Articles | Volume 21, issue 3
https://doi.org/10.5194/acp-21-1835-2021
© 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-21-1835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Feb 2021
Research article |
| 10 Feb 2021
| 10 Feb 2021
Effects of thermodynamics, dynamics and aerosols on cirrus clouds based on in situ observations and NCAR CAM6
Ryan Patnaude
Department of Meteorology and Climate Science, San Jose State
University, San Jose, CA 95192, USA
Department of Meteorology and Climate Science, San Jose State
University, San Jose, CA 95192, USA
Xiaohong Liu
Department of Atmospheric Sciences, Texas A&M University, College
Station, TX 77843, USA
Suqian Chu
Department of Atmospheric Science, University of Wyoming, Laramie, WY
82071, USA
Related authors
Derek Ngo, Minghui Diao, Ryan J. Patnaude, Sarah Woods, and Glenn Diskin
Atmos. Chem. Phys., 25, 7007–7036, https://doi.org/10.5194/acp-25-7007-2025, https://doi.org/10.5194/acp-25-7007-2025, 2025
Short summary
Short summary
Key controlling factors of cirrus clouds were individually quantified using machine learning models based on global-scale in situ observations from 12 campaigns at 67° S–87° N. Relative humidity shows much larger effects on cirrus occurrences and ice water content (IWC) fluctuations than vertical velocity. Aerosol–cloud interactions are seen for both large and small aerosols, with higher IWC and ice crystal number concentration under higher aerosol concentrations. Large aerosols are more impactful than small aerosols.
Ryan J. Patnaude, Kathryn A. Moore, Russell J. Perkins, Thomas C. J. Hill, Paul J. DeMott, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 24, 911–928, https://doi.org/10.5194/acp-24-911-2024, https://doi.org/10.5194/acp-24-911-2024, 2024
Short summary
Short summary
In this study we examined the effect of atmospheric aging on sea spray aerosols (SSAs) to form ice and how newly formed secondary marine aerosols (SMAs) may freeze at cirrus temperatures (< −38 °C). Results show that SSAs freeze at different relative humidities (RHs) depending on the temperature and that the ice-nucleating ability of SSA was not hindered by atmospheric aging. SMAs are shown to freeze at high RHs and are likely inefficient at forming ice at cirrus temperatures.
Flor Vanessa Maciel, Minghui Diao, and Ryan Patnaude
Atmos. Chem. Phys., 23, 1103–1129, https://doi.org/10.5194/acp-23-1103-2023, https://doi.org/10.5194/acp-23-1103-2023, 2023
Short summary
Short summary
Aerosol indirect effects on cirrus clouds are investigated during cirrus evolution, using global-scale in situ observations and climate model simulations. As cirrus evolves, the mechanisms to form ice crystals also change with time. Both small and large aerosols are found to affect cirrus properties. Southern Hemisphere cirrus appears to be more sensitive to additional aerosols. The climate model underestimates ice crystal mass, likely due to biases of relative humidity and vertical velocity.
Mingxuan Wu, Hailong Wang, Zheng Lu, Xiaohong Liu, Huisheng Bian, David D. Cohen, Yan Feng, Mian Chin, Didier A. Hauglustaine, Vlassis A. Karydis, Marianne T. Lund, Gunnar Myhre, Andrea Pozzer, Michael Schulz, Ragnhild B. Skeie, Alexandra P. Tsimpidi, Svetlana G. Tsyro, and Shaocheng Xie
Atmos. Chem. Phys., 25, 10049–10074, https://doi.org/10.5194/acp-25-10049-2025, https://doi.org/10.5194/acp-25-10049-2025, 2025
Short summary
Short summary
A key challenge in simulating the life cycle of nitrate aerosol in global models is accurately representing the mass size distribution of nitrate aerosol, which lacks sufficient observational constraints. We found that most global models underestimate the mass fraction of fine-mode nitrate at the surface in all regions. Our study highlights the importance of gas–aerosol partitioning parameterization and the simulation of dust and sea salt in correctly simulating the mass size distribution of nitrate.
Derek Ngo, Minghui Diao, Ryan J. Patnaude, Sarah Woods, and Glenn Diskin
Atmos. Chem. Phys., 25, 7007–7036, https://doi.org/10.5194/acp-25-7007-2025, https://doi.org/10.5194/acp-25-7007-2025, 2025
Short summary
Short summary
Key controlling factors of cirrus clouds were individually quantified using machine learning models based on global-scale in situ observations from 12 campaigns at 67° S–87° N. Relative humidity shows much larger effects on cirrus occurrences and ice water content (IWC) fluctuations than vertical velocity. Aerosol–cloud interactions are seen for both large and small aerosols, with higher IWC and ice crystal number concentration under higher aerosol concentrations. Large aerosols are more impactful than small aerosols.
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary
Short summary
This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, P. Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankararaman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Johann Engelbrecht, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbigniew Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gómez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal L. Weagle, and Xi Zhao
Atmos. Chem. Phys., 25, 4665–4702, https://doi.org/10.5194/acp-25-4665-2025, https://doi.org/10.5194/acp-25-4665-2025, 2025
Short summary
Short summary
Aerosol particles are an important part of the Earth system, but their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Here, we present a new compilation of PM2.5 and PM10 aerosol observations, focusing on the spatial variability across different observational stations, including composition, and demonstrate a method for comparing the data sets to model output.
Mariya Petrenko, Ralph Kahn, Mian Chin, Susanne E. Bauer, Tommi Bergman, Huisheng Bian, Gabriele Curci, Ben Johnson, Johannes W. Kaiser, Zak Kipling, Harri Kokkola, Xiaohong Liu, Keren Mezuman, Tero Mielonen, Gunnar Myhre, Xiaohua Pan, Anna Protonotariou, Samuel Remy, Ragnhild Bieltvedt Skeie, Philip Stier, Toshihiko Takemura, Kostas Tsigaridis, Hailong Wang, Duncan Watson-Parris, and Kai Zhang
Atmos. Chem. Phys., 25, 1545–1567, https://doi.org/10.5194/acp-25-1545-2025, https://doi.org/10.5194/acp-25-1545-2025, 2025
Short summary
Short summary
We compared smoke plume simulations from 11 global models to each other and to satellite smoke amount observations aimed at constraining smoke source strength. In regions where plumes are thick and background aerosol is low, models and satellites compare well. However, the input emission inventory tends to underestimate in many places, and particle property and loss rate assumptions vary enormously among models, causing uncertainties that require systematic in situ measurements to resolve.
