FRIDA-Clim v1.0.0: a Simple Climate Model with Process-Based Carbon Cycle used in the FRIDAv2.1 IAM

Abstract. The new global Feedback-based knowledge Repository for IntegrateD Assessments version 2.1 (FRIDAv2.1) Integrated Assessment Model (IAM) seeks to study the dynamics of the coupled human-Earth system. Connecting anthropogenic emissions to the resultant climate response is one part of this two-way feedback. This paper documents the Climate Module within FRIDAv2.1, of which a modified version is separately simulated as a standalone simple climate model termed FRIDA-Clim version 1.0. This approach, based loosely on the existing FaIR simple climate model, simulates the key radiative forcings and the resultant temperature response, with process-based representations of the carbon cycle across the ocean, land, and atmosphere. When connected within the FRIDA IAM, it features deep connections to the other modules, being affected by processes such as water use for irrigation and land use change. In both uses, coupled and uncoupled, its climate drivers are simplified as compared to FaIR, to allow for this reduced set of key drivers to be interactively simulated within FRIDA, tightly coupling the evolution of the social and climate systems within the full model. Both the Climate Module and FRIDA-Clim are fully calibrated to accurately reproduce observations of key climate variables, with a systematic exploration of the uncertainty in the climate response. Together with the rest of the FRIDA model, this module is used to incorporate climate change systematically in the FRIDA System Dynamics IAM. As a standalone climate model, FRIDA-Clim comprises a simple climate model, enabling fast calculation of the global climate response to forcing; to explore this, the response of the model to both idealised CO₂ emissions experiments and plausible future scenarios is also presented here. This setup will allow FRIDA-Clim to contribute to inter-model simple climate modelling initiatives, helping to explore the structural uncertainty in this modelling domain.