2009 Portland GSA Annual Meeting (18-21 October 2009)

Paper No. 5
Presentation Time: 9:00 AM-6:00 PM


EBELING, Carl W. and STEIN, Seth, Earth and Planetary Sciences, Northwestern University, 1850 Campus Drive, Evanston, IL 60208-2150, carl@earth.northwestern.edu

An ongoing debate within the climatological community centers on whether rising sea-surface temperatures due to global warming are changing the frequency or energy of North Atlantic hurricanes. The historical record makes it difficult to answer this question because before the advent of satellite-based observations in the 1960s, storms that did not make landfall may have gone unobserved, making an undercount likely.

To address this issue, we are developing a methodology to improve the record of the number and energy of North Atlantic hurricanes by analyzing their signals on decades of historical seismograms. Seismic noise—signals derived from natural sources and not related to earthquakes—is generated by atmospheric energy and so has been used as a proxy for oceanic wave climate and an indication of decadal-scale climate variability. Hence seismic noise should be usable to detect hurricanes that may have gone unobserved and to estimate their energy. As a first step in developing such a methodology, we are using digital data from the HRV (Harvard, MA) and SJG (San Juan, PR) seismic stations to calibrate seismic noise signals correlated with maximum wind speeds of well-characterized North Atlantic hurricanes and investigate the development of a hurricane discriminant.

Preliminary analysis of seismic noise power shows a variation by about two orders of magnitude between the low noise levels of the summer and the high noise levels between late September and May. Although a hurricane signature is not apparent in raw HRV power data, band-pass filtering of data recorded during hurricane Andrew (August 1992) reveals a signal correlatable with Andrew’s maximum storm wind speed. Because non-hurricane storms also generate signals in this band, we are investigating a discrimination algorithm combining data from the two distant sites.