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In addition, CORDEX data for CDS includes Persistent IDentifiers (PID) in their metadata which allows CDS users to report any error during the scientific analysis. The error will be at least documented on the ESGF Errata Service (http://errata.es-doc.org) but also planned to be documented in the CDS. The CDS do not aim to archive all dataset versions. Some version could be unpublished in the case of critical issues or outdated dataset. In general, the latest version of each dataset remains published on the CDS.
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Domains
The CDS-CORDEX subset at the moment consists of the following CORDEX experiments partly derived from the CMIP5 ones:
- evaluation: model simulations for the past with imposed "perfect" lateral boundary condition following ERA-Interim reanalyses (1979-2015).
- historical: model simulations for the past using lateral boundary conditions from Global Climate Models (GCMs). These experiments cover a period for which modern climate observations exist. These experiments show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future.
- scenario experiments RCP2.6, RCP4.5, RCP8.5: ensemble of CORDEX climate projection experiments driven by boundary conditions from GCMs using RCP (Representative Concentration Pathways) forcing scenarios. The scenarios used here are RCP 2.6, 4.5 and 8.5, they provide different pathways of the future climate forcing.
Driving Global Climate Models and Regional Climate Models
Regional Climate Model (RCM) simulations needs lateral boundary conditions from Global Climate Models (GCMs). At the moment the CDS-CORDEX subset boundary conditions are extracted from CMIP5 global projections. In general the CORDEX framework requires each RCM downscale a minimum of 3 GCMs for 2 scenarios (at least RCP8.5 and RCP2.6). The C3S-CORDEX subset aims to fill the gaps in this matrix between GCMs (aka "driving models), RCMs and RCPs. This will ensure better representation of uncertainties coming from GCMs, RCMs and RCP scenarios and make possible to study the regional climate change signals in a more comprehensive fashion.
The driving GCM and RCM models included in the CDS-CORDEX subset are detailed in the table below. These include 8 of the driving GCMs from the main CMIP5 archive and 13 of the RCMs from the main CORDEX archive. Please note that a small number of models were not included as those data have a research-only restriction on their use, while the data presented ion the CDS are released without any restriction.
European (and very soon Mediterranean) CORDEX domains (aka EURO-CORDEX; https://www.euro-cordex.net/ and Med-CORDEX). For the end of 2020 we are planning to add several new CORDEX model domains into the CDS. More details of the CORDEX domains can be found at https://cordex.org/domains/. Please note that those domains are not on regular grids. Projections may differ depending on the domain and the Regional Climate Model. The coordinates are the maximum and minimum values of the domain window. As a summary, the available domains are:
| Name | Short name | Minimum latitude | Maximum latitude | Minimum longitude | Maximum longitude | Horizontal resolution |
|---|---|---|---|---|---|---|
| Europe | EUR-11 | 27°N | 72°N | 22°W | 45°E | 0.11°x0.11° |
| Mediterranee | MED-11 | 25°N | 52°N | 21°W | 50°E | 0.11°x0.11° |
| MED-44 | 25°N | 52°N | 21°W | 50°E | 0.44°x0.44° | |
| North America | NAM-22 | 12°N | 59°N | 171°W | 24°W | 0.22°x0.22° |
| NAM-22 | 12°N | 59°N | 171°W | 24°W | 0.22°x0.22° | |
| Artic | ARC-22 | 46°N | 90°N | 180°W | 180°E | 0.22°x0.22° |
| ARC-44 | 46°N | 90°N | 180°W | 180°E | 0.44°x0.44° |
Experiments
The CDS-CORDEX subset consists of the following CORDEX experiments partly derived from the CMIP5 ones:
- evaluation: model simulations for the past with imposed "perfect" lateral boundary condition following ERA-Interim reanalyses (1979-2015).
- historical: model simulations for the past using lateral boundary conditions from Global Climate Models (GCMs). These experiments cover a period for which modern climate observations exist. These experiments show how the RCMs perform for the past climate when forced by GCMs and can be used as a reference period for comparison with scenario runs for the future.
- scenario experiments RCP2.6, RCP4.5, RCP8.5: ensemble of CORDEX climate projection experiments driven by boundary conditions from GCMs using RCP (Representative Concentration Pathways) forcing scenarios. The scenarios used here are RCP 2.6, 4.5 and 8.5, they provide different pathways of the future climate forcing.
