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Tropical cyclones (TCs) can transport birds and insects near their center of circulation. In this study, we examined the maximum altitude, area and density of the radar‐derived bioscatter signature across a set of 42 TC centers of circulation sampled from 2011 to 2020. All TC events contained at least one time when a bioscatter signature was present. More intense hurricanes with closed eyes typically had taller and denser bioscatter signatures, and sometimes larger areas dominated by bioscatter. This indicated a larger number of organisms within the circulation of more intense hurricanes, supporting the speculation that those storms were most likely to trap birds that do not want to risk flying through their eyewall thunderstorms. Larger and denser bioscatter signatures, indicating a larger number of birds, tend to occur when fall migration brings a large bird population to the Gulf and East Coasts where most storms were sampled. TC formation location was not related to bioscatter characteristics, but storms sampled in the Gulf of Mexico and Florida tended to have larger and denser bioscatter signatures.

Previous studies have linked interannual variability of tropical cyclone (TC) intensity in the North Atlantic basin (NA) to Sahelian rainfall, vertical shear of the environmental flow, and relative sea surface temperature (SST). In this study, the contribution of TC track changes to the interannual variations of intense hurricane activity in the North Atlantic basin is evaluated through numerical experiments. It is found that that observed interannual variations of the frequency of intense hurricanes during the period 1958–2017 are dynamically consistent with changes in the large-scale ocean/atmosphere environment. Track changes can account for ~50% of the interannual variability of intense hurricanes, while no significant difference is found for individual environmental parameters between active and inactive years. The only significant difference between active and inactive years is in the duration of TC intensification in the region east of 60°W. The duration increase is not due to the slow-down of TC translation. In active years, a southeastward shift of the formation location in the region east of 60°W causes TCs to take a westward prevailing track, which allows TCs to have a longer opportunity for intensification. On the other hand, most TCs in inactive years take a recurving track, leading to a shorter duration of intensification. This study suggests that the influence of track changes should be considered to understand the basin-wide intensity changes in the North Atlantic basin on the interannual time scale.

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. We would like to acknowledge the three anonymous reviewers and the editor, Chris Landsea, for helpful comments that significantly improved the manuscript. P. Klotzbach thanks the G. Unger Vetlesen Foundation for financial support that helped fund this research. K. Wood was supported by National Science Foundation Award AGS-2011812. M. Bell was supported by Office of Naval Research Award N000141613033. E. Gibney’s research was supported by NOAA’s Science Collaboration Program and administered by UCAR’s Cooperative Programs for the Advancement of Earth System Science (CPAESS) under Awards NA16NWS4620043 and NA18NWS4620043B. C. Schreck was supported by NOAA through the Cooperative Institute for Satellite Earth System Studies under Cooperative Agreement NA19NES4320002. We thank Christina Patricola for providing the ENSO longitude index and Chris Landsea for providing Fig. SB1.

The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High‐resolution prediction on Earth‐to‐Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skills as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4–5-day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.

The Paleogene history of biogenic opal accumulation in the North Atlantic provides insight into both the evolution of deepwater circulation in the Atlantic basin and weathering responses to major climate shifts. However, existing records are compromised by low temporal resolution and/or stratigraphic discontinuities. In order to address this problem, we present a multi-site, high-resolution record of biogenic silica (bioSiO2) accumulation from Blake Nose (ODP Leg 171B, western North Atlantic) spanning the early Paleocene to late Eocene time interval (∼65–34 Ma). This record represents the longest single-locality history of marine bioSiO2 burial compiled to date and offers a unique perspective into changes in bioSiO2 fluxes through the early to middle Paleogene extreme greenhouse interval and the subsequent period of long-term cooling. Blake Nose bioSiO2 fluxes display prominent fluctuations that we attribute to variations in sub-thermocline nutrient supply via cyclonic eddies associated with the Gulf Stream. Following elevated and pulsed bioSiO2 accumulation through the Paleocene to early Eocene greenhouse interval, a prolonged interval of markedly elevated bioSiO2 flux in the middle Eocene between ∼46 and 42 Ma is proposed to reflect nutrient enrichment at Blake Nose due to invigorated overturning circulation following an early onset of Northern Component Water export from the Norwegian–Greenland Sea at ∼49 Ma. Reduced bioSiO2 flux in the North Atlantic, in combination with increased bioSiO2 flux documented in existing records from the equatorial Pacific between ∼42 and 38 Ma, is interpreted to indicate diminished nutrient supply and reduced biosiliceous productivity at Blake Nose in response to weakening of the overturning circulation. Subsequently, in the late Eocene, a deepwater circulation regime favoring limited bioSiO2 burial in the Atlantic and enhanced bioSiO2 burial in the Pacific was established after ∼38 Ma, likely in conjunction with re-invigoration of deepwater export from the North Atlantic. We also observe that Blake Nose bioSiO2 fluxes through the middle Eocene cooling interval (∼48 to 34 Ma) are similar to or higher than background fluxes throughout the late Paleocene–early Eocene interval (∼65 to 48 Ma) of intense greenhouse warmth. This observation is consistent with a temporally variable rather than constant silicate weathering feedback strength model for the Paleogene, which would instead predict that marine bioSiO2 burial should peak during periods of extreme warming.

