Visualizing climate change with stripes

Climate change is abstract. We do not personally experience climate change in our day-to-day activities (although cimate change is detectable from any single day of weather at global scale, cf. Sippel 2020), and if we are to understand climate change, data – and in particular data visualisation – is crucial. I have recently been reading some literature on the relevance of visualisations and uncertainty in relation to climate change. There has, for example, been some work on the role of visual imagery on public attitudes towards climate change (e.g., Bolsen et al. 2019 and van der Linden et al. 2014) and how uncertainty may make people more likely to accept climate scientists’ predictions (see Howe et al. 2019).

Scientific evidence and data is not enough and we need to consider the best possible ways to visualise climate change. One of the most popular visualisations is the iconic #ShowYourStripes figure that shows global average annual temperatures from 1850 to 2019:

I believe it is a good visualisation but I have a few objections. First and foremost, I like numbers and I do not like how simplified the presentation is. What exactly are the numbers we are looking at here? Should I be concerned? If the dark blue is -0.01°C and the dark red is 0.01°C, is the story here one of change or stability? What is the average temperature in the sample and how much variation is there? Call me old-fashioned, but I don’t think a data visualisation is working if you are simply saying that something is increasing over time.

Interestingly, you can also download the figure with labels, but this provides no information on what values the colours are showing – only the time dimension:

The lack of a meaningful legend is an issue here. It would not make the visualisation more complicated but only help better understand the changes.

Second, I am not convinced that the tool is actually good if you want to show your stripes (and that is what we are being told to do afterall). How useful is the visualisation when we go beyond the global averages? To illustrate my concern, here is the visualisation I got for Denmark:

Sure, there is a story to tell, but I begin to miss certain details. Again, what are the values I am looking at? How much variation is there? And most importantly, how much uncertainty is there over time?

Third, I do not like the extreme colour scale used to illustrate the changes from 1850 (or 1901) to 2019. We know that the temperatures are going to increase in the future and the visualisation can give the false impression that we are already at a peak. I know this is not the message that the figure wants to convey, but people might look at the figure and conclude that we have seen the worst of what is to come.

It is not a bad visualisation. However, it is definitely not the best. You can check out the Vis for Future project from 2019 and find a lot of other great visualisations (the ‘Warming Stripes’ was one of the winners). I can also recommend a lot of the work by Neil Kaye, e.g. this and this. A recent example of a good visualisation is this visualisation from the National Oceanic and Atmospheric Administration on annual temperatures compared to the 20th-century average in the United States (notice how the legend is making it easier to see what we are actually looking at):

Climate change is abstract, but good visualisations with labels can help us better understand the global changes in our climate.

Global risks, climate change and COVID-19

On 15 January 2020, the World Economic Forum released The Global Risks Report 2020. The report was published before we talked about COVID-19 which makes it an even more interesting read. There are a lot of risks in the world. Weapons of mass destruction, food crises, natural disasters, climate change, data fraud, unemployment, asset bubbles etc. All of these risks differ in likelihood and potential impact.

Here is the global risks landscape of 2020 according to the report:

We can see a few things here. First, there is a positive correlation between likelihood and impact (with weapons of mass destruction being an outlier). Second, most of the likely risks with a high impact are related to climate change. This is in line with what the economist Dina D. Pomeranz wrote on 31 December 2019: “The key issue that can endanger much of the progress the world have achieved in so many areas is climate change.” Third, infectious diseases is not that likely, and it is even more likely that we would face a critical infrastructure failure rather than infectious diseases.

Of course, what we know now is that infectious diseases would be a severe problem in 2020. However, on the risks landscape above it is below average in terms of likelihood. This might simply reflect that we are not good at considering the tail risk of contagious diseases. The global risk report is based upon survey data on risk perceptions and will, for that reason, not reflect the actual likelihood of an event happening.

Tuesday, World Economic Forum launched their 2021 Global Risks Report. The top risks in terms of impact are now infectious diseases, climate action failure and weapons of mass destruction. Here is the new global risks landscape:

It is clear that the two risks with the biggest impact and likelihood are infectious diseases and climate action failure. But how should we think about these two risks? One possibility is to see them as unrelated. This would indicate that we should focus now on COVID-19 and then focus on climate change. However, I believe there are profound reasons for not see the risks as unrelated. On the contrary, these risks are correlated.

