  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.
