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Results from the Reflex experiment

This study aims to compare the strengths and weaknesses of various model-data fusion techniques for estimating carbon model parameters and predicting carbon fluxes. It also aims to quantify errors and biases introduced when extrapolating fluxes using earth observation data and models constrained by fusion methods.

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Results from the Reflex experiment

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  1. Results from the Reflex experiment Mathew Williams, Andrew Fox and the Reflex team

  2. Reflex Objectives • To compare the strengths and weaknesses of various model-data fusion techniques for estimating carbon model parameters and predicting carbon fluxes. • To quantify errors and biases introduced when extrapolating fluxes (and related measurements) made at flux tower sites in both space and time, using earth observation data and models constrained by model-data fusion methods.

  3. Participants

  4. Protocol • Inputs • Daily meteorological drivers • Initial C stocks • Daily NEE (gaps) and LAI (sparse) • Some synthetic, some observed • A simple C model • Outputs • Full C flux and stock estimates with uncertainty • Parameter estimates with uncertainty

  5. Identifying sources of error • Synthetic experiment - deals with observational and algorithmic error (and user error). • Real experiment – adds model error.

  6. Algorithm mean cumulative NEE

  7. Flux estimation - synthetic Uncertainty on retrieval of cumulative/integrated C dynamics. Time series of monthly means (shows uncertainty between algorithms from range of means) for deciduous (top) and evergreen (bottom) synthetic experments.

  8. Flux estimation - uncertainty Range of confidence intervals on retrieval of cumulative/integrated C dynamics. Time series for deciduous (top) and evergreen (bottom) synthetic experments.

  9. Flux estimation - observed

  10. Flux estimation

  11. Other analyses • Identification of parameter correlations from parameter error covariance matrices • Eigenvector analysis • Taylor diagrams (bias, phase, variability) • Test C state dynamics with CLs • Compare with gap-filled, use CLs

  12. Questions • Can parameters and their uncertainties be effectively determined? • We show different levels of uncertainty (DC) • Parameter figures, eigenvectors, CM (CT MvW) • Synthetic v true comparison (ET) • Can the full C cycle be described and forecast? • CLs on predictions for all years, fluxes, stocks (ZL, AF) • Taylor diagrams, Chi-squared test on years 1, 2, 3 (TQ, DR) • Gap filled estimates (AR) • Cumulative uncertainty on NEE predictions (MW) • GPP, Re, NEE predictions and uncertainty (AR) • What is the relative contribution to errors from observations, algorithms and model structure?

  13. Lessons for LSM calibration • Synthetic studies can show how data density and error can contribute information • A variety of DA methods show promise • Best constrained parameters are not intuitive • Difficult to identify model structural errors

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