Transcript JI Yano

new CRM diagnostics
dedicated to
convective parameterization
J-I Yano
P. Bechtold, J.-P. Chaboureau, F. Guichard,
J.-L. Redelsperger and J.-P. Lafore
LMD, CNRM-GAME & Laboratoire d’Aérologie,
Toulouse
• restitution of convective fluxes Yano et al. to appear
• potential energy convertibility (PEC)
CRMs-SCMs comparisons
A lot of diagnostics have been produced
domain average quantities
mean profiles of T, q, Q1, Q2, cloud water
convective fluxes, but not so much infact
sub-domain quantities, more delicate
often need to define criteria in CRMs (as in observations)
in-cloud properties
convective mass flux scheme
CRM simulations
MesoNH, 3D, Dx=2km
comeNH, 2D and 3D, Dx=500m to 2km
(nz ~ 50)
COARE cases : tropical convection over ocean
ARM case : and midlatitude convection over land
« segmentally constant » classification
knowing the mean properties of the different categories (up and down
convective, up and down stratiform…) is not enough to recover the total
convective fluxes
(more complex than shallow convection)
w' '    i w 
i
i
i
   i w 'i  'i
i
not negligeable
'
 c p w'
 (L v /c p ) w' q '
(only the resolved part of the convective flux above)
new model, new simulations, same conclusion
analysis in this « real » space:
in terms of mass-flux based parameterizations, the CRMderived estimates of mean properties within each category
alone are not useful to evaluate parameterized convective
fluxes
in the probability space
w' '

 p( w' , ' ) w' ' dw' d '
w’ < 0
’ > 0
w’ > 0
’ > 0
w’ < 0
’ < 0
w’ > 0
’ < 0
very wide range of
variability in  over
both half-planes
(w’> 0 & w’<0)
(from snapshot at z~5km)
u
u
d
d
(w , ) and (w , ) : mean values of (w, ) in the two 1/2 - planes (w' 0) & (w' 0)
u
(w ,
u
u-
u-
d
) , (w , ) , (w ,
see later
d
d-
d-
) and (w , ) : mean values of (w, ) in the 4 quadrants
same but for the convective area
The spread is not decreased much
u
u
pu w  
d d
pd w 
i i
 pi w 
u ,u,
d  ,d 
total
flux
improvement with 4 quadrants but still too low
p( w ' ,  ' )
p( w' ). p( ' )
higher correlations for higher values (4 corners)
the effective values contributing to fluxes are much
higher than simple means
use weighted average rather than simple average
We tried:
*
' 
 ' a '
'a
with parameter « a » , a = 0.25 hereafer
2 half-planes
4 quadrants
weighted 4 quadrants
sensible
heat
flux
latent
heat
flux
rms error for the 3 methods + standard deviation (solid)
same results for momentum flux
in terms of parameterization:
the decomposition in the (w’, ’) probability space
does not guaranty that the mass fluxes are the same
for all the variables (, q, u, v, tracers)
i wi for 
w
w
i wi for q
d-
w
d
w
u-
w
u
w
u-
u
combination of the 4 components into 2
 d 
*
 u   d 
*
u
*
*
positive drafts
negative drafts
i wi for 
nw
n
 pw
 pw
nw
p
n
*
*
*
*
  u wu   d  wd 
  u wu   d  wd 
i wi for q
 pw
p
nw
n
p
summary, discussion, in the context of today (GCSS)
one-to-one comparison between CRMs and SCMs not always
straightforward, e.g. convective fluxes
the method proposed here (with weighted averages) enables to
advance on this issue, by shifting to the probability space
behind is the idea that the mass flux formulation stands more as
an idealized framework rather than directly as a simplified picture
of the reality
(this view is coherent with recent LES analyses, e.g., showing the
weaknesses of this formulation to retrieve the variances)
…
to be further explored: the meaning of entrainment/detrainment
in this modified context
a generalization of CAPE with the
potential energy convertibility (PEC)
motivation:
CAPE is a very useful convective parameter but
CAPE has its own limitations in the context of
parameterization
e.g., extension of CAPE for entraining plumes
CAPE variations not simply linked to convection
e.g., diurnal cycle of deep convection over land, dry intrusion
periods over the warm pool
H
( w)* 
PEC 

ht
1
wb.dz
( w) * hs
PEC 
ht
hs b * .dz
1
H

( w)2dz
0
b*  wb /( w) *
CAPE 
ht
hs b.dz
PEC ~ rate of conversion of energy normalized
by a measure of the vertical momentum
Colors: 3 distinct COARE periods, from CRM runs
PEC far from perfect but provides a better measure of
moist convective instability than CAPE from the casestudies
possible strategy: to modify entrainment rates so that they
produce a buoyancy consistent with b* obtained from PEC
work in progress