Removal of bias introduced by considering calendar or rainfall day as 24‐hr period in daily minimum temperature series: Results from ACMANT approach

Abstract

AbstractThe automation of weather stations influences the climate observations in various ways, among them the hour of reading and reporting daily temperature extremes is usually altered. This alteration causes a systematic change, referred to as terminus change signal (TCS), in the recorded daily minimum temperatures (Tmin), in comparison with the earlier records. The TCS present in the Tmin data of the Meteorological Service of Catalonia is quantified accurately from the dates of the automation and hourly temperature observations. The TCS is very small for the annual means, but it has a characteristic, irregular‐shaped annual cycle with up to 0.3°C decrease of mean monthly Tmin from spring to autumn, and a change of the opposite sign in the winter months. The study explores how biases of the same seasonal behaviour as the TCS in Catalonia can be removed with statistical homogenization when the archived data and metadata are insufficient for the exact calculation of the bias properties. ACMANT, one of the most modern and most accurate homogenization methods is used on various test datasets with the TCS embedded. The test datasets are developed from two source datasets, one is a selected network of the observed Catalan Tmin series, while the other is a simulated Swedish Tmin dataset from the European project INDECIS. The test datasets include the TCS with varied parameterizations in order to obtain a more general picture about the skill of the statistical homogenization. While the signal‐to‐noise ratio and mean annual bias are varied by parameterization, the shape of the annual cycle of the TCS is fixed. The results show that ACMANT is notably more accurate in removing mean annual biases than mean monthly biases. The skill of removing a mean monthly bias widely varies and is influenced by several factors identified by the performed tests.