Articles | Volume 16, issue 7
https://doi.org/10.5194/essd-16-3233-2024
© 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/essd-16-3233-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 Jul 2024
Data description paper |
| 12 Jul 2024
| 12 Jul 2024
Visibility-derived aerosol optical depth over global land from 1959 to 2021
Hongfei Hao
Global Change and Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Institute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Chuanfeng Zhao
Institute of Carbon Neutrality, Department of Atmospheric and Oceanic Sciences, School of Physics, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Guocan Wu
Global Change and Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Institute of Carbon Neutrality, Department of Atmospheric and Oceanic Sciences, School of Physics, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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Caiwang Zheng, Chuanfeng Zhao, Yannian Zhu, Yang Wang, Xiaoqin Shi, Xiaolin Wu, Tianmeng Chen, Fang Wu, and Yanmei Qiu
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Gang Zhao, Chunsheng Zhao, Ye Kuang, Jiangchuan Tao, Wangshu Tan, Yuxuan Bian, Jing Li, and Chengcai Li
Atmos. Chem. Phys., 17, 12133–12143, https://doi.org/10.5194/acp-17-12133-2017, https://doi.org/10.5194/acp-17-12133-2017, 2017
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Guocan Wu and Xiaogu Zheng
Nonlin. Processes Geophys., 24, 329–341, https://doi.org/10.5194/npg-24-329-2017, https://doi.org/10.5194/npg-24-329-2017, 2017
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The accuracy of the assimilation results crucially relies on the estimate accuracy of forecast error covariance matrix in data assimilation. Ensemble Kalman filter estimates the forecast error covariance matrix as the sampling covariance matrix of the ensemble forecast states, which need to be further inflated. The experiment results on the Lorenz-96 model show that the analysis error is reduced and the analysis sensitivity to observations is improved using the proposed inflation technique.
Jizeng Du, Kaicun Wang, Jiankai Wang, and Qian Ma
Atmos. Chem. Phys., 17, 4931–4944, https://doi.org/10.5194/acp-17-4931-2017, https://doi.org/10.5194/acp-17-4931-2017, 2017
Fang Zhang, Zhanqing Li, Yanan Li, Yele Sun, Zhenzhu Wang, Ping Li, Li Sun, Pucai Wang, Maureen Cribb, Chuanfeng Zhao, Tianyi Fan, Xin Yang, and Qingqing Wang
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S. Zhang, X. Zheng, J. M. Chen, Z. Chen, B. Dan, X. Yi, L. Wang, and G. Wu
Geosci. Model Dev., 8, 805–816, https://doi.org/10.5194/gmd-8-805-2015, https://doi.org/10.5194/gmd-8-805-2015, 2015
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A Global Carbon Assimilation System based on the Ensemble Kalman filter (GCAS-EK) is developed for assimilating atmospheric CO2 data into an ecosystem model to simultaneously estimate the surface carbon fluxes and atmospheric CO2 distribution. This assimilation approach is similar to CarbonTracker, but with several new developments. The results showed that this assimilation approach can effectively reduce the biases and uncertainties of the carbon fluxes simulated by the ecosystem model.
J. Li, B. E. Carlson, O. Dubovik, and A. A. Lacis
Atmos. Chem. Phys., 14, 12271–12289, https://doi.org/10.5194/acp-14-12271-2014, https://doi.org/10.5194/acp-14-12271-2014, 2014
G. Wu, X. Yi, L. Wang, X. Liang, S. Zhang, X. Zhang, and X. Zheng
Nonlin. Processes Geophys., 21, 955–970, https://doi.org/10.5194/npg-21-955-2014, https://doi.org/10.5194/npg-21-955-2014, 2014
J. Li, B. E. Carlson, and A. A. Lacis
Atmos. Meas. Tech., 7, 2531–2549, https://doi.org/10.5194/amt-7-2531-2014, https://doi.org/10.5194/amt-7-2531-2014, 2014
X. Y. Wang and K. C. Wang
Atmos. Meas. Tech., 7, 1701–1709, https://doi.org/10.5194/amt-7-1701-2014, https://doi.org/10.5194/amt-7-1701-2014, 2014
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Domain: ESSD – Atmosphere | Subject: Atmospheric chemistry and physics
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A Global Classification Dataset of Daytime and Nighttime Marine Low-cloud Mesoscale Morphology Based on Deep Learning Methods
19th–20th century semi-quantitative surface ozone along subtropical Europe to tropical Africa Atlantic coasts
CREST: a Climate Data Record of Stratospheric Aerosols
Multiyear high-temporal-resolution measurements of submicron aerosols at 13 French urban sites: data processing and chemical composition
Large synthesis of in situ field measurements of the size distribution of mineral dust aerosols across their life cycles
A 10 km daily-level ultraviolet-radiation-predicting dataset based on machine learning models in China from 2005 to 2020
GHOST: a globally harmonised dataset of surface atmospheric composition measurements
Changes in air pollutant emissions in China during two clean-air action periods derived from the newly developed Inversed Emission Inventory for Chinese Air Quality (CAQIEI)
Version 1 NOAA-20/OMPS Nadir Mapper total column SO2 product: continuation of NASA long-term global data record
GERB Obs4MIPs: a dataset for evaluating diurnal and monthly variations in top-of-atmosphere radiative fluxes in climate models
Multiwavelength aerosol lidars at the Maïdo supersite, Réunion Island, France: instrument description, data processing chain, and quality assessment
PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022
MAP-IO: an atmospheric and marine observatory program on board Marion Dufresne over the Southern Ocean
Retrieving ground-level PM2.5 concentrations in China (2013–2021) with a numerical-model-informed testbed to mitigate sample-imbalance-induced biases
Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)
Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology
A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements
IPB-MSA&SO4: a daily 0.25° resolution dataset of in situ-produced biogenic methanesulfonic acid and sulfate over the North Atlantic during 1998–2022 based on machine learning
A long-term high-resolution air quality reanalysis with public facing air quality dashboard over the Contiguous United States (CONUS)
Global Methane Budget 2000–2020
Indicators of Global Climate Change 2023: annual update of key indicators of the state of the climate system and human influence
The Total Carbon Column Observing Network's GGG2020 data version
Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses
Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development
High-resolution physicochemical dataset of atmospheric aerosols over the Tibetan Plateau and its surroundings
Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research
The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020
PalVol v1: a proxy-based semi-stochastic ensemble reconstruction of volcanic stratospheric sulfur injection for the last glacial cycle (140 000–50 BP)
Ground- and ship-based microwave radiometer measurements during EUREC4A
Shortwave and longwave components of the surface radiation budget measured at the Thule High Arctic Atmospheric Observatory, Northern Greenland
Cloud condensation nuclei concentrations derived from the CAMS reanalysis
A merged continental planetary boundary layer height dataset based on high-resolution radiosonde measurements, ERA5 reanalysis, and GLDAS
12 years of continuous atmospheric O2, CO2 and APO data from Weybourne Atmospheric Observatory in the United Kingdom
CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations
Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets
High-resolution aerosol data from the top 3.8 kyr of the East Greenland Ice coring Project (EGRIP) ice core
A database of aircraft measurements of carbon monoxide (CO) with high temporal and spatial resolution during 2011–2021
A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements
A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products
Network for the Detection of Atmospheric Composition Change (NDACC) Fourier transform infrared (FTIR) trace gas measurements at the University of Toronto Atmospheric Observatory from 2002 to 2020
Deconstruction of tropospheric chemical reactivity using aircraft measurements: the Atmospheric Tomography Mission (ATom) data
Spatial variability of Saharan dust deposition revealed through a citizen science campaign
Radiative sensitivity quantified by a new set of radiation flux kernels based on the ECMWF Reanalysis v5 (ERA5)
Giulio Bongiovanni, Michael Matiu, Alice Crespi, Anna Napoli, Bruno Majone, and Dino Zardi
Earth Syst. Sci. Data, 17, 1367–1391, https://doi.org/10.5194/essd-17-1367-2025, https://doi.org/10.5194/essd-17-1367-2025, 2025
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EEAR-Clim is a new and unprecedented observational dataset gathering in situ daily measurements of air temperature and precipitation from a network of about 9000 weather stations covering the European Alps. Data collected, including time series from recordings up to 2020 and time series significantly enhancing data coverage at high elevations, were tested for quality and homogeneity. The dataset aims to serve as a powerful tool for better understanding climate change over the European Alpine region.
