DisclaimerThe Reduced Complexity Model Intercomparison Project (RCMIP) is about reduced-complexity, simple climate models and emulators. It focusses on evaluating these models by e.g. comparing them with observations of our climate system or examining their ability to emulate a range of CMIP6 coupled models. RCMIP is hence not one of the standard "MIPs" that are part of the Sixth Coupled Model Intercomparison Project's (CMIP6) (see CMIP6 protocols by the World Climate Research Programme here).

RCMIP's current status (November 2022)

RCMIP Phase 1 and 2 have been written up and published in the scientific literature and the outputs from these two phases are publicly available (see 'Phases, manuscripts and data' section below for details). Both those papers were cited in the Working Group 1 Contribution to the IPCC's Sixth Assessment Report (go looking in Chapter 7 to find them).

We are currently starting to plan for Phase 3 of RCMIP, which will focus on the carbon cycle. If you are a modelling group interested in joining RCMIP, please contact us (see contact details at the bottom of the page) and we will send you details when Phase 3 begins.

AGU 2022 Session: Making Climate Models & Data Actionable: Evaluating and Quantifying Uncertainty in Climate Models and Emulators

Closely related to RCMIP, there will be an AGU 2022 session which is partly on simple climate models. For anyone attending AGU 2022, the details are below:

  • Tuesday, December 13 Oral Session

  • Wednesday, December 14 in person posters and online posters

Session description

Increasingly there are calls to enhance the usability of climate science, often framed in terms of co-producing actionable science with stakeholder communities. Within the context of climate modeling, co-production with stakeholders is often difficult to achieve. Models are the result of research and labor distributed across large collections of practitioners and disciplines, often leading to a considerable gap between the producer and user communities. The evaluation, confirmation, and increase in credibility of global and regional models, in both abstract and applied settings, has received attention from philosophers and climate scientists alike. Compounding the difficulties of communicating between climate modelers and users of models, the information needs of the users usually differ from the research priorities of the modelers, especially in terms of scales, and values about what is represented in the models. Actionable knowledge is often localized and seasonal to decadal, while global climate models have traditionally provided information about globally oriented processes and conditions on longer than decadal time scales. This session focuses on presenting innovative and potentially generalizable approaches to characterizing uncertainties, and demonstrating and applying recent methods for climate model-building and evaluation, and climate attribution, including analysis of extreme events. Connecting methods for characterizing uncertainty or model support to implementation of climate-response policies to assess the risks posed by climate change to natural systems, are very welcome, all with the goal of informing effective future societal action.

EGU 2023 Session: simple climate models - development and applications

Closely related to RCMIP, there will be an EGU 2023 session on simple climate models – development and applications. Keep an eye out for announcements around EGU and please join us there!

Overview of RCMIP

Assessing how humans change the climate is a complex task, best investigated by complex Earth System Models. However, coupled atmosphere-ocean-biogeochemistry models are computationally expensive. Thus there is a need for emulators that are able to replicate some aggregate response characteristics of Earth System Models at a fraction of the computational cost. With such emulators, we can investigate uncertainties and simulate hundreds of possible future emission scenarios, rather than only a handful.

These emulators, ranging from one-line climate models to models with tens of thousands lines of code, are only useful if their inherent simplifications and parameterisations nonetheless produce reasonable behaviour (for their limited domains of interest). As a result, a systematic way to assess these emulators is required. This is what RCMIP is about.

RCMIP provides a standard protocol for one-line models, simple and reduced complexity models (henceforth we refer to the whole basket of models as RCMs) to perform experiments based on a common set of assumptions. Further, alongside a dedicated Python package (pyrcmip) and related CMIP6 data processing, RCMIP's protocol facilitates direct comparison with latest CMIP6 results. This provides a standardised test of the ability of RCMs to replicate ESM projections for e.g. surface air temperatures, ocean heat uptake, gas cycles, effective radiative forcing and sea level rise.

