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| Titel |
Investigating bias in the application of curve fitting programs to atmospheric time series |
| VerfasserIn |
P. A. Pickers, A. C. Manning |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 3 ; Nr. 8, no. 3 (2015-03-23), S.1469-1489 |
| Datensatznummer |
250116228
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| Publikation (Nr.) |
 copernicus.org/amt-8-1469-2015.pdf |
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| Zusammenfassung |
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 O3 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. |
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