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Cloud microphysical processes, such as droplet activation, condensational growth, and collisional growth, play a central role in the evolution of clouds and precipitation. Accurate representations of these processes in numerical models are challenging partially due to incomplete understanding of them at the process level arising from limited systematic observations. Most surface-based active remote sensors, including today's operational cloud radars and lidars, have a resolution on the order of tens of meters. This resolution is insufficient to resolve cloud microphysical processes that manifest at finer (meter and submeter) scales. A new set of ultra-high-resolution ground-based radar and lidar systems have been developed to address this observational gap. The newly developed 94-GHz cloud radar has a range resolution down to 2.8 m, or a factor of 10 finer than typical radars, using a large bandwidth and quadratic phase coding techniques. The lidar has a range resolution down to 10 cm, or a factor of 100 finer than typical lidars, using a time-gated time-correlated single-photon-counting technique. Such high-resolution observations were previously only achievable through in situ aircraft measurements. Even then, aircraft measurements do not permit continuous long-term cloud observation as is possible with ground-based remote sensing instruments. In this study, the first-light cloud observations from the new radar and lidar systems are shown to reveal detailed cloud structures that conventional sensors could only perceive in a bulk sense, thus providing new avenues to investigate cloud microphysical processes and their impact on weather and climate. SIGNIFICANCE STATEMENT: In this study, we introduced two novel remote sensing instruments capable of high-resolution cloud observations: a cloud radar system with a range resolution down to 2.8 m and a lidar system with a range resolution down to 10 cm. Observations show that the radar and lidar systems resolve detailed cloud structures and microphysical processes which would not be obtained from traditional remote sensing systems. The high-resolution measurements provide insights toward the understanding of cloud microphysics at a fundamental level. [ABSTRACT FROM AUTHOR]

How low clouds respond to warming constitutes a key uncertainty for climate projections. Here we observationally constrain low‐cloud feedback through a controlling factor analysis based on ridge regression. We find a moderately positive global low‐cloud feedback (0.45 W m−2 ${\mathrm{m}}^{-2}$K−1 ${\mathrm{K}}^{-1}$, 90% range 0.18–0.72 W m−2 ${\mathrm{m}}^{-2}$K−1 ${\mathrm{K}}^{-1}$), about twice the mean value (0.22 W m−2 ${\mathrm{m}}^{-2}$K−1 ${\mathrm{K}}^{-1}$) of 16 models from the Coupled Model Intercomparison Project. We link this discrepancy to a pervasive model mean‐state bias: models underestimate the low‐cloud response to warming because (a) they systematically underestimate present‐day tropical marine low‐cloud amount, and (b) the low‐cloud sensitivity to warming is proportional to this present‐day low‐cloud amount. Our results hence highlight the importance of reducing model biases in both the mean state of clouds and their sensitivity to environmental factors for accurate climate change projections. Plain Language Summary: Low clouds have a large impact on climate by reflecting a portion of incoming sunlight back to space. Hence any future changes in clouds under global warming could amplify or dampen climate change—a phenomenon known as "cloud feedback." Climate models however disagree on future low‐cloud changes, resulting in large uncertainty in future global‐warming projections. Here we perform a statistical analysis of global satellite observations of low clouds, using observed co‐variations between clouds and meteorology to constrain the feedback simulated by climate models. We find evidence of an amplifying feedback by low clouds, stronger than simulated by most climate models. We link this discrepancy to a low‐cloud deficit across wide swathes of the tropical oceans characterized by abundant low cloud cover in observations. Thus to reduce climate projection uncertainty, we propose it is important to understand and mitigate the low‐cloud deficit in climate models. Key Points: We implement a statistical learning‐based analysis to constrain low‐cloud feedback, with strong out‐of‐sample predictive skillThe observationally constrained global‐mean feedback is about double the mean value from an ensemble of 16 climate modelsThis discrepancy is consistent with a pervasive underestimate of mean‐state tropical marine low‐cloud amount in the models [ABSTRACT FROM AUTHOR]

Cloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called "vertical profile analysis of cloud feedback," which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method. Plain Language Summary: Rising temperature caused by global warming will depend to a large extent on how clouds will change in the future climate. It is therefore important to know in detail how clouds and radiation will change in the future, not only horizontally but also vertically, in order to understand the mechanism. We have developed a simple but useful method for analyzing the vertical profile of changes in radiation and clouds. This detailed information is useful for understanding the mechanism of changes in clouds and radiation. We have compared this analysis method with the conventional method and discussed the advantages and disadvantages of the two methods in detail, based on simulation results. Although our vertical profile analysis provides more detailed vertical profile information, both methods can be used effectively, depending on the purpose of the analysis. Key Points: A simple analysis method is proposed to obtain a detailed vertical profile of cloud feedbackApplications are shown for typical regimes including stratocumulus, shallow convection, mid‐latitude clouds, and tropical deep convectionThe advantages and disadvantages of this analysis method relative to the conventional method are presented [ABSTRACT FROM AUTHOR]

The detection and quantification of strong sea surface winds, reaching up to 25 m·s−1, whether associated with deep convection aloft or not, have been extensively discussed in previous studies. This method involves the combined observation of the same event from both low‐orbit altitude and geostationary (GEO) satellites. Strong surface winds observed by the Sentinel‐1 C‐band synthetic aperture radar (SAR) are robustly associated with deep convective clouds detected by GEO infrared sensors. The current paper aims to generalize the previous assessment of several convective wind events by collecting a larger dataset and comparing these data to in‐situ wind observations. To achieve this, we evaluated wind speeds retrieved from Sentinel‐1 SAR images against corresponding in‐situ wind measurements from all active buoys/stations in the Gulf of Mexico. Significant agreement between satellite‐based winds and in‐situ data was achieved, particularly for wind speeds exceeding 3 m·s−1, with even better agreement for wind speeds over 10 m·s−1. From this dataset, three specific convective cases were extracted to illustrate various stages of convective squall events: before, during, and after the occurrence of a squall peak. In each case, comparison with in‐situ measurements showed that SAR‐estimated wind speeds closely matched observed speeds, including the peak convective winds, which were estimated at 18.90 m·s−1 and measured at 20.69 m·s−1. Furthermore, combining these findings with GOES‐16 sequential images illustrates the temporal and spatial similarity between deep convection areas, estimated strong sea surface wind patterns, and measured wind speeds. [ABSTRACT FROM AUTHOR]

The Tropical Cloud Oscillation (TCO) in the Martian atmosphere is a shift of clouds in the northern spring and summer tropical cloud belt between the eastern and western hemispheres on an intra‐seasonal timescale of about 10–40 sols. The TCO is a significant intraseasonal variation and may strongly affect the Martian general circulation, water cycle, and dust cycle. We examine TCOs using multiple data sets with a focus on the clouds observed in Mars Daily Global Maps during Mars Year (MY) 29–35. One or more TCO cycles are observed in each MY and the phenomenon is most prominent during Ls = 135°–185°. Space‐time spectral analysis shows a variety of waves which appear to follow the theoretical dispersion relationships of equatorial waves, such as Kelvin waves, Rossby waves, and Mixed Rossby Gravity waves. The TCO appears to be controlled by zonal wavenumber one traveling waves with Kelvin and Rossby wave characteristics and exhibits a fine‐scale latitudinal structure that requires modeling with sufficient resolution. Issues with current data assimilation products for use in studies of Martian equatorial waves due to this fine‐scale structure are discussed. Plain Language Summary: During martian northern spring and summer, a relatively thick water ice cloud belt is observed in the tropics (approximately 25°S–25°N), which modifies large‐scale circulation, global water transport and the atmospheric dust cycle. We report on a Tropical Cloud Oscillation (TCO) that is a periodic shift in the thickness of this cloud belt between the eastern and western hemispheres on timescales (about 10–40 Mars days) longer than that of weather but shorter than that of seasons. Since the TCO involves changes in clouds and implies changes in circulation, it may also modulate the dust and water cycles. We examine TCOs using image data for Mars Year 29–35 (late 2007 through early 2021). One or more TCO cycles are observed in each Mars Year and the phenomenon is most prominent in late northern summer. A wide variety of atmospheric waves are found during TCOs; however, the most important are equatorial waves traveling eastward and westward with one peak and one trough around the planet. These waves have narrow latitudinal length scales and thus require high resolution numerical models to simulate and properly represent in data assimilation products. Key Points: The Martian tropical cloud belt exhibits eastern—western zonal oscillations that are most prominent in late northern summerTropical Cloud Oscillations are controlled by large‐scale low frequency equatorial Kelvin and Rossby wavesEquatorial wave dynamics may have an important effect on the Martian water and dust cycles [ABSTRACT FROM AUTHOR]

