## 1 Introduction: Challenges of Geovisual Analysis on Climatic Data  
In recent years, scientists from different communities, such as climatology, sociology and statistics, have been collaborating towards a better understanding of atmospheric-oceanic-glacial conditions [1, 2], long-term climate variations [3, 4], and interactions between climate changes and human society [5, 6]. Plenty of climate models have been developed as the results of these joint efforts and massive amounts of spatiotemporal data have been generated by running these models [7]. Various analyses using climate data are incorporated with IPCC assessment reports [8]. Moreover, as the climate change issue has become more salient and a growing number of open resources for climate study emerged, the public began to engage in climate research in different ways. For example, via the _climateprediction.net_ initiative, some people are contributing personal computing resources to run climate models [9, 10]. Data produced by climate models are simulated values of climate conditions over space and time. In general, these data have the following characteristics:  
1. The spatiotemporal data include hundreds of climate variables to describe complex components of atmosphere, ocean, cryosphere and land surface [11]. 2. These variables have different spatial dimensions ranging from one dimension to many dimensions [12]. 3. The data include information from global to regional scales. 4. Climate models also generate data in different temporal resolutions from daily to yearly. Climate analysis is usually based on the averaged values of data in basic time unit [13]. 5. To explicitly explore the structural uncertainty in the simulations, models are often run many times with different input parameter combinations [14]. Due to the characteristics of the complex climate data, petabytes of spatiotemporal data are produced from running climate models (Figure 1). Taking NASA Goddard Institute for Space Studies (GISS) ModelE as an example, outputs of ten-year monthly simulations from 300 model runs (one ensemble from thousands of ensembles) would yield 2.5 terabytes of data. Such large volumes of data are usually stored in distributed storage media. Geographically dispersed scientists and the public often access the data over the Internet. It is more convenient for data users to perform data visualization and analysis in a web-based environment. Visualizing and analyzing large spatiotemporal data in a web-based environment becomes a big challenge for climate researchers. Traditional numerical and statistical methods have been frequently employed to analyze spatiotemporal climate data. The analysis results are usually represented as numerical data. However, humans have a relatively weak vision and cognition capability on identifying underlying principles from the overwhelming amounts of spatiotemporal information presented as textual and numerical data [15]. Therefore, traditional approaches are not sufficient to visualize and analyze spatiotemporal data. In this case, approaches that represent information in visual products such as images are required to help researchers comprehend the information [16]. In the context of geospatial sciences, geovisualization (e.g., maps) has been proved to be an efficient method for prompt understanding of complex geospatial data [17]. Geovisual analytics integrates spatial analytical methods with geovisualization and is more powerful to reveal hidden patterns within geospatial data [15]. Considering this capability, geovisual analytics is a potential solution for analyzing climate data.