Paper Title: Binary climate data visuals amplify perceived impact of climate change

Author(s) and Year: Grace Liu, Jake C. Snell, Thomas L. Griffiths & Rachit Dubey (2025)

Journal: Nature Human Behaviour (closed access)

TL;DR: Presenting people with binary climate data (“lake frozen” vs. “lake not frozen”) increases their perceived impact of climate change more than continuous data (mean temperature). This effect can be used to design more meaningful climate communication that still presents data accurately.

Why I chose this paper: My interest in science communication began when I became more aware of the impacts of climate change, and the conversations we have — or don’t have — about them. Still, I see in myself how easy it is to grow “used to” the climate crisis. I am fortunate enough to not have experienced any extreme weather events or natural disasters firsthand so, to me, climate change mainly reveals itself as a yearly rise in temperature. And that’s what scares me — that we can learn to ignore the changes and not do anything about them. This study shows a way to challenge that inertia.

The effects of human-caused climate change on our planet are undeniable, but not everyone perceives them equally. While people in some regions experience dramatic impacts, such as extreme weather events, droughts, and wildfires, for many others climate change is just a minor issue bringing slow, gradual changes in their local climates. How do we represent climate data in a meaningful way that conveys its impacts but is still accurate?

The Background

Most people judge climate change based on their personal experiences and are very good at adapting to slowly worsening local conditions — what’s called the “boiling frog” effect. This can create a dangerous apathy towards the climate crisis, giving the public a false sense of security and preventing a strong enough collective motivation to act.

So how can we convey the actual urgency of the crisis? How do we tell the frog that it’s time to jump out of the pot?

The authors of this study wanted to understand how to counter the “boiling frog” effect through data visualization. Through a series of cognitive experiments, they examined whether people perceived climate impacts differently when presented with binary climate data compared to data in a continuous pattern.

The Methods

The research team introduced 766 randomly selected USA-based participants to a fictional town called Townsville, and told them about its typically cold winters and holiday tradition of ice-skating on the local lake. The participants were then randomly assigned to one of two groups. One group was shown a graph of the town’s mean winter temperature from 1939 to 2019 (continuous data), while the other group saw a graph showing whether the local lake had frozen or not in the winter of 1939 to 2019 (binary data). Afterwards, both groups were asked to rate (on a scale of 1-10) the perceived impact of climate change on the town and their perception of change in the town’s temperature and lake freezing frequency in the last 50 years.

A second experiment repeated the same procedure by showing the 235 participants real-world lake freeze and temperature data from five Northern Hemisphere lakes at risk of ice loss.

A third experiment with 392 participants reused the data from experiment 1, but added two more questions. The team asked both groups whether they observed a changepoint (a point where an obvious difference in the pattern popped up), and in which year they noticed the most pronounced shift.

The Results

In the first and second experiments, the group that saw the binary graphs about the lake freeze frequency rated their perceived impact of climate change significantly higher than the group who saw the continuous temperature change graphs (average rating 7.5 vs. 6.6 out of 10 for the fictional lake and 7.8 vs 6.7 for the real-world lake, with 10 being the highest). The binary group also perceived a stronger temperature increase and a stronger change in lake freeze frequency (all differences significant).

In the third experiment, 73% of participants shown the binary data perceived a changepoint, a significantly higher proportion than the 56% in the continuous data group. The binary data group’s answers about where they saw the changepoint also concentrated more around specific years. In both groups, those who saw a changepoint rated the impact of climate change higher than those who did not or were unsure.

The Impact

The study shows that people perceive the impact of climate change more strongly when they see binary graphs (which often create the illusion of sudden shifts even if the data they’re based on changes gradually) compared to continuous data.

This work is an important first step in figuring out what kinds of data and data visualizations are most effective in communicating the impacts of climate change. The next step, then, is understanding how to use this finding to drive concrete climate action.

Alt text: A climate stripes graphic showing global temperature change from 1850 to 2024. The x-axis shows the years from 1850 to 2024 in increments of 20 years. The stripes transition from dark blue on the left (19th century) to lighter blues and pale colors through the mid-20th century, then increasingly darker reds on the right towards the 2020s.

Figure 1: The “climate stripes” or “warming stripes” graphic (not included in the study) visually represents the rise in global average temperature between 1850 and 2024. Each stripe or bar represents the global temperature averaged over a year. A binary color code shows whether that particular year was warmer (red) or cooler (blue) than the average temperature between 1961-2010. Warming Stripes created by Professor Ed Hawkins (University of Reading). Source: https://showyourstripes.info . Used under CC BY 4.0

Binary visuals can be used to simplify complex data and make it more accessible. For example, the well-known “climate stripes” graphs (Figure 1) quickly and effectively convey the rise in average temperature in most regions of the world, without needing to show actual temperature values. This method is particularly useful when translating climate data to more tangible and emotion-evoking impacts for a local community, such as the loss of white Christmases, the inability to ice-skate on a lake in winter, or summer outdoor activities disrupted by wildfire smoke or extreme heat. By helping people better grasp the impact of climate change on their community and using that emotional connection to inspire action, this work can help prevent climate change from becoming background noise.

Written by Elena Reiriz Martínez

Edited by Clark Hickman and Sarah Ferguson