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.