1. Wenche Aas, Hilde Fagerli, Svetlana Tsyro, Sverre Solberg
Comparing trends from the benchmark data set (S,N,O3 and PM) with EMEP model results Part I. Benchmark dataset
Wenche Aas, Hilde Fagerli, Svetlana Tsyro, Sverre Solberg

2. Outline Calculating trends Benchmark dataset
the use of Mann Kendall statistics
How long time serie is needed to detect a trend?
Benchmark dataset
Some results on seasonal differences

3. Statistical analyses, Mann Kendall
Basic questions to ask are
To what extent will the method detect trends for the given time periods?
How reliable are the estimated Sen slopes?
What is the effect of missing data?
Constructed artificial time series reflecting two different processes leading to variability in atmospheric observational data:
A linear long-term trend due to changes in emissions and hemispheric baseline levels; and
A “natural variability” due to variations in meteorology from year to year
A note by Sverre Solberg available on wiki page)

4. Results from Mann Kendall statistics on artificial time series
annual trend of -1, -2, and -3 % /year
calculations of 1000 artificial time series with 11 and 23 years
natural variability corresponding to a st.dev of 0.05, 0.10 and 0.15 of the mean
the probability that the Mann Kendall detects a significant trend (p=0.05) and the average Sen’s slope

5. The length of the time series is critical for the performance of the Mann Kendall statistics.
The smaller the trend is relative to the inter-annual meteorological variability, the longer time series is needed in order to identify a significant trend.
With only 11 years of data, it is difficult to detect trends, except for the cases with a small annual variability and a marked trend (3 %/y or stronger in absolute level, risk of overestimated trends
Use less stringent sign. citeria for data with high variability?
With 23 years of data, the situation is very different, and the method will most certainly detect the trend, and the estimated slopes will be close to the real trend

6. First set of the benchmark dataset
Same sites as used in Tørseth et al (2012) though added the extra years
Uploaded in wiki.met.no in Dec 2014

7. Feedback and follow up Missing datasets (NL, DE44, RU, (SK))
No comments on the actual data used
Decided to include more sites:
not only sites with co-located air and precip measurements of sulphur
For the shorter periods ( and ) more sites than
Uploaded extended dataset and seasonal trends on wiki (in April)

8. Overview results (S,N,PM)

9. Seasonal differences in trends

10. Some remarks before conclusions
Time resolution (annual, season average) ok?
Higher time resolution (monthly, weekly, daily) may help detecting trends
For filtering based on sectors etc, original resolution needed
Stricter criteria for site selection based on quality?
Revisit “strange” data with national experts
Change in method may influence trends,
Wrong units
Change in surroundings
Use outlier test to exclude years or time series?