A harbor in Cabo San Lucas was damaged by Hurricane Genevieve.Tropical cyclone activity in the eastern North Pacific basin during the 2020 season was near average for total named storms but below a...

Hurricane Hanna storm damage in Texas.The 2020 hurricane season, in terms of the number of named storms, was the most active season on record, with 29 named tropical storms and 1 named subtropical ...

While considerable studies have proved that the track and intensity forecasts of tropical cyclone (TC) relied heavily on output from numerical weather prediction (NWP) model, few researches investigated how well NWP models forecast TC genesis in the western North Pacific (WNP) basin. In order to understand the characteristics of TC genesis forecast in WNP basin by NWP models, this study derives a set of criteria to identify the formation of TC using historical data and verifies it based on ECMWF model data between 2013 and 2015. The results show that the percentile values adopted as the criteria thresholds have a significant impact on the performance of algorithm based on the criteria. A reasonable adjustment of threshold in a specific interval can effectively improve the TC genesis prediction. For example, in the WNP basin the forecast results are most sensitive to small changes in the relative vorticity on the 850 hPa level. The results of forecast test of the optimal threshold combination scheme indicate that the turning point of performance lies between 24 and 48 hours with regard to the hit rate in the 12-72 hours prior to the formation of TC. For lead time less than 24 hours, the hit rate was basically maintained at a high level above 0.7 with a small decrease. After that, the performance drops sharply before stabilizing beyond 48 hours. In addition, the performance of the TC genesis prediction in ECMWF model varies significantly from year to year and also in different WNP regions. It performs better to the east of the Philippines than over the South China Sea (SCS). On the other hand, high false alarm (FA) rates are found in the central parts of the SCS up to the waters around the Philippines and the central part of the WNP. The significant discrepancy in ECMWF’s performance can also be observed between different basins. Within the 24 hours before the genesis of a TC, the forecasts for the WNP basin verify better than those for the Atlantic basin.

It was found in previous observational studies that the quasi-biennial oscillation (QBO) can modulate the teleconnection over the Atlantic basin related to the Madden–Julian Oscillation (MJO). In t...

The high diversity of fish species found in Brazilian streams has attracted scientific interest in recent years. However, it is not clear how studies have addressed biodiversity knowledge shortfalls. We conducted a scientometric analysis of stream fish studies in Brazil to identify trends and gaps in how objectives, spatial coverage, and biodiversity knowledge shortfalls have been studied. Our review covered 690 articles published between 1981 and 2019, and we found an increase in the number of publications, the spatial scale of studies, and the number of streams studied. We also found biases in the distribution of publications concerning regions, biomes, basins, and biodiversity knowledge shortfalls. The Southeast region, the Parana River basin, the Atlantic Forest biome, and the Hutchinsonian shortfall contributed to a greater number of studies while the Northeast region, the Pantanal biome, Parnaiba basin, Western Northeast Atlantic basin, and the Prestonian shortfall were less represented. Therefore, to improve our knowledge about stream fish, we recommend new collection efforts in under-sampled regions and new studies focused on filling less-addressed biodiversity knowledge shortfalls. Moreover, we emphasize the importance of standardized sampling protocols, a complete sharing of data, and an increase in scientific collaboration.

