HR: 0800h
AN: A41A-02    [Abstracts]
TI: Effects of ENSO, NAO (PVO), and PDO on Monthly Extreme Temperature and Precipitation
AU: * Brolley, J M
EM: jbrolley@coaps.fsu.edu
AF: COAPS - FSU, 2035 E. Paul Dirac Dr. R.M. Johnson Building - Suite 200, Tallahassee, FL 32310, United States
AU: O'Brien, J J
EM: lawtonprof@gmail.com
AF: COAPS - FSU, 2035 E. Paul Dirac Dr. R.M. Johnson Building - Suite 200, Tallahassee, FL 32310, United States
AB: 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 worst and best case scenarios for each phase and the combination of phases that produce the greatest monthly extremes. Data from North America are gathered from the Historical Climatology Network (HCN), and data from these stations are bootstrapped in order to expand the time series. Bootstrapping is the stochastic simulation of monthly data by the utilization of daily data with identical ENSO, PDO, and PVO (NAO) characteristics. 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 bootstrapped data are arranged, and the tenth and ninetieth percentiles are analyzed. It has been found that the magnitudes of temperature and precipitation anomalies are greatest in the western Canada and the southeastern United States during winter, and these anomalies are located near the Pacific North American (PNA) nodes. Summertime anomalies, on the other hand, are weak because temperature variance is low. The magnitudes of the anomalies and the corresponding phase combinations vary regionally and seasonally.
DE: 3305 Climate change and variability (1616, 1635, 3309, 4215, 4513)
DE: 3315 Data assimilation
SC: Atmospheric Sciences [A]
MN: 2007 Joint Assembly