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The development of a unified similarity scaling has so far failed over complex surfaces, as scaling studies show large deviations from the empirical formulations developed over flat and horizontally homogeneous terrain as well as large deviations between the different complex terrain data sets. However, a recent study of turbulence anisotropy for flat and horizontally homogeneous terrain has shown that separating the data according to the limiting states of anisotropy (isotropic, two‐component axisymmetric and one‐component turbulence) improves near‐surface scaling. In this paper we explore whether this finding can be extended to turbulence over inclined and horizontally heterogeneous surfaces by examining near‐surface scaling for 12 different data sets obtained over terrain ranging from flat to mountainous. Although these data sets show large deviations in scaling when all anisotropy types are examined together, the separation according to the limiting states of anisotropy significantly improves the collapse of data onto common scaling relations, indicating the possibility of a unified framework for turbulence scaling. A measure of turbulence complexity is developed, and the causes for the breakdown of scaling and the physical mechanisms behind the turbulence complexity encountered over complex terrain are identified and shown to be related to the distance to the isotropic state, prevalence of directional shear with height in mountainous terrain, and the deviations from isotropy in the inertial subrange., Key Points A pathway toward a unified theory of near‐surface atmospheric turbulence is providedThe separation according to the limiting states of anisotropy significantly improves the collapse of data onto common scaling relationsPhysical processes responsible for turbulence complexity are identified

The goal of this study is to understand the mechanisms controlling the isotopic composition of the water vapor near the surface of tropical oceans, at the scale of about a hundred kilometers and a month. In the tropics, it has long been observed that the isotopic compositions of rain and vapor near the surface are more depleted when the precipitation rate is high. This is called the “amount effect.” Previous studies, based on observations or models with parameterized convection, have highlighted the roles of deep convective and mesoscale downdrafts and rain evaporation. But the relative importance of these processes has never been quantified. We hypothesize that it can be quantified using an analytical model constrained by large‐eddy simulations. Results from large‐eddy simulations confirm that the classical amount effect can be simulated only if precipitation rate changes result from changes in the large‐scale circulation. We find that the main process depleting the water vapor compared to the equilibrium with the ocean is the fact that updrafts stem from areas where the water vapor is more enriched. The main process responsible for the amount effect is the fact that when the large‐scale ascent increases, isotopic vertical gradients are steeper, so that updrafts and downdrafts deplete the subcloud layer more efficiently., Key Points The precipitation is more depleted when the precipitation rate is higher only if it is associated with stronger large‐scale ascentUpdrafts are responsible for most of the depletion of the near‐surface water vapor relative to equilibrium with the oceanWith large‐scale ascent, the near‐surface water vapor is more depleted because updrafts export enriched water more efficiently

Stratocumulus clouds are the most common type of boundary layer cloud; their radiative effects strongly modulate climate. Large eddy simulations (LES) of stratocumulus clouds often struggle to maintain fidelity to observations because of the sharp gradients occurring at the entrainment interfacial layer at the cloud top. The challenge posed to LES by stratocumulus clouds is evident in the wide range of solutions found in the LES intercomparison based on the DYCOMS‐II field campaign, where simulated liquid water paths for identical initial and boundary conditions varied by a factor of nearly 12. Here we revisit the DYCOMS‐II RF01 case and show that the wide range of previous LES results can be realized in a single LES code by varying only the numerical treatment of the equations of motion and the nature of subgrid‐scale (SGS) closures. The simulations that maintain the greatest fidelity to DYCOMS‐II observations are identified. The results show that using weighted essentially non‐oscillatory (WENO) numerics for all resolved advective terms and no explicit SGS closure consistently produces the highest‐fidelity simulations. This suggests that the numerical dissipation inherent in WENO schemes functions as a high‐quality, implicit SGS closure for this stratocumulus case. Conversely, using oscillatory centered difference numerical schemes for momentum advection, WENO numerics for scalars, and explicitly modeled SGS fluxes consistently produces the lowest‐fidelity simulations. We attribute this to the production of anomalously large SGS fluxes near the cloud tops through the interaction of numerical error in the momentum field with the scalar SGS model., Key Points Fidelity of LES simulations of stratocumulus clouds depends on advection schemes and SGS closuresWENO schemes without explicit SGS closures simulate stratocumulus wellA simple model shows how advection schemes and SGS closures interact and affect cloud simulations

Very sparse data have previously limited observational studies of meteorological processes in the Sahara. We present an observed case of convectively driven water vapor transport crossing the Sahara over 2.5 days in June 2012, from the Sahel in the south to the Atlas in the north. A daily cycle is observed, with deep convection in the evening generating moist cold pools that fed the next day's convection; the convection then generated new cold pools, providing a vertical recycling of moisture. Trajectories driven by analyses were able to capture the direction of the transport but not its full extent, particularly at night when cold pools are most active, and analyses missed much of the water content of cold pools. The results highlight the importance of cold pools for moisture transport, dust and clouds, and demonstrate the need to include these processes in models in order to improve the representation of Saharan atmosphere., Key Points Observed case of convectively driven water vapor transport across the Sahara over 2.5 daysCycle of convection generating cold pools which feed next day's convection, enhancing transportAnalyses capture direction of transport but not its full extent due to missing cold pools

Atmospheric observations of greenhouse gases provide essential information on sources and sinks of these key atmospheric constituents. To quantify fluxes from atmospheric observations, representation of transport—especially vertical mixing—is a necessity and often a source of error. We report on remotely sensed profiles of vertical aerosol distribution taken over a 2 year period in Pasadena, California. Using an automated analysis system, we estimate daytime mixing layer depth, achieving high confidence in the afternoon maximum on 51% of days with profiles from a Sigma Space Mini Micropulse LiDAR (MiniMPL) and on 36% of days with a Vaisala CL51 ceilometer. We note that considering ceilometer data on a logarithmic scale, a standard method, introduces, an offset in mixing height retrievals. The mean afternoon maximum mixing height is 770 m Above Ground Level in summer and 670 m in winter, with significant day‐to‐day variance (within season σ = 220m≈30%). Taking advantage of the MiniMPL's portability, we demonstrate the feasibility of measuring the detailed horizontal structure of the mixing layer by automobile. We compare our observations to planetary boundary layer (PBL) heights from sonde launches, North American regional reanalysis (NARR), and a custom Weather Research and Forecasting (WRF) model developed for greenhouse gas (GHG) monitoring in Los Angeles. NARR and WRF PBL heights at Pasadena are both systematically higher than measured, NARR by 2.5 times; these biases will cause proportional errors in GHG flux estimates using modeled transport. We discuss how sustained lidar observations can be used to reduce flux inversion error by selecting suitable analysis periods, calibrating models, or characterizing bias for correction in post processing., Key Points Aerosol lidar maps LA mixing depth in space (pilot mobile study) and time (2 years data)Automatic mixing depth retrieval system finds daily variability far exceeds seasonal differencePBL heights in models used for GHG monitoring show biases that will carry over to flux estimates