Kai Lyu, Xiaohong Liu, and Bernd Kärcher
EGUsphere, https://doi.org/10.5194/egusphere-2024-4144, https://doi.org/10.5194/egusphere-2024-4144, 2025
Short summary
Short summary
Two nucleation schemes are used to study ice nucleation, focusing on three ice sources: mountains, turbulence and anvils. Ice from mountains is concentrated in mid- and high-latitudes, while ice from turbulence and anvils is more common in low and mid-latitudes. Both schemes simulate orographic cirrus clouds, with mountain ice as the dominant source. The schemes differ in how they handle ice source competition, causing turbulence and anvils to influence clouds differently.
Flor Vanessa Maciel, Minghui Diao, and Ching An Yang
Atmos. Meas. Tech., 17, 4843–4861, https://doi.org/10.5194/amt-17-4843-2024, https://doi.org/10.5194/amt-17-4843-2024, 2024
Short summary
Short summary
The partition between supercooled liquid water and ice crystals in mixed-phase clouds is investigated using aircraft-based in situ observations over the Southern Ocean. A novel method is developed to define four phases of mixed-phase clouds. Relationships between cloud macrophysical and microphysical properties are quantified. Effects of aerosols and thermodynamic and dynamical conditions on ice nucleation and phase partitioning are examined.
Allen Hu, Xiaohong Liu, Ziming Ke, Benjamin Wagman, Hunter Brown, Zheng Lu, Diana Bull, and Kara Peterson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2227, https://doi.org/10.5194/egusphere-2024-2227, 2024
Short summary
Short summary
Volcanic eruptions have a major effect on temperature throughout the atmosphere and can be studied as a proxy for geo-engineering. The aerosol module in the Energy Exascale Earth System Model (E3SM) was originally intended for simulation of tropospheric aerosols and has problems handling stratospheric sulfate aerosols due to volcanic eruptions. We have made alterations to the aerosol module to overcome these problems, with simulation results more closely reproducing observations.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
Short summary
Short summary
Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Johannes Mülmenstädt, Edward Gryspeerdt, Sudhakar Dipu, Johannes Quaas, Andrew S. Ackerman, Ann M. Fridlind, Florian Tornow, Susanne E. Bauer, Andrew Gettelman, Yi Ming, Youtong Zheng, Po-Lun Ma, Hailong Wang, Kai Zhang, Matthew W. Christensen, Adam C. Varble, L. Ruby Leung, Xiaohong Liu, David Neubauer, Daniel G. Partridge, Philip Stier, and Toshihiko Takemura
Atmos. Chem. Phys., 24, 7331–7345, https://doi.org/10.5194/acp-24-7331-2024, https://doi.org/10.5194/acp-24-7331-2024, 2024
Short summary
Short summary
Human activities release copious amounts of small particles called aerosols into the atmosphere. These particles change how much sunlight clouds reflect to space, an important human perturbation of the climate, whose magnitude is highly uncertain. We found that the latest climate models show a negative correlation but a positive causal relationship between aerosols and cloud water. This means we need to be very careful when we interpret observational studies that can only see correlation.
Hao Wang, Xiaohong Liu, Chenglai Wu, and Guangxing Lin
Atmos. Chem. Phys., 24, 3309–3328, https://doi.org/10.5194/acp-24-3309-2024, https://doi.org/10.5194/acp-24-3309-2024, 2024
Short summary
Short summary
We quantified different global- and regional-scale drivers of biogenic volatile organic compound (BVOC) emission trends over the past 20 years. The results show that global greening trends significantly boost BVOC emissions and deforestation reduces BVOC emissions in South America and Southeast Asia. Elevated temperature in Europe and increased soil moisture in East and South Asia enhance BVOC emissions. The results deepen our understanding of long-term BVOC emission trends in hotspots.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankarararman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Hannele Hakola, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbiginiw Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gomez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal Weagle, and Xi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-1, https://doi.org/10.5194/essd-2024-1, 2024
Preprint withdrawn
Short summary
Short summary
Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. Here we present a new compilation of aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model.
Ryan J. Patnaude, Kathryn A. Moore, Russell J. Perkins, Thomas C. J. Hill, Paul J. DeMott, and Sonia M. Kreidenweis
Atmos. Chem. Phys., 24, 911–928, https://doi.org/10.5194/acp-24-911-2024, https://doi.org/10.5194/acp-24-911-2024, 2024
Short summary
Short summary
In this study we examined the effect of atmospheric aging on sea spray aerosols (SSAs) to form ice and how newly formed secondary marine aerosols (SMAs) may freeze at cirrus temperatures (< −38 °C). Results show that SSAs freeze at different relative humidities (RHs) depending on the temperature and that the ice-nucleating ability of SSA was not hindered by atmospheric aging. SMAs are shown to freeze at high RHs and are likely inefficient at forming ice at cirrus temperatures.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev., 16, 6087–6125, https://doi.org/10.5194/gmd-16-6087-2023, https://doi.org/10.5194/gmd-16-6087-2023, 2023
Short summary
Short summary
We implemented an alternative aerosol scheme in the high- and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. This development enables the comparison of different aerosol schemes with different complexity in the same model framework. It identifies improvements compared to a range of observations in both the troposphere and stratosphere.
Flor Vanessa Maciel, Minghui Diao, and Ryan Patnaude
Atmos. Chem. Phys., 23, 1103–1129, https://doi.org/10.5194/acp-23-1103-2023, https://doi.org/10.5194/acp-23-1103-2023, 2023
Short summary
Short summary
Aerosol indirect effects on cirrus clouds are investigated during cirrus evolution, using global-scale in situ observations and climate model simulations. As cirrus evolves, the mechanisms to form ice crystals also change with time. Both small and large aerosols are found to affect cirrus properties. Southern Hemisphere cirrus appears to be more sensitive to additional aerosols. The climate model underestimates ice crystal mass, likely due to biases of relative humidity and vertical velocity.
