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The interaction between aerosols and clouds is one of the major uncertainties in past climate change, affecting the accuracy of future climate projections. Ship tracks, trails left in clouds through the addition of aerosol in the ship exhaust plume, have become a key observational tool for constraining aerosol‐cloud interactions. However, many expected tracks remain undetected, presenting a significant gap in current knowledge of aerosol forcing. Here we leverage a plume‐parcel model to simulate the impact of aerosol dispersion for 2,957 cases off California's coast on cloud droplet number concentration (CDNC) enhancements. Plume‐parcel models show a large sensitivity to updraft uncertainties, which are found to be a primary control on track formation. Using these plume‐parcel models, updraft values consistent with observed CDNC enhancements are recovered, suggesting that relying solely on cloud‐top radiative cooling may overestimate in‐cloud updrafts by around 50%, hence overstating the cloud sensitivity to aerosols. Plain Language Summary: Here we investigate the interactions between aerosols and clouds and their impact on climate change. Ship tracks were used to study these interactions, and aerosol emissions from ships were modeled. It was shown that the increase in cloud droplets from ship aerosol was highly sensitive to the speed of the updraft in the clouds. A method was developed to fit the updraft to the observed cloud enhancements; the resultant updrafts were smaller than current estimates, suggesting that the clouds may be less sensitive to aerosols than previously thought. Key Points: Using ship emission data, a plume‐parcel model was implemented to study aerosol availability and cloud sensitivity in ship track formationCloud droplet number enhancements are very sensitive to uncertainties in the in‐cloud updraftThe retrieved plume‐parcel‐based updrafts suggest that clouds may be less sensitive to aerosols than implied by other estimates [ABSTRACT FROM AUTHOR]

This paper presents a method for cloud water path retrieval from daytime MSU-GS measurements onboard the Russian hydrometeorological satellite Arktika-M 1. The technique is based on the physical principles of the interaction of electromagnetic radiation with cloud particles at wavelengths of 0.55 and 4.0 μm. Cloud water path estimates obtained from the MSU-GS radiometer are compared with similar estimates from the AMSU/MHS and AHI radiometer data. Based on the results of the comparison, the required estimates of the cloud water path of drop clouds are within the permissible limits of the measurement error, not exceeding 50 g/m2. At the same time, due to its design features, the MSU-GS radiometer does not allow retrieving the cloud water path of ice clouds with the required accuracy. On average, the cloud water path estimate of ice clouds according to the MSU-GS data is underestimated by 110 g/m2, and the root-mean-square error is 158 g/m2, when compared to the AHI radiometer data. The estimates of the cloud water path are introduced into the geographic information system Arktika-M, which provides access to the Arktika-M 1 data and the results of their thematic processing in a near real time mode. [ABSTRACT FROM AUTHOR]

The simulations of clouds and surface radiation from 10 CMIP6 models and their CMIP5 predecessors are compared to the ARM ground-based observations over different climate regions. Compared to the ARM radar-lidar derived total cloud fractions (CFT) and cloud fraction vertical distributions over the six selected sites, both CMIP5 and CMIP6 significantly underestimated CFT and low-level CF over the Northern Hemispheric midlatitude sites (SGPC1 and ENAC1), although the biases are generally smaller in CMIP6. Over the tropical oceanic site (TWPC2), 5 out of 10 CMIP6 models better simulated low-level CF than their CMIP5 predecessors. CMIP6 simulations generally agreed well with the ARM observations in CFT and cloud fraction vertical distributions over the tropical continental (MAOM1) and coastal (TWPC3) sites but missed the transitions between dry and wet seasons, similar to CMIP5 simulations. The improvements in downwelling shortwave fluxes (SWdn) at the surface from the majority of CMIP6 compared to CMIP5 primarily resulted from the improved cloud fraction simulations, especially over the SGPC1, ENAC1, and TWPC3 sites. By contrast, both CMIP5 and CMIP6 models exhibited diverse performances of clouds and shortwave radiation over the Arctic site (NSAC1), where CMIP6 models produced more clouds than CMIP5 models, especially for the low-level clouds. The comparisons between observations and CMIP5 and CMIP6 simulations provide valuable quantitative assessments of the accuracy of mean states and variabilities in the model simulations and shed light on general directions to improve climate models in different regions. [ABSTRACT FROM AUTHOR]

