of applying causal effect network analysis to identify sources of predictability on seasonal timescales.

Quantifying the Probability and Causes of the Surprisingly Active 2018 North Atlantic Hurricane Season

AbstractThe 2018 North Atlantic hurricane season was a destructive season with hurricanes Florence and Michael causing significant damage in the southeastern United States. In keeping with most destructive hurricane seasons, basinwide tropical cyclone activity was above average in 2018—by ~25% for named storm numbers, hurricane numbers, and Accumulated Cyclone Energy (ACE). In contrast to this above‐normal activity, the August–September tropical environmental fields that explain ~50% of the variance in Atlantic basin hurricane activity between 1950 and 2017 anticipated a well below‐average 2018 hurricane season. The surprisingly large mismatch between the observed and replicated levels of hurricane activity in 2018 is an extreme example of the uncertainty inherent in seasonal hurricane outlooks and highlights the need for these outlooks to be issued in terms of probability of exceedance. Such probabilistic information would better clarify the uncertainty associated with hurricane outlooks to the benefit of users. With retrospective knowledge of the August–September 2018 key tropical environmental fields, the chance that the observed 2018 Atlantic hurricane activity would occur is about 5%. The reasons for the surprisingly high hurricane activity in 2018 are a hurricane outbreak in early September and, in particular, the occurrence of unusually high tropical cyclone activity in the subtropical North Atlantic. The hyperactive subtropical activity was not anticipated because contemporary statistical models of seasonal Atlantic hurricane activity lack skill in anticipating subtropical ACE compared to tropical ACE.

Preparing individuals with spinal cord injury for extreme storms in the era of climate change

Preparing individuals with spinal cord injury for extreme storms in the era of climate change

Climate-driven Atlantic hurricanes pose rising threats for psychopathology

The 2019 Atlantic hurricane season is underway, heralding the prospect that extreme storms will bring psychological trauma and loss to island-based and coastal populations. Human activities are modifying the behaviour of hurricanes.1 Increased Atlantic hurricane activity has been observed since around the mid-1990s. Climate drivers, such as anomalously warm ocean temperatures, have generated storms that are stronger and wetter than in previous years, and that are stalling as they pass over populated areas.

Implementation of Tropical Cyclone Detection Scheme to CCAM model for Seasonel Tropical Cyclone Prediction over the Vietnam East Sea

Implementation of Tropical Cyclone Detection Scheme to CCAM model for Seasonel Tropical Cyclone Prediction over the Vietnam East Sea