Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices
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 detect...
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ftosti:oai:osti.gov:1816774 2023-07-30T04:01:56+02:00 Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices Nouri, Niloufar Devineni, Naresh Were, Valerie Khanbilvardi, Reza 2021-12-01 application/pdf http://www.osti.gov/servlets/purl/1816774 https://www.osti.gov/biblio/1816774 https://doi.org/10.1038/s41598-021-81143-5 unknown http://www.osti.gov/servlets/purl/1816774 https://www.osti.gov/biblio/1816774 https://doi.org/10.1038/s41598-021-81143-5 doi:10.1038/s41598-021-81143-5 54 ENVIRONMENTAL SCIENCES 2021 ftosti https://doi.org/10.1038/s41598-021-81143-5 2023-07-11T10:06:30Z 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. Other/Unknown Material Arctic North Atlantic North Atlantic oscillation SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Pacific Soi ENVELOPE(30.704,30.704,66.481,66.481) Scientific Reports 11 1 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Nouri, Niloufar Devineni, Naresh Were, Valerie Khanbilvardi, Reza Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
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. |
author |
Nouri, Niloufar Devineni, Naresh Were, Valerie Khanbilvardi, Reza |
author_facet |
Nouri, Niloufar Devineni, Naresh Were, Valerie Khanbilvardi, Reza |
author_sort |
Nouri, Niloufar |
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 |
publishDate |
2021 |
url |
http://www.osti.gov/servlets/purl/1816774 https://www.osti.gov/biblio/1816774 https://doi.org/10.1038/s41598-021-81143-5 |
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_relation |
http://www.osti.gov/servlets/purl/1816774 https://www.osti.gov/biblio/1816774 https://doi.org/10.1038/s41598-021-81143-5 doi:10.1038/s41598-021-81143-5 |
op_doi |
https://doi.org/10.1038/s41598-021-81143-5 |
container_title |
Scientific Reports |
container_volume |
11 |
container_issue |
1 |
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1772812675488677888 |