1. 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

2. Global 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?

3. 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.

4. 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)

5. GHG forcing: logarithmic CO 2 equivalent concentration

6. EOF-transformierte Säulendichten Sulfate forcing: The first 3 PCs of 8 zonal means of emission rates:

7. Variations of the Solar constant 1500 - 2001 after Lean:

8. Explosive volcanism: first three PCs out of 16 zonal means of volcanic radiative forcing after Grieser:

9. Southern-Oscillation-Index annual mean 1876 – 2001 (CRU)

10. NAO index 1850 - 2001 after P.D. Jones:

11. reservoir R pot forward selection: MLR with R i and R pot for each single R pot is the most significant reg. coeff. above threshold? backward elimination: MLR with all R i except R j for each single R j is the less significant reg. coeff. above threshold? end - model: no model: yes R d back to reservoir Stepwise Regression: deselection of R d no

12. Signal separation:

13. Significance test of the regression coefficients: t-test: :degrees of freedom r i.,part :partial correlation coefficient of R i j: lenght of time series n:number of regressors

14. global mean temperature 1878 – 2000, annual mean after P.D. Jones

15. GHG global mean temperature 1878 – 2000, annual mean after P.D. Jones

16. GHG + SOL global mean temperature 1878 – 2000, annual mean after P.D. Jones

17. GHG + SOL + SOI global mean temperature 1878 – 2000, annual mean after P.D. Jones

18. GHG + SOL + SOI + VUL explained variance: 78.9% global mean temperature 1878 – 2000, annual mean after P.D. Jones

19. explained variance of the complete model and and for single forcings on the global mean temperatur 1878 - 2000

20. Significance of signals: A signal has to be distuingished sufficiently from noise: Given a Gaussian distributed noise term, the significance of a signal to noise ratio can be computed.

21. significance of the greenhouse signal: For Gaussian distributed residuals: A signal has to be distuingished sufficiently from noise:

22. What is noise? Case 1:noise represents chance: To obtain the component representing chance, the residual is separated into a structured and unstructered component. The question to be answered here: Is the greenhouse signal distuingishable from chance?

23. 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?

24. Case 1:noise represents chance

29. Case 2:noise = natural variability + unexplained

30. Existing area means, temperature annual means 1901 - 2000 Data fields: Spatial distinction leads to area means which are not independent, because they all describe a part of the same field of meteorological data.

31. Transformation of the data field into principal components, which contain structures of the whole field: EOF-Transformation EOF:spacial components PC:time dependent components :Eigen value Empirical Orthogonal Functions:

32. data field EOF-Transformation PC Stepwise Regression backtransformation signal fields, residual field Treatment of data fields:

33. Eigen spectrum:

34. First PC and EOF of the global temperature field 1878 – 2000: 1. PC; e.V.: 40.7%Variance spectrum of the 1. PC 1. EOF

35. GHG signal field for the year 2000 relative to 1901 in [K]:

36. GHG signal field, seasonal means for 2000 relative to 1901 in [K]: NH winterNH spring NH summerNH autum

37. Solar signal field for 2000 relative to 1906 (first sunspot maximum analyzed) in [K]:

38. NAO signal field winter 1993 relative to mean values 1901-2000 in [K]:

39. Sulfate signal in the global temperature field 1901 - 2000: For 1970 relative to 1901 For 2000 relative to 1901

40. Explained variance of the full model and of single forcings for the global temperature data field 1878 - 2000

41. Significance of the GHG signal for 2000 relative to 1901 in percentages: Case 1:noise represents chance Case 2:noise = natural variability + unexplained

42. Significance of the GHG signal for 2000 relative to 1878 in percentages: Case 1:noise represents chance Case 2:noise = natural variability + unexplained

43. 1. PC; e.V.: 53,1%Varianzspektrum der 1. PC 1. EOF First PC and EOF of the european temperature field 1878 – 2000:

44. GHG signal field Europe for 2000 relative to 1878 in [K]:

45. Significance of the european GHG signal for 2000 relative to 1878 in percentages: Case 1:noise represents chance Case 2:noise = natural variability + unexplained

46. Explained variance of the full model and of single forcings for the european temperature data field 1878 - 2000

47. NAO in the european temperature field: NAO signal field in winter 1925 relative to mean values 1901-2000 in [K] Significance of the NAO signal in Winter 1925 in pecentages Case 2:complete residual and natural variability as noise (without NAO)

48. Signficance of the GHG signal in the german mean temperature 1878 - 2000: Case 1:noise represents chance

49. Signficance of the GHG signal in the german mean temperature 1878 - 2000: Case 1: noise = natural variability + unexplained

50. Explained variance of the full model and of single forcings for the german mean temperature 1878 - 2000

51. SLP Europe 1896 – 1995: GHG signal field annual mean 1995 relative to 1896 in [hPa] Significance of the GHG signal annual mean 1995 in percentages Case 1:unstructured residual component as noise

52. NAO in the european SLP field: NAO signal field winter 1989 relative to mean values 1896-1995 in [hPa] Significance of the GHG signal annual mean 1995 in percentages Case 1:unstructured residual component as noise

53. Explained variance of the full model and of single forcings for the european SLP field 1896 - 1995

54. Precipitation Europe 1900 – 1998: GHG signal field annual totals 1998 relative to 1900 in [mm] Significance of the GHG signal annual totals 1998 Case 1:unstructured residual component as noise

55. NAO in the european precipitation field: NAO signal field winter 1989 relative to mean values 1900-1998 in [mm] Significance of the NAO signal winter 1989 Case 1:unstructured residual component as noise

56. Explained variance of the full model and of single forcings for the european precipitation field 1900 - 1998

57. Comparism of explained variances for the full models and for single forcings for different temperature data sets 1878 - 2000:

58. Time moving analysis:

62. 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.