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Cloud microphysics is a critical aspect of the Earth's climate system, which involves processes at the nano‐ and micrometer scales of droplets and ice particles. In climate modeling, cloud microphysics is commonly represented by bulk models, which contain simplified process rates that require calibration. This study presents a framework for calibrating warm‐rain bulk schemes using high‐fidelity super‐droplet simulations that provide a more accurate and physically based representation of cloud and precipitation processes. The calibration framework employs ensemble Kalman methods including Ensemble Kalman Inversion and Unscented Kalman Inversion to calibrate bulk microphysics schemes with probabilistic super‐droplet simulations. We demonstrate the framework's effectiveness by calibrating a single‐moment bulk scheme, resulting in a reduction of data‐model mismatch by more than 75% compared to the model with initial parameters. Thus, this study demonstrates a powerful tool for enhancing the accuracy of bulk microphysics schemes in atmospheric models and improving climate modeling. Plain Language Summary: Cloud microphysics is a complex set of processes that determine the formation and evolution of particles in clouds, which affects the Earth's climate by regulating precipitation and cloud cover. However, the vast difference in scale between the microphysics and large‐scale atmospheric flows makes it impossible to simulate these processes in climate models directly. Instead, climate models use simplified methods to represent cloud microphysics, which can result in inaccuracies. In this study, we focus on calibrating the simplified models with more detailed simulations of cloud microphysics using the super‐droplet method. We demonstrate a framework for calibrating the simplified models using high‐fidelity simulations, which improves the accuracy of these models. Key Points: A calibration framework for warm‐rain bulk microphysics parameterizations is presentedThe framework relies on a library of super‐droplet simulations of a rain shaftCalibrating a single‐moment microphysics scheme with the calibration framework substantially reduces the model‐data mismatch [ABSTRACT FROM AUTHOR]

A key constraint of particle-based methods for modeling cloud microphysics is the conservation of total particle number, which is required for computational tractability. The process of collisional breakup poses a particular challenge to this framework, as breakup events often produce many droplet fragments of varying sizes, which would require creating new particles in the system. This work introduces a representation of collisional breakup in the so-called "superdroplet" method which conserves the total number of superdroplets in the system. This representation extends an existing stochastic collisional-coalescence scheme and samples from a fragment size distribution in an additional Monte Carlo step. This method is demonstrated in a set of idealized box model and single-column warm-rain simulations. We further discuss the effects of the breakup dynamic and fragment size distribution on the particle size distribution, hydrometeor population, and microphysical process rates. Box model experiments serve to characterize the impacts of properties such as coalescence efficiency and fragmentation function on the relative roles of collisional breakup and coalescence. The results demonstrate that this representation of collisional breakup can produce a stationary particle size distribution, in which breakup and coalescence rates are approximately equal, and that it recovers expected behavior such as a reduction in precipitate-sized particles in the column model. The breakup algorithm presented here contributes to an open-source pythonic implementation of the superdroplet method, PySDM, which will facilitate future research using particle-based microphysics. [ABSTRACT FROM AUTHOR]

A key constraint of particle-based methods for modeling cloud microphysics is the conservation of total particle number, which is required for computational tractability. The process of collisional breakup poses a particular challenge to this framework, as breakup events often produce many droplet fragments of varying sizes, which would require creating new particles in the system. This work introduces a representation of collisional breakup in the so-called "superdroplet" method which conserves the total number of superdroplets in the system. This representation extends an existing stochastic collisionalcoalescence scheme and samples from a fragment-size distribution in an additional Monte Carlo step. This method is demonstrated in a set of idealized box model and single-column warm-rain simulations. We further discuss the effects of the breakup dynamic and fragment-size distribution on the particle size distribution, hydrometeor population, and microphysical process rates. This representation of collisional breakup is able to produce a stationary particle-size distribution, in which breakup and coalescence rates are approximately equal, and it recovers expected behavior such as precipitation suppression in the column model. Furthermore, representing breakup has potential benefits that extend beyond warm rain processes, such as the ability to capture mechanisms of secondary ice production in the superdroplet method. The breakup algorithm presented here contributes to an open-source pythonic implementation of the superdroplet method, 'PySDM', which will facilitate future research using particle-based microphysics. [ABSTRACT FROM AUTHOR]

