Search

Understanding distributions of cloud thermodynamic phases is important for accurately representing cloud radiative effects and cloud feedback in a changing climate. Satellite‐based cloud phase data have been frequently used to compare with climate models, yet few studies validated them against in situ observations at a near‐global scale. This study aims to validate three satellite‐based cloud phase products using a compositive in situ airborne data set developed from 11 flight campaigns. Latitudinal‐altitudinal cross sections of cloud phase occurrence frequencies are examined. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) show the most similar vertical profiles of ice phase frequencies compared with in situ observations. The CloudSat data overestimate mixed‐phase frequencies up to 15 km but provide better sampling through cloud layers than lidar data. The DARDAR (raDAR/liDAR) data show a sharp transition between ice and liquid phase and overestimate ice phase frequency at most altitudes and latitudes. The satellite data are further evaluated for various latitudes, longitudes, and seasons, which show higher ice phase frequency in the extratropics in their respective wintertime and smaller impacts from longitudinal variations. The Southern Ocean shows a thicker mixing region where liquid and ice phases have similar frequencies compared with tropics and Northern Hemisphere (NH) extratropics. Two comparison methods with different spatiotemporal windows show similar results, which demonstrates the statistical robustness of these comparisons. Overall, this study develops a near global‐scale in situ observational data set to assess the accuracy of satellite‐based cloud phase products and investigates the key factors affecting the distributions of cloud phases. Plain Language Summary: Accurate representations of cloud thermodynamic phase (i.e., ice, liquid, and mixed phase) play an important role in climate prediction. Even though satellite observations have been used to improve climate model simulations of cloud phase, few studies have validated satellite‐based cloud phase distributions on a global scale. This work develops a large data set based on in situ aircraft‐based observations from 11 flight campaigns in various regions. Three satellite‐based cloud phase products are evaluated. Satellite observations that are either in proximity to the aircraft samples or in a similar domain are used for comparisons. CALISPO data show the best comparison results for representing the fraction of ice clouds among all types of cloud phases. CloudSat overestimates mixed phase frequency and DARDAR overestimates ice phase frequency, but they penetrate through cloud layers better when radar and lidar data are combined. The impacts of seasonal variability, spatial variability among various latitudes and longitudes, as well as temporal variability from a few hours to different seasons are examined. The results of this work help to identify the key factors affecting cloud phase distributions from a near‐global perspective. The methodology developed can also guide future validations of satellite data using aircraft‐based observations. Key Points: Ice phase frequencies vary largely with altitudes, latitudes, and seasons but not much with longitudes or small spatiotemporal mismatchesCALIPSO shows most similar ice phase frequency to in situ observations, CloudSat and DARDAR overestimate mixed and ice phase, respectivelyLiquid water transitions to ice gradually with increasing altitudes over Southern Ocean but more rapidly in tropics and NH extratropics [ABSTRACT FROM AUTHOR]

Abstract Cloud phase has important impacts on Arctic surface temperatures, and circumstantial evidence suggests that dust aerosols have strong regional impacts on Arctic cloud phase. We used 7 years of satellite observations and model and reanalysis products to control for co‐varying meteorology, and to assess how dust and other aerosols impact cloud phase and cloud radiative effects over the summertime sea ice. We focus on clouds at 3 km, where dust modeling is most accurate. There is strong indication that dust aerosols caused about 4.5% of clouds below −15°C to change phase, with smaller effects at higher temperatures. Sulfate has a smaller impact on cloud phase. Dust is associated with cloud‐mediated surface cooling of up to a 6.3 W m−2 below single‐layer clouds at ∼3 km in June. This is the first observational study to constrain likely dust‐related cloud radiative effects over the summertime Arctic sea ice.

We conduct a global assessment of the spatial heterogeneity of cloud phase within the temperature range where liquid and ice can coexist. Single‐shot Cloud‐Aerosol Lidar with Orthogonal Polarization lidar retrievals are used to examine cloud phase at scales as fine as 333 m, and horizontal heterogeneity is quantified according to the frequency of switches between liquid and ice along the satellite's path. In the global mean, heterogeneity is greatest between −15 and −4°C with a peak at −5°C, when small patches of ice are prevalent within liquid‐dominated clouds. Heterogeneity "hot spots" are typically found over the extratropical continents, whereas phase is relatively homogeneous over the Southern Ocean and the eastern subtropical ocean basins, where supercooled liquid clouds dominate. Even at a fixed temperature, heterogeneity undergoes a pronounced annual cycle that, in most places, consists of a minimum during autumn or winter and a maximum during spring or summer. Based on this spatial and temporal variability, it is hypothesized that heterogeneity is affected by the availability of ice nucleating particles. These results can be used to improve the representation of subgrid‐scale heterogeneity in general circulation models, which has the potential to reduce longstanding model biases in cloud phase partitioning and radiative fluxes. Plain Language Summary: At temperatures where ice and liquid can coexist within clouds, climate models tend to produce too much ice and too little liquid compared to satellite observations. This bias is likely caused by the assumption that liquid and ice are uniformly mixed, which results in the rapid conversion of liquid to ice for thermodynamic reasons. To reduce this bias, models need to account for the spatial heterogeneity ("patchiness") of liquid and ice that exists in the real atmosphere. The goal of this paper is to quantify this spatial heterogeneity using satellite‐based lidar observations of cloud phase. We find small pockets of ice in liquid‐dominated clouds to be more common than small pockets of liquid in ice‐dominated clouds. The greatest heterogeneity is found over the midlatitude continents, whereas phase is relatively uniform over the Southern Ocean and other maritime regions with extensive low cloud cover. In the mid and high latitudes, cloud phase tends to be more heterogeneous during spring and summer and more homogeneous during autumn and winter. These results can be used in the future to improve model representations of the thermodynamic processes responsible for biases in cloud phase. Key Points: Cloud phase heterogeneity is greatest at −5°C, when small ice patches form in majority‐liquid cloudsCloud phase is relatively homogeneous over the Southern Ocean and heterogeneous over the northern continentsFor a fixed temperature, extratropical phase heterogeneity is generally greatest during local spring and summer [ABSTRACT FROM AUTHOR]

The cloud type product 2B-CLDCLASS-LIDAR based on CloudSat and CALIPSO from June 2006 to May 2017 is used to examine the temporal and spatial distribution characteristics and interannual variability of eight cloud types (high cloud, altostratus, altocumulus, stratus, stratocumulus, cumulus, nimbostratus, and deep convection) and three phases (ice, mixed, and water) in the Arctic. Possible reasons for the observed interannual variability are also discussed. The main conclusions are as follows: (1) More water clouds occur on the Atlantic side, and more ice clouds occur over continents. (2) The average spatial and seasonal distributions of cloud types show three patterns: high clouds and most cumuliform clouds are concentrated in low-latitude locations and peak in summer; altostratus and nimbostratus are concentrated over and around continents and are less abundant in summer; stratocumulus and stratus are concentrated near the inner Arctic and peak during spring and autumn. (3) Regional averaged interannual frequencies of ice clouds and altostratus clouds significantly decrease, while those of water clouds, altocumulus, and cumulus clouds increase significantly. (4) Significant features of the linear trends of cloud frequencies are mainly located over ocean areas. (5) The monthly water cloud frequency anomalies are positively correlated with air temperature in most of the troposphere, while those for ice clouds are negatively correlated. (6) The decrease in altostratus clouds is associated with the weakening of the Arctic front due to Arctic warming, while increased water vapor transport into the Arctic and higher atmospheric instability lead to more cumulus and altocumulus clouds. [ABSTRACT FROM AUTHOR]

Abstract Understanding distributions of cloud thermodynamic phases is important for accurately representing cloud radiative effects and cloud feedback in a changing climate. Satellite‐based cloud phase data have been frequently used to compare with climate models, yet few studies validated them against in situ observations at a near‐global scale. This study aims to validate three satellite‐based cloud phase products using a compositive in situ airborne data set developed from 11 flight campaigns. Latitudinal‐altitudinal cross sections of cloud phase occurrence frequencies are examined. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) show the most similar vertical profiles of ice phase frequencies compared with in situ observations. The CloudSat data overestimate mixed‐phase frequencies up to 15 km but provide better sampling through cloud layers than lidar data. The DARDAR (raDAR/liDAR) data show a sharp transition between ice and liquid phase and overestimate ice phase frequency at most altitudes and latitudes. The satellite data are further evaluated for various latitudes, longitudes, and seasons, which show higher ice phase frequency in the extratropics in their respective wintertime and smaller impacts from longitudinal variations. The Southern Ocean shows a thicker mixing region where liquid and ice phases have similar frequencies compared with tropics and Northern Hemisphere (NH) extratropics. Two comparison methods with different spatiotemporal windows show similar results, which demonstrates the statistical robustness of these comparisons. Overall, this study develops a near global‐scale in situ observational data set to assess the accuracy of satellite‐based cloud phase products and investigates the key factors affecting the distributions of cloud phases.