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022, https://doi.org/10.5194/gmd-15-8181-2022, 2022
Short summary
Short summary
This study advances mineral dust parameterizations in the Community Atmospheric Model (CAM; version 6.1). Efforts include 1) incorporating a more physically based dust emission scheme; 2) updating the dry deposition scheme; and 3) revising the gravitational settling velocity to account for dust asphericity. Substantial improvements achieved with these updates can help accurately quantify dust–climate interactions using CAM, such as the dust-radiation and dust–cloud interactions.
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.
Susannah M. Burrows, Richard C. Easter, Xiaohong Liu, Po-Lun Ma, Hailong Wang, Scott M. Elliott, Balwinder Singh, Kai Zhang, and Philip J. Rasch
Atmos. Chem. Phys., 22, 5223–5251, https://doi.org/10.5194/acp-22-5223-2022, https://doi.org/10.5194/acp-22-5223-2022, 2022
Short summary
Short summary
Sea spray particles are composed of a mixture of salts and organic substances from oceanic microorganisms. In prior work, our team developed an approach connecting sea spray chemistry to ocean biology, called OCEANFILMS. Here we describe its implementation within an Earth system model, E3SM. We show that simulated sea spray chemistry is consistent with observed seasonal cycles and that sunlight reflected by simulated Southern Ocean clouds increases, consistent with analysis of satellite data.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Yang Shi, Xiaohong Liu, Mingxuan Wu, Xi Zhao, Ziming Ke, and Hunter Brown
Atmos. Chem. Phys., 22, 2909–2935, https://doi.org/10.5194/acp-22-2909-2022, https://doi.org/10.5194/acp-22-2909-2022, 2022
Short summary
Short summary
We perform a modeling study to evaluate the contribution to Arctic dust loading and ice-nucleating particle (INP) population from high-latitude local and low-latitude dust. High-latitude dust has a large contribution in the lower troposphere, while low-latitude dust dominates the upper troposphere. The high-latitude dust INPs result in a net cooling effect on the Arctic surface by glaciating mixed-phase clouds. Our results highlight the contribution of high-latitude dust to the Arctic climate.
Xi Zhao and Xiaohong Liu
Atmos. Chem. Phys., 22, 2585–2600, https://doi.org/10.5194/acp-22-2585-2022, https://doi.org/10.5194/acp-22-2585-2022, 2022
Short summary
Short summary
The goal of this study is to investigate the relative importance and interactions of primary and secondary ice production in the Arctic mixed-phase clouds. Our results show that the SIP is not only a result of ice crystals produced from ice nucleation, but also competes with the ice production; conversely, strong ice nucleation also suppresses SIP.
Ka Ming Fung, Colette L. Heald, Jesse H. Kroll, Siyuan Wang, Duseong S. Jo, Andrew Gettelman, Zheng Lu, Xiaohong Liu, Rahul A. Zaveri, Eric C. Apel, Donald R. Blake, Jose-Luis Jimenez, Pedro Campuzano-Jost, Patrick R. Veres, Timothy S. Bates, John E. Shilling, and Maria Zawadowicz
Atmos. Chem. Phys., 22, 1549–1573, https://doi.org/10.5194/acp-22-1549-2022, https://doi.org/10.5194/acp-22-1549-2022, 2022
Short summary
Short summary
Understanding the natural aerosol burden in the preindustrial era is crucial for us to assess how atmospheric aerosols affect the Earth's radiative budgets. Our study explores how a detailed description of dimethyl sulfide (DMS) oxidation (implemented in the Community Atmospheric Model version 6 with chemistry, CAM6-chem) could help us better estimate the present-day and preindustrial concentrations of sulfate and other relevant chemicals, as well as the resulting aerosol radiative impacts.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
Short summary
Short summary
Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao
Atmos. Meas. Tech., 14, 7079–7101, https://doi.org/10.5194/amt-14-7079-2021, https://doi.org/10.5194/amt-14-7079-2021, 2021
Short summary
Short summary
Many clouds with temperatures between 0 °C and −40 °C contain both liquid and ice particles, and the ratio of liquid to ice particles influences how the clouds interact with radiation and moderate Earth's climate. We use a machine learning method called random forest to classify images of individual cloud particles as either liquid or ice. We apply our algorithm to images captured by aircraft within clouds overlying the Southern Ocean, and we find that it outperforms two existing algorithms.
Xi Zhao, Xiaohong Liu, Vaughan T. J. Phillips, and Sachin Patade
Atmos. Chem. Phys., 21, 5685–5703, https://doi.org/10.5194/acp-21-5685-2021, https://doi.org/10.5194/acp-21-5685-2021, 2021
Short summary
Short summary
Arctic mixed-phase clouds significantly influence the energy budget of the Arctic. We show that a climate model considering secondary ice production (SIP) can explain the observed cloud ice number concentrations, vertical distribution pattern, and probability density distribution of ice crystal number concentrations. The mixed-phase cloud occurrence and phase partitioning are also improved.
Xi Zhao, Xiaohong Liu, Susannah M. Burrows, and Yang Shi
Atmos. Chem. Phys., 21, 2305–2327, https://doi.org/10.5194/acp-21-2305-2021, https://doi.org/10.5194/acp-21-2305-2021, 2021
Short summary
Short summary
Organic sea spray particles influence aerosol and cloud processes over the ocean. This study introduces the emission, cloud droplet activation, and ice nucleation (IN) of marine organic aerosol (MOA) into the Community Earth System Model. Our results indicate that MOA IN particles dominate primary ice nucleation below 400 hPa over the Southern Ocean and Arctic boundary layer. MOA enhances cloud forcing over the Southern Ocean in the austral winter and summer.
Mingxuan Wu, Xiaohong Liu, Hongbin Yu, Hailong Wang, Yang Shi, Kang Yang, Anton Darmenov, Chenglai Wu, Zhien Wang, Tao Luo, Yan Feng, and Ziming Ke
Atmos. Chem. Phys., 20, 13835–13855, https://doi.org/10.5194/acp-20-13835-2020, https://doi.org/10.5194/acp-20-13835-2020, 2020
Short summary
Short summary
The spatiotemporal distributions of dust aerosol simulated by global climate models (GCMs) are highly uncertain. In this study, we evaluate dust extinction profiles, optical depth, and surface concentrations simulated in three GCMs and one reanalysis against multiple satellite retrievals and surface observations to gain process-level understanding. Our results highlight the importance of correctly representing dust emission, dry/wet deposition, and size distribution in GCMs.
