This is FRIDA (v2.1): an introduction to the FRIDA GMD collection

FRIDA is a new contribution to the portfolio of integrated assessment models (IAMs) that address the climate – energy – economy – and society nexus. The FRIDA acronym stands for Feedback-based knowledge Repository for IntegrateD Assessments. Naming it a “knowledge repository” signals that the FRIDA model is never finished; it represents the current state of knowledge of the development team at any given time. FRIDA was developed through the European Horizon project WorldTrans – Transparent Assessments for Real People (2022–2026). The journal Geoscientific Model Development has given us space to document FRIDA, including its submodules and spin-offs, in a special GMD collection (https://gmd.copernicus.org/articles/collection12.html, last access: 18 May 2026). This brief paper is the introduction to the GMD FRIDA collection of papers. The purpose of the introductory paper, written by the project lead on behalf of the consortium, is to provide the conceptual and institutional context for the original model and to make explicit the initial design requirements that guide FRIDA's ongoing development as a living knowledge repository. FRIDA is implemented as a computationally efficient system-dynamics model and is accompanied by an Interactive Learning Environment. This combination makes it suitable not only for research, but also for education and broader outreach. In particular, FRIDA can be used in interdisciplinary climate science courses to show how individual disciplines (e.g. climatology, economics, demography) are tightly interwoven within the coupled climate–human system, thereby lowering the barrier to entry for users beyond the IAM community. What sets FRIDA apart from traditional IAMs is its shift from exogenous, narrative-based scenarios to a fully feedback-driven framework, in which human activities and climate change co-evolve within a single system of equations. By explicitly representing these two-way feedbacks, FRIDA accounts for the impacts that climate change has already begun to exert on human systems; without them, projections of the human activities that drive climate change will become increasingly unreliable. Preliminary results suggest that including these feedbacks lead to systematically less optimistic projections than conventional IAM baselines.