1. Impact of EOS MLS ozone data on medium-extended range ensemble forecasts Jacob C. H. Cheung 1, Joanna D. Haigh 1, David R. Jackson 2 1 Imperial College London 2 Met Office

2. Overview Motivation Methods Experimental period selected Impact on tropospheric forecasts Is there significant improvement?

3. Motivation Source: Mathison et al. 2007 Increase in forecast skill Decrease in forecast skill

4. Motivation Full tropospheric response to stratospheric thermal forcing is a two-stage process Improving representation of ozone will possibly improve medium- extended forecasts Source: Simpson et al. 2009 Forecast range considered by Mathison et al.

5. Aim Is the representation of stratospheric ozone important in medium-extended range tropospheric forecasts?

6. Methods Met Office Global and Regional Ensemble Prediction System (MOGREPS) Resolution: N216L85 31-day free running forecast 24 ensemble member Run IdLiShine (Control)MLS (Experiment) DatasetLi and Shine 95EOS MLS Ozone5-year monthly mean zonal mean Monthly mean zonal mean correspond to the chosen forecast date Case StudiesNorthern winter; Southern winter, spring Northern spring (March 2011)

7. Experimental period selected – March 2011 Source: NASA/Goddard

8. Ozone profiles

9. Results – Stratospheric temperature MLS-LiShine MLS forecast errorLiShine forecast error - General reduction in temperature forecast errors with MLS ozone - Temperature anomaly between runs is significant in stratosphere - Not much change in temperature in troposphere

10. Results – Tropospheric zonal wind MLS-LiShine MLS forecast errorLiShine forecast error - Tropospheric zonal wind anomaly between runs is weak compared to individual forecast errors - Response is statistically significant in some area

11. Results - SLP [hPa] MLS-LiShine NHSH

12. Temperature RMSE (10hPa) NH TR SH

13. Horizontal wind RMSE (250hPa)GPH RMSE (500hPa) NH TR SH

14. Horizontal wind RMSE (250hPa)GPH RMSE (500hPa) NH TR SH

15. Summary - Performed a case study in which the MLS ozone profile is much superior that of LiShine - Zonal wind and temperature response is sensitive to the ozone climatology in current NWP systems (in agreement with other ST coupling studies) - Tropospheric forecast errors are dominated by ensemble spread in medium-extended range forecasts -> In our experiments, using monthly mean zonal mean EOS MLS ozone data does not significantly improve medium-extended tropospheric forecasts

16. Questions?

17. Horizontal wind RMSE (50hPa)Temperature RMSE (50hPa) NH TR SH