Students analyze the stability and change of sea level after watching a visualization of sea level height around the world. 

For over 20 years, satellite instruments have measured the sea surface height of our ever-changing oceans.  This video of images shows the complicated patterns of rising and falling ocean levels across the globe from 1993 to 2015.

Students analyze four data visualizations focused on the topic of sea level.  They use a jigsaw method to explore and communicate their findings to their peers.

This Lesson Plan provides some generic maps, graphs, and data tables for use with the Data Literacy Cube. Because this is a differentiated resource, it is appropriate for multiple grade bands.

Information from satellites if often used to display information about objects. This information can include how things appear, as well as their contents. Explore how pixel data sequences can be used to create an image and interpret it.

Students consider the impact of changing conditions on the remote island of Little Diomede, Alaska after they investigate the relationship between seasonal trends in sea ice extent with shortwave and longwave radiation flux described in Earth’s energy budget.

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.

The advance-and-retreat cycle of snow cover drastically changes the whiteness and brightness of Earth. Using two maps created using NASA satellite data for 2017, students review the seasonal differences of snow and ice extent and answer questions on their observations.

Students will make a claim about whether changing albedo contributes to changes in Arctic habitats.

Students analyze two North Pole orthographic data visualizations produced from soil moisture data. After describing trends in the seasonal thaw of land surfaces, students demonstrate their understanding of Earth’s energy budget by explaining relationships and make predictions about the dataset.