2f:T58d,[_"More than 11,000 scientists declare a ‘climate emergency’ - They say the climate crisis is more severe than anticipated and threatens the fate of humanity."_](https://www.yahoo.com/lifestyle/2019-11-05-climate-change-emergency-global-scientists.html?guce_referrer=aHR0cHM6Ly93d3cuYmluZy5jb20v&guce_referrer_sig=AQAAADiJ-Lou1DjELBfLZGqHbwwIhH8LdNWz-XomSBQwLp6_-IBIG9Oq8QQpQKYbjesYGRW_q4Zaou8vDFFz5qjniwdyc-HM9-kyST1X3sARi8r1YwTTC5RRL_GvGaeCCqYDXjA1yydc8ZJUlAZvHCx3FKuwTo6areBsScDanxyV4gDQ&guccounter=2) There are countless headlines, such as the one above, raising questions like: _If the world is in extraordinary danger, aren't we hypocrites talking about saving the world but driving in a car, heating in the winter and eating food transported from far away? Is it really unquestionable science? Are there other problems that might have priority?_ **Since climate affects us all, we need to separate the wheat from the chaff.** In this report we compare claims about climate change made in the media with scientific sources. We focus on claims that seem to diverge significantly. We are convinced that climate change is an important topic that needs to be discussed in a scientific environment. **One needs to look rationally at the data.** Climate change is highly loaded with political interest and "end of the world fears". It can be dangerous to follow gut feeling or the opinions of others blindly. 30:T645,checkbox_emmissionsByCountry.includes("") //if the checkbox is ticked, execute: ? Plot.plot({ height: 500, marginLeft: 170, marginRight: 50, style: {fontSize: "13px"}, x: { axis: "top", //setting axis on top ticks: 0, label: "MtCO₂ per year 2021", labelOffset: 15 }, y: { domain: d3.sort(top_co2_emmision_country_2021, d => -d.CO2).map(d => d.country), //descending order tickSize: 0, label: null }, marks: [ // horizontal bars Plot.barX(top_co2_emmision_country_2021, { x: "CO2", y: "country", fill: "#969696" }), // labels (absolute numbers) Plot.text(top_co2_emmision_country_2021, { x: "CO2", y: "country", text: d => d3.format(",.0f")(d.CO2), textAnchor: "start", dx: 2, dy: 3 }) ] }) : Plot.plot({ //if the checkbox is unticked, execute: height: 500, marginLeft: 170, marginRight: 50, style: {fontSize: "13px"}, x: { axis: "top", //setting axis on top ticks: 0, label: "% of total MtCO₂ per year 2021", labelOffset: 15 }, y: { domain: d3.sort(top_co2_emmision_country_2021, d => -d.CO2).map(d => d.country), //descending order tickSize: 0, label: null }, marks: [ // horizontal bars Plot.barX(top_co2_emmision_country_2021, { x: "CO2", y: "country", fill: "#969696" }), // labels (%) Plot.text(top_co2_emmision_country_2021, { x: "CO2", y: "country", text: d => d3.format(",.2f")(d.CO2_perc), textAnchor: "start", dx: 2, dy: 3 }) ] })31:T483,Cutting Germany's CO2 emissions to zero would lower global emissions by less than 2%. China, India and Russia alone account for about 45% of global CO2 emissions with a growing trend. China alone [started constructing coal powered plants in 2022 with an output of 50 GW](https://energyandcleanair.org/publication/china-permits-two-new-coal-power-plants-per-week-in-2022/#:~:text=Key%20findings%20of%20the%20report&text=level%20since%202015.-,The%20coal%20power%20capacity%20starting%20construction%20in%20China%20was%20six,than%2050%25%20increase%20from%202021.) an increase of 50% compared with 2021 and even more are expected in 2023. Despite the pandemic, [Chinas CO2 emissions increased by 750 million tonnes between 2019 and 2021](https://www.iea.org/news/global-co2-emissions-rebounded-to-their-highest-level-in-history-in-2021), which is more than Germany is emitting in a year. A reasonable way to motivate countries to stop burning fossil fuels is to provide a cheaper energy source. Solar and wind power are already cheaper than fossil fuels in production, but storage costs are high, resulting in higher total costs compared to fossil fuels. 32:T4e7,_We created the visualization below by first using plot.barY() with the input data as an argument to plot the graph. Here we set the "index" column on the x-axis and the "Trend" column on the y-axis. We sorted the data according to the descending y-values and then coloured all the entries with an index larger than 101 in a lighter blue. This is because these entries are the models. We coloured the remaining entries (the observations) in a darker blue. Further, we added labels for the x- and y-axis and set the ticks so that the data is well visualized. Also, we set the grid of the y-axis to 'true' in order to make the horizontal grid lines visible. To match this chart dimensionally with the other charts of this report, we also set the dimensions of the visualization at the beginning of the code._ _To plot the horizontal average lines, we used ruleY and set the values to the respective averages, which we calculated before. We also coloured the dashed lines accordingly. Then we needed to use plot.text() to label the average lines. Here we set the font size and also coloured the text so that it matches the colour of the bars. An important point was to set the position of the text in a way that the text is above and near the dashed lines._33:T5ba,Plot.plot({ width: 1000, marginLeft: 40, y: { label: "↑ 10Y Warming Trend in °C", ticks: [0.0, 0.1, 0.2, 0.3, 0.4, 0.5], //custom ticks not to overload the chart grid: true }, x: { label: "Models and Observations", ticks: [] }, marks: [ // bars Plot.barY(models_vs_observations_indexed_sliced, { x: "index", y: "Trend", sort:{x: "y", reverse:true}, fill: d => d["index"] > 101 ? "#2f7ebc" : "#b5d4e9", // full color palette below //["#f7fbff","#e3eef9","#cfe1f2","#b5d4e9","#93c3df","#6daed5","#4b97c9","#2f7ebc","#1864aa","#0a4a90","#08306b"] title: d => `Name: ${d["Name"]} Trend: ${d["Trend"]}` }), // line: average models Plot.ruleY(models_vs_observations_indexed_sliced, { y: 0.3886, strokeDasharray: "10, 5", strokeWidth: 1, stroke: "#93c3df" }), // text: average models Plot.text(data_years540m_wrangled, { x: 50, y: 0.405, text: ["Avg Models: 0.389"], fontSize: 13, fill: "#93c3df" }), // line: average observations Plot.ruleY(models_vs_observations_indexed_sliced, { y: 0.142, strokeDasharray: "10, 5", strokeWidth: 1, stroke: "#1864aa" }), // text: average observations Plot.text(data_years540m_wrangled, { x: 50, y: 0.1584, text: ["Avg Observations: 0.142"], fontSize: 13, fill: "#1864aa" }), ] })34:T44c,_We plotted the visualization below in Tableau since the data for this chart includes gaps in either the temperature or the CO2 column. This is because