The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm−1, the slope of the fitted function of BT in the 900–1000 cm−1 interval, the BT difference (BTD) between 512 cm−1 and 726 cm−1, and the BTD between 550 cm−1 and 726 cm−1. Here, the performance of the proposed method is evaluated with Shupe's and Turner's method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner's method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe's method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner's method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor. [ABSTRACT FROM AUTHOR]

Shallow cloud decks residing in or near the boundary layer cover a large fraction of the Southern Ocean (SO) and play a major role in determining the amount of shortwave radiation reflected back to space from this region. In this article, we examine the macrophysical characteristics and thermodynamic phase of low clouds (tops <3 km) and precipitation using ground‐based ceilometer, depolarization lidar and vertically‐pointing W‐band radar measurements collected during the Macquarie Island Cloud and Radiation Experiment (MICRE) from April 2016 to March 2017. During MICRE, low clouds occurred ∼65% of the time on average (slightly more often in austral winter than summer). About 2/3 of low clouds were cold‐topped (temperatures ≤0°C). These were thicker and had higher bases on average than warm‐topped clouds. 83%–88% of cold‐topped low clouds were liquid phase at cloud base (depending on the season). The majority of low clouds had precipitation in the vertical range 150–250 m below cloud base, a significant fraction of which did not reach the surface. Phase characterization is limited to the period between April 2016 and November 2016. Small‐particle (low‐radar‐reflectivity) precipitation (which dominates precipitation occurrence) was mostly liquid below‐cloud, while large‐particle precipitation (which dominates total accumulation) was predominantly mixed/ambiguous or ice phase. Approximately 40% of cold‐topped clouds had mixed/ambiguous or ice phase precipitation below (with predominantly liquid phase cloud droplets at cloud base). Below‐cloud precipitation with radar reflectivity factors below about −10 dBZ were predominantly liquid, while reflectivity factors above about 0 dBZ were predominantly ice. Plain Language Summary: The Southern Ocean is covered by low altitude cloud decks the majority of the time. Properties like cloud occurrence frequency, particle phase and precipitation habits determine how much solar radiation clouds reflect and how much infrared radiation they emit, which in turn affects the balance of the planet's incoming and outgoing radiation. In this paper, we examine low cloud properties observed from the ground at Macquarie Island, including how frequently they occur and at what temperatures. We study particle thermodynamic phase (liquid, ice or mixed) at cloud base and in precipitation below‐cloud. A majority of low clouds are predominantly composed of liquid phase droplets, although frozen precipitation is frequently found below cloud base. Low clouds form precipitation more often than not, much of which evaporates before reaching the ground. In below‐freezing low clouds, the majority of large raindrops & snowflakes that do reach the ground originate as frozen precipitation directly below cloud base. This indicates that ice formation is frequently active in clouds composed predominantly of liquid‐phase droplets. Lastly, we build upon an established radar‐lidar relationship that particles with radar reflectivity factors below −10 dBZ are generally liquid, whereas above 0 dBZ are most often ice phase. Key Points: Ground observations at Macquarie Island indicate low clouds occur ∼65% of the time, with about 2/3 having cloud top temperatures below 0°CCold‐topped low clouds have liquid‐phase bases ∼85% of the time & precipitate ∼3/4 of the time; below‐cloud precip. is 40% ice/mixed phaseBelow‐cloud radar reflectivity factors <−10 dBZ are predominately due to liquid phase precipitation, while >0 dBZ are predominantly ice [ABSTRACT FROM AUTHOR]

The cloud thermodynamic phase is a crucial parameter to understand the Earth's radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ∘ C, followed by that for MP at −13 ∘ C. Between 0 and −27 ∘ C, the occurrence of SC and MP dominates IC, while below −27 ∘ C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites. [ABSTRACT FROM AUTHOR]

The decades-long Clouds and Earth's Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive comparison of Level 2.0 cloud properties between a Second-generation Global Imager (SGLI) aboard the GCOM-C satellite and a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, to better understand the qualities of cloud properties obtained from SGLI/GCOM-C launched on 23 December 2017. The cloud pixels identified as water phase by both satellite sensors are highly consistent to each other by more than 90%, although the consistency is only ~60% for ice phase cloud pixels. A comparison of cloud properties—cloud optical thickness (COT) and cloud particle effective radius (CER)—between these two satellite sensors reveals that water and ice cloud properties can have different degrees of agreement depending on underlying surface. The relative difference (RD) values of 22% (18%) and 37% (24%) for water cloud COT (CER) comparison over ocean and land surfaces and respective values of 35% (42%) and 35% (62%) for comparisons of ice cloud properties, and also other comparison metrics, suggest better agreements for water cloud properties than for ice cloud properties, and for ocean surface than for land surface. Though cloud properties differences between MODIS and SGLI can arise from inherent features of cloud retrieval algorithms, such as differences in ancillary data, surface reflectance, cloud droplet size distribution function, model for ice particle habit, etc., this study further identifies the important roles of cloud thickness and Sun and satellite positions for differences in cloud properties between SGLI and MODIS: the differences in cloud properties are found to increase for thinner clouds, higher solar zenith angle, and higher differences in viewing zenith and azimuth angles between these satellite sensors, and such differences are more distinct for water cloud properties than for ice cloud properties. [ABSTRACT FROM AUTHOR]

Cloud properties are essential for the Clouds and the Earth's Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms is critical for generating a multidecadal cloud and radiation record. Techniques used by CERES for retrievals from measurements by the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua platforms are adapted for the application to radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership to continue the CERES record beyond the MODIS era. The algorithm adjustments account for spectral and channel differences, use revised reflectance models, and set new thresholds for detecting thin cirrus clouds at night. Cloud amounts from VIIRS are less than their MODIS counterparts by 0.016 during the day and 0.026 at night, but trend consistently over the 2012–2020 period. The VIIRS mean liquid water cloud fraction differs by ~0.01 from the MODIS amount. The average cloud heights from VIIRS differ from the MODIS heights by less than 0.2 km, except the VIIRS daytime ice cloud heights, which are 0.4 km higher. The mean VIIRS nonpolar optical depths are 17% (1%) larger (smaller) than those from MODIS for liquid (ice) clouds. The VIIRS cloud hydrometeor sizes are generally smaller than their MODIS counterparts. Discrepancies between the MODIS and VIIRS properties stem from spectral and spatial resolution differences, new tests at night, calibration inconsistencies, and new reflectance models. Many of those differences will be addressed in future editions. [ABSTRACT FROM AUTHOR]

The cloud phase is one of the most important parameters of clouds. In this paper, we propose a method for cloud phase classification that synergistically utilizes the far- and thermal-infrared bands based on the Atmospheric Emitted Radiance Interferometer (AERI) at the Atmospheric Radiation Measurement West Antarctic Radiation Experiment (AWARE) observatory in 2016. The possible features in the far- and thermal-infrared bands are analyzed based on the differences in the simulated cloud brightness temperature (BT) spectra with different cloud phases. Using the support vector machine (SVM) algorithm, four features are determined to identify the cloud phase, which include the BT at 900 cm−1, the slope of the fitted function of BT in the 900–1000 cm−1 interval, the BT difference (BTD) between 512 cm−1 and 726 cm−1, and the BTD between 550 cm−1 and 726 cm−1. Here, the performance of the proposed method is evaluated with Shupe’s and Turner’s method. The monthly average accuracy of the proposed method, the method without the two far-infrared features, and Turner’s method are about 76%, 36%, and 49%, respectively, which infer the good performance of the proposed method and also indicate that the far-infrared band features can effectively enhance cloud phase classification. It is notable that, compared to Shupe’s method, the accuracy for the proposed method is only 61% during the Antarctic summer, which results from the definitions of cloud phase and radiative effect. In addition, the accuracy is only 44% for Turner’s method in seasons with a low frequency of mixed clouds due to the significant effect of water vapor.

