Articles | Volume 16, issue 4
https://doi.org/10.5194/os-16-895-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-16-895-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jul 2020
Research article |
| 27 Jul 2020
| 27 Jul 2020
Anomalous distribution of distinctive water masses over the Carlsberg Ridge in May 2012
Hailun He
CORRESPONDING AUTHOR
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Yuan Wang
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Xiqiu Han
CORRESPONDING AUTHOR
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Yanzhou Wei
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Pengfei Lin
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Zhongyan Qiu
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Yejian Wang
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
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Cited articles
Argo group: Argo float vertical profile, available at: http://www.argodatamgt.org, last access: 16 April 2017. a
Atlas, R., Hoffman, R. N., Ardizzone, J., Leidner, S. M., Jusem, J. C., Smith,
D. K., and Gombos, D.: A cross-calibrated, multiplatform ocean surface wind
velocity product for meteorological and oceanographic applications, B.
Am. Meteorol. Soc., 92, 157–174, 2011. a
Barnes, S. L.: Applications of the Barnes Objective Analysis Scheme. Part
I: Effects of undersampling, wave position, and station randomness, J.
Atmos. Ocean. Tech., 11, 1433–1448, 1994. a
Beal, L. M., Hormann, V., Lumpkin, R., and Foltz, G. R.: The response of the
surface circulation of the Arabian Sea to monsoonal forcing, J. Phys.
Oceanogr., 43, 2008–2022, 2013. a
Bower, A. S., Hunt, H. D., and Price, J. F.: Character and dynamics of the Red
Sea and Persian Gulf outflows, J. Geophys. Res.-Oceans, 105, 6387–6414,
https://doi.org/10.1029/1999JC900297, 2000. a, b
Carton, J. A. and Giese, B. S.: A reanalysis of ocean climate using Simple
Ocean Data Assimilation (SODA), Mon. Weather Rev., 136, 2999–3017,
2008. a
Chelton, D. B., Schlax, M. G., and Samelson, R. M.: Global observations of nonlinear
mesoscale eddies, Prog. Oceanogr., 91, 167–216, 2011. a
Chen, G. X., Han, W. Q., Li, Y. L., Wang, D. X., and McPhaden, M. J.:
Seasonal-to-interannual time scale dynamics of the equatorial undercurrent in
the Indian Ocean, J. Phys. Oceanogr., 54, 1532–1553, 2015. a
Cleveland, W. S. and Grosse, E.: Computational methods for local regression,
Stat. Comput., 1, 47–62, 1991. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.:
The Operational Sea Surface Temperature and Sea Ice analysis (OSTIA) system,
Remote Sens. Environ., 116, 140–158, 2012. a
Durgadoo, J. V., Rühs, S., Biastoch, A., and Böning, C. W. B.: Indian Ocean
sources of Agulhas leakage, J. Geophys. Res.-Oceans, 122, 3481–3499,
https://doi.org/10.1002/2016JC012676, 2017. a
Edwards, C. A., Moore, A. M., Hoteit, I., and Cornuelle, B. D.: Regional ocean
data assimilation, Annu. Rev. Mar. Sci., 7, 21–42, 2015. a
Emery, W. J.: Water Types and Water Masses, in: Encyclopedia of Ocean Sciences
(Second Edition), edited by: Steele, J. H., pp. 291–299, Academic Press,
Oxford, second edn.,
https://doi.org/10.1016/B978-012374473-9.00108-9, 2001. a, b, c, d
GHRSST project: Analysed foundation sea surface temperature over the global ocean: A merged multi-sensor L4 Foundation SST product (OSTIA), available at: http://ghrsst-pp.metoffice.com/pages/latest_analysis/index.htm, last access: 27 June 2017. a
