Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

Ghimire, Bardan; Riley, William J.; Koven, Charles D.; Mu, Mingquan; Randerson, James T.

doi:10.1002/2015MS000538

Title: Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions

Journal Article · Sun May 01 00:00:00 EDT 2016 · Journal of Advances in Modeling Earth Systems

DOI:https://doi.org/10.1002/2015MS000538· OSTI ID:1379371

Ghimire, Bardan ^[1]; Riley, William J. ^[1]; Koven, Charles D. ^[1]; Mu, Mingquan ^[2]; Randerson, James T. ^[2]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division
Univ. of California, Irvine, CA (United States). Dept. of Earth System Science

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1379371

Journal Information:: Journal of Advances in Modeling Earth Systems, Vol. 8, Issue 2; ISSN 1942-2466

Publisher:: American Geophysical Union (AGU)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 57 works

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