Within the realm of satellite remote sensing, optimal data acquisition to study natural phenomena under time, resource, and cost constraints is a well-known problem. Furthermore, since the sensors themselves are at remote locations with sparse ground connectivity, the optimal method must use a computationally light forecasting algorithm, which assimilates information from the observations at possibly irregular intervals, in near real time. In this article, we propose and demonstrate the ensemble -guided cyclone track forecasting (EGCTF) method for application in remote tropical cyclone tracking. The algorithm uses ensemble data produced by numerical weather prediction models to guide the forecasting process while assimilating measured cyclone center positions. The algorithm was tested and analyzed with the Global Ensemble Forecasting System (GEFS) data and the National Hurricane Center data for the 2018 year hurricanes within the Atlantic basin. Compared with a baseline method that uses the GEFS-issued mean ensemble track (AEMN) for forecasting and no data assimilation, the proposed algorithm exhibited positive forecast skill for more than 290 test cases over forecast periods spanning 6–48 h. The skill is seen to improve with lengthening forecast periods, with five test cases showing greater than 75% skill for a forecast period of 6 h to 247 test cases for the forecast period of 48 h.

Major Atlantic hurricanes Irma, Jose, and Maria of 2017 reached their peak intensity in September while traveling over the tropical North Atlantic Ocean and Caribbean Sea, where both atmospheric and ocean conditions were favorable for intensification. In situ and satellite ocean observations revealed that conditions in these areas exhibited (i) sea surface temperatures above 28°C, (ii) upper-ocean heat content above 60 kJ cm−2, and (iii) the presence of low-salinity barrier layers associated with a larger-than-usual extension of the Amazon and Orinoco riverine plumes. Proof-of-concept coupled ocean–hurricane numerical model experiments demonstrated that the accurate representation of such ocean conditions led to an improvement in the simulated intensity of Hurricane Maria for the 3 days preceding landfall in Puerto Rico, when compared to an experiment without the assimilation of ocean observations. Without the assimilation of ocean observations, upper-ocean thermal conditions were generally colder than observations, resulting in reduced air–sea enthalpy fluxes—enthalpy fluxes are more realistically simulated when the upper-ocean temperature and salinity structure is better represented in the model. Our results further showed that different components of the ocean observing system provide valuable information in support of improved TC simulations, and that assimilation of underwater glider observations alone enabled the largest improvement over the 24 h time frame before landfall. Our results, therefore, indicated that ocean conditions were relevant for more realistically simulating Hurricane Maria’s intensity. However, further research based on a comprehensive set of hurricane cases is required to confirm robust improvements to forecast systems.

Based on 19 years of precipitation data collected by the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission, a comparison of the rainfall produced by tropical cyclones (TCs) in different global basins is presented. A total of 1789 TCs were examined in the period from 1998 to 2016 by taking advantage of more than 47,737 observations of TRMM/GPM 3B42 multi-satellite derived rainfall amounts. The axisymmetric component of the TC rainfall is analyzed in all TC-prone basins. The resulting radial profiles show that major hurricanes in the Atlantic basin exhibit significantly heavier inner-core rainfall rates than those in any other basins. To explain the possible causes of this difference, rainfall distributions for major hurricanes are stratified according to different TC intensity and environmental variables. Based on the examination of these parameters, we found that the stronger rainfall rates in the Atlantic major hurricanes are associated with higher values of convective available potential energy, drier relative humidity in the low to middle troposphere, colder air temperature at 250hPa, and stronger vertical wind shear than other basins. These results have important implications in the refining of our understanding of the mechanisms of TC rainfall.

NOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in Hurricane Humberto during extratropical transition. Humberto was also a case where HAFS-globalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAA WP-3D to provide a more detailed evaluation of TC structure prediction. The results from this real-time experiment motivate several future model improvements, and highlight the promise of HAFS-globalnest for improved TC prediction.

The 1933 Atlantic hurricane season was extremely active, with 20 named storms and 11 hurricanes including 6 major (category 3+; 1-min maximum sustained winds ≥96 kt) hurricanes occurring. The 1933 hurricane season also generated the most accumulated cyclone energy (an integrated metric that accounts for frequency, intensity, and duration) of any Atlantic hurricane season on record. A total of 8 hurricanes tracked through the Caribbean in 1933—the most on record. In addition, two category 3 hurricanes made landfall in the United States just 23 h apart: the Treasure Coast hurricane in southeast Florida followed by the Cuba–Brownsville hurricane in south Texas. This manuscript examines large-scale atmospheric and oceanic conditions that likely led to such an active hurricane season. Extremely weak vertical wind shear was prevalent over both the Caribbean and the tropical Atlantic throughout the peak months of the hurricane season, likely in part due to a weak-to-moderate La Niña event. These favorable dynamic conditions, combined with above-normal tropical Atlantic sea surface temperatures, created a very conducive environment for hurricane formation and intensification. The Madden–Julian oscillation was relatively active during the summer and fall of 1933, providing subseasonal conditions that were quite favorable for tropical cyclogenesis during mid- to late August and late September to early October. The current early June and August statistical models used by Colorado State University would have predicted a very active 1933 hurricane season. A better understanding of these extremely active historical Atlantic hurricane seasons may aid in anticipation of future hyperactive seasons.

