ICTS

Indian Summer Monsoon is a multi scale system with predominant subseasonal and seasonal components. The relation between subseasonal and interannual variability are also intriguing especially in the context of how modulations by the tropical zonal modes of variability influence the monsoon. Monsoon onset is very much a synoptic timescale phenomenon but has some dependence on the seasonality of the subseasonal variability as well as decadal modulation. Withdrawal is not as abrupt as the onset but it is also modulated at interannual and decadal timescales. It is still unclear if subseasonal variability derives its energy from baroclinic or barotropic instability. Are there weather regimes that link the seemingly disparate timescales and local and remote influences on the monsoon system? It is important to explore which aspects of the monsoon dynamics may be reducible to theoretical approaches and how they maybe sewn back together to capt ure the monsoon in its full glory.

We propose a representation of the Indian summer monsoon rainfall in terms of a probabilistic model based on a Markov Random Field, consisting of discrete state variables representing low and high rainfall at grid-scale and daily rainfall patterns across space and in time. These discrete states are conditioned on observed daily gridded rainfall data from the period 2000- 2007. The model gives us a set of 10 spatial patterns of daily monsoon rainfall over India, which are robust over a range of user-chosen parameters as well as coherent in space and time. Each day in the monsoon season is assigned precisely one of the spatial patterns, that approximates the spatial distribution of rainfall on that day. Suc h approximations are quite accurate for nearly 95% of the days. Remarkably, these patterns are representative (with similar accuracy) of the monsoon seasons from 1901 to 2000 as well.

Normally, remote forcing (teleconnection) to Indian summer monsoon are quantified simultaneously. This includes connecting summer monsoon rainfall to summe r El Nino and Southern Oscillation (ENSO) condition. In this study we investigate the impact of slow evolution of the climate system from previous winter to present summer on the Indian summer monsoon rainfall.

Using past data, we showed that if previous winter was a La Nina, the rainfall in current summer over India would be less than normal (-ve anomaly). In other words, preceding winter La Nina increases the probability of a decrease in summer monsoon rainfall intensity. We showed that even if the present summer is ENSO neutral, the mean rainfall over India is on an average 4% below its long-term normal when it was preceded by La Nina. And if the present summer is El Nino (that normally causes decrease in rainfall), last winter's La Nina increases probability of drought. This wa s the first time we showed the asymmetry between La Nina and El Nino of the past season on Indian summer monsoon rainfall. Such delay in impact happens because of slow propagation of surface pressure anomalies in the subtropics as ENSO changes its state with season. Since the state of ENSO of the previous winter is known while providing seasonal rainfall outlook before summer, this result provides guidance to improve quantitative prediction of monsoon rainfall over India.

Reference: Chakraborty, A., 2018: Preceding winter La Niña reduces Indian summer monsoon rainfall. Environmental Research Letters, 13, 054030, doi:10.1088/1748-9326/aabdd5.