Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions. (1st May 2016)

• Record Type: Journal Article
• Authors: Ghimire, Bardan; Riley, William J.; Koven, Charles D.; Mu, Mingquan; Randerson, James T.
• Is Part Of: Journal of advances in modeling earth systems. Volume 8:Number 2(2016)
• Page Start: 598
• Page End: 613
• Publication Date: 2016-05-01
• Subjects: GPP -- carbon cycle -- nitrogen cycle -- leaf traits -- root traits -- CLM; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1002/2015MS000538
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: 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, CO2 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. Key Points: Improved representation of root and leaf physiological traits in Community Land Model Model changes led to an overall improvement in global carbon cycling predictions Model improved with mechanistic leaf‐level nitrogen allocation and root nitrogen uptake kinetics