MIMICA V5.1, user guide

This document introduces the MISU-MIT Cloud and Aerosol (MIMICA) model, an atmospheric model dedicated to the simulation of clouds and other atmospheric processes at high resolution. The document contains a detailed description of the code, as well as an extended section serving as a manual for new or experienced users

Large-eddy simulation of a two-layer boundary-layer cloud system from the Arctic Ocean 2018 expedition

Climate change is particularly noticeable in the Arctic. The most common type of cloud at these latitudes is mixed-phase stratocumulus. These clouds occur frequently and persistently during all seasons and play a critical role in the Arctic energy budget. Previous observations in the central (north of 80&deg; N) Arctic have shown a high occurrence of prolonged periods of a shallow, single-layer mixed-phase stratocumulus at the top of the boundary layer (BL; altitudes ~300 to 400 m). However, recent observations from the summer of 2018 instead showed a prevalence of a two-layer boundary-layer cloud system. Here we use large-eddy simulation to examine the maintenance of one of the cloud systems observed in the summer of 2018 as well as the sensitivity of the cloud layers to different micro- and macro-scale parameters. We find that the model generally reproduces the observed thermodynamic structure well, with two near-neutrally stratified layers in the BL caused by a low cloud (located within the first few hundred meters) capped by a lower temperature inversion, and an upper cloud layer (based around one km or slightly higher) capped by the main temperature inversion of the BL. The investigated cloud structure is persistent unless there are low aerosol number concentrations (&le; 5 cm-3), which cause the upper cloud layer to dissipate, or high large-scale wind speeds (greater than or equal 8.5 m s-1), which erode the lower inversion and the related cloud layer. These types of changes in cloud structure lead to a substantial reduction of the net longwave radiation at the surface due to a lower emissivity or higher altitude of the remaining cloud layer. The findings highlight the importance of better understanding and representing aerosol sources and sinks over the central Arctic Ocean. Furthermore, they underline the significance of meteorological parameters, such as the large-scale wind speed, for maintaining the two-layer boundary-layer cloud structure encountered in the lower atmosphere of the central Arctic.</p

The impact of secondary ice production on Arctic stratocumulus

In situ measurements of Arctic clouds frequently show that ice crystal number concentrations (ICNCs) are much higher than the number of available ice-nucleating particles (INPs), suggesting that secondary ice production (SIP) may be active. Here we use a Lagrangian parcel model (LPM) and a large-eddy simulation (LES) to investigate the impact of three SIP mechanisms (rime splintering, break-up from ice–ice collisions and drop shattering) on a summer Arctic stratocumulus case observed during the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) campaign. Primary ice alone cannot explain the observed ICNCs, and drop shattering is ineffective in the examined conditions. Only the combination of both rime splintering (RS) and collisional break-up (BR) can explain the observed ICNCs, since both of these mechanisms are weak when activated alone. In contrast to RS, BR is currently not represented in large-scale models; however our results indicate that this may also be a critical ice-multiplication mechanism. In general, low sensitivity of the ICNCs to the assumed INP, to the cloud condensation nuclei (CCN) conditions and also to the choice of BR parameterization is found. Finally, we show that a simplified treatment of SIP, using a LPM constrained by a LES and/or observations, provides a realistic yet computationally efficient way to study SIP effects on clouds. This method can eventually serve as a way to parameterize SIP processes in large-scale models

Intercomparison of Large-Eddy Simulations of Arctic Mixed-Phase Clouds: Importance of Ice Size Distribution Assumptions

Large-eddy simulations of mixed-phase Arctic clouds by 11 different models are analyzed with the goal of improving understanding and model representation of processes controlling the evolution of these clouds. In a case based on observations from the Indirect and Semi-Direct Aerosol Campaign (ISDAC), it is found that ice number concentration, Ni, exerts significant influence on the cloud structure. Increasing Ni leads to a substantial reduction in liquid water path (LWP), in agreement with earlier studies. In contrast to previous intercomparison studies, all models here use the same ice particle properties (i.e., mass-size, mass-fall speed, and mass-capacitance relationships) and a common radiation parameterization. The constrained setup exposes the importance of ice particle size distributions (PSDs) in influencing cloud evolution. A clear separation in LWP and IWP predicted by models with bin and bulk microphysical treatments is documented and attributed primarily to the assumed shape of ice PSD used in bulk schemes. Compared to the bin schemes that explicitly predict the PSD, schemes assuming exponential ice PSD underestimate ice growth by vapor deposition and overestimate mass-weighted fall speed leading to an underprediction of IWP by a factor of two in the considered case. Sensitivity tests indicate LWP and IWP are much closer to the bin model simulations when a modified shape factor which is similar to that predicted by bin model simulation is used in bulk scheme. These results demonstrate the importance of representation of ice PSD in determining the partitioning of liquid and ice and the longevity of mixed-phase clouds

