Periodic Reporting for period 3 - COAT (Collapse Of Atmospheric Turbulence)
Reporting period: 2019-01-01 to 2020-06-30
The interaction between the lower atmosphere and the surface is studied in detail, as this plays a
crucial role in the dynamics. Present generation forecasting models are incapable to predict whether
or not turbulence will survive or collapse under cold conditions. In nature, both situations frequently
occur and lead to completely different temperature signatures. As such, significant forecast errors
are made, particularly in arctic regions and winter conditions. Therefore, prediction of turbulence
collapse is highly relevant for weather and climate prediction.
Key innovation lies in our hypothesis. The collapse of turbulence is explained from a maximum
sustainable heat flux hypothesis which foresees in an enforcing positive feedback between the
atmosphere and the underlying surface. A comprehensive theory for the transition between the main
two nocturnal regimes would be ground-breaking in meteorological literature.
We propose an integrated approach, which combines in-depth theoretical work, simulation with
models of various hierarchy (DNS, LES, RANS), and observational analysis. Such comprehensive
methodology is new with respect to the problem at hand. An innovative element is the usage of
Direct Numerical Simulation in combination with dynamical surface interactions. This advanced
technique fully resolves turbulent motions up to their smallest scale without the need to rely on
subgrid closure assumptions. From a 10-year dataset (200m mast at Cabauw, Netherlands) nights
are classified according to their turbulence characteristics.
Multi-night composites are used as benchmark-cases to guide realistic numerical modelling. In the
validation phase, generality of the results with respect to both climate and surface characteristics is
assessed by comparison with the FLUXNET data-consortium, which operates on a long-term basis
over 240 sites across the globe.
Also, in accordance with the plan, we were successful in extending the maximum sustainable heat flux theory with land-atmosphere interactions, by introducing a new conceptual model (Van de Wiel et al., 2017; Figures below). As to contrast results from The Netherlands, also data from Antarctica (i.e. with snow coverage instead of grass) where analyzed with respect to collapsing turbulence and regime transitions in (Vignon et. al. 2017), and a comparison with the conceptual model was given in Van de Wiel et al. (2017). In the theoretical part of the project, simplified cases without atmospheric-surface interactions where studies using turbulence resolving techniques such as Direct Numerical Simulation in order to understand and predict the collapse of turbulence in those configurations. Successful ‘early warnings of critical transitions’ where reported in Van Hooijdonk et al. (2016). As, in reality, the collapse of turbulence is influenced by the turbulent state of the atmosphere during the preceding day and in the evening, we also investigated the impact of the evening transition in Van Hooijdonk et al. (2017). To enable turbulence resolving simulations of the full diurnal cycle, we currently investigate the potential of adaptive-grids in LES/DNS (Van Hooft et al., 2017). This technique appears to be promising in highly dynamic problems that suffer from a large-scale separation, such as our problem at hand. According to the research plan model results have been benchmarked against results reported in the literature. In summary: the project is executed according to the plan and significant progress is obtained, which has been disseminated in the scientific literature.
This enables comparison with atmospheric observational data. We will particularly study the 'snow-covered' case as for such case, strongest regime transitions are expected. Apart from modeling with advanced turbulence models, also simulations with state-of-the-art practical models is foreseen. Also the effect of collapsing turbulence on fog formation will be explored at more depth, with special emphasis on analysis of local surface characteristics and there influence on appearance disappearance of fog. Finally, (as stated above: to enable turbulence resolving simulations of the full diurnal cycle, we currently investigate the potential of adaptive-grids in LES/DNS (Van Hooft et al., 2017). This technique appears to be promising in highly dynamic problems that suffer from a large-scale separation, such as our problem at hand. In summary: we expect to make significant progress in the understanding of the cessation of turbulence in the evening boundary layer and also in understanding its practical implications.