Investigating a novel feature of Multi-Phase Rotated Empirical Orthogonal Function to capture Spatiotemporal Temperature Variations

Abstract Climate change has had a significant impact on the Northwest Himalayan (NWH) region, making it necessary to analyze its rapidly shifting space-time field using appropriate statistical methods. In order to represent systematic patterns and explain the localized characteristics of the region, we employ Rotated Empirical Orthogonal Functions (REOFs) on mean temperature data (1981-2021). The NWH region is divided into the snow and non-snow cover regions via REOF analysis, allowing for a more accurate representation of the data. In particular, the western parts of Uttarakhand (U.K.) and Himachal Pradesh (H.P.) showed significant fluctuations in the second REOF mode of mean temperature, which accounted for 38.7% of the entire variance. However, the principal component (PC) that was connected with it was unable to reflect the data pattern in these areas. Towards this, we have introduced Multi-phase REOF analysis for the first time to identify PCs that accurately represent the original data and provide insight into the specific temporal variations. We also investigated potential reasons for these variances in these specific NWH locations. Climate change modelling in the area can be enhanced by using the data's deduced physical properties and recognised temperature zones.