Copyright 2022-2023 Daniel Huppmann; this repository is released under the MIT License.
This repository holds a Jupyter notebook for a live-demo of the pyam package given as part of the hands-on tutorial sessions of the NAVIGATE-ENGAGE Summer School 2023 on Integrated-Assessment Modeling. This workshop was organized as part of the Horizon 2020 project ENGAGE (link).
The Jupyter notebook is based on the advanced assignment of the Modelling Lab, which was part of the Climate Risks Academy 2021 organized by the European University Institute (EUI) Florence School of Banking and Finance in cooperation with Oliver Wyman.
The slides for the related presentation are available at https://doi.org/10.5281/zenodo.8112529 (ZENODO).
The scenario data used in this tutorial notebook is taken from the NGFS Scenario Ensemble, Phase 3, see Richters et al, 2022 (link).
The data was downloaded from the following scenario database:
Emissions scenario database of the European Scientific Advisory Board on Climate Change, hosted by IIASA
Release 2.0
European Scientific Advisory Board on Climate Change, 2023
doi: https://doi.org/10.5281/zenodo.7660150
url: https://data.ece.iiasa.ac.at/eu-climate-advisory-board
This tutorial uses the Python package pyam, an open-source community toolbox for analysis & visualization of scenario data. The package was developed to facilitate working with timeseries scenario data conforming to the format developed by the Integrated Assessment Modeling Consortium (IAMC). The package is used in ongoing assessments by the IPCC and in many model comparison projects at the global and national level, including several Horizon 2020 projects.
Read the docs for more information!
To run the notebooks on your machine, please install Python version 3.7 or higher. To install the required packages and dependencies, download or git-clone this repository and run the following command in the root folder:
pip install -r requirements.txt
Then, you can start a Jupyter notebook using
jupyter notebook
