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Introduction
What are decadal climate predictictions?
Decadal climate predictions (not quite a forecast not quite a climate scenario but rather a middle ground).
The Decadal Climate Prediction Project (DCPP)
The Decadal Climate Prediction Project addresses a range of scientific issues involving (Boer et al., 2016) addresses the ability of the climate system to be predicted on annual, multi-annual to and 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.
DCPP, part of CMIP6
The DCPP as part of CMIP6
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 Data in the CDS
. The information generated by the DCPP and archived 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) is a contributing MIP (Model Intercomparison Project) to the 6th Coupled Model Intercomparison Project (CMIP6 – Eyring et al., 2016). The 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 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). Likewise, the decadal climate prediction project (DCPP) data in the Climate 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
Decadal Climate Prediction Project Data in the CDS
DCPP Experiments
The two DCPP experiments that are included in the C3S Climate Data Store, dcppA-hindcast and dcppB-forecast, are described in Table 2.1 below. The experiment descriptions are harvested from ES-DOC. We use content from the ES-DOC experiment extended_description attribute.The CDS provides data for 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-forecast 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.
DCPP Experiments
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's duration. dcppA-hindcast and dcppB-forecast are further described in the table below. The DCPP experiment descriptions presented here are 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 from 1960-2019 and performed with a coupled atmosphere-ocean general circulation model (AOGCM). Prior to the year 2020 the models running these simulations are forced with historical conditions that are consistent with observations, these conditions include atmospheric composition, land use, volcanic aerosols and solar forcing. For simulations that extend b beyond 2020 (e.g. 2015-2025) the models are forced with future conditions from the ssp245 scenario for the period from 2020 to the end of the simulation. There are 10 ensemble members for each start date and simulations run for 10 years. DCPP hindcasts can be used to assess and understand the historical decadal prediction skill of climate models. |
dcppB-forecast | forecast initialized from observations | dcppB-forecast is a set of quasi-real-time decadal forecasts that are initialised every year from 2019 in real time and ongoing. The forecasts are performed with the same coupled atmosphere-ocean general circulation model (AOGCM), which was used to generate the hindcast data. The models running these simulations are initialised using observed data, thereafter they are forced with future conditions from the ssp245 scenario that include atmospheric composition, land use, and solar forcing. There are 10 ensemble members for each start date and simulations run for 10 years. DCPP forecasts form a basis for potential operational forecast production. |
Models
Data for the dcppA-hindcast and dcppB-forecast experiments archived 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 DOIs held at the World Data Centre for Climate (WDCC)., further details can be found on the Earth System Documentation site ES-DOC.
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: 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. |
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: 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, 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: 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, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km. |
XX | The German Weather Service (Deutscher Wetterdienst, DWD) / Max Planck Institute for Meteorology (MPI-M) | XX |
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: 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, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 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.
Ensembles
Start Date Ensembles
For the DCPP dataset in the CDS each model runs a suite of overlapping simulations that are initialised annually throughout the experiment's duration. Each model has run the same experiment using the same model with slightly different settings 10 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 Each modelling centre typically run the same experiment using the same model with slightly different settings 10 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
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Data for dcppA-hindcast experiment and the dcppB-forecast experiments will include parameters at monthly and daily resolution as described in the tables below. The parameter descriptions are harvested from the CMIP6 Data Request via the CLIPC variable browser.
Monthly Parameters
| CDS parameter name for CMIP5 | ESGF variable id | units | Standard name | Long name | Description |
|---|---|---|---|---|---|
| 2m temperature | tas | K | air_temperature | Near-Surface Air Temperature | Near-surface (2 meter) air temperature |
| Mea precipitation flux | pr | kg m-2 s-1 | precipitation_flux | Precipitation | Includes both liquid and solid phases |
| Mean sea level pressure | psl | Pa | air_pressure_at_sea_level | Sea Level Pressure | Sea level pressure |
Daily Parameters
| CDS parameter name for CMIP5 | Variable name | units | Standard name | Long name | Description |
|---|---|---|---|---|---|
| Minimum 2m temperature in the last 24 hours | tasmin | K | air_temperature | Daily Minimum Near-Surface Air Temperature | Daily-minimum near-surface (2 meter) air temperature |
| Maximum 2m temperature in the last 24 hours | tasmax | K | air_temperature | Daily Maximum Near-Surface Air Temperature | Daily-maximum near-surface (2 meter) air temperature |
| Mea precipitation flux | pr | kg m |
precipitation_flux
Precipitation
Includes both liquid and solid phases
zg500
m
geopotential_height
Geopotential Height at 500hPa
-2 s-1 | precipitation_flux | Precipitation | Includes both liquid and solid phases | ||
| Geopotential height at 500 hPa | zg500 | m | geopotential_height | Geopotential Height at 500hPa | Geopotential height of the 500 hPa surface |
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). 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.
Data Format
The CDS subset of DCPP 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
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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 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:
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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)
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