Modelling Winter Atmospheric River Frequency over the Western Himalayas Using Large-Scale Climate Modes Via Statistical Approach
Year: 2026
Type: preprint
Abstract: Winter Atmospheric Rivers (ARs) contribute substantially to precipitation in the Western Himalayas, but how large-scale climate modes jointly influence their frequency remains unclear. In this study, we use a statistical method that combines correlation, phase-composite analysis of Integrated Vapor Transport (IVT), and predictive models such as linear regression, Generalized Linear Models (GLMs), and Generalized Additive Models (GAMs). We examine eighteen climate indices and data from 39 winters (1980 to 2018) and discover that the Arctic Oscillation, Pacific Decadal Oscillation, Pacific North American pattern, East Atlantic Western Russia pattern, and the Siberian High are strongly associated with winter AR frequency. The phase-composite IVT analysis reveals a consistent moisture-transport signal for each pattern. We also find that no one climate mode can account for the variability in ARs. However, including linear interaction terms in a GLM yields better predictions than a simple linear model. In contrast, a GAM that accounts for nonlinear relationships and key interactions, such as among the East Pacific–North Pacific, Western Pacific, and PNA patterns, provides considerably better predictive ability than a GLM. This indicates that AR frequency depends on nonlinear responses to the combined state of several climate modes. These results offer a statistical framework that clearly connects large-scale climate variability, including how different modes interact, to the frequency of winter ARs in the Western Himalayas. This connection helps improve seasonal forecasts and water resource planning in this challenging and data-limited region.
DOI: 10.31223/x5621b
Authors: Sheikh Fayaz, Munir Nayak, Mohammad Kanth
Institution: National Institute of Technology Srinagar
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7207616624