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Cloud microphysical properties are significantly affected by entrainment and mixing processes. However, it is unclear how the entrainment rate affects the relative dispersion of cloud droplet size distribution. Previously, the relationship between relative dispersion and entrainment rate was found to be positive or negative. To reconcile the contrasting relationships, the Explicit Mixing Parcel Model is used to determine the underlying mechanisms. When evaporation is dominated by small droplets, and the entrained environmental air is further saturated during mixing, the relationship is negative. However, when the evaporation of big droplets is dominant, the relationship is positive. Whether or not the cloud condensation nuclei are considered in the entrained environmental air is a key factor as condensation on the entrained condensation nuclei is the main source of small droplets. However, if cloud condensation nuclei are not entrained, the relationship is positive. If cloud condensation nuclei are entrained, the relationship is dependent on many other factors. High values of vertical velocity, relative humidity of environmental air, and liquid water content, and low values of droplet number concentration, are more likely to cause the negative relationship since new saturation is easier to achieve by evaporation of small droplets. Further, the signs of the relationship are not strongly affected by the turbulence dissipation rate, but the higher dissipation rate causes the positive relationship to be more significant for a larger entrainment rate. A conceptual model is proposed to reconcile the contrasting relationships. This work enhances the understanding of relative dispersion and lays a foundation for the quantification of entrainment-mixing mechanisms.

The organization and, for the first time, the three‐dimensional structure of clouds associated with the Southern Hemisphere cyclones are studied using active observations from the CloudSat and CALIPSO satellites. First, a composite cyclone is constructed from more than 800 individual cases in the years 2007 and 2008 using the cyclone centre as the composite reference point. It is shown that the three‐dimensional cloud distribution around the composite cyclone agrees well with conceptual models of extratropical cyclones. Composite mean fields of sea level pressure, vertical motion, potential temperature and relative humidity are superposed on the three‐dimensional cloud structure to better define the relationship between the clouds and dynamical properties of extratropical cyclones. The methodology used here reveals the relationship between dynamical and cloud processes in three dimensions around cyclones and provides the foundation for in‐depth evaluations of the ability of climate models to simulate the cloud and dynamical structures of Southern Hemisphere extratropical cyclones. Key Points The 3‐D cloud structure around cyclone resembles the conceptual models Dynamical, precipitation and radiation fields are in agreement with cloud field The used methodology provides an opportunity for evaluation of climate models

The atmospheres of ultra-hot Jupiters have been characterized in detail through recent phase curve and low- and high-resolution emission and transmission spectroscopic observations. Previous numerical studies have analyzed the effect of the localized recombination of hydrogen on the atmospheric dynamics and heat transport of ultra-hot Jupiters, finding that hydrogen dissociation and recombination lead to a reduction in the day-to-night contrasts of ultra-hot Jupiters relative to previous expectations. In this work, we add to previous efforts by also considering the localized condensation of clouds in the atmospheres of ultra-hot Jupiters, their resulting transport by the atmospheric circulation, and the radiative feedback of clouds on the atmospheric dynamics. To do so, we include radiatively active cloud tracers into the existing MITgcm framework for simulating the atmospheric dynamics of ultra-hot Jupiters. We take cloud condensate properties appropriate for the high-temperature condensate corundum from CARMA cloud microphysics models. We conduct a suite of general circulation model (GCM) simulations with varying cloud microphysical and radiative properties, and we find that partial cloud coverage is a ubiquitous outcome of our simulations. This patchy cloud distribution is inherently set by atmospheric dynamics in addition to equilibrium cloud condensation, and causes a cloud greenhouse effect that warms the atmosphere below the cloud deck. Nightside clouds are further sequestered at depth due to a dynamically induced high-altitude thermal inversion. We post-process our GCMs with the Monte Carlo radiative transfer code gCMCRT and find that the patchy clouds on ultra-hot Jupiters do not significantly impact transmission spectra but can affect their phase-dependent emission spectra.

Cloud processes relevant to radiative and precipitation properties depend on the shape of the cloud droplet size distribution. Recent holographic observations revealed that cloud droplet populations do not have the same size distribution shapes throughout but form regions of characteristic distributions with similar microphysical properties. We investigate the existence and properties of these characteristic distributions within Large‐Eddy Simulations of stratocumulus clouds using Lagrangian and bin microphysics schemes. Distribution types are identified, revealing localized characteristic distributions that vary on the scale of the largest convective cell for simulations with bin microphysics. The results from the Lagrangian microphysics scheme hint at similar behavior. Compared to observations, the simulated clouds are much more uniform. Analysis of the LES results suggests a connection to the local entrainment rate, so the poorly resolved entrainment interface in LES may be a cause of the uniformity. The uniformity of the large‐scale forcing could also be a factor.