André Ehrlich, Susanne Crewell, Andreas Herber, Marcus Klingebiel, Christof Lüpkes, Mario Mech, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Matthias Buschmann, Hans-Christian Clemen, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Andreas Giez, Sarah Grawe, Christophe Gourbeyre, Jörg Hartmann, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsófia Jurányi, Benjamin Kirbus, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Christian Mallaun, Johanna Mayer, Stephan Mertes, Guillaume Mioche, Manuel Moser, Hanno Müller, Veronika Pörtge, Nils Risse, Greg Roberts, Sophie Rosenburg, Johannes Röttenbacher, Michael Schäfer, Jonas Schaefer, Andreas Schäfler, Imke Schirmacher, Johannes Schneider, Sabrina Schnitt, Frank Stratmann, Christian Tatzelt, Christiane Voigt, Andreas Walbröl, Anna Weber, Bruno Wetzel, Martin Wirth, and Manfred Wendisch
Earth Syst. Sci. Data, 17, 1295–1328, https://doi.org/10.5194/essd-17-1295-2025, https://doi.org/10.5194/essd-17-1295-2025, 2025
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This paper provides an overview of the HALO–(AC)3 aircraft campaign data sets, the campaign-specific instrument operation, data processing, and data quality. The data set comprises in situ and remote sensing observations from three research aircraft: HALO, Polar 5, and Polar 6. All data are published in the PANGAEA database by instrument-separated data subsets. It is highlighted how the scientific analysis of the HALO–(AC)3 data benefits from the coordinated operation of three aircraft.
Laura Köhler, Julia Windmiller, Dariusz Baranowski, Michał Brennek, Michał Ciuryło, Lennéa Hayo, Daniel Kȩpski, Stefan Kinne, Beata Latos, Bertrand Lobo, Tobias Marke, Timo Nischik, Daria Paul, Piet Stammes, Artur Szkop, and Olaf Tuinder
Earth Syst. Sci. Data, 17, 633–659, https://doi.org/10.5194/essd-17-633-2025, https://doi.org/10.5194/essd-17-633-2025, 2025
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We present atmospheric and oceanic data from the Atlantic References and Convection ship campaign with the Maria S. Merian from Mindelo to Punta Arenas observed with the integrated ship sensors; humidity and temperature profiler; ceilometer; aerosol instruments (Calitoo, Microtops, and DustTrak); radiosondes; uncrewed aircraft vehicles; and conductivity, temperature, and depth scans. The data include three complete profiles of the Intertropical Convergence Zone and a storm in the South Atlantic.
Israel Silber, Jennifer M. Comstock, Michael R. Kieburtz, and Lynn M. Russell
Earth Syst. Sci. Data, 17, 29–42, https://doi.org/10.5194/essd-17-29-2025, https://doi.org/10.5194/essd-17-29-2025, 2025
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We present ARMTRAJ, a set of multipurpose trajectory datasets, which augments cloud, aerosol, and boundary layer studies utilizing the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility data. ARMTRAJ data include ensemble run statistics that enhance consistency and serve as uncertainty metrics for air mass coordinates and state variables. ARMTRAJ will soon become a near real-time product that will accompany past, ongoing, and future ARM deployments.
Cheng Chen, Xuefeng Lei, Zhenhai Liu, Haorang Gu, Oleg Dubovik, Pavel Litvinov, David Fuertes, Yujia Cao, Haixiao Yu, Guangfeng Xiang, Binghuan Meng, Zhenwei Qiu, Xiaobing Sun, Jin Hong, and Zhengqiang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-483, https://doi.org/10.5194/essd-2024-483, 2024
Revised manuscript accepted for ESSD
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POSP on board GF-5(02) satellite is the first space-borne UV-VIS-NIR-SWIR multi-spectral cross-track scanning polarimeter. Due to wide spectral range and polarimetric capabilities, POSP measurements provide rich information for aerosol and surface characterization. We present the aerosol/surface products generated from POSP first 18 months of operation using GRASP/Models, including spectral AOD, AODF, AODC, and AE, SSA, scale height, full surface BRDF, BPDF, black-/white-sky albedos, NDVI.
Peiyuan Jiao, Chengzhi Xing, Yikai Li, Xiangguang Ji, Wei Tan, Qihua Li, Haoran Liu, and Cheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-562, https://doi.org/10.5194/essd-2024-562, 2024
Revised manuscript accepted for ESSD
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This study provides a dataset of high-resolution vertical profiles of aerosol, NO2, and HCHO, observed over periods ranging from 5 months to 5 years at 32 sites across China between 2019 and 2023. The dataset captures the vertical distribution, diurnal pattern and seasonal variations of these compositions. It has been validated against TROPOMI satellite observations and ground-based CNEMC measurements, showing good correlations.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
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Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Yuanyuan Wu, Jihu Liu, Yannian Zhu, Yu Zhang, Yang Cao, Kang-En Huang, Boyang Zheng, Yichuan Wang, Yanyun Li, Quan Wang, Chen Zhou, Yuan Liang, Jianning Sun, Minghuai Wang, and Daniel Rosenfeld
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-536, https://doi.org/10.5194/essd-2024-536, 2024
Revised manuscript accepted for ESSD
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In this paper, based on deep learning method, we established a global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology. It aims to promote a comprehensive understanding of the cloud dynamics and cloud-climate feedback. Closed mesoscale cellular convection (MCC) clouds occur more frequently at night, while suppressed cumulus exhibit remarkable decrease. Solid stratus and MCC cloud types show clear seasonal variations.
