We investigate if mesoscale self-organisation of trade cumuli in 150 km-domain large-eddy simulations modifies the top-of-atmosphere radiation budget relative to 10 km-domain simulations, across 77 characteristic, idealized environments. In large domains, self-generated mesoscale circulations produce fewer, larger and deeper clouds, raising the cloud albedo. Yet they also precipitate more than small-domain cumuli, drying and warming the cloud layer, and reducing cloud cover. Consequently, large domains cool slightly less through the shortwave cloud-radiative effect, and slightly more through clear-sky outgoing longwave radiation, for a net cooling (−0.5 W (Formula presented.)). This cooling is generally smaller than the large-domain radiation's sensitivity to large-scale meteorological variability, which is similar in small-domain simulations and observations. Hence, mesoscale self-organisation would not alter weak trade-cumulus feedback estimates previously derived from small-domain simulations. We explain this with a symmetry hypothesis: ascending and descending branches of mesoscale circulations symmetrically increase and reduce cloudiness, weakly modifying the mean radiation budget.

Mesoscale numerical weather prediction models currently operate at kilometer-scale and even sub-kilometer-scale resolutions. Although shallow cumulus convection is partly resolved at these resolutions, it is still common to use a shallow cumulus parameterization (SCP). Within the context of the EUREC4A model intercomparison project, we evaluate how the modeled mesoscale cloud field in the trades responds to parameterized or explicit shallow convection in the mesoscale model HARMONIE-AROME. We simulate a region of 3,200 × 2,025 km2 east of Barbados using a grid spacing of 2.5 km for a 2 months period (1 January to 29 February 2020). We compare three configurations of HARMONIE-AROME: (a) one with an active SCP (control), (b) one without parameterized momentum transport by shallow convection, and (c) one with an inactive SCP. The experiments produce different responses in the cloud field that are not incremental. With the SCP inactive, the model produces a warmer lower troposphere with many smaller but deeper clouds that precipitate more. Along with stronger resolved eddy kinetic energy, wider and stronger shallow meridional overturning circulations develop. In the configuration without parameterized momentum transport by shallow convection, the eddy-diffusivity scheme effectively takes over the missing transport in the sub-cloud layer up to ~800 m. Above that level, horizontal wind variance increases as the total momentum flux decreases, enhancing eddy kinetic energy at scales of 2.5 km and larger. In contrast to the configuration with an inactive SCP, cloud top heights hardly deepen, but stratiform cloudiness below the inversion and mean cloud size increase.

Spectropolarimetry is a powerful tool for characterising planetary atmospheres and surfaces. For the design and operation of spectro(polari)metric instrumentation, numerically simulated signals of the measured radiation are essential. Here we present MONKI (Monte Carlo KNMI), an efficient and accurate radiative transfer code written in Fortran, based on the Monte Carlo method. MONKI computes both total and polarised radiances reflected and transmitted by a planetary atmosphere, fully accounting for the polarisation of light in all orders of scattering. MONKI can handle atmospheres that are horizontally homogeneous, as well as those with horizontal inhomogeneities, such as three-dimensional (3D) patchy clouds. We validate MONKI through comparisons with various other radiative transfer codes and demonstrate that it converges reliably even for optically thick and strongly polarising atmospheres. Finally, we present sample simulations of sunlight reflected by the Earth and Venus, and explain the total and polarised radiance features by analysing the altitudes at which the photons are scattered. We conclude that MONKI is a versatile and accurate tool, suitable for simulations and detailed analyses of locally reflected light by the Earth, Venus, and, in principle, any other planet.

