AustralieFrV1, PascalFrancis, Checkpoint, bibRecord, 001335

Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon-cycle data assimilation system

Identifieur interne : 001335 ( PascalFrancis/Checkpoint ); précédent : 001334; suivant : 001336

Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon-cycle data assimilation system

Auteurs : E. N. Koffi [France] ; P. J. Rayner [Australie] ; M. Scholze [Royaume-Uni] ; C. Beer [Allemagne]

Source :

Global biogeochemical cycles [ 0886-6236 ] ; 2012.

RBID : Pascal:12-0250236

Descripteurs français

Pascal (Inist)
- Productivité primaire, Ecosystème, Cycle carbone, Assimilation, Transport, Modèle, Biosphère, Atmosphère, Monde, Courant, Réseau, Etude expérimentale, Incertitude, Dioxyde de carbone, Probabilité, Problème inverse.
Wicri :
- topic : Biosphère, Atmosphère.

English descriptors

KwdEn :
- Carbon dioxide, assimilation, atmosphere, biosphere, carbon cycle, currents, ecosystems, experimental studies, global, inverse problem, models, networks, primary productivity, probability, transport, uncertainties.

Abstract

[1] This paper combines an atmospheric transport model and a terrestrial ecosystem model to estimate gross primary productivity (GPP) and net ecosystem productivity (NEP) of the land biosphere. Using atmospheric CO₂ observations in a Carbon Cycle Data Assimilation System (CCDAS) we estimate a terrestrial global GPP of 146 ± 19 GtC/yr. However, the current observing network cannot distinguish this best estimate from a different assimilation experiment yielding a terrestrial global GPP of 117 GtC/yr. Spatial estimates of GPP agree with data-driven estimates in the extratropics but are overestimated in the poorly observed tropics. The uncertainty analysis of previous studies was extended by using two atmospheric transport models and different CO₂ observing networks. We find that estimates of GPP and NEP are less sensitive to these choices than the form of the prior probability for model parameters. NEP is also found to be significantly sensitive to the transport model and this sensitivity is not greatly reduced compared to direct atmospheric transport inversions, which optimize NEP directly.

Affiliations:

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Le document en format XML

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<front><div type="abstract" xml:lang="en">[1] This paper combines an atmospheric transport model and a terrestrial ecosystem model to estimate gross primary productivity (GPP) and net ecosystem productivity (NEP) of the land biosphere. Using atmospheric CO<sub>2</sub>
 observations in a Carbon Cycle Data Assimilation System (CCDAS) we estimate a terrestrial global GPP of 146 ± 19 GtC/yr. However, the current observing network cannot distinguish this best estimate from a different assimilation experiment yielding a terrestrial global GPP of 117 GtC/yr. Spatial estimates of GPP agree with data-driven estimates in the extratropics but are overestimated in the poorly observed tropics. The uncertainty analysis of previous studies was extended by using two atmospheric transport models and different CO<sub>2</sub>
 observing networks. We find that estimates of GPP and NEP are less sensitive to these choices than the form of the prior probability for model parameters. NEP is also found to be significantly sensitive to the transport model and this sensitivity is not greatly reduced compared to direct atmospheric transport inversions, which optimize NEP directly.</div>
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Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon-cycle data assimilation system

Atmospheric constraints on gross primary productivity and net ecosystem productivity: Results from a carbon-cycle data assimilation system

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