...
A set of 26 core variables (17 for non-European domains, corresponding to surface fields, see the table below) from the CORDEX archive were identified for the CDS. These are the most used of the CORDEX data. These variables are provided from 5 CORDEX experiment types (evaluation, historical and 3 RCP scenarios) that are derived (downscaled) from the CMIP5 experiments. 3-hourly, daily and monthly information (GL Please check), whereas only daily information is provided for non-European domains.
ANDRAS: I think, we have to indicate here, that for the non-European domain we provide lass variables and maybe in the table below indicate, which ones are only for the EURO-CORDEX or meMed-CORDEX domains.
GL: That's difficult and also depends on the RCMs. I propose to at least indicate into the table of variable the one we "try" to provide for each domain (basically one additional column per domain).
ANDRAS: I thought the non-European domains will use the same set of variables, is that a wrong assumption? Jose, would you check this, please?
JOSE: The non-European domains include the set of 15 variables (plus land-sea mask and orography), when available (not all models provide all variables).
The CDS subset of CORDEX data have been through a metadata quality control procedure which ensures a high standard of reliability of the data. It may be for example that similar data can be found in the main CORDEX archive at the ESGF (Earth System Grid Federation) however these data come with no quality assurance and may have metadata errors or omissions. The quality-control process means that the CDS subset of CORDEX data is further reduced to exclude data that have metadata errors or inconsistencies. It is important to note that passing of the quality control should not be confused with validity: for example, it will be possible for a file to have fully compliant metadata but contain gross errors in the data that have not been noted. In other words, it means that the quality control is purely technical and does not contain any scientific evaluation (for instance consistency check).
ANDRAS: I think, here we have to mention that we also publish data, which had not been available so far in the ESGF. GL : Is there any dataset in this case? Euro-CORDEX comes from the ESGF, even the new simulations from PRINCIPLES that are published on the ESGF first. Med-CORDEX will be published on the ESGF in a second step after the 34b Lot 1. And for 34d data, non-ESGF data will be published on the ESGF and ESGF data have been just Qc-ed for CDS. So I don't see any dataset that exist on the CDS which is/will be not on the ESGF. ANDRAS: OK, maybe some text as proposed below would be sufficient.
We can mention the additional effort (of 34d) to find the additional data for non-European domains. Maybe the link to the IPCC Atlas should be also mentioned. GL: I agree. Jose, would you provide some additional text about the IPCC Atlas, a link, possibly?
JOSE: I included that information below, after describing the efforts (and funding) devoted to support CORDEX activities. Unfortunately there is no link yet since the information regarding the report is confidential (to prevent leaking) until it is releases (in July 2021).
Additional efforts (and funding) were devoted to support CORDEX activities by 1) providing support to archive in the ESGF relevant simulations available from the modeling centers for non-European domains not published in the ESGF earlier, and 2) making new simulations for the EURO-CORDEX domains. These activities which are contributing to a significant enhancement of the regional climate model matrix over different domains in terms of emission scenarios, global model forcing and regional climate models. For the non-European domains resources were put into finding simulations, which were not available before (and not published in the ESGF earlier).
The effort done by COPERNICUS to consolidate a World-wide CORDEX dataset is also contributing to the IPCC-AR6 WGI activities, providing a curated dataset to be assessed together with global climate information from CMIP experiments, in particular in the Interactive Atlas, a new product of the IPCC allowing exploration of observed and projected climate data to complement the assessment of relevant datasets undertaken in the WG I chapters.
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 aims to publish only the latest versions of the datasets.
Domains
We are aiming at publishing various CORDEX domains for the entire World. The CDS-CORDEX subset at the moment consists of the Europe (EURO), Mediterranean (MED), North America (NAM) and Arctic (ARC) CORDEX domains. More details of the entire list of CORDEX domains can be found at https://cordex.org/domains/; additionally more details for the EURO-CORDEX activities are available at https://www.euro-cordex.net/
Please note that the domains are not on regular grids. Projections may differ depending on the domain and the Regional Climate Model (RCM). The coordinates below are the maximum and minimum values of the domain window. As a summary, the available domains are:
...
The CDS subset of CORDEX data have been through a metadata quality control procedure which ensures a high standard of reliability of the data. It may be for example that similar data can be found in the main CORDEX archive at the ESGF (Earth System Grid Federation) however these data come with no quality assurance and may have metadata errors or omissions. The quality-control process means that the CDS subset of CORDEX data is further reduced to exclude data that have metadata errors or inconsistencies. It is important to note that passing of the quality control should not be confused with validity: for example, it will be possible for a file to have fully compliant metadata but contain gross errors in the data that have not been noted. In other words, it means that the quality control is purely technical and does not contain any scientific evaluation (for instance consistency check).
