Abstract Homogeneity is an important characteristic of time series that must be checked before doing any analysis. Breakpoints in meteorological time series are very common due to climatic jumps caused by natural forcing and/or human activity but also, and mainly, produced by inhomogeneities which are erratic in nature. In this work, five breakpoint tests are analyzed to evaluate their performance in detecting breakpoints for different lengths of time series and intensity of breakpoint, among other features, through the realization of numerical experiments of sensitivity. These tests are: Student's, Mann-Whitney, Buishand-R, Pettit and SNHT. The Student's and Mann-Whitney tests show high probability of false breakpoint detection and problems to reproduce the date when a breakpoint occurs. In addition, the Buishand-R and Pettit are more efficient to reproduce the date of breakpoint when it occurs in the middle of a time series while the SNHT does it for breakpoints in its borders. In this sense, the Pettit, Buishand-R and SNHT tests show better performance than the Mann-Whitney and Student's. Furthermore, an original methodology to detect multi-breakpoints based on the aforementioned tests is applied to precipitation time series from sixty-two rain-gauge stations in subtropical Argentina. A breakpoint around 1976 is detected in the wet season (austral warm season), highly likely linked to the well-documented 1976/77 climate transition, by all the used tests and for most of the stations. To a lesser extent, another breakpoint occurred in the mid-1950's. Other breakpoints are also detected in the early 1980s and the early 2000s, though in few stations and by one or two tests. For the breakpoints found in the mid-1950's and in the early 1980's, a strong relationship with two ENSO's indices is found which suggests that changes in long-term ENSO variability could be the cause of these breakpoints.

中文翻译：

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一种检测气候时间序列变化的多断点方法。阿根廷亚热带雨季降水的应用

摘要 同质性是时间序列的一个重要特征，在进行任何分析之前必须对其进行检查。由于自然强迫和/或人类活动引起的气候跳跃，气象时间序列中的断点非常常见，但也主要是由性质不稳定的不均匀性产生的。在这项工作中，通过实现灵敏度的数值实验，分析了五个断点测试，以评估它们在检测不同时间序列长度和断点强度等特征的断点方面的性能。这些测试是：Student's、Mann-Whitney、Buishand-R、Pettit 和 SNHT。Student 和 Mann-Whitney 检验显示错误断点检测的可能性很高，并且在断点发生时重现日期存在问题。此外，当断点发生在时间序列的中间时，Buihand-R 和 Pettit 可以更有效地重现断点的日期，而 SNHT 则是在其边界的断点处进行重现。从这个意义上说，Pettit、Buishand-R 和 SNHT 测试显示出比 Mann-Whitney 和 Student 更好的性能。此外，基于上述测试的原始多断点检测方法应用于阿根廷亚热带地区 62 个雨量站的降水时间序列。在雨季（南方暖季）检测到 1976 年左右的断点，通过所有使用的测试和大多数台站，很可能与有据可查的 1976/77 年气候转变有关。在较小程度上，另一个断点发生在 1950 年代中期。其他断点也在 1980 年代初和 2000 年代初被检测到，尽管在几个站点和通过一两次测试。对于在 1950 年代中期和 1980 年代初期发现的断点，发现与两个 ENSO 指数的强相关性，这表明 ENSO 长期变异性的变化可能是这些断点的原因。