paper) is extended to include precipitation. In it the subgrid scale disturbances are treated as stochastic series of monochromatic waves:

The stochastic method to treat GWs in Lott Guez and Maury (2013) (an EMBRACE

paper) is extended to include precipitation. In it the subgrid scale disturbances are treated as stochastic series of monochromatic waves:

w ' = ∑

^C

w '

∑

^C

=1

where

The C'

s generalised the intermittency coefficients of Alexander and Dunkerton (1995), and used in Beres et al. (2005).

The stochastic parameterization of GWs due to convection developped at IPSL and impact on the Eq. Strato.

To include convective forcings, we consider the subgrid scale precipitation as stochastic series

P '

= ∑

^C

^P

^{' where P}

^{' =ℜ} [ ^{P }

^e

]

We take ∣ ^{P }

∣ ^{= P}

The subgrid scale standard deviation of the precipitation equals the gridscale mean: White noise hypothesis!

Also, the k's and w's are chosen randomly.

G _uw tuning parameter of the CGWs amplitude

 F

=

k 

∣ k ^

∣

∣ k ^

∣

e

N 

G

 ^{ R L}

H c



^P

 z tuning parameter or scale of the heating depth Launching flux

deduced from forced wave

theory:

a) Precipitation Kg.s^-1.day^-1

b) Surface Stress amplitude (mPa)

30S 30N Eq 60N 90N

60S 90S

30S 30N Eq 60N 90N

60S

90S0 60E 120E 180 120W 60W 0

0

180 120W 60W

120E 60E

0

30S 30N Eq 60N 90N

60S 90S

30S 30N Eq 60N 90N

60S 90S

7 6 5 4 3 2 1 0

8 10 4 2

0 6

mPa Kg.s^-1.day^-1

30 26 22 16 12 8 4

42 36 30 24 18 12 6

Precipitations and surface stresses averaged over 1week (1-7 January 2000) Results for GPCP datas and ERAI

Offline tests with ERAI and GPCP

The CGWs stress is now well distributed along where there is strong precipitations

It is stronger on average in the tropical regions, but quite significant in the

midlatitudes.

The zonal mean stress comes from very large values issued from quite

few regions.

The stochastic parameterization of GWs due to convection developped at IPSL

and impact on the Eq. Strato.

On the benefit of having few large GWs rather than a large ensemble of small ones:

Offline it happens that the scheme can be used taking for the precip the zonal and temporal mean values.

Here are only shown the stress and tendencies of the waves with positive phase speed.

Lott and Guez, JGR 2013, submitted

CGWs stress

CGWs drag

Same zonal mean stress

Real precip. Stress amplitude (CI=2mPa) Uniformized precip. Stress amplitude (CI=2mPa)

Eq 30N 60N 90N

30S 60S

90S0 60E 120E 180E 60W 120W 0 60E 120E 180E 60W 120W

Real precip. du/dt *e(-z/2H), CI= 0.1 m/s/d Uniformized precip. du/dt *e(-z/2H), CI= 0.1 m/s/d

Eq 30N 60N 30S

60S 60S 30S Eq 30N 60N

More drag near and above stratopause Slightly less drag in the QBO region

50 60

40 30 20 10

50 60

40 30 20 10

0.15 0.25 0.35 0.45

0.05 0.05 0.15 0.25 0.35 0.45

The stochastic parameterization of GWs due to convection developped at IPSL

and impact on the Eq. Strato.

Online results:

LMDz version with 80 levels, dz<1km In the stratosphere

QBO of irregular period with mean around 26month, 20% too small amplitude

Westerly phase lacks of connection with the stratopause SAO

Also, no negative impacts on the SAO, subtropical winds,

ect...

(even slightly positive improvement of the SAO phase, and of the subtropical summer eastearlies

in the mesosphere)

Lott and Guez, JGR13, submitted

a) LMDz with convective GWs LMDz+CGWs

b) MERRA

1000 100

10 20 1 0.1 1000 100

10 20 1 0.1

1990 1992 1994 1996 1998

2 4 6 8

The stochastic parameterization of GWs due to convection developped at IPSL

and impact on the Eq. Strato.