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Global climate projections are simulations of the climate system performed with general circulation models which represent physical processes in the atmosphere, ocean, cryosphere and land surface. These models may cover the entire globe or a specific region and globe and use information about the external influences on the system. Such simulations have been generated by multiple independent climate research centres in an effort coordinated by the World Climate Research Program (WCRP) and assessed by the Intergovernmental Panel on Climate Change (IPCC). These climate projections underpin the conclusions of the IPCC Assessment Reports that “Continued emission of greenhouse gases will cause further warming and long-lasting changes in all components of the climate system, increasing the likelihood of severe, pervasive and irreversible impacts for people and ecosystems”.

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 Analysis of the CMIP data allows for improving

  • an improved understanding of

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  • the climate, including its variability and change,
  • an improved understanding of the societal and environmental implications of climate change in terms of impacts, adaptation and vulnerability,
  • informing the Intergovernmental Panel on Climate Change (IPCC) reports.

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  • determining why similarly forced models to produce a range of responses,
  • evaluating how realistic the different models are in simulating the recent past,
  • examining climate predictability.

CMIP6

The sixth phase of the Coupled Model Intercomparison Project (CMIP6) consists of 134 models from 53 modelling centres (Durack, 2020). CMIP6 data publication began in 2019 and the majority of the data publication will be completed by 2022. The scientific analyses from CMIP6 will be used extensively in the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6), due for release in 2021/22 (IPCC, 2020).

CMIP6 aims CMIP6 will aim to address 3 main questions:

  • How does the Earth system respond to forcing?
  • What are the origins and consequences of systematic model biases?
  • How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios ? (Eyring et al, 2016)?

There are some differences between the experimental design and organisation of CMIP6 and its predecessor CMIP5. It was decided that for CMIP6, a new and more federated structure would be used, consisting of the following three major elements:

  1. A handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850 – near-present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP;
  2. Common standards, coordination, infrastructure and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble;
  3. An ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases (World Climate Research Programme, 2020).

The CMIP6 data archive is distributed through the Earth System Grid Federation (ESGF) though many national centres have either a full or partial copy of the data. A quality-controlled subset of CMIP6 data are made available through the Climate Data Store (CDS) for the users of the Copernicus Climate Change Service (C3S).

Global climate projections in the CDS

The global climate projections in the Climate Data Store (CDS) are a quality-controlled subset of the wider CMIP6 data. These data represent only a small subset of CMIP6 archive. A set of 51 core variables from the CMIP6 archive were identified for the CDS. These variables are provided from 9 of the most popular CMIP6 experiments.

 The CDS subset of CMIP6 data have has been through a quality control procedure which ensures a high standard of dependability of the data. It may be for example, that similar data can be found in the main CMIP6 ESGF archive however these data come with very limited quality assurance and may have metadata errors or omissions.

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The SSP scenario experiments can be understood in terms of two pathways, a Shared Socioeconomic Pathway (SSP) and a Representative Concentration Pathway (RCP). The two pathways are represented by the three digits that make up the experiment’s name. The first digit represents the SSP storyline for the socio-economic mitigation and adaptation challenges that the experiment represents (Figure 1). The second and third digits represent the RCP climate forcing that the experiment follows. For example, experiment ssp245 follows SSP2, a storyline with intermediate mitigation and adaptation challenges, and RCP4.5 which leads to a radiative forcing of 4.5 Wm-2 by the year 2100.


Image AddedImage Removed

Figure 1 - The socioeconomic “Challenge Space” to be spanned by the CMIP6 SSP experiments (O’Neil et al. 2014).

 Experiments in the CDS

The CDS-CMIP6 subset consists of the following CMIP6 experiments:the CMIP6 experiments detailed in the table below.


Expand
titleClick here to expand... CMIP6 experiments included in the CDS


Experiment name

Extended Description

historical

The historical experiment is a simulation of the recent past from 1850 to 2014, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). In the historical simulations the model is forced with changing conditions (consistent with observations) which include atmospheric composition, land use and solar forcing. The initial conditions for the historical simulation are taken from the pre-industrial control simulation (piControl) at a point where the remaining length of the piControl is sufficient to extend beyond the period of the historical simulation to the end of any future "scenario" simulations run by the same model. The historical simulation is used to evaluate model performance against present climate and observed climate change.

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ssp585

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SSP5-8.5

SSP5-8.5 is a scenario experiment extending into the near future from 2015 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets.

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SSP5-8.5 is based on SSP5 in which climate change mitigation challenges dominate and RCP8.5, a future pathway with a radiative forcing of 8.5 W/m2 in the year 2100. The ssp585 scenario represents the high end of plausible future forcing pathways. 

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SSP5-8.5 is comparable to the CMIP5 experiment RCP8.5.

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ssp370

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SSP3-7.0

SSP3-7.0 is a scenario experiment extending into the near future from 2015 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets. SSP3-7.

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0 is based on SSP3 in which climate change mitigation and adaptation challenges are high and RCP7.0, a future pathway with a radiative forcing of 7.0 W/m2 in the year 2100. The

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SSP3-7.0 scenario represents the medium to high end of plausible future forcing pathways.

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SSP3-7.0 fills a gap in the CMIP5 forcing pathways that is particularly important because it represents a forcing level common to several (unmitigated) SSP baseline pathways.

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ssp245

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SSP2-4.5

SSP2-4.5 is a scenario experiment extending into the near future from 2015 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets. SSP2-4.

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5 is based on SSP2 with intermediate climate change mitigation and adaptation challenges and RCP4.5, a future pathway with a radiative forcing of 4.5 W/m2 in the year 2100. The ssp245 scenario represents the medium part of plausible future forcing pathways. SSP2-4.

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5 is comparable to the CMIP5 experiment RCP4.5.

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ssp126

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SSP1-2.6

SSP1-2.6 is a scenario experiment extending into the near future from 2015 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets.

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SSP1-2.6 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP2.6, a future pathway with a radiative forcing of 2.6 W/m2 in the year 2100. The

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SSP1-2.6 scenario represents the low end of plausible future forcing pathways. SSP1-2.

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6 depicts a "best case" future from a sustainability perspective.

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ssp460

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SSP4-6.0

SSP4-6.0 is a scenario experiment extending into the near future from 2015 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets. SSP4-6.

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0 is based on SSP4 in which climate change adaptation challenges dominate and RCP6.0, a future pathway with a radiative forcing of 6.0 W/m2 in the year 2100. The

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SSP4-6.0 scenario fills in the range of medium plausible future forcing pathways. SSP4-6.

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0 defines the low end of the forcing range for unmitigated SSP baseline scenarios.

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ssp434

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SSP4-3.4

SSP4-3.4 is a scenario experiment extending into the near future from 2015 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets. SSP4-3.

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4 is based on SSP4 in which climate change adaptation challenges dominate and RCP3.4, a future pathway with a radiative forcing of 3.4 W/m2 in the year 2100. The

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SSP4-3.4 scenario fills a gap at the low end of the range of plausible future forcing pathways. SSP4-3.

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4 is of interest to mitigation policy since mitigation costs differ substantially between forcing levels of 4.5 W/m2 and 2.6 W/m2.

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SSP5-

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3.4OS

...

SSP5-

...

3.4OS is a scenario experiment with simulations beginning in the mid-21st century running from 2040 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets.

...

SSP5-

...

3.4OS is based on SSP5 in which climate change mitigation challenges dominate and RCP3.4-over, a future pathway with a peak and decline in forcing towards an eventual radiative forcing of 3.4 W/m2 in the year 2100. The

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SSP5-

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3.4OS scenario branches from

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SSP5-8.5 in the year 2040 whereupon it applies substantially negative net emissions.

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SSP5-

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3.4OS explores the climate science and policy implications of a peak and decline in forcing during the 21st century. SSP5-3.

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4OS fills a gap in existing climate simulations by investigating the implications of a substantial overshoot in radiative forcing relative to a longer-term target.

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ssp119

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SSP1-1.9

SSP1-1.9 is a scenario experiment extending into the near future from 2015 to 2100, it is performed with a coupled atmosphere-ocean general circulation model (AOGCM). The forcing for the CMIP6 SSP experiments is derived from shared socioeconomic pathways (SSPs), a set of emission scenarios driven by different socioeconomic assumptions, paired with representative concentration pathways (RCPs), global forcing pathways which lead to specific end of century radiative forcing targets. SSP1-1.

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9 is based on SSP1 with low climate change mitigation and adaptation challenges and RCP1.9, a future pathway with a radiative forcing of 1.9 W/m2 in the year 2100. The

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SSP1-1.9 scenario fills a gap at the very low end of the range of plausible future forcing pathways.

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SSP1-1.9 forcing will be substantially below

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SSP1-2.6 in 2100. There is policy interest in low-forcing scenarios that would inform a possible goal of limiting global mean warming to 1.5°C above pre-industrial levels based on the Paris COP21 agreement

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.

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Models, grids and pressure levels

Models 

The models included in the CDS-CMIP5 CMIP6 subset are detailed in the table below , these include most of the models from the main CMIP5 archive. However a small number of models were not included as the data from the models have a research-only restriction on their use, all data in the CDS are released without restriction, therefore, the MIROC and MRI models from Japan are not included. The following table contains a list of the global climate models in use in the CDS and a brief description of the model where this information is readily including a brief description of the model where this information is readily available, further details can be found on the Earth System Documentation site.


