Extreme weather is becoming more frequent and intense as the climate warms, but one of the planet’s most important drivers of climate variability remains difficult to predict: the ocean. Covering roughly 70 percent of Earth’s surface, the ocean absorbs and redistributes enormous quantities of heat and carbon, shaping atmospheric conditions from one season to the next. Researchers at the Korea Institute of Science and Technology (KIST) have developed KIST-Ocean, a data-driven global ocean prediction model designed to reproduce three-dimensional ocean conditions and forecast how they will evolve. It predicts how these variables will change over five-day intervals and resolves ocean conditions down to a depth of 600 meters. Subject of Research: AI-based global ocean forecasting and atmosphere–ocean dynamicsArticle Title: Data-driven global ocean model resolving atmospherically forced ocean dynamicsNews Publication Date: 12-Jun-2026Web References: https://doi.org/10.1126/sciadv.aed1225References: Science Advances, DOI: 10.1126/sciadv.aed1225Image Credits: Korea Institute of Science and TechnologyKeywordsKIST-Ocean, artificial intelligence, ocean forecasting, climate prediction, El Niño, ocean dynamics, climate change, machine learning, Earth system models, seasonal forecasting