Random trend errors in climate station data due to inhomogeneities

Abstract

Inhomogeneities in station series are a large part of the uncertainty budget of long‐term temperature trend estimates. This article introduces two analytical equations for the dependence of the station trend uncertainty on the statistical properties of the inhomogeneities. One equation is for inhomogeneities that act as random deviations from a fixed baseline, where the deviation levels are random and independent. The second equation is for inhomogeneities, which behave like Brownian Motion (BM), where not the levels themselves but the jumps between them are independent. It shows that BM‐type breaks introduce much larger trend errors, growing linearly with the number of breaks. Using the information about type, strength, and frequency of the breaks for the United States and Germany, the random trend errors for these two countries are calculated for the period 1901–2000. An alternative and independent estimate is obtained by an empirical approach, exploiting the distance dependence of the trend variability for neighbouring station pairs. Both methods (empirical and analytical) find that the station trend uncertainty is larger in the United States (0.71 and 0.82°C per century, respectively) than in Germany (0.50 and 0.58°C per century, respectively). The good agreement of the analytical and the empirical estimate gives confidence in the methods to assess trend uncertainties, as well as in the method to determine the statistical properties of the break inhomogeneities.

Efficiency of Time Series Homogenization: Method Comparison with 12 Monthly Temperature Test Datasets

“…One conclusion of HOME was that multiple-break methods such as ACMANT and MASH generally perform better than hierarchic methods (Venema et al 2012), and the theoretical advantages of multiple-break techniques are widely discussed (Lindau and Venema 2013;Szentimrey et al 2014;Domonkos 2017). However, this study does not confirm the superiority of multiple-break techniques, at least not in all aspects.…”

“…ACMANT was developed from ''PRODIGE'' (Caussinus and Mestre 2004), keeping its principal detection and correction routines but adding new features. In ACMANT, the candidate series are compared with composite reference series (Peterson and Easterling 1994;Domonkos 2011b); step function fitting with the Caussinus-Lyazrhi criterion is applied for break detection (Caussinus and Lyazrhi 1997), and the ANOVA model is used for the correction of IHs (Caussinus and Mestre 2004;Mamara et al 2014;Lindau and Venema 2018). See further details of ACMANTv3 (AC3) in Domonkos and Coll (2017b).…”

Characterizing winter season severity in the Midwest United States, Part I: Climatology and recent trends

“…Discontinuities or inhomogeneities have been documented in the time series of ASOS and COOP station weather observations (Peterson et al ., 1998). These discontinuities are often associated with a station move, change in instrumentation, or change in observation techniques (Brown and DeGaetano, 2009) and can influence the apparent temporal trend in the time series (Allen and DeGaetano, 2000; Lindau and Venema, 2019). To ensure robust trend assessment, we first conducted a breakpoint analysis of winter total AWSSI, winter season average TMAX, TMIN, and SD, and winter season total SF at each station.…”

Reliability Evaluation of the Data Acquisition Potential of a Low-Cost Climatic Network for Applications in Agriculture

“…However, the data produced by these sensors are often contaminated by events other than variability, such as errors in taking or transmitting them, as well as changes in the instrument used, in the location of the node or in its environment. These alterations in the series of observations, called inhomogeneities, mask the real changes in the climate and cause the study of the series to lead to erroneous conclusions [ 24 ]. For years homogenisation methodologies have made it possible to eliminate or reduce these unwanted alterations as much as possible.…”