In recent years, artificial intelligence (AI) rapidly accelerated its
influence and is expected to promote the development of Earth system science
(ESS) if properly harnessed. In application of AI to ESS, a significant hurdle
lies in the interpretability conundrum, an inherent problem of black-box nature
arising from the complexity of AI algorithms. To address this, explainable AI
(XAI) offers a set of powerful tools that make the models more transparent. The
purpose of this review is twofold: First, to provide ESS scholars, especially
newcomers, with a foundational understanding of XAI, serving as a primer to
inspire future research advances; second, to encourage ESS professionals to
embrace the benefits of AI, free from preconceived biases due to its lack of
interpretability. We begin with elucidating the concept of XAI, along with
typical methods. We then delve into a review of XAI applications in the ESS
literature, highlighting the important role that XAI has played in facilitating
communication with AI model decisions, improving model diagnosis, and
uncovering scientific insights. We identify four significant challenges that
XAI faces within the ESS, and propose solutions. Furthermore, we provide a
comprehensive illustration of multifaceted perspectives. Given the unique
challenges in ESS, an interpretable hybrid approach that seamlessly integrates
AI with domain-specific knowledge appears to be a promising way to enhance the
utility of AI in ESS. A visionary outlook for ESS envisions a harmonious blend
where process-based models govern the known, AI models explore the unknown, and
XAI bridges the gap by providing explanations.

近年来，人工智能迅速加速其影响力，如果得到恰当利用，将促进地球系统科学的发展。解释性人工智能（XAI）提供了一套强大的工具，可以使模型更加透明。本文旨在为地球系统科学学者提供对 XAI 的基本理解，作为未来研究进展的基础，并鼓励地球系统科学专业人员接受人工智能的好处，克服解释性不足带来的偏见。希望通过融合 AI 和领域特定知识的可解释性混合方法来增强人工智能在地球系统科学中的实用性。

可解释人工智能在地球系统科学中的应用

Applications of Explainable artificial intelligence in Earth system  science

Advancements in Earth system science have seen a surge in diverse datasets.
Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this
influx of high-dimensional data. ESDCs offer a structured, intuitive framework
for data analysis, organising information within spatio-temporal grids. The
structured nature of ESDCs unlocks significant opportunities for Artificial
Intelligence (AI) applications. By providing well-organised data, ESDCs are
ideally suited for a wide range of sophisticated AI-driven tasks. An automated
framework for creating AI-focused ESDCs with minimal user input could
significantly accelerate the generation of task-specific training data. Here we
introduce cubo, an open-source Python tool designed for easy generation of
AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC)
that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates
ESDCs, requiring only central coordinates, spatial resolution, edge size, and
time range.

地球系统科学中的进展带来了各种各样的数据集，而地球系统数据立方体 (ESDCs) 的引入有效地处理了这些高维数据的涌入。ESDCs 提供了一个结构化、直观的数据分析框架，将信息组织在时空网格中。ESDCs 的结构化特性为人工智能 (AI) 应用提供了重大机遇。通过提供良好组织的数据，ESDCs 非常适合各种复杂的 AI 驱动任务。一个自动化的框架可以通过最少的用户输入来创建针对 AI 的 ESDCs，从而加速特定任务训练数据的生成。本文介绍了 cubo，这是一个用于轻松生成以 AI 为焦点的 ESDCs 的开源 Python 工具。cubo 利用存储为云优化地理标记格式 (COGs) 的时空资产目录 (STAC) 中的集合，以中心坐标、空间分辨率、边缘大小和时间范围为参数高效地创建 ESDCs。