Revisiting historical trends in the Eastern Boundary Upwelling Systems with a machine learning method

Overview & Synopsis

Eastern boundary upwelling systems (EBUS) host very productive marine ecosystems that provide services to many surrounding countries. The impact of global warming on their functioning is debated due to limited long-term observations, climate model uncertainties, and significant natural variability. This study utilizes the usefulness of a machine learning technique to document long-term variability in upwelling systems from 1993 to 2019, focusing on high-frequency synoptic upwelling events. Because the latter are modulated by the general atmospheric and oceanic circulation, it is hypothesized that changes in their statistics can reflect fluctuations and provide insights into the long-term variability of EBUS. A two-step approach using Self-Organizing Maps (SOM) and Hierarchical Agglomerative Clustering (HAC) algorithms was employed. These algorithms were applied to sets of upwelling events to characterize signatures in sea-level pressure, meridional wind, shortwave radiation, sea-surface temperature (SST), and Ekman pumping based on dominant spatial patterns. Results indicated that the dominant spatial pattern, accounting for 56%-75% of total variance, representing the seasonal pattern, due to the marked seasonality in along-shore wind activity. Findings showed that, except for the Canary-Iberian region, upwelling events have become longer in spring and more intense in summer. Southern Hemisphere systems (Humboldt and Benguela) had a higher occurrence of upwelling events in summer (up to 0.022 Events/km²) compared to spring (