CHELSAcruts – High resolution temperature and precipitation timeseries for the 20th century
CHELSAcruts is a delta change monthly climate dataset for the years 1901-2016 for mean monthly maximum temperatures, mean monthly minimum temperatures, and monthly precipitation sum. Here we use the delta change method by B-spline interpolation of anomalies (deltas) of the CRU TS 4.01 dataset. Anomalies were interpolated between all CRU TS grid cells and are then added (for temperature variables) or multiplied (in case of precipitation) to high resolution climate data from CHELSA V1.2 (Karger et al. 2017, Scientific Data). This method has the assumption that climate only varies on the scale of the coarser (CRU TS) dataset, and the spatial pattern (from CHELSA) is consistent over time. This is certainly a rather crude assumption, and for time periods for which more accurate data is available CHELSAcruts should be avoided if possible (e.g. use CHELSA V1.2 for 1979-2015). Different to CHELSA V1.2, CHELSAcruts does not take changing wind patterns, or temperature lapse rates into account, but rather expects them to be constant over time, and similar to the long term averages.
When to use CHELSAcruts over the CHELSA timeseries:
If you are in need of data that extents beyond the time 1979 to 2013, you can use CHELSAcruts. If not, the CHELSA timeseries is a better choice, as is has a higher accuracy due to its more mechanistic downscaling algorithm then CHELSAcruts, which is simply based on the delta change method.
As CHELSAcruts uses the temporal signal from CRU, its quality is influenced by this dataset as well. Problems arise in years before 1950, when weather station density was low. In general, the accuracy of the data gets lower, the further we go back in time.
The data can be downloaded here: DOWNLOADS
CITATION: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P. & Kessler, M. (2017) Climatologies at high resolution for the earth’s land surface areas. Scientific Data 4, 170122.