Articles | Volume 24, issue 16
https://doi.org/10.5194/acp-24-9101-2024
© Author(s) 2024. 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-24-9101-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Exploring aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean using the WRF-Chem–SBM model
Jianqi Zhao
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
Johannes Quaas
Leipzig Institute for Meteorology, Leipzig University, Leipzig, Germany
Hailing Jia
SRON Netherlands Institute for Space Research, Leiden, the Netherlands
Related authors
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Tong Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2555, https://doi.org/10.5194/egusphere-2025-2555, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We use the WRF-Chem-SBM model to investigate how the meteorological conditions impact aerosol-cloud interactions (ACI) under different pollution regimes for marine liquid-phase clouds. Our findings highlight the changes in aerosol effects on clouds and precipitation with thermodynamic conditions, as well as the sensitivity of ACI to meteorological conditions under different pollution regimes, which help to advance the understanding of ACI.
Jianqi Zhao, Xiaoyan Ma, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3662, https://doi.org/10.5194/egusphere-2024-3662, 2024
Preprint archived
Short summary
Short summary
We conduct a comparative analysis of aerosol-cloud responses in liquid-phase clouds under different aerosol and meteorological conditions based on simulations using the WRF-Chem-SBM model. Our findings highlight the different effects of aerosols on clouds and precipitation, as well as variations in the roles of aerosol and meteorological factors influencing aerosol-cloud interactions, in different environment.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
EGUsphere, https://doi.org/10.5194/egusphere-2023-331, https://doi.org/10.5194/egusphere-2023-331, 2023
Preprint archived
Short summary
Short summary
We improve the ability of WRF-Chem model to simulate aerosol-cloud physical and chemical processes by coupling a spectral-bin cloud microphysics scheme and online aerosol module, and consequently explore the aerosol-cloud interactions over eastern China and its adjacent ocean in boreal winter. Our study highlights the differences in aerosol-cloud interactions between land and ocean, precipitation clouds and non-precipitation clouds, and differentiates and quantifies their underlying mechanisms.
Rong Tian, Xiaoyan Ma, and Jianqi Zhao
Atmos. Chem. Phys., 21, 4319–4337, https://doi.org/10.5194/acp-21-4319-2021, https://doi.org/10.5194/acp-21-4319-2021, 2021
Short summary
Short summary
We improve the treatment of the dust emission process in GEOS-Chem by considering the effect of geographical variation of aerodynamic roughness length, smooth roughness length and soil texture, as well as the Owen effect and a more physically based formulation of sandblasting efficiency, which improve estimated threshold friction velocity and dust concentrations over China. Our study highlights the importance of incorporation of realistic land-surface properties into the dust emission scheme.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Tong Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2555, https://doi.org/10.5194/egusphere-2025-2555, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We use the WRF-Chem-SBM model to investigate how the meteorological conditions impact aerosol-cloud interactions (ACI) under different pollution regimes for marine liquid-phase clouds. Our findings highlight the changes in aerosol effects on clouds and precipitation with thermodynamic conditions, as well as the sensitivity of ACI to meteorological conditions under different pollution regimes, which help to advance the understanding of ACI.
Yusuf Bhatti, Duncan Watson-Parris, Leighton Regayre, Hailing Jia, David Neubauer, Ulas Im, Carl Svenhag, Nick Schutgens, Athanasios Tsikerdekis, Athanasios Nenes, Irfan Muhammed, Bastiaan van Diedenhoven, Ardit Arifi, Guangliang Fu, and Otto Hasekamp
EGUsphere, https://doi.org/10.5194/egusphere-2025-2848, https://doi.org/10.5194/egusphere-2025-2848, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Aerosols (small airborne particles) impact Earth's climate, but their extent is unknown. By running climate model simulations and emulating millions of additional variants with different settings, we found that natural emissions like sea spray and sulfur are key sources of uncertainty in climate predictions. Our work shows that understanding these natural processes better can help improve climate models and make future climate projections more accurate.
Sajedeh Marjani, Sina Mehrdad, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2025-1847, https://doi.org/10.5194/egusphere-2025-1847, 2025
Short summary
Short summary
A large part of aviation's climate impact comes from persistent contrails, not just carbon dioxide. This study examined how they evolve under different ice-supersaturated conditions, usually with background clouds. Using high-resolution simulations, we found that contrail growth depends on both the thickness of the supersaturated layer and available water vapor. Contrails can alter surrounding clouds and contribute to warming. Simple thresholds are not enough to predict their impact.
Goutam Choudhury, Karoline Block, Mahnoosh Haghighatnasab, Johannes Quaas, Tom Goren, and Matthias Tesche
Atmos. Chem. Phys., 25, 3841–3856, https://doi.org/10.5194/acp-25-3841-2025, https://doi.org/10.5194/acp-25-3841-2025, 2025
Short summary
Short summary
Aerosol particles in the atmosphere increase cloud reflectivity, thereby cooling the Earth. Accurate global measurements of these particles are crucial for estimating this cooling effect. This study compares and harmonizes two newly developed global aerosol datasets, offering insights for their future use and refinement. We identify pristine oceans as a significant source of uncertainty in the datasets and, therefore, in quantifying the role of aerosols in Earth's climate.
Johannes Mülmenstädt, Andrew S. Ackerman, Ann M. Fridlind, Meng Huang, Po-Lun Ma, Naser Mahfouz, Susanne E. Bauer, Susannah M. Burrows, Matthew W. Christensen, Sudhakar Dipu, Andrew Gettelman, L. Ruby Leung, Florian Tornow, Johannes Quaas, Adam C. Varble, Hailong Wang, Kai Zhang, and Youtong Zheng
Atmos. Chem. Phys., 24, 13633–13652, https://doi.org/10.5194/acp-24-13633-2024, https://doi.org/10.5194/acp-24-13633-2024, 2024
Short summary
Short summary
Stratocumulus clouds play a large role in Earth's climate by reflecting incoming solar energy back to space. Turbulence at stratocumulus cloud top mixes in dry, warm air, which can lead to cloud dissipation. This process is challenging for coarse-resolution global models to represent. We show that global models nevertheless agree well with our process understanding. Global models also think the process is less important for the climate than other lines of evidence have led us to conclude.
