High-Impact Extratropical Cyclones along the Northeast Coast of the United States in a Long Coupled Climate Model Simulation

Catalano, A. J.; Broccoli, Anthony J.; Kapnick, Sarah B.; Janoski, Tyler P.

doi:10.1175/jcli-d-18-0376.1

Cited by 7 publications

(8 citation statements)

References 35 publications

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“…Coastal areas surrounding Jamaica Bay, which are home to hundreds of thousands of New York residences, are highly susceptible to coastal flooding. Storm surges induced by tropical cyclones (TCs) and extratropical cyclones (ETCs) result in devastating flood events in this region, as best exemplified by historical TCs such as Hurricane Donna in 1960 and Sandy in 2012 and ETCs such as the Great Appalachian Storm of November 1950 and the December 1992 event (Catalano and Broccoli 2018;Catalano et al 2019). Climate change is expected to impact flood hazards, yet the compound impacts of sea level rise (SLR) and storm climatology change on flood hazards in Jamaica Bay are not fully understood.…”

Section: Introductionmentioning

confidence: 99%

Impacts of climate change on hurricane flood hazards in Jamaica Bay, New York

Marsooli

Lin

2020

Climatic Change

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Sea level rise (SLR) and tropical cyclone (TC) climatology change could impact future flood hazards in Jamaica Bay—an urbanized back-barrier bay in New York—yet their compound impacts are not well understood. This study estimates the compound effects of SLR and TC climatology change on flood hazards in Jamaica Bay from a historical period in the late twentieth century (1980–2000) to future periods in the mid- and late-twenty-first century (2030–2050 and 2080–2100, under RCP8.5 greenhouse gas concentration scenario). Flood return periods are estimated based on probabilistic projections of SLR and peak storm tides simulated by a hydrodynamic model for large numbers of synthetic TCs. We find a substantial increase in the future flood hazards, e.g., the historical 100-year flood level would become a 9- and 1-year flood level in the mid- and late-twenty-first century and the 500-year flood level would become a 143- and 4-year flood level. These increases are mainly induced by SLR. However, TC climatology change would considerably contribute to the future increase in low-probability, high-consequence flood levels (with a return period greater than 100 year), likely due to an increase in the probability of occurrence of slow-moving but intense TCs by the end of twenty-first century. We further conduct high-resolution coastal flood simulations for a series of SLR and TC scenarios. Due to the SLR projected with a 5% exceedance probability, 125- and 1300-year flood events in the late-twentieth century would become 74- and 515-year flood events, respectively, in the late-twenty-first century, and the spatial extent of flooding over coastal floodplains of Jamaica Bay would increase by nearly 10 and 4 times, respectively. In addition, SLR leads to larger surface waves induced by TCs in the bay, suggesting a potential increase in hazards associated with wave runup, erosion, and damage to coastal infrastructure.

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Section: Introductionmentioning

confidence: 99%

Impacts of climate change on hurricane flood hazards in Jamaica Bay, New York

Marsooli

Lin

2020

Climatic Change

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“…Reed et al (2020Reed et al ( , 2021 used TE to extract tracks of hurricanes Florence (2018) and Dorian (2019) and attribute human influence on these storms. TE has also been used for tracking storms in aquaplanet simulations (Chavas and Reed, 2019) so as to better understand how dynamic forcing impacts TC genesis and size. Recent work by Stansfield et al (2020) has also leveraged some of the more advanced capabilities in TE to filter fields (e.g., precipitation) in the vicinity of tracked features to evaluate model performance.…”

