NGFS CLIMATE SCENARIOS
The NGFS scenarios explore the transition and physical impacts of climate change under varying assumptions, with the aim of providing a common reference framework for central banks and supervisors.
The NGFS climate scenarios were selected in partnership with an academic consortium including the Potsdam Institute for Climate Impact Research (PIK), the International Institute for Applied Systems Analysis (IIASA), the Center for Global Sustainability at the University of Maryland (UMD) and Climate Analytics (CA).
This first iteration of the scenarios explores a set of 8 types of scenarios, which are consistent with the NGFS framework published in the First NGFS Comprehensive Report in April 2019. While developed primarily for use by central banks and supervisors, they may also be useful to broader financial, academic and corporate communities.
NGFS Scenarios and Scenario Analysis:
- For a high-level description of the NGFS scenarios, please consult the NGFS climate scenarios for central banks and supervisors.
- For a broad overview on how to perform scenario analysis for financial purposes, please refer to the NGFS Guide to climate scenario analysis for central banks and supervisors.
Databases and Technical documentation relating to the NGFS Scenarios.
The NGFS provides public access to the data underlying the NGFS Scenarios:
- The transition scenarios selected for the NGFS are available in the NGFS Scenario Explorer, hosted by IIASA. The Scenario Explorer is a web-based user interface, which provides intuitive visualisations and display of time series data. The scenario data is available for download as xlsx or csv files via the Scenario Explorer. Alternatively, the data can be accessed via a RestAPI or the open-source Python package pyam. More information is available at https://data.ene.iiasa.ac.at/ngfs/#/extra/API
- The physical impact data collected by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are directly downloadable here. To download larger quantity of datasets at the same time or to further process them with python, a separate readme on the installation steps and tutorial (jupyter notebook) can be found at the Climate Analytics gitlab.
Additionally, technical documentation have been prepared to help users access the datasets. It describes the models and variables, as well as provides detailed guidance for database users.
Updated on: 08/25/2020 16:31