Articles | Volume 17, issue 7
https://doi.org/10.5194/essd-17-3243-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-3243-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
04 Jul 2025
Data description paper |
| 04 Jul 2025
| 04 Jul 2025
A global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology based on deep-learning methods
Yuanyuan Wu
Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences, Nanjing University, Nanjing, 210023, China
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Nanjing-Helsinki Institute in Atmospheric and Earth System Sciences, Nanjing University, Nanjing, 210023, China
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Yu Zhang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Kang-En Huang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Boyang Zheng
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Yichuan Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Yanyun Li
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Quan Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Chen Zhou
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Yuan Liang
TianJi Weather Science and Technology Company, Beijing, 100000, China
Jianning Sun
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Minghuai Wang
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Joint International Research Laboratory of Atmospheric and Earth System Sciences & Institute for Climate and Global Change Research, Nanjing, 210023, China
Daniel Rosenfeld
School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Related authors
No articles found.
Qingmin Wang, Yincheng Liu, Lujun Zhang, and Chen Zhou
Atmos. Chem. Phys., 25, 6741–6755, https://doi.org/10.5194/acp-25-6741-2025, https://doi.org/10.5194/acp-25-6741-2025, 2025
Short summary
Short summary
Our research explores how SST (sea surface temperature) changes in non-polar regions impact the polar energy budget. Through idealized SST experiments, we found that warming in tropical and mid-latitude oceans raises polar temperatures through enhanced atmospheric energy transport, leading to surface warming and top-of-atmosphere cooling in polar areas. This study highlights the distinct impacts of tropical Pacific and Indian Ocean SST changes on Arctic and Antarctic climates.
Hanzheng Zhu, Yaman Liu, Man Yue, Shihui Feng, Pingqing Fu, Kan Huang, Xinyi Dong, and Minghuai Wang
Atmos. Chem. Phys., 25, 5175–5197, https://doi.org/10.5194/acp-25-5175-2025, https://doi.org/10.5194/acp-25-5175-2025, 2025
Short summary
Short summary
Dust-soluble iron deposition from East Asia plays an important role in the marine ecology of the Northwest Pacific. Using the developed model, our findings highlight a dual trend: a decrease in the overall deposition of soluble iron from dust but an increase in the solubility of the iron itself due to the enhanced atmospheric processing. The study underscores the critical roles of both dust emission and atmospheric processing in soluble iron deposition and marine ecology.
Xinyue Shao, Yaman Liu, Xinyi Dong, Minghuai Wang, Ruochong Xu, Joel A. Thornton, Duseong S. Jo, Man Yue, Wenxiang Shen, Manish Shrivastava, Stephen R. Arnold, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2025-1526, https://doi.org/10.5194/egusphere-2025-1526, 2025
Short summary
Short summary
Highly Oxygenated Organic Molecules (HOMs) are key precursors of secondary organic aerosols (SOA). Incorporating the HOMs chemical mechanism into a global climate model allows for a reasonable reproduction of observed HOM characteristics. HOM-SOA constitutes a significant fraction of global SOA, and its distribution and formation pathways exhibit strong sensitivity to uncertainties in autoxidation processes and peroxy radical branching ratios.
Wenxin Zhang, Yaman Liu, Man Yue, Xinyi Dong, Kan Huang, and Minghuai Wang
Atmos. Chem. Phys., 25, 3857–3872, https://doi.org/10.5194/acp-25-3857-2025, https://doi.org/10.5194/acp-25-3857-2025, 2025
Short summary
Short summary
Understanding long-term organic aerosol (OA) trends and their driving factors is important for air quality management. Our modeling revealed that OA in China increased by 5.6 % from 1990 to 2019, primarily due to a 32.3 % increase in secondary organic aerosols (SOAs) and an 8.1 % decrease in primary organic aerosols (POAs), both largely driven by changes in anthropogenic emissions. Biogenic SOA increased due to warming but showed little response to changes in anthropogenic nitrogen oxide emissions.
Chris J. Wright, Joel A. Thornton, Lyatt Jaeglé, Yang Cao, Yannian Zhu, Jihu Liu, Randall Jones II, Robert Holzworth, Daniel Rosenfeld, Robert Wood, Peter Blossey, and Daehyun Kim
Atmos. Chem. Phys., 25, 2937–2946, https://doi.org/10.5194/acp-25-2937-2025, https://doi.org/10.5194/acp-25-2937-2025, 2025
Short summary
Short summary
Aerosol particles influence clouds, which exert a large forcing on solar radiation and freshwater. To better understand the mechanisms by which aerosol influences thunderstorms, we look at the two busiest shipping lanes in the world, where recent regulations have reduced sulfur emissions by nearly an order of magnitude. We find that the reduction in emissions has been accompanied by a dramatic decrease in both lightning and the number of droplets in clouds over the shipping lanes.
