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 （CAL...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.展开更多
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 typ...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.展开更多
Cloud microphysical properties including liquid and ice particle number concentration （NC）, liquid water content （LWC）, ice water content （IWC） and effective radius （RE） were retrieved from CloudSat data for a...Cloud microphysical properties including liquid and ice particle number concentration （NC）, liquid water content （LWC）, ice water content （IWC） and effective radius （RE） were retrieved from CloudSat data for a weakly convective and a widespread stratus cloud. Within the mixed-phase cloud layers, liquid-phase fractions needed to be assumed in the data retrieval process, and one existing linear （Pl） and two exponential （P2 and P3） functions, which estimate the liquid-phase fraction as a function of subfreezing temperature （from -20℃ to 0℃）, were tested. The retrieved NC, LWC, IWC and RE using Pl were on average larger than airplane measurements in the same cloud layer, Function P2 performed better than p1 or P3 in retrieving the NCs of cloud droplets in the convective cloud, while function Pl performed better in the stratus cloud. Function P3 performed better in LWC estimation in both convective and stratus clouds. The REs of cloud droplets calculated using the retrieved cloud droplet NC and LWC were closer to the values of in situ observations than those retrieved directly using the Pl function. The retrieved NCs of ice particles in both convective and stratus clouds, on the assumption of liquid-phase fraction during the retrieval of liquid droplet NCs, were closer to those of airplane observations than on the assumption of function P1.展开更多
A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-...A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-hydroxyquinoline and Triton X-100 were used as the ligand and surfactant respectively. Nickel（Ⅱ） can form a hy-drophobic complex with 8-hydroxyquinoline, the complex can be extracted into the small volume surfactant rich phase at the cloud point temperature（CPT） for GFAAS determination. The factors affecting the cloud point extraction, such as pH, ligand concentration, surfactant concentration, and the incubation time were optimized. Under the optimal conditions, a detection limit of 12 ng/L and a relative standard deviation（RSD） of 2.9% were obtained for Ni（Ⅱ） determination. The enrichment factor was found to be 25. The proposed method was successfully applied to the determination of nickel（Ⅱ） in certified reference material and different types of water samples and the recovery was in a range of 95%―103%.展开更多
基金funded by Ko-rean Center for Atmospheric Sciences and Earthquake Re-search 2010–1178, and US Department of Energy grantDE-FG02-01ER63257
文摘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.
基金supported in part by the National Natural Science Foundation of China (Grant No. 42105127)the Special Research Assistant Project of the Chinese Academy of Sciencesthe National Key Research and Development Plans of China (Grant Nos. 2019YFC1510304 and 2016YFE0201900-02)。
文摘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.
基金funded by the National Natural Science Foundation of China(Grant No.41475035)the Natural Science Foundation of Jiangsu Province(Grant No.BK20131433)+1 种基金the Foundations from KLME of NUIST(Grant No.KLME1206)the Key Laboratory for Aerosol–Cloud–Precipitation of China Meteorological Administration of NUIST(Grant No.KDW1203)
文摘Cloud microphysical properties including liquid and ice particle number concentration （NC）, liquid water content （LWC）, ice water content （IWC） and effective radius （RE） were retrieved from CloudSat data for a weakly convective and a widespread stratus cloud. Within the mixed-phase cloud layers, liquid-phase fractions needed to be assumed in the data retrieval process, and one existing linear （Pl） and two exponential （P2 and P3） functions, which estimate the liquid-phase fraction as a function of subfreezing temperature （from -20℃ to 0℃）, were tested. The retrieved NC, LWC, IWC and RE using Pl were on average larger than airplane measurements in the same cloud layer, Function P2 performed better than p1 or P3 in retrieving the NCs of cloud droplets in the convective cloud, while function Pl performed better in the stratus cloud. Function P3 performed better in LWC estimation in both convective and stratus clouds. The REs of cloud droplets calculated using the retrieved cloud droplet NC and LWC were closer to the values of in situ observations than those retrieved directly using the Pl function. The retrieved NCs of ice particles in both convective and stratus clouds, on the assumption of liquid-phase fraction during the retrieval of liquid droplet NCs, were closer to those of airplane observations than on the assumption of function P1.
基金Supported by the National Natural Science Foundation of China(No.20075009)
文摘A new method based on the cloud point extraction（CPE） for separation and preconcentration of nickel（Ⅱ） and its subsequent determination by graphite furnace atomic absorption spectrometry（GFAAS） was proposed, 8-hydroxyquinoline and Triton X-100 were used as the ligand and surfactant respectively. Nickel（Ⅱ） can form a hy-drophobic complex with 8-hydroxyquinoline, the complex can be extracted into the small volume surfactant rich phase at the cloud point temperature（CPT） for GFAAS determination. The factors affecting the cloud point extraction, such as pH, ligand concentration, surfactant concentration, and the incubation time were optimized. Under the optimal conditions, a detection limit of 12 ng/L and a relative standard deviation（RSD） of 2.9% were obtained for Ni（Ⅱ） determination. The enrichment factor was found to be 25. The proposed method was successfully applied to the determination of nickel（Ⅱ） in certified reference material and different types of water samples and the recovery was in a range of 95%―103%.
