Tuesday, 25 January 2011: 9:30 AM
608 (Washington State Convention Center)
The definition of annual cycle has become an interesting topic in climate research. The annual cycles of climate variables, such as maximum and minimum temperature, often exhibit year to year variation due to the nonlinear and non-stationary nature of earth's climate system. Therefore, extracting amplitude-frequency modulated annual cycles may lead to new insights concerning climate variability on all timescales. In this study, we apply an adaptive and temporal local analysis method the Ensemble Empirical Mode Decomposition (EEMD) to decompose observed daily maximum and minimum temperature data from a set of 150 stations scattered throughout North Carolina, South Carolina, Georgia, Alabama, and Florida into climate variability of various naturally defined timescales. The analysis was carried out for the time period from 1954 through 2007. Further analysis of the decomposed components leads to different interpretations of the characteristics of surface temperature variability. For example, the widely recognized shift of the low frequency variability (including interannual and longer timescales) around mid-1970s can be alternatively interpreted as a phase-locked concurrence of individual quasi-oscillatory components of interannual to decadal timescales. The modulated annual cycle also facilitates the determination of the timing of a particular phase of annual cycle (such as spring onset) and its variability and trend. The results of the latter analysis, such as the variability and change of the spring onset date of the Southeastern United States, will all be presented in this study.
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