Ali, M., Singh, N., Kumar, M., Zheng, Y., Bourassa, M., Kishtawal, C., et al. (2018). Dominant Modes of Upper Ocean Heat Content in the North Indian Ocean.
Climate, 6(3), 71.
Abstract: The thermal energy needed for the development of hurricanes and monsoons as well as any prolonged marine weather event comes from layers in the upper oceans, not just from the thin layer represented by sea surface temperature alone. Ocean layers have different modes of thermal energy variability because of the different time scales of ocean-atmosphere interaction. Although many previous studies have focused on the influence of upper ocean heat content (OHC) on tropical cyclones and monsoons, no study thus farparticularly in the North Indian Ocean (NIO)has specifically concluded the types of dominant modes in different layers of the ocean. In this study, we examined the dominant modes of variability of OHC of seven layers in the NIO during 1998-2014. We conclude that the thermal variability in the top 50 m of the ocean had statistically significant semiannual and annual modes of variability, while the deeper layers had the annual mode alone. Time series of OHC for the top four layers were analyzed separately for the NIO, Arabian Sea, and Bay of Bengal. For the surface to 50 m layer, the lowest and the highest values of OHC were present in January and May every year, respectively, which was mainly caused by the solar radiation cycle.
Banks, R. (2006).
Variability of Indian Ocean Surface Fluxes Using a New Objective Method. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: A new objective technique is used to analyze monthly mean gridded fields of air and sea temperature, scalar and vector wind, specific humidity, sensible and latent heat flux, and wind stress over the Indian Ocean. A variational method produces a 1°x1° gridded product of surface turbulent fluxes and the variables needed to calculate these fluxes. The surface turbulent fluxes are forced to be physically consistent with the other variables. The variational method incorporates a state of the art flux model, which should reduce regional biases in heat and moisture fluxes. The time period is January 1982 to December 2003. The wind vectors are validated through comparison to monthly scatterometer winds. Empirical orthogonal function (EOF) analyses of the annual cycle emphasize significant modes of variability in the Indian Ocean. The dominant monsoon reversal and its connection with the southeast trades are linked in eigenmodes one and two of the surface fluxes. The third eigenmode of latent and sensible heat flux reveal a structure similar to the Indian Ocean Dipole (IOD) mode. The variability in surface fluxes associated with the monsoons and IOD are discussed. September-October-November composites of the surface fluxes during the 1997 positive IOD event and the 1983 negative IOD event are examined. The composites illustrate characteristics of fluxes during different IOD phases.
Misra, V., Mishra, A., & Bhardwaj, A. (2018). Simulation of the Intraseasonal Variations of the Indian Summer Monsoon in a Regional Coupled Ocean-Atmosphere Model.
J. Climate, 31(8), 3167–3185.
Moroni, D. F. (2008).
Global and Regional Diagnostic Comparison of Air-Sea Flux Parameterizations during Episodic Events. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: Twenty turbulent flux parameterizations are compared globally and regionally with a focus on the differences associated with episodic events. The regional focus is primarily upon the Gulf Stream and Drake Passage, as these two regions contain vastly different physical characteristics related to storm and frontal passages, varieties of sea-states, and atmospheric stability conditions. These turbulent flux parameterizations are comprised of six stress-related parameterizations [i.e., Large and Pond (1981), Large et al. (1994), Smith (1988), HEXOS (Smith et al. 1992, 1996), Taylor and Yelland (2001), and Bourassa (2006)] which are paired with a choice of three atmospheric stability parameterizations ['Neutral' assumption, Businger-Dyer (Businger 1966, Dyer 1967, Businger et al. 1971, and Dyer 1974) relations, and Beljaars-Holtslag (1991) with Benoit (1977)]. Two remaining turbulent flux algorithms are COARE version 3 (Fairall et al. 2003) and Kara et al. (2005), where Kara et al. is a polynomial curve fit approximation to COARE; these have their own separate stability considerations. The following data sets were used as a common input for parameterization: Coordinated Ocean Reference Experiment version 1.0, Reynolds daily SST, and NOAA WaveWatch III. The overlapping time period for these data sets is an eight year period (1997 through 2004). Four turbulent flux diagnostics (latent heat flux, sensible heat flux, stress, curl of the stress) are computed using the above parameterizations and analyzed by way of probability distribution functions (PDFs) and RMS analyses. The differences in modeled flux consistency are shown to vary by region and season. Modeled flux consistency is determined both qualitatively (using PDF diagrams) and quantitatively (using RMS differences), where the best consistencies are found during near-neutral atmospheric stratification. Drake Passage shows the least sensitivity (in terms of the change in the tails of PDFs) to seasonal change. Specific flux diagnostics show varying degrees of consistency between stability parameterizations. For example, the Gulf Stream's latent heat flux estimates are the most inconsistent (compared to any other flux diagnostic) during episodic and non-neutral conditions. In all stability conditions, stress and the curl of stress are the most consistent modeled flux diagnostics. Sea-state is also a very important source of modeled flux inconsistencies during episodic events for both regions.
Keywords: Parameterizations, Parameterization, Algorithm, Probability Density, Probability Distribution, Pdf, Drake Passage, Kuroshio, Gulf Stream Ect, Cold Tongue, Indian Ocean, Pacific Ocean, Southern Oceans, Atlantic Ocean, Tropics, Sea-State
Murty, V. S. N. (2004). A new technique for the estimation of sea surface salinity in the tropical Indian Ocean from OLR.
J. Geophys. Res., 109(C12).
Nakano, H., & Suginohara, N. (2002). Importance of the eastern Indian Ocean for the abyssal Pacific.
J. Geophys. Res., 107(C12), 12–1-12–14.
Nyadjro, E. S., Jensen, T. G., Richman, J. G., & Shriver, J. F. (2017). On the Relationship Between Wind, SST, and the Thermocline in the Seychelles-Chagos Thermocline Ridge.
IEEE Geosci. Remote Sensing Lett., 14(12), 2315–2319.
Shi, W. (2003). Estimation of heat and salt storage variability in the Indian Ocean from TOPEX/Poseidon altimetry.
J. Geophys. Res., 108(C7).
Shinoda, T., Han, W., Zamudio, L., Lien, R. - C., & Katsumata, M. (2017). Remote Ocean Response to the Madden-Julian Oscillation during the DYNAMO Field Campaign: Impact on Somali Current System and the Seychelles-Chagos Thermocline Ridge.
Atmosphere, 8(9), 171.
Subrahmanyam, B., Murty, V. S. N., Sharp, R. J., & O'Brien, J. J. (2005). Air-sea Coupling During the Tropical Cyclones in the Indian Ocean: A Case Study Using Satellite Observations.
Pure appl. geophys., 162(8-9), 1643–1672.