A new method to study inhomogeneities in climate records: Brownian Motion or Random Deviations?

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1002/joc.6105. This is version 2 of this Preprint.


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Ralf Lindau, Victor Venema


Climate data is 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 behavior. 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. US stations, on the other hand, show also the characteristics of a strong Brownian motion type component.




Climate, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Planetary Sciences


Brownian motion, climate stations, homogenization, number of breaks, random deviations, signal separation, spurious trends, variance of lag differences


Published: 2019-05-08 15:43

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