首页|Study Data from Universidad de Concepcion Provide New Insights into Machine Lear ning (Revisiting historical trends in the Eastern Boundary Upwelling Systems wit h a machine learning method)
Study Data from Universidad de Concepcion Provide New Insights into Machine Lear ning (Revisiting historical trends in the Eastern Boundary Upwelling Systems wit h a machine learning method)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from Concepcion, Chile, by NewsR x correspondents, research stated, “Eastern boundary upwelling systems (EBUS) ho st very productive marine ecosystems that provide services to many surrounding c ountries. The impact of global warming on their functioning is debated due to li mited long-term observations, climate model uncertainties, and significant natur al variability.” Funders for this research include Agencia Nacional De Investigacion Y Desarrollo . Our news journalists obtained a quote from the research from Universidad de Conc epcion: “This study utilizes the usefulness of a machine learning technique to d ocument long-term variability in upwelling systems from 1993 to 2019, focusing o n 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 a lgorithms were applied to sets of upwelling events to characterize signatures in sealevel pressure, meridional wind, shortwave radiation, sea-surface temperatu re (SST), and Ekman pumping based on dominant spatial patterns. Results indicate d that the dominant spatial pattern, accounting for 56%-75% of total variance, representing the seasonal pattern, due to the marked seasonal ity in along-shore wind activity. Findings showed that, except for the Canary-Ib erian region, upwelling events have become longer in spring and more intense in summer. Southern Hemisphere systems (Humboldt and Benguela) had a higher occurre nce 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, longterm changes in dominant spatial patterns w ere examined by dividing the time period in approximately two equally periods, t o compare past changes (1993-2006) with relatively new changes (2007-2019), reve aling shifts in key variables. These included poleward shifts in subtropical hig h-pressure systems (SHPS), increased upwelling-favorable winds, and SST drops to wards higher latitudes. The Humboldt Current System (HumCS) exhibited a distinct ive spring-to-summer pattern, with mid-latitude meridional wind weakening and co ncurrent SST decreases.”
Universidad de ConcepcionConcepcionC hileSouth AmericaCyborgsEmerging TechnologiesMachine Learning
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