A methodology and implementation of automated emissions harmonization for use in Integrated Assessment Models

Abstract

Emissions harmonization refers to the process used to match greenhouse gas (GHG) and air pollutant results from Integrated Assessment Models (IAMs) against a common source of historical emissions. To date, harmonization has been performed separately by individual modeling teams. For the hand-over of emission data for the Shared Socioeconomic Pathways (SSPs) to climate model groups, a new automated approach based on commonly agreed upon algorithms was developed. This work describes the novel methodology for determining such harmonization methods and an open-source Python software library implementing the methodology. A case study is presented for two example scenarios (with and without climate policy cases) using the IAM MESSAGE-GLOBIOM that satisfactorily harmonize over 96% of the total emissions trajectories while having a negligible effect on key long-term climate indicators. This new capability enhances the comparability across different models, increases transparency and robustness of results, and allows other teams to easily participate in intercomparison exercises by using the same, openly available harmonization mechanism.