we created a joined x-axis for both columns as a timescale. Tableau, therefore, allowed for a more informative visualisation, combining the two line charts._ _We plotted the data of the average temperature change in degrees Celsius and the CO2 ppm data separately as two visualizations and then used the dual axis function. This way we were able to show both lines in a joined visualization. We needed to change the ticks of both axes to enable a comparison of both lines since this is easier when the lines are not too far away from each other. For the x-axis, we also needed to delete the "k" after each number and instead included the word "Thousand" in the title of the axis._ _The vertical dotted lines enable the viewer to compare the two lines more easily, as we used this chart to show that CO2 lags temperature._ _Again, we used the title as a substitute for a legend by colouring the description of each line in the respective colour._35:T584, Plot.plot({ width: 620, height: 170, x: {label: "Million years ago →", domain: d3.sort(data_years540m_wrangled, d => -d["Year (Ma)"]).map(d=>d["Year (Ma)"]), // flipping the axis to have years go from 540 to 0 ticks: [500, 450, 400, 350, 300, 250, 200, 150, 100, 50], // custom ticks not to overload the chart grid: true }, y: { label: "↑ CO2 ppm", tickFormat: d3.format("~s") // 1k instead of 1000 on the y-axis }, marks: [ // CO2 line Plot.lineY(data_years540m_wrangled, { x: "Year (Ma)", y: "CO2_ppm", stroke: "dodgerblue" }), // average historic CO2 level Plot.ruleY(data_years540m_wrangled, { y: 3234, // y-coordinate of the line strokeDasharray: "10, 5", strokeWidth: 0.4, stroke: "#0a4a90" }), // current CO2 level Plot.ruleY(data_years540m_wrangled, { y: 420, // y-coordinate of the line strokeDasharray: "10, 5", strokeWidth: 0.4, stroke: "#0a4a90" }), // text to mark the average CO2 level Plot.text(data_years540m_wrangled, { x: 500, y: 3900, text: ["Avg CO2"], fontSize: 13, fill: "#0a4a90" }), // text to mark the historic CO2 level Plot.text(data_years540m_wrangled, { x: 500, y: 1000, text: ["Current CO2"], fontSize: 13, fill: "#0a4a90" }) ] })36:T71a,In their report, SINTEF reviewed the above-mentioned studies and comes to the conclusion that the statement that "'doubt has been eliminated' on AGW is plainly false". In contrast "the scientific debate may be considered healthy" and a "presentation of [a] two-sided war with a 97-98 % majority consensus and 2-3 % group of sceptics and “deniers” is flawed." (SINTEF report, page 65) In the case of Doran and Kendall Zimmermann 2009: The study is based on a survey, not on evidence. 3146 interviewees responded to the survey. 77 self-identified as climate specialists and answered the question "Do you think human activity is a significant contributing factor in changing mean global temperatures?". 75 of those said "yes" and 2 "no". Hence 97% agreed. In the case of Oreskes 2004 / 2005: She created a consensus arguing that “none of the papers disagreed with the consensus position”. A large part of the papers, which she counted towards the consensus, do not take an explicit position on the matter. (SINTEF report, page 22) Further, the report quotes a talk by Michael Crichton that he held at the California Institute of Technology in 2003: "Historically, the claim of consensus [...] is a way to avoid debate by claiming that the matter is already settled. [...] Let's be clear: the work of science has nothing to do with consensus. [...] Science [...] requires only one investigator who happens to be right, which means that he or she has results that are verifiable by reference to the real world. In science consensus is irrelevant. What is relevant is reproducible results. The greatest scientists in history are great precisely because they broke with the consensus. There is no such thing as consensus science. If it’s consensus, it isn’t science. If it’s science, it isn’t consensus."37:T4b6,After elaborating on each of these claims we found the differences between the media representation and science to be large. During our research, we came across several other facts that also seem to disagree with the media representation of climate change (listed below). This indicates that there is a bias within media to report dramatic findings more often, to exaggerate existing claims or to take findings out of context. That might not be surprising. Yet we find the intensity and magnitude of distortion in this topic worrisome. Also, there seem to be almost ideologic elements at play where the mere questioning of the catastrophic human-made climate change results in slander and defamation. When only one opinion is allowed, scientific work is not possible. Examples of other claims that seem to be misrepresented in the media: [The polar bear population has risen from about 5000 in 1950 to 22000 - 31000 in 2015](https://fee.org/articles/the-myth-that-the-polar-bear-population-is-declining/) [CO2 heat absorption is logarithmic and highly saturated, meaning that doubling CO2 now would only lead to a minimal increase in radiative forcing (page 13)](https://arxiv.org/pdf/2006.03098.pdf)38:T4b8,We noticed that the media distortion of climate change is not random and seems to follow a pattern. Please note that the following is obviously speculative. During our research, we noticed similar patterns as described by [Mike Hulme](https://mikehulme.org/about-me/), a well established climate scientist, who worked for the IPCC. He identifies [4 narratives](https://www.cbc.ca/news/canada/the-four-myths-behind-the-climate-change-debate-1.844967) that he connects with human emotions: 1. Lamenting Eden (nostalgia): Idealizing nature and the state before human influence 2. Presaging the Apocalypse (anxiety): Threatening with "end of the world" scenarios 3. Constructing Babel (pride): Insisting that we are in full control (God like) of the climate through CO2 emission 4. Celebrating Jubilee (need for justice): The need for repenting our sins We found that the claims that we investigated all resonate with one or more of these narratives, hinting at a religious-like mindset interfering with the climate science debate. 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2d:{"title":"Media's Distortion of Climate Change\n","cells":[{"id":3597,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":3490,"value":"displayCaution(\"This report exemplifies high-quality work from a previous year, earning a high grade. It features effective visualizations, clear writing, and thorough analysis. The views, analyses, and conclusions are those of the notebook authors and are not endorsed by FS or the course instructor. The author names are removed for anonymity.