Southern Africa produces almost a third of the Earth's biomass burning (BB) aerosol particles, yet the fate of these particles and their influence on regional and global climate is poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA EVS-2 (Earth Venture Suborbital-2) investigation with three intensive observation periods designed to study key atmospheric processes that determine the climate impacts of these aerosols. During the Southern Hemisphere winter and spring (June–October), aerosol particles reaching 3–5 km in altitude are transported westward over the southeast Atlantic, where they interact with one of the largest subtropical stratocumulus (Sc) cloud decks in the world. The representation of these interactions in climate models remains highly uncertain in part due to a scarcity of observational constraints on aerosol and cloud properties, as well as due to the parameterized treatment of physical processes. Three ORACLES deployments by the NASA P-3 aircraft in September 2016, August 2017, and October 2018 (totaling ∼350 science flight hours), augmented by the deployment of the NASA ER-2 aircraft for remote sensing in September 2016 (totaling ∼100 science flight hours), were intended to help fill this observational gap. ORACLES focuses on three fundamental science themes centered on the climate effects of African BB aerosols: (a) direct aerosol radiative effects, (b) effects of aerosol absorption on atmospheric circulation and clouds, and (c) aerosol–cloud microphysical interactions. This paper summarizes the ORACLES science objectives, describes the project implementation, provides an overview of the flights and measurements in each deployment, and highlights the integrative modeling efforts from cloud to global scales to address science objectives. Significant new findings on the vertical structure of BB aerosol physical and chemical properties, chemical aging, cloud condensation nuclei, rain and precipitation statistics, and aerosol indirect effects are emphasized, but their detailed descriptions are the subject of separate publications. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project and the dataset it produced.

Hurricane Irma was the most powerful Atlantic hurricane in recorded history, displacing 6 million and killing over 120 people in the state of Florida alone. Unpredictable disasters like Irma are associated with poor cognitive and health outcomes that can disproportionately impact children. This study examined the effects of Hurricane Irma on the hippocampus and memory processes previously related to unpredictable stress. We used an innovative application of an advanced diffusion-weighted imaging technique, restriction spectrum imaging (RSI), to characterize hippocampal microstructure (i.e., cell density) in 9- to 10-year-old children who were exposed to Hurricane Irma relative to a non-exposed control group (i.e., assessed the year before Hurricane Irma). We tested the hypotheses that the experience of Hurricane Irma would be associated with decreases in: (a) hippocampal cellularity (e.g., neurogenesis), based on known associations between unpredictable stress and hippocampal alterations; and (b) hippocampal-related memory function as indexed by delayed recall. We show an association between decreased hippocampal cellularity and delayed recall memory in children who experienced Hurricane Irma relative to those who did not. These findings suggest an important role of RSI for assessing subtle microstructural changes related to functionally significant changes in the developing brain in response to environmental events.

As climate change alters the behavior of Atlantic hurricanes, these storms are trending stronger, wetter, and slower moving over coastal and island populations. Hurricane Dorian exemplified all three attributes. Dorian's destructive passage over the Abaco Islands, Bahamas, on September 1, 2019, exposed residents of its capital, Marsh Harbour, to a prolonged encounter with the storm's core. After Dorian's fierce front eyewall and towering storm surge tore apart shanty town habitats and eviscerated concrete homesites, residents desperately sought refuge during the brief respite when Dorian's eye passed directly overhead. The category 5 winds then resumed abruptly and Dorian continued its relentless destruction. This article focuses on the storm's mental health consequences, drawing on observations of on-site clinicians as well as findings from previous research on the mental health effects of Atlantic hurricanes and the transformation of hurricane hazards resulting from climate change. To protect island and coastal populations against climate-driven storms, disaster planning policy should emphasize resilience-focused prevention and mitigation strategies. In the aftermath of these events, health system response should include community outreach, case finding, and evidence-based interventions that optimize the use of mental health professionals.

