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Introduction
What are decadal climate projections?
Decadal climate projections
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The Decadal Climate Prediction Project (DCPP)
The Decadal Climate Prediction Project (Boer et al., 2016) addresses the ability of the climate system to be predicted on annual, multi-annual and decadal timescales. The information generated by the DCPP and archived on the Earth System Grid Federation (ESGF) nodes that is made accessible in the CDS can provide a basis for socially relevant operational climate predictions on annual to decadal timescales.
DCPP: Part of CMIP6
The Decadal Climate Prediction Project (DCPP)
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The Climate is a contributing MIP (Model Intercomparison Project (CMIP) was established in 1995 by the World Climate Research Program (WCRP) to provide climate scientists with a database of coupled Global Circulation Model (GCM) simulations.
The CMIP process involves institutions (such as national meteorological centres or research institutes) from around the world running their climate models with an agreed set of input parameters (forcings). The modelling centres produce a set of standardised output. When combined, these produce a multi-model dataset that is shared internationally between modelling centres and the results compared.
Analysis of the CMIP data allows for
- an improved understanding of 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.
Comparison of different climate models allows for
- determining why similarly forced models 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 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:
- 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;
- Common standards, coordination, infrastructure and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble;
- 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).
Decadal Climate Prediction Project Experiments in the CDS
The decadal climate prediction project (DCPP) data in the Climate Data Store (CDS) are a quality-controlled subset of the wider DCPP data. The CDS provides data for two DCPP experiments from four modelling centres.
The CDS subset of CMIP6 data 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.
Experiments
Shared Socioeconomic Pathway (SSP) Experiments
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.
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 CMIP6 experiments detailed in the table below.
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| title | Click here to expand... CMIP6 experiments included in the CDS |
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Experiment name
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Extended Description
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historical
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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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SSP5-8.5
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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. 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. SSP5-8.5 is comparable to the CMIP5 experiment RCP8.5.
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SSP3-7.0
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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. SSP3-7.0 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 SSP3-7.0 scenario represents the medium to high end of plausible future forcing pathways. 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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SSP2-4.5
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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. SSP2-4.5 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. SSP2-4.5 is comparable to the CMIP5 experiment RCP4.5.
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SSP1-2.6
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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. 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 SSP1-2.6 scenario represents the low end of plausible future forcing pathways. SSP1-2.6 depicts a "best case" future from a sustainability perspective.
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SSP4-6.0
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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. SSP4-6.0 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 SSP4-6.0 scenario fills in the range of medium plausible future forcing pathways. SSP4-6.0 defines the low end of the forcing range for unmitigated SSP baseline scenarios.
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SSP4-3.4
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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. SSP4-3.4 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 SSP4-3.4 scenario fills a gap at the low end of the range of plausible future forcing pathways. SSP4-3.4 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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SSP5-3.4OS
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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 SSP5-3.4OS scenario branches from SSP5-8.5 in the year 2040 whereupon it applies substantially negative net emissions. SSP5-3.4OS explores the climate science and policy implications of a peak and decline in forcing during the 21st century. SSP5-3.4OS 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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SSP1-1.9
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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. SSP1-1.9 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 SSP1-1.9 scenario fills a gap at the very low end of the range of plausible future forcing pathways. SSP1-1.9 forcing will be substantially below 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.
Models, grids and pressure levels
Models
The models included in the CDS-CMIP6 subset are detailed in the table below including a brief description of the model where this information is readily available, further details can be found on the Earth System Documentation site.
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| title | Click here to expand...Global climate models included in the CDS |
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Model Name
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Modelling Centre
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Model Details
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ACCESS-CM2 (released in 2019)
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CSIRO-ARCCSS (Commonwealth Scientific and Industrial Research Organisation, Australian Research Council Centre of Excellence for Climate System Science)
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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.
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ACCESS-ESM1-5 (released in 2019)
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AWI-CM-1-1-MR (released in 2018)
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AWI (Alfred Wegener Institute)
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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.
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AWI-ESM-1-1-LR (released in 2018)
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BCC-CSM2-MR (released in 2017)
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BCC-ESM1 (released in 2017)
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CAMS-CSM1-0 (released in 2016)
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CanESM5 (released in 2019)
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CanESM5-CanOE (released in 2019)
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CAS-ESM2-0 (released in 2019)
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CESM2
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CESM2-FV2 (released in 2019)
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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.