A key constraint of particle-based methods for modeling cloud microphysics is the conservation of total particle number, which is required for computational tractability. The process of collisional breakup poses a particular challenge to this framework, as breakup events often produce many droplet fragments of varying sizes, which would require creating new particles in the system. This work introduces a representation of collisional breakup in the so-called 'superdroplet' method which conserves the total number of superdroplets in the system. This representation extends an existing stochastic collisional-coalescence scheme and samples from a fragment-size distribution in an additional Monte Carlo step. This method is demonstrated in a set of idealized box model and single-column warm-rain simulations. We further discuss the effects of the breakup dynamic and fragment-size distribution on the particle size distribution, hydrometeor population, and microphysical process rates. This representation of collisional breakup is able to produce a stationary particle-size distribution, in which breakup and coalescence rates are approximately equal, and it recovers expected behavior such as precipitation suppression in the column model. Furthermore, representing breakup has potential benefits that extend beyond warm rain processes, such as the ability to capture mechanisms of secondary ice production in the superdroplet method. The breakup algorithm presented here contributes to an open-source pythonic implementation of the superdroplet method, 'PySDM', which will facilitate future research using particle-based microphysics. [ABSTRACT FROM AUTHOR]

The small‐scale microphysical processes governing the formation of precipitation particles cannot be resolved explicitly by cloud resolving and climate models. Instead, they are represented by microphysics schemes that are based on a combination of theoretical knowledge, statistical assumptions, and fitting to data ("tuning"). Historically, tuning was done in an ad hoc fashion, leading to parameter choices that are not explainable or repeatable. Recent work has treated it as an inverse problem that can be solved by Bayesian inference. The posterior distribution of the parameters given the data—the solution of Bayesian inference—is found through computationally expensive sampling methods, which require over O105 $\mathcal{O}\left({10}^{5}\right)$ evaluations of the forward model; this is prohibitive for many models. We present a proof of concept of Bayesian learning applied to a new bulk microphysics scheme named "Cloudy," using the recently developed Calibrate‐Emulate‐Sample (CES) algorithm. Cloudy models collision‐coalescence and collisional breakup of cloud droplets with an adjustable number of prognostic moments and with easily modifiable assumptions for the cloud droplet mass distribution and the collision kernel. The CES algorithm uses machine learning tools to accelerate Bayesian inference by reducing the number of forward evaluations needed to O102 $\mathcal{O}\left({10}^{2}\right)$. It also exhibits a smoothing effect when forward evaluations are polluted by noise. In a suite of perfect‐model experiments, we show that CES enables computationally efficient Bayesian inference of parameters in Cloudy from noisy observations of moments of the droplet mass distribution. In an additional imperfect‐model experiment, a collision kernel parameter is successfully learned from output generated by a Lagrangian particle‐based microphysics model. Plain Language Summary: Clouds contain gazillions of cloud droplets, which grow by colliding and sticking together with each other, and eventually they become big enough to fall out as rain. Keeping track of every one of these droplets in weather and climate models is impossible, so the formation of rain has to be represented by simplified models, so‐called "microphysics schemes." These schemes have become a bit like black boxes, with baked‐in statistical assumptions and some empirical parameters whose values are somewhat obscure and not explainable. We show that we can use a method called Bayesian inference to determine the values of these parameters in a way that is both mathematically sound and reasonably fast. The idea of Bayesian inference is to come up with a first guess about the possible values of the parameters, and then to systematically refine that guess using observed data. We apply this method to a new microphysics scheme that we developed and named "Cloudy." To be honest, the data we use for our experiments are not real observations from, say, satellites, but are generated by Cloudy itself. With real observations, the problem becomes hairier, so what we do here is only a proof of concept—but hey, its a start! Key Points: Historically, microphysics schemes were tuned to data in an ad hoc way, resulting in parameter values that are not repeatable or explainableBayesian inference puts uncertainty quantification and parameter learning on solid mathematical grounds, but is computationally expensiveWe present a proof of concept of computationally efficient Bayesian learning applied to a new bulk microphysics scheme called "Cloudy" [ABSTRACT FROM AUTHOR]

