The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Some examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data are climate model intercomparison (CMIP) experiments; the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Observations for Model Intercomparison Projects (Obs4MIPs), Analysis for Model Intercomparison Projects (Ana4MIPs), and Collaborative Reanalysis Technical Environment-Intercomparison Project (CREATE-IP) activities; and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC). This article provides an overview of some of climate science's big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), which is the primary cyberinfrastructure currently supporting global climate research activities.

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气候科学中的大数据挑战：改进下一代网络基础设施

我们从气候科学研究中获得的知识取决于高质量数据的生成、传播和分析。这项工作包括技术实践和社会实践，两者都以其庞大的规模和全球影响力而著称。因此，涉及气候研究的数据量正以前所未有的速度增长。越来越需要改进网络基础设施来处理大量关键科学数据的活动类型的一些例子是气候模型比对 (CMIP) 实验；观测数据和气候再分析数据与气候模式输出的整合，如模式比对项目的观测（Obs4MIPs）、模式比对项目的分析（Ana4MIPs）中所见，和协作再分析技术环境比对项目 (CREATE-IP) 活动；以及政府间气候变化专门委员会 (IPCC) 的合作工作。本文概述了气候科学的一些大数据问题以及正在开发的技术解决方案，以推进数据发布、气候分析即服务以及地球系统网格联盟 (ESGF) 内的互操作性，ESGF 是目前支持全球的主要网络基础设施。气候研究活动。