Climate variability and change for China: Preparing for the next 30 years

1. Climate variability and change for China: Preparing for the next 30 years Uncertainties in 2xCO2 coupled GCM projections of future monsoon rainfall extremes Andrew Turner & Julia Slingo

2. Introduction

3. Active-break events on subseasonal timescales represent the largest variations of the Indian summer monsoon. 2002 2007 Introduction • Little is known about what will happen to extreme rainfall events in monsoon regions.

4. Outline • Introduction • Model set-up • Changes to rainfall extremes in HadCM3 • Comparison with the CMIP3 archive • Can we predict changes to the intensity of the most extreme events over monsoon regions? • HadCM3 vs. CMIP3 archive for China and India • Summary

5. Model set-up • Hadley Centre coupled model HadCM3: • Atmosphere 3.75 x 2.5 x 30 levels • Ocean 1.25 x 1.25 x 20 levels • 100-year pre-industrial control integration • 100-year (stable) 2xCO2 integration

6. Mean precipitation / change (JJAS) CMAP IMD 1 data HadCM3 1xCO2 2xCO2 minus 1xCO2

7. Rainfall distribution: model vs. obs. • Model has a tendency to drizzle (common with many convection schemes). • Cannot represent the upper tail correctly due to grid size. (each gridpoint) • Clear tendency for increased frequency of heavy events at 2xCO2, at the expense of moderate rainfall. • This trend is also noted in the IMD dataset1. 1B.N. Goswami, V. Venugopal, D. Sengupta, M.S. Madhusoodanan, P.K. Xavier (2006). Science314: 1442—1445.

8. Changing probability of extremes • Levels of precipitation associated with 95th and 99th percentiles are assessed at 1xCO2. • The change in probability of attaining these levels at 2xCO2 is plotted. 95th 99th • Chance more than doubles over northern India, head of Bay of Bengal and central China. From A.G. Turner & J.M. Slingo (2008). Submitted, QJRMS.

9. Changes in upper rainfall extremes • Precipitation 95th and 99th percentiles calculated at 1xCO2, 2xCO2 for each JJAS season, mean taken. 95th HadCM3 1xCO2 HadCM3 2xCO2 2xCO2 minus 1xCO2 99th • Extreme changes beyond mean changes, especially in northern India, south/central China and South/East China Seas. From A.G. Turner & J.M. Slingo (2008). Submitted, QJRMS.

10. Uncertainty in spatial distribution of extremes change • Projections of changes to the local distribution of precipitation extremes are very uncertain across GCMs. • Patterns of extreme change are strongly linked to mean changes  difficult to devise local strategies to deal with extreme change when there is such uncertainty in current GCM output. • IPCC AR4: • “Wet extremes more severe in models where mean precipitation increases” (Meehl et al. 2007). • “Projections concerning extreme events in the tropics remain uncertain” (Christensen et al. 2007). • Extreme percentiles examined in CMIP3 database daily output (picntrl & 1pctto2x experiments).

11. IPCC AR4 changes to mean rainfall intensity

12. Uncertainty in spatial distribution of extremes change: the AR4 models • Changes in 99th percentiles calculated from daily data in CMIP3 database in the same way as for HadCM3. • Wide variation in spatial distribution of summer extreme changes amongst IPCC AR4 models.

13. Uncertainty in spatial distribution of extremes change: the AR4 models – mean vs extreme

14. Regional precipitation extreme changes • Upper extremes are assessed at each gridpoint (after Allen & Ingram1) on a regional basis. tropics India • Maximum precipitation intensity increases broadly inline with Clausius-Clapeyron and measured climate sensitivity. 1M.R. Allen, W.J. Ingram (2002). Nature419: 224—232.

15. Regional precipitation extreme changes • Results for HadCM3 suggest remarkable predictability of maximum precipitation intensity based on purely thermodynamic arguments. • Allen & Ingram (2002) argued that tropics/monsoon regions could undergo larger increases due to feedbacks between latent heating and the large-scale flow. However HadCM3 undergoes no significant change in the Somali Jet at 2xCO2 (Turner et al. 20072). 2A.G. Turner, P.M. Inness, J.M. Slingo (2007). QJRMS133: 1143—1157.

16. Regional precipitation extreme changes • Upper percentile changes for monsoon regions assessed in daily data from the CMIP3 archive. • India (65–90E, 10–30N) & China (95–120E, 20–45N) in all models with daily data available. • Typical data input (for JJA and whole year): • 40yrs picntrl • 20yrs 1pctto2x at time of CO2 doubling (red) • 20yrs 1pctto2x from end of integration (blue, some models) • Surface air temperature used to measure climate sensitivity.

17. IPCC AR4 upper percentile intensity change (China)

18. IPCC AR4 upper percentile intensity change (China JJA)

19. Summary • For HadCM3: • Subseasonal extremes of precipitation increase beyond changes in the mean, and contribute a greater proportion of the seasonal rainfall total. • Maximum precipitation intensity for tropics and monsoon regions seem to increase based on the rate of atmospheric warming and thermodynamic arguments. • Compared with the CMIP3 archive: • Patterns of wet extreme change in monsoon regions follow quite closely changes in the mean • Some models show thermodynamic extreme repsonse. • Others have a strong dynamic component.

20. Uncertainties to resolve • We can’t say anything about local changes to extremes (needed to assess impacts of flooding on infrastructure, crop damage etc) until we have more convergence in patterns of mean change. • Need to understand dynamic vs. thermodynamic responses of extremes to climate warming, as well as any seasonally dependent response.

21. Thank you! a.g.turner@reading.ac.uk http://www.met.rdg.ac.uk/~sws05agt