Uncertainty in the spatial distribution of projected climate changes can be represented along with the magnitudes of those changes using coincident-bivariate maps. While these maps are popular in climate change assessment reports, limited empirical research has tested which combinations of colour (including variation in the visual variables of hue, lightness, and saturation) and pattern (including variation in the visual variables of size, shape, spacing, orientation, and arrangement) are best suited to mapping projected changes coincident with uncertainty. We evaluate eight coincident-bivariate techniques for mapping global temperature (CMIP5 ensemble using RCP8.5 and RCP4.5 scenarios) and its uncertainty, each using 20-classes: five temperature change classes combined with four uncertainty classes. Subjects ranked rectangular map regions by overall amounts of temperature change and uncertainty; response accuracy was evaluated using Kendall's Tau Distance. Maps using colour for representing temperature change and pattern for uncertainty appear most suitable for retaining separability of temperature and uncertainty information. Spot symbols for uncertainty (combining arrangement and lightness) overlaid on hue/lightness for temperature performed best for uncertainty ranking, second best for temperature ranking, and were map users' preferred representation. If colour alone must be used for both variables, then a classed overlay of lightness/saturation differences for uncertainty with hue/lightness for temperature change was preferred; such maps were strong performers on the uncertainty-ranking task but interfered with map users' ability to rank temperature change. Such a masking approach may be the best choice if the goal is to actively prevent map users from making erroneous inferences based on highly uncertain model output for climate change.
All Science Journal Classification (ASJC) codes
- Atmospheric Science