Morey, S. L., Dukhovskoy, D. S., & Bourassa, M. A. (2009). Connectivity of the Apalachicola River flow variability and the physical and bio-optical oceanic properties of the northern West Florida Shelf. Continental Shelf Research , 29 (9), 1264–1275.
Podestá, G., Letson, D., Messina, C., Royce, F., Ferreyra, R. A., Jones, J., et al. (2002). Use of ENSO-related climate information in agricultural decision making in Argentina: a pilot experience. Agricultural Systems , 74 (3), 371–392.
Jagtap, S. S., Jones, J. W., Hildebrand, P., Letson, D., O'Brien, J. J., Podestá, G., et al. (2002). Responding to stakeholder's demands for climate information: from research to applications in Florida. Agricultural Systems , 74 (3), 415–430.
Wallcraft, A. J., Kara, A. B., Hurlburt, H. E., Chassignet, E. P., & Halliwell, G. H. (2008). Value of bulk heat flux parameterizations for ocean SST prediction. Journal of Marine Systems , 74 (1-2), 241–258.
Karmel, T. (2016). Using multiple methodologies to explore variation in rainfall events in the southeastern United States . Bachelor's thesis, Florida State University, Tallahassee, FL.
Morey, S., Koch, M., Liu, Y., & Lee, S. - K. (2017). Florida's oceans and marine habitats in a changing climate. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.), Florida's climate: Changes, variations, & impacts (pp. 391–425). Gainesville, FL: Florida Climate Institute.
Smith, R. A. (2007). Trends in Maximum and Minimum Temperature Deciles in Select Regions of the United States . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Daily maximum and minimum temperature data from 758 COOP stations in nineteen states are used to create temperature decile maps. All stations used contain records from 1948 through 2004 and could not be missing more than 5 consecutive years of data. Missing data are replaced using a multiple linear regression technique from surrounding stations. For each station, the maximum and minimum temperatures are first sorted in ascending order for every two years (to reduce annual variability) and divided into ten equal parts (or deciles). The first decile represents the coldest temperatures, and the last decile contains the warmest temperatures. Patterns and trends in these deciles can be examined for the 57-year period. A linear least-squares regression method is used to calculate best-fit lines for each decile to determine the long-term trends at each station. Significant warming or cooling is determined using the Student's t-test, and bootstrapping the decile data will further examine the validity of significance. Two stations are closely examined. Apalachicola, Florida shows significant warming in its maximum deciles and significant cooling in its minimum deciles. The maximum deciles seem to be affected by some localized change. The minimum deciles are discontinuous, and the trends are a result of a minor station move. Columbus, Georgia has experienced significant warming in its minimum deciles, and this appears to be the result of an urban heat-island effect. The discontinuities seen in the Apalachicola case study illustrate the need for a quality control method. This method will eliminate stations from the regional analysis that experience large changes in the ten-year standard deviations within their time series. The regional analysis shows that most of the region is dominated by significant cooling in the maximum deciles and significant warming in the minimum deciles, with more variability in the lower deciles. Field significance testing is performed on subregions (based on USGS 2000 land cover data) and supports the findings from the regional analysis; it also isolates regions, such as the Florida peninsula and the Maryland/Delaware region, that appear to be affected by more local forcings.
Williams, M. (2010). Characterizing Multi-Decadal Temperature Variability in the Southeastern United States . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Prior studies of the long-term temperature record in the Southeastern United States (SE US) mostly discuss the long-term cooling trend, and the inter-annual variability produced by the region's strong ties to El Niño Southern Oscillation (ENSO). An examination of long-term temperature records in the SE US show clear multi-decadal variations in temperature, with relative warm periods in the 1920's through the mid 1950's and a cool period in the late 1950's through the late 1990's. This substantial shift in multi-decadal variability is not well understood and has not been fully investigated. It appears to account for the long-term downward trend in temperatures. An accurate characterization of this variability could lead to improved interannual and long-term forecasts, which would be useful for agricultural planning, drought mitigation, water management, and preparation for extreme temperature events. Statistical methods are employed to determine the spatial coherence of the observed variability on seasonal time scales. The goal of this study is to characterize the nature of this variability through the analysis of National Weather Service Cooperative Observer Program (COOP) station data in Florida, Georgia, Alabama, North Carolina, and South Carolina. One finding is a shift in the temperature Probability Distribution Function (PDF) between warm regimes and cool regimes.
Strazzo, S. (2011). Low-Frequency Minimum Temperature Variability Throughout the Southeastern United States during the 1970s: Regime Shift or Phase Coincidence? Master's thesis, Florida State University, Tallahassee, FL.
Michael, J. - P. (2010). ENSO Fidelity in Two Coupled Models . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: This study examines the fidelity of the ENSO simulation in two coupled model integrations and compares this with available global ocean data assimilation. The two models are CAM-HYCOM coupled model developed by the HYCOM Consortium and CCSM3.0. The difference between the two climate models is in the use of different ocean general circulation model (OGCM). The hybrid isopycnal-sigma-pressure coordinate ocean model Hybrid Coordinate Ocean Model (HYCOM) replaces the ocean model Parallel Ocean Program (POP) of the CCSM3.0. In both, the atmospheric general circulation model (AGCM) Community Atmosphere Model (CAM) is used. In this way the coupled systems are compared in a controlled setting so that the effects of the OGCM may be obtained. Henceforth the two models will be referred to as CAM-HYCOM and CAM-POP respectively. Comparison of 200 years of model output is used discarding the first 100 years to account for spin-up issues. Both models (CAM-HYCOM and CAM-POP) are compared to observational data for duration, intensity, and global impacts of ENSO. Based on the analysis of equatorial SST, thermocline depth, wind stress and precipitation, ENSO in the CAM-HYCOM model is weaker and farther east than observations while CAM-POP is zonal and extends west of the international dateline. CAM-POP also has an erroneous biennial cycle of the equatorial pacific SSTs. The analysis of the subsurface ocean advective terms highlights the problems of the model simulations.
