People wade through flooded street at Sonarpur town near Kolkata, Eastern India, on September 21. File Photo by Piyal Adhikary/EPA-EFE
Nov. 15 (UPI) -- A new forecasting approach has accurately predicted significant climate phenomena from El Niño to the Indian Summer Monsoon in central India much earlier than ever before, the researchers who developed it said Monday.
Unlike traditional weather and climate forecasting, the newer network-based forecasting model assesses "the connectivity between different geographic locations," the researchers wrote in a commentary published Monday by the Proceedings of the U.S. National Academy of Sciences.
The approach measures the similarity in the evolution of physical quantities such as air temperatures at these locations, they said.
In the case of El Niño, research has revealed a strong connection between changes in air temperatures in the tropical Pacific in the calendar year before the onset of the event, the researchers said.
By taking these changes into account, forecasters have been able to predict El Niño onset up to one full year early, compared with about six months with standard prediction methods, co-author Josef Ludescher said in a press release.
In addition, "the onset of the Indian Summer Monsoon in central India, vital for the economy in this region, was predicted more than a month in advance," said Ludescher, a senior scientist at the Potsdam Institute for Climate Impact Research in Germany.
Extreme events like floods, heatwaves or droughts often arrive with little or no warning time, making effective short-term adaption challenging, if not impossible, according to the researchers.
These events can have significant impacts on the affected regions, including crop loss, property damage and, in some cases, loss of life, a recent United Nations report noted.
Traditional weather and climate forecasting rely primarily on numerical models imitating atmospheric and oceanic processes, such as heat or humidity exchange, the researchers said.
These models, while very useful, are unable to perfectly simulate all underlying climate processes, meaning phenomena such as monsoons, floods or droughts may be predicted too late to take steps to offset their effects, they said.
Instead of merely looking at a number of local interactions, however, the new model assesses the connectivity between different geographical locations, which can span continents or oceans, the researchers said.
This connectivity is detected by measuring the similarity in the evolution of physical quantities such as air temperatures at these locations by analyzing large-scale patterns in observational data, according to the researchers.
These patterns, or the connectivity between the locations and their evolution in time, can provide critical new information for forecasting, hopefully making affected regions safer and saving billions of dollars in rescue, recovery and rebuilding costs, they said.
Similar approaches have been incorporated into new tools designed to predict floods and drought, according to recent studies.
"Currently, there is no reliable prediction of heavy rainfall in the Easter Central Andes leading to floods and landslides with devastating impacts for the inhabitants in that part of South America," co-author Jürgen Kurths said in a press release.
"Our network-based approach can predict those events up to two days in advance [and] that is crucial time for the people to prepare, save lives and limit damages," said Kurths, a senior adviser at the Potsdam Institute.