A new method to study inhomogeneities in climate records: Brownian motion or random deviations?. (13th May 2019)

• Record Type: Journal Article
• Authors: Lindau, Ralf; Venema, Victor
• Is Part Of: International journal of climatology. Volume 39:Number 12(2019)
• Page Start: 4769
• Page End: 4783
• Publication Date: 2019-05-13
• Subjects: Brownian motion -- climate stations -- homogenization -- number of breaks -- random deviations -- signal separation -- spurious trends -- variance of lag differences; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.6105
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: Climate data are affected by inhomogeneities due to historical changes in the way the measurements were performed. Understanding these inhomogeneities is important for accurate estimates of long‐term changes in the climate. These inhomogeneities are typically characterized by the number of breaks and the size of the jumps or the variance of the break signal, but a full characterization of the break signal also includes its temporal behaviour. This study develops a method to distinguish between two types of breaks: random deviations from a baseline and Brownian motion. Strength and frequency of both break types are estimated by using the variance of the spatiotemporal differences in the time series of two nearby stations as input. Thus, the result is directly obtained from the data without running a homogenization algorithm to estimate the break signal from the data. This opens the possibility to determine the total number of breaks and not only that of the significantly large ones. The application to German temperature observations suggests generally small inhomogeneities dominated by random deviations from a baseline. U.S. stations, on the other hand, also show the characteristics of a strong Brownian‐motion‐type component. Abstract : Climate data may contain inhomogeneities of two different types: free floating Brownian motion and random deviations from a fixed baseline. The variance of difference time series between two climate stations is analysed for different time lags. In this way, the two signals can be separated and quantified. Random‐deviation‐type inhomogeneities produce a saturating exponential function; Brownian motion breaks cause a linear increase, whereas the noise affects a lag independent offset. U.S. climate stations include a strong Brownian motion component.