CSIRO
Expand
titleClick here to expand...Global climate models included in the CDS


Model

name

Name

Modelling

centre

Centre

Model

details

Details 

ACCESS1-0

ACCESS1.3

ACCESS-CM2 (released in 2019)

CSIRO-ARCCSS (Commonwealth Scientific and Industrial Research Organisation,

Australia), and BOM (Bureau of Meteorology, Australia)

ACCESS1-0 2011. Atmosphere: AGCM v1.0 (N96 grid-point, 1.875 degrees EW x approx 1.25 degree NS, 38 levels); ocean: NOAA/GFDL MOM4p1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S, 50 levels); sea ice: CICE4.1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S); land: MOSES2 (1.875 degree EW x 1.25 degree NS, 4 levels;

Australian Research Council Centre of Excellence for Climate System Science)

The model includes the components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top-level 85 km), land: CABLE2.5, ocean: ACCESS-OM2 (GFDL-MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE5.1.2 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

ACCESS-ESM1-5 (released in 2019)

CSIRO 
(Commonwealth Scientific and Industrial Research Organisation
, Australia), and BOM (Bureau of Meteorology, Australia)

ACCESS1-3 2011. Atmosphere: AGCM v1.0 (N96 grid-point, 1.875 degrees EW x approx 1.25 degree NS, 38 levels); ocean: NOAA/GFDL MOM4p1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S, 50 levels); sea ice: CICE4.1 (nominal 1.0 degree EW x 1.0 degrees NS, tripolar north of 65N, equatorial refinement to 1/3 degree from 10S to 10 N, cosine dependent NS south of 25S); land: CABLE1.0 (1.875 degree EW x 1.25 degree NS, 6 levels;

bcc-csm1-1

Beijing Climate Center(BCC),China Meteorological Administration,China

bcc-csm1-1:atmosphere: BCC_AGCM2.1 (T42L26); land: BCC_AVIM1.0;ocean: MOM4_L40 (tripolar, 1 lon x (1-1/3) lat, L40);sea ice: SIS (tripolar,1 lon x (1-1/3) lat);

bcc-csm1-1-m

Beijing Climate Center(BCC),China Meteorological Administration,China

bcc-csm1-1-m:atmosphere: BCC_AGCM2.2 (T106L26); land: BCC_AVIM1.1;ocean: MOM4_L40v2 (tripolar, 1 lon x (1-1/3) lat, L40);sea ice: SIS (tripolar,1 lon x (1-1/3) lat);

BNU-ESM

GCESS,BNU,Beijing,China

BNU-ESM;

CanAM4

CCCma (Canadian Centre for Climate Modelling and Analysis, Victoria, BC, Canada)

CanAM4 2010 atmosphere: CanAM4 (AGCM15i, T63L35) land: CLASS2.7 (Note: Adjusted Land Cover and soil albedo relative to that used in CanESM2 and CanCM4);

CanCM4

CCCma (Canadian Centre for Climate Modelling and Analysis, Victoria, BC, Canada)

CanCM4 2010 atmosphere: CanAM4 (AGCM15i, T63L35) ocean: CanOM4 (OGCM4.0, 256x192L40) sea ice: CanSIM1 (Cavitating Fluid, T63 Gaussian Grid) land: CLASS2.7;

CanESM2

CCCma (Canadian Centre for Climate Modelling and Analysis, Victoria, BC, Canada)

CanESM2 2010 atmosphere: CanAM4 (AGCM15i, T63L35) ocean: CanOM4 (OGCM4.0, 256x192L40) and CMOC1.2 sea ice: CanSIM1 (Cavitating Fluid, T63 Gaussian Grid) land: CLASS2.7 and CTEM1;

CCSM4

NCAR (National Center for Atmospheric Research) Boulder, CO, USA

CCSM4 (repository tag: ccsm4_0_beta49 compset: BRCP26CN);

CESM1-BGC

NSF/DOE NCAR (National Center for Atmospheric Research) Boulder, CO, USA

CESM1-BGC;

CESM1-CAM5

NSF/DOE NCAR (National Center for Atmospheric Research) Boulder, CO, USA

CESM1-CAM5;

CESM1-FASTCHEM

NSF/DOE NCAR (National Center for Atmospheric Research) Boulder, CO, USA

CESM1-FASTCHEM;

CESM1-WACCM

NSF/DOE NCAR (National Center for Atmospheric Research) Boulder, CO, USA

CESM1-WACCM;

CMCC-CESM

CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna, Italy

CMCC-CESM;

CMCC-CM

CMCC - Centro Euro-Mediterraneo per i Cambiamenti

CMCC-CM;

CMCC-CMS

CMCC - Centro Euro-Mediterraneo per i Cambiamenti Climatici, Bologna, Italy

CMCC-CMS;

CNRM-CM5

CNRM (Centre National de Recherches Meteorologiques, Meteo-France, Toulouse,France) and CERFACS (Centre Europeen de Recherches et de Formation Avancee en Calcul Scientifique, Toulouse, France)

CNRM-CM5 2010 Atmosphere: ARPEGE-Climat (V5.2.1, TL127L31); Ocean: NEMO (nemo3.3.v10.6.6P, ORCA1degL42); Sea Ice: GELATO (V5.30); River Routing: TRIP (v1); Land: SURFEX (v5.1.c); Coupler : OASIS 3;

CNRM-CM5-2

CNRM (Centre National de Recherches Meteorologiques, Meteo-France, Toulouse, France) and CERFACS (Centre Europeen de Recherches et de Formation Avancee en Calcul Scientifique, Toulouse, France)

CNRM-CM5-2 2010 Atmosphere: ARPEGE-Climat (V5.2.3i, TL127L31); Ocean: NEMO (nemo3.2.v11.3, ORCA1degL42); Sea Ice: GELATO (V5.47f); River Routing: TRIP (v1); Land: SURFEX (v5.1.c); Coupler : OASIS 3;

CSIRO-Mk3-6-0

Australian Commonwealth Scientific and Industrial Research Organization (CSIRO) Marine and Atmospheric Research (Melbourne, Australia) in collaboration with the Queensland Climate Change Centre of Excellence (QCCCE) (Brisbane, Australia)

CSIRO-Mk3-6-0 2010 atmosphere: AGCM v7.3.8 (T63 spectral, 1.875 degrees EW x approx. 1.875 degrees NS, 18 levels); ocean: GFDL MOM2.2 (1.875 degrees EW x approx. 0.9375 degrees NS, 31 levels);

EC-EARTH

EC-EARTH (European Earth System Model)

EC-EARTH 2.3 (2011); atmosphere: IFS (cy31R1+modifications, T159L62); ocean: NEMO (version2+modifications, ORCA1-42lev); sea ice: LIM2; land: HTessel;

FGOALS_g2

IAP (Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China) and THU (Tsinghua University)

FGOALS_g2 2011 atmosphere: GAMIL (gamil2, 128x60L26); ocean: LICOM (licom2, 360x196L30); ice: CICE (cice4_lasg, 360x196L4); land: CLM (clm3, 128x60);

FGOALS-s2; IAP(Institute of Atmospheric Physics),CAS(Chinese Academy of Sciences),Beijing,China

FGOALS-s2 SAMIL 2-4-7

FIO-ESM

FIO(The First Institution of Oceanography,SOA,Qingdao,China)

FIO-ESM

GFDL-CM2p1

NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)

GFDL-CM2p1 2010 ocean: MOM4 (MOM4p1_x1_Z50_cCM2M,Tripolar360x200L50); atmosphere: AM2 (AM2p14,M45L24); sea ice: SIS (SISp2,Tripolar360x200L50); land: LM2 (LM2,M45);

GFDL-CM3

NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)

GFDL-CM3 2010 atmosphere: AM3 (AM3p9,C48L48); sea ice: SIS (SISp2,Tripolar360x200); land: LM3 (LM3p7_cCM3,C48); ocean: MOM4 (MOM4p1_x1_Z50_cCM3,Tripolar360x200L50);

GFDL-ESM2G

NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)

GFDL-ESM2G 2010 ocean: TOPAZ (TOPAZ1p2,Tripolar360x210L63); atmosphere: AM2 (AM2p14,M45L24); sea ice: SIS (SISp2,Tripolar360x210L63); land: LM3 (LM3p7_cESM,M45);

GFDL-ESM2M

NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)

GFDL-ESM2M 2010 ocean: MOM4 (MOM4p1_x1_Z50_cCM2M,Tripolar360x200L50); atmosphere: AM2 (AM2p14,M45L24); sea ice: SIS (SISp2,Tripolar360x200L50); land: LM3 (LM3p7_cESM,M45) Computing resources were provided by the Climate Simulation Laboratory at the NCAR Computational and Information Systems Laboratory (CISL),\n,;

GFDL-HIRAM-C180

NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)

GFDL-HIRAM-C180 2010 atmosphere: HIRAM (HIRAMp1,C180L32); land: LM3 (LM3p7_cHIRAM,C180);

GFDL-HIRAM-C360

NOAA GFDL(201 Forrestal Rd, Princeton, NJ, 08540)

GFDL-HIRAM-C360 2010 atmosphere: HIRAM (HIRAMp1,C360L32); land: LM3 (LM3p7_cHIRAM,C360);

GISS-E2-H

NASA/GISS (Goddard Institute for Space Studies) New York, NY

GISS-E2-H-Eh135f9b Atmosphere: GISS-E2; Ocean: H;

GISS-E2-H-CC

NASA/GISS (Goddard Institute for Space Studies) New York, NY

GISS-E2-H-CC-E4arobio_h8P Atmosphere: GISS-E2; Ocean: H;