\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":3591,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":66,"value":"# Media's Distortion of Climate Change \n\n_\"It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so.”_ (Mark Twain)\n\n **Testing 6 climate change claims from mass media**","pinned":false,"hidden":false,"mode":"md"},{"id":64,"value":"toc({skip: [\"Final Report Template\", \"Media's Distortion of Climate Change\"]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2268,"value":"# Prelude","pinned":false,"hidden":false,"mode":"md"},{"id":532,"value":"## Disclaimer\n\nAs this article touches on some sensitive issues, allow us to be very upfront.\n\nFrom our investigation, we come to the following conclusions:\n\n- The earth warmed by about 1.2 degrees Celsius in the last 150 years\n- CO2 levels rose from about 280 ppm to 420 ppm in the same timeframe\n- CO2 is a greenhouse gas with an absorption band centered at 15, 4.3, 2.7, and 2 μm, with the one at 15 μm being the most significant\n- CO2 is the second most significant greenhouse gas after water vapor\n- The rise in CO2 levels is in large part due to the burning of fossil fuels by humans\n- The CO2 emissions have contributed to some degree to the recent warming\n\nAll of these points seem scientifically sound and we have no intention to doubt them.\n","pinned":false,"hidden":false,"mode":"md"},{"id":7,"value":"## Climate Affects Us All","pinned":false,"hidden":false,"mode":"md"},{"id":239,"value":"$2f","pinned":false,"hidden":false,"mode":"md"},{"id":446,"value":"## Media Representation","pinned":false,"hidden":false,"mode":"md"},{"id":451,"value":"We focus on the following claims:\n\n1. We need to achieve \"Germany’s greenhouse gas emissions and energy transition targets\" to have an impact on climate change.\n2. The earth will warm by \"2 - 4 degrees Celsius by 2100.\"\n3. The climate was stable with a recent human made outburst (Hockey Stick claim).\n4. Al Gore's claim about the relationship between CO2 and temperature based on historic data.\n5. 2016 & 2020 were the hottest years on record and 2022 had the highest CO2 levels in human history.\n6. The science is settled.\n","pinned":false,"hidden":false,"mode":"md"},{"id":19,"value":"## The World We Grew Up In","pinned":false,"hidden":false,"mode":"md"},{"id":159,"value":"\n

\n","pinned":false,"hidden":false,"mode":"html"},{"id":1820,"value":"Since elementary school, we are taught that humans destroy the planet with CO2 emissions that cause the greenhouse effect.\nThe rational and noble conclusion is to do whatever is necessary to stop further destruction. This behavior is already put into practice by the many climate activists that glue themselves to the streets to save the world.\n\nThe representation of climate change in the media seems to fit with the narrative of identifying humankind as a form of cancer on the planet similar to the [discussion about humanity's rapid population growth](https://pubmed.ncbi.nlm.nih.gov/12291996/). An [absolute causality between human influence and climate change](https://www.edf.org/climate/9-ways-we-know-humans-triggered-climate-change) was built into our minds. \n","pinned":false,"hidden":false,"mode":"md"},{"id":3275,"value":"

","pinned":false,"hidden":false,"mode":"html"},{"id":3273,"value":"Photo by Markus Spiske on Unsplash","pinned":false,"hidden":false,"mode":"md"},{"id":24,"value":"# Testing the Claims","pinned":false,"hidden":false,"mode":"md"},{"id":179,"value":"We focused on different claims made by the media and compared them to relevant data from respective studies on these topics. Below you can find a link for each claim to see how the assertion is made in the media. Also, there are studies that guided us to describe the scientific point of view, as well as the data that we used to visualize the findings of the studies.\n\n_To find out how we constructed our datasets from the original sources, please look at the pre-appendix part._","pinned":false,"hidden":false,"mode":"md"},{"id":465,"value":"## Claim 1: We need to achieve [\"Germany’s greenhouse gas emissions and energy transition targets\"](https://www.cleanenergywire.org/factsheets/germanys-greenhouse-gas-emissions-and-climate-targets) to have an impact on climate change.\n","pinned":false,"hidden":false,"mode":"md"},{"id":1890,"value":"### What We Did","pinned":false,"hidden":false,"mode":"md"},{"id":1904,"value":"We accepted the claim of CO2 causing dangerous warming and explored the effects of cutting Germany's emissions to zero on global CO2 emissions.","pinned":false,"hidden":false,"mode":"md"},{"id":1905,"value":"### Studies and Data","pinned":false,"hidden":false,"mode":"md"},{"id":1925,"value":"_We used the following studies and datasets to test this claim:_\n\n[EDGAR - Emissions Database for Global Atmospheric Research](https://edgar.jrc.ec.europa.eu)\n[Data Scource](https://edgar.jrc.ec.europa.eu/booklet/EDGARv7.0_FT2021_fossil_CO2_booklet_2022.xlsx)","pinned":false,"hidden":false,"mode":"md"},{"id":1934,"value":"co2_20211 = await FileAttachment(\"co2_2021-1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1936,"value":"### Visualization and Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":2841,"value":"_We plotted the emissions by country using a bar chart since this is an easy way to compare the different emission levels. We listed the top 12 countries in descending order of CO2 emissions._\n\n_We also included a tickbox below the chart. In its default state, the chart shows the percentage of the total emissions by country on the right side of each bar. When ticking the box, the labels on the right side change into total amounts of CO2 produced in millions of tons._","pinned":false,"hidden":false,"mode":"md"},{"id":454,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1110,"value":"viewof checkbox_emmissionsByCountry = Inputs.checkbox([\"\"], {label: \"Select to show the emmissions in millions of US tons:\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":462,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":2155,"value":"### Conclusion","pinned":false,"hidden":false,"mode":"md"},{"id":2157,"value":"It seems that spending billions to reduce Germany's CO2 emissions is not affecting global CO2 emission levels in any significant way. The global need for cheap energy indicates that **investment in research and development to reduce energy storage cost can make alternative energy sources economically attractive** and thereby reduces emissions measurably. \n\n_Notable contributor of this argument: [Bjorn Lomberg](https://lomborg.com/) and nobel price laureate [William Nordhaus](https://williamnordhaus.com/)_","pinned":false,"hidden":false,"mode":"md"},{"id":1581,"value":"## Claim 2: [**The earth will warm by \"2 - 4 degrees Celsius by 2100.