European Space Agency's Mars Express (MEX) has been orbiting Mars for 20 years and its instruments have provided a plethora of observations of atmospheric dust and clouds. These observations have been analysed to produce many unique views of the processes leading to dust lifting and cloud formation, and a full picture of the climatologies of dust and clouds has emerged. Moreover, the orbit of MEX enables viewing the planet at many local times, giving a unique access to the diurnal variations of the atmosphere. This article provides an overview of the observations of dust and clouds on Mars by MEX, complemented by the Trace Gas Orbiter that has been accompanying MEX on orbit for some years. [ABSTRACT FROM AUTHOR]

We investigate the role of mesoscale organization for the response of trade cumulus (Tc) clouds to climate change. Among four recently identified states of Tc organization, the "Sugar" state has the lowest and the "Flower" state the highest cloud fraction and cloud radiative effect. Using large‐eddy simulations, we find that the Flower Tc state is more sensitive to climate change than the Sugar Tc state. In the considered case, the short‐wave cloud radiative effect weakens by 0.28 W m−2 in the Sugar state and by 1.5 W m−2 in the Flower state over the course of 21st century under the RCP8.5 emissions scenario. This is accompanied by a reduction of the short‐wave cloud radiative effect variance on the mesoscale. The primary mechanism is stabilization of the boundary layer by stronger long‐wave radiative heating at the inversion associated with higher greenhouse gas levels. This weakens the boundary layer mesoscale circulation that is responsible for aggregation of moisture and formation of the Flower Tc state. Thus, in the considered case, organization on the mesoscale amplifies the positive feedback of Tc clouds to climate change. Owing to the widespread occurrence of boundary layer mesoscale circulations in the Tc regime, this mechanism could modulate the Tc response to climate change in general. Plain Language Summary: Trade cumulus clouds cover large swaths of the oceans and cool the planet. Climate models struggle to capture these clouds, leading to uncertainty in climate projections. We show that among four recently identified organized types of trade cumulus clouds, the type with the highest cooling effect will be suppressed at the end of the 21st century and its cooling effect reduced, unlike less organized trade cumulus clouds with a lower cooling effect. The suppression is a direct consequence of cloud organization. Enhanced warming at higher greenhouse gas levels is the primary cause. Key Points: Mesoscale organization increases the sensitivity of trade cumulus clouds to climate changeThis is primarily caused by a stabilization of the boundary layer at higher greenhouse gas concentrationsThe stabilization of the boundary layer suppresses the mesoscale circulation that organizes trade cumulus clouds [ABSTRACT FROM AUTHOR]

The present study analyzes zonal mean cloud and radiation trends over the global oceans for the past 35 years from a suite of satellite datasets covering two periods. In the longer period (1984–2018) cloud properties come from the ISCCP-H, CLARA-A3, and PATMOS-x datasets and radiative properties from the ISCCP-FH dataset, while for the shorter period (2000–2018) cloud data from MODIS and CloudSat/CALIPSO and radiative fluxes from CERES-EBAF are added. Zonal mean total cloud cover (TCC) trend plots show an expansion of the subtropical dry zone, a poleward displacement of the midlatitude storm zone and a narrowing of the tropical intertropical convergence zone (ITCZ) region over the 1984–2018 period. This expansion of the 'low cloud cover curtain' and the contraction of the ITCZ rearrange the boundaries and extents of all major climate zones, producing a more poleward and narrower midlatitude storm zone and a wider subtropical zone. Zonal mean oceanic cloud cover trends are examined for three latitude zones, two poleward of 50 ° and one bounded within 50oS and 50oN, and show upward or near-zero cloud cover trends in the high latitude zones and consistent downward trends in the low latitude zone. The latter dominate in the global average resulting in TCC decreases that range from 0.72% per decade to 0.17% per decade depending on dataset and period. These contrasting cloud cover changes between the high and low latitude zones produce contrasting low latitude cloud radiative warming and high latitude cloud radiative cooling effects, present in both the ISCCP-FH and CERES-EBAF datasets. The global ocean mean trend of the short wave cloud radiative effect (SWCRE) depends on the balance between these contrasting trends, which in the CERES dataset materializes as a SW cloud radiative warming trend of 0.12 W/m2/decade coming from the dominance of the low-latitude positive SWCRE trends while in the ISCCP-FH dataset it manifests as a 0.3 W/m2/decade SW cloud radiative cooling trend coming from the dominance of the high latitude negative SWCRE trends. The CERES cloud radiative warming trend doubles in magnitude to 0.24 W/m2/decade when the period is extended from 2016 to 2022, implying a strong cloud radiative heating in the past 6 years coming from the low latitude zone. [ABSTRACT FROM AUTHOR]

Satellite imagery provides a global perspective for studying river hydrology and water quality, but clouds remain a fundamental limitation of optical sensors. Explicit studies of this problem were limited to specific locations or regions. In this study, we characterize the global severity of this limitation by analyzing 22 years of daily satellite cloud cover data and modeled river discharge for a global sample 21,642 river reaches of diverse sizes and climates. Our results show that the bias in observed river discharge is highly organized in space, particularly affecting Tropical and Arctic rivers. Given the fundamental nature of this cloud limitation, optical satellites will always provide a biased representation of river conditions. We discuss several strategies to mitigate bias, including modeling, data fusion, and temporal averaging, yet these methods introduce their own challenges and uncertainties. Plain Language Summary: This study examines how optical satellite imagery, which is vital for understanding rivers worldwide, is hindered by clouds specifically when observing rivers. We analyzed 22 years of daily data covering 21,642 sections of rivers of various sizes and climates. Our findings reveal that cloud cover significantly biases the distribution of river discharges we observe, especially for Tropical and Arctic rivers. This means that satellite images do not represent river conditions accurately. Our research provides the first comprehensive analysis of its extent and impact. Ultimately, while satellite technology continues to improve, clouds remain a challenge in obtaining precise river data, highlighting the need for innovative solutions. Key Points: We evaluated the impact of cloud cover on satellite optical remote sensing across a global sample of river reachesDirect optical satellite observations can present a highly biased, and geographically variable, view of river conditionsSeasonal relationships of clouds and discharge predict the ability of optical satellites to observe the distribution of river discharge [ABSTRACT FROM AUTHOR]

