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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 EmoriNational Institute for Environmental Studies

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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 motion Emori 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

* * *99 99 99 99( )

P PP P

Extreme Precipitation Change (99th percentile)

*99 99( )P P

0500hPa vertical velocity (upward)

exp

ect

ed

pre

cip

itatio

n

P99

P99+P99

*99 *99+*99

Dynamic Thermodynamic Covariation

ensemble mean of 4 CMIP3 CGCMs and 2 AGCMs

+50 [%] (relative to control)-50 0

Total

Total

Dynamic

Dynamic

Thermodynamic

Thermodynamic

Annual Mean Precipitation Change

99th percentile Precipitation Change

Results

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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 ＝ 29,900,800This enables calculation of very rare events (eg. 99.999%-itle)

– Focus on ocean grid points (avoid mountain effects)

– Composite various variables with respect to daily precipitation extremes

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MIROC-hires Tropics (30S-30N)

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Precipitable water

Precipitation

Precipitable water+ 500hPa omega

MIROC-hires Tropics (30S-30N)

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Approx. humidity budget

EPg

dpq

t

q

)(u

Pg

dp

dp

dq

P

P

a

a

W

W

500

500

P

P

b

b

500

Wgdp

pq

ag

dp

p

qb

e

*

*

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

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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.

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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.

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dT in denominator omitted

MIROC-hires Mid-latitudes (30N-60N, DJF)

• Change in vertical motion is small. • Precipitation change is mostly constrained by

thermodynamics.

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• Models disagree a lot.• 6 models: ΔP > Δ(precipitable water)

( ) in legend: Δ(precipitable water)

CMIP3 models Tropics

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• Models agree better.• Mostly constrained by

precipitable water.

( ) in legend: Δ(precipitable water)

CMIP3 models Mid-latitudes

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• 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