Han, W. and McCreary Jr., J. P.: Modeling salinity distributions in the Indian
Ocean, J. Geophys. Res.-Oceans, 106, 859–877, https://doi.org/10.1029/2000JC000316,
2001. a
HYCOM Project: HYCOM + NCODA Global 1/12∘ Reanalysis dataset, version GLBu0.08/expt_19.1, available at: https://www.hycom.org/data/glbu0pt08/expt-19pt1, last access: 25 August 2017. a
Lagerloef, G. S. E., Mitchum, G. T., Lukas, R. B., and Niiler, P. P.: Tropical
Pacific near-surface currents estimated from altimeter, wind, and drifter
data, J. Geophys. Res.-Oceans, 104, 23313–23326, 1999. a
Lapeyre, G. and Klein, P.: Dynamics of the upper cceanic layers in terms of
surface quasigeostrophy theory, J. Phys. Oceanogr., 36, 165–176,
https://doi.org/10.1175/JPO2840.1, 2006. a
Li, Y. L. and Han, W. Q.: Decadal sea level variations in the Indian Ocean
investigated with HYCOM: Roles of climate modes, ocean internal variability,
and stochastic wind forcing, J. Climate, 28, 9143–9165, 2015. a
Liu, L., Peng, S., Wang, J., and Huang, R. X.: Retrieving density and velocity
fields of the ocean's interior from surface data, J. Geophys. Res.-Oceans,
119, 8512–8529, https://doi.org/10.1002/2014JC010221, 2014. a
McCreary Jr., J. P.: Modeling equatorial ocean circulation, Annu. Rev. Fluid Mech.,
17, 359–409, 1985. a
Murton, B. J., Baker, E. T., Sands, C. M., and German, C. R.: Detection of an
unusually large hydrothermal event plume above the slow-spreading Carlsberg
Ridge: NW Indian Ocean, Geophys. Res. Lett., 33, L10608, https://doi.org/10.1029/2006GL026048, 2006. a
NODC: World Ocean Atlas 2013, version 2, available at: https://www.nodc.noaa.gov/, last access: 6 May 2017. a
Prasad, T. G. and Ikeda, M.: A numerical study of the seasonal variability of
Arabian Sea high-salinity water, J. Geophys. Res.-Oceans, 107, 3197,
https://doi.org/10.1029/2001JC001139, 2002. a, b
Rhines, P. B.: Waves and turbulence on a beta-plane, J. Fluid Mech., 69,
417–443, 1975. a
Rhines, P. B. and Young, W. R.: Homogenization of potential vorticity in
planetary gyres, J. Fluid Mech., 122, 347–367, 1982. a
Riser, S. C., Freeland, H. J., Roemmich, D., Wijffels, S., Troisi, A.,
Belbéoch, M., Gilbert, D., Xu, J., Pouliquen, S., Thresher, A., Le Traon,
P.-Y., Maze, G., Klein, B., Ravichandran, M., Grant, F., Poulain, P.-M.,
Suga, T., Lim, B., Sterl, A., Sutton, P., Mork, K.-A., Vélez-Belchí, P. J.,
Ansorge, I., King, B., Turton, J., Baringer, M., and Jayne, S. R.: Fifteen
years of ocean observations with the global Argo array, Nat. Clim.
Change, 6, 145–153, 2016. a
Saj, R. P.: Surface velocity estimates of the North Indian Ocean from satellite
gravity and altimeter missions, Int. J. Remote Sens., 38, 296–313, 2017. a
Saji, N. H., Goswami, B. N., Vinayachandran, P. N., and Yamagata, T.: A dipole
mode in the tropical Indian Ocean, Nature, 401, 360–363, 1999. a
Schott, F. A., Xie, S. P., and McCreary Jr., J. P.: Indian Ocean
circulation and climate variability, Rev. Geophys., 47, RG1002, https://doi.org/10.1029/2007RG000245, 2009. a, b, c
Shapiro, G. and Meschanov, S.: Distribution and spreading of Red Sea Water and
salt lens formation in the northwest Indian Ocean, Deep-Sea Res.-Pt. A, 38, 21–34,
https://doi.org/10.1016/0198-0149(91)90052-H, 1991. a, b, c
Sharma, G. S., Gouveia, A. D., and Satyendranath, S.: Incursion of the
Pacific Ocean water into the Indian Ocean, Proc. Indian Acad. Sci.,
87A, 29–45, 1978. a