Stefan Rahimi, Xiaohong Liu, Chun Zhao, Zheng Lu, and Zachary J. Lebo
Atmos. Chem. Phys., 20, 10911–10935, https://doi.org/10.5194/acp-20-10911-2020, https://doi.org/10.5194/acp-20-10911-2020, 2020
Short summary
Short summary
Dark particles emitted to the atmosphere can absorb sunlight and heat the air. As these particles settle, they may darken the surface, especially over snow-covered regions like the Rocky Mountains. This darkening of the surface may lead to changes in snowpack, affecting the local meteorology and hydrology. We seek to evaluate whether these light-absorbing particles more prominently affect this region through their atmospheric presence or their on-snow presence.
Cited articles
Barth, M. C., Cantrell, C. A., Brune, W. H., Rutledge, S. A., Crawford, J.
H., Huntrieser, H., Carey, L. D., MacGorman, D., Weisman, M., Pickering, K.
E., Bruning, E., Anderson, B., Apel, E., Biggerstaff, M., Campos, T.,
Campuzano-Jost, P., Cohen, R., Crounse, J., Day, D. A., Diskin, G., Flocke,
F., Fried, A., Garland, C., Heikes, B., Honomichl, S., Hornbrook, R.,
Gregory Huey, L., Jimenez, J. L., Lang, T., Lichtenstern, M., Mikoviny, T.,
Nault, B., O'Sullivan, D., Pan, L. L., Peischl, J., Pollack, I., Richter,
D., Riemer, D., Ryerson, T., Schlager, H., St. Clair, J., Walega, J.,
Weibring, P., Weinheimer, A., Wennberg, P., Wisthaler, A., Wooldridge, P. J.,
and Ziegler, C.: The Deep Convective Clouds and Chemistry (DC3) field
campaign, B. Am. Meteorol. Soc., 96, 1281–1310,
https://doi.org/10.1175/BAMS-D-13-00290.1, 2015.
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Craig, C.,
and Schanen, D. P.: Higher-order turbulence closure and its impact on
climate simulations in the community atmosphere model, J. Climate, 26,
9655–9676, https://doi.org/10.1175/JCLI-D-13-00075.1, 2013.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh,
S. K., Sherwood, S. C., Stevens, B., and Zhang, X.-Y.: Clouds and aerosols,
Clim. Chang. 2013 Phys. Sci. Basis Work. Gr. I Contrib. to Fifth Assess.
Rep. Intergov. Panel Clim. Chang., 9781107057, 571–658,
https://doi.org/10.1017/CBO9781107415324.016, 2013.
Brown, P. R. A. and Francis, P. N.: Improved Measurements of the Ice Water
Content in Cirrus Using a Total-Water Probe, J. Atmos. Ocean. Tech., 12,
410–414, https://doi.org/10.1175/1520-0426(1995)012<0410:IMOTIW>2.0.CO;2,
1995.
Chylek, P., Dubey, M. K., Lohmann, U., Ramanathan, V., Kaufman, Y. J.,
Lesins, G., Hudson, J., Altmann, G., and Olsen, S.: Aerosol indirect effect
over the Indian Ocean, Geophys. Res. Lett., 33, L06806,
https://doi.org/10.1029/2005GL025397, 2006.
Cziczo, D. J. and Froyd, K. D.: Sampling the composition of cirrus ice
residuals, Atmos. Res., 142, 15–31, https://doi.org/10.1016/j.atmosres.2013.06.012,
2014.
Cziczo, D. J., Froyd, K. D., Hoose, C., Jensen, E. J., Diao, M., Zondlo, M.
A., Smith, J. B., Twohy, C. H., and Murphy, D. M.: Clarifying the dominant
sources and mechanisms of cirrus cloud formation, Science,
340, 1320–1324, https://doi.org/10.1126/science.1234145, 2013.
D'Alessandro, J. J., Diao, M., Wu, C., Liu, X., Chen, M., Morrison, H.,
Eidhammer, T., Jensen, J. B., Bansemer, A., Zondlo, M. A., and DiGangi, J.
P.: Dynamical conditions of ice supersaturation and ice nucleation in
convective systems: A comparative analysis between in situ aircraft
observations and WRF simulations, J. Geophys. Res., 122, 2844–2866,
https://doi.org/10.1002/2016JD025994, 2017.
D'Alessandro, J. J., Diao, M., Wu, C., Liu, X., Jensen, J. B., and Stephens,
B. B.: Cloud phase and relative humidity distributions over the Southern
Ocean in austral summer based on in situ observations and CAM5 simulations,
J. Climate, 32, 2781–2805, https://doi.org/10.1175/JCLI-D-18-0232.1, 2019.
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D.,
Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting
global atmospheric ice nuclei distributions and their impacts on climate,
P. Natl. Acad. Sci. USA, 107, 11217–11222, https://doi.org/10.1073/pnas.0910818107,
2010.
Diao, M., Zondlo, M. A., Heymsfield, A. J., Beaton, S. P., and Rogers, D. C.:
Evolution of ice crystal regions on the microscale based on in situ
observations, Geophys. Res. Lett., 40, 3473–3478,
https://doi.org/10.1002/grl.50665, 2013.
Diao, M., Zondlo, M. A., Heymsfield, A. J., Avallone, L. M., Paige, M. E., Beaton, S. P., Campos, T., and Rogers, D. C.: Cloud-scale ice-supersaturated regions spatially correlate with high water vapor heterogeneities, Atmos. Chem. Phys., 14, 2639–2656, https://doi.org/10.5194/acp-14-2639-2014, 2014a.
Diao, M., Zondlo, M. A., Heymsfield, A. J., and Beaton, S. P.: Hemispheric
comparison of cirrus cloud evolution using in situ measurements in HIAPER
Pole-to-Pole Observations, Geophys. Res. Lett., 41, 1–8,
https://doi.org/10.1002/2014GL059873, 2014b.
Diao, M., Jensen, J. B., Pan, L. L., Homeyer, C. R., Honomichl, S., Bresch,
J. F., and Bansemer, A.: Distributions of ice supersaturation and ice
crystals from airborne observations in relation to upper tropospheric
dynamical boundaries, J. Geophys. Res., 120, 5101–5121,
https://doi.org/10.1002/2015JD023139, 2015.
