Estimating Seasonal Global Sea Surface Chlorophyll-a with Resource-Efficient Neural Networks

Abstract. Marine chlorophyll-a is an important indicator of ecosystem health, and accurate forecasting, even at the surface level, can have significant implications for climate studies and resource management. Traditionally, these predictions have relied on computationally intensive numerical models, which require extensive domain expertise and careful parameterization.

We propose a data-driven alternative: a lightweight, resource-efficient neural architecture based on the U-Net that reconstructs surface, near-global chlorophyll-a from four physical predictors. The model uses mixed layer depth, sea surface temperature, sea surface salinity, and sea surface height as input, all of which are known to influence phytoplankton distribution and nutrient availability. By leveraging publicly available seasonal forecasts of these variables, we can generate six-month chlorophyll-a predictions in a matter of minutes.

We first validated the quality of the reconstruction by using the GLORYS12 reanalysis as input. The reconstructed time series demonstrated strong agreement with the reference GlobColour observations, with an RMSE of 0.01 and a correlation of 0.95. Extending this approach to seasonal forecasting, we used six-month SEAS5 forecasts as input and found that our predictions maintained high skill globally, with low error rates and stable correlation coefficients throughout the forecast period.

Our model accurately captures spatial and temporal chlorophyll-a patterns across a variety of regions, with an accuracy that meets or exceeds that of the numerical model of reference while significantly reducing computational costs. This approach offers a scalable, efficient alternative for long-term chlorophyll-a forecasting.