English Free Article | Weather n Climate | VOL. 16, ISSUE 96, May-June 2016 |

Study claims early forecast of Indian monsoon

A study at the Potsdam Institute for Climate Impact Research (PIK), Germany claims the Indian monsoon’s yearly onset and withdrawal can now be forecasted significantly earlier than previously possible. A team of scientists, V Stolbova, E Surovyatkina, B Bookhagen and I Kurths, developed a novel prediction method based on a network analysis of regional weather data. Future climate change will likely affect monsoon stability and hence makes accurate forecasting even more relevant.

‘We can predict the beginning of the Indian monsoon two weeks earlier, and the end of it even six weeks earlier than before which is quite a breakthrough, given that for the farmers every day counts,’ says Stolbova from PIK and the lead-author of the study Tipping elements of the Indian monsoon: prediction of onset and withdrawal published in the Geophysical Research Letters. ‘The study found that in North Pakistan and the Eastern Ghats, changes of temperatures and humidity mark a critical transition to monsoon,’ explains Stolbova. Conventionally, the focus has been on the Kerala region on the southern tip of India.

Prior information about monsoon timing is key for Indian farmers to determine when to carry out the sowing. Crops like rice, soybean and cotton are normally grown during the monsoon season from June to September. Even a slight deviation of the rains can lead to droughts or floods. Also, the length of the monsoon is relevant for planning hydro power generation since the rains are necessary to fill the dams and reservoirs.

The scientists tested their method with historical monsoon data. It gives correct predictions for onset in more than 70 per cent and for withdrawal in more than 80 per cent of the considered years. The main advantage of the proposed approach is that it allows improvement in the time horizon of the prediction as compared to the methods currently used in India. In addition, the new scheme notably improves the forecasting of monsoon timing during years affected by the global weather phenomenon El Nino-Southern Oscillation (ENSO), particularly in its La Nina phase. This phenomenon significantly alters monsoon timing and decreases the prediction accuracy in existing methods.

‘We see the climate system as a network, just like the social networks such as Facebook or Twitter so many people are using in their everyday life,’ says co-author Jürgen Kurths. In the climate system, geographical regions are communicating in quite a complex manner and temperature and humidity get transported from one place to another by atmospheric flows, such as winds.

Using the network analysis of complex non-linear systems, an advanced mathematical approach, for monsoon forecasting is unprecedented yet the approach shows good results. The major innovation, the authors says, ‘is to combine the network analysis with the subtle statistical analyses of the early warning signals for the monsoon onset and withdrawal. These precursor phenomena are often buried by huge piles of weather data and hence get overlooked. We discovered how to use precursors in a new way to find regions where critical conditions for an occurrence of the Indian monsoon originate. In the future, this method can also help to unravel mysteries of other climate phenomena.’

Global warming due to greenhouse gas emissions from burning fossil fuels already affects the Indian monsoon and if unabated it is expected to get even worse in the future. The timing of Indian summer monsoon, on which the livelihoods of many million people depend, is likely to become more erratic as per the study. This makes early and accurate forecasting ever more crucial.

However, these conclusive claims are yet to be verified as the forecasts for this monsoon season had not arrived within 30-50 days prediction period. The delay in verification raises doubt about the efficacy of this method of prediction.

 

Inputs from Potsdam Institute for Climate Impact Research (PIK), Germany.

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