Coupled Model Intercomparison Project Phase 6 - CMIP6

Summary Description

The Coupled Model Intercomparison Project Phase 6 (CMIP6) is the latest iteration of the international effort of providing comparable, state of the art, global climate simulations from multiple modeling centers around the world. CMIP is coordinated by the World Climate Research Programme (WCRP) and is a cornerstone dataset for the Intergovernmental Panel on Climat Changes Assessment Reports (IPCC AR6). CMIP6 includes historical simulations, future scenarios (SSPs), and targeted experiments supporting studies on climate evolution, variability, extremes, and impacts. With the definition of simulation protocols CMIP enables the meaningful comparison of model responses. More than 140 models from 52 institutions representing 26 countries contributed to CMIP6.

Schematic of the CMIP/CMIP6 experiment design (Source: Eyring et al. (2016))

Schematic of the CMIP/CMIP6 experiment design
(Source: Eyring et al. (2016))

Conceptual visualisation of a Global Climate Model (GCM)

Conceptual visualisation of a Global Climate Model (GCM)

CMIP6 Global Climate Model Experiments

Dataset Characteristics

  • Current version: CMIP6 (2019–present)
  • Temporal coverage: 1850–2100+
  • Temporal resolution: model dependent, from hourly to monthly, all models provide daily.
  • Spatial coverage: Global (land + ocean)
  • Spatial resolution: ~1° to ~2° depending on model
  • Data type: Gridded NetCDF (multi-variable, multi-model)
  • Web references:
    CMIP Phase 6 on WCRP web page
    CMIP Phase 6 on PCMDI web page (includes a map of the locations of modeling centers)
  • Reference publications:
    See references below

Strengths and Limitations

Key Strengths of CMIP6

Strength Description
Reference Dataset CMIP is THE reference for simulations of earths climate.
Scenario Diversity Explores possible future climate under multiple Shared Socioeconomic Pathways (SSPs) and targeted experiments.
Multi-Model Ensemble The variety of models, emission scenarios and member simulations allows to explore the uncertainty associated with climate projections through ensemble approaches.
Global Coverage Provides climate projections for the entire globe, both land and ocean.
Alignment with IPCC CMIP experiments align with the Intergovernmental Panel’s on Climate Change (IPCC) Assessment Reports.

Key Limitations of CMIP6

Limitation Description
Coarse Resolution Grid spacing (~100 km) is often too coarse for local and regional climate change assessment.
Biases in Climate Models Models are imperfect and have biases and limitations in their process representations.
Raw data insufficiency Often requires downscaling and bias-correction for applied studies.
Data Size and Complexity Multi-model, multi-variable datasets require expansive computer resources.

Expert Guidance

Since its launch in 1995 the Coupled Model Intercomparison Project (CMIP) has coordinated international effort to improve understanding of past, present, and future climate. Each subsequent phase of the project provided ensembles of global climate model (GCM) simulations produced by research centers worldwide. These models have evolved through CMIP generations improving their representation of the physics, chemistry, and dynamics of the Earth system and are run under standardized experiments to explore key questions about climate variability and change, over Earth’s land and oceans.

Strengths

A key strength of CMIP is the global coordination and transparency, with CMIP6 being the latest most comprehensive and standardized collection of GCM experiments. Modeling centers follow shared protocols defined by the World Climate Research Programme (WCRP), ensuring that outputs are broadly comparable. All data are publicly available, well-documented, and widely used in research and assessments such as the IPCC Sixth Assessment Report.

CMIP is a compilation of multiple experiments, starting with the basic DECK experiments (Diagnostic, Evaluation and Characterization of Klima, where klima is greek for climate), and including various “MIPs” (Model Intercomparison Projects), such as historical forcing experiments (historical), future scenarios (ScenarioMIP), and specific processes (e.g., C4MIP for the carbon cycle, HighResMIP for resolution effects). With many different land surface, ocean and atmospheric variables available for time periods spanning from 1850 to 2100 and beyond, the diversity of CMIP6 enables analyses from long-term global projections to process-level studies.

The strength of the ensemble is that it allows for a multi-model perspective that provides a measure of robustness: when different models converge on a signal (e.g., global warming trends or precipitation shifts), confidence increases. The ensemble helps quantify uncertainty from structural differences among models. The process-based based models are Earth System representations with many CMIP6 models including coupled carbon cycles, interactive aerosols, and dynamic vegetation, enabling integrated assessments of feedbacks between climate, biogeochemistry, and ecosystems.

Limitations

Despite appearing as a large ensemble, CMIP6 is not exactly a “random sample.” Through the decade long history of the development of these highly complex models, many models share components or parameterizations, so their errors and biases may be correlated. Treating them as statistically independent may overstate the robustness of ensemble means. Nevertheless, CMIP GCM ensembles provide the most comprehensiver understanding of Earth’s atmosphere and climate.