Noteworthy, there are important temporal differences between the two risks. While the climate crisis is happening right now, the key difference between the two crises is, in the words of Lidskog et al. (2020), the “urgency of action to counter the rapid spread of the pandemic as compared to the slow and meager action to mitigate longstanding, well-documented, and accelerating climate change”.

Risks are correlated and we cannot talk about infectious diseases as something that is completely unrelated to climate change. However, I believe that these risks will not be identical over time. Specifically, my sense is that the risks are negatively correlated in the shorter term but positively correlated in the longer term.

What we have primarily seen in the coverage of climate change in the wake of the COVID-19 pandemic are stories about less pollution. We have all seen the various pictures of nature and subsequent “nature is healing” memes. COVID-19 led to a reduction in global CO2 emissions and air pollution, demonstrating certain short-term effects of the pandemic. Of course, this is not sustainable in the future and we cannot conclude that the solution to climate change is another pandemic.

On the contrary, climate change will make pandemics worse. Pandemics are related to the destruction of nature and will get worse because of climate change. Specifically, climate change is making it more likely that we will experience similar vira in the future, as described by Colin Carlson, an ecologist at Georgetown University, to Ed Yong: “the biggest factors behind spillovers are land-use change and climate change, both of which are hard to control. Our species has relentlessly expanded into previously wild spaces. Through intensive agriculture, habitat destruction, and rising temperatures, we have uprooted the planet’s animals, forcing them into new and narrower ranges that are on our own doorsteps. Humanity has squeezed the world’s wildlife in a crushing grip—and viruses have come bursting out.”

The key point is that we should treat the risk of pandemics and climate change within the same framework. Luckily, there has already been quite some attention to how the recovery from COVID-19 has to be green. In other words, it is not about returning to the world pre-COVID-19, but make a green recovery. The pandemic is a chance to do better for the climate and transition from a ‘brown’ to a ‘green’ economy over the next ten years. There are different ways to move forward and a strong green stimulus can meet global net-zero CO2 by 2050.

What we should care about is the long-term recovery and in particular the short-term costs to ensure long-term sustainability. One of the concerns is that climate change is already happening and COVID-19 is an inequality accelerator. For example, those who already experience the huge cost of climate change do more so during the pandemic. Again, the global risks related to pandemics and climate change are interconnected.

The good news is that, even in the midst of the pandemic, the public perceives climate change as a great threat – including in the United States and in Europe. The bad news is that there are important differences between a pandemic and climate change. Specifically, as Ramez Naam notes, “coronavirus is actually a much easier challenge for people to conceptualize”. Unsurprisingly, those who are more likely to be concerned about the pandemic and wear a mask will also be more likely to be concerned about climate change. For that reason, alas, I expect many of the challenges we have seen during the pandemic to still be relevant long after COVID-19.

We cannot self-isolate from the effects of climate change, but hopefully the experience with COVID-19 will have increased our understanding of global risks and enable scientists and politicians alike to address the risks of both infectious diseases and climate change.

The Effect of Question Wording: Climate Edition

It is well known that the question wording in a survey may influence the respondents answer. Take as an example the pioneer study (pdf) on framing effects by Amos Tversky and Daniel Kahneman, published in Science, which shows that presenting the same action with different words, in this case whether people will be saved or die, affects the respondents action choice.

A lot of different studies investigate the effects of different wordings in survey questions. An interesting paper by Jonathon P. Schuldt, Sara H. Konrath and Norbert Schwarz shows, that presenting respondents with either the words “global warming” or “climate change”, influence the evaluation of whether the climate is changing or not – but only for republicans. From the abstract:

Republicans were less likely to endorse that the phenomenon is real when it was referred to as “global warming” (44.0%) rather than “climate change” (60.2%), whereas Democrats were unaffected by question wording (86.9% vs. 86.4%).

The experimental design (as well as the whole paper) is simple (and easy to read), but I would like to see a clearer investigation of the causal mechanism, in order to explain the individual differences (being more than simply a democrat or republican), maybe by using some sort of within subjects design.