Remote Sensing and Wavelet Analysis for 30 Year Non-linear Non-stationary Teleconnection Signal Identification Between Sea Surface Temperature and Precipitation Regime in Central America

摘要

The effect that Sea Surface Temperature (SST) has on vegetation dynamics and precipitation throughout the world has been demonstrated widely. SST variations have been linearly linked with greenness and precipitation through ocean-atmospheric interactions such as El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillations (PDO) and Atlantic Multi-Decadal Oscillations (AMO) among others. Previous research has demonstrated that teleconnection can be used for climate prediction across a wide region at sub-continental scales. Although these studies are very important, the results are more difficult to interpret as linear analyses were used only to examine these relationships. In this paper 30-year, non-stationary signals are identified between SST at the Atlantic and Pacific oceans, and precipitation in the La Amistad International Park at Panama, Central America. The site was selected to avoid noise that can cause biased results. The methodology proposed for the teleconnection pattern identification has 3 major steps. First, the pre-processing of data, which involves the detrending by estimating the anomaly for the terrestrial and oceanic datasets. Furthermore, linear analysis was performed to the anomaly data in order to identify statistically significant regions of correlation between SST and the terrestrial site's precipitation. Indexes are selected in the regions of significant correlation. A second filter is applied by using a Stepwise Regression analysis to identify the most influential ocean regions. Finally, Wavelet analysis is used for the identification of non-stationary signals among the terrestrial dataset anomaly and SST anomaly. It was found that throughout the ocean regions there has been a link with ENSO, and during low ENSO years, with the NAO via atmospheric circulations. Also a link is found with the AMO and PDO. High frequency signals are also displayed in the time series which may coincide with the seasonal variations. These identified long-term teleconnection signals can aid for understanding the climate change impacts at local scales, and can aid to determine precipitation forecasts by establishing a relationship in the information identified.