A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

Measurements made in the Columbia River Basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP 2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modelled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin-Obukhov similarity and (ii) a modified Priestley-Taylor (P-T) algorithm with physically based ecophysiological constraints which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP 2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various timescales based on the modelled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP 2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

Asymptotic behaviour at the wall in compressible turbulent channels

The asymptotic behaviour of Reynolds stresses close to walls is well established in incompressible flows owing to the constraint imposed by the solenoidal nature of the velocity field. For compressible flows, thus, one may expect a different asymptotic behaviour, which has indeed been noted in the literature. However, the transition from incompressible to compressible scaling, as well as the limiting behaviour for the latter, is largely unknown. Thus, we investigate the effects of compressibility on the near-wall, asymptotic behaviour of turbulent fluxes using a large direct numerical simulation (DNS) database of turbulent channel flow at higher than usual wall-normal resolutions. We vary the Mach number at a constant friction Reynolds number to directly assess compressibility effects. We observe that the near-wall asymptotic behaviour for compressible turbulent flow is different from the corresponding incompressible flow even if the mean density variations are taken into account and semi-local scalings are used. For Mach numbers near the incompressible regimes, the near-wall asymptotic behaviour follows the well-known theoretical behaviour. When the Mach number is increased, turbulent fluxes containing wall-normal components show a decrease in the slope owing to increased dilatation effects. We observe that $R_{vv}$ approaches its high-Mach-number asymptote at a lower Mach number than that required for the other fluxes. We also introduce a transition distance from the wall at which turbulent fluxes exhibit a change in scaling exponents. Implications for wall models are briefly presented.

Evaluation of a Method for Calculating the Height of the Stable Boundary Layer based on Wind Profile Lidar and Turbulent Fluxes

The height of the stable boundary layer (SBL), known as the nocturnal boundary layer height, is controlled by numerous factors of different natures. The SBL height defines the state of atmospheric turbulence and describes the diffusion capacity of the atmosphere. Therefore, it is unsurprising that many alternative (sometimes contradictory) formulations for the SBL height have been proposed to date, and no consensus has been achieved. In our study, we propose an iterative algorithm to determine the SBL height h based on the flux–profile relationship using wind profiles and turbulent fluxes. This iterative algorithm can obtain temporally continuous, accurate estimates of h and is widely applicable. The predicted h presents relatively good agreement with four observation-derived SBL heights, hJ, h1, hi, and hθ (hJ: maximum wind speed height, h1: zero wind shear height, hi: temperature inversion height, and hθ: height at which 0.8 times the inversion strength appears for the first time), especially with hθ, which shows the best fit. In addition, h exhibits a low absolute difference and relative difference with hJ, which presents the second-best result. The agreement with hi and h1 may be satisfactory, but small differences are observed, and the one standard deviation of the mean relative difference is large. In addition, the predicted h is compared with other SBL height estimation methods, including diagnostic, λ1, λ2 and λ3 (three typical dimensional scale height parameters) and prognostic equation-based methods, λ(h) (an equation for the growth of h developed). The diagnostic formulas are found to be appropriate, especially under extremely stable conditions. Additionally, the equation of λ3 presents the best result of all the dimensional scale height parameters. However, the prognostic equation λ(h) in our study is very unsatisfactory.

Bidirectional Turbulent Fluxes of Fog at a Subtropical Montane Cloud Forest Covering a Wide Size Range of Droplets

