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Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS)

Identifieur interne : 00A199 ( Main/Exploration ); précédent : 00A198; suivant : 00A200

Two decades of terrestrial carbon fluxes from a carbon cycle data assimilation system (CCDAS)

Auteurs : P. J. Rayner [France, Australie] ; M. Scholze [Allemagne, Royaume-Uni] ; W. Knorr [Allemagne, Royaume-Uni] ; T. Kaminski [Allemagne] ; R. Giering [Allemagne] ; H. Widmann [Allemagne]

Source :

RBID : ISTEX:413296C7DA28931C48543C572645E46A8593516A

Descripteurs français

English descriptors

Abstract

This paper presents the space‐time distribution of terrestrial carbon fluxes for the period 1979–1999 generated by a terrestrial carbon cycle data assimilation system (CCDAS). CCDAS is based around the Biosphere Energy Transfer Hydrology model. We assimilate satellite observations of photosynthetically active radiation and atmospheric CO2 concentration observations in a two‐step process. The control variables for the assimilation are the parameters of the carbon cycle model. The optimized model produces a moderate fit to the seasonal cycle of atmospheric CO2 concentration and a good fit to its interannual variability. Long‐term mean fluxes show large uptakes over the northern midlatitudes and uptakes over tropical continents partly offsetting the prescribed efflux due to land use change. Interannual variability is dominated by the tropics. On interannual timescales the controlling process is net primary productivity (NPP) while for decadal changes the main driver is changes in soil respiration. An adjoint sensitivity analysis reveals that uncertainty in long‐term storage efficiency of soil carbon is the largest contributor to uncertainty in net flux.

Url:
DOI: 10.1029/2004GB002254


Affiliations:


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Le document en format XML

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<term>Initial values</term>
<term>Input parameters</term>
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<term>Large responses</term>
<term>Large shifts</term>
<term>Large values</term>
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<term>Maintenance respiration</term>
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<term>Ocean uptake</term>
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<term>Optimized</term>
<term>Optimized fluxes</term>
<term>Optimized model</term>
<term>Optimized parameter vector</term>
<term>Optimized parameters</term>
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<term>Parameter covariance</term>
<term>Parameter description</term>
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<term>Parameter vector</term>
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<term>Quantum efficiency</term>
<term>Quere</term>
<term>Quest department</term>
<term>Rayner</term>
<term>Respiration</term>
<term>Rich suite</term>
<term>Rodenbeck</term>
<term>Saclay orme</term>
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<term>Seasonal cycles</term>
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<term>Simulation</term>
<term>Slow carbon pool</term>
<term>Slow pool</term>
<term>Slow respiration</term>
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<term>Small shifts</term>
<term>Smaller increase</term>
<term>Soil carbon</term>
<term>Soil carbon pool</term>
<term>Soil moisture</term>
<term>Soil pool</term>
<term>Soil respiration</term>
<term>Soil temperature</term>
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<term>Spatial gradients</term>
<term>Standard deviation</term>
<term>Station density</term>
<term>Storage efficiency</term>
<term>Study period</term>
<term>Substantial uptake</term>
<term>Summer drawdown</term>
<term>Tellus</term>
<term>Temperature dependency</term>
<term>Temperature sensitivity</term>
<term>Temporal coverage</term>
<term>Terrestrial</term>
<term>Terrestrial biosphere</term>
<term>Terrestrial biosphere model</term>
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</keywords>
<keywords scheme="Wicri" type="topic" xml:lang="fr">
<term>Biomasse</term>
<term>Biosphère</term>
<term>Changement climatique</term>
<term>Sciences de la terre</term>
<term>écosystème</term>
<term>Simulation</term>
<term>Chiffre d'affaires</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract">This paper presents the space‐time distribution of terrestrial carbon fluxes for the period 1979–1999 generated by a terrestrial carbon cycle data assimilation system (CCDAS). CCDAS is based around the Biosphere Energy Transfer Hydrology model. We assimilate satellite observations of photosynthetically active radiation and atmospheric CO2 concentration observations in a two‐step process. The control variables for the assimilation are the parameters of the carbon cycle model. The optimized model produces a moderate fit to the seasonal cycle of atmospheric CO2 concentration and a good fit to its interannual variability. Long‐term mean fluxes show large uptakes over the northern midlatitudes and uptakes over tropical continents partly offsetting the prescribed efflux due to land use change. Interannual variability is dominated by the tropics. On interannual timescales the controlling process is net primary productivity (NPP) while for decadal changes the main driver is changes in soil respiration. An adjoint sensitivity analysis reveals that uncertainty in long‐term storage efficiency of soil carbon is the largest contributor to uncertainty in net flux.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Allemagne</li>
<li>Australie</li>
<li>France</li>
<li>Royaume-Uni</li>
</country>
<region>
<li>Hambourg</li>
<li>Île-de-France</li>
</region>
<settlement>
<li>Gif-sur-Yvette</li>
<li>Hambourg</li>
</settlement>
</list>
<tree>
<country name="France">
<noRegion>
<name sortKey="Rayner, P J" sort="Rayner, P J" uniqKey="Rayner P" first="P. J." last="Rayner">P. J. Rayner</name>
</noRegion>
<name sortKey="Rayner, P J" sort="Rayner, P J" uniqKey="Rayner P" first="P. J." last="Rayner">P. J. Rayner</name>
<name sortKey="Rayner, P J" sort="Rayner, P J" uniqKey="Rayner P" first="P. J." last="Rayner">P. J. Rayner</name>
</country>
<country name="Australie">
<noRegion>
<name sortKey="Rayner, P J" sort="Rayner, P J" uniqKey="Rayner P" first="P. J." last="Rayner">P. J. Rayner</name>
</noRegion>
</country>
<country name="Allemagne">
<region name="Hambourg">
<name sortKey="Scholze, M" sort="Scholze, M" uniqKey="Scholze M" first="M." last="Scholze">M. Scholze</name>
</region>
<name sortKey="Giering, R" sort="Giering, R" uniqKey="Giering R" first="R." last="Giering">R. Giering</name>
<name sortKey="Kaminski, T" sort="Kaminski, T" uniqKey="Kaminski T" first="T." last="Kaminski">T. Kaminski</name>
<name sortKey="Knorr, W" sort="Knorr, W" uniqKey="Knorr W" first="W." last="Knorr">W. Knorr</name>
<name sortKey="Widmann, H" sort="Widmann, H" uniqKey="Widmann H" first="H." last="Widmann">H. Widmann</name>
</country>
<country name="Royaume-Uni">
<noRegion>
<name sortKey="Scholze, M" sort="Scholze, M" uniqKey="Scholze M" first="M." last="Scholze">M. Scholze</name>
</noRegion>
<name sortKey="Knorr, W" sort="Knorr, W" uniqKey="Knorr W" first="W." last="Knorr">W. Knorr</name>
</country>
</tree>
</affiliations>
</record>

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