Investigating the European Energetic Situation Under a Potential Scenario that Marginally Meets Green Deal Requirements

The paper presents a potential energetic scenario that leads to near zero emissions for Europe in 2050, marginally meeting Green Deal requirements. Nevertheless, technologically wise it is an advanced implementation. It proposes a relatively high penetration of renewables (wind, solar, geothermal, biomass and nuclear), the increased use of electro-mobility, carbon capture and storage, hydrogen and other technologies. The simulations were performed using the open source Global Change Assessment Model (GCAM) and the simulation data and results are available online

Development of an Integrated Assessment Model at Provincial Level: GCAM-Korea

Integrated assessment modeling at a higher spatial scale is a prerequisite for deriving region-specific implications from the model. The Global Change Assessment Model (GCAM) was chosen for GCAM-Korea, a detailed integrated assessment model (IAM) of Korea’s socioeconomic and energy systems. GCAM-Korea is developed based on GCAM-USA. Data for 16 provinces have been collected from various sources. Some data have been pre-processed to fit within the specific structure of GCAM-USA data. Other types of data were newly added through new structures. The model results were validated to be compatible with historical trends. It was found that provincial energy plans or policies could be compiled in detail using the proposed model while maintaining consistency with national level modeling results. The cross-border air pollution issue in Northeast Asia could also be addressed by combining GCAM-Korea and air quality models in the future.

A HINDCAST EXPERIMENT USING THE GCAM 3.0 AGRICULTURE AND LAND-USE MODULE

We report results of a “hindcast” experiment focusing on the agricultural and land-use component of the Global Change Assessment Model (GCAM). We initialize GCAM to reproduce observed agriculture and land use in 1990 and forecast agriculture and land use patterns on one-year time steps to 2010. We report overall model performance for nine crops in 14 regions. We report areas where the hindcast is in relatively good agreement with observations and areas where the correspondence is poorer. We find that when given observed crop yields as input data, producers in GCAM implicitly have perfect foresight for yields leading to over compensation for year-to-year yield variation. We explore a simple model in which planting decisions are based on expectations but production depends on actual yields and find that this addresses the implicit perfect foresight problem. Second, while existing policies are implicitly calibrated into IAMs, changes in those policies over the period of analysis can have a dramatic effect on the fidelity of model output. Third, we demonstrate that IAMs can employ techniques similar to those used by the climate modeling community to evaluate model skill. We find that hindcasting has the potential to yield substantial benefits to the IAM community.

Downscaling land use and land cover from the Global Change Assessment Model for coupling with Earth system models

The Global Change Assessment Model (GCAM) is a global integrated assessment model used to project future societal and environmental scenarios, based on economic modeling and on a detailed representation of food and energy production systems. The terrestrial module in GCAM represents agricultural activities and ecosystems dynamics at the subregional scale, and must be downscaled to be used for impact assessments in gridded models (e.g., climate models). In this study, we present the downscaling algorithm of the GCAM model, which generates gridded time series of global land use and land cover (LULC) from any GCAM scenario. The downscaling is based on a number of user-defined rules and drivers, including transition priorities (e.g., crop expansion preferentially into grasslands rather than forests) and spatial constraints (e.g., nutrient availability). The default parameterization is evaluated using historical LULC change data, and a sensitivity experiment provides insights on the most critical parameters and how their influence changes regionally and in time. Finally, a reference scenario and a climate mitigation scenario are downscaled to illustrate the gridded land use outcomes of different policies on agricultural expansion and forest management. Several features of the downscaling can be modified by providing new input data or changing the parameterization, without any edits to the code. Those features include spatial resolution as well as the number and type of land classes being downscaled, thereby providing flexibility to adapt GCAM LULC scenarios to the requirements of a wide range of models and applications. The downscaling system is version controlled and freely available.