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- To better understand relevant regional/local climate phenomena, their variability and changes, through downscaling,
- To evaluate and improve regional climate downscaling models and techniques,
- To produce coordinated sets of regional downscaled projections worldwide,
- To foster communication and knowledge exchange with users of regional climate information.
A set of 25 26 core variables (16 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.
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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. These include 8 of the driving GCMs Note that the ensembles for different domains are formed by different GCM and RCM combinations from the main CMIP5 archive and 13 of the RCMs from the main CORDEX archive. Please note that a small number of models 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.
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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).
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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 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 | 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 pressure level. |
| 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 pressure level. |
| 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. |
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<variable>_<domain>_<driving-model>_<experiment>_<ensemble_member>_<rcm-model>_<rcm-run>_<time-frequency>_<temporal-range>.nc
Where
JOSE: 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”
- <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.
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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.
Various software tools have been used to check the metadata of the CDS CMIP5 data:
- The Quality Assurance compliance checking tool from DKRZ is used to check that:
- the file name adheres to the CORDEX file naming convention,
- the global attributes of the NetCDF file are consistent with filename,
- there are no omissions of required CORDEX metadata.
- The CF-Checker Climate and Forecast (CF) conventions checker (included in the QA-DKRZ) ensures that any metadata that is provided is consistent with the CF conventions.
The figure below shows a scheme that classifies the tests performed by the QA-DKRZ tool in twelve categories (in green) showing the specific tests/checks in each case.
- When possible (i.e., optional), the Time Axis checker developed by the IPSL is used to check the temporal dimension of the data:
- for individual files the time dimension of the data is checked to ensure it is valid and is consistent with the temporal information in the filename,
- where more than one file is required to generate a time-series of data, the files have been checked to ensure there are no temporal gaps or overlaps between the files.
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