Jianqi Zhao, Xiaoyan Ma, and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3662, https://doi.org/10.5194/egusphere-2024-3662, 2024
Preprint archived
Short summary
Short summary
We conduct a comparative analysis of aerosol-cloud responses in liquid-phase clouds under different aerosol and meteorological conditions based on simulations using the WRF-Chem-SBM model. Our findings highlight the different effects of aerosols on clouds and precipitation, as well as variations in the roles of aerosol and meteorological factors influencing aerosol-cloud interactions, in different environment.
Iris Papakonstantinou-Presvelou and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3293, https://doi.org/10.5194/egusphere-2024-3293, 2024
Preprint archived
Short summary
Short summary
As the Arctic warms and the sea ice retreats, more open ocean is exposed, changing how aerosols impact clouds. Our previous 10-year satellite analysis found higher ice crystal numbers over sea ice than over ocean. Using model simulations and aircraft observations we identify here two factors as potential causes at colder temperatures; ice nucleating particles over sea ice and blowing snow. With further sea ice loss, these processes may weaken, leading to fewer ice particles in the future.
Charlotte Lange and Johannes Quaas
EGUsphere, https://doi.org/10.5194/egusphere-2024-3229, https://doi.org/10.5194/egusphere-2024-3229, 2024
Short summary
Short summary
We studied how the Earth’s climate system adjusts to sudden changes in the energy budget, by analyzing data of four climate models, which simulated a 4 % reduction of incoming solar energy. We found rapid cooling of the atmosphere and shifts in cloud cover and atmospheric circulation patterns like land-sea-circulation. Our research helps to better understand cloud adjustments, which are a main source of uncertainty in climate models. This can improve future climate predictions.
Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi
Weather Clim. Dynam., 5, 1223–1268, https://doi.org/10.5194/wcd-5-1223-2024, https://doi.org/10.5194/wcd-5-1223-2024, 2024
Short summary
Short summary
This study introduces a novel deep learning (DL) approach to analyze how regional radiative forcing in Europe impacts the Arctic climate. By integrating atmospheric poleward energy transport with DL-based clustering of atmospheric patterns and attributing anomalies to specific clusters, our method reveals crucial, nuanced interactions within the climate system, enhancing our understanding of intricate climate dynamics.
Julien Lenhardt, Johannes Quaas, Dino Sejdinovic, and Daniel Klocke
EGUsphere, https://doi.org/10.5194/egusphere-2024-2724, https://doi.org/10.5194/egusphere-2024-2724, 2024
Short summary
Short summary
Clouds come in various shapes and sizes and constitute a fundamental element of the Earth’s climate system. Different cloud types show variable impacts on climate change. We present a new cloud type classification method called CloudViT relying on spatial patterns of cloud properties obtained from satellite data using machine learning. We can thus help understanding the effects of different cloud types on climate change.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Tong Sha, Siyu Yang, Qingcai Chen, Liangqing Li, Xiaoyan Ma, Yan-Lin Zhang, Zhaozhong Feng, K. Folkert Boersma, and Jun Wang
Atmos. Chem. Phys., 24, 8441–8455, https://doi.org/10.5194/acp-24-8441-2024, https://doi.org/10.5194/acp-24-8441-2024, 2024
Short summary
Short summary
Using an updated soil reactive nitrogen emission scheme in the Unified Inputs for Weather Research and Forecasting coupled with Chemistry (UI-WRF-Chem) model, we investigate the role of soil NO and HONO (Nr) emissions in air quality and temperature in North China. Contributions of soil Nr emissions to O3 and secondary pollutants are revealed, exceeding effects of soil NOx or HONO emission. Soil Nr emissions play an important role in mitigating O3 pollution and addressing climate change.
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.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Karoline Block, Mahnoosh Haghighatnasab, Daniel G. Partridge, Philip Stier, and Johannes Quaas
Earth Syst. Sci. Data, 16, 443–470, https://doi.org/10.5194/essd-16-443-2024, https://doi.org/10.5194/essd-16-443-2024, 2024
Short summary
Short summary
Aerosols being able to act as condensation nuclei for cloud droplets (CCNs) are a key element in cloud formation but very difficult to determine. In this study we present a new global vertically resolved CCN dataset for various humidity conditions and aerosols. It is obtained using an atmospheric model (CAMS reanalysis) that is fed by satellite observations of light extinction (AOD). We investigate and evaluate the abundance of CCNs in the atmosphere and their temporal and spatial occurrence.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Hao Luo, Johannes Quaas, and Yong Han
Atmos. Chem. Phys., 23, 8169–8186, https://doi.org/10.5194/acp-23-8169-2023, https://doi.org/10.5194/acp-23-8169-2023, 2023
Short summary
Short summary
Clouds exhibit a wide range of vertical structures with varying microphysical and radiative properties. We show a global survey of spatial distribution, vertical extent and radiative effect of various classified cloud vertical structures using joint satellite observations from the new CCCM datasets during 2007–2010. Moreover, the long-term trends in CVSs are investigated based on different CMIP6 future scenarios to capture the cloud variations with different, increasing anthropogenic forcings.
Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-69, https://doi.org/10.5194/amt-2023-69, 2023
Publication in AMT not foreseen
Short summary
Short summary
Insect numbers in the atmosphere can be calculated using polarimetric weather radar but they have to be identified and separated from other echoes, especially weather phenomena. Here, the separation is demonstrated using three machine-learning algorithms and insect count data from suction traps and the nature of radar measurements of different radar echoes is revealed. Random forest is the best separating algorithm and insect echoes radar measurements are distinct.
