Articles | Volume 21, issue 22
https://doi.org/10.5194/acp-21-16843-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-16843-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Midlatitude mixed-phase stratocumulus clouds and their interactions with aerosols: how ice processes affect microphysical, dynamic, and thermodynamic development in those clouds and interactions?
Seoung Soo Lee
CORRESPONDING AUTHOR
Earth System Science Interdisciplinary Center, University of Maryland,
College Park, Maryland, USA
Research Center for Climate Sciences, Pusan National University,
Busan, Republic of Korea
Kyung-Ja Ha
Research Center for Climate Sciences, Pusan National University,
Busan, Republic of Korea
Center for Climate Physics, Institute for Basic Science, Busan,
Republic of Korea
BK21 School of Earth and Environmental Systems, Pusan National
University, Busan, Republic of Korea
Manguttathil Gopalakrishnan Manoj
Advanced Centre for Atmospheric Radar Research, Cochin University of
Science and Technology, Kerala, India
Mohammad Kamruzzaman
School of Mathematical Sciences, University of Adelaide, Adelaide,
Australia
Natural and Built Environments Research Centre, Division of
Information Technology,
Engineering and the Environment (ITEE), University of South Australia,
Adelaide, Australia
Hyungjun Kim
Institute of Industrial Science, University of Tokyo, Tokyo, Japan
Moon Soul Graduate School of Future Strategy, Korea Advanced Institute
of Science and Technology, Daejeon, Republic of Korea
Department of Civil and Environmental Engineering, Korea Advanced
Institute of Science and Technology, Daejeon, Republic of Korea
Nobuyuki Utsumi
Nagamori Institute of Actuators, Kyoto University of Advanced
Science, Japan
Youtong Zheng
The Program in Atmospheric and Oceanic Sciences, Princeton
University, and National Oceanic and Atmospheric Administration/Geophysical
Fluid Dynamics Laboratory, Princeton, New Jersey, USA
Byung-Gon Kim
Department of Atmospheric Environmental Sciences, Gangneung–Wonju
National University, Gangneung, Republic of Korea
Chang Hoon Jung
Department of Health Management, Kyung-in Women's University, Incheon,
Republic of Korea
Junshik Um
Department of Atmospheric Sciences, Division of Earth Environmental
System, Busan, Republic of Korea
Jianping Guo
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Kyoung Ock Choi
Department of Atmospheric Sciences, University of Washington,
Seattle, Washington, USA
Department of Atmospheric Sciences, Yonsei University, Seoul,
Republic of Korea
Go-Un Kim
Marine Disaster Research Center, Korea Institute of Ocean Science and
Technology, Busan, Republic of Korea
Related authors
Seoung Soo Lee, Chang Hoon Jung, Jinho Choi, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, Sang-Keun Song, and Kyung-Ja Ha
Atmos. Chem. Phys., 25, 705–726, https://doi.org/10.5194/acp-25-705-2025, https://doi.org/10.5194/acp-25-705-2025, 2025
Short summary
Short summary
This study attempts to test a general factor that explains differences in the properties of different mixed-phase clouds using a modeling tool. Although this attempt is not to identify a factor that can perfectly explain and represent the properties of different mixed-phase clouds, we believe that this attempt acts as a valuable stepping stone towards a more complete, general way of using climate models to better predict climate change.
Jeonggyu Kim, Sungmin Park, Greg M. McFarquhar, Anthony J. Baran, Joo Wan Cha, Kyoungmi Lee, Seoung Soo Lee, Chang Hoon Jung, Kyo-Sun Sunny Lim, and Junshik Um
Atmos. Chem. Phys., 24, 12707–12726, https://doi.org/10.5194/acp-24-12707-2024, https://doi.org/10.5194/acp-24-12707-2024, 2024
Short summary
Short summary
We developed idealized models to represent the shapes of ice particles found in deep convective clouds and calculated their single-scattering properties. By comparing these results with in situ measurements, we discovered that a mixture of shape models matches in situ measurements more closely than single-form models or aggregate models. This finding has important implications for enhancing the simulation of single-scattering properties of ice crystals in deep convective clouds.
Seoung Soo Lee, Junshik Um, Won Jun Choi, Kyung-Ja Ha, Chang Hoon Jung, Jianping Guo, and Youtong Zheng
Atmos. Chem. Phys., 23, 273–286, https://doi.org/10.5194/acp-23-273-2023, https://doi.org/10.5194/acp-23-273-2023, 2023
Short summary
Short summary
This paper elaborates on process-level mechanisms regarding how the interception of radiation by aerosols interacts with the surface heat fluxes and atmospheric instability in warm cumulus clouds. This paper elucidates how these mechanisms vary with the location or altitude of an aerosol layer. This elucidation indicates that the location of aerosol layers should be taken into account for parameterizations of aerosol–cloud interactions.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
Short summary
Short summary
This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data, 17, 4023–4037, https://doi.org/10.5194/essd-17-4023-2025, https://doi.org/10.5194/essd-17-4023-2025, 2025
Short summary
Short summary
This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China's growing low-altitude economy, and is accessible at https://doi.org/10.5281/zenodo.14959025.
