OS4.2 | Ocean extremes: multi-scale dynamics through observations, models and machine learning techniques
EDI
Ocean extremes: multi-scale dynamics through observations, models and machine learning techniques
Convener: Antonio Ricchi | Co-conveners: Coline Poppeschi, Giovanni Liguori, Matjaz Licer, Baptiste Mourre

Marine extreme events, including phenomena such as storm surges, marine heatwaves, harmful algal blooms, jellyfish blooms, acidification, severe storms and temporally or spatially compounding events are becoming increasingly frequent under climate change. These events have significant impacts on marine ecosystems, coastal communities and global economies. Despite their profound socio-economic and environmental impact, extreme events in the marine environment remain largely understudied, poorly understood and difficult to simulate, making them difficult to predict. The dynamics of these events span a broad range of spatial and temporal scales and are often influenced by complex feedback mechanisms between the ocean and other components of the climate system. Fundamental research remains crucial in enhancing our understanding of these phenomena and in predicting their occurrence and related risks.

This session encourages contributions addressing dynamic mechanisms across an entire spectrum of atmospheric and ocean extremes, event attribution studies and projections under future climate. Relevant submissions also encompass new observation techniques, new modeling and machine learning methods to marine extremes forecasting, novel detection strategies and, finally, ecosystem or socio-economic impact assessments, relevant for prevention, mitigation and adaptation policies.