Use of satellite water vapour data sets for climate model evaluation & development

1. Use of satellite water vapour data sets for climate model evaluation & development Mark Ringer & Viju John, Met Office Hadley Centre, Exeter, UK GEWEX/ESA DUE GlobVapour Workshop, Frascati, 8-10 March 2011

2. Contents • Background & motivation • “Traditional” evaluation studies – total column moisture in the new Hadley Centre climate model, HadGEM2 • The forward modelling approach – simulation of HIRS/AMSU radiances • Climate model development & improvement • Summary & conclusions

3. Why do we use satellite data? • Evaluate the physical processes most relevant to reducing uncertainty in climate predictions • Inform & prioritise key areas for developing and improving climate models • Constrain climate change predictions – or at least try and determine if this is possible • Detection & attribution of observed variations to natural and anthropogenic forcings • Initialisation of models used for seasonal-to-decadal prediction

4. Three key questions • Can we use present-day observations to constrain climate feedbacks? • Can we use observations to improve the processes which contribute most to the range of uncertainty in climate projections? • Will future observations be suitable for evaluating our climate projections for the coming decades?

5. How do we use satellite data? • “Traditional” method – compare high-level products (e.g. radiative fluxes, total column moisture) with their model equivalents • “Model-to-satellite” approach – simulate what is actually measured (e.g. IR or microwave radiances, radar reflectivities, etc) • Development of new, process-based, evaluation techniques for using the data and combining with other information such as reanalyses – e.g. compositing in terms of dynamics, clustering • Apply similar techniques to analysis of climate change simulations and feedbacks • For “fast” processes such as clouds & precipitation we also use comparisons with the global NWP model (e.g. CloudSat) • In combination with in situ data from the global observation network, data from aircraft and other field campaigns

6. Radiative feedbacks in the IPCC AR4 models Water Vapour Clouds Albedo Lapse Rate WV + LR ALL (Bony et al. 2006)

7. Title We clearly need to remember the bigger picture… …and our desire to improve these processes in climate models. Images courtesy of the USGS (John M. Evans, USGS, Colorado District) and lasp.colorado.edu

8. Evaluation of TCWV in HadGEM2

9. Annual Mean TCWV

10. July Mean TCWV

11. El Niño: Anomalies in January 1998

12. La Niña: Anomalies in January 2000

13. Equatorial Anomalies: 1989-2005

14. Multiple data sets: TCWV & Clear-Sky OLR

15. The forward modelling approach

16. Allows direct comparison with measured radiances, etc • Avoids ambiguities associated with comparing to retrieved quantities • Example shows HIRS Channel 12 in previous version of Hadley Centre model • RTTOV is now part of the COSP simulator • For further details see: • http://cfmip.metoffice.com/COSP.html

17. Evaluation of HadGEM2 using AMSU-B January July Model OBS • Direct simulation of AMSU-B Channel 3 • Converted to UTH in model and observations • Further sub-sampled into times of large-scale descent

18. Long-term variability: Tropical mean anomalies in large-scale descending regions • Model reproduces observed variability very well • Indicates no significant trend in UTH

19. Evaluation of the global forecast model IR WV Model OBS

20. Climate model development

21. Development of HadGEM3: Relative Humidity New New-Old Old-Obs New-Obs

22. Development of HadGEM3: UTH New New-Old Old-Obs New-Obs

23. Development of HadGEM3: Clear-sky OLR New New-Old Old-Obs New-Obs

24. Summary

25. Summary – 1 • TCWV data sets need to demonstrate superiority compared to re-analyses, which are continually improving • Requirement for reliable vertical profiles of moisture to assess model biases • Increasing move towards forward modelling and away from retrieved quantities • Value of data is greatly enhanced in combination with other information such as radiative fluxes

26. Summary – 2 • Model resolutions – horizontal and vertical – are continually increasing • Increasing focus on seasonal-to-decadal prediction • Continued interest in understanding and trying to place observational constraints on feedbacks • Work on water vapour needs to be placed within the wider context of interest in the hydrological cycle: rainfall, clouds, etc.