GD
Geodynamics

Guest

Grace is a postdoctoral researcher at the Centre for Earth Evolution and Dynamics (CEED) at the University of Oslo, Norway. She works on linking plate tectonic reconstructions and mantle structure, especially in the Arctic and Pacific regions. Grace is part of the GD blog team as an Editor. You can reach Grace via email.

How good were the old forecasts of sea level rise?

How good were the old forecasts of sea level rise?

Professor Clint Conrad

The Geodynamics 101 series serves to showcase the diversity of research topics and methods in the geodynamics community in an understandable manner. We welcome all researchers – PhD students to Professors – to introduce their area of expertise in a lighthearted, entertaining manner and touch upon some of the outstanding questions and problems related to their fields. Our latest entry for the series is by Clinton P. Conrad, Professor of Geodynamics at the Centre for Earth Evolution and Dynamics (CEED), University of Oslo. Clint’s post reflects on the predictions of sea level rise since the first Intergovernmental Panel on Climate Change (IPCC) report in 1990 and the near three decades of observations and IPCC projections that have been made since then. Do you want to talk about your research area? Contact us!

This past week I flew over the North Atlantic with a direct flight to California from Europe. From the plane we had a beautiful view of glaciers on the western edge of the Greenland ice sheet, where the ice seems to be disintegrating into the ocean. We’ve been hearing lately that the ice sheets are slowly disintegrating – is this what that looks like? Using my mobile phone’s camera, I took a photo of the glacier that happened to be visible from my seat and compared it to images of the same glacier saved in Google Earth (Figure 1). This is an interesting exercise if you like looking at glaciers, but I can’t tell about the overall dynamics of the ice sheet this way.

Figure 1. A glacier on the west coast of Greenland on September 2, 2017 (left) taken with my iPhone. From my plane’s in-flight entertainment system, it seems that this glacier is between the villages of Upernavik and Niaqornat. For comparison, the image on the right is a screenshot of the same glacier from Google Maps.

Actually, we’ve been worried about ice sheet melting – and the sea level rise with it – for decades. I re-realized this during this past summer, as I finally started unpacking the boxes that we shipped to Oslo one year ago from Hawaii. Some of these boxes probably didn’t need to be unpacked, like the one labeled “High School Junk”, but it turns out there is interesting stuff in there! Here was my diploma, a baseball glove, some varsity letters, and a pile of old schoolwork – most of which I have no recollection of creating. But I did remember one of the items – a report on global warming that I wrote for Social Science class in 1989. In particular, I remember being fascinated by the prediction that human activity would eventually cause enough sea level rise to flood land areas around the world. For years, I have been personally crediting that particular high school report as being my first real introduction to the geosciences – but until this past summer I had never revisited that report to see what I actually wrote at the time. Now here it is – twelve yellowed pages of dot-matrix type, with side perforations still remaining from the printer feed strips that I tore off 28 years ago.

My report is entitled “Global Warming – What Must Government Do?” and now I can see that it is mostly a rehashing of reporting from a bunch of newspaper articles written in 1989. It was a bit disappointing that my younger self wasn’t more creative or inspirational, but the content of the report – really the content of the newspaper articles from 1989 – is fascinating because much of the material could have been written today. There is discussion of how the warmest years in recorded history have happened only recently, that climate skeptics were unwilling to attribute recent changes to human activity, and that a few obstinate countries (then, it was Japan, the USSR, and the USA) were standing in the way of international agreements to curb CO2 emissions. Another statement is also familiar: that “oceans could rise from 1.5 to 6.5 feet”. For those of you not familiar with that measurement system, that is about 0.5 to 2.0 meters! I know that recent predictions are not quite as dire as 2 m of rise (at least in the 2100 timeframe), although sea level acceleration has been getting more attention lately. Did people in 1989 consider 2 m of sea level rise a possibility? I checked the cited New York Times article from 1989, and indeed it seems that I dutifully reported the estimate correctly. The article says that 1.5 to 6.5 feet of sea level rise is expected “to occur gradually over the next century affecting coastal areas where a billion people, a quarter of the world’s population, now live”.