Additional efforts (and funding) were devoted to support CORDEX activities by 1) providing support to archive in the ESGF relevant simulations available from the modelling centres for non-European domains not published in the ESGF earlier, and 2) making new simulations for the EURO-CORDEX domains. These activities are contributing to a significant enhancement of the regional climate model matrix over different domains in terms of emission scenarios, global model forcing and regional climate models. For the non-European domains resources were put into finding simulations, which were not available before (and not published in the ESGF earlier).
The effort done by Copernicus to consolidate a World-wide CORDEX dataset is also contributing to the IPCC-AR6 WGI activities, providing a curated dataset to be assessed together with global climate information from CMIP experiments, in particular in the Interactive Atlas, a new product of the IPCC allowing exploration of observed and projected climate data to complement the assessment of relevant datasets undertaken in the WG I chapters.
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 aims to publish only the latest versions of the datasets.
Domains
We are aiming at publishing various CORDEX domains for the entire World. The CDS-CORDEX subset at the moment consists of the Europe (EURO), Mediterranean (MED), North America (NAM) and Arctic (ARC) CORDEX domains. More details of the entire list of CORDEX domains can be found at https://cordex.org/domains/; additionally more details for the EURO-CORDEX activities are available at https://www.euro-cordex.net/
Please note that the domains are not on regular grids. Projections may differ depending on the domain and the Regional Climate Model (RCM). The coordinates below are the maximum and minimum values of the domain window. As a summary, the available domains are:
| Name | Short name | Southernmost latitude | Northernmost latitude | Westernmost longitude | Easternmost longitude | Horizontal resolution (degrees) |
|---|---|---|---|---|---|---|
| Europe | EUR-11 | 27°N | 72°N | 22°W | 45°E | 0.11° x 0.11° |
| Mediterranean | MED-11 | 25°N | 52°N | 21°W | 50°E | 0.11° x 0.11° |
| MED-44 | 25°N | 52°N | 21°W | 50°E | 0.44° x 0.44° | |
| North America | NAM-22 | 12°N | 59°N | 171°W | 24°W | 0.22° x 0.22° |
| NAM-44 | 12°N | 59°N | 171°W | 24°W | 0.44° x 0.44° | |
| Arctic | ARC-22 | 46°N | 90°N | 180°W | 180°E | 0.22° x 0.22° |
| ARC-44 | 46°N | 90°N | 180°W | 180°E | 0.44° x 0.44° |
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.
The C3S-EURO-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 subsets for the different domains available are detailed in the table below. Note that the ensembles for different domains are formed by different GCM and RCM combinations from the main CMIP5 and CORDEX archives, respectively: These include 8 GCMs and 13 RCMs for EURO-CORDEX, 8 GCMs and 8 RCMs for North-America CORDEX, and 5 GCMs and 6 RCMs for the Arctic. 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 in the CDS are released without any restriction.
JOSE: I see that you haven't included non-comercial datasets at COPERNICUS. I guess this would be the same for all domains (we talked about this a couple of times, but I was not aware that this decision was made; I am in favor, no problem with that, just to know). This has no implications for NAM and ARC (all are unrestricted) but might have for other domains (I will check the impact and let you know).
| 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 | 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.
Ensembles
The boundary conditions used to run a RCM are also identified by the model member if the CMIP5 simulation used. Each modelling 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
The table below lists the variables provided (in boldface those provided for all domains, the rest are provided only for Europe) at 3-hourly, daily and monthly temporal scale (only daily for non European domains and for tasmin and tasmax for all domains). Note that sftlf and orog are static time independent fields.
| Name | Short name | Units | Description |
|---|---|---|---|
| 2m temperature | tas | K | The temperature of the air near the surface (or ambient temperature) |
...
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). ANDRAS: please check this, if this is still valid for the non-European domains. Jose, would you check this, please?
JOSE: I think that was not mandatory in the experimental proposal so we are not constraining the simulations on that (we include all simulations which provide historial + some scenario), so I propose removing this sentence.
The C3S-EURO-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-EURO-CORDEX CDS-CORDEX subsets for the different domains available are detailed in the table below. Note that the ensembles for different domains are formed by different GCM and RCM combinations from the main CMIP5 and CORDEX archives, respectively: These include 8 GCMs and 13 RCMs for EURO-CORDEX, 8 GCMs and 8 RCMs for North-America CORDEX, and 5 GCMs and 6 RCMs for the Arctic. 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 in the CDS are released without any restriction.