Over the Southern Ocean (SO, 40°S–70°S), climate models have consistently underestimated solar reflection. Here we evaluate the relationship between cloud profiles, cloud phase and radiation over the SO in Coupled Model Intercomparison Project Phase 6 (CMIP6) models against Clouds and the Earth's Radiant Energy System and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations. We find that the lack of solar reflection is slightly improved in CMIP6 models compared to CMIP5's, attributable to a better representation of cloud fraction and phase. We show that clouds have a different vertical structure and radiative effect south and north of where the 0°C isotherm meets the surface (∼55°S). Although the models capture the greater vertical extent of clouds south of 55°S, they fail to reproduce the observed increase in solar reflection, which we pinpoint to cloud phase biases. Increasing CMIP6 supercooled liquid cloud opacity should help reduce their persistent shortwave biases. Plain Language Summary: Over the Southern Ocean, defined as the latitudinal band between 40° and 70°S, climate models have consistently overestimated the amount of absorbed solar radiation, mostly because of biases in the representation of clouds. Such biases in climate models are particularly problematic because they may affect the radiative response of clouds to climate warming. We find that the newest generation of climate models better represents clouds than the previous one, compared to satellite observations. We show that clouds have a different vertical structure and radiative properties south and north of the 0°C surface isotherm, around 55°S. Although the models capture the greater vertical extent of clouds south of 55°S, they fail to reproduce the observed increase in solar reflection by clouds there. Finally, we report that increasing the opacity of liquid clouds at subzero temperatures (between 0° and −40°C) should help reduce persistent solar radiation biases attributable to clouds, south of 55°S, in the newest generation of climate models. Key Points: The lack of shortwave reflection is somewhat corrected in Coupled Model Intercomparison Project Phase 6 (CMIP6) models, attributable to a better representation of cloud fraction and cloud phaseThe cloud fraction and radiative effect behavior is different north and south of 55°S, which is where the 0°C isotherm meets the surfaceContrary to observations, CMIP6 clouds are less reflective south of 55°S, where boundary layer clouds are often topped by mid‐level clouds [ABSTRACT FROM AUTHOR]

The existence of clouds significantly increases or decreases the net radiation of the Earth. The influence of cloud type and cloud phase on radiation is as important as cloud amount, and the physical processes of different types of clouds are obviously different. In this study, the occurrence frequency of different cloud types (low transparent, low opaque, stratocumulus, broken cumulus, altocumulus transparent, altostratus opaque, cirrus, and deep convective) and cloud phases (ice and water) over China and its surrounding areas (0–55°N, 70–140°E) are calculated based on cloud vertical feature mask products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results show significant spatial differences and seasonal variations in the distribution of different cloud types and cloud phases. There are four prevailing cloud types over the whole year, among which cirrus and altocumulus transparent are the most widely distributed and have the highest occurrence frequency. Cirrus clouds are mainly distributed at altitudes above 6 km north of 30°N and south of 20°N. Altocumulus transparent clouds are mainly distributed over the Qinghai–Tibet Plateau and at an altitude of 3–6 km to the north of 40°N, and they are more widely distributed in winter than in summer. Water clouds are mainly distributed in the latitude range of 20°N–40°N and are obviously influenced by the Qinghai–Tibet Plateau. Water clouds are widely distributed in autumn and winter. Ice clouds are mainly distributed in the areas south of 20°N and north of 40°N. Regardless of the choice of altitude between 3 km and 7 km, the boundary between ice cloud and water cloud is always near the −14 °C isotherm, and when the −14 °C isotherm moves southward, the ice-cloud distribution range expands southward. The probability density functions of the temperature in the cloud always show the distribution characteristics of two peaks and one valley, which is particularly obvious in the middle and high clouds, and the peak temperature is warmer than the sub-peak temperature. The valley temperature and its corresponding latitude of all cloud types are different: the cirrus valley temperature is not significantly affected by the Qinghai–Tibet Plateau, but the plateau has an effect on the latitude of the valley temperature distribution of other types of cloud. The above conclusions lay the foundation for further research on the radiation effects of different clouds on China and its surrounding areas and also have certain indicating significance for weather effects caused by various cloud physical processes. [ABSTRACT FROM AUTHOR]

This study documents the three-dimensional structure and long-term trend of Arctic cloud cover with 14-year Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations from a seasonal perspective. Results show that Arctic Ocean presents larger low-level cloudiness and more evident vertical contrast between low and high-level cloud cover than Arctic land. Furthermore, Arctic Atlantic experiences the largest total and low-level cloud cover all year long, with relatively weak seasonal variation. In contrast, the low-level cloud cover over Arctic Pacific displays a pronounced bimodal annual cycle, as well as the low-level cloud cover over Arctic land. Arctic Atlantic with the maximum cloud fraction has the highest altitude of about 1.2 km above open sea in winter, which is near the surface in summer. The cloudiness in the lower troposphere over open water is significantly larger than that over ice except in winter, whereas, the liquid-containing cloud over open water is significantly larger than that over ice in the lower troposphere in four seasons. Moreover, though the liquid-containing cloud dominates the low-level cloud cover, its increasing trend is driven by liquid-containing and ice-containing cloud together. Instead, the ice-containing cloud dominates the high-level cloud cover and its increasing trend in all seasons over most Arctic regions, except a negative trend over Greenland in winter. Our work presents a more comprehensive picture of the Arctic cloud cover, which provides an insight into understanding of Arctic cloud and Arctic climate system. • Arctic Ocean presents larger low-level cloudiness and more evident vertical contrast between low and high-level cloud cover than Arctic land. • Arctic Atlantic with the maximum cloud fraction has the highest altitude above open sea. • The increasing trend of low-level cloud cover is driven by liquid-containing and ice-containing cloud together. • The ice-containing cloud dominates the high-level cloud cover and its increasing trend over most Arctic regions. [ABSTRACT FROM AUTHOR]

