31:T5ae,md` The visualization above shows so-called warming stripes. Each stripe represents a period of time, the color indicates the period's mean temperature compared to the global mean. Temperatures below mean are shown in blue, termperatures above it in red. Time is sorted where recent years are shown on the right. These kinds of visualizations were first introduced in a [blog post](http://www.climate-lab-book.ac.uk/2018/warming-stripes/) by [Ed Hawkins](https://twitter.com/ed_hawkins). They intentionally lack scales because their purpose is to show global warming as simple as possible. Warming stripes are available for a lot of regions of the world on [this website](https://showyourstripes.info/). I first saw warming stripes as part of the graphics used by the German [Scientists for Future](https://www.scientists4future.org/) group (most of their website is in German, but there's some [material in English](https://www.scientists4future.org/wp-content/uploads/2019/05/S4F-Postkarte-digital-140319-EN.pdf) too). I especially like these visualizations because they communicate their message clearly ("*It's getting warmer*") while also looking aesthetically pleasing. I wanted to play with the data and similar visualizations myself, so I created this document. It's a live computational notebook that runs in your browser. If you want, you can change the data, visualization options etc. and see how the output changes.
Let's dive in: `32:T3cf4,world_monthly = [-0.7, -0.286, -0.732, -0.563, -0.327, -0.213, -0.125, -0.237, -0.439, -0.451, -0.187, -0.257, -0.296, -0.356, -0.479, -0.441, -0.295, -0.197, -0.212, -0.157, -0.101, -0.057, -0.02, -0.051, -0.315, -0.477, -0.502, -0.557, -0.211, -0.04, -0.018, -0.202, -0.125, -0.216, -0.193, 0.073, -0.182, -0.327, -0.309, -0.355, -0.268, -0.175, -0.059, -0.148, -0.404, -0.362, -0.255, -0.437, -0.365, -0.282, -0.286, -0.353, -0.233, -0.219, -0.227, -0.167, -0.119, -0.192, -0.367, -0.233, -0.169, -0.401, -0.306, -0.223, -0.338, -0.167, -0.271, -0.163, -0.336, -0.217, -0.214, -0.518, -0.122, -0.376, -0.528, -0.38, -0.129, -0.291, -0.304, -0.309, -0.461, -0.391, -0.608, -0.449, -0.513, -0.345, -0.434, -0.649, -0.57, -0.31, -0.541, -0.329, -0.393, -0.474, -0.668, -0.358, -0.528, -0.708, -0.548, -0.512, -0.653, -0.582, -0.329, -0.278, -0.332, -0.211, -0.643, -0.304, -0.308, -0.189, -0.327, -0.195, -0.307, -0.248, -0.274, -0.1, -0.565, -0.249, -0.315, -0.363, -0.181, 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7.43, 7.55, 7.87, 8.43, 8.33, 7.52, 7.83, 8.34, 8.71, 8.43, 8.72, 7.92, 7.73, 8.48, 8.61, 8.22, 8.39, 8.09, 7.6, 7.69, 8.5, 9.3, 8.28, 8.77, 8.6, 8.54, 8.8, 7.76, 8.86, 7.8, 8.83, 9.46, 7.72, 8.57, 7.82, 8.49, 9.02, 8.39, 8.85, 8.01, 9.14, 7.88, 8.49, 8.17, 9.69, 8.81, 8.68, 9.06, 8.75, 8.58, 7.32, 7.73, 7.84, 9.17, 8.46, 9.35, 8.51, 9.3, 9.23, 9.57, 8.86, 8.96, 8.48, 9.15, 8.13, 7.92, 7.22, 8.96, 8.59, 9.63, 8.85, 9.34, 7.56, 7.39, 8.67, 7.86, 8.96, 9.01, 8.31, 8.26, 8.23, 8.7, 8.08, 8.66, 9.11, 9.12, 9.23, 8.92, 7.97, 8.11, 8.09, 8.55, 9.14, 9.24, 8.34, 7.76, 8.33, 7.97, 9.38, 9.7, 9.73, 8.74, 9.57, 8.98, 10.19, 9.48, 7.85, 9.3, 9.28, 9.78, 10.11, 9.55, 10.07, 10.14, 9.26, 9.58, 9.98, 10.14, 9.57, 9.61, 8.41, 10.22, 9.49, 9.01, 10.7, 10.28, 9.77, 10.03, 10.87], "Schleswig-Holstein": [7.12, 8.78, 8.18, 8.86, 7.62, 7.83, 7.34, 6.69, 7.73, 7.62, 7.61, 7.12, 7.85, 8.41, 7.35, 8.08, 7.97, 8.51, 8.38, 8.35, 7.94, 6.97, 8.39, 8.2, 8.05, 8.54, 7.59, 7.82, 7.34, 8.75, 9.08, 7.97, 8.82, 9.22, 7.63, 8.19, 7.74, 8.43, 7.2, 8.57, 9.08, 7.1, 7.59, 7.58, 8.51, 8.69, 8.03, 8.1, 7.11, 8.73, 7.71, 8.68, 8.29, 9.7, 8.65, 8.36, 8.43, 9.1, 8.64, 6.5, 7.11, 6.95, 