Articles | Volume 23, issue 13
https://doi.org/10.5194/acp-23-7823-2023
© Author(s) 2023. 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-23-7823-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jul 2023
Research article |
| 14 Jul 2023
| 14 Jul 2023
Multi-model ensemble projection of the global dust cycle by the end of 21st century using the Coupled Model Intercomparison Project version 6 data
Yuan Zhao
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Yang Cao
Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Jun Zhu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Chenguang Tian
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Climate Change Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yuwen Chen
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Yihan Hu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Weijie Fu
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Xu Zhao
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China
Related authors
No articles found.
Qingwei Zeng, Lei Liu, Lele Cai, Hao Zhou, Qinghui Li, and Xichuan Liu
EGUsphere, https://doi.org/10.5194/egusphere-2026-3143, https://doi.org/10.5194/egusphere-2026-3143, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Sea fog disrupts shipping but remains hard to forecast due to a lack of measurements inside fog. We flew a drone with a novel cloud sensor over the Yellow Sea, obtaining the first vertical profiles of temperature, humidity, and fog droplets through a full fog life cycle. Our results show how turbulent mixing and a stable layer above the fog trap moisture, causing fog to thicken. These findings provide a solid foundation to improve fog forecasts and maritime safety.
Hao Zheng, Jun Wang, Hao Zhou, Jingfeng Ding, Haijin Dai, Zhi Huang, Zishan Wang, Meirong Wang, Jianying Li, Hengmao Wang, Fei Jiang, and Weimin Ju
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-318, https://doi.org/10.5194/essd-2026-318, 2026
Preprint under review for ESSD
Short summary
Short summary
Lightning can trigger wildfires and severe storms, but long-term global records are limited. We reconstructed monthly land lightning density worldwide from 1979 to 2025 using weather data and machine-learning models trained on observations. The dataset captures observed lightning patterns and can support wildfire, climate, and air-quality studies.
Weijie Fu, Chenguang Tian, Yuan Zhao, Yihan Hu, Jingchao Huang, Haishan Chen, and Xu Yue
EGUsphere, https://doi.org/10.5194/egusphere-2026-1884, https://doi.org/10.5194/egusphere-2026-1884, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Land-atmosphere interaction is an important process for the exchange of matter and energy between land and atmosphere. In this work, we incorporated the interactive Model for Air Pollution and Land Ecosystems into the European Centre Hamburg general circulation model. Compare with original configuration, the new coupling model demonstrates better performance in simulating terrestrial carbon and water fluxes. Our research provides a useful tool for studying land-atmosphere interactions.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida Bagus Mandhara Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique M. Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe R. Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido R. van der Werf, Sebastiaan J. van de Velde, Anthony P. Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data, 18, 3211–3288, https://doi.org/10.5194/essd-18-3211-2026, https://doi.org/10.5194/essd-18-3211-2026, 2026
Short summary
Short summary
The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Jun Zhu, Yingying Wang, Xu Yue, Huizheng Che, Xiangao Xia, Xiaofei Lu, Chenguang Tian, and Hong Liao
Atmos. Chem. Phys., 26, 5679–5696, https://doi.org/10.5194/acp-26-5679-2026, https://doi.org/10.5194/acp-26-5679-2026, 2026
Short summary
Short summary
The radiative forcing (RF) of PM2.5 heavy pollution, its influencing factors and importance to precipitation in the Bohai Rim regions (China) during 2014–2023 is analyzed. The results show that the variations in PM2.5 and RF values under different temperature profiles are not consistent. Pollution RFs were as important as vertical winds to the total precipitation. The results may improve understanding of the radiative effect of pollution and provide some assistance in precipitation forecasting.
