Integrated Remote Sensing and Forecasting of Regional Terrestrial Precipitation with Global Nonlinear and Nonstationary Teleconnection Signals Using Wavelet Analysis

• Other Authors: Mullon, Lee (Author), Chang, Ni-bin (Committee Chair), Wang, Dingbao (Committee Member), Wanielista, Martin (Committee Member), University of Central Florida (Degree Grantor)
• Format: Text
• Language: English
• Published: University of Central Florida
• Subjects: teleconnection > climate change > artificial neural network > precipitation > greenness > forecasting > remote sensing; Pacific; Green Mountain; North Atlantic; North Atlantic oscillation
• Online Access: http://purl.flvc.org/ucf/fd/CFE0005535

Global sea surface temperature (SST) anomalies have a demonstrable effect on terrestrial climate dynamics throughout the continental U.S. SST variations have been correlated with greenness (vegetation densities) and precipitation via ocean-atmospheric interactions known as climate teleconnections. Prior research has demonstrated that teleconnections can be used for climate prediction across a wide region at sub-continental scales. Yet these studies tend to have large uncertainties in estimates by utilizing simple linear analyses to examine chaotic teleconnection relationships. Still, non-stationary signals exist, making teleconnection identification difficult at the local scale. Part 1 of this research establishes short-term (10-year), linear and non-stationary teleconnection signals between SST at the North Atlantic and North Pacific oceans and terrestrial responses of greenness and precipitation along multiple pristine sites in the northeastern 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 patterns across anomalous datasets found significant correlation regions between SST and the terrestrial sites. Non-stationary signals also exhibit salient co-variations 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 analysis of the combined ocean indices explained up to 50% of the greenness and 42% of the precipitation in the study sites. The identified short-term teleconnection signals improve the understanding and projection of climate change impacts at local scales, as well as harness the interannual periodicity information for future ...