Remote sensing has been a key resource for developing extensive and detailed datasets for studying and constraining aerosol-cloud-climate interactions. However, aerosol-cloud collocation challenges, algorithm limitations, as well as difficulties in unraveling dynamic from aerosol-related effects on cloud microphysics, have long challenged precise retrievals of cloud droplet number concentrations. By combining a series of remote sensing techniques and in situ measurements at ground level, we developed a semiautomated approach that can address several retrieval issues for a robust estimation of cloud droplet number for non-precipitating Planetary Boundary Layer (PBL) clouds. The approach is based on satellite retrievals of the PBL cloud droplet number (Ndsat) using the geostationary meteorological satellite data of the Optimal Cloud Analysis (OCA) product, which is obtained by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT). The parameters of the retrieval are optimized through closure with droplet number obtained from a combination of ground-based remote sensing data and in situ observations at ground level. More specifically, the remote sensing data are used to retrieve cloud-scale vertical velocity, and the in situ aerosol measurements at ground level were used constrain as input to a state-of-the-art droplet activation parameterization to predict the respective Cloud Condensation Nuclei (CCN) spectra, cloud maximum supersaturation and droplet number concentration (Nd), accounting for the effects of vertical velocity distribution and lateral entrainment. Closure studies between collocated Nd and Nd sat are then used to evaluate exising droplet spectral width parameters used for the retrieval of droplet number, and determine the optimal values for retrieval. This methodology, used to study aerosol-cloud interactions for non-precipitating clouds formed over the Athens Metropolitan Area (AMA), Greece, during the springtime period from March to May 2020, shows that droplet closure can be achieved to within ±33.4%, comparable to the level of closure obtained in many in situ studies. Given this, the ease of applying this approach with satellite data obtained from SEVIRI with high temporal (15 min) and spatial resolution (3.6 km Ã--4.6 km), opens the possibility of continuous and reliable Ndsat, giving rise to high value datasets for aerosol-cloud-climate interaction studies. [ABSTRACT FROM AUTHOR]

The article presents the correction for a article titled "Towards reliable retrievals of cloud droplet number for non-precipitating planetary boundary layer clouds and their susceptibility to aerosol" published on August 12, 2024.

Ice formation remains one of the most poorly represented microphysical processes in climate models. While primary ice production (PIP) parameterizations are known to have a large influence on the modeled cloud properties, the representation of secondary ice production (SIP) is incomplete and its corresponding impact is therefore largely unquantified. Furthermore, ice aggregation is another important process for the total cloud ice budget, which also remains largely unconstrained. In this study, we examine the impact of PIP, SIP, and ice aggregation on Arctic clouds, using the Norwegian Earth System Model, version 2 (NorESM2). Simulations with both prognostic and diagnostic PIP show that heterogeneous freezing alone cannot reproduce the observed cloud ice content. The implementation of missing SIP mechanisms (collisional breakup, drop shattering, and sublimation breakup) in NorESM2 improves the modeled ice properties, while improvements in liquid content occur only in simulations with prognostic PIP. However, results are sensitive to the description of collisional breakup. This mechanism, which dominates SIP in the examined conditions, is very sensitive to the treatment of the sublimation correction factor, a poorly constrained parameter that is included in the utilized parameterization. Finally, variations in ice aggregation treatment can also significantly impact cloud properties, mainly through their impact on collisional breakup efficiency. Overall, enhancement in ice production through the addition of SIP mechanisms and the reduction in ice aggregation (in line with radar observations of shallow Arctic clouds) result in enhanced cloud cover and decreased TOA radiation biases, compared to satellite measurements, especially during the cold months. Significance Statement: Arctic clouds remain a large source of uncertainty in projections of the future climate due to the poor representation of the microphysical processes that govern their life cycle. Ice formation is among the least understood processes. While it is widely recognized that better constraints on primary ice production (PIP) are needed to improve existing parameterizations, we show that secondary ice production (SIP) and ice aggregation can have also a significant impact on ice number concentrations. Constraining ice formation through the addition of missing SIP mechanisms and reducing ice aggregation can improve the representation of the cloud macrophysical properties and enhance total cloud cover in the Arctic region, which in turn contributes to decreased TOA radiation biases in the cold months. [ABSTRACT FROM AUTHOR]

• Machine learning (ML) techniques were pioneered to analyze non-linear relationships. • Algorithm's predictive power validated ML models, preventing spurious relations. • Recursive feature elimination explored predictor importance. • Presence of non-linear relationships is suggested, advising further investigation. • Acknowledgment of skepticism stresses rigorous analysis for long-term correlations. The Earth's climate is a complex system that can be influenced by various internal and external factors. Previous research has explored the relationship between climate and external forcings such as solar activity and GCR. However, the relations are intricate and intertwined into almost chaotic meteorological measurements. This paper delves deeper into understanding their interactions. GCR flux, Sunspot number (SSN), total solar irradiance (TSI), UV irradiance (UVI), and the Oceanic Niño Index (ONI) are the predictor variables, while total cloud amount (TCA) and low cloud amount (LCA) are the response variables. The analysis begins with standard statistical techniques and continues with multiple regression models including random forest, which is a machine learning (ML) method that can be used for non-linear regression. Subsequently, Recursive Feature Elimination (RFE) is employed to scrutinize the correlation among the predictor parameters. In the ML model, the final 25% of the dataset is held out and tested for validation, so, the predictive power of the algorithm is measured. Both geographical and temporal patterns have been investigated. This study suggests that a non-linear relationship might exist between the parameters, and should be investigated further, particularly in specific regions of the world. [ABSTRACT FROM AUTHOR]

The effect of leads in Arctic sea ice on clouds is a potentially important climate feedback. We use observations of clouds and leads from the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) to study the effects of leads on clouds. Both leads and clouds are strongly forced by synoptic weather conditions, with more clouds over both leads and sea ice at lower sea level pressure. Contrary to previous studies, we find that the overall lead effect on low-level cloud cover is −0.02, a weak cloud dissipating effect in cold months, after the synoptic forcing influence is removed. This is due to compensating contributions from the cloud dissipating effect by newly frozen leads under high pressure systems and the cloud enhancing effect by newly open leads under low pressure system. The lack of proper representation of lead effect on clouds in current climate models and reanalyses may impact their performance in winter months, such as in sea ice growth and Arctic cyclone development. [ABSTRACT FROM AUTHOR]

Novel observations of aerosol and clouds by means of ground-based remote sensing have been performed in Antarctica over the Ekström Ice Shelf on the coast of Dronning Maud Land at Neumayer Station III (70.67°S, 8.27°W) from January to December 2023. The deployment of the OCEANET-Atmosphere remote sensing observatory in the framework of the Continuous Observations of Aerosol-Cloud Interaction (COALA) campaign has brought Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) aerosol and cloud profiling capabilities next to meteorological and air chemistry in situ observations at the Antarctic station. We present an overview of the site, the instrumental setup, and data analysis strategy and introduce 3 scientific highlights from austral fall and winter, namely, 1) observations of a persistent mixed-phase cloud embedded in a plume of marine aerosol. Remote sensing–based retrievals of cloud-relevant aerosol properties and cloud microphysical parameters confirm that the free-tropospheric mixed-phase cloud layer formed in an aerosol-limited environment. 2) Two extraordinary warm air intrusions: one with intense snowfall produced the equivalent of 10% of the yearly snow accumulation and a second one with record-breaking maximum temperatures and heavy icing due to supercooled drizzle. 3) Omnipresent aerosol layers in the stratosphere. Our profiling capabilities could show that 50% of the 500-nm aerosol optical depth of 0.06 was caused by stratospheric aerosol, while the troposphere was usually pristine. As demonstrated by these highlights, the 1-yr COALA observations will serve as a reference dataset for the vertical structure of aerosol and clouds above the region, enabling future observational and modeling studies to advance understanding of atmospheric processes in Antarctica. [ABSTRACT FROM AUTHOR]

