Dynamical Downscaling Projections of Late 21st Century U.S. Landfalling Hurricane Activity

U.S. landfalling tropical cyclone (TC) activity was projected for late 21st century conditions using a two-step dynamical downscaling framework. A regional atmospheric model, run for 27 seasons, generated tropical storm cases. Each storm case was re-simulated (up to 15 days) using the higher resolution GFDL hurricane model. Thirteen CMIP3 or CMIP5 modeled climate change projections were explored as scenarios. Robustness of projections was assessed using statistical significance tests and comparing the sign of changes derived from different models. The proportion of TCs (tropical storms and hurricanes) making U.S. landfall increases for the warming scenarios (by order 50% or more). For category 1-3 hurricane frequency, a robust decrease is projected (basin-wide), but robust changes are not projected for U.S. landfalling cases. A relatively robust increase in U.S. landfalling category 4-5 hurricane frequency is projected, averaging about +400% across the models; 10 of 13 models/ensembles project an increase (statistically significant in three individual models), while three models projected no change. The most robust projections overall for U.S. landfalling TC activity are for increased near-storm rainfall rates: these increases average +18% (all tropical storms and hurricanes), +26% (all hurricanes), and +37% (major hurricanes). Landfalling hurricane wind speed intensities show no robust signal, in contrast to a ~5% increase in basin-averaged TC intensity; basin-wide Power Dissipation Index (PDI) is projected to decrease, partly due to decreased duration. TC translation speed increases a few percent in most simulations. A caveat is the framework’s low correlation of modeled U.S. TC landfalls vs. observed interannual variations (1980-2016).

Changes in Atlantic major hurricane frequency since the late-19th century

Atlantic hurricanes are a major hazard to life and property, and a topic of intense scientific interest. Historical changes in observing practices limit the utility of century-scale records of Atlantic major hurricane frequency. To evaluate past changes in frequency, we have here developed a homogenization method for Atlantic hurricane and major hurricane frequency over 1851–2019. We find that recorded century-scale increases in Atlantic hurricane and major hurricane frequency, and associated decrease in USA hurricanes strike fraction, are consistent with changes in observing practices and not likely a true climate trend. After homogenization, increases in basin-wide hurricane and major hurricane activity since the 1970s are not part of a century-scale increase, but a recovery from a deep minimum in the 1960s–1980s. We suggest internal (e.g., Atlantic multidecadal) climate variability and aerosol-induced mid-to-late-20th century major hurricane frequency reductions have probably masked century-scale greenhouse-gas warming contributions to North Atlantic major hurricane frequency.

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.

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.

Effects of Atlantic and Pacific Ocean Teleconnections on Corn Yield East of Puebla, Mexico: Case Studies

Ocean-atmospheric interactions have effects at different scales; forming microclimates, which can explain variations with climatic or natural anomalies, between meteorological processes. This research analyzes and identifies the relationship of the teleconnection hydrometeorological effects, which determine the distribution of precipitation in corn yield. The data were used from a semi-structured interview directed to corn producers, where seven years of case studies were identified for the eastern region of the state of Puebla, Mexico. The Graphics were made with “pentad scale distribution”. The results show the importance of geographical location for agricultural activities in relation to a valley with altitudinal gradient. In addition, the corn growth cycle is associated with tropical disturbances from east Puebla region as well as Hurricane activity. It was identified that the relationship of teleconnections and the distribution of rainfall are main factors that influence in the development good or bad of corn, showed in the yields, where the different phases of ENSO (EL NIÑO Southern Oscillation) have a differentiated impact on the availability of precipitation in this case studies of the present investigation.

Northeast Pacific annual accumulated cyclonic energy rank-profile

The ranking of events is a powerful way to study the complexity of rare catastrophic events as earthquakes and hurricanes. Hurricane activity can be quantified by the annual accumulated cyclone energy index (ACE), which contains the information of the maximum sustained wind speed, duration and frequency of the tropical cyclone season. Here, the ranking of the Northeast Pacific annual ACE is obtained and fitted using nonlinear regression with several two- and three-parameter ranking laws that fit the tail and head of the data, where lives the information of relevant events for human society. The logarithmic like function [Formula: see text] overperforms all other fits. A sliding window analysis of the parameters [Formula: see text] and [Formula: see text] of such a function shows that forcing and dissipation processes are anticorrelated.

Skilful predictions of multi-year US hurricane insured losses by decadal prediction systems

<p>North Atlantic tropical cyclones are the costliest natural hazard affecting the US, and are capable of causing hundreds of billions of dollars of insured losses in a single season.  Tropical cyclone activity has been observed to show considerable decadal variability, linked with variations in sea surface temperatures in regions of the North Atlantic such as the main hurricane development region (MDR) and sub-polar gyre (SPG).</p><p>In this presentation we show that a multi-model ensemble of decadal prediction systems can skilfully predict north Atlantic hurricane activity and consequent US insured losses on multi-annual timescales, with a correlation coefficient of greater than 0.7 for 5 year mean hurricane activity.  Rather than tracking tropical cyclones directly in the dynamical models, we make predictions using an index based on predicted temperatures over the north Atlantic.  The skill of the dynamical models outperforms persistence, and could aid decision making for the (re)insurance industry over the US.  As part of the Copernicus Climate Change Service, a publicly available probabilistic forecast of 5 year mean north Atlantic hurricane activity and US insured losses has been produced and will be presented here.</p>

Human arrival and landscape dynamics in the northern Bahamas

The first Caribbean settlers were Amerindians from South America. Great Abaco and Grand Bahama, the final islands colonized in the northernmost Bahamas, were inhabited by the Lucayans when Europeans arrived. The timing of Lucayan arrival in the northern Bahamas has been uncertain because direct archaeological evidence is limited. We document Lucayan arrival on Great Abaco Island through a detailed record of vegetation, fire, and landscape dynamics based on proxy data from Blackwood Sinkhole. From about 3,000 to 1,000 y ago, forests dominated by hardwoods and palms were resilient to the effects of hurricanes and cooling sea surface temperatures. The arrival of Lucayans by about 830 CE (2σ range: 720 to 920 CE) is demarcated by increased burning and followed by landscape disturbance and a time-transgressive shift from hardwoods and palms to the modern pine forest. Considering that Lucayan settlements in the southern Bahamian archipelago are dated to about 750 CE (2σ range: 600 to 900 CE), these results demonstrate that Lucayans spread rapidly through the archipelago in less than 100 y. Although precontact landscapes would have been influenced by storms and climatic trends, the most pronounced changes follow more directly from landscape burning and ecosystem shifts after Lucayan arrival. The pine forests of Abaco declined substantially between 1500 and 1670 CE, a period of increased regional hurricane activity, coupled with fires on an already human-impacted landscape. Any future intensification of hurricane activity in the tropical North Atlantic Ocean threatens the sustainability of modern pine forests in the northern Bahamas.