Cargando…

Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble

Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (...

Descripción completa

Detalles Bibliográficos
Autores principales: Jiang, Mingkai, Felzer, Benjamin S., Sahagian, Dork
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4947969/
https://www.ncbi.nlm.nih.gov/pubmed/27425819
http://dx.doi.org/10.1038/srep29962
_version_ 1782443261247356928
author Jiang, Mingkai
Felzer, Benjamin S.
Sahagian, Dork
author_facet Jiang, Mingkai
Felzer, Benjamin S.
Sahagian, Dork
author_sort Jiang, Mingkai
collection PubMed
description Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment.
format Online
Article
Text
id pubmed-4947969
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Nature Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-49479692016-07-26 Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble Jiang, Mingkai Felzer, Benjamin S. Sahagian, Dork Sci Rep Article Characterizing precipitation seasonality and variability in the face of future uncertainty is important for a well-informed climate change adaptation strategy. Using the Colwell index of predictability and monthly normalized precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensembles, this study identifies spatial hotspots of changes in precipitation predictability in the United States under various climate scenarios. Over the historic period (1950–2005), the recurrent pattern of precipitation is highly predictable in the East and along the coastal Northwest, and is less so in the arid Southwest. Comparing the future (2040–2095) to the historic period, larger changes in precipitation predictability are observed under Representative Concentration Pathways (RCP) 8.5 than those under RCP 4.5. Finally, there are region-specific hotspots of future changes in precipitation predictability, and these hotspots often coincide with regions of little projected change in total precipitation, with exceptions along the wetter East and parts of the drier central West. Therefore, decision-makers are advised to not rely on future total precipitation as an indicator of water resources. Changes in precipitation predictability and the subsequent changes on seasonality and variability are equally, if not more, important factors to be included in future regional environmental assessment. Nature Publishing Group 2016-07-18 /pmc/articles/PMC4947969/ /pubmed/27425819 http://dx.doi.org/10.1038/srep29962 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Jiang, Mingkai
Felzer, Benjamin S.
Sahagian, Dork
Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble
title Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble
title_full Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble
title_fullStr Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble
title_full_unstemmed Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble
title_short Predictability of Precipitation Over the Conterminous U.S. Based on the CMIP5 Multi-Model Ensemble
title_sort predictability of precipitation over the conterminous u.s. based on the cmip5 multi-model ensemble
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4947969/
https://www.ncbi.nlm.nih.gov/pubmed/27425819
http://dx.doi.org/10.1038/srep29962
work_keys_str_mv AT jiangmingkai predictabilityofprecipitationovertheconterminoususbasedonthecmip5multimodelensemble
AT felzerbenjamins predictabilityofprecipitationovertheconterminoususbasedonthecmip5multimodelensemble
AT sahagiandork predictabilityofprecipitationovertheconterminoususbasedonthecmip5multimodelensemble