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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 approximate maximum and minimum values of the domain window (see more details at https://cordex.org/domains/. As a summary, the available domains are:
Experiments
The CDS-CORDEX subset consists of the following CORDEX experiments partly derived from the CMIP5 ones:
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In the tables below, please see the GCM-RCM combinations for each published domains.
AFR-CORDEX:
ANT-CORDEX:
ARC-CORDEX:
AUS-CORDEX:
CAM-CORDEX:
CAS-CORDEX:
EAS-CORDEX:
EURO-CORDEX:
Med-CORDEX:
MENA-CORDEX:
NAM-CORDEX:
SAM-CORDEX:
WAS-CORDEX:
SEA-CORDEX:
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.
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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.
Note: on the data update of the 27th of May, 2021 for some non-European simulations only the six most demanded variables are published, which are 2m specific humidity, mean precipitation flux, 10m wind speed, 2m temperature, minimum and maximum temperature in the last 24 hours. The additional variables (indicated in bold face below) will be added in the next batch (anticipated latest in September, 2021).
| Name | Short name | Units | Description |
|---|---|---|---|
| 2m temperature | tas | K | The temperature of the air |
| 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. |
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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
EUROAFR-CORDEX:
- Nikulin G, Lennard C, Dosio A, et al. (2018) The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble, Environ. Res. Lett., doi:10.1088/1748-9326/aab2b4, http://iopscience.iop.org/article/10.1088/1748-9326/aab1b1
- Nikulin G., Jones, C. , Giorgi, F., et al. (2013) Precipitation Climatology in An Ensemble of CORDEX-Africa Regional Climate Simulations, J. Climate, 25, 6057–6078. http://doi.org/10.1175/JCLI-D-11-00375.1
ANT-CORDEX:
- Kittel, C., Amory, C., Agosta, C., et al. (2021): Diverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet, The Cryosphere, 15, 1215–1236Kotlarski, 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/gmdtc-715-12971215-2014, 20142021.Jacob, D
- Lenaerts, J. T. M., Teichmann, Cvan den Broeke, M. R., Sobolowskivan de Berg, SW. et al. Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community. Reg Environ Change 20, 51 (2020). https://J., van Meijgaard, E., and Kuipers Munneke, P. (2012), A new, high-resolution surface mass balance map of Antarctica (1979–2010) based on regional atmospheric climate modeling, Geophys. Res. Lett., 39, L04501, https://www.doi.org/10.1029/2011GL050713
- Mottram, R., Hansen, N., Kittel, C., et al. (2020) What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], 1007/s10113-020-01606-9Christensen, 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/10.10075194/s00382tc-0202019-05229-y Sørland SL, Schär C, Lüthi D, Kjellström E (2018) Bias patterns and climate 333
ARC-CORDEX:
Inoue, J., Sato, K., Rinke, A., et al. (2021) Clouds and radiation processes in regional climate models evaluated using observations over the ice‐free Arctic Ocean, J. Geophys. Res. Atm., 126, e2020JD033904, http
change signals in GCM-RCM model chains. Environ Res Lett 13(7):074017. https1088/1748-9326/aacc77Coppola E., Nogherotto R., Ciarlò J.M., Giorgi F., van Meijgaard E., Kadygrov N., Iles C., Corre L., Sandstad M., Somot S., Nabat P., Vautard R., Levavasseur G., Schwingshackl C., Sillmann J., Kjellström E., Nikulin G., Aalbers E., Lenderink G., Christensen O.B., Boberg F., Lund Sørland S., Demory M.-E., Bülow K., Teichmann C., Warrach-Sagi K., Wulfmeyer V. (2021) Assessment of the European climate projections as simulated by the large EURO-CORDEX regional and global climate model ensemble. Journal of Geophysical Research – Atmospheres, doi: 10.1029/2019JD032356
