| Summary: | Global sea surface temperature (SST) anomalies have an inherent effect on vegetation dynamics and precipitation processes throughout the continental United States (U.S.). SST variations have been correlated with precipitation patterns via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that understanding excitation mechanisms of the teleconnection patterns can be instrumental for climate prediction across a wide region at sub-continental scales, yet these studies tend to have large uncertainties in estimates by assuming linearity when examining teleconnection signals. The co-existence of non-stationary and nonlinear signals embedded in SST anomalies makes the identification of the teleconnection patterns difficult at the local scale. This study explores the short-term (10-year) frequencies (i.e., interannual and seasonal ) embedded in the non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and the responses of terrestrial greenness and precipitation along multiple pristine sites in northeast U.S., including (1) White Mountain National Forest - Pemigewasset Wilderness, (2) Green Mountain National Forest - Lye Brook Wilderness, and (3) Adirondack State Park - Siamese Ponds Wilderness. Each site was selected to avoid anthropogenic influences that may otherwise mask climate teleconnection signals. Lagged pixel-wise linear teleconnection analysis based on remote sensing satellite images across anomalous global SST datasets found significant correlation regions between SST and these terrestrial sites. With the aid of wavelet analyses including continuous wavelet transform, cross-wavelet analysis, and wavelet coherency analysis, nonlinear and non-stationary signals exhibit salient covariations at biennial and triennial frequencies between terrestrial responses and SST anomalies across oceanic regions in agreement with the El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) signals. Multiple regression ...
|
|---|