Climate science produces a wealth of complex, high-dimensional, multivariate data from observations and numerical models. These data are critical for understanding climate changes and their socioeconomic impacts. Climate scientists are continuously evaluating output from numerical models against observations. This model evaluation process provides useful guidance to improve the numerical models and subsequent climate projections. Exploratory visual analytics systems possess the potential to significantly reduce the burden on scientists for traditional spatiotemporal analyses. In addition, technology and infrastructure advancements are further facilitating broader access to climate data. Climate scientists today can access climate data in distributed analytic environments and render exploratory visualizations for analyses. Efforts are ongoing to optimize the computational efficiency of spatiotemporal analyses to enable efficient exploration of massive data. These advances present further opportunities for the visualization community to innovate over the full landscape of challenges and requirements raised by scientists. In this report, we provide a comprehensive review of the challenges, requirements, and current approaches for exploratory spatiotemporal visual analytics solutions for climate data. We categorize the visual analytic techniques, systems, and tools presented in the relevant literature based on task requirements, data sources, statistical techniques, interaction methods, visualization techniques, performance evaluation methods, and application domains. Moreover, our analytic review identifies trends, limitations, and key challenges in visual analysis. This report will advance future research activities in climate visualizations and enables the end-users of climate data to identify effective climate change mitigation strategies.

翻译：气候科学通过观测和数值模型产生了大量复杂、高维、多变量的数据。这些数据对于理解气候变化及其社会经济影响至关重要。气候科学家持续将数值模型的输出与观测数据进行比对评估。这一模型评估过程为改进数值模型及后续气候预测提供了重要指导。探索性可视化分析系统具有显著减轻科学家传统时空分析负担的潜力。此外，技术和基础设施的进步正进一步促进气候数据的广泛可及性。当今气候科学家可在分布式分析环境中访问气候数据，并生成探索性可视化结果进行分析。学界正持续优化时空分析的计算效率，以实现对海量数据的高效探索。这些进展为可视化研究社群在科学家提出的完整挑战与需求谱系中进行创新提供了更多机遇。本报告全面综述了面向气候数据的探索性时空可视化分析解决方案所面临的挑战、需求及现有方法。我们依据任务需求、数据来源、统计技术、交互方法、可视化技术、性能评估方法和应用领域，对相关文献中提出的可视化分析技术、系统与工具进行了分类。此外，我们的分析性综述识别了可视化分析领域的趋势、局限性与关键挑战。本报告将推动气候可视化领域的未来研究活动，并帮助气候数据的终端用户制定有效的气候变化缓解策略。