S44 Sensitivity of Strong Extratropical Cyclones to Large-Scale Climate Variability in the United States

Sunday, 10 January 2016
Hall E ( New Orleans Ernest N. Morial Convention Center)
Khara Lukancic, Southern Illinois University, Carbondale, IL; and J. T. Schoof

Handout (2.6 MB)

Extratropical cyclones are responsible for a substantial portion of mid-latitude climate variability and contribute to widespread impacts. The characteristics of extratropical cyclones, such as their spatial distribution and intensity, are thought to be dependent on the large scale circulation. The relationship between cyclone characteristics and modes of large-scale climate variability has been investigated in previous studies, but interactions between modes of climate variability have largely been ignored. Since extratropical cyclone characteristics may be related to interactions between phases, quantifying these relationships is an important step in improving the climatology of extratropical cyclones. The goal of this study is to quantify relationships between modes of climate variability and characteristics of strong cyclones in the contiguous United States. Using historical sea-level pressure data, gridded at 2.5° × 2.5° (NCEP/NCAR Reanalysis data), cyclones intensity, frequency, and spatial distribution are investigated using a cyclone definition that combines the requirement for low pressure (1000 hPa or lower) and positive (cyclonic) vorticity. The large scale modes of climate variability considered include El Niņo Southern Oscillation (ENSO), the Pacific North American (PNA) mode, and the Arctic Oscillation (AO). The analysis is divided into three phases focusing on (1) establishing the annual and seasonal cyclone climatology within the study area, (2) quantifying differences in cyclone characteristics between the positive and negative phases of the individual modes of climate variability, and (3) examining the interactions between the modes of climate variability as they relate to extratropical cyclone characteristics. The results are expected to provide an improved baseline for evaluation of coupled climate models and also have the potential to improve seasonal climate predictability.
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