GISS-E2-R

NASA/GISS (Goddard Institute for Space Studies) New York, NY

GISS-E2-R-E135OCNf9aF40 Atmosphere: GISS-E2;

GISS-E2-R-CC

NASA/GISS (Goddard Institute for Space Studies) New York, NY

GISS-E2-R-CC-E4arobio_g8RCP45 Atmosphere: GISS-E2; Ocean: R;

HadCM3

Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK, (http://www.metoffice.gov.uk)

HadCM3 - Hadley Centre Coupled Model Version 3 (2000) atmosphere: HadAM3 (N48L19); ocean: HadOM (lat: 1.25 lon: 1.25 L20); land-surface/vegetation: MOSES1;;

HadGEM2-A

Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK, (http://www.metoffice.gov.uk)

HadGEM2-A (2009) atmosphere: HadGAM2 (N96L38); land-surface/vegetation: MOSES2;

HadGEM2-CC

Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK, (http://www.metoffice.gov.uk)

HadGEM2-CC (2011) atmosphere: HadGAM2(N96L60); ocean: HadGOM2 (lat: 1.0-0.3 lon: 1.0 L40); land-surface/vegetation: MOSES2 and TRIFFID; ocean biogeochemistry: diat-HadOCC;

HadGEM2-ES

Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK, (http://www.metoffice.gov.uk)

HadGEM2-ES (2009) atmosphere: HadGAM2 (N96L38); ocean: HadGOM2 (lat: 1.0-0.3 lon: 1.0 L40); land-surface/vegetation: MOSES2 and TRIFFID; tropospheric chemistry: UKCA; ocean biogeochemistry: diat-HadOCC;

inmcm4

INM (Institute for Numerical Mathematics, Moscow, Russia)

inmcm4 (2009);

IPSL-CM5A-LR

IPSL (Institut Pierre Simon Laplace, Paris, France)

IPSL-CM5A-LR (2010) : atmos : LMDZ4 (LMDZ4_v5, 96x95x39); ocean : ORCA2 (NEMOV2_3, 2x2L31); seaIce : LIM2 (NEMOV2_3); ocnBgchem : PISCES (NEMOV2_3); land : ORCHIDEE (orchidee_1_9_4_AR5);

IPSL-CM5A-MR

IPSL (Institut Pierre Simon Laplace, Paris, France)

IPSL-CM5A-MR (2010) : atmos : LMDZ4 (LMDZ4_v5, 144x143x39); ocean : ORCA2 (NEMOV2_3, 2x2L31); seaIce : LIM2 (NEMOV2_3); ocnBgchem : PISCES (NEMOV2_3); land : ORCHIDEE (orchidee_1_9_4_AR5);

IPSL-CM5B-LR

IPSL (Institut Pierre Simon Laplace, Paris, France)

IPSL-CM5B-LR (2011) : atmos : LMDZ5 (LMDZ5_NPv3.1, 96x95x39); ocean : ORCA2 (NEMOV2_3, 2x2L31); seaIce : LIM2 (NEMOV2_3); ocnBgchem : PISCES (NEMOV2_3); land : ORCHIDEE (orchidee_1_9_4_AR5);

MPI-ESM-LR

Max Planck Institute for Meteorology

MPI-ESM-LR 2011; URL: https://www.mpimet.mpg.de/en/science/models/mpi-esm/; atmosphere: ECHAM6 (REV: 4418), T63L47; land: JSBACH (REV: 4418);;

MPI-ESM-MR

Max Planck Institute for Meteorology

MPI-ESM-MR 2011; URL: https://www.mpimet.mpg.de/en/science/models/mpi-esm/; atmosphere: ECHAM6 (REV: 4968), T63L47; land: JSBACH (REV: 4968);;

MPI-ESM-P

Max Planck Institute for Meteorology

MPI-ESM-P 2011; URL: https://www.mpimet.mpg.de/en/science/models/mpi-esm/; atmosphere: ECHAM6 (REV: 5051), T63L47; land: JSBACH (REV: 5051); ocean: MPIOM (REV: 5051), GR15L40; sea ice: 5051; marine bgc: HAMOCC (REV: 5051);;

NorESM1-M

Norwegian Climate Centre

NorESM1-M 2011 atmosphere: CAM-Oslo (CAM4-Oslo-noresm-ver1_cmip5-r112, f19L26); ocean: MICOM (MICOM-noresm-ver1_cmip5-r112, gx1v6L53); sea ice: CICE (CICE4-noresm-ver1_cmip5-r112); land: CLM (CLM4-noresm-ver1_cmip5-r112);

NorESM1-ME

Norwegian Climate Centre

NorESM1-ME 2011 atmosphere: CAM-Oslo (CAM4-Oslo-noresm-ver1_cmip5-r139, f19L26); ocean: MICOM (MICOM-noresm-ver1_cmip5-r139, gx1v6L53); ocean biogeochemistry: HAMOCC (HAMOCC-noresm-ver1_cmip5-r139, gx1v6L53); sea ice: CICE (CICE4-noresm-ver1_cmip5-r139); land: CLM (CLM4-noresm-ver1_cmip5-r139);

Pressure levels

For pressure level data the model output is available on the pressure levels according to the table below. Note that not all models provide the same pressure levels. 

...

Frequency

...

Number of Levels 

...

Pressure Levels (hPa)

...

Daily

...

8

...

1000., 850., 700., 500., 250., 100., 50., 10.

...

Monthly

...

17

...

1000., 925., 850., 700., 600., 500., 400., 300., 250., 200., 150., 100., 70., 50., 30., 20., 10.

Ensembles

Each modelling centre typically run the same experiment using the same model several times to confirm the robustness of results and inform sensitivity studies through the generation of statistical information. A model and its collection of runs is referred to as an ensemble. Within these ensembles, three different categories of sensitivity studies are done, and the resulting individual model runs are labelled by three integers indexing the experiments in each category. 

  • The first category, labelled “realization”, performs experiments which differ only in random perturbations of the initial conditions of the experiment. Comparing different realizations allow estimation of the internal variability of the model climate. 
  • The second category refers to variation in initialisation parameters. Comparing differently initialised output provides an estimate of how sensitive the model is to initial conditions. 
  • The third category, labelled “physics”, refers to variations in the way in which sub-grid scale processes are represented. Comparing different simulations in this category provides an estimate of the structural uncertainty associated with choices in the model design. 

Each member of an ensemble is identified by a triad of integers associated with the letters r, i and p which index the “realization”, “initialization” and “physics” variations respectively. For instance, the member "r1i1p1" and the member "r1i1p2" for the same model and experiment indicate that the corresponding simulations differ since the physical parameters of the model for the second member were changed relative to the first member. 

It is very important to distinguish between variations in experiment specifications, which are globally coordinated across all the models contributing to CMIP5, and the variations which are adopted by each modelling team to assess the robustness of their own results. The “p” index refers to the latter, with the result that values have different meanings for different models, but in all cases these variations must be within the constraints imposed by the specifications of the experiment. 

For the scenario experiments, the ensemble member identifier is preserved from the historical experiment providing the initial conditions, so RCP 4.5 ensemble member “r1i1p2” is a continuation of historical ensemble member “r1i1p2”.

Parameter listings

Table 1: CMIP5 data on pressure levels

...

titleTable 1

...

count

...

name

...

units

...

Daily data

...

1

...

temperature

...

2

...

geopotential_height

...

relative_humidity

...

specific_humidity

...

5

...

u_component_of_wind

...

v_component_of_wind

...

Table 2: CMIP5 data on single levels

...

titleTable 2

...

count

...

name

...

units

...

10m_wind_speed

...

10m_v_component_of_wind

...

10m_wind_speed

...

2m_temperature

...

daily_near_surface_relative_humidity

...

eastward_turbulent_surface_stress

...

evaporation

...

maximum_2m_temperature_in_the_last_24_hours

...

mean_precipitation_flux

...

mean_sea_level_pressure

...

minimum_2m_temperature_in_the_last_24_hours

...

near_surface_relative_humidity

...

near_surface_specific_humidity

...

northward_turbulent_surface_stress

...

runoff

...

sea_ice_fraction

...

sea_ice_plus_snow_amount

...

sea_ice_surface_temperature

...

sea_ice_thickness

...

sea_surface_height_above_geoid

...

sea_surface_salinity

...

sea_surface_temperature

...

skin_temperature

...

snow_depth_over_sea_ice

...

snowfall

...

soil_moisture_content

...

surface_latent_heat_flux

...

surface_pressure

...

surface_sensible_heat_flux

...

surface_snow_amount

...

surface_solar_radiation_downwards

...

)The model includes the components: aerosol: CLASSIC (v1.0), atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m), land: CABLE2.4, ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: WOMBAT (same grid as ocean), seaIce: CICE4.1 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, land: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

AWI-CM-1-1-MR (released in 2018)

AWI (Alfred Wegener Institute)

The model includes the components: atmos: ECHAM6.3.04p1 (T127L95 native atmosphere T127 gaussian grid; 384 x 192 longitude/latitude; 95 levels; top level 80 km), land: JSBACH 3.20, ocean: FESOM 1.4 (unstructured grid in the horizontal with 830305 wet nodes; 46 levels; top grid cell 0-5 m), seaIce: FESOM 1.4. The model was run in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

AWI-ESM-1-1-LR (released in 2018)

AWI (Alfred Wegener Institute)The model includes the components: atmos: ECHAM6.3.04p1 (T63L47 native atmosphere T63 gaussian grid; 192 x 96 longitude/latitude; 47 levels; top-level 80 km), land: JSBACH 3.20 with dynamic vegetation, ocean: FESOM 1.4 (unstructured grid in the horizontal with 126859 wet nodes; 46 levels; top grid cell 0-5 m), seaIce: FESOM 1.4. AWI-ESM 1.1 LR is an extension of the AWI-CM for earth system modelling. The model was run in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 50 km, seaIce: 50 km.

