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Long-term trend analysis of aerosol parameters at the Jungfraujoch

Long-term trend analysis of aerosol parameters at the Jungfraujoch. Martine COLLAUD COEN, MeteoSwiss, Switzerland Ernest WEINGARTNER, Stephan NYEKI and Urs BALTENSPERGER, Paul Scherrer Institute, Switzerland. martine.collaud@meteoswiss.ch. The Sphynx station at the Jungfraujoch.

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Long-term trend analysis of aerosol parameters at the Jungfraujoch

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  1. Long-term trend analysis of aerosol parameters at the Jungfraujoch Martine COLLAUD COEN, MeteoSwiss, Switzerland Ernest WEINGARTNER, Stephan NYEKI and Urs BALTENSPERGER, Paul Scherrer Institute, Switzerland martine.collaud@meteoswiss.ch

  2. The Sphynx station at the Jungfraujoch •3580 m asl •GAW station • Partially in free troposphere (FT) • Influenced by PBL • Remote, aged particles • 40% in-cloud

  3. Aerosol parametersat the Jungfraujoch: 1995-2005 dry aerosols

  4. 10.5 years of measurement (1995-2005) Daily median Monthly RM Yearly RM

  5. Seasonal Mann-Kendall test • The Mann-Kendall test is based on rank and allows to determine if a trend exists at a chosen confidence limit. • It is a non parametric test that can therefore be applied to all distributions • Seasonality are taken into account. • Missing values, ties in time (several measurements per season) and ties in values are allowed • The covariance is corrected by the Dietz and Killeen estimator. • The variance is corrected for data autocorrelation by the procedure described by Hamed and Rao (1998).

  6. Sen‘s slope estimator Sen‘s slope estimator is a non parametric estimate of the linear trend. • It allows missing values and ties. • The Sen‘s slope is given by the median of all Aij, with j>i , ti<>tj • Confidence limits at 90% have been evaluated (Gilbert 1998).

  7. Least mean square fit and number of years necessary to detect the trend Stationnary auto-regressive noise AR(1): Nt = Nt-1 +  Y(t) =  + St + (t/12) + Ut + Nt, t = 1,…n, Seasonal component: St= sin(2t/365.25) + sin(4t/365.25)+ cos(2t/365.25) LMQ fit has been applied on the log of the data when the data had a lognormal distribution. = constant Intervention Linear trend with slope  Number of years necessary to detect the estimated trend (Weatherhead, 2000) :

  8. LMQ fit of the monthly median of the scattering coefficient at 700 nm June-August November-December Ln(scattering coef) Slope= 4%/year Time

  9. Results of the seasonal Mann-Kendall test Significant at 90% Positive slope Significant at 95% Positive slope Significant at 90% Negative slope Significant at 95% Negative slope

  10. Seasonal cycle of the scattering coefficient Abs. coef. Scat. coef

  11. Sen’s slope of the scattering coefficients Significant trends at 90% Significant trends at 95%

  12. Sen’s slope of the backscattering fraction Significant trends at 90% Significant trends at 95%

  13. Results of the LMQ: Significant trends at 95%

  14. Conclusions • The scattering coefficients have a positive significant trends of 3-4% yr-1. • The autumn and winter are the periods with the most significant trends. • There is no trend in the summer months with the greatest PBL influence. • The particle size in the free tropospheric air masses decreases. • Increase of aerosol background concentration. martine.collaud@meteoswiss.ch

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