Frumkin, A. (2011).
Predictability of Dry Season Reforecasts over the Tropical South American Region. Master's thesis, Florida State University, Tallahassee, FL.
Fu, C. B., Qian, C., & Wu, Z. H. (2011). Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach.
Sci. China Earth Sci., 54(9), 1400–1406.
Gilford, D. M., Smith, S. R., Griffin, M. L., & Arguez, A. (2013). Southeastern U.S. Daily Temperature Ranges Associated with the El Niño-Southern Oscillation.
J. Appl. Meteor. Climatol., 52(11), 2434–2449.
Goto, Y. (2008).
Improved Vegetation Characterization and Freeze Statistics in a Regional Spectral Model for the Florida Citrus Farming Region. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: This study focused on the effective use of a numerical climate model for agriculture in Florida, especially in the citrus farming region of the Florida peninsula, because of the impact of agriculture to Florida's economy. For the analyses of the ensemble, the climate models used in this study were the FSU/COAPS Global Spectral Model and FSU/COAPS Regional Spectral Model (FSU/COAPS RSM) coupled with a land-surface model. The multi-convective scheme method and variable initial conditions were used for the ensembles. Severe freezes impacting agriculture in Florida were associated with some major climate patterns, such as El Niño and Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). In the first part of this study, seasonal ensemble integrations of the regional model were examined for the tendencies of freezes in the Florida peninsula during each ENSO or NAO phase is examined. Mean excess values of minimum temperatures from thresholds on the basis of the Generalized Pareto Distribution (GPD), which represents the extreme data in a dataset, were used to analyze the freezes in the regional model. According to some previous studies, El Niño winters obtain fewer freezes than the other ENSO phases. Although the ensemble comprised only 19 winters, the ensemble found variability patterns in minimum temperatures in each climate phase similar to the findings in the previous studies which were based on the observed data. The FSU/COAPS RSM was coupled with Community Land Model 2.0 (CLM2), to represent the land-surface conditions. Although the coupling improved the temperature forecast of the RSM, it still has a cold bias and simulates smaller diurnal temperature changes than actually occur in southern Florida. Among the prescribed surface data, Leaf Area Index (LAI) for southern Florida in the CLM2 is lower than those observed by MODIS (Moderate Resolution Imaging Spectroradiometer). In the first experiment of this part, the sensitivity of the temperature forecast to the LAI in the climate models was investigated, by modifying the LAI data in the CLM2 based on the monthly MODIS observations. In the second experiment, newly created prescribed datasets of LAI and plant functional types for the CLM2 based on the MODIS observations were applied to the RSM. The substitution increased the diurnal temperature change in southern Florida slightly but almost consistently.
Groenen, D. (2018). The Effects of Climate Change on the Pests and Diseases of Coffee Crops in Mesoamerica.
Journal of Climatology & Weather Forecasting, 6(3).
Abstract: Coffee is an in-demand commodity that is being threatened by climate change. Increasing temperatures and rainfall variability are predicted in the region of Mexico and Central America (Mesoamerica). This region is plagued with pests and diseases that have already caused millions of dollars in damages and losses to the coffee industry.This paper examines three pests that negatively affect coffee plants: the coffee borer beetle, the black twig borer,and nematodes. In addition, this paper examines three diseases that can destroy coffee crops: bacterial blight,coffee berry disease, and coffee leaf rust. This paper will review the literature on how these pests and diseases are predicted to affect coffee crops under climate change models. In general, increased temperatures will increase the spread of pest and disease in coffee crops. Projected decreased rainfall in Honduras and Nicaragua may decrease the spread of pest and disease. However, these are complex issues which still require further study.
Hong, S. - Y., Park, H., Cheong, H. - B., Kim, J. - E. E., Koo, M. - S., Jang, J., et al. (2013). The Global/Regional Integrated Model system (GRIMs).
Asia-Pacific J Atmos Sci, 49(2), 219–243.
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.
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.
Kirtman, B. P., Misra, V., Anandhi, A., Palko, D., & Infanti, J. (2017). Future Climate Change Scenarios for Florida. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.),
Florida's climate: Changes, variations, & impacts (pp. 533–555). Gainesville, FL: Florida Climate Institute.
Kirtman, B. P., Misra, V., Burgman, R. J., Infanti, J., & Obeysekera, J. (2017). Florida Climate Variability and Prediction. In E. P. Chassignet, J. W. Jones, V. Misra, & J. Obeysekera (Eds.),
Florida's climate: Changes, variations, & impacts (pp. 511–532). Gainesville, FL: Florida Climate Institute.