DOI: 10.1029/2018GB005934
论文题名: How Data Set Characteristics Influence Ocean Carbon Export Models
作者: Bisson K.M. ; Siegel D.A. ; DeVries T. ; Cael B.B. ; Buesseler K.O.
刊名: Global Biogeochemical Cycles
ISSN: 0886-6236
EISSN: 1944-9224
出版年: 2018
卷: 32, 期: 9 语种: 英语
英文关键词: carbon cycle
; carbon flux
; data set
; deep water
; net primary production
; optimization
; particulate organic carbon
; remote sensing
; spatiotemporal analysis
学科: carbon cycle
; carbon flux
; mechanistic model
; optimization
; remote sensing
中文摘要: Ocean biological processes mediate the transport of roughly 10 petagrams of carbon from the surface to the deep ocean each year and thus play an important role in the global carbon cycle. Even so, the globally integrated rate of carbon export out of the surface ocean remains highly uncertain. Quantifying the processes underlying this biological carbon export requires a synthesis between model predictions and available observations of particulate organic carbon (POC) flux; yet the scale dissimilarities between models and observations make this synthesis difficult. Here we compare carbon export predictions from a mechanistic model with observations of POC fluxes from several data sets compiled from the literature spanning different space, time, and depth scales as well as using different observational methodologies. We optimize model parameters to provide the best match between model-predicted and observed POC fluxes, explicitly accounting for sources of error associated with each data set. Model-predicted globally integrated values of POC flux at the base of the euphotic layer range from 3.8 to 5.5 Pg C/year, depending on the data set used to optimize the model. Modeled carbon export pathways also vary depending on the data set used to optimize the model, as well as the satellite net primary production data product used to drive the model. These findings highlight the importance of collecting field data that average over the substantial natural temporal and spatial variability in carbon export fluxes, and advancing satellite algorithms for ocean net primary production, in order to improve predictions of biological carbon export. ©2018. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160202
Appears in Collections: 气候变化与战略
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作者单位: Earth Research Institute, University of California, Santa Barbara, CA, United States; Massachusetts Institute of Technology, Cambridge, MA, United States; Woods Hole Oceanographic Institution, Woods Hole, MA, United States
Recommended Citation:
Bisson K.M.,Siegel D.A.,DeVries T.,et al. How Data Set Characteristics Influence Ocean Carbon Export Models[J]. Global Biogeochemical Cycles,2018-01-01,32(9)