To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models, we present here a first dual-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here, we interpret signals recorded in observed carbon dioxide (CO2) along with observed ratios of its stable isotopologues (CO2)-C-13/(CO2)-C-12 (delta C-13). The latter is in particular a valuable tracer to untangle CO2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of (CO2)-C-13 and (CO2)-C-12 molecules removed from the atmosphere by plants is dependent on moisture conditions. The dual-species CTDAS system varies the net exchange fluxes of both (CO2)-C-13 and CO2 in ocean and terrestrial biosphere models to create an ensemble of (CO2)-C-13 and CO2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated (CO2)-C-13 and CO2 mole fractions (and thus delta C-13) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions. With this system, we are able to estimate changes in terrestrial delta C-13 exchange on seasonal and continental scales in the Northern Hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO2 and delta C-13 constraints as done in this work. The additional constraints on surface CO2 exchange from delta C-13 observations neither affected the estimated carbon fluxes nor compromised our ability to match observed CO2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but also to potentially function as a data-driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO2, water, isotope, and energy balance.

http://dx.doi.org/10.5194/gmd-11-283-2018

Journal Article

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