Journal of Geophysical Research: Biogeosciences

Cover image for Vol. 119 Issue 12

Impact Factor: 3.44

ISI Journal Citation Reports © Ranking: 2013: 24/173 (Geosciences Multidisciplinary)

Online ISSN: 2169-8961

Associated Title(s): Journal of Geophysical Research

Improving model estimates of gross primary production


Balancing the global carbon budget is a daunting task complicated by the fact that even the most essential values elude direct observation. Gross primary production, the amount of carbon used by terrestrial vegetation to fuel its annual growth, is often estimated by extrapolation from observations using climate models, including the Community Land Model (CLM). Terrestrial ecosystems are an extremely important sink for atmospheric carbon, so any misrepresentations of gross primary production can have strong effects on the understanding of the global carbon budget and potentially affect lawmakers' ability to plan around it. The widely used model, which is now in its fourth major version and seeks to represent the interplay between vegetation and climate, is known to overestimate global gross primary production by around 35 Pg yr-1 when compared to estimates derived from observations and other models, a surplus equivalent to around 6 times the annual carbon emissions of the United States.To bring the aberrant estimates back into line, Bonan et al. (2011) used a novel data set of gross primary production derived from observations of a global network of microclimate detectors to reevaluate the model's representations of a number of key processes. The researchers updated the CLM's calculations for the temperature dependency of leaf behavior, the effects and abundance of direct versus diffuse sunlight, and the rate of photosynthesis for different plant types. The authors also adjusted how the activity of an individual leaf is interpreted on the scale of the whole canopy. On the basis of their suggested modifications, the authors were able to bring the estimates of gross primary production made by CLM to well within the range of estimates derived from the global observational network.

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