# Climate Model Tuning with Online Synchronization-Based Parameter Estimation

arXiv: https://arxiv.org/abs/2510.06180 · HTML: https://mlsanity.com/paper/2510.06180/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 47 references that are in the map.
- First posted on arXiv 7 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics](https://mlsanity.com/paper/2402.18484/) (2024)
- [RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models](https://mlsanity.com/paper/2408.16118/) (2024)
- [Semi-automatic tuning of coupled climate models with multiple intrinsic timescales: lessons learned from the Lorenz96 model](https://mlsanity.com/paper/2208.06243/) (2022)

## Abstract (arXiv)

In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of the system state and long integration times. Supermodelling is a technique which has shown the potential for reducing climate model biases by dynamically coupling multiple models together, and training their coupling on a short timescale. Here, we introduce a new approach called adaptive supermodeling, where the internal model parameters of the member of a supermodel are tuned. We perform three experiments. We first directly optimize the internal parameters of a climate model. We then optimize the weights between two members of a supermodel in a classical supermodel approach. For a case designed to challenge the two previous methods, we implement adaptive supermodeling, which achieves a performance similar to a perfect model.