Modeling feedbacks of varying scale to the human-Earth system on a per timestep basis using GCAMs Python API

Researchers and decision makers are increasingly interested in understanding the characteristics of future interactions between human and Earth systems under various conditions. For example, one may wish to explore how changes in population, income, or technology cost might alter crop production, energy demand, or water withdrawals at varying scales. These analyses are generally conducted with integrated models that capture the dynamics of multiple systems to produce a harmonized projection of future realizations over a range of time. It is well known that dynamics in the human-Earth system vary based on scale; thus, it is important to capture feedbacks that represent the response of local processes to regional projections to create a better representation of how these systems evolve into the future and how they may influence one another. We introduce gcamwrapper a new Python API to the Global Change Analysis Model (GCAM) which provides us with the ability to model high-resolution feedbacks in the context of global integrated modeling on a timestep-by-timestep basis. We will demonstrate how to use gcamwrapper by conducting a highly coordinated coupling with Demeter, a land use and land cover change disaggregation model, to show a comparison between modeling with and without the influence of locally-influenced feedbacks from crop yield outcomes.