Explore the vast range of titles available.

A satellite‐based temperature‐dependent parameterization of the Wegener‐Bergeron‐Findeisen (WBF) process that takes into account the subgrid‐scale variability of cloud thermodynamic phase within mixed‐phase clouds is developed and implemented in version 5.3 of the Community Atmosphere Model (CAM5.3). Its impact on cloud microphysical and macrophysical properties in experiments with prescribed sea surface temperature and sea ice concentrations as well as the cloud feedback response to a global warming perturbation is investigated. The parameterization significantly improves overestimates in the mass of ice within mixed‐phase clouds and ice effective radius relative to satellite observations, the former being superior to tuning the WBF process with a multiplicative constant. The parameterization also reduces overall biases in cloud fraction with respect to satellite observations, however, is due to compensating biases in existing simulated low biases in low‐level cloud cover and new increased biases in non‐low‐level cloud cover. The increased bias in non‐low‐level cloud cover is due to decreases in the rate of autoconversion of cloud ice that is a side effect of the WBF parameterization. While the WBF parameterization can significantly impact the magnitude of model biases in cloud properties and the cloud feedback, it does not significantly change their spatial distribution. Before observational constraints on WBF process rates become available, it is recommended that temperature‐dependent scalings of the WBF process are used to account for the subgrid‐scale variability of cloud phase rather than a constant scaling parameter as the former type of parameterization can more realistically simulate cloud properties relative to satellite observations. Plain Language Summary Global climate models are indispensable tools for climate projections. They predict how clouds change over large domains known as “grid cells” that are typically on the order of 100 km wide and 1 km tall. Clouds, however, vary on spatial scales that are much smaller. These finer features must be represented using large‐scale atmospheric variables in equations known as “parameterizations” that are key sources of uncertainty in climate projections. This study focuses on the assumption in global climate models that supercooled liquid droplets and ice crystals in clouds are uniformly distributed in grid cells despite that they have been observed to cluster in patches. A parameterization is developed using satellite observations and implemented to represent this spatial non‐uniformity. The parameterization reduces the overestimate of ice residing in mixed‐phase clouds and also improves biases in their sizes relative to satellite observations. It also improves biases in total cloud cover, however, is due to compensating side effects on different cloud types that are related to the slowdown of the conversion of ice crystals in clouds to precipitation. The parameterization also significantly impacts how cloud properties respond to global warming. Overall, the parameterization shows the potential to reduce certain model biases. Key Points A satellite‐based temperature‐dependent parameterization of the Wegener‐Bergeron‐Findeisen process was developed to represent subgrid‐scale cloud phase The parameterization reduces biases in the simulation of cloud ice mass, ice effective radius, and total cloud cover A slowdown of the autoconversion process for cloud ice is a side effect of the parameterization and increases mid‐ and high‐cloud cover

The influence of six CAM5.1 cloud microphysical parameters on the variance of phase partitioning in mixed-phase clouds is determined by application of a variance-based sensitivity analysis. The sensitivity analysis is based on a generalized linear model that assumes a polynomial relationship between the six parameters and the two-way interactions between them. The parameters, bounded such that they yield realistic cloud phase values, were selected by adopting a quasi–Monte Carlo sampling approach. The sensitivity analysis is applied globally, and to 20°-latitude-wide bands, and over the Southern Ocean at various mixed-phase cloud isotherms and reveals that the Wegener–Bergeron–Findeisen (WBF) time scale for the growth of ice crystals single-handedly accounts for the vast majority of the variance in cloud phase partitioning in mixed-phase clouds, while its interaction with the WBF time scale for the growth of snowflakes plays a secondary role. The fraction of dust aerosols active as ice nuclei in latitude bands, and the parameter related to the ice crystal fall speed and their interactions with the WBF time scale for ice are also significant. All other investigated parameters and their interactions with each other are negligible (<3%). Further analysis comparing three of the quasi–Monte Carlo–sampled simulations with spaceborne lidar observations by CALIOP suggests that the WBF process in CAM5.1 is currently parameterized such that it occurs too rapidly due to failure to account for subgrid-scale variability of liquid and ice partitioning in mixed-phase clouds.