Juan A. Añel, Juan-Carlos Antuña-Marrero, Antonio Cid Samamed, Celia Pérez-Souto, Laura de la Torre, Maria Antonia Valente, Yuri Brugnara, Alfonso Saiz-López, and Luis Gimeno
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-366, https://doi.org/10.5194/essd-2024-366, 2024
Revised manuscript accepted for ESSD
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Ozone, discovered in 1837, was first measured in 1847 using paper strips that reacted with ozone, providing an indication of its concentration based on color changes. Here we present the data, covering over sixty years of daily observations, conducted along the East Atlantic coast, spanning from the tropics to the northern extratropics.
Viktoria F. Sofieva, Alexei Rozanov, Monika Szelag, John P. Burrows, Christian Retscher, Robert Damadeo, Doug Degenstein, Landon A. Rieger, and Adam Bourassa
Earth Syst. Sci. Data, 16, 5227–5241, https://doi.org/10.5194/essd-16-5227-2024, https://doi.org/10.5194/essd-16-5227-2024, 2024
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Climate-related studies need information about the distribution of stratospheric aerosols, which influence the energy balance of the Earth’s atmosphere. In this work, we present a merged dataset of vertically resolved stratospheric aerosol extinction coefficients, which is derived from data of six limb and occultation satellite instruments. The created aerosol climate record covers the period from October 1984 to December 2023. It can be used in various climate-related studies.
Hasna Chebaicheb, Joel F. de Brito, Tanguy Amodeo, Florian Couvidat, Jean-Eudes Petit, Emmanuel Tison, Gregory Abbou, Alexia Baudic, Mélodie Chatain, Benjamin Chazeau, Nicolas Marchand, Raphaële Falhun, Florie Francony, Cyril Ratier, Didier Grenier, Romain Vidaud, Shouwen Zhang, Gregory Gille, Laurent Meunier, Caroline Marchand, Véronique Riffault, and Olivier Favez
Earth Syst. Sci. Data, 16, 5089–5109, https://doi.org/10.5194/essd-16-5089-2024, https://doi.org/10.5194/essd-16-5089-2024, 2024
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Long-term (2015–2021) quasi-continuous measurements have been obtained at 13 French urban sites using online mass spectrometry, to acquire the comprehensive chemical composition of submicron particulate matter. The results show their spatial and temporal differences and confirm the predominance of organics in France (40–60 %). These measurements can be used for many future studies, such as trend and epidemiological analyses, or comparisons with chemical transport models.
Paola Formenti and Claudia Di Biagio
Earth Syst. Sci. Data, 16, 4995–5007, https://doi.org/10.5194/essd-16-4995-2024, https://doi.org/10.5194/essd-16-4995-2024, 2024
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Particles from deserts and semi-vegetated areas (mineral dust) are important for Earth's climate and human health, notably depending on their size. In this paper we collect and make a synthesis of a body of these observations since 1972 in order to provide researchers modeling Earth's climate and developing satellite observations from space with a simple way of confronting their results and understanding their validity.
Yichen Jiang, Su Shi, Xinyue Li, Chang Xu, Haidong Kan, Bo Hu, and Xia Meng
Earth Syst. Sci. Data, 16, 4655–4672, https://doi.org/10.5194/essd-16-4655-2024, https://doi.org/10.5194/essd-16-4655-2024, 2024
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Limited ultraviolet (UV) measurements hindered further investigation of its health effects. This study used a machine learning algorithm to predict UV radiation with a daily and 10 km resolution of high accuracy in mainland China in 2005–2020. Then, uneven spatial distribution and population exposure risks as well as increased temporal trend of UV radiation were found in China. The long-term and high-quality UV dataset could further facilitate health-related research in the future.
Dene Bowdalo, Sara Basart, Marc Guevara, Oriol Jorba, Carlos Pérez García-Pando, Monica Jaimes Palomera, Olivia Rivera Hernandez, Melissa Puchalski, David Gay, Jörg Klausen, Sergio Moreno, Stoyka Netcheva, and Oksana Tarasova
Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, https://doi.org/10.5194/essd-16-4417-2024, 2024
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GHOST (Globally Harmonised Observations in Space and Time) represents one of the biggest collections of harmonised measurements of atmospheric composition at the surface. In total, 7 275 148 646 measurements from 1970 to 2023, from 227 different components, and from 38 reporting networks are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties.
Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Jie Li, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data, 16, 4351–4387, https://doi.org/10.5194/essd-16-4351-2024, https://doi.org/10.5194/essd-16-4351-2024, 2024
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A new long-term inversed emission inventory for Chinese air quality (CAQIEI) is developed in this study, which contains constrained monthly emissions of NOx, SO2, CO, PM2.5, PM10, and NMVOCs in China from 2013 to 2020 with a horizontal resolution of 15 km. Emissions of different air pollutants and their changes during 2013–2020 were investigated and compared with previous emission inventories, which sheds new light on the complex variations of air pollutant emissions in China.
Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris McLinden, Debora Griffin, Peter J. T. Leonard, Simon Carn, Colin Seftor, and Alexander Vasilkov
Earth Syst. Sci. Data, 16, 4291–4309, https://doi.org/10.5194/essd-16-4291-2024, https://doi.org/10.5194/essd-16-4291-2024, 2024
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Sulfur dioxide (SO2), a poisonous gas from human activities and volcanoes, causes air pollution, acid rain, and changes to climate and the ozone layer. Satellites have been used to monitor SO2 globally, including remote areas. Here we describe a new satellite SO2 dataset from the OMPS instrument that flies on the N20 satellite. Results show that the new dataset agrees well with the existing ones from other satellites and can help to continue the global monitoring of SO2 from space.
Jacqueline E. Russell, Richard J. Bantges, Helen E. Brindley, and Alejandro Bodas-Salcedo
Earth Syst. Sci. Data, 16, 4243–4266, https://doi.org/10.5194/essd-16-4243-2024, https://doi.org/10.5194/essd-16-4243-2024, 2024
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We present a dataset of top-of-atmosphere diurnally resolved reflected solar and emitted thermal energy for Earth system model evaluation. The multi-year, monthly hourly dataset, derived from observations made by the Geostationary Earth Radiation Budget instrument, covers the range 60° N–60° S, 60° E–60° W at 1° resolution. Comparison with two versions of the Hadley Centre Global Environmental Model highlight how the data can be used to assess updates to key model parameterizations.
Dominique Gantois, Guillaume Payen, Michaël Sicard, Valentin Duflot, Nelson Bègue, Nicolas Marquestaut, Thierry Portafaix, Sophie Godin-Beekmann, Patrick Hernandez, and Eric Golubic
Earth Syst. Sci. Data, 16, 4137–4159, https://doi.org/10.5194/essd-16-4137-2024, https://doi.org/10.5194/essd-16-4137-2024, 2024
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We describe three instruments that have been measuring interactions between aerosols (particles of various origin) and light over Réunion Island since 2012. Aerosols directly or indirectly influence the temperature in the atmosphere and can interact with clouds. Details are given on how we derived aerosol properties from our measurements and how we assessed the quality of our data before sharing them with the scientific community. A good correlation was found between the three instruments.