JOSE: I think the GCM and RCM numbers for Europe need to be updated. GL please check.
ANDRAS: we will need an additional paragraph here for the non-European domains and of course similar tables for the other domains below the EURO-CORDEX one.
GL: I let Manuel for the additional paragraph. I will update the table of simulations once we agreed on the different decisions to take (cf. our mail thread about this).
Jose, would you propose an additional paragraph, here?
Jose: I included details in the paragraph above (in red).
JOSE: I see that you haven't included non-comercial datasets at COPERNICUS. I guess this would be the same for all domains (we talked about this a couple of times, but I was not aware that this decision was made; I am in favor, no problem with that, just to know). This has no implications for NAM and ARC (all are unrestricted) but might have for other domains (I will check the impact and let you know).
...
Regional Climate Models
...
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)
ANDRAS: I think, this can be a bit confusing, since above we mention that we publish only the latest version. So somehow we have to explain clearly what is the difference between model version and dataset number.
GL: As said in my email, this could be delegate to the Errata Service and we can remove this paragraph. What do you think? I will create an issue on the Errata test instance to show how it looks like.
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.
DMIFor 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.
KNMIFor 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.
SMHITwo 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.
CNRMFor 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 ANDRAS: maybe here we can indicate with bold face those variables, which are available only for the EURO-CORDEX domain GL : see my above comment on this.
Jose: I have done that and included the paragraph below. Please check.
The table below lists the variables provided (in boldface those provided for all domains, the rest are provided for Europe) at 3-hourly, daily and monthly temporal scale (only daily for non European domains and for tasmin and tasmax for all domains). Note that sftlf and orog are static time independent fields.
| 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 pressure level. | ||||||||
| 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. ANDRAS: I guess this variable is available only for daily data, is that correct? Please check! JOSE: Yes for 34d (all are daily) | ||||||||
| 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. ANDRAS: I guess this variable is available only for daily data, is that correct? Please check! JOSE: Yes for 34d (all are daily) | ||||||||
| 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 over the aggregation period at 2m above the surface. | 2m surface specific humidity | huss | ||||||
| 200hPa temperature | ta200 | KDimensionless | The amount temperature of moisture in the air at 2m above the surface divided by the amount of air plus moisture at that location200hPa. The data represents the mean over the aggregation period at 2m above the surface.200hPa pressure level. | ||||||||
| Minimum 2m temperature in the last 24 hours | tasmin | K | The minimum temperature of the air near 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. | ||||||||
| daily minimum 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 at 2m above the surface. | ||||||||
| Mean precipitation flux | pr | kg.m-2 | 10m Wind Speed | sfcWind | m.s-1 | The | magnitude of the two-dimensional horizontal air velocitydeposition 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 | at 10m above the surface. | Surface solar radiation downwards | rsds | W.m-2 | The downward shortwave radiative flux of energy per unit area
| 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 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 | 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. Thedata represents the mean over the aggregation period at 2m above the surface. | ||||
| Surface | upwelling shortwave radiationpressure | rsus | W.m-2 | The upward shortwave radiative flux of energy from the surface per unit areaps | Pa | The air pressure at the lower boundary of the atmosphere. 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|
| 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. | 500hPa geopotential | |||||||
| 10m Wind Speed | sfcWind | zg500m.s-1 | The gravitational potential energy per unit mass normalized by the standard gravity at 500hPa at the same latitude. The magnitude of the two-dimensional horizontal air velocity. The data represents the mean over the aggregation period at 500hPa pressure level. | 10m u-component of wind | uas | m.s-1 | 10m above the surface. | ||||
| Surface solar radiation downwards | rsds | W.m-2 | The downward shortwave radiative flux of energy per unit area | The magnitude of the eastward component of the wind. The data represents the mean over the aggregation period at | 10m abovethe surface. | 10m v-component of wind | vas | m.s-1 | The magnitude of the northward component of the wind|||
| 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 | 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 | |
| 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 The magnitude of the northward component of the wind. The data represents the mean over the aggregation period at 200hPa pressure level. | ||||||||
| 500hPa geopotential | zg500 | m | The gravitational potential energy per unit mass normalized by the standard gravity at 500hPa at the same latitude. The | 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 500hPa pressure level.850hPa V | ||||
| 10m u-component of | thewind | va850uas | m.s-1 | The magnitude of the | northwardeastward component of the wind. The data represents the mean over the aggregation period at | 850hPa pressure level10m above the surface. | Total run-off flux | mrro | |||
| 10m v-component of wind | vas | m | kg.m-2.s-1 | The | mass of surface and sub-surface liquid water per unit area and time, which drains from land. Themagnitude of the northward component of the wind. The data represents the mean over the aggregation period at 10m above the surface. | Mean evaporation flux | evspsbl | kg.m-2||||
| 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 | mass of surface and sub-surface liquid water per unit area ant time, which evaporates from land. The data includes conversion to vapour phase from both the liquid and solid phase, i.e., includes sublimation, andrepresents the mean over the aggregation period at 200hPa pressure level. | |||||||
| 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 in the model. The data is time-independent. |
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
A CORDEX NetCDF file in the CDS contains:
- Global metadata: these fields can describe many different aspects of the file such as
- when the file was created,
- the name of the institution and model used to generate the file
- links to peer-reviewed papers and technical documentation describing the climate model,
- the persistent identifier used to track the file annotations,
- links to supporting documentation on the climate model used to generate the file,
- software used in post-processing.
