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Grade Level: 6-8, 9-12

In this activity, students make a claim about the cause of ocean currents and then develop a model to explain the role of temperature and density in deep ocean currents.  This lesson is modified from "Visit to an Ocean Planet" Caltech and NASA/Jet Propulsion Laboratory.



Grade Level: 6-8

In this activity, students will analyze a NASA sea surface height model of El Niño for December 27, 2015, and answer questions. Then they will be instructed to create a model of their own made from pudding to reflect the layers of El Niño.


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Grade Level: 6-8, 9-12

Students will examine a 2014-2015 El Niño Southern Oscillation (ENSO) event to identify relationships among sea surface height, sea surface temperature, precipitation, and wind vectors.



Grade Level: 6-8

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


Grade Level: 6-8

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.


Grade Level: 6-8, 9-12

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.


Grade Level: 6-8, 9-12

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.



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