Detection of inhomogeneities in daily climate records to study trends in extreme weather (daily stew project)

全球变化不仅可能影响长期平均气温和降水率，还可能导致极端天气的变化，即热浪和寒潮、干旱、洪水和风暴可能变得更加常见。测量记录提供了一些证据，表明可以发现上世纪极端降水的趋势。然而，目前尚不清楚气候记录的质量是否足以得出确切的结论。已知长期气候记录包含不均匀性，即不代表气候变化的变化，而是测量条件的变化（迁移、遮蔽物和仪器的变化等）。如果从Meta数据中已知这些不均匀性，则可以将其去除，但在大多数情况下，需要通过与相邻站点的比较来识别（统计相对均匀化）。目前，不均匀性通常是以气候变量的月至年平均值进行分析的。我们认为，许多不均匀性主要影响每日数据分布的尾部，因此可能无法在聚合数据中检测到。因此，对极端趋势最感兴趣的不均匀性检测得很差。纠正通常限于改变手段。大部分温度分布的校正算法是可用的，但需要高度相关的相邻站。由于变化，例如仪器的变化，往往是通过整个网络传播的，不均匀性不仅可能导致趋势估计的更大不确定性，而且可能导致偏差。为了提供更可靠的极端趋势估计，我们将开发一种新的温度和降水不均匀性检测算法，其目标是分布尾部的断裂。我们建议不纠正数据，而是开发趋势测试，知道的休息，明确忽略它们。因此，我们规避纠正不均匀的数据与不完美的方法。这将使我们能够更可靠地估计极端情况下的趋势。通过这些工具，我们将研究德国的气候趋势。

Global change may not only affect the long-term mean temperatures and precipitation rates, but may also lead to changes in weather extremes, i.e. heat and cold waves, droughts, floods and storms may become more common. The measurement record gives some evidence that trends in extreme precipitation can be found for the last century. However, it is not clear whether the current quality of the climate record is sufficient to draw firm conclusions. Long climate records are known to contain inhomogeneities, i.e. changes that do not represent climatic changes, but changes in the measurement conditions (relocations, changes in shelters and instruments, etc.). These inhomogeneities can be removed if they are known from meta data, but in most cases need to be identified by a comparison with neighbouring stations (statistical relative homogenisation). Currently inhomogeneities are typically analysed in monthly to yearly means of climatic variables. We will argue that many inhomogeneities mostly affect the tails of the distribution of the daily data and may thus not be detectable in aggregated data. Consequently, the inhomogeneities that are of most interest for trends in extremes are poorly detected. Corrections are typically limited to changing the means. Correction algorithms for the bulk of the temperature distribution are available, but require highly correlated neighbouring stations. Since changes, e.g. in instrumentation, are often spread through the entire network inhomogeneities may not only lead to larger uncertainties in trend estimates, but may also lead to biases. In order to provide more reliable estimates of trends in extremes, we will develop a new inhomogeneity detection algorithm for temperature and precipitation, which targets breaks in the tails of the distribution. We propose not to correct the data, but rather to develop trend tests that are aware of the breaks and explicitly ignore them. Thus, we circumvent correcting inhomogeneous data with imperfect methods. This will allow us to estimate trends in extremes much more reliably. With these tools, we will study climate trends for Germany.