BCC-CSM2-MR (released in 2017)

BCC (Beijing Climate Center)The model includes the components: atmos: BCC_AGCM3_MR (T106; 320 x 160 longitude/latitude; 46 levels; top level 1.46 hPa), land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. The model was run in native nominal resolutions: atmosphere: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.

BCC-ESM1 (released in 2017)

BCC  (Beijing Climate Center)The model includes the components: atmos: BCC_AGCM3_LR (T42; 128 x 64 longitude/latitude; 26 levels; top level 2.19 hPa), atmosChem: BCC-AGCM3-Chem, land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. The model was run in native nominal resolutions: atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, ocean: 50 km, seaIce: 50 km.

CAMS-CSM1-0 (released in 2016)

CAMS (Chinese Academy of Meteorological Sciences)The model includes the components: atmos: ECHAM5_CAMS (T106; 320 x 160 longitude/latitude; 31 levels; top level 10 mb), land: CoLM 1.0, ocean: MOM4 (tripolar; 360 x 200 longitude/latitude, primarily 1deg latitude/longitude, down to 1/3deg within 30deg of the equatorial tropics; 50 levels; top grid cell 0-10 m), seaIce: SIS 1.0. The model was run in native nominal resolutions: atmosphere: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

CanESM5 (released in 2019)

CCCMA (Canadian Centre for Climate Modelling and Analysis)The model includes the components: aerosol: interactive, atmos: CanAM5 (T63L49 native atmosphere, T63 Linear Gaussian Grid; 128 x 64 longitude/latitude; 49 levels; top-level 1 hPa), atmosChem: specified oxidants for aerosols, land: CLASS3.6/CTEM1.2, landIce: specified ice sheets, ocean: NEMO3.4.1 (ORCA1 tripolar grid, 1 deg with refinement to 1/3 deg within 20 degrees of the equator; 361 x 290 longitude/latitude; 45 vertical levels; top grid cell 0-6.19 m), ocnBgchem: Canadian Model of Ocean Carbon (CMOC); NPZD ecosystem with OMIP prescribed carbonate chemistry, seaIce: LIM2. The model was run in native nominal resolutions: aerosol: 500 km, atmosphere: 500 km, atmospheric chemistry: 500 km, land: 500 km, landIce: 500 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

CanESM5-CanOE (released in 2019)

CCCMA (Canadian Centre for Climate Modelling and Analysis)CanESM5-CanOE is identical to CanESM5, except that CMOC (Canadian Model of Ocean Carbon) was replaced with CanOE (Canadian Ocean Ecosystem model). The model was run in native nominal resolutions: aerosol: 500 km, atmos: 500 km, atmosChem: 500 km, land: 500 km, landIce: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

CAS-ESM2-0 (released in 2019)

CAS (Chinese Academy of Sciences)The model includes the components: aerosol: IAP AACM, atmos: IAP AGCM 5.0 (Finite difference dynamical core; 256 x 128 longitude/latitude; 35 levels; top level 2.2 hPa), atmosChem: IAP AACM, land: CoLM, ocean: LICOM2.0 (LICOM2.0, primarily 1deg; 362 x 196 longitude/latitude; 30 levels; top grid cell 0-10 m), ocnBgchem: IAP OBGCM, seaIce: CICE4. The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

CESM2

NCAR (National Center for Atmospheric Research)The model includes the components: aerosol: MAM4 (same grid as atmos), atmos: CAM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 32 levels; top level 2.25 mb), atmosChem: MAM4 (same grid as atmos), land: CLM5 (same grid as atmos), landIce: CISM2.1, ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m), ocnBgchem: MARBL (same grid as ocean), seaIce: CICE5.1 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 5 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

CESM2-FV2 (released in 2019)

NCAR (National Center for Atmospheric Research)

The model includes the components: aerosol: MAM4 (same grid as atmos), atmos: CAM6 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 32 levels; top level 2.25 mb), atmosChem: MAM4 (same grid as atmos), land: CLM5 (same grid as atmos), landIce: CISM2.1, ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m), ocnBgchem: MARBL (same grid as ocean), seaIce: CICE5.1 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, landIce: 5 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

CESM2-WACCM (released in 2018)

NCAR (National Center for Atmospheric Research)The model includes the components: aerosol: MAM4 (same grid as atmos), atmos: WACCM6 (0.9x1.25 finite volume grid; 288 x 192 longitude/latitude; 70 levels; top level 4.5e-06 mb), atmosChem: MAM4 (same grid as atmos), land: CLM5 (same grid as atmos), landIce: CISM2.1, ocean: POP2 (320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m), ocnBgchem: MARBL (same grid as ocean), seaIce: CICE5.1 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, landIce: 5 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

CESM2-WACCM-FV2 (released in 2019)

NCAR (National Center for Atmospheric Research)The model includes the components: aerosol: MAM4 (same grid as atmos), atmos: WACCM6 (1.9x2.5 finite volume grid; 144 x 96 longitude/latitude; 70 levels; top level 4.5e-06 mb), atmosChem: MAM4 (same grid as atmos), land: CLM5 (same grid as atmos), landIce: CISM2.1, ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m), ocnBgchem: MARBL (same grid as ocean), seaIce: CICE5.1 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, landIce: 5 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

CIESM (released in 2017)

THU (Tsinghua University - Department of Earth System Science)The model includes the components: aerosol: MAM4, atmos: CIESM-AM (FV/FD; 288 x 192 longitude/latitude; 30 levels; top level 2.255 hPa), atmosChem: trop_mam4, land: CIESM-LM (modified CLM4.5), ocean: CIESM-OM (FD, SCCGrid Displaced Pole; 720 x 560 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4. The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.

CMCC-CM2-SR5 (released in 2016)

CMCC (Centro Euro-Mediterraneo per I Cambiamenti Climatici)The model includes the components: aerosol: MAM3, atmos: CAM5.3 (1deg; 288 x 192 longitude/latitude; 30 levels; top at ~2 hPa), land: CLM4.5 (BGC mode), ocean: NEMO3.6 (ORCA1 tripolar primarly 1 deg lat/lon with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 50 vertical levels; top grid cell 0-1 m), seaIce: CICE4.0.  The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

CNRM-CM6-1  (released in 2017)

CNRM-CERFACS (National Center for Meteorological Research, Météo-France and CNRS laboratory, Climate Modeling and Global change)The model includes the components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top-level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

CNRM-CM6-1-HR (released in 2017)

CNRM-CERFACS (National Center for Meteorological Research, Météo-France and CNRS laboratory, Climate Modeling and Global change)The model includes the components: aerosol: prescribed monthly fields computed by TACTIC_v2 scheme, atmos: Arpege 6.3 (T359; Gaussian Reduced with 181724 grid points in total distributed over 360 latitude circles (with 720 grid points per latitude circle between 32.2degN and 32.2degS reducing to 18 grid points per latitude circle at 89.6degN and 89.6degS); 91 levels; top-level 78.4 km), atmosChem: OZL_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA025, tripolar primarily 1/4deg; 1442 x 1050 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: Gelato 6.1. The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

CNRM-ESM2-1 (released in 2017)

CNRM-CERFACS (National Center for Meteorological Research, Météo-France and CNRS laboratory, Climate Modeling and Global change)The model includes the components: aerosol: TACTIC_v2, atmos: Arpege 6.3 (T127; Gaussian Reduced with 24572 grid points in total distributed over 128 latitude circles (with 256 grid points per latitude circle between 30degN and 30degS reducing to 20 grid points per latitude circle at 88.9degN and 88.9degS); 91 levels; top-level 78.4 km), atmosChem: REPROBUS-C_v2, land: Surfex 8.0c, ocean: Nemo 3.6 (eORCA1, tripolar primarily 1deg; 362 x 294 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: Pisces 2.s, seaIce: Gelato 6.1. The model was run in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

E3SM-1-0 (released in 2018)

E3SM-Project LLNL (Energy Exascale Earth System Model, Lawrence Livermore National Laboratory)The model includes the components: aerosol: MAM4 with resuspension, marine organics, and secondary organics (same grid as atmos), atmos: EAM (v1.0, cubed sphere spectral-element grid; 5400 elements with p=3; 1 deg average grid spacing; 90 x 90 x 6 longitude/latitude/cubeface; 72 levels; top level 0.1 hPa), atmosChem: Troposphere specified oxidants for aerosols. Stratosphere linearized interactive ozone (LINOZ v2) (same grid as atmos), land: ELM (v1.0, cubed sphere spectral-element grid; 5400 elements with p=3; 1 deg average grid spacing; 90 x 90 x 6 longitude/latitude/cubeface; satellite phenology mode), MOSART (v1.0, 0.5 degree latitude/longitude grid), ocean: MPAS-Ocean (v6.0, oEC60to30 unstructured SVTs mesh with 235160 cells and 714274 edges, variable resolution 60 km to 30 km; 60 levels; top grid cell 0-10 m), seaIce: MPAS-Seaice (v6.0, same grid as ocean). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.