Dataset presented in the manuscript "The sensitivity of convective cloud ensemble statistics to horizontal resolution in idealized RCE simulations" by J. Savre and G. Craig

HASH(0x559ca9c43720

Simulation numérique instationnaire de la combustion turbulente au sein de foyers aéronautiques et prédiction des émissions polluantes

In order to simulate major pollutant formation inside realistic aeronautical combustion chambers, a detailed chemistry reduction technique (FPI), based on the construction of databases from elementary laminar premixed flame calculations, is adapted and coupled to the ONERA household CFD code : CEDRE. After a short validation of this model based on the numerical simulation of simplified laminar flames, the chemistry turbulence interactions are modeled under the laminar flamelet hypothesis, by assuming the shape of the FPI progress variable PDFs using beta functions. This comprehensive approach is then applied to the numerical simulation of the flow inside a realistic geometry :the PRECCINSTA combustion chamber. This well-known configuration has enabled the evaluation of the model’s abilities within an industrial framework using numerical/experimental results comparisons. It has especially allowed to test an extension of the model to partially premixed combution. Furthermore, the use of a tabulated chemistry model turns out to be particularly appropriate to predict pollutant species formation such as CO. However, when considering the formation of nitrogen oxides,FPI cannot be applied directly because of the slow dynamics of the chemical processes involved. Toovercome these limitations, two approaches allowing NO production modeling within complexe flowsare proposed, derived from the use of the tabulated data. The capacities of these models are finally analysed using computations performed on the PRECCINSTA chamber.Afin de pouvoir simuler la formation des principaux polluants au sein de foyers aéronautiques réalistes, un modèle de réduction de la chimie détaillée (FPI), basé sur la construction de tables à partir de calculs de flammes de prémélange laminaires élémentaires, est adapté et couplé au code d’aérothermochimie CEDRE de l’ONERA. Après une brève validation de ce modèle via la simulation de flammes laminaires canoniques, les interactions chimie/turbulence sont modélisées sous l’hypothèse des flammelettes, en approchant les PDF des paramètres d’entrée des tables par des fonctions beta. Cette approche complète est appliquée à la simulation numérique de l’écoulement au sein d’une configuration plus appliquée : la chambre PRECCINSTA. Ce cas bien connu a permis notamment l’évaluation des capacités du modèle dans un contexte plus industriel par comparaison des résultats de calcul aux données expérimentales disponibles. Il a en particulier permis de tester l’approche FPI étendue à la modélisation de la combustion partiellement prémélangée. Par ailleurs, l’utilisation d’un modèle de chimie réduite s’avère particulièrement appropriée pour prédire l’émission de substances polluantes, par exemple CO. Cependant, lorsque l’on considère la formation de NO, FPI ne peut pas être utilisé directement du fait de la lente dynamique chimique de cette espèce.Pour pallier à cette limitation, deux approches permettant de modéliser la production de NO au sein d’écoulements complexes sont proposées, fondées sur l’utilisation des tables chimiques FPI. Les capacités de ces modèles sont finalement analysées à l’aide de calculs effectués sur la configuration PRECCINSTA

Fitting Cumulus Cloud Size Distributions From Idealized Cloud Resolving Model Simulations