Xiaoran Guo, Jianping Guo, Deli Meng, Yuping Sun, Zhen Zhang, Hui Xu, Liping Zeng, Juan Chen, Ning Li, and Tianmeng Chen
Earth Syst. Sci. Data, 17, 3541–3552, https://doi.org/10.5194/essd-17-3541-2025, https://doi.org/10.5194/essd-17-3541-2025, 2025
Short summary
Short summary
Optimal atmospheric dynamic conditions are essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of a radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present ahead of rainfall events. This suggests that this dataset can help improve our understanding of the pre-storm environment and has the potential to be applied in weather forecasting.
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
Short summary
A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Rongkang Yang, Peng Zhang, Jianping Guo, Jincheng Wang, Die Xiao, Jianping Du, Zhongliang Sun, Shijun Liu, Sijie Chen, and Anfan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2012, https://doi.org/10.5194/egusphere-2025-2012, 2025
Short summary
Short summary
This study aims to introduce in-situ profiling techniques and cost-effective technology for upper-air observation—the Round-trip Drifting Sounding System (RDSS)—which reduces costs relative to intensive sounding and achieves three sounding phases: Ascent-Drift-Descent (ADD). The RDSS not only provides additional data for weather analysis and numerical prediction models but also makes substantial contributions to targeted observations.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Seoung Soo Lee, Chang Hoon Jung, Jinho Choi, Young Jun Yoon, Junshik Um, Youtong Zheng, Jianping Guo, Manguttathil G. Manoj, Sang-Keun Song, and Kyung-Ja Ha
Atmos. Chem. Phys., 25, 705–726, https://doi.org/10.5194/acp-25-705-2025, https://doi.org/10.5194/acp-25-705-2025, 2025
Short summary
Short summary
This study attempts to test a general factor that explains differences in the properties of different mixed-phase clouds using a modeling tool. Although this attempt is not to identify a factor that can perfectly explain and represent the properties of different mixed-phase clouds, we believe that this attempt acts as a valuable stepping stone towards a more complete, general way of using climate models to better predict climate change.
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024, https://doi.org/10.5194/essd-16-5753-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
Short summary
Short summary
This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Jeonggyu Kim, Sungmin Park, Greg M. McFarquhar, Anthony J. Baran, Joo Wan Cha, Kyoungmi Lee, Seoung Soo Lee, Chang Hoon Jung, Kyo-Sun Sunny Lim, and Junshik Um
Atmos. Chem. Phys., 24, 12707–12726, https://doi.org/10.5194/acp-24-12707-2024, https://doi.org/10.5194/acp-24-12707-2024, 2024
Short summary
Short summary
We developed idealized models to represent the shapes of ice particles found in deep convective clouds and calculated their single-scattering properties. By comparing these results with in situ measurements, we discovered that a mixture of shape models matches in situ measurements more closely than single-form models or aggregate models. This finding has important implications for enhancing the simulation of single-scattering properties of ice crystals in deep convective clouds.
Deli Meng, Jianping Guo, Xiaoran Guo, Yinjun Wang, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Haoran Li, Fan Zhang, Bing Tong, Hui Xu, and Tianmeng Chen
Atmos. Chem. Phys., 24, 8703–8720, https://doi.org/10.5194/acp-24-8703-2024, https://doi.org/10.5194/acp-24-8703-2024, 2024
Short summary
Short summary
The turbulence in the planetary boundary layer (PBL) over the Tibetan Plateau (TP) remains unclear. Here we elucidate the vertical profile of and temporal variation in the turbulence dissipation rate in the PBL over the TP based on a radar wind profiler (RWP) network. To the best of our knowledge, this is the first time that the turbulence profile over the whole TP has been revealed. Furthermore, the possible mechanisms of clouds acting on the PBL turbulence structure are investigated.
Xiaoran Guo, Jianping Guo, Tianmeng Chen, Ning Li, Fan Zhang, and Yuping Sun
Atmos. Chem. Phys., 24, 8067–8083, https://doi.org/10.5194/acp-24-8067-2024, https://doi.org/10.5194/acp-24-8067-2024, 2024
Short summary
Short summary
The prediction of downhill thunderstorms (DSs) remains elusive. We propose an objective method to identify DSs, based on which enhanced and dissipated DSs are discriminated. A radar wind profiler (RWP) mesonet is used to derive divergence and vertical velocity. The mid-troposphere divergence and prevailing westerlies enhance the intensity of DSs, whereas low-level divergence is observed when the DS dissipates. The findings highlight the key role that an RWP mesonet plays in the evolution of DSs.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
Short summary
Short summary
A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024, https://doi.org/10.5194/acp-24-4047-2024, 2024
Short summary
Short summary
Accurate wind profile estimation, especially for the lowest few hundred meters of the atmosphere, is of great significance for the weather, climate, and renewable energy sector. We propose a novel method that combines the power-law method with the random forest algorithm to extend wind profiles beyond the surface layer. Compared with the traditional algorithm, this method has better stability and spatial applicability and can be used to obtain the wind profiles on different land cover types.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
Short summary
Short summary
A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Hui Xu, Jianping Guo, Bing Tong, Jinqiang Zhang, Tianmeng Chen, Xiaoran Guo, Jian Zhang, and Wenqing Chen
Atmos. Chem. Phys., 23, 15011–15038, https://doi.org/10.5194/acp-23-15011-2023, https://doi.org/10.5194/acp-23-15011-2023, 2023
Short summary
Short summary
The radiative effect of cloud remains one of the largest uncertain factors in climate change, largely due to the lack of cloud vertical structure (CVS) observations. The study presents the first near-global CVS climatology using high-vertical-resolution soundings. Single-layer cloud mainly occurs over arid regions. As the number of cloud layers increases, clouds tend to have lower bases and thinner layer thicknesses. The occurrence frequency of cloud exhibits a pronounced seasonal diurnal cycle.
