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Using six flights observations in September 2015 over Hebei, China, this study shows a robust negative aerosol‐cloud droplet effective radius (re) relationship for liquid clouds, which is different from previous studies that found positive aerosol‐cloud re relationship over East China using satellite observations. A total of 27 cloud samples was analyzed with the classification of clean and polluted conditions using lower and upper 1/3 aerosol concentration at 200 m below the cloud bases. By normalizing the profiles of cloud droplet re, we found significant smaller values under polluted than under clean condition at most heights. Moreover, the averaged profiles of cloud liquid water content (LWC) show larger values under polluted than clean conditions, indicating even stronger negative aerosol‐cloud re relationship if LWC is kept constant. The droplet size distributions further demonstrate that more droplets concentrate within smaller size ranges under polluted conditions. Quantitatively, the aerosol‐cloud interaction is found around 0.10–0.19 for the study region. Key Points There are clearly more droplets within smaller size regime under polluted than under clean conditions The cloud re is significantly smaller for all in‐cloud heights even with less LWC under polluted than under clean conditions Robust negative aerosol‐cloud re relationship is found over Hebei, China region, with FIE values about 0.10–0.19

Cloud liquid water content (LWC) and droplet effective radius (re) have an important influence on cloud physical processes and optical characteristics. The microphysical properties of a three-layer pure liquid stratus were measured by aircraft probes on 26 April 2014 over a coastal region in Huanghua, China. Vertical variations in aerosol concentration (Na), cloud condensation nuclei (CCN) at supersaturation (SS) 0.3%, cloud LWC and cloud re are examined. Large Na in the size range of 0.1–3 μm and CCN have been found within the planetary boundary layer (PBL) below ~1150 m. However, Na and CCN decrease quickly with height and reach a level similar to that over marine locations. Corresponding to the vertical distributions of aerosols and CCN, the cloud re is quite small (3.0–6 μm) at heights below 1150 m, large (7–13 μm) at high altitudes. In the PBL cloud layer, cloud re and aerosol Na show a negative relationship, while they show a clear positive relationship in the upper layer above PBL with much less aerosol Na. It also shows that the relationship between cloud re and aerosol Na changes from negative to positive when LWC increases. These results imply that the response of cloud re to aerosol Na depends on the combination effects of water-competency and collision-coalescence efficiency among droplets. The vertical structure of aerosol Na and cloud re implies potential cautions for the study of aerosol-cloud interaction using aerosol optical depth for cloud layers above the PBL altitude.

The effects of aerosol on cloud droplet effective radius (R eff ), cloud optical thickness and cloud droplet number concentration (N d ) are analysed both from long-term direct ground-based in situ measurements conducted at the Puijo measurement station in Eastern Finland and from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra and Aqua satellites. The mean in situ N d during the period of study was 217 cm −3 , while the MODIS-based N d was 171 cm −3 . The absolute values, and the dependence of both N d observations on the measured aerosol number concentration in the accumulation mode (N acc ), are quite similar. In both data sets N d is clearly dependent on N acc , for N acc values lower than approximately 450 cm −3 . Also, the values of the aerosol-cloud-interaction parameter [ACI=(1/3)*d ln(N d )/d ln(N acc )] are quite similar for N acc <400 cm −3 with values of 0.16 and 0.14 from in situ and MODIS measurements, respectively. With higher N acc (>450 cm −3 ) N d increases only slowly. Similarly, the effect of aerosol on MODIS-retrieved R eff is visible only at low N acc values. In a sub set of data, the cloud and aerosol properties were measured simultaneously. For that data the comparison between MODIS-derived N d and directly measured N d , or the cloud droplet number concentration estimated from N acc values (N d,p ), shows a correlation, which is greatly improved after careful screening using a ceilometer to make sure that only single cloud layers existed. This suggests that such determination of the number of cloud layers is very important when trying to match ground-based measurements to MODIS measurements.

