Bove, M. C., & O'Brien, J. J. (1998). Impacts of ENSO on United States Tornadic Activity. In
The Ninth Symposium on Global Change Studies, 78th AMS Annual Meeting, Amer. Meteorol. Soc. (pp. 199–202).
Bove, M. C., & O'Brien, J. J.
PDO Modification of U.S ENSO Climate Impacts. COAPS Technical Report 00-3, 103 pp., Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, 32306-2840.
Bove, M. C., O'Brien, J. J., Eisner, J. B., Landsea, C. W., & Niu, X. (1998). Effect of El Niño on U.S. Landfalling Hurricanes, Revisited.
Bull. Amer. Meteor. Soc., 79(11), 2477–2482.
Bove, M. C., Zierden, D. F., & O'Brien, J. J. (1998). Are Gulf Landfalling Hurricanes Getting Stronger?
Bull. Amer. Meteor. Soc., 79(7), 1327–1328.
Bozec, A., E. Chassignet, Z. Garraffo, G. Halliwell, and S. Lozier. (2008).
On the Variability of the Mediterranean Outflow Water in North Atlantic HYCOM Simulations, Research Activities in Atmospheric and Ocean Modeling.
Bozec, A., Kageyama, M., Ramstein, G., Creepon, M. (2008). Impact of a Last Glacial Maximum sea-level drop on the Mediterranean Sea.
Earth and Planetary Science Letters, , submitted.
Bozec, A., Lozier, M. S., Chassignet, E. P., & Halliwell, G. R. (2011). On the variability of the Mediterranean Outflow Water in the North Atlantic from 1948 to 2006.
J. Geophys. Res., 116(C9).
Brassington, G. B., Martin, M. J., Tolman, H. L., Akella, S., Balmeseda, M., Chambers, C. R. S., et al. (2015). Progress and challenges in short- to medium-range coupled prediction.
Journal of Operational Oceanography, 8(sup2), s239–s258.
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.
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.