1. Andrew Turner UKCDS Bangladesh Delegation Visit Wednesday 3 March 2010 Key modes of climate variability over South Asia and uncertainties in future climate projections

2. Introduction Climate of Bangladesh is particularly influenced by the South Asian summer monsoon, in addition to tropical cyclones and other low-pressure systems. This talk particularly focuses on: –the skill with which monsoon processes on various timescales are modelled; –the uncertainty in future projections of the regional climate, particularly in the IPCC AR4 / CMIP3 database.

3. Outline Introduction How is the mean monsoon and its seasonal cycle simulated in models? How certain are projections of change to seasonal mean rainfall? What about modes of monsoon variability and their teleconnections? Can we say anything about changes to short-timescale variability? Summary

4. The mean monsoon... Few models can simulate the major precipitation centers and their interannual variation. from Annamalai et al. (2007) J. Climate

5. ...and its seasonal cycle Only 6 CMIP3 models can reasonably simulate both the spatial distribution and seasonal cycle of rainfall over South Asia! from Annamalai et al. (2007) J. Climate

6. Uncertainties in late 21 st century IPCC AR4 A1B mean projections In this scenario, reasonable agreement between coupled climate models on surface temperature changes in the South Asia region: 3 – 4˚C Inter-model spread of precipitation projections is large for all the monsoon systems, including South Asia. from Meehl et al. (2007) Global Climate Projections in the IPCC AR4

7. Uncertainties in late 21 st century IPCC AR4 A1B mean projections From Turner & Slingo (2009b) Atmos. Sci. Lett. 10 Large uncertainty in mean JJA rainfall change over Bangladesh?

8. Interannual variability and relationship with ENSO Climate models have difficulty with these teleconnections, thus inhibiting accurate seasonal prediction. Flooding in Bangladesh linked to the Southern Oscillation. El Niño events also influence upstream precipitation (e.g., Chowdhury, 2003, Theor. Appl. Climatol.)  need institutional collaboration. from Turner et al. (2005), Q. J. R. Meteorol. Soc. 131

9. Interannual variability and relationship with ENSO The ENSO-monsoon correlation waxes and wanes, and is currently in a weakened phase. Is this related to warming? Variation in teleconnections on decadal timescales needs to be considered when designing seasonal forecasts.

10. Boreal summer intraseasonal variability EOF reconstruction of OLR during the active-break cycle of the monsoon. Based on finding the patterns that explain the most variance of OLR in a 20-100 day period during the monsoon season Note the distinct NW-SE tilt of the bands of active and suppressed convection Taken from Annamalai & Sperber (2005) J. Atmos. Sci.

11. Daily OLR anomalies + ‘MJO’ modal projection (Matt Wheeler) ‘MJO’ mode by filtering in the zonal wavenumber / frequency domain, Wheeler & Weickmann (2001) SymmetricAnti-symmetric ‘MJO’ strongly implicated in 2009 summer: April to August

12. Model capability at simulating BSISV OBS  Northward propagation From Sperber & Annamalai (2008) Clim. Dyn. time latitude

13. OBS  eastward propagation Model capability at simulating BSISV time longitude

14. Very few coupled climate models are able to simulate the northward and eastward propagation inherent to intraseasonal variability during boreal summer. More needs to be understood about the physical processes involved before we can be sure how their climate responses can be assessed. Model capability at simulating BSISV

15. A possible projection of BSISV at 2xCO 2 ? This example uses the Hadley Centre model HadCM3, which is able to well capture the seasonal cycle of rainfall in the region and northward propagation of BSISV. Active and break modes of BSISV potentially intensified? Active convectionBreak convection 2xCO 2 minus control from Turner & Slingo (2009a), Q. J. R. Meteorol. Soc. 135

16. Spatial change in HadCM3: mean & subseasonal extremes Changes in response to 2xCO 2 in simulations with HadCM3: percentile extremes calculated for each JJA season then meaned. from Turner & Slingo (2009a), Q. J. R. Meteorol. Soc. 135 Mean precipitation change 99 th subseasonal percentile change Response of subseasonal extreme is closely tied to signal of seasonal mean change.

17. Uncertainty in spatial pattern of change to subseasonal rainfall extremes Considerable uncertainty in mean and extreme projections. Predominantly positive in sign. Qualitative similarity, although extreme changes are of larger magnitude.

18. Allen & Ingram (2002) suggested changes to the heaviest rainfall events could be predicted based on the climate sensitivity and increases in atmospheric moisture suggested by Clausius-Clapeyron. Turner & Slingo (2009a) showed that this could also apply locally for the South Asia region in HadCM3. Predictability in changes to the heaviest rainfall Maximum increase of around 13%, as predicted by atmospheric warming at 2xCO 2 and Clausius- Clapeyron. ratio 1xCO 2 2xCO 2

19. Predictability in changes to the heaviest events: 3 case studies We examine 3 model case studies of different behaviour in predictions vs. measured changes: 1) Changes above those predicted: gfdl_cm2_1 and 5 others (6/15). 2) Changes below predictions: ipsl_cm4 and 2 others (3/15). 3) Changes inline with predictions: cnrm_cm3 and 5 others (6/15), as well as HadCM3. Some models suggest remarkable predictability of changes to maximum precipitation intensity based on thermodynamic arguments. Allen & Ingram (2002) argued that monsoons could undergo larger increases due to feedbacks between latent heat release and moisture convergence.

20. Rainfall statistics CMIP3 models overestimate drizzle at the expense of heavy precipitation. Can we trust projections of extremes? observations

21. Summary State-of-the-art coupled models have some skill in simulating the monsoon and its seasonal cycle of rainfall in South Asia. Short timescale variability and teleconnections could be improved. Further research is required to narrow the range of uncertainty in climate projections.

22. Thank You Email: a.g.turner@reading.ac.uka.g.turner@reading.ac.uk Web: www.met.rdg.ac.uk/~sws05agtwww.met.rdg.ac.uk/~sws05agt

23. Inter-model spread in increases to heaviest monsoon rainfall over India Large model spread of increases in heaviest extremes. Appears to exceed Clausius-Clapeyron prediction. CO 2 doubling (15 models)CO 2 stabilization (9 models)