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Publication Date
1 May 2016

Representing Leaf and Root Physiological Traits in CLM Improves Global Carbon and Nitrogen Cycling Predictions



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 alloca-tion 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 pho-tosynthesis rates are governed by nitrogen that has been allocated to the physiological processes underpin-ning 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 represen-tations 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, CO2 fertilization, and climate also dif-fered from the baseline model. The mechanistic representation of leaf-level nitrogen allocation and a theo-retically consistent treatment of competition with belowground consumers led to overall improvements in global carbon cycling predictions.

“Representing Leaf And Root Physiological Traits In Clm Improves Global Carbon And Nitrogen Cycling Predictions”. 2016. Journal Of Advances In Modeling Earth Systems 8: 598-613. doi:10.1002/2015MS000538.
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