Exploring Regional Ocean Climate Variability: Insights from Integrated Clustering and Principal Component Analysis.

Abstract

Ocean regional climate variability is a part of the Earth's complex system that can influence the occurrence and intensity of extreme weather events. Variability in ocean temperature can either amplify or mitigate the impact of these events. For example, the El Niño phenomena affect weather conditions in various parts of the world, leading to droughts, floods, and altered precipitation patterns. Furthermore, regional climate variability is also linked to changes in sea level. Understanding regional variability is crucial for predicting how sea level changes will vary in different parts of the world, which has profound implications for coastal communities and infrastructure. To contribute to this understanding, we have developed a novel method that combines K-means clustering and Principal Component Analysis to extract ocean climate modes at a regional scale worldwide. This integrated approach automatically identifies regions of variability, allowing for the emulation of coastal and regional sea level variations across multiple timescales. It also has the potential to offer valuable insights into the significance of temperature across multiple depth layers extending up to 700 meters. The produced set of regional sea-level emulators are a complementary source of information in coastal areas, especially in situations where satellite altimetry encounters challenges and/or tide-gauge sensor records are incomplete, thereby supporting well-informed decision-making.