RCMIP takes place over multiple stages (see the Phases, manuscripts and data section). Participation in RCMIP is open to all modelling groups who have peer-reviewed scientific papers that document their models or applications. If you have an idea for a phase of RCMIP, please don't hestitate to reach out. All data submitted to RCMIP will be published in dedicated data archives under a Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license (see data archives in the Phases, manuscripts and data section).

To our knowledge, RCMIP constitutes the first systematic intercomparison project among reduced-complexity climate models. The longest tradition of systematic intercomparisons is without doubt those performed by the coupled model community while models of intermediate complexity (EMICs) have also performed systematic intercomparisons in the past. RCMIP is lucky to build on the work by many others to compare RCMs e.g. van Vuuren et al. Climatic Change 2011. Nonetheless, we believe that RCMIP presents a significant step forward in terms of standardisation, reproducibility and automation.

About

Governance

The RCMIP project is supervised by a Panel of international stakeholders (Malte Meinshausen, Maisa Corradi, Piers Forster, Jan Fuglestvedt, Joeri Rogelj, Steven Smith). The RCMIP secretariat is at the Climate & Energy College of the University of Melbourne, headed by Zebedee Nicholls, a modeller with experience developing both the FaIR and MAGICC reduced-complexity models, a developer of the OpenSCM toolbox and Contributing Author to multiple chapters in AR6 Working Group 1 and Working Group 3, and Malte Meinshausen, lead developer of MAGICC, contributor to multiple IPCC reports and Lead Author in the IPCC AR6 Working Group I.

Relation to CMIP

As stated in the disclaimer, RCMIP is NOT focussed on coupled climate models and hence is not one of CMIP6's endorsed MIPs. It relies on the results of CMIP6 and uses the 'MIP' ending because it a) makes sense (being a model intercomparison project) and b) pays homage to the incredible efforts of the CMIP community over the years.

Invited Modelling Groups

RCMIP is open to all modelling groups, which have scientific peer-reviewed papers that document their models or model applications. The invite remains open, so please don't hesitate to get in contact if you would like to join our mailing list.

Phases, manuscripts and data

Phase 1

Phase 1 focused on introducing the RCMIP concept, the different participating RCMs and evaluating the global-mean temperature response of the models in a range of experiments. Phase 1 ran from November 2019 until March 2020. After an extensive review process, the RCMIP Phase 1 manuscript was accepted in Geoscientific Model Development.

Phase 1 invite letter available here

Phase 2

Phase 2 focused on the ability of RCMs to reproduce ranges of a number of different climate metrics e.g. historical warming, equilibrium climate sensitivity, transient climate response, transient climate response to emissions and aerosol effective radiative forcing. Phase 2 begun in July 2020 and was published in May 2021.

RCMIP protocol

Overview

The design principle behind the RCMIP protocol is relatively simple: to evaluate reduced-complexity model behaviour for key climate metrics (like globally-averaged surface air temperature). To facilitate comparisons with CMIP6, RCMIP replicates the same scenario design as many of the CMIP6-endorsed MIPs.

Specific RCMIP Design

The RCMIP protocol consists of definitions of RCMIP scenario designs, output variable definitions and model version definitions. The output variable definitions follow a standard template that allows for simple data transfer and compatibility with the design specifications used among the Integrated Modelling Assessment Consortium (IAMC). We decided not to follow the CF-compliant netCDF standard used by the CMIP6 experiment as many simple models do not have the capability to write netCDF data and applying strict CF-compliance isn't appropriate for RCMs. The scenario design matches the scenario specifications used by the respective CMIP6-endorsed MIP protocols, adapted for simple climate models that only have a single global box or a few boxes. RCMIP also provides a standardised way for models to report the versions and settings used in their emulations.

If a model were to complete all available experiments, a data submission of approximately 50GB (multi-million data points) would result. However, all RCM modelling groups are free to submit a subset of the data, to match their own capability.