Hongfei Hao, Kaicun Wang, Guocan Wu, Jianbao Liu, and Jing Li
Earth Syst. Sci. Data, 16, 4051–4076, https://doi.org/10.5194/essd-16-4051-2024, https://doi.org/10.5194/essd-16-4051-2024, 2024
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In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine learning method at more than 5000 terrestrial sites in the Northern Hemisphere based on hourly atmospheric visibility data, which are extracted from the Meteorological Terminal Aviation Routine Weather Report (METAR).
Pierre Tulet, Joel Van Baelen, Pierre Bosser, Jérome Brioude, Aurélie Colomb, Philippe Goloub, Andrea Pazmino, Thierry Portafaix, Michel Ramonet, Karine Sellegri, Melilotus Thyssen, Léa Gest, Nicolas Marquestaut, Dominique Mékiès, Jean-Marc Metzger, Gilles Athier, Luc Blarel, Marc Delmotte, Guillaume Desprairies, Mérédith Dournaux, Gaël Dubois, Valentin Duflot, Kevin Lamy, Lionel Gardes, Jean-François Guillemot, Valérie Gros, Joanna Kolasinski, Morgan Lopez, Olivier Magand, Erwan Noury, Manuel Nunes-Pinharanda, Guillaume Payen, Joris Pianezze, David Picard, Olivier Picard, Sandrine Prunier, François Rigaud-Louise, Michael Sicard, and Benjamin Torres
Earth Syst. Sci. Data, 16, 3821–3849, https://doi.org/10.5194/essd-16-3821-2024, https://doi.org/10.5194/essd-16-3821-2024, 2024
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The MAP-IO program aims to compensate for the lack of atmospheric and oceanographic observations in the Southern Ocean by equipping the ship Marion Dufresne with a set of 17 scientific instruments. This program collected 700 d of measurements under different latitudes, seasons, sea states, and weather conditions. These new data will support the calibration and validation of numerical models and the understanding of the atmospheric composition of this region of Earth.
Siwei Li, Yu Ding, Jia Xing, and Joshua S. Fu
Earth Syst. Sci. Data, 16, 3781–3793, https://doi.org/10.5194/essd-16-3781-2024, https://doi.org/10.5194/essd-16-3781-2024, 2024
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Surface PM2.5 data have gained widespread application in health assessments and related fields, while the inherent uncertainties in PM2.5 data persist due to the lack of ground-truth data across the space. This study provides a novel testbed, enabling comprehensive evaluation across the entire spatial domain. The optimized deep-learning model with spatiotemporal features successfully retrieved surface PM2.5 concentrations in China (2013–2021), with reduced biases induced by sample imbalance.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data, 16, 3565–3577, https://doi.org/10.5194/essd-16-3565-2024, https://doi.org/10.5194/essd-16-3565-2024, 2024
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Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024, https://doi.org/10.5194/essd-16-3001-2024, 2024
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CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
David Winker, Xia Cai, Mark Vaughan, Anne Garnier, Brian Magill, Melody Avery, and Brian Getzewich
Earth Syst. Sci. Data, 16, 2831–2855, https://doi.org/10.5194/essd-16-2831-2024, https://doi.org/10.5194/essd-16-2831-2024, 2024
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Clouds play important roles in both weather and climate. In this paper we describe version 1.0 of a unique global ice cloud data product derived from over 12 years of global spaceborne lidar measurements. This monthly gridded product provides a unique vertically resolved characterization of the occurrence and properties, optical and physical, of thin ice clouds and the tops of deep convective clouds. It should provide significant value for cloud research and model evaluation.
Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi
Earth Syst. Sci. Data, 16, 2717–2740, https://doi.org/10.5194/essd-16-2717-2024, https://doi.org/10.5194/essd-16-2717-2024, 2024
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We propose and evaluate machine learning predictive algorithms to model freshly formed biogenic methanesulfonic acid and sulfate concentrations. The long-term constructed dataset covers the North Atlantic at an unprecedented resolution. The improved parameterization of biogenic sulfur aerosols at regional scales is essential for determining their radiative forcing, which could help further understand marine-aerosol–cloud interactions and reduce uncertainties in climate models
Rajesh Kumar, Piyush Bhardwaj, Cenlin He, Jennifer Boehnert, Forrest Lacey, Stefano Alessandrini, Kevin Sampson, Matthew Casali, Scott Swerdlin, Olga Wilhelmi, Gabriele G. Pfister, Benjamin Gaubert, and Helen Worden
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-180, https://doi.org/10.5194/essd-2024-180, 2024
Revised manuscript accepted for ESSD
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We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS) . The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, Pierre Regnier, Joseph G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihito Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joel Thanwerdas, Hanquin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido van der Werf, Doug E. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-115, https://doi.org/10.5194/essd-2024-115, 2024
Revised manuscript accepted for ESSD
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Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesize and update the budget of the sources and sinks of CH4. This edition benefits from important progresses in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Joshua L. Laughner, Geoffrey C. Toon, Joseph Mendonca, Christof Petri, Sébastien Roche, Debra Wunch, Jean-Francois Blavier, David W. T. Griffith, Pauli Heikkinen, Ralph F. Keeling, Matthäus Kiel, Rigel Kivi, Coleen M. Roehl, Britton B. Stephens, Bianca C. Baier, Huilin Chen, Yonghoon Choi, Nicholas M. Deutscher, Joshua P. DiGangi, Jochen Gross, Benedikt Herkommer, Pascal Jeseck, Thomas Laemmel, Xin Lan, Erin McGee, Kathryn McKain, John Miller, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Haris Riris, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Steven C. Wofsy, Minqiang Zhou, and Paul O. Wennberg
Earth Syst. Sci. Data, 16, 2197–2260, https://doi.org/10.5194/essd-16-2197-2024, https://doi.org/10.5194/essd-16-2197-2024, 2024
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This paper describes a new version, called GGG2020, of a data set containing column-integrated observations of greenhouse and related gases (including CO2, CH4, CO, and N2O) made by ground stations located around the world. Compared to the previous version (GGG2014), improvements have been made toward site-to-site consistency. This data set plays a key role in validating space-based greenhouse gas observations and in understanding the carbon cycle.
Antonin Soulie, Claire Granier, Sabine Darras, Nicolas Zilbermann, Thierno Doumbia, Marc Guevara, Jukka-Pekka Jalkanen, Sekou Keita, Cathy Liousse, Monica Crippa, Diego Guizzardi, Rachel Hoesly, and Steven J. Smith
Earth Syst. Sci. Data, 16, 2261–2279, https://doi.org/10.5194/essd-16-2261-2024, https://doi.org/10.5194/essd-16-2261-2024, 2024
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Anthropogenic emissions are the result of transportation, power generation, industrial, residential and commercial activities as well as waste treatment and agriculture practices. This work describes the new CAMS-GLOB-ANT gridded inventory of 2000–2023 anthropogenic emissions of air pollutants and greenhouse gases. The methodology to generate the emissions is explained and the datasets are analysed and compared with publicly available global and regional inventories for selected world regions.