Mixed‐phase clouds (MPCs), which consist of both supercooled cloud droplets and ice crystals, play an important role in the Earth's radiative energy budget and hydrological cycle. In particular, the fraction of ice crystals in MPCs determines their radiative effects, precipitation formation and lifetime. In order for ice crystals to form in MPCs, ice‐nucleating particles (INPs) are required. However, a large‐scale relationship between INPs and ice initiation in clouds has yet to be observed. By analyzing satellite observations of the typical transition temperature (T*) where MPCs become more frequent than liquid clouds, we constrain the importance of INPs in MPC formation. We find that over the Arctic and Southern Ocean, snow and sea ice cover significantly reduces T*. This indicates that the availability of INPs is essential in controlling cloud phase evolution and that local sources of INPs in the high‐latitudes play a key role in the formation of MPCs. Plain Language Summary: Mixed‐phase clouds (MPCs), which consist of both liquid droplets and ice crystals, play an important role for the Earth's climate system. For example, the number of ice crystals in MPCs determines how much sunlight is reflected by the cloud and how efficiently the cloud can form precipitation. The formation of ice crystals in MPCs requires a special subset of aerosol particles called ice‐nucleating particles (INPs). INPs are required for liquid cloud droplets to freeze at temperatures warmer than −36°C. However, a large‐scale relationship between INPs and ice formation in clouds has yet to be observed. Using satellite observations, we determine the transition temperature (T*) where MPCs become more frequent than liquid clouds and find that T* is strongly dependent on snow and sea ice cover over the Arctic and Southern Ocean. This indicates that sea ice and snow cover act as a lid that inhibits the emission of INPs from the ocean. In a warming world with retreating sea ice and snow cover, our results suggest that clouds in these regions will contain ice crystals at warmer temperatures than previously estimated and, thus, have potential implications for future warming predictions. Key Points: Ice‐nucleating particles (INPs) control ice formation in high‐latitude cloudsSea ice and snow cover inhibit the local emission of INPs, which directly influences cloud phase in the Arctic and Southern OceanThis has implications for the predicted negative cloud phase feedback with future warming and the associated sea ice and snow cover loss [ABSTRACT FROM AUTHOR]

Investigated in this study are the seasonal and semi‐diurnal variations of the cloud‐phase climatology over the southern Himalayas and adjacent regions by using 5 years of Himawari‐8 cloud‐product data (2016–2020). The four selected regions from south to north are the Gangetic Plains (I), Himalayan foothills (II), southern slope of the Himalayas (III), and the Himalayan–Tibetan Plateau region (IV), respectively. Results showed that the cloud frequency of cloud gradually increases from region I via II to III, then sharply decreases as the altitude increases up to IV. Ice‐phase clouds occurs more frequently over regions I/II (III/IV) during boreal summer (winter), while water‐phase cloud has a higher frequency over region III/IV (I/II) in summer (winter), respectively. All of the different cloud phases have a clear seasonal cycle. Furthermore, the semi‐diurnal variation shows that water‐phase clouds tends to have an occurrence frequency peak around noon (in the later afternoon) over regions I/II/III in summer (winter), whereas over region IV it only occurs more in the morning in both seasons. Ice‐phase cloud generally occurs more in the late afternoon in both boreal summer and winter, especially over region IV. Further analysis showed that the differences in the cloud‐phase characteristics over the southern Himalayas and adjacent regions are likely related to the interactions of the synoptic‐scale circulation, the topography, and the water vapor transport. Plain Language Summary: By using 5 years geostationary weather satellite cloud product, we investigated the seasonal and semi‐diurnal variations of the cloud‐phase climatology characteristics over the southern Himalayas and adjacent regions, including four select sectors of the plain, foothill, slope and plateau. We found that the cloud frequency gradually increases from plain via foothill to slope, then decreases as the terrain increases up to plateau. All different phase clouds have a clear seasonal cycle and semi‐diurnal variations. Such as ice‐phase cloud occurs more over the plain and the foothill during summer season, while it occurs more over the slope region and plateau in winter; water‐phase cloud has a peak at noon (later afternoon) over the plain/foothill/slope in summer (winter), while over the plateau, it only occurs more in the morning. The results are important for comprehend how the southern Himalayas impact regional climate. Key Points: Cloud frequency increases significantly from region I via II to III, then decreases as the elevation further increases up to region IVIce‐phase clouds occurs more frequently over regions I/II (III/IV) in summer (winter), which is opposite to the occurrence of water‐phase cloudWater‐phase clouds tend to occur more around noon (in the later afternoon) over regions I/II/III in summer (winter), while it only occurs more in the morning over region IV [ABSTRACT FROM AUTHOR]

The cloud thermodynamic phase is a crucial parameter to understand the Earth’s radiation budget, the hydrological cycle, and atmospheric thermodynamic processes. Spaceborne active remote sensing such as the synergistic radar-lidar DARDAR product is considered the most reliable method to determine cloud phase; however, it lacks large-scale observations and high repetition rates. These can be provided by passive instruments such as SEVIRI aboard the geostationary Meteosat Second Generation (MSG) satellite, but passive remote sensing of the thermodynamic phase is challenging and confined to cloud top. Thus, it is necessary to understand to what extent passive sensors with the characteristics of SEVIRI are expected to provide a relevant contribution to cloud phase investigation. To reach this goal, we collect five years of DARDAR data to model the cloud top phase (CTP) for MSG/SEVIRI and create a SEVIRI-like CTP through an elaborate aggregation procedure. Thereby, we distinguish between ice (IC), mixed-phase (MP), supercooled (SC), and warm liquid (LQ). Overall, 65% of the resulting SEVIRI pixels are cloudy, consisting of 49% IC, 14% MP, 13% SC, and 24% LQ cloud tops. The spatial resolution has a significant effect on the occurrence of CTP, especially for MP cloud tops, which occur significantly more often at the lower SEVIRI resolution than at the higher DARDAR resolution (9%). We find that SC occurs most frequently at high southern latitudes, while MP is found mainly in both high southern and high northern latitudes. LQ dominates in the subsidence zones over the ocean, while IC occurrence dominates everywhere else. MP and SC show little seasonal variability apart from high latitudes, especially in the south. IC and LQ are affected by the shift of the Intertropical Convergence Zone. The peak of occurrence of SC is at −3 ∘C, followed by that for MP at −13 ∘C. Between 0 and −27 ∘C, the occurrence of SC and MP dominates IC, while below −27 ∘C, IC is the most frequent CTP. Finally, the occurrence of cloud top height (CTH) peaks lower over the ocean than over land, with MP, SC, and IC being undistinguishable in the tropics but with separated CTH peaks in the rest of the MSG disk. Finally, we test the ability of a state-of-the-art AI-based ice cloud detection algorithm for SEVIRI named CiPS (Cirrus Properties for SEVIRI) to detect cloud ice. We confirm previous evaluations with an ice detection probability of 77.1% and find a false alarm rate of 11.6%, of which 68% are due to misclassified cloud phases. CiPS is not sensitive to ice crystals in MP clouds and therefore not suitable for the detection of MP clouds but only for fully glaciated (i.e., IC) clouds. Our study demonstrates the need for the development of dedicated cloud phase distinction algorithms for all cloud phases (IC, LQ, MP, SC) from geostationary satellites.

The decades-long Clouds and Earth’s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed.

This study presents a comprehensive comparison of Level 2.0 cloud properties between a Second-generation Global Imager (SGLI) aboard the GCOM-C satellite and a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, to better understand the qualities of cloud properties obtained from SGLI/GCOM-C launched on 23 December 2017. The cloud pixels identified as water phase by both satellite sensors are highly consistent to each other by more than 90%, although the consistency is only ~60% for ice phase cloud pixels. A comparison of cloud properties—cloud optical thickness (COT) and cloud particle effective radius (CER)—between these two satellite sensors reveals that water and ice cloud properties can have different degrees of agreement depending on underlying surface. The relative difference (RD) values of 22% (18%) and 37% (24%) for water cloud COT (CER) comparison over ocean and land surfaces and respective values of 35% (42%) and 35% (62%) for comparisons of ice cloud properties, and also other comparison metrics, suggest better agreements for water cloud properties than for ice cloud properties, and for ocean surface than for land surface. Though cloud properties differences between MODIS and SGLI can arise from inherent features of cloud retrieval algorithms, such as differences in ancillary data, surface reflectance, cloud droplet size distribution function, model for ice particle habit, etc., this study further identifies the important roles of cloud thickness and Sun and satellite positions for differences in cloud properties between SGLI and MODIS: the differences in cloud properties are found to increase for thinner clouds, higher solar zenith angle, and higher differences in viewing zenith and azimuth angles between these satellite sensors, and such differences are more distinct for water cloud properties than for ice cloud properties.