Vautard R., Kadygrov N., Iles C., Boberg F., Buonomo E., Bülow K., Coppola E., Corre L., van Meijgaard E., Nogherotto R., Sandstad M., Schwingshackl C., Somot S., Aalbers E., Christensen O.B., Ciarlo J.M., Demory M.-E., Giorgi F., Jacob D., Jones R.G., Keuler K., Kjellström E., Lenderink G., Levavasseur G., Nikulin G., Sillmann J., Solidoro C., Lund Sørland S., Steger C., Teichmann C., Warrach-Sagi K., Wulfmeyer V.(2021) Evaluation of the large EURO-CORDEX regional climate model ensemble. Journal of Geophysical Research – Atmospheres,doi: 10.1029/2019JD032344
MED-CORDEX:
Sedlar, J., M. Tjernström, A. Rinke, et al. (2020) Confronting Arctic troposphere, clouds and surface energy budget representations in regional climate models with observations, J. Geophys. Res. Atm., 124, http://doi.org/10.1029/2019JD031783
Akperov, M., A. Rinke, I.I. Mokhov, et al. (2019) Future projections of cyclone activity in the Arctic for the 21st century from regional climate models (Arctic-CORDEX), Glob. Planet. Change, http://doi.org/10.1016/j.gloplacha.2019.103005
Diaconescu, E.P., Mailhot, A., Brown, R. et al. (2018) Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas, Clim. Dyn., 50, 2061–2085, doi:doi.org/10.1007/s00382-017-3736-4
AUS-CORDEX:
- Di Virgilio, G., Evans, J.P., Di Luca, A. et al. (2019) Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors. Clim Dyn 53, 2985–3005. https://doi.org/10.1007/s00382-019-04672-w
- Evans, J.P., Di Virgilio, G., Hirsch, A.L. et al. (2020) The CORDEX-Australasia ensemble: evaluation and future projections. Clim Dyn. https://doi.org/10.1007/s00382-020-05459-0
- Di Virgilio, G., Evans, J.P., Di Luca, A. et al. (2020) Realised added value in dynamical downscaling of Australian climate change. Clim Dyn 54, 4675–4692. https://doi.org/10.1007/s00382-020-05250-1
CAM-CORDEX:
- Cavazos, T, Luna-Niño, R, Cerezo-Mota, R, et al. (2020) Climatic trends and regional climate models intercomparison over the CORDEX-CAM (Central America, Caribbean, and Mexico) domain. Int J Climatol.; 40: 1396– 1420. https://doi.org/10.1002/joc.6276
- Luna-Niño, R., Cavazos, T., Torres-Alavez, J.A. et al. (2020) Interannual variability of the boreal winter subtropical jet stream and teleconnections over the CORDEX-CAM domain during 1980–2010. Clim Dyn. https://doi.org/10.1007/s00382-020-05509-7
CAS-CORDEX:
- Top, S., Kotova, L., De Cruz, L., et al. (2021) Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain, Geosci. Model Dev., 14, 1267–1293, https://doi.org/10.5194/gmd-14-1267-2021
- Ozturk, T., Turp, M. T., Türkeş, M., & Kurnaz, M. L. (2017). Projected changes in temperature and precipitation climatology of Central Asia CORDEX Region 8 by using RegCM4.3.5. Atmospheric Research, 183, 296–307. https://doi.org/10.1016/j.atmosres.2016.09.008
EAS-CORDEX:
- Sun, H., Wang, A. et al (2018) Impacts of global warming of 1.5°C and 2.0°C on precipitation patterns in China by regional climate model (COSMO-CLM). Atmos Res 203:83–94. https://doi.org/10.1016/j.atmosres.2017.10.024
- Teichmann, C., Jacob, D., Remedio, A.R. et al. (2020) Assessing mean climate change signals in the global CORDEX-CORE ensemble. Clim Dyn. https://doi.org/10.1007/s00382-020-05494-x
EURO-CORDEX:
- Kotlarski, S., Keuler, K., Christensen, O. B., et al. (2014) 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
- Jacob, D., Teichmann, C., Sobolowski, S. et al. (2020) Regional climate downscaling over Europe: perspectives from the EURO-CORDEX community. Reg Environ Change 20, 51. https://doi.org/10.1007/s10113-020-01606-9
- 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
