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Markus Reichstein

CARBONFUSION workshop, University of Edinburgh, June 2008. 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.

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Markus Reichstein

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  1. CARBONFUSION workshop, University of Edinburgh, June 2008 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

  2. Nominal uncertainties from CCDAS Rayner et al. (2005)

  3. Real uncertainties? Rayner et al. (2005)

  4. Nominal uncertainties by flux tower inversion Parameter-based Knorr and Kattge (2005)

  5. Types of uncertainty in model-data fusion • Model • Parameters • Structure • Model set-up • Calibration data and drivers • Statistical error • (Selective) bias • Representation error

  6. A toy experiment with artificial data…

  7. Simple temperature response function with noise Respiration Temperature [°C]

  8. Distribution of prediction at 18°C Predicted respiration Temperature [°C] Estimating uncertainties via bootstrapping assuming a linear model

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

  10. Prediction uncertainty: confidence intervals Linear Linear or quadr.Linear to cubic Respiration Temperature [°C]

  11. Respiration Temperature [°C] Simulating systematic selective error Probability of 30% bias increasing from 10 to 5°C

  12. Density 9.27 5.02 8 6 3.15 4 Y 2 1.28 0.00 -0.58 5 10 15 20 X Effect of this error depends on ‘model’ Linear Linear or quadr.Linear to cubic Respiration Temperature [°C]

  13. 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. 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. Random errors: annual NEE Histogramm of confidence interval range for annual NEE Based solely on random error statistics

  16. 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. Random versus systematic errors: annual NEE Histogramm of confidence interval range for annual NEE Based on bootstrappedustar uncertainty Based solely on random error statistics

  19. Selective systematic error leads to selective parameter errors… CO2 flux constraint only CO2 and H2O constraint … but can be attenuated by multiple constraints… Lasslop et al. (2008)

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

  21. 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?

  22. Finally the 11 commandments… • Don’t kill your neighbor • … • … • … • … • … • … • … • … • … • Don’t understate uncertainties …

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