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A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series

Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human–environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating...

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Detalles Bibliográficos
Autores principales: Jin, Kai, Wang, Fei, Chen, Deliang, Liu, Huanhuan, Ding, Wenbin, Shi, Shangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668394/
https://www.ncbi.nlm.nih.gov/pubmed/31366934
http://dx.doi.org/10.1038/s41597-019-0143-1
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author Jin, Kai
Wang, Fei
Chen, Deliang
Liu, Huanhuan
Ding, Wenbin
Shi, Shangyu
author_facet Jin, Kai
Wang, Fei
Chen, Deliang
Liu, Huanhuan
Ding, Wenbin
Shi, Shangyu
author_sort Jin, Kai
collection PubMed
description Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human–environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970–2050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16 W/m(2) in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.
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spelling pubmed-66683942019-08-01 A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series Jin, Kai Wang, Fei Chen, Deliang Liu, Huanhuan Ding, Wenbin Shi, Shangyu Sci Data Data Descriptor Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human–environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970–2050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16 W/m(2) in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales. Nature Publishing Group UK 2019-07-31 /pmc/articles/PMC6668394/ /pubmed/31366934 http://dx.doi.org/10.1038/s41597-019-0143-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.
spellingShingle Data Descriptor
Jin, Kai
Wang, Fei
Chen, Deliang
Liu, Huanhuan
Ding, Wenbin
Shi, Shangyu
A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series
title A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series
title_full A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series
title_fullStr A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series
title_full_unstemmed A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series
title_short A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series
title_sort new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6668394/
https://www.ncbi.nlm.nih.gov/pubmed/31366934
http://dx.doi.org/10.1038/s41597-019-0143-1
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