Cabrera, V. E., D. Solis, G.A. Baigorria, and D. Letson. Managing climate risks to agriculture: evidence from El Nino . Gainesville, FL: SECC.
Cammarano, D., Basso, B., Stefanova, L., & Grace, P. (2012). Adapting wheat sowing dates to projected climate change in the Australian subtropics: analysis of crop water use and yield. Crop Pasture Sci. , 63 (10), 974.
Caron, J. (1997). The generation of synthetic sea surface temperature date for the equatorial Pacific Ocean . Master's thesis, Florida State University, Tallahassee, FL.
Caron, J. M., & O'Brien, J. J. (1998). The Generation of Synthetic Sea Surface Temperature Data for the Equatorial Pacific Ocean. Mon. Wea. Rev. , 126 (11), 2809–2821.
Carstens, J. (2019). Tropical Cyclogenesis from Self-aggregated Convection in Numerical Simulations of Rotating Radiative-convective Equilibrium . Florida State University - FCLA; ProQuest Dissertations & Theses Global.
Abstract: Organized convection is of critical importance in the tropical atmosphere. Recent advances in numerical modeling have revealed that moist convection can interact with its environment to transition from a quasi-random to organized state. This phenomenon, known as convective self-aggregation,is aided by feedbacks involving clouds, water vapor, and radiation that increase the spatial variance of column-integrated frozen moist static energy. Prior studies have shown self-aggregation to takeseveral different forms, including that of spontaneous tropical cyclogenesis in an environment of rotating radiative-convective equilibrium (RCE). This study expands upon previous work to address the processes leading to tropical cyclogenesis in this rotating RCE framework. More specifically,a three-dimensional, cloud-resolving numerical model is used to examine the self-aggregation of convection and potential cyclogenesis, and the background planetary vorticity is varied on an f-plane across simulations to represent a range of deep tropical and near-equatorial environments.Convection is initialized randomly in an otherwise homogeneous environment, with no background wind, precursor disturbance, or other synoptic-scale forcing.All simulations with planetary vorticity corresponding to latitudes from 10°to 20°generate intense tropical cyclones, with maximum wind speeds of 80 m s−1or above. Time to genesis varies widely, even within a five-member ensemble of 20°simulations, reflecting a potential degree of stochastic variability based in part on the initial random distribution of convection. Shared across this so-called “high-f” group is the emergence of a midlevel vortex in the days leading to genesis,which has dynamic and thermodynamic implications on its environment that facilitate the spinup of a low-level vortex. Tropical cyclogenesis is possible in this model even at values of Coriolis parameter as low as that representative of 1°. In these experiments, convection self-aggregates into a quasi-circular cluster, which then begins to rotate and gradually strengthen into a tropical storm, aided by near-surface inflow and shallow overturning radial circulations aloft within the aggregated cluster. Other experiments at these lower Coriolis parameters instead self-aggregate into an elongated band and fail to undergo cyclogenesis over the 100-day simulation. A large portion of this study is devoted to examining in greater detail the dynamic and thermodynamic evolution of cyclogenesis in these experiments and comparing the physical mechanisms to current theories.
Chan, S. C., & Misra, V. (2010). A Diagnosis of the 1979-2005 Extreme Rainfall Events in the Southeastern United States with Isentropic Moisture Tracing. Mon. Wea. Rev. , 138 (4), 1172–1185.
Chan, S. C., & Misra, V. (2011). Dynamic Downscaling of the North American Monsoon with the NCEP-Scripps Regional Spectral Model from the NCEP CFS Global Model. J. Climate , 24 (3), 653–673.
Chan, S. C., Misra, V., & Smith, H. (2011). A modeling study of the interaction between the Atlantic Warm Pool, the tropical Atlantic easterlies, and the Lesser Antilles: ATLANTIC WARM POOL, EASTERLIES, ISLANDS INTERACTIONS. J. Geophys. Res. , 116 (D21).
Chassignet, E., Cenedese, E., & Verron, J. (2012). Buoyancy-Drivenn Flows . Cambridge University Press.
Chassignet, E., Hurlburt, H., Metzger, E. J., Smedstad, O., Cummings, J., Halliwell, G., et al. (2009). US GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM). Oceanog. , 22 (2), 64–75.
