NPPCast: A Compact CNN Integrating Satellite Data for Global Ocean Net Primary Production Forecasts

Skillful prediction of marine net primary production (NPP) on seasonal to multi-year timescales is essential for assessing the ocean’s role in the global carbon cycle and managing marine resources. We introduce NPPCast, a compact convolutional neural network using causal dilated convolutions, and compare its performance with four representative UNet-family models (UNet, VNet, AttUNet, R2UNet). Each model is pre-trained on 36-month output from either Community Earth System Model version 2 forced-ocean–sea-ice (CESM2-FOSI) or interannual varying forcing (CESM2-GIAF) and fine-tuned using three satellite-derived NPP products (the Standard Vertically Generalized Production Model (SVGPM), the Eppley Vertically Generalized Production Model (EVGPM), and the Carbon-based Productivity Model (CbPM)) as well as their multi-product mean (MEAN). Across most tests, NPPCast outperforms the baselines, reducing global root mean square error (RMSE) by 30–56% on MEAN/EVGPM/SVGPM and improving the anomaly correlation coefficient (ACC) by 0.32–0.49 over the best UNet-based alternative. NPPCast also achieves the highest structural similarity to observations and low bias, as seen in scatter and spatial analyses, and attains the highest or tied-highest Nash–Sutcliffe efficiency (NSE) in three of four products. Crucially, NPPCast’s performance remains stable when switching between FOSI and GIAF pre-training datasets, with RMSE changing by at most 2.17%, whereas UNet-family models vary from −41.6% to +42.5%. We show that NPPCast consistently outperforms the Earth system model, sustaining significant predictive skill in contrast to the rapid decline observed in the latter. These results demonstrate that an architecture that maintains performance across different pre-training datasets (CESM2–FOSI and CESM2–GIAF) can yield more accurate and reliable long-range global NPP forecasts than UNet-family models.