Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

Abstract The annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and bett...

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Autores principales:	Niloufar Nouri, Naresh Devineni, Valerie Were, Reza Khanbilvardi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f0ae163f801c4af8a9926a31b9011bce
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spelling	oai:doaj.org-article:f0ae163f801c4af8a9926a31b9011bce2021-12-02T11:50:40ZExplaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices10.1038/s41598-021-81143-52045-2322https://doaj.org/article/f0ae163f801c4af8a9926a31b9011bce2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81143-5https://doaj.org/toc/2045-2322Abstract The annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.Niloufar NouriNaresh DevineniValerie WereReza KhanbilvardiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Niloufar Nouri Naresh Devineni Valerie Were Reza Khanbilvardi Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices
description	Abstract The annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.
format	article
author	Niloufar Nouri Naresh Devineni Valerie Were Reza Khanbilvardi
author_facet	Niloufar Nouri Naresh Devineni Valerie Were Reza Khanbilvardi
author_sort	Niloufar Nouri
title	Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices
title_short	Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices
title_full	Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices
title_fullStr	Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices
title_full_unstemmed	Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices
title_sort	explaining the trends and variability in the united states tornado records using climate teleconnections and shifts in observational practices
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f0ae163f801c4af8a9926a31b9011bce
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Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

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