Jiyeon Park, Hyojin Kang, Yeontae Gim, Eunho Jang, Ki-Tae Park, Sangjong Park, Chang Hoon Jung, Darius Ceburnis, Colin O'Dowd, and Young Jun Yoon
Atmos. Chem. Phys., 23, 13625–13646, https://doi.org/10.5194/acp-23-13625-2023, https://doi.org/10.5194/acp-23-13625-2023, 2023
Short summary
Short summary
We measured the number size distribution of 2.5–300 nm particles and cloud condensation nuclei (CCN) number concentrations at King Sejong Station on the Antarctic Peninsula continuously from 1 January to 31 December 2018. During the pristine and clean periods, 97 new particle formation (NPF) events were detected. For 83 of these, CCN concentrations increased by 2 %–268 % (median 44 %) following 1 to 36 h (median 8 h) after NPF events.
Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong
Atmos. Chem. Phys., 23, 3181–3193, https://doi.org/10.5194/acp-23-3181-2023, https://doi.org/10.5194/acp-23-3181-2023, 2023
Short summary
Short summary
Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Seoung Soo Lee, Junshik Um, Won Jun Choi, Kyung-Ja Ha, Chang Hoon Jung, Jianping Guo, and Youtong Zheng
Atmos. Chem. Phys., 23, 273–286, https://doi.org/10.5194/acp-23-273-2023, https://doi.org/10.5194/acp-23-273-2023, 2023
Short summary
Short summary
This paper elaborates on process-level mechanisms regarding how the interception of radiation by aerosols interacts with the surface heat fluxes and atmospheric instability in warm cumulus clouds. This paper elucidates how these mechanisms vary with the location or altitude of an aerosol layer. This elucidation indicates that the location of aerosol layers should be taken into account for parameterizations of aerosol–cloud interactions.
Kathrin Wehrli, Fei Luo, Mathias Hauser, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, Dim Coumou, Wilhelm May, Philippe Le Sager, Frank Selten, Olivia Martius, Robert Vautard, and Sonia I. Seneviratne
Earth Syst. Dynam., 13, 1167–1196, https://doi.org/10.5194/esd-13-1167-2022, https://doi.org/10.5194/esd-13-1167-2022, 2022
Short summary
Short summary
The ExtremeX experiment was designed to unravel the contribution of processes leading to the occurrence of recent weather and climate extremes. Global climate simulations are carried out with three models. The results show that in constrained experiments, temperature anomalies during heatwaves are well represented, although climatological model biases remain. Further, a substantial contribution of both atmospheric circulation and soil moisture to heat extremes is identified.
Fei Luo, Frank Selten, Kathrin Wehrli, Kai Kornhuber, Philippe Le Sager, Wilhelm May, Thomas Reerink, Sonia I. Seneviratne, Hideo Shiogama, Daisuke Tokuda, Hyungjun Kim, and Dim Coumou
Weather Clim. Dynam., 3, 905–935, https://doi.org/10.5194/wcd-3-905-2022, https://doi.org/10.5194/wcd-3-905-2022, 2022
Short summary
Short summary
Recent studies have identified the weather systems in observational data, where wave patterns with high-magnitude values that circle around the whole globe in either wavenumber 5 or wavenumber 7 are responsible for the extreme events. In conclusion, we find that the climate models are able to reproduce the large-scale atmospheric circulation patterns as well as their associated surface variables such as temperature, precipitation, and sea level pressure.
Seoung Soo Lee, Jinho Choi, Goun Kim, Kyung-Ja Ha, Kyong-Hwan Seo, Chang Hoon Jung, Junshik Um, Youtong Zheng, Jianping Guo, Sang-Keun Song, Yun Gon Lee, and Nobuyuki Utsumi
Atmos. Chem. Phys., 22, 9059–9081, https://doi.org/10.5194/acp-22-9059-2022, https://doi.org/10.5194/acp-22-9059-2022, 2022
Short summary
Short summary
This study investigates how aerosols affect clouds and precipitation and how the aerosol effects vary with varying types of clouds that are characterized by cloud depth in two metropolitan areas in East Asia. As cloud depth increases, the enhancement of precipitation amount transitions to no changes in precipitation amount with increasing aerosol concentrations. This indicates that cloud depth needs to be considered for a comprehensive understanding of aerosol-cloud interactions.
Peilin Song, Yongqiang Zhang, Jianping Guo, Jiancheng Shi, Tianjie Zhao, and Bing Tong
Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, https://doi.org/10.5194/essd-14-2613-2022, 2022
Short summary
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Kaixu Bai, Ke Li, Mingliang Ma, Kaitao Li, Zhengqiang Li, Jianping Guo, Ni-Bin Chang, Zhuo Tan, and Di Han
Earth Syst. Sci. Data, 14, 907–927, https://doi.org/10.5194/essd-14-907-2022, https://doi.org/10.5194/essd-14-907-2022, 2022
Short summary
Short summary
The Long-term Gap-free High-resolution Air Pollutant concentration dataset, providing gap-free aerosol optical depth (AOD) and PM2.5 and PM10 concentration with a daily 1 km resolution for 2000–2020 in China, is generated and made publicly available. This is the first long-term gap-free high-resolution aerosol dataset in China and has great potential to trigger multidisciplinary applications in Earth observations, climate change, public health, ecosystem assessment, and environment management.
