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Advancing Projections of Antarctic Ice-Ocean Interactions with Physics-Informed Neural Networks

CORDIS · observation · Publication date unknown

d governing physical equations to reconstruct the daily full-depth structure of temperature, salinity, and circulation across the Southern Ocean and Antarctic shelves. By combining the flexibility of machine learning with physical consistency, CAPTSA will overcome challenges of data sparsity, coarse resolution, poorly constrained parametrization schemes, and computational cost. Its time series predictions will be employed in the analysis of ASF dynamics, CDW intrusions, and cross-shelf exchanges across fresh, warm, and dense shelf regimes. Further, by linking the CAPTSA predictions with satellite- and radar-derived melt estimates, CAPTSA will allow causal attribution between external climate forcing, oceanic variability, and ice-shelf melt rates. This integrative approach will advance process-based understanding of ice-ocean feedbacks and provide a robust framework for regime-specific projections of basal melting and Antarctic contributions to sea-level rise. Antarctic ice sheet, Sea level rise, Ice-Ocean Interactions, Physics-informed neural networks, Predictive AI models, Basal me

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recordType

award

status

SIGNED

region

EU

value

251578.56

unit

EUR