long-term trend analysis of aerosol parameters at the jungfraujoch

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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

[email protected]



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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



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Aerosol parametersat the Jungfraujoch: 1995-2005

Scattering coefficient at = 450, 550, 700 nm

Nephelometer(TSI 3563)

Backscattering coefficient

at = 450, 550, 700 nm

Nephelometer(TSI 3563)

Backscattering fraction at = 450, 550, 700 nm

ratio

Scattering exponent fit

Absorption coefficient at 7 : 370 nm 950 nm

Spectrum Aethalometer (AE31)

Condensation Nuclei CN CPC (TSI 3010)

*a

spbspb /

sp

bsp

apdry aerosols



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10.5 years of measurement (1995-2005)

Daily median

Monthly RMYearly RM



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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).



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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

Sen‘s slope estimator

)(

)(

ji

jiij tt

CCA

j>i , ti<>tj

• Confidence limits at 90% have been evaluated (Gilbert 1998).



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Seasonal component:St= sin(2t/365.25) + sin(4t/365.25)+ cos(2t/365.25)

Least mean square fit and number of years necessary to detect the trend

Stationnary auto-regressive noise AR(1):

Nt = Nt-1 +

0

0

1

0

Ttif

TtifU t

Y(t) = + St + (t/12) + Ut + Nt, t = 1,…n,

= constantLinear trend with slope InterventionLMQ fit has been applied on the log of the data when the data had a

lognormal distribution.

2/1

3/2

*

)1(31

1*

1

1)2(

N

zn

Number of years necessary to detect the estimated trend (Weatherhead, 2000) :



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June-August

November-December

LMQ fit of the monthly median of the scattering coefficient at 700 nm

Time

Ln(s

catt

erin

g co

ef)

Slope= 4%/year



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Months 1 2 3 4 5 6 7 8 9 10 11 12

Scat

Back-scatt

CN

Abs white

Abs

B-

fraction

Scat exp

Significant at 90%Positive slope

Significant at 95%Negative slope

Significant at 95%Positive slope

Significant at 90%Negative slope

Results of the seasonal Mann-Kendall test



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Seasonal cycle of the scattering coefficient

Abs. coef.

Scat. coef



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Sen’s slope of the scattering coefficients

Significant trends at 90%




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Sen’s slope of the backscattering fraction




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Results of the LMQ:Significant trends at 95%

Slope of ln(data)

%/year

Slope of data

%/year

Nb of years n*

Scattering coef. 0.21 2.9 10.2

0.33 4.0 7.6

0.35 3.9 7.5

Backscattering coef.

0.18 3.2 8.9

0.24 4.0 7.6

0.17 3.5 8.8

Backscattering fraction

-2.3 6.5

-2.6 6.6

-2.8 6.6

Scattering exp. 5.0 3.7



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☺ 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.

Conclusions

[email protected]

long-term trend analysis of aerosol parameters at the jungfraujoch

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