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Etravirine (formerly TMC125) is a non-nucleoside reverse transcriptase inhibitor (NNRTI) with activity against wild-type and NNRTI-resistant strains of HIV-1. Etra virine has been approved in several countries for use as part of highly active antiretroviral therapy in treatment-experienced patients. In vivo , etravirine is a substrate for, and weak inducer of, the hepatic cytochrome P450 (CYP) isoenzyme 3A4 and a substrate and weak inhibitor of CYP2C9 and CYP2C19. Etravirine is also a weak inhibitor of P-glycoprotein. An extensive drug-drug interaction programme in HIV-negative subjects has been carried out to assess the potential for pharmacokinetic interactions between etravirine and a variety of non-antiretroviral drugs. Effects of atorvastatin, clarithromycin, methadone, omeprazole, oral contraceptives, paroxetine, ranitidine and sildenafil on the pharmacokinetic disposition of etravirine were of no clinical relevance. Likewise, etravirine had no clinically significant effect on the pharmacokinetics of fluconazole, methadone, oral contraceptives, paroxetine or voriconazole. No clinically relevant interactions are expected between etravirine and azithromycin or ribavirin, therefore, etravirine can be combined with these agents without dose adjustment. Fluconazole and voriconazole increased etravirine exposure 1.9- and 1.4-fold, respectively, in healthy subjects, however, no increase in the incidence of adverse effects was observed in patients receiving etravirine and fluconazole during clinical trials, therefore, etravirine can be combined with these antifungals although caution is advised. Digoxin plasma exposure was slightly increased when co-administered with etravirine. No dose adjustments of digoxin are needed when used in combination with etravirine, however, it is recommended that digoxin levels should be monitored. Caution should be exercised in combining rifabutin with etravirine in the presence of certain boosted HIV protease inhibitors due to the risk of decreased exposure to etravirine. Although adjustments to the dose of clarithromycin are unnecessary for the treatment of most infections, the use of an alternative macrolide (e.g. azithromycin) is recommended for the treatment of Mycobacterium avium complex infection since the overall activity of clarithromycin against this pathogen may be altered when co-administered with etravirine. Dosage adjustments based on clinical response are recommended for clopidogrel, HMG-CoA reductase inhibitors (e.g. atorvastatin) and for phosphodiesterase type-5 inhibitors (e.g. sildenafil) because changes in the exposure of these medications in the presence of co-administered etravirine may occur. When co-administered with etravirine, a dose reduction or alternative to diazepam is recommended. When combining etravirine with warfarin, the international normalized ratio (INR) should be monitored. Systemic dexamethasone should be co-administered with caution, or an alternative to dexamethasone be found as dexamethasone induces CYP3A4. Caution is also warranted when co-administering etravirine with some antiarrhythmics, calcineurin inhibitors (e.g. ciclosporin) and antidepressants (e.g. citalopram). Coadministration of etravirine with some antiepileptics (e.g. carbamazepine and phenytoin), rifampicin (rifampin), rifapentine or preparations containing St John’s wort ( Hypericum perforatum ) is currently not recommended as these are potent inducers of CYP3A and/or CYP2C and may potentially decrease etravirine exposure. Antiepileptics that are less likely to interact based on their known pharmacological properties include gabapentin, lamotrigine, levetiracetam and pregabalin. Overall, pharmacokinetic and clinical data show etravirine to be well tolerated and generally safe when given in combination with non-antiretroviral agents, with minimal clinically significant drug interactions and no need for dosage adjustments of etravirine in any of the cases, or of the non-antiretroviral agent in the majority of cases studied.

Aerosol-cloud-radiation interactions represent one of the largest uncertainties in the current climate assessment. Much of the complexity arises from the non-monotonic responses of clouds, precipitation and radiative fluxes to aerosol perturbations under various meteorological conditions. In this study, an aerosol-aware WRF model is used to investigate the microphysical and radiative effects of aerosols in three weather systems during the March 2000 Cloud Intensive Observational Period campaign at the US Southern Great Plains. Three simulated cloud ensembles include a low-pressure deep convective cloud system, a collection of less-precipitating stratus and shallow cumulus, and a cold frontal passage. The WRF simulations are evaluated by several ground-based measurements. The microphysical properties of cloud hydrometeors, such as their mass and number concentrations, generally show monotonic trends as a function of cloud condensation nuclei concentrations. Aerosol radiative effects do not influence the trends of cloud microphysics, except for the stratus and shallow cumulus cases where aerosol semi-direct effects are identified. The precipitation changes by aerosols vary with the cloud types and their evolving stages, with a prominent aerosol invigoration effect and associated enhanced precipitation from the convective sources. The simulated aerosol direct effect suppresses precipitation in all three cases but does not overturn the aerosol indirect effect. Cloud fraction exhibits much smaller sensitivity （typically less than 2%） to aerosol perturbations, and the responses vary with aerosol concentrations and cloud regimes. The surface shortwave radiation shows a monotonic decrease by increasing aerosols, while the magnitude of the decrease depends on the cloud type.

