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Indian monsoon: novel approach allows early forecasting

04/20/2016 - The Indian monsoon’s yearly onset and withdrawal can now be forecasted significantly earlier than previously possible. A team of scientists developed a novel prediction method based on a network analysis of regional weather data, and will propose this approach to the Indian Meteorological Department. The heavy summer rains are of vital importance for millions of farmers feeding the subcontinent’s population. Future climate change will likely affect monsoon stability and hence makes accurate forecasting even more relevant.
Indian monsoon: novel approach allows early forecasting

Indian rice farmers depend on monsoon rainfall. Photo: Hemera/thinkstock

“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 Veronika Stolbova from the Potsdam Institute for Climate Impact Research (PIK) and the University of Zurich, the lead-author of the study to be published in the Geophysical Research Letters. “We found that in North Pakistan and the Eastern Ghats, a mountain range close to the Indian Ocean, 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.

Rainfall timing is key for growing rice, but also for generating hydro power 

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 June to September monsoon rainy season. Even a slight deviation of the monsoon can lead to droughts or floods, causing damages. 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 percent and for withdrawal in more than 80 percent of the considered years. The main advantage of the proposed approach is that it allows to improve the time horizon of the prediction 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 Niño – Southern Oscillation (ENSO), particularly in its La Niña phase. This phenomenon significantly alters monsoon timing and decreases the prediction accuracy in existing methods.

Network analysis: “The climate system is just like Facebook” 

“We see the climate system as a network, just like the social networks so many people are using in their everyday life,” says co-author Jürgen Kurths, head of PIK’s research domain Transdisciplinary Concepts & Methods. “On Facebook or Twitter, you can follow how news is spreading, one posting leading to many others. In the climate system, not people but geographical regions are communicating – admittedly in a quite complex way.” Like Facebook postings or tweets that get shared over and over again, 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 say, 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,” says Elena Surovyatkina of the Russian Academy of Sciences’ Space Research Institute, currently a PIK guest scientist. "We discovered how to use precursors in a new way – to find regions where critical conditions for an occurence of the Indian monsoon originate.” This has been achieved in cooperation with co-author Bodo Bookhagen from the University of Potsdam. In the future, this method can also help to unravel mysteries of other climate phenomena. 

Climate change affects rainfall, making accurate predictions more important 

Global warming due to mankind’s greenhouse-gas emissions from burning fossil fuels already affects the Indian monsoon and – if unabated – is expected to do even more so in the future. “We’re seeing this in our data, and other research also points in this direction,” says project-lead Jürgen Kurths. “The timing of Indian summer monsoon, on which the livelihoods of many million people depend, is likely becoming more erratic. This makes early and accurate forecasting ever more crucial.”


Article: Stolbova, V., E. Surovyatkina, B. Bookhagen, and J. Kurths (2016): Tipping elements of the Indian monsoon: Prediction of onset and withdrawal. Geophys. Res. Lett., 43, 1–9 [doi:10.1002/2016GL068392]

Weblink to the article: http://onlinelibrary.wiley.com/doi/10.1002/2016GL068392/full


Update May 06, 2016: 
Forecast of the Onset date of Indian Summer Monsoon - 2016 over the Eastern Ghats (20N, 80E)

The Indian Summer Monsoon is likely (with a 73% probability) to set over The Eastern Ghats region (20N, 80E, Gadchiroli Forest Reserve) on or around 13th June (+/- 4 days). The onset of monsoon is a date of the arrival of monsoon over a particular region of the Indian subcontinent and represents the beginning of rainy season over the region. We estimate a date of monsoon onset over the EG using our recently developed method of long-range forecasting (more than 30 days in advance), which uses the following predictors: i) in the Eastern Ghats region, a daily mean surface air temperature falls, and a relative humidity rises to critical thresholds for the EG region; ii) in North Pakistan, a daily mean surface air temperature rises, and a relative humidity falls to the same values, as critical thresholds defined for the EG region.

Fig.1 3. Prediction of onset date (OD): case study 2016. Air temperature at 1000 hPa (A); relative humidity at 1000 hPa (B). Time series fromreference points (NCEP/NCAR data): previous 14 year mean (black) and 2016 values for NP (blue) and the EG (red). Grey lines show time seriesfrom the NP and EG for the training period of previous 14 years. Saturation temperature Tsat (A) and saturation humidity rhsat (B) are marked byhorizontal black solid lines (Tsat = Tonset, Tonset and rhsat calculated as intersection of mean time series for the training period from the EG andNP) and day of the saturation (dsat) (when temperature in the EG in 2016 reaches Tsat)—with dark blue. Orange lines indicate trends to the meantime series in the NP and EG for the training period, light blue—trends for 2016. Black solid lines indicate mean values of the OD (< OD>) forthe training period. Dotted grey line corresponds to the predicted onset (ODp).

Fig.1 3. Prediction of onset date (OD): case study 2016. Air temperature at 1000 hPa (A); relative humidity at 1000 hPa (B). Time series from reference points (NCEP/NCAR data): previous 14 year mean (black) and 2016 values for NP (blue) and the EG (red). Grey lines show time series from the NP and EG for the training period of previous 14 years. Saturation temperature Tsat (A) and saturation humidity rhsat (B) are marked by
horizontal black solid lines (Tsat = Tonset, Tonset and rhsat calculated as intersection of mean time series for the training period from the EG and NP) and day of the saturation (dsat) (when temperature in the EG in 2016 reaches Tsat)—with dark blue. Orange lines indicate trends to the mean time series in the NP and EG for the training period, light blue—trends for 2016. Black solid lines indicate mean values of the OD (< OD>) for the training period. Dotted grey line corresponds to the predicted onset (ODp).

Our estimation is valid in the case of no a bogus monsoon onset. Otherwise, the onset date will shift for the duration of a bogus monsoon. We will update our long-term forecasting as new data from NCEP/NCAR become available.

See also: Successful early forecasting of Indian Monsoon
https://www.pik-potsdam.de/news/in-short/early-forecasting-of-indian-monsoon-is-successful


For further information please contact:
PIK press office
Phone: +49 331 288 25 07
E-Mail: press@pik-potsdam.de
Twitter: @PIK_Climate
www.pik-potsdam.de

by Sarah Messina — last modified Oct 27, 2016 08:53 AM

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