113
slope of the mountains and the mountain top can be seen during excess years compared to the years in the other category. The excess precipitation years have more
moisture (q, g/kg) along the slope of the mountains (figure not shown). The difference in specific humidity in these two categories of years is about 1.2 g/kg. Positive
differences are seen up to about 400 hPa. The patterns of u, v and q indicate that
during excess precipitation years, more moisture is transported from south (the
Arabian Sea) and the presence of a westerly trough on the slopes of high mountains
make the conditions favorable for excess precipitation.
In order to further understand the precipitation process and the cloud microphysics over the high Himalaya, several other diagnostic parameters have been examined. In the context present set of simulation, the water in the domain can be divided
into three categories: (i) water vapor, (ii) hydrometeors and (iii) precipitated water
on the ground. The microphysics scheme in the model represents ice particles by
separating them into predefined categories (small ice, snow, graupel, hail, etc.) with
a priori specification with fixed bulk densities and fall speeds within a particular
range for each category. The hydrometeors can be separated into non-precipitating
particles having mass mixing ratios of cloud liquid water (QCLOUD, g/kg) and ice
(QICE, g/kg) and precipitating particles having mass mixing ratios of rain water,
snow and graupels (QRAIN, QSNOW and QGRAUP in g/kg, respectively). During
model simulation steps, the microphysics scheme redistributes the total mass of
atmospheric water among the different phases and particles of water.
Figure 8 has the vertical cross-section of composite differences in QCLOUD,
QRAIN, QSNOW, and QICE from the model simulations of excess and deficit
years. These differences have further been averaged for the longitude band of
70°E–79°E and averaged over the entire season. Increased amount of cloud liquid
water (QCLOUD) is seen from about 30°N to 35°N extending up to 500 hPa in
Fig. 8a during excess years. QRAIN (Fig. 8b) is confined to only lower heights and
above about 700 hPa, no large values are seen. QSNOW difference (Fig. 8c) has the
largest magnitude and it starts at about 700 hPa and extends up to about 400 hPa or
higher. Maximum value is seen around 32°N and 36°N. Similar to snow, ice also
forms only above 700 hPa with a maximum reaching at about 400 hPa during excess
years as compared to deficit years (Fig. 8d). A comparison of precipitation difference (Fig. 6b) with the hydrometeors (Fig. 8) indicates that the zone of maximum
precipitation coincides with the zone of maximum mixing ratios of rain water, and
snow. The model is able to bring out characteristic difference in cloud microphysics
when there is more or less precipitation over the western Himalaya.
3.2 The RegCM4 Model Simulations
As already mentioned, the RegCM4 model simulations have been carried out forcing
the model with a GCM product instead of observed reanalysis data as described earlier for the WRF model. Figure 9a shows the observed seasonal mean (DJF) precipitation climatology (mm/day) for the period 1982–2009. These are gridded
High-Resolution Dynamic Downscaling of Winter Climate over the Himalaya
slope of the mountains and the mountain top can be seen during excess years compared to the years in the other category. The excess precipitation years have more
moisture (q, g/kg) along the slope of the mountains (figure not shown). The difference in specific humidity in these two categories of years is about 1.2 g/kg. Positive
differences are seen up to about 400 hPa. The patterns of u, v and q indicate that
during excess precipitation years, more moisture is transported from south (the
Arabian Sea) and the presence of a westerly trough on the slopes of high mountains
make the conditions favorable for excess precipitation.
In order to further understand the precipitation process and the cloud microphysics over the high Himalaya, several other diagnostic parameters have been examined. In the context present set of simulation, the water in the domain can be divided
into three categories: (i) water vapor, (ii) hydrometeors and (iii) precipitated water
on the ground. The microphysics scheme in the model represents ice particles by
separating them into predefined categories (small ice, snow, graupel, hail, etc.) with
a priori specification with fixed bulk densities and fall speeds within a particular
range for each category. The hydrometeors can be separated into non-precipitating
particles having mass mixing ratios of cloud liquid water (QCLOUD, g/kg) and ice
(QICE, g/kg) and precipitating particles having mass mixing ratios of rain water,
snow and graupels (QRAIN, QSNOW and QGRAUP in g/kg, respectively). During
model simulation steps, the microphysics scheme redistributes the total mass of
atmospheric water among the different phases and particles of water.
Figure 8 has the vertical cross-section of composite differences in QCLOUD,
QRAIN, QSNOW, and QICE from the model simulations of excess and deficit
years. These differences have further been averaged for the longitude band of
70°E–79°E and averaged over the entire season. Increased amount of cloud liquid
water (QCLOUD) is seen from about 30°N to 35°N extending up to 500 hPa in
Fig. 8a during excess years. QRAIN (Fig. 8b) is confined to only lower heights and
above about 700 hPa, no large values are seen. QSNOW difference (Fig. 8c) has the
largest magnitude and it starts at about 700 hPa and extends up to about 400 hPa or
higher. Maximum value is seen around 32°N and 36°N. Similar to snow, ice also
forms only above 700 hPa with a maximum reaching at about 400 hPa during excess
years as compared to deficit years (Fig. 8d). A comparison of precipitation difference (Fig. 6b) with the hydrometeors (Fig. 8) indicates that the zone of maximum
precipitation coincides with the zone of maximum mixing ratios of rain water, and
snow. The model is able to bring out characteristic difference in cloud microphysics
when there is more or less precipitation over the western Himalaya.
3.2 The RegCM4 Model Simulations
As already mentioned, the RegCM4 model simulations have been carried out forcing
the model with a GCM product instead of observed reanalysis data as described earlier for the WRF model. Figure 9a shows the observed seasonal mean (DJF) precipitation climatology (mm/day) for the period 1982–2009. These are gridded
High-Resolution Dynamic Downscaling of Winter Climate over the Himalaya