Data visuals, such as graphs and diagrams, are essential tools in science communicators’ toolkits to communicate complex scientific data and concepts in an easily accessible format. Such data visuals can ‘augment’ cognition – they store and organise information for us, and provide visually salient cues about the relationships between variables. Without such visuals, we would have to exert additional mental effort to compute the raw information (e.g. numbers, text) into something meaningful. Effective communication of data and evidence is critical in the field of climate change – for example, policy makers need to know how different emission scenarios might affect temperatures at different points across the globe over time.
The Intergovernmental Panel on Climate Change (IPCC) use data visuals of various designs within their assessment reports to aid communication between scientists and policy makers. However, given the requirements and constrants involved in developing international scientific consensus, are the graphs and figures that make it into the reports designed as optimally as they could be? and how could they be improved? My research aims to improve our understanding of cognitive processes involved interpreting data visuals such as graphs, and translate these insights into practical applications to improve the design of data visuals to aid comprehension (and consequently decision-making), in climate change communications.