Multisatellite observations of smaller mesoscale eddy generation in the Kuroshio Extension
-
Abstract: Smaller mesoscale eddies (SMEs) have an important effect on the transmission of ocean temperatures, salinity, energy, and marine biochemical processes. However, traditional altimeters, the dominant sensors used to identify and track eddies, have made it challenging to observe SMEs accurately due to resolution limitations. Eddies drive local upwelling or downwelling, leaving signatures on sea surface temperatures (SSTs) and chlorophyll concentrations (Chls). SST can be observed by spaceborne infrared sensors, and Chl can be measured by ocean color remote sensing. Therefore, multisatellite observations provide an opportunity to obtain information to characterize SMEs. In this paper, an eddy detection algorithm based on SST and Chl images is proposed, which identifies eddies by characterizing the spatial and temporal distribution of SST and Chl data. The algorithm is applied to characterize and analyze SMEs in the Kuroshio Extension. Statistical results on their distribution and seasonal variability are shown, and the formation processes are preliminarily discussed. SMEs generation may be contributed by horizontal strain instability, the interaction of topographic obstacles and currents, and wind stress curl.
-
Figure 1. Geographic distribution of the main currents in the Kuroshio Extension (area bounded by a dashed line), the yellow line represents the Kuroshio Current spindle, and the white line represents Oyashio Current. The solid black box is the eddy high incidence area mentioned below. The base map shows the bathymetry data from General Bathymetric Chart of the Oceans (
https://www.gebco.net/data_and_products/gridded_bathymetry_data/ ).
Figure 2. Algorithm flow chart of eddy detection based on sea surface temperature (SST) and chlorophyll concentration (Chl) data.
Figure 3. Examples of cyclonic eddy (CE) and anticyclonic eddy (AE). Vectors are thermal-wind velocities, and the color is sea surface temperature anomaly (SSTA) from the Remote Sensing Systems. The solid lines are eddy boundaries.
Figure 4. An example of eddy identification from sea surface temperature (SST) and chlorophyll concentration (Chl) maps. a. Sea surface temperature anomaly (SSTA) map; b. Chl map; c. eddy shapes extracted from SSTA and Chl maps.
a b c
Figure 5. Flow chart of credibility assessment expresses how to detect eddy both in the extracted shapes from sea surface temperature (SST) and chlorophyll concentration (Chl). a. The extracted eddy features, AE means anticyclonic eddy, CE means cyclonic eddy; b. the marked grid, whose color is meaningless; c. the result after parameter calculation, red circles mean the grids satisfy conditions, black circles are the opposite; d. the final results.
Figure 6. Histograms of eddy diameter (a) and seasonal distribution (b) in the number of smaller mesoscale eddies.
Figure 7. Spatial distribution of the number of identified smaller mesoscale eddies in the Kuroshio Extension region: anticyclonic eddies (a) and cyclonic eddies (b). The bin size is 0.5°×0.5°. The black box marks an area with dense cyclonic eddies.
Figure 8. Upper panels: sea surface temperature anomaly (SSTA) field from NOAA on May 8 (a), May 11 (b), May 14 (c), May 16, 2014 (d), respectively. Lower panels: sea level anomaly (SLA) field (colors) superimposed current velocity field (black arrows) on the same date with upper panels (e−h). The black circles mark the locations of smaller mesoscale eddies identified by SSTA from NOAA. The solid one is the example observed above.
Figure 9. Observed chlorophyll a concentration snapshots on May 19, 2014: the snapshot of observed the smaller mesoscale eddy (a); an enlarged portion of the black box in Fig. 8a (b). The black circles mark the position of the eddy.
Figure 10. The trajectory information of drifter: the snapshot of sea surface temperature anomaly (SSTA) on May 16, 2014, superimposed sample points of drifter from May 8, 2014 to May 25, 2014, at a 6-h time interval (A); the color of points represents four parts of trajectory; the snapshot of SSTA superimposed sample points of drifter from May 13, 2014 to May 25, 2014, shown as an enlarged portion of A (B), calculated properties of the drifter, including sea surface temperature (SST) and velocity (V) (upper panels), vorticity and strain rate (lower panels) (C). The dashed lines (a, b, c) marked the times for the drifter to enter the eddy, leave the eddy center, and leave the eddy, respectively.