Coppola E., Nogherotto R., Ciarlò J.M. et al. (2021) Assessment of the European climate projections as simulated by the large EURO-CORDEX regional and global climate model ensemble. Journal of Geophysical Research – Atmospheres, https://doi.org/10.1029/2019JD032356
Vautard R., Kadygrov N., Iles C. et al. (2021) Evaluation of the large EURO-CORDEX regional climate model ensemble. Journal of Geophysical Research – Atmospheres, https://doi.org/10.1029/2019JD032344
MED-CORDEX:
Ruti PM, Somot S, Giorgi F, et al. Ruti PM, Somot S, Giorgi F, Dubois C, Flaounas E, Obermann A, Dell’Aquila A, Pisacane G, Harzallah A, Lombardi E, Ahrens B, Akhtar N, Alias A, Arsouze T, Aznar R, Bastin S, Bartholy J, Béranger K, Beuvier J, Bouffies-Cloché S, Brauch J, Cabos W, Calmanti S, Calvet J-C, Carillo A, Conte D, Coppola E, Djurdjevic V, Drobinski P, Elizalde-Arellano A, Gaertner M, Galàn P, Gallardo C, Gualdi S, Goncalves M, Jorba O, Jordà G, L'Heveder B, Lebeaupin-Brossier C, Li L, Liguori G, Lionello P, Maciàs D, Nabat P, Onol B, Raikovic B, Ramage K, Sevault F, Sannino G, Struglia MV, Sanna A, Torma C, Vervatis V(2016) MED-CORDEX initiative for Mediterranean Climate studies. Bull. Amer. Meteor. Soc.,97(7), 1187-1208,July 2016, doi: http://dx.doi.org/10.1175/BAMS-D-14-00176.1, http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-14-00176.1
- Somot S., Ruti P., Ahrens B. , Coppola E., Jordà G., Sannino G., Solmon Fet al. (2018). Editorial for the Med-CORDEX Special Issue. Clim. Dyn. 51(3):771-777, doi: 10.1007/s00382-018-4325-x, https://link.springer.com/article/10.1007/s00382-018-4325-x
- Med-CORDEX Special Issue, Climate Dynamics, volume 51, issue 3, 2018; https://link.springer.com/journal/382/volumes-and-issues/51-3
NAM-CORDEX:
- Bukovsky, M. S., and Mearns, L. O. (2020). Regional climate change projections from NA-CORDEX and their relation to climate sensitivity. Clim. Change 162, 645–665., 2018; https://link.springer.com/journal/article382/10.1007%2Fs10584volumes-020and-02835issues/51-x3
ARC-CORDEX:
Inoue, J., Sato, K., Rinke, A., Cassano, J. J., Fettweis, X., Heinemann, G., et al., 2021: Clouds and radiation processes in regional climate models evaluated using observations over the ice‐free Arctic Ocean, J. Geophys. Res. Atm., 126, e2020JD033904, doi:10.1029/2020JD033904
Sedlar, J., M. Tjernström, A. Rinke, A. Orr, J. Cassano, X. Fettweis, et al., 2020: Confronting Arctic troposphere, clouds and surface energy budget representations in regional climate models with observations, J. Geophys. Res. Atm., 124, doi:10.1029/2019JD031783
Akperov, M., A. Rinke, I.I. Mokhov, V.A. Semenov, M.R. Parfenova, H. Matthes, et al., 2019: Future projections of cyclone activity in the Arctic for the 21st century from regional climate models (Arctic-CORDEX), Glob. Planet. Change, doi:10.1016/j.gloplacha.2019.103005
Diaconescu, E.P., Mailhot, A., Brown, R. et al., 2018: Evaluation of CORDEX-Arctic daily precipitation and temperature-based climate indices over Canadian Arctic land areas, Clim. Dyn., 50, 2061–2085, doi:doi.org/10.1007/s00382-017-3736-4
AFR-CORDEX:
- Nikulin G, Lennard C, Dosio A, Kjellström E, Chen Y, Hänsler A, Kupiainen M, Laprise R, Mariotti L, Fox Maule C, van Meijgaard E, Panitz H-J, Scinocca J F and Somot S (2018) The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble, Environ. Res. Lett., doi:10.1088/1748-9326/aab2b4, http://iopscience.iop.org/article/10.1088/1748-9326/aab1b1
- Nikulin G., Jones, C. , Giorgi, F., Asrar, G., Büchner, M., Cerezo-Mota, R., Christensen, O. B., Déqué, M., Fernandez, J., Haensler, van Meijgaard, E., Samuelsson P., Sylla M. B., and Sushama L., 2013. Precipitation Climatology in An Ensemble of CORDEX-Africa Regional Climate Simulations, J. Climate, 25, 6057–6078. DOI: 10.1175/JCLI-D-11-00375.1
SAM-CORDEX:
MENA-CORDEX:
- Zittis, G., Hadjinicolaou, P. and Lelieveld, J. (2014) Comparison of WRF Model Physics Parameterizations over the MENA-CORDEX Domain. American Journal of Climate Change, 3, 490-511. http://doi.org/10.4236/ajcc.2014.35042
- Almazroui, M. (2016) RegCM4 in climate simulation over CORDEX-MENA/Arab domain: selection of suitable domain, convection and land-surface schemes. Int. J. Climatol., 36: 236-251. https://doi.org/10.1002/joc.4340
- Bucchignani, E., Mercogliano, P., Rianna, G. and Panitz, H.-J. (2016) Analysis of ERA-Interim- driven COSMO-CLM simulations over Middle East – North Africa domain at different spatial resolutions. Int. J. Climatol., 36: 3346–3369. http://doi.org/10.1002/joc.4559
NAM-CORDEX:
- Bukovsky, M. S., and Mearns, L. O. (2020). Regional climate change projections from NA-CORDEX and their relation to climate sensitivity. Clim. Change 162, 645–665. https://link.springer.com/article/10.1007%2Fs10584-020-02835-x
SAM-CORDEX:
- Blázquez, J., Silvina, A.S. Multiscale precipitation variability and extremes over South America: analysis of future changes from a set of CORDEX regional climate model simulations. Clim Dyn 55, 2089–2106 (2020). https://doi.org/10.1007/s00382-020-05370-8
- Llopart, M., Simões Reboita, M. & Porfírio da Rocha, R. Assessment of multi-model climate projections of water resources over South America CORDEX domain. Clim Dyn 54, 99–116 (2020). https://doi.org/10.1007/s00382-019-04990-z
- Solman, S.A., Blázquez, J. Multiscale precipitation variability over South America: Analysis of the added value of CORDEX RCM simulations. Clim Dyn 53, 1547–1565 (2019Blázquez, J., Silvina, A.S. Multiscale precipitation variability and extremes over South America: analysis of future changes from a set of CORDEX regional climate model simulations. Clim Dyn 55, 2089–2106 (2020). https://doi.org/10.1007/s00382-020019-0537004689-81
- LlopartFalco, M., Simões Reboita, M. & Porfírio da Rocha, R. Assessment of multi-model climate projections of water resources over South America CORDEX domain. Clim Dyn 54, 99–116 (2020Carril, A.F., Menéndez, C.G. et al. Assessment of CORDEX simulations over South America: added value on seasonal climatology and resolution considerations. Clim Dyn 52, 4771–4786 (2019). https://doi.org/10.1007/s00382-019018-049904412-zSolman, S.A., Blázquez, J. Multiscale precipitation variability over South America: Analysis of the added value of CORDEX RCM simulations. Clim Dyn 53, 1547–1565 (2019).
WAS-CORDEX:
- Sanjay J, Krishnan R, Shrestha AB, Rajbhandari R, Ren G-Y (2017) Downscaled climate change projections for the Hindu Kush Himalayan region using CORDEX South Asia regional climate models. Adv Clim Chang Res 8(3):185–198, https://doi.org/10.1016/j.accre.2017.08.003
SEA-CORDEX:
- Tangang, F., Chung, J.X., Juneng, L. et al. (2020) Projected future changes in rainfall in Southeast Asia based on CORDEX–SEA multi-model simulations. Clim Dyn 55, 1247–1267. 1007/s00382-019-04689-1Falco, M., Carril, A.F., Menéndez, C.G. et al. Assessment of CORDEX simulations over South America: added value on seasonal climatology and resolution considerations. Clim Dyn 52, 4771–4786 (2019). https://doi.org/10.1007/s00382-018020-441205322-z2
CORDEX-CORE (comprehensive and homogeneous projections across almost all CORDEX domains with 0.22º resolution):
- Teichmann, C., Jacob, D., Remedio, A.R. et al. (2020) Assessing mean climate change signals in the global CORDEX-CORE ensemble. Clim Dyn (2020). https://doi.org/10.1007/s00382-020-05494-x
- Coppola, E., Raffaele, F., Giorgi, F. et al. (2021) Climate hazard indices projections based on CORDEX-CORE, CMIP5 and CMIP6 ensemble. Clim Dyn. https://doi.org/10.1007/s00382-021-05640-z
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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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