Figure 2. Projections of sea level in 2100 (relative to 1990 sea level) for the five IPCC reports between 1990 and 2013, plotted as a function of IPCC report date. Shown are the minimum and maximum projections (range of red bars), and the mean of estimates (black circles).

I have contributed a little to sea level research in the intervening years, and am somewhat familiar with the current predictions. I know that the most recent (2013) report of the Intergovernmental Panel on Climate Change (IPCC) predicts up to about a meter of sea level rise by 2100, which was a large increase over the 2007 report that predicted up to about 0.6 meters. Thus, meter-scale sea level rise predictions seemed like a relatively recent development, and yet here was a prediction just as large from nearly 30 years ago. What did the IPCC have to say about sea level at the time?

I plotted the sea level projections of the five reports that the IPCC has released between 1990 and 2013 (Figure 2). Indeed, the 1990 report predicted slightly higher sea level for the year 2100 (31-110 cm higher) than did the most recent report from 2013 (28-98 cm higher). In fact, the IPCC projections for 2100 sea level declined from 1990 through 2007, until they increased again in the most recent report in 2013 (Figure 2). Why is this? Well, we have nearly 3 decades of observations that could help us to answer this question!

 

Figure 3. Sea level projection from the IPCC’s first assessment report (1990), showing that report’s low, best, and high estimates (blue lines) and predicted rates in mm/yr. Also shown is the University of Colorado sea level time series (red line), which is based on satellite altimetry observations from 1992-2016 and records a sea level rise rate of 3.4 ± 0.4 mm/yr.

First, let’s evaluate the initial predictions of the first IPCC report from 1990. Since 27 years have passed since the publication of that report, we can actually compare a sizeable fraction of those 1990 predictions to actual sea level observations. Left, I have plotted (Figure 3) the 1990 report’s sea level projection from 1990-2100 (Fig. 9.6 of that report) along with actual sea level observations made using satellite altimetry between 1992 and 2016, which have been nicely compiled by the University of Colorado’s Sea Level Research Group. The comparison shows (Figure 3) that the actual sea level change for the past 24 years has fallen slightly below the “best” estimate of the 1990 report, and well above the “low” estimate.

In retrospect, the 1990 predictions of future sea level change seem rather bold, because the 1990 IPCC report also concludes that “the average rate of rise over the last 100 years has been 1.0-2.0 mm/yr” and that “there is no firm evidence of accelerations in sea level rise during this century”. Yet, the 1990 report’s projection of 2.0-7.3 mm/yr of average sea level rise from 1990-2030 (Figure 2), represents a prediction that sea level rise would accelerate almost immediately – and this acceleration actually happened! Indeed, three recent studies (Hay et al., 2015; Dangendorf et al., 2017; Chen et al., 2017) have confirmed sea level acceleration after about 1990.

Thus, the IPCC’s 1990 sea level projection did a remarkably good job for the first three decades of its prediction timetable, and the next 8 decades don’t seem so unreasonable as a result. What did the 1990 report do right? Here the 1990 IPCC report helps us again, by breaking down its projection into contributions from four factors: thermal expansion of the seawater due to warming, the melting of mountain glaciers, and changes in the mass of the great ice sheets in Greenland and Antarctica. The 1990 report makes predictions for the changes in sea level caused by these factors for a 45-year timeframe of 1985-2030, and I have plotted these predictions as a rate (in mm/yr) in Figure 4. Thermal expansion and deglaciation in mountainous areas were predicted to be the largest contributors. Greenland was predicted to contribute only slightly, and Antarctica was predicted to gain ice, resulting in a drop in sea level.

Figure 4. Comparison of projections and observations of the various factors contributing to global mean sea level rise (GMSL, in mm/yr). Red bars show predictions that were made in 1990 (table 9.10 of the 1990 IPCC report) for the 45-year period 1985-2030 (range is given by red bars and best estimate is shown with a dark line). Blue bars show the actual contribution from each factor for the 17-year period 1993-2010, as detailed in table 13.1 of the 2013 IPCC report. Note both the sum of observed contributions and the direct observation of sea level change from satellite altimetry (bottom two blue bars) are consistent with recent analyses of tide gauge data (Hay et al., 2015; Dangendorf et al., 2017), within uncertainty.

