Estimate of contribution of near-inertial waves to the velocity shear in the Bay of Bengal based on mooring observations from 2013 to 2014

Shanwu Zhang Yun Qiu Hangyu Chen Junqiang Shen Junpeng Zhang Jing Cha Fuwen Qiu Chunsheng Jing

Shanwu Zhang, Yun Qiu, Hangyu Chen, Junqiang Shen, Junpeng Zhang, Jing Cha, Fuwen Qiu, Chunsheng Jing. Estimate of contribution of near-inertial waves to the velocity shear in the Bay of Bengal based on mooring observations from 2013 to 2014[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1743-0
Citation: Shanwu Zhang, Yun Qiu, Hangyu Chen, Junqiang Shen, Junpeng Zhang, Jing Cha, Fuwen Qiu, Chunsheng Jing. Estimate of contribution of near-inertial waves to the velocity shear in the Bay of Bengal based on mooring observations from 2013 to 2014[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1743-0

doi: 10.1007/s13131-021-1743-0

Estimate of contribution of near-inertial waves to the velocity shear in the Bay of Bengal based on mooring observations from 2013 to 2014

Funds: National Key Research and Development Program of China under contract No. 2016YFC1401403; State Oceanic Administration (SOA) Program on Global Change and Air-Sea Interactions under contract No. GASI-IPOVAI-02; Indian Ocean Ninety-east Ridge Ecosystem and Marine Environment Monitoring and Protection, Supported by the China Ocean Mineral Resources R & D Association under contract No. DY135-E2-4; Scientific Research Foundation of Third Institute of Oceanography, SOA under contract Nos 2018001, 2017012 and 2014028.
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  • Figure  1.  Distribution of annual-mean near-inertial energy flux (colors) in the BoB in 2013. The mooring location is indicated by the black triangle. Gray solid lines and color dots give the paths and strength levels of TCs in 2013, respectively. Dates (MMDD) are marked along the tracks besides the dots. The color dots represent the maximum sustained wind speed (in knots) of the TCs. The near-inertial energy flux (F) is computed by solving the Pollard-Millard slab model using the spectral method (Alford, 2003) with National Centers for Environmental Prediction (NCEP) Climate Forecast Version 2 (CSFv2) 6-hourly wind data (Saha et al., 2014) with the mixed layer depth in the slab model set to 15 m.

    Figure  2.  Mooring observations of the velocity and shear. Surface wind acquired from the NCEP CFSv2 6-hourly winds (a). Observed meridional velocity (b). Vertical mean of the total and near-inertial KE (c). Vertical gradient of the meridional velocity (d). Vertical mean of the magnitude of the total shear and near-inertial shear (e). The red dashed lines divide the observations into six periods: TC Viyaru (May 11–June 3, 2013), summer monsoon (June–September, 2013), post-monsoon (October–mid November, 2013), TC Madi (December 6–31, 2013), winter monsoon (January–March, 2014) and pre-monsoon (April 1–May 11, 2014).

    Figure  3.  Buoyancy frequency profile and the WKB-stretched depth. The annual-mean profile of buoyancy frequency from WOA2018(a). WKB-scale factor(b). The WKB-stretched depth versus actual depth(c).

    Figure  4.  Near-inertial shear and plane wave solutions. Bandpass filtered near-inertial meridional shear from April 9, 2013 to May 11, 2014 (a). Same as a, but for the bandpass filtered near-inertial meridional shear with WKB scaling (b). The gray lines give the areas where vector correlation coefficients between the total shear and the near-inertial shear exceeds 0.7. The black lines indicate the rays of NIWs propagating downward, and the group velocity is shown along the side. Snapshot of the TC Viyaru case between 112 m and 227 m (stretched depth) (c). The time ticks are in format mm/dd. Same as c, but for the TC Madi case between 77 m and 202 m (stretched depth) (d). The solid and dashed lines represent contours of 0.1 and –0.1 of the plane wave solutions, respectively.