\"**](https://dnr.wisconsin.gov/climatechange/science#:~:text=Since%201880%2C%20average%20global%20temperatures,7.2%20degrees%20Fahrenheit)","pinned":false,"hidden":false,"mode":"md"},{"id":1657,"value":"### What We Did","pinned":false,"hidden":false,"mode":"md"},{"id":1593,"value":"We used a study from the American Geophysical Union (AGU) that tested the observed warming in the last 60 years against the predicted warming of 102 models.","pinned":false,"hidden":false,"mode":"md"},{"id":1659,"value":"### Studies and Data","pinned":false,"hidden":false,"mode":"md"},{"id":408,"value":"_This is the study and the respective dataset:_\n\n[A Test of the Tropical 200- to 300-hPa Warming Rate in Climate Models](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2018EA000401):\n[_Data_](https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1029%2F2018EA000401&file=ess2204-sup-0001-SI-template_form.docx)\n\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":565,"value":"The model_vs_observations dataset compares modeled against measured warming trends in the atmosphere from the last 60 years of the tropical 200 to 300hPa layer.","pinned":false,"hidden":false,"mode":"md"},{"id":486,"value":"model_vs_observations = await FileAttachment(\"model_vs_observations.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1661,"value":"### Visualization and Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":2354,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":1391,"value":"**The warming forecasts are based on models that do not match observations **","pinned":false,"hidden":false,"mode":"md"},{"id":1216,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1730,"value":"This visualization compares the 10-year warming trend in degrees Celsius of each model and each observation. To show the magnitude of the discrepancy, we included the averages of the models and observations.","pinned":false,"hidden":false,"mode":"md"},{"id":1714,"value":"It can be seen that the average predicted 10-year warming of the models is about 2.7 times higher than the average of the observations. The claims made about future warming are built on these models, which show a strong warming bias. Claims built on models that do not match actual observations do not seem reliable. \n\n_Side note: The only model that almost got it right is the Russian one._","pinned":false,"hidden":false,"mode":"md"},{"id":2200,"value":"### Conclusion","pinned":false,"hidden":false,"mode":"md"},{"id":2203,"value":"Claims about future warming based on models that don't match the observations are not reliable and should not be used as the basis for policies.","pinned":false,"hidden":false,"mode":"md"},{"id":1611,"value":"## Claim 3: [**The climate was stable with a recent outburst (Hockey Stick claim).**](https://time.com/6088531/ipcc-climate-report-hockey-stick-curve/)","pinned":false,"hidden":false,"mode":"md"},{"id":1663,"value":"### What We Did","pinned":false,"hidden":false,"mode":"md"},{"id":1624,"value":"We rebuilt a version of the \"Hockey Stick\" by combining temperature reconstructions using tree ring proxies and temperature measurements. ","pinned":false,"hidden":false,"mode":"md"},{"id":1670,"value":"### Studies and Data","pinned":false,"hidden":false,"mode":"md"},{"id":413,"value":"_We used the following studies and datasets to test this claim:_\n\n[Briffa et al. 1998 Northern Hemisphere Temperature Reconstructions](https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=noaa-recon-6224):\n[_Data_](https://www.ncei.noaa.gov/pub/data/paleo/treering/reconstructions/n_hem_temp/briffa98_volcanic_data-noaa.txt)\n\n[GISS Surface Temperature Analysis (GISTEMP v4)](https://data.giss.nasa.gov/gistemp/):\n[_Data_](https://data.giss.nasa.gov/gistemp/tabledata_v4/GLB.Ts+dSST.csv)\n\n[Loehle and McCulloch (2008) \"Correction to: A 2000-Year Global Temperature Reconstruction Based on Non-Tree Ring Proxies,\"](https://www.asc.ohio-state.edu/mcculloch.2/AGW/Loehle/): [Data](https://www.asc.ohio-state.edu/mcculloch.2/AGW/Loehle/LoehleMcC.txt)","pinned":false,"hidden":false,"mode":"md"},{"id":435,"value":"The dataset that we used for the first two visualizations is a combination of the temperature reconstructions from tree rings for the last 600 years and the thermometer temperature measurements since the 1880s. ","pinned":false,"hidden":false,"mode":"md"},{"id":280,"value":"data_years600 = await FileAttachment(\"600years@3.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1749,"value":"The dataset below includes temperature reconstructions from non-tree-ring proxies.","pinned":false,"hidden":false,"mode":"md"},{"id":528,"value":"pollen_2000_years_loehle = await FileAttachment(\"Pollen_2000_years_loehle.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1678,"value":"### Visualization and Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":2357,"value":"_We plotted the visualization below by using the dataset which contains the combined data of both measurements. We set the \"year\" column on the x-axis and set the colour of the line to \"olivedrab\"._\n\n_Then we set the x- and y-labels of the axes and set the y-axis grid to true. We used the axis variable and set it to \"right\" so that the y-axis is plotted on the right side. This is needed to better read the values of a recent temperature outburst._","pinned":false,"hidden":false,"mode":"md"},{"id":1696,"value":"**Creating the notion of a recent and unprecedented outburst in temperature**:","pinned":false,"hidden":false,"mode":"md"},{"id":1408,"value":"Plot.plot({\n width: 1200,\n x: {\n label: \"Year →\", \n tickFormat: d3.format(\".0f\") // changing the tick format for better readability\n },\n y: {\n label: \"↑ Avg. Temperature Change °C\", \n grid: true,\n axis: \"right\",\n },\n // combined misleading line\n marks: [ \n Plot.lineY(data_years600_wrangled_ma, {\n x: \"year\", \n y: \"misleading_chart_data\", \n stroke: \"olivedrab\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2362,"value":"_The visualization below is mainly plotted like the one above. However, the main difference is that we used another lineY method to plot the tree