IMD expects 'normal' monsoon but uncertainty looms

Threat of El Nino phenomenon adds element of uncertainty

April 18, 2017 05:24 pm | Updated 11:30 pm IST - New Delhi

The southern peninsula had registered deficient rainfall and several parts of Tamil Nadu, Karnataka and Kerala.

The southern peninsula had registered deficient rainfall and several parts of Tamil Nadu, Karnataka and Kerala.

India is likely to get ‘normal’ monsoon rains, according to the first official forecast of the season by the India Meteorological Department (IMD).

Rains are likely to be 96% of the 50-year average of 89cm for the monsoon season of June to September. They are expected to fan out favourably and “help agriculture.”

There is, however, a significant element of uncertainty in this forecast.

For one, there’s the looming threat of El Nino. Secondly, the IMD has adopted a new weather model this year, and it is still a work-in-progress.

Officials told The Hindu that they would be more confident by June, when the IMD updates its numbers.

The IMD’s estimate of 96% rains falls at the bottom edge of what it considers ‘normal’ monsoon rains. Every number forecast by the IMD has a built-in 5% error margin.

India saw drought years in 2014 and 2015. As for 2016, it received 3% less than the 89 cm average, despite an IMD forecast of ‘above normal’ rains.

Given the deficient pre-monsoon rains over large parts of Karnataka, Tamil Nadu and Kerala, good rains are essential this year to trap enough soil moisture for a healthy kharif crop, which is vital to keep inflation down and rural consumption up.

The El Nino — characterised by surface waters of the equatorial Pacific warming up more than half a degree — is known to dry up monsoon rains every six out of 10 years. This year, international weather models as well as the IMD’s own dynamical global climate forecasting system model indicate that El Nino conditions might set in during the “latter part of the monsoon.”

Another climate phenomenon, called the Indian Ocean Dipole (IOD), which refers to a swing in the sea surface temperatures in the western and eastern Indian oceans, is also known to influence the Indian monsoon. A ‘positive’ IOD can counter an ominous El Nino. In its assessment, the IMD expects “weak positive IOD” to develop in the latter half of the monsoon, which means that it’s unlikely to be a potent ally this year.

“We are watching these variables and expect more clarity around June,” said K.J. Ramesh, Director General, Meteorology, IMD. “But monsoon will be good for agriculture this time.”

In a departure from the norm, the IMD didn’t quantify the likelihood of monsoon rains being ‘below normal,’ or ‘deficient’, with Mr. Ramesh only letting on that there was a 38% probability of ‘near normal’ rains. In any given year, the odds of ‘near-normal’ monsoon rains are 33%.

An official familiar with the IMD’s forecast preparation said that the dynamical model had a “different definition” for normal rains and wasn’t yet completely synchronised to the IMD’s monsoon forecasting system. Last year, the dynamical model in April had forecast excess rains (more than 110%) and India ended up with 3% less.

The traditional, so-called April forecast model, is prepared by the IMD measuring five climate “predictors”, including the sea surface temperature in the south Indian Ocean in February, and the volume of warm water in the equatorial Pacific in February and March.

Permutations of these numbers are crunched and statistically compared to IMD’s century-old data bank of monsoon rainfall to arrive at a consensus figure of what monsoon rains are likely to be.

Since 2012, the April forecast has never been able to forecast the monsoon numbers right. In 2015, for instance, it said monsoon rains would be 93%, but India ended up with 86%. In 2014, it predicted 95% and the country ended up with 88%. On both occasions, the forecasts failed to signal the magnitude of the monsoon failure.

Last month, private weather forecasting company, Skymet had said that the monsoon was likely to be ‘below normal’ at 95% of the Long Period Average.

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