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO₂, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C. Clouds are among the leading causes of this uncertainty. Here we show that the ECS can be up to 1.3°C higher in simulations where mixed-phase clouds consisting of ice crystals and supercooled liquid droplets are constrained by global satellite observations. The higher ECS estimates are directly linked to a weakened cloud-phase feedback arising from a decreased cloud glaciation rate in a warmer climate. We point out the need for realistic representations of the supercooled liquid fraction in mixed-phase clouds in GCMs, given the sensitivity of the ECS to the cloud-phase feedback.

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect technique The Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate models The median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback

Marine cold‐air outbreak (MCAO) clouds begin as shallow cloud streets near the ice edge and evolve into broken open‐cell convection downstream. Their rapid transitions in cloud structure and microphysics are closely linked to vertical motions, but this interplay has not previously been observed from space. The EarthCARE satellite, carrying the first spaceborne Doppler radar (Cloud Profiling Radar, CPR), now measures hydrometeor sedimentation velocity and vertical air motion. Using CPR observations over the Norwegian and Barents Seas (December 2024–May 2025), we analyze MCAO and non‐MCAO clouds. We find that strong updrafts in MCAO clouds coincide with larger supercooled liquid water (SLW) path (LWP). These conditions are accompanied by increases in sedimentation velocity and its vertical gradient, reflecting rapid riming‐driven ice growth. In contrast, non‐MCAO clouds show lower riming efficiency at similar LWP due to relatively weaker dynamical support. This emphasizes the importance of dynamics in shaping polar cloud structure and microphysics.

It is noteworthy to highlight that ammonia nitrogen contamination in wastewater has been reported to pose a great threat to the environment. This conventional method of remediating ammonia nitrogen contamination in wastewater applies the packed bed tower technology. Nevertheless, this technology appears to pose several application issues. Over the years, researchers have tested various types of ammonia stripping process to overcome the shortcomings of the conventional ammonia stripping technology. Along this line, the present study highlights the recent development of ammonia stripping process for industrial wastewater treatment. In addition, this study reviews ammonia stripping application for varied types of industrial wastewater and several significant operating parameters. Furthermore, this paper discusses some issues related to the conventional ammonia stripper for industrial treatment application. Finally, this study explicates the future prospects of the ammonia stripping method. This review, hence, contributes by enhancing the ammonia stripping treatment efficiency and its application for industrial wastewater treatment.

Oral mucosal abnormalities considered to have malignant potential may involve a large area of mucosa. Standard care histopathological investigation is invasive, limited to site selection and subject to variation in assessment between pathologists. A quantitative, minimally invasive, rapidly collected, oral mucosal site-specific assessment will assist in decision making and increase diagnostic precision. This study aimed to validate a workflow to analyse oral scrape derived cancer-associated microRNA analysis for mucosal site-specific assessment as a surrogate biomarker to histopathological diagnosis. Forty-one oral scrapes were collected from 33 patients undergoing investigation at the Royal Dental Hospital of Melbourne before mucosal biopsy. RNA from oral scrapes was used to investigate ten cancer-associated microRNAs. An algorithm for categorical high or low-risk based on histopathological diagnosis was developed. The novel risk stratification algorithm utilised two microRNAs and categorised all cases of carcinoma and severe dysplasia as high-risk and accurately distinguished all non-potentially malignant disorders as low-risk lesions. This study provides proof-of-concept that oral scrapes can be predictably collected in a clinical workflow to assess the expression of cancer-associated microRNA specific to an oral mucosal site with minimal invasiveness.