AbstractWhereas it is now widely accepted that cumulus cloud sizes are power‐law distributed, characteristic exponents reported in the literature vary greatly, generally taking values between 1 and &gt;3. Although these differences might be explained by variations in environmental conditions or physical processes organizing the cloud ensembles, the use of improper fitting methods may also introduce large biases. To address this issue, we propose to use a combination of maximum likelihood estimation and goodness‐of‐fit tests to provide more robust power‐law fits while systematically identifying the size range over which these fits are valid. The procedure is applied to cloud size distributions extracted from two idealized high‐resolution simulations displaying different organization characteristics. Overall, power‐laws are found to be outperformed by alternative distributions in almost all situations. When clouds are identified based on a condensed water path threshold, using power‐laws with an exponential cutoff yields the best results as it provides superior fits in the tail of the cloud size distributions. For clouds identified using a combination of water content and updraft velocity thresholds in the free troposphere, no substantial improvement over pure power‐laws can be found when considering more complex two‐parameter distributions. In this context however, exponential distributions provide results that are as good as, if not better than power‐laws. Finally, it is demonstrated that the emergence of scale free behaviors in cloud size distributions is related to exponentially distributed cloud cores merging as they are brought closer to each other by underlying organizing mechanisms.Plain Language Summary: Clouds constitute an important element of the climate system reflecting incoming solar radiation and emitting infra‐red radiation that heats the atmosphere. The net radiative impact of clouds however depends on many factors including their size. It is thus of prime importance to characterize the size of clouds, in particular convective clouds, and understand the underlying processes controlling them. In this study, a numerical model is used to simulate two convective situations at horizontal resolutions providing a fine description of cloud processes. After identifying individual clouds and calculating their size, statistical methods are employed to characterize the cloud size distributions. Depending on the situation, cloud size distributions are found to be best represented by either power‐laws with an exponential cutoff or exponential functions. Pure power‐laws, which constitute the most popular model used to represent cloud size distributions, are generally found to yield poorer fits. Finally, it is demonstrated that power‐laws in cloud size distributions emerge when individual cloud cores, that are exponentially distributed in size, are brought closer to each other and merge as the cloud ensemble organizes.Key Points: A combination of statistical methods is used to fit cloud size distributions from two simulated convective cloud ensembles. Depending on the situation, exponential distributions and power‐laws with an exponential cutoff may constitute superior alternatives to pure power‐laws. The merging of individual cloud cores is found to control the emergence of power‐law cloud size distributions. https://bitbucket.org/julien_savre/pycloudfit/src/master/https://doi.org/10.5281/zenodo.700514

Unstationnary numerical simulations of turbulent combustion inside aeronautical burners and pollutant formation modeling

Afin de pouvoir simuler la formation des principaux polluants au sein de foyers aéronautiques réalistes, un modèle de réduction de la chimie détaillée (FPI), basé sur la construction de tables à partir de calculs de flammes de prémélange laminaires élémentaires, est adapté et couplé au code d’aérothermochimie CEDRE de l’ONERA. Après une brève validation de ce modèle via la simulation de flammes laminaires canoniques, les interactions chimie/turbulence sont modélisées sous l’hypothèse des flammelettes, en approchant les PDF des paramètres d’entrée des tables par des fonctions beta. Cette approche complète est appliquée à la simulation numérique de l’écoulement au sein d’une configuration plus appliquée : la chambre PRECCINSTA. Ce cas bien connu a permis notamment l’évaluation des capacités du modèle dans un contexte plus industriel par comparaison des résultats de calcul aux données expérimentales disponibles. Il a en particulier permis de tester l’approche FPI étendue à la modélisation de la combustion partiellement prémélangée. Par ailleurs, l’utilisation d’un modèle de chimie réduite s’avère particulièrement appropriée pour prédire l’émission de substances polluantes, par exemple CO. Cependant, lorsque l’on considère la formation de NO, FPI ne peut pas être utilisé directement du fait de la lente dynamique chimique de cette espèce.Pour pallier à cette limitation, deux approches permettant de modéliser la production de NO au sein d’écoulements complexes sont proposées, fondées sur l’utilisation des tables chimiques FPI. Les capacités de ces modèles sont finalement analysées à l’aide de calculs effectués sur la configuration PRECCINSTA.In order to simulate major pollutant formation inside realistic aeronautical combustion chambers, a detailed chemistry reduction technique (FPI), based on the construction of databases from elementary laminar premixed flame calculations, is adapted and coupled to the ONERA household CFD code : CEDRE. After a short validation of this model based on the numerical simulation of simplified laminar flames, the chemistry turbulence interactions are modeled under the laminar flamelet hypothesis, by assuming the shape of the FPI progress variable PDFs using beta functions. This comprehensive approach is then applied to the numerical simulation of the flow inside a realistic geometry :the PRECCINSTA combustion chamber. This well-known configuration has enabled the evaluation of the model’s abilities within an industrial framework using numerical/experimental results comparisons. It has especially allowed to test an extension of the model to partially premixed combution. Furthermore, the use of a tabulated chemistry model turns out to be particularly appropriate to predict pollutant species formation such as CO. However, when considering the formation of nitrogen oxides,FPI cannot be applied directly because of the slow dynamics of the chemical processes involved. Toovercome these limitations, two approaches allowing NO production modeling within complexe flowsare proposed, derived from the use of the tabulated data. The capacities of these models are finally analysed using computations performed on the PRECCINSTA chamber