The Earth System Satellite Images, along with the Data Literacy Cubes, help the learner visualize how different Earth system variables change over time, identify patterns, and determine relationships among two variables in three months.
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This activity was developed by NASA's Global Precipitation Measurement (GPM) team as an introductory experience to a series of lessons about water resources on Earth.
Helping students build their understanding of Earth's spheres and how they are connected is difficult. Review the graphics to help identify the parts of the Earth System and the processes that connect them at the local, regional, and global scales.
This graphic organizer may be used to help students analyze the processes and components of Earth System phenomena.
Check out the Arctic and Earth SIGNs video to explore how climate models are used in climate change research.
In this story map students will learn about the different components of the Earth's Energy Budget, where in the Earth System energy is being absorbed and reflected, and how features of the Earth such as clouds, aerosols, and greenhouse gases, can cause variations in the flow of energy into and out of Earth Systems. In the final section, students make a claim as to why the Earth's Energy Budget is currently out of balance and provide evidence to support their reasoning.
Students will use coloring sheets to create a color coded model of El Niño, then make comparisons using the actual model by answering questions. If the Data Literacy Map Cube is used with this, students will color their models first.
This lesson plan provides some generic maps, graphs, and data tables for use with the Data Literacy Cube. Because it is a differentiated resource, this lesson plan is appropriate for multiple grade bands.
Students will analyze a pie chart (circle graph) showing the distribution of different parts of the Earth system's absorption and reflection of energy.
The Earth System Satellite Images help students observe and analyze global Earth and environmental data, understand the relationship among different environmental variables, and explore how the data change seasonally and over longer timescales.