Linye Song, Shangfeng Chen, Wen Chen, Jianping Guo, Conglan Cheng, and Yong Wang
Atmos. Chem. Phys., 22, 1669–1688, https://doi.org/10.5194/acp-22-1669-2022, https://doi.org/10.5194/acp-22-1669-2022, 2022
Short summary
Short summary
This study shows that in most years when haze pollution (HP) over the North China Plain (NCP) is more (less) serious in winter, air conditions in the following spring are also worse (better) than normal. Conversely, there are some years when HP in the following spring is opposed to that in winter. It is found that North Atlantic sea surface temperature (SST) anomalies play important roles in HP evolution over the NCP. Thus North Atlantic SST is an important preceding signal for NCP HP evolution.
Tianning Su, Youtong Zheng, and Zhanqing Li
Atmos. Chem. Phys., 22, 1453–1466, https://doi.org/10.5194/acp-22-1453-2022, https://doi.org/10.5194/acp-22-1453-2022, 2022
Short summary
Short summary
To enrich our understanding of coupling of continental clouds, we developed a novel methodology to determine cloud coupling state from a lidar and a suite of surface meteorological instruments. This method is built upon advancement in our understanding of fundamental boundary layer processes and clouds. As the first remote sensing method for determining the coupling state of low clouds over land, this methodology paves a solid ground for further investigating the coupled land–atmosphere system.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26, https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
Short summary
Short summary
Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is the comparison of wind speed on a large scale between the Aeolus, ERA5 and RS , shedding important light on the data application of Aeolus wind products.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Jianping Guo, Jian Zhang, Kun Yang, Hong Liao, Shaodong Zhang, Kaiming Huang, Yanmin Lv, Jia Shao, Tao Yu, Bing Tong, Jian Li, Tianning Su, Steve H. L. Yim, Ad Stoffelen, Panmao Zhai, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, https://doi.org/10.5194/acp-21-17079-2021, 2021
Short summary
Short summary
The planetary boundary layer (PBL) is the lowest part of the troposphere, and boundary layer height (BLH) is the depth of the PBL and is of critical importance to the dispersion of air pollution. The study presents the first near-global BLH climatology by using high-resolution (5-10 m) radiosonde measurements. The variations in BLH exhibit large spatial and temporal dependence, with a peak at 17:00 local solar time. The most promising reanalysis product is ERA-5 in terms of modeling BLH.
Daisuke Tokuda, Hyungjun Kim, Dai Yamazaki, and Taikan Oki
Geosci. Model Dev., 14, 5669–5693, https://doi.org/10.5194/gmd-14-5669-2021, https://doi.org/10.5194/gmd-14-5669-2021, 2021
Short summary
Short summary
We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
Ifeanyichukwu C. Nduka, Chi-Yung Tam, Jianping Guo, and Steve Hung Lam Yim
Atmos. Chem. Phys., 21, 13443–13454, https://doi.org/10.5194/acp-21-13443-2021, https://doi.org/10.5194/acp-21-13443-2021, 2021
Short summary
Short summary
This study analyzed the nature, mechanisms and drivers for hot-and-polluted episodes (HPEs) in the Pearl River Delta, China. A total of eight HPEs were identified and can be grouped into three clusters of HPEs that were respectively driven (1) by weak subsidence and convection induced by approaching tropical cyclones, (2) by calm conditions with low wind speed in the lower atmosphere and (3) by the combination of both aforementioned conditions.
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.
Jianping Guo, Boming Liu, Wei Gong, Lijuan Shi, Yong Zhang, Yingying Ma, Jian Zhang, Tianmeng Chen, Kaixu Bai, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys., 21, 2945–2958, https://doi.org/10.5194/acp-21-2945-2021, https://doi.org/10.5194/acp-21-2945-2021, 2021
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China have thus far not been evaluated by in situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future research and applications.
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-41, https://doi.org/10.5194/acp-2021-41, 2021
Revised manuscript not accepted
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. Aeolus is the first satellite mission to directly observe wind profile information on a global scale. However, Aeolus wind products over China were thus far not evaluated by in-situ comparison. This work is expected to let the public and science community better know the Aeolus wind products and to encourage use of these valuable data in future researches and applications.
Richard Essery, Hyungjun Kim, Libo Wang, Paul Bartlett, Aaron Boone, Claire Brutel-Vuilmet, Eleanor Burke, Matthias Cuntz, Bertrand Decharme, Emanuel Dutra, Xing Fang, Yeugeniy Gusev, Stefan Hagemann, Vanessa Haverd, Anna Kontu, Gerhard Krinner, Matthieu Lafaysse, Yves Lejeune, Thomas Marke, Danny Marks, Christoph Marty, Cecile B. Menard, Olga Nasonova, Tomoko Nitta, John Pomeroy, Gerd Schädler, Vladimir Semenov, Tatiana Smirnova, Sean Swenson, Dmitry Turkov, Nander Wever, and Hua Yuan
The Cryosphere, 14, 4687–4698, https://doi.org/10.5194/tc-14-4687-2020, https://doi.org/10.5194/tc-14-4687-2020, 2020
Short summary
Short summary
Climate models are uncertain in predicting how warming changes snow cover. This paper compares 22 snow models with the same meteorological inputs. Predicted trends agree with observations at four snow research sites: winter snow cover does not start later, but snow now melts earlier in spring than in the 1980s at two of the sites. Cold regions where snow can last until late summer are predicted to be particularly sensitive to warming because the snow then melts faster at warmer times of year.