Driving Global Climate Models and Regional Climate Models
Regional Climate Model (RCM) simulations needs lateral boundary conditions from Global Climate Models (GCMs). At the moment the CDS-CORDEX subset boundary conditions are extracted from CMIP5 global projections. In general the CORDEX framework requires each RCM downscale a minimum of 3 GCMs for 2 scenarios (at least RCP8.5 and RCP2.6). The C3S-CORDEX subset aims to fill the gaps in this matrix between GCMs (aka "driving models), RCMs and RCPs. This will ensure better representation of uncertainties coming from GCMs, RCMs and RCP scenarios and make possible to study the regional climate change signals in a more comprehensive fashion.
The driving GCM and RCM models included in the CDS-CORDEX subset are detailed in the table below. These include 8 of the driving GCMs from the main CMIP5 archive and 13 of the RCMs from the main CORDEX archive. Please note that a small number of models were not included as those data have a research-only restriction on their use, while the data presented ion the CDS are released without any restriction.
| Driving Global Coupled Models | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HadGEM2-ES | EC-EARTH | CNRM-CM5 | NorESM1-M | MPI-ESM-LR | IPSL-CM5A-MR | CanESM2 | MIROC5 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Regional Climate Models | RCA4 (SMHI | Driving Global Coupled Models | HadGEM2-ES | EC-EARTH | CNRM-CM5 | NorESM1-M | MPI-ESM-LR | IPSL-CM5A-MR | CanESM2 | MIROC5 | Regional Climate Models | RCA4 (SMHI) | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | CCLM-8-17 (ETH) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | crCLIM-v1-1-1 (ETH) | 1 | 1 | 2 | REMO2009 (GERICS) | 2 | 2 | 2 | 1 | REMO2015 (GERICS) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | RACMO22E (KNMI) | 1 | 1 | 1 | 1 | 21 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | HIRHAM5 (DMI) | 1 | 2 | 2 | 2 | 4 | 1 | 2 | 2 | 1 | WRF361H (UHOH) | 1 | WRF381P (IPSL) | 1 | 1 | 1 | 1 | 1 | 1 | ALADIN53 (CNRM) | 1 | 1 | 1 | ALADIN63 (CNRM) | 1 | 1 | 1 | 1 | 1 | RegCM4.6.1 (ICTP) | HadGEM3-GA7-05 (MOHC) | 1 | 1 | RCP26 | RCP45 | RCP85 | [0-9] = Number of simulations | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Dataset numbers (simulation version)
On a general level in the CDS form for the RCM simulations “v” enumerates runs and NOT model versions. For the DMI, KNMI and SMHI runs numbers different from “v1” means new simulations relative to the first “v1” one. It might not mean a new version. Hereafter we describe the meaning of the different dataset numbers for those models, which have some.
- DMI
- For the EC-EARTH r3i1p1 forced HIRHAM simulation “v2” is a new simulation where proper GHG concentrations changing with time are used as a contrast to “v1” that erroneously used the constant control level throughout the simulation. Therefore users should use v2.
- As it is for the previous point “v2” is used for the HIRHAM simulation forced by MOHC-HadGEM2-ES.
- As for the previous two points but here “v3” is used for the NorESM driven simulation. A previous “v2” version including also an error in the vertical interpolation when preparing the boundary files also exists.
- KNMI
- For the MOHC-HadGEM2-ES forced RACMO simulation v2 is a new simulation where a big error in SST-remapping from the HadGEM-grid to the RCM-grid in v1 was corrected. The erroneous v1-simulation has been unpublished from the ESGF.
- For the CNRM-CM5 simulation v2 is a new simulation replacing the old now with input data taken from pressure levels instead of model levels. The originally provided model level fields from CNRM were wrong.
- SMHI
- Two MPI-driven scenario runs were rerun in 2016 as there had been problems with a restart file and as there was an error in the snow diagnostics in the original run. The reruns were labelled v1a.
- CNRM
- For the CNRM-CM5 simulation v2 is a new simulation replacing the old now with input data taken from pressure levels instead of model levels. The original provided model level fields from CNRM were wrong.
Ensembles
The boundary conditions used to run a RCM are also identified by the model member if the CMIP5 simulation used. Each modeling centre typically run the same experiment using the same GCM 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 initialization parameters. Comparing differently initialized 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 modeling 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”.
For CORDEX data, the ensemble member is equivalent to the ensemble member of the CMIP5 simulation used to extract boundary conditions.
Domains
The CDS-CORDEX subset at the moment consists of the European (and very soon Mediterranean) CORDEX domains (aka EURO-CORDEX; https://www.euro-cordex.net/ and Med-CORDEX). For the end of 2020 we are planning to add several new CORDEX model domains into the CDS. More details of the CORDEX domains can be found at https://cordex.org/domains/.
