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

检测每日气候记录中的不均匀性以研究极端天气的趋势(每日炖项目)

基本信息

  • 批准号:
    186717436
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 依托单位国家:
    德国
  • 项目类别:
    Research Grants
  • 财政年份:
    2010
  • 资助国家:
    德国
  • 起止时间:
    2009-12-31 至 2013-12-31
  • 项目状态:
    已结题

项目摘要

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.
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,表明极端降水的趋势可以在上个世纪找到。然而,目前尚不清楚当前气候记录的质量是否足以得出确定的结论。众所周知,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(重新安置、庇护所和仪器的变化等)。如果从元数据中知道这些不均匀性,则可以消除它们,但在大多数情况下,需要通过与邻近站点的比较来识别(统计相对均匀化)。目前,不均匀性通常是用气候变量的月至年平均值来分析的。我们将论证,许多不均匀性主要影响日常数据分布的尾部,因此可能无法在汇总数据中检测到。因此,对极端趋势最感兴趣的非均质性很少被发现。修正通常限于改变方法。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,例如在仪器中,经常通过整个网络传播,不均匀性不仅可能导致趋势估计中更大的不确定性,而且还可能导致偏差。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非均匀性检测算法,其目标是分布尾部的断裂。我们建议不要纠正数据,而是开发趋势测试,它可以意识到中断并明确地忽略它们。因此,我们避免了用不完善的方法校正非齐次数据。这将使我们能够更可靠地估计极端事件的趋势。有了这些工具,我们将研究德国的气候趋势。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
The uncertainty of break positions detected by homogenization algorithms in climate records
气候记录中均质化算法检测到的断裂位置的不确定性
The joint influence of break and noise variance on the break detection capability in time series homogenization
时间序列均质化中断裂和噪声方差对断裂检测能力的联合影响
  • DOI:
    10.5194/ascmo-4-1-2018
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Lindau;Victor Venema
  • 通讯作者:
    Victor Venema
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Dr. Victor Venema其他文献

Dr. Victor Venema的其他文献

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{{ truncateString('Dr. Victor Venema', 18)}}的其他基金

Daily HUME: Daily Homogenization, Uncertainty Measures and Extremes
每日休谟:每日同质化、不确定性测量和极值
  • 批准号:
    259061279
  • 财政年份:
    2014
  • 资助金额:
    --
  • 项目类别:
    Research Grants
Improved algorithms to generate 3-dimensional cloud fields for use in radiative transfer modelling
改进的算法生成用于辐射传输建模的 3 维云场
  • 批准号:
    34100140
  • 财政年份:
    2007
  • 资助金额:
    --
  • 项目类别:
    Research Grants

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