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 (1993-2006) with relatively new changes (2007-2019), 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.

中文翻译：

---

用机器学习方法重温东部边界上升流系统的历史趋势


东部边界上升流系统（EBUS）拥有非常富有成效的海洋生态系统，为许多周边国家提供服务。由于长期观测有限、气候模型不确定性和显着的自然变化，全球变暖对其功能的影响存在争议。本研究利用机器学习技术来记录 1993 年至 2019 年上升流系统的长期变化，重点关注高频天气上升流事件。由于后者受到一般大气和海洋环流的调节，因此假设其统计数据的变化可以反映波动并提供对 EBUS 长期变化的见解。采用了使用自组织映射 (SOM) 和层次聚合聚类 (HAC) 算法的两步方法。这些算法应用于一组上升流事件，以根据主要空间模式来表征海平面压力、经向风、短波辐射、海面温度 (SST) 和埃克曼泵送的特征。结果表明，由于沿岸风活动具有明显的季节性，主导空间格局占总方差的56%-75%，代表季节格局。调查结果显示，除加那利-伊比利亚地区外，上升流事件在春季变得更长，在夏季变得更加强烈。与春季（<0.016 事件/km²）相比，南半球系统（洪堡和本格拉）在夏季上升流事件的发生率更高（高达 0.022 事件/平方公里），而北半球系统（<0.012 事件/平方公里）则相反。平方公里）。 此外，通过将时间段分为大约两个相等的时期来检查主导空间格局的长期变化，以比较过去的变化（1993-2006年）与相对较新的变化（2007-2019年），揭示关键变量的变化。其中包括副热带高压系统（SHPS）向极地移动、上升流有利风增加以及海温向高纬度地区下降。洪堡洋流系统（HumCS）表现出独特的春夏模式，中纬度经向风减弱，同时海表温度下降。最后，对 EBUS 内的上升流中心进行了比较，重点关注压力和温度梯度的变化、经向风、混合层深度、纬向埃克曼输运和埃克曼泵送，没有发现任何证据支持巴贡的假设。这些指标的时间变化在 EBUS 内部和之间各不相同，表明不同地点的影响和反应存在差异。