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Organized Turbulence in a Cold-Air Outbreak: Evaluating a Large-Eddy Simulation with Respect to Airborne Measurements

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Abstract

Cold-air outbreaks (CAO) lead to intense air–sea interactions, the appropriate representation of which are fundamental for climate modelling and numerical weather forecasting. We analyze a CAO event with low-level wind speeds of approximately 25 m s\(^{-1}\) observed in the north-western Mediterranean Sea. The marine atmospheric boundary layer (MABL) was sampled with an aircraft equipped for turbulence measurements, revealing the organization of the MABL flow in coherent structures oriented along the mean wind direction, which was then simulated in two steps. First, a one-dimensional simulation enabled the determination of the forcing terms (particularly horizontal advection) required to adequately reproduce the vertical structure of the MABL flow. These terms were computed from a limited-area forecast model in operation during the entire field campaign. Then, a large-eddy simulation (LES) was performed during the well-established phase of the CAO event. The LES output is validated with respect to airborne data, not only with respect to the mean wind-speed and thermodynamic profiles, but also the turbulence statistics and coherent structures. The validated LES results enable description of the turbulent field as well as the coherent structures. The main discrepancy is a considerable underestimation of the simulated evaporation (computed with a parametrization of the turbulent surface fluxes), and hence of the moisture fluctuations throughout the boundary layer. Several possible explanations may explain this underestimation. The structure of the boundary layer is nonetheless well reproduced by the LES model, including the organized structures and their characteristic scales, such as the structure wavelength, orientation, and aspect ratio, which closely agree with observations. A conditional-sampling analysis enables determination of the contribution of the coherent structures to the vertical exchange. Although they occupy a limited fractional area, organized structures are the primary contributors to the turbulent exchange.

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Acknowledgements

Many people were involved in the realization of the aircraft mission of HyMeX-SOP2 and in the data processing. The aircraft was operated by the Service des Avions Français Instrumentés pour la Recherche en Environnement (SAFIRE). We also thank the TRAMM team from CNRM (Centre National de la Recherche Météorologique) at Météo-France for their help in computing the data. This work is a contribution to the HyMeX program (HYdrological cycle in the Mediterranean EXperiment, www.hymex.org) through the ASICS-MED project (Air–Sea Interaction and Coupling with Submesoscale structures in the MEDiterranean), ANR-12-BS06-0003). The authors acknowledge Météo-France for supplying the data and the HyMeX database teams (ESPRI/IPSL and SEDOO/Observatoire Midi-Pyrénées) for their help in accessing the data (accessible on http://mistrals.sedoo.fr/HyMeX/). We also gratefully thank C. Lac, F. Couvreux and T. Bergot for their help with the numerical simulations and M.-N. Bouin (CMM/CRNM) for providing us the buoy-derived bulk fluxes.

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Correspondence to Pierre-Etienne Brilouet.

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Appendix 1: Cloud Cover

Appendix 1: Cloud Cover

We present here some illustrations of the cloud cover for the period of time during which the observations and simulations were performed. People on board the aircraft reported cloud streets observed at the MABL top. Unfortunately, as the shortwave and longwave broadband radiation sensors installed on the aircraft malfunctioned, we examined satellite observations. Presented in Fig. 16 are photos from the MODIS instrumentation aboard the AQUA and TERRA satellites in the mornings of 13 and 14 March 2013. The meteorological conditions evolved rapidly during 13 March, with a progressive development of the CAO event associated with an increasing wind speed from 0000 UTC to the early afternoon. The MODIS image taken at 1010 UTC exhibits a large cloud cover, which is denser in the southern half of the domain explored by the aircraft. Unfortunately, there is no image available during the afternoon period analyzed here. We might however assume that the cloud cover disaggregated in connection with the dry-air advection observed during the CAO event. In this way, the MODIS image for the following morning (Fig. 16b) may be more representative of the situation analyzed here, because the meteorological conditions are approximately consistent from the afternoon of the 13 March to the evening of 14 March (see Brilouet et al. (2017), Fig. 1). In the latter image, cloud organization along the mean wind direction is clearly visible.

Fig. 16
figure16

Photos from MODIS instrumentation over the Gulf of Lion on, a 13 March 2013 at 1010 UTC, and b 14 March 2013 at 1050 UTC. The red square represents the area where the aircraft observations were performed

It is easier to examine the cloud cover from the output fields of the LES. It is characterized in Fig. 17 from the time series of the fractional area covered by clouds, and of the lifting condensation level (LCL), computed at each horizontal grid point of the model from the pressure, temperature and moisture values at the height of 50 m above the surface. The LCL represents the height at which an air parcel, adiabatically raised while keeping its specific humidity, becomes saturated. The LCL time series represented in the figure is the average value over the horizontal domain of the model (25 km \(\times \) 10 km). Figure 17 shows that clouds were present all along the LES, with an average cloud fractional area of 64%. LCL values vary little during the simulation (around 690 m on average), and are close to the MABL height, indicating that clouds form at the top of the MABL. It is easier to examine the cloud cover from the output fields of the LES model. Figure 17 presents the time series of the fractional area covered by clouds, and of the lifting condensation level (LCL) computed at each horizontal grid point of the model from the pressure, temperature and moisture values at the height of 50 m above the surface. The LCL represents the height at which an air parcel adiabatically raised at constant specific humidity becomes saturated. The LCL time series is the average value over the horizontal domain of the model (25 km \(\times \) 10 km). Figure 17 reveals that the clouds were present throughout the LES run, with an average cloud fractional area of 64%. The LCL values vary little during the simulation (around 690 m on average), and are close to the MABL height, indicating that clouds form at the MABL top.

Fig. 17
figure17

Time series of the cloud fractional area computed from the LES results, and of the LCL between 1430 and 1700 UTC. At each timestep of the LES run, the LCL is the average of the values computed for the air parcels at 50 m above the surface

Figure 18 presents the horizontal field at 1545 UTC of the maximum value along the vertical coordinate of the liquid water content. A non-zero value means that liquid water is present somewhere above the grid point considered. Let us recall that the horizontal LES domain is oriented in such a way that the mean MABL wind direction is parallel to the long axis of the domain. The cloud structure is clearly elongated along the mean wind direction, with stripes evoking both the periodic structure of the cross-wind observations and the horizontal cross-sections of the vertical velocity component and moisture fields of the LES results (Figs. 3 and 10). This is consistent with clouds forming at the top of the ascending branches of the convective rolls.

Fig. 18
figure18

Simulated horizontal field at 1545 UTC of the liquid water content. The represented value is the maximum over the vertical coordinate, regardless of the height at which it is computed

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Brilouet, P., Durand, P., Canut, G. et al. Organized Turbulence in a Cold-Air Outbreak: Evaluating a Large-Eddy Simulation with Respect to Airborne Measurements. Boundary-Layer Meteorol (2020). https://doi.org/10.1007/s10546-019-00499-4

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Keywords

  • Cold-air outbreak
  • Large-eddy simulation
  • Marine atmospheric boundary layer
  • Turbulence organization