气候变化领域集成服务门户(CCPortal): How Data Set Characteristics Influence Ocean Carbon Export Models

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DOI	10.1029/2018GB005934
	How Data Set Characteristics Influence Ocean Carbon Export Models
	Bisson K.M.; Siegel D.A.; DeVries T.; Cael B.B.; Buesseler K.O.
发表日期	2018
ISSN	0886-6236
EISSN	1944-9224
起始页码	1312
结束页码	1328
卷号	32 期号:9
英文摘要	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.
英文关键词	carbon cycle; carbon flux; mechanistic model; optimization; remote sensing
语种	英语
scopus关键词	carbon cycle; carbon flux; data set; deep water; net primary production; optimization; particulate organic carbon; remote sensing; spatiotemporal analysis
来源期刊	Global Biogeochemical Cycles
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129798
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
推荐引用方式 GB/T 7714	Bisson K.M.,Siegel D.A.,DeVries T.,et al. How Data Set Characteristics Influence Ocean Carbon Export Models[J],2018,32(9).
APA	Bisson K.M.,Siegel D.A.,DeVries T.,Cael B.B.,&Buesseler K.O..(2018).How Data Set Characteristics Influence Ocean Carbon Export Models.Global Biogeochemical Cycles,32(9).
MLA	Bisson K.M.,et al."How Data Set Characteristics Influence Ocean Carbon Export Models".Global Biogeochemical Cycles 32.9(2018).