10.48364/ISIMIP.836809.1
Dirk N. Karger
Dirk N.
Karger
0000-0001-7770-6229
Stefan Lange
Stefan
Lange
0000-0003-2102-8873
Chantal Hari
Chantal
Hari
0000-0003-2507-8747
Christopher P. O. Reyer
Christopher P. O.
Reyer
0000-0003-1067-1492
Niklaus E. Zimmermann
Niklaus E.
Zimmermann
0000-0003-3099-9604
CHELSA-W5E5 v1.0: W5E5 v1.0 downscaled with CHELSA v2.0
ISIMIP Repository
2022
EARTH SCIENCE > ATMOSPHERE > PRECIPITATION
EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC RADIATION > SHORTWAVE RADIATION
EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATURE > SURFACE TEMPERATURE
Matthias Büchner
Matthias
Büchner
0000-0002-1382-7424
Jochen Klar
Jochen
Klar
0000-0002-5883-4273
Iliusi Vega del Valle
Iliusi
Vega del Valle
0000-0001-6902-2257
Jan Volkholz
Jan
Volkholz
Jacob Schewe
Jacob
Schewe
0000-0001-9455-4159
Stefan Lange
Stefan
Lange
0000-0003-2102-8873
Franziska Piontek
Franziska
Piontek
0000-0003-4305-7552
Christopher P. O. Reyer
Christopher P. O.
Reyer
0000-0003-1067-1492
Matthias Mengel
Matthias
Mengel
0000-0001-6724-9685
María del Rocío Rivas López
María del Rocío
Rivas López
0000-0002-1984-3070
Christian Otto
Christian
Otto
0000-0001-5500-6774
Bjoern Soergel
Bjoern
Soergel
0000-0002-2630-7081
Anne Gädeke
Anne
Gädeke
0000-0003-0514-2908
Martin Park
Martin
Park
0000-0002-2467-3256
Katja Frieler
Katja
Frieler
0000-0003-4869-3013
Potsdam Institute for Climate Impact Research
03e8s1d88
2022-01-09
eng
Input dataset
https://doi.org/10.1038/s41597-021-01084-6
https://doi.org/10.5194/essd-12-2097-2020
https://doi.org/10.1016/S0166-2481(08)00008-1
https://doi.org/10.1038/sdata.2017.122
https://doi.org/10.1002/qj.3803
https://doi.org/10.3133/ofr20111073
https://doi.org/10.5880/pik.2019.023
https://doi.org/10.24381/cds.bd0915c6
https://doi.org/10.48364/ISIMIP.836809
https://doi.org/10.48364/ISIMIP.836809.2
application/x-netcdf
1.0.1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
The CHELSA-W5E5 dataset was created to serve as observational climate input data for the impact assessments carried out in phase 3a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a).<br>Version 1.0 of the CHELSA-W5E5 dataset covers the entire globe at 30 arcsec horizontal and daily temporal resolution from 1979 to 2016. Data sources of CHELSA-W5E5 are version 1.0 of WFDE5 over land merged with ERA5 over the ocean (W5E5; Lange, 2019; Cucchi et al., 2020), the ERA5 global reanalysis (Hersbach et al. 2020) and the Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010; Danielson and Gersch, 2011).<br>Variables (with short names and units in brackets) included in the CHELSA-W5E5 dataset are Daily Mean Precipitation (pr, kg m-2 s-1), Daily Mean Surface Downwelling Shortwave Radiation (rsds, W m-2), Daily Mean Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and the CHELSA-W5E5 land-sea mask (mask, 1).<br>Version 1.0.1 of this dataset resolved the caveat described at https://data.isimip.org/caveats/6/.
CHELSA-W5E5 v1.0 is a downscaled version of the W5E5 v1.0 dataset, where the downscaling is done with the CHELSA v2.0 algorithm (Karger et al. 2017, Karger et al. 2021). In the following we outline how this algorithm works.<br>The CHELSA algorithm applies topographic adjustments based on the surface altitude, orog, from GMTED2010. Since it does not add any value over the ocean, all values over the ocean are masked.<br>The CHELSA algorithm is applied day by day. CHELSA-W5E5 tas is obtained by applying a lapse rate adjustment to W5E5 tas, using differences between CHELSA-W5E5 orog and W5E5 orog in combination with temperature lapse rates from ERA5. Those lapse rates are calculated based on atmospheric temperature, ta, at 950 hPa and 850 hPa, and the geopotential height, zg, of those pressure levels. The lapse rate used for the adjustment is calculated as the daily mean of hourly values of (ta_850-ta_950)/(zg_850-zg_950). The variables tasmax and tasmin are downscaled in the same way, using the same lapse rate value.<br>Precipitation downscaling uses daily mean zonal and meridional wind components from ERA5 to calculate the orographic wind effect and combines that with the height of the planetary boundary layer to approximate the total orographic effect on precipitation intensity. Using that, precipitation from W5E5 is downscaled such that precipitation fluxes are preserved at the original 0.5° resolution of W5E5. More details are given in Karger et al. (2021).<br>Surface downwelling shortwave radiation, rsds, at 30 arcsec resolution is strongly influenced by topographic features such as aspect or terrain shadows, that are less pronounced at 0.5° resolution. The downscaling algorithm combines such geometric effects with orographic effects on cloud cover for an orographic adjustment of rsds. Geometric effects are considered by computing 30 arcsec clear-sky radiation estimates using the method described in Böhner and Antonic (2009) and a simplified, uniform atmospheric transmittance of 80 %. These effects include shadowing from surrounding terrain, diffuse radiation based on reflectance from surrounding terrain, and terrain aspect. To include how orographic effects on cloud cover influence rsds, the clear-sky radiation estimates are adjusted using downscaled ERA5 total cloud cover. The cloud cover downscaling uses ERA5 cloud cover at all pressure levels and the orographic wind field. For details see Karger et al. (2022, in preparation). Finally, the clear-sky radiation estimates adjusted for cloud cover are rescaled such that they match W5E5 rsds, B-spline interpolated to 30 arcsec.
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