#climatechangecrochet – The global warming blanket.

Q. What do you get when you cross crochet and climate science?

A. A lot of attention on Twitter.

At the weekend I like to crochet. Last weekend I finished my latest project and posted the picture on Twitter. And then had to turn the notifications off because it all went a bit noisy. The picture of my “global warming blanket” rapidly became my top tweet ever, with more retweets and likes than anything else. Apparently I had found a creative way to visualise trends in global mean temperature. I particularly liked the “this is the most frightening knitwear I have seen all year” comment. Given the interest on Twitter I thought I had better answer a few of the questions in this blog. Also, it would be great if global warming blankets appeared all over the world.

How did you get the idea?

The global warming blanket was based on “temperature” blankets made by crocheters around the world. Their blankets consist of one row, or square, of crochet each day, coloured according to the temperature at their location  . They look amazing and show both the annual cycle and day-to-day variability. Other people make “sky” blankets where the colours are based on the sky colour of the day – this results in a more muted grey-blue-white colour palette.

I wondered what the global temperature series would look like as a blanket. Also, global warming is often explained as greenhouse gases acting like a blanket, trapping infrared radiation and keeping the Earth warm. So that seemed like an interesting link. I also had done several rainbow themed blankets in the past and had a lot of yarn left that needed using.

Where did the data come from?

I used the annual and global mean temperature anomaly compared to 1900-2000 mean as a reference period as available from NOAA https://www.ncdc.noaa.gov/cag/time-series/global/globe/land_ocean/ytd/12/1880-2016. This is what the data looks like shown more conventionally.


I then devised a colour scale using 15 different colours each representing a 0.1 °C data bin. So everything between 0 and 0.099 was in one colour for example. Making a code for these colours, the time series can be rewritten as in the table below. It is up to the creator to then choose the colours to match this scale, and indeed which years to include. I was making a baby sized blanket so chose the last 100 years, 1916-2016.


1929              1940-46        1954-56                                       1991/2  1997/8

If you look closely you can see the 1997-1998 El Nino (relatively warm yellow stripe), 1991/92 Pinatubo eruption (relatively cool pink year) as well as cool periods 1929, and 1954-56 and the relatively warm 1940-46. Remember that these are global temperature anomalies and may not match your own personal experience at a given location!

Because of these choices, and the long reference period, much of the blanket has relatively muted colour differences that tend to emphasise the last 20 years or so. There are other data sets available, and other reference periods and it would be interesting to see what they looked like. Also the colours I used were determined mainly by what I had available; if I were to do another one, I might change a few around (dark pink looks too much like red in the photograph and needed a darker blue instead of purple for the coldest colour), or even use a completely different colour palette – especially as rainbow colour scales aren’t great as they can distort data and render it meaningless if you are colour blind Ed Hawkins kindly provided me with a more user friendly colour scale which I love and may well turn into a scarf for myself (much quicker than a blanket!).


How can I recreate this?

If you want to create something similar, you will need 15 different colours if you want to do the whole 1880-2016 period. You will need relatively more yarn in colours 3-7 than other colours (if, like me you are using your stash). You can use any stitch or pattern but since you want the colour changes to be the focus of the blanket, I would choose something relatively simple. I used rows of treble crochet (UK terms) and my 100 years ended up being about 90 cm by 110 cm. You can of course choose any width you like for your blanket, or make a scarf by doing a much shorter foundation row. It goes without saying that it could also be knitted. Or painted. Or woven. Or, whatever your particular craft is.



How long did it take?

I used a very simple stitch, so for a blanket this size, it was a couple of months (note I only crochet in the evenings 2 or 3 evenings a week for a couple of hours with more at some weekends). It helped that the Champions League was on during this time as other members of the household were happy to sit around watching football whilst I crocheted. Weave the ends in as you go. There are a lot of them, and I had to do them all at the end. The time flies because….

Why do I crochet?

I like crochet because you can do simple projects whilst thinking about other things, watching TV or listening to podcasts, or, you can do more complicated things which require your full attention and divert your brain from all other things. There is also something meditative about crochet, as has been discussed here (https://www.theguardian.com/lifeandstyle/2013/may/16/knitting-yoga-perfect-bedfellows) I find it a good way to destress. Additionally, a lot of what I make is for gifts or for charities and that is a really good feeling.

What’s next?

Suggestions have come in for other time series blankets e.g. greys for aerosol optical depth punctuated by red for volcanic eruptions, oranges and yellows punctuated by black for solar cycle (black being high sun spot years), a central England temperature record. Blankets take time, but scarves could be quicker so I might test a few of these ideas out over the next few months. Would love to hear and see more ideas, or perhaps we could organise a mass “global warming blanket” make-athon around the world and then donate them to communities in need.

And finally.

