Periodic Reporting for period 4 - COAT (Collapse Of Atmospheric Turbulence) Reporting period: 2020-07-01 to 2020-12-31 Summary of the context and overall objectives of the project This project aims to predict the cessation of continuous turbulence in the evening boundary layer.The interaction between the lower atmosphere and the surface is studied in detail, as this plays acrucial role in the dynamics. Present generation forecasting models are incapable to predict whetheror not turbulence will survive or collapse under cold conditions. In nature, both situations frequentlyoccur and lead to completely different temperature signatures. As such, significant forecast errorsare made, particularly in arctic regions and winter conditions. Therefore, prediction of turbulencecollapse is highly relevant for weather and climate prediction.Key innovation lies in our hypothesis. The collapse of turbulence is explained from a maximumsustainable heat flux hypothesis which foresees in an enforcing positive feedback between theatmosphere and the underlying surface. A comprehensive theory for the transition between the maintwo nocturnal regimes would be ground-breaking in meteorological literature.We propose an integrated approach, which combines in-depth theoretical work, simulation withmodels of various hierarchy (DNS, LES, RANS), and observational analysis. Such comprehensivemethodology is new with respect to the problem at hand. An innovative element is the usage ofDirect Numerical Simulation in combination with dynamical surface interactions. This advancedtechnique fully resolves turbulent motions up to their smallest scale without the need to rely onsubgrid closure assumptions. From a 10-year dataset (200m mast at Cabauw, Netherlands) nightsare classified according to their turbulence characteristics.Multi-night composites are used as benchmark-cases to guide realistic numerical modelling. In thevalidation phase, generality of the results with respect to both climate and surface characteristics isassessed by comparison with the FLUXNET data-consortium, which operates on a long-term basisover 240 sites across the globe. Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far Goal of the project is to “predict of the collapse of turbulence in the evening boundary layer and to understand the nonlinear coupling between the atmosphere and its underlying surface.” The plan of the proposal was followed in phase I: a 10-year dataset from Cabauw was used to classify nocturnal boundary layer regimes (and their degree of turbulence) in terms of the external forcings (large-scale pressure gradient, degree of cloud coverage). The resulting composites, published in Van der Linden et al. (2017) serve as a benchmark for numerical modeling. In Baas et al. (2017) we assessed the state-of-the-art of practical modeling with respect to this classification, using RACMO (basically the IFS scheme of the European Centre for Medium-range Weather Forecast). In the next stage, the impact of regime transition on fog formation was assessed. 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) and Van Hooft et al. (2019). As stated in the proposal special emphasis has been given to conceptualization of land-atmosphere interactions and a generalized description of the diurnal cycle of the dry atmospheric boundary layer was given in Van Hooft et al. (2019), where indeed it was shown that (nighttime) boundary layer response was very different for different forcings. To enable turbulence resolving simulations of the full diurnal cycle, we also investigated 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 was executed according to the plan and significant progress was obtained, which has been disseminated in the scientific literature. Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far) We primarily followed the path foreseen in the plan: theoretical modeling was augmented with fully interactive surface schemes and besides turbulence also radiative effects were accounted for. This enables comparison with atmospheric observational data. Also the 'snow-covered' case was investigated 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 were made (e.g. Baas et al. 2017, 2019). Also the effect of collapsing turbulence on fog formation was explored in depth, with special emphasis on analysis of local surface characteristics and their influence on appearance disappearance of fog (Izett et al. 2018, 2019). Finally (as stated above): to enable turbulence resolving simulations of the full diurnal cycle, we investigated 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 made significant progress in the understanding of the cessation of turbulence in the evening boundary layer and also in understanding its practical implications. Collapsing turbulence leads to large temperature differences for weak winds. Collaps of turbulence in nature: beautifully stratified fog