A preliminary look at alternative analyses of trends in EMEP measured concentrations

1. A preliminary look at alternative analyses of trends in EMEP measured concentrations Ron Smith Centre for Ecology and Hydrology, Edinburgh Marian Scott (University of Glasgow) Marco Giannitrapani (University of Palermo)

2. Introduction : reasons for our interest in trend analysis • trends in UK did not follow ‘expected’ pattern of decrease in concentration with decreased emission • in parameterising deposition processes, differences in pattern of changing pollution climate over Europe may help with understanding model parameterisation • recent statistical publications have suggested new approaches to detecting trends and spatial patterns in trends – primarily following the interest in climate change • (Richard) Smith & Holland, Kyriakidis & Journel, Civerolo & Rao, Host

3. Introduction : reasons for trend analysis (1) What is a trend? Long-term variation in the statistical properties of a process, where ‘long-term’ depends on the application (Richard Chandler, UCL) usually refers to the mean, but can equally apply to the variance or the extremes interest is often triggered by an observation that ‘something has happened’ – e.g. ‘we have just had the three wettest summers in living memory, so is there a trend?’

4. Introduction : reasons for trend analysis (2) • Why analyse for trend? (Richard Chandler, UCL) • to describe past behaviour of a process • quantify nature and extent of climate change • to try and understand mechanisms behind change • human activity and/or natural variability • to extrapolate and provide policy guidance • where are we going? what should we do about it? • to monitor policy application • are policies having desired impacts? • remove trend to focus on more interesting relationships • find relationships between variables which have all increased/decreased over time

5. Introduction : reasons for trend analysis (3) different methods are appropriate for different aims EMEP series are ‘short’ time series non-parametric v. parametric more assumptions give narrower confidence bands (non-parametric tends to have lower power) smoothing window – subjective choice filtering – residuals (high pass); smooth (low pass) BUT linked, so residuals have structure autocorrelation is a big problem

6. Introduction : reasons for trend analysis (4) simple methods are very useful e.g. annual means be aware of strengths and limitations of method note replication issues a year is not repeatable (smoothing window choice) what are the real degrees of freedom? complex statistical models do perform better, but they also require more experience to use them well

7. Trends in EMEP monitored data (1) Aim: build spatial – temporal model temporal autocorrelation smooth temporal change spatial covariance structure (may be time dependent) meteorological and other dependences Reason: this gives best estimates, particularly for non monitored locations

8. Trends in EMEP monitored data (2) Note CCC report UK work in NEGTAP: allow data to choose ‘regions’ – similarity measure followed by cluster analysis and dendrograms significant trends at regional level, not site level differences with SOx, NOx, NHx

9. Conclusions Making progress with developing a non-parametric approach to a spatial-temporal model Multivariate approaches will be considered in future Good for exploration but no simple inferential methods