Inter-comparison and similarity analysis to gauge consensus among multiple simulation models is a critical problem for understanding climate change patterns. Climate models represent time and space variable ecosystem processes, like, simulations of photosynthesis and respiration, using algorithms and driving variables such as climate and land use. It is widely accepted that effective use of visualization techniques can enable scientists to better explore model similarity from different perspectives and different granularity of space and time. But currently there is a lack of such visualization tools. To fills this gap we first analyzed the state-of-the-art of visualization techniques for the climate science domain. In collaboration with climate scientists, we systematically analyzed a sample of static visualizations from research papers and presentations, and suggested solutions to visualization design problems that interfered with the scientists’ intents. Second, based on the scientists’ needs for analyzing multifaceted climate data, we developed an exploratory visualization tool that facilitates similarity comparison of models across different scales of space and time.