Cloud shadows can be detected in the radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite due to its high spatial resolution and could possibly affect its air quality products. The cloud-shadow-induced signatures are, however, not always apparent and may depend on various cloud and scene parameters. Hence, the quantification of the cloud shadow impact requires the analysis of large data sets. Here we use the cloud shadow detection algorithm DARCLOS to detect cloud shadow pixels in the TROPOMI absorbing aerosol index (AAI) product over Europe during 8 months. For every shadow pixel, we automatically select cloud- and shadow-free neighbour pixels in order to estimate the cloud-shadow-induced signature. In addition, we simulate the measured cloud shadow impact on the AAI with our newly developed three-dimensional (3D) radiative transfer algorithm MONKI. Both the measurements and simulations show that the average cloud shadow impact on the AAI is close to zero (0.06 and 0.16, respectively). However, the top-of-atmosphere reflectance ratio between 340 and 380 nm, which is used to compute the AAI, is significantly increased in 95 % of the shadow pixels. So, cloud shadows are bluer than surrounding non-shadow pixels. Our simulations explain that the traditional AAI formula intrinsically already corrects for this cloud shadow effect via the lower retrieved scene albedo. This cancellation of cloud shadow signatures is not always perfect, sometimes yielding second-order low and high biases in the AAI which we also successfully reproduce with our simulations. We show that the magnitude of those second-order cloud shadow effects depends on various cloud parameters which are difficult to determine for the shadows measured with TROPOMI. We conclude that a potential cloud shadow correction strategy for the TROPOMI AAI would therefore be complicated if not unnecessary.

Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-The-Art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025°ĝ€¯×ĝ€¯0.025° covering an area surrounding the North Sea for the years 2019-2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

For a number of years research has been carried out in several centres which has demonstrated the potential benefits of 100-m scale models for a range of meteorological phenomena. More recently, some meteorological services have started to consider seriously the operational implementation of practical hectometric models. Many, but by no means all, of the applications are likely to relate to urban areas, where the enhanced resolution has obvious benefits. This article is concerned with the issues that need to be addressed to bridge the gap between research at 100-m scales and practical models. We highlight a number of key issues that need to be addressed, with suggestions of important avenues for future development. An overarching issue is the high computational cost of these models. Although some ideas to reduce this are presented, it will always be a serious constraint. This means that the benefits of these models over lower resolution ones, or other techniques for generating high-resolution forecasts, will need to be clearly understood, as will the trade-offs with resolution. We discuss issues with model dynamical cores and physics–dynamics coupling. There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear. Observational data at these scales present a challenge and novel types of observations will need to be considered. Data assimilation will be needed for short-range forecasts, but there is currently little knowledge of this, although some of the likely issues are clear. An ensemble approach will be essential in many cases (e.g., convection), but research is needed into ensembles at these scales and significant work on post-processing systems is required to make the best use of models at these grid lengths.

Recent observations of the trade-wind regions highlight the covariability between cold-pool properties and mesoscale cloud organization. Given the covariability of organization with cloud cover and albedo, this suggests a potential impact of cold pools on the cloud radiative effect (CRE). To explore this, we use an ensemble of 103 large-domain, high-resolution, large-eddy simulations and investigate how the variability in cold pools is determined by large-scale external cloud-controlling factors (CCFs) and shaped by processes within the mesoscale. It is demonstrated that the size and frequency of occurrence of cold pools are strongly influenced by the near-surface horizontal wind speed and large-scale subsidence. The temporal evolution of cold pools is strongly correlated with the diurnality in radiation. Even without external variability, we find a strong intermittent behaviour in the evolution of cold pools, governed by a complex interplay between cold pools and clouds which expresses itself in the form of shallow squall lines. These squall lines result from precipitating downdrafts, cold pool outflows and the resulting gust fronts, reinforcing parent clouds. Cold pools influence the CRE of trade cumuli, but only when they exist during the day. This emphasizes the importance of the synchronization between cold-pool events and the diurnal cycle of insolation for the dependence of the CRE on cold pools.