Diao, M., Bryan, G. H., Morrison, H., and Jensen, J. B.: Ice nucleation
parameterization and relative humidity distribution in idealized squall-line
simulations, J. Atmos. Sci., 74, 2761–2787, https://doi.org/10.1175/JAS-D-16-0356.1,
2017.
Eidhammer, T., Morrison, H., Bansemer, A., Gettelman, A., and Heymsfield, A. J.: Comparison of ice cloud properties simulated by the Community Atmosphere Model (CAM5) with in-situ observations, Atmos. Chem. Phys., 14, 10103–10118, https://doi.org/10.5194/acp-14-10103-2014, 2014.
Eidhammer, T., Morrison, H., Mitchell, D., Gettelman, A., and Erfani, E.:
Improvements in global climate model microphysics using a consistent
representation of ice particle properties, J. Climate, 30, 609–629,
https://doi.org/10.1175/JCLI-D-16-0050.1, 2017.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan,
K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A.,
da Silva, A. M., Gu, W., Kim, G. K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M.,
Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective
analysis for research and applications, version 2 (MERRA-2), J. Climate,
30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Gettelman, A. and Morrison, H.: Advanced two-moment bulk microphysics for
global models. Part I: Off-line tests and comparison with other schemes, J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015.
Gettelman, A., Liu, X., Ghan, S. J., Morrison, H., Park, S., Conley, A. J.,
Klein, S. A., Boyle, J., Mitchell, D. L., and Li, J. L. F.: Global
simulations of ice nucleation and ice supersaturation with an improved cloud
scheme in the Community Atmosphere Model, J. Geophys. Res.-Atmos., 115,
1–19, https://doi.org/10.1029/2009JD013797, 2010.
Gettelman, A., Bardeen, C. G., McCluskey, C. S., Järvinen, E., Stith, J., Bretherton, C., McFarquhar, G., Twohy, C., D'Alessandro, J., and Wu, W.: Simulating Observations of Southern Ocean
Clouds and Implications for Climate, J. Geophys. Res.-Atmos., 125, e2020JD032619,
https://doi.org/10.1029/2020JD032619, 2020.
Golaz, J. C., Larson, V. E., and Cotton, W. R.: A PDF-Based Model for
Boundary Layer Clouds. Part I: Method and Model Description, J. Atmos.
Sci., 59, 3540–3551, 2002.
Heymsfield, A. J.: Precipitation Development in Stratiform Ice Clouds: A
Microphysical and Dynamical Study, J. Atmos. Sci., 367–381, 1977.
Heymsfield, A. J., Winker, D., and van Zadelhoff, G. J.: Extinction-ice water
content-effective radius algorithms for CALIPSO, Geophys. Res. Lett.,
32, 1–4, https://doi.org/10.1029/2005GL022742, 2005.
Heymsfield, A. J., Krämer, M., Wood, N. B., Gettelman, A., Field, P. R.,
and Liu, G.: Dependence of the Ice Water Content and Snowfall Rate on
Temperature, Globally: Comparison of in Situ Observations, Satellite Active
Remote Sensing Retrievals, and Global Climate Model Simulations, J. Appl.
Meteorol. Climatol., 56, 189–215, https://doi.org/10.1175/JAMC-D-16-0230.1, 2017.
Hoose, C., Kristjánsson, J. E., Chen, J. P., and Hazra, A.: A
classical-theory-based parameterization of heterogeneous ice nucleation by
mineral dust, soot, and biological particles in a global climate model, J.
Atmos. Sci., 67, 2483–2503, https://doi.org/10.1175/2010JAS3425.1, 2010.
Jensen, E. J., Toon, O. B., Vay, S. A., Ovarlez, J., May, R., Bui, T. P.,
Twohy, C. H., Gandrud, B. W., Pueschel, R. F., and Schumann, U.: Prevalence
of ice-supersaturated regions in the upper troposphere: Implications for
optically thin ice cloud formation, J. Geophys. Res.-Atmos., 106,
17253–17266, https://doi.org/10.1029/2000JD900526, 2001.
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, 1–5, https://doi.org/10.1029/2011GL048532, 2011.
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud
formation: Homogeneous freezing of supercooled aerosols, J. Geophys. Res.,
107, D2, https://doi.org/10.1029/2001JD000470, 2002.
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud
formation: Heterogeneous freezing, J. Geophys. Res., 107, 4402,
https://doi.org/10.1029/2002JD003220, 2003.
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based
parameterization of cirrus cloud formation for use in global atmospheric
models, J. Geophys. Res.-Atmos., 111, D01205, https://doi.org/10.1029/2005JD006219,
2006.
Koop, T., Luo, B., Tsias, A., and Peter, T.: Water activity as the
determinant for homogeneous ice nucleation in aqueous solutions, Nature,
406, 611–614, https://doi.org/10.1038/35020537, 2000.
Kooperman, G. J., Pritchard, M. S., Ghan, S. J., Wang, M., Somerville, R. C.
J., and Russell, L. M.: Constraining the influence of natural variability to
improve estimates of global aerosol indirect effects in a nudged version of
the Community Atmosphere Model 5, J. Geophys. Res.-Atmos., 117, 1–16,
https://doi.org/10.1029/2012JD018588, 2012.
Krämer, M., Schiller, C., Afchine, A., Bauer, R., Gensch, I., Mangold, A., Schlicht, S., Spelten, N., Sitnikov, N., Borrmann, S., de Reus, M., and Spichtinger, P.: Ice supersaturations and cirrus cloud crystal numbers, Atmos. Chem. Phys., 9, 3505–3522, https://doi.org/10.5194/acp-9-3505-2009, 2009.
Krämer, M., Rolf, C., Luebke, A., Afchine, A., Spelten, N., Costa, A., Meyer, J., Zöger, M., Smith, J., Herman, R. L., Buchholz, B., Ebert, V., Baumgardner, D., Borrmann, S., Klingebiel, M., and Avallone, L.: A microphysics guide to cirrus clouds – Part 1: Cirrus types, Atmos. Chem. Phys., 16, 3463–3483, https://doi.org/10.5194/acp-16-3463-2016, 2016.
Krämer, M., Rolf, C., Spelten, N., Afchine, A., Fahey, D., Jensen, E., Khaykin, S., Kuhn, T., Lawson, P., Lykov, A., Pan, L. L., Riese, M., Rollins, A., Stroh, F., Thornberry, T., Wolf, V., Woods, S., Spichtinger, P., Quaas, J., and Sourdeval, O.: A microphysics guide to cirrus – Part 2: Climatologies of clouds and humidity from observations, Atmos. Chem. Phys., 20, 12569–12608, https://doi.org/10.5194/acp-20-12569-2020, 2020.