Now 27 years later, we have actual observations of the world’s oceans, glaciers, and ice sheets that we can use to evaluate the predictions of 1990 report. Many of these observations are based on measurements made using satellites, which can now remotely measure ocean temperatures, changes in the mass of land ice (mountain glaciers and ice sheets) and even changes in groundwater volumes, over time. The IPCC report from 2013 (the most recent report) shows these contributions in the timeframe of 1993-2010, which are 17 years during the 45-year outlook predicted by the IPCC’s 1990 report. I have plotted these observations in Figure 4, and we can see how the 1990 predictions compare so far – remembering that the prediction and observation timescales do not exactly align.

First, we see that 1990 report overpredicted the contribution from thermal expansion, and slightly overpredicted the contribution from mountain glaciers. Of course, there is still time before 2030 for these factors to increase some more toward the predictions made in 1990. However, we also see that Greenland melting has already matched the 1990 report’s prediction for 2030, and that the prediction of a sea level drop from Antarctica did not materialize – Antarctica contributed almost as much sea level rise as Greenland did by 2010 (Figure 4). Furthermore, there is another significant contributor to sea level rise – land water, which represents the transfer of liquid water from the continents into the oceans. This occurs because groundwater that is mined for human activities eventually ends up in the ocean. According to the 2013 report, land water caused more sea level rise than ice sheet melting from Antarctica.

Thus, in 2010 the predicted rates of sea level rise from two factors (thermal expansion and mountain glaciers) had not yet reached the 2030 predictions of the 1990 report, but the contributions from Greenland, Antarctica, and land water loss have already nearly met or exceeded the predictions of 1990. Indeed, recent satellite observations between 2002 and 2014 show an acceleration of melting in Antarctica (Harig et al., 2015) and especially in Greenland (Harig et al., 2016). The recognition that Antarctica and Greenland may contribute significantly more to sea level rise in the future compared to earlier estimates is reflected in the 2013 IPCC report (Figure 2).

Figure 5. A dike near the town of Putten in the Netherlands, where the recent EGU-sponsored “Nethermod” meeting was held in late August 2017. This dike is one of many in the Netherlands that protect negative-elevation land (left) from a higher water level (right).

So far, it seems that the IPCC’s 1990 sea level projection has stood the test of 27 years remarkably well (Figure 3). It is rather disheartening to realize that we are on track for the ~60 cm of sea level rise that the 1990 report predicted for the year 2100, or more if the early underestimates of ice sheet contributions prove to be more significant than any overestimates of thermal expansion (Figure 4). Looking at my own high school report from the same time, it is also disappointing that to realize that the warmest years in recorded history have again happened only recently, that climate change skeptics are still unwilling to attribute recent changes to human activity, and that there are still obstinate countries (well, one country) standing in the way of international agreements to curb CO2 emissions. On the other hand, high school students writing reports on this topic today will likely find discussions of dropping beachfront real estate prices, governmental planning for future sea level rise, and engineering techniques for managing future sea level rise (Figure 5). I hope that these students save copies of their reports in a format that they can examine decades later, because it is interesting to consider how predictions of future sea level rise have changed over time, and how society has been responding to the challenges of this geodynamic phenomenon that is operating on the timescale of a human lifetime. One day in the 2040s these students may want to scrutinize another quarter century of data against the projections of the next IPCC report, to be completed by 2022. I wonder what they will find?

 

NetherMod Day 5 – Putten an end to Nethermod: interviews with attendees

NetherMod Day 5 – Putten an end to Nethermod: interviews with attendees

Today is the fifth and final day of the XVth International Workshop on Modelling of Mantle and Lithosphere Dynamics, or “Nethermod”, here in Putten, The Netherlands. Despite the overcast conditions outside, the lively scientific program included keynotes by Paul Tackley and Carolina Lithgow-Bertelloni in the morning and Clint Conrad and Louise Kellogg in the afternoon. With over 120 attendees, and a program built around selected keynote presentations with plenty of time for posters and discussions, Nethermod offers a unique meeting format. Today’s post includes interviews with three attendees at different stages of their career – student, mid career and more established – and asks about their experiences of the workshop and their perspective on the future of geodynamics.


Kiran Chotalia here at Putten

— Early Career Researcher —
Kiran Chotalia (University College London).
Kiran is entering the third year of her PhD, and is a first-time attendee to the workshop.