8.97, 8.61, 9.13, 8.22, 8, 9.08, 9.31, 8.62, 8.68, 7.75, 9.15, 7.85, 7.78, 7.21, 8.67, 8.06, 9.07, 8.33, 8.94, 7.38, 7.13, 7.95, 7.54, 8.16, 9.09, 8.43, 7.76, 7.55, 8.64, 8, 8.57, 8.91, 9.16, 8.4, 8.6, 7.95, 7.23, 7.69, 8.03, 8.69, 9.12, 8.21, 7.36, 7.78, 7.32, 9.08, 9.6, 9.73, 8.46, 9.53, 8.17, 9.31, 8.86, 7.16, 8.96, 8.99, 9.65, 9.81, 8.85, 9.62, 9.2, 9.12, 9.24, 9.88, 10.04, 9.74, 9.28, 7.67, 9.4, 8.8, 8.78, 10.48, 9.71, 9.61, 9.57, 10.18], "Sachsen": [6.71, 8.12, 7.46, 8.21, 7.73, 7.85, 6.72, 6.66, 7.11, 7.2, 7.36, 7.36, 7.63, 7.94, 7.3, 7.39, 7.85, 8.63, 7.91, 8.26, 7.5, 6.91, 8.43, 8.29, 7.82, 8.22, 7.71, 7.36, 7.3, 8.36, 9.02, 7.66, 8.4, 8.39, 7.74, 8.44, 7.38, 8.53, 7.09, 8.48, 8.75, 6.91, 7.88, 7.46, 8.34, 8.63, 7.8, 8.03, 7.14, 8.6, 7.42, 8.13, 7.15, 9.57, 8.22, 8.25, 8.42, 8.46, 8.07, 6.17, 6.68, 7.05, 8.79, 8.14, 8.92, 8.17, 8.16, 9.11, 8.93, 8.46, 8.87, 7.78, 9.06, 7.48, 7.23, 6.38, 8.49, 8.17, 8.84, 8.21, 8.89, 7.1, 7.22, 8, 7.39, 8.51, 8.9, 8.05, 7.53, 7.45, 8.33, 7.82, 8, 8.66, 8.88, 8.14, 8.49, 7.64, 7.67, 7.23, 8.02, 9, 9.03, 7.72, 7.33, 7.79, 7.01, 8.78, 9.52, 9.42, 8.16, 9.3, 8.26, 9.45, 8.54, 6.63, 8.46, 8.89, 9.18, 9.82, 8.62, 9.2, 9.01, 8.62, 8.59, 9.24, 9.68, 9.41, 8.91, 7.46, 9.37, 8.86, 8.39, 10.14, 9.93, 9.37, 9.39, 10.29], "Sachsen-Anhalt": [7.46, 8.81, 8.32, 8.94, 8.07, 8.36, 7.5, 7.29, 7.96, 7.85, 8, 7.89, 8.32, 8.63, 7.84, 8.1, 8.33, 9.01, 8.56, 8.81, 8.1, 7.27, 8.98, 8.81, 8.42, 8.95, 8.13, 7.93, 7.8, 8.87, 9.66, 8.24, 9.06, 9.11, 8.2, 8.8, 7.98, 8.95, 7.66, 9.02, 9.41, 7.44, 8.21, 8.01, 8.94, 9.17, 8.4, 8.58, 7.64, 9.18, 8.16, 8.93, 7.96, 10.27, 8.97, 8.9, 8.96, 9.24, 8.87, 6.73, 7.42, 7.62, 9.33, 8.97, 9.18, 8.77, 8.57, 9.69, 9.69, 9, 9.17, 8.22, 9.61, 8.1, 7.82, 7.1, 9.04, 8.63, 9.41, 8.78, 9.35, 7.54, 7.53, 8.41, 7.88, 8.85, 9.47, 8.59, 7.97, 7.97, 8.93, 8.22, 8.73, 9.36, 9.52, 8.77, 9.08, 8.28, 7.98, 7.92, 8.62, 9.55, 9.63, 8.41, 7.88, 8.2, 7.53, 9.56, 10.12, 10.07, 8.8, 9.91, 8.75, 9.97, 9.29, 7.28, 9.26, 9.59, 10.1, 10.39, 9.35, 9.7, 9.63, 9.34, 9.42, 10.02, 10.34, 10.01, 9.54, 7.99, 9.98, 9.44, 9.07, 10.68, 10.32, 10.08, 10.03, 10.88], "Thüringen": [6.66, 7.77, 7.31, 7.89, 7.16, 7.42, 6.22, 6.22, 6.73, 6.69, 6.85, 6.92, 7.27, 7.46, 6.68, 6.83, 7.34, 7.94, 7.39, 7.75, 6.72, 6.36, 7.85, 7.82, 7.32, 7.67, 7.19, 6.82, 6.75, 7.78, 8.48, 7.3, 7.85, 7.78, 7.28, 7.85, 6.85, 7.98, 6.61, 7.99, 8.23, 6.41, 7.44, 6.99, 7.78, 8.16, 7.4, 7.63, 6.8, 8.19, 7.04, 7.75, 6.89, 9.1, 7.83, 7.83, 8.09, 7.95, 7.67, 5.93, 6.53, 6.74, 8.4, 7.71, 8.5, 7.8, 7.87, 8.53, 8.55, 7.97, 8.26, 7.41, 8.61, 7.17, 6.84, 6.19, 8.1, 7.72, 8.6, 7.83, 8.42, 6.48, 6.55, 7.59, 6.91, 8.01, 8.36, 7.47, 7.1, 7.1, 7.83, 7.16, 7.54, 8.18, 8.34, 7.86, 8.08, 7.16, 7.06, 6.95, 7.53, 8.45, 8.4, 7.25, 6.76, 7.27, 6.63, 8.46, 8.95, 8.83, 7.67, 8.7, 7.79, 9.11, 8.22, 6.39, 8.25, 8.54, 8.87, 9.36, 8.45, 8.84, 8.74, 8.29, 8.4, 8.87, 9.28, 8.95, 8.58, 7.2, 9.12, 8.59, 8.08, 9.79, 9.5, 9.03, 9.1, 9.91], })35:[] 2f:{"title":"Warming Stripes","cells":[{"id":358,"value":"md`\n# Warming Stripes\n*Simple visualizations showing how the earth is getting warmer over time.*\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":276,"value":"to_svg(partition_data(world_monthly, 6), 400)","pinned":false,"hidden":false,"mode":"ojs"},{"id":289,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":76,"value":"md`## World\n\nFirst, I wanted to recreate the most popular set of warming stripes that show the world's average yearly temperature. I've used the same data as Ed Hawkings, which is the HadCRUT dataset. This notebook contains both the monthly and yearly datasets (covering 1850-2021):\n\n**Data Source:** [The HadCRUT Dataset](https://www.metoffice.gov.uk/hadobs/hadcrut4/)
