Random trend errors in climate station data due to inhomogeneities. (21st October 2019)

• Record Type: Journal Article
• Authors: Lindau, Ralf; Venema, Victor
• Is Part Of: International journal of climatology. Volume 40:Number 4(2020)
• Page Start: 2393
• Page End: 2402
• Publication Date: 2019-10-21
• Subjects: break types -- Brownian motion -- climate stations -- homogenization -- inhomogeneities -- random deviations -- trend errors; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.6340
• Languages: English
• ISSNs: 0899-8418

Abstract

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. Abstract : To quantify the effect of inhomogeneities on station trends two independent methods are applied. An empirical approach studies how the variance of the trend differences between two stations depends on the distance between them (fat curves). The double error variance for Germany (G) and United States of America (U) can be read at zero distance. For the second method, we derive analytical equations for the trend uncertainty based on the statistics of the inhomogeneities. The results of both methods largely match.