(31m) Climate Change-Induced Thermodynamic Alterations in Surface Seawater: A Novel Investigation of CO2 Solubility Dynamics

The escalating global climate destabilization necessitates a fundamental reassessment of oceanic thermodynamic transformations. This investigation presents a comprehensive investigation of Arabian Sea surface seawater, specifically examining the correlation between varying temperature and salinity conditions with CO₂ solubility dynamics. Through precise experimental measurements of ultrasonic velocity and density across varying temperature and salinity conditions, we present novel investigation of critical thermophysical parameters including internal pressure, cohesive energy density, and solubility parameters - metrics previously unexplored in natural seawater systems. The study integrates advanced machine learning algorithms including Random Forest (RF), Gradient Booster (GB), Stacked Ensemble Machine Learning (SEML), and AdaBoost (AB) models. The SEML framework achieved exceptional predictive accuracy (>99%), with AdaBoost demonstrating 95%. Our findings reveal significant molecular restructuring under thermal stress: enhanced kinetic energy facilitates expanded intermolecular spacing, resulting in density reduction and augmented CO₂ solubility. This mechanism potentially creates a positive feedback loop in oceanic warming cycles, carrying profound implications for global climate dynamics. These insights provide crucial quantitative foundations for evidence-based environmental policy formulation and intervention strategies.