Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction

TL;DR

This paper introduces ORCA-DL, a novel data-driven 3D ocean model that improves seasonal to decadal global ocean predictions by accurately simulating ocean dynamics and outperforming traditional models in capturing extreme climate events.

Contribution

The paper presents the first data-driven 3D ocean model for long-term prediction, demonstrating superior performance over existing dynamical models in forecasting extreme oceanic phenomena.

Findings

Outperforms state-of-the-art models in predicting El Niño and heatwaves.

Accurately simulates ocean dynamics at decadal timescales.

Shows potential for climate projections and long-term forecasting.

Abstract

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 Ni\~no-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.