High-impact climate events – better adaptation through earlier prediction
The prediction of high impact climate phenomena can be substantially improved by a new mathematical approach that analyses the connectivity and patterns between geographical locations, scientists say in a new publication. This could save many lives and substantially reduce economic losses.
Prediction times for events like El Niño, monsoons, droughts or extreme rainfall could be increased substantially, to a month or, in some cases, even a year in advance, depending on the type of the event. The new framework can thus become key to improving adaptation to the global warming crisis.
“The new forecasting approach has, in several instances over the past years, proven to be highly efficient in predicting different climate phenomena much earlier than before. El Niño for instance could be predicted up to one full year early, compared to about six months with the standard prediction methods,” explains PIK’s Josef Ludescher, lead author of the 'Perspective' article to be published in the Proceedings of the US National Academy of Sciences (PNAS). “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, much earlier than the forecast currently used, thanks to the new approach.“
Extreme events like floods, heatwaves or droughts often arrive with little or no warning time at all, making effective short-term adaptation challenging if not impossible. The new prediction framework fundamentally improves this, as PIK’s Jürgen Kurths, a pioneer of network application to climate-phenomena forecasting and co-author of the paper, underlines. “Currently, for instance, there is no reliable prediction of heavy rainfall in the Eastern Central Andes leading to floods and landslides with devastating impacts for the inhabitants in that part of South America,” Kurths says. “Our network-based approach can predict those events up to two days in advance – that is crucial time for the people to prepare, save lives and limit damages.”
A mathematical approach to help save lives
Traditional weather and climate forecasting largely involves numerical models imitating atmospheric and oceanic processes. These models, while generally very useful, can’t perfectly simulate all underlying processes – and phenomena like monsoon onsets, floods or droughts might be predicted too late.
This is where network-based forecasting comes into play. „As opposed to looking at a huge number of local interactions, which represent physical processes like heat or humidity exchange, we focus directly on the connectivity between different geographical locations, which can span continents or oceans. This connectivity is detected by measuring the similarity in the evolution of physical quantities like air temperatures at these locations,” Ludescher explains. “For instance, in the case of El Niño, a strong connectivity in the tropical Pacific tends to build up in the calendar year before the onset of the event.”
Kurths adds: “That’s a fundamentally different approach from traditional numerical modelling used in weather and climate forecasts. It does not simulate the entire Earth system, but analyses large-scale connectivity patterns in observational data.”
“These patterns, that is the connectivity between the locations and their evolution in time, can provide critical new information for forecasting – and, so we hope, make the respective regions safer,” states co-author Maria Martin, also at PIK.
Hans-Joachim Schellnhuber, former Director of the institute, concludes: “With this Perspective, we have brought together several success stories that demonstrate the scientific power of the network approach for forecasting – and, in consequence, for potentially saving thousands of lives and avoiding billions of euros in terms of economic costs.”
Josef Ludescher, Maria Martin, Niklas Boers, Armin Bunde, Catrin Ciemer, Jingfang Fan, Shlomo Havlin, Marlene Kretschmer, Jürgen Kurths, Jakob Runge, Veronika Stolbova, Elena Surovyatkina, Hans Joachim Schellnhuber (2021): Network-based forecasting of climate phenomena. PNAS. [DOI: 10.1073/pnas.1922872118]