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CESM2-WACCM (released in 2018)
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CESM2-WACCM-FV2 (released in 2019)
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CIESM (released in 2017)
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CMCC-CM2-SR5 (released in 2016)
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CNRM-CM6-1 (released in 2017)
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CNRM-CM6-1-HR (released in 2017)
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CNRM-ESM2-1 (released in 2017)
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E3SM-1-0 (released in 2018)
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E3SM-1-1 (released in 2019)
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E3SM-1-1-ECA (released in 2019)
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EC-Earth3 (released in 2019)
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EC-Earth3-LR (released in 2019)
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EC-Earth3-Veg (released in 2019)
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EC-Earth3-Veg-LR (released in 2019)
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FGOALS-f3-L (released in 2017)
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FGOALS-g3 (released in 2017)
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FIO-ESM-2-0 (released in 2018)
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GFDL-AM4 (released in 2018)
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GFDL-CM4 (released in 2018)
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GFDL-ESM4 (released in 2018)
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GISS-E2-1-G (released in 2019)
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GISS-E2-1-H (released in 2019)
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GISS-E2-2-G (released in 2019)
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HadGEM3-GC31-LL (released in 2016)
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HadGEM3-GC31-MM (released in 2016)
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IITM-ESM (released in 2015)
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INM-CM4-8 (released in 2016)
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INM-CM5-0 (released in 2016)
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IPSL-CM6A-LR (released in 2017)
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KACE-1-0-G (released in 2018)
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KIOST-ESM (released in 2018)
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MCM-UA-1-0 (released in 1991)
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MIROC6 (released in 2017)
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MIROC-ES2L (released in 2018)
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MPI-ESM-1-2-HAM (released in 2017)
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MPI-ESM1-2-HR (released in 2017)
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MPI-ESM1-2-LR (released in 2017)
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MRI-ESM2-0 (released in 2017)
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NESM3 (released in 2016)
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NorCPM1 (released in 2019)
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NorESM1-F (released in 2018)
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NorESM2-LM (released in 2017)
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NorESM2-MM (released in 2017)
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SAM0-UNICON (released in 2017)
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SNU (Seoul National University)
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TaiESM1 (released in 2018)
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UKESM1-0-LL (released in 2018)
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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.
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Frequency
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Number of Levels
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Pressure Levels (hPa)
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Daily
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8
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1000., 850., 700., 500., 250., 100., 50., 10.
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Monthly
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19
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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 *
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| title | List of parameters |
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CDS parameter name for CMIP5
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ESGF variable id
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Units
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2m temperature
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tas
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Kelvin
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Maximum 2m temperature in the last 24 hours
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tasmax
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Kelvin
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Minimum 2m temperature in the last 24 hours
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tasmin
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Kelvin
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kg m-2 s-1
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kg m-2 s-1
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Surface upwelling longwave radiation
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Surface solar radiation downwards
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Surface upwelling shortwave radiation
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TOA incident solar radiation
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) to the 6th Coupled Model Intercomparison Project (CMIP6 – Eyring et al., 2016), which is running as part of the World Climate Research Programme (WCRP). DCPP addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.
The CMIP6 data archive is distributed through the ESGF. A quality-controlled subset of CMIP6 global climate projection data are made available through the Climate Data Store (CDS) for the users of the Copernicus Climate Change Service (C3S). Dedicated ESGF data nodes are used for C3S in France (at IPSL) and in Germany (DKRZ). Similarly, the decadal climate prediction project (DCPP) data in the Climate Data Store (CDS) are a targeted, quality-controlled subset of the DCPP commissioned by C3S.
The published datasets are the ones which took part on the C3S sectoral demonstrator service. This demonstrator provided decadal prediction products tailored to specific users from the agriculture, energy, infrastructure and insurance sectors (see details at https://climate.copernicus.eu/sectoral-applications-decadal-predictions). The data were used in these demonstrators following processing procedures necessary to extract valid information (e.g., bias adjustment); details on this processing are available in the technical appendix at https://climate.copernicus.eu/sites/default/files/2021-09/Technical_appendix_2020.pdf. Any application - similar to or different from these examples - needs to consider and apply the required data processing with care.
