IPCC (Intergovernmental Panel on Climate Change) AR4 Synthesis Report
Learn more about Teaching Climate Literacy and Energy Awareness»
See how this Static Visualization supports the Next Generation Science Standards»
Middle School: 2 Cross Cutting Concepts, 1 Science and Engineering Practice
High School: 1 Disciplinary Core Idea, 2 Cross Cutting Concepts, 3 Science and Engineering Practices
About Teaching Climate Literacy
Other materials addressing 4d
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 |
- For background on this figure, see it in the context of the IPCC AR4 Synthesis Report see http://www.ipcc.ch/publications_and_data/ar4/syr/en/spms1.html
- Figure caption reads: Observed changes in (a) global average surface temperature; (b) global average sea level from tide gauge (blue) and satellite (red) data and (c) Northern Hemisphere snow cover for March-April. All differences are relative to corresponding averages for the period 1961-1990. Smoothed curves represent decadal averaged values, while circles show yearly values. The shaded areas are the uncertainty intervals estimated from a comprehensive analysis of known uncertainties (a and b) and from the time series (c).
About the Science
- This visualization of several critical data sets is from the IPCC Fourth Assessment (AR4) Synthesis Report from 2007 based on figures and data sources in the Working Group 1 report.
- Comments from expert scientist: Figure contains important data and is accurate, but page contains no caption, references or other info to help the user.
About the Pedagogy
- These datasets provide the critical evidence for the primary impacts of the recent rise in atmospheric carbon dioxide concentrations. They will be central to any discussion of climate change.
- Teacher will need to explain the vertical axis label (difference from 1961-1990) to younger students.
Technical Details/Ease of Use
- This is a simple and direct visualization of three main climate change datasets with yearly values, decadal averaged values, and uncertainty intervals.
Related URLs These related sites were noted by our reviewers but have not been reviewed by CLEANFor background on this figure see it in the context of the IPCC AR4 Synthesis Report - http://www.ipcc.ch/publications_and_data/ar4/syr/en/spms1.html.
Next Generation Science Standards See how this Static Visualization supports:
Cross Cutting Concepts: 2
MS-C2.1:Relationships can be classified as causal or correlational, and correlation does not necessarily imply causation.
MS-C2.3:Phenomena may have more than one cause, and some cause and effect relationships in systems can only be described using probability.
Disciplinary Core Ideas: 1
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.
Cross Cutting Concepts: 2
HS-C2.1:Empirical evidence is required to differentiate between cause and correlation and make claims about specific causes and effects.
HS-C2.2:Cause and effect relationships can be suggested and predicted for complex natural and human designed systems by examining what is known about smaller scale mechanisms within the system.
Science and Engineering Practices: 3
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.
HS-P4.3:Consider limitations of data analysis (e.g., measurement error, sample selection) when analyzing and interpreting data