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).
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).
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).
GET THE DATA HERE.
CITATION: Dirk N. Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Niklaus E. Zimmermann (2021):CHELSA-W5E5 v1.0: W5E5 v1.0 downscaled with CHELSA v2.0. ISIMIP Repository. https://doi.org/10.48364/ISIMIP.836809
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.
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.
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.
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).
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.
- M. Cucchi, G. P. Weedon, A. Amici, N. Bellouin, S. Lange, H. Müller Schmied, H. Hersbach, and C. Buontempo (2020) WFDE5: bias adjusted ERA5 reanalysis data for impact studies, Earth System Science Data https://doi.org/10.5194/essd-12-2097-2020
- Karger, D. N., Wilson, A. M., Mahony, C., Zimmermann, N. E., and Jetz, W.: Global daily 1km land surface precipitation based on cloud cover-informed downscaling, 2021. Scientific Data. → https://arxiv.org/abs/2012.10108
- Böhner, J. and Antonic, O. (2009). Land-Surface Parameters Specific to Topo-Climatology. In T. Hengl, & H. I. Reuter (Eds.), GEOMORPHOMETRY: CONCEPTS, SOFTWARE, APPLICATIONS (pp. 195-226). Elsevier Science., in: in T. Hengl, & H. I. Reuter (eds.) Geomorphometry: Concepts, Software, Applications, Elsevier Science, 195–226, 2009. https://doi.org/10.1016/S0166-2481(08)00008-1
- Karger, D., Conrad, O., Böhner, J. et al. Climatologies at high resolution for the earth’s land surface areas. Sci Data 4, 170122 (2017). https://doi.org/10.1038/sdata.2017.122
- Hersbach, H, Bell, B, Berrisford, P, et al. The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146: 1999– 2049. https://doi.org/10.1002/qj.3803
- Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010), US Geological Survey, 2011. https://doi.org/10.3133/ofr20111073