| Abstrakt: |
Regional-scale climate variability has far-reaching implications for both local and global climate systems, impacting factors like temperature, precipitation patterns, oceanic circulation, and the occurrence of extreme weather events. However, despite these influences, there is currently no universal methodology for the automated identification of regional-scale variability modes, including those less dominant globally, and for simultaneously exploring the influence of various ocean depth layers in characterizing these modes and diagnosing regional sea level variations. The presented innovative approach addresses these critical region-specific needs by assisting in the extraction of novel regional depth-layered variability modes and establishing their correlation with regional sea level fluctuations, employing tailored machine-learning techniques. This dual-purpose is achieved through the utilization of an optimized k-means clustering method for the automatic identification of regions with shared variability patterns across all global oceans, revealing previously unexplored regional variability modes. Additionally, guided by an EOF/PC analysis, the approach facilitates an automatic exploration of depth layers that significantly contribute to explaining sea level variability, providing insights into diverse climatic regions. Furthermore, the methodology is specifically designed for a multi-scale analysis, enabling the examination of climate variability spanning from months to several years. The results obtained through this approach have the potential to support informed decision-making regarding local climate-related changes. |
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