Kun Wang, Xiaoyan Ma, Rong Tian, and Fangqun Yu
Atmos. Chem. Phys., 23, 4091–4104, https://doi.org/10.5194/acp-23-4091-2023, https://doi.org/10.5194/acp-23-4091-2023, 2023
Short summary
Short summary
From 12 March to 6 April 2016 in Beijing, there were 11 typical new particle formation days, 13 non-event days, and 2 undefined days. We first analyzed the favorable background of new particle formation in Beijing and then conducted the simulations using four nucleation schemes based on a global chemistry transport model (GEOS-Chem) to understand the nucleation mechanism.
Jianqi Zhao, Xiaoyan Ma, Johannes Quaas, and Hailing Jia
EGUsphere, https://doi.org/10.5194/egusphere-2023-331, https://doi.org/10.5194/egusphere-2023-331, 2023
Preprint archived
Short summary
Short summary
We improve the ability of WRF-Chem model to simulate aerosol-cloud physical and chemical processes by coupling a spectral-bin cloud microphysics scheme and online aerosol module, and consequently explore the aerosol-cloud interactions over eastern China and its adjacent ocean in boreal winter. Our study highlights the differences in aerosol-cloud interactions between land and ocean, precipitation clouds and non-precipitation clouds, and differentiates and quantifies their underlying mechanisms.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
Short summary
Short summary
Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Yiwen Hu, Zengliang Zang, Xiaoyan Ma, Yi Li, Yanfei Liang, Wei You, Xiaobin Pan, and Zhijin Li
Atmos. Chem. Phys., 22, 13183–13200, https://doi.org/10.5194/acp-22-13183-2022, https://doi.org/10.5194/acp-22-13183-2022, 2022
Short summary
Short summary
This study developed a four-dimensional variational assimilation (4DVAR) system based on WRF–Chem to optimise SO2 emissions. The 4DVAR system was applied to obtain the SO2 emissions during the early period of the COVID-19 pandemic over China. The results showed that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts.
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, https://doi.org/10.5194/acp-22-12221-2022, 2022
Short summary
Short summary
Pollution particles cool climate and offset part of the global warming. However, they are washed out by rain and thus their effect responds quickly to changes in emissions. We show multiple datasets to demonstrate that aerosol emissions and their concentrations declined in many regions influenced by human emissions, as did the effects on clouds. Consequently, the cooling impact on the Earth energy budget became smaller. This change in trend implies a relative warming.
Ovid O. Krüger, Bruna A. Holanda, Sourangsu Chowdhury, Andrea Pozzer, David Walter, Christopher Pöhlker, Maria Dolores Andrés Hernández, John P. Burrows, Christiane Voigt, Jos Lelieveld, Johannes Quaas, Ulrich Pöschl, and Mira L. Pöhlker
Atmos. Chem. Phys., 22, 8683–8699, https://doi.org/10.5194/acp-22-8683-2022, https://doi.org/10.5194/acp-22-8683-2022, 2022
Short summary
Short summary
The abrupt reduction in human activities during the first COVID-19 lockdown created unprecedented atmospheric conditions. We took the opportunity to quantify changes in black carbon (BC) as a major anthropogenic air pollutant. Therefore, we measured BC on board a research aircraft over Europe during the lockdown and compared the results to measurements from 2017. With model simulations we account for different weather conditions and find a lockdown-related decrease in BC of 41 %.
Mahnoosh Haghighatnasab, Jan Kretzschmar, Karoline Block, and Johannes Quaas
Atmos. Chem. Phys., 22, 8457–8472, https://doi.org/10.5194/acp-22-8457-2022, https://doi.org/10.5194/acp-22-8457-2022, 2022
Short summary
Short summary
The impact of aerosols emitted by the Holuhraun volcanic eruption on liquid clouds was assessed from a pair of cloud-system-resolving simulations along with satellite retrievals. Inside and outside the plume were compared in terms of their statistical distributions. Analyses indicated enhancement for cloud droplet number concentration inside the volcano plume in model simulations and satellite retrievals, while there was on average a small effect on both liquid water path and cloud fraction.
Hailing Jia, Johannes Quaas, Edward Gryspeerdt, Christoph Böhm, and Odran Sourdeval
Atmos. Chem. Phys., 22, 7353–7372, https://doi.org/10.5194/acp-22-7353-2022, https://doi.org/10.5194/acp-22-7353-2022, 2022
Short summary
Short summary
Aerosol–cloud interaction is the most uncertain component of the anthropogenic forcing of the climate. By combining satellite and reanalysis data, we show that the strength of the Twomey effect (S) increases remarkably with vertical velocity. Both the confounding effect of aerosol–precipitation interaction and the lack of vertical co-location between aerosol and cloud are found to overestimate S, whereas the retrieval biases in aerosol and cloud appear to underestimate S.
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.
