Overview of Camborne, UK and Jungfraujoch, Switzerland Field Campaigns
Long-term trend analysis of aerosol parameters at the Jungfraujoch
description
Transcript of 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
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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%
Significant trends at 95%
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Significant trends at 90%
Sen’s slope of the backscattering fraction
Significant trends at 95%
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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