DOI: 10.1002/2015MS000538
Scopus记录号: 2-s2.0-84992302742
论文题名: Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions
作者: Ghimire B ; , Riley W ; J ; , Koven C ; D ; , Mu M ; , Randerson J ; T
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2016
卷: 8, 期: 2 起始页码: 598
结束页码: 613
语种: 英语
英文关键词: Carbon dioxide
; Climate models
; Ecology
; Forecasting
; Forestry
; Nutrients
; Photosynthesis
; Physiology
; Productivity
; Carbon cycles
; Leaf traits
; Nitrogen availability
; Nitrogen cycles
; Physiological process
; Root traits
; Soil mineral nitrogen
; Water use efficiency
; Nitrogen
; biomass
; carbon cycle
; climate modeling
; growth rate
; leaf area
; leaf area index
; nitrogen cycle
; nutrient availability
; nutrient limitation
; photosynthesis
; physiological response
; root system
; soil nitrogen
; water use
; water use efficiency
英文摘要: 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. © 2016. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75896
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States; Department of Earth System Science, University of California, Irvine, CA, United States
Recommended Citation:
Ghimire B,, Riley W,J,et al. Representing leaf and root physiological traits in CLM improves global carbon and nitrogen cycling predictions[J]. Journal of Advances in Modeling Earth Systems,2016-01-01,8(2)