– What is your PhD project about and what did you present here?
My project looks into the effects of water on mantle circulation, firstly using parameterized models and then 2-D models. I had a poster on day 2 which presented some parameterized models that included a time-lag to simulated delayed mixing.

– The conference is aimed at leaving extra time to develop student-keynote interactions. What have your impressions been? Do you have any suggestions for changes for future workshops?
Considering the format and length of the lectures (45 mins + 15 questions), I think that 30 mins with the keynotes presenters was sufficient. However, there are around 40-50 other student attendees so perhaps the option to write some anonymous questions to help find consensus within the broader group’s needs could be incorporated. This would also help achieve a more overview style session, which cannot be covered in the lectures.

– What are your plans for after the PhD – is the Brexit process seriously weighing into your decision making?
I am definitely now more open to considering the idea of moving outside the UK. In any case, with time I have felt more integrated with the geodynamics community, and have a broader picture of who else is also out there and what the cutting-edge ideas are.

– What are your takeaway potatoes of wisdom from the meeting?
Dave Stegman’s comment – “Don’t always believe what you read” has stuck with me. It is important to be reminded of this fact! Science is only the best description at that time, and also considering the current state of pressure to publish, it is easy loose this perspective. It is also nice to be at a smaller, manageable conference with other researchers doing similar things. It is a chance to meet and talk to those beyond the home institute and I feel more up-to-date with what others are up to.

 


Fanny Garel at the poster of her PhD student Manar Alsaif

— Mid Career Researcher —
Fanny Garel (Géosciences Montpellier)
Fanny is a lecturer at Montpellier, and was an invited keynote presenter from the “Subduction and mantle flow modelling” session. Nethermod is her third workshop of this series.

– Your presentation built upon work from your earlier paper (Garel et al., 2014) that has had quite a reception in the community. Can you please summarize the talk for us?
My presentation was on numerical models that (re)produce the seismically imaged slab morphologies by varying different parameters and understanding the physical controls (e.g., slab sinking and bending).

– From your perspective as a keynote speaker, how did the daily student question session go?
Half an hour was perhaps slightly too short. The session is a key opportunity to open the discussion to more of the limitations and assumptions of the model. Students can ask more about the basics which you cannot fit into a presentation. Teaching is not just presenting, and vice versa. It is also helpful for speakers in terms of feedback for their own presentations!

– What are some of the biggest and outstanding questions in the modelling community?
Understanding when present-day subduction zones initiated, is one. Exploring a self-consistent interaction between single subduction zone specific-scales and global scales, both spatially and temporally, is still outstanding.

– Any tips or suggestions for ECS researchers at the end of the thesis and thinking of whether to continue for a postdoc?
You are already experts of your PhD subject, so keep your options open and try to change topic or your approach for a postdoc. There are plenty of different scales in geodynamics to explore, and perhaps it is best to change tools rather than objects. Consider your longer term view too and what the hot topics are that will lead to new opportunities in the next ca. 5 years.

– What are your takeaway potatoes of wisdom from the meeting?
There is always the requirement for simple models to help understand the Earth. Models can have lots of complexity but we can lack a fundamental and first-order understanding of problems – including the physics and relevance of feedbacks e.g. the Marianas trench, and surface and deep dynamics.
The generation and modelling of melt was also discussed, including the different ways of approaching it, and the links chemical evolution and dynamic evolution in a self-consistent manner.

 


Dave and the sunset

— Established Career Researcher —
Dave Stegman (Scripps Institute of Oceanography)
Dave is an Associate Professor of Geophysics and was a keynote speaker within the “Global modelling of Early and recent Earth” theme.

– This is the 15th time it has been run – how many conferences within the series have you attended?
My first in the series was in 2001 as a grad student and I have missed two of them since then. Some of the fellow students I met there the first time are here this week, so there is a real community building aspect to this meeting series and it is really important for the fabric of the community.

– How has it evolved since your first conference?
The format similar is pretty similar and has become increasingly student focused. The attendance of a high proportion of students makes the meeting fresh and dynamic.

