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- Author or Editor: Kentaroh Suzuki x
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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.
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.
Uncertainty Characteristics of Total Water Path Retrievals in Shallow Cumulus Derived from Spaceborne Radar/Radiometer Integral ConstraintsAbstract
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Abstract
Information about microphysical processes in warm clouds embedded in satellite measurements must be untangled to be used to improve the parameterization in global models. In this paper, the relationship between vertical profiles of horizontally averaged radar reflectivity Z m and cloud optical depth from cloud top τ d was investigated using a hybrid cloud microphysical model and a forward simulator of satellite measurements. The particle size distributions were explicitly simulated using a bin method in a kinematic framework. In contrast to previous interpretations of satellite-observed data, three patterns of the Z m –τ d relationship related to microphysical processes were identified. The first is related to the autoconversion process, which causes Z m to increase upward with decreasing τ d . Before the initiation of surface precipitation, Z m increases downward with τ d in the upper part of the cloud, which is considered to be a second characteristic pattern and is caused by the accretion process. The appearance of this pattern corresponds to the initiation of efficient production of raindrops in the cloud. The third is related to the sedimentation and evaporation of raindrops causing Z m to decrease downward with τ d in the lower part of the Z m –τ d relationship. It was also found that the bulk collection efficiency has a partially positive correlation with the slope factor of Z m with regard to τ d and that the slope factor could be a gross measure of the collection efficiency in partial cases. This study also shows that differences in the aerosol concentration modulate the duration of these three patterns and change the slope factor of Z m .
Abstract
Information about microphysical processes in warm clouds embedded in satellite measurements must be untangled to be used to improve the parameterization in global models. In this paper, the relationship between vertical profiles of horizontally averaged radar reflectivity Z m and cloud optical depth from cloud top τ d was investigated using a hybrid cloud microphysical model and a forward simulator of satellite measurements. The particle size distributions were explicitly simulated using a bin method in a kinematic framework. In contrast to previous interpretations of satellite-observed data, three patterns of the Z m –τ d relationship related to microphysical processes were identified. The first is related to the autoconversion process, which causes Z m to increase upward with decreasing τ d . Before the initiation of surface precipitation, Z m increases downward with τ d in the upper part of the cloud, which is considered to be a second characteristic pattern and is caused by the accretion process. The appearance of this pattern corresponds to the initiation of efficient production of raindrops in the cloud. The third is related to the sedimentation and evaporation of raindrops causing Z m to decrease downward with τ d in the lower part of the Z m –τ d relationship. It was also found that the bulk collection efficiency has a partially positive correlation with the slope factor of Z m with regard to τ d and that the slope factor could be a gross measure of the collection efficiency in partial cases. This study also shows that differences in the aerosol concentration modulate the duration of these three patterns and change the slope factor of Z m .
Abstract
This study investigates the correlation patterns between cloud droplet effective radius (CDR) and cloud optical thickness (COT) of warm clouds with a nonhydrostatic spectral bin microphysics cloud model. Numerical experiments are performed with the model to simulate low-level warm clouds. The results show a positive and negative correlation pattern between CDR and COT for nondrizzling and drizzling stages of cloud development, respectively, consistent with findings of previous observational studies. Only a positive correlation is simulated when the collection process is switched off in the experiment, whereas both the positive and negative correlations are reproduced in the simulation with collection as well as condensation processes. The positive and negative correlations can also be explained in terms of an evolution pattern of the size distribution function due to condensation and collection processes, respectively.
Sensitivity experiments are also performed to examine how the CDR–COT correlation patterns are influenced by dynamical and aerosol conditions. The dynamical effect tends to change the amplitude of the CDR–COT plot mainly through changing the liquid water path, whereas the aerosol amount significantly modifies the correlation pattern between CDR and COT mainly through changing the cloud particle number concentration. These results suggest that the satellite-observed relationships between CDR and COT can be interpreted as being formed through microphysical particle growth processes under various dynamical and aerosol conditions in the real atmosphere.
Abstract
This study investigates the correlation patterns between cloud droplet effective radius (CDR) and cloud optical thickness (COT) of warm clouds with a nonhydrostatic spectral bin microphysics cloud model. Numerical experiments are performed with the model to simulate low-level warm clouds. The results show a positive and negative correlation pattern between CDR and COT for nondrizzling and drizzling stages of cloud development, respectively, consistent with findings of previous observational studies. Only a positive correlation is simulated when the collection process is switched off in the experiment, whereas both the positive and negative correlations are reproduced in the simulation with collection as well as condensation processes. The positive and negative correlations can also be explained in terms of an evolution pattern of the size distribution function due to condensation and collection processes, respectively.
Sensitivity experiments are also performed to examine how the CDR–COT correlation patterns are influenced by dynamical and aerosol conditions. The dynamical effect tends to change the amplitude of the CDR–COT plot mainly through changing the liquid water path, whereas the aerosol amount significantly modifies the correlation pattern between CDR and COT mainly through changing the cloud particle number concentration. These results suggest that the satellite-observed relationships between CDR and COT can be interpreted as being formed through microphysical particle growth processes under various dynamical and aerosol conditions in the real atmosphere.
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.
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.
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.
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.