E3SM-1-1 (released in 2019)

E3SM-Project RUBISCO (Energy Exascale Earth System Model, Reducing Uncertainty in Biogeochemical Interactions through Synthesis and COmputation)The model includes the components: aerosol: MAM4 with resuspension, marine organics, and secondary organics (same grid as atmos), atmos: EAM (v1.1, cubed sphere spectral-element grid; 5400 elements with p=3; 1 deg average grid spacing; 90 x 90 x 6 longitude/latitude/cubeface; 72 levels; top-level 0.1 hPa), atmosChem: Troposphere specified oxidants for aerosols. Stratosphere linearized interactive ozone (LINOZ v2) (same grid as atmos), land: ELM (v1.1, same grid as atmos; active biogeochemistry using the Converging Trophic Cascade plant and soil carbon and nutrient mechanisms to represent carbon, nitrogen and phosphorus cycles), MOSART (v1.1, 0.5 degree latitude/longitude grid), ocean: MPAS-Ocean (v6.0, oEC60to30 unstructured SVTs mesh with 235160 cells and 714274 edges, variable resolution 60 km to 30 km; 60 levels; top grid cell 0-10 m), ocnBgchem: BEC (Biogeochemical Elemental Cycling model, NPZD-type with C/N/P/Fe/Si/O; same grid as ocean), seaIce: MPAS-Seaice (v6.0; same grid as ocean). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, ocean: 50 km, ocean biogeochemistry: 50 km, seaIce: 50 km.

E3SM-1-1-ECA (released in 2019)

E3SM-Project  (Energy Exascale Earth System Model)The model includes the components: aerosol: MAM4 with resuspension, marine organics, and secondary organics (same grid as atmos), atmos: EAM (v1.1, cubed sphere spectral-element grid; 5400 elements with p=3; 1 deg average grid spacing; 90 x 90 x 6 longitude/latitude/cubeface; 72 levels; top-level 0.1 hPa), atmosChem: Troposphere specified oxidants for aerosols. Stratosphere linearized interactive ozone (LINOZ v2) (same grid as atmos), land: ELM (v1.1, same as atmos; active biogeochemistry using the Equilibrium Chemistry Approximation to represent plant and soil carbon and nutrient mechanisms especially carbon, nitrogen and phosphorus limitation), MOSART (v1.1, 0.5 degree latitude/longitude grid), ocean: MPAS-Ocean (v6.0, oEC60to30 unstructured SVTs mesh with 235160 cells and 714274 edges, variable resolution 60 km to 30 km; 60 levels; top grid cell 0-10 m), ocnBgchem: BEC (Biogeochemical Elemental Cycling model, NPZD-type with C/N/P/Fe/Si/O; same grid as ocean), seaIce: MPAS-Seaice (v6.0; same grid as ocean). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, ocean: 50 km, ocean biogeochemistry: 50 km, seaIce: 50 km.

EC-Earth3 (released in 2019)

EC-Earth-ConsortiumThe model includes the components: atmos: IFS cy36r4 (TL255, linearly reduced Gaussian grid equivalent to 512 x 256 longitude/latitude; 91 levels; top-level 0.01 hPa), land: HTESSEL (land surface scheme built-in IFS), ocean: NEMO3.6 (ORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: LIM3. The model was run in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

EC-Earth3-LR  (released in 2019)

EC-Earth-ConsortiumThe model includes the components: atmos: IFS cy36r4 (TL159, linearly reduced Gaussian grid equivalent to 320 x 160 longitude/latitude; 62 levels; top-level 5 hPa), land: HTESSEL (land surface scheme built-in IFS), ocean: NEMO3.6 (ORCA1 tripolar primarily 1 degree with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: LIM3. The model was run in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

EC-Earth3-Veg (released in 2019)

EC-Earth-ConsortiumThe model includes the components: atmos: IFS cy36r4 (TL255, linearly reduced Gaussian grid equivalent to 512 x 256 longitude/latitude; 91 levels; top-level 0.01 hPa), land: HTESSEL (land surface scheme built-in IFS) and LPJ-GUESS v4, ocean: NEMO3.6 (ORCA1 tripolar primarily 1 degree with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: LIM3. The model was run in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

EC-Earth3-Veg-LR (released in 2019)

EC-Earth-ConsortiumThe model includes the components: atmos: IFS cy36r4 (TL159, linearly reduced Gaussian grid equivalent to 320 x 160 longitude/latitude; 62 levels; top-level 5 hPa), land: HTESSEL (land surface scheme built-in IFS) and LPJ-GUESS v4, ocean: NEMO3.6 (ORCA1 tripolar primarily 1 degree with meridional refinement down to 1/3 degree in the tropics; 362 x 292 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: LIM3. The model was run in native nominal resolutions: atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

FGOALS-f3-L (released in 2017)

CAS (Chinese Academy of Sciences)The model includes the components: atmos: FAMIL2.2 (Cubed-sphere, c96; 360 x 180 longitude/latitude; 32 levels; top level 2.16 hPa), land: CLM4.0, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run in native nominal resolutions: atmosphere: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

FGOALS-g3 (released in 2017)

CAS (Chinese Academy of Sciences)The model includes the components: atmos: GAMIL3 (180 x 80 longitude/latitude; 26 levels; top level 2.19hPa), land: CAS-LSM, ocean: LICOM3.0 (LICOM3.0, tripolar primarily 1deg; 360 x 218 longitude/latitude; 30 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run in native nominal resolutions: atmosphere: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

FIO-ESM-2-0 (released in 2018)

FIO-QLNM (First Institute of Oceanography (FIO) and Qingdao National Laboratory for Marine Science and Technology (QNLM))The model includes the components: aerosol: Prescribed monthly fields, atmos: CAM4 (0.9x1.25 finite volume grid; 192 x 288 longitude/latitude; 26 levels; top level ~2 hPa), land: CLM4.0 (same grid at atmos), ocean: POP2-W (POP2 coupled with MASNUM surface wave model, Displaced Pole; 320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m), seaIce: CICE4.0 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

GFDL-AM4 (released in 2018)

NOAA-GFDL (National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory)The model includes the components: aerosol: interactive, atmos: GFDL-AM4.0 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa), atmosChem: fast chemistry, aerosol only, land: GFDL-LM4.0, landIce: GFDL-LM4.0. The model was run in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km.

GFDL-CM4 (released in 2018)

NOAA-GFDL (National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory)The model includes the components: aerosol: interactive, atmos: GFDL-AM4.0.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 33 levels; top level 1 hPa), atmosChem: fast chemistry, aerosol only, land: GFDL-LM4.0.1 (1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 20 levels; bottom level 10m); land-Veg:unnamed (dynamic vegetation, dynamic land use); land-Hydro:unnamed (soil water and ice, multi-layer snow, rivers and lakes), landIce: GFDL-LM4.0.1, ocean: GFDL-OM4p25 (GFDL-MOM6, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-BLINGv2, seaIce: GFDL-SIM4p25 (GFDL-SIS2.0, tripolar - nominal 0.25 deg; 1440 x 1080 longitude/latitude; 5 layers; 5 thickness categories). The model was run in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, landIce: 100 km, ocean: 25 km, ocnBgchem: 25 km, seaIce: 25 km.

GFDL-ESM4 (released in 2018)

NOAA-GFDL (National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory)The model includes the components: aerosol: interactive, atmos: GFDL-AM4.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 49 levels; top level 1 Pa), atmosChem: GFDL-ATMCHEM4.1 (full atmospheric chemistry), land: GFDL-LM4.1, landIce: GFDL-LM4.1, ocean: GFDL-OM4p5 (GFDL-MOM6, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: GFDL-COBALTv2, seaIce: GFDL-SIM4p5 (GFDL-SIS2.0, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 5 layers; 5 thickness categories). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, landIce: 100 km, ocean: 50 km, ocean biogeochemistry: 50 km, seaIce: 50 km.

GISS-E2-1-G (released in 2019)

NASA-GISS  (National Oceanic and Atmospheric Administration, Goddard Institute for Space Studies)The model includes the components: aerosol: Varies with physics-version (p==1 none, p==3 OMA, p==4 TOMAS, p==5 MATRIX), atmos: GISS-E2.1 (2.5x2 degree; 144 x 90 longitude/latitude; 40 levels; top level 0.1 hPa), atmosChem: Varies with physics-version (p==1 Non-interactive, p>1 GPUCCINI), land: GISS LSM, ocean: GISS Ocean (GO1, 1 degree; 360 x 180 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: GISS SI. The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, ocean: 100 km, seaIce: 250 km.

GISS-E2-1-H (released in 2019)

NASA-GISS  (National Oceanic and Atmospheric Administration, Goddard Institute for Space Studies)The model includes the components: aerosol: Varies with physics-version (p==1 none, p==3 OMA, p==4 TOMAS, p==5 MATRIX), atmos: GISS-E2.1 (2.5x2 degree; 144 x 90 longitude/latitude; 40 levels; top level 0.1 hPa), atmosChem: Varies with physics-version (p==1 Non-interactive, p>1 GPUCCINI), land: GISS LSM, ocean: HYCOM Ocean (~1 degree tripolar grid; 360 x 180 longitude/latitude; 32 levels; top grid cell 0-10 m), seaIce: GISS SI. The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, ocean: 100 km, seaIce: 250 km.

GISS-E2-2-G (released in 2019)

NASA-GISS  (National Oceanic and Atmospheric Administration, Goddard Institute for Space Studies)The model includes the components: aerosol: varies with physics-version (p==1 none, p==3 OMA, p==4 TOMAS, p==5 MATRIX), atmos: GISS-E2.2 (High-top, 2 x 2.5 degrees; 144 x 90 longitude/latitude; 102 levels; top level 0.002 hPa), atmosChem: varies with physics-version (p==1 Non-interactive, p>1 GPUCCINI), land: GISS LSM, landIce: Fixed, ocean: GISS Ocean (GO1, 1 degree; 360 x 180 longitude/latitude; 40 levels; top grid cell 0-10m), seaIce: GISS SI. The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, seaIce: 100 km.