– Can you summarize your presentation from earlier this week?
The take home message of my presentation was to shift our mindset in order to allow for scenarios that permit a molten lower mantle and a magnetic field in places outside the core. It was provocative, sure, but ensured a healthy scientific discussion.

– Origins of the geodynamo and core formation is a shift from your earlier work. Can you comment on the interdisciplinary aspects of modelling on vastly different temporal and spatial scales? 
This work really integrates geo- and paleo-magnetism, geodynamics and mineral physics, which really inform each other. Collaboration is required to progress.

– The “Geodynamics Liberation Front” was a big success and you are rolling out a new geodynamics themed t-shirt. Can you tell us more?
It will come in different sizes. The first was wildly popular – some more community themed fabric. Email me if you are interested in knowing more, else I’ll be bringing a suitcase full to AGU.

– What are some of the biggest or outstanding questions in the modelling community generally?
Those regarding Earth’s evolution and it’s entire history. The roadmap that makes most sense to me is to firstly calibrate our models of plate tectonics to present-day or recent timescales before going back in time, or to exoplanets.

– As a non-EU based researcher, do you have many active collaborations with researchers back on this side of the Atlantic?
Science is an international and you need to follow problems wherever they take you. Our community is really open to collaborations and in-person opportunities are important; they add much more value to Skype level meetings.

– Any tips for the next generation of ECS members of the geodynamics community, or those PhD students not sure whether to transition to postdoc?
Don’t underestimate yourself. The skills required to accomplish a PhD are valued in many settings… persistence, attention to detail, the ability to think at different levels, time management. Students often don’t realize they have these abilities and they are a starting point for many paths.

– Finally, what are your takeaway potatoes of wisdom from the meeting?
The informality of the venue and meeting format enables everyone to expand from their comfort zone. This is critical for learning as you cannot be inhibited to ask questions and start discussions. The financial support to bring so many students to the meeting really tips the scales… when students and ECSs are the dominant force they do not feel intimidated to make the most of it.


Thanks Dave, Fanny, and Kiran for your time!  

The Rainbow Colour Map (repeatedly) considered harmful

The Rainbow Colour Map (repeatedly) considered harmful

This week’s “Wit and Wisdom” post is a guest entry by researcher Fabio Crameri from the Centre for Earth Evolution of Dynamics (CEED), University of Oslo. Many of us are guilty of creating figures using the colours of the rainbow in their full glory – it’s bold, exciting, and justifies the golden data contained within, right? Wrong! As Fabio explains, the rainbow scheme is misleading and should be abandoned – unless you are a unicorn, of course.

Fabio Crameri – in a near rainbow jumper?

Visualization is, together with problem description, data production and data post-processing, one of the four supporting pillars of science. If new findings cannot be shared with the community through some sort of visualization technique, the knowledge is lost; science becomes useless. The same happens, if the chosen means of visualization is not good enough. In fact, poor vizualisation can even alter your data and mislead readers. To the contrary, good visualization is making your knowledge accessible and comprehensible to the broader community in an unperturbed and hence scientifically valid way. And that is how it should be.

Now, the following statement is terribly difficult to digest for a scientist’s belly: There is not one single right way of visualizing your data and consequently, no simple one-to-one instruction to follow to create the next figure or movie.

But, there is an easier approach. There are easy-to-follow, crystal-clear rules to prevent the most dangerous of visualization pitfalls (see, for example, Rougier et al., 2014). Here, I am writing about the most-basic, most-broken, most-stated, Rule #1.

The first rule of the science club is:
do not use the rainbow colour map!

The easiest thing to start with, is to prevent the one and only, most terribly-persisting pitfall: The rainbow colour map. The rainbow colour map is also known as ‘jet’ and, be aware, it comes in various forms and mutations.

The human eye

In contrast to its other, social meaning of equality matters, the scientific rainbow colour map is not equal at all. Even when ordered in a physically-consistent manner by the wavelength of the individual colours, the rainbow colour sequence appears highly unequal. The reason for this inequality lies within the human visual apparatus: Our eyes (see example in Figure 1).

Figure 1. Can we scientifically trust our eyes? Can you see all the twelve black dots? (From Imgur)

The human eye has evolved to facilitate our very survival in the dynamic and ever-changing environment we live in. This is like filling your backpack for the next field trip: To still be able to move and carry out our science, we pack the important, relevant things (e.g., laptops, geologic hammers), but leave the unnecessary things at home (e.g., ties).