A review of studies on the atmospheres of planets in the solar system performed by Russian scientists in 2019–2022 is presented. This review was prepared in the Commission on Planetary Atmospheres of the National Geophysical Committee for the National Report on Meteorology and Atmospheric Science at the 28th General Assembly of the International Union of Geodesy and Geophysics (IUGG 2023) in Berlin. [ABSTRACT FROM AUTHOR]

Results of Russian studies on cloud physics, precipitation, and weather modification in 2019–2022 are presented based on a survey prepared for the Russian National Report on Meteorology and Atmospheric Sciences to the 28th General Assembly of the International Union of Geodesy and Geophysics. Results concerning general issues of the observation and modeling of clouds and precipitation, including convective clouds; issues of studying the microphysical and optical characteristics of clouds; and the weather modification are discussed. [ABSTRACT FROM AUTHOR]

Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement: This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster's ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center. [ABSTRACT FROM AUTHOR]

Anthropogenic emissions affect vegetation photosynthesis and carbon flux through meteorological variations induced by aerosols and clouds. However, the insufficient consideration of meteorological conditions limits the understanding of relevant mechanisms, and further inhibits the projection of future terrestrial carbon balance. Based on multiple sets of model simulations, we characterized changes in gross primary production (GPP) due to three typical individual pollutants emissions (black carbon, organic carbon, and sulfate), quantified the relative contributions of co-varied environmental factors, and explored the regulatory roles of background meteorological conditions across China. Our results showed that the heterogeneous GPP enhancement induced by emissions was dominated by cloud cover (CC) change. During its short-term effect, air temperature (Tair), vapor pressure deficit (VPD), and radiation (both quality and quantity) played a collectively non-negligible role in GPP variation, among which the universal diffuse radiation fertilization effect was generally far less than the benefits of brighter, cooler, and wetter environmental conditions. However, the sensitivity of GPP to an individual environmental variable was also altered by background meteorological gradients, whose changing pattern differed substantially among factors, indicating that the meteorological-regulated vegetation optimal photosynthetic range was a trade-off among heat, water, and light instead of being controlled by the univariable. This study implies that a deeper understanding of concurrent environmental variables is an effective way to reduce uncertainties in assessing the terrestrial carbon cycle perturbation exerted by human-induced emissions, especially under future scenarios with ongoing climate change. [ABSTRACT FROM AUTHOR]

A postulate that relates global warming to higher entropy generation rate demand in the tropospheric is offered and tested. This article introduces a low-complexity model to calculate the entropy generation rate required in the troposphere. The entropy generation rate per unit volume is noted to be proportional to the square of the Earth's average surface temperature for a given positive rate of surface warming. The main postulate is that the troposphere responds with mechanisms to provide for the entropy generation rate that involves specific cloud morphologies and wind behavior. A diffuse-interface model is used to calculate the entropy generation rates of clouds. Clouds with limited vertical development, like the high-altitude cirrus or mid-altitude stratus clouds, are close-to-equilibrium clouds that do not generate much entropy but contribute to warming. Clouds like the cumulonimbus permit rapid vertical cloud development and can rapidly generate new entropy. Several extreme weather events that the Earth is experiencing are related to entropy-generating clouds that discharge a high rate of rain, hail, or transfer energy in the form of lightning. The water discharge from a cloud can cool the surface below the cloud but also add to the demand for a higher entropy generation rate in the cloud and troposphere. The model proposed predicts the atmospheric conditions required for bifurcations to severe-weather clouds. The calculated vertical velocity of thunderclouds associated with high entropy generation rates matches the recorded observations. The scale of instabilities for an evolving diffuse interface is related to the entropy generation rate per unit volume. Significant similarities exist between the morphologies and the entropy generation rate correlations in vertical cloud evolution and directionally solidified grainy microstructures. Such similarities are also explored to explore a generalized framework of pattern evolution and establish the relationships with the corresponding entropy generation rate. A complex system like the troposphere can invoke multiple phenomena that dominate at different spatial scales to meet the demand for an entropy generation rate. A few such possibilities are presented in the context of rapid and slow changes in weather patterns. [ABSTRACT FROM AUTHOR]

