Modelling the discrimination of 13CO2 above and within a temperate broad-leaved forest canopy on hourly to seasonal time scales



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    1. Ecosystem Science Division, Department of Environmental Science, Policy and Management, 151 Hilgard Hall, University of California, Berkeley, CA 94720, USA and
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    1. Department of Biology, 257 S 1400 East, University of Utah, Salt Lake City, UT 84112, USA
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Correspondence: Dennis D. Baldocchi. E-mail:


Fluxes and concentrations of carbon dioxide and 13CO2 provide information about ecosystem physiological processes and their response to environmental variation. The biophysical model, CANOAK, was adapted to compute concentration profiles and fluxes of 13CO2 within and above a temperate deciduous forest (Walker Branch Watershed, Tennessee, USA). Modifications to the model are described and the ability of the new model (CANISOTOPE) to simulate concentration profiles of 13CO2, its flux density across the canopy–atmosphere interface and leaf-level photosynthetic discrimination against 13CO2 is demonstrated by comparison with field measurements. The model was used to investigate several aspects of carbon isotope exchange between a forest ecosystem and the atmosphere. During the 1998 growing season, the mean photosynthetic discrimination against 13CO2, by the deciduous forest canopy (Δcanopy), was computed to be 22·4‰, but it varied between 18 and 27‰. On a diurnal basis, the greatest discrimination occurred during the early morning and late afternoon. On a seasonal time scale, the greatest diurnal range in Δcanopy occurred early and late in the growing season. Diurnal and seasonal variations in Δcanopy resulted from a strong dependence of Δcanopy on photosynthetically active radiation and vapour pressure deficit of air. Model calculations also revealed that the relationship between canopy-scale water use efficiency (CO2 assimilation/transpiration) and Δcanopy was positive due to complex feedbacks among fluxes, leaf temperature and vapour pressure deficit, a finding that is counter to what is predicted for leaves exposed to well-mixed environments.