No abstract

Condensation in cumulus clouds plays a key role in structuring the mean, non-precipitating trade-wind boundary layer. Here, we summarise how this role also explains the spontaneous growth of mesoscale (> О(10) km) fluctuations in clouds and moisture around the mean state in a minimal-physics, large-eddy simulation of the undisturbed period during BOMEX on a large (О(100) km) domain. Small, spatial anomalies in latent heating in cumulus clouds, which form on top of small moisture fluctuations, give rise to circulations that transport moisture, but not heat, from dry to moist regions, and thus reinforce the latent heating anomaly. We frame this positive feedback as a linear instability in mesoscale moisture fluctuations, whose time scale depends only on i) a vertical velocity scale and ii) the mean environment’s vertical structure. In our minimal-physics setting, we show both ingredients are provided by the shallow cumulus convection itself: It is intrinsically unstable to length scale growth. The upshot is that energy released by clouds at kilometre scales may play a more profound and direct role in shaping the mesoscale trade-wind environment than is generally appreciated, motivating further research into the mechanism’s relevance.

Shallow cloud fields over the subtropical ocean exhibit many spatial patterns. The frequency of occurrence of these patterns can change under global warming. Hence, they may influence subtropical marine clouds’ climate feedback. While numerous metrics have been proposed to quantify cloud patterns, a systematic, widely accepted description is still missing. Therefore, this study suggests one. We compute 21 metrics for 5,000 satellite scenes of shallow clouds over the subtropical Atlantic Ocean and translate the resulting data set to its principal components (PCs). This yields a unimodal, continuous distribution without distinct classes, whose first four PCs explain 82% of all 21 metrics’ variance. The PCs correspond to four interpretable dimensions: Characteristic length, void size, directional alignment, and horizontal cloud top height variance. These dimensions span a space in which an effective pattern description can be given, which may be used to better understand the patterns’ underlying physics and feedback on climate.

Vegetation and atmosphere processes are coupled through a myriad of interactions linking plant transpiration, carbon dioxide assimilation, turbulent transport of moisture, heat and atmospheric constituents, aerosol formation, moist convection, and precipitation. Advances in our understanding are hampered by discipline barriers and challenges in understanding the role of small spatiotemporal scales. In this perspective, we propose to study the atmosphere-ecosystem interaction as a continuum by integrating leaf to regional scales (multiscale) and integrating biochemical and physical processes (multiprocesses). The challenges ahead are (1) How do clouds and canopies affect the transferring and in-canopy penetration of radiation, thereby impacting photosynthesis and biogenic chemical transformations? (2) How is the This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Abstract. Atmospheric boundary layers and other wall-bounded flows are often simulated with the large-eddy simulation (LES) technique, which relies on subgrid-scale (SGS) models to parameterize the smallest scales. These SGS models often make strong simplifying assumptions. Also, they tend to interact with the discretization errors introduced by the popular LES approach where a staggered finite-volume grid acts as an implicit filter. We therefore developed an alternative LES SGS model based on artificial neural networks (ANNs) for the computational fluid dynamics MicroHH code (v2.0). We used a turbulent channel flow (with friction Reynolds number Reτ=590) as a test case. The developed SGS model has been designed to compensate for both the unresolved physics and instantaneous spatial discretization errors introduced by the staggered finite-volume grid. We trained the ANNs based on instantaneous flow fields from a direct numerical simulation (DNS) of the selected channel flow. In general, we found excellent agreement between the ANN-predicted SGS fluxes and the SGS fluxes derived from DNS for flow fields not used during training. In addition, we demonstrate that our ANN SGS model generalizes well towards other coarse horizontal resolutions, especially when these resolutions are located within the range of the training data. This shows that ANNs have potential to construct highly accurate SGS models that compensate for spatial discretization errors. We do highlight and discuss one important challenge still remaining before this potential can be successfully leveraged in actual LES simulations: we observed an artificial buildup of turbulence kinetic energy when we directly incorporated our ANN SGS model into a LES simulation of the selected channel flow, eventually resulting in numeric instability. We hypothesize that error accumulation and aliasing errors are both important contributors to the observed instability. We finally make several suggestions for future research that may alleviate the observed instability.

