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Shuyun Zhao and Kentaroh Suzuki

Abstract

This study explores the effects of black carbon (BC) and sulfate (SO4) on global and tropical precipitation with a climate model. Results show that BC causes a decrease in global annual mean precipitation, consisting of a large negative tendency of a fast precipitation response scaling with instantaneous atmospheric absorption and a small positive tendency of a slow precipitation response scaling with the BC-caused global warming. SO4 also causes a decrease in global annual mean precipitation, which is dominated by a slow precipitation response corresponding to the surface cooling caused by SO4. BC causes a northward shift of the intertropical convergence zone (ITCZ), mainly through a fast precipitation response, whereas SO4 causes a southward shift of the ITCZ through a slow precipitation response. The displacements of the ITCZ caused by BC and SO4 are found to linearly correlate with the corresponding changes in cross-equatorial heat transport in the atmosphere, with a regression coefficient of about −3° PW−1, implying that the ITCZ shifts occur as manifestations of the atmospheric cross-equatorial heat transport changes in response to the BC and SO4 forcings. The atmospheric cross-equatorial heat transport anomaly caused by BC is basically driven by the BC-induced interhemispheric contrast in instantaneous atmospheric absorption, whereas the atmospheric cross-equatorial heat transport anomaly caused by SO4 is mostly attributable to the response of evaporation. It is found that a slab-ocean model exaggerates the cross-equatorial heat transport response in the atmosphere and the ITCZ shift both for BC and SO4, as compared with an ocean-coupled model. This underscores the importance of using an ocean-coupled model in modeling studies of the tropical climate response to aerosols.

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Matthew D. Lebsock and Kentaroh Suzuki

Abstract

A precipitating marine cumulus cloud simulation is coupled to radiation propagation models to simulate active and passive microwave observations at 94 GHz. The simulations are used to examine the error characteristics of the total water path retrieved from the integral constraints of the passive microwave brightness temperature or the path-integrated attenuation (PIA) using a spatial interpolation technique. Three sources of bias are considered: 1) the misdetection of cloudy pixels as clear, 2) the systematic differences in the column water vapor between cloudy and clear skies, and 3) the nonuniform beamfilling effects on the observables. The first two sources result in biases on the order of 5–10 g m−2 of opposite signs that tend to cancel. The third source results in a bias that increases monotonically with the water path that approaches 50%. Nonuniform beamfilling is sensitive to footprint size. Random error results from both instrument measurement precision and the natural variability in the relationship between the water path and the observables. Random errors for the retrievals using the CloudSat PIA are estimated to be the larger of either 20 g m−2 or 30%. A radar/radiometer system with a measurement precision of 0.3 K or 0.05 dB could reduce this error to the larger of either 10 g m−2 or 30%. All error mechanisms reported here result from variability in either the spatial structure of the atmosphere or the hydrometeor drop size distribution. The results presented here are specific to the cloud simulation and in general the magnitude will vary globally.

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Xianwen Jing, Kentaroh Suzuki, and Takuro Michibata

Abstract

Global climate models (GCMs) have been found to share the common too-frequent bias in the warm rain formation process. In this study, five different autoconversion schemes are incorporated into a single GCM, to systematically evaluate the warm rain formation processes in comparison with satellite observations and investigate their effects on the aerosol indirect effect (AIE). It is found that some schemes generate warm rain less efficiently under polluted conditions in the manner closer to satellite observations, while the others generate warm rain too frequently. Large differences in AIE are found among these schemes. It is remarkable that the schemes with more observation-like warm rain formation processes exhibit larger AIEs that far exceed the uncertainty range reported in IPCC AR5, to an extent that can cancel much of the warming trend in the past century, whereas schemes with too-frequent rain formations yield AIEs that are well bounded by the reported range. The power-law dependence of the autoconversion rate on the cloud droplet number concentration β is found to affect substantially the susceptibility of rain formation to aerosols: the more negative β is, the more difficult it is for rain to be triggered in polluted clouds, leading to larger AIE through substantial contributions from the wet scavenging feedback. The appropriate use of a droplet size threshold can mitigate the effect of a less negative β. The role of the warm rain formation process on AIE in this particular model has broad implications for others that share the too-frequent rain-formation bias.

