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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
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