This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at https://github.com/p3jitnath/climate-rl.

本研究针对气候科学中的关键参数化挑战，探讨了将强化学习（RL）与理想化气候模型相结合的方法。目前的气候模型依赖复杂的数学参数化，可能引入较大的不确定性。研究结果表明，不同的RL方法在不同气候情境中表现出色，支持将基于RL的参数化方案集成到全球气候模型中，从而提高气候动态捕捉的准确性和效率。

RAIN：提高数值天气和气候模型的强化学习算法