The conditions in Venus' upper mesosphere at around 120 km have some similarities to the upper mesosphere of Earth and Mars where ice clouds form. Here we show, using published satellite products and numerical modeling, that the upper mesosphere of Venus can be sufficiently cold that both H2O and CO2 may condense to form particles. We show that amorphous solid water particles (ASW) are likely to nucleate both heterogeneously on meteoric smoke and also homogeneously, resulting in clouds of nano‐scaled particles at around 120 km that will occur globally. The temperatures may then become sufficiently low, below ∼90 K, that CO2 particles can nucleate on ASW particles. Given the uncertainty associated with retrievals of temperature in the upper mesosphere, it is unclear how frequently this occurs, but it could be >30% of the time poleward of 60°. Since the main component of Venus' tenuous atmosphere is CO2, any CO2 crystals that form will grow and sediment on a timescale of 10–20 min. Mie calculations show that these Venusian mesospheric clouds (VMCs) should be observable by contemporary satellite instruments, although their short lifetime means that the probability of detection is small. We suggest that VMCs are important for the redistribution of meteoric smoke and may serve as a cold‐trap, removing some water vapor from the very upper mesosphere of Venus through the growth and sedimentation of cloud particles, and possibly reducing the loss of water to space. Plain Language Summary: Venus is renowned for extreme heat at the surface and clouds composed of sulfuric acid encircling the planet; however, there are regions of Venus's atmosphere that are sufficiently cold to harbor ice clouds. In fact, the temperatures frequently fall below 100 K at around 120 km altitude and under the right conditions, we have shown that ice clouds composed of both water and carbon dioxide ices can form. We have used published data from satellites that orbit Venus to show that clouds composed of nanometer sized water ice particles may encircle the planet. The temperatures are so low in this part of Venus' atmosphere that the ice in these water ice particles likely lacks any crystalline structure, i.e., it has an amorphous (liquid‐like) structure. Furthermore, if the temperature falls below about 90 K, we have shown that carbon dioxide ice crystals can form on top of water ice particles. Since the atmosphere of Venus is mainly made of carbon dioxide, these carbon dioxide crystals grow and then sediment rapidly in the thin atmosphere. If we were lucky enough to see one of these short‐lived sporadic clouds, it would look a bit like mares' tail cloud on Earth. Key Points: The upper mesosphere of Venus can be supersaturated with respect to both amorphous solid water (ASW) and crystalline carbon dioxide iceThere is a persistent layer of nano‐scale ASW particles encircling VenusShort‐lived carbon dioxide ice clouds may sporadically form in the upper mesosphere [ABSTRACT FROM AUTHOR]

Earth observation (EO) significantly increased in the second half of the 20th century and continues to advance rapidly, with remote sensing being a key component for gathering Earth-related information. Nowadays, satellites, manned aircraft, helicopters, UAVs and drones are used to capture aerial imagery in a periodic or schedule-based manner. This paper examine the feasibility of creating a novel remote sensing system by mounting cameras on commercial flights. The study evaluates flight coverage, including spatial and temporal resolutions, and considers the impact of clouds on image usability. We have compared flight coverage with cloud-inclusive flight coverage, which represents reduced flight coverage based on cloud quantity. Results show that entire country of Croatia is covered between 95% and 100% during the day and night. However, when clouds are included in the calculation, it is important to consider different altitudes and periods of the year because their distribution is not the same. In a less cloudy month (August), the highest differences between flight coverage and cloud-inclusive flight coverage for high-altitude flights are 70% for the worst-case scenario and 25% for the best-case scenario. Results show it is feasible to use commercial flights as a new remote sensing system. [ABSTRACT FROM AUTHOR]