Figure 11. Observed sea surface temperature snapshots from NOAA data: the color background represents the sea surface temperature anomaly (SSTA), the black line and circles (derived from NOAA) manifest the location of the smaller mesoscale eddy (SME) (a, b, d, and e); enlarged sea level anomaly (SLA) field superimposed velocity derived from Copernicus Marine Environment Monitoring Service data, the color represents SLA and the arrows represent current vectors (c); observed chlorophyll a concentration snapshot on March 4, 2014 (f).
Figure 12. Sea level anomaly (SLA) field (colors) superimposed current velocity from Copernicus Marine Environment Monitoring Service data (black arrows, derived from Absolute Dynamic Topography) (a−c); sea surface temperature anomaly (SSTA) fields from NOAA (d−f). The black circles represent the locations of the smaller mesoscale eddies.
Figure 13. Chlorophyll a concentration map from OceanColor (a), wind stress curl distribution from Advanced Scatterometer (b, c). The black circles represent the locations of the smaller mesoscale eddies.
Table 1. Algorithm results for the 12 days are used for valuation
Parameters Value P 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 N 139 116 92 72 50 38 25 18 8 Nt 75 61 49 42 34 28 23 17 8 DA 53.96% 52.59% 53.26% 58.33% 68.00% 73.68% 92.00% 94.44% 100% Note: N is the total number of eddies detected by the algorithm; Nt is the number of true eddies identified by manual detection; DA is the detection accuracy of the algorithm. 
下载: 导出CSV
-
[1] Abhishek P, Sil S. 2019. Validation of multi-scale ultra-high resolution (MUR) sea surface temperature with coastal buoys observations and applications for coastal fronts in the Bay of Bengal. In: Proceedings of 2019 URSI Asia-Pacific Radio Science Conference (AP-RASC). New Delhi, India: IEEE [2] Alpers W, Brandt P, Lazar A, et al. 2013. A small-scale oceanic eddy off the coast of West Africa studied by multi-sensor satellite and surface drifter data. Remote Sensing of Environment, 129: 132–143. doi: 10.1016/j.rse.2012.10.032 [3] Capet X, McWilliams J C, Molemaker M J, et al. 2008. Mesoscale to submesoscale transition in the California current system. Part I: flow structure, eddy flux, and observational tests. Journal of Physical Oceanography, 38(1): 29–43. doi: 10.1175/2007JPO3671.1 [4] Chelton D B, Gaube P, Schlax M G, et al. 2011. The influence of nonlinear mesoscale eddies on near-surface oceanic chlorophyll. Science, 334(6054): 328–332. doi: 10.1126/science.1208897 [5] Chen Ge, Tang Junwu, Zhao Chaofang, et al. 2019. Concept design of the “Guanlan” science mission: China’s novel contribution to space oceanography. Frontiers in Marine Science, 6: 194. doi: 10.3389/fmars.2019.00194 [6] Chen Baiyang, Xie Lingling, Zheng Quanan, et al. 2020. Seasonal variability of mesoscale eddies in the Banda Sea inferred from altimeter data. Acta Oceanologica Sinica, 39(12): 11–20. doi: 10.1007/s13131-020-1665-2 [7] Dandapat S, Chakraborty A. 2016. Mesoscale eddies in the western Bay of Bengal as observed from satellite altimetry in 1993–2014: statistical characteristics, variability and three-dimensional properties. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11): 5044–5054. doi: 10.1109/JSTARS.2016.2585179 [8] Dong Changming, Nencioli F, Liu Yu, et al. 2011. An automated approach to detect oceanic eddies from satellite remotely sensed sea surface temperature data. IEEE Geoscience and Remote Sensing Letters, 8(6): 1055–1059. doi: 10.1109/LGRS.2011.2155029 [9] Durand M, Fu Lee-Lueng, Lettenmaier D P, et al. 2010. The surface water and ocean topography mission: observing terrestrial surface water and oceanic submesoscale eddies. Proceedings of the IEEE, 98(5): 766–779. doi: 10.1109/JPROC.2010.2043031 [10] Frenger I, Gruber N, Knutti R, et al. 2013. Imprint of Southern Ocean eddies on winds, clouds and rainfall. Nature Geoscience, 6(8): 608–612. doi: 10.1038/ngeo1863 [11] Fu Lee Lueng, Le Traon P Y. 2006. Satellite altimetry and ocean dynamics. Comptes Rendus Geoscience, 338(14−15): 1063–1076. doi: 10.1016/j.crte.2006.05.015 [12] Gaultier L, Ubelmann C, Fu Lee-Lueng. 