HadGEM3-GC31-LL (released in 2016)

MOHC NERC (Met Office Hadley Centre, Natural Environmental Research Council)The model includes the components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

HadGEM3-GC31-MM (released in 2016)

MOHC (Met Office Hadley Centre)The model includes the components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.

IITM-ESM  (released in 2015)

CCCR-IITM (Centre for Climate Change Research, Indian Institute of Tropical Meteorology)The model includes the components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, land: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

INM-CM4-8 (released in 2016)

INM (Institute of Numerical Mathematics)The model includes the components: aerosol: INM-AER1, atmos: INM-AM4-8 (2x1.5; 180 x 120 longitude/latitude; 21 levels; top level sigma = 0.01), land: INM-LND1, ocean: INM-OM5 (North Pole shifted to 60N, 90E; 360 x 318 longitude/latitude; 40 levels; sigma vertical coordinate), seaIce: INM-ICE1. The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

INM-CM5-0 (released in 2016)

INM (Institute of Numerical Mathematics)The model includes the components: aerosol: INM-AER1, atmos: INM-AM5-0 (2x1.5; 180 x 120 longitude/latitude; 73 levels; top level sigma = 0.0002), land: INM-LND1, ocean: INM-OM5 (North Pole shifted to 60N, 90E. 0.5x0.25; 720 x 720 longitude/latitude; 40 levels; vertical sigma coordinate), seaIce: INM-ICE1. The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km.

IPSL-CM6A-LR (released in 2017)

IPSL (Institut Pierre‐Simon Laplace)The model includes the components: atmos: LMDZ (NPv6, N96; 144 x 143 longitude/latitude; 79 levels; top level 80000 m), land: ORCHIDEE (v2.0, Water/Carbon/Energy mode), ocean: NEMO-OPA (eORCA1.3, tripolar primarily 1deg; 362 x 332 longitude/latitude; 75 levels; top grid cell 0-2 m), ocnBgchem: NEMO-PISCES, seaIce: NEMO-LIM3. The model was run in native nominal resolutions: atmosphere: 250 km, land: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

KACE-1-0-G (released in 2018)

NIMS-KMA (National Institute of Meteorological Sciences/Korea Met. Administration)The model includes the components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: MOM4p1 (tripolar primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), seaIce: CICE-HadGEM3-GSI8 (tripolar primarily 1deg; 360 x 200 longitude/latitude). The model was run in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

KIOST-ESM (released in 2018)

KIOST (Korea Institute of Ocean Science and Technology)The model includes the components: atmos: GFDL-AM2.0 (cubed sphere (C48); 192 x 96 longitude/latitude; 32 vertical levels; top level 2 hPa), atmosChem: Simple carbon aerosol model (emission type), land: NCAR-CLM4, landIce: NCAR-CLM4, ocean: GFDL-MOM5.0 (tripolar - nominal 1.0 deg; 360 x 200 longitude/latitude; 52 levels; top grid cell 0-2 m; NK mixed layer scheme), ocnBgchem: TOPAZ2, seaIce: GFDL-SIS. The model was run in native nominal resolutions: atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

MCM-UA-1-0 (released in 1991)

UA (University of Arizona - Department of Geosciences)The model includes the components: aerosol: Modifies surface albedoes (Haywood et al. 1997, doi: 10.1175/1520-0442(1997)010<1562:GCMCOT>2.0.CO;2), atmos: R30L14 (3.75 X 2.5 degree (long-lat) configuration; 96 x 80 longitude/latitude; 14 levels; top level 0.015 sigma, 15 mb), land: Standard Manabe bucket hydrology scheme (Manabe 1969, doi: 10.1175/1520-0493(1969)097<0739:CATOC>2.3.CO;2), landIce: Specified location - invariant in time, has high albedo and latent heat capacity, ocean: MOM1.0 (MOM1, 1.875 X 2.5 deg; 192 x 80 longitude/latitude; 18 levels; top grid cell 0-40 m), seaIce: Thermodynamic ice model (free drift dynamics). The model was run in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: 250 km, ocean: 250 km, seaIce: 250 km.

MIROC6 (released in 2017)

MIROC (Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI))The model includes the components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, seaIce: 100 km.

MIROC-ES2L (released in 2018)

MIROC (Atmosphere and Ocean Research Institute (AORI), Centre for Climate System Research - National Institute for Environmental Studies (CCSR-NIES) and Atmosphere and Ocean Research Institute (AORI))The model includes the components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T42; 128 x 64 longitude/latitude; 40 levels; top level 3 hPa), land: MATSIRO6.0+VISIT-e ver.1.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), ocnBgchem: OECO ver.2.0; NPZD-type with C/N/P/Fe/O cycles, seaIce: COCO4.9. The model was run in native nominal resolutions: aerosol: 500 km, atmos: 500 km, land: 500 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

MPI-ESM-1-2-HAM (released in 2017)

HAMMOZ-Consortium (Swiss Federal Institute of Technology Zurich (ETH-Zurich), Max Planck Institute for Meteorology (MPI-M), Forschungszentrum Jülich, University of Oxford, Finnish Meteorological Institute (FMI), Leibniz Institute for Tropospheric Research (IfT) and Center for Climate Systems Modeling (C2SM) at ETH Zurich)The model includes the components: aerosol: HAM2.3, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), atmosChem: sulfur chemistry (unnamed), land: JSBACH 3.20, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

MPI-ESM1-2-HR (released in 2017)

MPI-M DWD DKRZ (Max Planck Institute for Meteorology (MPI-M), German Meteorological Service (DWD), German Climate Computing Center (DKRZ))The model includes the components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocean biogeochemistry: 50 km, seaIce: 50 km.

MPI-ESM1-2-LR (released in 2017)

MPI-M AWI (Max Planck Institute for Meteorology (MPI-M), AWI (Alfred Wegener Institute))The model includes the components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocean biogeochemistry: 250 km, seaIce: 250 km.

MRI-ESM2-0 (released in 2017)

MRI (Meteorological Research Institute, Japan)The model includes the components: aerosol: MASINGAR mk2r4 (TL95; 192 x 96 longitude/latitude; 80 levels; top level 0.01 hPa), atmos: MRI-AGCM3.5 (TL159; 320 x 160 longitude/latitude; 80 levels; top level 0.01 hPa), atmosChem: MRI-CCM2.1 (T42; 128 x 64 longitude/latitude; 80 levels; top level 0.01 hPa), land: HAL 1.0, ocean: MRI.COM4.4 (tripolar primarily 0.5 deg latitude/1 deg longitude with meridional refinement down to 0.3 deg within 10 degrees north and south of the equator; 360 x 364 longitude/latitude; 61 levels; top grid cell 0-2 m), ocnBgchem: MRI.COM4.4, seaIce: MRI.COM4.4. The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 100 km, atmospheric chemistry: 250 km, land: 100 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

NESM3 (released in 2016)

NUIST (Nanjing University of Information Science and Technology) The model includes the components: atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa), land: JSBACH v3.1, ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m), seaIce: CICE4.1. The model was run in native nominal resolutions: atmosphere: 250 km, land: 2.5 km, ocean: 100 km, seaIce: 100 km.

NorCPM1 (released in 2019)

NCC (Norwegian Climate Centre)The model includes the components: aerosol: OsloAero4.1 (same grid as atmos), atmos: CAM-OSLO4.1 (2 degree resolution; 144 x 96 longitude/latitude; 26 levels; top level ~2 hPa), atmosChem: OsloChemSimp4.1 (same grid as atmos), land: CLM4 (same grid as atmos), ocean: MICOM1.1 (1 degree resolution; 320 x 384 longitude/latitude; 53 levels; top grid cell 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1 (same grid as ocean), seaIce: CICE4 (same grid as ocean). The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

NorESM1-F (released in 2018)

NCC (Norwegian Climate Centre)The model includes the components: atmos: CAM4 (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), land: CLM4, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC5.1, seaIce: CICE4. The model was run in native nominal resolutions: atmosphere: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

NorESM2-LM (released in 2017)

NCC (Norwegian Climate Centre)The model includes the components: aerosol: OsloAero, atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. The model was run in native nominal resolutions: aerosol: 250 km, atmospheric: 250 km, atmospheric chemistry: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

NorESM2-MM (released in 2017)

NCC (Norwegian Climate Centre)The model includes the components: aerosol: OsloAero, atmos: CAM-OSLO (1 degree resolution; 288 x 192; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, landIce: 100 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.

SAM0-UNICON (released in 2017)

SNU (Seoul National University)

The model includes the components: aerosol: MAM3, atmos: CAM5.3 with UNICON (1deg; 288 x 192 longitude/latitude; 30 levels; top level ~2 hPa), land: CLM4.0, ocean: POP2 (Displaced Pole; 320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m), seaIce: CICE4.0. The model was run in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.

TaiESM1 (released in 2018)

AS-RCEC (Research Center for Environmental Changes)The model includes the components: aerosol: SNAP (same grid as atmos), atmos: TaiAM1 (0.9x1.25 degree; 288 x 192 longitude/latitude; 30 levels; top level ~2 hPa), atmosChem: SNAP (same grid as atmos), land: CLM4.0 (same grid as atmos), ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m), seaIce: CICE4. The model was run in native nominal resolutions: aerosol: 100 km, atmosphere: 100 km, atmospheric chemistry: 100 km, land: 100 km, ocean: 100 km, seaIce: 50 km.