ring data separately from the thermometer data. It is important to notice that we used the moving average columns of both measurements to reduce the variability of both lines. We coloured both lines differently and matched the describing words in the title respectively. This way we did not need any additional legend._","pinned":false,"hidden":false,"mode":"md"},{"id":1422,"value":"**Let us look at the tree ring data and thermometer measurements separately:**","pinned":false,"hidden":false,"mode":"md"},{"id":325,"value":"Plot.plot({\n width: 1200,\n x: {\n label: \"Year →\", \n tickFormat: d3.format(\".0f\")\n },\n y: {\n label: \"↑ Avg. Temperature Change °C\", \n grid: true,\n axis: \"right\"\n },\n marks: [ \n // tree ring line\n Plot.lineY(data_years600_wrangled_ma, {\n x: \"year\", \n y: \"tree_ring_movAvg\", \n stroke: \"olivedrab\"\n }),\n // thermometer measurements line\n Plot.lineY(data_years600_wrangled_ma, {\n x: \"year\", \n y: \"thermometer_movAvg\", \n stroke: \"orange\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2243,"value":"When looking at the data separately, it can be seen that the tree ring data shows a temperature decline while the thermometer measurements show a sharp increase, indicating that a similar outburst could have happened before without being reflected in the tree ring data.\n\nLet us compare the data with temperature reconstructions from other sources.","pinned":false,"hidden":false,"mode":"md"},{"id":2365,"value":"_We created the chart below by almost the same steps as the first chart of this claim. However, the main difference is that we used the dataset containing pollen data instead of the combined measurement data._","pinned":false,"hidden":false,"mode":"md"},{"id":1753,"value":"**Temperature reconstruction based on non-tree-ring proxies suggest much more climate variability**\n\nPlease note that the base period (the 0 tick) of this chart and the chart above are different and are therefore not directly comparable.","pinned":false,"hidden":false,"mode":"md"},{"id":1736,"value":"Plot.plot({\n width: 1200,\n x: {\n label: \"Year →\", \n tickFormat: d3.format(\".0f\")\n },\n y: {\n label: \"↑ Avg. Temperature Change °C\", \n grid: true,\n axis: \"right\"\n },\n marks: [ \n // pollen data\n Plot.lineY(pollen_2000_years_loehle, {\n x: \"Year\", \n y: \"Temp_Anom._Pollen\", \n stroke: \"olivedrab\"\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2260,"value":"### Conclusion","pinned":false,"hidden":false,"mode":"md"},{"id":2262,"value":"Based on the fact that the tree ring data did not capture the recent warming trend and that non-tree-ring proxies show much more climate variability, the claim that the climate was stable with a recent outburst is inaccurate.","pinned":false,"hidden":false,"mode":"md"},{"id":1646,"value":"## Claim 4: [**Al Gore's claim about the relationship between CO2 and temperature based on historic data** (at 3:35)](https://www.youtube.com/watch?v=-JIuKjaY3r4)","pinned":false,"hidden":false,"mode":"md"},{"id":1764,"value":"### What We Did","pinned":false,"hidden":false,"mode":"md"},{"id":2264,"value":"We compared the temperature and CO2 reconstructions of the last 800 thousand years to find out whether there are indications for a causal relationship.","pinned":false,"hidden":false,"mode":"md"},{"id":1767,"value":"### Studies and Data","pinned":false,"hidden":false,"mode":"md"},{"id":416,"value":"\n\n\n\n[EPICA Dome C - 800KYr Deuterium Data and Temperature Estimates](https://www.ncei.noaa.gov/access/paleo-search/study/6080):\n[_Data_](https://www.ncei.noaa.gov/pub/data/paleo/icecore/antarctica/epica_domec/edc3deuttemp2007.txt)\n\n[High-resolution carbon dioxide concentration record 650,000–800,000 years before present](https://www.nature.com/articles/nature06949#MOESM30):\n[Data - Sheet 3. Composite CO2](https://static-content.springer.com/esm/art%3A10.1038%2Fnature06949/MediaObjects/41586_2008_BFnature06949_MOESM31_ESM.xls)\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":575,"value":"The dataset \"800kYears\" comprises the temperature deviation from the average of the last 1000 years (base line of 0 degrees Celsius) and the CO2 parts per million (ppm) for the last 800 thousand years.","pinned":false,"hidden":false,"mode":"md"},{"id":498,"value":"kyears = await FileAttachment(\"800kYears.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1782,"value":"### Visualization and Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":2839,"value":"$34","pinned":false,"hidden":false,"mode":"md"},{"id":2220,"value":"

","pinned":false,"hidden":false,"mode":"html"},{"id":2279,"value":"We zoomed in at 160 thousand years in order to have a better comparison. Based on visual analysis it is possible to see a lag of CO2 levels behind temperature at times. We found two studies that quantified the lag of CO2 behind temperature. One study from 1999 estimates the [temperature lag at 600 +/- 400 years](http://ruby.fgcu.edu/courses/twimberley/EnviroPhilo/IceCoreRecords2.pdf) and one from 2012 argued that the [temperature lag is likely less than 400 years](https://cp.copernicus.org/articles/8/1213/2012/cp-8-1213-2012.pdf).","pinned":false,"hidden":false,"mode":"md"},{"id":2298,"value":"### Conclusion","pinned":false,"hidden":false,"mode":"md"},{"id":2300,"value":"Based on the ice core drillings that Al Gore refers to, it seems that changes in CO2 follow temperature changes most of the time, indicating a possible causal relationship between temperature and CO2 and not vice versa.","pinned":false,"hidden":false,"mode":"md"},{"id":1649,"value":"## Claim 5: [**2016 & 2020 were the hottest years on record**](https://www.noaa.gov/news/2020-was-earth-s-2nd-hottest-year-just-behind-2016#:~:text=NASA%20scientists%2C%20who%20conducted%20a,sharing%20the%20first%2Dplace%20spot.) and [2022 had the highest CO2 levels in human history](https://www.axios.com/2022/05/04/april-sets-record-highest-co2-levels)","pinned":false,"hidden":false,"mode":"md"},{"id":1776,"value":"### What We Did","pinned":false,"hidden":false,"mode":"md"},{"id":1788,"value":"We compared the temperature and CO2 reconstructions from the last 540 million years with current temperature and CO2 levels.","pinned":false,"hidden":false,"mode":"md"},{"id":1779,"value":"### Studies and Data","pinned":false,"hidden":false,"mode":"md"},{"id":1790,"value":"_We built the visualization with data from this source:_","pinned":false,"hidden":false,"mode":"md"},{"id":419,"value":"\n\n[A high-resolution climate simulation dataset for the past 540 million years](https://www.nature.com/articles/s41597-022-01490-4):\n[_Data_](https://www.nature.com/articles/s41597-022-01490-4/tables/2)","pinned":false,"hidden":false,"mode":"md"},{"id":441,"value":"To analyse this claim, we used the dataset from \"A high-resolution climate simulation dataset for the past 540 million years - 2022\". It contains CO2 and temperature reconstructions from the last 540 million years, with one datapoint every 10 million years.","pinned":false,"hidden":false,"mode":"md"},{"id":288,"value":"data_years540m = await FileAttachment(\"A high-resolution climate simulation dataset for the past 540 million years - 2022.