Declan L. Finney, Alan M. Blyth, Martin Gallagher, Huihui Wu, Graeme J. Nott, Michael I. Biggerstaff, Richard G. Sonnenfeld, Martin Daily, Dan Walker, David Dufton, Keith Bower, Steven Böing, Thomas Choularton, Jonathan Crosier, James Groves, Paul R. Field, Hugh Coe, Benjamin J. Murray, Gary Lloyd, Nicholas A. Marsden, Michael Flynn, Kezhen Hu, Navaneeth M. Thamban, Paul I. Williams, Paul J. Connolly, James B. McQuaid, Joseph Robinson, Zhiqiang Cui, Ralph R. Burton, Gordon Carrie, Robert Moore, Steven J. Abel, Dave Tiddeman, and Graydon Aulich
Earth Syst. Sci. Data, 16, 2141–2163, https://doi.org/10.5194/essd-16-2141-2024, https://doi.org/10.5194/essd-16-2141-2024, 2024
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The DCMEX (Deep Convective Microphysics Experiment) project undertook an aircraft- and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar signals, thermodynamics, dynamics, electric fields, and weather. The project's objectives included the utilisation of these data with satellite observations to study the anvil cloud radiative effect.
Jianzhong Xu, Xinghua Zhang, Wenhui Zhao, Lixiang Zhai, Miao Zhong, Jinsen Shi, Junying Sun, Yanmei Liu, Conghui Xie, Yulong Tan, Kemei Li, Xinlei Ge, Qi Zhang, and Shichang Kang
Earth Syst. Sci. Data, 16, 1875–1900, https://doi.org/10.5194/essd-16-1875-2024, https://doi.org/10.5194/essd-16-1875-2024, 2024
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A comprehensive aerosol observation project was carried out in the Tibetan Plateau (TP) and its surroundings in recent years to investigate the properties and sources of atmospheric aerosols as well as their regional differences by performing multiple intensive field observations. The release of this dataset can provide basic and systematic data for related research in the atmospheric, cryospheric, and environmental sciences in this unique region.
Xiaoyong Zhuge, Xiaolei Zou, Lu Yu, Xin Li, Mingjian Zeng, Yilun Chen, Bing Zhang, Bin Yao, Fei Tang, Fengjiao Chen, and Wanlin Kan
Earth Syst. Sci. Data, 16, 1747–1769, https://doi.org/10.5194/essd-16-1747-2024, https://doi.org/10.5194/essd-16-1747-2024, 2024
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The Himawari-8/9 level-2 operational cloud product has a low spatial resolution and is available only during the daytime. To supplement this official dataset, a new dataset named the NJIAS Himawari-8/9 Cloud Feature Dataset (HCFD) was constructed. The NJIAS HCFD provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the years 2016–2022 by 30 retrieved cloud variables. The NJIAS HCFD has been demonstrated to outperform the official dataset.
Honglin Pan, Jianping Huang, Jiming Li, Zhongwei Huang, Minzhong Wang, Ali Mamtimin, Wen Huo, Fan Yang, Tian Zhou, and Kanike Raghavendra Kumar
Earth Syst. Sci. Data, 16, 1185–1207, https://doi.org/10.5194/essd-16-1185-2024, https://doi.org/10.5194/essd-16-1185-2024, 2024
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We applied several correction procedures and rigorously checked for data quality constraints during the long observation period spanning almost 14 years (2007–2020). Nevertheless, some uncertainties remain, mainly due to technical constraints and limited documentation of the measurements. Even though not completely accurate, this strategy is expected to at least reduce the inaccuracy of the computed characteristic value of aerosol optical parameters.
Julie Christin Schindlbeck-Belo, Matthew Toohey, Marion Jegen, Steffen Kutterolf, and Kira Rehfeld
Earth Syst. Sci. Data, 16, 1063–1081, https://doi.org/10.5194/essd-16-1063-2024, https://doi.org/10.5194/essd-16-1063-2024, 2024
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Volcanic forcing of climate resulting from major explosive eruptions is a dominant natural driver of past climate variability. To support model studies of the potential impacts of explosive volcanism on climate variability across timescales, we present an ensemble reconstruction of volcanic stratospheric sulfur injection over the last 140 000 years that is based primarily on tephra records.
Sabrina Schnitt, Andreas Foth, Heike Kalesse-Los, Mario Mech, Claudia Acquistapace, Friedhelm Jansen, Ulrich Löhnert, Bernhard Pospichal, Johannes Röttenbacher, Susanne Crewell, and Bjorn Stevens
Earth Syst. Sci. Data, 16, 681–700, https://doi.org/10.5194/essd-16-681-2024, https://doi.org/10.5194/essd-16-681-2024, 2024
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This publication describes the microwave radiometric measurements performed during the EUREC4A campaign at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January 2020 and 14 February 2020.
Daniela Meloni, Filippo Calì Quaglia, Virginia Ciardini, Annalisa Di Bernardino, Tatiana Di Iorio, Antonio Iaccarino, Giovanni Muscari, Giandomenico Pace, Claudio Scarchilli, and Alcide di Sarra
Earth Syst. Sci. Data, 16, 543–566, https://doi.org/10.5194/essd-16-543-2024, https://doi.org/10.5194/essd-16-543-2024, 2024
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Solar and infrared radiation are key factors in determining Arctic climate. Only a few sites in the Arctic perform long-term measurements of the surface radiation budget (SRB). At the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W) in Northern Greenland, solar and infrared irradiance measurements were started in 2009. These data are of paramount importance in studying the impact of the atmospheric (mainly clouds and aerosols) and surface (albedo) parameters on the SRB.
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
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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.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
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A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Karina E. Adcock, Penelope A. Pickers, Andrew C. Manning, Grant L. Forster, Leigh S. Fleming, Thomas Barningham, Philip A. Wilson, Elena A. Kozlova, Marica Hewitt, Alex J. Etchells, and Andy J. Macdonald
Earth Syst. Sci. Data, 15, 5183–5206, https://doi.org/10.5194/essd-15-5183-2023, https://doi.org/10.5194/essd-15-5183-2023, 2023
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We present a 12-year time series of continuous atmospheric measurements of O2 and CO2 at the Weybourne Atmospheric Observatory in the United Kingdom. These measurements are combined into the term atmospheric potential oxygen (APO), a tracer that is not influenced by land biosphere processes. The datasets show a long-term increasing trend in CO2 and decreasing trends in O2 and APO between 2010 and 2021.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
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This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
Sandip S. Dhomse and Martyn P. Chipperfield
Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, https://doi.org/10.5194/essd-15-5105-2023, 2023
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There are no long-term stratospheric profile data sets for two very important greenhouse gases: methane (CH4) and nitrous oxide (N2O). Along with radiative feedback, these species play an important role in controlling ozone loss in the stratosphere. Here, we use machine learning to fuse satellite measurements with a chemical model to construct long-term gap-free profile data sets for CH4 and N2O. We aim to construct similar data sets for other important trace gases (e.g. O3, Cly, NOy species).