Zhe Song, Shaocai Yu, Pengfei Li, Ningning Yao, Lang Chen, Yuhai Sun, Boqiong Jiang, and Daniel Rosenfeld
Atmos. Chem. Phys., 25, 2473–2494, https://doi.org/10.5194/acp-25-2473-2025, https://doi.org/10.5194/acp-25-2473-2025, 2025
Short summary
Short summary
Our results with injected sea salt aerosols for five open oceans show that sea salt aerosols with low injection amounts dominate shortwave radiation, mainly through indirect effects. As indirect aerosol effects saturate with increasing injection rates, direct effects exceed indirect effects. This implies that marine cloud brightening is best implemented in areas with extensive cloud cover, while aerosol direct scattering effects remain dominant when clouds are scarce.
Xinyue Shao, Minghuai Wang, Xinyi Dong, Yaman Liu, Stephen R. Arnold, Leighton A. Regayre, Duseong S. Jo, Wenxiang Shen, Hao Wang, Man Yue, Jingyi Wang, Wenxin Zhang, and Ken S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-4135, https://doi.org/10.5194/egusphere-2024-4135, 2025
Short summary
Short summary
This study uses a global chemistry-climate model to investigate how new particle formation (NPF) from highly oxygenated organic molecules (HOMs) contributes to cloud condensation nuclei (CCN) in both preindustrial (PI) and present-day (PD) environments, and its impact on aerosol indirect radiative forcing. The findings highlight the crucial role of biogenic emissions in climate change, providing new insights for carbon-neutral scenarios and enhancing understanding of aerosol-cloud interactions.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Xinyue Shao, Minghuai Wang, Xinyi Dong, Yaman Liu, Wenxiang Shen, Stephen R. Arnold, Leighton A. Regayre, Meinrat O. Andreae, Mira L. Pöhlker, Duseong S. Jo, Man Yue, and Ken S. Carslaw
Atmos. Chem. Phys., 24, 11365–11389, https://doi.org/10.5194/acp-24-11365-2024, https://doi.org/10.5194/acp-24-11365-2024, 2024
Short summary
Short summary
Highly oxygenated organic molecules (HOMs) play an important role in atmospheric new particle formation (NPF). By semi-explicitly coupling the chemical mechanism of HOMs and a comprehensive nucleation scheme in a global climate model, the updated model shows better agreement with measurements of nucleation rate, growth rate, and NPF event frequency. Our results reveal that HOM-driven NPF leads to a considerable increase in particle and cloud condensation nuclei burden globally.
Renmin Yuan, Hongsheng Zhang, Jiajia Hua, Hao Liu, Peizhe Wu, Xingyu Zhu, and Jianning Sun
Atmos. Meas. Tech., 17, 2089–2102, https://doi.org/10.5194/amt-17-2089-2024, https://doi.org/10.5194/amt-17-2089-2024, 2024
Short summary
Short summary
Previously, a new method for atmospheric aerosol flux was proposed, and a large-aperture scintillometer was developed for experimental measurements, but the method was consistently not validated. In this paper, eddy correlation experiments for aerosol vertical transport fluxes were conducted to verify the reliability of the previous large-aperture scintillometer method. The experimental results also show that urban green land is a sink area for aerosol particles.
Yawen Liu, Yun Qian, Philip J. Rasch, Kai Zhang, Lai-yung Ruby Leung, Yuhang Wang, Minghuai Wang, Hailong Wang, Xin Huang, and Xiu-Qun Yang
Atmos. Chem. Phys., 24, 3115–3128, https://doi.org/10.5194/acp-24-3115-2024, https://doi.org/10.5194/acp-24-3115-2024, 2024
Short summary
Short summary
Fire management has long been a challenge. Here we report that spring-peak fire activity over southern Mexico and Central America (SMCA) has a distinct quasi-biennial signal by measuring multiple fire metrics. This signal is initially driven by quasi-biennial variability in precipitation and is further amplified by positive feedback of fire–precipitation interaction at short timescales. This work highlights the importance of fire–climate interactions in shaping fires on an interannual scale.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-36, https://doi.org/10.5194/amt-2024-36, 2024
Preprint withdrawn
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Yuchen Wang, Xvli Guo, Yajie Huo, Mengying Li, Yuqing Pan, Shaocai Yu, Alexander Baklanov, Daniel Rosenfeld, John H. Seinfeld, and Pengfei Li
Atmos. Chem. Phys., 23, 5233–5249, https://doi.org/10.5194/acp-23-5233-2023, https://doi.org/10.5194/acp-23-5233-2023, 2023
Short summary
Short summary
Substantial advances have been made in recent years toward detecting and quantifying methane super-emitters from space. However, such advances have rarely been expanded to measure the global methane pledge because large-scale swaths and high-resolution sampling have not been coordinated. Here we present a versatile spaceborne architecture that can juggle planet-scale and plant-level methane retrievals, challenge official emission reports, and remain relevant for stereoscopic measurements.