Weijie Fu, Xu Yue, Chenguang Tian, Rongbin Xu, and Yuming Guo
Atmos. Chem. Phys., 25, 13103–13121, https://doi.org/10.5194/acp-25-13103-2025, https://doi.org/10.5194/acp-25-13103-2025, 2025
Short summary
Short summary
Stratospheric aerosol geoengineering could cool Earth by blocking sunlight, but its impacts on extreme droughts are unclear. Our analysis of global climate simulations shows this approach might reduce extreme droughts overall. However, benefits are uneven: poorer nations face far higher drought risks compared to wealthier regions. These findings suggest that stratospheric aerosol geoengineering strategies may induce the risk of unintentionally worsening regional hydroclimatic disparities.
Yonghang Hu, Chenguang Tian, Xu Yue, Yadong Lei, Yang Cao, Rongbin Xu, and Yuming Guo
Earth Syst. Sci. Data, 17, 3741–3756, https://doi.org/10.5194/essd-17-3741-2025, https://doi.org/10.5194/essd-17-3741-2025, 2025
Short summary
Short summary
We develop a global dataset of daily fire-sourced PM2.5 concentrations at a spatial resolution of 0.25° for 2000–2023, using a chemical transport model driven with two fire emission inventories and a machine learning approach trained with ground measurements from over 9000 sites. The dataset shows significant spatiotemporal variations of fire PM2.5 in the past decades, serving a useful tool for exploring the impacts of fire-related air pollutants on climate, ecosystems, and public health.
Yuxin Zheng, Xu Yue, Xiaofei Lu, and Jun Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2025-1515, https://doi.org/10.5194/egusphere-2025-1515, 2025
Preprint archived
Short summary
Short summary
The sensitivity of gross primary productivity (GPP) to climate varies across regions and ecosystem types. Our study shows that warming enhances GPP in boreal regions but suppresses it in the tropics. Increased precipitation generally promotes GPP but has limited effects on tree species. However, current models tend to overestimate GPP responses to precipitation, resulting in greater GPP variability during drought events.
Yufen Wang, Ke Li, Xi Chen, Zhenjiang Yang, Minglong Tang, Pascoal M. D. Campos, Yang Yang, Xu Yue, and Hong Liao
Atmos. Chem. Phys., 25, 4455–4475, https://doi.org/10.5194/acp-25-4455-2025, https://doi.org/10.5194/acp-25-4455-2025, 2025
Short summary
Short summary
The impacts of biomass burning and anthropogenic emissions on high tropospheric ozone levels are not well studied in southern Africa. We combined model simulations with recent observations at the surface and from space to quantify tropospheric ozone and its drivers in southern Africa. Our work focuses on the impact of emissions from different sources at different spatial scales, contributing to a comprehensive understanding of air pollution drivers and their uncertainties in southern Africa.
Ling Kang, Hong Liao, Ke Li, Xu Yue, Yang Yang, and Ye Wang
Atmos. Chem. Phys., 25, 3603–3621, https://doi.org/10.5194/acp-25-3603-2025, https://doi.org/10.5194/acp-25-3603-2025, 2025
Short summary
Short summary
Using the model of Global Change and Air Pollution version 2.0, climate change over 2010–2045 under a carbon neutrality scenario is simulated to increase summertime maximum daily 8 h average ozone (O3) levels in eastern China, the North China Plain, and the Yangtze River Delta by 2.3, 4.7, and 3.0 ppbv, respectively. Temperature, radiation, and relative humidity are the key meteorological parameters and net chemical production is the key process in climate-driven O3 increases in eastern China.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Xinyi Zhou, Xu Yue, Chenguang Tian, and Xiaofei Lu
Atmos. Chem. Phys., 24, 9923–9937, https://doi.org/10.5194/acp-24-9923-2024, https://doi.org/10.5194/acp-24-9923-2024, 2024
Short summary
Short summary
With a climate–vegetation–chemistry coupled model, we explore global climatic responses to the ozone–vegetation interactions of the present day. We find strong warming and drying effects due to the ozone-induced inhibition on plant stomatal conductance, especially over polluted regions such as the eastern US and China. These climatic perturbations further enhance surface ozone by decreasing dry deposition but reduce aerosol optical depth by increasing cloudiness and the drought tendency.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
Short summary
Short summary
We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
Short summary
Short summary
For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Jiachen Cao, Xu Yue, and Mingrui Ma
Atmos. Chem. Phys., 24, 3973–3987, https://doi.org/10.5194/acp-24-3973-2024, https://doi.org/10.5194/acp-24-3973-2024, 2024
Short summary
Short summary
We implemented two widely used ozone damage schemes into a same regional model. Although the two schemes yielded distinct ozone vegetation damages, they predicted similar feedbacks to surface air temperature and ozone air quality in China. Our results highlighted the significance of ozone pollution control given its detrimental impacts on ecosystem functions, contributions to global warming, and amplifications of ozone pollution through ozone–vegetation coupling.
