Abstract
Abstract. Many of the key processes represented in global terrestrial carbon models remain largely unconstrained. For instance, plant allocation patterns and residence times of carbon pools are poorly known globally, except perhaps at a few intensively studied sites. As a consequence of data scarcity, carbon models tend to be underdetermined, and so can produce similar net fluxes with very different parameters and internal dynamics. To address these problems, we propose a series of ecological and dynamic constraints (EDCs) on model parameters and initial conditions, as a means to constrain ecosystem variable inter-dependencies in the absence of local data. The EDCs consist of a range of conditions on (a) carbon pool turnover and allocation ratios, (b) steady-state proximity, and (c) growth and decay of model carbon pools. We use a simple ecosystem carbon model in a model–data fusion framework to determine the added value of these constraints in a data-poor context. Based only on leaf area index (LAI) time series and soil carbon data, we estimate net ecosystem exchange (NEE) for (a) 40 synthetic experiments and (b) three AmeriFlux tower sites. For the synthetic experiments, we show that EDCs lead to an overall 34% relative error reduction in model parameters, and a 65% reduction in the 3 yr NEE 90% confidence range. In the application at AmeriFlux sites all NEE estimates were made independently of NEE measurements. Compared to these observations, EDCs resulted in a 69–93% reduction in 3 yr cumulative NEE median biases (–0.26 to +0.08 kg C m−2), in comparison to standard 3 yr median NEE biases (–1.17 to −0.84 kg C m−2). In light of these findings, we advocate the use of EDCs in future model–data fusion analyses of the terrestrial carbon cycle.
Highlights
Terrestrial ecosystem carbon exchange is a fundamental part of the global carbon cycle link to biosphere processes
We found an overall reduction in the posterior Metropolis Hastings Markov Chain Monte Carlo (MHMCMC) ecological and dynamic constraints (EDCs) parameter vector errors E(xsEDC), relative to both the standard MHMCMC errors E(xsSTA) and the randomly sampled parameter vector errors E(xsRAN): we found an improvement of IEDC = 34 % associated with using EDCs (Fig. 2c)
At each AmeriFlux site, we found that EDCs led to an increased confidence and a largely reduced net ecosystem exchange (NEE) bias; our DALEC2 model analyses suggests that the use of EDCs regionally and globally could significantly enhance our ability to estimate ecosystem state variables in the absence of direct observational constraints
Summary
Terrestrial ecosystem carbon exchange is a fundamental part of the global carbon cycle link to biosphere processes. Atmospheric CO2 measurements indicate the presence of a global land C sink, i.e. uptake by the terrestrial biosphere exceeds losses. Relative to all major terms in the global carbon budget, the global land sink exhibits both the largest inter-annual variability and the largest uncertainty (Le Quéré et al, 2013). The terrestrial carbon budget uncertainty stems largely from unknowns in the size, spatial distribution and temporal dynamics of the major terrestrial carbon pools. There is little agreement among modelled land sink projections for the 21st century (Todd-Brown et al, 2013; Friend et al, 2013), reflecting uncertainty in knowledge on the current state of the terrestrial C cycle and its dynamics. A range of ecosystem carbon models and data sets have been brought together in model–data fusion (MDF) frameworks to produce an enhanced analy-
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