Benova and Cenova Models in the Homogenization of Climatic Time Series

For the correct evaluation of climate trends and climate variability, it is important to remove non-climatic biases from the observed data. Such biases, referred to as inhomogeneities, occur for station relocations or changes in the instrumentation or instrument installation, among other reasons. Most inhomogeneities are related to a sudden change (break) in the technical conditions of the climate observations. In long time series (>30 years), usually multiple breaks occur, and their joint impact on the long-term trends and variability is more important than their individual evaluation. Benova is the optimal method for the joint calculation of correction terms for removing inhomogeneity biases. Cenova is a modified, imperfect version of Benova, which, however, can also be used in discontinuous time series. In the homogenization of section means, the use of Benova should be preferred, while in homogenizing probability distribution, only Cenova can be applied. This study presents the Benova and Cenova methods, discusses their main properties and compares their efficiencies using the benchmark dataset of the Spanish MULTITEST project (2015–2017), which is the largest existing dataset of this kind so far. The root mean square error (RMSE) of the annual means and the mean absolute trend bias were calculated for the Benova and Cenova results. When the signal-to-noise ratio (SNR) is high, the errors in the Cenova results are higher, from 14% to 24%, while when the SNR is low, or concerted inhomogeneities in several time series occur, the advantage of Benova over Cenova might disappear.