Document Type : Research Paper
Author
Department of Irrigation and reclamation- Faculty of Agriculture and Nature Resource (Alborz)-Tehran University-Karaj-Iran
Abstract
Keywords
Main Subjects
Snowpack acts as a delayed reservoir in Iran’s mountainous basins, governing spring streamflow, hydropower generation and multi‑sectoral water security. Over recent decades, observational and reanalysis records indicate pronounced declines and increased volatility in fresh snowfall, mean maximum snow depth and seasonal peak snow depth, coincident with accelerated warming over Southwest Asia. Yet the extent to which this behavior is orchestrated by large‑scale teleconnections, and how different modes co‑modulate distinct snow metrics across complex orography, remains only partially understood. Addressing this gap is crucial for translating global climate signals into actionable seasonal outlooks for Iran.
This study employs a recently proposed Rock‑PCA framework to diagnose spatiotemporally coherent patterns of snow variability. The approach first constructs complex analytic signals via Hilbert transforms of monthly snow time series at each station, then projects them into linear or nonlinear reproducing‑kernel Hilbert spaces before extracting eigenmodes and applying Varimax/Promax rotation. In doing so, Rock‑PCA simultaneously encodes phase, amplitude and potential nonlinearity, yielding dynamically interpretable modes. For each rotated component, station‑wise Spearman rank correlations are computed against an ensemble of atmosphere–ocean indices—including AMO, NAO, EAWR, SCAND, EP–NP, AMM, PDO, ONI and MEI—to quantify teleconnection fingerprints.
Analyses focus on winter (January–March, JFM) during 2000–2025, a period that coincides with the onset of accelerated warming highlighted in the IPCC Sixth Assessment Report and strong shifts in Atlantic and Pacific background states. The snow dataset comprises three monthly metrics—fresh snowfall, mean maximum snow depth and maximum snow depth—at 37 synoptic stations operated by the Iran Meteorological Organization. Stations are concentrated over north‑western Iran, the Alborz and central Zagros ranges and selected north‑eastern locations, spanning elevations from roughly 955 to almost 3000 m and climates from cold‑mountainous to semi‑arid. Teleconnection indices are obtained from NOAA and related centers and aggregated to monthly and seasonal means consistent with the snow record.
Rock‑PCA isolates a small number of leading components that together capture the bulk of interannual snow variability across Iran. Correlation analysis reveals the Atlantic Multidecadal Oscillation (AMO) as the dominant control: seasonal mean correlations between AMO and the three snow metrics reach −0/59 on average across ten stations, with several mountainous sites in Sahneh, Sepidan, Sahand and Varzaghan exhibiting |ρ| greater than 0/65. Extreme positive AMO phases are systematically associated with suppressed snow accumulation and shallow snowpacks, whereas negative phases favor anomalous snow‑rich winters. The Scandinavian pattern (SCAND) emerges as a second‑order but dynamically coherent influence, especially over Piranshahr, Sanandaj and neighbouring stations, where correlations between SCAND and snow metrics approach −0/60, redolent of colder, cloudier and snowier states during its positive phase.
Regionally confined yet statistically significant teleconnections are also detected. The East Atlantic–Western Russia pattern (EAWR) exerts strong control over fresh snow anomalies in Abali, Khorramabad and Kouhdasht, while EP–NP predominantly impacts maximum snow depth in Quchan and Varzaghan. Positive NAO phases coincide with enhanced fresh snowfall and deeper snowpacks in Ardabil during March, consistent with a south‑eastward extension of Atlantic storm tracks and strengthened Mediterranean cyclogenesis. These statistical relationships are corroborated by composite diagnostics of 500‑ and 250‑hPa geopotential height, 2‑m temperature and multi‑level cloudiness, conditioned on ENSO and teleconnection phases. Negative AMO and positive SCAND regimes favor equatorward‑shifted jets, deep troughing over the eastern Mediterranean and the Black Sea, negative temperature anomalies over north‑western Iran and pervasive low‑level cloud fields supportive of widespread snowfall. In contrast, the recent sequence of record‑warm, snow‑poor winters during 2022–2024 aligns with an extreme positive AMO state, amplified Hadley‑cell ridging and suppressed storm‑track activity over Iran.
By jointly considering multiple snow metrics, a diverse suite of teleconnections and a physically informed multivariate decomposition, this study demonstrates that a limited set of large‑scale modes—foremost AMO, SCAND, EAWR, EP–NP and NAO—explain a substantial fraction of observed winter snow variability over Iran. The Rock‑PCA framework provides a compact, interpretable basis in which teleconnection signals become more salient, thereby enhancing the prospects for robust, teleconnection‑conditioned seasonal prediction systems. From an applied perspective, integrating AMO and SCAND phase information into operational outlooks could materially improve anticipatory management of snowmelt‑driven floods, reservoir rule curves and downstream water allocation. The results further motivate extending the present framework to high‑resolution climate model ensembles, ultimately enabling stress‑testing of Iran's water‑resource infrastructure under plausible future teleconnection and warming scenarios.
Statement: During the preparation of this work the author(s) used [Chatgpt5 / SERVICE] in order to [better quality of article]. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.
This declaration does not apply to the use of basic tools for checking grammar, spelling, references, etc. If there is nothing to disclose, there is no need to add a statement.
Data available on request from the author.
The author avoided data fabrication, falsification, plagiarism, and misconduct.
)