I don’t see trees of green, but red roses I do.

During our evolution, our environment was mostly green; green trees with green leaves on top of green grass. Different shades of green, but that did not really matter. What mattered then – and still does now – were those tasty but scarce red berries. Individuals that developed eyes with a strong contrast for the orange-red part of the light had a better chance to survive, as they had this little extra help in hunting out the good stuff more clearly. Long story short, the 21st-Century human eye now features a stronger contrast for the yellow-orange-red part of the colour spectrum than for the blue-green part. Therefore, we wear a yellow jacket when we bike on streets, we wear orange vests on construction sites, and we paint all important warning signs in red and the less important in blue.

When viewing scientific figures that use the rainbow colour map, we tend to only see where the tasty yellow-orange-red part of the plot starts and ends, and ignore everything in the comparatively boring blue-green areas (see example in Figure 2). It is therefore why some Geodynamicists may start to see imaginary gaps in observed slabs, miss seismic reflectors, and interpret large topographic gradients to occur in the wrong areas (see example in Figure 3).

Figure 2. Have you ever seen the ocean seafloor age map in non-rainbow colours? (a) The rainbow colour scheme adds randomly (at least) two strong artificial boundaries to the data: One along the red-yellow transition (at 0.4 of the colour bar range) and one along the blue-cyan transition (at 0.7 of the colour bar range). At the same time, it hides the local data gradients in the greenish parts of the colour map making it impossible to spot, for example, the large area of same age just north of the Tonga subduction zone. Moreover, it is unreadable for people with most forms of colour blindness, and when printed in black and white. The perceptually-uniform colour maps (b) lajolla and (c) vik prevent all of these problems, where the latter is so-called zero-centred and can be used to specifically highlight the old (brownish) and young (blueish) parts of the ocean floor with a boundary (white) occurring naturally in the middle of the data range.

 

Figure 3. The same simulations but with a slight shift of the lower colour bar limit for (a,b) the non-uniform rainbow and (c,d) the perceptually-uniform davos colour map. Significant visual manipulation of the data results when using the rainbow colour map, while the simulation looks, for both cases, factually the same with the davos colour map. (a) The upper-mantle transition zone (enlarged in the square magnifier) and upper-mantle convection appear to be absent, while an artificial, globe-spanning pile of high-viscosity material seems to exist in the lower mantle reaching up to about mid-mantle depth. (b) The upper-mantle transition zone is strongly highlighted and convection appears to be only taking place in the upper mantle, while the mid-mantle appears to be uniformly viscous, with no small-scale structures.

Finding the end of the Rainbow

This is probably not the first time you have heard about it, and is certainly not the first time someone has written about it. Finding the end of the rainbow can be illustrated with a short journey through a long time-period…

At the end of the 19th Century, a German scientist named Arthur König pointed out that the human ability to differentiate between different wavelengths in the blue-green region of the colour spectrum was impaired (König and Dieterici 1983; König 1894). Later, in the mid 20th Century, studies like the one of Thomson and Wright (1947) confirmed this finding and followed up with refined investigations on the colour sensitivity of the retina. Then, along with the rise of coloured figures in scientific publications, more and more studies were published investigating the influence of our impaired colour perception on various scientific colour schemes (Pizer and Zimmerman 1983; Ware 1988; Rheingans 1992). At first, these studies had objective, how-to-do titles like “Choosing Effective Colors for Data Visualization” (Healey 1996).

Obviously, not everyone followed this guidance and so later studies became more subjective to the more and more common, misleading use of colours. These studies were then published under predominantly how-not-to-do titles like “How Not to Lie with Visualization” (Rogowitz and Treinish 1996). However, it became quickly clear that the main culprit in scientific visualization was the rainbow colour map. Titles therefore became more specific, like “Data Visualization: The End of the Rainbow” (Rogowitz and Treinish 1998).

One would think that after the latter publication, with its clear title and content, scientific authors, reviewers, and editors would step up and put an end to the rainbow colour scheme. They did not. At least not the majority.