Recent observations of the trade-wind regions highlight the covariability between cold-pool properties and mesoscale cloud organization. Given the covariability of organization with cloud cover and albedo, this suggests a potential impact of cold pools on the cloud radiative effect (CRE). To explore this, we use an ensemble of 103 large-domain, high-resolution, large-eddy simulations and investigate how the variability in cold pools is determined by large-scale external cloud-controlling factors and shaped by processes within the mesoscale. It is demonstrated that the size and frequency of occurrence of cold pools are strongly influenced by the near-surface horizontal wind speed and large-scale subsidence. The temporal evolution of cold pools is strongly correlated with the diurnality in radiation. Even without external variability, we find a strong intermittent behavior in the evolution of cold pools, governed by a complex interplay between cold pools and clouds which expresses itself in the form of shallow squall lines. These squall lines result from precipitating downdrafts, cold pool outflows and the resulting gust fronts, reinforcing parent clouds. Cold pools influence the CRE of trade cumuli, but only when they exist during the day. This emphasizes the importance of the synchronization between cold-pool events and the diurnal cycle of insolation for the dependence of the CRE on cold pools.

Shallow trade cumuli over subtropical oceans are a persistent source of uncertainty in climate projections. Mesoscale organization of trade cumulus clouds has been shown to influence their cloud radiative effect (CRE) through cloud cover. We investigate whether organization can explain CRE variability independently of cloud-cover variability. By analyzing satellite observations and high-resolution simulations, we show that more clustered cloud fields feature geometrically thicker clouds with larger domain-averaged liquid water paths, smaller cloud droplets, and consequently larger cloud optical depths. The relationships between these variables are shaped by the mixture of deep cloud cores and shallower interstitial clouds or anvils that characterize cloud organization. Eliminating cloud-cover effects, more clustered clouds reflect up to 20 W/m2 more instantaneous shortwave radiation back to space.

Open boundary conditions were developed for atmospheric large-eddy simulation (LES) models and implemented into the Dutch Atmospheric Large-Eddy Simulation model. The implementation was tested in a “Big Brother”-like setup, in which the simulation with open boundary conditions was forced by an identical control simulation with periodic boundary conditions. The results show that the open boundary implementation has minimal influence on the solution. Both the mean state and the turbulent structures are close to the control simulation, and disturbances at the in- and outflow boundaries are negligible. To emulate a setup in which the LES is coupled to a coarser model, the influence of coarse boundary input was tested by smoothing the output of the periodic control simulation both temporally and spatially before feeding it as input to the simulation with open boundary conditions. When smoothing is applied over larger spatial and longer temporal scales, disturbances start to form at the inflow boundary and an area exists where turbulence needs to develop. Adding synthetic turbulence to the smoothed input reduces the size of this area and the magnitude of the disturbances.

Recent observations of the trades highlight the covariability between cold pool (CP) properties and cloud cover, suggesting a potential impact of CPs on the cloud radiative effect (CRE). To explore this, we use an ensemble of 103 large-domain, high-resolution, large-eddy simulations (Cloud Botany). We investigate the extent to which the variability in CPs is driven by external conditions or convective self-organization. Our findings show that CPs are notably controlled by large-scale conditions, specifically (horizontal) wind speed and subsidence. The temporal evolution of CPs is tightly related to the diurnality in radiation. To understand the extent to which CPs vary with self-organization, we switch off the diurnality in radiation. Despite the absence of the diurnal cycle, CP time series still exhibit fluctuations. These fluctuations result from the recharge-discharge of thermodynamic and dynamic properties of the sub-cloud layer owing to CP-cloud interactions. Our results demonstrate that circulations induced by CPs reinforce the parent clouds, resulting in deepening and scale growth, followed by mesoscale arcs enclosing clear-sky areas. Finally, we show that CPs influence CRE, but only when they exist during the day. Our findings emphasize the importance of the relationship between the timescales of self-organization and the diurnal cycle of external conditions, greatly influencing the CRE dependency on self-organizing CPs.

Clouds affected by solar eclipses could influence the reflection of sunlight back into space and might change local precipitation patterns. Satellite cloud retrievals have so far not taken into account the lunar shadow, hindering a reliable spaceborne assessment of the eclipse-induced cloud evolution. Here we use satellite cloud measurements during three solar eclipses between 2005 and 2016 that have been corrected for the partial lunar shadow together with large-eddy simulations to analyze the eclipse-induced cloud evolution. Our corrected data reveal that, over cooling land surfaces, shallow cumulus clouds start to disappear at very small solar obscurations (~15%). Our simulations explain that the cloud response was delayed and was initiated at even smaller solar obscurations. We demonstrate that neglecting the disappearance of clouds during a solar eclipse could lead to a considerable overestimation of the eclipse-related reduction of net incoming solar radiation. These findings should spur cloud model simulations of the direct consequences of sunlight-intercepting geoengineering proposals, for which our results serve as a unique benchmark.