On September 20, 2017, Maria, the eleventh-most intense Atlantic hurricane ever recorded, made landfall at 6:15 am local time, the second category 5 hurricane to strike the island in as many weeks. What followed was one of the most challenging recovery situations since Hurricane Katrina struck New Orleans in 2005. Using 402 newspaper articles from The New York Times and the Wall Street Journal, I performed a deductive and inductive analysis of print news media frames to demonstrate the complex ways in which the discourse and actions of key social agents and institutions shape disaster risk for the archipelago before, during, and after Maria. Findings suggest media framing facilitated a lack of accountability for key institutions obliged to provide response and recovery. Key powerful actors, including President Donald Trump, shifted blame for the disaster from FEMA and other key institutions to Puerto Rico, effectively protecting the legitimacy of the Trump administration and its response in Puerto Rico. I argue that these processes are owed to economic factors. Here, Beck’s concept of an institutionally dependent “industrial society” is reconstituted in economically vital urban centers. In contrast, the failure of key institutions causes rural spaces to abandon them. This individualization of risk marks what Beck refers to as the emergence into a “risk society.” This article offers important implications for the study of media as a key site for the selective preservation of institutional legitimacy during disasters and the particular and contingent development of a risk society.

The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models including both short and long lags perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution and the different time scales of the two phases. As a spinoff of the modelling procedure, we are able to deduce that the interannual dependence of the NAO mostly affects the positive phase and that timescales of one to three weeks are more dominant for the negative phase. The statistical properties of the model makes it useful for the generation of realistic climate noise.

Risks associated with natural hazards such as hurricanes are increasingly communicated on social media. For hurricane risk communication, visual information products—graphics—generated by meteorologists and scientists at weather agencies portray forecasts and atmospheric conditions and are offered to parsimoniously convey predictions of severe storms. This research considers risk interactivity by examining a particular hurricane graphic which has shown in previous research to have a distinctive diffusion signature: the ‘spaghetti plot’, which contains multiple discrete lines depicting a storm’s possible path. We first analyzed a large dataset of microblog interactions around spaghetti plots between members of the public and authoritative weather sources within the US during the 2017 Atlantic hurricane season. We then conducted interviews with a sample of the weather authorities after preliminary findings sketched the role that experts have in such communications. Findings describe how people make sense of risk dialogically over graphics, and show the presence of a fundamental tension in risk communication between accuracy and ambiguity. The interactive effort combats the unintended declarative quality of the graphical risk representation through communicative acts that maintain a hazard’s inherent ambiguity until risk can be foreclosed. We consider theoretical and practice-based implications of the limits and potentials of graphical risk representations and of widely diffused scientific communication, and offer reasons we need CSCW attention paid to the larger enterprise of risk communication.

This essay examines the role of Atlantic islands as food production and maritime provisioning centers during early European expansion into the Atlantic basin, roughly 1400-1550. It offers an overvi...

Tropical Cyclone (TC) formation regions are analysed in twelve CMIP5 models using a recently developed diagnostic that provides a model-performance summary in a single image for the mid-summer TC season. A subjective assessment provides an indication of how well the models perform in each TC basin throughout the globe, and which basins can be used to determine possible changes in TC formation regions in a warmer climate. The analysis is necessarily succinct so that seven basins in twelve models can be examined. Consequently, basin performance was reduced to an assessment of two common problems specific to each basin. Basins that were not too adversely affected were included in the projection exercise. The North Indian basin was excluded because the mid-summer analysis period covers a lull in TC activity. Surprisingly, the North Atlantic basin also had to be excluded, because all twelve models failed the performance assessment. A slight poleward expansion in the western North Pacific and an expansion towards the Hawaiian Islands in the eastern North Pacific is plausible in the future, while a contraction in the TC formation regions in the eastern South Indian and western South Pacific basins would reduce the Australian region TC formation area. More than half the models were too active in the eastern South Pacific and South Atlantic basins. However, projections based on the remaining models suggest these basins will remain hostile for TC formation in the future. These southern hemisphere changes are consistent with existing projections of fewer southern hemisphere TCs in a future warming world

