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Author Bove, M. C.; O'Brien, J. J. url  openurl
  Title PDO Modification of U.S ENSO Climate Impacts Type $loc['typeReport']
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  Series Editor Series Title COAPS Technical Report 00-3, 103 pp., Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, 32306-2840 Abbreviated Series Title  
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  Funding Approved $loc['no']  
  Call Number COAPS @ mfield @ Serial 796  
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Author Bove, M. C.; O'Brien, J. J. openurl 
  Title Impacts of ENSO on United States Tornadic Activity Type $loc['typeConference Article']
  Year 1998 Publication The Ninth Symposium on Global Change Studies, 78th AMS Annual Meeting, Amer. Meteorol. Soc. Abbreviated Journal  
  Volume Issue Pages 199-202  
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  Call Number COAPS @ mfield @ Serial 751  
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Author Bove, M.C.; O'Brien, J.J.; Eisner, J.B.; Landsea, C.W.; Niu, X. url  doi
openurl 
  Title Effect of El Niño on U.S. Landfalling Hurricanes, Revisited Type $loc['typeJournal Article']
  Year 1998 Publication Bulletin of the American Meteorological Society Abbreviated Journal Bull. Amer. Meteor. Soc.  
  Volume 79 Issue 11 Pages 2477-2482  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0003-0007 ISBN Medium  
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  Funding Approved $loc['no']  
  Call Number COAPS @ mfield @ Serial 535  
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Author Bove, M.C.; Zierden, D.F.; O'Brien, J.J. url  doi
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  Title Are Gulf Landfalling Hurricanes Getting Stronger? Type $loc['typeJournal Article']
  Year 1998 Publication Bulletin of the American Meteorological Society Abbreviated Journal Bull. Amer. Meteor. Soc.  
  Volume 79 Issue 7 Pages 1327-1328  
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  Series Volume Series Issue Edition  
  ISSN 0003-0007 ISBN Medium  
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  Funding Approved $loc['no']  
  Call Number COAPS @ mfield @ Serial 537  
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Author Bozec, A.; Lozier, M.S.; Chassignet, E.P.; Halliwell, G.R. url  doi
openurl 
  Title On the variability of the Mediterranean Outflow Water in the North Atlantic from 1948 to 2006 Type $loc['typeJournal Article']
  Year 2011 Publication Journal of Geophysical Research Abbreviated Journal J. Geophys. Res.  
  Volume 116 Issue C9 Pages  
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  Series Volume Series Issue Edition  
  ISSN 0148-0227 ISBN Medium  
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  Call Number COAPS @ mfield @ Serial 290  
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Author Bozec, A., E. Chassignet, Z. Garraffo, G. Halliwell, and S. Lozier openurl 
  Title On the Variability of the Mediterranean Outflow Water in North Atlantic HYCOM Simulations, Research Activities in Atmospheric and Ocean Modeling Type $loc['typeReport']
  Year 2008 Publication CAS/JSC Working Group on Numerical Experimentation Abbreviated Journal  
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  Call Number COAPS @ mfield @ Serial 687  
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Author Bozec, A., Kageyama, M., Ramstein, G., Creepon, M. openurl 
  Title Impact of a Last Glacial Maximum sea-level drop on the Mediterranean Sea Type $loc['typeJournal Article']
  Year 2008 Publication Earth and Planetary Science Letters Abbreviated Journal  
  Volume Issue Pages submitted  
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  Call Number COAPS @ mfield @ Serial 679  
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Author Brassington, G.B.; Martin, M.J.; Tolman, H.L.; Akella, S.; Balmeseda, M.; Chambers, C.R.S.; Chassignet, E.; Cummings, J.A.; Drillet, Y.; Jansen, P.A.E.M.; Laloyaux, P.; Lea, D.; Mehra, A.; Mirouze, I.; Ritchie, H.; Samson, G.; Sandery, P.A.; Smith, G.C.; Suarez, M.; Todling, R. url  doi
openurl 
  Title Progress and challenges in short- to medium-range coupled prediction Type $loc['typeJournal Article']
  Year 2015 Publication Journal of Operational Oceanography Abbreviated Journal Journal of Operational Oceanography  
  Volume 8 Issue sup2 Pages s239-s258  
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  Series Volume Series Issue Edition  
  ISSN 1755-876X ISBN Medium  
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  Funding Approved $loc['no']  
  Call Number COAPS @ mfield @ Serial 96  
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Author Briggs, K. url  openurl
  Title ENSO Event Reproduction: A Comparison of an EOF vs. A Cyclostationary (CSEOF) Approach Type $loc['typeManuscript']
  Year 2006 Publication Abbreviated Journal  
  Volume Issue Pages  
  Keywords EOF, Autoregression, Wind Stress, Sea Level Height, SST, ENSO, Regression, CSEOF, Cyclostationary  
  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.  
  Address Department of Meteorology  
  Corporate Author Thesis $loc['Master's thesis']  
  Publisher Florida State University Place of Publication Tallahassee, FL Editor  
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  Funding Approved $loc['no']  
  Call Number COAPS @ mfield @ Serial 614  
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Author Brolley, J. M. url  openurl
  Title Experimental Forest Fire Threat Forecast Type $loc['typeManuscript']
  Year 2004 Publication Abbreviated Journal  
  Volume Issue Pages  
  Keywords Forest Fire, El Nino, ENSO, Seasonal Forecast, KBDI, Keetch-Byram Drought Index, Bootstrapping  
  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.  
  Address Department of Meteorology  
  Corporate Author Thesis $loc['Master's thesis']  
  Publisher Florida State University Place of Publication Tallahassee, FL Editor  
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  Funding Approved $loc['no']  
  Call Number COAPS @ mfield @ Serial 622  
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