Matthew W. Christensen, Andrew Gettelman, Jan Cermak, Guy Dagan, Michael Diamond, Alyson Douglas, Graham Feingold, Franziska Glassmeier, Tom Goren, Daniel P. Grosvenor, Edward Gryspeerdt, Ralph Kahn, Zhanqing Li, Po-Lun Ma, Florent Malavelle, Isabel L. McCoy, Daniel T. McCoy, Greg McFarquhar, Johannes Mülmenstädt, Sandip Pal, Anna Possner, Adam Povey, Johannes Quaas, Daniel Rosenfeld, Anja Schmidt, Roland Schrödner, Armin Sorooshian, Philip Stier, Velle Toll, Duncan Watson-Parris, Robert Wood, Mingxi Yang, and Tianle Yuan
Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, https://doi.org/10.5194/acp-22-641-2022, 2022
Short summary
Short summary
Trace gases and aerosols (tiny airborne particles) are released from a variety of point sources around the globe. Examples include volcanoes, industrial chimneys, forest fires, and ship stacks. These sources provide opportunistic experiments with which to quantify the role of aerosols in modifying cloud properties. We review the current state of understanding on the influence of aerosol on climate built from the wide range of natural and anthropogenic laboratories investigated in recent decades.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
Short summary
Short summary
The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Rong Tian, Xiaoyan Ma, and Jianqi Zhao
Atmos. Chem. Phys., 21, 4319–4337, https://doi.org/10.5194/acp-21-4319-2021, https://doi.org/10.5194/acp-21-4319-2021, 2021
Short summary
Short summary
We improve the treatment of the dust emission process in GEOS-Chem by considering the effect of geographical variation of aerodynamic roughness length, smooth roughness length and soil texture, as well as the Owen effect and a more physically based formulation of sandblasting efficiency, which improve estimated threshold friction velocity and dust concentrations over China. Our study highlights the importance of incorporation of realistic land-surface properties into the dust emission scheme.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
Short summary
Short summary
Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Jan Kretzschmar, Johannes Stapf, Daniel Klocke, Manfred Wendisch, and Johannes Quaas
Atmos. Chem. Phys., 20, 13145–13165, https://doi.org/10.5194/acp-20-13145-2020, https://doi.org/10.5194/acp-20-13145-2020, 2020
Short summary
Short summary
This study compares simulations with the ICON model at the kilometer scale to airborne radiation and cloud microphysics observations that have been derived during the ACLOUD aircraft campaign around Svalbard, Norway, in May/June 2017. We find an overestimated surface warming effect of clouds compared to the observations in our setup. This bias was reduced by considering subgrid-scale vertical motion in the activation of cloud condensation nuclei in the two-moment microphysical scheme used.
Martina Krämer, Christian Rolf, Nicole Spelten, Armin Afchine, David Fahey, Eric Jensen, Sergey Khaykin, Thomas Kuhn, Paul Lawson, Alexey Lykov, Laura L. Pan, Martin Riese, Andrew Rollins, Fred Stroh, Troy Thornberry, Veronika Wolf, Sarah Woods, Peter Spichtinger, Johannes Quaas, and Odran Sourdeval
Atmos. Chem. Phys., 20, 12569–12608, https://doi.org/10.5194/acp-20-12569-2020, https://doi.org/10.5194/acp-20-12569-2020, 2020
Short summary
Short summary
To improve the representations of cirrus clouds in climate predictions, extended knowledge of their properties and geographical distribution is required. This study presents extensive airborne in situ and satellite remote sensing climatologies of cirrus and humidity, which serve as a guide to cirrus clouds. Further, exemplary radiative characteristics of cirrus types and also in situ observations of tropical tropopause layer cirrus and humidity in the Asian monsoon anticyclone are shown.
Tong Sha, Xiaoyan Ma, Jun Wang, Rong Tian, Jianqi Zhao, Fang Cao, and Yan-Lin Zhang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-760, https://doi.org/10.5194/acp-2020-760, 2020
Preprint withdrawn
Short summary
Short summary
Most numerical models perform poorly on simulating the inorganic chemical components in PM2.5 (sulfate, nitrate, and ammonium (SNA)), generally underestimate sulfate but overestimate nitrate concentrations in haze events. Our work aims at investigating the role of cloud water in simulating SNA. We find that the uncertainties of cloud water can lead to model bias in simulating SNA, and can be reduced by constraining the modeled cloud water with MODIS satellite observations.
Cited articles
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Arias, P., Bellouin, N., Coppola, E., Jones, R., Krinner, G., Marotzke, J., Naik, V., Palmer, M., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J., Storelvmo, T., Thorne, P., Trewin, B., Achutarao, K., Adhikary, B., Allan, R., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J. G., Cassou, C., Cherchi, A., Collins, W. D., Collins, W. J., Connors, S., Corti, S., Cruz, F., Dentener, F. J., Dereczynski, C., Di Luca, A., Diongue Niang, A., Doblas-Reyes, P., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V., Fischer, E. M., Forster, P., Fox-Kemper, B., Fuglestvedt, J., Fyfe, J., Gillett, N., Goldfarb, L., Gorodetskaya, I., Gutierrez, J. M., Hamdi, R., Hawkins, E., Hewitt, H., Hope, P., Islam, A. S., Jones, C., Kaufmann, D., Kopp, R., Kosaka, Y., Kossin, J., Krakovska, S., Li, J., Lee, J.-Y., Masson-Delmotte, V., Mauritsen, T., Maycock, T., Meinshausen, M., Min, S.-K., Ngo Duc, T., Otto, F., Pinto, I., Pirani, A., Raghavan, K., Ranasighe, R., Ruane, A., Ruiz, L., Sallée, J.-B., Samset, B. H., Sathyendranath, S., Monteiro, P. S., Seneviratne, S. I., Sörensson, A. A., Szopa, S., Takayabu, I., Treguier, A.-M., van den Hurk, B., Vautard, R., Von Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Technical Summary, The Intergovernmental Panel on Climate Change AR6, Remote, 33–144, https://doi.org/10.1017/9781009157896.002, 2021.
Arola, A., Lipponen, A., Kolmonen, P., Virtanen, T. H., Bellouin, N., Grosvenor, D. P., Gryspeerdt, E., Quaas, J., and Kokkola, H.: Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors, Nat. Commun., 13, 1–8, https://doi.org/10.1038/s41467-022-34948-5, 2022.
