Data assimilation as a tool for C cycle studies Collaborators: P Stoy, J Evans, C Lloyd, A Prieto Blanco, M Disney, L Street, A Fox (Sheffield) M Van Wijk.

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Presentation transcript:

Data assimilation as a tool for C cycle studies 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) Mathew Williams, University of Edinburgh

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

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?

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

SWEDEN 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?

A multiscale approach Arctic Biosphere Atmosphere Coupling at multiple Scales

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

Shaver et al. J. Ecol. (2007)

GPPC root C wood C litter C SOM/CWD RaRa ArAr AwAw C foliage AfAf LfLf LrLr LwLw RhRh D Temperature controlled 5 model pools 9 model fluxes 9 unknown parameters 2 data time series Net Ecosystem Exchange of CO 2 C = carbon pools A = allocation L = litter fall R = respiration (auto- & heterotrophic) NDVI DALEC

The Kalman Filter in practice DALEC model AtAt F t+1 NEE NDVI LAI-NDVI fit A t+1 NEE NDVI Assimilation Initial stateForecast Predictions Analysis Parameters Met. drivers Light response curves Harvest calibration Flux tower Skye sensor

Data time series Time (day of year 2007)

Analysis

Stocks

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)

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

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

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

Thank you

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  mol 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)

Indirect, continuous LAI calibration NDVI

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