1. Laurent Misson*; Baldocchi DD; Black TA; Blanken PD; Brunet Y; Curiel Yuste J; Dorsey JR; Falk M; Granier A; Irvine MR; Jarosz N; Lamaud E; Launiainen S; Law BE; Longdoz B; Loustau D; McKay M; Paw U KT; Vesala T; Vickers D; Wilson KB; Goldstein AH * University of California, Berkeley and soon at CNRS, Montpellier Funded by: Kearney Foundation of Soil Science, UC Agricultural Experiment Station, US Department of Energy (NIGEC) Partitioning Forest Carbon Fluxes with Over- and Understory Eddy-Covariance

2. Most forests are vertically complex Pinus ponderosa Ceanothus cordulatus Soil Overstory Understory

3. Most forests are vertically complex Pinus ponderosa Ceanothus cordulatus Soil Overstory Understory CO 2 Photosynthesis Respiration

4. CO 2 Photosynthesis Questions 1/ how canopy density influences the coupling between overstory and understory meteo? Respiration

5. CO 2 Photosynthesis Questions 1/ how canopy density influences the coupling between overstory and understory meteo? Respiration 2/ how different forest types, structures, and climates influence CO 2 flux partitioning?

6. CO 2 Photosynthesis Questions 1/ how canopy density influences the coupling between overstory and understory meteo? 3/ what factors control understory CO 2 fluxes for these different forests? Respiration 2/ how different forest types, structures, and climates influence CO 2 flux partitioning?

7. Blodgett Hesse Le Bray Tonzi Hyytiala Wind River Metolius Aspen Jackpine Walker Branch Synthesis Based on FLUXNET Data

8. 6 evergreen / 4 deciduous 3 boreal, 4 temperate, 3 (semi)-arid LAI overstory [ 1 - 9.0 ] m 2 m -2 LAI understory [ 0 - 3.2 ] m 2 m -2 10 Sites

9. CO 2 Aubinet et al. (2000) and Baldocchi et al. (2001) 1 year of summertime data at each site NEE above includes storage term (not below) GPP and respiration were separated using Q 10 10 Sites Methodology 6 evergreen / 4 deciduous 3 boreal, 4 temperate, 3 (semi)-arid LAI overstory [ 1 - 9.0 ] m 2 m -2 LAI understory [ 0 - 3.2 ] m 2 m -2

10. Results 1/ Micrometeorology 2/ Flux partitionning 3/ Controlling factors

11. How canopy density influences temperature stratification ?

12. T under > T over for low LAI DAY LAI T over – T under

13. How canopy density influences temperature stratification ? T under > T over for low LAI Open forest: good mixing Closed forest: weaker mixing DAY LAI T over – T under

14. How canopy density influences temperature stratification ? T under > T over for low LAI Open forest: good mixing Closed forest: weaker mixing T under < T over for low LAI DAY LAI T over – T under NIGHT

15. How canopy density influences temperature stratification ? T under > T over for low LAI Open forest: good mixing Closed forest: weaker mixing T under < T over for low LAI Open forest: strong inversion Closed forest: good mixing DAY LAI T over – T under NIGHT

16. How canopy density influences wind deflection ? LAI Wind Dir over – Wind Dir under (º)

17. How canopy density influences wind deflection ? LAI Wind Dir over – Wind Dir under (º) Wind is strongly defleted in dense forests probably because of stronger drag force

18. How canopy density influences wind deflection ? LAI Wind Dir over – Wind Dir under (º) Wind is strongly defleted in dense forests probably because of stronger drag force Overstory and understory flux footpint may be different

19. How much is the understory contribution to whole ecosystem fluxes ? in % Understory Contribution

20. GPP R (%) How much is the understory contribution to whole ecosystem fluxes ?

21. Understory Contribution GPP R Evergreen = Deciduous (14%) Semi-Arid > Temperate > Boreal 20%13%6% (%) How much is the understory contribution to whole ecosystem fluxes ?

22. Understory Contribution GPP R Evergreen = Deciduous (14%) Semi-Arid > Temperate > Boreal 20%13%6% Deciduous (62%) > Evergreen (49%) Soil C:N = 16Soil C:N = 31 (%) How much is the understory contribution to whole ecosystem fluxes ?

23. Understory Contribution GPP R Evergreen = Deciduous (14%) Semi-Arid > Temperate > Boreal 20%13%6% Deciduous (62%) > Evergreen (49%) Semi-Arid < Temperate = Boreal 44%60% Soil C:N = 16Soil C:N = 31 (%) How much is the understory contribution to whole ecosystem fluxes ?

24. What controls understory respiration fluxes across different forests ?

25. Soil temperature (ºC) Mean summertime respiration flux (µmol m -2 s -1 ) NS

26. Normalized flux for soil temperature and soil moisture

27. Normalized flux for soil temperature and soil moisture Soil temperature (ºC) R 2 = 0.64 Flux T,SM

28. Soil temperature (ºC) R 2 = 0.64 Flux T,SM Soil C (g C m -2 ) R 2 = 0.82 Flux T,SM Normalized flux for soil temperature and soil moisture

29. Soil temperature (ºC) R 2 = 0.64 Flux T,SM Soil C (g C m -2 ) R 2 = 0.82 Flux T,SM Uncorrelated Normalized flux for soil temperature and soil moisture

30. Soil temperature (ºC) R 2 = 0.64 Flux T,SM Soil C (g C m -2 ) R 2 = 0.82 Flux T,SM Uncorrelated Partial evidence that respiration acclimates to temperature Zogg et al. 1997, Zhang et al. 2005, Atkin et al. 2005 Normalized flux for soil temperature and soil moisture

31. Relative soil moisture R 2 = 0.67 Flux T,C Normalized flux for soil temperature and soil carbon

32. Relative soil moisture R 2 = 0.67 Microbial metabolic activity limited by soil moisture Normalized flux for soil temperature and soil carbon Flux T,C

33. GPP ecosystem (µmol m -2 s -1 ) R 2 = 0.78 Mean summertime respiration flux (µmol m -2 s -1 ) Slope = 0.23

34. GPP ecosystem (µmol m -2 s -1 ) R 2 = 0.78 Understory respiration is linked to gross primary productivity Mean summertime respiration flux (µmol m -2 s -1 ) Slope = 0.23

35. Conclusion

36. Eddy-Covariance method: able to measure understory fluxes for a wide range of forest types, structures and climates

37. Conclusion Eddy-Covariance method: able to measure understory fluxes for a wide range of forest types, structures and climates Problems: open forests  night inversion dense forests  different flux footprint

38. Conclusion Eddy-Covariance method: able to measure understory fluxes for a wide range of forest types, structures and climates Problems: open forests  night inversion dense forests  different flux footprint Understory can contribute significantly to whole ecosystem CO 2 sinks and sources, but variations across sites are important

39. Conclusion Eddy-Covariance method: able to measure understory fluxes for a wide range of forest types, structures and climates Problems: open forests  night inversion dense forests  different flux footprint Understory can contribute significantly to whole ecosystem CO 2 sinks and sources, but variations across sites are important Understory LAI and light penetration are important factors influencing understory GPP

40. Conclusion Eddy-Covariance method: able to measure understory fluxes for a wide range of forest types, structures and climates Problems: open forests  night inversion dense forests  different flux footprint Understory can contribute significantly to whole ecosystem CO 2 sinks and sources, but variations across sites are important Understory LAI and light penetration are important factors influencing understory GPP Substrate availability and quality, soil temperature and soil moisture are important factors for understory respiration