1. Detection and attribution of temperature change in the lower stratosphere Nathan Gillett, Ben Santer

2. Model data Will start by using IPCC 20 th century simulations with at least ozone depletion and volcanic aerosol. Will start by using IPCC 20 th century simulations with at least ozone depletion and volcanic aerosol. Ben Santer has calculated T4 temperatures from all forced runs of these models, and ensembles of the PCM with multiple forcings. Ben Santer has calculated T4 temperatures from all forced runs of these models, and ensembles of the PCM with multiple forcings. Model and observational data both available for the period 19801999. Model and observational data both available for the period 19801999.

3. Temperature anomaly (K) Global mean T4 MSU4 temperatures in 20 th century simulations of IPCC AR4 models with at least greenhouse gases, volcanic aerosols and ozone. 80°S - 80°N.

4. Power spectra of global mean T4 Variability in global mean T4 realistically simulated in all-forced runs. Variability in global mean T4 realistically simulated in all-forced runs. Variability underestima ted in control. Variability underestima ted in control. Most variability at >1yr is forced in model. Most variability at >1yr is forced in model.

5. T4 trend patterns – 1980-1999 RSS UAH IPCC AR4 models Trends in K/year

6. Detection and attribution Detection and attribution techniques allow us to objectively test model-observations consistency, and test for evidence of the response to particular forcings in the observations. Detection and attribution techniques allow us to objectively test model-observations consistency, and test for evidence of the response to particular forcings in the observations. We need simulations with individual forcings in order to attribute to separate forcings. We need simulations with individual forcings in order to attribute to separate forcings. Method applied here using PCM data (model top ~47km, 7 stratospheric levels), but could be applied using other models. Method applied here using PCM data (model top ~47km, 7 stratospheric levels), but could be applied using other models.

7. Calculation of attributable trends Data are filtered to retain large-scale, long- timescale variability. I used 2-yr means of 25 T4 spherical harmonics. Data are filtered to retain large-scale, long- timescale variability. I used 2-yr means of 25 T4 spherical harmonics. Observations are regressed onto model response patterns for each forcing in a multiple regression. Observations are regressed onto model response patterns for each forcing in a multiple regression. Regression coefficients and uncertainties are used to scale cooling/warming due to each forcing. Regression coefficients and uncertainties are used to scale cooling/warming due to each forcing.

8. T4 timeseries in PCM Temperature Anomaly (K)

9. RSS GHGs Ozone ALL

10. Detection results

11. Attributable temperature trends in T4