Potential vorticity (PV) is one of the most important quantities in atmospheric science. In the absence of dissipative processes, the PV of each fluid parcel is known to be conserved, for a dry atmosphere. However, a parcel's PV is not conserved if clouds or phase changes of water occur. Recently, PV conservation laws were derived for a cloudy atmosphere, where each parcel's PV is not conserved but parcel‐integrated PV is conserved, for integrals over certain volumes that move with the flow. Hence a variety of different statements are now possible for moist PV conservation and non‐conservation, and in comparison to the case of a dry atmosphere, the situation for moist PV is more complex. Here, in light of this complexity, several different definitions of moist PV are compared for a cloudy atmosphere. Numerical simulations are shown for a rising thermal, both before and after the formation of a cloud. These simulations include the first computational illustration of the parcel‐integrated, moist PV conservation laws. The comparisons, both theoretical and numerical, serve to clarify and highlight the different statements of conservation and non‐conservation that arise for different definitions of moist PV. [ABSTRACT FROM AUTHOR]

Detection of clouds and shadows in remote sensing imagery is important due to its wide range of applications. There are a lot of applications in remote sensing images such as monitoring of the environment, change detection etc. It is an important and booming research area. Ineffective and inaccurate cloud and cloud shadow masking will cause undesirable effects on different task that can be performed by using remote sensing images. Because of high spectral conglomeration and the spectral and temperature discrepancy of the underlying surface the detection of clouds and associated shadows is not candid. In this paper, we propose CSDUNet a modified U-Net network for precise pixel-wise semantic segmentation of cloud and its associated shadow from optical remote sensing images. It uses an encoder network and a decoder network. This method concatenated feature maps at different scales. We have proposed a novel network for cloud detection, which extract features corresponding cloud and shadow at different scales from multilevel layers to generate sharp boundaries. Which will help to detect clouds in heterogeneous landscape, under complex underlying surfaces with varying geometry. Experimental analysis on the Landsat satellite dataset proves that the proposed CSDUNet achieves a dice coefficient of 95.05%. Our method got 95.93% precision, recall of 94.71% and Jaccard index of 97.29%. CSDUNet achieves accurate detection accuracy and surpass several traditional methods. [ABSTRACT FROM AUTHOR]

Diurnal variations in precipitation, clouds and other related fields are of interest for many applications. Here I analyze surface and satellite observations and ERA5 data to quantify these variations and evaluate ERA5's performance. Results show that ERA5 captures the observed seasonal climatology of precipitation and cloud amount remarkably well. Surface observations show that warm-season precipitation exhibits a robust diurnal cycle with an amplitude of ~ 20 to 50% of the daily mean and a peak around 14–18 local solar time (LST) over most land areas and 04–08 LST over most oceans. ERA5 approximately reproduces these features with a slightly earlier peak (by ~ 2 h) over both land and ocean and a stronger amplitude over land, mainly due to biases in its convective precipitation. The IMERG satellite product captures mainly the diurnal cycle of convective precipitation with a peak around 16–20 LST during the warm season. ERA5 oceanic precipitation shows robust diurnal variations that are comparable to observations despite its dampened marine surface diurnal cycle due to its use of daily-mean SST. This suggests a free-tropospheric control of oceanic precipitation diurnal cycle. Surface and satellite observations show more clouds (mainly from low clouds) during daytime (nighttime) over land (ocean). ERA5 total cloud diurnal anomalies are more comparable to surface observations than to ISCCP satellite product. Cloud base height shows a minimum in early afternoon and a maximum around midnight with a diurnal amplitude of ~ 150 m over warm-season land in surface observations; ERA5 approximately captures this diurnal cycle with a slightly stronger amplitude and earlier phase. Land planetary boundary layer height (PBLH) in ERA5 is around 250 m at night but increases after sunrise to a peak around 14–15 LST of about 1500–1900 m in the warm season and ~ 650 to 1100 m in the cold season, with largest diurnal amplitudes over summer drylands. ERA5 marine PBLH is higher in the cold season (~ 1000 m) than in the warm season (~ 530 m) in the extra-tropics, suggesting a dominant role by low-level wind-induced mixing. ERA5 CAPE shows out-of-phase diurnal variations over land and ocean, with near-noontime peak (minimum) and an early morning minimum (peak) over land (ocean). ERA5 CIN's diurnal cycle is approximately out of phase with CAPE. ERA5 captures well the diurnal cycles and their land–ocean and seasonal differences in surface net shortwave and longwave (LWnet) radiation seen in CERES satellite product, with a near-noontime peak in land LWnet. A near-noontime peak is also seen in ERA surface sensible and latent heat fluxes over land, while oceanic PBLH, LWnet and heat fluxes show little diurnal variation in ERA5, which may be partly due to its use of daily-mean SST. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Numerical simulations of turbulent moist Rayleigh–Bénard convection driving CCN activation and droplet growth in the laboratory Pi chamber are discussed. Supersaturation fluctuations come from isobaric mixing of warm and humid air rising from the lower boundary with colder air featuring lower water vapor concentrations descending from the upper boundary. Lagrangian particle–based microphysics is used to represent the growth of haze CCN and cloud droplets with kinetic, solute, and surface tension effects included. Dry CCN spectra in the range between 2- and 200-nm radii from field observations are considered. Increasing the total CCN concentration from pristine to polluted conditions results in an increase in the droplet concentration and reduction in the mean droplet radius and spectral width. These are in agreement with Pi chamber observations and numerical simulations, as well as with numerous past studies of CCN cloud-base activation in natural clouds. The key result is that a relatively small fraction of the available CCN is activated in the Pi chamber fluctuating supersaturations, from about a half in the pristine case to only a 10th in the polluted case. The activation fraction as a function of the dry CCN radius is similar in all simulations, close to zero at the CCN small end, increasing to a maximum at CCN radius around 50 nm, and decreasing to close to zero at the large CCN end. This is explained as too small supersaturations to activate small CCN as in natural clouds and insufficient time to allow large CCN reaching the critical radius. Significance Statement: Impact of turbulence on the formation and growth of cloud droplets is an important cloud physics problem. Laboratory experiments in the Michigan Technological University cloud chamber provide key insights into this problem. Numerical simulations of cloud chamber processes discussed in this paper complement laboratory experiments by providing insights difficult or impossible to obtain in the laboratory. The study contrasts the formation and growth of cloud droplets in the laboratory cloud chamber with processes taking place in natural clouds. The differences documented in this paper pose questions concerning the impact of turbulence on the formation and growth of cloud droplets as a result of interactions of clouds with their environment. [ABSTRACT FROM AUTHOR]

Remote Patient Monitoring (RPM) is a healthcare solution that uses technology to monitor patients outside conventional healthcare settings. It is especially useful for people with chronic conditions or needing regular monitoring. One of the main reasons for the increase in the number of deaths each year is the increase in cardiovascular diseases, including hypertension. Online blood pressure monitoring offers many advantages but also potential challenges. This work reviews the key communication technologies and research challenges in the real-time transmission of measured blood pressure data. Delay in these systems is not tolerated as it involves human lives. To conduct the experimental studies, a prototype of an experimental intelligent system was created to study the delay and processor load of the RPI4 gateway. The measured blood pressure data is sent to the Things Board cloud using the AMQP and CoAP protocols. The experimental results are particularly useful for RPM system designers. The results of this research facilitate an informed decision on the choice of protocol that transmits the data from the gateway to the cloud in the process of designing remote patient monitoring systems. [ABSTRACT FROM AUTHOR]

