1. Improving understanding and forecasts of the terrestrial carbon cycle Mathew Williams School of GeoSciences, University of Edinburgh With input from: BE Law, A Fox, RF Fisher, J Grace, J Moncrieff, T Hill, P Meir, REFLEX team

2. Motivation  How is the Earth changing?  What are the consequences of these changes for life on Earth?

3. Fossil Fuels (7 per yr) & volcanoes Atmosphere Vegetation Ocean Sediments Soils The Global Carbon Cycle – a simple model Litterfall/ sedimentation Respiration Photosynthesis Combustion The Carbon Cycle Understanding, prediction and control of the Carbon cycle Climate

4. Research Vision  To use EO data to test, constrain, modify and evolve models of the terrestrial biosphere  To focus on uncertainty throughout the process of linking observations to models  To guide experimental and observational science towards critical areas of uncertainty  To generate global bottom-up estimates of the terrestrial C cycle with quantified uncertainty

5. Outline  The problems  Progress so far  Challenges for the future

6. Friedlingstein et al 2006: C4MIP Intercomparison of 11 coupled carbon climate models

7. Matrix of R 2 for simulations of mean annual GPP for 36 major watersheds in Europe from different process- and data oriented models Williams et al. 2009, BGD

8. Space (km) time s hr day month yr dec 0.11.010100100010000 Flask Site Time and space scales in ecological processes Physiology Climate change Succession Growth and phenology Adaptation Disturbance Photosynthesis and respiration Climate variability Nutrient cycling

9. GOSAT Space (km) time s hr day month yr dec 0.11.010100100010000 Flux Tower Aircraft Flask Site Flask Site Field Studies MODIS Time and space scales in ecological observations Tall tower

10. Williams et al. 2009, BGD

11. Progress so far in MDF  Model-data fusion with multiple constraints to improve analyses of C dynamics (Williams et al. 2005, GCB)  Assimilating EO data to improve C model state estimation (Quaife et al. 2008, RSE)  REFLEX: Intercomparison experiment on parameter estimation using synthetic and observed flux data (Fox et al, in press, AFM)  “Improving land surface models with FLUXNET data” (Williams et al 2009, BGD)

12. C cycling in Ponderosa Pine, OR Flux tower (2000-2) Sap flow Soil/stem/leaf respiration LAI, stem, root biomass Litter fall measurements

13. Time (days since 1 Jan 2000) Williams et al GCB (2005) Chambers Sap-flow A/Ci EC Chambers

14. Time (days since 1 Jan 2000)

15. GPPC root C wood C foliage C litter C SOM/CWD RaRa AfAf ArAr AwAw LfLf LrLr LwLw RhRh D Photosynthesis & plant respiration Phenology & allocation Senescence & disturbance Microbial & soil processes Climate drivers Non linear f(T)Simple linear functionsFeedback from C f

16. The Kalman Filter MODEL AtAt F t+1 F´ t+1 OPERATOR A t+1 D t+1 Assimilation Initial stateForecast Observations Predictions Analysis P Drivers

17. Time (days since 1 Jan 2000) Williams et al GCB (2005)  = observation — = mean analysis | = SD of the analysis

18. Time (days since 1 Jan 2000) Williams et al GCB (2005) = observation — = mean analysis | = SD of the analysis

19. Data bring confidence & test the model Williams et al, GCB (2005)  = observation — = mean analysis | = SD of the analysis

20. REFLEX experiment  Objectives: To compare the strengths and weaknesses of various MDF techniques for estimating C model parameters and predicting C fluxes.  Evergreen and deciduous models and data  Real and synthetic observations  Multiple MDF techniques  Links between stocks and fluxes are explicit www.carbonfusion.org

21. Parameter constraint Consistency among methods Confidence intervals constrained by the data Consistent with known “truth” “truth” Fox et al. in press

22. A tolab GPP CrCr CwCw CfCf C lit C SOM RaRa AfAf ArAr AwAw LfLf LrLr LwLw R h1 D C lab A fromlab R h2 DALEC Model Fox et al. in press

24. Problems with SOM and wood Fox et al. in press

25. Problems so far  Varied estimation of confidence intervals  Equifinality  Problems in defining priors  Multiple time scales of response

26. Challenges for the future Quantifying model skill across biomes Williams et al. 2009, BGD FLUXNET

27. Arctic Biosphere-Atmosphere Coupling across multiple Scales ABACUS WP1 Plants WP2 Soils WP3 Fluxes WP4 Towers WP Moss WP York WP5 Airborne WP6 Earth observation

28. Other data constraints?  Tree rings  FPAR, NDVI, EVI time series  Stem inventories  chronosequences  Phenology observations  Soil moisture, LE, stream-flow  Surface temperature  Soil chambers

29. Manipulation Experiments

30. 5 Drought : R 2 =0.75 Control : R 2 =0.81 SPA model output vs. data Soil-Root Resistance (modelled) R p  lmin  v K  v  LAI Root Met. Fisher et al. 2007

32. Links to atmospheric CO2 observations…

33. Atmos. transport Calibration/ Validation Satellite X CO2 vs Models Flasks/aircraft Ground X CO2 Satellite X CO2 Model intercomparison Assimilation Flux analysis Error/bias characterisation MODIS Fire Science questions Workflow for interpretation of GOSAT, flask, aircraft and tall tower data Model X CO2 Global C fluxes Science questions Aircraft/ ground X CO2 Land surface model

34. Thank you Funding support: NERC NASA DOE

35. Information content of data (——) aircraft soundings + flux data ( ‑ ‑ ‑ ‑ ) flux data only; (— — —) aircraft soundings only Hill et al. in prep.

36. Spadavecchia et al. in prep. Quantifying driver uncertainty in carbon flux predictions

37. Parameter retrieval from a synthetic experiment using the DALEC model using EnKF Williams et al. 2009, BGD