Abstract
This study examines the seasonal and intraseasonal modulation of near-inertial wind power associated with fluctuations in unidirectional wind speed in the Bay of Bengal (BoB). For that purpose, we use concurrent measurements of high-resolution in situ near-surface current and wind speed from six moorings in the BoB. It is found that the annual mean of near-inertial wind power in the BoB shows roughly similar magnitude (0.25–0.35 mW m−2) at all the mooring locations. However, in response to the seasonal evolution of monsoonal wind forcing, near-inertial wind power shows significant annual variability, with a maximum during summer (~ 0.4–0.5 mW m−2) and fall (~ 0.3–0.4 mW m−2) and a minimum during winter (~ 0.1 mW m−2) and spring (~ 0.2 mW m−2). In addition, it is also found that modulation of near-inertial wind power due to summer monsoon intraseasonal oscillation (MISO), such as its magnitude, reaches as large as ~ 1 mW m−2 at the mooring in the northern BoB during phases 3–4 of MISO. Using a high vertical resolution of current profile data, the near-inertial kinetic energy (NIKE) budget in the mixed layer in the northern BoB shows good temporal correspondence with the magnitude of the rate of change of NIKE and near-inertial wind power, with a maximum magnitude of the rate of change of NIKE lags the wind power by 24 hr. The NIKE budget also indicates that a significant portion of near-inertial wind power dissipates in the mixed layer and rarely energises the depth regime underneath the mixed layer.
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Data availability
The OMNI mooring data is made available by INCOIS https://odis.incois.gov.in/portal/datainfo/mb.jsp, and data is accessible on request https://odis.incois.gov.in/portal/datainfo/dicontact.jsp. CCMP version-3.0 vector wind analyses are produced by Remote Sensing Systems. Data is available at www.remss.com. The quality-controlled RAMA mooring data is distributed by GTMBA Project Office of NOAA/PMEL, and data is available at https://www.pmel.noaa.gov/tao/drupal/disdel. The Ssalto/Duacs altimeter products were produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS), and data are available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-sea-level-global.
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Acknowledgements
The encouragement and support provided by the Director, INCOIS, Student Coordinator, INCOIS, and Group Director, ODICT, INCOIS is gratefully acknowledged. Graphics are generated using PyFerret and Python. The OMNI mooring network is maintained by NIOT, Chennai. The INCOIS OMM-mooring is procured as part of the Ministry of Earth Sciences (MoES) funded Monsoon Mission project, ‘Coupled Physical Processes in the Bay of Bengal and Monsoon Air-Sea Interaction (Ocean Mixing and Monsoon)’. This work is carried out as part of Aswathy’s M.Sc. dissertation work. We thank Dr. Vijay P., Scientist, INCOIS, for providing MISO index data. We thank two anonymous reviewers for their valuable suggestions on an earlier version of this manuscript. This is INCOIS contribution 505.
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Appendix. Sensitivity of the results
Appendix. Sensitivity of the results
Note that, at the RAMA mooring locations, the current measurement at 10 m is used in contrast to measurements at 1 m at OMNI moorings for wind power estimates. Though the current measurement at 10 m is close to the sea surface, the MLD at the mooring location may occasionally be shallower than the mooring current meter deployment depth. These conditions are more common in the spring (where 50% of MLD are shallower than 10 m) and less common in the summer (where 10% of MLD are shallower than 10 m) (Table S1). In this case, the current measurement below the shallow MLD cannot be considered as mixed layer currents. However, after eliminating data points when MLD is shallower than 10 m, the assessment of seasonal and intraseasonal variability of wind power at RAMA mooring locations indicates that the result will not change significantly (Figures S4 and S5). Hence, we followed an assumption, such as the measurements at 10 m were representative of mixed layer current.
Following Liu et al. (2019), a band-pass filter with a cutoff ± 0.25 f0 (0.75 f0 and 1.25 f0) from theoretical inertial frequency is adopted in the present study. As depicted in Fig. 3, these bands are either slightly broad or narrow at particular mooring locations. However, previous studies have also used a slightly narrow band (± 0.2 f0) to filter the data in near-inertial frequency (Lin et al. 2022; Liu et al. 2018; Majumder et al. 2015; von Storch and Lüschow 2023) (Figure S7). The seasonal and intraseasonal average of near-inertial wind power estimated using our default choice (0.75 f0 and 1.25 f0) is compared to an estimation based on the narrow band and arbitrarily selected band to examine how sensitive our results are to the choice of the frequency band (Figures S4-S6; Table S2). The seasonal and intraseasonal variability of wind power estimated using the narrow-band and arbitrary bands is shown to be insignificantly different from our default choice (Figures S4-S6). As a result, we focused on the wind power estimation based on the frequency band 0.75 f0–1.25 f0 in the present study.
Previous research in the BoB used a higher value of density increment (0.2 kg m−3; density change equivalent to a temperature change of magnitude 0.8 °C) for MLD calculation (Thangaprakash et al. 2016; Vijith et al. 2020). However, MLD estimation by this criterion is deeper compared to the base of the surface isopycnal layer (Figure S2). However, it is found that the NIKE budget estimation based on this higher threshold for MLD does not differ considerably from our default decision (0.125 kg m−3) (Figure S7).
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Aswathy, V.S., Girishkumar, M.S. & Athulya, K. Seasonal and intraseasonal modulation of near-inertial wind power associated with fluctuations in unidirectional wind speed in the Bay of Bengal. Ocean Dynamics 74, 81–95 (2024). https://doi.org/10.1007/s10236-023-01589-1
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DOI: https://doi.org/10.1007/s10236-023-01589-1