Accelerating exploration of Marine Cloud Brightening impacts on tipping points Using an AI Implementation of Fluctuation-Dissipation Theorem

TL;DR

This paper introduces AiBEDO, an AI model applying the Fluctuation-Dissipation Theorem to rapidly project climate responses to Marine Cloud Brightening interventions, enabling efficient assessment of tipping point risks.

Contribution

The paper presents AiBEDO, a novel AI-based approach that leverages FDT and existing climate data to efficiently simulate climate responses to MCB, reducing reliance on costly Earth System Models.

Findings

AiBEDO accurately projects climate responses to MCB forcing.

AiBEDO-FDT enables optimization of MCB interventions to minimize tipping point risks.

The approach significantly accelerates climate impact assessments.

Abstract

Marine cloud brightening (MCB) is a proposed climate intervention technology to partially offset greenhouse gas warming and possibly avoid crossing climate tipping points. The impacts of MCB on regional climate are typically estimated using computationally expensive Earth System Model (ESM) simulations, preventing a thorough assessment of the large possibility space of potential MCB interventions. Here, we describe an AI model, named AiBEDO, that can be used to rapidly projects climate responses to forcings via a novel application of the Fluctuation-Dissipation Theorem (FDT). AiBEDO is a Multilayer Perceptron (MLP) model that uses maps monthly-mean radiation anomalies to surface climate anomalies at a range of time lags. By leveraging a large existing dataset of ESM simulations containing internal climate noise, we use AiBEDO to construct an FDT operator that successfully projects climate responses to MCB forcing, when evaluated against ESM simulations. We propose that AiBEDO-FDT can be used to optimize MCB forcing patterns to reduce tipping point risks while minimizing negative side effects in other parts of the climate.