Aerosol increases over the 20th century delayed the rate at which Earth warmed as a result of increases in greenhouse gases (GHGs). Aggressive aerosol mitigation policies arrested aerosol radiative forcing from ∼1980 to ∼2010. Recent evidence supports decreases in forcing magnitude since then. Using the approximate partial radiative perturbation (APRP) method, future shortwave aerosol effective radiative forcing changes are isolated from other shortwave changes in an 18‐member ensemble of ScenarioMIP projections from phase 6 of the Coupled Model Intercomparison Project (CMIP6). APRP‐derived near‐term (2020–2050) aerosol forcing trends are correlated with published model emulation values but are 30%–50% weaker. Differences are likely explained by location shifts of aerosol‐impacting emissions and their resultant influences on susceptible clouds. Despite weaker changes, implementation of aggressive aerosol cleanup policies will have a major impact on global warming rates over 2020–2050. APRP‐derived aerosol radiative forcings are used together with a forcing and impulse response model to estimate global temperature trends. Strong mitigation of GHGs, as in SSP1‐2.6, likely prevents warming exceeding 2C since preindustrial but the strong aerosol cleanup in this scenario increases the probability of exceeding 2C by 2050 from near zero without aerosol changes to 6% with cleanup. When the same aerosol forcing is applied to a more likely GHG forcing scenario (i.e., SSP2‐4.5), aggressive aerosol cleanup more than doubles the probability of reaching 2C by 2050 from 30% to 80%. It is thus critical to quantify and simulate the impacts of changes in aerosol radiative forcing over the next few decades. Plain Language Summary: Over the 20th century, fossil fuel burning led to increased concentrations of greenhouse gases (GHGs) and small particles known as aerosols. Aerosols scatter sunlight back to space and enhance cloud brightness and longevity, thus cooling Earth. The amount of warming since the pre‐industrial depends upon both GHGs and aerosol. After ∼1980, air quality improvements led to a reduction in this cooling effect, unmasking some of the GHG potential to warm the planet. This has reinforced the importance of understanding the interplay between aerosols and GHGs in climate projections. To examine this sensitivity, we use a set of global climate model simulations forced by a variety of future GHG and aerosol concentration based on plausible socioeconomic pathways. Shifts in the location of regional aerosol emissions has an impact on the global climate, influencing the accuracy of our predictions for Earth's future warming as measured by the probability of increasing global temperatures by 2C by 2050 compared to pre‐industrial. Under plausible GHG scenarios, aggressive aerosol cleanup policies can more than double the probability of crossing this threshold. This emphasizes the urgency of improving our simulations in order to accurately predict and quantify the impact of aerosols over the next few decades. Key Points: Aerosol forcing changes are isolated from CMIP6 projected scenarios using an approximate partial radiative perturbation methodAerosol‐cloud interactions dominate future aerosol forcing changes from cleanup policies over the period 2020–2050Likelihood of maintaining global warming to below 2C will be strongly reduced by implementing aggressive near‐term aerosol cleanup policies [ABSTRACT FROM AUTHOR]

Abstract How low clouds respond to warming constitutes a key uncertainty for climate projections. Here we observationally constrain low‐cloud feedback through a controlling factor analysis based on ridge regression. We find a moderately positive global low‐cloud feedback (0.45 W m−2 K−1, 90% range 0.18–0.72 W m−2 K−1), about twice the mean value (0.22 W m−2 K−1) of 16 models from the Coupled Model Intercomparison Project. We link this discrepancy to a pervasive model mean‐state bias: models underestimate the low‐cloud response to warming because (a) they systematically underestimate present‐day tropical marine low‐cloud amount, and (b) the low‐cloud sensitivity to warming is proportional to this present‐day low‐cloud amount. Our results hence highlight the importance of reducing model biases in both the mean state of clouds and their sensitivity to environmental factors for accurate climate change projections.

Businesses that want to benefit from cloud computing must choose a Cloud Service Provider (CSP). Cost, performance, Reliability, security, and SLAs must be evaluated during the decision process. CSP assessment is tough because of uncertainties and erroneous data. Fuzzy logic and the firefly optimization technique have been proposed in this paper to achieve optimal results based on diverse components. The proposed methodology uses consumer, service provider, and public reviews based on the three elements. These components' ratings can be used to analyze efficiency. Simple fuzzy logic is inferior to optimized fuzzy logic, according to experiments. The Firefly Optimized Fuzzy DSS is compared against non-optimized fuzzy decision-making systems and standard optimization methods. The results show that the proposed model is better for selecting the best CSP based on many parameters and managing assessment uncertainty. Fuzzy logic and optimization methods provide more nuanced and precise decision-making that accounts for subjective assessments and confusing facts. Businesses can make informed choices and ensure their CSP needs are satisfied with the approach. Finally, the Firefly Optimized Fuzzy Decision Support System offers a new perspective on cloud service provider selection by merging fuzzy logic with optimization. The system's ability to handle poor evaluations and ambiguity makes it ideal for CSP selection's complex decision-making process. This paper helps build decision support systems for choosing a cloud service provider and has substantial implications for firms seeking successful cloud computing solutions. This research work's conclusions have major implications for corporations and organizations searching for the finest cloud service providers. CSP-related real-world datasets are tested experimentally.

Rising temperatures entail important changes in the soil hydrologic processes of the northern permafrost zone. Using the ICON‐Earth System Model, we show that a large‐scale thaw of essentially impervious frozen soil layers may cause a positive feedback by which permafrost degradation amplifies the causative warming. The thawing of the ground increases its hydraulic connectivity and raises drainage rates which facilitates a drying of the landscapes. This limits evapotranspiration and the formation of low‐altitude clouds during the snow‐free season. A decrease in summertime cloudiness, in turn, increases the shortwave radiation reaching the surface, hence, temperatures and advances the permafrost degradation. Our simulations further suggest that the consequences of a permafrost cloud feedback may not be limited to the regional scale. For a near‐complete loss of the high‐latitude permafrost, they show significant temperature impacts on all continents and northern‐hemisphere ocean basins that raise the global mean temperature by 0.25 K. Plain Language Summary: Landscapes in the Arctic and subarctic zone are often very wet with highly water saturated soils and an extensive lake‐ and wetland cover. To some extent, this is due to the perennially frozen soil layers that underlay large parts of these regions and inhibit the movement of water through the ground. Thus, a thawing of the frozen soils, caused by rising temperatures, may ultimately lead to a drying of the landscapes. Here, we use simulations with the ICON‐Earth System Model to show that such a drying increases regional temperatures via an atmospheric feedback: During the warm season, dryer conditions at the surface reduce the moisture transport into the atmosphere. This decreases the relative humidity in the boundary layer and the low‐altitude cloud cover. Since clouds reflect more sunlight than the snow‐free land surface, the reduced cloudiness increases the available energy, hence, temperatures and advances the thawing of the ground. Higher temperatures in the Arctic and subarctic zone, in turn, have important consequences for the net energy exchange between equatorial and polar regions. Thus, the effects of a large‐scale drying of high‐latitude soils may not be limited to the regional scale but could notably increase global mean temperatures. Key Points: Advanced degradation of permafrost may facilitate large‐scale landscape dryingDependency of clouds on terrestrial hydrology allows for feedback between permafrost thaw, diminished cloudiness and rising temperaturesThis feedback could amplify global warming notably [ABSTRACT FROM AUTHOR]

