Bayesian analysis of climate change impacts in phenology
Bayesian analysis of climate change impacts in phenology
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DOI：
10.1111/j.1529-8817.2003.00731.x
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发表时间：
2004-02-01
影响因子：
11.6
通讯作者：
Menzel, A
中科院分区：
文献类型：
--
作者：
Dose, V;Menzel, A
查明与气候变化假设有关的观测数据的变化仍然是一个极为重要的议题。特别是，迫切需要科学上合理和严格的方法来检测变化。在本文中，我们开发了一个贝叶斯方法来非参数函数估计。将该方法应用于欧洲甜樱桃（Prunus avium L.）的开花时间序列，雪花莲和椴树（Tilia platyphyllos SCOP）。这些系列的功能行为是由三个不同的模型：常数模型，线性模型和一个变点模型。在所有三个数据集中，一个变点模型是首选的，与其他替代品有相当大的区别。除了功能行为外，还计算了每年天数的变化率。我们还获得了函数估计和变化率的不确定性裕度。我们的研究结果提供了一个定量的表示，以前推断出相同的数据，较少涉及的方法。
The identification of changes in observational data relating to the climate change hypothesis remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for detecting changes are urgently needed. In this paper, we develop a Bayesian approach to nonparametric function estimation. The method is applied to blossom time series of Prunus avium L., Galanthus nivalis L. and Tilia platyphyllos SCOP. The functional behavior of these series is represented by three different models: the constant model, the linear model and the one change point model. The one change point model turns out to be the preferred one in all three data sets with considerable discrimination of the other alternatives. In addition to the functional behavior, rates of change in terms of days per year were also calculated. We obtain also uncertainty margins for both function estimates and rates of change. Our results provide a quantitative representation of what was previously inferred from the same data by less involved methods.