ERC CoG UNICORN: Developing a novel framework for understanding near-surface turbulence in complex terrain
Atmospheric turbulence is the main mechanism with which the heat, moisture, mass (e.g., CO2, water vapor, pollutants) and momentum are transported from the Earth’s surface into the atmosphere and therefore turbulence impacts atmospheric processes as diverse as climate, storm systems, hydrological cycle, air pollution, and glacial melt. Still, turbulence is one of the last unknowns of classical physics. Due to its chaotic nature that spans multiple orders of magnitude it is impossible to directly model turbulence for any practical purpose. We therefore have to resort to statistical approaches. The most famous and widely used is the Monin-Obukhov similarity theory (MOST) that stands as the cornerstone of our understanding of turbulence over flat and horizontally homogenous terrain and forms the basis of parametrizations of turbulence in all weather and climate models. The Earth, however, is not flat and over 70% of Earth’s land surface can be classified as complex heterogeneous terrain. This has a profound effect on turbulence which is deformed by processes such as terrain slope, adverse pressure gradients, secondary circulations etc., and therefore over complex surfaces, such as mountains, MOST fails. Thus, for the majority of our planetary surface no viable theory of turbulence is available, and approaches that are known to be inadequate are nevertheless applied.
Unicorn addresses this knowledge gap and aims to create a novel scaling framework that will extend MOST to complex terrain. This novel framework is based on the hypothesis that anisotropy, which provides the information on the directionality of turbulent exchange, encodes the boundary conditions and therefore provides a unifying element in scaling over all surface types.
Unicorn will use a synergy of measurements, coupled with machine learning approaches, sensitivity studies using state-of-the-art high resolution numerical modelling, and reduced order theoretical derivations to identify the key physical processes that cause anisotropy in complex terrain to differ from that over flat terrain. This synergistic approach incorporating the effects of complex terrain into a framework based on turbulence anisotropy will bring a much-needed breakthrough for understanding turbulence in complex terrain. The new generalized similarity theory has the potential to revolutionize near-surface turbulence representation in high resolution numerical modelling, leading to better predictive capability in numerous societally and scientifically relevant topics, such as climate, extreme weather and air pollution.
European Research Council
01/06/2021 to 31/05/2026