1 / 13

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. ?. + Disturbances. Climate, soil.

lewis-chase
Download Presentation

Meta-analysis of eddy covariance carbon fluxes data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. ? + Disturbances Climate, soil Schulze et al. 2000

  3. Euroflux and Carboeuroflux datasets used

  4. 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)

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

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

  7. How do we chose the radiation threshold? Performance of the simple regression model

  8. 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

  9. 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 press Thanks Ivan for the slide!

  10. 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.

  11. Latuitude - NEE relation Valentini et al. 2000

  12. 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

More Related