Six years later, some scientists felt the need to reiterate on the problems of the rainbow colour map with an EOS article titled: “The End of the Rainbow? Color Schemes for Improved Data Graphics” (Light and Bartlein 2004). But the picture did not change even another three years later: “Rainbow Color Map (still) considered harmful” (Borland and Tailor 2007).

Another 5 years later, after all this immense effort over more than a century, the author of this EGU blog entry publishes a scientific study (Crameri et al., 2012) – using the rainbow colour map…

Unfortunately, it is not the only recent study using the still-harmful, repeatedly-ended and steadily-reincarnated rainbow colour map. In fact, it is the most common colour scheme used in present-day geoscience publications; it is omnipresent in presentations during major international conferences, often features in the broken pillar of published high-impact journals, and even mocks us as “featured figures” on various journal homepages. The rainbow keeps arching in all its glory above all the informative and somewhat alerting publications outlined above; its end is seemingly impossible to find.

And anyway, what does the current blog author have to add to this discussion that has not already been said? – Well, nothing really. He just kind of feels bad about publishing with the rainbow colour map and thought that ending it is, in the name of science, well worth another try.

Sleep, try-to-end-rainbow, repeat!

Why is Rainbow used at all?

It might be a good idea to try and understand why the rainbow colour map is so common still. There are multiple reasons that make the rainbow colour map appear attractive for scientists.

First of all, the rainbow colour map looks kind of peppy: with the varying contrasts and multiple colours, it appears to create the most interesting, lively look. The multiple strong and varying contrasting parts along the rainbow colour map seem to highlight the large but also the smaller variations in our data very well. Given that the rainbow colour map contains all standard colours, it consequently provides the highest contrast between neighbouring data values. Another reason comes from the fact that a scientist naturally tends to adjust his or her way of working to his or her teacher, supervisor, mentor, and peer. And finally, it is always the most convenient way to simply take the default colour map provided by the applied software. And the rainbow colour map was and, in some unfortunate cases, still is the default in many scientific computer programs and codes, and even appears in the logo of a widely-used visualisation software.

One would think that these points are all good things, but here are the problems related to each of the above points:

The locally-strong contrast of the rainbow colour map is not uniform all along the colour bar, meaning that some parts of the data are highlighted, while other parts are masked (see Figure 4). The final effect is factually equal to manipulating the numbers of the data by hand – something that cannot be considered the scientific way, can it?

Figure 4. Scientific colour map comparison. Continuous ripples on top of each colour bar indicate the appearance of low-contrast data variations in different parts of the colour bar. The graph indicates the (CIE76) lightness difference (dE) along a colour map: The flatter the curve, the lesser the data distortion, the better the colour map. Diagnostics after Kovesi 2015. The perceptually-uniform colour maps devon, davos, oslo and broc produce nearly-flat curves and are available from www.fabiocrameri.ch/visualisation.

Also, scientific methodologies often become outdated quickly, and there is therefore no validity in continuing applying a tool – or a colour map – against better reasoning. And finally, the reason for the rainbow colour map to be the default of a software is not due to the fact that the developers have thought carefully about scientific visualisation. In fact, they almost certainly did not: MatLab has, for example, only recently changed their default from Rainbow (i.e., jet) to Parula in version 2014b (see Figure 4). Using the default is, even though convenient, therefore never an excuse.

So, in conclusion, there is, from a scientific point of view, no good reason left over supporting the use of the rainbow colour map. But despair not: There are plenty of other colour schemes waiting in the ranks.

What are the Alternatives?

 For scientific figures, the best choice is always perceptually-uniform colour maps. Or let’s call them hero colour maps to be in line with the naming convention of a previous blog entry.

In perceptual-uniformity we trust! 

Perceptually-uniform, hero colour maps handle local data variations equally all along the colour bar. Hero colour maps can be read even after being printed in black and white. As such, hero colour maps are also readable by people with a form of colour vision deficiency.