Abstract There is growing evidence that marine microorganisms may influence cloud cover over the ocean through their impact on sea spray and trace gas emissions, further forming cloud droplets or ice crystals. However, evidence of a robust causal relationship based on observations is still pending. In this study, we use 4 years of multi‐instrument satellite data to segregate low‐level clouds into ice‐containing and liquid‐water clouds to obtain clear relationships between cloud types and ocean biological tracers, especially with nanophytoplankton cell abundances. Results suggest that microorganisms may be involved in compensating effects on cloud properties, increasing the frequency of occurrence of warm‐liquid clouds, and decreasing the occurrence of ice‐containing clouds in most regions during springtime. The relationships observed in most regions do not apply to the South Pacific Ocean in the 40°S–50°S latitude band. These results shed light on overlooked potential compensating effects of ocean microorganisms on cloud cover.

Abstract Anthropogenic activity drives extensive tropical deforestation, particularly in Southeast Asia where 16% of total forest cover was lost between 2000 and 2020. While land surface changes significantly affect the atmosphere, their net impact on convective clouds is not well‐constrained. Here, we use satellite data to demonstrate long‐term deforestation in Southeast Asia robustly alters cloud properties and provide the first observational evidence that the magnitude of this response depends on the atmospheric environment. Deforestation drives a shift toward more widespread, shallower clouds during the daytime, with amplified effects in dry inland areas compared with moist coastal regions. Aerosols only weakly modulate the cloud fraction response, but offset the cloud top height response to deforestation, suggesting the influence of aerosol indirect effects. We conclude that the local signature of forest loss is not uniform, and regional differences in climatology must be considered when assessing deforestation impacts on clouds and the climate system.

Current operational satellite retrievals of cloud optical and microphysical properties go back to the Nakajima-King technique developed at the end of the 1980 s. This technique is based on library calculations for plane-parallel homogeneous clouds. It often works well for overcast skies but leads to substantial errors for inhomogeneous and broken cloud fields where the plane-parallel-geometry assumption is no longer valid. The basic concept of a new technique was first introduced in nuclear physics to quantify the critical conditions of reactors. Based on the eigenvalues of the radiative transfer equation, their approach provides a powerful means to parameterize the structure of 3D media. This parameterization was later successfully applied to relate surface reflectance spectra to 3D canopy structure and is known as the spectrally-invariant approximation. The proposed approach adapts this technique to the remote sensing of cloud properties such as droplet single scattering albedo and the average number of scatterings (which are the fundamental parameters in radiative transfer theory), with an emphasis on quantifying the associated errors and uncertainties. This retrieval is free from the plane-parallel homogeneous cloud assumption.

Abstract Observations of marine stratus clouds in clean air off the Californian coast reveal a functional relationship between the number of cloud condensation nuclei (CCN) and supersaturation. Satellite‐derived liquid droplet density estimates the number density of CCN. Combining the estimated supersaturation using Köhler theory, global maps of supersaturation and the critical activation size of CCN are estimated. Here, we show that high supersaturation >0.5% persists over the oceans with a critical CCN size of 25–30 nm, which is smaller than the conventional wisdom of 60 nm. Independent support for such high supersaturation in the marine cloud is obtained from CCN measurements provided by the “Atmospheric Tomography Mission.” Higher supersaturation implies smaller activation size for CCN making cloud formation more sensitive to changes in aerosol nucleation.

Abstract Understanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions.

Abstract This study addresses a critical gap in understanding anthropogenic influences on tropical climate dynamics by investigating the impact of aerosol‐cloud interactions on large‐scale circulation. Despite extensive research on greenhouse gas‐induced warming and its effects on tropical circulation, the impact of aerosols, particularly their interactions with clouds, on large‐scale circulation remains understudied. Utilizing large‐domain radiative convective equilibrium cloud‐resolving simulations, this research demonstrates that increasing aerosol concentration intensifies tropical overturning circulation, evaluated at the mid‐troposphere (I), strongly correlating with domain mean cloud and radiative properties. Employing a weak temperature gradient approximation, I attribute variations in I to changes in clear‐sky radiative cooling rather than stability. These radiative cooling changes are linked to humidity changes driven by warm rain suppression by aerosols. This study's findings underscore the need to take into account microphysical changes, particularly aerosol concentrations, when studying anthropogenic effects on tropical circulation.

