1. Edinburgh, June 2008Markus Reichstein Critical issues when using flux data for reducing Land Surfcace Model uncertainties – towards full uncertainty accounting? Markus Reichstein Biogeochemical Model-Data Integration Group Max-Planck Institute for Biogeochemistry CARBONFUSION workshop, University of Edinburgh, June 2008

2. Edinburgh, June 2008Markus Reichstein Nominal uncertainties from CCDAS Rayner et al. (2005)

3. Edinburgh, June 2008Markus Reichstein Real uncertainties? Rayner et al. (2005)

4. Edinburgh, June 2008Markus Reichstein Nominal uncertainties by flux tower inversion Knorr and Kattge (2005) Parameter-based

5. Edinburgh, June 2008Markus Reichstein Types of uncertainty in model-data fusion Model –Parameters –Structure –Model set-up Calibration data and drivers –Statistical error –(Selective) bias –Representation error

6. Edinburgh, June 2008Markus Reichstein A toy experiment with artificial data…

7. Edinburgh, June 2008Markus Reichstein Simple temperature response function with noise Temperature [°C] Respiration

8. Edinburgh, June 2008Markus Reichstein Estimating uncertainties via bootstrapping assuming a linear model Temperature [°C] Predicted respiration Distribution of prediction at 18°C

9. Edinburgh, June 2008Markus Reichstein Introducing ‘model uncertainty’: use polynomials of higher order Prediction uncertainty at 18°C Respiration at 18°C Linear model ‘correct’ Linear or quadratic Linear, quadratic, or cubic

10. Edinburgh, June 2008Markus Reichstein Prediction uncertainty: confidence intervals Temperature [°C] Respiration Linear Linear or quadr. Linear to cubic

11. Edinburgh, June 2008Markus Reichstein Simulating systematic selective error Temperature [°C] Respiration Probability of 30% bias increasing from 10 to 5°C

12. Edinburgh, June 2008Markus Reichstein Effect of this error depends on ‘model’ Linear Linear or quadr. Linear to cubic Temperature [°C] Respiration

13. Edinburgh, June 2008Markus Reichstein What does that mean in our context (constraining LSMs with eddy-flux data)? Random error rel. well characterized (Richardson et al. 2006, Lasslop et al. 2007) More important and less well understood: systematic errors (e.g. night-time fluxes, energy balance closure…) LSMs far from perfect or unique…..

14. Edinburgh, June 2008Markus Reichstein Random error well characterized and ‘relatively’ unproblematic Almost normal distribution in most cases Fast decay of autocorrelation, almost no cross-correl Lasslop et al. (2008)

15. Edinburgh, June 2008Markus Reichstein Random errors: annual NEE Based solely on random error statistics Histogramm of confidence interval range for annual NEE

16. Edinburgh, June 2008Markus Reichstein Assessing the syst. error: Uncertain u* - threshold Bootstrapping technique is used to assess the uncertainty in the ustar threshold selection BE-Vie 2001 cf. Reichstein et al. 2005, Papale et al. 2006

17. ‘Barford’ plot, as sent before, blue triangle now show 95% confidence intervalls in u*threshold and NEE estimate, based on our bootstrapping Box plots for NEE estimate and u*thresholds based on bootstrapping u* threshold, x-axis labels are years and annual NEE_fqcok. Boxes are 25-75 percentile, whiskers 5-95 perc. ~90% conf. intervall

18. Edinburgh, June 2008Markus Reichstein

19. Edinburgh, June 2008Markus Reichstein NEE

20. Edinburgh, June 2008Markus Reichstein Random versus systematic errors: annual NEE Based solely on random error statistics Based on bootstrapped ustar uncertainty Histogramm of confidence interval range for annual NEE

21. Edinburgh, June 2008Markus Reichstein Selective systematic error leads to selective parameter errors… … but can be attenuated by multiple constraints… Lasslop et al. (2008) CO 2 flux constraint onlyCO 2 and H 2 O constraint

22. Edinburgh, June 2008Markus Reichstein Ideal model-data integration cycle (bottom-up) Model (re)formulation (Definition of model structure) Model characterization (Forward runs, consistency check, sensitivity, uncert. analysis) Model parameter estimation (Multiple constraint) Parameter interpretation (Thinking) Generalization (‘up-scaling’) Model validation (against indep. data, by scale or quantity) Model application DATA

23. Edinburgh, June 2008Markus Reichstein Addressing and reducing these uncertainties: ideas and questions Not only ‘formal uncertainties’; explore full range of uncertainty by data and model resampling strategies (‘data ensembles’) Disentangle parts of the system, i.e. look at sub-processes –Physiology, phenology, long-term dynamics (  different time scales) –e.g. first constrain and evaluate GPP, preferably while knowing APAR, then constrain phenology parameters Combine process-oriented and data-mining approaches (e.g. finding patterns in residuals) Pattern-oriented modelling (only compare against ‘robust unbiased data patterns’, not the noise) Multiple-constraint approaches (but what if constraints contradict each other…?) Can Bayesian approaches help or does does it just ‘hide’ uncertainties?

24. Edinburgh, June 2008Markus Reichstein Finally the 11 commandments… 1.Don’t kill your neighbor 2.… 3.… 4.… 5.… 6.… 7.… 8.… 9.… 10.… 11.Don’t understate uncertainties …