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Statistical separation of natural and anthropogenic signals
in observed surface air temperature time series
T. Staeger, J. Grieser and C.-D. Schönwiese
Meteorological Environmental Research / Climatology
Institute for Meteorology and Geophysics J.W. Goethe-University, Frankfurt /M., Germany
1860 1880 1900 1920 1940 1960 1980 2000
tem
pera
ture
ano
mal
ies
in [K
]
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6Global mean temperature 1856 – 2003 after P.D. Jones et al.
Which parts of the variations in observed temperature are assignable to natural and anthropogenic forcings?
Are anthropogenic signals distuingishable from noise?
Approach:
Causes for the structures in the time series under consideration are being postulated.
A pool of potential regressor time series is collected out of the forcings / processes considered.
A selection routine is applied to obtain a multiple linear regression model.
Stepwise Regression
The effects are seen to be linear and additive.
Forcings / processes considered:
- Greenhouse gases (GHG)
- El Niño - Southern Oscillation (SOI)
- Explosive volcanism (VUL)
- Solar forcings (SOL)
- North atlantic oscillation (NAO)
- Tropospheric sulphate aerosol (SUL)
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
T-A
no
ma
lien
in K
-0,7
-0,6
-0,5
-0,4
-0,3
-0,2
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
-0,7
-0,6
-0,5
-0,4
-0,3
-0,2
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
GHG + SOL + SOI + VUL
explained variance: 78.9%
global mean temperature 1878 – 2000, annual mean after P.D. Jones
Ges. GHG SUL SOL SOI VUL NAO
proz
ent
uale
erk
lärt
e V
aria
nz
0
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
60
70
80
90
100
explained variance of the complete model and and for single forcings on the global mean temperatur 1878 - 2000
What is noise?
Case 1: noise represents chance:
To obtain the component representing chance, the residual is separated into a structured and unstructered component.
txnoisetxRtxRtxR polytrend ,,,,
The question to be answered here:
Is the greenhouse signal distuingishable from chance?
What is noise?
Case 2: noise comprises of natural variability and unexplained variance
The question to be ansewered here:
Is the greenhouse signal distuingishable from variability of non-anthropogenic origin?
txStxRtxnoise nat ,,,
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
T-A
no
mal
ie in
K
-0,5
-0,4
-0,3
-0,2
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6GHGSOLVULSOI
99.9%
99.9%
99%
99%
95%
95%
srsch
srsch
Case 1: noise represents chance
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
T-A
no
mal
ie in
K
-0,5
-0,4
-0,3
-0,2
-0,1
0,0
0,1
0,2
0,3
0,4
0,5
0,6GHGSOLVULSOI
99.9%
99%
99%
95%
95%
srsch
srsch
Case 2: noise = natural variability + unexplained
data field EOF-Transformation
PC
Stepwise Regression
backtransformation
signal fields,
residual field
Treatment of data fields:
GHG signal field for the year 2000 relative to 1901 in [K]:
GHG signal field, seasonal means for 2000 relative to 1901 in [K]:
NH winter NH spring
NH summer NH autum
Ges. GHG SOL SOI VUL NAO
proz
ent
uale
erk
lärt
e V
aria
nz
0
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
60
70
80
90
100
Explained variance of the full model and of single forcings for the global temperature data field 1878 - 2000
Significance of the GHG signal for 2000 relative to 1901 in percentages:
Case 1: noise represents chance
Case 2: noise = natural variability + unexplained
GHG signal field Europe for 2000 relative to 1878 in [K]:
Significance of the european GHG signal for 2000 relative to 1878 in percentages:
Case 1: noise represents chance
Case 2: noise = natural variability + unexplained
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
T-A
no
mal
ie in
K
-1,0
-0,8
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
GHGSOLNAO
90%
90%
srsch
srsch
Signficance of the GHG signal in the german mean temperature 1878 - 2000:
Case 1: noise represents chance
1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
T-A
no
mal
ie in
K
-1,0
-0,8
-0,6
-0,4
-0,2
0,0
0,2
0,4
0,6
0,8
1,0
GHGSOLNAO
90%
90%
srsch
srsch
Signficance of the GHG signal in the german mean temperature 1878 - 2000:
Case 1: noise = natural variability + unexplained
Time moving analysis:
Global mean temperature 1856 - 2003, window width: 100 yr
0
10
20
30
40
50
60
70
80
1856
-195
5
1861
-196
0
1866
-196
5
1871
-197
0
1876
-197
5
1881
-198
0
1886
-198
5
1891
-199
0
1896
-199
5
1901
-200
0
data window
ex
pla
ine
d v
ari
an
ce
[%
]
GES
GHG
NAT
SOL
SOI
Conclusions:
Explained variance is highest in global and hemispheric mean temperatures (ca. 70% - 80%) and is reduced in data sets with high spacial resolution.
On the global scale, GHG forcing is most important and significant.
On the european scale NAO is dominant – GHG forcing is not significant.
Time moving analysis shows a growing meaning of GHG forcing compared to natural forcings, especially since around 1985 on the global scale.