Scenarios / Experiment

Experiments currently implemented in the RCMIP protocol focus on concentration and emissions driven scenarios as well as some basic benchmarking tests (e.g. 1pctCO2, abrupt-4xCO2, historical). Further experiments examine the behaviour of the models in other idealised settings e.g. (1pctCO2-bgc, 1pctCO2-rad, esm-1pct-brch-1000PgC, esm-bell-1000PgC). The full list of proposed scenarios can be found in the RCMIP data submission template on the sheet "scenario_info". The specific descriptions and links to datasources for these CMIP6 scenarios are documented in https://search.es-doc.org/ (Set the "Project" Filter to "CMIP6" and the "Document Type" Filter to "Experiment" to see the full list of experiments).

Output variables

The full list of requested output variables is provided on the sheet "variable_definitions" of the RCMIP data submission template. Those comprise emissions, carbon cycle fluxes, radiative forcings, effective radiative forcings, surface air temperatures, (ocean) surface temperatures and (ocean) heat uptake. While there are 10s of key variables (labelled as Tier 1), the total number of potential variables that a model could (and ideally should) report on is currently around 300. The bulk of those are emissions, concentrations, and forcings of the various greenhouse gases and aerosols.

Datasets for RCMIP experiment protocol

The experiment protocol is in a comma-separated variable (csv) format which is compatible with scmdata and pyam.

Latest

Citation

If you use this data, please cite (bibtex available from https://gmd.copernicus.org/articles/13/5175/2020/gmd-13-5175-2020.bib)

Nicholls, Z. R. J., Meinshausen, M., Lewis, J., Gieseke, R., Dommenget, D., Dorheim, K., Fan, C.-S., Fuglestvedt, J. S., Gasser, T., Golüke, U., Goodwin, P., Hartin, C., Hope, A. P., Kriegler, E., Leach, N. J., Marchegiani, D., McBride, L. A., Quilcaille, Y., Rogelj, J., Salawitch, R. J., Samset, B. H., Sandstad, M., Shiklomanov, A. N., Skeie, R. B., Smith, C. J., Smith, S., Tanaka, K., Tsutsui, J., and Xie, Z.: Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response, Geosci. Model Dev., 13, 5175–5190, https://doi.org/10.5194/gmd-13-5175-2020, 2020.

As described in Supplementary Section S2 of Nicholls et al. GMD 2020, our data is compiled from a number of other data sources. Please cite the appropriate original resources too if you use the data (all the references are available in the bibtex format at https://gitlab.com/rcmip/rcmip/-/blob/master/paper-final/bibliography.bib, alternately in paper-final/bibliography.bib of the zip file available at https://doi.org/10.5281/zenodo.4016613).

CMIP6 emissions

A notebook documenting the entire emissions dataset compilation is available at (https://gitlab.com/rcmip/rcmip/-/blob/master/notebooks/protocol-generation/emissions.ipynb, alternately in notebooks/protocol-generation/emissions.ipynb of the zip file available at https://doi.org/10.5281/zenodo.4016613).

a) 2015-2100 emissions of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), black carbon (BC), carbon monoxide (CO), ammonia (NH3), nitrogen oxides (NOx), organic carbon (OC), sulfur oxides (SOx), (non-methane) volatile organic compounds (VOCs), CF4, C2F6, and SF6.

Raw data available from https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=60.

Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geoscientific Model Development, 12, 1443–1475, https://doi.org/10.5194/gmd-12- 1443-2019, 2019

b) 2015-2100 emissions of HFC32, HFC125, HFC134a, HFC143a, HFC152a, HFC227ea, HFC236fa, HFC245fa, HFC365mfc, HFC4310mee.

Inverse emissions based on Meinshausen et al. GMD 2020. The Meinshausen et al. GMD 2020 concentrations are derived using a combination of Gidden et al. GMD 2019 and Velders et al. Atmos. Env. 2015.

Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geoscientific Model Development, 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, https://gmd.copernicus.org/articles/13/3571/2020/, 2020

Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geoscientific Model Development, 12, 1443–1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019

Velders. G. J. M., Fahey, D. W., Daniel, J. S., Andersen, S. O. and McFarland, M.: Future atmospheric abundances and climate forcings from scenarios of global and regional hydrofluorocarbon (HFC) emissions, Atmospheric Environment, 123, 200-209, https://doi.org/10.1016/j.atmosenv.2015.10.071, 2015

c) Post-2100 emissions of all the above species follow the description in Meinshausen et al. GMD 2020.

Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geoscientific Model Development, 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, https://gmd.copernicus.org/articles/13/3571/2020/, 2020

d) 1750-2014 anthropogenic (excluding biomass burning) emissions of carbon dioxide (CO2), methane (CH4), black carbon (BC), carbon monoxide (CO), ammonia (NH3), nitrogen oxides (NOx), organic carbon (OC), sulfur oxides (SOx) and (non-methane) volatile organic compounds (VOCs).

Raw data available in the supplementary material of Hoesly et al. GMD 2018

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., Bond, T. C., Dawidowski, L., Kholod, N., Kurokawa, J.-i., Li, M., Liu, L., Lu, Z., Moura, M. C. P., O’Rourke, P. R., and Zhang, Q.: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS), Geoscientific Model Development, 11, 369–408, https://doi.org/10.5194/gmd-11-369-2018, 2018.

e) 1750-2014 biomass burning emissions of methane (CH4), black carbon (BC), carbon monoxide (CO), ammonia (NH3), nitrogen oxides (NOx), organic carbon (OC), sulfur oxides (SOx) and (non-methane) volatile organic compounds (VOCs).

Raw data available in the supplementary material of van Marle et al. GMD 2017

van Marle, M. J. E., Kloster, S., Magi, B. I., Marlon, J. R., Daniau, A.-L., Field, R. D., Arneth, A., Forrest, M., Hantson, S., Kehrwald, N. M., Knorr, W., Lasslop, G., Li, F., Mangeon, S., Yue, C., Kaiser, J. W., and van der Werf, G. R.: Historic global biomass burning emissions for CMIP6 (BB4CMIP) based on merging satellite observations with proxies and fire models (1750–2015), Geoscientific Model Development, 10, 3329–3357, https://doi.org/10.5194/gmd-10-3329-2017, 2017.

f) 1750-2014 carbon dioxide (CO2) land-use emissions

1959-2014 emissions from Global Carbon Budget 2016 (Quéré et al. ESSD 2016, raw data in supplementary material), combined with regional information and 1850-1959 emissions from PRIMAP-hist Version 1.0 (Gütschow et al. 2016, raw data at https://doi.org/10.5880/PIK.2016.003). 1750-1850 historical emissions derived by assuming a constant relative rate of decline based on 1850-1860.

Quéré, C. L., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Peters, G. P., Manning, A. C., Boden, T. A., Tans, P. P., Houghton, R. A., Keeling, R. F., Alin, S., Andrews, O. D., Anthoni, P., Barbero, L., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Currie, K., Delire, C., Doney, S. C., Friedlingstein, P., Gkritzalis, T., Harris, I., Hauck, J., Haverd, V., Hoppema, M., Goldewijk, K. K., Jain, A. K., Kato, E., Körtzinger, A., Landschützer, P., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Melton, J. R., Metzl, N., Millero, F., Monteiro, P. M. S., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-i., O’Brien, K., Olsen, A., Omar, A. M., Ono, T., Pierrot, D., Poulter, B., Rödenbeck, C., Salisbury, J., Schuster, U., Schwinger, J., Séférian, R., Skjelvan, I., Stocker, B. D., Sutton, A. J., Takahashi, T., Tian, H., Tilbrook, B., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., and Zaehle, S.: Global Carbon Budget 2016, Earth System Science Data, 8, 605–649, https://doi.org/10.5194/essd-8-605-2016, 2016.

