1. Gap Filling Comparison Workshop, September 18-20, 2006, Jena, Germany Corinna Rebmann Olaf Kolle Max-Planck-Institute for Biogeochemistry Jena, Germany Eddy covariance measurements and their shortcomings for the determination of the net ecosystem exchange of carbon dioxide

2. Outline Introduction of measurement site and advection experiment Reasons for data gaps  Special features of open path analyser Consequences for final flux data Summary

3. Measurement Site: Wetzstein, Thuringia, Germany, flux measurements established end of 2001 main tower tower C tower B tower D tower A measuring heights: Main tower: 30.0m Tower C: 29.4m ADVEX’06 (April 11– June 19, 2006) flux measurements for , H, E, CO 2 CO 2, wind and temperature profiles

4. The ADVEX Experiment Advection experiment CarboEurope-IP: 4 towers around the main tower: A, B, C, D: profiles of [CO 2 ], T, u‘, v‘, w‘, T‘, tower B with CO 2 -fluxes below canopy, tower C and main tower with CO 2 -fluxes above canopy 60m

5. Why care about advection? Eddy covariance theory is derived from tracer conservation equation with many simplifications which are only valid under homogeneous conditions

6. Flux Calculation All fluxes are calculated in the following steps: Calculation of planar fit planes (Wilzcak et al., 2001) → comparison with 2D-rotation Determination of CO 2 - and H 2 O-lags for closed path analysers Determination of H 2 O-lag dependency on VPD → modelling Determination of spectral correction according to Eugster & Senn (1995) for closed path analysers, Webb et al. (1980) correction for open path analysers

7. Data gaps are due to Maintenance interruptions, power failures, ice coating Instrumental problems Non-turbulent conditions Unfavoured wind directions (tower effects, heterogeneous terrain) Precipitation, fog events (open path analyser) high wind speeds

8. Wetzstein, main tower data gaps (closed-path analyser) Jan 1 – Aug 24, 2006

9. Wetzstein, main tower and tower C Apr 11 – Jun 19, 2006 data gaps caused by maintenance, power failures etc.

10. stepMain tower (TM) Tower C (TC) 1 (maintenance etc) 3.6%4.8% 2 (after pre-selection) 3 (after stationarity test 1)

11. Wetzstein, main tower and tower C Apr 11 – Jun 19, 2006 time series of CO 2 -fluxes after pre-selection (eg Vickers & Mahrt 1997, JAOT14)

12. Wetzstein, main tower and tower C Apr 11 – Jun 19, 2006 data gaps after pre-selection stepMain tower (TM) Tower C (TC) 1 (maintenance etc) 3.6%4.8% 2 (after pre-selection) 4.5%30.2% 3 (after stationarity test 1) 30.2%

13. Wetzstein, main tower and tower C which data are rejected in case of open path-analyser? 24 of 624 half-hours (3.8%) rejected April 30 – May 12, 2006, dry period

14. Wetzstein, main tower and tower C which data are rejected in case of open path-analyser? 263 of 630 half-hours (41.7%) rejected!!! May 13 – 28, 2006, rainy period

15. Wetzstein, main tower and tower C Apr 11 – Jun 19, 2006 consequences for dependencies on meteorological variables Michalis-Menten-relationship: see Falge et al. 2001, AFM107 NEE: net ecosystem exchange (µmol CO 2 m −2 s −1 ) PPFD: photosynthetic photon flux density (µmol quantum m −2 s −1 ) a: ecosystem quantum yield (µmol CO 2 ) / (µmol quantum) F GPP,sat : gross primary productivity at saturating light (µmol CO 2 m −2 s −1 ) R day : ecosystem respiration during the day (µmol CO 2 m −2 s −1 ) TMTC a0.0570.061 F GPP,sat 24.627.6 R day 5.44.1 r2r2 0.660.58

16. Wetzstein, main tower and tower C Apr 11 – Jun 19, 2006 consequences for dependencies on meteorological variables Michalis-Menten-relationship: see Falge et al. 2001, AFM107 NEE: net ecosystem exchange (µmol CO 2 m −2 s −1 ) PPFD: photosynthetic photon flux density (µmol quantum m −2 s −1 ) a: ecosystem quantum yield (µmol CO 2 ) / (µmol quantum) F GPP,sat : gross primary productivity at saturating light (µmol CO 2 m −2 s −1 ) R day : ecosystem respiration during the day (µmol CO 2 m −2 s −1 ) TMTM, TC av TC a0.0570.0630.061 F GPP,sat 24.623.227.6 R day 5.45.14.1 r2r2 0.660.590.58

17. Wetzstein, main tower and tower C time series of CO 2 -fluxes with stationarity tests May 8 – 14, 2006

18. Wetzstein, main tower and tower C Apr 11 – Jun 19, 2006 When do instationaries occur? Instationarities occur mainly at low or zero radiation conditions

19. Wetzstein, main tower and tower C Apr 11 – Jun 19, 2006 data gaps summary stepMain tower (TM) Tower C (TC) 13.6%4.8% 2 (after pre-selection) 4.5%30.2% rainy, moist conditions 3 (after stationarity test 1) 9.8%33.2% Low radiation conditions

20. Do we have perfect data now? Are these data reliable as input for gap filling procedures? Still missing: advective processes night flux treatment reliability check

21. Hainich Drainage/advective fluxes Data from W. Kutsch

22. Night-flux problem Weak turbulence Instrumental problems, large footprints, gravity waves Turbulent flux is influenced by other transport/storage processes →Site dependent see eg: Lee, 1998 Aubinet et al, 2003, 2005 Staebler and Fitzjarrald, 2004 Feigenwinter et al, 2004

23. Night-flux corrections Empirical: Separate calm and turbulent periods, remove calm periods, fill the gap u*-criterion mostly used

24. Aubinet et al. AER30, 2000 NEE night versus u*

25. Wetzstein NEE 2005, unrealistic high night-time fluxes

26. Wetzstein when do high fluxes occur? u*>0.4m s-1 wind direction between 200° and 280° or 30° and 40° neutral atmospheric conditions: stability parameter: -0.0625<ζ<0.0625 (determined by M. Zeri) → turbulent upwind mixing from the valley

27. Wetzstein NEE 2005 after application of MZ criteria for 2005: 72% data available 58% data available

28. Wetzstein NEE 2005 after application of MZ criteria

29. Wetzstein night-time NEE 2005 after application of MZ criteria R 10 =3.9 R 10 =3.0

30. Wetzstein NEE comparison during advection experiment after application of MZ criteria

31. Summary Amount of data gaps strongly depending on: site type of quality check (still no common agreement in CarboEurope-IP!) type of analyser, weather pattern threshold criteria for u* (have to be objective, Gu et al. AFM128, 2005) Derived dependencies on meteorological variables vary with data left after selection → biased datasets Reliability has to be tested against chamber and biometric measurements

32. Thanks for your attention! Questions?