Suite for energy model diagnostics. Latest release can be found on Zenodo:
For the purpose of comparing any single scenario (e.g., from your own model) to any given reference scenario dataset (e.g., from a project), we created some code in the folder Interactive Fingerprinting. A step-by-step manual and recordings of a workshop where this is used will be shared soon. There is also a Compute_own_fingerprint.ipynb notebook in the Calculations folder that can be used to analyze scenarios similar to in the paper.
You can set up the code by using conda create --name <env> --file requirements.txt in your command prompt, where <env> is the name of your conda environment you want to create for this. (The requirements file may contain a number of packages that are not used here.)
The code in this repository reads in scenario output of eight energy models (most of which are integrated assessment models) from the ECEMF project. These scenarios are tailored to be diagnostic and reveal model behavior. The analysis yields a set of diagnostic indicators and model fingerprint diagrams in which model behavior can be distinguished.
Information on the scenarios can be found publicly on Zenodo, both the dataset and the protocol. In our code, we read in the scenario data automatically from the IIASA database, using the pyam package. No credentials are needed for the public version of this database. To obtain the up-to-date ECEMF internal database, the user can adapt the config.ini file.
In config.yaml, you can set general settings for the calculations. The file Main.ipynb first initializes class class_indicatorcalculation.py that downloads the scenario data and reformats this into a netcdf file called XRdata.nc, accessible and saved into the Data directory. Subsequently, in Main.ipynb, the class class_indicatorcalculation.py computes the diagnostic indicators from this netcdf file, producing another netcdf file XRindicators.nc, which includes all indicators by model and scenario. The plotting scripts can be found in the Plotting directory, and they read the aforementioned netcdf files, storing the figures in the Figures directory.
The paper in Nature Energy can be found here: https://www.nature.com/articles/s41560-023-01399-1
This work was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 101022622 (European Climate and Energy Modelling Forum ECEMF).