Ensemble Seasonal Prediction of Indian Summer Monsoon with
Atmospheric General Circulation Model
M.K. Soman
Climate and Global Modelling Division
Indian Institute of Tropical Meteorology, Pune 411 008, INDIA.
ABSTRACT
The interannual variability of the Indian summer monsoon simulated by
ensemble integrations of Hadley Centre Climate Model is examined to assess the
possibility of using the model for dynamical seasonal forecasting of monsoon rainfall.
Seasonal integrations with observed initial conditions and sea surface temperatures for
15 years are used in the study. Each year, 9 member ensemble runs starting from 23
May to 31 May initial conditions were made.
The model simulates the mean monsoon circulation and precipitation quite
well. However, the interannual variability is not adequately handled. Taking average
of ensembles substantially reduces the interannual variability of the Indian Monsoon.
The Co-efficient of variation (CV) of monsoon rainfall from a 17-year long
integration of the same model with observed SSTs was 7.3% whereas average rainfall
from the seasonal integration with 9 initial conditions gave a CV of 3.4%. The
observed CV of Indian summer monsoon rainfall is 10%. The standard deviation
(SD) among the members of ensemble of an year varies between 45 to 65 mm (CV=
6-8%). From the above it is clear that giving a quantitative forecast of areal average
monsoon rainfall is difficult. However, it is seen that if majority of the ensembles
indicate either positive or negative departure from normal, the qualitative forecast can
be of some use.
An interesting fact that came out of these analysis is that the members of each
ensemble show much larger variability in El Nino years compared to La Nina years.
The larger spread of monsoon rainfall over India is due to long delays in the monsoon
onset over India in some members of the El Nino ensembles.
Traditionally in India statistical methods are used for operational seasonal
forecasting of summer monsoon rainfall. During the last three years experimental
attempts have been made to use dynamical models for seasonal forecasting. Ensemble
integrations with persisted SST anomalies are carried out. The results of these
experiments will be presented.