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ZENODO
Software . 2026
Data sources: ZENODO
ZENODO
Software . 2026
Data sources: Datacite
ZENODO
Software . 2026
Data sources: Datacite
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Data used in the figures of the paper "The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7) " by van Vuuren et al (2026)

Authors: Sanderson, Benjamin; Sandstad, Marit; Smith, Chris; Kikstra, Jarmo; Bauer, Nico; Chini, Louise; Eyring, Veronika; +37 Authors

Data used in the figures of the paper "The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7) " by van Vuuren et al (2026)

Abstract

Archival release of scenariomip-paper-plots, the code used to produce the FaIR v2.2 climate simulations and figures.DATA ARE INDICATIVE AND FOR ILLUSTRATIVE PURPOSES ONLY. Please use the final projections of the ScenarioMIP pathways for CMIP7 in your research here: https://scenariomip.apps.ece.iiasa.ac.at What this release contains A reproducible pipeline that runs FaIR v2.2 over 1750–2501 across the seven ScenarioMIP-CMIP7 emissions pathways (VL, LN, L, ML, M, H, HL), spanning very low to very high / high-legacy emissions futures. A single-source plotting module (scripts/plotting.py) used by both a headless CLI (scripts/make_plots.py) and an interactive Jupyter notebook (notebooks/0505_extensions_plotting.ipynb) to regenerate every figure in the paper. Input emissions/forcing data version-controlled in data/; AR6 FaIR calibration parameters fetched from Zenodo at runtime; cached ensemble output written to data/fair-outputs/fair_run.nc. Scientific context ScenarioMIP-CMIP7 defines a set of emissions-driven scenarios spanning high-end, current-policy, and Paris-aligned futures, including pathways that peak and decline in greenhouse gas concentrations during the 21st century. The simulations archived here illustrate the climate response of these scenarios in a reduced-complexity model (FaIR v2.2 with the AR6 calibrated ensemble) and underpin the figures in the companion GMD paper. Reproducing the figures git clone https://github.com/benmsanderson/scenariomip-paper-plots cd scenariomip-paper-plots python3 -m venv .venv && source .venv/bin/activate pip install -r requirements.txt python scripts/make_plots.py # writes all figures to plots/ Requires Python 3.10+. See the README for the notebook workflow and a memory-limited 5-member test ensemble. Citation If you use this code, please cite both the paper and this Zenodo archive. Paper: Van Vuuren, D. P., O'Neill, B. C., Tebaldi, C., Sanderson, B. M., Chini, L. P., Friedlingstein, P., Hasegawa, T., Riahi, K., Govindasamy, B., Bauer, N., Eyring, V., Fall, C. M. N., Frieler, K., Gidden, M. J., Gohar, L. K., Högner, A., Jones, A. D., Kikstra, J., King, A., Knutti, R., Kriegler, E., Lawrence, P., Lennard, C., Lowe, J., Mathison, C., Mehmood, S., Nicholls, Z., Prado, L. F., Zhang, Q., Rose, S. K., Ruane, A. C., Sandstad, M., Schleussner, C.-F., Seferian, R., Sillmann, J., Smith, C., Sörensson, A. A., Panickal, S., Tachiiri, K., Vaughan, N., Vishwanathan, S. S., Yokohata, T., Zecchetto, M., and Ziehn, T.: The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7), Geosci. Model Dev., 19, 2627–2656, https://doi.org/10.5194/gmd-19-2627-2026, 2026. Archive: Sanderson, B. M., Sandstad, M., Smith, C., Kikstra, J., Bauer, N., Chini, L. P., Eyring, V., Fall, C. M. N., Friedlingstein, P., Frieler, K., Gidden, M. J., Gohar, L. K., Govindasamy, B., Hasegawa, T., Högner, A., Jones, A. D., King, A., Knutti, R., Kriegler, E., Lawrence, P., Lennard, C., Lowe, J., Mathison, C., Mehmood, S., Nicholls, Z., O'Neill, B. C., Panickal, S., Prado, L. F., Riahi, K., Rose, S. K., Ruane, A. C., Schleussner, C.-F., Seferian, R., Sillmann, J., Sörensson, A. A., Tachiiri, K., Tebaldi, C., Van Vuuren, D. P., Vaughan, N., Vishwanathan, S. S., Yokohata, T., Zecchetto, M., Zhang, Q., and Ziehn, T.: Data used in the figures of the paper "The Scenario Model Intercomparison Project for CMIP7 (ScenarioMIP-CMIP7) " by van Vuuren et al (2026), Zenodo, https://zenodo.org/doi/10.5281/zenodo.20713982, 2026. License Released under the MIT License — see LICENSE in the repository.

If you use this software, please cite the associated paper below.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average