final_program_abstracts[1]

11 IMSC Session Program
Trend evaluation including level shifts and overlapping data
Tuesday - Poster Session 8
S. Mieruch, S. Noël, H. Bovensmann and J. P. Burrows
Institute of Environmental Physics (IUP), University of Bremen, Germany
The detection of trends is difficult and depends on the length of the time series, the
magnitude of variability and autocorrelation of the data. The trends can be influenced
by level shifts inside the time series from instrument changes or new instrumental
calibration etc. Short time series as well as high variability, autocorrelation and level
shifts in the data increase the uncertainty of trend detection. Weatherhead et al. (1998)
showed how to consider autocorrelations of the noise and level shifts in time series. In
this presentation we extend the methods from Weatherhead et al. (1998) in three
ways. First, an amplitude change is considered at the position of the level shift
(Mieruch et al., 2008) to reliably estimate the seasonal component of the time series.
Second, the introduction of level shifts for homogenisation is expanded by allowing
the data to overlap. Instead of removing level shifts from data, they are considered as
a source of uncertainty during the trend analysis and error estimation. Third, we apply
the method to a combined data set consisting of three segments of data, hence
demonstrating the potential of implementing multiple level shifts for overlapping
data.
The method is applied to monthly global satellite observations of water vapour total
column data from the Global Ozone Monitoring Experiment (GOME) (since 1995),
SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY
(SCIAMACHY) (2002–now) and GOME-2 (since 2006). Hence we have to
implement 3 level shifts to combine the data for the subsequent trend analysis.
Mieruch, S., Noël, S., Bovensmann, H., and Burrows, J. P.: Analysis of global water
vapour trends from satellite measurements in the visible spectral range, Atmos. Chem.
Phys., 8, 491–504, 2008.
Weatherhead, E. C., Reinsel, G. C., Tiao, G. C., Meng, X.-L., Choi, D., Cheang, W.-
K., Keller, T., DeLuisi, J., Wuebbles, D. J., Kerr, J. B., Miller, A. J., Oltmans, S. J.,
and Frederick, J. E.: Factors affecting the detection of trends: Statistical
considerations and applications to environmental data, Journal of Geophysical
Research, 103, 17,149–17,161, 1998.
Abstracts 110