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Maximum and minimum daily temperatures from the second half of the twentieth century are examined using a high resolution dataset of 833 grid cells across the state of Florida. A bivariate extreme value analysis point process approach is used to model characteristics including the frequency, magnitude, duration, and timing of periods or heat waves during which both daily maximum and minimum temperatures exceed their respective 90th percentile thresholds. The temperature dataset is combined with indices of the El Niño-Southern Oscillation (ENSO) and the Atlantic multi-decadal oscillation (AMO) to explore the influence of these oscillations on heat wave characteristics in Florida. In order to investigate the influence of a time varying signal (ENSO and AMO) on heat waves the signals are introduced into non-stationary models as covariates in the location and log-transformed scale parameters. The improvements to the model obtained by introducing covariates are examined using the deviance statistic whereby the difference in negative log-likelihood values between two models is tested for significance using a Chi squared distribution. Significant improvements in the non-stationary models with ENSO and AMO covariates indicate spatially varying impacts in the frequency, magnitude, and duration of heat waves. In particular, the warm phase of the AMO brings heat waves earlier in the summertime while also increasing their magnitude, frequency, and duration. [ABSTRACT FROM AUTHOR]

Following rapid population growth and urbanization in Florida, there is an increased demand for energy. The state currently gets more than 50% of its electricity from burning imported natural gas. As the future of fossil fuels is uncertain and their impact on climate has proven negative, one could expect great potential and interest in further developing the solar industry, which utilizes the most prominent of renewable energy sources in Florida. Solar energy production is dependent on the supply of clear skies and plentiful insolation. This paper seeks to explain variations in the number of clear sunny days by identifying the months with clearest (cloudiest) sky conditions during the years 1950-2009 in relation to some of the most dominant low frequency climate patterns of variability in the Northern Hemisphere. The hypergeometric distribution is used to test for significant association between the phases of El Niño Southern Oscillation (ENSO), North Atlantic Oscillation, Pacific- North American, and Atlantic Multi-decadal Oscillation (AMO) and cloud cover in Florida. We find that ENSO and AMO significantly impact the occurrence of clear/ cloudy skies with the warm phase of ENSO associated with cloudier conditions across much of the state and the warm phase of AMO bringing clearer conditions to northern stations. [ABSTRACT FROM AUTHOR]

Summary Large-scale climate can provide predictive information for streamflow forecasts in many parts of the world. However, the optimal selection of predictors can be problematic when focusing on a localized region. This work evaluated multiple gridded climate datasets in order to determine optimal predictors of seasonal streamflow in West-Central Florida. Using persistence in streamflow, existing indices of climate, and sea surface temperature (SST) expansion coefficient time-series from singular value decomposition (SVD) analysis, this work developed probability of exceedance streamflow forecasts for multiple stations, seasons, and lead-times. Forecasts were found to be generally skillful between the September–November and April–June seasons with this range narrowing as lead time increased and skill was mainly related to the impact of the El Niño-Southern Oscillation (ENSO) on the region. Using multiple indices of ENSO that were determined by correlation and composite analyses to track its evolution from the west Pacific at long lead-times to the east Pacific at short lead-times was not found to appreciably improve forecasts over using the Niño 3.4 index alone. Using SST expansion coefficient time-series from SVD analysis was found to capture the evolution of ENSO from west to east and to provide skillful forecasts of streamflow at earlier leads (up to 7 months in advance) compared to that found by pre-defined indices, indicating the importance of predictor selection in achieving optimal forecast skill. [ABSTRACT FROM AUTHOR]

The wet season of Florida is well defined and is invariably centered in the boreal summer season of June-July-August. In this observational study we objectively define the Length of the Wet Season (LOWS) for Florida and examine its variations with respect to El Niño and the Southern Oscillation (ENSO) and the Atlantic Warm Pool (AWP). Our study reveals that ENSO variability has a profound influence on the LOWS especially over south Florida and parts of panhandle Florida prior to 1976. In the post-1976 era the influence of ENSO has significantly diminished. Our results show that in this pre-1976 era, warm (cold) ENSO events in the boreal winter are followed by long (short) LOWS over the region. This variation is consistent with warm (cold) ENSO events influencing early (late) onset of the wet season in the region. There is significant relationship of the LOWS in south and northeast Florida with the variation of the AWP. Unlike the teleconnection with ENSO the relationship of the demise of the wet season with AWP is stronger in the post-1976 period compared to the pre-1976 period. Furthermore the variability of the LOWS has increased in the post-1976 period. [ABSTRACT FROM AUTHOR]

This study evaluates the relationships between El Niño-Southern Oscillation (ENSO) indices and South Florida hydrology and proposes applications to water management decision making. ENSO relations to the Upper Kissimmee Basin rainfall, watershed for Lake Okeechobee, and cumulative sea surface temperature (SST) anomalies at Niño 3.4 were evaluated. Additionally, relationship between ENSO and Lake Okeechobee inflows, Arbuckle Creek and Josephine Creek flows were analyzed. Hydrology of the northern watersheds of the South Florida water management system is linked to ENSO events. Dry season (November-May) rainfall and flows are higher than average during El Niño years and lower during La Niña years, at the 90% confidence level or higher. The relationship is strongest when the ENSO event is strong as shown with analysis of correlation. ENSO prediction has more certainty than hydrologic prediction for a region. Identifying ENSO and hydrologic relationships can aid water management decision making by providing a lead-time of months to mitigate drought or flood impacts. The ENSO tracking method, which was published in a previous study, is presented to track ENSO strength and event type to provide supplemental outlook on dry season rainfall for Lake Okeechobee operations. Lake Okeechobee, which is the main storage in the South Florida water management system, is regulated by a schedule with a limited band of stage fluctuation because of susceptibility of the Herbert Hoover Dike to wave erosion and seepage at high stages. An early decision making approach to storage management with respect to ENSO related hydrology, is presented based on tracking the strength of ENSO events. [ABSTRACT FROM AUTHOR]