1. CAMELS- uncertainties in data Bart Kruijt, Isabel van den Wyngaert, Ronald Hutjes, Celso von Randow, Jan Elbers, Eddy Moors...

2. Types of data Land use, Site parameters accuracy representativity driving variables (weather) instrument error/precision technical/ operational error siting error validation/optimisation data (fluxes) stochastic error technical/ operational error calculation/conceptual uncertainty representation of surface day night vegetation height, LAI, d, z 0, rooting … heterogeneity, sampling cup anemometer stalling, hygrometers.. calibration, dew on radiation sensor,.. Sheltering, shading, …  =(w 2 c 2 ) *T/ Tscale --> fourth moments calibration, pump maintenance, window cleaning averaging time, coordinate rotation, freq. corr footprint models, heterogeneity, win direction calm nights drainage, return fluxes

3. CO 2 ? F c = .w.c NEE = F c +  z (  c/  t) Eddy correlation

4. hopeless?

5. Each processing step carries uncertainty

6. Time Sensitivity to flux calculation methods Rotation: correction for tilt of mean streamlines Detrending and averaging: removing non-stationarity

7. CO 2 Fluxes (SW Amazon) - Scale contributions ‘Turbulent’‘Meso-scale’

8. Summary effects of rotation and averaging Relative effects of averaging time and rotation on daily total fluxes, Amazon

9. Finnigan, Malhi, 2002 Longer averaging times --> better energy closure?

10. Total uncertainty from rotation and averaging over the day

12. Uncertainty in calibration Calibration a posteriori causes problems and uncertainty

13. Eddy flux, storage flux and Ecosystem (‘biotic’) flux Windy nights Calm nights

14. Eddy correlation integrates everything but misses advection Morning CO 2 stored in valleys CO 2 return ? Night CO 2 drainage ? Rs Manaus, Amazon

15. Total one-sided error for AMAZON on annual totals is, apart from night-time error, between 12.5% and 32%, or 1-2 t ha -1.

16. Systematic or random error? Error depends on measuerement height, surface type, time of day, weather Random error vanishes when the number of independent samples increases. BUT: when are atmospheric samples independent? Systematic error is persistent. What if maintenance varies or calibration drifts? What if low frequencies vary with weather or season? ---> when do systematic errors become random?

17. Bias. Example from the SW Amazon, with cold periods

18. Other bias : transient periods (morning, early evening) are non- stationary and carry high uncertainty rainy periods carry high uncertainty ideal weather associated with specific wind directions

19. Estimates for CAMELS

20. Rebmann et al - CARBOEUROFLUX footprint-quality analysis

21. Discussion: How to avoid bias when applying uncertainties to model fitting? Include more processes? Look at daily totals where day-night cross contamination occurs? Can we eliminate bias by better matching models and measurements? How to fine-tune uncertainties for specific sites or conditions?

22. U* lm Fc=f(C,u*,lm,R,Ps) Advection=f(C) Advection Consider the area beneath the sensor a leaky, sloshing vessel and fit both physiological and micrometeorological parameters R, Ps=alpha.PAR To be tested …. C=sum(R-Ps-Fc-advection)

23. Some early results look good

25. Effect of spikes in one channel only 5 ppm and 50 ppm spike on CO2. Effect is random relative uncertainty, increasing with spike/signal ratio

27. Uncertainty in tube delay calculations

29. Summary effects of rotation and averaging Variation in sensitivities to treatments Relative effects of averaging time and rotation

32. Frequency corrections Zero-plane, tube NOT important. Low frequencies ARE important.

34. Conversion ppm m s -1 to area based fluxes Small potential errors average out over days

36. Similarity relations - representativity for surface Filtering for poor similarity will discard important periods such as early morning

37. Uncertainty as a function of the percentage good data - Rebio Jaru

38. Uncertainty on annual totals from (well distributed) data gaps

39. And finally….