Machine Learning for Decadal Ocean Prediction - Exploring the Feasibility of Capturing Climate Memory in the Upper Ocean

Decadal climate predictions are essential for climate adaptation, yet remain challenging due to the complex interplay of initial conditions and external forcings. A key factor in achieving skillful forecasts is the upper ocean, which plays a central role in modulating decadal-scale climate variability, including phenomena such as ENSO, the Atlantic Multidecadal Variability, and the Indian Ocean Dipole. Accurately capturing the ocean’s memory is therefore critical, but traditional numerical models are computationally demanding and often exhibit systematic biases. While machine learning has shown promise in improving medium-range weather forecasts, its application to decadal climate prediction remains limited.

This work explores the feasibility of using machine learning to predict sea surface temperature (SST) and ocean heat content (OHC) on decadal timescales. We develop an autoregressive model based on a UNet-like convolutional neural network, trained on 1,000 years of data from a pre-industrial control run from the fully coupled MPI-ESM. This simulation provides a controlled environment to study predictability arising from internal ocean dynamics. Inputs include SST, OHC, a land-sea mask, and top-of-atmosphere solar radiation to encode the seasonal cycle. We conduct a systematic study of input design, to assess how the representation of past states influences model stability and predictive skill. Our results suggest that machine learning can be a viable and flexible approach for decadal ocean prediction. Additionally, we find that longer input windows and coarser resolution may improve long-term stability, potentially offering new insights into how climate memory is encoded.