This paper documents a study to examine the sensitivity to cloud droplet effective radius and liquid water path and the alleviation the energy imbalance at the top of the atmosphere and at the surface in the latest version of the Grid-point Atmospheric Model of the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics (IAP) (GAMIL1.1.0). Considerable negative biases in all flux components, and thus an energy imbalance, are found in GAMIL1.1.0. In order to alleviate the energy imbalance, two modifications, namely an increase in cloud droplet effective radius and a decrease in cloud liquid water path, have been made to the cloud properties used in GAMIL. With the increased cloud droplet effective radius, the single scattering albedo of clouds is reduced, and thus the reflection of solar radiation into space by clouds is reduced and the net solar radiation flux at the top of the atmosphere is increased. With the reduced cloud optical depth, the net surface shortwave radiation flux is increased, causing a net warming over the land surface. This results in an increase in both sensible and latent heat fluxes over the land regions, which is largely balanced by the increased terrestrial radiation fluxes. Consequently, the energy balance at the top of atmosphere and at the surface is achieved with energy flux components consistent with available satellite observations.

The effective radius of cloud droplets ( [Formula omitted]), representing the ratio of the third to the second moment of the droplet size distribution, is a pivotal factor in inferring the cloud optical properties, such as cloud optical thickness, and single scattering albedo, and consequently plays a vital role in the calculation of radiative properties. Effective radius holds significance not only in assessing the global radiation budget but also in incorporating the aerosol indirect effect within climate models. Hence, refining the parameterization of cloud droplet's effective radius in earth system models (ESMs) has emerged as a critical endeavor for assessing climate change. Understanding the relationship between the gradient (k) or effective radius ratio ( [Formula omitted]), a dimensionless parameter, and mean diameter and relative dispersion ( [Formula omitted]) is essential for the parameterization of effective radius. Fundamental theoretical studies using the Eulerian-Lagrangian particle-based model, guided by airborne observations, yield a spectrum of effective radius ratios ( [Formula omitted]) aimed at improving the parameterization of [Formula omitted]. Notably, [Formula omitted] or the gradient (k) exhibit significant variations across shallow and convective clouds, with k ranging from 0.61 to 0.84 (mean of 0.72 at a relative dispersion of 0.35). The analysis of CMIP6 models indicate that adopting appropriate [Formula omitted] value in the effective radius parameterization is crucial to mitigate the bias in [Formula omitted]. Thus, better parameterization of the cloud effective radius remains essential for elucidating the radiative impacts in ESMs.

Previous studies have shown that accurate descriptions of the cloud droplet effective radius （Re） and the autoconversion process of cloud droplets to raindrops （At） can effectively improve simulated clouds and surface precipitation, and reduce the uncertainty of aerosol indirect effects in GCMs. In this paper, we implement cloud microphysical schemes including two-moment Ar and Re considering relative dispersion of the cloud droplet size distribution into version 4.1 of the Institute of Atmospheric Physics＇s atmospheric GCM （IAP AGCM 4.1）, which is the atmospheric component of the Chinese Academy of Sciences＇ Earth System Model. Analysis of the effects of different schemes shows that the newly implemented schemes can improve both the simulated shortwave and longwave cloud radiative forcings, as compared to the standard scheme, in lAP AGCM 4.1. The new schemes also effectively enhance the large-scale precipitation, especially over low latitudes, although the influences of total precipitation are insignificant for different schemes. Further studies show that similar results can be found with the Community Atmosphere Model, version 5.1.

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.