Yang Yang, Yang Zhou, Hailong Wang, Mengyun Li, Huimin Li, Pinya Wang, Xu Yue, Ke Li, Jia Zhu, and Hong Liao
Atmos. Chem. Phys., 24, 1177–1191, https://doi.org/10.5194/acp-24-1177-2024, https://doi.org/10.5194/acp-24-1177-2024, 2024
Short summary
Short summary
This study reveals that extreme ozone pollution over the North China Plain and Yangtze River Delta is due to the chemical production related to hot and dry conditions, and the regional transport explains the ozone pollution over the Sichuan Basin and Pearl River Delta. The frequency of meteorological conditions of the extreme ozone pollution increases from the past to the future. The sustainable scenario is the optimal path to retaining clean air in China in the future.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
Short summary
Short summary
Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Huibin Dai, Hong Liao, Ke Li, Xu Yue, Yang Yang, Jia Zhu, Jianbing Jin, Baojie Li, and Xingwen Jiang
Atmos. Chem. Phys., 23, 23–39, https://doi.org/10.5194/acp-23-23-2023, https://doi.org/10.5194/acp-23-23-2023, 2023
Short summary
Short summary
We apply the 3-D global chemical transport model (GEOS-Chem) to simulate co-polluted days by O3 and PM2.5 (O3–PM2.5PDs) in Beijing–Tianjin–Hebei in 2013–2020 and investigate the chemical and physical characteristics of O3–PM2.5PDs by composited analyses of such days that are captured by both the observations and the model. We report for the first time the unique features in vertical distributions of aerosols during O3–PM2.5PDs and the physical and chemical characteristics of O3–PM2.5PDs.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Chenguang Tian, Xu Yue, Jun Zhu, Hong Liao, Yang Yang, Yadong Lei, Xinyi Zhou, Hao Zhou, Yimian Ma, and Yang Cao
Atmos. Chem. Phys., 22, 12353–12366, https://doi.org/10.5194/acp-22-12353-2022, https://doi.org/10.5194/acp-22-12353-2022, 2022
Short summary
Short summary
We quantify the impacts of fire aerosols on climate through direct, indirect, and albedo effects. In atmosphere-only simulations, we find global fire aerosols cause surface cooling and rainfall inhibition over many land regions. These fast atmospheric perturbations further lead to a reduction in regional leaf area index and lightning activities. By considering the feedback of fire aerosols on humidity, lightning, and leaf area index, we predict a slight reduction in fire emissions.
Jiyuan Gao, Yang Yang, Hailong Wang, Pinya Wang, Huimin Li, Mengyun Li, Lili Ren, Xu Yue, and Hong Liao
Atmos. Chem. Phys., 22, 7131–7142, https://doi.org/10.5194/acp-22-7131-2022, https://doi.org/10.5194/acp-22-7131-2022, 2022
Short summary
Short summary
China has been implementing a sequence of policies for clean air since the year 2013. The aerosol decline produced a 0.09 ± 0.10°C warming during 2013–2017 estimated in this study, and the increase in ozone in the lower troposphere during this time period accelerated the warming, leading to a total 0.16 ± 0.15°C temperature increase in eastern China. Residential emission reductions led to a cooling effect because of a substantial decrease in light-absorbing aerosols.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Yu Zheng, Huizheng Che, Yupeng Wang, Xiangao Xia, Xiuqing Hu, Xiaochun Zhang, Jun Zhu, Jibiao Zhu, Hujia Zhao, Lei Li, Ke Gui, and Xiaoye Zhang
Atmos. Meas. Tech., 15, 2139–2158, https://doi.org/10.5194/amt-15-2139-2022, https://doi.org/10.5194/amt-15-2139-2022, 2022
Short summary
Short summary
Ground-based observations of aerosols and aerosol data verification is important for satellite and climate model modification. Here we present an evaluation of aerosol microphysical, optical and radiative properties measured using a multiwavelength photometer with a highly integrated design and smart control performance. The validation of this product is discussed in detail using AERONET as a reference. This work contributes to reducing AOD uncertainties in China and combating climate change.
