Climate Intervention Analysis using AI Model Guided by Statistical
Physics Principles
- URL: http://arxiv.org/abs/2302.03258v1
- Date: Tue, 7 Feb 2023 05:09:10 GMT
- Title: Climate Intervention Analysis using AI Model Guided by Statistical
Physics Principles
- Authors: Soo Kyung Kim, Kalai Ramea, Salva R\"uhling Cachay, Haruki Hirasawa,
Subhashis Hazarika, Dipti Hingmire, Peetak Mitra, Philip J. Rasch, Hansi A.
Singh
- Abstract summary: We propose a novel solution by utilizing a principle from statistical physics known as the Fluctuation-Dissipation Theorem (FDT)
By leveraging, we are able to extract information encoded in a large dataset produced by Earth System Models.
Our model, AiBEDO, is capable of capturing the complex, multi-timescale effects of radiation perturbations on global and regional surface climate.
- Score: 6.824166358727082
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The availability of training data remains a significant obstacle for the
implementation of machine learning in scientific applications. In particular,
estimating how a system might respond to external forcings or perturbations
requires specialized labeled data or targeted simulations, which may be
computationally intensive to generate at scale. In this study, we propose a
novel solution to this challenge by utilizing a principle from statistical
physics known as the Fluctuation-Dissipation Theorem (FDT) to discover
knowledge using an AI model that can rapidly produce scenarios for different
external forcings. By leveraging FDT, we are able to extract information
encoded in a large dataset produced by Earth System Models, which includes 8250
years of internal climate fluctuations, to estimate the climate system's
response to forcings. Our model, AiBEDO, is capable of capturing the complex,
multi-timescale effects of radiation perturbations on global and regional
surface climate, allowing for a substantial acceleration of the exploration of
the impacts of spatially-heterogenous climate forcers. To demonstrate the
utility of AiBEDO, we use the example of a climate intervention technique
called Marine Cloud Brightening, with the ultimate goal of optimizing the
spatial pattern of cloud brightening to achieve regional climate targets and
prevent known climate tipping points. While we showcase the effectiveness of
our approach in the context of climate science, it is generally applicable to
other scientific disciplines that are limited by the extensive computational
demands of domain simulation models. Source code of AiBEDO framework is made
available at https://github.com/kramea/kdd_aibedo. A sample dataset is made
available at https://doi.org/10.5281/zenodo.7597027. Additional data available
upon request.
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