- variable dimensions: such as time, latitude, longitude and height
- variable data: the gridded data
- variable metadata: e.g. the variable units, averaging period (if relevant) and additional descriptive data
The metadata provided in NetCDF files adhere to the Climate and Forecast (CF) conventions. The rules within the CF-conventions ensure consistency across data files, for example ensuring that the naming of variables is consistent and that the use of variable units is consistent.
File naming conventions ANDRAS: please check, if this convention is also held for the non-European domains. I also think that there might be some amendment needed, because of the domain name and the resolution. Jose, would you check this, please?
When you download a CORDEX file from the CDS it will have a naming convention that is as follows:
<variable>_<domain>_<driving-model>_<experiment>_<ensemble_member>_<rcm-model>_<rcm-run>_<time-frequency>_<temporal-range>.nc
| 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 pressure level. |
| 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 pressure level. |
| 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 vapour phase from both the liquid and solid phase, i.e., includes sublimation, and represents 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 in the model. The data is time-independent. |
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
A CORDEX NetCDF file in the CDS contains:
- Global metadata: these fields can describe many different aspects of the file such as
- when the file was created,
- the name of the institution and model used to generate the file
- links to peer-reviewed papers and technical documentation describing the climate model,
- the persistent identifier used to track the file annotations,
- links to supporting documentation on the climate model used to generate the file,
- software used in post-processing.
- variable dimensions: such as time, latitude, longitude and height
- variable data: the gridded data
- variable metadata: e.g. the variable units, averaging period (if relevant) and additional descriptive data
The metadata provided in NetCDF files adhere to the Climate and Forecast (CF) conventions. The rules within the CF-conventions ensure consistency across data files, for example ensuring that the naming of variables is consistent and that the use of variable units is consistent.
File naming conventions
When you download a CORDEX file from the CDS it will have a naming convention that is as follows:
<variable>_<domain>_<driving-model>_<experiment>_<ensemble_member>_<rcm-model>_<rcm-run>_<time-frequency>_<temporal-range>.ncJOSE: The CORDEX DRS indicates that the name of the institution should be an appendix of the <driving-model> and <rcm-model>. In 34d we are homogenizing this, including the institution in cases that it is missing. Thus, we have "ICHEC-EC-EARTH_r12i1p1" and not "EC-EARTH_r12i1p1" and also "NCAR-WRF_v3.5.1" and not "WRF_v3.5.1". Therefore the GCMs and the RCMs will be listed in the widget in the full form. We could change that if needed, but we would need to keep coherence (some of the current ESGF datasets use one approach and some use the other; that will be harmonized for the CDS). The advantage of leaving the institution is that we could have the same RCM driven by different institutions and that is useful information for users. Maybe the institution name in the case of the GCM can be dropped (it is less relevant and may be confusing, since there are many cases where the model has the same name as the institution).
Where
- <variable> is a short variable name, e.g. “tas” for ”temperature at the surface”
- <driving-model> is the name of the model that produced the boundary conditions
- <experiment> is the name of the experiment used to extract the boundary conditions
- <ensemble-member> is the ensemble identifier in the form “r<X>i<Y>p<Z>”, X, Y and Z are integers
- <rcm-model> is the name of the model that produced the data
- <rcm-run> is the version run of the model in the form of "vX" where X is integer
- <time-frequency> is the time series frequency (e.g., monthly, daily, seasonal)
- 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-CORDEX
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subset
The CDS subset of the CORDEX 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-CORDEX subset or if possible the error is corrected and a note made in the history attribute of the file. The quality control of the CDS-CORDEX subset checks for metadata errors or inconsistencies against the Climate and Forecast (CF) Conventions and a set of CORDEX specific file naming and file global metadata conventions.
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