Hao Zhou, Xu Yue, Yadong Lei, Chenguang Tian, Jun Zhu, Yimian Ma, Yang Cao, Xixi Yin, and Zhiding Zhang
Atmos. Chem. Phys., 22, 693–709, https://doi.org/10.5194/acp-22-693-2022, https://doi.org/10.5194/acp-22-693-2022, 2022
Short summary
Short summary
Aerosols enhance plant photosynthesis by increasing diffuse radiation. In this study, we found that the aerosol impacts are quite different for varied species. Scattering aerosols such as sulfate and organic carbon promote photosynthesis while absorbing aerosols such as black carbon have negative impacts. Earth system models should consider the impacts of cloud and aerosol species on terrestrial ecosystems so as to better predict carbon cycles under different emission scenarios.
Yadong Lei, Xu Yue, Hong Liao, Lin Zhang, Yang Yang, Hao Zhou, Chenguang Tian, Cheng Gong, Yimian Ma, Lan Gao, and Yang Cao
Atmos. Chem. Phys., 21, 11531–11543, https://doi.org/10.5194/acp-21-11531-2021, https://doi.org/10.5194/acp-21-11531-2021, 2021
Short summary
Short summary
We present the first estimate of ozone enhancement by fire emissions through ozone–vegetation interactions using a fully coupled chemistry–vegetation model (GC-YIBs). In fire-prone areas, fire-induced ozone causes a positive feedback to surface ozone mainly because of the inhibition effects on stomatal conductance.
Cited articles
Aryal, Y. N. and Evans, S.:
Global Dust Variability Explained by Drought Sensitivity in CMIP6 Models, J. Geophys. Res.-Earth, 126, e2021JF006073, https://doi.org/10.1029/2021JF006073, 2021.
Bauer, S. E. and Koch, D.:
Impact of heterogeneous sulfate formation at mineral dust surfaces on aerosol loads and radiative forcing in the Goddard Institute for Space Studies general circulation model, J. Geophys. Res., 110, D17202, https://doi.org/10.1029/2005jd005870, 2005.
Breuning-Madsen, H. and Awadzi, T. W.:
Harmattan dust deposition and particle size in Ghana, Catena, 63, 23–38, https://doi.org/10.1016/j.catena.2005.04.001, 2005.
Checa-Garcia, R., Balkanski, Y., Albani, S., Bergman, T., Carslaw, K., Cozic, A., Dearden, C., Marticorena, B., Michou, M., van Noije, T., Nabat, P., O'Connor, F. M., Olivié, D., Prospero, J. M., Le Sager, P., Schulz, M., and Scott, C.:
Evaluation of natural aerosols in CRESCENDO Earth system models (ESMs): mineral dust, Atmos. Chem. Phys., 21, 10295–10335, https://doi.org/10.5194/acp-21-10295-2021, 2021.
Csavina, J., Field, J., Félix, O., Corral-Avitia, A. Y., Sáez, A. E., and Betterton, E. A.:
Effect of wind speed and relative humidity on atmospheric dust concentrations in semi-arid climates, Sci. Total Environ., 487, 82–90, https://doi.org/10.1016/j.scitotenv.2014.03.138, 2014.