Decadal Climate Prediction Project Data in the CDS
DCPP Experiments
The CDS provides data access to two DCPP experiments: dcppA-hindcast which consists of retrospective decadal forecasts that can be used to assess historical decadal prediction skill, and dcppB-forecast which are experimental quasi-real-time decadal forecasts that form a basis for potential operational forecast production. For these DCPP experiments, each model performs multiple overlapping simulations that are initialised annually throughout the experiment. The dcppA-hindcast and dcppB-forecast experiments are further described in the table below. The DCPP experiment descriptions presented here are based on information harvested from Earth System Documentation (ES-DOC).
Experiment Name | Experiment Long Name | Extended Description |
|---|---|---|
dcppA-hindcast | hindcasts initialized from observations with historical forcing | dcppA-hindcast is a set of retrospective decadal forecasts (known as hindcasts) that are initialised every year mostly from 1960-2019 and performed with a coupled atmosphere-ocean general circulation model (AOGCM). The hindcasts begin in November to allow for DJF (December, January, February) seasonal averages to be calculated. There are 10 hindcasts for each start date and hindcasts run for 10 years. The models running these hindcasts are initialised using observed data. Prior to the year 2020, the models are forced with historical conditions that are consistent with observations, these conditions include atmospheric composition, land use, volcanic aerosols and solar forcing. When hindcasts extend beyond 2020, the models are forced with future conditions from the ssp245 scenario from 2020 until the end of the simulation. DCPP hindcast experiments can be used to assess and understand the historical decadal prediction skill of climate models. |
dcppB-forecast | forecasts initialised from observations with ssp245 scenario forcing | dcppB-forecast is a set of quasi-real-time decadal forecasts that are initialised every year from 2019 in real time and ongoing (although only the data used in the secotoral demonstrator service is available from the CDS). The forecasts are performed with the same coupled atmosphere-ocean general circulation model (AOGCM), which was used to generate the hindcast data. The forecasts begin in November to allow DJF (December, January, February) seasonal averages to be calculated. There are 10 forecasts for each start date and forecasts run for 10 years. The models running these forecasts are initialised using observed data. Prior to the year 2020, the models are forced with historical conditions that are consistent with observations, these conditions include atmospheric composition, land use, volcanic aerosols and solar forcing. When forecasts extend beyond 2020, the models are forced with future conditions from the ssp245 scenario from 2020 until the end of the simulation. DCPP forecast experiments form a basis for potential operational decadal forecast production. |
Models
Data for the dcppA-hindcast and dcppB-forecast experiments published in the CDS are generated from simulations run by the models described in the table below. The model descriptions presented here are harvested from the dataset DOI pages held at the World Data Centre for Climate (WDCC), further model details can be found on the ES-DOC. The EC-Earth3, MPI-ESM1-2-HR, MPI-ESM1-2-LR and HadGEM3-GC31-MM models were configured with 360-day years (where every month has 30 days), whereas the CMCC-CM2-SR5 model was configured with a 365 day year (with an irregular number of days in each month).
Model | Centre | Description |
|---|---|---|
EC-Earth3 | EC Earth Consortium | The model used in climate research named EC Earth 3.3, released in 2019, includes the components:
The model was run in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km. |
CMCC-CM2-SR5 | The Euro-Mediterranean Center on Climate Change (Centro Euro-Mediterraneo per I Cambiamenti Climatici, CMCC) | The model used in climate research named CMCC-CM2-SR5, released in 2016, includes the components:
The model was run in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km. |
MPI-ESM1-2-HR | The German Weather Service (Deutscher Wetterdienst, DWD) / Max Planck Institute for Meteorology (MPI-M) | The model used in climate research named MPI-ESM1.2-HR, released in 2017, includes the components:
The model was run in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km. |
MPI-ESM1-2-LR | The German Weather Service (Deutscher Wetterdienst, DWD) / Max Planck Institute for Meteorology (MPI-M) | The model used in climate research named MPI-ESM1.2-LR, released in 2017, includes the components:
The model was run in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km. https://www.wdc-climate.de/ui/cmip6?input=CMIP6.DCPP.MPI-M.MPI-ESM1-2-LR |
HadGEM3-GC31-MM | Met Office Hadley Centre (MOHC) | The model used in climate research named HadGEM3-GC3.1-N216ORCA025, released in 2016, includes the components:
The model was run in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km. |
Start-Date Ensembles
The DCPP experiments published in the CDS, are a suite of overlapping simulations that are initialised every year throughout the duration of the start-date range specified by the experiment. The simulations begin in November to allow for DJF (December, January, February) seasonal averages to be calculated. There are 10 simulations (ensemble members) for each start-date (called "Base year" in the CDS form), except for the MPI-ESM1-2-LR model which has 16 ensemble members.