A closed-cell marine stratocumulus case during the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) aircraft field campaign is selected to examine the heterogeneities of cloud and drizzle microphysical properties and the aerosol-cloud-precipitation interactions. The spatial and vertical variabilities of cloud and drizzle microphysics are found in two different sets of flight legs: Leg-1 and Leg-2, which are parallel and perpendicular to the cloud propagation, respectively. The cloud along Leg-2 was close to adiabatic, where cloud-droplet effective radius and liquid water content linearly increase from cloud base to cloud top with less drizzle. The cloud along Leg-1 was sub-adiabatic with lower cloud-droplet number concentration and larger cloud-droplet effective, but higher drizzle droplet number concentration, larger drizzle droplet median diameter and drizzle liquid water content. The heavier drizzle frequency and intensity on Leg-1 were enhanced by the collision-coalescence processes within cloud due to strong turbulence. The sub-cloud precipitation rate on Leg-1 was significantly higher than that along Leg-2. As a result, the sub-cloud accumulation mode aerosols and CCN on Leg-1 were depleted, but the coarse model aerosols increased. This further leads to a counter-intuitive phenomenon that the CCN is less than cloud-droplet number concentration for Leg-1. The average CCN loss rates are −3.89 cm −3 h −1 and −0.77 cm −3 h −1 on Leg-1 and Leg-2, respectively. The cloud and drizzle heterogeneities inside the same stratocumulus can significantly alter the sub-cloud aerosols and CCN budget. Hence it should be treated with caution in the aircraft assessment of aerosol-cloud-precipitation interactions.

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ∼5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ∼5%, while at the same time, reducing misjudgment by ∼3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.

The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.

An undersea volcano at Hunga Tonga-Hunga Ha’apai (HTHH) near the South Pacific island nation of Tonga, erupted violently on 15 January 2022. Potential climate impact of the HTHH volcanic eruption is of great concern to the public; here, we intend to size up the impact of the HTHH eruption from a historical perspective. The influence of historical volcanic eruptions on the global climate are firstly reviewed, which are thought to have contributed to decreased surface temperature, increased stratospheric temperature, suppressed global water cycle, weakened monsoon circulation and El Niño-like sea surface temperature. Our understanding of the impacts of past volcanic eruptions on global-scale climate provides potential implication to evaluate the impact of the HTHH eruption. Based on historical simulations, we estimate that the current HTHH eruption with an intensity of 0.4 Tg SO 2 injection will decrease the global mean surface temperature by only 0.004°C in the first year after eruption, which is within the amplitude of internal variability at the interannual time scale and thus not strong enough to have significant impacts on the global climate.

Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T -matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (∼127 GB) to that of the DNN parameters (∼20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.

The deployment of the U.S. Atmospheric Radiation Measurement mobile facility in Shouxian from May to December 2008 amassed the most comprehensive set of measurements of atmospheric, surface, aerosol, and cloud variables in China. This deployment provided a unique opportunity to investigate the aerosol-cloud interactions, which are most challenging and, to date, have not been examined to any great degree in China. The relationship between cloud droplet effective radius （CER） and aerosol index （AI） is very weak in summer because the cloud droplet growth is least affected by the competition for water vapor. Mean cloud liquid water path （LWP） and cloud optical depth （COD） significantly increase with increasing AI in fall. The sensitivities of CER and LWP to aerosol loading increases are not significantly different under different air mass conditions. There is a significant correlation between the changes in hourly mean AI and the changes in hourly mean CER, LWP, and COD. The aerosol first indirect effect （FIE） is estimated in terms of relative changes in both CER （FIEcER） and COD （FIEcoD） with changes in AI for different seasons and air masses. FIEcoD and FIEcER are similar in magnitude and close to the typical FIE value of - 0.23, and do not change much between summer and fall or between the two different air mass conditions. Similar analyses were done using spaceborne Moderate Resolution Imaging Spectroradiometer data. The satellite-derived FIE is contrary to the FIE estimated from surface retrievals and may have large uncertainties due to some inherent limitations.

The treatment of aerosols, clouds, radiation, and precipitation in climate models, in addition to their interactions and as- sociated feedbacks, has long been one of the largest sources of uncertainty in predicting any potential future climate changes. Although many improvements have been made in CMIP5, aerosols, clouds, radiation, and their feedbacks are still a problem in climate models, as concluded in IPCC AR5 and published papers. Many studies have shown that modeled aerosols, clouds, radiation, and precipitation agree with observations within a certain range on a global scale; however, large biases occur at the regional scale. Characterizing the effects of aerosols and clouds on energy and the hydrological cycle and understanding the interactions among aerosols, clouds, radiation, and precipitation, are critical for weather forecasting and climate models. Significant improvements are needed, which require advanced observations and modeling at a range of spatial and temporal scales.