Guidance for representing uncertainty on global temperature change maps

Abstract

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.

Climate vulnerability mapping: A systematic review and future prospects

“…Example of a sequential color scheme combined with one way of representing uncertainty from Reprinted with permission from Retchless and Brewer (). Copyright 2016 Wiley…”

“…With additional understanding of the local context, researchers are able to understand the shocks (e.g., weather, food prices, financial, or health) that are most important to households, and determine appropriate responses (McCusker et al, 2016). F I G U R E 8 Example of a sequential color scheme combined with one way of representing uncertainty from Reprinted with permission from Retchless and Brewer (2016). Copyright 2016 Wiley This underscores an important point: a map can serve to point out differential vulnerability in a given area, but deeper field research is almost always required to develop appropriate adaptation responses.…”

Visualizations of Projected Rainfall Change in the United Kingdom: An Interview Study about User Perceptions

“…Similarly, in the United Kingdom, the Met Office UK produced Research from the cognitive and behavioral sciences suggests that, compared to text or number formats, visualizations can help individuals to better understand information about complex issues, such as climate change [11,25]. However, most evidence about perceptions of visualizations comes from the health domain, where these have been used to improve individuals' understanding about risks [11,[26][27][28][29][30] and to promote self-protective behaviors [26]. However, findings from the domain of health may not always transfer to climate because climate projections may be more complex, more inherently uncertain, occur across longer time periods, and have consequences that vary widely between individuals or institutions.…”

“…Uncertainty resulting from model disagreement has also been represented through patterns, such as hatching, stippling, or color [12]. Alternatively, change and associated uncertainty have also been displayed in two separate maps [30]. Map and associated caption displaying summer and winter rainfall change in the United Kingdom, as prepared for Met Office communications and presented to participants.…”

Visualizing Spatiotemporal Models with Virtual Reality: from Fully Immersive Environments to Applications in Stereoscopic View

“…Because it is challenging for our visual memory to assimilate multiple sources of information at the same time, an appropriate choice of visual tool can facilitate the comprehension of complex and simultaneous information indexed in space (Few, 2009), and there is increasing interest in embedding uncertainty in maps (Bonneau et al, 2014). Whereas a uniformly accepted solution is yet to be agreed (MacEachren et al, 2005), several solutions have been devised, especially in the area of census data (Lucchesi and Wikle, 2017), from choropleth maps (Tufte, 1986;MacEachren et al, 2005;Retchless and Brewer, 2015) to map pixellation (Ewans, 1997;MacEachren et al, 2005) and glyph rotation (Wittenbrink et al, 1996;MacEachren et al, 2005).…”