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Human Energy Systems | Lesson 2 - Describing Patterns in Large-Scale Data

Lesson 2: Finding Patterns in Large-Scale Data

Using a jigsaw activity, students discuss generalizability, representation, and short-term variability using four different large-scale data sets related to climate change: global temperature, sea level rise, long-term atmospheric CO2 concentration, and short-term atmospheric CO2 annual cycle.

Guiding Question

What is happening to global temperature, atmospheric carbon dioxide, and sea level?

Activities in this Lesson

  • Activity 2.1: Home Groups: Four Considerations for Large-Scale Data (45 min)
  • Activity 2.2: Expert Groups: Analysis of Large-Scale Data (45 min)
  • Activity 2.3: Home Groups: Share Expertise (60 minutes)
  • Activity 2.4: Evidence-Based Arguments for Earth Systems (30 min)


  • Explain how data are sampled and represented in different representations of large-scale data sets (e.g., graphs, maps, videos).
  • Use large-scale data sets related to climate change (sea level rise, global temperature, atmospheric CO2 long-term trend, and atmospheric CO2 short-term variability) to make predictions about the future.
  • Distinguish between short-term variability and long-term trends in large-scale data sets.
  • Distinguish between local signals and global trends in large-scale data sets.

NGSS Performance Expectations

Middle School

  • Earth and Human Activity. MS-ESS3-5. Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past century.

High School

  • Earth’s Systems. HS-ESS2-2. Analyze geoscience data to make the claim that one change to Earth’s surface can create feedbacks that cause changes to other Earth systems.
  • Earth’s Systems. HS-ESS2-2. Analyze geoscience data to make the claim that one change to Earth’s surface can create feedbacks that cause changes to other Earth systems.
  • Weather and Climate. HS-ESS2-4. Use a model to describe how variations in the flow of energy into and out of Earth’s systems result in changes in climate.
  • Earth’s Systems. HS-ESS2-6. Develop a quantitative model to describe the cycling of carbon among the hydrosphere, atmosphere, geosphere, and biosphere.
  • Earth and Human Activity. HS-ESS3-5. Analyze geoscience data and the results from global climate models to make an evidence-based forecast of the current rate of global or regional climate change and associated future impacts to Earth systems.
  • Earth and Human Activity. HS-ESS3-6. Use a computational representation to illustrate the relationships among Earth systems and how those relationships are being modified due to human activity.

Background Information

In Activity 2.1, students are introduced to the first of five different large-scale data sets dealing with different phenomena in the Earth’s system. The first data set deals with arctic sea ice extent. When scientists interpret any data set, there are certain pieces of information that are crucial for helping them make sense of the data. We focus on four of those in this activity: representation, generalizability, short-term variability, and long-term trends. The first is generalizability. One reason climate change is so difficult for the public to understand is that scientists use a combination of many global and local data sets to find patterns that are not always clear locally. Using other sources, they determine when and how local signals may or may not be reflective of a global trend. The second is representation. Scientists also need to examine what time period and data are being represented in the table, so they know if it is generalizable to other times and places or not. Third, scientists also need to distinguish between short-term variability and long-term trends. Short-term variability is predictable in some data sets (like atmospheric CO2 concentrations rising each winter and falling each summer), and unpredictable and stochastic in other data sets (like arctic sea ice extent, which is subject to many factors in the earth’s climate system). Finally, scientists also use long-term trends in data to understand what has happened in the past and to predict what might happen in the future. In this activity students are introduced to these four considerations. Students fill in the first row in the 2.1 Finding Patterns Tool together as a class.

In Activity 2.2, students begin a jigsaw activity to examine the other four large-scale data sets:, sea level, global temperatures, historic atmospheric CO2 concentrations, and annual patterns in atmospheric CO2 concentration. The ideas introduced in this Jigsaw are extended through the rest of the Lesson. For more information about the Jigsaw discussion strategy, see To begin, students form home groups and discuss the goals for the Lesson. Then, students form expert groups to examine a large-scale data set that represents a global phenomenon. They read about their expert group topic and work with their groups to discuss the “four considerations” covered in Activity 2.1 for their own phenomenon. They fill in their expert group’s corresponding row on the 2.1 Finding Patterns Tool. At this point in the Unit, the students may have a difficult time understanding why these considerations are valuable, and this activity aims to help establish why these considerations are important.

In Activity 2.3, students return to their home groups to share their expertise about their phenomena, using the four considerations as a frame for their presentation, and fill in the remaining rows in their Finding Patterns Tool. This provides yet another context for helping students work through the challenges of interpreting large-scale data sets. Why, for example, is a measurement of carbon dioxide concentrations in the atmosphere representative of a global pattern? How do scientists know that? Some of these questions will remain unanswered at the end of this activity, which is intentional. Students will revisit these questions later in the unit.

Finally, in Activity 2.4 students use the Finding Patterns Tool for Earth Systems to identify patterns across data sets and discuss what may be causing these patterns. At this point in the unit, students will have collected evidence of various global trends related to climate change. However, they may not have evidence that explains that the driving factor of all of these trends is the increase in carbon dioxide in the atmosphere due to human activity (primarily the combustion of fossil fuels). The unanswered questions in this tool set students up for learning that takes place in the following lessons, when the students delve deeper into the Keeling Curve and learn about the driving forces for all phenomena they studied in this lesson.

Unit Map

Unit Map for Lesson 1