Ž .
y1
Ž .
y1
higher than Lk and k rH
RT . This is observed for H O scavenged by small
t t
eff 2
2
drops of 10 mm radius when no SO chemistry is involved as can be seen on Fig. 3a.
2
For big drops, the total content of H O in a single drop may become so important in
2 2
phase equilibrium, that the resulting equilibration time seriously exceeds the chemical .
equilibration times and the model timestep , even with fast exchanges at the interface of Ž
. the drops Lk term . In such case, deviations from Henry’s law is observed with q - 1
t
at the onset of the cloud and q 1 during the dissipation stage of the cloud. Fast exchanges are also revealed through the high variations in the q factor.
Ž .
In the second run without SO the value of D of the hydrogen peroxide is small
2 g
compared to Lk for both 10 mm and 100 mm cases, as D is small compared to
t aq
Ž .
Ž Ž
.. k rH
RT. It is noteworthy that both D q Lk
and D q k rH RT
decreases
t eff
gas t
aq t
eff
by a factor of 100 between the 10 mm and 100 mm cases, meaning that the equilibration time is much longer for big drops. When sulfur dioxide is added, Lk is still higher than
t
D , while D becomes higher than k rH
RT by a factor of 2000 in the case of small
g aq
t eff
Ž .
drops and only by a factor of 2 up to 8 in the big drop case. The driving time scale in Ž
.
y1
the aqueous phase equilibrium is then k rH
RT . Hence, the method assuming
t eff
instantaneous Henry’s law equilibrium is not applicable anymore, and the q factor is Ž
. always less than 1 Fig. 3 .
4. Deviations from Henry’s law for hydrogen peroxide in a polydisperse cloud
Ž .
The same type of study Audiffren et al., 1996 has been performed in the framework of a mesoscale model simulating orographic clouds formed from different air masses
Ž .
continental vs. maritime . This mesoscale meteorological model is coupled with the chemical module described in Section 3, except that the chemical equation system is
Ž solved with a QSSA type solver more appropriate see Audiffren et al., 1998 for more
. details . A complete description of the meteorological mountain wave scenario can be
Ž .
found in Chaumerliac et al. 1987, 1990 . The half width of the mountain is 25 km and its height is 1 km. The initial wind is horizontal and homogeneous with a velocity of 20
mrs. The initial atmosphere consists in a layer of constant lapse rate with 80 relative humidity up to 250 mb, which is topped by a dry isothermal layer. The mesoscale model
Ž
. Nickerson et al., 1986 covers an horizontal domain of 300 km with a grid spacing of
10 km. The timestep is 10 s and there are 16 vertical levels spaced in a terrain-following coordinate. The dynamical, microphysical and chemical processes are fully interactive in
the model. The simulation lasts 3 h and is two-dimensional. The microphysical Ž
. parameterization is based upon the work from Berry and Reinhardt 1974 .
Two runs are performed with different types of orographic clouds, originating from Ž
. two different air masses Fig. 4 . A continental air mass gives rise to a cloud composed
of many small droplets, non-precipitating, while the maritime cloud formed in a maritime air mass contains less, but larger drops that can precipitate. The total liquid
water content is identical in both cases and only the partitioning between cloud water and rainwater, due to the autoconversion process differs. This provides us with an
idealized situation for comparing chemical contents of such clouds. Chemical species
Fig. 4. Vertical cross-sections of the cloud water and rainwater mixing ratios for a continental non-participat- ing cloud and a maritime precipitating cloud.
dissolve both in cloud and rain phases and once included in the drops they can undergo transfer through microphysical processes. This last characteristic complicates the previ-
ous situation, where only the time evolution of a cloud was considered, and make the cloud chemistry more realistic.
Ž .
Winiwarter et al. 1992 reported that sampling in a chemically homogeneous droplet population in a cloud system where the liquid water content varies with time leads to
subsaturation of the aqueous phase for nondissociating species. Pandis and Seinfeld Ž
. 1991 gave additional explanations for the results of these samplings. Independent of
sampling duration, the variation with time of the liquid water content is, in their case, the main reason for producing an apparent deviation from Henry’s law equilibrium due
Ž .
to the nonlinearity of the aqueous sampling samplings on discontinuous time intervals . Hence, measurements in polydisperse clouds can lead to deviations from Henry’s law
for the bulk sampling which vary with the location in the cloud. In monodisperse clouds Ž
. continental cloud in our case for which all the droplets have the same size one may
surmise that sampling in the middle of the cloud or on the edges would give the same results. This is what we seek to verify. As seen before, the ratio q s C rLH
RTC
aq eff
g
gives a measure of the deviation from Henry’s law. C can be either the cloud water
aq
Ž .
concentration or the rainwater concentration mole per cubic centimeter air and L the corresponding liquid water content. This ratio has been plotted for both the soluble
Ž .
species H O Fig. 5 . Fig. 5a and b shows the evolution of this q factor along the
2 2
mountain slope for the continental and maritime cloud, while Fig. 5c refers to the maritime rainwater. We clearly observe that the cloud droplets are always subsaturated
upwind of the mountain top and supersaturated downwind where cloud evaporation occurs. Henry’s law equilibrium is attained in the middle of the cloud and not on cloud
edges. This can be explained by the fact that surrounding air acts as an important gaseous source for hydrogen peroxide on cloud edges by entrainment. In the case of the
maritime cloud also, deviations from Henry’s law occur in parts of the cloud where rain is present. Vertically, the cloud water content decreases not only because of evaporation,
but mainly through conversion of cloud into rain. This can be related to an open system
Ž .
as studied by Pandis and Seinfeld 1991 , where a decrease in cloud water leads to an
Fig. 5. Evolution as a function of the distance from the mountain top of the factor q sC r LH RTC
aq eff
g
Ž . Ž .
Ž . showing the deviations from Henry’s law in case of a a continental cloud, b a maritime cloud, and c the
maritime rain field and for several heights.
increase in the q factor. In contrast, in maritime rainwater, drops on the upwind side are supersaturated in the upper part of the cloud. In all remaining parts of the precipitating
Ž cloud, raindrop concentrations of H O are very low under Henry’s law value q is less
2 2
. than 1 even equal to 0.2 near the ground . This net deviation from Henry’s law is likely
due to the raindrop size, which increases drastically on that side of the mountain. In conclusion, dealing with a mesoscale meteorological model simulating orographic
cloud has highlighted some more features about chemical species scavenging, compared to what we already showed with a box model. In particular, the box model did not
include effects of microphysical processes that link a small drop population to a large drop population in the maritime case. It seems that microphysical processes that
transport H O
from the cloud to the rain phase act in the mesoscale model as the
2 2
reagent SO was acting in the box model, displacing the equilibrium below Henry’s law
2
Ž .
value. Also for big drops like the ones in the maritime case , we observe a high variability of the q factor, related to fast exchanges compared with in the box model.
Finally, precipitation efficiently removes H O , while in the box model, a decrease in
2 2
the liquid water content allows a complete release of the chemical species into the gas phase.
5. Conclusion