This study investigates momentum transport in shallow cumulus clouds as simulated with the Dutch Atmospheric Large Eddy Simulation (DALES) for a 150 3 150 km2 domain east of Barbados during 9 days of EUREC4A. DALES is initialized and forced with the mesoscale weather model HARMONIE-AROME and subjectively reproduces observed cloud patterns. This study examines the evolution of momentum transport, which scales contribute to it, and how they modulate the trade winds. Daily-mean momentum flux profiles show downgradient zonal momentum transport in the subcloud layer, which turns countergradient in the cloud layer. The meridional momentum transport is nontrivial, with mostly downgradient transport throughout the trade wind layer except near the top of the surface layer and near cloud tops. Substantial spatial and temporal heterogeneity in momentum flux is observed with much stronger tendencies imposed in areas of organized convection. The study finds that while scales < 2 km dominate momentum flux at 200 m in unorganized fields, submesoscales O(2-20) km carry up to 50% of the zonal momentum flux in the cloud layer in organized fields. For the meridional momentum flux, this fraction is even larger near the surface and in the subcloud layer. The scale dependence of the momentum flux is not explained by changes in convective or boundary layer depth. Instead, the results suggest the importance of spatial heterogeneity, increasing horizontal length scales, and countergradient transport in the presence of organized convection.

Numerical simulations of the tropical mesoscales often exhibit a self-reinforcing feedback between cumulus convection and shallow circulations, which leads to the self-aggregation of clouds into large clusters. We investigate whether this basic feedback can be adequately captured by large-eddy simulations (LESs). To do so, we simulate the non-precipitating, cumulus-topped boundary layer of the canonical “BOMEX” case over a range of numerical settings in two models. Since the energetic convective scales underpinning the self-aggregation are only slightly larger than typical LES grid spacings, aggregation timescales do not converge even at rather high resolutions (<100 m). Therefore, high resolutions or improved sub-filter scale models may be required to faithfully represent certain forms of trade-wind mesoscale cloud patterns and self-aggregating deep convection in large-eddy and cloud-resolving models, and to understand their significance relative to other processes that organize the tropical mesoscales.

Condensation in cumulus clouds plays a key role in structuring the mean, nonprecipitating trade wind boundary layer. Here, we summarize how this role also explains the spontaneous growth of mesoscale [.O(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 [O(100) km] domain. Small, spatial anomalies in condensation in cumulus clouds, which form on top of small moisture fluctuations, power circulations that transport moisture, but not heat, from dry to moist regions, and thus reinforce the condensation 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 kilometer 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.

Small shallow cumulus clouds (less-than 1 km) over the tropical oceans appear to possess the ability to self-organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self-organized convection, we present Cloud Botany, an ensemble of 103 large-eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger-scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger-scale environment and patterns in mesoscale, self-organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross-wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross-sections, as well as post-processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time-series). The data set is directly accessible from Python through the use of the EUREC4A intake catalog.

A new generation of operational atmospheric models operating at horizontal resolutions in the range 200 m ∼ 2 km is becoming increasingly popular for operational use in numerical weather prediction and climate applications. Such grid spacings are becoming sufficiently fine to resolve a fraction of the turbulent transports. Here we analyze Large-eddy simulation results of a convective boundary layer obtained by coarsening horizontal grid spacings up to 800 m. The aim is to explore the dependency of the mean state and turbulent fluxes on the grid resolution. Both isotropic and anisotropic eddy diffusion approaches are evaluated, where in the latter case the horizontal and vertical eddy diffusivities differ in accord with their horizontal and vertical grid spacings. For coarsening horizontal grid sizes entrainment at the top of the boundary layer tends to get slightly enhanced for isotropic diffusion, whereas for the anisotropic diffusion approach the vertically well-mixed boundary-layer structure becomes severely degraded. An analysis of the energy spectrum shows that anisotropic diffusion causes relatively more dissipation of variance at smaller length scales. This leads, in turn, to a shift of spectral energy toward larger length scales that also becomes apparent from a rather different kind of spatial organization of convection. The present study therefore suggests that details with regards to the representation of processes at small scales might impact the organization at length scales much larger than the smallest scales that can be resolved by the model.

