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Identifying the effects of global change requires identifying global-scale signals in local data. We are seeking evidence of climate signals such as the Southern Oscillation Index (SOI) and the North Atlantic Oscillation Index (NAOI) in long-term data for the Georgia coast. The NAOI leads the SOI by 1 month and the two are very weakly correlated, so these two signals could explain different patterns in local data. Monthly standardized anomalies of Altamaha River discharge and Georgia coastal precipitation were constructed by normalizing, deseasonalizing, and detrending those data. Climate indices were also transformed if necessary, and all series were prewhitened prior to cross-correlation analyses to determine the most appropriate lags between series. Altamaha discharge and coastal precipitation are weakly negatively correlated, suggesting that coastal and inland precipitation patterns are different. However, climate signals explain very little of the variability in the local data (R2 < 0.02). This analysis will be extended to other datasets for coastal Georgia.

Sheldon, J.E. and Burd, A.B. 2007. Poster: Detecting climate signals in river discharge and precipitation data for the central Georgia coast. 2007 AERS/SEERS Meeting, March 15-17, 2007, Pine Knoll Shores, NC.