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E., Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C., Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S. M., Guo, H., Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R., Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A., Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T., Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B., Zeng, Y., and Zhao, M.:
The GFDL Earth System Model Version 4.1 (GFDL-ESM 4.1): Overall Coupled Model Description and Simulation Characteristics, J. Adv. Model. Earth Sy., 12, e2019MS002015, https://doi.org/10.1029/2019MS002015, 2020.
Evans, S., Ginoux, P., Malyshev, S., and Shevliakova, E.:
Climate-vegetation interaction and amplification of Australian dust variability, Geophys. Res. Lett., 43, 11823–11830, https://doi.org/10.1002/2016GL071016, 2016.
Feng, H. and Zhang, M.:
Global land moisture trends: drier in dry and wetter in wet over land, Sci. Rep., 5, 18018, https://doi.org/10.1038/srep18018, 2015.
Fernandes, R., Dupont, S., and Lamaud, E.:
Origins of Turbulent Transport Dissimilarity Between Dust and Momentum in Semiarid Regions, J. Geophys. Res.-Atmos., 125, e2019JD031247, https://doi.org/10.1029/2019jd031247, 2020.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., Raga, G., Schulz, M., Dorland, R. V., Bodeker, G., Etheridge, D., Foukal, P., Fraser, P., Geller, M., Joos, F., Keeling, C. D., Keeling, R., Kinne, S., Lassey, K., Oram, D., O'Shaughnessy, K., Ramankutty, N., Reid, G., Rind, D., Rosenlof, K., Sausen, R., Schwarzkopf, D., Solanki, S. K., Stenchikov, G., Stuber, N., Takemura, T., Textor, C., Wang, R., Weiss, R., Whorf, T., Nakajima, T., Ramanathan, V., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., and Dorland, R. V.:
Changes in Atmospheric Constituents and in Radiative Forcing, Climate Change 2007: The Physical Science Basis, Cambridge University Press, Cambridge, UK, 2008.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.:
Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res.-Atmos., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Ginoux, P., Prospero, J., Torres, O., and Chin, M.:
Long-term simulation of global dust distribution with the GOCART model: correlation with North Atlantic Oscillation, Environ. Modell. Softw., 19, 113–128, https://doi.org/10.1016/s1364-8152(03)00114-2, 2004.
Goudie, A. S.:
Desert dust and human health disorders, Environ. Int., 63, 101–113, https://doi.org/10.1016/j.envint.2013.10.011, 2014.
Jickells, T. D., An, Z. S., Andersen, K. K., Baker, A. R., Bergametti, G., Brooks, N., Cao, J. J., Boyd, P. W., Duce, R. A., Hunter, K. A., Kawahata, H., Kubilay, N., laRoche, J., Liss, P. S., Mahowald, N., Prospero, J. M., Ridgwell, A. J., Tegen, I., and Torres, R.:
Global Iron Connections Between Desert Dust, Ocean Biogeochemistry, and Climate, Science, 308, 67–71, https://doi.org/10.1126/science.1105959, 2005.
Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., Ackerman, A. S., Aleinov, I., Bauer, M., Bleck, R., Canuto, V., Cesana, G., Cheng, Y., Clune, T. L., Cook, B. I., Cruz, C. A., Del Genio, A. D., Elsaesser, G. S., Faluvegi, G., Kiang, N. Y., Kim, D., Lacis, A. A., Leboissetier, A., LeGrande, A. N., Lo, K. K., Marshall, J., Matthews, E. E., McDermid, S., Mezuman, K., Miller, R. L., Murray, L. T., Oinas, V., Orbe, C., Garcia-Pando, C. P., Perlwitz, J. P., Puma, M. J., Rind, D., Romanou, A., Shindell, D. T., Sun, S., Tausnev, N., Tsigaridis, K., Tselioudis, G., Weng, E., Wu, J., and Yao, M. S.:
GISS-E2.1: Configurations and Climatology, J. Adv. Model. Earth Sy., 12, e2019MS002025, https://doi.org/10.1029/2019MS002025, 2020.
