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Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles

Environmental Studies and Forestry

Climate Intervention Analysis using AI Model Guided by Statistical Physics Principles

S. K. Kim, K. Ramea, et al.

Discover AiBEDO, a groundbreaking AI model that utilizes the Fluctuation-Dissipation Theorem to revolutionize climate intervention analysis. Developed by a team of experts including Soo Kyung Kim and Kalai Ramea from Palo Alto Research Center, this model drastically reduces the evaluation time for strategies like Marine Cloud Brightening, helping us tackle critical climate challenges swiftly.

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Playback language: English
Abstract
This study introduces AiBEDO, an AI model leveraging the Fluctuation-Dissipation Theorem (FDT) to analyze climate intervention scenarios. AiBEDO is trained on a large Earth System Model (ESM) dataset to predict climate responses to external forcings, significantly accelerating the evaluation of interventions like Marine Cloud Brightening (MCB). The model effectively captures complex, multi-timescale effects, enabling rapid prototyping and optimization of MCB strategies to achieve regional climate targets and prevent tipping points. The methodology is generally applicable to other computationally intensive scientific domains.
Publisher
Not specified in the provided text
Published On
Jan 01, 2023
Authors
Soo Kyung Kim, Kalai Ramea, Rühling Salva, Haruki Hirasawa, Subhashis Hazarika, Dipti Hingmire, Peetak Mitra, Philip J Rasch, Hansi A Singh
Tags
AiBEDO
Fluctuation-Dissipation Theorem
climate intervention
Marine Cloud Brightening
Earth System Model
climate response
tipping points
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