End of the rainbow

Note: Better Figures is proud to support Ed Hawkins at Climate Lab Book, in calling for an end to the “Rainbow” palette. The text of an open letter to the climate science community, led by Ed, is reproduced below.

An open letter to the climate science community

Ed Hawkins, Doug McNeall, David Stephenson, Jonny Williams & Dave Carlson

Dear colleagues,

This is a heartfelt plea.

A plea to you all to help rid climate science of colour scales that can distort, mislead and confuse. Colour scales that are often illegible to those who are colour blind.

The main culprit is, of course, the ‘rainbow’:

We have all likely used it, and we have all certainly seen it – presentations, posters, papers, blogs and news articles full of figures with similar colour scales.

However, the most commonly used rainbow colour scales can distort perceptions of data and alter meaning by creating false boundaries between values. There are numerous blogs and published papers from visualisation experts illustrating these issues. In one example, changing to a non-rainbow scale even improved accuracy of heart disease diagnoses.

And, if you use a rainbow colour scale, you will have a friend or colleague that is colour blind and may confuse the colours.

This is not the first such plea.

A decade ago an article appeared in EOS, demonstrating that contrasting red with green can render a figure illegible to the 8% of the male and 0.4% of the female population who are colour blind. The EOS article suggested that journals should do more to improve the colour accessibility of figures.

But, the problem is now worse than a decade ago. Most issues of every major climate journal have figures which are potentially misleading and colour inaccessible. Maps, line graphs and histograms can all have confusing colour combinations.

Journals, rightly, do not tolerate poor grammar, incorrect spelling, or muddy descriptions of scientific methods. It should be no different for visual communication. We should be equally intolerant to poor use of the grammar of graphics as we are to its written equivalent.

It is not just the journals who need to act. As scientists increase their efforts to make their work accessible to the public through the media, blogs and social media, there are more opportunities to show poor figures.

What are the possible solutions?

We need to be more willing to discuss and criticise the visualisation of the science as well as the science itself.

Authors should be responsible about the colour choices they make. Journals might add colour accessibility to their existing guidelines for acceptable figure types. Reviewers could recommend revision if such colour scales are used. Editors should not accept papers which use inaccessible and potentially misleading colour scales. And, the media might reconsider using such figures from published work.

We know ‘rainbow’ is the default colour scale in many commonly used programming languages, but that doesn’t make it the best. Resources are easily available to change colour scales for R, IDL (& here), MATLAB & Python.

There are numerous websites and online tools giving advice and recommending safe and better colour scales (such as Color Brewer). You can even test online how your figures might appear to those who are colour blind. Adding different shape markers in line graphs might also aid interpretation.

Choosing a good colour scale is not difficult – it just takes awareness and a few moments of effort. The best choice will probably depend on the situation, so ask yourself why you have chosen that particular colour scale.

We take heart from some recent progress.

The journal BAMS recently took a step forward by publishing an article pointing out the flaws with rainbow colour scales. MATLAB have just announced that they are changing the default rainbow colour scale, giving a comprehensive explanation considering colour accessibility and perception issues.

All of us could do more in improving the clarity of our figures, the authors of this open letter included. More needs to be done. And, it needs all of us to do more.

So, we undertake this pledgeto never again be an author on a paper which uses a rainbow colour scale.

If you agree to make this pledge (or disagree), please comment below this post. Or email us. And tell your colleagues.

We hope that you will join us.

We encourage the climate science community to communicate this letter widely. To spread the word on twitter, please use #endrainbow. Short URL: http://tiny.cc/endoftherainbow

Other climate-related case studies:

Example of simulating colour blindness with different colour scales & MATLAB software: Which colour scale is best for you?

Considering different colour scales for sea-level change maps: Better palettes

And, it is not just climate. One of the iconic images in astronomy – the Cosmic Microwave Background (CMB) – is normally in rainbow.

Many thanks to all those who have patiently commented on these issues.


  1. If the commonest form of colour blindness is red-green. What idiot chose traffic lights to be red-green?

  2. Philip Bett · · Reply

    I agree!

    But with a caveat – it’s the continuous HSV rainbow that’s the killer. Alternatives are the Munsell or HCL colour spaces (if that’s the correct technical term) – see the colorspace (doi:10.1016/j.csda.2008.11.033, but http://statmath.wu-wien.ac.at/~zeileis/papers/ for a preprint) and munsell packages in R; the scales package is also useful.

    But in practice, you might want a set of ordered high-contrast colours – that is, a discrete palette, where you can look at a colour and read off the number (range) it corresponds to, but with a logical order. The categorical Brewer palettes are unordered (deliberately I guess), so I can end up making my own frequency-ordered palette, picking the interpolation points carefully so that I don’t end up with a big low-contrast segments. But contrasts in hue and brightness are what I’d be aiming for in such a palette, because you can pack in more distinct categories then than just ramping one of H/C/L. (people sometimes say that rainbow palettes don’t have a logical order, but I find they do, very strongly – it is frequency/wavelength order after all)

    To me, the main thing is whether the colour scheme highlights the features you want highlighting – did you mean to blur together all those green regions? Is that yellow band really picking out an important feature? How would a colour-blind person interpret it? Think about it, rather than picking the defaults, and make your own wrapper functions to make it easier.

    And, most importantly how to ask people to follow these kind of standard guidelines, without sounding patronising and pompus? 🙂

    1. Hi Philip, thanks for the notes and support. I agree on your last point, and I hope we don’t sound too pompous 🙂 I think the key is finding attractive, useful and *easy* alternative default palettes for the major scripting languages.

  3. Great initiative.

    The EOS article was an Eureka moment for me, and got me interested in this topic.

    Another interesting paper is Borland and Taylor (2007 – Rainbow color map (still) considered harmful: IEEE Computer Graphics and Applications, 27, no. 2, 14–17). One of the things that strikes me is their observation that more practitioners in the field of medical imaging and analysis reject the default rainbow (many use grayscale) compared to other disciplines: their statistics for the IEEE Visualization Conference proceedings from 2001 to 2005 show that over the five years, 51 percent of papers including medical images used rainbow, but the percentage increased to 61 percent without the papers including the medical images, with a peak difference in 2003 of 52 percent and 71 percent, respectively. I think these practitioners have more motivation to look beyond the rainbow default colormaps and make an extra effort, as shown for example by Borkin et al. (2011 – Evaluation of artery visualizations for heart disease
    diagnosis. IEEE Transactions on Visualization and Computer Graphics 17, no. 12,
    2479–2488, //dx.doi.org/10.1109/TVCG.2011.192), who argue that using rainbow in artery visualization has a negative impact on task performance, and may cause more heart disease misdiagnoses.

    I’ve written a post before where I show an attempt to fix the rainbow with a dynamically variable stretch function based on the human vision hue discrimination curve:
    The method worked only on the green-yellow-red portion of the rainbow. Moreover, even with such a correction in that case we lose contrast (intended as the rate of change of lightness). With the results of this experiment, I concluded the article calling the rainbow hopeless for scientific visualization.

  4. […] end to the use of the dreaded “Rainbow” colour palette for scientific visualisation (mirrored over at my data viz blog Better Figures). It was the busiest day ever at both CLB and BF, and we got lots of great feedback on twitter and […]

  5. […] visualisations, but one thing that this event does highlight is just how many different types of rainbow colour palette there are. There are different data sets, but many of these are being used to encode the same […]

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