Human-Earth feedbacks in E3SM-GCAM successfully simulate the evolution of a combined human-Earth system

Understanding human-environment feedbacks is becoming increasingly important as climate change mitigation and adaptation strategies continue to diversify, target new areas, and grow in extent. Incorporating these feedbacks into models is critical for assessing the effectiveness of such strategies and how they may change in response to a changing climate. For example, projected forest expansion varies with changing climate because climate-driven changes in forest productivity affect the cost-effectiveness of reforestation strategies. Including human-environment feedbacks in models can dramatically change the projected scenario as human systems respond to the changing environment, which in turn affects the Earth system projection.

 

We have incorporated human-Earth feedbacks in a synchronously coupled system comprising the Global Change Analysis Model (GCAM) and the Energy Exascale Earth System Model (E3SM). GCAM is the core model in a new E3SM human component that is at the same level as the Earth model components (land, atmosphere, ocean, etc.) and interacts with them through the shared coupling software. Terrestrial productivity is passed from E3SM to GCAM to make climate-responsive land use and CO₂ emission projections for the next five-year period, which are interpolated and passed to E3SM annually. Previous experiments with a similar model have shown that the incorporation of these feedbacks affects land use/cover change, crop prices, terrestrial carbon, local surface temperature, and land carbon-atmosphere feedbacks. Preliminary results indicate that this newly coupled system is robust in relation to the previous experiments. The human scenario is altered by terrestrial feedbacks, which in turn changes the Earth system projections. Regional differences are more pronounced than global differences due to regional shifts in land use. This new coupling addresses inconsistency across models, enables a new type of scenario development, and provides a modeling framework that is more easily updated and expanded.