Table of Contents maxLevel 5
Introduction
Global Climate Models (GCM) can provide reliable climate information on global, continental and large regional scales covering what could be a vastly differing landscape (from very mountainous to flat coastal plains for example) with greatly varying potential for floods, droughts or other extreme events. Horizontal resolution limits the possibility to address smaller scale ranging from regional to local. Regional Climate Models (RCM) applied with higher spatial resolution over a limited area and driven by GCMs can provide more appropriate information on such smaller scales supporting more detailed impact and adaptation assessment and planning. Therefore Regional Climate Models (RCMs) have an important role to play by providing projections with much greater detail and more accurate representation of localized extreme events.
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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 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/
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| 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:
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- 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.
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The 13 Regional Climate Models that ran simulations over the 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.
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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 (the bold face items are available for all domains, the rest is only for Europe) at 3-hourly, 6-hourly, daily, monthly and seasonal temporal scale (for non-European domains only daily data are available). Note that orography and land area fraction variables are time independent model 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 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.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. |
| 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. |
| 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 fraction (in percentage) of grid cell occupied by land surface. The data is time-independent. |
| Orography | orog | m | The height above the geoid (being 0.0 over the ocean). 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 modelling community. See the more details: What are NetCDF files and how can I read them
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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:
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- <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 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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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
All known issues about CORDEX data are documented through the ES-DOC Errata Service : https://errata.es-doc.org/. The Errata Service also includes a command-line interface and an API to request the issue database for a specific dataset or file.
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- Please have a look on the Errata Service to be warned about deprecated CORDEX runs that will be retracted in the future.
- 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.
Background documents and user guides
There is a very useful User Guide prepared by the EURO-CORDEX community which is providing guidance how to use EURO-CORDEX climate projection data. Please note that the data download part of this document at this stage refers only to access the data from the ESGF directly. Certainly the data can be also downloaded from the CDS and this information will be soon provided in that document. This EURO-CORDEX User Guide is available at https://www.euro-cordex.net/imperia/md/content/csc/cordex/euro-cordex-guidelines-version1.0-2017.08.pdf
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C3S is aiming to build a EURO-CORDEX ensemble which is as complete as possible. By doing this, C3S will fill some of the missing elements of the EURO-CORDEX GCM-RCM-RCP uncertainty matrix. As we will have more simulations available (and these being complete sub-matrices, for instance), we are in a better position to assess how the full matrix can be reproduced when based on fewer available model simulations. In addition, we can determine how the missing model elements can be built. This unique study gives valuable insights into the optimal design of such ensemble systems in the future.
References
- Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K., and Wulfmeyer, V.: Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble, Geosci. Model Dev., 7, 1297–1333, https://doi.org/10.5194/gmd-7-1297-2014, 2014.
- Jacob, D., Teichmann, C., Sobolowski, S. et al. Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community. Reg Environ Change 20, 51 (2020). https://doi.org/10.1007/s10113-020-01606-9
- Article using model simulations prepared by C3S funding:
Christensen, O.B., Kjellström, E. Partitioning uncertainty components of mean climate and climate change in a large ensemble of European regional climate model projections. Clim Dyn (2020). https://doi.org/10.1007/s00382-020-05229-y - Sørland SL, Schär C, Lüthi D, Kjellström E (2018) Bias patterns and climate change signals in GCM-RCM model chains. Environ Res Lett 13(7):074017. https://doi.org/10.1088/1748-9326/aacc77
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This document has been produced in the context of the Copernicus Climate Change Service (C3S).The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.The user thereof uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission and the European Centre for Medium-Range Weather Forecasts has no liability in respect of this document, which is merely representing the authors view. |
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