Mat Disney and Shaun Quegan

1. EO data assimilation in land process models Mat Disney and Shaun Quegan No one trusts a model except the man who wrote it; everyone trusts an observation except the man who made it (Harlow Shapley)

2. Concept for Global Carbon Data Assimilation SystemNB carbon and water are inextricably linked, so this is a more generalised vegetation – soil – water- atmosphere scheme Ciais et al. 2003 IGOS-P Integrated Global Carbon Observing Strategy

3. Terrestrial Component Which model(s) should go here? + Water components: SWE soil moisture

4. Land process models Land models need to deal with transfers of - energy - matter - momentum between the land surface and the atmosphere. Three classes of land (coupled carbon-water) models: • Models driven by radiation (light use efficiency models) • Dynamic Vegetation Models: climate driven • Simple box models Some models emphasise hydrology (not discussed here)

5. Light Use Efficiency models Incoming PAR CO2 LUE Absorption fAPAR Photo- synthesis Respiration GPP NPP Efficiency coefficient: LUE The LUE may depend on biome, soil moisture, temperature, nutrients, age, Modeled or measured by satellites Measured by satellites

6. Notes on LUE models • Models built by ecologists tend to focus on leaves as the functional element (e.g. Leaf Area Index). • Models built by remote sensors tend to focus on radiation. • LUE models are driven by EO data, rather than geared to assimilating data.

7. Properties of DVMs DVMs originally designed to examine long-term trends under climate change so… • Data-independent, except for varying climate data and static soil texture data • Comprehensive description of biophysics • All processes internalised, parameterised • Complex, non-linear, non-differentiable, (discontinuities, thresholds) • Expensive to run

8. The Structure of a Dynamic Vegetation Model Parameters Climate Sn Sn+1 • DVM Soil texture Testing Processes

9. Phenology Snow water Burnt area Processes Climate • DVM Land cover Forest age Sn+1 Sn Testing: Radiance fAPAR Soils Observable Possible feedback How EO data can affect DVM calculations fAPAR Parameters

10. Calibration– boreal budburst Offline setting of global parameters can be thought of as a form of DA, but is better described as model calibration. In the following e.g, we use new EO observations that are unaffected by snow-melt to parameterise the spring warming boreal phenology model.

11. The SDGVM budburst algorithm min(0, T – T0) > Threshold, budburst occurs. When The sum is the red area. Optimise over the 2 parameters, Threshold and T0 (minimum effective temperature). T0 Start of budburst

12. The Date of budburst derived from minimum NDWI (VGT sensor, 2000) N. Delbart, CESBIO Day of year

13. Testing SDGVM with EO data SDGVM can predict satellite ‘observations’ since it contains a canopy model and the concept of radiation interception

14. Model “skill” 1999 SDGVM fAPAR AVHRR NDVI Skill Bad Good

15. Are derived parameters the problem? Is the problem the SDGVM or the derived parameter from the EO signal? The next slide shows the fAPAR derived from Seawifs (JRC) and from MODIS for a site in the UK. The large bias between the two is a general feature of these two datasets.

16. Biases in derived parameters

17. Assimilating products Assumptions Observations Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) Assumptions Observations MODEL Assumptions For example: soil moisture from SMOS, surface temperature, LAI from MODIS

18. Low-level vs derived products • similar products give substantially different values; • assumptions used to derive products usually inconsistent with biospheric models; • Product uncertainties are poorly known • Can we use low-level products (Reflectance? BOA radiance? TOA radiance?)

19. Observation Operator MODEL Assumptions Assumptions Assimilating reflectance Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) Observations Observations e.g. reflectance, backscatter, etc… Assumptions in the observation operator are made to be consistent with those in the model

20. Observation operators This approach needs observation operators: translate ecosystem model state vector into observable properties e.g. • reflectance data assimilated into DALEC; • predicting radar coherence in ERS Tandem data from the SPA model; • relating snowpack properties to SSM/I radiometer data; • recognising burnt area and severity of burn.

