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Many of the key processes represented in global terrestrial carbon modelsremain largely unconstrained. For instance, plant allocation patterns andresidence times of carbon pools are poorly known globally, except perhaps ata few intensively studied sites. As a consequence of data scarcity, carbonmodels tend to be underdetermined, and so can produce similar net fluxes withvery different parameters and internal dynamics. To address these problems,we propose a series of ecological and dynamic constraints (EDCs) on modelparameters and initial conditions, as a means to constrain ecosystem variableinter-dependencies in the absence of local data. The EDCs consist of a rangeof conditions on (a) carbon pool turnover and allocation ratios, (b) steady-state proximity, and (c) growth and decay of model carbon pools. We usea simple ecosystem carbon model in a model–data fusion framework to determinethe added value of these constraints in a data-poor context. Based only onleaf area index (LAI) time series and soil carbon data, we estimate netecosystem exchange (NEE) for (a) 40 synthetic experiments and (b) threeAmeriFlux tower sites. For the synthetic experiments, we show that EDCs leadto an   overall 34% relative error reduction in model parameters, anda 65% reduction in the 3 yr NEE 90% confidence range. Inthe application at AmeriFlux sites all NEE estimates were made independentlyof NEE measurements. Compared to these observations, EDCs resulted ina 69–93% reduction in 3 yr cumulative NEE median biases(–0.26 to +0.08 kg C m<sup>−2</sup>), in comparison to standard3 yr median NEE biases (–1.17 to −0.84 kg C m<sup>−2</sup>). Inlight of these findings, we advocate the use of EDCs in future model–datafusion analyses of the terrestrial carbon cycle

biogeosciences

Constraining ecosystem carbon dynamics in a data-limited world: integrating ecological &amp;quot;common sense&amp;quot; in a model–data fusion framework