Long‐term area‐mean temperature series for Switzerland—Combining homogenized station data and high resolution grid data. (7th March 2018) - Begert, Michael; Frei, Christoph
Abstract : In this study, we derive new time‐series of monthly‐mean surface air temperature for Switzerland that range back to 1864 and represent area‐mean conditions over the country and three major sub‐regions. The methodology integrates data from a small sample (19 stations) of homogenized long‐term series and from a high‐resolution (2 km) grid dataset over a short (20 years) period. The statistical combination defines an objective weighting of station data that delivers reliable and time‐consistent area‐mean estimates, despite coarse and biased coverage with stations in early years. The methodology also quantifies the uncertainty of the estimates. Validation of the method reveals plausible patterns of station weights, and estimation errors of about 0.1 °C, much smaller than inter‐annual variations. The new series suggest a warming in Switzerland of almost 1.5 °C from the early‐industrial period (1864–1900) till the latest WMO standard period (1981–2010), with a linear trend of 1.29 °C per 100 years between 1864 and 2016. The warming is found to be larger in autumn than in other seasons, larger to the north of the Alps than to the south, and larger below (above) 1000 m asl in winter (summer). In all series, the warming is modulated by inter‐decadal variations. Current global temperature datasets exhibit less warming for Switzerland than the present analysis. The pattern of disagreement suggests that a network‐wide change in Swiss temperature measurements around 1980 may have been missed in the homogeneity adjustments at global data archives. It is desirable that these archives are better aligned with the latest quality processing of the original data owners. Abstract : The study derives new time‐series of monthly‐mean surface air temperature for Switzerland that range back to 1864 and represent area‐mean conditions over the country and three major sub‐regions. The method integrates data from a small sample of homogenized long‐term series and from a high‐resolution grid dataset over a short 20‐year period. The statistical combination defines an objective weighting of station data that delivers reliable and time‐consistent area‐mean estimates together with uncertainty information.