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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ftdoajarticles:oai:doaj.org/article:f0ae163f801c4af8a9926a31b9011bce 2023-05-15T15:10:16+02:00 Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices Niloufar Nouri Naresh Devineni Valerie Were Reza Khanbilvardi 2021-01-01T00:00:00Z https://doi.org/10.1038/s41598-021-81143-5 https://doaj.org/article/f0ae163f801c4af8a9926a31b9011bce EN eng Nature Portfolio https://doi.org/10.1038/s41598-021-81143-5 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-021-81143-5 2045-2322 https://doaj.org/article/f0ae163f801c4af8a9926a31b9011bce Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021) Medicine R Science Q article 2021 ftdoajarticles https://doi.org/10.1038/s41598-021-81143-5 2022-12-31T09:23:29Z 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. Article in Journal/Newspaper Arctic North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Arctic Pacific Soi ENVELOPE(30.704,30.704,66.481,66.481) Scientific Reports 11 1 |
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Directory of Open Access Journals: DOAJ Articles |
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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 |
topic_facet |
Medicine R Science Q |
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 in Journal/Newspaper |
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://doi.org/10.1038/s41598-021-81143-5 https://doaj.org/article/f0ae163f801c4af8a9926a31b9011bce |
long_lat |
ENVELOPE(30.704,30.704,66.481,66.481) |
geographic |
Arctic Pacific Soi |
geographic_facet |
Arctic Pacific Soi |
genre |
Arctic North Atlantic North Atlantic oscillation |
genre_facet |
Arctic North Atlantic North Atlantic oscillation |
op_source |
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021) |
op_relation |
https://doi.org/10.1038/s41598-021-81143-5 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-021-81143-5 2045-2322 https://doaj.org/article/f0ae163f801c4af8a9926a31b9011bce |
op_doi |
https://doi.org/10.1038/s41598-021-81143-5 |
container_title |
Scientific Reports |
container_volume |
11 |
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1 |
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1766341314492760064 |