    Figure  5.  Wavenumber-frequency spectra for different periods. Pre-monsoon (a), post-monsoon (b), summer monsoon (c), winter monsoon (d),TC Viyaru (e), TC Madi (f). The division of the periods is the same as that presented in Fig. 2. The gray dashed lines represent different frequencies, and the corresponding frequencies are shown. The abscissa is wave frequency, σ, in cycle per day (cpd), and the ordinate is vertical wavenumber, kz, in cycle per meter (cpm).

    Figure  6.  Rotary frequency spectra for different periods. Pre-monsoon (a), post-monsoon (b), summer monsoon (c), winter monsoon (d), TC Viyaru (e), TC Madi (f). The red dashed lines indicate the near-inertial frequency bands determined by the ratio of clockwise and counterclockwise components exceeding 3 (see text). Horizontal gray lines give the confidence intervals of the spectra, with degrees of freedom varying with increasing frequency.

    Figure  7.  Histogram of the percentage of shear variances in total variances. The error bars were estimated by the corresponding 95% confidence intervals given in Fig. 6.

    Figure  8.  Mooring temperature observations and SLA. SLA and surface geostrophic currents correspond to the dates indicated by the dashed lines in the lower panel (a–f). The green triangle represents the location of the mooring. Contours of temperature data beneath 280 m (e). The black solid lines give four of the six periods indicated by the red dashed lines in Fig. 2.

    Figure  9.  Backrotated shear and internal tide displacements. M2 tidal components of the isotherm of 11°C for TC Viyaru (May 2013) (a) and TC Madi (December 2013) (b). The corresponding real part of the backrotated shear (Sbr) between 50 m and 200 m with internal tide displacements superimposed (black solid lines) (b, d).

    Table  1.   Percentage of shear variances in the total variances. The columns give the estimates for near-inertial bands (NIB), subinertial bands (SubIB) and M2 internal tide bands. The values in the parentheses give the percentage of the downward-propagating shear in the total shear