Paolo Dandini, Céline Cornet, Renaud Binet, Laetitia Fenouil, Vadim Holodovsky, Yoav Y. Schechner, Didier Ricard, and Daniel Rosenfeld
Atmos. Meas. Tech., 15, 6221–6242, https://doi.org/10.5194/amt-15-6221-2022, https://doi.org/10.5194/amt-15-6221-2022, 2022
Short summary
Short summary
3D cloud envelope and development velocity are retrieved from realistic simulations of multi-view
CLOUD (C3IEL) images. Cloud development velocity is derived by finding matching features
between acquisitions separated by 20 s. The tie points are then mapped from image to space via 3D
reconstruction of the cloud envelope obtained from 2 simultaneous images. The retrieved cloud
topography as well as the velocities are in good agreement with the estimates obtained from the
physical models.
Mengying Li, Shaocai Yu, Xue Chen, Zhen Li, Yibo Zhang, Zhe Song, Weiping Liu, Pengfei Li, Xiaoye Zhang, Meigen Zhang, Yele Sun, Zirui Liu, Caiping Sun, Jingkun Jiang, Shuxiao Wang, Benjamin N. Murphy, Kiran Alapaty, Rohit Mathur, Daniel Rosenfeld, and John H. Seinfeld
Atmos. Chem. Phys., 22, 11845–11866, https://doi.org/10.5194/acp-22-11845-2022, https://doi.org/10.5194/acp-22-11845-2022, 2022
Short summary
Short summary
This study constructed an emission inventory of condensable particulate matter (CPM) in China with a focus on organic aerosols (OAs), based on collected CPM emission information. The results show that OA emissions are enhanced twofold for the years 2014 and 2017 after the inclusion of CPM in the new inventory. Sensitivity cases demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to primary, secondary, and total OA concentrations.
Feiyue Mao, Ruixing Shi, Daniel Rosenfeld, Zengxin Pan, Lin Zang, Yannian Zhu, and Xin Lu
Atmos. Chem. Phys., 22, 10589–10602, https://doi.org/10.5194/acp-22-10589-2022, https://doi.org/10.5194/acp-22-10589-2022, 2022
Short summary
Short summary
Previous studies generally ignored the faint aerosols undetected by the CALIPSO layer detection algorithm because they are too optically thin. Here, we retrieved the faint aerosol extinction based on instantaneous CALIPSO observations with the constraint of SAGE data. The correlation and normalized root-mean-square error of the retrievals with independent SAGE data are 0.66 and 100.6 %, respectively. The minimum retrieved extinction at night can be extended to 10-4 km-1 with 125 % uncertainty.
