Students analyze diagrams showing the effects of clouds on Earth’s Radiation and answer the questions that follow. This mini lesson is designed to help students analyze the interaction between clouds and Earth's incoming and outgoing energy. 

Using an infographic, students describe differences in electromagnetic radiation that is part of a model of Earth’s energy budget by applying the defined terms of Shortwave Radiation and Longwave Radiation.

Students will analyze a graph showing the variation of energy imbalance on Earth over the year along different latitudinal zones and answer the questions that follow.

In this mini lesson, students analyze a bar graph showing the relative forcings from natural and human factors that affect Earth's climate. They use information from this graph to assess the relative importance of these factors.

This lesson is designed to help students analyze the interaction between different cloud heights and Earth's incoming and outgoing energy. 

Students watch a short video to gather information about sources of methane emissions and then extend their understanding of these sources to evaluate monthly trends in the Alaska region, ultimately making connections to Earth’s energy budget.

Students differentiate between data sets of monthly shortwave radiation and monthly cloud coverage to discover a relationship between radiation and clouds by answering analysis questions.

Students will examine how radiation, conduction, and convection work together as a part of Earth’s Energy Budget to heat the atmosphere.

Students will investigate the role of clouds and their contribution (if any) to global warming. Working in cooperative groups, students will make a claim about the future role clouds will play in Earth’s Energy Budget if temperatures continue to increase.

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.