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How N changes the C-cycle

How N changes the C-cycle. Unravelling nitrogen deposition effects on carbon cycling in forest. N deposition and NEP: Magnani et al. 2007 (Nature). Carbo-Age and literature chronosequences (n = 20), account for age effects and climate

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How N changes the C-cycle

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  1. How N changes the C-cycle

  2. Unravelling nitrogen deposition effects on carbon cycling in forest N deposition and NEP: Magnani et al. 2007 (Nature) • Carbo-Age and literature chronosequences (n = 20), account for age effects and climate • Maps of N deposition from measurements (Europe, Namerica) or models • 1 g total N deposition added ~ 210 g C sequestration Magnani et al. 2007, Nature Federico Magnani and Sebastiaan Luyssaert and contributions of 50+ research groups

  3. Unravelling nitrogen deposition effects on carbon cycling in forest Conclusion • N-deposition : • Strengthened the CO2 sink capacity of N-limited forests • Stimulated GPP with a proportional increase in woody biomass production, but a disproportionately high production of short-lived tissues • Strongly retarded heterotrophic respiration and associated carbon losses from the soil • Mankind ultimately controls the carbon balance of temperate and boreal forests • Environmental policy implications Federico Magnani and Sebastiaan Luyssaert and contributions of 50+ research groups

  4. Way forward • Reconciling the differences between our result and the Nitroeurope view & Swedish experiment • Understanding the mechanism ! • Putting this process into the bottom-up models- at present they don’t capture this

  5. Researchers will find new things

  6. Resumé of Forest Activity

  7. Luyssaert et al (2007) Global Change Biology Key to flux diagrams Fluxes are g m-2 annum-1

  8. Models should be able to: • Match the Luyssaert et al data set • Show the N-response, match observational data • Simulate the management process. How to make this happen- are we on track- do we need workshops- three synthesis papers are possible

  9. G. LeMaire S. Zaehle N. Viovy Next model generation Go from a representation of a mean tree to a statistical representation of the forest: • Age classes (LPJ,ORCHIDEE) • Diameter classes by age (ORCHIDEE)

  10. Second step: Definition of thinning and felling rules • Estimation of self-thinning • Thinning evaluated relative to self-thinning To prevent it • Estimation of final felling Defined from different possible indices on productivity and demand

  11. General scheme in ORCHIDEE ∆B ∆VOL δheighti 7 1 2 circi circ class δvoli (tree) Stand-level averages: H, D, Ind, BA + LAI max δcirci Tree cut rules  circi =0 Trees die  circi =0 6 4 Biomass dead goes to soil or export Dg=f(circi + δcirci ) rdi calculation Self-thinning Ind=f(Dg) ∆Ind (n. dead tr.) 3 no yes Management ? Test on rdi 5

  12. Some tasks • Disturbance data, bring it together • Canopy matters- getting LAI right, harmonisation of methods- needs workshop and a bit more effort • Identify sites as good targets for forthcoming satellite missions, do some preliminary work to link this community to biomass sensing and stress-sensing

  13. Loose ends • Phenology (canopy movies- synthesis of phenological data, autumn and spring) • Growth rings (useful archive) • Advertise some of our unique data sets

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