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Data Assimilation for Carbon Cycle Studies: Upscaling, Arctic Landscapes & Forecasting

Explore data assimilation as a key tool for studying carbon cycles, upscaling fluxes, Arctic landscapes, and forecasting future changes. Discover the REFLEX project and its goal to compare Multiple Data Fusion techniques. Learn about the Kalman Filter theory and practice, stock analysis, and next steps in the field.

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Data Assimilation for Carbon Cycle Studies: Upscaling, Arctic Landscapes & Forecasting

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  1. www.abacus-ipy.org Data assimilation as a tool for C cycle studies Mathew Williams, University of Edinburgh Collaborators: P Stoy, J Evans, C Lloyd, A Prieto Blanco, M Disney, L Street, A Fox (Sheffield) M Van Wijk (Wageningen), E B Rastetter (MBL), G Shaver (MBL)

  2. Transferring information across scales • The upscaling problem and data assimilation • An Arctic C cycle application • REFLEX – a comparison of DA approaches for C flux estimation

  3. Upscaling C fluxes • How do we cope with spatial variation? • What are the critical feedbacks over longer time scales? • How can model/parameters be improved? • How can multiple data be combined? • How trustworthy are such combinations?

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

  5. What is the carbon balance of an Arctic landscape? How will C balance change in the future? What measurements should we take to improve understanding and forecast skills? SWEDEN

  6. A multiscale approach Arctic Biosphere Atmosphere Coupling at multiple Scales

  7. Observation operator: NDVI-LAI LAI harvest calibrates indirect measurement (NDVI) Van Wijk & Williams, 2005

  8. Shaver et al. J. Ecol. (2007)

  9. Af Lf Cfoliage Net Ecosystem Exchange of CO2 NDVI Rh Ra Clitter Ar Lr GPP Croot D 5 model pools 9 model fluxes 9 unknown parameters 2 data time series Aw Lw Cwood CSOM/CWD C = carbon pools A = allocation L = litter fall R = respiration (auto- & heterotrophic) Temperature controlled DALEC

  10. The Kalman Filter in practice Flux tower Skye sensor Harvest calibration Initial state Met. drivers Forecast Predictions At Ft+1 NEE NDVI NEE NDVI DALEC model LAI-NDVI fit Parameters Assimilation At+1 Light response curves Analysis

  11. Data time series Time (day of year 2007)

  12. Analysis

  13. Stocks

  14. Next steps • Isotopic tracer experiments • C14 for SOM turnover • Automated chambers • Field determination of NPP (rhizotrons, harvests) • Spatial NDVI sampling (field and aircraft) • PBL measurements (aircraft)

  15. 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

  16. 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

  17. 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

  18. Thank you

  19. Time series data • Eddy covariance measurements at 3 m, open path LICOR 7500 • EC: logical filter and U* filter (0.2 m s-1) applied • EC: error assumed constant at 1 mmol m-2 s-1 • Being actively explored • NDVI sensor at 2 m (Skye 2-channel) logged at 20 mins and averaged daily, with estimated 10% error (tbc)

  20. Indirect, continuous LAI calibration NDVI

  21. How good is the model? Are the parameters well known? How accurate are the observations? Are there complementary observations? Observer

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