Kai Zhang, Wentao Zhang, Hui Wan, Philip J. Rasch, Steven J. Ghan, Richard C. Easter, Xiangjun Shi, Yong Wang, Hailong Wang, Po-Lun Ma, Shixuan Zhang, Jian Sun, Susannah M. Burrows, Manish Shrivastava, Balwinder Singh, Yun Qian, Xiaohong Liu, Jean-Christophe Golaz, Qi Tang, Xue Zheng, Shaocheng Xie, Wuyin Lin, Yan Feng, Minghuai Wang, Jin-Ho Yoon, and L. Ruby Leung
Atmos. Chem. Phys., 22, 9129–9160, https://doi.org/10.5194/acp-22-9129-2022, https://doi.org/10.5194/acp-22-9129-2022, 2022
Short summary
Short summary
Here we analyze the effective aerosol forcing simulated by E3SM version 1 using both century-long free-running and short nudged simulations. The aerosol forcing in E3SMv1 is relatively large compared to other models, mainly due to the large indirect aerosol effect. Aerosol-induced changes in liquid and ice cloud properties in E3SMv1 have a strong correlation. The aerosol forcing estimates in E3SMv1 are sensitive to the parameterization changes in both liquid and ice cloud processes.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Suleiman Mostamandi, Paul A. Kucera, Duncan Axisa, William I. Gustafson Jr., and Yannian Zhu
Atmos. Chem. Phys., 22, 8659–8682, https://doi.org/10.5194/acp-22-8659-2022, https://doi.org/10.5194/acp-22-8659-2022, 2022
Short summary
Short summary
Rainfall affects the distribution of surface- and groundwater resources, which are constantly declining over the Middle East and North Africa (MENA) due to overexploitation. Here, we explored the effects of dust on rainfall using WRF-Chem model simulations. Although dust is considered a nuisance from an air quality perspective, our results highlight the positive fundamental role of dust particles in modulating rainfall formation and distribution, which has implications for cloud seeding.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
Short summary
Short summary
Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Ramon Campos Braga, Barbara Ervens, Daniel Rosenfeld, Meinrat O. Andreae, Jan-David Förster, Daniel Fütterer, Lianet Hernández Pardo, Bruna A. Holanda, Tina Jurkat-Witschas, Ovid O. Krüger, Oliver Lauer, Luiz A. T. Machado, Christopher Pöhlker, Daniel Sauer, Christiane Voigt, Adrian Walser, Manfred Wendisch, Ulrich Pöschl, and Mira L. Pöhlker
Atmos. Chem. Phys., 21, 17513–17528, https://doi.org/10.5194/acp-21-17513-2021, https://doi.org/10.5194/acp-21-17513-2021, 2021
Short summary
Short summary
Interactions of aerosol particles with clouds represent a large uncertainty in estimates of climate change. Properties of aerosol particles control their ability to act as cloud condensation nuclei. Using aerosol measurements in the Amazon, we performed model studies to compare predicted and measured cloud droplet number concentrations at cloud bases. Our results confirm previous estimates of particle hygroscopicity in this region.
Linhui Jiang, Yan Xia, Lu Wang, Xue Chen, Jianjie Ye, Tangyan Hou, Liqiang Wang, Yibo Zhang, Mengying Li, Zhen Li, Zhe Song, Yaping Jiang, Weiping Liu, Pengfei Li, Daniel Rosenfeld, John H. Seinfeld, and Shaocai Yu
Atmos. Chem. Phys., 21, 16985–17002, https://doi.org/10.5194/acp-21-16985-2021, https://doi.org/10.5194/acp-21-16985-2021, 2021
Short summary
Short summary
This paper establishes a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current inventories. The results show that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74 kg, 40 kg, 8 kg, and 2 kg, respectively. Integrating our traffic-monitoring-based approach with urban measurements, we could address major data gaps between urban air pollutant emissions and concentrations.
Ramon Campos Braga, Daniel Rosenfeld, Ovid O. Krüger, Barbara Ervens, Bruna A. Holanda, Manfred Wendisch, Trismono Krisna, Ulrich Pöschl, Meinrat O. Andreae, Christiane Voigt, and Mira L. Pöhlker
Atmos. Chem. Phys., 21, 14079–14088, https://doi.org/10.5194/acp-21-14079-2021, https://doi.org/10.5194/acp-21-14079-2021, 2021
Short summary
Short summary
Quantifying the precipitation within clouds is crucial for our understanding of the Earth's hydrological cycle. Using in situ measurements of cloud and rain properties over the Amazon Basin and Atlantic Ocean, we show here a linear relationship between the effective radius (re) and precipitation water content near the tops of convective clouds for different pollution states and temperature levels. Our results emphasize the role of re to determine both initiation and amount of precipitation.
Xin Lu, Feiyue Mao, Daniel Rosenfeld, Yannian Zhu, Zengxin Pan, and Wei Gong
Atmos. Chem. Phys., 21, 11979–12003, https://doi.org/10.5194/acp-21-11979-2021, https://doi.org/10.5194/acp-21-11979-2021, 2021
Short summary
Short summary
In this paper, a novel method for retrieving cloud base height and geometric thickness is developed and applied to produce a global climatology of boundary layer clouds with a high accuracy. The retrieval is based on the 333 m resolution low-level cloud distribution as obtained from the CALIPSO lidar data. The main part of the study describes the variability of cloud vertical geometrical properties in space, season, and time of the day. Resultant new insights are presented.
