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Table of Contents
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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.

...

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 In this case the users will have the option to reproduce their results using the old, outdated datasets too.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:

...

  • 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. 

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Driving Global Coupled Models


HadGEM2-ESEC-EARTHCNRM-CM5NorESM1-MMPI-ESM-LRIPSL-CM5A-MRCanESM2MIROC5

Regional Climate Models

RCA4 (SMHI)111113
111
2111
11





CCLM-8-17 (ETH)
11111
11


111




1

1
CCLM-GPU (ETH)










1

3








REMO 09&15 (GERICS)1
11
1

1

1223




1

1
RACMO22E (KNMI)111123

2

1











HIRHAM5 (DMI)

2113

1
11











WRF361H

1

1





1
1
1






WRF381P

1







1




2





ALADIN53 (CNRM)





111














ALADIN63 (CNRM)

1




1














RegCM4.6.1 (ICTP)

1







1

1








HadGEM3-RA (MOHC)

1

1













































RCP26RCP45RCP85
[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. 

...

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

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

...

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> 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. 

...

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 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

...

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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