1 Investigating mechanisms of future changes in precipitation extremes simulated in GCMs I’d like to thank Dr. M. Sugiyama (CRIEPI), Dr. H. Shiogama (NIES), and Dr. S. Brown (UKMO). Seita Emori National Institute for Environmental Studies

2 Research on physical basis of precipitation extreme changes Research on mostly statistical analyses are excluded Precipitable water (Clausius-Clapeyron) Trenberth (1999) Allen and Ingram (2002) Precipitable water + vertical motionEmori and Brown (2005) Pall et al. (2007) Lenderink & van Meijgaard (2008) Precipitable water + vertical motion + vertical profile + temperature when precipitating O’Gorman and Schneider (2009a, 2009b) Sugiyama et al. (2009)

Precipitable water + vertical motion (Emori and Brown, 2005) Use daily mean 500hPa vertical velocity (  )  as a proxy of ‘dynamic disturbance’ at each grid/day Composite daily precipitation for each  -class to give ‘expected’ precipitation for given  at each grid Is the change in precipitation due to: –Change in  ? (dynamic change) –Change in expected precipitation for given  ? (non-dynamic or ‘thermodynamic’ change) Cf. Bony et al. (2004) for cloud-radiation analysis

Extreme Precipitation Change (99 th percentile) 0 500hPa vertical velocity (upward) expected precipitation P 99 P 99 +  P 99  * 99  * 99 +  * 99 Dynamic Thermodynamic Covariation

ensemble mean of 4 CMIP3 CGCMs and 2 AGCMs +50 [%] (relative to control)-500 Total Dynamic Thermodynamic Annual Mean Precipitation Change 99 th percentile Precipitation Change Results

6 Precipitable water + vertical motion + vertical profile (Sugiyama et al., 2009) Space-time CDF of daily precipitation (Allen and Ingram 2002, Pall et al. 2007) –Create CDF by combining space and time for very rare events Sample size (30S-30N, MIROC medres ， ocean + land): 128 (long.) X 32 (lat.) X 365 days X 20 years ＝ 29,900,800 This enables calculation of very rare events (eg %-itle) –Focus on ocean grid points (avoid mountain effects) –Composite various variables with respect to daily precipitation extremes

7 MIROC-hires Tropics (30S-30N)

8 Precipitable water Precipitation Precipitable water + 500hPa omega MIROC-hires Tropics (30S-30N)

9 Approx. humidity budget b: O’Gorman and Schneider (2009) Condensation, assuming vertical motion follows a pseudoadiabatic lapse rate a: gross moisture stratificaiton (e.g., Chou et al. 2009) Parameter that characterizes vertical profiles of humidity and vertical motion

10 dT in denominator omitted MIROC-hires Tropics (30S-30N) Change in ‘a’ is negative and suppressing the overestimation of the scaling by precipitable water + vertical motion, especially for higher percentiles. Negative change in ‘a’ is due to changes in vertical profiles of humidity (moist adiabat) and vertical motion.

11 MIROC-hires Tropics (30S-30N) The profile of vertical motion shifts upward under global warming. Change in omega is smaller in lower layers than at 500hPa.

12 dT in denominator omitted MIROC-hires Mid-latitudes (30N-60N, DJF) Change in vertical motion is small. Precipitation change is mostly constrained by thermodynamics.

13 Models disagree a lot. 6 models: Δ P > Δ (precipitable water) ( ) in legend: Δ (precipitable water) CMIP3 models Tropics

14 Models agree better. Mostly constrained by precipitable water. ( ) in legend: Δ(precipitable water) CMIP3 models Mid-latitudes

15 Mid-latitude precipitation extremes: mostly thermodynamic –With correction on vertical profiles (moist adiabat) Tropical precipitation extremes: require full knowledge of vertical motion (strength and vertical profile) Precipitation extremes exceeding the Clausius- Clapeyron prediction might occur, as shown in MIROC and some CMIP3 models. –Reproducing them in GCM is challenging because of significance of disturbances like tropical cyclones. Conclusions