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1785,"value":"### Visualization and Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":2845,"value":"_By plotting two different visualizations and putting them close together, we visualized them as one chart. The visualization above shows the temperature levels of the surface and the chart below shows the CO2 in ppm. The timeframe for both visualizations is 540 million years. \nWe also added vertical lines to make it easier to compare the two lines. Here, it was important to set both charts to the same size so that the lines are comparable on the timeline. We also added y-axis labels for both charts, but only one x-axis label for the chart below. This way it is also easier to see the two visualizations as one._\n\n_We added the current average temperature as a dashed line in the first chart and the current CO2 level for the second chart. We also added an average CO2 dashed line for the second chart._","pinned":false,"hidden":false,"mode":"md"},{"id":1492,"value":"**Natural temperature and CO2 levels were much higher throughout most of Earth's history than in the current ice age**","pinned":false,"hidden":false,"mode":"md"},{"id":348,"value":"Plot.plot({\n width: 600,\n height: 170,\n y: {\n label: \"↑ Surface Temperature °C\"\n },\n x: {axis: false,\n domain: d3.sort(data_years540m_wrangled, d => -d[\"Year (Ma)\"]).map(d=>d[\"Year (Ma)\"]),\n // flipping the axis to have years go from 540 to 0\n ticks: [500, 450, 400, 350, 300, 250, 200, 150, 100, 50],\n grid: true},\n marks: [ \n // temperature line\n Plot.lineY(data_years540m_wrangled, {\n x: \"Year (Ma)\", \n y: \"Surface temperature (°C)\", \n stroke: \"orange\"\n }),\n // current temperature\n Plot.ruleY(data_years540m_wrangled, {\n y: 14, // y-coordinate of the line\n strokeDasharray: \"10, 5\",\n strokeWidth: 0.4,\n stroke: \"orange\"\n }),\n // text to mark the current temperature\n Plot.text(data_years540m_wrangled, {\n x: 450,\n y: 15,\n text: [\"Current temperature\"],\n fontSize: 13,\n fill: \"orange\"\n }),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":351,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":2331,"value":"The current temperature and CO2 levels are very low when compared to the last 540 million years. We are actually in the Holocene interglacial period of the Quaternary ice age. The term ice age implies the existence of substantial ice sheets in the northern and southern hemispheres. For the majority of the Earth's history, there was no ice on the poles. The low levels of CO2 in recent times lead to CO2 [starvation in plants](https://royalsocietypublishing.org/doi/10.1098/rstb.1998.0198) with a large impact on the ecosystem. Most [crops reach CO2 saturation between 1000 - 1300 ppm](https://www.ontario.ca/page/supplemental-carbon-dioxide-greenhouses#:~:text=For%20most%20crops%20the%20saturation,well%20as%20for%20lettuce%20production.). That is about three times the current level. The current rise in CO2 led to the [greening of the earth according to Nasa](https://www.nasa.gov/feature/goddard/2016/carbon-dioxide-fertilization-greening-earth).","pinned":false,"hidden":false,"mode":"md"},{"id":3222,"value":"

","pinned":false,"hidden":false,"mode":"html"},{"id":3224,"value":"This image shows the change in leaf area across the globe from 1982-2015.\n\nCredits: Boston University/R. Myneni","pinned":false,"hidden":false,"mode":"md"},{"id":2293,"value":"### Conclusion","pinned":false,"hidden":false,"mode":"md"},{"id":2360,"value":"Even though the claims \"2016 & 2020 were the hottest years on record and 2022 had the highest CO2 levels in human history\" are factually true when looking at the timeframe that is \"on record\" or in \"human history\". Yet, it seems misleading to present it as something that has never happened before or is inherently bad. In particular, higher CO2 levels have many benefits for plants. ","pinned":false,"hidden":false,"mode":"md"},{"id":470,"value":"## Claim 6: [The science is settled.](https://time.com/4308518/climate-change-settled-science/)","pinned":false,"hidden":false,"mode":"md"},{"id":2100,"value":"_[\"A scientific consensus is not an opinion, survey, popularity contest, etc. among scientists. It is entirely dictated by the quality and quantity of evidence published in peer-reviewed journals.\"](https://www.intelligentspeculation.com/blog/the-scientific-consensus)_\n","pinned":false,"hidden":false,"mode":"md"},{"id":2083,"value":"### What We Did","pinned":false,"hidden":false,"mode":"md"},{"id":2102,"value":"We looked at different media claims about the consensus on climate change and identified two studies which are among others repeatedly [quoted as references](https://climate.nasa.gov/scientific-consensus/) in that context: [Oreskes 2004 / 2005](https://www.science.org/doi/10.1126/science.1103618) and [Doran and Kendall Zimmermann 2009](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009EO030002).\nWe compared the consensus claim with the [Sintef Report on\nConsensus and Controversy](https://www.sintef.no/globalassets/upload/teknologi_og_samfunn/teknologiledelse/sintef-report-a24071-consensus-and-controversy.pdf). Sintef is Norway's largest research institute for energy and climate technology and has no apparent financial incentive to go against the \"consensus\".