IAMfingerprints

Suite for energy model diagnostics. Latest release can be found on Zenodo:

Setup

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.)

Introduction

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.

Data

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.

Reproduce paper results

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.

Create your own model fingerprint

For modellers outside of ECEMF, it may be interesting to use this code and scenario ensemble to evaluate their own fingerprint. We provide a means to do so. Follow these steps:

1. Add your scenario data to the folder Data/Own_model folder. Take note of the format of the example file that we provided there (IMAGE_example.xlsx). Make sure that you have a unique model name, other than that is already inside the ECEMF database. Also, it is currently required that you use one of the diagnostic scenario names - running a completely different scenario would be comparing apples and pears.
2. Navigate to the Compute_own_fingerprint notebook in the Calculations folder. At the top, you can insert some general parameters such as the year and region of your scope. The defaults are Europe in 2050. Also the name of the file that you added to the Data/Own_model folder needs to be inserted here.
3. Run all cells in the notebook, and at the bottom you will find your model fingerprint.

References

The paper in Nature Energy can be found here (from Nov 6th and on): https://www.nature.com/articles/s41560-023-01399-1

The preprint on Research Square is found here: https://www.researchsquare.com/article/rs-2638588/v1

Acknowledgments

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).