Bangert, M., Kottmeier, C., Vogel, B., and Vogel, H.: Regional scale effects of the aerosol cloud interaction simulated with an online coupled comprehensive chemistry model, Atmos. Chem. Phys., 11, 4411–4423, https://doi.org/10.5194/acp-11-4411-2011, 2011.
Bao, J. W., Feng, J. M., and Wang, Y. L.: Dynamical downscaling simulation and future projection of precipitation over China, J. Geophys. Res.-Atmos., 120, 8227–8243, https://doi.org/10.1002/2015jd023275, 2015.
Brenguier, J. L., Pawlowska, H., Schüller, L., Preusker, R., Fischer, J., and Fouquart, Y.: Radiative properties of boundary layer clouds: Droplet effective radius versus number concentration, J. Atmos. Sci., 57, 803–821, https://doi.org/10.1175/1520-0469(2000)057<0803:RPOBLC>2.0.CO;2, 2000.
Bretherton, C. S., Blossey, P. N., and Uchida, J.: Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo, Geophys. Res. Lett., 34, L03813, https://doi.org/10.1029/2006gl027648, 2007.
Buchholz, R. R., Emmons, L. K., Tilmes, S., and The CESM2 Development Team: CESM2.1/CAM-chem Instantaneous Output for Boundary Conditions, UCAR/NCAR – Atmospheric Chemistry Observations and Modeling Laboratory [data set], https://doi.org/10.5065/NMP7-EP60, 2019.
Carslaw, K. S., Boucher, O., Spracklen, D. V., Mann, G. W., Rae, J. G. L., Woodward, S., and Kulmala, M.: A review of natural aerosol interactions and feedbacks within the Earth system, Atmos. Chem. Phys., 10, 1701–1737, https://doi.org/10.5194/acp-10-1701-2010, 2010.
Chen, Y. C., Christensen, M. W., Stephens, G. L., and Seinfeld, J. H.: Satellite-based estimate of global aerosol-cloud radiative forcing by marine warm clouds, Nat. Geosci., 7, 643–646, https://doi.org/10.1038/ngeo2214, 2014.
Chen, Y. Y., Yang, K., Zhou, D. G., Qin, J., and Guo, X. F.: Improving the Noah Land Surface Model in Arid Regions with an Appropriate Parameterization of the Thermal Roughness Length, J. Hydrometeorol., 11, 995–1006, https://doi.org/10.1175/2010jhm1185.1, 2010.
China National Environmental Monitoring Center: National Urban Air Quality Real-time Publishing Platform, https://air.cnemc.cn:18007 (last access: 19 March 2023), 2023.
Christensen, M. W. and Stephens, G. L.: Microphysical and macrophysical responses of marine stratocumulus polluted by underlying ships: 2. Impacts of haze on precipitating clouds, J. Geophys. Res.-Atmos., 117, D11203, https://doi.org/10.1029/2011jd017125, 2012.
Christensen, M. W., Chen, Y. C., and Stephens, G. L.: Aerosol indirect effect dictated by liquid clouds, J. Geophys. Res.-Atmos., 121, 14636–614650, https://doi.org/10.1002/2016JD025245, 2016.
Christensen, M. W., Gettelman, A., Cermak, J., Dagan, G., Diamond, M., Douglas, A., Feingold, G., Glassmeier, F., Goren, T., Grosvenor, D. P., Gryspeerdt, E., Kahn, R., Li, Z., Ma, P.-L., Malavelle, F., McCoy, I. L., McCoy, D. T., McFarquhar, G., Mülmenstädt, J., Pal, S., Possner, A., Povey, A., Quaas, J., Rosenfeld, D., Schmidt, A., Schrödner, R., Sorooshian, A., Stier, P., Toll, V., Watson-Parris, D., Wood, R., Yang, M., and Yuan, T.: Opportunistic experiments to constrain aerosol effective radiative forcing, Atmos. Chem. Phys., 22, 641–674, https://doi.org/10.5194/acp-22-641-2022, 2022.
Church, J., Clark, P., Cazenave, A., Gregory, J., and Unnikrishnan, A.: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Computational Geometry, https://doi.org/10.1016/S0925-7721(01)00003-7, 2013.
Emmons, L. K., Schwantes, R. H., Orlando, J. J., Tyndall, G., Kinnison, D., Lamarque, J., Marsh, D., Mills, M. J., Tilmes, S., and Bardeen, C.: The chemistry mechanism in the community earth system model version 2 (CESM2), J. Adv. Model. Earth Syst., 12, e2019MS001882, https://doi.org/10.1029/2019MS001882, 2020.
Fan, J. W., Leung, L. R., Li, Z. Q., Morrison, H., Chen, H. B., Zhou, Y. Q., Qian, Y., and Wang, Y.: Aerosol impacts on clouds and precipitation in eastern China: Results from bin and bulk microphysics, J. Geophys. Res.-Atmos., 117, D00K36, https://doi.org/10.1029/2011jd016537, 2012.
Fan, J. W., Liu, Y. C., Xu, K. M., North, K., Collis, S., Dong, X. Q., Zhang, G. J., Chen, Q., Kollias, P., and Ghan, S. J.: Improving representation of convective transport for scale-aware parameterization: 1. Convection and cloud properties simulated with spectral bin and bulk microphysics, J. Geophys. Res.-Atmos., 120, 3485–3509, https://doi.org/10.1002/2014jd022142, 2015.
Fast, J. D., Gustafson, W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model, J. Geophys. Res.-Atmos., 111, D21305, https://doi.org/10.1029/2005jd006721, 2006.
Fuentes, E., Coe, H., Green, D., and McFiggans, G.: On the impacts of phytoplankton-derived organic matter on the properties of the primary marine aerosol – Part 2: Composition, hygroscopicity and cloud condensation activity, Atmos. Chem. Phys., 11, 2585–2602, https://doi.org/10.5194/acp-11-2585-2011, 2011.
