1. Some challenges of model-data- integration a collection of issues and ideas based on model evaluation excercises Martin Jung, Miguel Mahecha, Markus Reichstein, Model Simulations by: Guerric Le Maire, Maarten Braakhekke, Sönke Zaehle, Mona Vetter

2. Forest age Gross productivity Ecosystem respiration Net productivity Ecosystem respiration Net productivity After Odum (1969), modified by from Alex Knohl Model world Real world Carbon balance simulated by process models is most likely biased Models may be useful to study variability of the carbon balance (anomalies, processes, …) Variability of the carbon balance results from variability of big constituent fluxes (GPP, TER, …) Models need to be quite precise at the constituent fluxes to get variability of the carbon balance right Implications for data assimilation and model evaluations! Models in steady-state (see contribution by Nuno Carlvalhais)

3. Correlation of NEP residuals with GPP and TER residuals (based on site-level runs, monthly data) If NEP is wrong it can be because of: -GPP -TER If GPP is wrong it can be because of: -some parameter -LAI/fpar -soil water dynamics -temperature sensitivity function -sensitivity of gcan to VPD and soil moisture -coupling of Gcan and photosynthesis... How to handle confounding effects? Isolating model components as much as possible for evaluation/assimilation excercises?! Sensitivity experiments?!

4. Agreement among models regarding inter-annual variability of GPP Biome-BGC vs Orchidee & LPJ 1-R2 Based on annual GPP from 1981-2000 Models were run with the same input data!

5. APAR Biophysical vs. ecophysiological control of GPP interannual variability in the models GPP = APAR x RUE APAR: Absorbed Photosynthetic Active Radiation [MJ/m2/yr] RUE: Radiation Use Efficiency [gC/MJ] Simulated LAI Input Radiation Fraction of absorbed radiation (FAPAR): 1 - exp(-0.5 x LAI) Coefficient of variation (%) Correlation maps of GPP vs APAR and GPP vs RUE Interannual variations of radiation use efficiency are the primary cause of GPP interannual varibaility Jung et al., GBC, 2007

6. Correlation and sensitivity of summer (JJA) meteorology with GPP Reducing meteorological variable space (radiation, temperature, vapour pressure deficit, and precipitation) to principal components PCA1 explains 84% of variance of the summer meteorological data PCA1 weights: RAD (-0.28), TEMP (-0.28), VPD (-0.28), RAIN (0.24) Temperature/Radiation limited Moisture limited Does nitrogen dynamics influence interannual variations of GPP?! Effects of water stress on photosynthesis largely control GPP interannual variability  canopy conductance and coupling with carbon assimilation  representation of soil, roots, below ground processes Jung et al., GBC, 2007

7. Do the models have a systematic bias during drought? (Model_site_month_DryYear – Model_site_month_WetYear) – (Eddy_site_month_DryYear – Eddy_site_month_WetYear) Drought effect too strong Drought effect too weak n.s. significant Site-level runs

8. The models response to meteorology - How to tackle equifinality? 21 day sliding correlation window between C-fluxes and Temp, Rad, VPD, SWC Consistency Response of simulated NEP to meteo is more consistent with site data than the gross fluxes  ‘equifinality’ or artifact of flux separation? Largest differences with respect to TER Consistency: how often does the simulated flux correlate with the same meteo driver as the eddy-based flux sum(Var_maxR_site == Var_maxR_model)/sum(significant correlations) Site-level runs Confounding effects because meteo variabels are co-linear

9. Model RMSE as a function of time scale Mahecha et al. In prep. RMSE (norm by data range) High frequency components & seasonal cycle work better than inter- and intra-annual components Significance of changing pools & ecosystem properties? Inter-annual components of GPP vs Gcan

10. What is an adequate model? ‚scatter‘ is ok, bias not (data are noisy, simulations not) RMSE, R2,... are not really good measures of model performance Looking for robust patterns in the FLUXNET data! Can ‚patterns‘ be assimilated into models? Jung et al 2007, Biogeosciences

11. What about using patterns from upscaled carbon fluxes? Advantages: noise goes away; no issues of ‚site specific pecularities‘; no representation bias; matches the scale of the models Disadvantages: uncertainties from drivers (meteo data, remote sensing products); model specific sensitivity to meteo; no effects from changing pools (  IAV)

12. Comparison of European mean GPP pattern: Process- vs. data-oriented models Process oriented models Data driven models R2 Mean annual GPP patterns from data- oriented models are becoming sufficiently robust for benchmarking process-oriented models

13. 2003 GPP anomaly from different data-oriented models Inter-annual variability from data-oriented models is not sufficiently robust for benchmarking process-oriented models Jung et al., GCB, in press

14. Final Remarks/Questions How to deal with important input data that are usually not available (effective rooting depth, water holding capacity)? To what extent are parameters allowed to compensate for inadequate structure? What is an adequate model structure? How to identify not adequate structure components? Should we concentrate on ‚patterns‘ rather than on ‚values‘?