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In this lesson, Observing Earth’s Seasonal Changes, students observe patterns of average snow and ice amounts as they change from one month to another, as well as connect the concepts of the tilt and orbit of the Earth (causing the changing of seasons) with monthly snow/ice data from January 2008
NASA visualizers take data – numbers, codes – and turn them into animations people can see and quickly understand. You can become a data visualizer by creating your own flipbook animations using maps of science variables that NASA scientists commonly study to better understand the Earth System.
Through guided inquiry, students will identify interactions of the four major scientific spheres on Earth: biosphere, atmosphere, hydrosphere and geosphere. They will then identify how these systems are represented and interact in their classroom aquarium.
Check out this the Arctic and Earth SIGNs video to explore how climate models are used in climate change research.
This activity is designed to introduce students to geologic processes on Earth and how to identify geologic features in images. It will also introduce students to how scientists use Earth to gain a better understanding of other planetary bodies in the solar system.
Students analyze map visualizations representing the amount of Sun’s energy received on the Earth as indicated by the amount that is reflected back to space, known as “albedo”.
Check out this hands-on demonstration of the El Niño Effect, trade winds, and upwelling provided by NASA's Jet Propulsion Lab
Credit: JPL's Sea Level Program
In Part A of this lab, students will examine a variety of images and maps of the whole Earth in order to identify the major components of the Earth system at a global scale.
This story map is intended to be used with students who have access to a computing device in a 1:1 or 1:2 setting. Using various visualizations (i.e., images, charts, and graphs), students will explore the urban heat island effect using land surface temperature and vegetation data.
Explore the spatial patterns observed in meteorological data and learn how this information is used to predict weather and understand climate behavior. By observing patterns in data we can classify our observations and investigate underlying cause and effect relationships.