Three climate models are evaluated using in situ airborne observations from the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) campaign. The evaluation targets cloud phases, microphysical properties, thermodynamic conditions, and aerosol indirect effects from −40°C to 0°C. Compared with 580‐s averaged observations (i.e., 100 km horizontal scale), the Community Atmosphere Model version 6 (CAM6) shows the most similar result for cloud phase frequency distribution and allows more liquid‐containing clouds below −10°C compared with its predecessor—CAM5. The Energy Exascale Earth System Model (E3SM) underestimates (overestimates) ice phase frequencies below (above) −20°C. CAM6 and E3SM show liquid and ice water contents (i.e., LWC and IWC) similar to observations from −25°C to 0°C, but higher LWC and lower IWC than observations at lower temperatures. Simulated in‐cloud RH shows higher minimum values than observations, possibly restricting ice growth during sedimentation. As number concentrations of aerosols larger than 500 nm (Na500) increase, observations show increases of LWC, IWC, liquid, and ice number concentrations (Nliq, Nice). Number concentrations of aerosols larger than 100 nm (Na100) only show positive correlations with LWC and Nliq. From −20°C to 0°C, higher aerosol number concentrations are correlated with lower glaciation ratio and higher cloud fraction. From −40°C to −20°C, large aerosols show positive correlations with glaciation ratio. CAM6 shows small increases of LWC and Nliq with Na500 and Na100. E3SM shows small increases of Nice with Na500. Overall, CAM6 and E3SM underestimate aerosol indirect effects on ice crystals and supercooled liquid droplets over the Southern Ocean. Plain Language Summary: Clouds can be collections of entirely liquid droplets, ice particles, or both. Whether clouds are liquid, ice, or a mixture, particularly over the Southern Ocean, contributes to large uncertainties in climate model simulations. This study uses aircraft observations to evaluate the performance of three climate models. The evaluation compares model simulations with the observation data in terms of environmental conditions (temperature and relative humidity), small‐scale properties (amount of liquid and ice), and the relationship between aerosols and clouds at temperatures from −40°C to 0°C. A commonly used model (CAM5) does not allow supercooled liquid water at temperatures <−10°C, while a newer version of it (CAM6), produces distributions of the three cloud phases comparable to observations. Another climate model (E3SM), on the other hand, produces too many liquid clouds and too few ice clouds from −35°C to −20°C. CAM6 and E3SM show an insufficient amount of ice and a surplus of supercooled liquid water compared with observations between −40°C and −25°C. As aerosol number concentrations increase, observations show more ice crystals and supercooled liquid droplets; such trends are not as evident in simulations. Key Points: CAM6 and E3SM show similar liquid water content (LWC) and ice water content (IWC) compared to observations from −25°C to 0°C, but show higher (lower) LWC (IWC) at lower temperaturesHigher LWC, IWC, Nliq, and Nice were observed with higher aerosol concentrations, yet such correlations are weaker in modelsCAM6 shows the most similar ice, liquid, and mixed phase frequency compared to 100‐km observations; CAM5 and E3SM show less similarity [ABSTRACT FROM AUTHOR]

Cloud properties are essential for the Clouds and the Earth’s Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms is critical for generating a multidecadal cloud and radiation record. Techniques used by CERES for retrievals from measurements by the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua platforms are adapted for the application to radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership to continue the CERES record beyond the MODIS era. The algorithm adjustments account for spectral and channel differences, use revised reflectance models, and set new thresholds for detecting thin cirrus clouds at night. Cloud amounts from VIIRS are less than their MODIS counterparts by 0.016 during the day and 0.026 at night, but trend consistently over the 2012–2020 period. The VIIRS mean liquid water cloud fraction differs by ~0.01 from the MODIS amount. The average cloud heights from VIIRS differ from the MODIS heights by less than 0.2 km, except the VIIRS daytime ice cloud heights, which are 0.4 km higher. The mean VIIRS nonpolar optical depths are 17% (1%) larger (smaller) than those from MODIS for liquid (ice) clouds. The VIIRS cloud hydrometeor sizes are generally smaller than their MODIS counterparts. Discrepancies between the MODIS and VIIRS properties stem from spectral and spatial resolution differences, new tests at night, calibration inconsistencies, and new reflectance models. Many of those differences will be addressed in future editions.

The existence of clouds significantly increases or decreases the net radiation of the Earth. The influence of cloud type and cloud phase on radiation is as important as cloud amount, and the physical processes of different types of clouds are obviously different. In this study, the occurrence frequency of different cloud types (low transparent, low opaque, stratocumulus, broken cumulus, altocumulus transparent, altostratus opaque, cirrus, and deep convective) and cloud phases (ice and water) over China and its surrounding areas (0–55°N, 70–140°E) are calculated based on cloud vertical feature mask products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results show significant spatial differences and seasonal variations in the distribution of different cloud types and cloud phases. There are four prevailing cloud types over the whole year, among which cirrus and altocumulus transparent are the most widely distributed and have the highest occurrence frequency. Cirrus clouds are mainly distributed at altitudes above 6 km north of 30°N and south of 20°N. Altocumulus transparent clouds are mainly distributed over the Qinghai–Tibet Plateau and at an altitude of 3–6 km to the north of 40°N, and they are more widely distributed in winter than in summer. Water clouds are mainly distributed in the latitude range of 20°N–40°N and are obviously influenced by the Qinghai–Tibet Plateau. Water clouds are widely distributed in autumn and winter. Ice clouds are mainly distributed in the areas south of 20°N and north of 40°N. Regardless of the choice of altitude between 3 km and 7 km, the boundary between ice cloud and water cloud is always near the −14 °C isotherm, and when the −14 °C isotherm moves southward, the ice-cloud distribution range expands southward. The probability density functions of the temperature in the cloud always show the distribution characteristics of two peaks and one valley, which is particularly obvious in the middle and high clouds, and the peak temperature is warmer than the sub-peak temperature. The valley temperature and its corresponding latitude of all cloud types are different: the cirrus valley temperature is not significantly affected by the Qinghai–Tibet Plateau, but the plateau has an effect on the latitude of the valley temperature distribution of other types of cloud. The above conclusions lay the foundation for further research on the radiation effects of different clouds on China and its surrounding areas and also have certain indicating significance for weather effects caused by various cloud physical processes.

The surprising increase of Earth's climate sensitivity in the most recent coupled model intercomparison project (CMIP) models has been largely attributed to extratropical cloud feedback, which is thought to be driven by greater supercooled water in present‐day cloud phase partitioning (CPP). Here, we report that accounting for precipitation in the Goddard Institute for Space Studies ModelE3 radiation scheme, neglected in more than 60% of CMIP6 and 90% of CMIP5 models, systematically changes its apparent CPP and substantially increases its cloud feedback, consistent with results using CMIP models. Including precipitation in the comparison with cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning, and simulate cloud feedbacks. Our findings suggest that making radiation schemes precipitation‐aware (missing in most CMIP6 models) should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity. Plain Language Summary: The surprising increase of Earth's climate sensitivity–a proxy for future global warming–in the most recent climate models (CMIP6) has been largely attributed to the response of extratropical low clouds to warming. This cloud‐climate feedback is thought to be driven by greater supercooled water in present‐day cloud phase partitioning. Here we report that accounting for precipitation in climate model radiation schemes–neglected in more than 60% of CMIP6 and 90% of CMIP5 models–profoundly changes their apparent cloud phase partitioning and substantially increases their cloud‐climate feedbacks, which has not been reported before. Including precipitation in the comparison with observations and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning and simulate cloud‐climate feedbacks. Our novel findings suggest that making radiation schemes precipitation‐aware, which is missing in most CMIP6 models, should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity Key Points: Accounting for snow in a lidar simulator to compare with observations systematically reduces the simulated apparent supercooled liquidAllowing radiative schemes to be snow‐aware in climate models greatly increases their net cloud feedbackThe mean climate sensitivity is greater for recently coupled model intercomparison project models with snow‐aware radiative schemes compared to those without [ABSTRACT FROM AUTHOR]

