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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.

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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 The erroneous v1-simulation has been unpublished from the ESGF.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. 

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• 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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