Advances in high‐performance computing have enabled large‐eddy simulations (LES) of turbulence, convection, and clouds. However, their potential to improve parameterizations in global climate models (GCMs) is only beginning to be harnessed, with relatively few canonical LES available so far. The purpose of this paper is to begin creating a public LES library that expands the training data available for calibrating and evaluating GCM parameterizations. To do so, we use an experimental setup in which LES are driven by large‐scale forcings from GCMs, which in principle can be used at any location, any time of year, and in any climate state. We use this setup to create a library of LES of clouds across the tropics and subtropics, in the present and in a warmer climate, with a focus on the transition from stratocumulus to shallow cumulus over the East Pacific. The LES results are relatively insensitive to the choice of host GCM driving the LES. Driven with large‐scale forcing under global warming, the LES simulate a positive but weak shortwave cloud feedback, adding to the accumulating evidence that low clouds amplify global warming. Plain Language Summary: Clouds remain one of the largest uncertainties in our understanding and in predictions of climate change because it is challenging to represent their small‐scale dynamics in climate models. However, high‐resolution simulations can provide faithful simulations of clouds and turbulence in limited areas, and these can be used to calibrate climate models. So far, only a limited set of simulations has been used for calibration of climate models, with focus on a few specific locations. This study presents an experimental setup that allows the high‐resolution simulations to be run anywhere on the globe, in any climate state, driven by output from different climate models. The setup is used to create a library of high‐resolution simulations of clouds across the tropics and subtropics in both the current and a warmer climate. The library substantially expands the data set available for the calibration and evaluation of climate models. Key Points: A library of high‐resolution simulations of clouds is created with large‐eddy simulations (LES) driven by a few global climate models (GCMs)Clouds simulated by LES driven by different GCMs are more similar to one another than the corresponding GCM‐simulated cloudsUnder global warming, LES‐simulated low clouds exhibit a weak but positive feedback on warming [ABSTRACT FROM AUTHOR]

Because of their limited spatial resolution, numerical weather prediction and climate models have to rely on parameterizations to represent atmospheric turbulence and convection. Historically, largely independent approaches have been used to represent boundary layer turbulence and convection, neglecting important interactions at the subgrid scale. Here we build on an eddy‐diffusivity mass‐flux (EDMF) scheme that represents all subgrid‐scale mixing in a unified manner, partitioning subgrid‐scale fluctuations into contributions from local diffusive mixing and coherent advective structures and allowing them to interact within a single framework. The EDMF scheme requires closures for the interaction between the turbulent environment and the plumes and for local mixing. A second‐order equation for turbulence kinetic energy (TKE) provides one ingredient for the diffusive local mixing closure, leaving a mixing length to be parameterized. Here, we propose a new mixing length formulation, based on constraints derived from the TKE balance. It expresses local mixing in terms of the same physical processes in all regimes of boundary layer flow. The formulation is tested at a range of resolutions and across a wide range of boundary layer regimes, including a stably stratified boundary layer, a stratocumulus‐topped marine boundary layer, and dry convection. Comparison with large eddy simulations (LES) shows that the EDMF scheme with this diffusive mixing parameterization accurately captures the structure of the boundary layer and clouds in all cases considered. Plain Language Summary: Turbulence and convection transport heat and moisture in the atmosphere and are ultimately responsible for the formation of clouds. However, they act on scales far too small to be resolved in current global atmosphere models. Instead, parameterizations have to be used to approximate their average effect on the finite volumes that are resolved in a global model. These parameterizations are often tailored to specific atmospheric conditions and fail when those conditions are not met. Here we propose a parameterization that aims to reproduce the average effect of turbulent heat and moisture transport under all atmospheric conditions. Numerical simulations demonstrate the accuracy of the parameterization in simulating turbulence in atmospheric boundary layers under stable and convective conditions, including the simulation of stratocumulus clouds. Key Points: EDMF schemes represent boundary layer (BL) turbulence and convection separately yet consistentlyA mixing length model based on kinetic energy constraints represents turbulent fluxes in EDMF schemes wellThe resulting EDMF scheme captures dynamic regimes ranging from stable BLs to stratocumulus‐topped BLs [ABSTRACT FROM AUTHOR]