Yaman Liu, Xinyi Dong, Minghuai Wang, Louisa K. Emmons, Yawen Liu, Yuan Liang, Xiao Li, and Manish Shrivastava
Atmos. Chem. Phys., 21, 8003–8021, https://doi.org/10.5194/acp-21-8003-2021, https://doi.org/10.5194/acp-21-8003-2021, 2021
Short summary
Short summary
Secondary organic aerosol (SOA) is considered one of the most important uncertainties in climate modeling. We evaluate SOA performance in the Community Earth System Model version 2.1 (CESM2.1) configured with the Community Atmosphere Model version 6 with chemistry (CAM6-Chem) through a long-term simulation (1988–2019) with observations in the United States, which indicates monoterpene-formed SOA contributes most to the overestimation of SOA at the surface and underestimation in the upper air.
Tianmeng Chen, Zhanqing Li, Ralph A. Kahn, Chuanfeng Zhao, Daniel Rosenfeld, Jianping Guo, Wenchao Han, and Dandan Chen
Atmos. Chem. Phys., 21, 6199–6220, https://doi.org/10.5194/acp-21-6199-2021, https://doi.org/10.5194/acp-21-6199-2021, 2021
Short summary
Short summary
A convective cloud identification process is developed using geostationary satellite data from Himawari-8.
Convective cloud fraction is generally larger before noon and smaller in the afternoon under polluted conditions, but megacities and complex topography can influence the pattern.
A robust relationship between convective cloud and aerosol loading is found. This pattern varies with terrain height and is modulated by varying thermodynamic, dynamical, and humidity conditions during the day.
Yuwei Zhang, Jiwen Fan, Zhanqing Li, and Daniel Rosenfeld
Atmos. Chem. Phys., 21, 2363–2381, https://doi.org/10.5194/acp-21-2363-2021, https://doi.org/10.5194/acp-21-2363-2021, 2021
Short summary
Short summary
Impacts of anthropogenic aerosols on deep convective clouds (DCCs) and precipitation are examined using both the Morrison bulk and spectral bin microphysics (SBM) schemes. With the SBM scheme, anthropogenic aerosols notably invigorate convective intensity and precipitation, causing better agreement between the simulated DCCs and observations; this effect is absent with the Morrison scheme, mainly due to limitations of the saturation adjustment approach for droplet condensation and evaporation.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
Short summary
Short summary
Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Liqiang Wang, Shaocai Yu, Pengfei Li, Xue Chen, Zhen Li, Yibo Zhang, Mengying Li, Khalid Mehmood, Weiping Liu, Tianfeng Chai, Yannian Zhu, Daniel Rosenfeld, and John H. Seinfeld
Atmos. Chem. Phys., 20, 14787–14800, https://doi.org/10.5194/acp-20-14787-2020, https://doi.org/10.5194/acp-20-14787-2020, 2020
Short summary
Short summary
The Chinese government has made major strides in curbing anthropogenic emissions. In this study, we constrain a state-of-the-art CTM by a reliable data assimilation method with extensive chemical and meteorological observations. This comprehensive technical design provides a crucial advance in isolating the influences of emission changes and meteorological perturbations over the Yangtze River Delta (YRD) from 2016 to 2019, thus establishing the first map of the PM2.5 mitigation across the YRD.
Jiwen Fan, Yuwei Zhang, Zhanqing Li, Jiaxi Hu, and Daniel Rosenfeld
Atmos. Chem. Phys., 20, 14163–14182, https://doi.org/10.5194/acp-20-14163-2020, https://doi.org/10.5194/acp-20-14163-2020, 2020
Short summary
Short summary
We investigate the urbanization-induced land and aerosol impacts on convective clouds and precipitation over Houston. We find that Houston urbanization notably enhances storm intensity and precipitation, with the anthropogenic aerosol effect more significant. Urban land effect strengthens sea-breeze circulation, leading to a faster development of warm cloud into mixed-phase cloud and earlier rain. The anthropogenic aerosol effect accelerates the development of storms into deep convection.
Cited articles
Bony, S., Schulz, H., Vial, J., and Stevens, B.: Sugar, Gravel, Fish, and Flowers: Dependence of Mesoscale Patterns of Trade-Wind Clouds on Environmental Conditions, Geophys. Res. Lett., 47, e2019GL085988, https://doi.org/10.1029/2019GL085988, 2020.