\n**Direct link** (*accessed 01/11/2022*): [monthly](https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.6.0.0.monthly_ns_avg.txt ), [annual](https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.6.0.0.annual_ns_avg.txt)\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":156,"value":"md`### Annual\n\nUsing the *world_annual* data and about ten lines of JavaScript, it's possible to create a visualization that almost exactly matches the [original](http://www.climate-lab-book.ac.uk/files/2018/05/globalcore.png):\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":150,"value":"to_svg(world_annual, 200)","pinned":false,"hidden":false,"mode":"ojs"},{"id":159,"value":"md`\n\nPretty neat, huh? But it's getting better:\n\n### Monthly\n\nUsing the *world_monthly* data, it's possible to create a more detailed visualization. The plot below shows the world's average temperatures *per month*. In the initial setting, each stripe represents a single month. While this may not look impressive on a phone screen, it's pretty nice on a larger monitor.\n\nBecause this is a live notebook, you can change the plot using the slider below. Dragging it to the right increases the number of months represented by a single stripe. \n\n> *Tip: Try using a slider value of 12 to produce a yearly plot!*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":163,"value":"viewof world_monthly_slider = gen_slider(1, \"months(s)\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":168,"value":"to_svg(partition_data(world_monthly, world_monthly_slider), 200)","pinned":false,"hidden":false,"mode":"ojs"},{"id":313,"value":"md`\nWhat's your favorite setting? I think mine is somewhere around 6. BTW, the image at the top of this post also uses stripes that each represent half a year.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":465,"value":"### Per Year\n\nThe following plot shows the warming stripes for a single year. Use the drop-down menu to select a year!","pinned":false,"hidden":false,"mode":"md"},{"id":469,"value":"viewof year_selector = select(year_numbers)","pinned":false,"hidden":false,"mode":"ojs"},{"id":475,"value":"to_svg(world_monthly.slice((year_selector-1850) * 12, (year_selector-1850+1) * 12), 200, Math.min.apply(Math, world_monthly), Math.max.apply(Math, world_monthly))","pinned":false,"hidden":false,"mode":"ojs"},{"id":80,"value":"md`## Germany`","pinned":false,"hidden":false,"mode":"ojs"},{"id":187,"value":"md`\nIn addition to global temperatures, let's also look at some regional data. I've chosen Germany since that's where I'm from, but you could change this notebook to include warming stripes for any region of your choice.\n\nI've used temperature data from the Deutscher Wetterdienst which provides annual temperature averages for each federal state (1881-2018):\n\n**Data Source:** [DWD Climate Data Center](https://www.dwd.de/EN/klimaumwelt/cdc/cdc_node.html)
\n**Data Set:** Annual regional averages of air temperature (annual mean) in °C (2 m above ground) - RGN_DEU_P1Y_TM (*accessed 9/10/2019*)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":195,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":227,"value":"md`Using the data above, Germany's average yearly temperature can easily be calculated by averaging over all federal states:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":205,"value":"ger_avg_annual = {\n let keys = Object.keys(ger_annual);\n let res = [];\n for (var i=0; i < ger_annual[keys[0]].length; i++) {\n var elmt = 0;\n keys.forEach(k => {\n elmt += ger_annual[k][i];\n });\n res.push(elmt/keys.length);\n }\n return res;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":231,"value":"md`\nThe plot below shows the Germany's average temperatures per year. Like with the previous example, you can use the slider to alternate the plot.