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Statistical Separation of Natural and Anthropogenic Signals in Temperature Time Series

Investigating the impact of natural and human-induced factors on observed temperature variations, using a stepwise regression approach and various climatic parameters. Analyzing data from 1856-2003 to distinguish anthropogenic signals from noise.

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Statistical Separation of Natural and Anthropogenic Signals in Temperature Time Series

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  1. Meteorological Environmental Research / Climatology Institute for Meteorology and Geophysics J.W. Goethe-University, Frankfurt /M., Germany Statistical separation of natural and anthropogenic signalsin observed surface air temperature time series T. Staeger, J. Grieser and C.-D. Schönwiese

  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. Stepwise Regression Approach: Causes for the structures in the time series under consideration are being postulated. The effects are seen to be linear and additive. 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.

  4. Forcings / processes considered: - Greenhouse gases (GHG) - Tropospheric sulphate aerosol (SUL) - El Niño - Southern Oscillation (SOI) - Explosive volcanism (VUL) - Solar forcings (SOL) - North atlantic oscillation (NAO)

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

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

  7. 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?

  8. 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?

  9. Case 1: noise represents chance

  10. Case 2: noise = natural variability + unexplained

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

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

  13. GHG signal field, seasonal means for 2000 relative to 1901 in [K]: NH winter NH spring NH summer NH autum

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

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

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

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

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

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

  20. Time moving analysis:

  21. 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.

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