This is a simulation that illustrates how temperature will be affected by global CO2 emission trajectories. It addresses the issue that even if global emissions begin to decrease, the atmospheric concentration of CO2 will continue to increase, resulting in increased global temperatures.
This graphic depicts evidence for a human fingerprint on climate change based on multiple sets of independent observations. The graphic is available to study at three levels - basic, intermediate, and advanced understanding, with substantial support for students to investigate the evidence themselves and draw their own conclusions.
In this short video, host Dr. Ryan interviews graduate student Amy Steiker at the Institute of Arctic and Alpine Research about her research, using isotopes of nitrous oxide, connecting human activity to greenhouse gas emissions.
In this activity, students explore the increase in atmospheric carbon dioxide over the past 40 years with an interactive online model. They use the model and observations to estimate present emission rates and emission growth rates. The model is then used to estimate future levels of carbon dioxide using different future emission scenarios. These different scenarios are then linked by students to climate model predictions also used by the Intergovernmental Panel on Climate Change.
These graphs show carbon dioxide measurements at the Mauna Loa Observatory, Hawaii. The graphs display recent measurements as well as historical long term measurements. The related website summarizes in graphs the recent monthly CO2, the full CO2 Record, the annual Mean CO2 Growth Rate, and gives links to detailed CO2 data for this location, which is one of the most important CO2 sites in the world.
C-Learn is a simplified version of the C-ROADS simulator. Its primary purpose is to help users understand the long-term climate effects (CO2 concentrations, global temperature, sea level rise) of various customized actions to reduce fossil fuel CO2 emissions, reduce deforestation, and grow more trees. Students can ask multiple, customized what-if questions and understand why the system reacts as it does.