AR6 Scenario Explorer and Database
- Initial Release:04.2022
- Referent:Ed Byers
- Referent e-mail:firstname.lastname@example.org
As part of the IPCC’s Sixth Assessment Report (AR6), authors from Working Group III on Mitigation of Climate Change undertook a comprehensive exercise to collect and assess quantitative, model-based scenarios related to the mitigation of climate change. Building on previous assessments, such as those undertaken for the Fifth Assessment Report (AR5) and the Special Report on Global Warming of 1.5°C (SR15), the calls for scenarios in AR6 have been expanded to include energy, emissions, and sectoral scenarios from global to national scales, thus more broadly supporting the assessment across multiple chapters (see Annex III, Part 2 of the WG III Report and the About tab for more details).
The compilation and assessment of the scenario ensemble was conducted by authors of the IPCC AR6 report, and the resource is hosted by the International Institute for Applied Systems Analysis (IIASA) as part of a cooperation agreement with Working Group III of the IPCC. The scenario ensemble contains 3,131 quantitative scenarios with data on socio-economic development, greenhouse gas emissions, and sectoral transformations across energy, land use, transportation and industry. These scenarios derive from 188 unique modelling frameworks and 95+ model families that are either globally comprehensive, national, multi-regional or sectoral. The criteria for submission included that the scenario is presented in a peer-reviewed journal accepted for publication no later than October 11th, 2021, or published in a report determined by the IPCC to be eligible grey literature by the same date.
The IAMC Scientific Working Group on Data Protocols and Management and numerous authors of the AR6 WGIII Report who are members of the IAMC contributed to enable consistent reporting across research groups and the development of a common data template. IAMC was also involved in the Scenario Submission Portal for IPCC AR6 WGIII used to collect quantitative scenario data.