Meta-analysis of eddy covariance carbon fluxes data
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Meta-analysis of eddy covariance carbon fluxes data. Dario Papale, Markus Reichstein, Riccardo Valentini Marc Aubinet, Christian Bernhofer, Alessandro Cescatti, Alexander Knohl, Tuomas Laurila, Anders Lindroth, Eddy Moors, Kim Pilegaard, Günther Seufert. ?. + Disturbances. Climate, soil.

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Meta-analysis of eddy covariance carbon fluxes data

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Meta analysis of eddy covariance carbon fluxes data

Meta-analysis of eddy covariance carbon fluxes data

Dario Papale, Markus Reichstein, Riccardo Valentini

Marc Aubinet, Christian Bernhofer, Alessandro Cescatti, Alexander Knohl, Tuomas Laurila, Anders Lindroth, Eddy Moors, Kim Pilegaard, Günther Seufert


Meta analysis of eddy covariance carbon fluxes data

?

+ Disturbances

Climate, soil

Schulze et al. 2000


Meta analysis of eddy covariance carbon fluxes data

Euroflux and Carboeuroflux datasets used


Meta analysis of eddy covariance carbon fluxes data

Pot. Rad. > 8.8 TJ m-2 y-1

Pot. Rad. < 8.8 TJ m-2 y-1

IWA (Index Water Availability): ratio of actual and potential evapotranspiration

MAT: Mean annual temperature

The data set was split into two populations by a threshold of potentially available radiation energy of 8.8 TJ m-2 yr-1 (52°N)


Meta analysis of eddy covariance carbon fluxes data

Pot. Rad. > 8.8 TJ m-2 y-1

Pot. Rad. < 8.8 TJ m-2 y-1


Meta analysis of eddy covariance carbon fluxes data

Best-fit modeled versus observed annual (a) GPP, (b) TER, (c) NEP


Meta analysis of eddy covariance carbon fluxes data

How do we chose the radiation threshold?

Performance of the simple regression model


Meta analysis of eddy covariance carbon fluxes data

Are the results robust against errors?

(advection, footprint, quality, gapfilling, partitioning…)

Results from 500 Monte-Carlo simulations where randomly plus or minus 200 gC m-2 were added to GPP, TER and NEP


Meta analysis of eddy covariance carbon fluxes data

Decomposition = enzymatic process that follows Michaelis-Menten kinetics (1913)

At high [S], Km = insignificant

and Q10 of R = Q10 of Vmax (Arrhenius kinetics)

At low [S] : Km becomes important

Also Km increases with Temp

At low [S]: Q10 of R << Q10 of Vmax

Davidson et al., Global Change Biology, in pressThanks Ivan for the slide!


Meta analysis of eddy covariance carbon fluxes data

Apparent Q10 depends on soil moisture

In opposite direction of what models predict

Under ‘standard’ conditions (15°C; RSWC=0.6) the emergent Q10 of model and data are similar

Along decreasing/increasing water availability data and model behave completely differently with respect to how Q10 changes.


Meta analysis of eddy covariance carbon fluxes data

Latuitude - NEE relation

Valentini et al. 2000


Conclusions

Conclusions

  • GPP and TER compensate each other canceling out single climate factor effects

  • Water availability plays an important role in GPP and TER not only in the Mediterranean region but also in central Europe

  • Ecosystem carbon balance modeling approaches should abandon the convenient climate-NPP analogy and better account for carbon-water cycle interactions and non-climatic factors affecting respiration

  • Flux tower data are a unique source of information that play an important role in process understanding and model development


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