Kuebbeler, M., Lohmann, U., Hendricks, J., and Kärcher, B.: Dust ice nuclei effects on cirrus clouds, Atmos. Chem. Phys., 14, 3027–3046, https://doi.org/10.5194/acp-14-3027-2014, 2014.
Lin, S. J.: A “vertically Lagrangian” finite-volume dynamical core for
global models, Mon. Weather Rev., 132, 2293–2307,
https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2,
2004.
Liu, X. and Penner, J. E.: Ice nucleation parameterization for global
models, Meteorol. Zeitschrift, 14, 499–514,
https://doi.org/10.1127/0941-2948/2005/0059, 2005.
Liu, X., Penner, J. E., Ghan, S. J., and Wang, M.: Inclusion of ice
microphysics in the NCAR Community Atmospheric Model version 3 (CAM3), J. Climate, 20, 4526–4547, https://doi.org/10.1175/JCLI4264.1, 2007.
Liu, X., Shi, X., Zhang, K., Jensen, E. J., Gettelman, A., Barahona, D., Nenes, A., and Lawson, P.: Sensitivity studies of dust ice nuclei effect on cirrus clouds with the Community Atmosphere Model CAM5, Atmos. Chem. Phys., 12, 12061–12079, https://doi.org/10.5194/acp-12-12061-2012, 2012.
Liu, X., Ma, P.-L., Wang, H., Tilmes, S., Singh, B., Easter, R. C., Ghan, S. J., and Rasch, P. J.: Description and evaluation of a new four-mode version of the Modal Aerosol Module (MAM4) within version 5.3 of the Community Atmosphere Model, Geosci. Model Dev., 9, 505–522, https://doi.org/10.5194/gmd-9-505-2016, 2016.
Luebke, A. E., Avallone, L. M., Schiller, C., Meyer, J., Rolf, C., and Krämer, M.: Ice water content of Arctic, midlatitude, and tropical cirrus – Part 2: Extension of the database and new statistical analysis, Atmos. Chem. Phys., 13, 6447–6459, https://doi.org/10.5194/acp-13-6447-2013, 2013.
Luebke, A. E., Afchine, A., Costa, A., Grooß, J.-U., Meyer, J., Rolf, C., Spelten, N., Avallone, L. M., Baumgardner, D., and Krämer, M.: The origin of midlatitude ice clouds and the resulting influence on their microphysical properties, Atmos. Chem. Phys., 16, 5793–5809, https://doi.org/10.5194/acp-16-5793-2016, 2016.
Mace, G. G. and Wrenn, F. J.: Evaluation of the hydrometeor layers in the
East and West Pacific within ISCCP cloud-top pressure-optical depth bins
using merged CloudSat and CALIPSO data, J. Climate, 26, 9429–9444,
https://doi.org/10.1175/JCLI-D-12-00207.1, 2013.
Mcfarquhar, G. M. and Heymsfield, A. J.: Parameterization of tropical cirrus
ice crystal size distributions and implications for radiative transfer:
Results from CEPEX, J. Atmos. Sci., 54, 2187–2200,
https://doi.org/10.1175/1520-0469(1997)054<2187:POTCIC>2.0.CO;2,
1997.
Minikin, A., Petzold, A., Ström, J., Krejci, R., Seifert, M., van Velthoven, P., Schlager, H., and Schumann, U.: Aircraft observations of the upper tropospheric fine particle aerosol in the Northern and Southern Hemispheres at midlatitudes, Geophys. Res. Lett., 30, 1503, https://doi.org/10.1029/2002GL016458, 2003.
Mitchell, D. L., Garnier, A., Pelon, J., and Erfani, E.: CALIPSO (IIR–CALIOP) retrievals of cirrus cloud ice-particle concentrations, Atmos. Chem. Phys., 18, 17325–17354, https://doi.org/10.5194/acp-18-17325-2018, 2018.
Montgomery, M. T., Davis, C., Dunkerton, T., Wang, Z., Velden, C., Torn, R.,
Majumdar, S. J., Zhang, F., Smith, R. K., Bosart, L., Bell, M. M., Haase, J.
S., Heymsfield, A., Jensen, J., Campos, T., and Boothe, M. A.: The
pre-depression investigation of cloud-systems in the tropics (PREDICT)
experiment: Scientific basis, new analysis tools, and some first results,
B. Am. Meteorol. Soc., 93, 153–172, https://doi.org/10.1175/BAMS-D-11-00046.1,
2012.
Morrison, H. and Gettelman, A.: A new two-moment bulk stratiform cloud
microphysics scheme in the community atmosphere model, version 3 (CAM3).
Part I: Description and numerical tests, J. Climate, 21, 3642–3659,
https://doi.org/10.1175/2008JCLI2105.1, 2008.
Muhlbauer, A., Ackerman, T. P., Comstock, J. M., Diskin, G. S., Evans, S.
M., Lawson, R. P., and Marchand, R. T.: Impact of large-scale dynamics on the
microphysical properties of midlatitude cirrus, J. Geophys. Res., 119,
3976–3996, https://doi.org/10.1002/2013JD020035, 2014a.
Muhlbauer, A., Kalesse, H., and Kollias, P.: Vertical velocities and
turbulence in midlatitude anvil cirrus: A comparison between in situ
aircraft measurements and ground-based Doppler cloud radar retrievals,
Geophys. Res. Lett., 41, 7814–7821, https://doi.org/10.1002/2014GL062279, 2014b.
Murphy, D. M. and Koop, T.: Review of the vapour pressures of ice and
supercooled water for atmospheric applications, Q. J. Roy. Meteor. Soc.,
131, 1539–1565, https://doi.org/10.1256/qj.04.94, 2005.
Pan, L. L., Bowman, K. P., Atlas, E. L., Wofsy, S. C., Zhang, F., Bresch, J.
F., Ridley, B. A., Pittman, J. V., Homeyer, C. R., Romashkin, P., and Cooper,
W. A.: The stratosphere-troposphere analyses of regional transport 2008
experiment, B. Am. Meteorol. Soc., 91, 327–342,
https://doi.org/10.1175/2009BAMS2865.1, 2010.