We demonstrate that an extended eddy‐diffusivity mass‐flux (EDMF) scheme can be used as a unified parameterization of subgrid‐scale turbulence and convection across a range of dynamical regimes, from dry convective boundary layers, through shallow convection, to deep convection. Central to achieving this unified representation of subgrid‐scale motions are entrainment and detrainment closures. We model entrainment and detrainment rates as a combination of turbulent and dynamical processes. Turbulent entrainment/detrainment is represented as downgradient diffusion between plumes and their environment. Dynamical entrainment/detrainment is proportional to a ratio of a relative buoyancy of a plume and a vertical velocity scale, that is modulated by heuristic nondimensional functions which represent their relative magnitudes and the enhanced detrainment due to evaporation from clouds in drier environment. We first evaluate the closures off‐line against entrainment and detrainment rates diagnosed from large eddy simulations (LESs) in which tracers are used to identify plumes, their turbulent environment, and mass and tracer exchanges between them. The LES are of canonical test cases of a dry convective boundary layer, shallow convection, and deep convection, thus spanning a broad rangeof regimes. We then compare the LES with the full EDMF scheme, including the new closures, in a single‐column model (SCM). The results show good agreement between the SCM and LES in quantities that are key for climate models, including thermodynamic profiles, cloud liquid water profiles, and profiles of higher moments of turbulent statistics. The SCM also captures well the diurnal cycle of convection and the onset of precipitation. Plain Language Summary: The dynamics of clouds and turbulence are too small in scale to be resolved in global models of the atmosphere, yet they play a crucial role in controlling weather and climate. These models rely on parameterizations for representing clouds and turbulence. Inadequacies in these parameterizations have hampered especially climate models for decades; they are the largest source of physical uncertainties in climate predictions. It has proven challenging to represent the wide rangeof cloud and turbulence regimes encountered in nature in a single parameterization. Here we present such a parameterization that does capture a wide range of cloud and turbulence regimes within a single, unified physical framework, with relatively few parameters that can be adjusted to fit data. The framework relies on a decomposition of turbulent flows into coherent updraft and downdraft (i.e., plumes) and random turbulence in their environment. A key contribution of this paper is to show how the exchange of mass and properties between the plumes and their turbulent environment—the so‐called entrainment and detrainment of air into and out of plumes—can be modeled. We show that the resulting parameterization represents well the most important features of dry convective boundary layers, shallow cumulus convection, and deep cumulonimbus convection. Key Points: An extended eddy‐diffusivity mass‐flux (EDMF) scheme successfully captures diverse regimes of convective motionsUnified closures are presented for entrainment and detrainment across the different convective regimesWith the unified closures, the EDMF scheme can simulate dry convection, shallow cumulus, and deep cumulus [ABSTRACT FROM AUTHOR]

This paper introduces a new scheme available in the library of algorithms for representing cloud microphysics in numerical models named libcloudph++. The scheme extends the particle-based microphysics scheme with a Monte Carlo coalescence available in libcloudph++ to the aqueous-phase chemical processes occurring within cloud droplets. The representation of chemical processes focuses on the aqueous-phase oxidation of the dissolved SO2 by O3 and H2O2. The particle-based microphysics and chemistry scheme allows for tracking of the changes in the cloud condensation nuclei (CCN) distribution caused by both collisions between cloud droplets and aqueous-phase oxidation. The scheme is implemented in C++ and equipped with bindings to Python. The scheme can be used on either a CPU or a GPU, and is distributed under the GPLv3 license. Here, the particle-based microphysics and chemistry scheme is tested in a simple 0-dimensional adiabatic parcel model and then used in a 2-dimensional prescribed flow framework. The results are discussed with a focus on changes to the CCN sizes and comparison with other model simulations discussed in the literature. [ABSTRACT FROM AUTHOR]

