Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction
This addresses the problem of improving seasonal to decadal ocean forecasting for climate science and prediction applications, representing a novel approach rather than an incremental improvement.
The paper tackles the challenge of predicting global ocean variability over multi-year scales by proposing ORCA-DL, a data-driven 3D ocean model that accurately simulates ocean dynamics and outperforms state-of-the-art dynamical models in capturing extreme events like El Niño-Southern Oscillation and upper ocean heatwaves.
Accurate ocean dynamics modeling is crucial for enhancing understanding of ocean circulation, predicting climate variability, and tackling challenges posed by climate change. Despite improvements in traditional numerical models, predicting global ocean variability over multi-year scales remains challenging. Here, we propose ORCA-DL (Oceanic Reliable foreCAst via Deep Learning), the first data-driven 3D ocean model for seasonal to decadal prediction of global ocean circulation. ORCA-DL accurately simulates three-dimensional ocean dynamics and outperforms state-of-the-art dynamical models in capturing extreme events, including El Niño-Southern Oscillation and upper ocean heatwaves. This demonstrates the high potential of data-driven models for efficient and accurate global ocean forecasting. Moreover, ORCA-DL stably emulates ocean dynamics at decadal timescales, demonstrating its potential even for skillful decadal predictions and climate projections.