DOI: 10.1111/gcb.12474
论文题名: Climate-driven uncertainties in modeling terrestrial gross primary production: A site level to global-scale analysis
作者: Barman R. ; Jain A.K. ; Liang M.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2014
卷: 20, 期: 5 起始页码: 1394
结束页码: 1411
语种: 英语
英文关键词: Gross primary production (GPP)
; Integrated science assessment model (ISAM)
; Land surface model
; Uncertainty
Scopus关键词: air temperature
; broad-leaved forest
; climate effect
; eddy covariance
; evergreen forest
; grassland
; humidity
; primary production
; savanna
; shrubland
; terrestrial ecosystem
; uncertainty analysis
; water stress
; carbon cycle
; climate
; ecosystem
; meteorological phenomena
; plant physiology
; theoretical model
; uncertainty
; Carbon Cycle
; Climate
; Ecosystem
; Meteorological Concepts
; Models, Theoretical
; Plant Physiological Phenomena
; Uncertainty
英文摘要: We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis-driven GPP bias was significantly positive with respect to the observed meteorology-driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m-2 yr-1 across sites (ca. 15% of site level GPP). At the northern mid-/high-latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20-30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short-wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site-level reanalysis-driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate-driven uncertainties in carbon, energy, and water fluxes using a single modeling framework. © 2013 John Wiley & Sons Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/62035
Appears in Collections: 影响、适应和脆弱性
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作者单位: Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Civil and Environmental Engineering, Princeton University, Princeton, NJ, United States
Recommended Citation:
Barman R.,Jain A.K.,Liang M.. Climate-driven uncertainties in modeling terrestrial gross primary production: A site level to global-scale analysis[J]. Global Change Biology,2014-01-01,20(5)