Application of homogenization methods for Ireland's monthly precipitation records: Comparison of break detection results

Abstract

Time series homogenization for 299 of the available precipitation records for the island of Ireland (IENet) was performed. Four modern relative homogenization methods, that is, HOMER, ACMANT, CLIMATOL and AHOPS were applied to this network of station series where contiguous intact monthly records range from 30 to 70 years within the base period 1941–2010. Break detection results are compared between homogenization methods, and coincidences with available documentary information (metadata) were analysed. The lowest (highest) number of breaks were detected with HOMER (ACMANT). Large differences of break frequency were found, namely ACMANT and AHOPS detected 8 times as many breaks than HOMER, while the break frequency with CLIMATOL was intermediate. Also, the ratio of series classified to be homogeneous varies widely between the methods. It is 85% with HOMER, 60% with CLIMATOL, 31% with AHOPS, while only 22% with ACMANT. In a further experiment, all the available time series for Ireland and Northern Ireland, (910 series) were used with ACMANT and CLIMATOL to explore the stability of break frequency for the same 299 series examined in the base experiment. While overall break frequency slightly increased (by 6–13%), the break positions notably changed for individual time series. The number of breaks changed for 59% (23%) of the series with ACMANT (CLIMATOL). For the breaks detected coincidentally by at least three methods including ACMANT and CLIMATOL in the base experiment, the second experiment confirmed the break positions in 86–87% of the breaks. The consequences of these results in relation to the reliability of statistical homogenization are discussed. Sometimes markedly different step functions provide comparable good approaches. However, the accuracy of homogenized time series cannot be related directly to the instability of break detection results.

Uncertainty evaluation of Climatol's adjustment algorithm applied to daily air temperature time series

“…The R package Climatol is a homogenization software that has been widely used in recent years for removing inhomogeneities from collections of raw time series of different climate variables and different time resolution (e.g., Mamara et al ., 2013; Sanchez‐Lorenzo et al ., 2015; Guijarro et al ., 2018; Meseguer‐Ruiz et al ., 2018; Azorin‐Molina et al ., 2019; Coll et al ., 2020; Dumitrescu et al ., 2020). The effectiveness of the software has been evaluated in several benchmark tests (Venema et al ., 2012; Killick, 2016; Guijarro et al ., 2017; Guijarro et al ., 2019) where it demonstrated good results, which are comparable in terms of accuracy to other well established and tested homogenization algorithms.…”

“…As noted in Coll et al . (2020), such metrics can provide useful indications in relation to the strengths and weaknesses of homogenization methods used.…”

“…An alternative approach is to use specially elaborated statistical metrics or indicators (e.g., Trewin, 2018;Vincent et al, 2018). As noted in Coll et al (2020), such metrics can provide useful indications in relation to the strengths and weaknesses of homogenization methods used.…”

“…Both sub‐problems might produce a certain number of common errors, which deviate the homogenized data from the true climate signal. Evaluation of the efficiency of the detection algorithms has been performed in many works (e.g., Ducré‐Robitaille et al ., 2003; DeGaetano, 2006; Reeves et al ., 2007; Domonkos, 2011; Kuglitsch et al ., 2012; Venema et al ., 2012; Willett et al ., 2014; Killick, 2016; Yozgatligil and Yazici, 2016; Coll et al ., 2020). Assessment of the performance of the adjustment methods has also been considered (e.g., Della‐Marta and Wanner, 2006; Mestre et al ., 2011; Trewin, 2013; Squintu et al, 2020).…”

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

“…In addition, the use of ACMANT is straightforward and it treats well data gaps (Domonkos and Coll, 2019). Relying on these properties of ACMANT, its use should be widespread, but what we can see in fact is a quite modest increase in the frequency of its use (Kezoudi and Tymvios, 2017; Mamara et al ., 2017; Yosef et al ., 2018, 2019; Fioravanti et al ., 2019; Coll et al ., 2020).…”