Intrinsic Mode-Based Network Approach to Examining Multiscale Characteristics of Sea Surface Temperature Variability

Abstract

Variability of sea surface temperature (SST), characterized by various spatiotemporal scales, is a proxy of climate change. A network analysis combined with empirical mode decomposition is newly presented for examining scale-dependent spatial patterns of SST variability. Our approach is applied to SST anomaly variability in the East/Japan Sea (EJS), consisting of satellite-derived daily datasets of 0.25° × 0.25° resolution from 1981 to 2023. Through the spatial distribution of instantaneous energy in intrinsic modes and features of intrinsic-mode networks, scale-dependent spatiotemporal features are found. The season-specific spatial pattern of energy density is observed only for weekly to semiannual modes, while a persistent high-energy distribution in the tongue-shaped region from East Korea Bay (EKB) to the Sub-Polar Front (SPF) is observed only for annual-to-decadal modes. The seasonality is apparent in the time evolution of energy only for weekly-to-annual modes, with a peak in summer and an increasing trend since the 2010s. Hubs of intrinsic-mode networks are observed in the whole southern area (some northern part) of EJS during the summer (winter), only for monthly to semiannual modes. Regional communities are observed only for weekly to seasonal modes, while there is an inter-basin community with annual-to-biennial modes, incorporating two pathways of East Sea Intermediate Water (ESIW).

Keywords

• sea-surface-temperature anomaly
• empirical mode decomposition
• intrinsic mode
• complex network theory