Oral cancer detection is based on biopsy histopathology, however with digital microscopy imaging technology there is real potential for rapid multi-site imaging and simultaneous diagnostic analysis. Fifty-nine patients with oral mucosal abnormalities were imaged in vivo with a confocal laser endomicroscope using the contrast agents acriflavine and fluorescein for the detection of oral epithelial dysplasia and oral cancer. To analyse the 9168 images frames obtained, three tandem applied pre-trained Inception-V3 convolutional neural network (CNN) models were developed using transfer learning in the PyTorch framework. The first CNN was used to filter for image quality, followed by image specific diagnostic triage models for fluorescein and acriflavine, respectively. Images were categorised based on a histopathological diagnosis into 4 categories: no dysplasia, lichenoid lesions, low-grade dysplasia and high-grade dysplasia/oral squamous cell carcinoma (OSCC). The quality filtering model had an accuracy of 89.5%. The acriflavine diagnostic model performed well for identifying lichenoid (AUC = 0.94) and low-grade dysplasia (AUC = 0.91) but poorly for identifying no dysplasia (AUC = 0.44) or high-grade dysplasia/OSCC (AUC = 0.28). In contrast, the fluorescein diagnostic model had high classification performance for all diagnostic classes (AUC range = 0.90–0.96). These models had a rapid classification speed of less than 1/10th of a second per image. Our study suggests that tandem CNNs can provide highly accurate and rapid real-time diagnostic triage for in vivo assessment of high-risk oral mucosal disease.

In this study, it is shown that CMIP5 global climate models (GCMs) that convert supercooled water to ice at relatively warm temperatures tend to have a greater mean‐state cloud fraction and more negative cloud feedback in the middle and high latitude Southern Hemisphere. We investigate possible reasons for these relationships by analyzing the mixed‐phase parameterizations in 26 GCMs. The atmospheric temperature where ice and liquid are equally prevalent (T5050) is used to characterize the mixed‐phase parameterization in each GCM. Liquid clouds have a higher albedo than ice clouds, so, all else being equal, models with more supercooled liquid water would also have a higher planetary albedo. The lower cloud fraction in these models compensates the higher cloud reflectivity and results in clouds that reflect shortwave radiation (SW) in reasonable agreement with observations, but gives clouds that are too bright and too few. The temperature at which supercooled liquid can remain unfrozen is strongly anti‐correlated with cloud fraction in the climate mean state across the model ensemble, but we know of no robust physical mechanism to explain this behavior, especially because this anti‐correlation extends through the subtropics. A set of perturbed physics simulations with the Community Atmospheric Model Version 4 (CAM4) shows that, if its temperature‐dependent phase partitioning is varied and the critical relative humidity for cloud formation in each model run is also tuned to bring reflected SW into agreement with observations, then cloud fraction increases and liquid water path (LWP) decreases with T5050, as in the CMIP5 ensemble. Key Points: Cloud cover and mixed‐phase parameterizations have compensating effects on planetary albedo in GCMs. Models that maintain liquid to lower temperatures have less cloud cover. This compensation affects both the climate mean‐state and cloud feedback.

Palm oil processing is a multi-stage operation which generates large amount of palm oil mill effluent (POME). Due to its potential to cause environmental pollution, POME must be treated prior to discharge. Electrocoagulation (EC), adsorption (AD), combined EC and AD, and EC integrated with AD have demonstrated great potential to remove various organic and inorganic pollutants from wastewater. Up to date, no study has been found on POME treatment using EC-AD hybrid process. Therefore, this study aims to investigate the feasibility of applying EC-AD hybrid process as an alternative treatment for POME. The EC-AD hybrid process achieved higher removal of total suspended solid (TSS), chemical oxygen demand (COD) and colour as compared to EC and AD stand-alone processes. The EC-AD hybrid process reduced 79% of TSS, 44% of COD and 89% of colour from POME. The adsorption kinetics of TSS, COD and colour were best interpreted using pseudo-second-order model, which indicated that the adsorption rate was mainly controlled by chemisorption. Overall, the EC-AD hybrid process could be recommended as an alternative treatment for POME.