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 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

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.

This NASA visualization shows sea surface salinity observations (September 2011-September 2014). Students review the video and answer questions.

Hands-on demonstration of the El Niño Effect, trade winds, and upwelling provided by NASA's Jet Propulsion Lab.

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 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 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 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.