Gao, W. H., Fan, J. W., Easter, R. C., Yang, Q., Zhao, C., and Ghan, S. J.: Coupling spectral-bin cloud microphysics with the MOSAIC aerosol model in WRF-Chem: Methodology and results for marine stratocumulus clouds, J. Adv. Model. Earth Syst., 8, 1289–1309, https://doi.org/10.1002/2016ms000676, 2016.
Gryspeerdt, E., Goren, T., Sourdeval, O., Quaas, J., Mülmenstädt, J., Dipu, S., Unglaub, C., Gettelman, A., and Christensen, M.: Constraining the aerosol influence on cloud liquid water path, Atmos. Chem. Phys., 19, 5331–5347, https://doi.org/10.5194/acp-19-5331-2019, 2019.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
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, https://doi.org/10.1175/1520-0442(1992)005<1281:Teocto>2.0.Co;2, 1992.
Heyn, I., Block, K., Mulmenstadt, J., Gryspeerdt, E., Kuhne, P., Salzmann, M., and Quaas, J.: Assessment of simulated aerosol effective radiative forcings in the terrestrial spectrum, Geophys. Res. Lett., 44, 1001–1007, https://doi.org/10.1002/2016gl071975, 2017.
Hu, Y., Zang, Z., Ma, X., Li, Y., Liang, Y., You, W., Pan, X., and Li, Z.: Four-dimensional variational assimilation for SO2 emission and its application around the COVID-19 lockdown in the spring 2020 over China, Atmos. Chem. Phys., 22, 13183–13200, https://doi.org/10.5194/acp-22-13183-2022, 2022.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 1 day 0.1 degree × 0.1 degree V06, edited by: Savtchenko, A., Greenbelt, MD [data set], https://doi.org/10.5067/GPM/IMERGDF/DAY/06, 2019.
Islam, T., Srivastava, P. K., Rico-Ramirez, M. A., Dai, Q., Gupta, M., and Singh, S. K.: Tracking a tropical cyclone through WRF-ARW simulation and sensitivity of model physics, Nat. Hazard., 76, 1473–1495, https://doi.org/10.1007/s11069-014-1494-8, 2015.
Jia, H., Ma, X., Quaas, J., Yin, Y., and Qiu, T.: Is positive correlation between cloud droplet effective radius and aerosol optical depth over land due to retrieval artifacts or real physical processes?, Atmos. Chem. Phys., 19, 8879–8896, https://doi.org/10.5194/acp-19-8879-2019, 2019a.
Jia, H., Ma, X., and Liu, Y.: Exploring aerosol–cloud interaction using VOCALS-REx aircraft measurements, Atmos. Chem. Phys., 19, 7955–7971, https://doi.org/10.5194/acp-19-7955-2019, 2019b.
Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A., and Phillips, V.: Simulation of Effects of Atmospheric Aerosols on Deep Turbulent Convective Clouds Using a Spectral Microphysics Mixed-Phase Cumulus Cloud Model. Part I: Model Description and Possible Applications, J. Atmos. Sci., 61, 2963–2982, https://doi.org/10.1175/jas-3350.1, 2004.
Khain, A., Lynn, B., and Dudhia, J.: Aerosol effects on intensity of landfalling hurricanes as seen from simulations with the WRF model with spectral bin microphysics, J. Atmos. Sci., 67, 365–384, https://doi.org/10.1175/2009JAS3210.1, 2010.
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O., Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., van den Heever, S. C., and Yano, J. I.: Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization, Rev. Geophys., 53, 247–322, https://doi.org/10.1002/2014rg000468, 2015.
Lebo, Z. J., Morrison, H., and Seinfeld, J. H.: Are simulated aerosol-induced effects on deep convective clouds strongly dependent on saturation adjustment?, Atmos. Chem. Phys., 12, 9941–9964, https://doi.org/10.5194/acp-12-9941-2012, 2012.
Levy, R., Hsu, C., Sayer, A., Mattoo, S., and Lee, J.: MODIS Atmosphere L2 Aerosol Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA [data set], https://doi.org/10.5067/MODIS/MOD04_L2.061, 2017.
Li, G. H., Wang, Y., and Zhang, R. Y.: Implementation of a two-moment bulk microphysics scheme to the WRF model to investigate aerosol-cloud interaction, J. Geophys. Res.-Atmos., 113, D15211, https://doi.org/10.1029/2007jd009361, 2008.
Li, X., Choi, Y., Czader, B., Roy, A., Kim, H., Lefer, B., and Pan, S.: The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign, Atmos. Chem. Phys., 16, 3127–3144, https://doi.org/10.5194/acp-16-3127-2016, 2016.
Liu, Y., Bourgeois, A., Warner, T., Swerdlin, S., and Hacker, J.: Implementation of observation-nudging based FDDA into WRF for supporting ATEC test operations, WRF/MM5 Users' Workshop, https://www2.mmm.ucar.edu/wrf/users/workshops/WS2005/abstracts/Session10/7-Liu.pdf, (last access: 7 August 2024), 2005.
Maussion, F., Scherer, D., Finkelnburg, R., Richters, J., Yang, W., and Yao, T.: WRF simulation of a precipitation event over the Tibetan Plateau, China – an assessment using remote sensing and ground observations, Hydrol. Earth Syst. Sci., 15, 1795–1817, https://doi.org/10.5194/hess-15-1795-2011, 2011.
Michibata, T., Suzuki, K., Sato, Y., and Takemura, T.: The source of discrepancies in aerosol–cloud–precipitation interactions between GCM and A-Train retrievals, Atmos. Chem. Phys., 16, 15413–15424, https://doi.org/10.5194/acp-16-15413-2016, 2016.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Mulmenstadt, J. and Feingold, G.: The Radiative Forcing of Aerosol-Cloud Interactions in Liquid Clouds: Wrestling and Embracing Uncertainty, Curr. Clim. Change Rep., 4, 23–40, https://doi.org/10.1007/s40641-018-0089-y, 2018.
