Monsoon depressions, that form during the Indian summer monsoon season (June to September) are
known to be baroclinic disturbances (horizontal scale 2000 to 3000 km) and are driven by deep
convection that carries a very large vertical slope towards cold air aloft in the upper troposphere. Deep convection is nearly always organized around the scale of these depressions. In the maintenance of the
monsoon depression the generation of eddy kinetic energy on the scale of the monsoon depression is
largely governed by the “in scale” covariance of heating and temperature and of vertical velocity and
temperature over the region of the monsoon depression. There are normally about 6 to 8 monsoon
depressions during a summer monsoon season. Recent years 2009, 2010 and 2011 saw very few (around
1, 0 and 1 per season respectively). The best numerical models such as those from ECMWF and US
(GFS) carried many false alarms in their 3 to 5 day forecasts, more like 6 to 8 disturbances. Even in
recent years with fewer observed monsoon depressions a much larger number of depressions is noted in
ECMWF forecasts. These are fairly comprehensive models that carry vast data sets (surface and satellite
based), detailed data assimilation, and are run at very high resolutions. The monsoon depression is well
resolved by these respective horizontal resolutions in these models (at 15 and 35km). These
models carry complete and detailed physical parameterizations. The false alarms in their
forecasts leads us to suggest that some additional important ingredient may be missing in these current
best state of the art models. This paper addresses the effects of pollution for the enhancement of cloud
condensation nuclei and the resulting disruption of the organization of convection in monsoon
depressions. Our specific studies make use of a high resolution mesoscale model (WRF/CHEM) to
explore the impacts of the first and second aerosol indirect effects proposed by Twomey and
Albrecht. We have conducted preliminary studies including examination of the evolution of radar
reflectivity (computed inversely from the model hydrometeors) for normal and enhanced CCN effects
(arising from enhanced monsoon pollution). The time lapse histories show a major disruption in the organization of convection of the monsoon depressions on the time scale of a week to ten days in these
enhanced CCN scenarios.
This is a study on seasonal climate forecasts for the Asian Monsoon region. The unique
aspect of this study is that it became possible to use the forecast results from as many as 16 state of
the art coupled atmosphere-ocean models. A downscaling component, with respect to observed
rainfall estimates uses data sets from TRMM and a dense rain gauge distriburion; this enables the
forecasts of each model to be bias corrected to a common 25 km resolution. The downscaling
statistics for each model, at each grid location is developed during a training phase of the model
forecasts; the forecasts from all of the member models use the downscaling coefficients of the
training phase. These forecasts are next used for the construction of a multimodel superensemble. A
major result of this paper is on the climatology of the model rainfall. From the downscaled
multimodel superensemble which shows a correlation of nearly 1.0 with respect to the observed
climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are
defined in terms of departures from the observed (rather than a model based) climatology.
The second part of this study addresses seasonal climate forecasts of Asian monsoon
precipitation anomalies. Seasonal climate forecasts over the larger monsoon Asia domain and over
the regional belts are evaluated. The superensemble forecasts invariably carry the highest skill
compared to the member models globally and regionally. This relates largely to the presence of large
systematic errors in models that carry low seasonal prediction skills. Such models carry persistent
signatures of systematic errors, and their errors are recognized by the multimodel superensemble.
One of the conclusions of this study is that given the uncertainties in current modeling for seasonal
rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology,
seems to provide the most promising results for the rainfall anomaly forecasts.
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