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Time History of Atmospheric CO2

NOAA Earth System Research Laboratory, Carbon Tracker Program

This animated visualization represents a time history of atmospheric carbon dioxide in parts per million (ppm) from 1979 to 2016, and then back in time to 800,000 years before the present.

Animation length: 3:15 minutes.

Learn more about Teaching Climate Literacy and Energy Awareness»

ngssSee how this Simulation/Interactive supports the Next Generation Science Standards»
Middle School: 2 Disciplinary Core Ideas, 4 Cross Cutting Concepts, 5 Science and Engineering Practices
High School: 6 Disciplinary Core Ideas, 4 Cross Cutting Concepts, 4 Science and Engineering Practices

Climate Literacy
About Teaching Climate Literacy

Observations are the foundation for understanding the climate system
About Teaching Principle 5
Other materials addressing 5b
Increased GHG concentrations in atmosphere will remain high for centuries and affect future climate
About Teaching Principle 6
Other materials addressing 6b
Human activities have increased GHG levels and altered global climate patterns
About Teaching Principle 6
Other materials addressing 6c

Notes From Our Reviewers The CLEAN collection is hand-picked and rigorously reviewed for scientific accuracy and classroom effectiveness. Read what our review team had to say about this resource below or learn more about how CLEAN reviews teaching materials
Teaching Tips | Science | Pedagogy | Technical Details

Teaching Tips

  • There are multiple data sets represented in the visualization and thus it will need explanation by the teacher.
  • This visualization would best be shown to an entire class with narration by the teacher as opposed to students looking at the visualization by themselves. College-level students would not need as much support.

About the Science

  • Visualization begins with data from 1979, showing the seasonal variations in atmospheric CO2 as well as the global distribution through the year. The animation continues to the present, showing the rise in CO2. Alongside the main graph, a cumulative plot of CO2 animates the Keeling curve over time. The animation then moves into exploring pre-industrial and ice age atmospheric CO2 concentrations.
  • This animation is called the "pumphandle" because the seasonal up/down of CO2 resembles the pumping of an old-fashioned well handle.
  • At the time of review, the data was current (2016). This resource may update automatically as new data becomes available.
  • Comments from expert scientist: This is a nice animation showing the measurements of atmospheric CO2, and the increase through time since measurements began at NOAA in 1979. It clearly shows the increase through time and the overlying seasonal cycle in the Northern hemisphere. This animation shows the actual measurements made at NOAA/ESRL CO2 measurement sites around the world that have been and continue to be used to determine the atmospheric CO2 concentration.

About the Pedagogy

  • Animation is data-rich and students will likely need multiple viewings. Even if students do not understand ppm, the visual impact of the increase in CO2 concentration since pre-industrial times is hard to miss.
  • Comment from expert scientist: This presents the data nicely, but includes very little information about what it means. Educators wanting to use this resource may have to look elsewhere (e.g. on the linked website) to find more information.
  • See http://www.esrl.noaa.gov/gmd/ccgg/carbontracker/ for more information on the Carbon Tracker.

Technical Details/Ease of Use

  • Excellent technical quality. Site recommends viewing in full screen at HD.
  • Other tabs above the graph show related graphics and data.

Next Generation Science Standards See how this Simulation/Interactive supports:

Middle School

Disciplinary Core Ideas: 2

MS-ESS3.D1:Human activities, such as the release of greenhouse gases from burning fossil fuels, are major factors in the current rise in Earth’s mean surface temperature (global warming). Reducing the level of climate change and reducing human vulnerability to whatever climate changes do occur depend on the understanding of climate science, engineering capabilities, and other kinds of knowledge, such as understanding of human behavior and on applying that knowledge wisely in decisions and activities.

MS-ESS3.D:Global Climate Change

Cross Cutting Concepts: 4

Stability and Change, Patterns, Cause and effect, Scale, Proportion and Quantity

MS-C1.4:Graphs, charts, and images can be used to identify patterns in data.

MS-C2.3:Phenomena may have more than one cause, and some cause and effect relationships in systems can only be described using probability.

MS-C3.1:Time, space, and energy phenomena can be observed at various scales using models to study systems that are too large or too small.

MS-C7.3:Stability might be disturbed either by sudden events or gradual changes that accumulate over time.

Science and Engineering Practices: 5

Analyzing and Interpreting Data, Asking Questions and Defining Problems

MS-P1.1:Ask questions that arise from careful observation of phenomena, models, or unexpected results, to clarify and/or seek additional information.

MS-P1.3:Ask questions to determine relationships between independent and dependent variables and relationships in models.

MS-P4.1:Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships.

MS-P4.2:Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships.

MS-P4.4:Analyze and interpret data to provide evidence for phenomena.

High School

Disciplinary Core Ideas: 6

HS-ESS2.D2:Gradual atmospheric changes were due to plants and other organisms that captured carbon dioxide and released oxygen.

HS-ESS2.D3:Changes in the atmosphere due to human activity have increased carbon dioxide concentrations and thus affect climate.

HS-ESS2.D4:Current models predict that, although future regional climate changes will be complex and varied, average global temperatures will continue to rise. The outcomes predicted by global climate models strongly depend on the amounts of human-generated greenhouse gases added to the atmosphere each year and by the ways in which these gases are absorbed by the ocean and biosphere.

HS-ESS3.D2:Through computer simulations and other studies, important discoveries are still being made about how the ocean, the atmosphere, and the biosphere interact and are modified in response to human activities.

HS-LS2.B3:Photosynthesis and cellular respiration are important components of the carbon cycle, in which carbon is exchanged among the biosphere, atmosphere, oceans, and geosphere through chemical, physical, geological, and biological processes.

HS-PS3.D2:The main way that solar energy is captured and stored on Earth is through the complex chemical process known as photosynthesis.

Cross Cutting Concepts: 4

Patterns, Cause and effect, Scale, Proportion and Quantity, Stability and Change

HS-C1.5:Empirical evidence is needed to identify patterns.

HS-C2.1:Empirical evidence is required to differentiate between cause and correlation and make claims about specific causes and effects.

HS-C3.1:The significance of a phenomenon is dependent on the scale, proportion, and quantity at which it occurs.

HS-C7.2:Change and rates of change can be quantified and modeled over very short or very long periods of time. Some system changes are irreversible.

Science and Engineering Practices: 4

Asking Questions and Defining Problems, Analyzing and Interpreting Data

HS-P1.1:Ask questions that arise from careful observation of phenomena, or unexpected results, to clarify and/or seek additional information.

HS-P1.3:ask questions to determine relationships, including quantitative relationships, between independent and dependent variables

HS-P4.1:Analyze data using tools, technologies, and/or models (e.g., computational, mathematical) in order to make valid and reliable scientific claims or determine an optimal design solution.

HS-P4.2:Apply concepts of statistics and probability (including determining function fits to data, slope, intercept, and correlation coefficient for linear fits) to scientific and engineering questions and problems, using digital tools when feasible.

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