Kok, J. F., Storelvmo, T., Karydis, V. A., Adebiyi, A. A., Mahowald, N. M., Evan, A. T., He, C., and Leung, D. M.:
Mineral dust aerosol impacts on global climate and climate change, Nature Reviews Earth & Environment, 4, 71–86, https://doi.org/10.1038/s43017-022-00379-5, 2023.
Lawrence, D. M., Fisher, R. A., Koven, C. D., Oleson, K. W., Swenson, S. C., Bonan, G., Collier, N., Ghimire, B., Kampenhout, L. v., Kennedy, D., Kluzek, E., Lawrence, P. J., Li, F., Li, H., Lombardozzi, D., Riley, W. J., Sacks, W. J., Shi, M., Vertenstein, M., Wieder, W. R., Xu, C., Ali, A. A., Badger, A. M., Bisht, G., Broeke, M. v. d., Brunke, M. A., Burns, S. P., Buzan, J., Clark, M., Craig, A., Dahlin, K., Drewniak, B., Fisher, J. B., Flanner, M. G., Fox, A. M., Gentine, P., Hoffman, F., Keppel-Aleks, G., Knox, R., Kumar, S., Lenaerts, J., Leung, L. R., Lipscomb, W. H., Lu, Y., Pandey, A., Pelletier, J. D., Perket, J., Randerson, J. T., Ricciuto, D. M., Sanderson, B. M., Slater, A., Subin, Z. M., Tang, J., Thomas, R. Q., Martin, M. V., and Zeng, X.:
The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty, J. Adv. Model. Earth Sy., 11, 4245–4287, https://doi.org/10.1029/2018MS001583, 2019.
Le, T. and Bae, D.-H.:
Causal influences of El Niño–Southern Oscillation on global dust activities, Atmos. Chem. Phys., 22, 5253–5263, https://doi.org/10.5194/acp-22-5253-2022, 2022.
Li, J., Hao, X., Liao, H., Yue, X., Li, H., Long, X., and Li, N.:
Predominant Type of Dust Storms That Influences Air Quality Over Northern China and Future Projections, Earths Future, 10, e2022EF002649, https://doi.org/10.1029/2022ef002649, 2022.
Li, W. and Wang, Y.:
Reduced surface fine dust under droughts over the southeastern United States during summertime: observations and CMIP6 model simulations, Atmos. Chem. Phys., 22, 7843–7859, https://doi.org/10.5194/acp-22-7843-2022, 2022.
Liu, X., Yin, Z., Zhang, X., and Yang, X.:
Analyses of the spring dust storm frequency of northern China in relation to antecedent and concurrent wind, precipitation, vegetation, and soil moisture conditions, J. Geophys. Res., 109, D16210, https://doi.org/10.1029/2004JD004615, 2004.
Mahowald, N. M., Baker, A. R., Bergametti, G., Brooks, N., Duce, R. A., Jickells, T. D., Kubilay, N., Prospero, J. M., and Tegen, I.:
Atmospheric global dust cycle and iron inputs to the ocean, Global Biogeochem. Cy., 19, GB4025, https://doi.org/10.1029/2004GB002402, 2005.
Marticorena, B., Bergametti, G., Aumont, B., Callot, Y., N'Doum, C., and Legrand, M.:
Modeling the atmospheric dust cycle 2. Simulation of Saharan dust sources, J. Geophys. Res., 102, 4387–4404, https://doi.org/10.1029/96JD02964, 1997.
Middleton, N. J.:
Desert dust hazards: A global review, Aeolian Res., 24, 53–63, https://doi.org/10.1016/j.aeolia.2016.12.001, 2017.
Munkhtsetseg, E., Shinoda, M., Gillies, J. A., Kimura, R., King, J., and Nikolich, G.:
Relationships between soil moisture and dust emissions in a bare sandy soil of Mongolia, Particuology, 28, 131–137, https://doi.org/10.1016/j.partic.2016.03.001, 2016.
