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Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i

Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclim...

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Autores principales: Berio Fortini, Lucas, Kaiser, Lauren R., Xue, Lulin, Wang, Yaping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486033/
https://www.ncbi.nlm.nih.gov/pubmed/36148215
http://dx.doi.org/10.1016/j.dib.2022.108572
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author Berio Fortini, Lucas
Kaiser, Lauren R.
Xue, Lulin
Wang, Yaping
author_facet Berio Fortini, Lucas
Kaiser, Lauren R.
Xue, Lulin
Wang, Yaping
author_sort Berio Fortini, Lucas
collection PubMed
description Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclimatic variables have been developed on a global scale (e.g., WorldClim) but rarely represent the finer regional scale pattern of climate in Hawai'i. Recognizing the value of having such regionally downscaled products, we integrated more detailed projections from recent climate models developed for Hawai'i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (T(min), T(mean), T(max)) and mean precipitation (P(mean)) data at 250 m resolution. We used the most up-to-date dynamically downscaled projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two climate projections into a suite of 19 standard bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions for the Hawaiian Islands. These bioclimatic variables are available for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under representative concentration pathway (RCP) 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections) climate scenarios. The resulting dataset provides a more robust set of climate products that can be used for modeling purposes, impact studies, and management planning.
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spelling pubmed-94860332022-09-21 Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i Berio Fortini, Lucas Kaiser, Lauren R. Xue, Lulin Wang, Yaping Data Brief Data Article Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclimatic variables have been developed on a global scale (e.g., WorldClim) but rarely represent the finer regional scale pattern of climate in Hawai'i. Recognizing the value of having such regionally downscaled products, we integrated more detailed projections from recent climate models developed for Hawai'i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (T(min), T(mean), T(max)) and mean precipitation (P(mean)) data at 250 m resolution. We used the most up-to-date dynamically downscaled projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two climate projections into a suite of 19 standard bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions for the Hawaiian Islands. These bioclimatic variables are available for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under representative concentration pathway (RCP) 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections) climate scenarios. The resulting dataset provides a more robust set of climate products that can be used for modeling purposes, impact studies, and management planning. Elsevier 2022-09-02 /pmc/articles/PMC9486033/ /pubmed/36148215 http://dx.doi.org/10.1016/j.dib.2022.108572 Text en Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Berio Fortini, Lucas
Kaiser, Lauren R.
Xue, Lulin
Wang, Yaping
Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i
title Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i
title_full Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i
title_fullStr Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i
title_full_unstemmed Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i
title_short Bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in Hawai'i
title_sort bioclimatic variables dataset for baseline and future climate scenarios for climate change studies in hawai'i
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9486033/
https://www.ncbi.nlm.nih.gov/pubmed/36148215
http://dx.doi.org/10.1016/j.dib.2022.108572
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