Geoscientific Instrumentation Methods and Data Systems Analyzing time varying trends in stratospheric ozone time series using state space approach

Abstract

Published by Copernicus Publications on behalf of the European Geosciences Union. 20503 Abstract We describe a hierarchical statistical state space model for ozone profile time series. The time series are from satellite measurements by the SAGE II and GOMOS instru-ments spanning years 1984–2011. The original data sets are combined and gridded monthly using 10 • latitude bands, and covering 25–55 km with 1 km vertical spacing. 5 In the analysis, mean densities are studied separately for 25–35 km, 35–45 km, and 45–55 km layers, also. Model components include level, trend and seasonal effect with solar activity and Quasi-Biennial Oscillations as proxy variables. We will show how the chosen statistical model is well suited for trend analysis of atmospheric time series that are not stationary but can exhibit both slowly varying and 10 abrupt changes in the distributional properties. The dynamic linear model state space approach provides well defined statistical model for assessing the long term back-ground changes in the ozone time series. The modelling assumptions can be eval-uated and the method provides realistic uncertainty estimates for the model based statements on the quantities of interest. We discuss the methodological challenges 15 and practical implementation. The modelling result agree with the hypothesized trend change point for stratospheric ozone at around the year 1997 for mid latitude regions. This is a companion article to Kyrölä et al. (2013).