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Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest

The terrestrial biosphere is a key player in slowing the accumulation of carbon dioxide in the atmosphere. While quantification of carbon fluxes at global land scale is important for mitigation policy related to climate and carbon, measurements are only available at sites scarcely distributed in the...

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Autores principales: Zeng, Jiye, Matsunaga, Tsuneo, Tan, Zheng-Hong, Saigusa, Nobuko, Shirai, Tomoko, Tang, Yanhong, Peng, Shushi, Fukuda, Yoko
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518252/
https://www.ncbi.nlm.nih.gov/pubmed/32973132
http://dx.doi.org/10.1038/s41597-020-00653-5
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author Zeng, Jiye
Matsunaga, Tsuneo
Tan, Zheng-Hong
Saigusa, Nobuko
Shirai, Tomoko
Tang, Yanhong
Peng, Shushi
Fukuda, Yoko
author_facet Zeng, Jiye
Matsunaga, Tsuneo
Tan, Zheng-Hong
Saigusa, Nobuko
Shirai, Tomoko
Tang, Yanhong
Peng, Shushi
Fukuda, Yoko
author_sort Zeng, Jiye
collection PubMed
description The terrestrial biosphere is a key player in slowing the accumulation of carbon dioxide in the atmosphere. While quantification of carbon fluxes at global land scale is important for mitigation policy related to climate and carbon, measurements are only available at sites scarcely distributed in the world. This leads to using various methods to upscale site measurements to the whole terrestrial biosphere. This article reports a product obtained by using a Random Forest to upscale terrestrial net ecosystem exchange, gross primary production, and ecosystem respiration from FLUXNET 2015. Our product covers land from −60°S to 80°N with a spatial resolution of 0.1° × 0.1° every 10 days during the period 1999–2019. It was compared with four existing products. A distinguishable feature of our method is using three derived variables of leaf area index to represent plant functional type (PFT) so that measurements from different PFTs can be mixed better by the model. This product can be valuable for the carbon-cycle community to validate terrestrial biosphere models and cross check datasets.
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spelling pubmed-75182522020-10-08 Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest Zeng, Jiye Matsunaga, Tsuneo Tan, Zheng-Hong Saigusa, Nobuko Shirai, Tomoko Tang, Yanhong Peng, Shushi Fukuda, Yoko Sci Data Data Descriptor The terrestrial biosphere is a key player in slowing the accumulation of carbon dioxide in the atmosphere. While quantification of carbon fluxes at global land scale is important for mitigation policy related to climate and carbon, measurements are only available at sites scarcely distributed in the world. This leads to using various methods to upscale site measurements to the whole terrestrial biosphere. This article reports a product obtained by using a Random Forest to upscale terrestrial net ecosystem exchange, gross primary production, and ecosystem respiration from FLUXNET 2015. Our product covers land from −60°S to 80°N with a spatial resolution of 0.1° × 0.1° every 10 days during the period 1999–2019. It was compared with four existing products. A distinguishable feature of our method is using three derived variables of leaf area index to represent plant functional type (PFT) so that measurements from different PFTs can be mixed better by the model. This product can be valuable for the carbon-cycle community to validate terrestrial biosphere models and cross check datasets. Nature Publishing Group UK 2020-09-24 /pmc/articles/PMC7518252/ /pubmed/32973132 http://dx.doi.org/10.1038/s41597-020-00653-5 Text en © The Author(s) 2020 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
Zeng, Jiye
Matsunaga, Tsuneo
Tan, Zheng-Hong
Saigusa, Nobuko
Shirai, Tomoko
Tang, Yanhong
Peng, Shushi
Fukuda, Yoko
Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest
title Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest
title_full Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest
title_fullStr Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest
title_full_unstemmed Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest
title_short Global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest
title_sort global terrestrial carbon fluxes of 1999–2019 estimated by upscaling eddy covariance data with a random forest
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518252/
https://www.ncbi.nlm.nih.gov/pubmed/32973132
http://dx.doi.org/10.1038/s41597-020-00653-5
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