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Hemantha W. Wijesekera, W. J. Teague, David W. Wang, Z. R. Hallock, Conrad A. Luecke, Ewa Jarosz, H. J. S. Fernando, S. U. P. Jinadasa, Tommy G. Jensen, Adam Rydbeck, and Maria Flatau

.g., Sengupta et al. 2001 ; Krishnamurti et al. 2017 ). The importance of the MISO in the predictability of the Asian monsoon has been well documented ( Fu et al. 2003 , 2007 ; Flatau et al. 2003 ) and includes the upscale feedback of subseasonal oscillations onto the seasonal cycle of precipitation in the region. The general view of atmosphere–ocean interactions within MISO is that the surface latent heat flux is critical for the positive SST anomalies ( Sengupta et al. 2001 ). Based on coupled model

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Patrick Orenstein, Baylor Fox-Kemper, Leah Johnson, Qing Li, and Aakash Sane

seasonal monsoon rainfall trend, which gradually increases over the course of the summer, peaks around late July, then decreases to its off-season intensity ( Krishnamurti and Ardanuy 1980 ). This is generated in part by the annual north–south movement of the monsoonal intertropical convergence zone ( Goswami and Mohan 2001 ). As a result, MISO events occur in an extremely complex circulation context, making them difficult to predict more than a few weeks in advance ( Mo 2001 ). Nonetheless, they

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C. A. Luecke, H. W. Wijesekera, E. Jarosz, D. W. Wang, J. C. Wesson, S. U. P. Jinadasa, H. J. S. Fernando, and W. J. Teague

how mixing processes regulate and influence air–sea interactions in the southern BoB are not well defined. Within the BoB, the seasonal reversal of the winds due to the monsoonal cycle ( Kumar et al. 2012 ) is the dominant seasonal feature that defines the large-scale circulation. From May through September, strong southwesterly winds impart wind stress-curl forces, spinning up the Southwest Monsoon Current (SMC), also known as the Summer Monsoon Current. During this time, a semistationary

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K. Jossia Joseph, Amit Tandon, R. Venkatesan, J. Thomas Farrar, and Robert A. Weller

. 1992 ; Pinker et al. 2009 ; Venugopal et al. 2016 ; Ramesh Kumar et al. 2017 ). The earlier studies of surface heat flux in the Indian Ocean addressed by Hastenrath and Lamb (1979) and Jones et al. (1995) used monthly mean values, which could not address frequencies higher than seasonal variability. Tomita and Kubota (2004) analyzed the heat flux variability and the relative importance of its different components using the ship-based COADS dataset in the Indian Ocean and emphasized the

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B. Praveen Kumar, Eric D’Asaro, N. Sureshkumar, E. Pattabhi Rama Rao, and M. Ravichandran

quantities. MO scaling accurately describes conditions in the atmospheric boundary layer ( Businger et al. 1971 ) especially under unstable conditions or weakly stable conditions. In the ocean, its usefulness is less well established ( LG89 ; D’Asaro 2014 ; Zheng et al. 2021 ) due to the complicating effects of surface wave forcing and radiation absorption which introduce additional length scales. The complications due to penetrative radiation do not occur at night; this analysis thus focuses mostly on

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Michael B. Natoli and Eric D. Maloney

seasonal cycle removed) are projected back onto the EEOF patterns in Fig. 3 . Since unfiltered OLR anomalies make up the PCs, it must be assured that they still capture the 10–20-day time scale well, as we do allow for other time scales to project on the index. Spectra for both PC1 and PC2 ( Figs. 3d,h ) show strong, statistically significant peaks in spectral power on 10–20-day time scales. While there is some bleeding to both higher and lower frequencies, no distinct peak can be seen elsewhere in

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Kenneth G. Hughes, James N. Moum, Emily L. Shroyer, and William D. Smyth

Taylor–Goldstein equation. This linear stability analysis (LSA) predicts the growth rate, wavelength, and vertical structure of the instability. For a thermally stratified fluid, instabilities are assumed to have vertical velocity and temperature perturbations ( w ′, T ′) of the following form: (1) w ′ = Real [ w ^ ⁡ ( z )   exp ⁡ ( σ t + i k x ) ] and (2) T ′ = Real [ T ^ ⁡ ( z )   exp ⁡ ( σ t + i k x ) ] . In stable flow, neglecting molecular effects, σ is purely imaginary and the expressions

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D. A. Cherian, E. L. Shroyer, H. W. Wijesekera, and J. N. Moum

output ( Gadgil and Rupa Kumar 2006 ), lending significant social relevance to the problem of understanding air–sea interaction and near-surface ocean dynamics that influence the Bay’s SST. The Bay’s physical oceanography is characterized by two major features. First, its circulation reverses seasonally under the influence of the Indian Ocean monsoon—the seasonal reversal of winds north of approximately 10°S in the Indian Ocean basin. Second, it receives an immense amount of freshwater—more than 50

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Corinne B. Trott, Bulusu Subrahmanyam, Heather L. Roman-Stork, V. S. N. Murty, and C. Gnanaseelan

eddy variability ( Schott et al. 2009 ; Dandapat and Chakraborty 2016 ; Mahadevan et al. 2016a , b ). Highly dynamic heat and moisture fluxes drive the ISOs in the BoB and bring in seasonal and complex subseasonal variability ( Goswami et al. 2016 ; Weller et al. 2016 ; Sanchez-Franks et al. 2018 ). The ISOs of the BoB can be categorized into three major components of atmospherically driven coupled air–sea oscillations: the 30–90-day signal associated with the monsoon ISO (MISO) and Madden

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Wei-Ting Chen, Chien-Ming Wu, and Hsi-Yen Ma

cumulus parameterization, such as the modification of the triggering function, to see if the improvement of the diurnal cycle of land conditions can improve the local circulation. Experiments with higher horizontal resolution can also be carried out to investigate the effects when local topography is better resolved. The present results also give prominence to the importance of land–ocean–convection interactions and coastal convection systems when simulating the precipitation during the seasonal

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