Students will observe monthly satellite data of the North Atlantic to identify relationships among key science variables that include sea surface salinity (SS), air temperature at the ocean surface (AT), sea surface temperature (ST), evaporation (EV), precipitation (PT), and evaporation minus pre

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 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.

Students compare climographs for two locations to determine the most likely months to expect the emergence of mosquitoes in each location.

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.

Students will analyze the mapped plot of the historic Ocean Chlorophyll Concentrations at key locations around the world for the period of 1998-2018.

The ocean's surface is not level, and sea levels change in response to changes in chemistry and temperature. Sophisticated satellite measurements are required for scientists to document current sea level rise.

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 move through a series of short activities to explore and evaluate global solar radiation data from NASA satellites.  In this process, students make qualitative and quantitative observations about seasonal variations in net energy input to the Earth System.

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.