The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data

A. Fox, M. Williams, A.D. Richardson, D. Cameron, J.H. Gove, T. Quaife, D. Ricciuto, M. Reichstein, E. Tomelleri, C.M. Trudinger, M.T. van Wijk

Research output: Contribution to journalArticleAcademicpeer-review

94 Citations (Scopus)

Abstract

We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals
Original languageEnglish
Pages (from-to)1597-1615
JournalAgricultural and Forest Meteorology
Volume149
Issue number10
DOIs
Publication statusPublished - 2009

Keywords

  • parameter-estimation
  • data assimilation
  • carbon-dioxide
  • uncertainty
  • climate
  • forest
  • productivity
  • variability
  • simulation
  • feedbacks

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