A Bayesian normal homogeneity test for the detection of artificial discontinuities in climatic series

Abstract

A Bayesian Normal Homogeneity Test (BNHT) for the detection of artificial discontinuities in climatic series is presented. The test is simple to use and allows the integration of prior knowledge on the date of change from various sources of information (e.g. metadata or expert belief) in the analysis. The performance of the new test was evaluated on synthetic series with similar statistical properties as observed total annual precipitation in the southern and central parts of the province of Quebec, Canada. Different priors were used to investigate the sensitivity of the test to the choice of priors. It was found that (1) high-prior probability of no change yields low false detection rates on the homogeneous series; (2) the test has a very high power of detection on series with a single shift (the best power of detection if compared with previous methods applied to the same synthetic series); (3) shifts having a small magnitude are detectable with a low prior probability of no change and (4) when applied to series with multiple shifts with a segmentation procedure and a high probability of no change, the test proved to be performing well in detecting multiple shifts (as performing as the best techniques previously applied to the same synthetic series). An example of application to total annual precipitation in Quebec City, Canada is also presented to illustrate (1) a case for which the results are not affected by the choice of the prior parameters and (2) a case for which information about potential changes found in the metadata was integrated in the analysis and allowed the detection of a change that would not have been detected with a non-informative prior.

Climate and water availability indicators in Canada: Challenges and a way forward. Part II – Historic trends

“…station automation, site relocation, etc.) have led to homogeneity testing and adjustment of temperature and precipitation time series in order to detect and correct artificial shifts and create homogenized climate data (Beaulieu et al 2008(Beaulieu et al , 2010Vincent et al 2012). Testing for homogeneity and homogenization of hydro-climate time series data should be key methodological steps prior to testing for trends to remove potential bias and increase robustness of the analysis.…”

Homogeneidade de séries climatológicas em Minas Gerais

“…As não homogeneidades podem ser alterações causadas, por exemplo, pelo crescimento de vegetação ou pela urbanização na proximidade das estações ou, ainda, por mudança na localização ou descalibração nos instrumentos de medida e até mesmo por hábitos de observação (Aguilar et al, 2003;Caussinus & Mestre, 2004;Brunet et al, 2006;Costa & Soares, 2009;Beaulieu et al, 2010;Pandžiae & Likso, 2010;Rienzner & Gandolfi, 2011).…”

A Bayes Factor Model for Detecting Artificial Discontinuities via Pairwise Comparisons

“…Consequently, many algorithms have been developed to detect the discontinuities. A list of representative publications includes Alexandersson (1986), Vincent (1998), Lund and Reeves (2002), Caussinus and Mestre (2004), Della-Marta and Wanner (2006), Lund et al (2007), Reeves et al (2007), , Wang (2008a,b), Menne and Williams (2009), Hannart and Naveau (2009), Beaulieu et al (2010), and Lu et al (2010). Caussinus and Mestre (2004) and Menne and Williams (2009) adopt a pairwise comparison approach that is argued to have advantages in terms of avoiding the detection of true climate signals and utilizing difference series to increase signal-to-noise ratios (SNR) and improve hit rates (HRs).…”

“…Menne and Williams (2009) solve the location uncertainty issue empirically. Hannart and Naveau (2009) and Beaulieu et al (2010) address the location uncertainty issue from a Bayesian perspective. Hannart and Naveau (2009) propose a method based on Bayesian decision theory.…”

“…Their method identifies subsequences containing a unique discontinuity by minimizing average posterior cost functions recursively. Beaulieu et al (2010) develop a framework based on Bayesian normal homogeneity test (BNHT) and apply BNHT recursively on the series to detect multiple discontinuities. However, such uncertainty can also be addressed from a Bayesian model selection perspective.…”