Kaixu Bai, Ke Li, Chengbo Wu, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 12, 3067–3080, https://doi.org/10.5194/essd-12-3067-2020, https://doi.org/10.5194/essd-12-3067-2020, 2020
Short summary
Short summary
PM2.5 data from the national air quality monitoring network in China suffered from significant inconsistency and inhomogeneity issues. To create a coherent PM2.5 concentration dataset to advance our understanding of haze pollution and its impact on weather and climate, we homogenized this PM2.5 dataset between 2015 and 2019 after filling in the data gaps. The homogenized PM2.5 data is found to better characterize the variation of aerosol in space and time compared to the original dataset.
Pengguo Zhao, Zhanqing Li, Hui Xiao, Fang Wu, Youtong Zheng, Maureen C. Cribb, Xiaoai Jin, and Yunjun Zhou
Atmos. Chem. Phys., 20, 13379–13397, https://doi.org/10.5194/acp-20-13379-2020, https://doi.org/10.5194/acp-20-13379-2020, 2020
Short summary
Short summary
We discussed the different aerosol effects on lightning in plateau and basin regions of Sichuan, southwestern China. In the plateau area, the aerosol concentration is low, and aerosols (via microphysical effects) inhibit the process of warm rain and stimulate convection and lightning activity. In the basin region, however, aerosols tend to show a significant radiative effect (reducing the solar radiation reaching the surface by absorbing and scattering) and inhibit the lightning.
Yang Yang, Min Chen, Xiujuan Zhao, Dan Chen, Shuiyong Fan, Jianping Guo, and Shaukat Ali
Atmos. Chem. Phys., 20, 12527–12547, https://doi.org/10.5194/acp-20-12527-2020, https://doi.org/10.5194/acp-20-12527-2020, 2020
Short summary
Short summary
This study analyzed the impacts of aerosol–radiation interaction on radiation and meteorological forecasts using the offline coupling of WRF and high-frequency updated AOD simulated by WRF-Chem. The results revealed that aerosol–radiation interaction had a positive influence on the improvement of predictive accuracy, including 2 m temperature (~ 73.9 %) and horizontal wind speed (~ 7.8 %), showing potential prospects for its application in regional numerical weather prediction in northern China.
Ruqian Miao, Qi Chen, Yan Zheng, Xi Cheng, Yele Sun, Paul I. Palmer, Manish Shrivastava, Jianping Guo, Qiang Zhang, Yuhan Liu, Zhaofeng Tan, Xuefei Ma, Shiyi Chen, Limin Zeng, Keding Lu, and Yuanhang Zhang
Atmos. Chem. Phys., 20, 12265–12284, https://doi.org/10.5194/acp-20-12265-2020, https://doi.org/10.5194/acp-20-12265-2020, 2020
Short summary
Short summary
In this study we evaluated the model performances for simulating secondary inorganic aerosol (SIA) and organic aerosol (OA) in PM2.5 in China against comprehensive datasets. The potential biases from factors related to meteorology, emission, chemistry, and atmospheric removal are systematically investigated. This study provides a comprehensive understanding of modeling PM2.5, which is important for studies on the effectiveness of emission control strategies.
Boming Liu, Jianping Guo, Wei Gong, Lijuan Shi, Yong Zhang, and Yingying Ma
Atmos. Meas. Tech., 13, 4589–4600, https://doi.org/10.5194/amt-13-4589-2020, https://doi.org/10.5194/amt-13-4589-2020, 2020
Short summary
Short summary
Vertical wind profiles are crucial to a wide range of atmospheric disciplines. However, the wind profile across China remains poorly understood. Here we reveal the salient features of winds from the radar wind profile of China, including the main instruments, spatial coverage and sampling frequency. This work is expected to allow the public and scientific community to be more familiar with the nationwide network and encourage the use of these valuable data in future research and applications.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-378, https://doi.org/10.5194/hess-2020-378, 2020
Revised manuscript not accepted
Cited articles
Ackerman, A. S., Kirkpatrick, M. P., Stevens, D. E., and Toon, O. B.: The
impact of humidity above stratiform clouds on indirect aerosol climate
forcing, Nature, 432, 1014–1017, 2004.
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness,
Science, 245, 1227–1230, 1989.
Bergeron, T.: On the physics of clouds and precipitation. Proces Verbaux de
l`Association de Meteorologie, International Union of Geodesy and
Geophysics, 156–178, 1935.
Bodas-Salcedo, A., Hill, P. G., Furtado, K., Williams, K. D., Field, P. R.,
Manners, J. C., Hyder, P., and Kato, S.: Large contribution of supercooled
liquid clouds to the solar radiation budget of the Southern Ocean, J.
Climate, 29, 4213–4228, https://doi.org/10.1175/JCLI-D-15-0564.1, 2016.
