Work

Solar Geoengineering Strategies Based on Reinforcement Learning

Year: 2025

Type: article

Abstract: Solar geoengineering via stratospheric aerosol injection (SAI) poses an optimization problem. How exactly should aerosol be deployed to maximize its benefits while minimizing undesirable side‐effects, such as shifts in rainfall patterns? Previous work explored this problem using feedback control based on linear algorithms. Here, we investigate an alternative approach, which also naturally incorporates feedback. We let a reinforcement learning (RL) algorithm control the distribution of stratospheric aerosol concentration in an idealized global climate model (GCM). Within several dozen GCM simulations, RL learns to produce stable and plausible strategies. RL also learns that the optimal geoengineering strategy depends on the time when geoengineering is initiated, which we further explain using a simple energy‐balance model. Our results provide a first proof‐of‐concept that RL can identify promising SAI strategies.

Source: Journal of Geophysical Research Atmospheres

DOI: 10.1029/2025jd044319

Authors: Heng Quan, Daniel D. B. Koll, Nicholas Lutsko, Janni Yuval

Institutions: Princeton University, Peking University, Scripps Institution of Oceanography, Massachusetts Institute of Technology

Language: English

Topic: Climate Change and Geoengineering

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: bronze

Funder: Peking University