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Published: 13 November 2017  
Related theme(s) and subtheme(s)
EnvironmentClimate & global change
Research policySeventh Framework Programme
Special CollectionsDisaster reduction
Countries involved in the project described in the article
Germany  |  Iceland  |  Netherlands  |  Spain
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Modelling techniques boost climate knowledge

EU-funded researchers are transferring modelling and analysis techniques used in other disciplines to climate science in a bid to improve predictions of climate events like El Niño. The research feeds into efforts to better understand complex weather patterns and their impact on the environment, economic activities and society.

© Sergey Nivens - fotolia.com

The Earth’s climate is a highly complex system that scientists are trying to unpick. Research by the EU-funded project LINC is helping to advance state-of-the-art climate know-how with a new approach that draws from fields such as transport and complex networks.

LINC joined the dots between techniques used in different fields and applied them to climate science. It aimed to improve the forecasting of major climate events, including El Niño. The researchers developed a new approach for climate modelling and data analysis. They also developed new modelling software for use by the scientific community and for assessing the predictability of extreme weather.

“The results of the LINC project have improved our understanding of natural climate variability. This will certainly have an impact on the degree of belief of future climate model projections and hence on policymaking,” says project coordinator Cristina Masoller, associate professor at the Universitat Politècnica de Catalunya in Barcelona, Spain.

Modelling El Niño

The main innovation of the project was to apply complex networks and nonlinear data analysis tools already used to model systems including transport networks, social networks, brain networks, and the internet and ecosystems, and to study climate phenomena, she adds.

Using the complex network approach combined with nonlinear time-series analysis, the project analysed how El Niño – Southern Oscillation (ENSO) the most important dynamic phenomena in our climate – affects climate.

“Our climate is made up of a huge number of nonlinear subsystems with mutual nonlinear interactions and feedback loops active on a wide-range of spatial scales – from several metres to thousands of kilometres, and time scales from several hours to many years. Applying useful methods from other disciplines can help us understand these linkages,” says Masoller.

While El Niño originates in the Pacific Ocean, its effects are global and include altering the frequency of hurricanes in the Atlantic and the monsoon in India. The new methodology helped model this, potentially improving predictions of ENSO’s effects.

LINC researchers came up with new methods to identify geographical regions that share similar climate dynamics. These regions – known as climatic communities – are not necessarily geographically close.

Extreme weather forecasting

LINC also developed new software – PyUnicorn and Par@Graph – which will be used extensively by the complex system scientific community. These tools could also be used to assess the sub-seasonal predictability of extreme events such as flooding, heatwaves and cold surges.

“Extreme weather forecasting at the sub-seasonal time scale is extremely challenging because climate phenomena like the Madden-Julian Oscillation and atmospheric blockings remain poorly understood. We are convinced that the methods developed by the LINC project have huge potential to advance the sub-seasonal predictability of extreme events,” says Masoller.

Under the EU’s Marie SkÅ‚odowska-Curie fellowship programme, LINC trained 12 early stage researchers and 3 young experienced researchers who are now ready to undertake a career in physics and geosciences with expertise in climatology, networks and complex systems.

Project details

  • Project acronym: LINC
  • Participants: Spain (Coordinator), Germany, Israel, The Netherlands, Uruguay
  • Project N°: 289447
  • Total costs: € 3 715 523
  • EU contribution: € 3 715 523
  • Duration: January 2014 to December 2017

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