On the Estimation of Lagrangian Diffusivity: Influence of Nonstationary Mean Flow
Qian, YK; Peng, SQ; Liang, CX; Lumpkin, R; speng@scsio.ac.cn
2014
发表期刊JOURNAL OF PHYSICAL OCEANOGRAPHY
ISSN0022-3670
卷号44期号:10页码:2796-2811
摘要Eddy mean flow decomposition is crucial to the estimation of Lagrangian diffusivity based on drifter data. Previous studies have shown that inhomogeneous mean flow induces shear dispersion that increases the estimated diffusivity with time. In the present study, the influences of nonstationary mean flows on the estimation of Lagrangian diffusivity, especially the asymptotic behavior, are investigated using a first-order stochastic model, with both idealized and satellite-based oceanic mean flows. Results from both experiments show that, in addition to inhomogeneity, nonstationarity of mean flows that contain slowly varying signals, such as a seasonal cycle, also cause large biases in the estimates of diffusivity within a time lag of 2 months if a traditional binning method is used. Therefore, when assessing Lagrangian diffusivity over regions where a seasonal cycle is significant [e.g., the Indian Ocean (IO) dominated by monsoon winds], inhomogeneity and nonstationarity of the mean flow should be simultaneously taken into account in eddy mean flow decomposition. A temporally and spatially continuous fit through the Gauss Markov (GM) estimator turns out to be very efficient in isolating the effects of inhomogeneity and nonstationarity of the mean flow, resulting in estimates that are closest to the true diffusivity, especially in regions where strong seasonal cycles exist such as the eastern coast of Somalia and the equatorial IO.
部门归属[Qian, Yu-Kun ; Peng, Shiqiu] Chinese Acad Sci, South China Sea Inst Oceanol, State Key Lab Trop Oceanog, Guangzhou, Guangdong, Peoples R China ; [Liang, Chang-Xia] State Ocean Adm, South China Sea Marine Predict Ctr, Guangzhou, Guangdong, Peoples R China ; [Lumpkin, Rick] NOAA, Atlantic Oceanog & Meteorol Lab, Miami, FL 33149 USA
学科领域Oceanography
资助者Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory
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收录类别sci
资助项目LTO
资助者Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory ; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA11010304]; MOST of China [2011CB403505, 2010CB950302]; Knowledge Innovation Program of the Chinese Academy of Sciences [SQ201305]; National Natural Science Foundation of China [41376021, 41306013]; Hundred Talent Program of the Chinese Academy of Sciences; NOAA's Climate Program Office; Atlantic Oceanographic and Meteorological Laboratory
WOS记录号WOS:000342861100012
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文献类型期刊论文
条目标识符http://ir.scsio.ac.cn/handle/344004/10503
专题热带海洋环境国家重点实验室(LTO)
通讯作者speng@scsio.ac.cn
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GB/T 7714
Qian, YK,Peng, SQ,Liang, CX,et al. On the Estimation of Lagrangian Diffusivity: Influence of Nonstationary Mean Flow[J]. JOURNAL OF PHYSICAL OCEANOGRAPHY,2014,44(10):2796-2811.
APA Qian, YK,Peng, SQ,Liang, CX,Lumpkin, R,&speng@scsio.ac.cn.(2014).On the Estimation of Lagrangian Diffusivity: Influence of Nonstationary Mean Flow.JOURNAL OF PHYSICAL OCEANOGRAPHY,44(10),2796-2811.
MLA Qian, YK,et al."On the Estimation of Lagrangian Diffusivity: Influence of Nonstationary Mean Flow".JOURNAL OF PHYSICAL OCEANOGRAPHY 44.10(2014):2796-2811.
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