What is going on with methane?

Human activities have significantly altered the Earth’s carbon cycle and energy budget through the release of anthropogenic greenhouse gases (GHG), particularly carbon dioxide (CO₂) and methane (CH₄). While we have a good understanding of anthropogenic sources of CO2 at national to continental scales, uncertainties surrounding CH4 are an order of magnitude higher, as sources range from agriculture and waste management to fossil fuel production and use (Turner, Frankenberg, and Kort 2019; Saunois et al. 2017). The recent acceleration in the methane growth rate creates an urgency to act, yet we do not even understand the relative contribu- tions of natural climate feedbacks (Zhang et al. 2023) and anthropogenic sources (Jackson et al. 2020).

CH₄ data from NOAA

Ground based in-situ data provide the most accurate information on the recent rise in global methane. Here you can observe the global rise, stagnation and recent acceleration of the global methane abundances, obtained directly from NOAA’s Monthly CH₄ concentration data, globally averaged per month (black) and a 12 months running mean. Stagnation, renewed increase and acceleration periods are indicated in blue, yellow, and red, respectively.

fileUrl = 'https://corsproxy.io/?' + encodeURIComponent('https://gml.noaa.gov/webdata/ccgg/trends/ch4/ch4_mm_gl.csv');
//fileUrl = `/data/subset_data.csv`;

csvText = d3.text(fileUrl)

processedData = {
  const lines = csvText.split('\n').filter(line => line.trim().length > 0 && !line.startsWith('#'));
  return d3.csvParse(lines.join('\n'),d3.autoType);
}

processedData2 = processedData.map(d => ({
  ...d,
  Date: new Date(`${d.year}-${d.month}-01`) // Parsing year and month into a Date
}));

start = new Date(1980, 0)
end   = new Date(2024,0)

viewof slider = rangeSlider({
  min: 0,
  max: d3.timeDay.count(start, end),
  step: 1,
  format: d => formatDate(d3.timeDay.offset(start, d)),
  title: 'Pick a date range',
})

startDate = d3.timeDay.offset(start, slider[0])
stopDate  = d3.timeDay.offset(start, slider[1])

filtered = processedData2.filter(function(s2) {
  return s2.Date >= startDate && s2.Date <= stopDate;
})

import { rangeSlider as rangeSlider } from '@mootari/range-slider'

formatDate = d3.timeFormat("%Y-%m-%d")

yMin = d3.min(filtered, d => d.average); // Get the minimum value of the 'average' field
yMax = d3.max(filtered, d => d.average);

stagnationDate = new Date("2005-01-01") 
increaseDate   = new Date("2010-01-01") 
accelerationDate = new Date("2015-01-01") 
topMargin= 1750

function adjustTextPosition(plotDomain, fixedDate, defaultPosition) {
  if (fixedDate < plotDomain[0] || fixedDate > plotDomain[1]) {
    // Adjust the position to be within the domain
    // For example, set it to the start or end of the domain
    return plotDomain[0]; // or plotDomain[1]
  }
  return defaultPosition;
}

Plot.plot({
    width,
  y: {
    grid: true,
    label: "CH₄ (ppb)",
    domain: [yMin, yMax] // Set the y-axis domain
  },
  x: {
    grid: true,
    label: "time",
    domain: [d3.min(filtered, d => d.Date), d3.max(filtered, d => d.Date)] // Set the y-axis domain
  },
  color: { legend: true },
  style: { fontSize: "12px",},
  marks: [
    Plot.rectY([{}], { 
      x1: new Date("2000-01-01"), // lower x bound of your overlay
      x2: new Date("2007-01-01"), // upper x bound of your overlay
      y1: yMin,
      y2: yMax,
      fill: "blue", // color of the overlay
      fillOpacity: 0.1
    }),
    Plot.rectY([{}], { 
      x1: new Date("2007-01-01"), // lower x bound of your overlay
      x2: new Date("2015-01-01"), // upper x bound of your overlay
      y1: yMin,
      y2: yMax,
      fill: "yellow", // color of the overlay
      fillOpacity: 0.1
    }),
    Plot.rectY([{}], { 
      x1: new Date("2015-01-01"), // lower x bound of your overlay
      x2: new Date("2024-01-01"), // upper x bound of your overlay
      y1: yMin,
      y2: yMax,
      fill: "red", // color of the overlay
      fillOpacity: 0.1
    }),
    Plot.line(filtered, { 
      x: "Date", 
      y: "average",
      title: (d) =>
        `${formatDate(d.Date)} \n CH₄: ${d.average} +/- ${d.average_unc} ppb`, // \n makes a new line
    }),
    Plot.lineY(filtered, Plot.windowY({k: 12, reduce: "mean"}, {x: "Date", y: "average", stroke: "red"})),
    Plot.text([{}], {
      x: stagnationDate,
      y: 1820,
      textAnchor: "middle",
      dy: "-0.5em", // Adjust vertical position
      fontWeight: "bold",
      text: "Stagnation Period"
    })
  ]
})

import {Plot} from "@mkfreeman/plot-tooltip"

References

Jackson, R B, M Saunois, P Bousquet, J G Canadell, B Poulter, A R Stavert, P Bergamaschi, Y Niwa, A Segers, and A Tsuruta. 2020. “Increasing Anthropogenic Methane Emissions Arise Equally from Agricultural and Fossil Fuel Sources.” Environmental Research Letters 15 (7): 071002. https://doi.org/10.1088/1748-9326/ab9ed2.

Saunois, Marielle, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G Canadell, Edward J Dlugokencky, et al. 2017. “Variability and Quasi-Decadal Changes in the Methane Budget over the Period 2000–2012.” Atmospheric Chemistry and Physics 17 (18): 11135–61. https://doi.org/10.5194/essd-2016-25-rc2.

Turner, Alexander J, Christian Frankenberg, and Eric A Kort. 2019. “Interpreting Contemporary Trends in Atmospheric Methane.” Proceedings of the National Academy of Sciences 116 (8): 2805–13. https://doi.org/10.1073/pnas.1814297116.

Zhang, Zhen, Benjamin Poulter, Andrew F. Feldman, Qing Ying, Philippe Ciais, Shushi Peng, and Xin Li. 2023. “Recent Intensification of Wetland Methane Feedback.” Nature Climate Change 13 (5): 430–33. https://doi.org/10.1038/s41558-023-01629-0.