Tobias Erhardt, Camilla Marie Jensen, Florian Adolphi, Helle Astrid Kjær, Remi Dallmayr, Birthe Twarloh, Melanie Behrens, Motohiro Hirabayashi, Kaori Fukuda, Jun Ogata, François Burgay, Federico Scoto, Ilaria Crotti, Azzurra Spagnesi, Niccoló Maffezzoli, Delia Segato, Chiara Paleari, Florian Mekhaldi, Raimund Muscheler, Sophie Darfeuil, and Hubertus Fischer
Earth Syst. Sci. Data, 15, 5079–5091, https://doi.org/10.5194/essd-15-5079-2023, https://doi.org/10.5194/essd-15-5079-2023, 2023
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The presented paper provides a 3.8 kyr long dataset of aerosol concentrations from the East Greenland Ice coring Project (EGRIP) ice core. The data consists of 1 mm depth-resolution profiles of calcium, sodium, ammonium, nitrate, and electrolytic conductivity as well as decadal averages of these profiles. Alongside the data a detailed description of the measurement setup as well as a discussion of the uncertainties are given.
Chaoyang Xue, Gisèle Krysztofiak, Vanessa Brocchi, Stéphane Chevrier, Michel Chartier, Patrick Jacquet, Claude Robert, and Valéry Catoire
Earth Syst. Sci. Data, 15, 4553–4569, https://doi.org/10.5194/essd-15-4553-2023, https://doi.org/10.5194/essd-15-4553-2023, 2023
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To understand tropospheric air pollution at regional and global scales, an infrared laser spectrometer called SPIRIT was used on aircraft to rapidly and accurately measure carbon monoxide (CO), an important indicator of air pollution, during the last decade. Measurements were taken for more than 200 flight hours over three continents. Levels of CO are mapped with 3D trajectories for each flight. Additionally, this can be used to validate model performance and satellite measurements.
Goutam Choudhury and Matthias Tesche
Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, https://doi.org/10.5194/essd-15-3747-2023, 2023
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Aerosols in the atmosphere that can form liquid cloud droplets are called cloud condensation nuclei (CCN). Accurate measurements of CCN, especially CCN of anthropogenic origin, are necessary to quantify the effect of anthropogenic aerosols on the present-day as well as future climate. In this paper, we describe a novel global 3D CCN data set calculated from satellite measurements. We also discuss the potential applications of the data in the context of aerosol–cloud interactions.
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
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We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
Shoma Yamanouchi, Stephanie Conway, Kimberly Strong, Orfeo Colebatch, Erik Lutsch, Sébastien Roche, Jeffrey Taylor, Cynthia H. Whaley, and Aldona Wiacek
Earth Syst. Sci. Data, 15, 3387–3418, https://doi.org/10.5194/essd-15-3387-2023, https://doi.org/10.5194/essd-15-3387-2023, 2023
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Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO; 43.66° N, 79.40° W; 174 m.a.s.l.) are presented. These are retrieved from Fourier transform infrared (FTIR) solar absorption spectra recorded with a spectrometer from May 2002 to December 2020. The retrievals have been optimized for fourteen species: O3, HCl, HF, HNO3, CH4, C2H6, CO, HCN, N2O, C2H2, H2CO, CH3OH, HCOOH, and NH3.
Michael J. Prather, Hao Guo, and Xin Zhu
Earth Syst. Sci. Data, 15, 3299–3349, https://doi.org/10.5194/essd-15-3299-2023, https://doi.org/10.5194/essd-15-3299-2023, 2023
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The Atmospheric Tomography Mission (ATom) measured the chemical composition in air parcels from 0–12 km altitude on 2 km horizontal by 80 m vertical scales for four seasons, resolving most scales of chemical heterogeneity. ATom is one of the first missions designed to calculate the chemical evolution of each parcel, providing semi-global diurnal budgets for ozone and methane. Observations covered the remote troposphere: Pacific and Atlantic Ocean basins, Southern Ocean, Arctic basin, Antarctica.
Marie Dumont, Simon Gascoin, Marion Réveillet, Didier Voisin, François Tuzet, Laurent Arnaud, Mylène Bonnefoy, Montse Bacardit Peñarroya, Carlo Carmagnola, Alexandre Deguine, Aurélie Diacre, Lukas Dürr, Olivier Evrard, Firmin Fontaine, Amaury Frankl, Mathieu Fructus, Laure Gandois, Isabelle Gouttevin, Abdelfateh Gherab, Pascal Hagenmuller, Sophia Hansson, Hervé Herbin, Béatrice Josse, Bruno Jourdain, Irene Lefevre, Gaël Le Roux, Quentin Libois, Lucie Liger, Samuel Morin, Denis Petitprez, Alvaro Robledano, Martin Schneebeli, Pascal Salze, Delphine Six, Emmanuel Thibert, Jürg Trachsel, Matthieu Vernay, Léo Viallon-Galinier, and Céline Voiron
Earth Syst. Sci. Data, 15, 3075–3094, https://doi.org/10.5194/essd-15-3075-2023, https://doi.org/10.5194/essd-15-3075-2023, 2023
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Saharan dust outbreaks have profound effects on ecosystems, climate, health, and the cryosphere, but the spatial deposition pattern of Saharan dust is poorly known. Following the extreme dust deposition event of February 2021 across Europe, a citizen science campaign was launched to sample dust on snow over the Pyrenees and the European Alps. This campaign triggered wide interest and over 100 samples. The samples revealed the high variability of the dust properties within a single event.
Han Huang and Yi Huang
Earth Syst. Sci. Data, 15, 3001–3021, https://doi.org/10.5194/essd-15-3001-2023, https://doi.org/10.5194/essd-15-3001-2023, 2023
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We present a newly generated set of ERA5-based radiative kernels and compare them with other published kernels for the top of the atmosphere and surface radiation budgets. For both, the discrepancies in sensitivity values are generally of small magnitude, except for temperature kernels for the surface, likely due to improper treatment in the perturbation experiments used for kernel computation. The kernel bias is not a major cause of the inter-GCM (general circulation model) feedback spread.
Cited articles
Ackerman, A. S., Hobbs, P. V., and Toon, O. B.: A model for particle microphysics, turbulent mixing, and radiative transfer in the stratocumulus-topped marine boundary layer and comparisons with measurements, J. Atmos. Sci., 52, 1204–1236, https://doi.org/10.1175/1520-0469(1995)052<1204:AMFPMT>2.0.CO;2, 1995.
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Anderson, T. L., Charlson, R. J., Bellouin, N., Boucher, O., Chin, M., Christopher, S. A., Haywood, J., Kaufman, Y. J., Kinne, S., Ogren, J. A., Remer, L. A., Takemura, T., Tanre, D., Torres, O., Trepte, C. R., Wielicki, B. A., Winker, D. M., and Yu, H. B.: An “A-Train” strategy for quantifying direct climate forcing by anthropogenic aerosols, B. Am. Meteorol. Soc., 86, 1795, https://doi.org/10.1175/Bams-86-12-1795, 2005.
