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Analysis of the terrestrial carbon cycle through data assimilation and remote sensing

Analysis of the terrestrial carbon cycle through data assimilation and remote sensing. Mathew Williams, University of Edinburgh Collaborators L Spadavecchia, M Van Wijk. B Law, J Irvine, P Schwarz, M Kurpius, T Quaife, P Lewis M Disney G Shaver, L Street.

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Analysis of the terrestrial carbon cycle through data assimilation and remote sensing

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  1. Analysis of the terrestrial carbon cycle through data assimilation and remote sensing Mathew Williams, University of Edinburgh Collaborators L Spadavecchia, M Van Wijk. B Law, J Irvine, P Schwarz, M Kurpius, T Quaife, P Lewis M Disney G Shaver, L Street

  2. Sampling at 3397 meters, well mixed free troposphere Source: CD Keeling, NOAA/ESRL

  3. Harvard Forest Data since 1989

  4. Hourly data ~5 m above canopy Source: Wofsy et al, Harvard Forest LTER

  5. Talk outline What are the uncertainties in temporal and spatial extrapolation of C cycle estimates? • Using multiple time series data to constrain C cycle analyses • Use multiscale spatial studies to determine up-scaling uncertainties

  6. PART 1: Time

  7. 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

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

  9. 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

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

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

  12. Time (days since 1 Jan 2000)

  13. Rtotal & Net Ecosystem Exchange of CO2 Af Lf Cfoliage Rh Ra Ar Lr GPP Croot Clitter D 5 model pools 10 model fluxes 11 parameters 10 data time series Aw Lw Cwood CSOM/CWD C = carbon pools A = allocation L = litter fall R = respiration (auto- & heterotrophic) Temperature controlled

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

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

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

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

  18. GPP results No assimilation Assimilating MODIS (bands 1 and 2) Quaife et al, RSE (in press)

  19. Summary: time • Multiple time series data generate powerful constraints on analyses • For improved predictions, better constraints on long time constant processes are required • Error characterisation is vital • EO data can be assimilated with appropriate observation operators

  20. PART 2: Space

  21. (Street et al 2007, Shaver et al 2007)

  22. (Van Wijk & Williams 2005)

  23. Height of sensor and field of view  3.0 m  2.0 m  1.5 m  1.0 m  0.5 m  0.2 m 4.5 m 3.0 m 2.35 m 1.5 m 0.75 m 0.1 m

  24. A multi-scale experimental design macroscale microscale Distance (m) Distance (m) (Williams et al. in press)

  25. Microscale study: Scale invariance Linear averaged Skye NDVIs (collected at 0.2 x 02 m resolution with diffuser off) versus measured NDVIs at coarser spatial scales with diffuser on

  26. Microscale study: Scale invariance Relationships between estimated LAI (using both Skye NDVI and LI-COR LAI-2000 observations at 0.2 m resolution, linearly averaged for upscaling) versus Skye NDVI at different spatial scales.

  27. Frequency histograms for LAI estimates in the microscale site at a range of resolutions. (Williams et al. in press)

  28. Semi-variogram for LAI in the microscale study

  29. Macroscale study: Frequency histograms Inferred from ground NDVI Measured in a ground survey, 2004 Satellite overpass, ETM+, August 2001

  30. A significant but poor correlation with LandSat data

  31. Macroscale study: Semivariograms Inferred from ground NDVI Measured in a ground survey, 2004 Satellite overpass, ETM+, August 2001

  32. Extrapolation models

  33. Landsat IDW Kriging Kriging Error (Williams et al. in press)

  34. Summary: space • Scale invariance in LAI-NDVI relationships at scales > vegetation patches • However spatial variability is high so Kriging has limited usefulness • Over scales >50 m interpolation error was of similar magnitude to the uncertainty in the Landsat NDVI calibration to LAI • Characterisation of spatial LAI errors provides key data for spatial data assimilation

  35. Key challenges and opportunities • Coping with variable data richness • Identifying and removing model bias • Estimating representation and data errors • Making use of remote sensing (optical and XCO2) • Links to atmospheric CO2 using CTMs. • Designing experimental network • Boundaries in natural systems

  36. Funding support: NERC NASA DOE Thank you

  37. REFLEX: GOALS • To identify and compare the strengths and weaknesses of various MDF techniques • To quantify errors and biases introduced when extrapolating fluxes made at flux tower sites using EO data • Closing date for contributions: 31 October www.carbonfusion.org

  38. Regional Flux Estimation Experiment, stage 1 Flux data MODIS LAI Training Runs - FluxNet data - synthetic data Assimilation MDF DALEC model Deciduous forest sites Coniferous forest sites Output Full analysis Model parameters Forecasts www.carbonfusion.org

  39. Figure by Andrew Fox  observations (with noise)  truth  predictions  uncertainty Synthetic evergreen forest 2 years obs., 1 year prediction

  40. REFLEX, stage 2 Flux data MODIS LAI Testing predictions With only limited EO data MDF Flux data DALEC model testing MDF Model parameters Analysis Assimilation MODIS LAI

  41. FluxNet – Integrating worldwide CO2 flux measurements How to upscale from site locations to regions and the globe?

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