Bretherton, C. S., McCoy, I. L., Mohrmann, J., Wood, R., Ghate, V., Gettelman, A., Bardeen, C. G., Albrecht, B. A., and Zuidema, P.: Cloud, Aerosol, and Boundary Layer Structure across the Northeast Pacific Stratocumulus–Cumulus Transition as Observed during CSET, Mon. Weather Rev., 147, 2083–2103, https://doi.org/10.1175/MWR-D-18-0281.1, 2019.
Cao, Y., Zhu, Y., Wang, M., Rosenfeld, D., Zhou, C., Liu, J., Liang, Y., Huang, K.-E., Wang, Q., Bai, H., Wang, Y., Wang, H., and Zhang, H.: Improving Prediction of Marine Low Clouds Using Cloud Droplet Number Concentration in a Convolutional Neural Network, J. Geophys. Res.-Machine Learning and Computation, 1, e2024JH000355, https://doi.org/10.1029/2024JH000355, 2024.
Climate Data Store: Data sets, Climate Data Store, https://cds.climate.copernicus.eu/datasets, last access: 1 July 2025.
Cox, D. T. C., Maclean, I. M. D., Gardner, A. S., and Gaston, K. J.: Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index, Glob. Change Biol., 26, 7099–7111, https://doi.org/10.1111/gcb.15336, 2020.
Dai, A.: Global Precipitation and Thunderstorm Frequencies. Part II: Diurnal Variations, J. Climate, 14, 1112–1128, https://doi.org/10.1175/1520-0442(2001)014<1112:GPATFP>2.0.CO;2, 2001.
Dai, A., Trenberth, K. E., and Karl, T. R.: Effects of Clouds, Soil Moisture, Precipitation, and Water Vapor on Diurnal Temperature Range, J. Climate, 12, 2451–2473, https://doi.org/10.1175/1520-0442(1999)012<2451:EOCSMP>2.0.CO;2, 1999.
Dai, A., Lin, X., and Hsu, K.-L.: The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes, Clim. Dynam., 29, 727–744, https://doi.org/10.1007/s00382-007-0260-y, 2007.
Davy, R., Esau, I., Chernokulsky, A., Outten, S., and Zilitinkevich, S.: Diurnal asymmetry to the observed global warming, Int. J. Climatol., 37, 79–93, https://doi.org/10.1002/joc.4688, 2017.
Eastman, R., McCoy, I. L., and Wood, R.: Wind, Rain, and the Closed to Open Cell Transition in Subtropical Marine Stratocumulus, J. Geophys. Res.-Atmos., 127, e2022JD036795, https://doi.org/10.1029/2022JD036795, 2022.
Eytan, E., Koren, I., Altaratz, O., Kostinski, A. B., and Ronen, A.: Longwave radiative effect of the cloud twilight zone, Nat. Geosci., 13, 669–673, https://doi.org/10.1038/s41561-020-0636-8, 2020.
Feingold, G., Koren, I., Wang, H., Xue, H., and Brewer, W. A.: Precipitation-generated oscillations in open cellular cloud fields, Nature, 466, 849–852, https://doi.org/10.1038/nature09314, 2010.
Goodfellow, I., Bengio, Y., and Courville, A.: Deep Learning, MIT Press, http://www.deeplearningbook.org (last access: 1 July 2025), 2016.
Goren, T., Kazil, J., Hoffmann, F., Yamaguchi, T., and Feingold, G.: Anthropogenic Air Pollution Delays Marine Stratocumulus Breakup to Open Cells, Geophys. Res. Lett., 46, 14135–14144, https://doi.org/10.1029/2019GL085412, 2019.
Guzel, M., Kalkan, M., Bostanci, E., Acici, K., and Asuroglu, T.: Cloud type classification using deep learning with cloud images, PeerJ Comput. Sci., 10, e1779, https://doi.org/10.7717/peerj-cs.1779, 2024.
Herman, G. and Goody, R.: Formation and Persistence of Summertime Arctic Stratus Clouds, J. Atmos. Sci., 33, 1537–1553, https://doi.org/10.1175/1520-0469(1976)033<1537:FAPOSA>2.0.CO;2, 1976.
Klein, S. A. and Hartmann, D. L.: The Seasonal Cycle of Low Stratiform Clouds, J. Climate, 6, 1587–1606, https://doi.org/10.1175/1520-0442(1993)006<1587:TSCOLS>2.0.CO;2, 1993.
Koonce, B.: ResNet 50, in: Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization, Apress, Berkeley, CA, 63–72, https://doi.org/10.1007/978-1-4842-6168-2_6, 2021.