More seriously, whilst lots of the initial comments on Twitter were from climate scientists, there are also a lot from a far more diverse set of folks. I think this is a good example of how if we want to reach out, we need to explore different ways of doing so. There are only so many people who respond to graphs and charts. And if we can find something we are passionate about as a way of doing it, then all the better.

Scientist? Brilliant? Masculine?

brilliant_tToday  the news has been full of a study in the USA which reported that girls as young as 6 or 7 start to remove themselves from challenges associated with being “really really smart”. The research, by Lin Bian, Sarah-Jane Leslie and Andrew Cimpian of the Universities of Illinois, New York University and Princeton  found that around the ages of 6-7, there started to emerge a difference in the way boys and girls viewed being “really really smart” in relation to their own gender.

Wanting to explore the origin of the widespread “brilliance = male” stereotype that has been used to explain the lack of women in many occupations including science and engineering, and been demonstrated in reference writing, (more studies), they used younger participants than previous studies. Over 4 different studies they discovered that 5 year olds were equally likely to rate members of their own gender as being brilliant, but that by age 6-7, girls were statistically significantly less likely to rate members of their own gender as brilliant. A corresponding question about rating pictures of people as ” really really nice” started to reveal the opposite stereotype about women being nicer than men. The older children in the study also started to dissociate high school marks with “being clever” – they identified that girls got better marks in class than boys but did not associate this with girls being clever. (Actually the rest of us could probably learn something from this- some of the cleverest people I know would not “look” clever on paper as they may have finished formal education early and learnt in other ways – too often smart= good marks).

So these studies showed that children are influenced by gender stereotypes in relation to brilliance and niceness sufficiently such that they start to show these stereotypes around the ages of 6-7 (it should be noted that this study included mainly middle-class children of whom 75% were white, therefore it would be interesting to see how the conclusions differ across different cohorts). But it also showed some evidence that it influences choices made. Given a choice of two games, one presented as for those who work really hard, and one for those who are really really smart, both genders showed similar interest in the “try hard” game at all ages, but girls showed significantly less interest in the “really smart” game at ages 6 and 7.

In order to tell if these results actually have an influence on career paths, we would need to complete a longitudinal study of many many children and their influences. One such study is underway as part of the ASPIRES project run from Professor Louise Archer’s team at Kings College London. Whilst we wait for the second phase of that study to take us all the way from 10-18, we can perhaps start to piece together the new work with even younger children.

The ASPIRES work with 10-14 year olds suggests that children of this age and their parents strongly associate science with masculinity and science with cleverness. Whilst girls claim to enjoy science, they can’t see themselves in science careers. Those girls who are defined as “science keen” either by themselves or others often struggle to combine this interest with other stereotypical views of femininity or “girliness” – needing to engage in “identity work” to feel comfortable with their choices. “Science-keen” girls in the Archer et al (2012,2013) studies come in two flavours – those who also excel in other areas, e.g. sport, music etc and take pains to emphasise their “roundedness” and those who adopt the “blue-stocking” or nerdy approach. All the science-keen girls in this study were middle-class.

There are many many studies of  how stereotype threat affects college-age students and beyond, brilliantly collected in “Whistling Vivaldi” which broadens the discussion from gender to other characteristics such as race and ethnicity – or indeed the ivivaldintersectionality of gender and race. A highly recommended read for evidence based studies over a range of conditions and subject areas, you can hear Claude Steele talk about how he came to write the book, or watch a longer Claude Steele lecture.

Given the compelling number of studies demonstrating the awareness of stereotypes at an ever younger age, and the studies of older students showing real effect on subject choice and career path, it would be easy as someone who cares passionately about all children having as many doors open to them as possible to get disheartened and think “it will ever be thus”. However, if stereotypes are starting to take hold and influence choices at 6 and 7 then it is probably also a good time to intervene. Talking to some primary school teachers and children in year 3 and 4 i.e. ages 8-9 it is clear that it is possible at this age to intervene appropriately and reset stereotypes at least in the School environment. My 9 year-old can explain that “it used to be thought women weren’t intelligent enough to make decisions like voting but now we know that’s not true at all”. It is clear to me that we need to begin our work with much younger age groups than we work with traditionally.

And finally, we should perhaps try to convey that “brilliance” has several definitions. Yes, it can be defined as ” exceptionally clever or talented” but it can also mean “of light, radiant, blazing, beaming” . Now that might be something to aspire to for all of us.

