Аннотация
Abstract The present study investigated the uncertainty associated with Climatol's adjustment algorithm applied to daily minimum and maximum air temperature. The uncertainty quantification was performed based on several numerical experiments and the benchmark data that were created in the framework of the INDECIS project. Using a complex approach, the uncertainty was evaluated at different levels of detail (day‐to‐day evaluation through formalism of random functions and six statistical metrics) and time resolution (daily and yearly). However, only the main source of potential residual errors was considered, namely station signals introduced into a raw data set to be homogenized/adjusted. Other influencing factors, such as the averaged correlation between a candidate and references, were removed from the analysis or kept almost unchanged. According to our calculations, the Climatol's adjustment uncertainty, evaluated on the daily scale, varies over time. The width of the residual errors distribution in summer months is substantially less compared with wintertime. The slight seasonality is also observed in the means of the residual errors. The uncertainty evaluation based on the statistical metrics usually neglect such seasonal non‐stationarity of the residual errors providing only assessments averaged over time. On the other hand, the metrics provide detailed information regarding both types of the residual errors, systematic and scatter. The metrics values confirmed good capability of the Climatol software to remove the systematic errors related to jumps in the means, while the scatter errors are removed from the raw time series less efficiently. On the yearly scale, the uncertainty evaluation was performed for the yearly temperature data and several climate extreme indices. Both types of errors are removed well in the yearly time series of the air temperature and the extreme indices. The metrics values showed a significant reduction of the Climatol's adjustment uncertainty. A substantial decrease of the linear trend errors in the yearly time series can also be observed.