Development and validation of a high-resolution monthly gridded temperature and precipitation data set for Switzerland (1951-2000)

Abstract

A 5 km gridded temperature and precipitation data set was constructed for the topographically complex region of Switzerland in the European Alps. The data set consists of 1961-1990 mean fields for monthly mean temperature (T̄) and monthly total precipitation (P̄), plus monthly anomaly fields ΔT and ΔP for 1951-2000. All data are point estimates and come with extensive statistics on interpolation errors as a function of geographical location, elevation and time of the year. A novel interpolation method was employed that accounted for possible orographic effects at different spatial scales and allowed for regionally and seasonally varying relief-climate relationships. The accuracy of the interpolations was quantified by means of cross-validation. The proposed method was found to be superior to linear regression employing elevation as the only predictor for P̄, and better than inverse distance weighting (IDW) interpolation for September to February ΔT. It was worse than IDW interpolation for springtime ΔT and for March to September ΔP. The areal mean cross-validation errors obtained for the new method were generally close to zero. The annually averaged mean absolute error for T̄ was 0.6°C and for P̄ it was 10.5 mm mo-1 (or 11 %). The average proportion of temporal variance explained by the cross-validated monthly 1951-2000 station time series was 89 % for ΔT and 81 % for ΔP. The average proportion of spatial variance of the monthly anomaly fields explained was 13 % for ΔT and 40 % for ΔP. The largest cross-validation errors were generally found at regions of lower station density in south-southeast Switzerland and at elevations above ∼2000 m above sea level. All error variances showed distinct annual cycles. The ΔT and ΔP fields and the derived trend fields showed substantial small-scale variability, which was not well reproduced and deserves further study. The individual gridpoint estimates should therefore be interpreted with care.