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MAR-MPI-ESM1-2-HR SSP585 European Alps (2015-2100)

<p>This deposit contains MAR simulations over the European Alps domain (7 kilometers resolution) forced by the MPI-ESM1-2-HR GCM (CMIP6 version) for the SSP585 projection (2015 to 2100). The version of the MAR model used is v.3.10, model set-up is described in detail in Beaumet et al., 2021 (https://doi.org/10.1007/s10113-021-01830-x) Contact person : Julien Beaumet (beaumetjulien@gmail.com), Martin Menegoz (martin.menegoz@univ-grenoble-alpes.fr)<br> The realization used is r1i1p1f1.<br> Data are available at the daily frequency, with one variable per file (10 years of data per file).<br> The available variables in this deposit are :</p> <p><strong>CC:</strong> Cloud cover, [0-1]<br> <strong>MB:</strong>&nbsp; Total snow water equivalent, [mm.We]<br> <strong>MBrr:</strong> Daily rainfall, [mm.We] (5)<br> <strong>MBsf:</strong> Daily snowfall, [mm.We] (5)<br> <strong>QQz:&nbsp;</strong> Near-surface specific humidity at constant height, [g/kg]&nbsp; (2)</p> <p><strong>TTmax:</strong> Near-surface maximum air temperature for the first model level above the surface (constant sigma), [C]<br> <strong>TTmin:</strong> Near-surface minimum air temperature for the first model level above the surface (constant sigma),[C]</p> <p><strong>TTz: </strong>Near-surface mean air temperature at constant-height, [C]<br> <strong>UUz:</strong> Near-surface zonal component of wind speed at constant height, [m/s]<br> <strong>VVz:</strong> Near surface Meridional component of wind speed at constant height, [m/s]<br> <strong>ZN3:</strong> Total snow height, [m]</p> <p>Other variables are available upon request (see email above).<br> AL : Surface albedo, [0-1]<br> CD: Low level Cloud cover, [0-1]<br> CM: Middle level Cloud cover, [0-1]<br> CU: High level Cloud cover, [0-1]<br> SP:&nbsp; Surface pressure, [hPa]<br> ST:&nbsp; Surface temperature, [C]<br> TT:&nbsp; Near-surface mean air temperature for the first three model level above the surface (constant sigma), [C] (1)<br> TTp: Constant pressure-level mean air temperature, [C] (4)<br> ZZ:&nbsp; Surface geopotential for the first three model level above the surface (constant sigma), [m]</p> <p>LWD: Surface downward longwave radiation, [W/m2]<br> LWU: Surface upward longwave radiation, [W/m2]</p> <p>SWD: Surface downward shortwave radiation, [W/m2]<br> SWU: Surface upward shortwave radiation, [W/m2]</p> <p>SHF: Surface sensible heat flux, [W/m2]&nbsp; LHF: Surface latent heat flux, [W/m2]</p> <p>* Latitude(LAT),longitude(LON)and surface elevation (SH) of each grid point can be read in MARgrid_EUy.nc file</p> <p>(1) For variable TT, ZZ model constant sigma level of 0.9997479<br> (2) For variables TTz, QQz constant height level is : 2m<br> (3) For variables UUz, VVz constant height level are : 10m<br> (4) Variables TTp, UUp, VVp available at pressure level : 925, 850, 800, 700, 600, 500, 200 hPa<br> (5) Snow height and snow water equivalent are available for three sectors which corresponds to three different vegetation type : The three vegetation type used can be readen in the file MARgrid_EUy.nc, with the variable VEG and their respectibe fraction for each grid point is given by the variable FRV. The third vegetation type (sector=3) mostly corresponds to bare soil or low crops by default, but sometimes its fraction=0, which gives unrealistic low values of snow height. In this case, using the max. value on the axis sector often gives the best results.<br> Legend of the vegetation type for the VEG variables : 0:NO_VEGETATION&nbsp;&nbsp; 1:CROPS_LOW&nbsp;&nbsp; 2:CROPS_MEDIUM&nbsp;&nbsp; 3:CROPS_HIGH&nbsp;&nbsp; 4:GRASS_LOW&nbsp;&nbsp; 5:GRASS_MEDIUM&nbsp;&nbsp; 6:GRASS_HIGH&nbsp;&nbsp; 7:BROADLEAF_LOW&nbsp;&nbsp; 8:BROADLEAF MEDIUM&nbsp;&nbsp; 9:BROADLEAF_HIGH&nbsp; 10:NEEDLELEAF_LOW&nbsp; 11:NEEDLELEAF MEDIUM&nbsp; 12:NEEDLELEAF_HIGH&nbsp; 13:City<br> ~&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p>

ShareScore

44/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
20
Reuse readiness
8
Engagement
4

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