Verfälschen Inhomogenitäten den globalen beobachteten Temperaturtrend?
Zusammenfassung der Projektergebnisse
Meteorological observations from climate stations suffer from inhomogeneities caused by relocations or changes in the measuring technique. They are known to introduce sudden jumps into the time series, which have the potential to falsify the global temperature trend observed during the past century. This global temperature trend bias was the main topic of the project. Several homogenization algorithms exist to detect and correct such inhomogeneities. They use difference time series of neighboring stations in order to largely eliminate the regional climate signal. Only some noise remains caused by the anyhow existing small climatic differences between the stations so that the actually interesting inhomogeneity signal is better detectable. The signal-to-noise ratio (SNR) is a key parameter to assess the theoretical ability to detect the breakpoints. SNRs below 1 carry the risk of producing nonsensical results because segmentations according to the noise are preferred, which are seemingly significant since much of the variance is explained. The first paper published during the project term determines the SNR for different continents using the autocovariance function. As the noise has no impact here, the magnitude of the pure break variance is readable at zero time lag. However, noise does affect the variance so that the difference between variance and covariance provides a measure for the noise amount. As the distance between the stations increases, the climatic differences and thus the noise increases, causing the SNR to shrink. The maximum permissible distance is reached where the SNR falls below 1. In North America this distance is found to be about 500 km, in Europe only half of that value. Homogenization algorithms consist of two parts, detection and correction. A commonly used correction scheme adjusts the inhomogeneities in a stepwise manner, break after break. In a second paper, we examined the performance of this standard correction scheme. While the trend bias itself is corrected satisfactorily, a large uncertainty is introduced, because errors are accumulated due to the stepwise procedure. A further reason for the large scatter is the mutual use of the stations, which produces largely dependent solutions so that averaging helps less than expected to reduce the noise. A slight modification of the algorithm is proposed, which reduces the error variance almost to the value of a seldom used optimal but complex correction scheme. In the third paper, a method is presented that is able to derive the temperature trend bias directly from the data without running a homogenization algorithm. The usual proceeding is to detect first the breaks by a homogenization algorithm, which are then statistically evaluated to extract the temperature trend bias. The disadvantage of this procedure is that it depends on the capability of the homogenization algorithm used. In our method the Composite Reference (CR) method is applied, where the mean of all neighbors is subtracted from the target station. In this way, not only the common climate signal is eliminated, but also the common trend bias. However, we do not use the CR data directly, but consecutive differences of it. Every break occurring in the target station arises also in the reference of all other stations, where it is attenuated by averaging and with opposite sign, as the reference is subtracted. In this way, every inhomogeneity produces one large jump and many small ones with reversed sign so that the median is shifted. This effect is exploited in the proposed method.
Projektbezogene Publikationen (Auswahl)
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Estimation of Break and Noise Variance and the Maximum Distance of Climate Stations Allowed in Relative Homogenisation of Annual Temperature Anomalies. International Journal of Climatology, 45(3).
Lindau, Ralf
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Calculating the Temperature Trend Bias Induced by Inhomogeneities Into Climate Data Without Running a Homogenization Algorithm. International Journal of Climatology, 45(9).
Lindau, Ralf
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Evaluation of the Stepwise Correction Module Used in the Pairwise Homogenisation Algorithm. International Journal of Climatology, 45(9).
Lindau, Ralf
