Students observe monthly images of changing vegetation patterns, looking for seasonal changes occurring throughout 2017. These data can be used by students to develop their own models of change.

In this mini lesson, students use in-water profiles of historical ocean data to analyze how sea surface salinity varies with depth. 

This mini lesson focuses on the 2015-2016 El Niño event and how its weather conditions triggered regional disease outbreaks throughout the world. Students will review a NASA article and watch the associated video to use as a tool to compare with maps related to 2015-2016 rainfall and elevated disease risk, and answer the questions.

Students interpret a double bar/column chart comparing the number of tropical cyclones in different locations.

Students will analyze a projected map of the April 8, 2024 total solar eclipse across the US, with an accompanying data table of the locations and times, to explain how people in different locations experience a solar eclipse.  

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.

Exploring salinity patterns is a great way to better understand the relationships between the water cycle, ocean circulation, and climate. In this mini lesson, students analyze sea surface salinity mapped plots created from the Earth System Data Explorer, paired with questions (and answers) from the Aquarius Mission. Credit: Aquarius Education

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.

In this mini-lesson, students analyze soil moisture quantities associated with Hurricane Harvey around Houston, Texas on August 25, 2017.

Students differentiate between data sets of monthly shortwave radiation and monthly cloud coverage to discover a relationship between radiation and clouds by answering analysis questions.