Code and supplementary data for "Fast energy transitions can reduce climate tipping risks"

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:GX researchers should note the modeling framework linking transition speed to tipping risks, which is useful for scenario analysis and policy advocacy.

🏢実務担当者:Corporate sustainability teams can use this to advocate for aggressive internal decarbonization timelines aligned with climate science.

🏛政策担当者:Policymakers should consider the urgency highlighted by this study when setting nationally determined contributions and transition plans.

This repository provides code and data to reproduce results of the article ### *Fast energy transitions can reduce climate tippin risks* Hauke Schlesier, Nico Wunderling, Gonzalo Guillen Gosalbez, Harald Desing ## Files _______________ ### CODE * `3machines.InsightMaker` Insight Maker 3machine energy transition model. * `00_emission_assembly.py` Python code to produce emission trajectories. * `01_FaIR_emulator.py` Python code to convert emission trajectories to temperature trajectories. * `02_temp_extension.py` Python code to extent temperature trajectories. * (`02_provide_extension.py` Python code to extent provide temperature trajectories, optional.) * `03_temp_conversion.py` Python code to convert temperature trajectories to .txt files for 05_multiprocess_pycascade.py. * `04_monte_carlo_ensemble.py` Python code to produce the ensemble members to propagate tipping related uncertainties. * `05_multiprocess_pycascade.py` Python code to calculate tipping risks. * `MAIN.py` Python code to run 00 to 05. * `core` pycascades model scripts. * `earth_sys` pycascades model scripts. * `loader.py` Python code to load tipping results. * `overshoot_evalution.ipynb` Python code to plot results. Paper: H. Schlesier, N. Wunderling, G. Guillen Gosalbez, H. Desing, [Fast energy transitions can reduce climate tipping risks](link), Journal (2026). ## DATA ### INPUT data: * `3machines/3machines.InsightMaker`: 3-Machine energy transition model for import in InsightMaker * `3machines/3machines_parametrization.xlsx`: 3-Machine energy transition model parametrization * `3machines/All-in_EL_{Tconv}_{year}.csv`: 3-Machine energy transition emissions * `provide/tier1_temperature_summary.csv`: PROVIDEv1.2 temperature trajectories * `assembly/ar6_snapshot_1773335475_update.xlsx`: Current Policies AR6 Database scenario emissions * `assembly/C1C2_emission_data.csv`: AR6 Database C1/C2 AFOLU emissions * `assembly/volcanic_solar_ext.csv`: Background radiative forcing * `fair_calibration/calibrated_constrained_parameters_calibration1.4.1.csv`: FaIR emulator climate calibration * `fair_calibration/species_configs_properties_calibration1.4.1`: FaIR emulator GHG species properties from [The 3-Machines Energy Transition Model: Exploring the Energy Frontiers for Restoring a Habitable Climate](https://doi.org/10.1029/2022EF002875) (Desing et al., 2022) from [Scenario emissions and temperature data for PROVIDE project](https://zenodo.org/record/7194542) (Robin Lamboll, Joeri Rogelj, Carl-Friedrich Schleussner, 2022) from [AR6 Scenario Explorer and Database hosted by IIASA] (https://data.ece.iiasa.ac.at/ar6/) (Byers et al., 2022) from [Finite-amplitude Impulse Response simple climate model] (https://github.com/OMS-NetZero/FAIR/tree/master/examples/data/calibrated_constrained_ensemble) [Leach et al., 2021] ## Required modules _______________ python: * numpy * pandas * matplotlib * cycler * glob * re * sys * os * pyDOE * scipy * seaborn * time * itertools * netCDF4 * networkx * tqdm * pathlib * fair * importlib * multiprocessing ## Description _______________ The 3-Machines energy transition model can be run by importing `/model/data/3machines/3machines.InsightMaker` into InsightMaker (https://insightmaker.com) and importing the parameters for the individual scenario parameters from the `scenario` sheet in `/model/data/3machines/3machines_parametrization.xlsx`. For reproducing our results, the emission timeseries of our energy transition scenarios can be found in `/model/data/3machines/`. The executable python scripts need to be run in the indicated order either individually or by running `MAIN.py`. For execution, `core` and `earth_sys` need to be saved in the same folder as `05_multiprocess_pycascade.py`. DEMO: By default, `MAIN.py` is set to run with a test system of all ensemble members on a limited pycascade simulation time of 1000 years, one network configuration, one coupling strengh, and for 100 Monte Carlo runs. This works as a demo on an ordinary pc. Running all scripts in demo mode takes about 5 minutes. Expected output is shared in `results_1000demo`. For the full ensemble of model simulations, scripts need to be run with demo mode diabled. For this, switch the variable mode from 'demo' to '' in all Python scripts 01 to 05. This code was implemented in Python 3.14.0 and run on an Apple M4 Pro v15.6.1. For each script, it is advised to first check dependencies. _________________________ H. Schlesier, 28.05.2026

• Zenodo https://zenodo.org/records/20426450first seen 2026-05-29 04:14:24 · last seen 2026-05-31 04:14:05