    PeriodNIBSubIBM2
    pre-monsoon44.5 (32.5)9.05.9 (2.6)
    post-monsoon27.2 (17.8)30.36.3 (3.8)
    summer monsoon33.9 (27.6)35.63.6 (2.4)
    winter monsoon42.6 (34.1)14.84.9 (2.7)
    TC Viyaru80.2 (78.5)7.71.1 (0.9)
    TC Madi74.8 (72.2)4.31.2 (0.90)
    whole record49.5 (44.5)20.33.4 (2.2)
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  • [1] Alford M H. 2003. Improved global maps and 54-year history of wind‐work on ocean inertial motions. Geophysical Research Letters, 30: 1424
    [2] Alford M H, Cronin M F, Klymak J M. 2012. Annual cycle and depth penetration of wind-generated near-inertial internal waves at Ocean Station Papa in the northeast Pacific. Journal of Physical Oceanography, 42: 889–909. doi: 10.1175/JPO-D-11-092.1
    [3] Alford M H, Gregg M C. 2001. Near-inertial mixing: Modulation of shear, strain and microstructure at low latitude. Journal of Geophysical Research: Oceans, 106: 16947–16968. doi: 10.1029/2000JC000370
    [4] Alford M H, MacKinnon J A, Pinkel R, et al. 2017. Space-time scales of shear in the North Pacific. Journal of Physical Oceanography, 47: 2455–2478. doi: 10.1175/JPO-D-17-0087.1
    [5] Alford M H, MacKinnon J A, Simmons, H L, et al. 2016. Near-inertial internal gravity waves in the ocean. Annual review of marine science, 8: 95–123. doi: 10.1146/annurev-marine-010814-015746
    [6] Breaker L C, Gemmill W H, Dewitt P W, et al. 2003. A curious relationship between the winds and currents at the western entrance of the Santa Barbara Channel. Journal of Geophysical Research, 108: 3132. doi: 10.1029/2002JC001458
    [7] Cairns J L, Williams G O. 1976. Internal wave observations from a midwater float. Journal of Geophysical Research, 81: 1943–1950. doi: 10.1029/JC081i012p01943
    [8] Cao Anzhou, Guo Zheng, Wang Shuya, et al. 2019. Upper ocean shear in the northern South China Sea. Journal of Oceanography, 75: 525–539. doi: 10.1007/s10872-019-00520-x
    [9] Cherian D, Shroyer E, Wijesekera H, et al. 2020. The seasonal cycle of upper-ocean mixing at 8°N in the Bay of Bengal. Journal of Physical Oceanography, 50: 323–342. doi: 10.1175/JPO-D-19-0114.1
    [10] Chowdary J S, Parekh A, Ojha S, et al. 2016. Impact of upper ocean processes and air-sea fluxes on seasonal SST biases over the tropical Indian Ocean in the NCEP Climate Forecasting System. International Journal of Climatology, 36: 188–207. doi: 10.1002/joc.4336
    [11] Garrett C. 2001. What is the “near-inertial” band and why is it different from the rest of the internal wave spectrum?. Journal of Physical Oceanography, 31: 962–971. doi: 10.1175/1520-0485(2001)031<0962:WITNIB>2.0.CO;2
    [12] Garrett C, Munk W. 1975. Space‐time scales of internal waves: A progress report. Journal of Geophysical Research, 80: 291–297. doi: 10.1029/JC080i003p00291
    [13] Gao Jing, Wang Jianing, Wang Fan. 2019. Response of near-Inertial shear to wind stress curl and sea level. Scientific Reports, 9: 1–11
    [14] Girishkumar M, Suprit K, Chiranjivi J, et al. 2014. Observed oceanic response to tropical cyclone Jal from a moored buoy in the south-western Bay of Bengal. Ocean Dynamics, 64: 325–335. doi: 10.1007/s10236-014-0689-6
    [15] Goswami B, Rao S A, Sengupta D, et al. 2016. Monsoons to mixing in the Bay of Bengal: Multiscale air-sea interactions and monsoon predictability. Oceanography, 29: 18–27
    [16] Jochum M, Briegleb B P, Danabasoglu G, et al. 2013. The impact of oceanic near-inertial waves on climate. Journal of Climate, 26: 2833–2844. doi: 10.1175/JCLI-D-12-00181.1
    [17] Johnston T S, Chaudhuri D, Mathur M, et al. 2016. Decay mechanisms of near-inertial mixed layer oscillations in the Bay of Bengal. Oceanography, 29: 180–191. doi: 10.5670/oceanog.2016.50
    [18] Köhler J, Völker G S, Walter M. 2018. Response of the internal wave field to remote wind forcing by tropical cyclones. Journal of Physical Oceanography, 48: 317–328. doi: 10.1175/JPO-D-17-0112.1
    [19] Leaman K D, Sanford T B. 1975. Vertical energy propagation of inertial waves: A vector spectral analysis of velocity profiles. Journal of Geophysical Research, 80: 1975–1978. doi: 10.1029/JC080i015p01975
    [20] Majumder S, Tandon A, Rudnick D L, et al. 2015. Near inertial kinetic energy budget of the mixed layer and shear evolution in the transition layer in the Arabian Sea during the monsoons. Journal of Geophysical Research: Oceans, 120: 6492–6507. doi: 10.1002/2014JC010198
    [21] Pinkel R. 2008. Advection, Phase Distortion, and the Frequency Spectrum of Finescale Fields in the Sea. Journal of Physical Oceanography, 38: 291–313. doi: 10.1175/2007JPO3559.1
    [22] Rimac A, von Storch J S, Eden C, et al. 2013. The influence of high‐resolution wind stress field on the power input to near‐inertial motions in the ocean. Geophysical Research Letters, 40: 4882–4886. doi: 10.1002/grl.50929
    [23] Saha S, Coauthors. 2014. The NCEP Climate Forecast System Version 2. Journal of Climate, 27: 2185–2208. doi: 10.1175/JCLI-D-12-00823.1
    [24] Schott F A, Xie S P, McCreary J P. 2009. Indian Ocean circulation and climate variability. Reviews of Geophysics, 47: RG1002
    [25] Varkey M J, Murty V S, Suryanarayana A. 1996. Physical oceanography of the Bay of Bengal and Andaman Sea. Oceanography and Marine Biology, 34: 1–70
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出版历程
  • 收稿日期:  2020-08-15
  • 录用日期:  2020-09-12
  • 网络出版日期:  2021-06-18

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