Modeling weather and climate is an essential endeavor to understand the near- and long-term impacts of climate change, as well as inform technology and policymaking for adaptation and mitigation efforts. In recent years, there has been a surging interest in applying data-driven methods based on machine learning for solving core problems such as weather forecasting and climate downscaling. Despite promising results, much of this progress has been impaired due to the lack of large-scale, open-source efforts for reproducibility, resulting in the use of inconsistent or underspecified datasets, training setups, and evaluations by both domain scientists and artificial intelligence researchers. We introduce ClimateLearn, an open-source PyTorch library that vastly simplifies the training and evaluation of machine learning models for data-driven climate science. ClimateLearn consists of holistic pipelines for dataset processing (e.g., ERA5, CMIP6, PRISM), implementation of state-of-the-art deep learning models (e.g., Transformers, ResNets), and quantitative and qualitative evaluation for standard weather and climate modeling tasks. We supplement these functionalities with extensive documentation, contribution guides, and quickstart tutorials to expand access and promote community growth. We have also performed comprehensive forecasting and downscaling experiments to showcase the capabilities and key features of our library. To our knowledge, ClimateLearn is the first large-scale, open-source effort for bridging research in weather and climate modeling with modern machine learning systems. Our library is available publicly at https://github.com/aditya-grover/climate-learn.

翻译：天气与气候建模是理解气候变化近期及长期影响、并为适应与减缓措施的科技与政策制定提供依据的关键研究。近年来，基于机器学习的数据驱动方法在天气预报和气候降尺度等核心问题中的应用引起了广泛关注。尽管取得了令人鼓舞的成果，但由于缺乏大规模开源的可复现性工作，领域科学家与人工智能研究者所采用的数据集、训练方案和评估标准往往不一致或未充分说明，这阻碍了相关进展。我们推出ClimateLearn——一个开源PyTorch库，可极大简化数据驱动气候科学中机器学习模型的训练与评估流程。该库包含数据集处理（如ERA5、CMIP6、PRISM）的完整流程、先进深度学习模型（如Transformer、ResNet）的实现，以及针对标准天气与气候建模任务的定量与定性评估工具。我们通过详尽的文档、贡献指南和快速入门教程补充这些功能，以降低使用门槛并促进社区发展。此外，我们开展了全面的预报与降尺度实验，展示了该库的能力与核心特性。据我们所知，ClimateLearn是首个连接天气与气候建模研究与现代机器学习系统的大规模开源项目。本库公开访问地址为：https://github.com/aditya-grover/climate-learn。