Yatagai, A., Krishnamurti, T. N., Kumar, V., Mishra, A. K., & Simon, A. (2014). Use of APHRODITE Rain Gauge-Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method. J. Climate , 27 (3), 1062–1069.
Zheng, Y., Zhang, R., & Bourassa, M. A. (2014). Impact of East Asian Winter and Australian Summer Monsoons on the Enhanced Surface Westerlies over the Western Tropical Pacific Ocean Preceding the El Niño Onset. J. Climate , 27 (5), 1928–1944.
Lim, Y. - K., & Kim, K. - Y. (2006). A New Perspective on the Climate Prediction of Asian Summer Monsoon Precipitation. J. Climate , 19 (19), 4840–4853.
Qian, C., Fu, C., Wu, Z., & Yan, Z. (2011). The role of changes in the annual cycle in earlier onset of climatic spring in northern China. Adv. Atmos. Sci. , 28 (2), 284–296.
Krishnamurthy, V., & Misra, V. (2011). Daily atmospheric variability in the South American monsoon system. Clim Dyn , 37 (3-4), 803–819.
Misra, V., & Li, H. (2014). The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system. Clim Dyn , 42 (9-10), 2491–2507.
Krishnamurti, T. N., Kumar, V., Simon, A., Thomas, A., Bhardwaj, A., Das, S., et al. (2017). March of buoyancy elements during extreme rainfall over India. Clim Dyn , 48 (5-6), 1931–1951.
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.
Pantina, P. (2010). Characterizing the Variability of the Indian Monsoon: Changes in Evaporative Sources for Summertime Rainfall Events . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: This study focuses on the interannual and intraseasonal variability of evaporative sources for rainfall events during the Indian monsoon. The monsoon is an important part of the economy and lifestyle in India, thus, any improvements in our understanding of its mechanisms would be directly beneficial to society. We first discuss the use of evaporative sources for rainfall events as an important tool to help increase our knowledge of the variations of the monsoon. We then outline the variability of the monsoon on an interannual (wet and dry years) and intraseasonal (active and break periods) time scale. We use three reanalyses (NCEP-R2, CFSR, and MERRA) and an IMD gridded rainfall dataset to trace the location and strength of evaporative sources via a quasi-isentropic back trajectory program. The program uses reanalysis winds and evaporation, among other parameters, to estimate these sources back in time. We discuss the differences in parameters between the datasets on a seasonal, interannual, and intraseasonal time scale. We then thoroughly investigate the strength and location of evaporative sources between datasets on interannual and intraseasonal time scales, and we attempt to explain the variations by analyzing the differences in the input parameters and circulation mechanisms themselves. The study finds that the evaporative sources for given interannual or intraseasonal rainfall events do vary in strength and location. Interannually, the strongest change in evaporative source occurs over central India and the Arabian Sea, suggesting that the overall monsoon flow contributes moisture for Indian rainfall on this time scale. Intraseasonally, the strongest change in evaporative source occurs over the Bay of Bengal, suggesting that low pressure systems contribute moisture for Indian rainfall on this time scale. All three reanalyses yield similar fields of evaporative source. We conclude that accurate prediction of the Indian monsoon requires improved understanding of both interannual and intraseasonal oscillations since the sources of moisture for these events are unique.
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.