UKESM1-0-LL (released in 2018)

MOHC, NERC, NIMS-KMA, NIWA  (Met Office Hadley Centre, Natural Environmental Research Council,  National Institute of Meteorological Science / Korean Meteorological Administration (NIMS-KMA), National Institute of Weather and Atmospheric Research (NIWA)) The model includes the components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N96; 192 x 144 longitude/latitude; 85 levels; top level 85 km), atmosChem: UKCA-StratTrop, land: JULES-ES-1.0, ocean: NEMO-HadGEM3-GO6.0 (eORCA1 tripolar primarily 1 deg with meridional refinement down to 1/3 degree in the tropics; 360 x 330 longitude/latitude; 75 levels; top grid cell 0-1 m), ocnBgchem: MEDUSA2, seaIce: CICE-HadGEM3-GSI8 (eORCA1 tripolar primarily 1 deg; 360 x 330 longitude/latitude). The model was run in native nominal resolutions: aerosol: 250 km, atmosphere: 250 km, atmospheric chemistry: 250 km, land: 250 km, ocean: 100 km, ocean biogeochemistry: 100 km, seaIce: 100 km.


Grids

CMIP6 data is reported either on the model’s native grid or re-gridded to one or more target grids with data variables generally provided near the centre of each grid cell (rather than at the boundaries).  For CMIP6 there is a requirement to record both the native grid of the model and the grid of its output (archived in the CMIP6 repository) as a “nominal_resolution”.  The "nominal_resolution” enables users to identify which models are relatively high resolution and have data that might be challenging to download and store locally. Information about the grids can be found in the model table above, under 'Model Details' and within the NetCDF file metadata.

Pressure levels

For pressure level data the model output is available on the pressure levels according to the table below. Note that since the model output is standardised all models produce the data on the same pressure levels.

Frequency

Number of Levels

Pressure Levels (hPa)

Daily

8

1000., 850., 700., 500., 250., 100., 50., 10.

Monthly

19

1000., 925., 850., 700., 600., 500., 400., 300., 250., 200., 150., 100., 70., 50., 30., 20., 10., 5., 1.

Ensembles

Each modelling centre typically run the same experiment using the same model with slightly different settings several times to confirm the robustness of results and inform sensitivity studies through the generation of statistical information. A model and its collection of runs is referred to as an ensemble. Within these ensembles, four different categories of sensitivity studies are done, and the resulting individual model runs are labelled by four integers indexing the experiments in each category

e.g. r<W>i<X>p<Y>f<Z>, where W, X, Y and Z are positive integers as defined below:

  • The first category, labelled realization_index (referred to with letter r), performs experiments which differ only in random perturbations of the initial conditions of the experiment. Comparing different realizations allow estimation of the internal variability of the model climate.
  • The second category, labelled initialization_index (referred to with letter i), refers to variation in initialisation parameters. Comparing differently initialised output provides an estimate of how sensitive the model is to initial conditions.
  • The third category, labelled physics_index (referred to with letter p), refers to variations in the way in which sub-grid scale processes are represented. Comparing different simulations in this category provides an estimate of the structural uncertainty associated with choices in the model design.
  • The fourth category labelled forcing_index (referred to with letter f) is used to distinguish runs of a single CMIP6 experiment, but with different forcings applied.

Parameter listings

Time-Independent parameters are marked with a *

Expand
titleList of parameters


CDS parameter name for CMIP5

 ESGF variable id

Long name to be used in CMIP6

 Units

2m temperature

 tas

Near-Surface Air Temperature

Kelvin

Maximum 2m temperature in the last 24 hours

 tasmax

Daily Maximum Near-Surface Air Temperature

Kelvin

Minimum 2m temperature in the last 24 hours 

 tasmin

Daily Minimum Near-Surface Air Temperature

Kelvin

Maximum 10m wind speed in the last 24 hourssfcwindmaxDaily Maximum Near-Surface Wind Speedm s-1
Skin temperaturetsSurface TemperatureKelvin
Mean sea level pressurepslSea Level PressurePa
Surface pressurepsSurface Air PressurePa
10m u component of winduasEastward Near-Surface Windm s-1
10m v component of windvasNorthward Near-Surface Windm s-1
10m wind speedsfcWindNear-Surface Wind Speedm s-1
2m relative humidityhursNear-Surface Relative Humidity1
2m specific humidityhussNear-Surface Specific Humidity1
Mean precipitation fluxprPrecipitationkg m-2s-1
SnowfallprsnSnowfall Flux

kg m-2 s-1

EvaporationevspsblEvaporation Including Sublimation and Transpiration

kg m-2 s-1

Atmosphere water vapor contentprwAtmosphere Water Vapor Contentkg m-2
Eastward turbulent surface stresstauuSurface Downward Eastward Wind StressPa
Northward turbulent surface stresstauvSurface Downward Northward Wind StressPa
Surface latent heat fluxhflsSurface Upward Latent Heat FluxW m-2
Surface sensible heat fluxhfssSurface Upward Sensible Heat FluxW m-2 
Surface thermal radiation downwardsrldsSurface Downwelling Longwave RadiationW m-2

Surface upwelling longwave radiation

rlusSurface Upwelling Longwave RadiationW m-2

Surface solar radiation downwards

rsdsSurface Downwelling Shortwave RadiationW m-2

Surface upwelling shortwave radiation

rsusSurface Upwelling Shortwave RadiationW m-2

TOA incident solar radiation

rsdtTOA Incident Shortwave RadiationW m-2
TOA outgoing shortwave radiationrsutTOA Outgoing Shortwave RadiationW m-2
TOA outgoing longwave radiationrlutTOA Outgoing Longwave RadiationW m-2
TOA outgoing clear-sky shortwave radiationrsutcsTOA Outgoing Shortwave Flux Assuming Clear SkyW m-2
TOA outgoing clear-sky longwave radiationrlutcsTOA Outgoing Longwave Flux Assuming Clear SkyW m-2
Total cloud covercltTotal Cloud Cover Percentage1
Air temperaturetaAir TemperatureK
U-component of winduaEastward Windm s-1
V-component of windvaNorthward Windm s-1
Relative humidityhurRelative Humidity1
Specific humidity husSpecific Humidity1
Geopotential heightzgGeopotential Heightm
Surface snow amountsnwSurface Snow Amountkg m-2
Snow depthsndSnow Depthm
Surface runoffmrrosSurface Runoff Fluxkg m-2 s-1
RunoffmrroTotal Runoffkg m-2 s-1
Soil moisture contentmrsosMoisture in Upper Portion of Soil Columnkg m-2
Sea-ice area percentagesiconcSea-Ice Area Percentage (Ocean Grid)1
Sea ice thicknesssithickSea Ice Thicknessm
Sea ice plus snow amountsimassSea-Ice Mass per Areakg m-2
Sea ice surface temperaturesitemptopSurface Temperature of Sea IceK
Sea surface temperaturetosSea Surface TemperatureK
Sea surface salinitysosSea Surface SalinityPSU
Sea surface height above geoidzosSea Surface Height Above Geoidm
Grid-cell area for ocean variablesareacelloGrid-Cell Area for Ocean Variables*m2
Sea area percentagesftofSea Area Percentage*%
Grid-cell area for atmospheric grid variablesareacellaGrid-Cell Area for Atmospheric Grid Variables*m2
Capacity of soil to store water (field capacity)mrsofcCapacity of Soil to Store Water (Field Capacity)*kg m-2
Percentage of grid cell occupied by land (including lakes)sftlfPercentage of the Grid Cell Occupied by Land (Including Lakes)*%
Land ice area percentagesftgifLand Ice Area Percentage*1
OrographyorogSurface Altitude*m


Data Format

The CDS subset of CMIP6 data are provided as NetCDF files. NetCDF (Network Common Data Form) is a file format that is freely available and commonly used in the climate modelling community. See more details:  What are NetCDF files and how can I read them

 A CMIP6 NetCDF file in the CDS contains:

  • Global metadata: these fields can describe many different aspects of the file such as
    • when the file was created
    • the name of the institution and model used to generate the file
    • Information on the horizontal grid and regridding procedure
    • links to peer-reviewed papers and technical documentation describing the climate model,
    • links to supporting documentation on the climate model used to generate the file,
    • software used in post-processing.
  • variable dimensions: such as time, latitude, longitude and height
  • variable data: the gridded data
  • variable metadata: e.g. the variable units, averaging period (if relevant) and additional descriptive data

File naming conventions

When you download a CMIP6 file from the CDS it will have a naming convention that is as follows:

<variable_id>_<table_id>_<source_id>_<experiment_id>_<variant_label>_<grid_label>_<time_range>.nc

 Where:

  • variable_id: variable is a short variable name, e.g. “tas” for “temperature at the surface”.
  • table_id: this refers to the MIP table being used. The MIP tables are used to organise the variables. For example, Amon refers to monthly atmospheric variables and Oday contains daily ocean data.
  • source_id: this refers to the model used that produced the data.
  • experiment_id: refers to the set of experiments being run for CMIP6. For example, PiControl, historical and 1pctCO2 (1 percent per year increase in CO2).
  • variant_label: is a label constructed from 4 indices (ensemble identifiers) r<W>i<X>p<Y>f<Z>, where W, K, Y and Z are integers.
  • grid_label: this describes the model grid used. For example, global mean data (gm), data reported on a model's native grid (gn) or regridded data reported on a grid other than the native grid and other than the preferred target grid (gr1).
  • time_range: the temporal range is in the form YYYYMM[DDHH]-YYYY[MMDDHH], where Y is year, M is the month, D is day and H is hour. Note that day and hour are optional (indicated by the square brackets) and are only used if needed by the frequency of the data. For example, daily data from the 1st of January 1980 to the 31st of December 2010 would be written 19800101-20101231.