The Venusian atmosphere hosts a 10 km deep convective layer that has been studied by various spacecrafts. However, the impact of the strong vertical mixing on the chemistry of this region is still unknown. This study presents the first realistic coupling between resolved small‐scale turbulence and a chemical network. The resulting vertical mixing is different for each species: those with longer chemical timescales will tend to be well‐mixed. Vertical eddy diffusion due to resolved convection motions was estimated, ranging from 102 to 104 m2/s for the 48–55 km convective layer, several orders of magnitude above the typically used value. In the 48–55 km convective layer, the impact of the small‐scale turbulence on the cloud layer boundaries was between 200 m and 1 km. The impact of turbulence on cloud chemistry is consistent with Venus Express/Visible and Infrared Thermal Imaging Spectrometer observations. The observability at the cloud‐top of small‐scale turbulence by VenSpec‐U spectrometer would be challenging. Plain Language Summary: Venus hosts a global sulfuric acid cloud layer between 45 and 70 km. A convective layer is present between roughly 50 and 60 km, with its variability in latitude and local time assessed by observation, with a thicker layer at high latitude and at night. One question that remains unclear is how this turbulence mixes momentum, heat, and chemical species. Especially, the impact of the strong vertical mixing on the chemistry of this region is still unknown. To investigate this topic, we use a convection‐resolving model coupled for the first time with a realistic chemical network. The resulting vertical mixing is different for each species: those with longer chemical timescales will tend to be well‐mixed. 1D and global circulation models use the so‐called vertical eddy diffusion approach to represent turbulent motion, quantified in our model and underestimated in chemistry models. The small‐scale turbulence in the cloud layer causes a variation in the altitude of the top and bottom boundaries of the cloud. Our model shows that the impact of turbulence on cloud chemistry corresponds well to what has been observed by satellites. In the future, the EnVision mission will be able to observe chemical species at the small turbulence scales. Key Points: Estimation for the first time of the spatial and temporal variability of chemical species due to vertical mixingQuantification of the vertical eddy diffusion coefficient, order of magnitude above typical used valuesCloud‐top altitudes change by 0.2–1 km due to vertical convective mixing and gravity waves [ABSTRACT FROM AUTHOR]

Over the past century, rapid urbanization has greatly altered landscapes and affected atmospheric conditions causing impacts across a wide range of sectors including human welfare, infrastructure, and ecosystems. As a result, there is a growing imperative to improve understanding of urbanization impacts on local and regional weather events and hydroclimates. This study investigates how urbanization affects precipitation and cloud fraction (CF) in the vicinity of Indianapolis. We employ multi‐month simulations using the Weather Research and Forecasting model to: (a) assess the impact of an urban area on precipitation and CF, (b) quantify how this impact varies with urban growth, and (c) examine the main mechanisms through which the city alters the local hydroclimate. Specifically, two perturbed simulations, where the urban land cover is either replaced by croplands or is increased in size, are also performed for the two rainiest months. Comparisons of the control run with no city against the perturbed runs indicate statistically significant impacts of the urban area in enhancing precipitation amounts, frequency and low‐level CF, particularly within the first 100 km radius downwind of the city boarder. The urban environment is found to increase precipitation efficiency over the city and in downwind regions. Temperature at 2 m height, planetary boundary layer height, turbulent kinetic energy, and convective available potential energy, are also enhanced in the perturbed runs and drive changes in vertical mixing, downwind precipitation amounts, frequency and low‐level height CF. All these changes appear to be a non‐linear function of the city size. Plain Language Summary: Over the past century, rapid urbanization has reshaped landscapes, influencing atmospheric circulation, and leaving profound impacts on human welfare, infrastructure, and ecosystems. Recognizing the urgency to comprehend these effects, this study contributes to an improved understanding of the influence of urbanization on precipitation amounts and cloud fraction (CF) in the surrounding areas of a metropolitan area in the United States. This work utilizes a modeling approach based on an ensemble of simulations of the Weather Research and Forecasting model. The design of our modeling experiment allows us to investigate the role of different degrees of urbanization and to decouple the urban signature from the background climate in terms of precipitation and cloud properties. Our findings reveal a statistically significant increase in low‐level CF and precipitation, particularly within the first 100 km radius downwind of the city border. Further, we identify a non‐linear relationship between size of the urban area and the associated changes in precipitation amounts, frequency, and CF. Key Points: Urban areas may modify the regional climate by altering cloud fraction (CF), rainfall patterns, and rain efficiencyStatistically significant increases in downwind low‐level CF, precipitation amounts, intensity, and frequency are identifiedUrban areas enhance turbulent mixing, convective available potential energy and rain efficiency, over the city, and in downwind regions [ABSTRACT FROM AUTHOR]

Dimethyl sulfide (DMS) is the largest source of natural sulfur in the atmosphere and undergoes oxidation reactions resulting in gas‐to‐particle conversion to form sulfate aerosol. Climate models typically use independent chemical schemes to simulate these processes, however, the sensitivity of sulfate aerosol to the schemes used by CMIP6 models has not been evaluated. Current climate models offer oversimplified DMS oxidation pathways, adding to the ambiguity surrounding the global sulfur burden. Here, we implemented seven DMS and sulfate chemistry schemes, six of which are from CMIP6 models, in an atmosphere‐only Earth system model. A large spread in aerosol optical depth (AOD) is simulated (0.077), almost twice the magnitude of the pre‐industrial to present‐day increase in AOD. Differences are largely driven by the inclusion of the nighttime DMS oxidation reaction with NO3, and in the number of aqueous phase sulfate reactions. Our analysis identifies the importance of DMS‐sulfate chemistry for simulating aerosols. We suggest that optimizing DMS/sulfur chemistry schemes is crucial for the accurate simulation of sulfate aerosols. Plain Language Summary: Dimethyl sulfide (DMS) is a sulfur‐bearing gas predominantly emitted from marine biological activity. DMS is the largest natural contributor to the global sulfur cycle, but its contribution is highly uncertain. Representing the complex chemical conversion of DMS to form natural sulfur atmospheric particles accurately in Earth System Models is difficult. Complex atmospheric chemistry is expensive to implement, therefore simplistic approaches to represent the chemistry are used. Here we examine the variability between different chemistry schemes. To achieve this, we employ a state‐of‐the‐art Earth System Model to compare seven simulations with differing sulfur‐related chemical reactions. We show that sulfate chemistry contributes to large uncertainties in aerosol and cloud formation. This work underscores the need to improve sulfur chemistry to improve the accuracy of cloud and aerosol projections in a warming world. Key Points: The simulated spread in aerosol optical depth and cloud droplet number concentration is more than twice as large as the change from pre‐industrial to present‐daySimulations with similar Dimethyl sulfide burdens have very different sulfate burdens driven by the different sulfate mechanisms/oxidation pathwaysConstraining the chemistry of atmospheric sulfur is critical to constrain aerosol‐cloud interactions [ABSTRACT FROM AUTHOR]

In this paper, we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devise a novel method for calculating the cloud adjustments for the abrupt solar forcing simulations that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere‐only, abrupt CO2 forced experiments that have prescribed sea‐surface temperature. Our main findings are that (a) there are substantial differences in the responses of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and (b) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating and differences in the pattern of sea‐surface temperature change (for a fixed global mean temperature). These cloud adjustments contribute significantly to the total net cloud radiative effect, even after 150 years of simulation. Plain Language Summary: In climate change, clouds change due to a variety of mechanisms including surface temperature, dynamical circulations, and radiative forcing. In this paper, we examine the latter: how clouds respond to radiative forcing. We study this topic using climate model simulations where the brightness of the sun is abruptly increased by 4% and compare those with simulations where CO2 concentration is abruptly quadrupled. In doing so we find that, there are differences in the cloud response to changes in solar and CO2 forcing which include the occurrence of thick and thin high cloud, as well as the amount and height of low and mid‐level clouds. Key Points: Increasing CO2 causes a reduction and lowering of mid‐level and low‐level clouds which does not occur from solar forcingThere is large reduction in optically thin high clouds from solar forcing, especially as compared with CO2Even after 150 years adjustments make a significant contribution to the total net cloud radiative effect [ABSTRACT FROM AUTHOR]