Because a change in the frequency (number/year) of hurricanes could be a result of climate change, we analyzed the historical record of Atlantic basin and US landfalling hurricanes, as well as US continental accumulated cyclone energy to evaluate issues related to trend detection. Hurricane and major hurricane landfall counts exhibited no significant overall trend over 167 years of available data, nor did accumulated cyclone energy over the continental USA over 119 years of available data, although shorter-term trends were evident in all three datasets. Given the χ2 distribution evinced by hurricane and major hurricane counts, we generated synthetic series to test the effect of segment length, demonstrating that shorter series were increasingly likely to exhibit spurious trends. Compared to synthetic data with the same mean, the historical all-storm data were more likely to exhibit short-term trends, providing some evidence for long-term persistence at timescales below 10 years. Because this might be due to known climate modes, we examined the relationship between the Atlantic multidecadal oscillation (AMO) and hurricane frequency in light of these short-term excursions. We found that while ratios of hurricane counts with AMO phase matched expectations, statistical tests were less clear due to noise. Over a period of 167 years, we found that an upward trend of roughly 0.7/century is sufficient to be detectable with 80% confidence over the range from 1 to 21 storms/year. Storm energy data 1900–2018 over land were also analyzed. The trend was again zero. The pattern of spurious trends for short segments was again found. Results for AMO periods were similar to count data. Atlantic basin all storms and major storms (1950–2018) did not exhibit any trend over the whole period or after 1990. Major storms 1950–1989 exhibited a significant downward trend. All-storm basin scale storms exhibited short-term trends matching those expected from a Poisson process. A new test for Poisson series was developed based on the 95% distribution of slopes for simulated data across a range of series lengths. Because short data series are inherently likely to yield spurious trends, care is needed when interpreting hurricane trend data.

Heavy rainfall events, typically associated with tropical cyclones (TCs), provoke intense flooding, consequently causing severe losses to life and property. Therefore, the amount and distribution of rain associated with TCs must be forecasted precisely within a reasonable time to guarantee the protection of lives and goods. In this study, the skill of the Numerical Tool for Hurricane Forecast (NTHF) for determining rainfall pattern, average rainfall, rainfall volume, and extreme amounts of rain observed during TCs is evaluated against Tropical Rainfall Measuring Mission (TRMM) data. A sample comprising nine systems formed in the North Atlantic basin from 2016 to 2018 is used, where the analysis begins 24 h before landfall. Several statistical indices characterising the abilities of the NTHF and climatology and persistence model for rainfalls (R-CLIPER) for forecasting rain as measured by the TRMM are calculated at 24, 48, and 72 h forecasts for each TC and averaged. The model under consideration presents better forecasting skills than the R-CLIPER for all the attributes evaluated and demonstrates similar performances compared with models reported in the literature. The proposed model predicts the average rainfall well and presents a good description of the rain pattern. However, its forecast of extreme rain is only applicable for 24 h.

This study examines climatological potential vorticity streamer (PVS) activity associated with Rossby wave breaking (RWB), which can impact TC activity in the subtropical North Atlantic (NATL) basin via moisture and wind anomalies. PVSs are identified along the 2-PVU (1 PVU = 10−6 K kg−1 m2 s−1) contour on the 350-K isentropic surface, using a unique identification technique that combines previous methods. In total, 21 149 individual PVS instances are identified from the ERA-Interim (ERAI) climatology during June–November over 1979–2015 with a peak in July–August. The total number of PVSs identified in this study is more than previous PVS climatologies for this region, since the new technique identifies a wider range of cases. Variations in PVS size and intensity prompt the development of a new PVS activity index (PVSI), which provides an integrated measure of PVS activity that can improve comparisons with TC activity. For instance, PVSI has a stronger negative correlation with seasonal TC activity (r = −0.55) relative to PVS frequency, size, or intensity alone. PVSI in June–July is also positively correlated with PVSI in August–November (r = 0.67), suggesting predictive capability. Compared to the ERAI and Japan Meteorological Agency 55-Year Reanalysis (JRA-55) climatology, there are more PVSs in the Climate Forecast System Reanalysis (CFSR) but these have weaker average intensity overall. While no long-term trend in PVSI is observed in the ERAI or JRA-55 climatologies, a negative trend is observed in CFSR, which could be related to differences in near tropopause static stability early in the climatological period (1979–86) between the CFSR and ERAI datasets.

The co-occurrence of the 2020 Atlantic hurricane season and the ongoing coronavirus disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations – proven life-saving protective measures that gather evacuees together inside durable, enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies, including lockdowns, sheltering at home, physical distancing, donning personal protective equipment, conscientious handwashing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, health care providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and the resurgence of COVID-19 cases among hurricane evacuees who shelter together.