Andrews, E., Sheridan, P. J., Ogren, J. A., Hageman, D., Jefferson, A., Wendell, J., Alástuey, A., Alados-Arboledas, L., Bergin, M., and Ealo, M.: Overview of the NOAA/ESRL federated aerosol network, B. Am. Meteorol. Soc., 100, 123–135, https://doi.org/10.1175/BAMS-D-17-0175.1, 2019.
Bergstrom, R. W., Pilewskie, P., Russell, P. B., Redemann, J., Bond, T. C., Quinn, P. K., and Sierau, B.: Spectral absorption properties of atmospheric aerosols, Atmos. Chem. Phys., 7, 5937–5943, https://doi.org/10.5194/acp-7-5937-2007, 2007.
Berk, R. A.: Classification and Regression Trees (CART), in: Statistical Learning from a Regression Perspective, Springer New York, New York, NY, 1–65, https://doi.org/10.1007/978-0-387-77501-2_3, 2008.
Bescond, A., Yon, J., Girasole, T., Jouen, C., Rozé, C., and Coppalle, A.: Numerical investigation of the possibility to determine the primary particle size of fractal aggregates by measuring light depolarization, J. Quant. Spectrosc. Ra., 126, 130–139, https://doi.org/10.1016/j.jqsrt.2012.10.011, 2013.
Boers, R., van Weele, M., van Meijgaard, E., Savenije, M., Siebesma, A. P., Bosveld, F., and Stammes, P.: Observations and projections of visibility and aerosol optical thickness (1956–2100) in the Netherlands: impacts of time-varying aerosol composition and hygroscopicity, Environ. Res. Lett., 10, 015003, https://doi.org/10.1088/1748-9326/10/1/015003, 2015.
Bösenberg, J., Linné, H., Matthias, V., Böckmann, C., Mironova, I., Schpeidenbach, L., Kirsche, A., Mekler, A., Wiegner, M., Freudenthaler, V., Stachlewska, l., Kump, W., Pappalardo, G.,Amdeo, A., Mona, L., Pandolfi, M., Balis, D., Amoiridis, V., Zerefos, C., Ansmann, A., Mattis,l., Wandinger, U., Maller,D., Spinelli, N., Wang, X., Boselli, A., Chaikovsky, A., Comeron, A., Rocadenbosch, F., Pérez, C., Baldasano, J. M., Pelon, J., Sauvage,L., Perone, R. M., Ferdinando de Tomasi, Eixmann,R., Mitev, V., Matthey, R., Hapard, A., Persson, R., Carlsson, G., Rizi, V., Iarlori, M., Vaughan, G., Trickl,T., Kreipl, S., Giehl, H., Simeonov, V., Resendes, D. P., Rodrigues, J. A., Sobolewski, P., Nickovic, Music, S., Zavrtanik, M., Stoyanov, D., Grigorov, L., Kolarov, G., and Papayannis, A.: EARLINET: A European Aerosol Research Lidar Network to Establish an Aerosol Climatology, Max Planck Institut für Meteorologie, 348, ISSN 0937 1060, 2003.
Bokoye, A. I., Royer, A., O'Neil, N., Cliche, P., Fedosejevs, G., Teillet, P., and McArthur, L.: Characterization of atmospheric aerosols across Canada from a ground-based sunphotometer network: AEROCAN, Atmos. Ocean, 39, 429–456, https://doi.org/10.1080/07055900.2001.9649687, 2001.
Bright, J. M. and Gueymard, C. A.: Climate-specific and global validation of MODIS Aqua and Terra aerosol optical depth at 452 AERONET stations, Sol. Energy, 183, 594–605, https://doi.org/10.1016/j.solener.2019.03.043, 2019.
Browne, M. W.: Cross-validation methods, J. Math. Psychol., 44, 108–132, https://doi.org/10.1006/jmps.1999.1279, 2000.
Calvo, A. I., Alves, C., Castro, A., Pont, V., Vicente, A. M., and Fraile, R.: Research on aerosol sources and chemical composition: Past, current and emerging issues, Atmos. Res., 120, 1–28, https://doi.org/10.1016/j.atmosres.2012.09.021, 2013.
Chafe, Z. A., Brauer, M., Klimont, Z., Van Dingenen, R., Mehta, S., Rao, S., Riahi, K., Dentener, F., and Smith, K. R.: Household Cooking with Solid Fuels Contributes to Ambient PM2.5 Air Pollution and the Burden of Disease, Environ. Health Persp., 122, 1314–1320, https://doi.org/10.1289/ehp.1206340, 2014.
Che, H., Zhang, X., Chen, H., Damiri, B., Goloub, P., Li, Z., Zhang, X., Wei, Y., Zhou, H., Dong, F., Li, D., and Zhou, T.: Instrument calibration and aerosol optical depth validation of the China Aerosol Remote Sensing Network, J. Geophys. Res.-Atmos., 114, D03206, https://doi.org/10.1029/2008jd011030, 2009.
Che, H., Xia, X., Zhu, J., Li, Z., Dubovik, O., Holben, B., Goloub, P., Chen, H., Estelles, V., Cuevas-Agulló, E., Blarel, L., Wang, H., Zhao, H., Zhang, X., Wang, Y., Sun, J., Tao, R., Zhang, X., and Shi, G.: Column aerosol optical properties and aerosol radiative forcing during a serious haze-fog month over North China Plain in 2013 based on ground-based sunphotometer measurements, Atmos. Chem. Phys., 14, 2125–2138, https://doi.org/10.5194/acp-14-2125-2014, 2014.
Chen, A., Zhao, C., and Fan, T.: Spatio-temporal distribution of aerosol direct radiative forcing over mid-latitude regions in north hemisphere estimated from satellite observations, Atmos. Res., 266, 105938, https://doi.org/10.1016/j.atmosres.2021.105938, 2022.
Cherian, R. and Quaas, J.: Trends in AOD, clouds, and cloud radiative effects in satellite data and CMIP5 and CMIP6 model simulations over aerosol source regions, Geophys. Res. Lett., 47, e2020GL087132, https://doi.org/10.1029/2020GL087132, 2020.
Chin, M., Diehl, T., Tan, Q., Prospero, J. M., Kahn, R. A., Remer, L. A., Yu, H., Sayer, A. M., Bian, H., Geogdzhayev, I. V., Holben, B. N., Howell, S. G., Huebert, B. J., Hsu, N. C., Kim, D., Kucsera, T. L., Levy, R. C., Mishchenko, M. I., Pan, X., Quinn, P. K., Schuster, G. L., Streets, D. G., Strode, S. A., Torres, O., and Zhao, X.-P.: Multi-decadal aerosol variations from 1980 to 2009: a perspective from observations and a global model, Atmos. Chem. Phys., 14, 3657–3690, https://doi.org/10.5194/acp-14-3657-2014, 2014.
Chu, D., Kaufman, Y., Ichoku, C., Remer, L., Tanré, D., and Holben, B.: Validation of MODIS aerosol optical depth retrieval over land, Geophys. Res. Lett., 29, MOD2-1-MOD2-4, https://doi.org/10.1029/2001GL013205, 2002.
