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.
Regional climate projections are results from regional climate model simulations which have been generated by multiple independent climate research centres in the framework of the Coordinated Regional Climate Downscaling (CORDEX) supported by the World Climate Research Program (WCRP) and assessed by the Intergovernmental Panel on Climate Change (IPCC). These regional climate projections underpin the conclusion of the IPCC 5th Assessment Report (published in 2003) that “Continued emission of greenhouse gases will cause further warming and long-lasting changes in all components of the climate system, increasing the likelihood of severe, pervasive and irreversible impacts for people and ecosystems”.
The regional climate projections in the Climate Data Store (CDS) are a quality-controlled subset of the wider (CORDEX) data over Europe. The CORDEX vision is to advance and coordinate the science and application of regional climate downscaling through global partnerships. It aims to evaluate regional climate model performance through a set of experiments aiming at producing regional climate projections. The goals of CORDEX are:
- 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 30 core variables 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.
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).
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. In this case the users will have the option to reproduce their results using the old, outdated datasets too.
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 12 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) | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | |||||||||
| CCLM-8-17 (ETH) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |||||||||||||
| CCLM-GPU (ETH) | 1 | 3 | |||||||||||||||||||||||
| REMO 09&15 (GERICS) | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 3 | 1 | 1 | ||||||||||||||
| RACMO22E (KNMI) | 1 | 1 | 1 | 1 | 2 | 3 | 2 | 1 | |||||||||||||||||
| HIRHAM5 (DMI) | 2 | 1 | 1 | 3 | 1 | 1 | 1 | ||||||||||||||||||
| WRF361H | 1 | 1 | 1 | 1 | 1 | ||||||||||||||||||||
| WRF381P | 1 | 1 | 2 | ||||||||||||||||||||||
| ALADIN53 (CNRM) | 1 | 1 | 1 | ||||||||||||||||||||||
| ALADIN63 (CNRM) | 1 | 1 | |||||||||||||||||||||||
| RegCM4.6.1 (ICTP) | 1 | 1 | 1 | ||||||||||||||||||||||
| HadGEM3-RA (MOHC) | 1 | 1 | |||||||||||||||||||||||
| RCP26 | RCP45 | RCP85 | [0-9] = Number of simulations | ||||||||||||||||||||||
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 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
count | name | units | Variable name in CDS |
1 | 10m Wind Speed | m s-1 | 10m_wind_speed |
2 | 2m air temperature | K | 2m_air_temperature |
3 | Mean precipitation flux | kg m-2 s-1 | mean_precipitation_flux |
4 | Mean sea level pressure | Pa | mean_sea_level_pressure |
5 | Near surface relative humidity | % | near_surface_relative_humidity |
6 | Surface solar radiation downwards | W m-2 | surface_solar_radiation_downwards |
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>.nc
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 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.
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.
- 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.
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 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.
Background documents
The documents below were provided by the data supplier as background information on the creation of the CORDEX data stored in the Climate Data Store (CDS) for the benefit of the CORDEX data users.
- Experimental design for the GCM/RCM Matrix (29/06/2018)
This report documents how the experiment is designed in terms of which GCM-RCM-RCP combinations to run in the project. This was produced quite some time ago, therefore the presented information is not fully up-to-date, but nevertheless provides a fairly good idea about the concept for designing new experiments.
This report documents results from the 34 EURO-CORDEX RCP8.5 simulations. For a number of European subregions we present patterns describing the regional climate change in relation to the change in global mean temperature. These patterns are derived as the linear fit between regional climate change and change in global mean temperature. This is a commonly used method and can be seen as the standard definition of pattern scaling used in the scientific literature. For the calculation of these patterns the climate change signal was derived for three different time windows (2011-2040, 2041-2070 and 2071-2100) w.r.t. the control climate (1971-2000)
Internal variability is an intrinsic character of the climate system and it is also present in climate models. The design to run new RCM experiments took into account the intention that the internal variability can be studied. This report present some early investigations on these aspects.
- Synthesis on the existing simulations at M16 (02/10/2019)
In this report we review the state of the EURO-CORDEX ensemble as valid at the beginning of 2019. The report indicates climate change findings what can be deduced with the help of the larger RCM ensemble available. Regular updates of such reports are planned and will be published here.
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
