NDLI: Methodological uncertainties in multi-regression analyses of middle-atmospheric data series

Content Provider	Royal Society of Chemistry (RSC)
Author	Kerzenmacher, Tobias E. Hauchecorne, Alain Chanin, Marie-Lise Keckhut, Philippe
Copyright Year	2006
Abstract	Multi-regression analyses have often been used recently to detect trends, in particular in ozone or temperature data sets in the stratosphere. The confidence in detecting trends depends on a number of factors which generate uncertainties. Part of these uncertainties comes from the random variability and these are what is usually considered. They can be statistically estimated from residual deviations between the data and the fitting model. However, interferences between different sources of variability affecting the data set, such as the Quasi-Biennal Oscillation (QBO), volcanic aerosols, solar flux variability and the trend can also be a critical source of errors. This type of error has hitherto not been well quantified. In this work an artificial data series has been generated to carry out such estimates. The sources of errors considered here are: the length of the data series, the dependence on the choice of parameters used in the fitting model and the time evolution of the trend in the data series. Curves provided here, will permit future studies to test the magnitude of the methodological bias expected for a given case, as shown in several real examples. It is found that, if the data series is shorter than a decade, the uncertainties are very large, whatever factors are chosen to identify the source of the variability. However the errors can be limited when dealing with natural variability, if a sufficient number of periods (for periodic forcings) are covered by the analysed dataset. However when analysing the trend, the response to volcanic eruption induces a bias, whatever the length of the data series. The signal to noise ratio is a key factor: doubling the noise increases the period for which data is required in order to obtain an error smaller than 10%, from 1 to 3–4 decades. Moreover, if non-linear trends are superimposed on the data, and if the length of the series is longer than five years, a non-linear function has to be used to estimate trends. When applied to real data series, and when a breakpoint in the series occurs, the study reveals that data extending over 5 years are needed to detect a significant change in the slope of the ozone trends at mid-latitudes.
Starting Page	682
Ending Page	690
Page Count	9
File Format	HTM / HTML PDF
ISSN	14640325
Volume Number	8
Issue Number	7
Journal	Journal of Environmental Monitoring
DOI	10.1039/b603750j
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Subscribed
Subject Keyword	Ozone Stratosphere Dependent and independent variables
Content Type	Text
Resource Type	Article
Subject	Public Health, Environmental and Occupational Health Management, Monitoring, Policy and Law

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