Experiment Setup and Forcings Guidance: vl-cf
Experiment Setup and Forcings Guidance: vl-cf¶
Counterfactual emissions pathway that is as physically consistent as possible, while aiming for global surface air
temperature to peak at 1.5C and stabilise or slowly decline.
Run with prescribed carbon dioxide concentrations (for prescribed carbon dioxide emissions, see esm-vl-cf).
- Responsible activity: PolMIP
- Tier: 2
- MIP co-chair review: In progress see https://github.com/WCRP-CMIP/cmip7-guidance/issues/226
- Tags:
This page is intended to help with implementation. If you notice something that is unclear, please raise an issue.
For the full background of the experiment, please see the following references:
- Zhong, J., Zhang, X., Zhang, D., Wang, D., Guo, L., Peng, H., Huang, X., Wang, Z., Lei, Y., Lu, Y., Qu, C., Zhang, X., & Miao, C. (2025). Plausible global emissions scenario for 2 °C aligned with China’s net-zero pathway. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-62983-5
- Zhang, X., Zhong, J., Zhang, X., Zhang, D., Miao, C., Wang, D., & Guo, L. (2025). China Can Achieve Carbon Neutrality in Line with the Paris Agreement’s 2 °C Target: Navigating Global Emissions Scenarios, Warming Levels, and Extreme Event Projections. Engineering, 44, 207–214. https://doi.org/10.1016/j.eng.2024.11.023
- Zhang, D., Huang, X.-D., Zhong, J.-T., Guo, L.-F., Guo, S.-Y., Wang, D.-Y., Miao, C.-H., Zhang, X.-L., & Zhang, X.-Y. (2023). A representative CO2 emissions pathway for China toward carbon neutrality under the Paris Agreement’s 2 °C target. Advances in Climate Change Research, 14(6), 941–951. https://doi.org/10.1016/j.accre.2023.11.004
- Lu, Y., Jin, L., Zhong, J., Zhang, X., Zhang, Y., Wu, F., Zhang, F., Wang, Z., Zhang, J., Xin, X., Wu, T., Wang, D., Zhang, D., Wang, T., & Hua, W. (2025). Earth system responses under a global 2 °C-target scenario aligned with China’s carbon neutrality pledge. Environmental Research Letters, 20(10), 104049. https://doi.org/10.1088/1748-9326/adfbfb
- Högner, A., Sandstad, M., Kikstra, J., Nauels, A., Nicholls, Z., Sanderson, B., Smith, C., Zecchetto, M., & Schleussner, C.-F. (2026). The CMIP7 VL-CF counterfactual emissions pathway dataset v1.1.1 documentation [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21487424
Experiment set up¶
Parent experiment and branching¶
The vl-cf experiment branches from the historical experiment (part of CMIP). The parent experiment's MIP era is CMIP7.
Branch from historical at the start of year 2016 (i.e. 2016-01-01).
Output time axis¶
Your output time axis must start on 2016-01-01 and must end on 2100-12-31. You must perform the full simulation i.e. 85 simulation years.
Minimum ensemble size¶
Only one ensemble member is required.
Forcings¶
The following information will help you identify the forcings to use. However, we can't define every single detail because there can be lots of subjective steps between the raw forcings data and model inputs (e.g. interpolation, re-aggregation, supplementation with other information). If further guidance would be helpful, please raise an issue.
General headlines¶
The vl-cf experiment is a transient forcings experiment.
Data¶
Here we make a distinction between data that is described on other experiment pages, data that is described on other experiment pages with modifications you have to make yourself, data available via ESGF's input4MIPs project and data distributed via other channels.
Data described on other experiment pages¶
For the following data, please see these other experiment pages:
- historical for anthropogenic emissions, biomass burning emissions, land use, stratospheric aerosol forcing, ozone, nitrogen deposition, solar, aerosol optical properties, population density
- scen7-vl for anthropogenic emissions, biomass burning emissions, land use, stratospheric aerosol forcing, ozone, nitrogen deposition, solar, aerosol optical properties, population density
Data described on other experiment pages with modifications you have to make¶
No data described on other experiment pages requires modifications by you. Please see the other data sub-sections for details of the forcings data to use for this experiment.
Data available via input4MIPs¶
Versions to use¶
For each forcing available via input4MIPs, we provide the version(s), called 'source ID(s)' in the file's metadata, which should be used when running this simulation. The recommended version(s) are the version(s) we recommend using. Any acceptable versions can be used (you are not obliged to re-run simulations that used them). Please see the guidance pages linked under each forcing for full details.
- greenhouse gas concentrations
- recommended source IDs: CR-vl-cf-1-1-0
- further guidance: input4mips-cvs.readthedocs.io/dataset-overviews/greenhouse-gas-concentrations
JSON¶
For easier parsing with machines, we also present the information given above as JSON.
{
"greenhouse-gas-concentrations": {
"human_readable_name": "greenhouse gas concentrations",
"recommended_versions": [
"CR-vl-cf-1-1-0"
],
"acceptable_versions": []
}
}
Download via esgpull¶
The data is on ESGF and searchable via metagrid, although this method of finding and downloading the data can involve a lot of clicking.
If you install esgpull, you can download all the data associated with the recommended source IDs above using the script given below. Note that this will download all the data associated with these source IDs, which is likely to be much more data than you actually need to run your model.
#!/bin/bash
EXPERIMENT_NAME="vl-cf"
## You may need to run the below if you haven't already done it once with esgpull
# esgpull self install
## You may also need to run this step to get the data to download
# esgpull config api.index_node esgf-node.ornl.gov/esgf-1-5-bridge
esgpull add --track --tag ${EXPERIMENT_NAME} source_id:CR-vl-cf-1-1-0
esgpull update --tag ${EXPERIMENT_NAME} --yes
esgpull download --tag ${EXPERIMENT_NAME}
Data not available via input4MIPs¶
No data that is not input4MIPs-based is described specifically on this page. Please see the other data sub-sections for details of the forcings data to use for this experiment.