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":63,"value":"viewof ger_avg_annual_slider = gen_slider(1, \"year(s)\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":86,"value":"to_svg(partition_data(ger_avg_annual, ger_avg_annual_slider), 200)","pinned":false,"hidden":false,"mode":"ojs"},{"id":244,"value":"md`\nIn comparison to the global warming stripes, the plot above contains much more variance and therefore doesn't look as nice. The variance can be reduced by taking the average of different locations (see the global plots) or by averaging over time. The latter can be done by dragging the slider to the right. Watch how the image gets smoother and the trend becomes more obvious!\n\n### Federal states\n\nHow have average temperatures changed in each federal state of Germany? Select a federal state below to find out! As always, you can use the slider to group multiple years.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":247,"value":"viewof selector = select(Object.keys(ger_annual))","pinned":false,"hidden":false,"mode":"ojs"},{"id":258,"value":"viewof ger_fed_annual_slider = gen_slider(1, \"year(s)\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":261,"value":"to_svg(partition_data(ger_annual[selector], ger_fed_annual_slider), 200)","pinned":false,"hidden":false,"mode":"ojs"},{"id":324,"value":"md`\nDid you see the federal state that got colder over the last century? \n\n*Just kiddin', they all got warmer significantly!*\n\n

\n#ShowYourStripes\n\n

\n\n*How did you like this notebook? Tell me on [twitter](https://twitter.com/FKohlgrueber) or send me an [e-mail](mailto:felix.kohlgrueber@gmail.com). You can also take a look at my [blog](https://fkohlgrueber.github.io/) where I (very) occasionally post about visualizations, technology and whatever else I find interesting. If you'd like to know how this notebook works, take a look at the appendix below.*\n\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":239,"value":"md`

\n## Appendix\n\nThis section contains the code that makes everything work ;-)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"function gen_slider(default_val, text){\n let form = html``;\n form.oninput = () => {\n form.value = form.s.valueAsNumber;\n form.os.value = `${form.value}`;\n };\n form.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":52,"value":"function partition_data(data, num_elmts){\n let array = [];\n for (let i=0; i a + b, 0)/slice.length);\n }\n return array;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"function to_svg(data, height, min, max){\n if (min === undefined || max === undefined) {\n min = Math.min.apply(Math, data);\n max = Math.max.apply(Math, data);\n }\n const fill = d3.scaleSequential(d3.interpolateViridis).domain([-2, 2]);\n var color = d3.scaleQuantize()\n .domain([min, max])\n .range(color_scale);\n return html``;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"color_scale = [\"#08306b\", \"#08519c\", \"#2171b5\", \"#4292c6\", \"#6baed6\", \"#9ecae1\", \"#c6dbef\", \"#deebf7\", \"#fee0d2\", \"#fcbba1\", \"#fc9272\", \"#fb6a4a\", \"#ef3b2c\", \"#cb181d\", \"#a50f15\", \"#67000d\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":471,"value":"year_numbers = Array(172).fill(1850).map((x, y) => x + y)","pinned":false,"hidden":false,"mode":"ojs"},{"id":250,"value":"import { select } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}