Abstract. Atmospheric boundary layers and other wall-bounded flows are often simulated with the large-eddy simulation (LES) technique, which relies on subgrid-scale (SGS) models to parameterize the smallest scales. These SGS models often make strong simplifying assumptions. Also, they tend to interact with the discretization errors introduced by the popular LES approach where a staggered finite-volume grid acts as an implicit filter. We therefore developed an alternative LES SGS model based on artificial neural networks (ANNs) for the computational fluid dynamics code MicroHH (v2.0), which can be run in direct numerical simulation (DNS) and LES mode. We used a turbulent channel flow (with a friction Reynolds number Reτ = 590) as a test case. The developed SGS model has been designed to require fewer simplifying assumptions, and to compensate for the instantaneous discretization errors introduced by the staggered finite-volume grid. We trained the ANNs based on instantaneous flow fields from a direct numerical simulation (DNS) of the selected channel flow. In general, we found excellent agreement between the ANN predicted SGS fluxes and the SGS fluxes derived from DNS for flow fields not used during training (with the correlation coefficient ρ mostly varying between 0.6 and 1.0), showing the potential ANNs may have to construct highly accurate SGS models. However, we observed an artificial build-up of turbulence kinetic energy at high wave modes when we directly incorporated our ANN SGS model into a LES simulation of the selected channel flow, eventually resulting in numeric instability. We hypothesized that error accumulation and aliasing errrors, were both important contributors to the observed instability. Several obstacles therefore remain before the a priori promise of our ANN LES SGS model, can be successfully leveraged in practical applications.

A striking feature of idealized simulations of the tropical atmosphere in radiative-convective equilibrium (RCE) is the spontaneous aggregation of their column-integrated moisture and convection into large clusters (Bretherton et al., 2005;Muller & Held, 2012). Many mechanisms have been proposed to explain this, including the collision and convective triggering of horizontally expanding and colliding cold pools of evaporated precipitation (Böing, 2016;Haerter, 2019;Tompkins, 2001) and gravity wave-convection interactions (Yang, 2021). Yet, perhaps the strongest consensus is on the importance of shallow circulations (Muller et al., 2022;Shamekh et al., 2020), configured to transport moisture from dry to moist columns.These circulations can be traced to differential, radiative cooling between moist regions, which trap outgoing longwave radiation in their moisture-rich lower atmosphere and under high clouds, and dry regions, which more readily radiate their thermal energy to space (Muller & Held, 2012). Such heating anomalies give rise to ascent in moist columns and descent in dry columns, and may be framed as a moisture-radiation instability (Beucler & Cronin, 2016;Emanuel et al., 2014) with negative moist gross stability (Bretherton et al., 2005;Raymond et al., 2009). However, the circulations may also be reinforced by turbulent mixing at cloud edges, which deposits moisture in the free troposphere and thus raises the livelihood and vigor of any subsequent convection; differential convection may then itself result in a net ascent of moist, convecting regions and descent in

The toxicity and oxidative stress responses of 19-day old Arabidopsis seedlings induced by U (66 M) and Cd (20 M) alone or in a binary mixture set-up (equitoxic mixture) are studied in function of time. After 48h a significant decrease in root and shoot growth and a simultaneous increase in anthocyanin production was evident in all treated plants. Production of O .− 2 or H 2 O 2 was visualized by staining freshly harvested leaves with nitrobluetetrazolium or diaminobezidine, respectively. With this method production of O .− 2 was only significantly measurable after 168 h treatment which coincides with a significant decrease in biomass production and probably also plant cell death. For Cd treated plants a significant increase in H 2 O 2 production was measurable from 24h onwards. In contrast, a similar H 2 O 2 production could not be measured in U or U + Cd treated plants. Both water and lipophilic soluble antioxidants significantly increased in U treated plants after 48 h. These high antioxidant levels might detoxify potential H 2 O 2 produced in the U treated plants. In contrast for Cd treated plants only after 168h a significant increase in water soluble antioxidants was measured whereas no difference in the lipophilic fraction was visible.