Supplementary material to "The Role of El Niño Southern Oscillation in driving Coastal Hazards in the U.S. Pacific Northwest"
Year: 2025
Type: supplementary-materials
Abstract: Information 1) Methods a) Annual Weather Type Construction Annual Weather Types (AWTs) are constructed through a multi-step process following the methodology of Anderson et al. (2019) that begins with the extraction of monthly sea surface temperature anomalies (SSTAs) from the Nino3.4bounding box.Hovmöller diagrams are constructed by taking the longitudinal average of SSTAs within the bounding box for each month of a boreal year (June to May).Empirical Orthogonal Function (EOF) analysis is applied to the Hovmöller diagrams, extracting dominant spatial modes and temporal behavior of observed ENSO dynamics.The three leading principal components (PCs) of the EOF analysis are clustered into six types (using K-means clustering) associated with canonical ENSO patterns (EP El Niño, CP El Niño, three transition/ neutral phases, and La Niña).This custom ENSO index preserves orthogonal modes of variability that are critical for representing ENSO diversity and complexity (Almar et al., 2023;Williams and Patricola, 2018).The leading EOF captures the dominant seesaw of interannual SSTA variability represented by traditional ENSO indices (R 2 >0.9 for both the ONI and Nino3.4 indices), while the second and third EOFs depict spatial and temporal patterns linked to shifting seasonal anomalies, and Kelvin wave propagation of SSTAs across the Pacific Basin, respectively (Anderson et al., 2019).By capturing more variance than classical ENSO indices (+19% compared to those that rely only on the leading EOF), this methodology is better able to represent the diversity of ENSO behavior on sub-seasonal to interannual scales.' * # %,# ; and the influence of previous weather type states is introduced via a Markov chain, parameterized as: 𝜋 % () = < 𝑌 !"/ 𝛾 / 0 /,#
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
OpenAlex ID: W7084058895
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