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Adapted Caussinus-Mestre detection Algo-rithm for homogenising Networks of Tempera-ture series (ACMANT) Peter Domonkos Centre for Climate Change University Rovira i Virgili, Tortosa, Spain. Introduction.
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Adapted Caussinus-Mestre detection Algo-rithm for homogenising Networks of Tempera-ture series (ACMANT)Peter DomonkosCentre for Climate ChangeUniversity Rovira i Virgili, Tortosa,Spain
Introduction • The seasonal cycle of radiation intensity often causes marked seasonal cycle in the inhomogeneities (IHs) of observed temperature time series, thus the magnitudes of temperature IHs tend to be larger in summer than in winter. – ACMANT is a method developed for homogenising temperature datasets from the mid and high latitudes. It includes segments for data-gap filling, deriving reference series, outlier-filtering, IH-detection and correction of biases. Its operation is fully automatic.
ACMANT: Innovations • The ACMANT adapts many elements of some earlier developed homogenisation methods. The basis for the IH-detection part was step-function fitting with the Caussinus-Mestre method. In this presentation some innovations for the ACMANT are shown. • When data-series cover different periods, often various reference series are built for different sections of the candidate series. The automatic selection of the components for building reference series aims to find an optimum according to the number and spatial correlations of the available series. • The detection process intensively exploits the fact that most of the IHs have sinusoid-form seasonal changes.
ACMANT: Detection process • The detection process has two parts: “Main detection” and “Secondary detection”. In the Main detection, annual climatic characteristics are examined only (annual mean and summer-winter difference), and IHs with minimum 3 year duration are searched. • Secondary detection is applied, only when accumulated anomalies exceed some predefined thresholds in the series corrected according to the results of the Main detection.
ACMANT: Detection process • In the Secondary detection 60 month long sub-series of monthly temperatures around the maximum of accumulated anomalies are examined. In the dynamic programming algorithm subsection-means are substituted with a sinusoid curve of annual cycle for subsections of minimum 10 months. The optimum mean shift and sinus-amplitude are searched with iteration. • Semi-empirical parameterisation
Fitting sinusoid annual cycles with sinusoid annual cycle
Semi-empirical parameterisation Semi-empirical parameterisation of the penalty-term: The most usual values of p is 0.75 in the Main detection and 1.0 in the Secondary detection, but higher p values are applied when the number of the components of the reference series is below 4.
Statistics of detection frequency IH(all) = all the IHs detected with ACMANT IH(S) = IHs detected with the Secondary detection The frequency of short-term IHs is lower in the simulated datasets than in the observed datasets.
Results of Temp-Sur 01 dataset1) Timings of IHs (red) and outliers (blue)
Annual anomalies: homogenised and filtered common fluctuations
Concluding ideas • ACMANT supposes that the examined climatic element has a sinusoid-form annual cycle, therefore it is applicable only for temperature datasets from the mid and high latitudes. • Some segments of ACMANT (building reference series, outlier-filtering, etc.) can be adapted in the homogenisation of other elements. On the other hand, further development is possible for some segments, or for the parameterisation of detection parts. • The present form is fully automatic, and does not allow the use of metadata.