Simulations of a marine cold air outbreak with the mesoscale weather model HARMONIE suggest that parameterized shallow convective momentum transport acts to diminish circulations that accompany cellular cloud structures. In this study we test this hypothesis in the trade-wind region where various type of shallow cumulus cloud patterns occur using a hierarchy of model simulations of the EUREC4A campaign. We select a ten-day period within EUREC4A and run the Dutch Atmospheric Large-Eddy Simulation (DALES) on a 150 km x 150 km domain with a resolution of 100 m. Its boundaries are forced hourly with dynamical tendencies from the mesoscale weather model HARMONIE. HARMONIE is also run on a 3200 km x 2000 km domain with 2.5 km resolution, in runs with shallow convective momentum transport on and off. First, we validate the model output with observations. Observations from EUREC4A suggest that in early February, deeper cumulus mediocris and larger cloud structures are associated with a different profile of eddy momentum flux divergence than days with shallower cumulus humilis. Second, we analyze the profiles of eddy momentum flux associated with turbulence, convection, and mesoscale flows. We show the momentum budget associated with different patterns of cloud organization and, lastly, we evaluate whether momentum transport has a significant influence on shallow cloud organization.

The parameterised description of subgrid-scale processes in the clear and cloudy boundary layer has a strong impact on the performance skill in any numerical weather prediction (NWP) or climate model and is still a prime source of uncertainty. Yet, improvement of this parameterised description is hard because operational models are highly optimised and contain numerous compensating errors. Therefore, improvement of a single parameterised aspect of the boundary layer often results in an overall deterioration of the model as a whole. In this paper, we will describe a comprehensive integral revision of three parameterisation schemes in the High Resolution Local Area Modelling - Aire Limitée Adaptation dynamique Développement InterNational (HIRLAM-ALADIN) Research on Mesoscale Operational NWP In Europe - Applications of Research to Operations at Mesoscale (HARMONIE-AROME) model that together parameterise the boundary layer processes: the cloud scheme, the turbulence scheme, and the shallow cumulus convection scheme. One of the major motivations for this revision is the poor representation of low clouds in the current model cycle. The newly revised parametric descriptions provide an improved prediction not only of low clouds but also of precipitation. Both improvements can be related to a stronger accumulation of moisture under the atmospheric inversion. The three improved parameterisation schemes are included in a recent update of the HARMONIE-AROME configuration, but its description and the insights in the underlying physical processes are of more general interest as the schemes are based on commonly applied frameworks. Moreover, this work offers an interesting look behind the scenes of how parameterisation development requires an integral approach and a delicate balance between physical realism and pragmatism.

In atmospheric modeling, superparameterization (SP) has gained popularity as a technique to improve cloud and convection representations in large-scale models by coupling them locally to cloud-resolving models. We show how the different representations of cloud water in the local and the global models in SP lead to a suppression of cloud advection and ultimately to a systematic underrepresentation of the cloud amount in the large-scale model. We demonstrate this phenomenon in a regional SP experiment with the global model OpenIFS coupled to the local model Dutch Atmospheric Large Eddy Simulation, as well as in an idealized setup, where the large-scale model is replaced by a simple advection scheme. As a starting point for mitigating the problem of suppressed cloud advection, we propose a scheme where the spatial variability of the local model's total water content is enhanced in order to match the global model's cloud condensate amount. The proposed scheme enhances the cloud condensate amount in the test cases, however a large discrepancy remains, caused by rapid dissipation of the clouds added by the proposed scheme.