Homogenising meteorological variables: Impact on trends and associated climate indices

Abstract

Daily precipitation, maximum and minimum air temperature series are homogenised over the Lake Chad Basin between 1979 and 2020 using two conceptually different homogenisation methods; the Adapted Caussinus-Mestre Algorithm for homogenising Networks of Temperature series (ACMANT) and the iterated standard normal homogeneity (CLIMATOL). Results show the existence of unnatural breakpoints for most of the station series. However, the two methods show a general improvement in the quality of climate series. The trend estimation of the homogenised series based on the modified Mann Kendall methodology shows different modifications of the trend’s magnitude for different periods. Overall, CLIMATOL and ACMANT exhibit nearly similar trend patterns, suggesting the credibility of both homogenisation methods. Relative to the base period of analysis (1981–2010), the anomaly classification for the entire basin between 1979 and 2020 into dry-warm, wet-warm, dry-cold and wet-cold are misrepresented by the raw series compared to the homogenised series. Such erroneous representations resulting from inhomogeneities in raw climate series could misinform decisions akin to climate change assessment and water resources management strategies, thereby reducing the adaptive-capability of the basin’s inhabitants to climate change effects. Our study demonstrates the importance of robust homogenisation of climate series to mitigate inhomogeneity errors and improve the quality of information when observations are used in climate and hydrological studies.