2016. The challenge of using future SWOT data for oceanic field reconstruction. Journal of Atmospheric and Oceanic Technology, 33(1): 119–126. doi: 10.1175/JTECH-D-15-0160.1 [13] Gula J, Molemaker M J, McWilliams J C. 2015. Topographic vorticity generation, submesoscale instability and vortex street formation in the Gulf Stream. Geophysical Research Letters, 42(10): 4054–4062. doi: 10.1002/2015GL063731 [14] Gula J, Molemaker M J, McWilliams J C. 2016. Topographic generation of submesoscale centrifugal instability and energy dissipation. Nature Communications, 7(1): 12811. doi: 10.1038/ncomms12811 [15] Hansen D V, Poulain P M. 1996. Quality control and interpolations of WOCE-TOGA drifter data. Journal of Atmospheric and Oceanic Technology, 13(4): 900–909. doi: 10.1175/1520-0426(1996)013<0900:QCAIOW>2.0.CO;2 [16] He Yinghui, Feng Ming, Xie Jieshuo, et al. 2017. Spatiotemporal variations of mesoscale eddies in the Sulu Sea. Journal of Geophysical Research: Oceans, 122(10): 7867–7879. doi: 10.1002/2017JC013153 [17] Hsu P C, Ho C Y, Lee H J, et al. 2020. Temporal variation and spatial structure of the Kuroshio-induced submesoscale island vortices observed from GCOM-C and Himawari-8 data. Remote Sensing, 12(5): 883. doi: 10.3390/rs12050883 [18] Hu Chuanmin, Lee Zhongping, Franz B. 2012. Chlorophyll a algorithms for oligotrophic oceans: a novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(C1): C01011 [19] Huang Xueping, Zhou Xinhua, Wang Zhiqiang, et al. 2012. CD99 triggers upregulation of mir-9-modulated PRDM1/BLIMP1 in Hodgkin/Reed-Sternberg cells and induces redifferentiation. International Journal of Cancer, 131(4): E382–E394. doi: 10.1002/ijc.26503 [20] Isoguchi O, Shimada M, Sakaida F, et al. 2009. Investigation of Kuroshio-induced cold-core eddy trains in the lee of the Izu islands using high-resolution satellite images and numerical simulations. Remote Sensing of Environment, 113(9): 1912–1925. doi: 10.1016/j.rse.2009.04.017 [21] Ji Jinlin, Dong Changming, Zhang Biao, et al. 2018. Oceanic eddy characteristics and generation mechanisms in the Kuroshio Extension region. Journal of Geophysical Research: Oceans, 123(11): 8548–8567. doi: 10.1029/2018JC014196 [22] Jia Yinglai, Chen Longjing, Liu Qinyu, et al. 2019. The role of background wind and moisture in the atmospheric response to oceanic eddies during winter in the Kuroshio Extension region. Atmosphere, 10(9): 5279 [23] Klein P, Lapeyre G. 2009. The oceanic vertical pump induced by mesoscale and submesoscale turbulence. Annual Review of Marine Science, 1: 351–375. doi: 10.1146/annurev.marine.010908.163704 [24] Kubryakov A A, Stanichny S V. 2015. Seasonal and interannual variability of the black sea eddies and its dependence on characteristics of the large-scale circulation. Deep-Sea Research Part I: Oceanographic Research Papers, 97: 80–91. doi: 10.1016/j.dsr.2014.12.002 [25] Li Jianing, Dong Jihai, Yang Qingxuan, et al. 2019a. Spatial-temporal variability of submesoscale currents in the South China Sea. Journal of Oceanology and Limnology, 37(2): 474–485. doi: 10.1007/s00343-019-8077-1 [26] Li Jitao, Liang Yongquan, Zhang Jie, et al. 2019b. A new automatic oceanic mesoscale eddy detection method using satellite altimeter data based on density clustering. Acta Oceanologica Sinica, 38(5): 134–141. doi: 10.1007/s13131-019-1447-x [27] Li Yuheng, Sun Weifu, Zhang Jie, et al. 2021. Reconstruction of arctic SST data and generation of multi-source satellite fusion products with high temporal and spatial resolutions. Remote Sensing Letters, 12(7): 695–703. doi: 10.1080/2150704X.2021.1931531 [28] Luo Shihao, Jing Zhiyou, Qi Yiquan. 