A comparative analysis between observational data from McMurdo Station, Antarctica and the Community Atmosphere Model version 6 (CAM6) simulation is performed focusing on cloud characteristics and their thermodynamic conditions. Ka‐band Zenith Radar (KAZR) and High Spectral Resolution Lidar (HSRL) retrievals are used as the basis of cloud fraction and cloud phase identifications. Radiosondes released at 12‐h increments provide atmospheric profiles for evaluating the simulated thermodynamic conditions. Our findings show that the CAM6 simulation consistently overestimates (underestimates) cloud fraction above (below) 3 km in four seasons of a year. Normalized by total in‐cloud samples, ice and mixed phase occurrence frequencies are underestimated and liquid phase frequency is overestimated by the model at cloud fractions above 0.6, while at cloud fractions below 0.6 ice phase frequency is overestimated and liquid‐containing phase frequency is underestimated by the model. The cloud fraction biases are closely associated with concurrent biases in relative humidity (RH), that is, high (low) RH biases above (below) 2 km. Frequencies of correctly simulating ice and liquid‐containing phase increase when the absolute biases of RH decrease. Cloud fraction biases also show a positive correlation with RH biases. Water vapor mixing ratio biases are the primary contributor to RH biases, and hence, likely a key factor controlling the cloud biases. This diagnosis of the evident shortfalls of representations of cloud characteristics in CAM6 simulation at McMurdo Station brings new insight in improving the governing model physics therein. Plain Language Summary: Global climate models (GCMs) historically struggle to accurately estimate the amounts and types of clouds over the polar regions. Cloud cover and thermodynamic phase directly influence Earth's radiation budget and the accuracy of future climate prediction. Particularly, Antarctic ice sheet is vulnerable to a changing climate through interactions with atmosphere and ocean, and the impacts of clouds are still not well understood. In this study, shortcomings of cloud representations in the CAM6 model were diagnosed by comparing with observational data (ground‐based remote sensing and radiosondes), which encompassed a year of measurements at McMurdo Station, Antarctica. Cloud fraction and phase as well as thermodynamic variables were examined to identify model biases. The model overestimates cloud cover above 3 km and underestimates it below that altitude. In cases where cloud cover is greater than 60%, the model also produces excessively large percentages of liquid clouds. These model biases are well correlated with biases in relative humidity, which is further dominated by biases in water vapor concentrations. Thus, these findings indicate that improving representations of water vapor concentrations in the model is a key step toward improving the simulations of cloud characteristics in Antarctica. Key Points: CAM6 shows higher (lower) cloud fraction above (below) 3 km than observations at McMurdo Station, AntarcticaCAM6 underestimates (overestimates) ice phase proportion among all cloud samples at cloud fraction ≥0.6 (<0.6)Model RH biases are dominated by water vapor biases instead of temperature biases, and correlate with cloud fraction and cloud phase biases [ABSTRACT FROM AUTHOR]

Assessments of the Clouds and the Earth’s Radiant Energy System Edition 4 (Ed4) cloud retrievals are critical for climate studies. Ed4 cloud parameters are evaluated using instruments in the A-Train Constellation. Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) retrievals are compared with Ed4 retrievals from the Aqua Moderate-Resolution Imaging Spectroradiometer (MODIS) as a function of the CALIOP horizontal averaging (HA) scale. Regardless of the HA scale, MODIS daytime (nighttime) water cloud fraction (CF) is greater (less) than that from CALIOP. MODIS ice CF is less than CALIOP overall, with the largest differences in polar regions. Ed4 and CALIOP retrieve the same cloud phase in 70%–98% of simultaneous observations depending on the time of day, surface conditions, HA scales, and type of cloud vertical structure. Mean cloud top height (CTH) differences for single-layer water clouds over snow-/ice-free surfaces are less than 100 m. Base altitude positive biases of 170–460 m may be impacted by CPR detection limitations. Average MODIS ice CTHs are underestimated by 70 m for some deep convective clouds and up to ~2.2 km for thin cirrus. Ice cloud base altitudes are typically underestimated (overestimated) during daytime (nighttime). MODIS and CALIOP cirrus optical depths over oceans are within 46% and 5% for daytime and nighttime observations, respectively. Ice water path differences depend on the CALIOP retrieval version and warrant further investigation. Except for daytime cirrus optical depth, Ed4 cloud property retrievals are at least as accurate as other long-term operational cloud property retrieval systems. [ABSTRACT FROM AUTHOR]

The detection of clouds from the latest-generation satellites is crucial for the accuracy of downstream remote sensing products. Classic threshold-based algorithms or machine-learning-based algorithms have limitations in all-day cloud detection, usually sacrificing measurements from visible bands to develop all-day cloud detections. It is difficult to find a day-night consistent cloud detection that takes advantage of all band measurements even if there is an abrupt absence of visible bands on one sides of the terminator. This study explains a cloud detection algorithm for the Cloud, Atmospheric Radiation, and renewal Energy application (CARE) datasets that synergistically uses threshold-based tests and an extra randomized tree (ERT) model to detect all-day clouds from Himawari-8 full-disk measurements. First, we developed a visible-to-infrared band daytime cloud detection algorithm (D algorithm) to generate high-precision training datasets, based on which an infrared band all-day cloud detection algorithm (DN algorithm) is built with an ERT model. Second, we merge the D and DN algorithm into CARE algorithm by weighting the results of the two algorithms differently according to day/night and land/sea conditions. In developing D algorithm, we use two-layer curved-surface thresholds from a look-up-table (LUT), that can effectively improve the dusk-dawn cloud detection accuracy over high latitudes and reduce the misclassification of clouds over sunglint regions. Validations against CALIPSO measurements show that the hit rate (HR) of CARE results (daytime cloudy HR 76.95%, clear HR 87.97%, nighttime cloudy HR 73.19%, clear HR 81.77%) outperform MODIS (daytime cloudy HR 77.35%, clear HR 66.09%, nighttime cloudy HR 78.08%, clear HR 59.72%) especially in nighttime clear sky detections, daytime CARE results show lower and higher HR in water/ice cloud detections than JAXA and MODIS results, nighttime CARE (water cloud HR 47.82%, ice cloud HR 93.59%) results show higher HR in both water and ice clouds than MODIS (water cloud HR 43.39%, ice cloud HR 74.41%). The comparison between the CARE and JAXA products shows high consistency during the daytime, and the CARE products have better cloud detection in the sunglint center and in dusk-dawn scenarios. • A hybrid cloud detection using classic threshold-based tests and machine learning. • A 24 h cloud detection without sacrificing daytime-only visible band measurements. • Reduce sun-glint and dusk-dawn induced bias using two-layer curved thresholds. [ABSTRACT FROM AUTHOR]

The Edition 2 (Ed2) cloud property retrieval algorithm system was upgraded and applied to the MODerate-resolution Imaging Spectroradiometer (MODIS) data for the Clouds and the Earth’s Radiant Energy System (CERES) Edition 4 (Ed4) products. New calibrations for solar channels and the use of the 1.24-μm channel for cloud optical depth (COD) over snow improve the daytime consistency between Terra and Aqua MODIS retrievals. Use of additional spectral channels and revised logic enhanced the cloud-top phase retrieval accuracy. A new ice crystal reflectance model and a CO2-channel algorithm retrieved higher ice clouds, while a new regional lapse rate technique produced more accurate water cloud heights than in Ed2. Ice cloud base heights are more accurate due to a new cloud thickness parameterization. Overall, CODs increased, especially over the polar (PO) regions. The mean particle sizes increased slightly for water clouds, but more so for ice clouds in the PO areas. New experimental parameters introduced in Ed4 are limited in utility, but will be revised for the next CERES edition. As part of the Ed4 retrieval evaluation, the average properties are compared with those from other algorithms and the differences between individual reference data and matched Ed4 retrievals are explored. Part II of this article provides a comprehensive, objective evaluation of selected parameters. More accurate interpretation of the CERES radiation measurements has resulted from the use of the Ed4 cloud properties. [ABSTRACT FROM AUTHOR]

