Pre-industrial Temperature Variability on the Swiss Plateau Derived from the Instrumental Daily Series of Bern and Zurich

Abstract

Abstract. We describe the compilation of two early instrumental daily temperature series from Bern and Zurich, Switzerland, starting from 1760 and 1756, respectively. The series are a combination of numerous small segments from different observers at different locations, within and outside the two cities, converted to modern units and homogenized. In addition, we introduce a methodology to estimate the errors affecting daily and monthly mean values derived from early instrumental observations. Given the frequent small data gaps, we merge the two daily series into a more complete series representing the central Swiss Plateau. We finally compare the homogenized monthly series with other temperature reconstructions for Switzerland. We find significant differences before 1860 pointing to biases affecting some of the most widely used instrumental data sets. In general, the homogenization of temperature measurements at the transition between the early instrumental and the national weather services eras remains a problematic issue in historical climatology and has significant implications for other fields of climate research.

Two new early instrumental records of air pressure and temperature for the southern European Alps

“…9). Jennings et al (2019) give for the modern-day station of Bolzano a snowfall frequency of 50% at 0.5 • C. We find the same frequency at about 1.5 • C, suggesting a positive bias in the order of 1 • C. Such a bias is very common in early instrumental temperature records (e.g., Brugnara et al, 2022a) and is usually related to the exposure of the thermometer. However, this result should be interpreted with caution because the early precipitation observations might not be fully consistent with the modern ones.…”

“…The calculation of daily means must take into account the diurnal cycle of pressure and temperature, particularly when only one measurement per day is available as in the case of Rovereto. For temperature, we use the multiple linear regression (MLR) approach described in Brugnara et al (2022a). In short, we train a MLR model on modern-day sub-hourly data to reproduce true daily means from only one or two measurements in a day.…”

“…To do so we calculated monthly corrections by comparing our data with the ensemble mean of the EKF400 paleo-reanalysis at the nearest grid point. The monthly corrections are then transformed into daily corrections by fitting the first two harmonics of a Fourier series (Brugnara et al, 2022a). In order to minimize modifications to the original record, the data are adjusted with respect to the longest homogeneous segment.…”

“…We estimate standard errors for each daily and monthly mean value following Brugnara et al (2022a). Daily errors are given by:…”

“…For temperature, e 2 is the standard deviation of the residuals from Eq. 1 aggregated by month, while e 3 is calculated from the modern-day mean diurnal cycle of Rovereto assuming a time uncertainty of 90 minutes (see also Brugnara et al, 2022a).…”