2020. Submesoscale flows associated with convergent strain in an anticyclonic eddy of the Kuroshio Extension: a high-resolution numerical study. Ocean Science Journal, 55(2): 249–264. doi: 10.1007/s12601-020-0022-x [29] McGillicuddy Jr D J. 2016. Mechanisms of physical-biological-biogeochemical interaction at the oceanic mesoscale. Annual Review of Marine Science, 8: 125–159. doi: 10.1146/annurev-marine-010814-015606 [30] McWilliams J C, Colas F, Molemaker M J. 2009. Cold filamentary intensification and oceanic surface convergence lines. Geophysical Research Letters, 36(18): L18602. doi: 10.1029/2009GL039402 [31] Munk W. 2001. Spirals on the sea. Scientia Marina, 65(S2): 193–198. doi: 10.3989/scimar.2001.65s2193 [32] Nencioli F, Dong Changming, Dickey T, et al. 2010. A vector geometry-based eddy detection algorithm and its application to a high-resolution numerical model product and high-frequency radar surface velocities in the Southern California Bight. Journal of Atmospheric and Oceanic Technology, 27(3): 564–579. doi: 10.1175/2009JTECHO725.1 [33] Qian Sijia, Yu Fangjie, Chen Ge. 2021. Distribution characteristics of eddies with a scale of 50~100 km in the Kuroshio Extension Region. Marine Sciences, 45(11): 10–19 [34] Qiu Bo, Chen Shuiming. 2005. Eddy-induced heat transport in the subtropical North Pacific from Argo, TMI, and altimetry measurements. Journal of Physical Oceanography, 35(4): 458–473. doi: 10.1175/JPO2696.1 [35] Qiu Bo, Chen Shuiming. 2010. Eddy-mean flow interaction in the decadally modulating Kuroshio Extension system. Deep-Sea Research Part II: Topical Studies in Oceanography, 57(13−14): 1098–1110. doi: 10.1016/j.dsr2.2008.11.036 [36] Qiu Bo, Kelly K A, Joyce T M. 1991. Mean flow and variability in the Kuroshio Extension from Geosat altimetry data. Journal of Geophysical Research: Oceans, 96(C10): 18491–18507. doi: 10.1029/91JC01834 [37] Rubio A, Caballero A, Orfila A, et al. 2018. Eddy-induced cross-shelf export of high Chl-a coastal waters in the SE Bay of Biscay. Remote Sensing of Environment, 205: 290–304. doi: 10.1016/j.rse.2017.10.037 [38] Sasai Y, Richards K J, Ishida A, et al. 2010. Effects of cyclonic mesoscale eddies on the marine ecosystem in the Kuroshio Extension region using an eddy-resolving coupled physical-biological model. Ocean Dynamics, 60(3): 693–704. doi: 10.1007/s10236-010-0264-8 [39] Sasaki H, Klein P, Qiu Bo, et al. 2014. Impact of oceanic-scale interactions on the seasonal modulation of ocean dynamics by the atmosphere. Nature Communications, 5(1): 5636. doi: 10.1038/ncomms6636 [40] Shafeeque M, Balchand A N, Shah P, et al. 2021. Spatio-temporal variability of chlorophyll-a in response to coastal upwelling and mesoscale eddies in the South Eastern Arabian Sea. International Journal of Remote Sensing, 42(13): 4840–4867 [41] Shan Xuan, Jing Zhao, Sun Bingrong, et al. 2020. Impacts of ocean current-atmosphere interactions on mesoscale eddy energetics in the Kuroshio Extension region. Geoscience Letters, 7(1): 3. doi: 10.1186/s40562-020-00152-w [42] Su Zhan, Wang Jinbo, Klein P, et al. 2018. Ocean submesoscales as a key component of the global heat budget. Nature Communications, 9: 775. doi: 10.1038/s41467-018-02983-w [43] Sun Shuangwen, Fang Yue, Liu Baochao, et al. 2016. Coupling between SST and wind speed over mesoscale eddies in the South China Sea. Ocean Dynamics, 66(11): 1467–1474. doi: 10.1007/s10236-016-0993-4 [44] Taburet G, Sanchez-Roman A, Ballarotta M, et al. 2019. DUACS DT2018: 25 