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Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data

Accurate terrestrial biosphere model (TBM) simulations of gross carbon uptake (gross primary productivity – GPP) are essential for reliable future terrestrial carbon sink projections. However, uncertainties in TBM GPP estimates remain. Newly-available satellite-derived sun-induced chlorophyll fluore...

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Autores principales: MacBean, Natasha, Maignan, Fabienne, Bacour, Cédric, Lewis, Philip, Peylin, Philippe, Guanter, Luis, Köhler, Philipp, Gómez-Dans, Jose, Disney, Mathias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5792553/
https://www.ncbi.nlm.nih.gov/pubmed/29386626
http://dx.doi.org/10.1038/s41598-018-20024-w
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author MacBean, Natasha
Maignan, Fabienne
Bacour, Cédric
Lewis, Philip
Peylin, Philippe
Guanter, Luis
Köhler, Philipp
Gómez-Dans, Jose
Disney, Mathias
author_facet MacBean, Natasha
Maignan, Fabienne
Bacour, Cédric
Lewis, Philip
Peylin, Philippe
Guanter, Luis
Köhler, Philipp
Gómez-Dans, Jose
Disney, Mathias
author_sort MacBean, Natasha
collection PubMed
description Accurate terrestrial biosphere model (TBM) simulations of gross carbon uptake (gross primary productivity – GPP) are essential for reliable future terrestrial carbon sink projections. However, uncertainties in TBM GPP estimates remain. Newly-available satellite-derived sun-induced chlorophyll fluorescence (SIF) data offer a promising direction for addressing this issue by constraining regional-to-global scale modelled GPP. Here, we use monthly 0.5° GOME-2 SIF data from 2007 to 2011 to optimise GPP parameters of the ORCHIDEE TBM. The optimisation reduces GPP magnitude across all vegetation types except C4 plants. Global mean annual GPP therefore decreases from 194 ± 57 PgCyr(−1) to 166 ± 10 PgCyr(−1), bringing the model more in line with an up-scaled flux tower estimate of 133 PgCyr(−1). Strongest reductions in GPP are seen in boreal forests: the result is a shift in global GPP distribution, with a ~50% increase in the tropical to boreal productivity ratio. The optimisation resulted in a greater reduction in GPP than similar ORCHIDEE parameter optimisation studies using satellite-derived NDVI from MODIS and eddy covariance measurements of net CO(2) fluxes from the FLUXNET network. Our study shows that SIF data will be instrumental in constraining TBM GPP estimates, with a consequent improvement in global carbon cycle projections.
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spelling pubmed-57925532018-02-12 Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data MacBean, Natasha Maignan, Fabienne Bacour, Cédric Lewis, Philip Peylin, Philippe Guanter, Luis Köhler, Philipp Gómez-Dans, Jose Disney, Mathias Sci Rep Article Accurate terrestrial biosphere model (TBM) simulations of gross carbon uptake (gross primary productivity – GPP) are essential for reliable future terrestrial carbon sink projections. However, uncertainties in TBM GPP estimates remain. Newly-available satellite-derived sun-induced chlorophyll fluorescence (SIF) data offer a promising direction for addressing this issue by constraining regional-to-global scale modelled GPP. Here, we use monthly 0.5° GOME-2 SIF data from 2007 to 2011 to optimise GPP parameters of the ORCHIDEE TBM. The optimisation reduces GPP magnitude across all vegetation types except C4 plants. Global mean annual GPP therefore decreases from 194 ± 57 PgCyr(−1) to 166 ± 10 PgCyr(−1), bringing the model more in line with an up-scaled flux tower estimate of 133 PgCyr(−1). Strongest reductions in GPP are seen in boreal forests: the result is a shift in global GPP distribution, with a ~50% increase in the tropical to boreal productivity ratio. The optimisation resulted in a greater reduction in GPP than similar ORCHIDEE parameter optimisation studies using satellite-derived NDVI from MODIS and eddy covariance measurements of net CO(2) fluxes from the FLUXNET network. Our study shows that SIF data will be instrumental in constraining TBM GPP estimates, with a consequent improvement in global carbon cycle projections. Nature Publishing Group UK 2018-01-31 /pmc/articles/PMC5792553/ /pubmed/29386626 http://dx.doi.org/10.1038/s41598-018-20024-w Text en © The Author(s) 2018 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/.
spellingShingle Article
MacBean, Natasha
Maignan, Fabienne
Bacour, Cédric
Lewis, Philip
Peylin, Philippe
Guanter, Luis
Köhler, Philipp
Gómez-Dans, Jose
Disney, Mathias
Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
title Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
title_full Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
title_fullStr Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
title_full_unstemmed Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
title_short Strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
title_sort strong constraint on modelled global carbon uptake using solar-induced chlorophyll fluorescence data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5792553/
https://www.ncbi.nlm.nih.gov/pubmed/29386626
http://dx.doi.org/10.1038/s41598-018-20024-w
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