Actions for Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions [electronic resource].

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Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Science, 2016.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
pages 598-613 : digital, PDF file
Additional Creators
Lawrence Berkeley National Laboratory, United States. Department of Energy. Office of Science, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
In many ecosystems, nitrogen is the most limiting nutrient for plant growth and productivity. However, current Earth System Models (ESMs) do not mechanistically represent functional nitrogen allocation for photosynthesis or the linkage between nitrogen uptake and root traits. The current version of CLM (4.5) links nitrogen availability and plant productivity via (1) an instantaneous downregulation of potential photosynthesis rates based on soil mineral nitrogen availability, and (2) apportionment of soil nitrogen between plants and competing nitrogen consumers assumed to be proportional to their relative N demands. However, plants do not photosynthesize at potential rates and then downregulate; instead photosynthesis rates are governed by nitrogen that has been allocated to the physiological processes underpinning photosynthesis. Furthermore, the role of plant roots in nutrient acquisition has also been largely ignored in ESMs. We therefore present a new plant nitrogen model for CLM4.5 with (1) improved representations of linkages between leaf nitrogen and plant productivity based on observed relationships in a global plant trait database and (2) plant nitrogen uptake based on root-scale Michaelis-Menten uptake kinetics. Our model improvements led to a global bias reduction in GPP, LAI, and biomass of 70%, 11%, and 49%, respectively. Furthermore, water use efficiency predictions were improved conceptually, qualitatively, and in magnitude. The new model's GPP responses to nitrogen deposition, CO 2 fertilization, and climate also differed from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theoretically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.
Report Numbers
E 1.99:1379371
Subject(s)
Note
Published through SciTech Connect.
05/01/2016.
"ark:/13030/qt38c6b3tf"
Journal of Advances in Modeling Earth Systems 8 2 ISSN 1942-2466 AM
Bardan Ghimire; William J. Riley; Charles D. Koven; Mingquan Mu; James T. Randerson.
Funding Information
AC02-05CH11231
View MARC record | catkey: 23774947