Pan, L. L., Atlas, E. L., Salawitch, R. J., Honomichl, S. B., Bresch, J. F.,
Randel, W. J., Apel, E. C., Hornbrook, R. S., Weinheimer, A. J., Anderson,
D. C., Andrews, S. J., Baidar, S., Beaton, S. P., Campos, T. L., Carpenter,
L. J., Chen, D., Dix, B., Donets, V., Hall, S. R., Hanisco, T. F., Homeyer,
C. R., Huey, L. G., Jensen, J. B., Kaser, L., Kinnison, D. E., Koenig, T.
K., Lamarque, J.-F., Liu, C., Luo, J., Luo, Z. J., Montzka, D. D., Nicely,
J. M., Pierce, R. B., Riemer, D. D., Robinson, T., Romashkin, P.,
Saiz-Lopez, A., Schauffler, S., Shieh, O., Stell, M. H., Ullmann, K.,
Vaughan, G., Volkamer, R., and Wolfe, G.: The Convective Transport of Active
Species in the Tropics (CONTRAST) Experiment, B. Am. Meteorol. Soc.,
98, 106–128, https://doi.org/10.1175/bams-d-14-00272.1, 2017.
Patnaude, R. and Diao, M.: Aerosol indirect effects on cirrus clouds based on global aircraft observations, Geophys. Res. Lett., 47, e2019GL086550, https://doi.org/10.1029/2019GL086550, 2020.
Penner, J. E., Chen, Y., Wang, M., and Liu, X.: Possible influence of anthropogenic aerosols on cirrus clouds and anthropogenic forcing, Atmos. Chem. Phys., 9, 879–896, https://doi.org/10.5194/acp-9-879-2009, 2009.
Penner, J. E., Zhou, C., Garnier, A., and Mitchell, D. L.: Anthropogenic
Aerosol Indirect Effects in Cirrus Clouds, J. Geophys. Res.-Atmos., 123,
11652–11677, https://doi.org/10.1029/2018JD029204, 2018.
Prenni, A. J., Petters, M. D., Faulhaber, A., Carriço, C. M., Ziemann,
P. J., Kreidenweis, S. M., and DeMott, P. J.: Heterogeneous ice nucleation
measurements of secondary organic aerosol generated from ozonolysis of
alkenes, Geophys. Res. Lett., 36, 1–5, https://doi.org/10.1029/2008GL036957, 2009.
Righi, M., Hendricks, J., Lohmann, U., Beer, C. G., Hahn, V., Heinold, B., Heller, R., Krämer, M., Ponater, M., Rolf, C., Tegen, I., and Voigt, C.: Coupling aerosols to (cirrus) clouds in the global EMAC-MADE3 aerosol–climate model, Geosci. Model Dev., 13, 1635–1661, https://doi.org/10.5194/gmd-13-1635-2020, 2020.
Sassen, K., Wang, Z., and Liu, D.: Global distribution of cirrus clouds from
CloudSat/cloud-aerosol lidar and infrared pathfinder satellite observations
(CALIPSO) measurements, J. Geophys. Res.-Atmos., 113, 1–12,
https://doi.org/10.1029/2008JD009972, 2008.
Schiller, C., Krämer, M., Afchine, A., Spelten, N., and Sitnikov, N.: Ice
water content of Arctic, midlatitude, and tropical cirrus, J. Geophys. Res.-Atmos., 113, 1–12, https://doi.org/10.1029/2008JD010342, 2008.
Shi, X. and Liu, X.: Effect of cloud-scale vertical velocity on the
contribution of homogeneous nucleation to cirrus formation and radiative
forcing, Geophys. Res. Lett., 43, 6588–6595, https://doi.org/10.1002/2016GL069531,
2016.
Shi, X., Liu, X., and Zhang, K.: Effects of pre-existing ice crystals on cirrus clouds and comparison between different ice nucleation parameterizations with the Community Atmosphere Model (CAM5), Atmos. Chem. Phys., 15, 1503–1520, https://doi.org/10.5194/acp-15-1503-2015, 2015.
Stephens, B. B., Long, M. C., Keeling, R. F., Kort, E. A., Sweeney, C.,
Apel, E. C., Atlas, E. L., Beaton, S., Bent, J. D., Blake, N. J., Bresch, J.
F., Casey, J., Daube, B. C., Diao, M., Diaz, E., Dierssen, H., Donets, V.,
Gao, B.-C., Gierach, M., Green, R., Haag, J., Hayman, M., Hills, A. J.,
Hoecker-Martínez, M. S., Honomichl, S. B., Hornbrook, R. S., Jensen, J.
B., Li, R.-R., McCubbin, I., McKain, K., Morgan, E. J., Nolte, S., Powers,
J. G., Rainwater, B., Randolph, K., Reeves, M., Schauffler, S. M., Smith,
K., Smith, M., Stith, J., Stossmeister, G., Toohey, D. W., and Watt, A. S.:
The
Stephens, G. and Webster, P.: Clouds and climate: Sensitivity of simple
systems, J. Atmos. Sci., 38, 235–247, 1981.
Storelvmo, T. and Herger, N.: Cirrus cloud susceptibility to the injection
of ice nuclei in the upper troposphere, J. Geophys. Res., 119,
2375–2389, https://doi.org/10.1002/2013JD020816, 2014.
Tan, X., Huang, Y., Diao, M., Bansemer, A., Zondlo, M. A., DiGangi, J. P.,
Volkamer, R., and Hu, Y.: An assessment of the radiative effects of ice
supersaturation based on in situ observations, Geophys. Res. Lett., 43,
11039–11047, https://doi.org/10.1002/2016GL071144, 2016.
Thorsen, T. J., Fu, Q., Comstock, J. M., Sivaraman, C., Vaughan, M. A.,
Winker, D. M., and Turner, D. D.: Macrophysical properties of tropical
cirrus clouds from the CALIPSO satellite and from ground-based micropulse
and Raman lidars, J. Geophys. Res.-Atmos., 118, 9209–9220,
https://doi.org/10.1002/jgrd.50691, 2013.
Tseng, H.-H. and Fu, Q.: Temperature control of the variability of tropical
tropopause layer cirrus clouds, J. Geophys. Res.-Atmos., 122, 11062–11075, https://doi.org/10.1002/2017JD027093,
2017.