Borys, R. D., Lowenthal, D. H., Cohn, S. A. and Brown, W. O. J.: Mountaintop
and radar measurements of anthropogenic aerosol effects on snow growth and
snowfall rate, Geophys. Res. Lett., 30, 1538, https://doi.org/10.1029/2002GL016855, 2003.
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly,
A.: Unified modeling and prediction of weather and climate: A 25-year
journey, B. Am Meteorol. Soc., 93, 1865–1877, 2012.
Chen, F. and Dudhia, J.: Coupling an advanced land-surface hydrology model
with the Penn State-NCAR MM5 modeling system. Part I: Model description and
implementation, Mon. Weather Rev., 129, 569–585, 2001.
Dong, X. and Mace, G. G.: Arctic stratus cloud properties and radiative
forcing derived from ground-based data collected at Barrow, Alaska, J.
Climate, 16, 445–461, https://doi.org/10.1175/1520-0442(2003)016<0445:ASCPAR>2.0.CO;2,
2003.
Eun, S.-H., Kim, B.-G., Lee, K.-M., and Park, J.-S.: Characteristics of
recent severe haze events in Korea and possible inadvertent weather
modification, SOLA, 12, 32–36, 2016.
Faller, K.: MTSAT-1R: A multifunctional satellite for Japan and the
Asia-Pacific region, Proceedings of the 56th IAC 2005, Fukuoda, Japan, 17–21 October
2005, IAC-05-B3.2.04, 2005.
Fan, J., Yuan, T., Comstock, J. M., Ghan, S., Khain, A., Leung, L. R., Li, Z., Martins, V. J., and Ovchinnikov, M.: Dominant role by vertical wind
shear in regulating aerosol effects on deep convective clouds, J. Geophys.
Res., 114, D22206, https://doi.org/10.1029/2009JD012352, 2009.
Findeisen, W.: Kolloid-meteorologische Vorgange bei Neiderschlagsbildung,
Meteorol. Z., 55, 121–133, 1938.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., and Van Dorland, R.: Changes in atmospheric constituents and in radiative
forcing, in: Climate change 2007: the physical science basis, Contribution
of working group I to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: SSolomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge Univ.
Press, New York, 2007.
Fouquart, Y. and Bonnel, B.: Computation of solar heating of the Earth's
atmosphere: a new parameterization, Beitr. Phys. Atmos., 53, 35–62, 1980.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost,
G., Skamarock, W. C., and Eder, B.: Fully coupled online chemistry in the
WRF model, Atmos. Environ., 39, 6957–6976, 2005.
Guo, J., Deng, M., Lee, S. S., Wang, F., Li, Z., Zhai, P., Liu, H., Lv, W., Yao, W.,
and Li, X.: Delaying precipitation and lightning by air pollution over the
Pearl River Delta. Part I: Observational analyses, J. Geophys. Res.-Atmos.,
121, 6472–6488, https://doi.org/10.1002/2015JD023257, 2016.
Ha, K.-J., Nam, S., Jeong, J.-Y, Moon, I.-J., Lee, M., Yun, J., Jang, C. J., Kim, Y. S., Byun, D.-S., Heo, K.-Y., and Shim, J.-S.: Observations utilizing Korean
ocean research stations and their applications for process studies, B.
Am. Meteorol. Soc., 100, 2061–2075, 2019.
Hahn, C. J. and Warren, S. G.: A gridded climatology of clouds over land
(1971–96) and ocean (1954–97) from surface observations worldwide. Numeric
Data Package NDP-026EORNL/CDIAC-153, CDIAC, Department of Energy, Oak Ridge,
TN, 2007.
Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: The effect of
cloud type on earth's energy balance – Global analysis, J. Climate, 5,
1281–1304, 1992.
Holben, B., Tanre, D., Smirnov, A., Eck, T., Slutsker, I., Abuhassen, N., Newcomb, W., Schafer, J., Chatenet, B., Lavenue, F., Kaufman, Y., Vande Castle, J., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karnieli, A., O`Neill, N., Pietras, C., Pinker, R., Voss, K., and Zibordi, G.: An emerging ground-based
aerosol climatology: Aerosol optical depth from AERONET, J. Geophys. Res.,
106, 12067–12097, 2001.
Hu, Y., Rodier, S., Xu, K.-M., Sun, W., Huang, J., Lin, B., Zhai, P., and
Josset, D.: Occurrence, liquid water content and fraction of supercooled
water clouds from combined CALIOP/IIR/MODIS measurements, J. Geophys. Res.,
115, D00H34, https://doi.org/10.1029/2009JD012384, 2010.
Huang, Y., Siems, S. T., Manton, M. J., Protat, A., and Delanöe, J.: A
study on the low-altitude clouds over the Southern Ocean using the
DARDAR-MASK, J. Geophys. Res., 117, D18204, https://doi.org/10.1029/2012JB009424, 2012.
Intrieri, J. M., Shupe, M. D., Uttal, T., and McCarty, B. J.: An annual cycle
of Arctic cloud characteristics observed by radar and lidar at SHEBA, J.
Geophys. Res., 107, 8030, https://doi.org/10.1029/2000jc000423, 2002.