Quality control of the CDS-CMIP6 subset

The CDS subset of the CMIP6 data have been through a set of quality control checks before being made available through the CDS. The objective of the quality control process is to ensure that all files in the CDS meet a minimum standard. Data files were required to pass all stages of the quality control process before being made available through the CDS. Data files that fail the quality control process are excluded from the CDS-CMIP6 subset, data providers are contacted and if they are able to release a new version of the data with the error corrected then providing this data passes all remaining QC steps may be available for inclusion in the next CMIP6 data release.

 The main aim of the quality control procedure is to check for metadata and gross data errors in the CMIP6 files and datasets. A brief description of each of the QC checks is provided here:

  1. CF-Checks: The CF-checker tool checks that each NetCDF4 file in a given dataset is compliant with the Climate and Forecast (CF) conventions, compliance ensures that the files are interoperable across a range of software tools.
  2. PrePARE: The PrePARE software tool is provided by PCMDI (Program for Climate Model Diagnosis and Intercomparison) to verify that CMIP6 files conform to the CMIP6 data protocol. All CMIP6 data should meet this required standard however this check is included to ensure that all data supplied to the CDS have passed this QC test.
  3. nctime: The nctime checker checks the temporal axis of the NetCDF files. For each NetCDF file the temporal element of the file is compared with the time axis data within the file to ensure consistency. For a time-series of data comprised of several NetCDF files nctime ensures that the entire timeseries is complete, that there are no temporal gaps or overlaps in either the filename or in the time axes within the files.
  4. Errata: The dataset is checked to ensure that no outstanding Errata record exists.
  5. Data Ranges: A set of tests on the extreme values of the variables are performed, this is used to ensure that the values of the variables fall into physically realistic ranges.
  6. Handle record consistency checks: This check ensures that the version of the dataset used is the most recently published dataset by the modelling centre, it also checks for any inconsistency in the ESGF publication and excludes any datasets that may have an inconsistent ESGF publication metadata.
  7. Exists at all partner sites: It is asserted that each dataset exists at all three partner sites CEDA, DKRZ and IPSL.

It is important to note that passing these quality control tests should not be confused with validity: for example, it will be possible for a file to pass all QC steps but contain errors in the data that have not been identified by either data providers or data users.

 In cases where the quality control picks up errors that are related to minor technical details of the conventions, or behavior that is in line with expectations for climate model output despite being unexpected in a physical system, the data will be published with details of the errors referenced in the documentation. An example of the 2nd type of error is given by negative salinity values which occur in one model as a result of rapid release of fresh water from melting sea-ice. These negative values are part of the noise associated with the numerical simulation and reflect what is happening in the numerical model.

Citation and license information

The CMIP6 data Citation Service provides information for data users on how to cite CMIP6 data and on the data license. The long-term availability and long-term accessibility are granted by the use of DOIs for the landing page e.g . http://doi.org/10.22033/ESGF/CMIP6.1317.

Available CMIP6 data citations are discoverable in the ESGF or in the Citation Search at: http://bit.ly/CMIP6_Citation_Search.

Known issues

CDS users will be directed to the CMIP6 ES-DOC Errata Service for known issues with the wider CMIP6 data pool. Data that is provided to the CDS should not contain any errors or be listed in the Errata service, however this will still be a useful resource for CDS users as data they may be looking for but cannot access may have been withheld from the CDS for justifiable reasons.

Subsetting and downloading data

CDS users will now be able to apply subsetting operations to CMIP6 datasets. This mechanism (the "roocs" WPS framework) that runs at each of the partner sites: CEDA, DKRZ and IPSL. The WPS can receive requests for processing based on dataset identifiers, a temporal range, a bounding box and a range of vertical levels. Each request is converted to a job that is run asynchronously on the processing servers at the partner sites. NetCDF files are generated and the response contains download links to each of the files. Users of the CDS will be able to make subsetting selections using the web forms provided by the CDS catalogue web-interface. More advanced users will be able to define their own API requests in the CDS Toolbox that will call the WPS. Output files will be automatically retrieved so that users can access them directly within the CDS.

References

Durack, P J. (2020) CMIP6_CVs. v6.2.53.5. Available at: https://github.com/WCRP-CMIP/CMIP6_CVs (Accessed: 26 October 2020).

Eyring, V. et al. (2016) ‘Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization’, Geoscientific Model Development, 9(5), pp. 1937–1958. doi: 10.5194/gmd-9-1937-2016.

IPCC (2020) Sixth Assessment Report. Available at: https://www.ipcc.ch/assessment-report/ar6/ (Accessed: 26 October 2020).

Moss, R. et al. (2008) ‘Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies’, Intergovernmental Panel on Climate Change, Geneva, pp. 132.

O’Neill, B.C. et al. (2014) ‘A new scenario framework for climate change research: the concept of shared socioeconomic pathways.’, Climatic Change, 122, pp. 387–400. doi: https://doi.org/10.1007/s10584-013-0905-2

World Climate Research Programme (2020) CMIP Phase 6 (CMIP6): Overview CMIP6 Experimental Design and Organization. Available at: https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 (Accessed: 2 November 2020).

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Data availability matrix

A data availability matrix for the C3S CMIP5 exists at: https://cp-availability.ceda.ac.uk.

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Data Format

The CDS subset of CMIP5 data are provided as NetCDF files. NetCDF (Network Common Data Form) is a file format that is freely available and commonly used in the climate modelling community. See more details:  What are NetCDF files and how can I read them

A CMIP5 NetCDF file in the CDS contains: 

  • Global metadata: these fields can describe many different aspects of the file such as
    • when the file was created
    • the name of the institution and model used to generate the file
    • links to peer-reviewed papers and technical documentation describing the climate model,
    •  links to supporting documentation on the climate model used to generate the file, 
    • software used in post-processing. 
  • variable dimensions: such as time, latitude, longitude and height
  • variable data: the gridded data
  • variable metadata: e.g. the variable units, averaging period (if relevant) and additional descriptive data

The metadata provided in NetCDF files adhere to the Climate and Forecast (CF) conventions (v1.4 for CMIP5 data). The rules within the CF-conventions ensure consistency across data files, for example ensuring that the naming of variables is consistent and that the use of variable units is consistent.

File naming conventions

When you download a CMIP5 file from the CDS it will have a naming convention that is as follows:

<variable>_<cmor_table>_<model>_<experiment>_<ensemble_member>_<temporal_range>.nc

Where

  • variable is a short variable name, e.g. “tas” for ”temperature at the surface”
  • cmor_table is a reference to the realm (an earth system component such as atmosphere or ocean) and frequency of the variable, e.g. “Amon” indicates that a variable is present in the atmosphere realm at a monthly frequency (link to list of these)
  • model is the name of the model that produced the data
  • ensemble member is the ensemble identifier in the form “r<X>i<Y>p<Z>”, X, Y and Z are integers
  • the temporal range is in the form YYYYMM[DDHH]-YYYY[MMDDHH], where Y is year, M is the month, D is day and H is hour. Note that day and hour are optional (indicated by the square brackets) and are only used if needed by the frequency of the data. For example daily data from the 1st of January 1980 to the 31st of December 2010 would be written 19800101-20101231.

Quality control of the CDS-CMIP5 subset

The CDS subset of the CMIP5 data have been through a set of quality control checks before being made available through the CDS. The objective of the quality control process is to ensure that all files in the CDS meet a minimum standard. Data files were required to pass all stages of the quality control process before being made available through the CDS. Data files that fail the quality control process are excluded from the CDS-CMIP5 subset or if possible the error is corrected and a note made in the history attribute of the file. The quality control of the CDS CMIP5 subset checks for metadata errors or inconsistencies against the Climate and Forecast (CF) Conventions and a set of CMIP5 specific file naming and file global metadata conventions. 

Various software tools have been used to check the metadata of the CDS CMIP5 data:

  • The Centre for Environmental Data Analysis (CEDA) compliance checking tool CEDA-CC is used to check that: 
    • the file name adheres to the CMIP5 file naming convention, 
    • the global attributes of the NetCDF file are consistent with filename,
    • there are no omissions of required CMIP5 metadata.
  • The CF-Checker Climate and Forecast (CF) conventions checker ensures that any metadata that is provided is consistent with the CF conventions. 
  • A time-axis-checker is used to check the temporal dimension of the data:
    • for individual files the time dimension of the data is checked to ensure it is valid and is consistent with the temporal information in the filename, 
    • where more than one file is required to generate a time-series of data, the files have been checked to ensure there are no temporal gaps or overlaps between the files.

The data within the files were not individually checked however where it was known that a variable from a given model had a gross error, e.g in the sign convention of a flux, then these data were also omitted from the CDS-CMIP5 subset. 

It is important to note that passing of these quality control tests should not be confused with validity: for example, it will be possible for a file to be fully CF compliant and have fully compliant CMIP5 metadata but contain gross errors in the data that have not been noted. 

For a detailed description of all the quality control of the data please see the accompanying documentation  

Known issues

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This document has been produced in the context of the Copernicus Climate Change Service (C3S).
The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.
The users thereof use the information at their sole risk and liability. For the avoidance of all doubt, the European Commission and the European Centre for Medium-Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

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