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Coastal upwelling systems are vital to ocean productivity and regional climate regulation, yet quantifying the influence of wind forcing on the persistence of upwelling remains a significant challenge. This study presents a supervised learning framework to predict Upwelling Stability Periods (USPs) by linking daily wind to Sea Surface Temperature (SST)-derived labels in the Canary Upwelling System. Building on the Core-Shell clustering approach, which identifies USPs via unsupervised SST segmentation, we reformulate the problem as a multiclass classification task. A fuzzification–defuzzification scheme is employed to generate USP labels in a manner that reconciles the temporal resolution mismatch between SST-based clustering and daily wind fields. We construct three progressively enriched feature sets from wind and SST data. Decision Trees (DTs) and Random Forests (RFs) are used to model the relationship between wind and USPs. Over a 16-year period, SST-enhanced features yield substantial gains in classification accuracy (AUC > 0.94). DTs achieve strong predictive performance while maintaining interpretability through rule-based structures. RFs consistently deliver robust performance reinforcing their role as a reliable benchmark for classification.

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Learning Coastal Upwelling Patterns from Wind Velocity via Interpretable Tree Models

  • 会议论文

  • 发表日期：

    2026年

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  • Susana Nascimento,
  • Pedro Caldeirão,
  • Paulo Relvas,
  • Boris Mirkin

摘要

Coastal upwelling systems are vital to ocean productivity and regional climate regulation, yet quantifying the influence of wind forcing on the persistence of upwelling remains a significant challenge. This study presents a supervised learning framework to predict Upwelling Stability Periods (USPs) by linking daily wind to Sea Surface Temperature (SST)-derived labels in the Canary Upwelling System. Building on the Core-Shell clustering approach, which identifies USPs via unsupervised SST segmentation, we reformulate the problem as a multiclass classification task. A fuzzification–defuzzification scheme is employed to generate USP labels in a manner that reconciles the temporal resolution mismatch between SST-based clustering and daily wind fields. We construct three progressively enriched feature sets from wind and SST data. Decision Trees (DTs) and Random Forests (RFs) are used to model the relationship between wind and USPs. Over a 16-year period, SST-enhanced features yield substantial gains in classification accuracy (AUC > 0.94). DTs achieve strong predictive performance while maintaining interpretability through rule-based structures. RFs consistently deliver robust performance reinforcing their role as a reliable benchmark for classification.