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.
Spiesberger, J. L., Hurlburt, H. E., Johnson, M., Keller, M., Meyers, S., & O'Brien, J. (1998). Acoustic thermometry data compared with two ocean models: the importance of Rossby waves and ENSO in modifying the ocean interior.
Dynamics of Atmospheres and Oceans, 26(4), 209–240.
Petraitis, D. C. (2006).
Long-Term ENSO-Related Winter Rainfall Predictions over the Southeast U.S. Using the FSU Global Spectral Model. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Rainfall patterns over the Southeast U.S. have been found to be connected to the El Niño-Southern Oscillation (ENSO). Warm ENSO events cause positive precipitation anomalies and cold ENSO events cause negative precipitation anomalies. With this level of connection, models can be used to test the predictability of ENSO events. Using the Florida State University Global Spectral Model (FSUGSM), model data over a 50-year period will be evaluated for its similarity to observations. The FSUGSM is a global spectral model with a T63 horizontal resolution (approximately 1.875°) and 17 unevenly spaced vertical levels. Details of this model can be found in Cocke and LaRow (2000). The experiment utilizes two runs using the Naval Research Laboratory (NRL) RAS convection scheme and two runs using the National Centers for Environmental Prediction (NCEP) SAS convection scheme to comprise the ensemble. The simulation was done for 50 years, from 1950 to 1999. Reynolds and Smith monthly mean sea surface temperatures (SSTs) from 1950-1999 provide the lower boundary condition. Atmospheric and land conditions from January 1, 1987 and January 1, 1995 were used as the initial starting conditions. The observational precipitation data being used as the basis for comparison is a gridded global dataset from Willmott and Matsuura (2005). Phase precipitation differences show higher precipitation amounts for El Niño than La Niña in all model runs. Temporal correlations between model runs and the observations show southern and eastern areas with the highest correlation values during an ENSO event. Skill scores validate the findings of the model/observation correlations, with southern and eastern areas showing scores close to zero. Temporal correlations between tropical Pacific SSTs and Southeast precipitation further confirm the model's ability to predict ENSO precipitation patterns over the Southeast U.S. The inconsistency in the SST/precipitation correlations between the models can be attributed to differences in the 200-mb jet stream and 500-mb height anomalies. Slight differences in position and strength for both variables affect the teleconnection between tropical Pacific SSTs and Southeast.
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.
Culin, J. C. (2006).
Wintertime ENSO Variability in Wind Direction Across the Southeast United States. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Changes in wind direction in association with the phases of the El Niño-Southern Oscillation (ENSO) are identified over the Southeast region of the United States during the winter season (December-February). Wind roses, which depict the percentage of time the wind comes from each direction and can graphically identify the prevailing wind, are computed according to a 12-point compass for 24 stations in the region. Unfolding the wind rose into a 12-bin histogram visually demonstrates the peak frequencies in wind direction during each of the three (warm, cold and neutral) phases of ENSO. Normalized values represent the number of occurrences (counts) per month per ENSO phase, and comparison using percent changes illustrates the differences between phases. Based on similarities in wind direction characteristics, regional topography and results from a formal statistical test, stations are grouped into five geographic regions, with a representative station used to describe conditions in that region. Locations in South Florida show significant differences in the frequencies in wind direction from easterly directions during the cold phase and northerly directions during the warm phase. North Florida stations display cold phase southerly directions, and westerly and northerly directions during the warm phase, both of which are significant for much of the winter. Coastal Atlantic stations reveal winds from westerly directions for both phases. The Piedmont region demonstrates large variability in wind direction due to the influence from the Appalachian Mountains, but generally identifies warm phase and cold phase winds with more zonal influences rather than just from south or north. The Mountainous region also indicates southerly cold phase winds and northerly warm phase winds, but also reveals less of an influence from ENSO or significantly different distributions. Comparisons between observed patterns and those obtained using the NCEP/NCAR Reanalysis data reveal how the model-derived observations resolve the ENSO influence on surface wind direction at selected locations. Overall, resolution of the strength of the signals is not achieved, though the depiction of the general pattern is fair at two of the three locations. Connections between the synoptic flow and surface wind direction are examined via relationships to the storm track associated with the 250 hPa jet stream and sea level pressure patterns during each extreme ENSO phase. Discussion of reasons the NCEP reanalysis illustrates surface wind direction patterns different from those derived from observations is included.
Brolley, J. M. (2007).