List of published parameters
| 1 | |||||||||||||||||||||||||
| CCLM-8-17 (ETH) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||||||
| crCLIM-v1-1-1 (ETH) | 1 | 1 | 2 | ||||||||||||||||||||||
| REMO2009 (GERICS) | 2 | 2 | 2 | 1 | |||||||||||||||||||||
| REMO2015 (GERICS) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||||||||||||
| RACMO22E (KNMI) | 1 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||
| HIRHAM5 (DMI) | 1 | 2 | 2 | 2 | 4 | 1 | 2 | 2 | 1 | ||||||||||||||||
| WRF361H (UHOH) | 1 | ||||||||||||||||||||||||
| WRF381P (IPSL) | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||||||||||
| ALADIN53 (CNRM) | 1 | 1 | 1 | ||||||||||||||||||||||
| ALADIN63 (CNRM) | 1 | 1 | 1 | 1 | 1 | ||||||||||||||||||||
| RegCM4.6.1 (ICTP) | |||||||||||||||||||||||||
| HadGEM3-GA7-05 (MOHC) | 1 | 1 | |||||||||||||||||||||||
| RCP26 | RCP45 | RCP85 | [0-9] = Number of simulations | ||||||||||||||||||||||
The 13 Regional Climate Models that ran simulations over European domain will be documented through the Earth-System Documentation (ES-DOC) which provides a standardised and easy way to document climate models.
Dataset numbers (simulation version)
On a general level in the CDS form for the RCM simulations “v” enumerates runs and NOT model versions. For the DMI, KNMI and SMHI runs numbers different from “v1” means new simulations relative to the first “v1” one. It might not mean a new version. Hereafter we describe the meaning of the different dataset numbers for those models, which have some.
- DMI
- For the EC-EARTH r3i1p1 forced HIRHAM simulation “v2” is a new simulation where proper GHG concentrations changing with time are used as a contrast to “v1” that erroneously used the constant control level throughout the simulation. Therefore users should use v2.
- As it is for the previous point “v2” is used for the HIRHAM simulation forced by MOHC-HadGEM2-ES.
- As for the previous two points but here “v3” is used for the NorESM driven simulation. A previous “v2” version including also an error in the vertical interpolation when preparing the boundary files also exists.
- KNMI
- For the MOHC-HadGEM2-ES forced RACMO simulation v2 is a new simulation where a big error in SST-remapping from the HadGEM-grid to the RCM-grid in v1 was corrected. The erroneous v1-simulation has been unpublished from the ESGF.
- For the CNRM-CM5 simulation v2 is a new simulation replacing the old now with input data taken from pressure levels instead of model levels. The originally provided model level fields from CNRM were wrong.
- SMHI
- Two MPI-driven scenario runs were rerun in 2016 as there had been problems with a restart file and as there was an error in the snow diagnostics in the original run. The reruns were labelled v1a.
- CNRM
- For the CNRM-CM5 simulation v2 is a new simulation replacing the old now with input data taken from pressure levels instead of model levels. The original provided model level fields from CNRM were wrong.
Ensembles
The boundary conditions used to run a RCM are also identified by the model member if the CMIP5 simulation used. Each modeling centre typically run the same experiment using the same GCM 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 initialization parameters. Comparing differently initialized 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 modeling 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”.
For CORDEX data, the ensemble member is equivalent to the ensemble member of the CMIP5 simulation used to extract boundary conditions.
