Statistical analysis of relations between monthly teleconnection indices

• Other Authors: Jones, Ma'Ko'Quah (author), Hartten, Leslie (contributor), Penland, Cécile (contributor), White, Allen (contributor)
• Format: Manuscript
• Language: English
• Published: 2012
• Subjects: Pacific; Soi; North Atlantic; North Atlantic oscillation
• Online Access: http://nldr.library.ucar.edu/repository/collections/SOARS-000-000-000-418; https://doi.org/10.5065/y4nz-bt34

Weather conditions in one region can often be linked to conditions in another region thousands of miles away. These long-distance relationships are known as teleconnections and play a large role in our ocean-atmosphere system, including global weather patterns. Some recurring deviations, or anomalies, in regional weather patterns are commonly referred to as oscillations. This project focused on common spatial oscillations that affect North American weather patterns: the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO) and the El Niño/Southern Oscillation (ENSO). These phenomena are monitored using climate indices, which are calculated monthly from relevant physical variables representing the state of the oscillation over a specific region. Contemporaneous and lagged correlation analysis was used to investigate relationships among four indices: the NAO index, the PDO index, the Southern Oscillation Index (SOI) and the Niño3.4 SST index. Results show the autocorrelation function of the PDO is highly significant (at the 5% level) for at least one year and still significant out to at least seven years, which was the length of time allowed by the 60-year (1951-2011) dataset. The Niño3.4 autocorrelation is negative (significant at the 5% level) around 24 months, which indicates a transition in the ENSO from a positive to a negative phase, and significantly positive again around 50 months. The maximum correlation between the PDO Index and the Niño3.4 SST Index is 0.52 (highly significant), with the PDO leading the Niño3.4 by two months. Understanding correlations and whether there exists relationships between teleconnections may improve seasonal weather forecasting.