Mesoscale Predictability in Moist Mid-Latitude Cyclones Is Not Sensitive to the Slope of the Background Kinetic Energy Spectrum

We investigate the sensitivity of mesoscale atmospheric predictability to the slope of the background kinetic energy spectrum E by adding initial errors to simulations of idealized moist midlatitude cyclones at several wavenumbers k for which the slope of E(k) is significantly different. These different slopes arise from 1) differences in the E(k) generated by cyclones growing in two different moist baroclinically unstable environments, and 2) differences in the horizontal scale at which initial perturbations are added, with E(k) having steeper slopes at larger scales. When small-amplitude potential temperature perturbations are added, the error growth through the subsequent 36-hour simulation is not sensitive to the slope of E(k) nor to the horizontal scale of the initial error. In all cases with small-amplitude perturbations, the error growth in physical space is dominated by moist convection along frontal boundaries. As such, the error field is localized in physical space and broad in wavenumber (spectral) space. In moist mid-latitude cyclones, these broadly distributed errors in wavenumber space limit mesoscale predictability by growing up-amplitude rather than by cascading upscale to progressively longer wavelengths. In contrast, the error distribution in homogeneous turbulence is broad in physical space and localized in wavenumber space, and dimensional analysis can be used to estimate the error growth rate at a specific wavenumber k from E(k). Predictability estimates derived in this manner, and from the numerical solutions of idealized models of homogeneous turbulence, depend on whether the slope of E(k) is shallower or steeper than k−3, which differs from the slope-insensitive behavior exhibited by moist mid-latitude cyclones.

Variabilidade temporal dos fluxos noturnos determinados a partir de duas diferentes metodologias no nível de 325 m acima da floresta Amazônica

The present study aimed to analyze and compare the temporal variability of the nocturnal fluxes of CO2, sensitive and latent heat, calculated from two different methodologies: one with a 5-minute temporal window (using the eddy covariance technique), and another with 109 minutes (from multiresolution decomposition). For this, night series of 25 nights were used between October and November 2015. The analyzes were made for two groups of distinct turbulence patterns: one with intermittent regime and the other with homogeneous turbulence. The results showed that the fluxes obtained by the classical method of eddy covariance were dependent on the intensity of the turbulence. On the other hand, the fluxes calculated from the multiresolution decomposition technique showed significant fluctuations in the temporal evolution of all scalars analyzed, with the largest percentage differences between the two approaches occurring in the homogeneous turbulence regime group, which was characterized by predominantly weak turbulent activity throughout the night. In the comparison made, the methodology employed in the 109-minute window showed greater efficiency in the estimates of exchanges at 325 m in the ATTO tower, especially during conditions of low turbulent activity.

Lagrangian Statistics of Heat Transfer in Homogeneous Turbulence Driven by Boussinesq Convection

The movement of heat in a convecting system is typically described by the nondimensional Nusselt number, which involves an average over both space and time. In direct numerical simulations of turbulent flows, there is considerable variation in the contributions to the Nusselt number, both because of local spatial variations due to plumes and because of intermittency in time. We develop a statistical approach to more completely describe the structure of heat transfer, using an exit-distance extracted from Lagrangian tracer particles, which we call the Lagrangian heat structure. In a comparison between simulations of homogeneous turbulence driven by Boussinesq convection, the Lagrangian heat structure reveals significant non-Gaussian character, as well as a clear trend with Prandtl number and Rayleigh number. This has encouraging implications for simulations performed with the goal of understanding turbulent convection in natural settings such as Earth’s atmosphere and oceans, as well as planetary and stellar dynamos.