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Colleen M. Kaul, João Teixeira, and Kentaroh Suzuki

Abstract

Arctic mixed-phase stratocumulus clouds are maintained by feedbacks between microphysical and dynamical phenomena, but the details of these interactions are incompletely understood. Although large-eddy simulations are a promising means of elucidating microphysics–turbulence relationships, the use of sophisticated microphysical schemes complicates analysis of their results. Here, the ability of a simplified one-moment scheme to capture basic features of this cloud type is investigated through simulations based on Mixed-Phase Arctic Cloud Experiment (MPACE), SHEBA/FIRE-ACE, and Indirect and Semi-Direct Aerosol Campaign (ISDAC) intercomparison studies. The results of the simple scheme show reasonable agreement with liquid and ice water path predictions reported by models using schemes of similar or greater complexity. Additional tests are performed to evaluate the sensitivity of the results to three main parameters of the scheme: the snow and ice size distribution intercept parameters and the exponent appearing in the temperature-dependent phase-partition function, which is used to diagnose cloud condensate amounts. Sensitivities of the SHEBA and ISDAC cases, both of which have low surface heat fluxes and low precipitation rates, tend to be similar, while the MPACE case, with higher surface fluxes and precipitation rates, shows somewhat different trends. Results of all three cases are found to be sensitive to the snow size distribution intercept parameter, but this quantity can be adequately estimated using a recently developed diagnostic expression based on observations of Arctic clouds.

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Haruka Hotta, Kentaroh Suzuki, Daisuke Goto, and Matthew Lebsock

Abstract

This study investigates how subgrid cloud water inhomogeneity within a grid spacing of a general circulation model (GCM) links to the global climate through precipitation processes. The effect of the cloud inhomogeneity on autoconversion rate is incorporated into the GCM as an enhancement factor using a prognostic cloud water probability density function (PDF), which is assumed to be a truncated skewed-triangle distribution based on the total water PDF originally implemented. The PDF assumption and the factor are evaluated against those obtained by global satellite observations and simulated by a global cloud-system-resolving model (GCRM). Results show that the factor implemented exerts latitudinal variations, with higher values at low latitudes, qualitatively consistent with satellite observations and the GCRM. The GCM thus validated for the subgrid cloud inhomogeneity is then used to investigate how the characteristics of the enhancement factor affect global climate through sensitivity experiments with and without the factor incorporated. The latitudinal variation of the factor is found to have a systematic impact that reduces the cloud water and the solar reflection at low latitudes in the manner that helps mitigate the too-reflective cloud bias common among GCMs over the tropical oceans. Due to the limitation of the factor arising from the PDF assumption, however, no significant impact is found in the warm rain formation process. Finally, it is shown that the functional form for the PDF in a GCM is crucial to properly characterize the observed cloud water inhomogeneity and its relationship with precipitation.

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Jussi Leinonen, Matthew D. Lebsock, Graeme L. Stephens, and Kentaroh Suzuki

Abstract

A revised version of the CloudSat–MODIS cloud liquid water retrieval algorithm is presented. The new algorithm, which combines measurements of radar reflectivity and cloud optical depth, addresses issues discovered in the current CloudSat–MODIS cloud water content (CWC) product. This current product is shown to be underconstrained by observations and to be too dependent on prior information incorporated into the Bayesian optimal-estimation algorithm. The most significant change made to the algorithm in this study was decreasing the number of independent variables to allow the observations to constrain the retrieved values better. The retrieval was also reformulated for improved compliance with the mathematical assumptions of the optimal-estimation algorithm. To validate the accuracy of the revised algorithm, the path-integrated attenuation (PIA) of the CloudSat radar signal was computed from the algorithm results. These modeled values were compared with independent measurements of the PIA that were obtained using a surface reference technique. This comparison shows that the cloud liquid water retrieved by the algorithm is close to being unbiased. The revised algorithm was also found to be an improvement over the current CloudSat CWC product and, to a lesser degree, the MODIS-derived cloud liquid water path.

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Takashi M. Nagao, Kentaroh Suzuki, and Takashi Y. Nakajima

