# Surface Conditioning for Zero-Shot Multivariate Correction in a Marine Biogeochemistry Emulator

**Authors:** Gabriela Martínez Balbontín, Anastase Charantonis, Dominique Béréziat, Stefano Ciavatta  
**Published:** 29 Oct 2026  
**Links:** [arXiv](https://arxiv.org/abs/2610.39777) · [PDF](https://arxiv.org/pdf/2610.39777) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.39777.html)

## Abstract

Bias correction in process-based models usually means iterative data assimilation with explicit covariance modelling. We propose a lightweight alternative based on a neural emulator of marine biogeochemistry (BGC), showing that the conditioning pathway of its autoencoder can serve as an observation injection point. The decoder is conditioned on surface variables via Feature-wise Linear Modulation (FiLM), originally designed to incorporate physical boundary forcings. We extend these inputs to include surface chlorophyll-a and replace the simulated field with satellite observations at inference time. The surface substitution propagates through the water column, improving agreement with depth-resolved observations at all depths, and spreads to other output variables in a pattern consistent with expected trophic relationships.

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