There are many uncertainties in future climate, including how the Earth may react to different types of radiative forcing, such as CO2, aerosols, and even geoengineered changes in the amount of sunlight absorbed by Earth's surface. Here, we analyze model simulations where the climate system is subjected to an abrupt change of the solar constant by ±4%, and where the atmospheric CO2 concentration is abruptly changed to quadruple and half its preindustrial value. Using these experiments, we examine how clouds respond to changes in solar forcing, compared to CO2, and feedback on global surface temperature. The total cloud response can be decomposed into those responses driven by changes in global surface temperature, called the temperature mediated cloud feedbacks, and responses driven directly by the forcing that are independent of the global surface temperature. In this paper, we study the temperature mediated cloud changes to answer two primary questions: (a) How do temperature mediated cloud feedbacks differ in response to abrupt changes in CO2 and solar forcing? And (b) Are there symmetrical (equal and opposite) temperature mediated cloud feedbacks during global warming and global cooling? We find that temperature mediated cloud feedbacks are similar in response to increasing solar and increasing CO2 forcing, and we provide a short review of recent literature regarding the physical mechanisms responsible for these feedbacks. We also find that cloud responses to warming and cooling are not symmetric, due largely to non‐linearity introduced by phase changes in mid‐to‐high latitude low clouds and sea ice loss/formation. Plain Language Summary: As the global mean temperature changes, there are changes in cloud amount, location, and thickness, which can all impact the radiative balance of the Earth. Cloud changes driven directly by global temperature change are called temperature mediated cloud feedbacks. In this paper, we study the temperature mediated cloud feedbacks that occur in model simulations where the amount of sunlight incident upon the Earth is increased or decreased abruptly, and then held constant for 150 years. We compare the cloud changes in these experiments with experiments where the CO2 concentration is similarly increased or decreased abruptly and held constant for 150 years. In doing so we find that the temperature mediated cloud feedbacks following abrupt changes in solar radiation are characteristically similar to those occurring following CO2 increase. There are however substantial differences in the temperature mediated cloud feedbacks that occur while the climate is warming versus cooling. Key Points: The temperature mediated cloud changes and feedbacks incurred by changes in solar and CO2 forcing are similarOptical depth changes at high latitudes produce substantial differences in cloud feedbacks in cooling and warming experimentsLikewise, tropical circulations respond differently in models to cooling and warming, with a stronger change in the Walker circulation in warming experiments [ABSTRACT FROM AUTHOR]

The cloud properties and governing processes in Southern Ocean marine boundary layer clouds have emerged as a central issue in understanding the Earth's climate sensitivity. While our understanding of Southern Ocean cloud feedbacks have evolved in the most recent climate model intercomparison, the background properties of simulated summertime clouds in the Southern Ocean are not consistent with measurements due to known biases in simulating cloud condensation nuclei concentrations. This paper presents several case studies collected during the Capricorn 2 and Marcus campaigns held aboard Australian research vessels in the Austral Summer of 2018. Combining the surface‐observed cases with MODIS data along forward and backward air mass trajectories, we demonstrate the evolution of cloud properties with time. These cases are consistent with multi‐year statistics showing that long trajectories of air masses over the Antarctic ice sheet are critical to creating high droplet number clouds in the high latitude summer Southern Ocean. We speculate that secondary aerosol production via the oxidation of biogenically derived aerosol precursor gasses over the high actinic flux region of the high latitude ice sheets is fundamental to maintaining relatively high droplet numbers in Southern Ocean clouds during Summer. Plain Language Summary: The amount of warming the Earth will experience because of increasing carbon dioxide levels in the atmosphere is sensitive to the properties of clouds that occur over the Southern Ocean. The atmosphere over the circumpolar Southern Ocean is poorly understood and presents significant challenges to climate models. Here we document the properties of the ubiquitous Southern Ocean low‐level clouds that exert a strong influence on the albedo of this region. We find that high cloud droplet number concentrations are associated with air masses that have taken paths over the high‐altitude ice sheets. The chemistry of the aerosol on which the cloud droplets form suggests that aerosols that have recently condensed from gasses emitted by phytoplankton in the highly productive waters near Antarctica are an important component of the cloud properties that must be correctly simulated in models. Key Points: High cloud droplet number concentrations are associated with air masses that have recently passed over continental AntarcticaCloud droplet number concentrations decrease with time as clouds evolve in over‐water trajectories due to scavenging by precipitationKatabatic flows bring high concentrations of cloud condensation nuclei to the marine boundary layer where they influence cloud properties [ABSTRACT FROM AUTHOR]

Although afforestation is a potential strategy to mitigate climate change by sequestering carbon, its potential biophysical effects on climate, such as regulating surface albedo, evapotranspiration, and energy balance, have not been fully incorporated into climate change mitigation strategies. This is partly due to the challenges associated with modeling the complex bidirectional interactions between vegetation and climate. In this study, we assess the impact of afforestation on low cloud cover using a regional climate model (RCM) and Earth observation data, applying a space‐for‐time approach to overcome limitations that may arise from comparing satellite and RCM results, such as different background climate conditions or different extents of land cover change. Our results show a consistent increase in low cloud cover in Europe due to afforestation in both datasets (3.71% and 3.56% on average, respectively), but the magnitude and direction of this effect depend on various factors, including location, seasonality, and forest type. These results suggest that afforestation can have important feedbacks on the climate system, and that its biophysical effects must be considered in climate change mitigation strategies. Furthermore, we emphasize the role of the modeling community in developing accurate and reliable approaches to assess the biophysical effects of land cover change on climate. Plain Language Summary: Although forests can help combat climate change by capturing carbon dioxide, we haven't fully considered the potential impact they can have on the climate in terms of biophysical factors such as surface reflectivity, evaporation, and energy balance. This is because it's challenging to accurately model the complex interactions between forests and climate. In this study, we used a regional climate model and Earth observation data to examine how forests affect the amount of low‐lying clouds. We used a method that compares satellite and model data to overcome limitations arising from different climate conditions and changes in land cover. Our findings consistently show that forests increase the coverage of low clouds in Europe. However, the specific effects depend on factors such as the location, season, and forest type. These results indicate that forests can have significant impacts on the climate system, and it is important to consider these effects when developing climate change strategies. We also emphasize the need for accurate modeling to better understand how changes in forest cover influence the climate. Key Points: Forests increase the amount of low clouds in EuropeForests impact climate beyond carbon sequestration [ABSTRACT FROM AUTHOR]

