My initial concept maps focused on the long-term and short-term indicators of climate change, particularly how we distinguish between human versus natural causes. Scientists tend to consider how do you detect change: for example, in "paleorecords" (e.g., ice cores, sediment cores) and from instruments that measure temperature, precipitation, etc. Then they compare these records to computer model outputs to see how well they match (i.e., "attribution"). This iterative matching process is designed to reveal the best climate models which, in turn, allows us to simulate future conditions. In cases of undesirable model outcomes, researchers can begin to develop strategies to either mitigate change and/or adapt to the new climate conditions.

Working with educators, we re-configured my initial map to follow a conceptual flow from "scientific concepts" to the equations that drive "climate models." We illustrated the types of data that are used to create the equations (i.e., records, instrument and satellite data) and how these are either verified -- or not verified -- by running a series of simulations. An important part of the map is how the "Evidence" for climate change is produced by analyzing computer model outputs that separate "natural factors" from "human factors."