1. Biases in land surface models Yingping Wang CSIRO Marine and Atmospheric Research

2. Model residuals Differences between predictions and data, and result from errors –Data: representation and precision –Model formulation –State (time varying) –Parameters (time independent)

3. Errors in state space Total errors: Errors due to model structure: Errors due to incorrect state and parameters values

4. How big are those errors? Abramowitz et al. 2006 Averaging window size (day) Parameter error Systematic error Random error

5. Some errors can not be accounted for by parameter tuning Use the improved CBM (CABLE) Eight parameters varied within their reasonable ranges Grey region shows PDF of ensemble predictions From Abramowitz et al. 2008

6. Model errors If systematic model errors are not modeled the SE of optimized model parameters can are too optimistic; Estimates of model parameters can be biased;

7. Systematic model errors Inaccurate inputs Missing processes Low sensitivities Incorrect formulations

8. Incorrect inputs of LW to the model

9. Why does CABLE predict incorrect energy partitioning ? Haverd unpublished data

10. Modeling variance in the data statistically Braswell et al. 2005 8 of 11 optimized photosynthesis parameters are well constrained. But the model still failed capturing a significant fraction of seasonal and inter- annual variations in NEE data.

11. Analyzing errors in frequency domain From Braswell et al. 2005 Inter-annual Seasonal Daily

12. S ET/ET m Katual et al. 2007 Incorrect response to soil water

13. Explaining the variance in the data Any variability that can not be modeled deterministically.. must be.. modeled statistically (Enting 2002) Analysis model residuals in both time and frequency domains

14. Analyzing model residual in t and f domains Time domain (t)Frequency domain (f) Residual plots SOFM Wavelet analysis Intuitive Clues for when and why models failed Separation of what the models should and should not explain at different time scales Difficult to resolve some complex interactions at different time scales Little information about why the models fail

15. Conclusions How many models should be calibrated? One or many? –Many. How do we address the initial condition problem? –Treat initial state values as model parameters. How do we detect and address model flaws? –SOFM, –State-space formulation –Analysis model residuals in both t and f domain, –data-model fusion as a sensitivity analysis tool –etc.

17. Deficiencies in land surface models Inadequate representation of canopy and soil Inaccurate formulations

18. Deficiencies in land surface models Overestimate heat fluxes, and because of –insulation by litter –canopy heat storage Incorrect response to soil water, and because of –incorrect model parameters –model structure

19. The Kalman-gain (g) Kalman gain (g)  cov( x )/cov( z ); Larger errors in data give smaller g; Lower sensitivity to z to x gives smaller gain; We need to separate model structural errors and from state and parameter errors Errors must be accounted by statistical models

20. Fast vs slow process Variance in EC data is dominated by the variation at diurnal and seasonal scales. Fitting LSM to EC data then gives better constraints on parameters for fast process than those for slow processes

21. Analyzing model residuals in frequency domain: the Bayesian approach

22. A consistent framework for studying model residuals

23. Fast biophysical processes Canopy conductance photosynthesis, leaf respiration Carbon transfer, Soil temp. & moisture availibity Slow biogeographical processes Vegetation dynamics & disturbance Land-use and land-cover change Vegetation change Autotrophic and Heterotrophic respiration Allocation Intermediate timescale biogeochemical processes Phenology Turnoover Nutrient cycle Solution of SEB; canopy and ground temperatures and fluxes Soil heat and moisture Surface water balance Update LAI, Photosyn- thesis capacity Physical- chemical forcing T,u,Pr,q, R s, R l, CO 2 Radiation water, heat, & CO2 fluxes dayyears Biogeo- chemical forcing Time scale of biosphere-atmosphere interactions Atmosphere hour

24. Limitations of current land surface models What is PFT? Do all plants in the same PFT truly have same parameter values? Mismatch between model and data, soil T and q for example. Spatial heterogeneity in canopy and soil Litter layer

25. Why EC data cannot constrain soil BGC processes? Sensitivity of turnover rate of slow pools to C fluxes is low; Soil C has a spectrum of turnover rate as substrate quality changes with time; Soil C has long memory (disturbance history, weather history etc) The parameters you obtained have limited applicability in predicting response to future climate change

26. State and parameter estimation

27. What eddy flux data can constrain effectively? Sensitivity of Biogeochemical processes (particularly slow pools) Plant phenology Vegetation dynamics

29. Schimel’s Figure