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Forecasted attribution of the human influence on Hurricane Florence

Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropic...

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Autores principales: Reed, K. A., Stansfield, A. M., Wehner, M. F., Zarzycki, C. M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938700/
https://www.ncbi.nlm.nih.gov/pubmed/31911940
http://dx.doi.org/10.1126/sciadv.aaw9253
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author Reed, K. A.
Stansfield, A. M.
Wehner, M. F.
Zarzycki, C. M.
author_facet Reed, K. A.
Stansfield, A. M.
Wehner, M. F.
Zarzycki, C. M.
author_sort Reed, K. A.
collection PubMed
description Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm’s core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones.
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spelling pubmed-69387002020-01-07 Forecasted attribution of the human influence on Hurricane Florence Reed, K. A. Stansfield, A. M. Wehner, M. F. Zarzycki, C. M. Sci Adv Research Articles Changes in extreme weather, such as tropical cyclones, are one of the most serious ways society experiences the impact of climate change. Advance forecasted conditional attribution statements, using a numerical model, were made about the anthropogenic climate change influence on an individual tropical cyclone, Hurricane Florence. Mean total overland rainfall amounts associated with the forecasted storm’s core were increased by 4.9 ± 4.6% with local maximum amounts experiencing increases of 3.8 ± 5.7% due to climate change. A slight increase in the forecasted storm size of 1 to 2% was also attributed. This work reviews our forecasted attribution statement with the benefit of hindsight, demonstrating credibility of advance attribution statements for tropical cyclones. American Association for the Advancement of Science 2020-01-01 /pmc/articles/PMC6938700/ /pubmed/31911940 http://dx.doi.org/10.1126/sciadv.aaw9253 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Reed, K. A.
Stansfield, A. M.
Wehner, M. F.
Zarzycki, C. M.
Forecasted attribution of the human influence on Hurricane Florence
title Forecasted attribution of the human influence on Hurricane Florence
title_full Forecasted attribution of the human influence on Hurricane Florence
title_fullStr Forecasted attribution of the human influence on Hurricane Florence
title_full_unstemmed Forecasted attribution of the human influence on Hurricane Florence
title_short Forecasted attribution of the human influence on Hurricane Florence
title_sort forecasted attribution of the human influence on hurricane florence
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938700/
https://www.ncbi.nlm.nih.gov/pubmed/31911940
http://dx.doi.org/10.1126/sciadv.aaw9253
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