Jackson, R. C., McFarquhar, G. M., Korolev, A. V., Earle, M. E., Liu, P. S. K., Lawson, R. P., Brooks, S., Wolde, M., Laskin, A., and Free, M.: The dependence of Arctic mixed-phase stratus
ice cloud microphysics on aerosol concentration using observations acquired
during ISDAC and M-PACE, J. Geophys. Res., 117, D15207,
https://doi.org/10.1029/2012JD017668, 2012.
Kanitz, T., Seifert, P., Ansmann, A., Engelmann, R., Althausen, D.,
Casiccia, C. and Rohwer, E. G.: Contrasting the impact of aerosols at
northern and southern midlatitudes on heterogeneous ice formation, Geophys.
Res. Lett., 38, L17802, https://doi.org/10.1029/2011GL048532, 2011.
Khain, A., BenMoshe, N., and Pokrovsky, A.: Factors determining the impact of
aerosols on surface precipitation from clouds: Attempt of classification, J.
Atmos. Sci., 65, 1721–1748, 2008.
Khain, A., Pokrovsky, A., Rosenfeld, D., Blahak, U., and Ryzhkoy, A.: The
role of CCN in precipitation and hail in a mid-latitude storm as seen in
simulations using a spectral (bin) microphysics model in a 2D dynamic
frame, Atmos. Res., 99, 129–146, 2011.
Klemp, J. B., Skamarock, W. C., and Dudhia, J.: Conservative split-explicit
time integration methods for the compressible nonhydrostatic equations, Mon.
Weather Rev., 135, 2897–2913, 2007.
Korea Meteorological Administration: Seoul, South Korea, National Climate Data Center [data set], available at: https://data.kma.go.kr, last access: 10 November 2021.
Korolev, A.: Limitations of the Wegener–Bergeron–Findeisen Mechanism in the Evolution of
Mixed-Phase Clouds, J. Atmos. Sci., 64, 3372–3375, 2007.
Koop, T., Luo, B. P., Tsias, A., and Peter, T.: Water activity as the
determinant for homogeneous ice nucleation in aqueous solutions, Nature,
406, 611–614, 2000.
Lance, S., Brock, C. A., Rogers, D., and Gordon, J. A.: Water droplet calibration of the Cloud Droplet Probe (CDP) and in-flight performance in liquid, ice and mixed-phase clouds during ARCPAC, Atmos. Meas. Tech., 3, 1683–1706, https://doi.org/10.5194/amt-3-1683-2010, 2010.
Lebo, Z. J. and Morrison, H.: Dynamical effects of aerosol perturbations on
simulated idealized squall lines, Mon. Weather Rev., 142, 991–1009, 2014.
Lee, S., Ho, C.-H., Lee, Y. G., Choi, H.-J., and Song, C.-K.: Influence of
transboundary air pollutants from China on the high-PM10 episode in Seoul,
Korea for the period October 16–20, 2008, Atmos. Environ., 77, 430–439,
2013.
Lee, S. S., Donner, L. J., Phillips, V. T. J., and Ming, Y.: The dependence
of aerosol effects on clouds and precipitation on cloud-system organization,
shear and stability. J. Geophys. Res., 113, D16202,
https://doi.org/10.1029/2007JD009224, 2008.
Lee, S. S., Kim, B.-G., Yum, S. S., Seo, K.-H., Jung, C.-H., Um, J., Li, Z., Hong, J., Chang, K.-H., and Jeong, J.-Y.: Effect of aerosol on
evaporation, freezing and precipitation in a multiple cloud system, Clim.
Dynam., 48, 1069–1087, 2016.
Lohmann, U.: Aglaciation indirect aerosol effect caused by soot aerosols,
Geophys. Res. Lett., 29, 1052, https://doi.org/10.1029/2001GL014357, 2002.
Lohmann, U. and Diehl, K.: Sensitivity studies of the importance of dust ice nuclei for the
indirect aerosol effect on stratiform mixed-phase clouds, J. Atmos. Sci., 63, 968–982,
2006.
Michalakes, J., Chen, S., Dudhia, J., Hart, L., Klemp, J., Middlecoff, J.,
and Skamarock, W.: Development of a next generation regional weather
research and forecast model, in: Developments in Teracomputing: Proceedings
of the Ninth ECMWF Workshop on the Use of High Performance Computing in
Meteorology, edited by: Zwieflhofer, W. and Kreitz, N., World
Sci., Singapore, 269–276, 2001.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.,
102, 1663–1668, 1997.
Morrison, A. E., Siems, S. T., and Manton, M. J.: A three year climatology of
cloud-top phase over the Southern Ocean and North Pacific, J. Climate, 24,
2405–2418, https://doi.org/10.1175/2010JCLI3842.1, 2011.
Morrison, H. and Grabowski, W. W.: Cloud-system resolving model simulations of aerosol indirect effects on tropical deep convection and its thermodynamic environment, Atmos. Chem. Phys., 11, 10503–10523, https://doi.org/10.5194/acp-11-10503-2011, 2011.
Möhler, O., Field, P. R., Connolly, P., Benz, S., Saathoff, H., Schnaiter, M., Wagner, R., Cotton, R., Krämer, M., Mangold, A., and Heymsfield, A. J.: Efficiency of the deposition mode ice nucleation on mineral dust particles, Atmos. Chem. Phys., 6, 3007–3021, https://doi.org/10.5194/acp-6-3007-2006, 2006.