Nabat, P., Somot, S., Mallet, M., Michou, M., Sevault, F., Driouech, F., Meloni, D., di Sarra, A., Di Biagio, C., Formenti, P., Sicard, M., Léon, J.-F., and Bouin, M.-N.:
Dust aerosol radiative effects during summer 2012 simulated with a coupled regional aerosol–atmosphere–ocean model over the Mediterranean, Atmos. Chem. Phys., 15, 3303–3326, https://doi.org/10.5194/acp-15-3303-2015, 2015.
Oleson, K. W., Lawrence, D. M., Flanner, M. G., Kluzek, E., Levis, S., Swenson, S. C., Thornton, E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Lamarque, J. F., Niu, G., Qian, T., Running, S., Sakaguchi, K., Slater, A., Stöckli, R., Wang, A., Yang, L., Zeng, X., and Zeng, X.:
Technical Description of version 4.0 of the Community Land Model (CLM)(No. NCAR/TN-478+STR), University Corporation for Atmospheric Research, 266, https://doi.org/10.5065/D6FB50WZ, 2010.
Penner, J. E., Quaas, J., Storelvmo, T., Takemura, T., Boucher, O., Guo, H., Kirkevåg, A., Kristjánsson, J. E., and Seland, Ø.:
Model intercomparison of indirect aerosol effects, Atmos. Chem. Phys., 6, 3391–3405, https://doi.org/10.5194/acp-6-3391-2006, 2006.
Pu, B. and Ginoux, P.:
How reliable are CMIP5 models in simulating dust optical depth?, Atmos. Chem. Phys., 18, 12491–12510, https://doi.org/10.5194/acp-18-12491-2018, 2018.
Sabade, S. S., Kulkarni, A., and Kripalani, R. H.:
Projected changes in South Asian summer monsoon by multi-model global warming experiments, Theor. Appl. Climatol., 103, 543–565, https://doi.org/10.1007/s00704-010-0296-5, 2011.
Schepanski, K.:
Transport of Mineral Dust and Its Impact on Climate, Geosciences, 8, 151, https://doi.org/10.3390/geosciences8050151, 2018.
Shao, Y., Wyrwoll, K. H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H., Mikami, M., Tanaka, T. Y., Wang, X., and Yoon, S.:
Dust cycle: An emerging core theme in Earth system science, Aeolian Res., 2, 181–204, https://doi.org/10.1016/j.aeolia.2011.02.001, 2011.
Stefanski, R. and Sivakumar, M. V. K.:
Impacts of sand and dust storms on agriculture and potential agricultural applications of a SDSWS, IOP Conf. Ser. Earth Environ. Sci, 7, 012016, https://doi.org/10.1088/1755-1307/7/1/012016, 2009.
Tegen, I., Werner, M., Harrison, S. P., and Kohfeld, K. E.:
Relative importance of climate and land use in determining present and future global soil dust emission, Geophys. Res. Lett., 31, L05105, https://doi.org/10.1029/2003GL019216, 2004.
Wang, S., Wang, J., Zhou, Z., and Shang, K.:
Regional characteristics of three kinds of dust storm events in China, Atmos. Environ., 39, 509–520, https://doi.org/10.1016/j.atmosenv.2004.09.033, 2005.
Wang, T., Miao, J., Sun, J., and Fu, Y.:
Intensified East Asian summer monsoon and associated precipitation mode shift under the 1.5 ∘C global warming target, Adv. Clim. Change Res., 9, 102–111, https://doi.org/10.1016/j.accre.2017.12.002, 2018.
Wittmann, M., Groot Zwaaftink, C. D., Steffensen Schmidt, L., Guðmundsson, S., Pálsson, F., Arnalds, O., Björnsson, H., Thorsteinsson, T., and Stohl, A.:
Impact of dust deposition on the albedo of Vatnajökull ice cap, Iceland, The Cryosphere, 11, 741–754, https://doi.org/10.5194/tc-11-741-2017, 2017.