We quantified effects of dust load on the cloud top ice cloud fraction (ICF) in terms of the dust extinction coefficient (σext). We analyzed 3‐year data sets obtained from an active satellite sensor over middle to high latitudes in the northern hemisphere for temperatures (T) between 230 and 273 K and σext values between 0.005 and 0.145 km−1. At about 250 K, ICF changed by about 30% in response to the above range of σext, whereas at extreme T values, ICF was relatively insensitive to σext. Thus, we concluded that ICF was primarily determined by T, with substantial influence of σext at about 250 K, likely due to increased opportunities for freezing as σext increases. Sensitivity of ICF was the lowest both at the largest σext and lowest T and at the smallest σext and highest T, while it was the highest at about 0.03 km−1 of σext and about 250 K. If there are any physical parameters that influence the ICF except temperature (T), how much does this parameter influence ICF in a given T? Dust particles have been long known as efficient ice nucleating particles. Although previous studies suggested that more dust particles increased ICF, they did not use quantitative parameters of the dust amount, but less‐quantitative indicators such as relative dust frequency. Therefore, we used the dust extinction coefficient (σext) as a quantitative parameter of dust amount and examined the relationship between the dust amount and ICF for T between 230 and 273 K. We observed the following phenomena from satellite data. At about 250 K, ICF substantially depended on σext likely due to increased opportunities for freezing as σext increases. However, at extreme T values, ICF was relatively insensitive to σext. Moreover, we found that sensitivity of ICF was the lowest both at the largest σext and lowest T and at the smallest σext and highest T, while it was the highest at about 0.03 km−1 of σext and about 250 K. These behaviors of the ICF sensitivity could be understood from characteristics of T (the lower, the easier for freezing) and σext (the larger, the easier for freezing). Key Points: Ice cloud fraction is primarily determined by temperature, with substantial influence of dust extinction coefficient at about 250 KThe larger dust extinction coefficient is, the larger ice cloud fraction is in the same temperature range by higher freezing chancesThe variation of ice cloud fraction is the highest at dust extinction coefficient of about 0.03 km−1and temperature of about 250 K [ABSTRACT FROM AUTHOR]

Surface melting on Antarctic Peninsula ice shelves can influence ice shelf mass balance, and consequently sea level rise. We show that summertime cloud phase on the Larsen C ice shelf on the Antarctic Peninsula strongly influences the amount of radiation received at the surface and can determine whether or not melting occurs. While previous work has separately evaluated cloud phase and the surface energy balance (SEB) during summertime over Larsen C, no previous studies have examined this relationship quantitatively. Furthermore, regional climate models frequently produce surface radiation biases related to cloud ice and liquid water content. This study uses a high‐resolution regional configuration of the UK Met Office Unified Model (MetUM) to assess the influence of cloud ice and liquid properties on the SEB, and consequently melting, over the Larsen C ice shelf. Results from a case‐study show that simulations producing a vertical cloud phase structure more comparable to aircraft observations exhibit smaller surface radiative biases. A configuration of the MetUM adapted to improve the simulation of cloud phase reproduces the observed surface melt most closely. During a five‐week simulation of summertime conditions, model melt biases are reduced to <2 W·m−2: a four‐fold improvement on a previous study that used default MetUM settings. This demonstrates the importance of cloud phase in determining summertime melt rates on Larsen C. [ABSTRACT FROM AUTHOR]

The accurate recognition of the cloud phase has a great influence on the retrieval of the cloud top height. In order to improve the accuracy of obtaining the cloud top height with OCO-2, we proposed a cloud phase recognition algorithm based on the threshold of parameter α; α is defined as the reflectivity ratio of the region with weak continuous absorption of the oxygen A band to the region with weak continuous absorption of the CO2 1.6 µm band. The α under different solar zenith angles and different ground albedos was calculated. The results show the following: under the same surface albedo and solar zenith angle, α was large for ice clouds and small for water clouds. Under the same surface albedo, the greater the solar zenith angle, the smaller the α of the ice cloud, and the larger the α of the water cloud. Under the same solar zenith angle, the greater the surface albedo, the smaller the α; when the solar zenith angle was less than 70°, α can be used to effectively distinguish between the ice cloud and water cloud. This study used OCO-2 data of a single orbit over ocean to verify the feasibility of the algorithm through comparison with the CALIOP cloud phase product, which provided a basis for OCO-2 cloud top height estimation.

Inaccurate mixed‐phase cloud parameterizations over the Southern Ocean remain one of the largest sources of disagreement among global models in determining shortwave cloud radiative feedbacks. Suitable global observations supporting model improvements are currently unavailable. The conventional satellite cloud phase retrieval from passive radiometers is strongly biased toward cloud top without further information on the subcloud phase. Mixed‐phase clouds with the liquid‐top mixed‐phase (LTMP) structures are often classified simply as supercooled liquid. This paper presents a daytime multispectral detection algorithm for LTMP clouds, based on differential absorption between liquid and ice in shortwave infrared bands (1.61 and 2.25 μm). The LTMP algorithm, previously developed for polar‐orbiting sensors, is applied to Himawari‐8 Advanced Himawari Imager (the first of the next‐generation geostationary satellites) to probe subcloud phase for mixed‐phase clouds over the Southern Ocean. The results are compared with spaceborne active sensor data from CloudSat and CALIPSO. Ship‐based field experiment measurements are examined for selected cases to provide a more direct assessment of algorithm performance. The results show that applying the LTMP algorithm to geostationary satellites has potential to provide advanced time‐resolved observations for mixed‐phase clouds globally with improved sublayer cloud phase information that can support enhancement and validation of global models. Key Points: A multispectral algorithm for daytime liquid‐top mixed‐phase cloud characterization is applied to Himawari‐8 AHISubcloud‐top phase probing results over the Southern Ocean are compared with spaceborne and shipborne observationsGlobal time‐resolved observations for mixed‐phase clouds with the advanced phase information are enabled from geostationary satellites [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • Preprocess all POLDER3, CloudSat, CALIPSO, MODIS data; these data are reduced and projected respectively. • The multi-objective dynamic optimal rules are constructed and a dynamic spatial fusion algorithm is proposed. • The experimental data are processed by the spatial data fusion algorithm. • Comparison and analysis of cloud phase fusion results with other products; close to the cloud top phase of CALIPSO. Abstract Cloud phase is one of the important parameters of weather and climate research and core elements of atmospheric cloud parametric inversion. The accuracy of its recognition directly influences the inversion precision of cloud optical thickness, spherical albedo, effective particle radius, ice/liquid water content and other parameters. In this paper, we combine with cloud phase products of spaceborne multi-angle polarimetric radiometer, polarized laser radar, and millimeter wave radar. Next, we put forward a Dynamic Spatial Optimal Fusion (DSOF) algorithm. In this algorithm we construct the spatial optimal fusion rules of Cloud Top Phase (CTP) for multi-source data. Finally, we realize the cloud phase spatial fusion using these rules. We used the typhoon "Lupit" as the research object and verified this method. The standard deviations are 4.67%, 7.18%, 40.14% and 36.51%, respectively, compared the results of fused CTP to that of CALIPSO, CloudSat, POLDER3 and MODIS. The fusion results are most close to the CTP of CALIPSO. The results show that this method can effectively achieve multi-source cloud phase inversion, and provide new technology for the development and data synergy of spaceborne multi-sensor satellite. [ABSTRACT FROM AUTHOR]

One of the emergencies rescue crews have to face is toxic gas leakages. The characteristics of the gas leakages differ with regard to their leakage duration. Long-term releases have plume-like behaviors that can be described by utilizing mean concentrations at individual exposed locations. In contrast, ensemble statistics of individual cloud characteristics are needed for short-term releases with puff-like behaviors to ensure fully aware risk assessment. The reason is that the time evolution of the concentration of short-term gas releases can differ wildly under the same mean ambient and leakage conditions. The duration from which the release can be classified as plume-like can be found only by studying the releases of different durations, which is the main aim of this paper. To investigate gas releases of different durations, wind tunnel experiments of gas releases in an idealized urban area were conducted. The results present a new method by which concentration signals of releases can be divided into three cloud phases: the arrival, the central and the departure cloud phase. The characteristics (e.g., lengths, mean concentrations) of the individual cloud phases are explored. The results indicate that the finite-duration releases for which the central cloud phase exists have the plume-like behavior for this cloud part.

