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. 

Students analyze and compare satellite data of Ocean Chlorophyll Concentrations with Sea Surface Temperatures, beginning with the North Atlantic region, while answering questions about the global patterns of these phenomenon.

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 watch a short video that explains albedo and how it plays an important role in Earth’s Energy Budget. Applying what they learned from the video, students will analyze a bar graph that lists the albedos of common surfaces found on Earth to answer critical thinking questions.

Students use albedo values of common surfaces along with photographic images of Earth taken from the International Space Station to make an argument about specific anthropogenic activities that impact Earth’s albedo.

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.

Students review the NASA video showing biosphere data over the North Atlantic Ocean as a time series animation displaying a decade of phytoplankton blooms and answer questions.

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 lesson, "Awenasa Goes to Camp!," is a data analysis activity that presents maps of NASA Earth satellite data for a variety of locations across the United States for four unidentified months throughout the year.  Each location represents a real science camp th

Students observe monthly images of changing vegetation patterns, looking for seasonal changes occurring throughout 2017. These data can be used by students to develop their own models of change.