气候变化领域集成服务门户(CCPortal): Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS)

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DOI	10.1016/j.rse.2020.111719
	Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS)
	Wu M.; Scholze M.; Kaminski T.; Voßbeck M.; Tagesson T.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	The terrestrial carbon cycle is an important component of the global carbon budget due to its large gross exchange fluxes with the atmosphere and their sensitivity to climate change. Terrestrial biosphere models show large uncertainties in simulating carbon fluxes, which impact global carbon budget assessments. The land surface carbon cycle is tightly controlled by soil moisture through plant physiological processes. Accurate soil moisture observations thereby have the potential to improve the modeling of carbon fluxes in a model-data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate six years of surface soil moisture provided by the SMOS satellite in combination with global-scale observations of atmospheric CO2 concentrations. We find that assimilation of SMOS soil moisture exhibits better performance on soil hydrology modeling at both global and site-level than only assimilating atmospheric CO2 concentrations, and it improves the soil moisture simulation particularly in mid- to high-latitude regions where the plants suffer from water stress frequently. The optimized model also shows good agreements with inter-annual variability in simulated Net Primary Productivity (NEP) and Gross Primary Productivity (GPP) from an atmospheric inversion (Jena CarboScope) and the up-scaled eddy covariance flux product (FLUXNET-MTE), respectively. Correlation between SIF (Solar Induced Fluorescence) and optimized GPP also shows to be the highest when soil moisture and atmospheric CO2 are simultaneously assimilated. In general, CCDAS obtains smaller annual mean NEP values (1.8 PgC/yr) than the atmospheric inversion and an ensemble of Dynamic Global Vegetation Models (DGVMs), but larger GPP values (167.8 PgC/yr) than the up-scaled eddy covariance dataset (FLUXNET-MTE) and the MODIS based GPP product for the years 2010 to 2015. This study demonstrates the high potential of constraining simulations of the terrestrial biosphere carbon cycle on inter-annual time scales using long-term microwave observations of soil moisture. © 2020 Elsevier Inc.
英文关键词	Data assimilation; Inter-annual variability; SMOS L3 soil moisture; Terrestrial biosphere carbon fluxes; Uncertainty evaluation
语种	英语
scopus关键词	Biospherics; Budget control; Carbon; Carbon dioxide; Climate change; Data fusion; Ecosystems; Moisture control; Photosynthesis; Phytoplankton; Soil moisture; Carbon fluxes; Data assimilation; Gross primary productivity; Interannual variability; Net primary productivity; Solar-induced fluorescences; Terrestrial carbon cycle; Uncertainty evaluation; Soil surveys; annual variation; biosphere; carbon flux; data assimilation; eddy covariance; MODIS; sampling; satellite altimetry; SMOS; soil moisture; uncertainty analysis
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179398
作者单位	International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China; Department of Physical Geography and Ecosystem Science, Lund University, Lund, 22362, Sweden; The Inversion Lab, Hamburg, Germany; Department of Geosciences and Natural Resource Management, University of Copenhagen, ?ster Voldgade 10, Copenhagen, DK-1350, Denmark
推荐引用方式 GB/T 7714	Wu M.,Scholze M.,Kaminski T.,等. Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS)[J],2020,240.
APA	Wu M.,Scholze M.,Kaminski T.,Voßbeck M.,&Tagesson T..(2020).Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS).Remote Sensing of Environment,240.
MLA	Wu M.,et al."Using SMOS soil moisture data combining CO2 flask samples to constrain carbon fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System (CCDAS)".Remote Sensing of Environment 240(2020).