Investigating bias in the application of curve fitting programs to atmospheric time series
Investigating bias in the application of curve fitting programs to atmospheric time series
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DOI：
10.5194/amt-8-1469-2015
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发表时间：
2015-01-01
影响因子：
3.8
通讯作者：
Manning, A. C.
中科院分区：
文献类型：
--
作者：
Pickers, P. A.;Manning, A. C.
将大气层时间序列分解成其组成部分是从数据集中查明和分离出有关变化的一个基本工具，广泛用于获取关于对气候重要的气体的源、汇和趋势的信息。这些程序涉及将适当的数学函数拟合到数据。然而，已经证明，这种曲线拟合程序的应用可能会引入偏差，从而影响对数据集的科学解释。我们调查的潜在偏差与应用程序的三个曲线拟合程序，被称为HP样条，CCGCRV和STL，使用多年的记录CO2，CH 4和O-3的数据从三个大气监测站。这三种曲线拟合程序在温室气体测量界被广泛用于分析大气时间序列，但以前没有进行过广泛的比较，这些程序经过严格的测试，以准确地表示大气时间序列的显着特征，他们的能力，以科普异常值和数据中的差距，并为每个程序所需的输入参数的值的敏感性。我们发现这些程序可以产生显着不同的曲线拟合，并且这些曲线拟合可能取决于所选择的输入参数。有显着的差异所产生的结果由三个程序的时间序列的许多分解组件，如季节性周期特征和长期（多年）的增长率的代表性。这些方案在对时间序列中的差距和离群值的反应方面也有很大差异。总的来说，我们发现这三个方案都没有上级，每个方案都有其长处和短处。因此，我们提供了一个关于适当使用这三种曲线拟合程序的建议列表，用于某些类型的数据集，以及某些类型的分析和应用。此外，我们建议在任何研究中使用曲线拟合程序进行敏感性测试，以确保结果不会受到输入平滑参数选择的不当影响，我们的研究结果也有以前的研究依赖于一个单一的曲线拟合程序来解释大气时间序列测量的影响。通过使用另外两个曲线拟合程序来复制Piao等人（2008年）关于大气CO2季节循环的零交叉分析以调查陆地生物圈变化的工作，可以证明这一点。我们强调使用多个程序的重要性，以确保结果一致，可重复，无偏倚。
The decomposition of an atmospheric time series into its constituent parts is an essential tool for identifying and isolating variations of interest from a data set, and is widely used to obtain information about sources, sinks and trends in climatically important gases. Such procedures involve fitting appropriate mathematical functions to the data. However, it has been demonstrated that the application of such curve fitting procedures can introduce bias, and thus influence the scientific interpretation of the data sets. We investigate the potential for bias associated with the application of three curve fitting programs, known as HPspline, CCGCRV and STL, using multi-year records of CO2, CH4 and O-3 data from three atmospheric monitoring field stations. These three curve fitting programs are widely used within the greenhouse gas measurement community to analyse atmospheric time series, but have not previously been compared extensively.The programs were rigorously tested for their ability to accurately represent the salient features of atmospheric time series, their ability to cope with outliers and gaps in the data, and for sensitivity to the values used for the input parameters needed for each program. We find that the programs can produce significantly different curve fits, and these curve fits can be dependent on the input parameters selected. There are notable differences between the results produced by the three programs for many of the decomposed components of the time series, such as the representation of seasonal cycle characteristics and the long-term (multi-year) growth rate. The programs also vary significantly in their response to gaps and outliers in the time series. Overall, we found that none of the three programs were superior, and that each program had its strengths and weaknesses. Thus, we provide a list of recommendations on the appropriate use of these three curve fitting programs for certain types of data sets, and for certain types of analyses and applications. In addition, we recommend that sensitivity tests are performed in any study using curve fitting programs, to ensure that results are not unduly influenced by the input smoothing parameters chosen.Our findings also have implications for previous studies that have relied on a single curve fitting program to interpret atmospheric time series measurements. This is demonstrated by using two other curve fitting programs to replicate work in Piao et al. (2008) on zero-crossing analyses of atmospheric CO2 seasonal cycles to investigate terrestrial biosphere changes. We highlight the importance of using more than one program, to ensure results are consistent, reproducible, and free from bias.