The start-date ensemble is reflected in the DCPP data naming convention with the addition of a s<yyyy> start-date ensemble identifier. Please note that the conventional CMIP6 ripf ensemble identifiers are omitted for this particular dataset since all the ensemble members are concatenated into one file.
See some more more details in the File naming conventions and In-file metadata modifications sections below.
Practical details of the published data
In the table below some practical details of the data is shown including the base year (or start year) period covered and the number of ensemble members. For each start year there are (at least) 10 years of corresponding hindcast or forecast data available. Hindcast and forecast start years are not distinguished in the CDS form. Please note that the ensemble members are not available individually, but they are concatenated into one file while the data is downloaded, and generally users are encouraged to use all members instead of selecting one member of the predictions.
| Hindcast start years* | Forecast start years* | Ensemble members | Nominal resolution | Monthly variables | Daily variables | |
|---|---|---|---|---|---|---|
| CMCC (Italy) | 1960 -2018 | 2019 - 2020 | 10 | 100 km | Near surface air temperature, precipitation, sea level pressure | --- |
| EC-EARTH (Europe) | 1960 - 2018 | 2019 - 2020 | 10 | 100 km | Near surface air temperature, precipitation, sea level pressure | 500 hPa geopotential height, daily maximum near surface air temperature, daily minimum near surface air temperature, near surface air temperature, precipitation, sea level pressure |
| HadGEM3 (UK) | 1960 - 2018 | 2019 - 2020 | 10 | 100 km | Near surface air temperature, precipitation, sea level pressure | 500 hPa geopotential height, daily minimum near surface air temperature, precipitation |
| MPI-ESM1-2-HR (Germany) | 1960 - 2018 | --- | 10 | 100 km | Near surface air temperature, precipitation, sea level pressure | 500 hPa geopotential height, daily maximum near surface air temperature, daily minimum near surface air temperature, precipitation |
| MPI-ESM1-2-LR (Germany) | 1960 - 2018 | 2019 - 2021 | 16 | 250 km | Near surface air temperature, precipitation, sea level pressure | Daily maximum near surface air temperature, daily minimum near surface air temperature |
*Note: Since hindcast and forecast data begins in November, the actual period the data covers includes only November and December for each start year, however the last year includes November and December. For example, for the 1960 start year, 1960 includes November and December and 1961 - 1970 have full coverage.
Parameter listings
Data for the dcppA-hindcast experiments and the dcppB-forecast experiments will include parameters at monthly and daily resolution as described in the tables below. The parameter descriptions presented here are harvested from the CMIP6 Data Request via the CLIPC variable browser.
| CDS parameter name | ESGF variable id | units | Standard name (CF) | Long name | Description |
|---|---|---|---|---|---|
| 500 hPa geopotential height | zg500 | m | geopotential_height | Geopotential Height at 500hPa | Gravitational potential energy per unit mass normalised by the standard gravity at 500hPa at the same latitude. |
| Daily maximum near-surface air temperature | tasmax | K | air_temperature | Daily Maximum Near-Surface Air Temperature | Daily maximum temperature of air at 2m above the surface of land, sea or inland waters. |
| Daily minimum near-surface air temperature | tasmin | K | air_temperature | Daily Minimum Near-Surface Air Temperature | Daily minimum temperature of air at 2m above the surface of land, sea or inland waters. |
| Near-surface air temperature | tas | K | air_temperature | Near-Surface Air Temperature | Temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions. |
| Precipitation | pr | kg m-2 s-1 | precipitation_flux | Precipitation | The sum of liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation. This parameter does not include fog, dew or the precipitation that evaporates in the atmosphere before it lands at the surface of the Earth. This variable represents amount of water per unit area and time. |
| Sea level pressure | psl | Pa | air_pressure_at_sea_level | Sea Level Pressure | The pressure (force per unit area) of the atmosphere at the surface of the Earth, adjusted to the height of sea level. It is a measure of the weight that all the air in a column vertically above a point on the Earth's surface would have, if the point were located at sea level. It is calculated over all surfaces - land, sea and inland water. |
Grids
DCPP data like the rest of CMIP6 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). A grid_label (found in the file name following the ensemble identifier and also in the file's global metadata attributes) indicates whether the data is provided on the model's native grid (gn) or has been re-gridded (gr) to a target grid. For DCPP data in the CDS, only data from the EC-Earth3 model has been re-gridded to a target grid, data from the other models are provided on each model's native grid. The file's "nominal_resolution" global metadata attribute gives an indication of the resolution of the data, for the DCPP data in the CDS the nominal resolution of the models is 100km (except for the MPI-ESM1-2-LR model, which is 250 km).
