気候科学の革新におけるAIと機械学習の交差点 (At the Intersection of Climate and AI, Machine Learning is Revolutionizing Climate Science)

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2025-01-22 ジョージア工科大学 (Georgia Tech)

機械学習(ML)は、気候科学の分野で革命的な役割を果たしており、特に観測データの欠損補完、気候モデルの強化、そして気象予測の精度向上に寄与しています。ジョージア工科大学のアナリーサ・ブラッコ教授らの国際研究チームは、MLが気候物理学の理解と予測能力を飛躍的に高めていると指摘しています。具体的には、MLを活用することで、従来の手法では困難だった時間的・空間的スケールでの気候現象の解析が可能となり、気候変動の理解と予測に新たな道を開いています。しかし、MLには限界もあり、特に将来の傾向予測や新たなデータ収集の面では、引き続き科学者によるデータ収集と問題解決が必要とされています。

<関連情報>

気候物理学のための機械学習 Machine learning for the physics of climate

Annalisa Bracco,Julien Brajard,Henk A. Dijkstra,Pedram Hassanzadeh,Christian Lessig & Claire Monteleoni
Nature Reviews Physics  Published:11 November 2024
DOI:https://doi.org/10.1038/s42254-024-00776-3

気候科学の革新におけるAIと機械学習の交差点 (At the Intersection of Climate and AI, Machine Learning is Revolutionizing Climate Science)

Abstract

Climate science has been revolutionized by the combined effects of an exponential growth in computing power, which has enabled more sophisticated and higher-resolution simulations to be made of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit. Big data and associated algorithms, coalesced under the field of machine learning (ML), offer the opportunity to study the physics of the climate system in ways, and with an amount of detail, that were previously infeasible. Additionally, ML can ask causal questions to determine whether one or more variables cause or affect one or more outcomes and improve prediction skills beyond classical limits. Furthermore, when paired with modelling experiments or robust research on model parameterizations, ML can accelerate computations, increasing accuracy and generating very large ensembles with a fraction of the computational cost of traditional systems. In this Review, we outline the accomplishments of ML in climate physics. We discuss how ML has been used to tackle long-standing problems in the reconstruction of observational data, representation of sub-grid-scale phenomena and climate (and weather) prediction. Finally, we consider the benefits and major challenges of exploiting ML in studying complex systems.

Key points

  • Advances in machine learning for climate physics have extended observational data records in time, space and observables, making them longer, more global and more complete.
  • Innovative approaches that use machine learning to learn parameterizations from data or high-resolution simulations could contribute to hybrid models that will be able to provide more detailed, physically consistent simulations of the climate system.
  • The use of machine learning has enabled classical predictability barriers to be broken in forecasts ranging from weather to phenomena at interannual scales such as the El Niño Southern Oscillation, leading to higher forecast skill at larger lead times using orders-of-magnitude less computing resource.
1702地球物理及び地球化学
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