In Earth System Science, underling factors affecting observable phenomena can be difficult to identify and describe.  The Iceberg Diagram diagram uses the metaphor of an iceberg to demonstrate the idea of visible vs hidden as it relates to Earth science phenomena.  This teaching strategy helps students to see beyond the obvious and to develop their awareness of the underlying causes, relationships, and/or conditions that can contribute to phenomenological events. It also provides a framework for digging deeper into phenomena-driven lessons in Earth Science.

Students observe seasonal images of Monthly Leaf Area, looking for any changes that are occurring throughout the year.

Students can interact with NASA data to build a custom visualizations of local, regional, or global plant growth patterns over time, using the Earth System Data Explorer to generate plots of satellite data as they develop models of this phenomenon.

This mini lesson engages students by watching a NASA video related to plant growth activity around the world using data from the NASA/NOAA Suomi NPP satellite and answering questions on these stability and change relationships.

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 identify patterns in chlorophyll concentration data to formulate their explanations of phytoplankton distribution. 

Students will identify and describe the relationship between watersheds and phytoplankton distribution. 

Students analyze historic plant growth data (i.e., Peak Bloom dates) of Washington, D.C.’s famous cherry blossom trees, as well as atmospheric near surface temperatures as evidence for explaining the phenomena of earlier Peak Blooms in our nation’s capital.

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.