The Pacific Decadal Oscillation (PDO), the leading mode of Pacific decadal sea surface temperature variability, arises mainly from combinations of regional air-sea interaction within the North Pacific Ocean and remote forcing, such as from the tropical Pacific and the Atlantic. Because of such a combination of mechanisms, a question remains as to how much PDO variability originates from these regions. To better understand PDO variability, the equatorial Pacific and the Atlantic impacts on the PDO are examined using several 3-dimensional partial ocean data assimilation experiments conducted with two global climate models: the CESM1.0 and MIROC3.2m. In these partial assimilation experiments, the climate models are constrained by observed temperature and salinity anomalies, one solely in the Atlantic basin and the other solely in the equatorial Pacific basin, but are allowed to evolve freely in other regions. These experiments demonstrate that, in addition to the tropical Pacific’s role in driving PDO variability, the Atlantic can affect PDO variability by modulating the tropical Pacific climate through two proposed processes. One is the equatorial pathway, in which tropical Atlantic sea surface temperature (SST) variability causes an El Niño-like SST response in the equatorial Pacific through the reorganization of the global Walker circulation. The other is the north tropical pathway, where low-frequency SST variability associated with the Atlantic Multidecadal Oscillation induces a Matsuno-Gill type atmospheric response in the tropical Atlantic-Pacific sectors north of the equator. These results provide a quantitative assessment suggesting that 12–29% of PDO variance originates from the Atlantic Ocean and 40–44% from the tropical Pacific. The remaining 27–48% of the variance is inferred to arise from other processes such as regional ocean-atmosphere interactions in the North Pacific and possibly teleconnections from the Indian Ocean.

A wave energy assessment is performed in the Canary Islands, based on a 33-year hindcast, between 1979 and 2011. The third-generation wave models WAVEWATCH III and SWAN are used to study the generation of the waves in the North Atlantic basin and the transformation of the waves in the Canary Islands, respectively. The hindcast system was validated in a prior study, showing a relevant wave energy resource distribution with an average annual energy availability of 25–30 kW/m. This study intends to offer a detail description of the wave climate, combining the previous results with the new studies. The results show that the seasonal mean distribution of wave energy varies between 15 and 20 kW/m, in the spring and 25–30 kW/m, in the winter, while in the less energetic areas the seasonal average varies between 10 and 15 kW/m, in the spring, and 15–20 kW/m, in the winter. Also, the temporal variability indexes suggest that the East coast of the islands presents less variability and that the North/Northwest coast of the islands exhibits greater energy availability.

Although extreme events are inevitable, the associated cost to infrastructure and human life is not. We can mitigate these costs through improving the information available to emergency responders during and after crisis events via social media. Recent research has identified a correlation between spikes of Twitter activity and the infrastructural damage incurred during natural disasters. This research, however, overlooks emergencies occurring in areas in which people have lost power, lack the ability to connect to the internet, or, due to differences in social media perceptions, are uncompelled to Tweet during a disaster. To assess the prevalence of Twitter activity decreases and the relative importance of those decreases in detecting areas in crisis, we study crisis-driven Twitter activity deviations from “normal” in nine cities affected by the 2017 Atlantic hurricane season. In analyzing more than 1.1 million Tweets across the season, we find that there is a stronger, more significant correlation between infrastructure damage and a metric that prioritizes both increases and decreases in Twitter activity than one that prioritizes only Twitter activity increases. These findings indicate that social media drop-offs could be representative of significant distress, and accounting for the apparent survivorship bias in social media will be critical to the equitable use of social media in crisis applications.

GOES-16 visible satellite image of Hurricane Dorian near peak intensity on September 1.The 2019 Atlantic hurricane season featured above-normal activity. Eighteen named storms formed, of which six ...

