Paper featuring John Coll of ICARUS. Post written by John Coll
International Journal of Climatology July 2016 doi: 10.1002/joc.4822
Time series homogenisation and the multiple breaks problem
During the long period of climatic observations, station location, instrumentation and several other conditions of the observation may change, resulting in non-climatic temporal variation in the observed data. Such non-climatic changes “inhomogeneities” affect the usability of observed data to the detection of climate change and climate variability. One important present task of climate science is to provide accurate regional and global mean temperature trend estimates (Rohde et al., 2013; Rennie et al., 2014; Venema et al., 2015), and homogenisation significantly contributes to that. The most frequent way of time series homogenisation is the use of statistical procedures. The direct aim is to identify and correct statistically significant shifts in the section means (they are the estimated timings of technical changes, often referred as breaks or change points). To separate the inhomogeneities from the true climatic variation (the latter never should be removed from the data), homogeneity tests are usually applied to the differences between a candidate series and other series of the same climatic area (“relative homogenisation”), rather than directly to the candidate series (“absolute homogenisation”).
We have the general experience that climatic time series contain about 5 breaks per 100 years on average (Venema et al. 2012). Although statistical homogenisation has a century long history, the theory and development of multiple break homogenisation offering mathematically higher level solutions appeared only in the 1990s coincident with the more widespread use of personal computers. One early representative of multiple break methods was PRODIGE (Caussinus and Mestre 2004).
During the European project COST ES0601 (known as ‘HOME’, 2007–2011) two new multiple break methods were created based on PRODIGE: one is the fully automatic ACMANT (Adapted Caussinus–Mestre Algorithm for homogenising Networks of Temperature series, Domonkos, 2011) and the other is Homogenisation software in R (HOMER, Mestre et al., 2013), the interactive homogenisation method officially recommended by HOME. Both HOMER and ACMANT include the optimal step function fitting with dynamic programming for break detection and the network wide minimization of residual variance for correcting inhomogeneities (ANOVA, Caussinus and Mestre, 2004; Domonkos, 2015). Both HOMER and ACMANT provide additional functionality relative to the parent method PRODIGE, and they are assumed to be the most efficient homogenization methods nowadays.
ACMANT3 development and discussion
This paper describes the theoretical background of ACMANT and the recent developments, which extend the capabilities, and hence, the application of the method. The most important novelties in ACMANT3 are: the ensemble pre-homogenisation with the exclusion of one potential reference composite in each ensemble member; the use of ordinary kriging for weighting reference composites; the assessment of seasonal cycle of temperature biases in case of irregular-shaped seasonal cycles. ACMANT3 also allows for homogenisation on the daily scale including for break timing assessment, gap filling and analysis of ANOVA application on the daily time scale.
ACMANT3 is a complex software package incorporating six programmes, these are: temperature homogenisation with a sinusoid annual cycle of biases; temperature homogenisation with an irregular annual cycle of biases; precipitation homogenisation. Each of the preceding three has monthly and daily homogenisation versions (http://www.c3.urv.cat/data.html); and in total the six programmes incorporate 174 sub-routines. The software package also includes auxiliary files to support network construction. However, despite its complicated structure, ACMANT provides the fastest method implementation among all the available automatic homogenisation methods.
Considering the similarities of the theoretical background of HOMER and ACMANT, the choice between HOMER and ACMANT for particular homogenisation tasks should be based on the dataset characteristics. The use of ACMANT is particularly recommended for (1) datasets with little or no metadata; (2) datasets from dense networks with large numbers of time series and where there are high spatial correlations; (3) very large datasets (>∼200 time series) for which the use of automatic methods is the most feasible and easily managed solution.
Figure 1: Errors of raw data and residual errors of ACMANT homogenised data in a test dataset of simulated air surface temperatures. AC1, AC2, AC3 mean the first, second and third generation of ACMANT. Upper left: root mean squared error (RMSE) of monthly values, upper right: RMSE of annual values, bottom left: trend bias for individual series, bottom right: network mean trend bias. Smean means systematic trend bias.
The efficiency tests presented in this paper provide firm indications that ACMANT3 can considerably reduce initial regional trend biases at any spatial scale, although the efficiency achieved depends both on the spatial density and the extent of the intact record of the observational data. Further research is needed in this important and emerging area, for both the development and testing of statistical methods (Domonkos and Guijarro, 2015) and alongside an analysis of the causes of possible systematic biases in temperature records, with parallel measurements (http://www.surface
The authors also have another ongoing collaboration as part of the Irish Environmental Protection Agency funded “HOMERUN” project (e.g. Coll et al., 2015a,b) which aims to homogenise the large and dense Irish precipitation dataset with ACMANT and HOMER and explore more details about the practical application of these methods. More details are available from
The ACMANT3 software package together with its manual is freely accessible from http://www.c3.urv.cat/ data.html.
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