Abstract Large-scale dynamics of the oceans and atmosphere are governed by primitive equations (PEs). Due to nonlinearity nonlocality, numerical study PEs is generally challenging. Neural networks have been shown be a promising machine learning tool tackle this challenge. In work, we employ physics-informed neural (PINNs) approximate solutions error estimates. We first establish higher-order re...