In this interactive, students will identify and describe the different components and flows of energy of the Earth's Energy Budget diagram as well as the imbalances that exist in Earth's Energy Budget.
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This story map allows students to explore the urban heat island effect using land surface temperature and vegetation data in a 5 E-learning cycle. Students investigate the processes that create differences in surface temperatures, as well as how human activities have led to the creation of urban heat islands.
Students will analyze nitrogen dioxide concentration in the atmosphere at different spatial and temporal scales, and describe the stability of nitrogen dioxide as it relates to changes in human behavior.
Students will identify and describe the relationship between land cover classification and surface temperature as they relate to the urban heat island effect. Students will also describe patterns between population density and the locations of urban heat islands.
Students will describe how the spread of COVID-19 is affected by population density and explain why patterns in the spread of COVID-19 are happening over time.
In this story map students will learn about the different components of the Earth's Energy Budget, where in the Earth System energy is being absorbed and reflected, and how features of the Earth such as clouds, aerosols, and greenhouse gases, can cause variations in the flow of energy into and out of Earth Systems. In the final section, students make a claim as to why the Earth's Energy Budget is currently out of balance and provide evidence to support their reasoning.
Using various visualizations (i.e., images, charts, and graphs), students will explore changes in sea ice extent as it relates to other spheres within the Earth System. 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.
By matching pie charts with dates between 2002 and 2020, students will predict how air quality has changed over the past two decades. They will then use color-coded Air Quality Index signatures to assess the accuracy of their predictions.