AI for Climate Stabilization: Forecasting, Optimization, and Autonomous Mitigation Systems - Preprint

[Geoengineering News]

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7319241

Authors: Hunter Hughes

Date Written: August 20, 2026

Abstract

Artificial intelligence is becoming the core operating system for planetary climate stabilization, replacing the latency‑bound, parameterization‑heavy workflows of classical Earth System Models with real‑time, physics‑informed emulation and cyber‑physical control. Modern neural operators and foundation climate models—such as Fourier Neural Operators, GraphCast, ClimaX, and NeuralGCM—deliver 10,000× faster global forecasts while resolving sub‑grid turbulence and convective cloud microphysics with physically constrained stability.

Across infrastructure systems, deep reinforcement learning and model predictive control enable dynamic grid balancing, synthetic inertia injection, virtual power plant coordination, and industrial thermodynamic optimization, achieving up to 87% renewable curtailment mitigation and 15–30% energy reductions in hard‑to‑abate sectors.

In carbon removal, generative materials models (Crystal Diffusion VAEs) discover high‑performance MOFs that cut DAC regeneration energy from 1,200 to 650 kWh/tCO₂, while multimodal satellite‑IoT MRV systems provide cryptographically verifiable carbon accounting with sub‑50 m leak localization and 95% credible interval bounds across oceanic and geological sinks.

The report further establishes multi‑objective AI guardrails for Solar Radiation Management, reducing hydrological distortion by 65% and constraining termination shock risk through autonomous radiative forcing caps.

Collectively, mature climate AI systems can abate 4.8–6.2 Gt CO₂e annually by 2030, scaling to 12.8 Gt CO₂e by 2040, with an Enablement Ratio exceeding 45:1, demonstrating that optimized AI‑driven mitigation overwhelmingly outweighs its computational footprint.

Source: SSRN