The tentative detection of ppb levels of phosphine (PH3) in the clouds of Venus was extremely surprising, as this reduced gas was not expected to be a component of Venus’ oxidized atmosphere. Despite potential confirmation in legacy Pioneer Venus mass spectrometry data, the detection remains controversial. Here we review the potential production of phosphine by gas reactions, surface and sub-surface geochemistry, photochemistry, and other nonequilibrium processes. None of these potential phosphine production pathways is sufficient to explain the presence of phosphine in Venus atmosphere at near the observed abundance. The source of atmospheric PH3 could be unknown geo- or photochemistry, which would imply that the consensus on Venus’ chemistry is significantly incomplete. An even more extreme possibility is that a strictly aerial microbial biosphere produces PH3. The detection of phosphine adds to the complexity of chemical processes in the Venusian environment and motivates better quantitation of the gas phase chemistry of phosphorus species and in situ follow-up sampling missions to Venus.

Unique airborne meteorological in-situ measurements have been used to study the impact of offshore wind farms on the latent heat flux in the marine boundary layer. 22 out of the total 42 flights collected during the WIPAFF project in the years 2016 and 2017 allowed for such an evaluation during various atmospheric conditions. The measurements revealed a significant increase of the vertical latent heat flux, mainly in the upward direction above the offshore wind farm cluster Amrumbank West, Nordsee Ost, Meerwind Süd/Ost or Godewind located in the German Bight. For thermally stable conditions, eight out of nine flights showed an enhanced turbulent latent heat flux downstream of the wind farms at hub height, with an increase of up to +70 W m−2 compared to the undisturbed flow. For flights during neutral thermal conditions, 10 out of 13 cases showed an increase, with the largest difference with respect to undisturbed flow of +600 W m−2 above the wind farm. Our observations suggest that the impact of offshore wind farms on the latent heat flux in the marine boundary layer is more frequent during stable conditions, but more prominent during neutral conditions and may trigger cloud development in the vicinity of the farm.

Recent years have witnessed the rapid deployment of smart homes; most of them are controlled by remote servers in the cloud. Such designs raise security and privacy concerns for end users. In this article, we describe the design of Sovereign, a home Internet of Things (IoT) system framework that provides end users complete control of their home IoT systems. Sovereign lets home IoT devices and applications communicate via application-named data and secures data directly. This approach enables direct, secure, one-to-one, and one-to-many Device-to-Device communication over wireless broadcast media. Sovereign utilizes semantic names to construct usable security solutions. We implement Sovereign as a publish-subscribe-based development platform together with a prototype home IoT controller. Our preliminary evaluation shows that Sovereign provides a systematic, easy-to-use solution to user-controlled, self-contained smart homes running on existing IoT hardware without imposing noticeable overhead.

Clouds and other data artefacts frequently limit the retrieval of key variables from remotely sensed Earth observations. We train a natural language processing (NLP)-inspired algorithm with high-fidelity ocean simulations to accurately reconstruct masked or missing data in sea surface temperature (SST) fields—one of 54 essential climate variables identified by the Global Climate Observing System. We demonstrate that the resulting model, referred to as Enki, repeatedly outperforms previously adopted inpainting techniques by up to an order of magnitude in reconstruction error, while displaying exceptional performance even in circumstances where the majority of pixels are masked. Furthermore, experiments on real infrared sensor data with masked percentages of at least 40% show reconstruction errors of less than the known uncertainty of this sensor (root mean square error (RMSE) ≲0.1 K). We attribute Enki’s success to the attentive nature of NLP combined with realistic SST model outputs—an approach that could be extended to other remotely sensed variables. This study demonstrates that systems built upon Enki—or other advanced systems like it—may therefore yield the optimal solution to mitigating masked pixels in in climate-critical ocean datasets sampling a rapidly changing Earth.