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.
Brolley, J. M., O'Brien, J. J., Schoof, J., & Zierden, D. (2007). Experimental drought threat forecast for Florida.
Agricultural and Forest Meteorology, 145(1-2), 84–96.
Feng, J., Wu, Z., & Zou, X. (2014). Sea Surface Temperature Anomalies off Baja California: A Possible Precursor of ENSO.
J. Atmos. Sci., 71(5), 1529–1537.
Fraisse, C. W., Breuer, N. E., Zierden, D., Bellow, J. G., Paz, J., Cabrera, V. E., et al. (2006). AgClimate: A climate forecast information system for agricultural risk management in the southeastern USA.
Computers and Electronics in Agriculture, 53(1), 13–27.
Izaurralde, R. C., Rosenberg, N. J., Brown, R. A., Legler, D. M., Tiscareño López, M., & Srinivasan, R. (1999). Modeled effects of moderate and strong 'Los Niños' on crop productivity in North America.
Agricultural and Forest Meteorology, 94(3-4), 259–268.
Kara, A. B., Rochford, P. A., & Hurlburt, H. E. (2002). Air-Sea Flux Estimates And The 1997-1998 Enso Event.
Boundary-Layer Meteorology, 103(3), 439–458.
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.
Michael, J. - P., Misra, V., & Chassignet, E. P. (2013). The El Niño and Southern Oscillation in the historical centennial integrations of the new generation of climate models.
Reg Environ Change, 13(S1), 121–130.
Murty, V. S. N. (2004). A new technique for the estimation of sea surface salinity in the tropical Indian Ocean from OLR.
J. Geophys. Res., 109(C12).
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.