Do Inhomogeneities Shift the Global Observed Temperature Trend? (DISGOT Trend)

不均匀性是否会改变全球观测到的温度趋势？

• 批准号: 441689576
• 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
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/441689576?language=en
• 关键词: Do; Inhomogeneities; Shift; Global; Observed

Temperature trends in climate data are affected by inhomogeneities, which are typically caused by relocation of the stations or changes in observation methods. It is undisputed that these breaks have the potential to severely falsify the individual trend of a climate station. However, only if the introduced jumps are biased, i.e. being non-zero on average, they induce a mean trend bias into the data and not only some scatter that largely cancels out by averaging. Consequently, such biased inhomogeneities are most important, but they are also hardest to detect with relative homogenization methods. Thus, the main goals of the proposed project are to verify or reject the existence of a global mean trend bias due to inhomogeneities and, if applicable, to estimate its magnitude and uncertainty.In the previous project, we tested prototypes of commonly used homogenization algorithms that consist of a combination of those components, which we expect from the literature to be crucial. These are the applied detection and correction modules or the method to build difference time series. The generally good performance of the modern homogenization algorithms, found by earlier Benchmark studies, could be confirmed. However, in the past the focus was often on break detection scores and the improvement of station series, not on the network-mean trend bias. Our preliminary work concentrated just on this specific issue and found mixed results. One of the two principal algorithm types seems to have general problems with the mean trend bias, the other works satisfactory only if dense station networks are available. Thus, there are strong indications that the mean trend bias, at least on global scale, is difficult to detect by current methods, because the station density might be too low in large areas worldwide. However, a full coverage is indispensable for the determination of the global climate trend.The proposed project is structured in four major Work Packages. In WP 1, we will analyze the preliminary indications that the mean trend bias is hardly detectable by current methods and identify the responsible shortcomings of each prototype. WP 2 derives the signal-to-noise ratio (SNR) for different regions of the world, which is the key parameter for the reasonable functioning of any homogenization algorithm. WP 3 tests the real algorithms whether they have similar problems as their prototype counterparts. WP 4 proposes five alternative approaches to determine the mean trend bias and tests their ability to overcome the problems.

气候数据中的温度趋势受到不均匀性的影响，这种不均匀性通常是由台站迁移或观测方法改变造成的。毫无疑问，这些突变有可能严重歪曲气候站的个别趋势。然而，只有当引入的跳跃是有偏差的，即平均值为非零时，它们才会在数据中引入平均趋势偏差，而不仅仅是一些分散，这些分散在很大程度上被平均值抵消。因此，这种有偏的不均匀性是最重要的，但它们也是最难用相对均匀化方法检测的。因此，拟议项目的主要目标是验证或拒绝存在的全球平均趋势偏差，由于不均匀性，并在适用的情况下，估计其幅度和不确定性。在以前的项目中，我们测试了常用的均匀化算法的原型，包括这些组件的组合，我们期望从文献中是至关重要的。这些是应用的检测和校正模块或建立差分时间序列的方法。早期Benchmark研究发现的现代均匀化算法的总体良好性能可以得到证实。然而，在过去的重点往往是突破检测分数和改善站系列，而不是在网络平均趋势偏差。我们的初步工作只集中在这一具体问题上，结果好坏参半。两个主要的算法类型之一似乎有一般的平均趋势偏差的问题，另一个工程令人满意，只有当密集的站网络。因此，有明显的迹象表明，平均趋势偏差，至少在全球范围内，很难用目前的方法检测，因为在世界各地的大面积地区，台站密度可能太低。然而，要确定全球气候趋势，全面覆盖是必不可少的。建议的项目分为四个主要工作包。在WP 1中，我们将分析当前方法难以检测到平均趋势偏差的初步迹象，并确定每个原型的责任缺陷。WP 2推导出世界不同区域的信噪比（SNR），这是任何均匀化算法合理运行的关键参数。WP 3测试了真实的算法是否存在与原型算法类似的问题。WP 4提出了五种确定均值趋势偏差的替代方法，并测试了它们克服这些问题的能力。