Prediction of steady states in a marine ecosystem model by a machine learning technique

TL;DR

This paper demonstrates that machine learning, specifically a CVAE, can predict steady states in a marine ecosystem model, significantly reducing computational time for model spin-up processes.

Contribution

The study introduces a CVAE-based approach to predict steady states, reducing spin-up iterations by up to 95%, which is a novel application in marine ecosystem modeling.

Findings

CVAE predictions approximate steady states reasonably well.

Using CVAE predictions reduces spin-up iterations by up to 95%.

The method significantly decreases computational time for model convergence.

Abstract

We used precomputed steady states obtained by a spin-up for a global marine ecosystem model as training data to build a mapping from the small number of biogeochemical model parameters onto the three-dimensional converged steady annual cycle. The mapping was performed by a conditional variational autoencoder (CVAE) with mass correction. Applied for test data, we show that the prediction obtained by the CVAE already gives a reasonable good approximation of the steady states obtained by a regular spin-up. However, the predictions do not reach the same level of annual periodicity as those obtained in the original spin-up data. Thus, we took the predictions as initial values for a spin-up. We could show that the number of necessary iterations, corresponding to model years, to reach a prescribed stopping criterion in the spin-up could be significantly reduced compared to the use of the originally uniform, constant initial value. The amount of reduction depends on the applied stopping criterion, measuring the periodicity of the solution. The savings in needed iterations and, thus, computing time for the spin-up ranges from 50 to 95\%, depending on the stopping criterion for the spin-up. We compared these results with the use of the mean of the training data as an initial value. We found that this also accelerates the spin-up, but only by a much lower factor.