The potential to narrow uncertainty in regional climate predictions Ed Hawkins, Rowan Sutton

1. The potential to narrow uncertainty in regional climate predictions Ed Hawkins, Rowan Sutton NCAS-Climate, University of Reading IMSC 11 – July 2010

2. Motivation • Adaptation planners would like quantitative projections of future climate on regional scales, especially for the next few decades • these projections exist but have (large) uncertainties • Questions: • what are the largest sources of climate uncertainty on regional scales? • does this vary with region, lead time and climate variable? • are the dominant uncertainties potentially reducible?

3. European temperature projections

4. European temperature predictions

5. European temperature projections

6. European temperature projections

7. European temperature projections

8. European temperature projections

9. European temperature predictions

10. Uncertainty in temperature projections Internal variability Global mean temperature Scenario uncertainty CMIP3 projections Model uncertainty Relative to 1971-2000 Internal variability – spread in residuals from smooth fits to projections Scenario uncertainty – spread between multi-model mean of smooth fits Model uncertainty – spread around multi-model means of smooth fits Hawkins & Sutton, BAMS, 2009 – also see Cox & Stephenson (2007)

11. Uncertainty in temperature projections Internal variability British Isles (UK) mean temperature Scenario uncertainty CMIP3 projections Model uncertainty Relative to 1971-2000 Internal variability – spread in residuals from smooth fits to projections Scenario uncertainty – spread between multi-model mean of smooth fits Model uncertainty – spread around multi-model means of smooth fits Hawkins & Sutton, BAMS, 2009 – also see Cox & Stephenson (2007)

12. A different representation Global mean temperature Hawkins & Sutton, 2010, Clim. Dyn.

13. A different representation British Isles mean temperature

14. Maps of uncertainty – temperature Hawkins & Sutton, BAMS, 2009

15. Precipitation uncertainties Global mean precipitation Hawkins & Sutton, 2010, Clim. Dyn.

16. Precipitation uncertainties Global, decadal mean European DJF, decadal mean Internal variability Scenario uncertainty Model uncertainty Sahel JJA, decadal mean SE Asia JJA, decadal mean

17. Maps of uncertainty – DJF precipitation

18. Signal-to-noise ratios Signal-to-noise ratio (S/N) for JJA projections Hawkins & Sutton, 2010, Clim. Dyn.

19. Signal-to-noise ratios Signal-to-noise ratio (S/N) for JJA projections with model uncertainty without model uncertainty Hawkins & Sutton, 2010, Clim. Dyn.

20. Longer time means

21. Uncertainty estimates only 3 scenarios used only 15 models used Internal variability estimate relies on GCMs Wide range in GCM estimates Caveats

22. Internal variability in CMIP3 GCMs Discussion: www.met.reading.ac.uk/~ed/blog

23. Uncertainty estimates only 3 scenarios used only 15 models used Internal variability estimate relies on GCMs Wide range in GCM estimates Spread ≠ skill Progress in climate science may increase uncertainty carbon cycle feedbacks, ice sheet and land-use change uncertainties… Simple trend model used Caveats

24. Using ANOVA instead Thanks to Stan Yip, Chris Ferro, David Stephenson

25. Uncertainty in global ozone recovery Global mean ozone CCMVal-2 intercomparison Charlton-Perez et al. (2010), ACPD

26. Uncertainty in tropical evergreen tree cover for the Amazon Poulter et al., (2010), Glob. Change Bio.

27. June 1991 June 1995 June 2001 Thanks to Jon Robson Reducing uncertainty – decadal climate prediction Retrospectively predicting North Atlantic upper ocean heat content Observations GCM predictions Decadal climate prediction allows us to test our climate models in making predictions, to identify processes causing errors and may help predict some internal variability for up to a decade

28. Summary Interactive website: http://ncas-climate.nerc.ac.uk/research/uncertainty/ • Model uncertainty and internal variability are the dominant sources of uncertainty in regional climate projections for next few decades. • Uncertainty is potentially reducible with progress in climate science • Internal variability more important for precipitation than temperature • Scenario uncertainty is almost negligible in the tropics for precipitation • Potential for reduction in uncertainty for precipitation appears smaller • Adaptation decisions will need to be made with low S/N predictions for precipitation, even with a perfect model! • Climate impact modellers need to use more than one GCM! • Could estimate potential value of climate science investments to reduce uncertainty, compared to economic savings from less costly adaptation

29. Robustness of internal variability CONTROL TRANSIENT

30. uncertainty mean signal Fractional uncertainty comparison Cox & Stephenson schematic Using CMIP3 projections • Using CMIP3 data, model uncertainty is clearly the dominant contribution for decadal predictions Hawkins & Sutton, BAMS, 2009