Identifying how aerosol particles interact with atmospheric water is critical to understand climate and precipitation. Ice‐nucleating particles (INP) trigger the formation of atmospheric ice crystals at higher temperatures than pure water. They are difficult to characterize because of their scarce occurrence, and variability, in the atmosphere, especially at temperatures above −20°C. It has been demonstrated that at these temperatures, biological aerosol particles can contribute significantly to INP number concentration. This study incorporates a series of offline, size‐segregated measurements of INPs collected at the Puy de Dôme station (PUY, 1,465 m a.s.l.) over a 6 month period from October to May, covering the transitions from autumn, winter, to spring. These measurements show a general trend of decreasing particle number concentrations during the winter months and higher concentration during autumn and spring. INP concentrations measured in the range of −5 and −18°C, had concentrations of 0.001 INP/Lair at the warmest temperatures, and between 0.01 and 0.1 INP/Lair at the coldest temperatures. The majority of INP measured at temperatures warmer than −15°C were heat labile, suggesting a biological or organic origin. The INP variability was compared with collocated aerosol physical and chemical properties, allowing us to associate highest INP concentrations with local and marine origins. Following these comparisons, we use aerosol total number concentration to develop a new parameterization. In addition, this parameterization is specifically optimized for warmer temperature INP measurements, and demonstrated a good performance when tested on independent data sets. Plain Language Summary: Understanding how tiny particles in the air (aerosol particles) interact with water in the atmosphere is crucial for studying climate and precipitation. Ice Nuclei Particles (INP) play a role in forming ice crystals in the atmosphere, especially at higher temperatures. This is important because it's been found that biological aerosol particles can significantly contribute to INP at temperatures above −20°C. The study conducted offline measurements of INPs over 6 months at the Puy de Dôme station, covering the transition from autumn to spring. Results showed a general trend of fewer particles during winter and more during autumn and spring. The INP concentrations were highest at temperatures between −5 and −18°C, with warmer temperatures having 0.001 INP per liter of air and colder temperatures having 0.01 to 0.1 INP per liter of air. Most INPs at temperatures above −15°C were heat‐sensitive, indicating a biological or organic origin. This study linked high INP concentrations to local and marine sources by comparing them with aerosol properties. A new parameterization based on total aerosol number concentration, specifically optimized for warmer temperatures, performed well when tested on independent data sets. Key Points: Size‐segregated airborne ice‐nucleating particles (INP) concentrations were measured at an altitude station in Central France over 6 monthsSeasonal variations are observed with a minimum in winter. The majority of INPs is biological and activated above −18°CWe developed a parameterization for predicting INP concentrations at warm temperatures based on total aerosol concentrations [ABSTRACT FROM AUTHOR]

The viable bacterial assemblages in clouds at Réunion Island (Indian Ocean) were examined through culture-based approach. A total of 176 isolates were recovered from 15 independent cloud events collected during 3 field campaigns, and identified to the species level through full length 16S rRNA gene sequencing. As often in atmospheric samples, Alpha-, Beta- and Gamma-proteobacteria dominated, along with Actinobacteria, Firmicutes, and Bacteroidetes, depicting these as the backbone of the global aeromicrobiome. A comparative analysis with similar work from a cloud sampling site in Central France revealed site-specificities, and numerous common species. These latter included members of Pseudomonas, Sphingomonas, Bacillus, Staphylococcus, Rhodococcus and others, whose such widespread presence in clouds supports the existence of a pan-atmospheric microbiome. This also confirms that cultures remain powerful methods in the description of the viable microbial diversity by allowing deep taxonomic affiliation. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. Plain Language Summary: Humans are driving widespread deforestation in the tropics. Changes to the land surface following forest loss are generally known to affect the atmosphere, but it is hard to tell how deforestation will impact clouds in a given area. Here, we focus on Southeast Asia, a region of the world facing dramatic large‐scale deforestation. We use two decades of satellite data to estimate how the loss of tropical forests impacts cloud properties. On average, we find that deforestation leads to more widespread and shallower clouds. We then look further into how this cloud response to deforestation depends on other environmental factors like moisture and aerosols. This gives us a better idea of which regions are most sensitive to changes in forest cover. Overall, our results show there is an observable cloud response to deforestation, but this response may be stronger in some regions than in others depending on underlying moisture and aerosol conditions. As forest loss continues in Southeast Asia and across the world, it is important to further study these region‐dependent interactions between the atmosphere and the land surface so we can better understand the impacts of human‐driven deforestation on weather and climate. Key Points: Deforestation in Southeast Asia drives a robust shift toward more widespread and shallower clouds on an annual timescaleThis effect has been debated in modeling studies, but we demonstrate this observationally using two decades of satellite dataSome regions are especially vulnerable to deforestation‐driven changes in clouds, depending on atmospheric moisture and aerosol loading [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Climate is governed by interactions between the ocean and the atmosphere. While physical interactions such as exchanges of heat and water vapor are fairly well understood, the role of biology, that is, the living marine microorganisms, on atmospheric processes, is a lot more complex. For instance, marine microorganisms may influence the number and the chemical composition of sea sprays and also emit trace gasses that will form tiny particles. Sea sprays and newly formed particles can then serve as nuclei on which cloud droplets or ice crystals form, therefore influencing cloud properties and climate. These chains of processes are theoretical, and there are few clear linkages between ocean biology and cloud properties derived from observational data. This study uses new satellite retrievals to establish relationships between cloud phase occurrence (ice, warm‐liquid, mixed‐phase or supercooled‐liquid clouds) and the biological activity of the ocean in different regions of the southern ocean. For a given month, locations of higher abundance of phytoplankton corresponds to a higher warm‐liquid cloud cover but lower ice cloud cover. These results suggest compensating effects of marine microorganisms on cloud lifetime via their potential to impact the formation of particles able to become water droplets or ice crystals. Key Points: Nanophytoplankton biomass shows more relations to cloud occurrences than Chlorophyll‐a or Particulate Organic Carbon concentrationsHigher nanophytoplankon abundance is positively linked to warm‐liquid cloud frequency of occurrence in spring in most regions of 40°S–60°SHigher nanophytoplankton abundance is linked to a decrease in the ice‐containing cloud frequency of occurrence in most regions [ABSTRACT FROM AUTHOR]

Significant variability in climate predictions originates from the simulated cloud cover over the Southern Ocean. Historically, Southern Ocean cloud and aerosol properties have been less studied than their northern hemisphere counterparts, and cloud‐sea‐ice interactions over the Southern Ocean also remain largely unexamined. We used data from combined radar, lidar, radiometer, radiosonde, and ERA5 reanalysis profiles to investigate cloud property relationships to cloud temperature, sea‐ice concentration, and boundary layer stability. Our findings show correlations between both cloud macrophysical properties and radiative effects and sea‐ice concentration, and that the marine atmospheric boundary layer is more stable over higher sea‐ice concentrations. Mixed‐phase cloud frequency of occurrence was highest over the sea‐ice zone at 15%, three times higher than over cold water south of the Antarctic Polar Front. For temperatures greater than −15°C, low‐level, single‐layer clouds were more likely to precipitate ice if they were coupled to cold‐water or sea‐ice surfaces than if they were decoupled from these surfaces, with the highest percentage of clouds precipitating ice observed over sea ice. These findings suggest a surface source of ice‐nucleating particles at high southern latitudes that increases cloud glaciation probability. We discuss the implications of our results for future studies into the relationship between cloud properties, aerosols, sea ice, and boundary layer stability at high latitudes over the Southern Ocean. Plain Language Summary: The atmosphere above the Southern Ocean remains difficult for most modern climate models to simulate well. Clouds containing liquid water below 0°C, also known as supercooled liquid water, are likely responsible for much of the simulation difficulties. In this study we investigated observations of the atmosphere made by several different instruments during a shipborne campaign from 2017 to 2018, as well as satellite observations of sea ice. We found that clouds containing supercooled liquid water were more likely to occur above sea ice than above open ocean at high latitudes, and that the lowest layer of the atmosphere is more stable above sea ice than above open ocean. We also found that, over sea ice, clouds containing supercooled liquid water were less likely to contain and precipitate ice when these clouds were separated from the lowest layer of the atmosphere. Key Points: Colder, drier air over Antarctic sea ice is associated with increased inversion strength and stability of the atmospheric boundary layerThe prevalence of mixed‐phase clouds at high southerly latitudes is greatest over sea iceFor temperatures greater than −10°C, clouds coupled to the surface are more likely to contain ice [ABSTRACT FROM AUTHOR]