Clouds are largely responsible for the spread of climate models predictions. Here we focus on the uncertainties in cloud shortwave radiative effect due to the parameterization of liquid cloud single scattering properties (SSPs) from liquid water content (LWC) and droplet number concentration (N), named parameterization of cloud optical properties. Uncertainties arise from not accounting for the droplet size distribution (DSD)—which affects the estimation of the effective radius (reff) and modulates the reff‐dependency of the SSPs—and from averaging SSPs over wide spectral bands. To assess these uncertainties a series of reff‐dependent SSPs parameterizations corresponding to various DSDs and spectral averaging methods are derived and implemented in a radiative code. Combined with the DSD‐dependent estimation of reff they are used to compute the bulk radiative properties (reflectance, transmittance, absorptance) of various clouds (defined in terms of LWC and N), including a homogeneous cloud, more realistic case studies, and outputs of a climate model. The results show that the cloud radiative forcing can vary up to 20% depending on the assumed DSD. Likewise, differences up to 20% are obtained for heating rates. The estimation of reff is the main source of uncertainty, while the SSPs parameterization contributes to around 20% of the total uncertainty. Spectral averaging is less an issue, except for atmospheric absorption. Overall, global shortwave cloud radiative effect can vary by 6 W m−2 depending on the assumed DSD shape, which is about 13% of the best observational estimate. Plain Language Summary Climate predictions differ a lot from one model to another, and the difficulty to simulate how clouds will behave in a warmer world is largely responsible for that. The radiative effect of clouds depends on the size of the individual droplets forming a cloud, a quantity that is not explicitly represented in climate models. In this study we investigate how not accounting for the detailed droplet size distribution affects the capability of climate models to reliably predict the radiative effect of clouds. By assuming a variety of droplet size distributions in a set of simulations, we observe that apparently similar clouds in climate models can have very different radiative impacts depending on the assumed distribution. This is primarily attributed to the estimation of the effective radius of cloud droplets, a key quantity that drives cloud radiative properties. Differences up to 20% are observed on critical quantities such as fluxes at top‐of‐atmosphere and at the surface. The absorption of radiation within clouds is also significantly altered. The impact on the global estimate of shortwave cloud radiative effect is around 13%, which highlights the need to improve the representation of the microphysical characteristics of clouds to run more reliable climate predictions. Key Points The estimation of cloud single scattering properties (SSPs) is affected by not accounting for the droplet size distribution (DSD) and by spectral averaging over wide bands The impact of the DSD shape on the estimation of reff is significant, and slightly offset by the weak dependency of SSPs to DSD shape The global cloud radiative effect simulated by a climate model can vary up to 13% depending on the assumed DSD

Geostationary satellites can retrieve the cloud droplet effective radius (re) but suffer biases from cloud inhomogeneities, internal retrieval nonlinearities, and 3-D scattering/shadowing from neighboring clouds, among others. A 1-D retrieval method was applied to Geostationary Operational Environmental Satellite 13 (GOES-13) imagery, over large areas in South America (5∘ N–30∘ S; 20∘–70∘ W), the Southeast Pacific (5∘ N–30∘ S; 70∘–120∘ W), and the Amazon (2∘ N–7∘ S; 54∘–73∘ W), for four months in each year from 2014–2017. Results were compared against in situ aircraft measurements and the Moderate Resolution Imaging Spectroradiometer cloud product for Terra and Aqua satellites. Monthly regression parameters approximately followed a seasonal pattern. With up to 108,009 of matchups, slope, intercept, and correlation for Terra (Aqua) ranged from about 0.71 to 1.17, −2.8 to 2.5 μm, and 0.61 to 0.91 (0.54 to 0.78, −1.5 to 1.8 μm, 0.63 to 0.89), respectively. We identified evidence for re overestimation (underestimation) correlated with shadowing (enhanced reflectance) in the forward (backscattering) hemisphere, and limitations to illumination and viewing configurations accessible by GOES-13, depending on the time of day and season. A proposition is hypothesized to ameliorate 3-D biases by studying relative illumination and cloud spatial inhomogeneity.

Liquid-drop clouds play a significant role in the evolution of cloud systems and the formation of the Earth’s radiation balance. Determination of their optical and microphysical characteristics is one of the most important problems of optics and atmospheric physics. The paper is devoted to assessing the applicability of an artificial neural network to processing synthetic data of passive satellite measurements of reflected solar radiation of low and medium spatial resolution in the visible and short-wave infrared spectral regions in order to simultaneously retrieve the optical thickness and effective radius of droplets of horizontally inhomogeneous cloudiness. The network is trained using the Monte Carlo calculated values of radiance in marine stratocumulus clouds generated by a fractal model. Through a nonlinear approximation of the dependence of optical and microphysical parameters of clouds on radiation characteristics, the tested algorithm allows taking into account the effects of horizontal radiative transfer, unlike classical IPA/NIPA (Independent Pixel Approximation/Nonlocal Independent Pixel Approximation) schemes. It is shown that the errors in solving the inverse problem can be reduced by assimilating data in adjacent pixels, reducing spatial resolution, and using radiance data received at small solar zenith angles. The high correlation between the test and retrieved optical thickness and effective radius indicate the possibility of using a neural network approach to interpreting satellite measurement data.