Data assimilation as a tool for biogeochemical studies

1. Data assimilation as a tool for biogeochemical studies Mathew Williams University of Edinburgh

2. The carbon problem • Friedlingstein P., et al. 2006. Journal of Climate. • 11 coupled climate-carbon models predicted very different future C dynamics • Conclusion – our models are flawed • Solution – better model testing against data, and better use of multiple data sets to test the representation of process interactions

3. Talk outline • Assimilating C flux and stocks data to improve analyses of C dynamics • Assimilating reflectance data • Assimilating latent energy flux data to deconvolve net carbon fluxes

4. FUSION ANALYSIS ANALYSIS + Complete + Clear confidence limits + Capable of forecasts Improving estimates of C dynamics MODELS MODELS + Capable of interpolation & forecasts - Subjective & inaccurate? OBSERVATIONS +Clear confidence limits - Incomplete, patchy - Net fluxes OBSERVATIONS

5. A prediction-correction system Time update “predict” Measurement update “correct” Initial conditions

6. The Kalman Filter Initial state Drivers Forecast Observations Predictions At Ft+1 Dt+1 F´t+1 MODEL OPERATOR P Assimilation Ensemble Kalman Filter At+1 Analysis

7. Observations – Ponderosa Pine, OR (Bev Law) Flux tower (2000-2) Sap flow Soil/stem/leaf respiration LAI, stem, root biomass Litter fall measurements

8. Af Lf Cfoliage Rh Ra Ar Lr GPP Croot Clitter D Aw Lw Cwood CSOM/CWD C = carbon pools A = allocation L = litter fall R = respiration (auto- & heterotrophic)

9. Rtotal & Net Ecosystem Exchange of CO2 Af Lf Cfoliage Rh Ra Ar Lr GPP Croot Clitter D 6 model pools 10 model fluxes 9 parameters 10 data time series Aw Lw Cwood CSOM/CWD C = carbon pools A = allocation L = litter fall R = respiration (auto- & heterotrophic) Temperature controlled

10. Time (days since 1 Jan 2000) Williams et al (2005)

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

12. Time (days since 1 Jan 2000) Williams et al (2005)

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

14. Data brings confidence =observation — = mean analysis | = SD of the analysis Williams et al (2005)

15. At Ft+1 Reflectancet+1 MODISt+1 DALEC Radiative transfer DA At+1 Assimilating EO reflectance data

16. EO assimilation to improve photosynthesis predictions Model only Assimilating MODIS NDVI • = observation — = mean analysis | = SD of the analysis Quaife et al. (RSE in press)

17. Constraining the C cycle via hydrology Carbon Hydrology ET Ra Rh Ppt Cf WS1 Cl GPP Cr WS2 WS3 Cw Csom

18. Deconvolving net C fluxes • NEE = Reco– GPP • Eddy flux towers also measure LE • LE  GPP (some complications…) • Use a model of coupled C-water fluxes… • Assimilate LE and NEE data, and use LE to constrain GPP • Improved flux deconvolution • Improved model diagnosis and prognosis

19. Demonstration study • Generate a “true” system with a complex model • Sample the “truth” and generate observations (with errors) • Attempt to reconstruct the truth through assimilating the observations into a simple model • Experiment with NEE data alone, and NEE + LE data

20. Fluxes Truth Obs. Analysis

21. Residuals Obs. Truth

22. Stocks Truth Obs. Analysis

23. Summary • Data assimilation techniques are powerful tools for ecological research • Time series data are most useful • For improved predictions, better constraints on long time constant processes are required • Error characterisation is vital • EO data can be assimilated • Hydrological assimilation can decompose net C fluxes into components.

24. Acknowledgements: Bev Law Tris Quaife Thank you