SODA Project: SODA3 ocean climate reanalysis dataset, version 3.3.1, available at: https://www.atmos.umd.edu/~ocean/, last access: 27 August 2017. a
SSALTO/DUACS: Delayed-time merged all satellites Global Ocean Gridded Absolute Dynamic Topography L4 product, available at: http://www.aviso.altimetry.fr, last access: 3 March 2017. a
Vecchi, G. A., Xie, S. P., and Fischer, A. S.: Ocean-atmosphere covariability
in the Western Arabian Sea, J. Climate, 17, 1213–1224, 2004. a
Vitale, S. S., DiMarco, S. F., Seidel, H. F., and Wang, Z. K.: Circulation
analysis in the northwest Indian Ocean using ARGO floats and surface
drifter observations, and SODA reanalysis output, Dynam. Atmos. Oceans, 78,
57–70, 2017. a
Wang, J., Flierl, G. R., LaCasce, J. H., McClean, J. L., and Mahadevan, A.:
Reconstructing the ocean's interior from surface data, J. Phys. Oceanogr.,
43, 1611–1626, https://doi.org/10.1175/JPO-D-12-0204.1, 2013. a
Wang, S., Zhu, W. J., Ma, J., Ji, J. L., Yang, J. S., and Dong, C. M.:
Variability of the Great Whirl and its impacts on atmospheric processes,
Remote Sens., 11, 322, https://doi.org/10.3390/rs11030322, 2019. a
Webster, P. J., Moore, A. M., Loschnigg, J. P., and Leben, R. R.: Coupled
ocean-atmosphere dynamics in the Indian Ocean during 1997–98, Nature,
401, 356–360, 1999. a
Wentz, F. J., Scott, J., Hoffman, R., Leidner, M., Atlas, R., and Ardizzone, J.: Remote Sensing Systems Cross-Calibrated Multi-Platform (CCMP) 6-hourly ocean vector wind analysis product on 0.25 deg grid, Version 2.0, Remote Sensing Systems, Santa Rosa, CA, available at: http://www.remss.com/measurements/ccmp (last access: 28 June 2017), 2015. a
Xie, S. P., Annamalai, H., Schott, F. A., and McCreary Jr., J. P.: Structure
and mechanisms of south Indian Ocean climate variability, J. Climate, 15,
864–878, 2002. a
Yan, H., Wang, H., Zhang, R., Chen, J., Bao, S., and Wang, G.: A
dynamical-statistical approach to retrieve the ocean interior structure from
surface data: SQG-mEOF-R, J. Geophys. Res.-Oceans, 125, e2019JC015840,
https://doi.org/10.1029/2019JC015840, 2020. a
Yang, G., He, H. L., Wang, Y., Han, X. Q., and Wang, Y. J.: Evaluating a
satellite-based sea surface temperature by shipboard survey in the
Northwest Indian Ocean, Acta Oceanol. Sin., 35, 52–58, 2016. a
Yin, X. Q., Qiao, F. L., Yang, Y. Z., Xia, C. S., and Chen, X. Y.: Argo data
assimilation in ocean general circulation model of Northwest Pacific Ocean,
Ocean Dynam., 62, 1059–1071, 2012. a
You, Y.: Intermediate water circulation and ventilation of the Indian Ocean
derived from water-mass contributions, J. Mar. Res., 56, 1029–1067,
https://doi.org/10.1357/002224098765173455, 1998. a
Zhu, J., Zhou, G. Q., Yan, C. X., Fu, W. W., and You, X. B.: A three-dimensional
variational ocean data assimilation system: Scheme and preliminary results,
Science in China Series D: Earth Sciences, 49, 1212–1222, 2006. a
Short summary
Ocean profiling observation in the Indian Ocean is not sufficient. We conducted a hydrographic survey on the Carlsberg Ridge, which is a mid-ocean ridge in the northwest Indian Ocean, to obtain snapshots of sectional temperature, salinity, and density fields by combining the ARGO data. The results show mesoscale eddies located along the specific ridge and the existence of a west-propagating planetary wave. The results provide references in the regional ocean circulation.
Ocean profiling observation in the Indian Ocean is not sufficient. We conducted a hydrographic...