Previous studies have noticed that the Coupled Model Intercomparison Project Phase 6 (CMIP6) models with a stronger cooling from aerosol‐cloud interactions (ACI) also have an enhanced warming from positive cloud feedback, and these two opposing effects are counter‐balanced in simulations of the historical period. However, reasons for this anti‐correlation are less explored. In this study, we perturb the cloud ice microphysical processes to obtain cloud liquid of varying amounts in two Earth System Models (ESMs). We find that the model simulations with a larger liquid water path (LWP) tend to have a stronger cooling from ACI and a stronger positive cloud feedback. More liquid clouds in the mean‐state present more opportunities for anthropogenic aerosol perturbations and also weaken the negative cloud feedback at middle to high latitudes. This work, from a cloud state perspective, emphasizes the influence of the mean‐state LWP on effective radiative forcing due to ACI (ERFACI). Plain Language Summary Since the preindustrial era, emissions of greenhouse gases (GHGs) and aerosols have both increased substantially. Planetary warming from the elevated GHGs causes changes in cloud distribution and properties, thereby imposing feedbacks on the climate system. At the same time, aerosols from air pollution exert a cooling effect by modifying cloud properties and lifetime. Cloud feedback and aerosol‐cloud interactions (ACI) are two critical factors for understanding the past and projecting the future climate change. In this study, we examine the relationships of ACI and cloud feedback with the mean‐state cloud liquid water amount predicted from simulations with two different Earth system models. We find that both the effective radiative forcing due to aerosol‐cloud interactions (ERFACI) and the cloud feedback are modulated by mean‐state liquid water path (LWP). The warming induced by cloud feedback and cooling by ACI counteracts each other. Our study suggests that the mean‐state LWP is an important factor influencing both ERFACI and the cloud feedback, and is a useful index for the future climate projection. Key Points Effective radiative forcing due to aerosol‐cloud interactions strengthens with the increase of mean‐state LWP in two Earth System Models Cloud radiative feedback increases monotonically with the increase of mean‐state LWP Mean‐state LWP is a good predictor for both ERFACI and cloud radiative feedback

The outgoing longwave radiation (OLR) emitted to space is a fundamental component of the Earth’s energy budget. There are numerous, entangled physical processes that contribute to OLR and that are responsible for driving, and responding to, climate change. Spectrally resolved observations can disentangle these processes, but technical limitations have precluded accurate space-based spectral measurements covering the far infrared (FIR) from 100 to 667 cm−1 (wavelengths between 15 and 100 μm). The Earth’s FIR spectrum is thus essentially unmeasured even though at least half of the OLR arises from this spectral range. The region is strongly influenced by upper-tropospheric–lower-stratospheric water vapor, temperature lapse rate, ice cloud distribution, and microphysics, all critical parameters in the climate system that are highly variable and still poorly observed and understood. To cover this uncharted territory in Earth observations, the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission has recently been selected as ESA’s ninth Earth Explorer mission for launch in 2026. The primary goal of FORUM is to measure, with high absolute accuracy, the FIR component of the spectrally resolved OLR for the first time with high spectral resolution and radiometric accuracy. The mission will provide a benchmark dataset of global observations which will significantly enhance our understanding of key forcing and feedback processes of the Earth’s atmosphere to enable more stringent evaluation of climate models. This paper describes the motivation for the mission, highlighting the scientific advances that are expected from the new measurements.

Planetary climates are strongly affected by planetary orbital parameters such as obliquity, eccentricity, and precession. In exoplanetary systems, exoterrestrial planets should have various obliquities. High-obliquity planets would have extreme seasonal cycles due to the seasonal change of the distribution of the insolation. Here, we introduce the Non-hydrostatic ICosahedral Atmospheric Model (NICAM), a global cloud-resolving model, to investigate the climate of high-obliquity planets. This model can explicitly simulate a three-dimensional cloud distribution and vertical transports of water vapor. We simulated exoterrestrial climates with high resolution using the supercomputer FUGAKU. We assumed aqua-planet configurations with 1 bar of air as a background atmosphere, with four different obliquities (0°, 23.5°, 45°, and 60°). We ran two sets of simulations: (1) low resolution (∼220 km mesh as the standard resolution of a general circulation model for exoplanetary science) with parameterization for cloud formation, and (2) high resolution (∼14 km mesh) with an explicit cloud microphysics scheme. Results suggest that high-resolution simulations with an explicit treatment of cloud microphysics reveal warmer climates due to less low cloud fraction and a large amount of water vapor in the atmosphere. It implies that treatments of cloud-related processes lead to a difference between different resolutions in climatic regimes in cases with high obliquities.