Gütschow, J., Jeffery, M. L., Gieseke, R., Gebel, R., Stevens, D., Krapp, M., and Rocha, M.: The PRIMAP-hist national historical emissions time series, Earth System Science Data, 8, 571–603, https://doi.org/10.5194/essd-8-571-2016, 2016.

g) 1750-2014 nitrous oxide (N2O) emissions

1850-2014 emissions from PRIMAP-hist Version 1.0 (Gütschow et al. 2016, raw data at https://doi.org/10.5880/PIK.2016.003). 1750-1850 historical emissions derived by assuming a constant relative rate of decline based on 1850-1860.

Gütschow, J., Jeffery, M. L., Gieseke, R., Gebel, R., Stevens, D., Krapp, M., and Rocha, M.: The PRIMAP-hist national historical emissions time series, Earth System Science Data, 8, 571–603, https://doi.org/10.5194/essd-8-571-2016, 2016.

h) Emissions of species controlled under the Montreal Protocol: CFC11, CFC12, CFC113, CFC114, CFC115, HCFC22, HCFC141b, HCFC142b, CH3Br, CH3Cl, CH3CCl3, CCl4, Halon1202, Halon1211, Halon1301 and Halon2402.

WMO: Scientific Assessment of Ozone Depletion: 2006, World Meteorological Organization, Geneva, Switzerland, 572 pp. 2006.

WMO: Scientific Assessment of Ozone Depletion: 2014, World Meteorological Organization, Geneva, Switzerland, 416 pp., 2014.

i) Any species and time periods not elsewhere discussed.

Inverse emissions based on Meinshausen et al. GMD 2020 (and the methodology described therein).

Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geoscientific Model Development, 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, https://gmd.copernicus.org/articles/13/3571/2020/, 2020

CMIP6 concentrations

Raw data available from https://esgf-node.llnl.gov/projects/input4mips/ with further information available at https://greenhousegases.science.unimelb.edu.au/

Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geoscientific Model Development, 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571- 2020, https://gmd.copernicus.org/articles/13/3571/2020/, 2020

CMIP6 effective radiative forcings

Note that the effective radiative forcings are only one estimate of the effective radiative forcings arising from CMIP6 concentrations and emissions. Other studies report different results and the quantification used here is only used where models would otherwise exclude the relevant effect.

Raw data available from https://doi.org/10.5281/zenodo.3515339

Smith, Christopher J. (2019, October 21). Effective Radiative Forcing from Shared Socioeconomic Pathways (Version v0.3.1). Zenodo. http://doi.org/10.5281/zenodo.3515339

CMIP5 emissions, concentrations and radiative forcings

Raw data available from http://www.pik-potsdam.de/~mmalte/rcps/, citation

Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M., and van Vuuren, D. P.: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109, 213–241, https://doi.org/10.1007/s10584-011-0156-z, 2011.

Download

Emissions (46MB): https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv

Concentrations (20MB): https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-concentrations-annual-means-v5-1-0.csv

Radiative forcing (5MB): https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-radiative-forcing-annual-means-v5-1-0.csv

Version 5.1.0, 21st September 2020 (also archived at Zenodo)

RCMIP protocol and data output template

The output data template is in a spreadsheet format. The latest version is available below. You can submit data from your reduced complexity climate model, simple model and/or emulator with that data output template.

RCMIP data output template and protocol (82KB). Download below.

https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-data-submission-template-v5-1-0.xlsx

Version 5.1.0, 21st September 2020 (also archived at Zenodo)

Contact & Submission

Before submitting your data, please email Zebedee Nicholls and cc datasubmission@rcmip.org to get details of how best to transfer the data. Instructions for preparing a submission can be found at https://pyrcmip.readthedocs.io/en/latest/submitting_results.html.

Zebedee Nicholls, zebedee.nicholls@climate-energy-college.org

Malte Meinshausen, malte.meinshausen@unimelb.edu.au

Organisors and Supporters

RCMIP is currently an unfunded activity, supported by the Climate & Energy College at the University of Melbourne. In case you want to become a partner organisation and can support RCMIP, we would be glad to hear from you.