David F. Bustos ^1,2* Diego A. Narváez ^2,3 Boris Dewitte ^4,5,6 Vera Oerder ^2,7 Mabel Vidal ⁸ Fabian Tapia ^2,3

• ¹ Programa de Posgrado en Oceanografía, Departamento de Oceanografía, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Casilla 160-C, Concepción, Chile, Concepcion, Chile
• ² Departamento de Oceanografía, Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción, Concepción, Chile, Concepcion, Chile
• ³ Centro de Investigación Oceanográfica COPAS Coastal, Universidad de Concepción, Concepción, Chile, concepcion, Chile
• ⁴ Centro de Estudios Avanzados en Zonas Áridas (CEAZA), Coquimbo, Chile, Coquimbo, Chile
• ⁵ Centro de Ecología y Gestión Sostenible de Islas Oceánicas (ESMOI), Departamento de Biología Marina, Facultad de Ciencias del Mar, Universidad Católica del Norte, Antofagasta, Chile, Antofagasta, Chile
• ⁶ Clima, Medio Ambiente, Acoplamientos e Incertidumbres (CECI), Universidad de Toulouse, CERFACS/CNRS, Toulouse, Francia, Toulouse, France
• ⁷ Instituto Milenio de Oceanografía (IMO), Universidad de Concepción, Concepción, Chile, Concepcion, Chile
• ⁸ Departamento de Ciencias de la Computación, Universidad de Concepción, Concepción, Chile, Concepcion, Chile

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 (<0.016 Events/km²), contrasting with Northern Hemisphere systems (<0.012 Events/km²). Furthermore, long-term changes in dominant spatial patterns were examined by dividing the time period in approximately two equally periods, to compare past changes (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) with relatively new changes (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019), revealing shifts in key variables. These included poleward shifts in subtropical high-pressure systems (SHPS), increased upwelling-favorable winds, and SST drops towards higher latitudes. The Humboldt Current System (HumCS) exhibited a distinctive spring-to-summer pattern, with mid-latitude meridional wind weakening and concurrent SST decreases. Finally, a comparison of upwelling centers within EBUS, focusing on changes in pressure and temperature gradients, meridional wind, mixed-layer depth, zonal Ekman transport, and Ekman pumping, found no evidence supporting Bakun's hypothesis. Temporal changes in these metrics varied within and across EBUS, suggesting differential impacts and responses in different locations.