GCMs require massive parallel computing resources and simulations may take several months to complete. Computing power is a key constraint to the spatial resolution at which GCMs are operated. With a resolution of around 100 km (model dependent) GCM capture inadequately local processes such as topography, coastal effects, or convection which reduces their regional skill. Downscaling approaches (statistical or dynamical) are often required for regional or impact studies (see ESPO-G6-R2 , CanDCS-M6 and CRCM5-CMIP6 .

Furthermore, models differ in their representation of clouds, aerosols, ocean circulation, and land–atmosphere coupling which are all processes crucial to climate sensitivity. Some CMIP6 models have been shown to exhibit higher equilibrium climate sensitivities (>5°C per CO2 doubling), which may not be realistic.

While CMIP ensembles reflect diversity in model structures their sampling of uncertainty may still be inclomplete regarding possible parameter choices or internal variability. Single model initial-condition large ensembles (SMILEs) (Maher et al. (2021)) seek to fill that gap by providing multiple runs from a single model to estimate and study internal variability.

Overall, CMIP6 is a powerful resource for understanding climate change at global to regional scales. While for most uses downscaled and/or bias-adjusted versions of the CMIP6 simulations are preferable, the source for these derived versions may be of interest for specific studies, exploration of otherwise unavailable variables, regions or time periods.

Example Applications

Links to Electricity Sector Activities:

Variables available in CMIP6

The IPCC list of standard output from Coupled Ocean-Atmosphere GCMs comprises far over 100 different variables, and many more may be produced by a model (see the Climate and Forecast (CF) Standard Name Table). Depending on the model and its realizations, the respective list of available variables will be determined by the modeling center’s available resources and research focus. Hence, from some models many atmospheric and surface variables will be available, however the common denominator for most CMIP6 model simulations will be the variables listed below.

Click on variable groups to uncollapse

Temperature variables are provided at hourly to daily intervals, depending on the model and realization.

  • mean temperature (day, 6hr, 3hr, 1hr) [K]
  • daily minimum temperature [K]
  • daily maximum temperature [K]

Precipitation is commonly provided as the sum of solid and liquid precipitation or as snowfall and variables are provided at hourly to daily intervals, depending on the model and realization.

  • Precipitation flux (day, 6hr, 3hr, 1hr) [kg m-2 s-1]
  • Snowfall flux [kg m-2 s-1]

Other variables often provided by a large number of models include:

  • Wind (estward, westward) [m s-1]
  • Specific or relative humidity [%]
  • Longwave and shortwave radiation (downwelling and upwelling) [W m-2]

Further variables related to evaporation, runoff, and cloud cover may be available depending on the model.

Data Access

Raw CMIP6 data are distributed through the Earth System Grid Federation (ESGF) and can be downloaded from one of their Federated Metagrid Nodes. In North America the closest download points are the node operated by the Oak Ridge National Laboratory (ORNL) and the Lawrence Livermore National Laboratory (LLNL) node.

The CMIP6 ensemble’s raw data forms the basis for post-processed and bias-adjusted ensemble data such as ESPO-G6-R2 and CanDCS-M6.

References

Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., Taylor, K.E., 2016. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9, 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016
Maher, N., Milinski, S., Ludwig, R., 2021. Large ensemble climate model simulations: Introduction, overview, and future prospects for utilising multiple types of large ensemble. Earth System Dynamics 12, 401–418. https://doi.org/10.5194/esd-12-401-2021
Tebaldi, C., Debeire, K., Eyring, V., Fischer, E., Fyfe, J., Friedlingstein, P., Knutti, R., Lowe, J., O’Neill, B., Sanderson, B., Vuuren, D. van, Riahi, K., Meinshausen, M., Nicholls, Z., Tokarska, K.B., Hurtt, G., Kriegler, E., Lamarque, J.-F., Meehl, G., Moss, R., Bauer, S.E., Boucher, O., Brovkin, V., Byun, Y.-H., Dix, M., Gualdi, S., Guo, H., John, J.G., Kharin, S., Kim, Y., Koshiro, T., Ma, L., Olivié, D., Panickal, S., Qiao, F., Rong, X., Rosenbloom, N., Schupfner, M., Séférian, R., Sellar, A., Semmler, T., Shi, X., Song, Z., Steger, C., Stouffer, R., Swart, N., Tachiiri, K., Tang, Q., Tatebe, H., Voldoire, A., Volodin, E., Wyser, K., Xin, X., Yang, S., Yu, Y., Ziehn, T., 2021. Climate model projections from the scenario model intercomparison project (ScenarioMIP) of CMIP6. Earth System Dynamics 12, 253–293. https://doi.org/10.5194/esd-12-253-2021