List of published parameters
| Name | Short name | Units | Description |
|---|---|---|---|
| 2m temperature | tas | K | The temperature of the air near the surface (or ambient temperature). The data represents the mean over the aggregation period at 2m above the surface. |
| 200hPa temperature | ta200 | K | The temperature of the air at 200hPa. The data represents the mean over the aggregation period at 200hPa above the surface. |
| Minimum 2m temperature in the last 24 hours | tasmin | K | The minimum temperature of the air near the surface. The data represents the daily minimum over the aggregation period at 2m above the surface. |
| Maximum 2m temperature in the last 24 hours | tasmax | K | The maximum temperature of the air near the surface. The data represents the daily maximum over the aggregation period at 2m above the surface. |
| Mean precipitation flux | pr | kg.m-2.s-1 | The deposition of water to the Earth's surface in the form of rain, snow, ice or hail. The precipitation flux is the mass of water per unit area and time. The data represents the mean over the aggregation period. |
| 2m surface relative humidity | hurs | % | The relative humidity is the percentage ratio of the water vapour mass to the water vapour mass at the saturation point given the temperature at that location. The data represents the mean of the amount of moisture divided by the maximum amount of moisture that could exist in the air at a specific temperature and location, over the aggregation period at 2m above the surface. |
| 2m surface specific humidity | huss | Dimensionless | The amount of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location. The data represents the mean over the aggregation period at 2m above the surface. |
| Surface pressure | ps | Pa | The air pressure at the lower boundary of the atmosphere. The data represents the mean over the aggregation period. |
| Mean sea level pressure | psl | Pa | The air pressure at sea level. In regions where the Earth's surface is above sea level the surface pressure is used to compute the air pressure that would exist at sea level directly below given a constant air temperature from the surface to the sea level point. The data represents the mean over the aggregation period. |
| 10m Wind Speed | sfcWind | m.s-1 | The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 10m above the surface. |
| Surface solar radiation downwards | rsds | W.m-2 | The downward shortwave radiative flux of energy per unit area. The data represents the mean over the aggregation period at the surface. |
| Surface thermal radiation downward | rlds | W.m-2 | The downward longwave radiative flux of energy inciding on the surface from the above per unit area. The data represents the mean over the aggregation period. |
| Surface upwelling shortwave radiation | rsus | W.m-2 | The upward shortwave radiative flux of energy from the surface per unit area. The data represents the mean over the aggregation period at the surface. |
| Total cloud cover | clt | Dimensionless | Total refers to the whole atmosphere column, as seen from the surface or the top of the atmosphere. Cloud cover refers to fraction of horizontal area occupied by clouds. The data represents the mean over the aggregation period. |
| 500hPa geopotential | zg500 | m | The gravitational potential energy per unit mass normalized by the standard gravity at 500hPa at the same latitude. The data represents the mean over the aggregation period at 500hPa over the surface. |
| 10m u-component of wind | uas | m.s-1 | The magnitude of the eastward component of the wind. The data represents the mean over the aggregation period at 10m above the surface. |
| 10m v-component of wind | vas | m.s-1 | The magnitude of the northward component of the wind. The data represents the mean over the aggregation period at 10m above the surface. |
| 200hPa u-component of the wind | ua200 | m.s-1 | The magnitude of the eastward component of the wind. The data represents the mean over the aggregation period at 200hPa above the surface. |
| 200hPa v-component of the wind | va200 | m.s-1 | The magnitude of the northward component of the wind. The data represents the mean over the aggregation period at 200hPa above the surface. |
| 850hPa U-component of the wind | ua850 | m.s-1 | The magnitude of the eastward component of the wind. The data represents the mean over the aggregation period at 850hPa above the surface. |
| 850hPa V-component of the wind | va850 | m.s-1 | The magnitude of the northward component of the wind. The data represents the mean over the aggregation period at 850hPa above the surface. |
| Total run-off flux | mrro | kg.m-2.s-1 | The mass of surface and sub-surface liquid water per unit area and time, which drains from land. The data represents the mean over the aggregation period. |
| Mean evaporation flux | evspsbl | kg.m-2.s-1 | The mass of surface and sub-surface liquid water per unit area ant time, which evaporates from land. The data includes conversion to vapor phase from both the liquid and solid phase, i.e., includes sublimation, and reprends the mean over the aggregation period. |
| Land area fraction | sftlf | % | The percentage of the surface occupied by land, aka land/sea mask. The data is time-independent. |
| Orography | orog | m | The surface altitude. The data is time-independent. |
count
name
units
1
10m Wind Speed
m s-1
2
2m air temperature
K
3
Mean precipitation flux
kg m-2 s-1
4
Mean sea level pressure
Pa
5
Near surface relative humidity
%
6
Surface solar radiation downwards
W m-2
Data Format
The CDS subset of CORDEX 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 modeling community. See the more details: What are NetCDF files and how can I read them
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The data within the files were not individually checked, therefore 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 metadata but contain gross errors in the data that have not been revealed.
Known issues
- Please not that not all combinations of models and domains exists. This feature is due to the different CORDEX initiatives/consortiums that do not involved the same data producers using the same RCMs.
- Please note that not all the combinations of models and variables exist. This feature is inherited from the ESGF system, where the main target is to publish as much as possible data and even publish incomplete datasets, which might be of use. This allows to have more data available with the price that not everything is fully complete.
- Sometimes there are some inconsistencies in the NetCDF files, for instance the header information is not in agreement with the content. An example is the case of ALADIN Regional Climate Model, where in the file header it is indicated that the 2m temperature data units are Kelvin, though instead the data are listed in Celsius. This kind of discrepancies come from the data producers and they are very difficult to rectify, since the producers are usually reluctant to update datasets, which were provided quite some time ago. . Such kind of issue should be documented through the errata service.
Background documents and user guides
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