Carbon isotope discrimination of arctic and boreal biomes inferred from remote atmospheric measurements and a biosphere-atmosphere model

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Abstract

Estimating discrimination against 13C during photosynthesis at landscape, regional, and biome scales is difficult because of large-scale variability in plant stress, vegetation composition, and photosynthetic pathway. Here we present estimates of 13C discrimination for northern biomes based on a biosphere-atmosphere model and on National Oceanic and Atmospheric Administration Climate Monitoring and Diagnostics Laboratory and Institute of Arctic and Alpine Research remote flask measurements. With our inversion approach, we solved for three ecophysiological parameters of the northern biosphere (13C discrimination, a net primary production light use efficiency, and a temperature sensitivity of heterotrophic respiration (a Q10 factor)) that provided a best fit between modeled and observed δ13C and CO2. In our analysis we attempted to explicitly correct for fossil fuel emissions, remote C4 ecosystem fluxes, ocean exchange, and isotopic disequilibria of terrestrial heterotrophic respiration caused by the Suess effect. We obtained a photosynthetic discrimination for arctic and boreal biomes between 19.0 and 19.6‰. Our inversion analysis suggests that Q10 and light use efficiency values that minimize the cost function covary. The optimal light use efficiency was 0.47 gC MJ-1 photosynthetically active radiation, and the optimal Q10 value was 1.52. Fossil fuel and ocean exchange contributed proportionally more to month-to-month changes in the atmospheric growth rate of δ13C and CO2 during winter months, suggesting that remote atmospheric observations during the summer may yield more precise estimates of the isotopic composition of the biosphere.

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Randerson, J. T., Still, C. J., Ballé, J. J., Fung, I. Y., Doney, S. C., Tans, P. P., … Denning, A. S. (2002). Carbon isotope discrimination of arctic and boreal biomes inferred from remote atmospheric measurements and a biosphere-atmosphere model. Global Biogeochemical Cycles, 16(3), 1-1-1–15. https://doi.org/10.1029/2001gb001435

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