LAADS: Your Source for Level-1 and Atmospheric Data, https://ladsweb.modaps.eosdis.nasa.gov/, last access: 1 July 2025.
Lang, F., Ackermann, L., Huang, Y., Truong, S. C. H., Siems, S. T., and Manton, M. J.: A climatology of open and closed mesoscale cellular convection over the Southern Ocean derived from Himawari-8 observations, Atmos. Chem. Phys., 22, 2135–2152, https://doi.org/10.5194/acp-22-2135-2022, 2022.
Lensky, I. M. and Rosenfeld, D.: Clouds-Aerosols-Precipitation Satellite Analysis Tool (CAPSAT), Atmos. Chem. Phys., 8, 6739–6753, https://doi.org/10.5194/acp-8-6739-2008, 2008.
Li, X., Qiu, B., Cao, G., Wu, C., and Zhang, L.: A Novel Method for Ground-Based Cloud Image Classification Using Transformer, Remote Sens., 14, 3978, https://doi.org/10.3390/rs14163978, 2022.
Luo, H., Quaas, J., and Han, Y.: Diurnally asymmetric cloud cover trends amplify greenhouse warming, Sci. Adv., 10, eado5179, https://doi.org/10.1126/sciadv.ado5179, 2024.
McCoy, I. L., Wood, R., and Fletcher, J. K.: Identifying Meteorological Controls on Open and Closed Mesoscale Cellular Convection Associated with Marine Cold Air Outbreaks, J. Geophys. Res.-Atmos., 122, 11678–11702, https://doi.org/10.1002/2017JD027031, 2017.
McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., and Bender, F. A.-M.: The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback, Geophys. Res. Lett., 50, e2022GL101042, https://doi.org/10.1029/2022GL101042, 2023.
Mohrmann, J., Wood, R., Yuan, T., Song, H., Eastman, R., and Oreopoulos, L.: Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications, Atmos. Chem. Phys., 21, 9629–9642, https://doi.org/10.5194/acp-21-9629-2021, 2021.
Muhlbauer, A., McCoy, I. L., and Wood, R.: Climatology of stratocumulus cloud morphologies: microphysical properties and radiative effects, Atmos. Chem. Phys., 14, 6695–6716, https://doi.org/10.5194/acp-14-6695-2014, 2014.
Petters, M. D., Snider, J. R., Stevens, B., Vali, G., Faloona, I., and Russell, L. M.: Accumulation mode aerosol, pockets of open cells, and particle nucleation in the remote subtropical Pacific marine boundary layer, J. Geophys. Res.-Atmos., 111, D02206, https://doi.org/10.1029/2004JD005694, 2006.
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE T. Geosci. Remote Sens., 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017.
Rosenfeld, D., Kaufman, Y. J., and Koren, I.: Switching cloud cover and dynamical regimes from open to closed Benard cells in response to the suppression of precipitation by aerosols, Atmos. Chem. Phys., 6, 2503–2511, https://doi.org/10.5194/acp-6-2503-2006, 2006.
Savic-Jovcic, V. and Stevens, B.: The Structure and Mesoscale Organization of Precipitating Stratocumulus, J. Atmos. Sci., 65, 1587–1605, https://doi.org/10.1175/2007JAS2456.1, 2008.
Schulz, H., Eastman, R., and Stevens, B.: Characterization and Evolution of Organized Shallow Convection in the Downstream North Atlantic Trades, J. Geophys. Res.-Atmos., 126, e2021JD034575, https://doi.org/10.1029/2021JD034575, 2021.
Segal Rozenhaimer, M., Nukrai, D., Che, H., Wood, R., and Zhang, Z.: Cloud Mesoscale Cellular Classification and Diurnal Cycle Using a Convolutional Neural Network (CNN), Remote Sens., 15, 1607, https://doi.org/10.3390/rs15061607, 2023.
Stevens, B., Vali, G., Comstock, K., Wood, R., van Zanten, M. C., Austin, P. H., Bretherton, C. S., and Lenschow, D. H.: POCKETS OF OPEN CELLS AND DRIZZLE IN MARINE STRATOCUMULUS, B. Am. Meteorol. Soc., 86, 51–58, https://doi.org/10.1175/BAMS-86-1-51, 2005.
Tornow, F., Ackerman, A. S., and Fridlind, A. M.: Preconditioning of overcast-to-broken cloud transitions by riming in marine cold air outbreaks, Atmos. Chem. Phys., 21, 12049–12067, https://doi.org/10.5194/acp-21-12049-2021, 2021.
