Abstract
Abstract. This paper describes the statistical analysis of annual trends in long term datasets of greenhouse gas measurements taken over ten or more years. The analysis technique employs a bootstrap resampling method to determine both the long-term and intra-annual variability of the datasets, together with the uncertainties on the trend values. The method has been applied to data from a European network of ground-based solar FTIR instruments to determine the trends in the tropospheric, stratospheric and total columns of ozone, nitrous oxide, carbon monoxide, methane, ethane and HCFC-22. The suitability of the method has been demonstrated through statistical validation of the technique, and comparison with ground-based in-situ measurements and 3-D atmospheric models.
Highlights
Global climate change is one of the most important environmental issues facing the world today
This paper describes the development and implementation of a trend analysis method to determine the annual trend and associated uncertainties, based on a statistical model that makes minimal assumptions about uncertainty distributions associated with the raw data
The final panel shows that the method is appropriate in the cases where there is little intra-annual variability as in this example of tropospheric nitrous oxide measured at the Jungfraujoch
Summary
Global climate change is one of the most important environmental issues facing the world today. A key element of this issue is understanding the atmospheric behaviour of radiatively active gases (direct greenhouse gases), and gases involved in the chemical production of greenhouse gases (indirect greenhouse gases). Long-term measurements of such gases provide the experimental data to study the evolution of these gases and the changing sources and sinks. These data are often expressed in terms of an annual trend in the amount of a particular gas. In order for these trend results to be used appropriately it is vital that the uncertainty associated with the trend value is properly quantified. An accurate determination of the trend value is challenging due to influence of large seasonal variations and other effects reflected in the data (Oltmans et al, 1998)
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