Woodward, S.:
Mineral Dust in HadGEM2, Hadley Centre Technical Note, 087, Met Office Hadley Centre, Fitzroy Road, Exeter, EX1 3PB, UK, https://library.metoffice.gov.uk/Portal/Default/en-GB/RecordView/Index/2522462011 (last access: 20 April 2023), 2011.
Woodward, S., Sellar, A. A., Tang, Y., Stringer, M., Yool, A., Robertson, E., and Wiltshire, A.:
The simulation of mineral dust in the United Kingdom Earth System Model UKESM1, Atmos. Chem. Phys., 22, 14503–14528, https://doi.org/10.5194/acp-22-14503-2022, 2022.
World Climate Research Programme (WCRP): CMIP6, World Climate Research Programme [data set], https://esgf-node.llnl.gov/search/cmip6/, last access: 20 April 2023.
Wu, C., Lin, Z., He, J., Zhang, M., Liu, X., Zhang, R., and Brown, H.:
A process-oriented evaluation of dust emission parameterizations in CESM: Simulation of a typical severe dust storm in East Asia, J. Adv. Model. Earth Sy., 8, 1432–1452, https://doi.org/10.1002/2016MS000723, 2016.
Wu, C., Lin, Z., and Liu, X.:
The global dust cycle and uncertainty in CMIP5 (Coupled Model Intercomparison Project phase 5) models, Atmos. Chem. Phys., 20, 10401–10425, https://doi.org/10.5194/acp-20-10401-2020, 2020.
Wu, Q., Li, Q., Ding, Y., Shen, X., Zhao, M., and Zhu, Y.:
Asian summer monsoon responses to the change of land-sea thermodynamic contrast in a warming climate: CMIP6 projections, Adv. Clim. Change Res., 13, 205–217, https://doi.org/10.1016/j.accre.2022.01.001, 2022.
Yue, X., Wang, H., Wang, Z., and Fan, K.:
Simulation of dust aerosol radiative feedback using the Global Transport Model of Dust: 1. Dust cycle and validation, J. Geophys. Res., 114, D10202, https://doi.org/10.1029/2008JD010995, 2009.
Zakey, A. S., Solmon, F., and Giorgi, F.:
Implementation and testing of a desert dust module in a regional climate model, Atmos. Chem. Phys., 6, 4687–4704, https://doi.org/10.5194/acp-6-4687-2006, 2006.
Zhang, D., Gao, X., Zakey, A., and Giorgi, F.:
Effects of climate changes on dust aerosol over East Asia from RegCM3, Adv. Clim. Change Res., 7, 145–153, https://doi.org/10.1016/j.accre.2016.07.001, 2016.
Zhang, J., Shao, Y., and Huang, N.:
Measurements of dust deposition velocity in a wind-tunnel experiment, Atmos. Chem. Phys., 14, 8869–8882, https://doi.org/10.5194/acp-14-8869-2014, 2014.
Zhao, A., Ryder, C. L., and Wilcox, L. J.:
How well do the CMIP6 models simulate dust aerosols?, Atmos. Chem. Phys., 22, 2095–2119, https://doi.org/10.5194/acp-22-2095-2022, 2022.
Zong, Q., Mao, R., Gong, D., Wu, C., Pu, B., Feng, X., and Sun, Y.:
Changes in Dust Activity in Spring over East Asia under a Global Warming Scenario, Asia-Pac. J. Atmos. Sci., 57, 839–850, https://doi.org/10.1007/s13143-021-00224-7, 2021.
Short summary
We project the future changes of dust emissions and loading using an ensemble of model outputs from the Coupled Model Intercomparison Project version 6 under four scenarios. We find increased dust emissions and loading in North Africa, due to increased drought and strengthened surface wind, and decreased dust loading over Asia, following enhanced precipitation. Such a spatial pattern remains similar, though the regional intensity varies among different scenarios.
We project the future changes of dust emissions and loading using an ensemble of model outputs...
Altmetrics
Final-revised paper
Preprint