Vose, R. S., Easterling, D. R., and Gleason, B.: Maximum and minimum temperature trends for the globe: An update through 2004, Geophys. Res. Lett., 32, L23822, https://doi.org/10.1029/2005GL024379, 2005.
Wang, H. and Feingold, G.: Modeling Mesoscale Cellular Structures and Drizzle in Marine Stratocumulus. Part I: Impact of Drizzle on the Formation and Evolution of Open Cells, J. Atmos. Sci., 66, 3237–3256, https://doi.org/10.1175/2009JAS3022.1, 2009.
Wang, Q., Zhou, C., Zhuge, X., Liu, C., Weng, F., and Wang, M.: Retrieval of cloud properties from thermal infrared radiometry using convolutional neural network, Remote Sens. Environ., 278, 113079, https://doi.org/10.1016/j.rse.2022.113079, 2022 (code available at: https://github.com/WgQuan/cloud-property-retrievals, last access: 2021).
Watson-Parris, D., Sutherland, S. A., Christensen, M. W., Eastman, R., and Stier, P.: A Large-Scale Analysis of Pockets of Open Cells and Their Radiative Impact, Geophys. Res. Lett., 48, e2020GL092213, https://doi.org/10.1029/2020GL092213, 2021.
Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423, https://doi.org/10.1175/MWR-D-11-00121.1, 2012.
Wood, R. and Hartmann, D. L.: Spatial Variability of Liquid Water Path in Marine Low Cloud: The Importance of Mesoscale Cellular Convection, J. Climate, 19, 1748–1764, https://doi.org/10.1175/JCLI3702.1, 2006.
Wu, Y., Liu, J., Zhu, Y., Zhang, Y., Cao, Y., Huang, K.-E., Zheng, B., Wang, Y., Wang, Q., Zhou, C., Liang, Y., Wang, M., and Rosenfeld, D.: Global Classification Dataset of Daytime and Nighttime Marine Low-cloud Mesoscale Morphology, Zenodo [data set], https://doi.org/10.5281/zenodo.13801408, 2024.
Wu, Y., Liu, J., Zhu, Y., Zhang, Y., Cao, Y., Huang, K.-E., Zheng, B., and Wang, Y.: Cloud-morphology-dataset, Zenodo [code], https://doi.org/10.5281/zenodo.15776240, 2025.
Wyant, M. C., Bretherton, C. S., Rand, H. A., and Stevens, D. E.: Numerical Simulations and a Conceptual Model of the Stratocumulus to Trade Cumulus Transition, J. Atmos. Sci., 54, 168–192, https://doi.org/10.1175/1520-0469(1997)054<0168:NSAACM>2.0.CO;2, 1997.
Xue, H., Feingold, G., and Stevens, B.: Aerosol Effects on Clouds, Precipitation, and the Organization of Shallow Cumulus Convection, J. Atmos. Sci., 65, 392–406, https://doi.org/10.1175/2007JAS2428.1, 2008.
Yamaguchi, T. and Feingold, G.: On the relationship between open cellular convective cloud patterns and the spatial distribution of precipitation, Atmos. Chem. Phys., 15, 1237–1251, https://doi.org/10.5194/acp-15-1237-2015, 2015.
Yuan, T., Song, H., Wood, R., Mohrmann, J., Meyer, K., Oreopoulos, L., and Platnick, S.: Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology, Atmos. Meas. Tech., 13, 6989–6997, https://doi.org/10.5194/amt-13-6989-2020, 2020.
Zhang, J., Liu, P., Zhang, F., and Song, Q.: CloudNet: Ground-Based Cloud Classification With Deep Convolutional Neural Network, Geophys. Res. Lett., 45, 8665–8672, https://doi.org/10.1029/2018GL077787, 2018.
Zhu, Y., Liu, J., Wang, M., and Rosenfeld, D.: Cloud Susceptibility to Aerosols: Comparing Cloud-Appearance vs. Cloud-Controlling Factors Regimes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4059, https://doi.org/10.5194/egusphere-egu24-4059, 2024.
Short summary
Based on a deep-learning method, we established a global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology. This aims to promote a comprehensive understanding of cloud dynamics and cloud–climate feedback. Closed mesoscale cellular convection (MCC) clouds occur more frequently at night, while suppressed cumulus clouds exhibit remarkable decreases. Solid stratus and MCC cloud types show clear seasonal variations.
Based on a deep-learning method, we established a global classification dataset of daytime and...
Altmetrics
Final-revised paper
Preprint