National Meteorological Centre of China: National Meteorological Centre official website, http://www.nmc.cn (last access: 19 March 2023), 2009.
NCEP (National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce): NCEP ADP Global Surface Observational Weather Data, NCAR [data set], https://doi.org/10.5065/4F4P-E398, 2004.
NCEP (National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce): NCEP GDAS/FNL 0.25 Degree Global Tropospheric Analyses and Forecast Grids, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, NCAR [data set], https://doi.org/10.5065/D65Q4T4Z, 2015.
Ngan, F. and Stein, A. F.: A Long-Term WRF Meteorological Archive for Dispersion Simulations: Application to Controlled Tracer Experiments, J. Appl. Meteorol. Climatol., 56, 2203–2220, https://doi.org/10.1175/jamc-d-16-0345.1, 2017.
Niu, X., Ma, X., and Jia, H.: Analysis of cloud-type distribution characteristics over major aerosol emission regions in the Northern Hemisphere by using CloudSat/CALIPSO satellite data, J. Meteorol. Sci., 42, 467–480, https://doi.org/10.12306/2021jms.0006, 2022.
Pahlow, M., Parlange, M. B., and Porté-Agel, F.: On Monin–Obukhov similarity in the stable atmospheric boundary layer, Bound. Lay. Meteorol., 99, 225–248, https://doi.org/10.1023/A:1018909000098, 2001.
Platnick, S., Ackerman, S., King, M., Wind, G., Meyer, K., Menzel, P., Frey, R., Holz, R., Baum, B., and Yang, P.: MODIS atmosphere L2 cloud product (06_L2), NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA [data set], https://doi.org/10.5067/MODIS/MOD06_L2.061, 2017.
Quaas, J., Boucher, O., and Lohmann, U.: Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data, Atmos. Chem. Phys., 6, 947–955, https://doi.org/10.5194/acp-6-947-2006, 2006.
Quaas, J., Boucher, O., Jones, A., Weedon, G. P., Kieser, J., and Joos, H.: Exploiting the weekly cycle as observed over Europe to analyse aerosol indirect effects in two climate models, Atmos. Chem. Phys., 9, 8493–8501, https://doi.org/10.5194/acp-9-8493-2009, 2009.
Quaas, J., Arola, A., Cairns, B., Christensen, M., Deneke, H., Ekman, A. M. L., Feingold, G., Fridlind, A., Gryspeerdt, E., Hasekamp, O., Li, Z., Lipponen, A., Ma, P.-L., Mülmenstädt, J., Nenes, A., Penner, J. E., Rosenfeld, D., Schrödner, R., Sinclair, K., Sourdeval, O., Stier, P., Tesche, M., van Diedenhoven, B., and Wendisch, M.: Constraining the Twomey effect from satellite observations: issues and perspectives, Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, 2020.
Rogers, R. E., Deng, A. J., Stauffer, D. R., Gaudet, B. J., Jia, Y. Q., Soong, S. T., and Tanrikulu, S.: Application of the Weather Research and Forecasting Model for Air Quality Modeling in the San Francisco Bay Area, J. Appl. Meteorol. Climatol., 52, 1953–1973, https://doi.org/10.1175/jamc-d-12-0280.1, 2013.
Roh, W., Satoh, M., Hashino, T., Okamoto, H., and Seiki, T.: Evaluations of the thermodynamic phases of clouds in a cloud-system-resolving model using CALIPSO and a satellite simulator over the Southern Ocean, J. Atmos. Sci., 77, 3781–3801, https://doi.org/10.1175/JAS-D-19-0273.1, 2020.
Rosenfeld, D., Zhu, Y. N., Wang, M. H., Zheng, Y. T., Goren, T., and Yu, S. C.: Aerosol-driven droplet concentrations dominate coverage and water of oceanic low-level clouds, Science, 363, eaav0566, https://doi.org/10.1126/science.aav0566, 2019.
Saponaro, G., Kolmonen, P., Sogacheva, L., Rodriguez, E., Virtanen, T., and de Leeuw, G.: Estimates of the aerosol indirect effect over the Baltic Sea region derived from 12 years of MODIS observations, Atmos. Chem. Phys., 17, 3133–3143, https://doi.org/10.5194/acp-17-3133-2017, 2017.
Satellite Services Division: Office of Satellite Data Processing and Distribution/NESDIS/NOAA/U.S. Department of Commerce, and National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP ADP Global Upper Air Observational Weather Data, NCAR [data set], https://doi.org/10.5065/39C5-Z211, 2004.
Sha, T., Ma, X. Y., Jia, H. L., Tian, R., Chang, Y. H., Cao, F., and Zhang, Y. L.: Aerosol chemical component: Simulations with WRF-Chem and comparison with observations in Nanjing, Atmos. Environ., 218, 116982, https://doi.org/10.1016/j.atmosenv.2019.116982, 2019.
Sha, T., Ma, X. Y., Wang, J., Tian, R., Zhao, J. Q., Cao, F., and Zhang, Y. L.: Improvement of inorganic aerosol component in PM2.5 by constraining aqueous-phase formation of sulfate in cloud with satellite retrievals: WRF-Chem simulations, Sci. Total Environ., 804, 150229, https://doi.org/10.1016/j.scitotenv.2021.150229, 2022.
Shin, H. H., Hong, S. Y., and Dudhia, J.: Impacts of the Lowest Model Level Height on the Performance of Planetary Boundary Layer Parameterizations, Mon. Weather Rev., 140, 664–682, https://doi.org/10.1175/mwr-d-11-00027.1, 2012.