This study investigates the sensitivity of winter seasonal rainfall over the Arabian Peninsula (AP) to different convective physical parameterization schemes using a high-resolution WRF Model. Three different parameterization schemes, Kain–Fritch (KF), Betts–Miller–Janjić (BMJ), and Grell–Freitas (GF), are used in winter simulations from 2001 to 2016. Results from seasonal simulations suggest that simulated AP winter rainfall with KF is in best agreement with observed rainfall in terms of spatial distribution and intensity. Higher spatial correlation coefficients and fewer biases with observations are also obtained with KF. In addition, the regional moisture transport, cloud distribution, and cloud microphysical responses are better simulated by KF. The AP low-level circulation, characterized by the Arabian anticyclone, is well captured by KF and BMJ, but its position is displaced in GF. KF is furthermore successful at simulating the moisture distribution in the lower atmosphere and atmospheric water plumes in the middle troposphere. The higher skill of rainfall simulation with the KF (and to some extent BMJ) is attributed to a better representation of the Arabian anticyclone and subtropical westerly jet, which guides the upper tropospheric synoptic transients and moisture. In addition, the vertical profile of diabatic heating from KF is in better agreement with the observations. Discrepancies in representing the diabatic heating profile by BMJ and GF show discrepancies in instability and in turn precipitation biases. Our results indicate that the selection of subgrid convective parameterization in a high-resolution atmospheric model over the AP is an important factor for accurate regional rainfall simulations.

One of the challenges in representing warm rain processes in global climate models (GCMs) is related to the representation of the subgrid variability of cloud properties, such as cloud water and cloud droplet number concentration (CDNC), and the effect thereof on individual precipitation processes such as autoconversion. This effect is conventionally treated by multiplying the resolved-scale warm rain process rates by an enhancement factor (Eq) which is derived from integrating over an assumed subgrid cloud water distribution. The assumed subgrid cloud distribution remains highly uncertain. In this study, we derive the subgrid variations of liquid-phase cloud properties over the tropical ocean using the satellite remote sensing products from Moderate Resolution Imaging Spectroradiometer (MODIS) and investigate the corresponding enhancement factors for the GCM parameterization of autoconversion rate. We find that the conventional approach of using only subgrid variability of cloud water is insufficient and that the subgrid variability of CDNC, as well as the correlation between the two, is also important for correctly simulating the autoconversion process in GCMs. Using the MODIS data which have near-global data coverage, we find that Eq shows a strong dependence on cloud regimes due to the fact that the subgrid variability of cloud water and CDNC is regime dependent. Our analysis shows a significant increase of Eq from the stratocumulus (Sc) to cumulus (Cu) regions. Furthermore, the enhancement factor EN due to the subgrid variation of CDNC is derived from satellite observation for the first time, and results reveal several regions downwind of biomass burning aerosols (e.g., Gulf of Guinea, east coast of South Africa), air pollution (i.e., East China Sea), and active volcanos (e.g., Kilauea, Hawaii, and Ambae, Vanuatu), where the EN is comparable to or even larger than Eq, suggesting an important role of aerosol in influencing the EN. MODIS observations suggest that the subgrid variations of cloud liquid water path (LWP) and CDNC are generally positively correlated. As a result, the combined enhancement factor, including the effect of LWP and CDNC correlation, is significantly smaller than the simple product of Eq⋅EN. Given the importance of warm rain processes in understanding the Earth's system dynamics and water cycle, we conclude that more observational studies are needed to provide a better constraint on the warm rain processes in GCMs.

Using the satellite-infrared-based Simple Convective Aggregation Index (SCAI) to determine the degree of aggregation, 5 years of CloudSat–CALIPSO cloud profiles are composited at a spatial scale of 10 degrees to study the relationship between cloud vertical structure and aggregation. For a given large-scale vertical motion and domain-averaged precipitation rate, there is a large decrease in anvil cloud (and in cloudiness as a whole) and an increase in clear sky and low cloud as aggregation increases. The changes in thick anvil cloud are proportional to the changes in total areal cover of brightness temperatures below 240K [cold cloud area (CCA)], which is negatively correlated with SCAI. Optically thin anvil cover decreases significantly when aggregation increases, even for a fixed CCA, supporting previous findings of a higher precipitation efficiency for aggregated convection. Cirrus, congestus, and midlevel clouds do not display a consistent relationship with the degree of aggregation. Lidar-observed low-level cloud cover (where the lidar is not attenuated) is presented herein as the best estimate of the true low-level cloud cover, and it is shown that it increases as aggregation increases. Qualitatively, the relationships between cloud distribution and SCAI do not change with sea surface temperature, while cirrus clouds are more abundant and low-level clouds less at higher sea surface temperatures. For the observed regimes, the vertical cloud profile varies more evidently with SCAI than with mean precipitation rate. These results confirm that convective scenes with similar vertical motion and rainfall can be associated with vastly different cloudiness (both high and low cloud) and humidity depending on the degree of convective aggregation.