Air, Water, Land, & Life: A Global Perspective

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

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.

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.

Students review Earth System phenomena that are affected by soil moisture.  They analyze and evaluate maps of seasonal global surface air temperature and soil moisture data from NASA satellites. Building from their observations, students will select a location in the U.S.

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.

The Earth System Poster activity walks learners through global patterns and illuminates how each of the spheres is interconnected across the world. We will divide into small groups to look at maps of different parts of the earth system that have been observed by NASA satellites.

By investigating the data presented in a model that displays extreme summer air temperatures, students explain energy transfer in the Earth system and consider the impact of excessive heat on local communities.

This investigation is part of the NASA: Mission Geography Module "What are the causes and consequences of climate change?" that guides students through explorations in climatic variability and evidence for global climate change. 

The Earth System Satellite Images, help the learner visualize how different Earth system variables change over time, establish cause and effect relationships for a specific variable, identify patterns, and determine relationships among variables over one year.