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

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

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

4. Sites and years used

5. Papale et al. 2006 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)

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

7. Papale et al. 2006 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%)

8. Papale et al. 2006 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%)

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

10. Richardson et al. 2006 B – Uncertainty due to gapfilling & random errors in the half hourly data 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. Random error frequency distribution for three different US sites (double-exponential distribution)

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

12. B – Uncertainty due to gapfilling Moffat et al. 2007 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

13. Desai et al. 2007, in press 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

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

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

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