Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning

[2601.04268] Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2601.04268 (cs) [Submitted on 7 Jan 2026 ( v1 ), last revised 14 Aug 2026 (this version, v4)] Title: Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning Authors: Pritthijit Nath , Sebastian Schemm , Henry Moss , Peter Haynes , Emily Shuckburgh , Mark J. Webb View a PDF of the paper titled Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning, by Pritthijit Nath and 5 other authors View PDF HTML (experimental) Abstract: Weather and climate models rely on parametrisations to represent unresolved sub-grid processes. Traditional schemes rely on fixed coefficients that are weakly constrained and tuned offline, contributing to persistent biases that limit their ability to adapt to underlying physics. This study presents a framework that learns components of parametrisation schemes online as a function of the evolving model state using reinforcement learning (RL) and evaluates policy-driven parameter updates across idealised testbeds spanning a simple climate bias correction (SCBC), a radiative-convective equilibrium (RCE), and a zonal mean energy balance model (EBM) with single-agent and federated multi-agent settings. Across nine RL algorithms, Truncated Quantile Critics (TQC), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3) achieved the highest skill and stable convergence, with performance assessed against a static baseline using area-weighted RMSE, temperature and pressure-level diagnostics. For the EBM, single-agent RL outperformed static parameter tuning with the strongest gains in tropical and mid-latitude bands, while federated RL on multi-agent setups enabled specialised control and faster convergence, with a six-agent DDPG configuration using frequent aggregation yielding the lowest area-weighted RMSE across the tropics and mid-latitudes. The learnt corrections were also physically meaningful as agents modulated EBM radiative parameters to reduce meridional biases, adjusted RCE lapse rates to match vertical temperature errors, and stabilised heating increments to limit drift. Overall, results show that RL can learn skilful state-dependent parametrisation components in idealised settings, offering a scalable pathway for online learning within numerical models and a starting point for evaluation in weather and climate models. Comments: 79 pages, 24 figures Subjects: Machine Learning (cs.LG) ; Atmospheric and Oceanic Physics (physics.ao-ph) Cite as: arXiv:2601.04268 [cs.LG] (or arXiv:2601.04268v4 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2601.04268 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Journal of Advances in Modeling Earth Systems (JAMES) 18 (8), e2026MS005745 Related DOI : https://doi.org/10.1029/2026MS005745 Focus to learn more DOI(s) linking to related resources Submission history From: Pritthijit Nath [ view email ] [v1] Wed, 7 Jan 2026 11:19:16 UTC (5,959 KB) [v2] Wed, 8 Apr 2026 17:27:09 UTC (20,080 KB) [v3] Fri, 24 Jul 2026 17:38:20 UTC (19,632 KB) [v4] Fri, 14 Aug 2026 23:42:47 UTC (19,417 KB) Full-text links: Access Paper: View a PDF of the paper titled Replacing Tunable Parameters in Weather and Climate Models with State-Dependent Functions using Reinforcement Learning, by Pritthijit Nath and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-01 Change to browse by: cs physics physics.ao-ph References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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