Size-resolved turbulent fluxes of fog droplets are investigated above a subtropical montane cloud forest in Taiwan. By integrating an aerosol spectrometer into an eddy-covariance set-up, we measure droplet number fluxes and liquid water fluxes in a size range of aerosol particles and droplets with diameters ranging from 0.25 $${\upmu }\!\mathrm{m}$$ μ m to 17.3 $${\upmu }\!\mathrm{m}$$ μ m . We find two flux-direction changes within this size range: a downward flux occurs for accumulation-mode aerosols of diameters between 0.25 $${\upmu }\!\mathrm{m}$$ μ m and 0.83 $${\upmu }\!\mathrm{m}$$ μ m , an upward flux occurs for hydrated aerosols with diameters between 1.1 $${\upmu }\!\mathrm{m}$$ μ m and 2.4 $${\upmu }\!\mathrm{m}$$ μ m , and a downward flux occurs again for activated fog droplets between diameters of 3 $${\upmu }\!\mathrm{m}$$ μ m and 17.3 $${\upmu }\!\mathrm{m}$$ μ m . The droplet size distributions can be modelled by a trimodal log-normal distribution, and the modes correlate with the different flux directions. The formation of the three modes and the establishment of the respective flux directions can be explained by combining the Köhler theory on the basis of measured ion concentrations in fog with the turbulent transport of droplets. Finally, from the combined analysis of droplet fluxes and size distributions, we infer relevant processes of droplet development and dissolving during various phases of the life cycles of the fog events.

Intrinsic non-stationarity of carbon dioxide turbulent fluxes in the urban boundary layer

Stationarity is a critical assumption in the eddy-covariance method that is widely used to calculate turbulent fluxes. Many methods have been proposed to diagnose non-stationarity attributed to external non-turbulent flows. In this paper, we focus on intrinsic non-stationarity (IN) attributed to turbulence randomness. The detrended fluctuation analysis is used to quantify IN of CO2 turbulent fluxes in the downtown of Beijing. Results show that the IN is widespread in CO2 turbulent fluxes and is a small-scale phenomenon related to the inertial sub-range turbulence. The small-scale IN of CO2 turbulent fluxes can be simulated by the Ornstein-Uhlenbeck (OU) process as a first approximation. Basing on the simulation results, we find that the average time should be greater than 27 s to avoid the effects of IN. Besides, the non-stationarity diagnosis methods that do not take into account IN would possibly make a wrong diagnosis with some parameters.

On Warm and Moist Air Intrusions into Winter Arctic

In this study, warm and moist air intrusions (WaMAI) over the Arctic Ocean sectors of Barents, Kara, Laptev, East Siberian, Chukchi and Beaufort Seas in recent 40 winters (from 1979 to 2018) are identified from ERA5 reanalysis using both Eulerian and Lagrangian views. The analysis shows that WaMAIs, fuelled by Arctic blockings, causes a relative surface warming and hence a sea ice reduction by exerting positive anomalies of net thermal irradiances and turbulent fluxes to the surface. Over Arctic Ocean sectors with land-locked sea ice in winter, such as Laptev, East Siberian, Chukchi and Beaufort Seas, total surface energy budget is dominated by net thermal irradiance. From a Lagrangian perspective, total water path (TWP) increases linearly with the downstream distance from the sea ice edge over the completely ice-covered sectors, inducing almost linearly increasing net thermal irradiance and total surface energy-budget. However, over the Barents Sea, with an open ocean to the south, total net surface energy-budget is dominated by the surface turbulent flux. With the energy in the warm-and-moist air continuously transported to the surface, net surface turbulent flux gradually decreases with distance, especially within the first 2 degrees north of the ice edge, inducing a decreasing but still positive total surface energy budget. The boundary-layer energy-budget patterns over the Barents Sea can be categorized into three classes: radiation-dominated, turbulence-dominated and turbulence-dominated with cold dome, comprising about 52 %, 40 % and 8 % of all WaMAIs, respectively. Statistically, turbulence-dominated cases with or without cold dome occur along with one order of magnitude larger large-scale subsidence than the radiation-dominated cases. For the turbulence-dominated category, larger turbulent fluxes are exerted to the surface, probably because of stronger wind shear. In radiation-dominated WaMAIs, stratocumulus develops more strongly and triggers intensive cloud-top radiative cooling and related buoyant mixing that extends from cloud top to the surface, inducing a thicker well-mixed layer under the cloud. With the existence of cold dome, fewer liquid water clouds were formed and less or even negative turbulent fluxes could reach the surface.