Abstract
Carbon storage dynamics in vegetation and soil are determined by the balance of carbon influx and turnover. Estimates of these opposing fluxes differ markedly among different empirical datasets and models leading to uncertainty and divergent trends. To trace the origin of such discrepancies through time and across major biomes and climatic regions, we used a model-data fusion framework. The framework emulates carbon cycling and its component processes in a global dynamic ecosystem model, LPJ-GUESS, and preserves the model-simulated pools and fluxes in space and time. Thus, it allows us to replace simulated carbon influx and turnover with estimates derived from empirical data, bringing together the strength of the model in representing processes, with the richness of observational data informing the estimations. The resulting vegetation and soil carbon storage and global land carbon fluxes were compared to independent empirical datasets. Results show model-data agreement comparable to, or even better than, the agreement between independent empirical datasets. This suggests that only marginal improvement in land carbon cycle simulations can be gained from comparisons of models with current-generation datasets on vegetation and soil carbon. Consequently, we recommend that model skill should be assessed relative to reference data uncertainty in future model evaluation studies.
Highlights
Terrestrial ecosystems can be a sink or a source of carbon (C) depending on the balance between primary production (C-influx) and the rate of return of vegetation and soil C to the atmosphere through respiration, biomass burning, and other minor release fluxes (C turnover)
Vegetation and soil C stocks are dependent on the influx of C from Gross Primary Production (GPP), for which estimates differ between models[6] and between models and empirical datasets[7,8]
The fully dynamic simulation is already at or very close to the baseline knowledge, suggesting that the increase in agreement should be interpreted with caution, and may not represent improvements in prediction accuracy
Summary
Terrestrial ecosystems can be a sink or a source of carbon (C) depending on the balance between primary production (C-influx) and the rate of return of vegetation and soil C to the atmosphere through respiration, biomass burning, and other minor release fluxes (C turnover). The Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) reported that a majority of Earth System Models (ESMs) project a continued net C uptake under all future CO2 emission scenarios, yet some models simulate a net C emission due to the combined effect of climate change and land use change[2] Different process representations both of C-influx and residence time in different models have been demonstrated to explain these model differences[3]. Three major factors may contribute to the model-data disagreement, (i) poor representation of ecological processes in the model, e.g., C assimilation, vegetation and soil turnover, (ii) uncertainties in, and the resolution of, data used to force models Such data are not limited to climatic data and include environmental data (e.g., information on land use), and (iii) uncertainties in present reference data from empirical observations, e.g. maps of aboveground biomass (AGB, the C stored in leaf and woody compartments) and soil C storage, against which model improvements are evaluated. By comparing outputs before and after replacing a simulated flux with an empirically-estimated one, we can identify the processes (e.g., photosynthesis or vegetation turnover) responsible for discrepancies between model predictions and observation-based estimates
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