Monsoon prediction: are dynamical models getting better than statistical models?

The Indian summer monsoon rainfall (ISMR) has remained remarkably stable during the last 140 years. The ISMR has varied between 70% and 120% of the long-term average of about 85 cm. Monsoon seasons with ISMR of less than 90% of the average are considered to be droughts, whereas those with more than 110% rainfall are considered as excess rainfall seasons. Although the variation is not large (Figure 1), it has a substantive impact on our agriculture and gross domestic product (GDP). We, therefore, can never take the monsoon for granted and every April/May with the summer heat scorching the countryside, we start worrying about the upcoming monsoon. In April 2012, a paper on monsoon prediction by Delsole and Shukla generated considerable interest. It was shown that the skill of predicting ISMR with coupled atmosphere–ocean models initialized in May is statistically significant and is much higher than empirical prediction from sea surface temperatures (SSTs) of May. Does this imply that dynamical models are getting better than statistical models? We consider here the implications of this paper and recent developments in monsoon prediction. Monsoon is predicted either by statistical models based on analysis of historical data to determine the relationship of ISMR, to a variety of atmospheric and oceanic variables over different parts of the world prior to the summer monsoon season, or by dynamical models based on the laws of physics. During 1932–2004, the operational statistical models evolved with changes in the predictors and finally use of power regression instead of linear regression, but the forecast skill did not improve much. However, nonlinear models developed recently have shown very high skill in the prediction of ISMR. With the rapid development of complex models of the atmosphere and coupled atmosphere–ocean system, and enhanced availability of computer power, in the last decade, it became possible to generate predictions of the monsoon with these dynamical models. These predictions are generated by integrating the governing equations from an initial state prior to the season. For many of us, dynamical models, based on laws of physics, are more appealing than statistical models which are based on the past behaviour of the system. However, until 2005, the skill of monsoon prediction by the dynamical models was too poor for them to be useful. Since then, there has been considerable improvement in the skill of the dynamical models. As the dynamical models are based on laws of physics, understanding why there are many models and how the models can be improved is important. The resolution of dynamical models is coarse and hence they do not model clouds from first principles. The relationship between clouds and other atmospheric parameters (called convective parameterization) is different in different models. In addition, parameterization of the energy fluxes between the interface of the atmosphere and ocean/land and land surface processes also vary. These parameterizations are empirical (with several tuneable parameters) and can be made more accurate when there are more field observations. During the last 40 years, extensive observations about clouds have been obtained from satellites and hence convective parameterizations have improved. When important facets of the phenomenon of interest are elucidated and theoretical studies lead to more insight into the underlying mechanisms, efforts are made to tune the models to represent the features/mechanisms realistically. In this way, the models can be developed to a level at which they yield better simulation of the phenomenon and higher skill in prediction. The first breakthrough in seasonal forecasting over the tropics was the development of models that could simulate and predict El Niño Southern Oscillation (ENSO). This was achieved because of the elucidation of the nature of ENSO with data from a new observational system of buoys over the equatorial Pacific and satellite data, and unravelling of the underlying mechanisms. As there is a strong link between the Indian monsoon and ENSO with a high propensity of droughts during the warm phase (El Niño), it was expected that skillful predictions of ISMR would be generated by dynamical models. However, until recently, the skill of prediction by the atmospheric or coupled general circulation models (AGCMs and CGCMs respectively) was poor. For example, almost all AGCMs in an international Atmospheric Model Inter-comparison project (AMIP) simulated deficit ISMR for the excess monsoon season of 1994 (Figure 2 a). Yet, almost all AGCMs could simulate at least the correct sign of the ISMR anomaly (whether deficit or excess) for the droughts or excess monsoon seasons associated with ENSO (e.g. 1988, Figure 2 b). In addition to variations in ENSO, variations in SST of the Indian Ocean and the winds and rainfall over this ocean