Adjustment of Temperature Trends in Land stations After Homogenization (ATTILAH)

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Relocations of climate stations and changes in observation practices induce inhomogeneities into the observed temperature records. There are some indications that these spurious jumps tend to be downward on average so that they introduce a cooling trend into the data. Homogenization algorithms are commonly applied to remove these spurious trends. However, for principle reasons a full correction is impossible, analogous to the only partly explainable variance in regression techniques. Especially for low signal-to-noise ratios (SNRs), when the noise variance is large compared to the variance introduced by the inhomogeneities, the actually necessary trend correction is only rudimentary applied. Such low SNRs prevail in data sparse periods and regions of the world, where stations to compare with are far away. For the global average, these isolated stations get unfortunately a large weight because they represent large areas. Thus, we expect the global temperature trend even after applied homogenization to be heavily undercorrected. Using simulated data we will determine the performance of two commonly-used correction schemes. The correlation between attained and necessary correction will be determined for different realistic SNRs. However, also the detection part of homogenization algorithms may influence indirectly the performance of the correction. Therefore, the combined effect of detection and correction is assessed by testing eight prototype algorithms that represent the full variety of the commonly-used homogenization methods. Finally, this information will be used to estimate and correct temperature trend biases of an existing, widely-used, and already homogenized global dataset and to estimate the uncertainties in these unbiased trend estimates.