Naud, C., Booth, J. F., and Del Genio, A. D.: Evaluation of ERA-Interim
andMERRA cloudiness in the Southern Ocean, J. Climate, 27, 2109–2124,
https://doi.org/10.1175/JCLI-D-13-00432.1, 2014.
Oh, H.-R., Ho, C.-H., Kim, J., Chen, D., Lee, S., Choi, Y.-S., Chang, L.-S.,
and Song, C.-K.: Long-range transport of air pollutants originating in
China: A possible major cause of multi-day high-PM10 episodes during cold
season in Seoul, Korea, Atmos. Environ., 109, 23–30, 2015.
Ovchinnikov, M., Korolev, A., and Fan, J.: Effects of ice number
concentration on dynamics of a shallow mixed-phase stratiform cloud, J.
Geophys. Res., 116, D00T06, https://doi.org/10.1029/2011JD015888, 2011.
Possner, A., Ekman, A. M. L., and Lohmann, U.: Cloud response and feedback
processes in stratiform mixed-phase clouds perturbed by ship exhaust,
Geophys. Res. Lett., 44, 1964–1972, https://doi.org/10.1002/2016GL071358, 2017.
Pruppacher, H. R. and Klett, J. D.: Microphysics of clouds and
precipitation, D. Reidel, 714 pp., 1978.
Ramaswamy, V., Boucher, O., Haigh, J., Hauglustaine, D., Haywood, J., Myhre, G., Nakajima, T., Shi, G. Y., and Solomon, S.: Radiative forcing of climate change, in: Climate
Change 2001: The Scientific Basis, edited by: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai, X., Maskell, K., and Johnson, C. A.,
Cambridge Univ. Press, New York, 349–416, 2001.
Rangno, A. L. and Hobbs, P. V.: Ice particles in stratiform clouds in the
Arctic and possible mechanisms for the production of high ice
concentrations, J. Geophys. Res., 106, 15065–15075,
https://doi.org/10.1029/2000JD900286, 2001.
Shupe, M. D., Uttal, T., Matrosov, S. Y., and Rrisch, A. S.: Cloud water
contents and hydrometeor sizes during the FIRE Arctic clouds experiment, J.
Geophys. Res., 106, 15015–15028, https://doi.org/10.1029/2000JD900476, 2001.
Shupe, M. D., Uttal, T., and Matrosov, S. Y.: Arctic cloud microphysics
retrievals from surface-based remote sensors at SHEBA, J. Appl. Meteorol., 44,
1544–1562, https://doi.org/10.1175/JAM2297.1, 2005.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M.,
Duda, M. G., Wang, W., and Powers, J. G.: A description of the advanced
research WRF version 3, NCAR Tech. Note NCAR/TN-475+STR, Boulder,
Colo., 113 pp., 2008.
Stephens, G. L. and Greenwald, T. J.: Observations of the Earth's radiation
budget in relation to atmospheric hydrology. Part II: Cloud effects and
cloud feedback, J. Geophys. Res., 96, 15325–15340, 1991.
Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and
precipitation in a buffered system, Nature, 461, 607–613, 2009.
Twomey, S.: Pollution and the Planetary Albedo, Atmos. Environ., 8, 1251–1256,
1974.
Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J.
Atmos. Sci., 34, 1149–1152, 1977.
Wang, H., Skamarock, W. C., and Feingold, G.: Evaluation of scalar advection
schemes in the Advanced Research WRF model using large-eddy simulations of
aerosol-cloud interactions, Mon. Weather Rev., 137, 2547–2558, 2009.
Warren, S. G., Hahn, C. J., London, J., Chervin, R. M., and Jenne, R. L.:
Global distribution of total cloud cover and cloud types over land, NCAR
Tech. Note NCAR/TN-273+STR, National Center for Atmospheric Research,
Boulder, CO, 29 pp. + 200 maps, 1986.
Warren, S. G., Hahn, C. J., London, J., Chervin, R. M., and Jenne, R. L.:
Global distribution of total cloud cover and cloud type amounts
over the ocean, in NCAR/TN-317+STR, National Center
for Atmospheric Research, Boulder, CO, 42 pp. +170 maps, 1988.
Wegener, A.: Thermodynamik der Atmosphäre, J. A. Barth, 311 pp., 1911.
Wood, R.: Stratocumulus clouds, Mon. Weather Rev., 140, 2373–2423, 2012.
Young, G., Connolly, P. J., Jones, H. M., and Choularton, T. W.: Microphysical sensitivity of coupled springtime Arctic stratocumulus to modelled primary ice over the ice pack, marginal ice, and ocean, Atmos. Chem. Phys., 17, 4209–4227, https://doi.org/10.5194/acp-17-4209-2017, 2017.
Zuidema, P., Westwater, E. R., Fairall, C., and Hazen, D.: Ship-based liquid
water path estimates in marine stratocumulus, J. Geophys. Res., 110, D20206,
https://doi.org/10.1029/2005JD005833, 2005.
Short summary
Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found that cloud mass in the system becomes very low due to interactions between ice and liquid particles compared to that in the absence of ice particles. It is also found that interactions between cloud mass and aerosols lead to a reduction in cloud mass in the system, and this is contrary to an aerosol-induced increase in cloud mass in the absence of ice particles.
Using a modeling framework, a midlatitude stratocumulus cloud system is simulated. It is found...
Altmetrics
Final-revised paper
Preprint