DETECTING TRENDS AND BIASES IN TIME-SERIES OF OZONESONDE DATA

In this paper, we discuss the complexities associated with the analysi s and interpretation of trends in time series of ozonesonde data, focu ssing on approaches to deal with the special constraints imposed by th e irregular nature of the ozonesonde data set. To improve upon earlier studies which have used monthly mean values and parametric techniques on ozonesonde data, a non-parametric statistical method is introduced to enable us to work with data from individual flights rather than wi th monthly mean values. To this end, ozone time series data are separa ted into their long-term, seasonal and short-term components to proper ly characterize the various scales of motion (climatic, annual and syn optic scale) embedded in the data set. We show that the statistical me thod used here meets the requirements for a reliable analysis of ozone sonde data. It is shown further that this approach enables us to estim ate trends in the ozonesonde data with a very high degree of confidenc e. We then introduce a non-parametric technique for discerning sudden changes in time series data and discuss its usefulness in detecting po tential biases in ozonesonde time series data, introduced by changes i n instrumentation, flight time, preflight preparation and data reducti on procedures. The results show that the method is able to detect disc ontinuities in the ozonesonde data which are supported by station hist ories. It is shown that the long-term trend estimates can be significa ntly affected by the presence of biases in the data. Although further research is necessary to adequately account for artificial breaks in t he data at all heights and stations, there is an indication that the e stimated upward trend in the raw tropospheric ozone data at Payerne, H ohenpeissenberg and Edmonton might be attributable, in part, to the pr esence of biases in the data. (C) 1998 Elsevier Science Ltd. All right s reserved.