Viridis, Magma, and Inferno (https://bids.github.io/colormap/) are examples of perceptually-uniform, hero colour maps that have been recently picked up and used more-widely. However, they are predominantly adopted only within python programs. A novel set of perceptually-uniform, hero colour maps including Devon, Davos, Oslo and Broc (see Figure 4), are now available freely on www.fabiocrameri.ch/visualisation: Each of these colour maps are conveniently provided in .mat, .py, .xml, .cpt, and .svg formats. This means, they are all ready to be used with the most-common scientific visualisation software applied in the field of Geodynamics, including MatLab, Python, Paraview, GMT, GPlates, and QGIS. Unlike other colour maps to date, they are all provided together with the corresponding scientific tests (e.g., for the CIE76 lightness differences along the colour map) to ensure their quality (see e.g., Figure 4). This allows for a confident statement about a careful, scientifically-proofed choice of colours in upcoming scientific publications.

Can we reach the end of the rainbow?

Changing the colour scheme for a figure does not seem to be such a huge deal.

Advising authors not to submit figures using the rainbow colour scheme does not seem to be such a huge deal. Pointing out bad colour schemes in a review does not seem to be such a huge deal. So, let’s deal with it!

What do you plot with, my lovely?

 

#endrainbow

 

REFERENCES

Crameri, F., P. J. Tackley, I. Meilick, T. V. Gerya, and B. J. P. Kaus (2012b), A free plate surface and weak oceanic crust produce single-sided subduction on Earth, Geophys. Res. Lett., 39, L03306, doi:10.1029/2011GL050046.


Borland, D. and R.M. Tailor (2007), “Rainbow Color Map (still) considered harmful,” IEEE Computer Society, vol. 07, 2007, p. 14-17.

Festinger, L. (1956), When prophecy fails, University of Minnesota Press, https://books.google.no/books?id=L700AAAAMAAJ

Healey, C.G. (1996), “Choosing Effective Colors for Data Visualization”, Proc. IEEE Visualization, IEEE CS Press, 1996, pp. 263-270.

König, A., & Dieterici, C. (1893). Die Grundempfindungen in normalen und anomalen Farbensystemen und ihre Intensitätsverteilung im Spektrum. Zeitschrift für Psychologie und Physiologie der Sinnesorgane, 4, 241–347.

König, A. (1894). S.B. Akad. Wiss. Berlin, 21 June, p. 577.

Kovesi, P. Good Colour Maps: How to Design Them. arXiv:1509.03700 [cs.GR] 2015

Light, A. and P.J. Bartlein (2004), “The End of the Rainbow? Color Schemes for Improved Data Graphics,” EOS Trans. Amer. Geophysical Union, vol. 85, no. 40, 2004, p. 385.

Pizer, S.M. and J.B. Zimmerman (1983), “Color Display in Ultrasonography,” Ultrasound in Medicine and Biology, vol. 9, no. 4, 1983, pp. 331-345.

Rogowitz, B.E. and L.A. Treinish (1998), “Data Visualization: The End of the Rainbow,” IEEE Spectrum, vol. 35, no. 12, 1998, pp. 52-59.

Rogowitz, B.E. and L.A. Treinish (1996), “How Not to Lie with Visualization,” Computers in Physics, vol. 10, no. 3, 1996, pp. 268-273.

Rougier, NP, Droettboom M, Bourne PE (2014) Ten Simple Rules for Better Figures. PLoS Comput Biol 10(9): e1003833. https://doi.org/10.1371/journal.pcbi.1003833

Rheingans, P. (1992), “Color, Change, and Control for Quantitative Data Display,” Proc. IEEE Visualization, IEEE CS Press, 1992, pp. 252-259.

Thomson, L. C., Wright, W. D., (1947), The colour sensitivity of the retina within the central fovea of man. The Journal of Physiology, 105 doi: 10.1113/jphysiol.1947.sp004173.

Ware, C. (1988), “Color Sequences for Univariate Maps: Theory, Experiments, and Principles,” IEEE Computer Graphics and Applications, vol. 8, no.5, 1988, pp. 41-49.

Watson, A.B., H.B. Barlow, and J.G. Robson (1983). What does the eye see best? Nature, 302:419–422.

 

FURTHER READING

#endrainbow

https://earthobservatory.nasa.gov/blogs/elegantfigures/2013/08/05/subtleties-of-color-part-1-of-6/

https://www.mathworks.com/tagteam/81137_92238v00_RainbowColorMap_57312.pdf

https://www.climate-lab-book.ac.uk/2014/end-of-the-rainbow/

http://colorbrewer2.org

 

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