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Graphing the Extent of Sea Ice in the Arctic and Antarctic

Randy Russell, Windows to the Universe

In this activity, students learn about sea ice extent in both polar regions (Arctic and Antarctic). They start out by forming a hypothesis on the variability of sea ice, testing the hypothesis by graphing real data from a recent 3-year period to learn about seasonal variations and over a 25-year period to learn about longer-term trends, and finish with a discussion of their results and predictions.

Activity takes about 30-45 minutes.

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Climate Literacy
About Teaching Climate Literacy

Global warming and especially arctic warming is recorded in natural geological and historic records
About Teaching Principle 4
Other materials addressing 4e
Effects of climate change on water cycle and freshwater availability
About Teaching Principle 7
Other materials addressing 7b

Excellence in Environmental Education Guidelines

2. Knowledge of Environmental Processes and Systems:2.1 The Earth as a Physical System:A) Processes that shape the Earth
Other materials addressing:
A) Processes that shape the Earth.

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

  • Intro to activity: Ideally educator would start activity by introducing how lesson fits into climate science (albedo, ocean circulations, migration patterns).
  • Wrapping up the activity: "Why do we care about sea ice extent? How does this affect life on Earth?"
  • Information on sea ice and sea ice formation should be provided by the educator as well as information on what the role of sea ice is for global warming and the thermohaline circulation.
  • Educators might want to copy and paste data into Excel format to include a technology piece.
  • Up-to-date data and imagery is available from the National Snow and Ice Data Center (NSIDC) site and can augment this activity.

About the Science

  • Carefully designed activity that introduces students to the concept of seasonality of sea ice and its extent, both in terms of seasonal variations and longer term trends.
  • Quality of data is excellent (well-referenced, up-to-date).
  • Information on more current data is provided in activity.
  • Great practice - have the students make predictions on the graph before plotting the data. This will address the misconceptions that the maximum sea ice extent occurs during the coldest month (December) and the minimum sea ice extent occurs during the warmest month (June), which is not the case.
  • Lesson provides a "teachable moment" to address the misconception of similar seasons in the two hemispheres.

About the Pedagogy

  • Students are using the scientific process of forming a hypothesis, collecting data, and interpreting the results.
  • Forming a hypothesis, graphing data and discussing results will engage students with different learning styles.
  • Very thorough background materials and educator's notes provided.
  • Great extrapolation at the end of the activity of predicting sea ice extent into the future.

Technical Details/Ease of Use

  • Clear, concise writing and well thought out and organized activity - ready to use.

Disciplinary Core Ideas

MS-ESS2.D1: Weather and climate are influenced by interactions involving sunlight, the ocean, the atmosphere, ice, landforms, and living things. These interactions vary with latitude, altitude, and local and regional geography, all of which can affect oceanic and atmospheric flow patterns.

HS-ESS2.E1: The many dynamic and delicate feedbacks between the biosphere and other Earth systems cause a continual co-evolution of Earth’s surface and the life that exists on it.

Science and Engineering Practices

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.

MS-P4.7: Analyze and interpret data to determine similarities and differences in findings.

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-P4.3: Consider limitations of data analysis (e.g., measurement error, sample selection) when analyzing and interpreting data

HS-P4.4: Compare and contrast various types of data sets (e.g., self-generated, archival) to examine consistency of measurements and observations.

HS-P4.5: Evaluate the impact of new data on a working explanation and/or model of a proposed process or system.

Cross-Cutting Concepts

MS-C5.2: Within a natural or designed system, the transfer of energy drives the motion and/or cycling of matter.

MS-C7.1: Explanations of stability and change in natural or designed systems can be constructed by examining the changes over time and forces at different scales, including the atomic scale.

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

MS-C2.2: Cause and effect relationships may be used to predict phenomena in natural or designed systems.

HS-C1.4: Mathematical representations are needed to identify some patterns.

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

HS-C3.5: Algebraic thinking is used to examine scientific data and predict the effect of a change in one variable on another (e.g., linear growth vs. exponential growth).

HS-C5.2: Changes of energy and matter in a system can be described in terms of energy and matter flows into, out of, and within that system.

HS-C7.1: Much of science deals with constructing explanations of how things change and how they remain stable.

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.

HS-C7.3: Feedback (negative or positive) can stabilize or destabilize a system.

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Graphing the Extent of Sea Ice in the Arctic and Antarctic --Discussion  

The Fall of 2010 was the third lowest sea ice extent since records have been kept, beginning in the early 1970s, according to the National Snow and Ice Data Center: http://nsidc.org/news/press/20101004_minimumpr.html

This activity from Windows to the Universe (or W2U to those familiar with it) is one of several in the CLEAN collection, and it's a great intro that has learners using real data and getting an idea of how scientists measure the seasonal changes in sea ice.

Other sea ice activities that could potentially be very complementary include the Teachers Domain multimedia activity, http://cleanet.org/resources/41836.html, and the Earth Exploration Toolbook chapter: Wither Sea Ice? http://cleanet.org/resources/41826.html

Has anyone had a chance to use these activities? If so, do you have any feedback? If you haven't, try them out and let us know what you find.


Share edittextuser=91 post_id=13191 initial_post_id=0 thread_id=3862

Depending on when these topics are taught in your state's math curriculum (Grade 8 in TN), you might have students create scatterplots and estimate trend lines for each of the months with data in Tables 3 or 4. (Trend lines can help students ignore the "fluctuations" in the data from year to year.)


Share edittextuser=4078 post_id=13203 initial_post_id=0 thread_id=3862

Mary- Thanks for the ideas. Do your middle school students know how to analyze scatterplots? If so, what sort of tips could you offer to help them grasp the relevance of the data. Your point about trend lines is really spot on.

Anyone else have thoughts about making sea ice data accessible to students in classrooms?


Share edittextuser=91 post_id=13338 initial_post_id=0 thread_id=3862

Even college students understand trend lines better if they plot their own scatterplots on graph paper instead of using Excel, even though the trend lines that they estimate are not as accurate.


Share edittextuser=4078 post_id=13344 initial_post_id=0 thread_id=3862

Mary - interesting you say that... there is scientific evidence that one of the single best predictors of student success in college-level science classes is the frequency of self-plotting of data in middle school and high school... it's a study done by the Science Ed department at the Harvard Smithsonian Center for Astrophysics called FICSS.


Share edittextuser=3705 post_id=13360 initial_post_id=0 thread_id=3862

This is a nice thing about this exercise is that it gets students using real data and doing their own analysis. Unlike a lot of real scientific data, it is simple enough for students to easily use and understand - it can be imported into Excel or, as mentioned above, simply plotted by hand.

Another benefit is that, in addition to learning about climate, there is lot that can be conveyed about climate variability and statistics - how the sea ice varies through the year, and from year-to-year; when a trend becomes meaningful(e.g. 'statistical significance'), uncertainties in the data, etc. These are concepts that are valuable for many things - e.g., margin of error in political polls.

Along with Mark and others, I helped develop the "Whither Arctic Sea Ice" module, which allows students to actually work with and analyze the raw data. This takes quite a bit more effort, but can be useful for specific case studies.


Share edittextuser=1173 post_id=13365 initial_post_id=0 thread_id=3862

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