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

均质化后陆地站温度趋势的调整（ATTILAH）

基本信息

批准号：
298651550
负责人：
Dr. Ralf Lindau
金额：
--
依托单位：
Abteilung Meteorologie
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2019-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/298651550?language=en
关键词：
Adjustment Temperature Trends Land stations

项目摘要

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.

气候站的搬迁和观测方法的改变会导致观测到的温度记录的不均匀性。有一些迹象表明，这些虚假的跳跃往往是向下的平均水平，使他们引入一个冷却趋势的数据。均匀化算法通常用于去除这些虚假趋势。然而，由于原则上的原因，完全校正是不可能的，类似于回归技术中只能部分解释的方差。特别是对于低信噪比（SNR），当噪声方差比由不均匀性引入的方差大时，实际上必要的趋势校正仅被初步应用。在世界上数据稀少的时期和地区，这种低信噪比普遍存在，因为与之比较的台站很远。对于全球平均值，这些孤立的站点不幸地得到了很大的权重，因为它们代表了很大的区域。因此，我们预计全球温度趋势，即使在应用均匀化严重不足。使用模拟数据，我们将确定两个常用的校正方案的性能。将针对不同的实际SNR确定所获得的校正与所需校正之间的相关性。然而，均匀化算法的检测部分也可能间接影响校正的性能。因此，检测和校正的综合效果进行评估，通过测试八个原型算法，代表了各种常用的均匀化方法。最后，这些信息将用于估计和校正现有的，广泛使用的，已经同质化的全球数据集的温度趋势偏差，并估计这些无偏趋势估计的不确定性。