SEAMLESS is a research project funded under the FED-tWIN programme, a federal initiative of the Belgian Science Policy Office aimed at fostering sustainable and long-term collaboration between Belgian universities and Federal Scientific Institutions.

The general scientific framework of SEAMLESS is “Modelling and forecasting the climate system from seasonal to decadal timescales over Europe using state-of-the-art models and tools from non-linear sciences” (FED-tWIN2019-prf069). The project specifically targets improved understanding and prediction of climate variability at regional scales, with a strong focus on Belgium.

SEAMLESS develops cutting-edge research on regional climate variability and predictability at seasonal-to-decadal timescales. The project focuses on Belgium and its surrounding regions, combining advanced climate modelling, data assimilation, causal analysis, and modern prediction techniques.

The research is structured around three main objectives:

1. Reconstruction of past regional climate (last 200 years)
The project will reconstruct the regional climate of Belgium and neighbouring countries over the past two centuries using long instrumental climate records. These observations will be assimilated into global climate models and dynamically and statistically downscaled to the regional scale. The resulting dataset will provide an unprecedented perspective on long-term climate variability and extremes in Belgium, and how they relate to large-scale climate dynamics.

2. Understanding the drivers of climate variability
Using this long climate reconstruction, SEAMLESS will investigate the physical origins of seasonal-to-decadal climate variations affecting Belgium. Advanced causal analysis methods will be applied to identify the mechanisms linking large-scale circulation patterns to regional climate variability and extremes.

3. Development of a regional climate prediction system
Building on this improved understanding of regional climate dynamics, the project will develop and evaluate a prediction system for Belgium. Statistical and machine-learning approaches will be tested over the past century to assess their skill in forecasting climate variability and extremes on seasonal to decadal timescales.

SEAMLESS is embedded in an international research network, collaborating with leading climate research centres and universities developing advanced modelling, data assimilation, and prediction methodologies.

At the same time, the project is strongly oriented towards climate services. Its outcomes will contribute to improved climate information for Belgium, supporting decision-making by public authorities, the private sector, and the general public in the context of climate variability and change.

Dr. Francisca Aguirre Correa, Postdoctoral Researcher at RMI and UCLouvain