Document Type : Original Article
Authors
1 Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran
2 Associate Prof., Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran.
Abstract
Teleconnections—statistically coherent, dynamically mediated couplings—shape Iranian temperature extremes across spatial and temporal scales. Leveraging a homogenized archive of monthly mean temperatures from 140 synoptic stations (1979–2023) alongside NOAA teleconnection indices, this study develops a phase‑lag‑aware ROCK–PCA framework that unites robust outlier handling, complex Hilbert embeddings and varimax‑type rotation with ERA5‑based composite diagnostics. The method isolates interpretable axes that map onto canonical modes (AMO/AMM/TNA, ENSO, PDO, EP–NP/PNA, NAO/EAWR, AO/SCAND) and quantifies their seasonally contingent leads/lags for Iran’s topographically diverse subregions. Results reveal an Atlantic‑led control: AMO/TNA dominate annual and warm‑season variability, peaking at two–three‑month leads; EP–NP emerges as the principal monthly‑scale driver at short lags; NAO/EAWR exert immediate wintertime synoptic influence; AO/SCAND contribute weeklies. Station‑resolved loadings highlight a southeast–east cluster (RPC7) during summer, implicating the Iranian thermal low and monsoon‑adjacent circulations, while lagged maps indicate a subsequent pivot toward the Persian‑Gulf littoral and, at longer lags, a sign reversal along the Caspian–Zagros windward belt. Extremal behavior, characterized via peaks‑over‑threshold statistics, exhibits systematic reorganization under positive AMO/AMM and TNA, with compounded warm‑season heat risk in southeastern Iran. Cross-validation between the rotated principal components and the gridded field composites, together with rank-based dependence metrics (e.g., Spearman/Kendall), demonstrates the robustness of our results despite known near-surface reanalysis biases. The framework delivers a minimal, maximally predictive subset of teleconnections and actionable lead‑time windows for sub‑seasonal‑to‑seasonal outlooks, thereby supporting risk‑aware planning across energy, agriculture, and public.
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