21. Which is the right model? • Complex DVM-type models never designed for DA • So, pursuing another approach with a simplified box model designed from the start for DA • DALEC

22. EO data (e.g. LAI, VI, reflectance) Ensemble Kalman Filter Observation model The Structure of a Data Assimilation Model (DALEC) Blue lines indicate integration of EO data with DALEC Ppt ET Ra Rh WS1 Cf Cl GPP Cr WS2 Cw Cs Q WSn Stocks and fluxes of carbon (left) and water (right)

23. Observation operator: simple RT model + snow

24. Canopy foliage results No assimilation Assimilating MODIS (bands 1 and 2)

25. Canopy foliage results Assimilating MODIS exc. snow Assimilating MODIS inc. snow Quaife, Williams, Disney et al. RSE in press

26. EO land cover and carbon • All EO land cover the same? • DGVMs use land cover indirectly • How do we translate land cover classes to PFTs? Quaife, Quegan, Disney et al., submitted

27. EO land cover and carbon Quaife, Quegan, Disney et al., submitted

28. How do we find best model-data framework? • Use ‘God’ models to test assumptions of simpler models • DVMs + DALEC-type models • Model-data fusion inter-comparison e.g. REFLEX: Regional Flux Estimation Experiment • www.carbonfusion.org • Compare strengths/weaknesses of various model-data fusion techniques • Quantify errors/biases introduced when extrapolating fluxes in both space and time using a model constrained by model-data fusion methods.

29. Key issues for DA in land models 1 • Models • Simple enough for effective DA but complex enough to capture biophysics • Suitable interface with observation operators • preferably differentiable

30. Key issues for DA in land models 2 • Data • Same meaning of observed parameters as used in models • Proper characterisation of uncertainty i.e. PDFs • Use OOs to make best use of all available data e.g. optical, LiDAR, RADAR, thermal …. • We are still searching for the best model-data structure.

31. Key issues for DA in land models 3 • DA through observation operators not only answer, for various practical reasons. • Also pursue general concepts of how EO data can reduce the uncertainty in land models • Calibration, testing etc.

32. Thank you

33. Severity of disagreement – AVHRR/SDGVM 1998 r > 0.497 OR r.m.s.e < 0.2 r < 0.497 AND r.m.s.e > 0.2 r < 0.497 AND r.m.s.e > 0.3

34. Severity of disagreement – example Mid Europe

35. Severity of disagreement – example SW China

36. Lesson • The DVM as currently formulated only supports a simple observation operator. This allows meaningful estimates of time series of observables; absolute values of the observables are of dubious value. • These time series permit the model to be interrogated with satellite data, and model failures to be identified.

37. Detecting incorrect land cover Crop class incorrectly set Crop class correctly set 0.9 0.0 Pearson’s product moment Temporal correlation

38. Lesson • Forward operators may prove a powerful tool in land cover mapping

39. Impact on Carbon Calculations 1 day advance: NPP increases by 10.1 gCm-2yr-1 15 days advance: 38% bias in annual NPP Calibrated model is unbiased, unlike methods based on NDVI Observations calibrate Carbon Calculation Dynamic Vegetation Model Phenology model Picard et al.,GCB, 2005

40. Comparison Model-EO: RMSE Model needs to be region specific, here include chilling requirement ?

41. NDVI predicted by SDGVM 1998 1999 1 0 0 1

42. A Dynamic Vegetation Model (SDGVM) ATMOSPHERIC CO2 Photosynthesis GPP BIOPHYSICS NPP Soil Litter Fire Mortality GROWTH Thinning Disturbance NBP Biomass LEACHED

43. Assimilating reflectance Observations Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) The real world MODEL Assumptions But how do we use a non-linear observation operator?

44. Comparing model and measured fAPAR August 99 May 99 Seawifs SDGVM

45. Model and predicted fAPAR Average over the whole of Europe for 1999 and 2000 Note: if SDGVM were driven by the Seawifs values, most model forests would die

46. Experiments • State and parameter estimation. DE1 and EV1 sites, 3 years driving data, all available obs • As 1. but using synthetic data (DE2 and EV2) • Within site forecasting. Another year of driving data for DE1 and EV1, but no observations • As 3. but using synthetic data (DE2 and EV2) • Between site extrapolation. DE3 and EV3 sites, 4 years driving data, MODIS LAI only

47. Integrated flux predictions

48. REFLEX data sets • “Paired” sites to test extrapolation/estimation • Brasschaat (DE2) and Vielsalm (EV2) (MF) • Hainich (DE3) and Hesse (DE1) (DBF) • Loobos (EV1) and Tharandt (EV3) (ENF) • Meteorological drivers, fluxes, MODIS LAI and stocks • Attempting to estimate “uncertainty” in fluxes and MODIS LAI

49. REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS Training Runs Assimilation MDF DALEC model Output Deciduous forest sites Coniferous forest sites Full analysis Model parameters

50. REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS Testing site forecasts with limited EO data MDF FluxNet data DALEC model testing MDF Full analysis Model parameters Analysis Assimilation MODIS