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CAMELS- uncertainties in data. Bart Kruijt, Isabel van den Wyngaert, Ronald Hutjes, Celso von Randow, Jan Elbers, Eddy Moors. Types of data. vegetation height, LAI, d, z 0 , rooting … heterogeneity, sampling cup anemometer stalling, hygrometers.. calibration, dew on radiation sensor,..
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CAMELS- uncertainties in data Bart Kruijt, Isabel van den Wyngaert, Ronald Hutjes, Celso von Randow, Jan Elbers, Eddy Moors...
Types of data vegetation height, LAI, d, z0, rooting … heterogeneity, sampling cup anemometer stalling, hygrometers.. calibration, dew on radiation sensor,.. Sheltering, shading, … =(w2c2) *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 • 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
Fc =.w.c NEE = Fc + z(c/ t) Eddy correlation ? CO2
Eddy correlation hopeless?
Time Sensitivity to flux calculation methods Rotation: correction for tilt of mean streamlines Detrending and averaging: removing non-stationarity
CO2 Fluxes (SW Amazon) - Scale contributions ‘Turbulent’ ‘Meso-scale’
Summary effects of rotation and averaging Relative effects of averaging time and rotation on daily total fluxes, Amazon
Longer averaging times --> better energy closure? Finnigan, Malhi, 2002
Uncertainty in calibration Calibration a posteriori causes problems and uncertainty
Eddy flux, storage flux and Ecosystem (‘biotic’) flux Windy nights Calm nights
Eddy correlation integrates everything but misses advection Morning Rs CO2 return ? Rs Rs Night CO2 drainage ? Rs Manaus, Amazon CO2 stored in valleys
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.
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?
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
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?
Consider the area beneath the sensor a leaky, sloshing vessel and fit both physiological and micrometeorological parameters Fc=f(C,u*,lm,R,Ps) U* • lm C=sum(R-Ps-Fc-advection) Advection=f(C) Advection R, Ps=alpha.PAR To be tested ….
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
Summary effects of rotation and averaging Variation in sensitivities to treatments Relative effects of averaging time and rotation
Frequency corrections Zero-plane, tube NOT important. Low frequencies ARE important.
Conversion ppm m s-1 to area based fluxes Small potential errors average out over days
Similarity relations - representativity for surface Filtering for poor similarity will discard important periods such as early morning
Uncertainty as a function of the percentage good data - Rebio Jaru
Uncertainty on annual totals from (well distributed) data gaps