This graphic organizer may be used to help students analyze the processes and components of Earth System phenomena.
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Students review a video showing how the ocean is warmed by solar energy. This is the first video of a four-part series on the water cycle, which follows the journey of water from the ocean to the atmosphere, to the land, and back again to the ocean.
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
The activities in this guide will help students understand variations in environmental parameters by examining connections among different phenomena measured on local, regional and global scales.
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.
In this experiment, students make a claim about the cause of ocean currents and then develop a model to explain the role of salinity 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.
Students evaluate graphs and images of sea ice and relate them to changes in albedo. Students make a claim about the interaction of albedo and sea ice extent.
This mini-lesson features time-series graphs of mean salinity at the surface for the Arctic and Antarctic regions. A series of questions guides students in their analysis.
The Earth System Satellite Images help students observe and analyze global Earth and environmental data, understand the relationship among different environmental variables, and explore how the data change seasonally and over longer timescales.