Data Format
The CDS subset of DCPP
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 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 DCPP 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)the data.
- 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:
- 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.
- 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.
- 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.
- Errata: The dataset is checked to ensure that no outstanding Errata record exists.
- 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.
- 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.
- 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.
...
- the start year of the simulation as s<yyyy>, where yyyy is the start year.
Quality control of the CDS-CMIP6-DCPP subset
The CDS subset of the DCPP 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-DCPP 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 DCPP 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:
- 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.
- 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.
- 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.
- Errata: The dataset is checked to ensure that no outstanding Errata record exists.
- 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.
- 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.
- Exists at both partner sites: It is asserted that each dataset exists at both partner ESGF data nodes at IPSL and DKRZ.
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.
In-file metadata modifications
Some updates have been applied to the DCPP netCDF files in the CDS. These conform with the CF Metadata Conventions and improve the usability of the time dimension when multiple overlapping decadal experiments are used together with different start dates (adding additional time coordinates facilitates to use multiple datasets in parallel and enables unambiguous selection of time). The specific details of the updates include the following modifications:
- A “realization” variable is added, to represent the ensemble member
- The “sub_experiment_id” global attribute is adjusted to include the start year and month of the simulation
- A “reftime” variable is added, representing the start time of the simulation
- A “leadtime” coordinate variable is added, which is the prediction range of the forecasts: this is calculated from the “reftime” and the valid times from the existing time variable
- The "long_name" attribute of the "time" coordinate is updated to "valid_time".
Citation 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.on how to cite CMIP6 DCPP data and on the data license. Available CMIP6 data citations are discoverable in the ESGF or in the Citation Search at: http://bit.ly/CMIP6_Citation_Search (search for DCPP at the top of the page).
Known issues
CDS users will be directed to the CMIP6 ES-DOC Errata Service (see dcppA-hindcast and dcppB-forecast for experiment ID) 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.have been withheld from the CDS for justifiable reasons.
Particularly, the "daily maximum near-surface air temperature" variable is missing for the HadGEM3-GC31-MM model due to the fact that in this model "grid point single time step spikes leading to excessively large daily maximum temperature value" were found. Details of the problem can be found at https://errata.es-doc.org/static/view.html?uid=76b3f818-d65f-c76b-bfd8-cae5bc27825c
Subsetting and downloading data
CDS users will now be are able to apply subsetting operations to CMIP6 decadal datasets. This mechanism (the "roocs" WPS framework) that runs at each of the partner sites: CEDA, IPSL and 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
DurackBoer, P G. 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.
, D. M. Smith, C. Cassou, F. Doblas-Reyes, G. Danabasoglu, B. Kirtman, Y. Kushnir, M. Kimoto, G. A. Meehl, R. Msadek, W. A. Mueller, K. E. Taylor, F. Zwiers, M. Rixen, Y. Ruprich-Robert, R. Eade (2016), The Decadal Climate Prediction Project (DCPP) contribution to CMIP6, Geosci. Model Dev., 9, 3751-3777 doi.org/10.5194/gmd-9-3751-2016
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.
Climate Change 2021: The Physical Science Basis, the Working Group I contribution to the Sixth Assessment Report. Available at: https://www.ipcc.ch/report/sixth-assessment-report-working-group-i/ (Accessed: 14 September 2021)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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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 agreementAgreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). 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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