Accelerating exploration of Marine Cloud Brightening impacts on tipping points Using an AI Implementation of Fluctuation-Dissipation Theorem. (arXiv:2302.01957v1 [physics.ao-ph])

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.