Current passive sensors fail to accurately identify cloud phase, thus largely limiting the quantification of radiative contributions and precipitation of different cloud phases over the Tibet Plateau (TP), especially for the mixed-phase and supercooled water clouds. By combining the 4 years of (January 2007–December 2010) cloud phase (2B-CLDCLASS-LIDAR), radiative fluxes (2B-FLXHR-LIDAR), and precipitation (2C-PRECIP-COLUMN) products from CloudSat, this study systematically quantifies the radiative contribution of cloud phases and precipitation over the TP. Statistical results indicate that the ice cloud frequently occurs during the cold season, while mixed-phase cloud fraction is more frequent during the warm season. In addition, liquid clouds exhibit a weak seasonal variation, and the relative cloud fraction is very low, but supercooled water cloud has a larger cloud distribution (the value reaches about 0.24) than those of warm water clouds in the eastern part of the TP during the warm season. Within the atmosphere, the ice cloud has the largest radiative contribution during the cold season, the mixed-phase cloud is the second most important cloud phase for the cloud radiative contribution during the warm season, and supercooled water clouds’ contribution is particularly important during the cold season. In particular, the precipitation frequency over the TP is mainly dominated by the ice and mixed-phase clouds and is larger over the southeastern part of the TP during the warm season.

The impact of Asian dust on the determination of cloud phase is analyzed over dust sources and downwind using cloud phase products from cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO), atmospheric infrared sounder (AIRS), moderate resolution imaging spectroradiometer (MODIS), and polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a lidar (PARASOL). The results show that the presence of dust greatly affects determination of cloud phase in both source and downwind regions. CALIPSO products demonstrate better ice cloud discrimination of about 70% and 60% over source and downwind regions, respectively, compared to passive sensors. These results suggest that semi-direct effects of dust, which act to warm clouds and evaporate large cloud particles, may play a role in cloud phase determination. This study suggests that the presence of dust tends to increase misclassification of ice clouds as water or uncertain phase by AIRS, MODIS, and PARASOL.

The cloud phase composition of cold clouds in the Antarctic atmosphere is explored using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instruments for the period 2000-2006. We used the averaged fraction of liquid-phase clouds out of the total cloud amount at the cloud tops since the value is comparable in the two measurements. MODIS data for the winter months (June, July, and August) reveal liquid cloud fraction out of the total cloud amount significantly decreases with decreasing cloud-top temperature below 0°C. In addition, the CALIOP vertical profiles show that below the ice clouds, low-lying liquid clouds are distributed over ∼20% of the area. With increasing latitude, the liquid cloud fraction decreases as a function of the local temperature. The MODIS-observed relation between the cloud-top liquid fraction and cloud-top temperature is then applied to evaluate the cloud phase parameterization in climate models, in which condensed cloud water is repartitioned between liquid water and ice on the basis of the grid point temperature. It is found that models assuming overly high cut-offs (≫ −40°C) for the separation of ice clouds from mixed-phase clouds may significantly underestimate the liquid cloud fraction in the winter Antarctic atmosphere. Correction of the bias in the liquid cloud fraction would serve to reduce the large uncertainty in cloud radiative effects. [ABSTRACT FROM AUTHOR]

Abstract: Cloud phase is a key parameter in cloud microphysics. The vertical profile of cloud phase has a major role in precipitation formation processes and cloud electrification. Active sensors, such as Lidar, can be used to derive cloud phase vertical profile, but these measurements are limited due to the narrow spatial coverage and low repetition time. Passive sensors, such as MSG-SEVIRI, have much better spatial coverage and temporal resolution but retrieve phase information only from cloud tops. Here we present a methodology to gain information on cloud phase vertical profile of cloud system from MSG data. The 15min cycle of MSG is used to monitor vertical phase development providing additional information on precipitation and electrification. An application is presented to lucid the usage of the cloud phase vertical profile. Clouds growing over polluted air mass are shown to have more supercooled water tops and wider mixed phase region, while full glacition take place at extreme temperatures. [ABSTRACT FROM AUTHOR]

The cloud structure associated with two frontal passages over the Southern Ocean and Tasmania is investigated. The first event, during August 2006, is characterized by large quantities of supercooled liquid water and little ice. The second case, during October 2007, is more mixed phase. The Weather Research and Forecasting model (WRFV2.2.1) is evaluated using remote sensed and in situ observations within the post frontal air mass. The Thompson microphysics module is used to describe in-cloud processes, where ice is initiated using the Cooper parameterization at temperatures lower than −8°C or at ice supersaturations greater than 8%. The evaluated cases are then used to numerically investigate the prevalence of supercooled and mixed-phase clouds over Tasmania and the ocean to the west. The simulations produce marine stratocumulus-like clouds with maximum heights of between 3 and 5 km. These are capped by weak temperature and strong moisture inversions. When the inversion is at temperatures warmer than −10°C, WRF produces widespread supercooled cloud fields with little glaciation. This is consistent with the limited in situ observations. When the inversion is at higher altitudes, allowing cooler cloud tops, glaciated (and to a lesser extent mixed phase) clouds are more common. The simulations are further explored to evaluate any orographic signature within the cloud structure over Tasmania. No consistent signature is found between the two cases. [ABSTRACT FROM AUTHOR]

This study discusses a method for deriving the vertical profiles of absorption and scattering coefficients in integrated atmospheric column for radiative models. It then presents these profiles in the case of cloudy layers using details of the distributions of cloud water content and the vertical description of other radiatively active atmospheric species in the shortwave spectrum for Yaounde (11°51′ E, 3°86′ N). It was shown that both quantities globally exhibit spatial and temporal variabilities and therefore should be used with caution in radiative transfer calculations. The proposed method duly incorporates these variabilities using existing knowledge on atmospheric optics and radiative constituents (gas, clouds and aerosols) for appropriate representation of the actual state of the atmosphere. The case study combines data of cloud water content and effective radius as described by Akana and Njomo (2010b) as well as HITRAM vertical profile for gas (Rothman et al., 2010) and aerosols vertical properties as proposed by Kneizys et al. (1996). From the present analysis, it was observed that the highest values obtained near the surface were followed by a decrease in altitude. The minima are obtained slightly above the mixed-phase cloud layer. The scattering coefficient close to the surface is globally smaller than the absorption coefficient but this tendency is reversed at mid-altitude in the mixed-phase cloud layer generally between 500 and 600 mbar. The single scattering albedo (SSA) deduced from these column-integrated profiles show very small values near the surface and gradual increase towards high altitudes with the smallest value (less than 0.15) found during rainy season. This increase is rapid in mixed-phase cloud where it reaches 0.79 at the upper limits. Assessing the impact of gases and aerosol on these profiles, it was observed that SSA increase by 153% in gas-free atmosphere while a drop of 3% was noticed in aerosol-free atmosphere close to the surface. [ABSTRACT FROM AUTHOR]