UCAR/NCAR – Earth Observing Laboratory: Vertical Cavity Surface
Emitting Laser Hygrometer (VCSEL), Version 1.0, UCAR/NCAR – Earth Observing
Laboratory, https://doi.org/10.5065/D6Z31X06, 2009.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data (NetCDF),
Version 3.0, UCAR/NCAR – Earth Observing Laboratory, https://doi.org/10.5065/D6BC3WKB, 2018a.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
1.2, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D6TX3CK0, 2018b.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
1.1, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D65T3HWR, 2018c.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
2.0, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D6NZ85Z4, 2019a.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
5.0, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D6JW8C64, 2019b.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
5.0, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D6QF8R6R, 2019c.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
3.0, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D6V40SK6, 2019d.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
3.0, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D6CZ35HX, 2019e.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
2.0, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D61R6NV5, 2019f.
UCAR/NCAR – Earth Observing Laboratory: Low Rate (LRT – 1 sps)
Navigation, State Parameter, and Microphysics Flight-Level Data, Version
3.0, UCAR/NCAR – Earth Observing Laboratory,
https://doi.org/10.5065/D6668BHR, 2019g.
Volkamer, R., Baidar, S., Campos, T. L., Coburn, S., DiGangi, J. P., Dix, B., Eloranta, E. W., Koenig, T. K., Morley, B., Ortega, I., Pierce, B. R., Reeves, M., Sinreich, R., Wang, S., Zondlo, M. A., and Romashkin, P. A.: Aircraft measurements of BrO, IO, glyoxal, NO2, H2O, O2–O2 and aerosol extinction profiles in the tropics: comparison with aircraft-/ship-based in situ and lidar measurements, Atmos. Meas. Tech., 8, 2121–2148, https://doi.org/10.5194/amt-8-2121-2015, 2015.
Wang, M. and Penner, J. E.: Cirrus clouds in a global climate model with a statistical cirrus cloud scheme, Atmos. Chem. Phys., 10, 5449–5474, https://doi.org/10.5194/acp-10-5449-2010, 2010.
Wang, M., Liu, X., Zhang, K., and Comstock, J. M.: Aerosol effects on cirrus
through ice nucleation in the Community Atmosphere Model CAM5 with a
statistical cirrus scheme, J. Adv. Model. Earth Syst., 6, 513–526,
https://doi.org/10.1002/2014MS000339, 2014a.
Wang, Y., Liu, X., Hoose, C., and Wang, B.: Different contact angle distributions for heterogeneous ice nucleation in the Community Atmospheric Model version 5, Atmos. Chem. Phys., 14, 10411–10430, https://doi.org/10.5194/acp-14-10411-2014, 2014b.
Wofsy, S. C.: HIAPER Pole-to-Pole Observations (HIPPO): Fine-grained,
global-scale measurements of climatically important atmospheric gases and
aerosols, Philos. T. R. Soc. A, 369,
2073–2086, https://doi.org/10.1098/rsta.2010.0313, 2011.
Wolf, V., Kuhn, T., Milz, M., Voelger, P., Krämer, M., and Rolf, C.: Arctic ice clouds over northern Sweden: microphysical properties studied with the Balloon-borne Ice Cloud particle Imager B-ICI, Atmos. Chem. Phys., 18, 17371–17386, https://doi.org/10.5194/acp-18-17371-2018, 2018.
Wu, C., Liu, X., Diao, M., Zhang, K., Gettelman, A., Lu, Z., Penner, J. E., and Lin, Z.: Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations, Atmos. Chem. Phys., 17, 4731–4749, https://doi.org/10.5194/acp-17-4731-2017, 2017.
Zhang, G. J. and McFarlane, N. A.: Sensitivity of climate simulations to the
parameterization of cumulus convection in the canadian climate centre
general circulation model, Atmos.-Ocean, 33, 407–446,
https://doi.org/10.1080/07055900.1995.9649539, 1995.
Zhang, K., Liu, X., Wang, M., Comstock, J. M., Mitchell, D. L., Mishra, S., and Mace, G. G.: Evaluating and constraining ice cloud parameterizations in CAM5 using aircraft measurements from the SPARTICUS campaign, Atmos. Chem. Phys., 13, 4963–4982, https://doi.org/10.5194/acp-13-4963-2013, 2013.
Zhang, Y., MacKe, A., and Albers, F.: Effect of crystal size spectrum and
crystal shape on stratiform cirrus radiative forcing, Atmos. Res., 52,
59–75, https://doi.org/10.1016/S0169-8095(99)00026-5, 1999.
Zhao, B., Liou, K.-N., Gu, Y., Jiang, J. H., Li, Q., Fu, R., Huang, L., Liu, X., Shi, X., Su, H., and He, C.: Impact of aerosols on ice crystal size, Atmos. Chem. Phys., 18, 1065–1078, https://doi.org/10.5194/acp-18-1065-2018, 2018.
Zhao, B., Wang, Y., Gu, Y., Liou, K. N., Jiang, J. H., Fan, J., Liu, X., Huang, L., and Yung, Y. L.: Ice nucleation by aerosols from anthropogenic pollution, Nat. Geosci., 12, 602–607, https://doi.org/10.1038/s41561-019-0389-4, 2019.
Zhou, C., Penner, J. E., Lin, G., Liu, X., and Wang, M.: What controls the low ice number concentration in the upper troposphere?, Atmos. Chem. Phys., 16, 12411–12424, https://doi.org/10.5194/acp-16-12411-2016, 2016.
Zondlo, M. A., Paige, M. E., Massick, S. M., and Silver, J. A.: Vertical
cavity laser hygrometer for the National Science Foundation Gulfstream-V
aircraft, J. Geophys. Res.-Atmos., 115, 1–14, https://doi.org/10.1029/2010JD014445,
2010.
Short summary
A comprehensive, in situ observation dataset of cirrus clouds was developed based on seven field campaigns, ranging from 87° N–75° S. The observations were compared with a global climate model. Several key factors for cirrus cloud formation were examined, including thermodynamics, dynamics, aerosol indirect effects and geographical locations. Model biases include lower ice mass concentrations, smaller ice crystals and weaker aerosol indirect effects.
A comprehensive, in situ observation dataset of cirrus clouds was developed based on seven field...
Altmetrics
Final-revised paper
Preprint