\n","pinned":false,"hidden":false,"mode":"md"},{"id":2108,"value":"### Studies and Data","pinned":false,"hidden":false,"mode":"md"},{"id":2125,"value":"[Oreskes 2004 / 2005](https://www.science.org/doi/10.1126/science.1103618)\n\n[Doran and Kendall Zimmermann 2009](https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009EO030002)\n\n[Sintef Report on Consensus and Controversy](https://www.sintef.no/globalassets/upload/teknologi_og_samfunn/teknologiledelse/sintef-report-a24071-consensus-and-controversy.pdf)","pinned":false,"hidden":false,"mode":"md"},{"id":2129,"value":"### Exploration and Analysis","pinned":false,"hidden":false,"mode":"md"},{"id":2131,"value":"$36","pinned":false,"hidden":false,"mode":"md"},{"id":2239,"value":"### Conclusion","pinned":false,"hidden":false,"mode":"md"},{"id":2242,"value":"Based on the presented data, we do not find the claim that science is settled accurate. On the contrary given the data presented in Claim 2 (models vs. observations), it seems that we are not able to model the climate with a satisfying degree of accuracy, implying that we still don't understand it well.","pinned":false,"hidden":false,"mode":"md"},{"id":42,"value":"## Impact of the Findings Above","pinned":false,"hidden":false,"mode":"md"},{"id":2842,"value":"$37","pinned":false,"hidden":false,"mode":"md"},{"id":44,"value":"# Conclusion","pinned":false,"hidden":false,"mode":"md"},{"id":3120,"value":"$38","pinned":false,"hidden":false,"mode":"md"},{"id":29,"value":"# Pre-Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":596,"value":"## Preprocessing","pinned":false,"hidden":false,"mode":"md"},{"id":1249,"value":"toc({\n title: null,\n headers: \"h4, h5\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1514,"value":"#### Claim 1","pinned":false,"hidden":false,"mode":"md"},{"id":1516,"value":"##### Original Dataset(s)","pinned":false,"hidden":false,"mode":"md"},{"id":1521,"value":"The original dataset is the Excel sheet \"fossil_CO2_totals_by_country\" from this [source](https://edgar.jrc.ec.europa.eu/booklet/EDGARv7.0_FT2021_fossil_CO2_booklet_2022.xlsx). We copied the 2021 CO2 emissions in a separate CSV-file.","pinned":false,"hidden":false,"mode":"md"},{"id":371,"value":"co2_2021 = await FileAttachment(\"co2_2021-1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1535,"value":"##### Preprocessing steps","pinned":false,"hidden":false,"mode":"md"},{"id":1528,"value":"Then we loaded the file into Observable and used the Wrangler to filter for the most emitting countries. We picked 456 as the lower bound so that Germany and some other countries are included as reference points. Then we ordered the countries by descending CO2 emissions, renamed the column and derived the percentage of total emissions. Further, we filtered for values smaller than 35860 so we did not include the total amount. Also, we filtered out \"International Shipping\" and the combined \"EU27\".","pinned":false,"hidden":false,"mode":"md"},{"id":374,"value":"top_co2_emmision_country_2021 =\naq.from(co2_2021)\n\t.filter(d => d[\"2021\"] > 456)\n\t.orderby(aq.desc(\"2021\"))\n\t.rename({2021: \"CO2\"})\n\t.derive({CO2_perc: d => (d.CO2 / 37857.576)*100})\n\t.filter(d => d[\"CO2\"] < 35860)\n\t.rename({Country: \"country\"})\n\t.filter(d => d[\"country\"] !== \"International Shipping\")\n\t.filter(d => d[\"country\"] !== \"EU27\")\n\t.objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1537,"value":"_This is the final dataset:_","pinned":false,"hidden":false,"mode":"md"},{"id":1539,"value":"top_co2_emmision_country_2021","pinned":true,"hidden":false,"mode":"ojs"},{"id":1024,"value":"#### Claim 2","pinned":false,"hidden":false,"mode":"md"},{"id":1226,"value":"##### Original Dataset(s)","pinned":false,"hidden":false,"mode":"md"},{"id":1028,"value":"The original data was in a Word document. We copied the data from the Word document into an Excel file and saved it as a CSV-file. The file was not correctly loaded into Observable as you can see below. The original file has 7 columns that were squashed into 3.","pinned":false,"hidden":false,"mode":"md"},{"id":1037,"value":"modelvsobs3 = await FileAttachment(\"ModelvsObs@4.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1230,"value":"##### Preprocessing steps","pinned":false,"hidden":false,"mode":"md"},{"id":1084,"value":"We added a title \"Name\" for the first column and deleted all columns except the \"Name\" and \"Trend\" columns. Also, we added the average of the models and observations in Excel as new rows. In the dataset, there are several entries for models with the same name. For Tableau we grouped the \"Name\" field by the different names and then calculated the average. However, in Observable, we plotted every row so that there are more entries in the visualization in Observable than in Tableau.\n\n_This is the final dataset after the preprocessing in Excel:_","pinned":false,"hidden":false,"mode":"md"},{"id":1082,"value":"model_vs_observations1 = await FileAttachment(\"model_vs_observations.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1200,"value":"We added indexes to each model to plot all the measurements separately, as some models contain several measurements.","pinned":false,"hidden":false,"mode":"md"},{"id":1186,"value":"models_vs_observations_indexed = model_vs_observations.map((d, i) => ({ ...d, index: i }));","pinned":true,"hidden":false,"mode":"ojs"},{"id":1295,"value":"avg_models = models_vs_observations_indexed","pinned":true,"hidden":false,"mode":"ojs"},{"id":1252,"value":"We also removed the last two rows (averages of models and observations) to plot the bar chart.","pinned":false,"hidden":false,"mode":"md"},{"id":1247,"value":"models_vs_observations_indexed_sliced = models_vs_observations_indexed.slice(0, -2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1062,"value":"#### Claim 3","pinned":false,"hidden":false,"mode":"md"},{"id":1173,"value":"##### Original Dataset(s)\nFor this dataset we used three datasets in different formats:\n\nThe first dataset is the tree ring data in txt format.","pinned":false,"hidden":false,"mode":"md"},{"id":1098,"value":"nhemtemp_dataNoaa = FileAttachment(\"nhemtemp_data-noaa.txt\").text()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1114,"value":"The second was an Excel file with the temperature measurements. For Observable to read this dataset correctly, we needed to delete the first row. Afterwards, we saved the data as a CSV-file and loaded it into Observable.","pinned":false,"hidden":false,"mode":"md"},{"id":1101,"value":"Global_temp = await FileAttachment(\"GLB@1.Ts+dSST.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1158,"value":"The third dataset \"Pollen_2000_years_loehle1\" is a csv-file.","pinned":false,"hidden":false,"mode":"md"},{"id":3262,"value":"pollen_2000_years_loehle1 = await FileAttachment(\"Pollen_2000_years_loehle.