Bhopal, July 20 (IANS) Scientists at the Indian Institute of Science Education and Research (IISER) Bhopal have developed a statistical model that can predict the temperature of the Indian summer season for the months of March-April-May and extended climate anomalies in India, using the weather data from the preceding winter.
The model has also helped in understanding the relationships among various weather parameters and how they have dynamically co-evolved over the past 69 years.
“With climate change and global warming being increasingly recognised as a threat to the ecosystem, socio-economy and, perhaps, life itself, it is important to understand and be able to predict seasonal patterns for better preparedness,” said lead researcher Pankaj Kumar, Assistant Professor, Department of Earth and Environmental Sciences, IISER Bhopal, in a statement.
“We have used a multi-linear statistical technique called Canonical Correlation Analysis to predict summer temperatures and understand the relationships among the various weather parameters,” explained Aditya Kumar Dubey, a research scholar at IISER.
The researchers have used parameters such as the sea surface temperature, sea level pressure, zonal wind, precipitation, and maximum, minimum, and average air temperatures from the previous winter, to predict the summer temperatures throughout India.
“We have found that the summer temperature has seen a significant increase, especially in North India, during recent decades,” said Kumar. The research has been recently published in the International Journal of Climatology.
The researchers have also shown that the summer temperature predictability is better for South India than North, due to the former’s proximity to the ocean and the greater impact of the sea surface temperature on summer heat in the subcontinent.
Because of the effect of the sea surface temperature, South India has been found to be warmer during El-Nino years and cooler during La-Nina. The North Indian summer, on the other hand, is affected by the high pressure and circulation systems at upper levels (around 5.5-12.5 km), which form a heat dome and lead to adiabatic heating, thereby pushing up the summer temperature irrespective of the El-Nino or La-Nina effect.
The model has been able to predict summer (March-April-May) temperatures a season ahead. The scientists have considered the role of all possible parameters in developing the predictive model and plan to elucidate the mechanisms behind their interplay.
“Timely, actionable and reliable climate prediction is crucial for policy making to help manage development opportunities and risks, and for adaptation and mitigation activities,” said Kumar.