Random trend errors in climate station data due to inhomogeneities
Random trend errors in climate station data due to inhomogeneities
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DOI：
10.1002/joc.6340
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发表时间：
2019-10-21
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影响因子：
--
通讯作者：
Venema, Victor
中科院分区：
文献类型：
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作者：
Lindau, Ralf;Venema, Victor
台站序列的不均匀性是长期温度趋势估计的不确定性预算的很大一部分。本文介绍了两个分析方程，用于说明台站趋势不确定性对不均匀性统计特性的依赖性。一个方程适用于作为相对于固定基线的随机偏差的不均匀性，其中偏差水平是随机且独立的。第二个方程用于非均匀性，其行为类似于布朗运动 (BM)，其中水平本身不是独立的，而是水平之间的跳跃是独立的。它表明 BM 型突破引入了更大的趋势误差，并随着突破次数线性增长。使用有关美国和德国中断类型、强度和频率的信息，计算这两个国家 1901-2000 年期间的随机趋势误差。通过经验方法获得替代且独立的估计，利用相邻站对的趋势变异性的距离依赖性。两种方法（经验方法和分析方法）都发现美国的台站趋势不确定性（分别为每世纪 0.71 和 0.82 摄氏度）大于德国（分别为每世纪 0.50 和 0.58 摄氏度）。分析估计和实证估计的良好一致性使人们对评估趋势不确定性的方法以及确定断裂不均匀性的统计特性的方法充满信心。
Inhomogeneities in station series are a large part of the uncertainty budget of long-term temperature trend estimates. This article introduces two analytical equations for the dependence of the station trend uncertainty on the statistical properties of the inhomogeneities. One equation is for inhomogeneities that act as random deviations from a fixed baseline, where the deviation levels are random and independent. The second equation is for inhomogeneities, which behave like Brownian Motion (BM), where not the levels themselves but the jumps between them are independent. It shows that BM-type breaks introduce much larger trend errors, growing linearly with the number of breaks. Using the information about type, strength, and frequency of the breaks for the United States and Germany, the random trend errors for these two countries are calculated for the period 1901-2000. An alternative and independent estimate is obtained by an empirical approach, exploiting the distance dependence of the trend variability for neighbouring station pairs. Both methods (empirical and analytical) find that the station trend uncertainty is larger in the United States (0.71 and 0.82 degrees C per century, respectively) than in Germany (0.50 and 0.58 degrees C per century, respectively). The good agreement of the analytical and the empirical estimate gives confidence in the methods to assess trend uncertainties, as well as in the method to determine the statistical properties of the break inhomogeneities.