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1220,"value":"##### Preprocessing steps","pinned":false,"hidden":false,"mode":"md"},{"id":1135,"value":"To process the data we used Wrangler on Global_temp to select the \"Year\" and \"J-D\" (temperature change within a year) columns. We saved the result of Wrangler as \"Yearly_temp_change\".","pinned":false,"hidden":false,"mode":"md"},{"id":1128,"value":"Yearly_temp_change = aq.from(Global_temp)\n\t.select('Year','J-D')\n\t.objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1109,"value":"Then we combined the NHLMT (surface air temperature reconstruction based on tree rings) from the txt-file with the \"J-D\" column from Yearly_temp_change and the \"MixR\" Column from \"Global Mean CO2 Mixing Ratios (ppm): Observations\". We joined the columns on \"Year\" and \"age_CE\" in Excel. \n\nWe used the result of this join for the next preprocessing steps with Wrangler.\nHere we renamed the columns respectively.","pinned":false,"hidden":false,"mode":"md"},{"id":291,"value":"data_years600_wrangled = aq.from(data_years600)\n .rename({\"Year\": \"year\"})\n .rename({\"Briffa et al. 1998\": \"temperature_tree_ring\"})\n .rename({\"Temp Measurements\": \"temperature_thermometer\"})\n\t.objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":306,"value":"In order to focus on the trend and to minimize the noise, we calculated a 10-year moving average column for tree ring data:","pinned":false,"hidden":false,"mode":"md"},{"id":311,"value":"column_tree_ring_movAvg = fc.indicatorMovingAverage()\n .period(10)\n .value(d => d.temperature_tree_ring)\n (data_years600_wrangled)","pinned":true,"hidden":false,"mode":"ojs"},{"id":314,"value":"We did the same for the thermometer data:","pinned":false,"hidden":false,"mode":"md"},{"id":316,"value":"column_thermometer_movAvg = fc.indicatorMovingAverage()\n .period(10)\n .value(d => d.temperature_thermometer)\n (data_years600_wrangled) ","pinned":true,"hidden":false,"mode":"ojs"},{"id":319,"value":"Then we mapped the newly created moving average columns to our data_years600_wrangled dataset:","pinned":false,"hidden":false,"mode":"md"},{"id":321,"value":"data_years600_wrangled_ma = data_years600_wrangled.map( (d, i) => ({ \n ...d,\n tree_ring_movAvg: column_tree_ring_movAvg[i], // tree ring moving average data\n thermometer_movAvg: column_thermometer_movAvg[i], // thermometer moving average data\n misleading_chart_data: d.year < 1940 ? column_tree_ring_movAvg[i] : column_thermometer_movAvg[i]\n // another joint column to plot an alternative 'misleading' chart \n }))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1238,"value":"_This is the final dataset that we used for our visualizations:_","pinned":false,"hidden":false,"mode":"md"},{"id":1243,"value":"data_years600_wrangled_ma","pinned":true,"hidden":false,"mode":"ojs"},{"id":1233,"value":"#### Claim 4","pinned":false,"hidden":false,"mode":"md"},{"id":1337,"value":"##### Original Dataset(s)","pinned":false,"hidden":false,"mode":"md"},{"id":1293,"value":"For the visualization of this topic, we combined two different datasets.\n\nThe first dataset is the temperature reconstruction from EPICA Dome C Ice Core 800KYr Deuterium Data and Temperature Estimates.","pinned":false,"hidden":false,"mode":"md"},{"id":1318,"value":"edc3deuttemp2007 = FileAttachment(\"edc3deuttemp2007.txt\").text()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1299,"value":"The second dataset is the sheet \"3. Composite CO2\" from the [supplementary Excel file in the study \"High-resolution carbon dioxide concentration record 650,000-800,000 years before present\"](https://static-content.springer.com/esm/art%3A10.1038%2Fnature06949/MediaObjects/41586_2008_BFnature06949_MOESM31_ESM.xls). This sheet contains the CO2 in ppmv for the last 800 thousand years.","pinned":false,"hidden":false,"mode":"md"},{"id":1333,"value":"composite_co2_800k = FileAttachment(\"Composite_CO2_800k.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1352,"value":"##### Preprocessing steps","pinned":false,"hidden":false,"mode":"md"},{"id":1389,"value":"To create the dataset, we deleted the empty rows from the txt file. Then we copied the \"Age\" and \"Temperature\" columns from the txt file into an Excel file. Below we copied the \"EDC3_gas_a (yr)\" and the \"CO2 (ppmv)\" columns. We put the year values into the same column and then ordered all values by ascending age.","pinned":false,"hidden":false,"mode":"md"},{"id":1417,"value":"_This is the final dataset:_","pinned":false,"hidden":false,"mode":"md"},{"id":1415,"value":"data_years800k = await FileAttachment(\"800kYears.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":338,"value":"#### Claim 5","pinned":false,"hidden":false,"mode":"md"},{"id":1302,"value":"##### Original Dataset(s)","pinned":false,"hidden":false,"mode":"md"},{"id":340,"value":"We copied the [data](https://www.nature.com/articles/s41597-022-01490-4/tables/2) for 540m years into a CSV and uploaded it into observable.\n\n_This is the original dataset:_","pinned":false,"hidden":false,"mode":"md"},{"id":1284,"value":"data_years540m","pinned":true,"hidden":false,"mode":"ojs"},{"id":1304,"value":"##### Preprocessing steps","pinned":false,"hidden":false,"mode":"md"},{"id":1289,"value":"We wrangled the \"data_years540m\" dataset and renamed the column \"CO2 (x280 ppmv)\" to \"CO2-multiples\". This is because we wanted to show this column in the absolute ppmv amount. For this, we also needed to multiply the values of the column by 280.","pinned":false,"hidden":false,"mode":"md"},{"id":343,"value":"data_years540m_wrangled = aq.from(data_years540m)\n\t.rename({\"CO2 (×280 ppmv)\": \"CO2_multiples\"})\n\t.derive({CO2_ppm: d => d.CO2_multiples * 280}) // converting CO2 measurements to parts per million\n\t.objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1312,"value":"_This is the final dataset that we used for our visualizations:_","pinned":false,"hidden":false,"mode":"md"},{"id":1310,"value":"data_years540m_wrangled","pinned":true,"hidden":false,"mode":"ojs"},{"id":51,"value":"# Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":53,"value":"import {toc} from \"@nebrius/indented-toc\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":72,"value":"import {howTo} from \"@clokman/howto\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":294,"value":"import {Wrangler, op} from \"@observablehq/data-wrangler\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":3267,"value":"fc = require('d3fc')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1458,"value":"import {rangeSlider} from '@mootari/range-slider'","pinned":true,"hidden":false,"mode":"ojs"},{"id":3585,"value":"import {displayCaution} from \"@mkfreeman/utilities\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W39","stdlib":"1","titling":"auto"}