Baigorria, G., Jones, J., Shin, D., Mishra, A., & Ingram, K. T., Jones, J. W., O'Brien, J. J., Roncoli, M. C., Fraisse, C., Breuer, N. E., Bartels, W.-L., Zierden, D. F., Letson, D. (2007). Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs.
Clim. Res., 34, 211–222.
Bastola, S., & Misra, V. (2015). Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States.
Environmental Modelling & Software, 73, 90–102.
Bastola, S., Misra, V., & Li, H. (2013). Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons.
Earth Interact., 17(25), 1–22.
Brolley, J. M. (2004).
Experimental Forest Fire Threat Forecast. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Climate shifts due to El Niño (warmer than normal ocean temperatures in the tropical Pacific Ocean) and La Niña (cooler than normal) are well known and used to predict seasonal temperature and precipitation trends up to a year in advance. These climate shifts are particularly strong in the Southeastern United States. During the winter and spring months, El Niño brings plentiful rainfall and cooler temperatures to Florida. Recent los Niños occurred in 1997-1998, one of the strongest on record, with another mild El Niño in 2002-2003. Conversely, La Niña is associated with warm and dry winter and spring seasons in Florida. Temperature and precipitation affect wildfire activity; interannual drivers of climate, like ENSO, have an influence on wildfire activity. Studies have shown a strong connection between wildfires in Florida and La Niña, with the more than double the average number of acres burned (O'Brien et al 2002; Jones et al. 1999). While this relationship is important and lends a degree of predictability to the relative activity of future wildfire seasons, human activities such as effective suppression, prescribed burns, and ignition can play an equally important role in wildfire risks. This study forecasts wildfire potential rather than actual burn statistics to avoid complications due to human interactions. This wildfire threat potential is based upon the Keetch-Byram Drought Index (KBDI). The KBDI is well suited as a seasonal forecast medium. It is based on daily temperature and rainfall measurements and responds to changing climate and weather conditions on time scales of days to months, and this index is high during dry warm weather patterns and low during wet cool patterns. The KBDI has been widely used in forestry in the Southeastern United States since its development in the 1970's, with foresters and firefighters have a good level of familiarity with the index and its applications. The KBDI is calculated daily and used as an index by wildfire managers. This study calculates wildfire potential using a statistical method known as bootstrapping. Many datasets contain approximately a half-century of data, and the limited dataset will introduce biases. Bootstrapping can remedy bias by simulating thousands of years of data, which retain the climatology for the past half-century. Bootstrapping preserves the mean but not the variance. By incorporating this method, this study will improve long-term forest fire risks that will become useful for those living or working near forests and assist in managing forests and wildfires. The Southeast Climate Consortium will also be issuing wildfire risk forecast for Florida and parts of Alabama and Georgia based on ENSO phase and the KBDI. Climate information and ENSO predictions are better served by incorporating them with known climate indices that are routinely used in the forestry sector. Wildfire managers and foresters operationally use the KBDI to monitor and predict wildfire activity (O'Brien et al. 2002). Meteorologists at the Florida Division of Forestry have demonstrated the validity of the KBDI as an indicator of potential wildfire activity in Florida (Long 2004). They showed that the value of the KBDI is not as important as the deviation from the monthly average. The wildfire risk forecast is based on the probabilities of KBDI anomalies and will present the probabilities associated with large deviations from the seasonal normal.
Conlon, K. C., Kintziger, K. W., Jagger, M., Stefanova, L., Uejio, C. K., & Konrad, C. (2016). Working with Climate Projections to Estimate Disease Burden: Perspectives from Public Health.
Int J Environ Res Public Health, 13(8).
Abstract: There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida.
Dukhovskoy, D. S., Myers, P. G., Platov, G., Timmermans, M. - L., Curry, B., Proshutinsky, A., et al. (2016). Greenland freshwater pathways in the sub-Arctic Seas from model experiments with passive tracers.
J. Geophys. Res. Oceans, 121(1), 877–907.
Engelman, M. B. (2008).
A Validation of the FSU/COAPS Climate Model. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: This study examines the predictability of the Florida State University/Center for Oceanic and Atmospheric Prediction Studies (FSU/COAPS) climate model, and is motivated by the model's potential use in crop modeling. The study also compares real-time ensemble runs (created using persisted SST anomalies) to hindcast ensemble runs (created using weekly updated SST) to asses the effect of SST anomalies on forecast error. Wintertime (DJF, 2 month lead time) surface temperature and precipitation forecasts over the southeastern United States (Georgia, Alabama, and Florida) are evaluated because of the documented links between tropical Pacific SST anomalies and climate in the southeastern United States during the winter season. The global spectral model (GSM) runs at a T63 resolution and then is dynamically downscaled to a 20 x 20 km grid over the southeastern United States using the FSU regional spectral model (RSM). Seasonal, monthly, and daily events from the October 2004 and 2005 model runs are assessed. Seasonal (DJF) plots of real-time forecasts indicate the model is capable of predicting wintertime maximum and minimum temperatures over the southeastern United States. The October 2004 and 2005 real-time model runs both produce temperature forecasts with anomaly errors below 3°C, correlations close to one, and standard deviations similar to observations. Real-time precipitation forecasts are inconsistent. Error in the percent of normal precipitation vary from greater than 100% in the 2004/2005 forecasts to less than 35% error in the 2005/2006 forecasts. Comparing hindcast runs to real-time runs reveals some skill is lost in precipitation forecasts when using a method of SST anomaly persistence if the SST anomalies in the equatorial Pacific change early in the forecast period, as they did for the October 2004 model runs. Further analysis involving monthly and daily model data as well as Brier scores (BS), relative operating characteristics (ROC), and equitable threat scores (ETS), are also examined to confirm these results.
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.
Jia, Y., & Chassignet, E. P. (2011). Seasonal variation of eddy shedding from the Kuroshio intrusion in the Luzon Strait.
J Oceanogr, 67(5), 601–611.