Raw data (20 Hz)

1. Uncertainty in eddy covariance datasetsDario Papale, Markus Reichstein, Antje Moffat, Ankur Desai, many others..

2. Tower setup IMECC Footprint problems Goeckede et al. in prep Advection ADVEX Random errors Richardson et al. Corrections, filtering, etc. IMECC Filtering (u*, spike, qc) and corrections (storage) A Gap filling B Partitioning C Raw data (20 Hz) Errors or uncertainties?? Half hourly data Half hourly, daily, monthly, annual data

3. This is not uncertainty, this is an error This is uncertainty

4. Sites and years used

5. A – Uncertainty due to quality check and filtering Difference between minimum and maximum value obtained at different time resolution using different correction setting (u* thresholds, spike thresholds, storage measurement) Papale et al. 2006

6. A – Uncertainty due to quality check and filtering NEE Uncertainty due to different corrections settings (u* thresholds, spike thresholds, storage measurement) at annual scale. < 100 gC m-2 (50 gC m-2) Papale et al. 2006

7. A – Uncertainty due to quality check and filtering GPP Uncertainty due to different corrections settings (u* thresholds, spike thresholds, storage measurement) at annual scale. ~/< 100 gC m-2 (5-10%) Papale et al. 2006

8. A – Uncertainty due to quality check and filtering TER Uncertainty due to different corrections settings (u* thresholds, spike thresholds, storage measurement) at annual scale. ~/< 100 gC m-2 (5-10%) Papale et al. 2006

9. B – Uncertainty due to gapfilling (15 methods, 50 artificial gaps scenarios) RMSE for different sites and different methods (50 scenarios) Moffat et al. 2007

10. B – Uncertainty due to gapfilling & random errors in the half hourly data Random error frequency distribution for three different US sites (double-exponential distribution) Richardson et al. estimated the random errors in eddy covariance measurements comparing data acquired by two systems in the same footprint and also comparing half hourly data acquired at the same site, under the same meteorological conditions but at different time. Richardson et al. 2006

11. B – Uncertainty due to gapfilling MAE boxplot of the different techniques and random uncertainty estimation using Richardson et al. method Moffat et al. 2007

12. B – Uncertainty due to gapfilling RMSE in function of different methods and gaps length At annual bases the average uncertainty introduced by the “good” methods has been estimated to be +/- 25 gC m-2 year-1 Moffat et al. 2007

13. C – Uncertainty due to partitioning (23 methods, 10 artificial gaps scenario) GPP and RE boxplot for each dataset using all the methods. Large part of the methods are in about 100 gC m.2 yr-1 Desai et al. 2007, in press

14. C – Uncertainty due to partitioning Annual sun bias due to artificial gaps. Each boxplot is based on 10 gaps scenarios Desai et al. 2007, in press

15. C – Uncertainty due to partitioning GPP and RE monthly boxplot RE GPP Desai et al. 2007, in press

16. Conclusions Big effort is ongoing to assess uncertainty in the eddy covariance measurements in CarboeuropeIP and other projects. There are uncertain assumptions in all steps of data acquisition and processing Standardization of data processing helps to reduce uncertainty particularly in multi sites synthesis analysis There is still a lot to do in the uncertainty definition due for example to advection and footprint We need to discuss with the modeling community about how to incorporate the uncertainty in the measurement in the model parameterization