This paper introduces a new scheme available in the library of algorithms for representing cloud microphysics in numerical models named libcloudph++. The scheme extends the Lagrangian microphysics scheme available in libcloudph++ to the aqueous phase chemical processes occurring within cloud droplets. The representation of chemical processes focuses on the aqueous phase oxidation of the dissolved SO2 by O3 and H2O2. The Lagrangian Microphysics and Chemistry (LMC) scheme allows tracking the changes in the cloud condensation nuclei (CCN) distribution caused by both collisions between cloud droplets and aqueous phase oxidation. The scheme is implemented in C++ and equipped with bindings to Python which allow reusing the created scheme from models implemented in other programming languages. The scheme can be used on either CPU or GPU, and is distributed under the GPL3 license. Here, the LMC scheme is tested in a simple 0-dimensional adiabatic parcel model and then used in a 2-dimensional prescribed flow framework. The results are discussed with the focus on changes to the CCN sizes and compared with other model simulations discussed in the literature. [ABSTRACT FROM AUTHOR]

Three object-oriented implementations of a prototype solver of the advection equation are introduced. The presented programs are based on Blitz++ (C++), NumPy (Python) and Fortran's built-in array containers. The solvers constitute implementations of the Multidimensional Positive-Definite Advective Transport Algorithm (MPDATA). The introduced codes serve as examples for how the application of object-oriented programming (OOP) techniques and new language constructs from C++11 and Fortran 2008 allow to reproduce the mathematical notation used in the literature within the program code. A discussion on the tradeoffs of the programming language choice is presented. The main angles of comparison are code brevity and syntax clarity (and hence maintainability and auditability) as well as performance. All performance tests are carried out using free and open-source compilers. In the case of Python, a significant performance gain is observed when switching from the standard interpreter (CPython) to the PyPy implementation of Python. Entire source code of all three implementations is embedded in the text and is licensed under the terms of the GNU GPL license. [ABSTRACT FROM AUTHOR]

Many complex molecular interactions are involved in the process of craniofacial development. Consequently, the network is sensitive to genetic mutations that may result in congenital malformations of varying severity. The most common birth anomalies within the head and neck are orofacial clefts (OFCs) and prognathism. Orofacial clefts are disorders with a range of phenotypes such as the cleft of the lip with or without cleft palate and isolated form of cleft palate with unilateral and bilateral variations. They may occur as an isolated abnormality (nonsyndromic—NSCLP) or coexist with syndromic disorders. Another cause of malformations, prognathism or skeletal class III malocclusion, is characterized by the disproportionate overgrowth of the mandible with or without the hypoplasia of maxilla. Both syndromes may be caused by the presence of environmental factors, but the majority of them are hereditary. Several mutations are linked to those phenotypes. In this review, we summarize the current knowledge regarding the genetics of those phenotypes and describe genotype–phenotype correlations. We then present the animal models used to study these defects. [ABSTRACT FROM AUTHOR]

Acetylation and deacetylation are posttranslational modifications (PTMs) which affect the regulation of chromatin structure and its remodeling. Acetylation of histone 3 at lysine placed on position 18 (H3K18Ac) plays an important role in driving progression of many types of cancer, including breast, colon, lung, hepatocellular, pancreatic, prostate, and thyroid cancer. The aim of this review is to analyze and discuss the newest findings regarding the role of H3K18Ac and acetylation of other histones in carcinogenesis. We summarize the level of H3K18Ac in different cancer cell lines and analyze its association with patients' outcomes, including overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Finally, we describe future perspectives of cancer therapeutic strategies based on H3K18 modifications. [ABSTRACT FROM AUTHOR]