On the reduction of trend errors by the ANOVA joint correction scheme used in homogenization of climate station records. (23rd August 2018)

• Record Type: Journal Article
• Authors: Lindau, Ralf; Venema, Victor
• Is Part Of: International journal of climatology. Volume 38:Number 14(2018)
• Page Start: 5255
• Page End: 5271
• Publication Date: 2018-08-23
• Subjects: adjustment -- climate stations -- correction scheme -- homogenization -- temperature trends; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.5728
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract : Inhomogeneities in climate data are the main source of uncertainty for secular warming estimates. To reduce the influence of inhomogeneities in station data statistical homogenization compares a candidate station to its neighbours to detect and correct artificial changes in the candidate. Many studies have quantified the performance of statistical break detection tests used in this comparison. Also, full homogenization methods have been studied numerically, but correction methods by themselves have not been studied much. We analyse the ANOVA (analysis of variance) joint correction method, which is expected to be the most accurate published method. We find that, if all breaks are known, this method produces unbiased trend estimates and that in this case the uncertainty in the trend estimates is not determined by the variance of the inhomogeneities, but by the variance of the weather and measurement noise. For low signal‐to‐noise ratios and high numbers of breaks, the correction may also worsen the data by increasing the original random unbiased trend error. Any uncertainty in the break dates leads to a systematic undercorrection of the trend errors and in this more realistic case the variance of the inhomogeneities is also important. Abstract : Inhomogeneities in climate station data may produce spurious temperature trends. After the detection of the breaks the data have to be adjusted by a correction scheme. We analyse simulated data with known properties to assess their skill. The scatterplot compares the trend error before and after the adjustment. The original trend error is largely reduced, but not completely removed.