Sicard, P., Crippa, P., De Marco, A., Castruccio, S., Giani, P., Cuesta, J., Paoletti, E., Feng, Z. Z., and Anav, A.: High spatial resolution WRF-Chem model over Asia: Physics and chemistry evaluation, Atmos. Environ., 244, 118004, https://doi.org/10.1016/j.atmosenv.2020.118004, 2021.
Small, J. D., Chuang, P. Y., Feingold, G., and Jiang, H. L.: Can aerosol decrease cloud lifetime?, Geophys. Res. Lett., 36, L16806, https://doi.org/10.1029/2009gl038888, 2009.
Tian, R., Ma, X., and Zhao, J.: A revised mineral dust emission scheme in GEOS-Chem: improvements in dust simulations over China, Atmos. Chem. Phys., 21, 4319–4337, https://doi.org/10.5194/acp-21-4319-2021, 2021.
Toll, V., Christensen, M., Quaas, J., and Bellouin, N.: Weak average liquid-cloud-water response to anthropogenic aerosols, Nature, 572, 51–55, https://doi.org/10.1038/s41586-019-1423-9, 2019.
Tuccella, P., Curci, G., Visconti, G., Bessagnet, B., Menut, L., and Park, R. J.: Modeling of gas and aerosol with WRF/Chem over Europe: Evaluation and sensitivity study, J. Geophys. Res.-Atmos., 117, D03303, https://doi.org/10.1029/2011jd016302, 2012.
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, https://doi.org/10.1175/1520-0469(1977)034<1149:Tiopot>2.0.Co;2, 1977.
University Corporation for Atmospheric Research (UCAR): WRF Source Codes and Graphics Software Download Page [code], https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html (last access: 22 June 2024), 2024.
Wang, F., Guo, J. P., Zhang, J. H., Huang, J. F., Min, M., Chen, T. M., Liu, H., Deng, M. J., and Li, X. W.: Multi-sensor quantification of aerosol-induced variability in warm clouds over eastern China, Atmos. Environ., 113, 1–9, https://doi.org/10.1016/j.atmosenv.2015.04.063, 2015.
Wang, Y., Fan, J. W., Zhang, R. Y., Leung, L. R., and Franklin, C.: Improving bulk microphysics parameterizations in simulations of aerosol effects, J. Geophys. Res.-Atmos., 118, 5361–5379, https://doi.org/10.1002/jgrd.50432, 2013.
Wilcox, L. J., Highwood, E. J., and Dunstone, N. J.: The influence of anthropogenic aerosol on multi-decadal variations of historical global climate, Environ. Res. Lett., 8, 024033, https://doi.org/10.1088/1748-9326/8/2/024033, 2013.
Wild, O., Zhu, X., and Prather, M. J.: Fast-J: Accurate simulation of in-and below-cloud photolysis in tropospheric chemical models, J. Atmos. Chem., 37, 245–282, https://doi.org/10.1023/A:1006415919030, 2000.
Xu, W. F., Liu, P., Cheng, L., Zhou, Y., Xia, Q., Gong, Y., and Liu, Y. N.: Multi-step wind speed prediction by combining a WRF simulation and an error correction strategy, Renew. Energy, 163, 772–782, https://doi.org/10.1016/j.renene.2020.09.032, 2021.
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, D13204, https://doi.org/10.1029/2007jd008782, 2008.
Zhang, Y., Fan, J., Li, Z., and Rosenfeld, D.: Impacts of cloud microphysics parameterizations on simulated aerosol–cloud interactions for deep convective clouds over Houston, Atmos. Chem. Phys., 21, 2363–2381, https://doi.org/10.5194/acp-21-2363-2021, 2021a.
Zhang, X., Wang, H., Che, H. Z., Tan, S. C., Yao, X. P., Peng, Y., and Shi, G. Y.: Radiative forcing of the aerosol-cloud interaction in seriously polluted East China and East China Sea, Atmos. Res., 252, 105405, https://doi.org/10.1016/j.atmosres.2020.105405, 2021b.
Zhang, Z. B., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue, H. W.: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large-eddy simulations, J. Geophys. Res.-Atmos., 117, D19208, https://doi.org/10.1029/2012jd017655, 2012.
Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson Jr., W. I., Fast, J. D., and Easter, R.: The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: modeling sensitivities to dust emissions and aerosol size treatments, Atmos. Chem. Phys., 10, 8821–8838, https://doi.org/10.5194/acp-10-8821-2010, 2010.
Zhao, J. Q., Ma, X. Y., Wu, S. Q., and Sha, T.: Dust emission and transport in Northwest China: WRF-Chem simulation and comparisons with multi-sensor observations, Atmos. Res., 241, 104978, https://doi.org/10.1016/j.atmosres.2020.104978, 2020.
Zhong, X. H., Ruiz-Arias, J. A., and Kleissl, J.: Dissecting surface clear sky irradiance bias in numerical weather prediction: Application and corrections to the New Goddard Shortwave Scheme, Sol. Energy, 132, 103–113, https://doi.org/10.1016/j.solener.2016.03.009, 2016.
Zhou, C. and Penner, J. E.: Why do general circulation models overestimate the aerosol cloud lifetime effect? A case study comparing CAM5 and a CRM, Atmos. Chem. Phys., 17, 21–29, https://doi.org/10.5194/acp-17-21-2017, 2017.
Short summary
We explore aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean in winter based on the WRF-Chem–SBM model, which couples a spectral-bin microphysics scheme and an online aerosol module. Our study highlights the differences in aerosol–cloud interactions between land and ocean and between precipitation clouds and non-precipitation clouds, and it differentiates and quantifies their underlying mechanisms.
We explore aerosol–cloud interactions in liquid-phase clouds over eastern China and its adjacent...
Altmetrics
Final-revised paper
Preprint