years of reprocessed sea level altimetry products. Ocean Science, 15(5): 1207–1224. doi: 10.5194/os-15-1207-2019 [45] Tedesco P, Gula J, Ménesguen C, et al. 2019. Generation of submesoscale frontal eddies in the Agulhas current. Journal of Geophysical Research: Oceans, 124(11): 7606–7625. doi: 10.1029/2019JC015229 [46] Trott C B, Subrahmanyam B, Nyadjro E S. 2019. Influence of mesoscale features on mixed layer dynamics in the Arabian Sea. Journal of Geophysical Research: Oceans, 124(5): 3361–3377. doi: 10.1029/2019JC014965 [47] Uchiyama Y, Suzue Y, Yamazaki H. 2017. Eddy-driven nutrient transport and associated upper-ocean primary production along the Kuroshio. Journal of Geophysical Research: Oceans, 122(6): 5046–5062. doi: 10.1002/2017JC012847 [48] Wang Jiahao, Mao Kefeng, Chen Xi, et al. 2020. Evolution and structure of the Kuroshio Extension front in spring 2019. Journal of Marine Science and Engineering, 8(7): 502. doi: 10.3390/jmse8070502 [49] Williams R G. 2011. Ocean eddies and plankton blooms. Nature Geoscience, 4(11): 739–740. doi: 10.1038/ngeo1307 [50] Yang Yang, San Liang X. 2018. On the seasonal eddy variability in the Kuroshio Extension. Journal of Physical Oceanography, 48(8): 1675–1689. doi: 10.1175/JPO-D-18-0058.1 [51] Yang Xiao, Xu Guangjun, Liu Yu, et al. 2020. Multi-source data analysis of mesoscale eddies and their effects on surface chlorophyll in the bay of Bengal. Remote Sensing, 12(21): 3485. doi: 10.3390/rs12213485 [52] Yasuda I, Okuda K, Hirai M. 1992. Evolution of a Kuroshio warm-core ring-variability of the hydrographic structure. Deep-Sea Research Part A. Oceanographic Research Papers, 39(S1): S131–S161 [53] Zatsepin A, Kubryakov A, Aleskerova A, et al. 2019. Physical mechanisms of submesoscale eddies generation: evidences from laboratory modeling and satellite data in the Black Sea. Ocean Dynamics, 69(2): 253–266. doi: 10.1007/s10236-018-1239-4 [54] Zhang Yingjun, Hu Chuanmin, Liu Yonggang, et al. 2019a. Submesoscale and mesoscale eddies in the Florida Straits: observations from satellite ocean color measurements. Geophysical Research Letters, 46(22): 13262–13270. doi: 10.1029/2019GL083999 [55] Zhang Zhengguang, Qiu Bo. 2018. Evolution of submesoscale ageostrophic motions through the life cycle of oceanic mesoscale eddies. Geophysical Research Letters, 45(21): 11847–11855. doi: 10.1029/2018GL080399 [56] Zhang Zhengguang, Qiu Bo. 2020. Surface chlorophyll enhancement in mesoscale eddies by submesoscale spiral bands. Geophysical Research Letters, 47(14): e2020GL088820 [57] Zhang Zhengguang, Qiu Bo, Klein P, et al. 2019b. The influence of geostrophic strain on oceanic ageostrophic motion and surface chlorophyll. Nature Communications, 10(1): 2838. doi: 10.1038/s41467-019-10883-w [58] Zhang Zhiwei, Tian Jiwei, Qiu Bo, et al. 2016. Observed 3D structure, generation, and dissipation of oceanic mesoscale eddies in the South China Sea. Scientific Reports, 6: 24349. doi: 10.1038/srep24349 [59] Zhang Chunhua, Xi Xiaoliang, Liu Songtao, et al. 2014. A mesoscale eddy detection method of specific intensity and scale from SSH image in the South China Sea and the Northwest Pacific. Science China Earth Sciences, 57(8): 1897–1906. doi: 10.1007/s11430-014-4839-y [60] Zhang Zhiwei, Zhang Yuchen, Qiu Bo, et al. 2020. Spatiotemporal characteristics and generation mechanisms of submesoscale currents in the northeastern South China Sea revealed by numerical simulations. Journal of Geophysical Research: Oceans, 125(2): e2019JC015404 [61] Zheng Shaojun, Du Yan, Li Jiaxun, et al. 2015. Eddy characteristics in the South Indian Ocean as inferred from surface drifters. Ocean Science, 11(3): 361–371. doi: 10.5194/os-11-361-2015 -
点击查看大图
计量
- 文章访问数: 886
- HTML全文浏览量: 325
- PDF下载量: 51
- 被引次数: 0