Abstract

This study examines the impact of in-cloud vertical inhomogeneity on cloud droplet effective radii (CDERs) of water-phase cloud retrieved from 1.6-, 2.1-, and 3.7-μm-band measurements (denoted by r 1.6, r 2.1, and r 3.7, respectively). Discrepancies between r 1.6, r 2.1, and r 3.7 due to in-cloud vertical inhomogeneity are simulated by using a spectral bin microphysics cloud model and one-dimensional (1D) remote sensing simulator under assumptions that cloud properties at the subpixel scale have horizontal homogeneity and 3D radiative transfer effects can be ignored. Two-dimensional weighting functions for the retrieved CDERs with respect to cloud optical depth and droplet size are introduced and estimated by least squares fitting to the relation between the model-simulated droplet size distribution functions and the retrieved CDERs. The results show that the 2D weighting functions can explain CDER discrepancies due to in-cloud vertical inhomogeneity and size spectrum characteristics. The difference between r 1.6 and r 2.1 is found to primarily depend on the vertical difference in droplet size distribution because the peak widths of their weighting functions differ in terms of cloud optical depth. The difference between r 3.7 and r 2.1, in contrast, is highly dependent on r 2.1 because the magnitude of its weighting function is always greater than that of r 3.7 over the entire range of optical depths and droplet sizes, except for the cloud top. The overestimation of retrieved CDER compared with in situ CDER in a typical adiabatic cloud case is also interpreted in terms of in-cloud vertical inhomogeneity based on the 2D weighting functions and simulation results.

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Takashi Y. Nakajima, Kentaroh Suzuki, and Graeme L. Stephens

Abstract

This study examines the sensitivity of the retrieved cloud droplet radii (CDR) to the vertical inhomogeneity of droplet radii, including the existence of a drizzle mode in clouds. The focus of this study is warm water-phase clouds. Radiative transfer simulations of three near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) channels centered on wavelengths of 1.6, 2.1, and 3.7 μm reveal that the retrieved CDR are strongly influenced by the vertical inhomogeneity of droplet size including (i) the existence of small cloud droplets at the cloud top and (ii) the existence of the drizzle mode. The influence of smaller droplets at cloud top affects the 3.7-μm channel most, whereas the presence of drizzle influences radiances of both the 2.1- and 1.6-μm channels more than the 3.7-μm channel. Differences in the CDR obtained from MODIS 1.6-, 2.1-, and 3.7-μm channels that appear in global analysis of MODIS retrievals and the CDR derived from data collected during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) intensive observation period in 1987 can be explained by the results obtained from the sensitivity experiments of this study.

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Takashi Y. Nakajima, Kentaroh Suzuki, and Graeme L. Stephens

Abstract

Hydrometeor droplet growth processes are inferred from a combination of Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) cloud particle size observations and CloudSat/Cloud Profiling Radar (CPR) observations of warm water clouds. This study supports the inferences of a related paper () (i) that MODIS-retrieved cloud droplet radii (CDR) from the 3.7-μm channel (R37) are influenced by the existence of small droplets at cloud top and (ii) that the CDR obtained from 1.6- (R16) and 2.1-μm (R21) channels contain information about drizzle droplets deeper into the cloud as well as cloud droplets. This interpretation is shown to be consistent with radar reflectivities when matched to CDR that were retrieved from MODIS data. This study demonstrates that the droplet growth process from cloud to rain via drizzle proceeds monotonically with the evolution of R16 or R21 from small cloud drops (on the order of 10–12 μm) to drizzle (CDR greater than 14 μm) to rain (CDR greater than 20 μm). Thus, R16 or R21 is an indicator of hydrometeor droplet growth processes whereas R37 does not contain information about coalescence. A new composite analysis, the contoured frequency diagram, is introduced to combine CloudSat/CPR reflectivity profiles and reveals a distinct trimodal population of reflectivities corresponding to cloud, drizzle, and rain modes.

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Kentaroh Suzuki, Takashi Y. Nakajima, and Graeme L. Stephens

Abstract

This study describes an approach for combining CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations to investigate the microphysical processes of warm clouds on the global scale. MODIS column optical thickness is vertically distributed between the cloud top and cloud bottom according to adiabatic and condensational growth assumptions and used as a vertical coordinate system to analyze profiles of CloudSat-observed radar reflectivity. The reflectivity profiles thus rescaled as a function of the in-cloud optical depth clearly depict how the cloud-to-rain particle growth processes take place within the cloud layer and how these processes vary systematically with variations in MODIS-derived effective particle radius. It is also found that the effective radii retrieved using two different wavelengths of MODIS tend to trace the microphysical change of reflectivity profiles in a different way because of the difference in the layer depth that characterizes these two effective radii.

The reflectivity profiles as a function of optical depth are also interpreted in terms of drop collection processes based on the continuous collection model. The slope of the reflectivity change with optical depth provides a gross measure of the collection efficiency factor. The systematic changes of reflectivity profiles with MODIS-derived particle sizes are then interpreted as demonstrating a strong dependency of the collection efficiency on particle size. These results provide a quantitative insight into the drop collection process of warm clouds in the real atmosphere.

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