Made available in DSpace on 2022-04-29T08:33:07Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-02-01 Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Petrobras The South Atlantic Ocean (SAO) is characterized by the development of different types of synoptic scale cyclones, which affect the weather and climate of South America. For the first time, we obtained the long term trend of subtropical cyclones (SCs) climatology over the SAO through two ensembles under RCP8.5 scenario. Regional Climate Model version 4 (RegCM4) projections were driven by three global climate models (GCMs) from CMIP5. SCs are obtained by applying three algorithms: (1) for tracking all cyclones based on relative vorticity; (2) to describe the thermal structure of the cyclones; and (3) for selecting only the SCs. Ensemble means are able to capture the main SCs characteristics shown by ERA-Interim reanalysis in the present climate (1979–2005), such as the main region of formation (near the southeastern Brazilian coast), track density, seasonality (higher frequency in austral summer) and lifetime (~ 3 days). The RegCM4 and GCMs ensembles project a negative and statistically significant trend in the frequency of SCs in the future climate (2050–2080) near the southeastern coast of Brazil. The projections also indicate a greater negative trend of SCs than for all cyclones. This would be a response to the future increase in the mean sea level pressure (expansion of South Atlantic subtropical anticyclone), which in turn leads to a change in the low-level circulation acting to decrease the moisture transport to the main region of SCs development. Though the SCs frequency will decrease in the future, they are projected to be more intense due to stronger convective forcing. Departamento de Ciências Atmosféricas Instituto de Astronomia Geofísica e Ciências Atmosféricas Universidade de São Paulo, SP Instituto de Recursos Naturais Universidade Federal de Itajubá, MG Bauru Meteorological Centre (IPMET) Faculdade de Ciências Universidade Estadual Paulista (Unesp), SP Bauru Meteorological Centre (IPMET) Faculdade de Ciências Universidade Estadual Paulista (Unesp), SP

Coastal areas of State of New Jersey in the Northeastern United States are exposed to extreme wind waves generated by tropical cyclones in the Atlantic Ocean. Past studies suggest that the frequency and intensity of major hurricanes in the Atlantic basin would increase under high greenhouse gas emission scenarios. Furthermore, sea level observations have revealed that the local mean sea level along the coast of New Jersey is rising at a rate higher than that of the global sea level rise. The objective of this study is to quantify the combined influence of sea level rise (SLR) and hurricane climatology change on wave heights induced by major hurricanes off the coast of New Jersey. To this end, a coupled hydrodynamic-wave model is utilized to simulate wind waves for synthetic hurricanes generated for the climate conditions in the historical period of 1980–2000 and future period of 2080–2100 under the RCP8.5 high emission scenario. The synthetic storms are generated by a hurricane model for the climate conditions obtained from four different global climate models. The projections of future wave heights show statistically significant increases in the wave heights induced by major hurricanes. Under the combined effects of hurricane climatology change and a SLR of 1.19 m, the increase in the extreme wave heights 15% in back-bays and shallow waters of the nearshore zone and up to 10% in deeper coastal waters. It is found that SLR alone would result in a significant increase in the hurricane-induced wave heights in the present-day surf zone.

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., If the frequency and intensity of tropical cyclones have changed over the last century, it is not well known, given the lack of reliable data before the mid-20th century. Here, the author uses a statistical-dynamical model to show an increase in tropical cyclone activity in the Atlantic since the 19th century.

North Atlantic Tropical Cyclones (TCs) are major atmospheric hazards that can cause large disruptions to coastal and near-coastal societies. Although most studies focus on those areas with highest impact (e.g., Caribbean Islands, the Gulf and western coast of United States), there is an increasing interest in characterizing changes in occurrence and impacts in areas usually less affected by TCs, particularly in the framework of a changing climate. Here we provide a long-term context evaluating changes in the frequency of TC in the Northeast Atlantic (NEA) basin during the 1978–2019 period. In the last decades, scattered information has shown an impact both from TCs and Post-Tropical Cyclones (PTC) in the NEA. We compute several complementary linear trends and show a significant (p ≤ 0.1) increase in the number of stronger storms in the entire North Atlantic basin, and the amount of TCs and PTCs that reach the NEA, in agreement with previous works. A highly significant relation (p ≤ 0.05) is found between the Atlantic Multidecadal Oscillation (AMO) index and TC activity in both the entire North Atlantic and the NEA basin. Sea surface temperature anomaly maps are produced to better encapsulate the annual variability without the multidecadal oscillation effects and, important cold (warm) pools in cyclogenesis and development zones are found in years with low (high) TC activity. It is also found that the sea surface temperature field plays a minor role in the guiding of storms into the NEA sector. Long-term trends as well as high/low seasonal activity analysis suggest that atmospheric circulation (vertical wind shear, lapse rate, mean sea level pressure and upper-level steering) is more relevant than sea surface temperature in the NEA region.