Effects of ENSO, NAO (PVO), and PDO on Monthly Extreme Temperatures and Precipitation. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: The El Nino-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), and the Polar Vortex Oscillation (PVO) produce conditions favorable for monthly extreme temperatures and precipitation. These climate modes produce upper-level teleconnection patterns that favor regional droughts, floods, heat waves, and cold spells, and these extremes impact agriculture, energy, forestry, and transportation. The above sectors prefer the knowledge of the worst (and sometimes the best) case scenarios. This study examines the extreme scenarios for each phase and the combination of phases that produce the greatest monthly extremes. Data from Canada, Mexico, and the United States are gathered from the Historical Climatology Network (HCN). Monthly data are simulated by the utilization of a Monte Carlo model. This Monte Carlo method simulates monthly data by the stochastic selection of daily data with identical ENSO, PDO, and PVO (NAO) characteristics. In order to test the quality of the Monte Carlo simulation, the simulations are compared with the observations using only PDO and PVO. It has been found that temperatures and precipitation in the simulation are similar to the model. Statistics tests have favored similarities between simulations and observations in most cases. Daily data are selected in blocks of four to eight days in order to conserve temporal correlation. Because the polar vortex occurs only during the cold season, the PVO is used during January, and the NAO is used during other months. The simulated data are arranged, and the tenth and ninetieth percentiles are analyzed. The magnitudes of temperature and precipitation anomalies are the greatest in the western Canada and the southeastern United States during winter, and these anomalies are located near the Pacific North American (PNA) extrema. Western Canada has its coldest (warmest) Januaries when the PDO and PVO are low (high). The southeastern United States has its coldest Januaries with high PDO and low PVO and warmest Januaries with low PDO and high PVO. Although extremes occur during El Nino or La Nina, many stations have the highest or lowest temperatures during neutral ENSO. In California and the Gulf Coast, the driest (wettest) Januaries tend to occur during low (high) PDO, and the reverse occurs in Tennessee, Kentucky, Ohio, and Indiana. Summertime anomalies, on the other hand, are weak because temperature variance is low. Phase combinations that form the wettest (driest) Julies form spatially incoherent patterns. The magnitudes of the temperature and precipitation anomalies and the corresponding phase combinations vary regionally and seasonally. Composite maps of geopotential heights across North America are plot for low, median, and high temperatures at six selected sites and for low, median, and high precipitation at the same sites. The greatest fluctuations occur near the six sites and over some of the loci of the PNA pattern. Geopotential heights tend to decrease (increase) over the target stations during the cold (warm) cases, and the results for precipitation are variable.
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.
Briggs, K. (2006).
ENSO Event Reproduction: A Comparison of an EOF vs. A Cyclostationary (CSEOF) Approach. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: In past studies, El Niño-Southern Oscillation (ENSO) events have been linked to devastating weather extremes. Climate modeling of ENSO is often dependent on limited records of the pertinent physical variables, thus longer records of these variables is desirable. Noisy signals, such as monthly sea surface temperature, are good candidates for reproduction by several existing auto-regression techniques. Through auto-regression, influential principal component modes are broken down into a series of time points that are each dependent upon an optimal weighting of the surrounding points. Using these unique numerical relationships, a noisy signal can be reproduced by thus processing the leading modes and adding an artificial record of properly distributed noise. Statistical measures of important ENSO regions suggest that the nature of oceanic and atmospheric anomalous events is cyclic with respect to certain timescales; for example, the monthly timescale. To detect ENSO signals in the presence of a varying background noise field, the detection method should take into account the signal's strong phase-locking with this nested variation. Cyclostationary Emperical Orthogonal Functions (CSEOFs) are built upon the idea of nested cycles, unlike traditional EOFs, which incorporate a design that is better detailed for stationary processes. In this study, both EOF and CSEOF modes of a 50-year Pacific SST record are processed using an auto-regression technique, and several sets of artificial SST records are constructed. Appropriate statistical indices are applied to these artificial time series to ensure an acceptable consistency with the real record, and then artificial data is produced using the artificial time series. In all cases, the cyclostationary approach produces more realistic warm ENSO events with respect to timing, strength, and other traits than does the stationary approach. However, both methods produce only a fair representation of cold events, suggesting that further study is necessary for improvement of La Niña modeling. Shorter records of variables such as sea level height and Pacific wind stress anomalies can hinder the usefulness of auto-regression, owing to time point dependence on surrounding points. Using a regression technique to find an evolutionary consistency (i.e. physically consistent patterns) between one of these variables and a variable with a longer record (such as SST) can eliminate this problem. Once a regression relationship is found between two variables, the variable with the shorter record can be re-written to match the time evolution of the variable with the longer record. Here regression, both EOF and CSEOF, is performed on both sea surface temperature and sea level height (a 20-year record), and sea surface temperature and wind stress (a 39-year record). Once the regression relationships are found, artificial SST time series are incorporated in place of the original time series to produce several artificial 50-year SLH and wind stress data sets. 5 Pacific regions are chosen, and statistics and behavior of the artificial sets within these regions are compared to those of the original data. Once again the cyclostationary approach fares better than the stationary. In particular the EOF assumption of cross correlational symmetry fails to capture the direction-dependence of ENSO evolution, causing inconsistent ENSO behavior. This renders an EOF method insufficient for climate modeling and prediction, and implies that a better aim is to incorporate physical cyclic features via a cyclostationary method.
Michael, J. - P. (2014).
On Initializing CGCMs for Seasonal Predictability of ENSO. Ph.D. thesis, Florida State University, Tallahassee, FL.
McNaught, C. (2014).
The Increasing Intensity and Frequency of ENSO and its Impacts to the Southeast U.S. Bachelor's thesis, Florida State University, Tallahassee, FL.