https://arxiv.org/abs/2302.03258
*Authors* Soo Kyung Kim <https://arxiv.org/search/cs?searchtype=author&query=Kim%2C+S+K>, Kalai Ramea <https://arxiv.org/search/cs?searchtype=author&query=Ramea%2C+K>, Salva Rühling Cachay <https://arxiv.org/search/cs?searchtype=author&query=Cachay%2C+S+R>, Haruki Hirasawa <https://arxiv.org/search/cs?searchtype=author&query=Hirasawa%2C+H> , Subhashis Hazarika <https://arxiv.org/search/cs?searchtype=author&query=Hazarika%2C+S>, Dipti Hingmire <https://arxiv.org/search/cs?searchtype=author&query=Hingmire%2C+D> , Peetak Mitra <https://arxiv.org/search/cs?searchtype=author&query=Mitra%2C+P>, Philip J. Rasch <https://arxiv.org/search/cs?searchtype=author&query=Rasch%2C+P+J>, Hansi A. Singh <https://arxiv.org/search/cs?searchtype=author&query=Singh%2C+H+A> *7 February 2023 * *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 this https URL <https://github.com/kramea/kdd_aibedo>. A sample dataset is made available at this https URL <https://doi.org/10.5281/zenodo.7597027>. Additional data available upon request. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2302.03258 <https://arxiv.org/abs/2302.03258> [cs.LG] (or arXiv:2302.03258v1 <https://arxiv.org/abs/2302.03258v1> [cs.LG] for this version) https://doi.org/10.48550/arXiv.2302.03258 -- You received this message because you are subscribed to the Google Groups "geoengineering" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/geoengineering/CAHJsh98OLbFRs0r6rmOVEsoQmgU__rKTxoK3-LH%3DgkfsZfru1w%40mail.gmail.com.
