Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
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DOI：
10.1038/s42256-021-00374-3
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发表时间：
2021-08-01
影响因子：
23.8
通讯作者：
Saynisch-Wagner, Jan
中科院分区：
文献类型：
--
作者：
Irrgang, Christopher;Boers, Niklas;Saynisch-Wagner, Jan
地球系统模型（ESM）是我们量化地球物理状态和预测未来在持续人为强迫下地球可能如何变化的主要工具。然而，近年来，人工智能（AI）方法越来越多地被用于增强甚至取代传统的ESM任务，这让人们对AI能够解决气候科学的一些重大挑战寄予了希望。在这一视角中，我们调查了基于过程的模型和人工智能在地球系统和气候研究中的最新成就和局限性，并提出了一种方法论转型，其中深度神经网络和ESM被拆除为单独的方法，并重新组装为学习，自我验证和可解释的ESM网络混合体。沿着这条道路，我们创造了术语神经地球系统建模。我们研究了神经地球系统建模的并发潜力和陷阱，并讨论了人工智能是否可以支持ESM甚至最终使其过时的公开问题。在过去的几年里，人工智能方法已被用于增强地球和气候建模。该观点探讨了进一步发展的机会，并从头开始构建混合系统，该系统集成了基于物理过程知识的人工智能工具和模型，以更有效地利用日常观测数据流。
Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth and predicting how it might change in the future under ongoing anthropogenic forcing. In recent years, however, artificial intelligence (AI) methods have been increasingly used to augment or even replace classical ESM tasks, raising hopes that AI could solve some of the grand challenges of climate science. In this Perspective we survey the recent achievements and limitations of both process-based models and AI in Earth system and climate research, and propose a methodological transformation in which deep neural networks and ESMs are dismantled as individual approaches and reassembled as learning, self-validating and interpretable ESM-network hybrids. Following this path, we coin the term neural Earth system modelling. We examine the concurrent potential and pitfalls of neural Earth system modelling and discuss the open question of whether AI can bolster ESMs or even ultimately render them obsolete. In the past few years, AI approaches have been used to enhance Earth and climate modelling. This Perspective examines the opportunity to go further, and build from scratch hybrid systems that integrate AI tools and models based on physical process knowledge to make more efficient use of daily observational data streams.