Section: Examples From the Existing Literaturementioning

confidence: 99%

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

et al. 2021

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Abstract. TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets on either rectilinear or unstructured/native grids. Version 2.1 of the TE framework now provides extensive support for examining both nodal (i.e., pointwise) and areal features, including tropical and extratropical cyclones, monsoonal lows and depressions, atmospheric rivers, atmospheric blocking, precipitation clusters, and heat waves. Available operations include nodal and areal thresholding, calculations of quantities related to nodal features such as accumulated cyclone energy and azimuthal wind profiles, filtering data based on the characteristics of nodal features, and stereographic compositing. This paper describes the core algorithms (kernels) that have been added to the TE framework since version 1.0, including algorithms for editing pointwise trajectory files, composition of fields around nodal features, generation of areal masks via thresholding and nodal features, and tracking of areal features in time. Several examples are provided of how these kernels can be combined to produce composite algorithms for evaluating and understanding common atmospheric features and their underlying processes. These examples include analyzing the fraction of precipitation from tropical cyclones, compositing meteorological fields around extratropical cyclones, calculating fractional contribution to poleward vapor transport from atmospheric rivers, and building a climatology of atmospheric blocks.

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“…underestimating their actual frequency). While these results regarding return levels and time series were valuable, Catalano et al (2019) did not distinguish between the cold season and the warm season of each year, which could also have led to biased results.…”

Section: Prior Studies Of Extreme Natural Hazardsmentioning

confidence: 99%

“…According to a study by Catalano et al (2019) of highimpact extratropical cyclones (ETCs) on the northeastern coast of the Unites States, limited data caused by these storms' rarity made it difficult to predict the damage they would cause or analyse their frequency. To overcome this, they utilized 1505 years' worth of simulations derived from a long coupled model, GFDL FLOR, to estimate these extreme events' exceedance probabilities and compared the results against those of short-term time series estimation.…”

Section: Prior Studies Of Extreme Natural Hazardsmentioning

confidence: 99%

Estimation of the non-exceedance probability of extreme storm surges in South Korea using tidal-gauge data

Yum

Wei

Jang

2021

Nat. Hazards Earth Syst. Sci.

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Abstract. Global warming, one of the most serious aspects of climate change, can be expected to cause rising sea levels. These have in turn been linked to unprecedentedly large typhoons that can cause flooding of low-lying land, coastal invasion, seawater flows into rivers and groundwater, rising river levels, and aberrant tides. To prevent typhoon-related loss of life and property damage, it is crucial to accurately estimate storm-surge risk. This study therefore develops a statistical model for estimating such surges' probability based on surge data pertaining to Typhoon Maemi, which struck South Korea in 2003. Specifically, estimation of non-exceedance probability models of the typhoon-related storm surge was achieved via clustered separated peaks-over-threshold simulation, while various distribution models were fitted to the empirical data for investigating the risk of storm surges reaching particular heights. To explore the non-exceedance probability of extreme storm surges caused by typhoons, a threshold algorithm with clustering methodology was applied. To enhance the accuracy of such non-exceedance probability, the surge data were separated into three different components: predicted water level, observed water level, and surge. Sea-level data from when Typhoon Maemi struck were collected from a tidal-gauge station in the city of Busan, which is vulnerable to typhoon-related disasters due to its geographical characteristics. Fréchet, gamma, log-normal, generalized Pareto, and Weibull distributions were fitted to the empirical surge data, and the researchers compared each one's performance at explaining the non-exceedance probability. This established that Weibull distribution was better than any of the other distributions for modelling Typhoon Maemi's peak total water level. Although this research was limited to one city on the Korean Peninsula and one extreme weather event, its approach could be used to reliably estimate non-exceedance probabilities in other regions where tidal-gauge data are available. In practical terms, the findings of this study and future ones adopting its methodology will provide a useful reference for designers of coastal infrastructure.

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High-Impact Extratropical Cyclones along the Northeast Coast of the United States in a Long Coupled Climate Model Simulation

Cited by 7 publications

References 35 publications

Impacts of climate change on hurricane flood hazards in Jamaica Bay, New York

Impacts of climate change on hurricane flood hazards in Jamaica Bay, New York

TempestExtremes v2.1: a community framework for feature detection, tracking, and analysis in large datasets

Estimation of the non-exceedance probability of extreme storm surges in South Korea using tidal-gauge data

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