Reducing Model Biases With Machine Learning Corrections Derived From Ocean Data Assimilation Increments

Large ocean data assimilation (DA) increments are concentrated in the mixed layer, indicating persistent biases in vertical mixing. These increments can therefore be used to derive bias corrections. Previous studies developed full-depth climatological temperature and salinity corrections from these increments, known as the Ocean Tendency Adjustment (OTA). In this work, we develop a machine learning (ML) model trained on DA outputs from the GFDL SPEAR system to predict state-dependent temperature corrections within three mixed layer depths (3 × MLD), targeting vertical-mixing biases. The trained ML model is implemented online in MOM6 to produce both DA simulations and free runs within the coupled SPEAR system. Compared with OTA, the ML scheme more effectively reduces upper-ocean analysis increments in DA runs. In 10-year free runs, the ML approach better represents mixed layer depth, reducing overly deep biases by capturing restratification. However, applying ML corrections alone degrades sea surface temperature (SST) climatology due to subsurface drift, reflecting processes not targeted by the mixed-layer-focused ML scheme. This drift can be mitigated by applying residual OTA corrections, which represent climatological DA increments not captured by the ML scheme. With this treatment, the ML correction scheme improves long-term SST climatology and outperforms OTA. The workflow developed here is generalizable to any climate model with an ocean DA system and offers a promising pathway for reducing long-standing model biases.