Additional resources


Whistling Vivaldi: How Stereotypes Affect Us and What We Can Do (Issues of Our Time) by Claude Steel (2011)

Pink Brain, Blue Brain: How Small Differences Grow Into Troublesome Gaps – And What We Can Do About It (2012) by Lise Eliot

Parenting Beyond Pink and Blue: How to Raise Your Kids Free of Gender Stereotypes, Christia Spears Brown (2014) Paperback

Ada Twist, Scientist and Rosie Revere, Engineer by Andrea Beatty and Dave Roberts

Videos and web links for resources:

How do we know it’s working? Book two tracking changes in pupils attitudes. A global citizenship toolkit by RISC, 2015 available from www.risc.org.uk/toolkit   Fantastic classroom ideas covering diversity and equality alongside other global citizenship issues.

http://www.amightygirl.com/     Very good for links to books and facebook feed showcasing important women, many of them scientists and engineers.


Research papers and similar:

Opening Doors, A guide to good practice in countering gender stereotyping in schools. Institute of Physics Report, October 2015

Gender stereotypes in Science Education Resources: A visual content analysis (2016) Kerkhoven, Russo, Land-Zandstra, Saxena and Rodenburg, PLOS ONE DOI:10.1371/journal.pone.0165037

‘Not girly, not sexy, not glamorous’ primary school girls’ and parents’ reconstructions of science aspirations (2013) Archer, DeWitt, Osborne, Dillon, Willis and Wong, Pedagogy, Culture and Society, 21:1, 171-194, DOI:10.1080/14681366.2012.748676 ASPIRES project

“Balancing Acts”: Elementary School Girls’ Negotiations of Femininity, Acheivement and Science (2012) Archer, DeWitt, Osborne, Dillon, Willis and Wong, Science Education, 96, 967-989




STEAMing ahead


54805-kettle_teaserI have spent an inspiring couple of days at the Association for Science Education Conference held here at Reading, picking up ideas (and freebies) for my outreach work. A strong theme emerging across several sessions that I have attended is the potential for learning opportunities that could be gained by working across traditional “arts” and “science” boundary. The newest additional to my acronym dictionary is therefore STEAM, being Science, Technology, Engineering, Arts and Mathematics.

Two sessions were particularly inspiring. Carole Kenrick @Lab13_Gillespie described her time as a “scientist/inventor” in residence in a state primary school, running Lab_13. Amongst the many fantastic activities and initiatives she set up during this time which included helping with curriculum and staff CPD, supporting students to run a science committee and doing some original research with the students that reached the national press, Carole also started a STEAM club. She described how this had evolved from Science Club, to STEM club and finally to STEAM, entraining and enthusing more and more children and parents as it made the transition. By bringing creative arts and science together through, for example, designing robot costumes, backpacks, growing and producing their own plant-based dyes and then using these to make textiles, children who “didn’t like science” began to involve themselves in it, and “science geeks” found new creative talents and skills. Carole has written this up for teachwire and as a blog.

The second STEAM themed session I attended was a keynote lecture by Marcus du Sautoy on The Art of Mathematics and the Mathematics of Art. In a well attended and thought provoking lecture, he focused on particular examples where mathematics is linked, either knowingly or unknowingly, to arts. Firstly music, considering the work of Oliver Messiaen who used repeats of rhythm and chords with different prime numbers in each to great effect in the piece he wrote for a prisoner of war camp quartet, “Quatuor pour la fin du temps (“Quartet for the end of time”) . Also in music, I was intrigued to discover that Indian musicians appear to have been aware of the Fibonacci sequence way before Fibonacci – it describes the number of patterns you can make with successive numbers of quaver beats for example. quaver

The connection between music and maths has often been made, but perhaps the other examples were less familiar. Firstly, in the visual arts, du Sautoy considered the success of Jackson Pollock paintings, attributed to them being fractals, and more than that, having similar fractal dimensions to those that we see in nature. This characteristic means that the level of complexity doesn’t change, no matter how much you “zoom in” to a Pollock painting, or in the natural world, trees. We also found out that to fake this you need to paint as a chaotic pendulum, one where the pivot moves as well as the pendulum. Apparently Pollock was able to do this through a natural combination of drunkenness and bad balance….

And finally to literature. The example used here was The Library of Babel by Jorge Luis Borges. Slightly different from the other examples, it is thought that this was a deliberate attempt by Borges to try to understand Poincare’s mathematics via literature. It dectorusdescribes a library “that some call the universe” and discusses whether it is finite or not, some of the most challenging questions still being addressed in science today.

To my mind, science is already a creative subject. What could be more creative than dreaming up hypotheses, designing experiments, designing technology and equipment to deliver them and making visualisations of our data and results? Recent emphasis on novel visualisations of climate data for example have attracted much attention and featured in Olympic games opening ceremonies. But it is probably true that the majority of people beginning their science journey don’t see it this way. The explicit A in STEAM could help us to demonstrate that aspect and perhaps attract some new interest. It might also encourage the creative side in career scientists, although many of them already demonstrate this.

So, are you ready to put the A into STEM?