Chuang, P.-J. and Huang, P.-Y.: B-VAE: a new dataset balancing approach using batched Variational AutoEncoders to enhance network intrusion detection, J. Supercomput., 79, 13262–13286, https://doi.org/10.1007/s11227-023-05171-w, 2023.
Deuzé, J., Goloub, P., Herman, M., Marchand, A., Perry, G., Susana, S., and Tanré, D.: Estimate of the aerosol properties over the ocean with POLDER, J. Geophys. Res.-Atmos., 105, 15329–15346, https://doi.org/10.1029/2000JD900148, 2000.
Dhanya, R., Paul, I. R., Akula, S. S., Sivakumar, M., and Nair, J. J.: F-test feature selection in Stacking ensemble model for breast cancer prediction, Procedia. Comput. Sci., 171, 1561–1570, https://doi.org/10.1016/j.procs.2020.04.167, 2020.
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E., Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., and Gerstl, S. A. W.: Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview, IEEE T. Geosci. Remote, 98, 1072–1087, https://doi.org/10.1109/36.700992, 1998.
Dong, Y., Li, J., Yan, X., Li, C., Jiang, Z., Xiong, C., Chang, L., Zhang, L., Ying, T., and Zhang, Z.: Retrieval of aerosol single scattering albedo using joint satellite and surface visibility measurements, Remote Sens. Environ., 294, 113654, https://doi.org/10.1016/j.rse.2023.113654, 2023.
Dubovik, O., Smirnov, A., Holben, B. N., King, M. D., Kaufman, Y. J., Eck, T. F., and Slutsker, I.: Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements, J. Geophys. Res.-Atmos., 105, 9791–9806, https://doi.org/10.1029/2000jd900040, 2000.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanré, D., and Slutsker, I.: Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations, J. Atmos. Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002a.
Dubovik, O., Holben, B., Eck, T. F., Smirnov, A., Kaufman, Y. J., King, M. D., Tanré, D., and Slutsker, I.: Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atmos. Sci., 59, 590–608, https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2, 2002b.
Eck, T. F., Holben, B. N., Reid, J. S., Sinyuk, A., Giles, D. M., Arola, A., Slutsker, I., Schafer, J. S., Sorokin, M. G., and Smirnov, A.: The extreme forest fires in California/Oregon in 2020: Aerosol optical and physical properties and comparisons of aged versus fresh smoke, Atmos. Environ., 305, 119798, https://doi.org/10.1016/j.atmosenv.2023.119798, 2023.
Elterman, L.: Relationships between vertical attenuation and surface meteorological range, Appl. Optics, 9, 1804–1810, https://doi.org/10.1364/AO.9.001804, 1970.
Fan, H., Zhao, C., Yang, Y., and Yang, X.: Spatio-Temporal Variations of the
Fernández, A., Garcia, S., Herrera, F., and Chawla, N. V.: SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary, J. Artif. Intell. Res., 61, 863–905, https://doi.org/10.1613/jair.1.11192, 2018.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., and Myhre, G.: Changes in atmospheric constituents and in radiative forcing, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the 4th Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press ISBN 9780521880091, 2007.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Giglio, L., Randerson, J. T., and Van Der Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013.
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Global Modeling and Assimilation Office (GMAO): MERRA-2 tavgM_2d_aer_Nx: 2d, Monthly mean, Time-averaged, Single-Level, Assimilation, Aerosol Diagnostics V5.12.4, Goddard Earth Sciences Data and Information Services Center (GES DISC) [dataset], https://doi.org/10.5067/FH9A0MLJPC7N, 2015.
Gras, J., Jensen, J., Okada, K., Ikegami, M., Zaizen, Y., and Makino, Y.: Some optical properties of smoke aerosol in Indonesia and tropical Australia, Geophys. Res. Lett., 26, 1393–1396, https://doi.org/10.1029/1999GL900275, 1999.
Guerrero-Rascado, J. L., Landulfo, E., Antuña, J. C., Barbosa, H. d. M. J., Barja, B., Bastidas, Á. E., Bedoya, A. E., da Costa, R. F., Estevan, R., and Forno, R.: Latin American Lidar Network (LALINET) for aerosol research: Diagnosis on network instrumentation, J. Atmos. Sol.-Terr. Phys., 138, 112–120, https://doi.org/10.1016/j.jastp.2016.01.001, 2016.
Guo, J., Zhang, J., Yang, K., Liao, H., Zhang, S., Huang, K., Lv, Y., Shao, J., Yu, T., Tong, B., Li, J., Su, T., Yim, S. H. L., Stoffelen, A., Zhai, P., and Xu, X.: Investigation of near-global daytime boundary layer height using high-resolution radiosondes: first results and comparison with ERA5, MERRA-2, JRA-55, and NCEP-2 reanalyses, Atmos. Chem. Phys., 21, 17079–17097, https://doi.org/10.5194/acp-21-17079-2021, 2021.
Hao, H., Wang, K., and Wu, G.: Visibility-derived aerosol optical depth over global land (1980–2021), National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.300822, 2023.
He, H., Bai, Y., Garcia, E. A., and Li, S.: ADASYN: Adaptive synthetic sampling approach for imbalanced learning, in: IEEE World Congress on Computational Intelligence, 1322–1328, https://doi.org/10.1109/IJCNN.2008.4633969, 2008.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersey, S. P., Garland, R. M., Crosbie, E., Shingler, T., Sorooshian, A., Piketh, S., and Burger, R.: An overview of regional and local characteristics of aerosols in South Africa using satellite, ground, and modeling data, Atmos. Chem. Phys., 15, 4259–4278, https://doi.org/10.5194/acp-15-4259-2015, 2015.
Holben, B. N., Eck, T. F., Slutsker, I., Tanre, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET - A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/s0034-4257(98)00031-5, 1998.
Hsu, N., Jeong, M. J., Bettenhausen, C., Sayer, A., Hansell, R., Seftor, C., Huang, J., and Tsay, S. C.: Enhanced Deep Blue aerosol retrieval algorithm: The second generation, J. Geophys. Res.-Atmos., 118, 9296–9315, https://doi.org/10.1002/jgrd.50712, 2013.
Hsu, N., Lee, J., Sayer, A., Carletta, N., Chen, S. H., Tucker, C., Holben, B., and Tsay, S. C.: Retrieving near-global aerosol loading over land and ocean from AVHRR, J. Geophys. Res.-Atmos., 122, 9968–9989, https://doi.org/10.1002/2017JD026932, 2017.
Hsu, N. C., Tsay, S.-C., King, M. D., and Herman, J. R.: Deep blue retrievals of Asian aerosol properties during ACE-Asia, IEEE T. Geosci. Remote, 44, 3180–3195, https://doi.org/10.1109/tgrs.2006.879540, 2006.
Hsu, N. C., Gautam, R., Sayer, A. M., Bettenhausen, C., Li, C., Jeong, M. J., Tsay, S.-C., and Holben, B. N.: Global and regional trends of aerosol optical depth over land and ocean using SeaWiFS measurements from 1997 to 2010, Atmos. Chem. Phys., 12, 8037–8053, https://doi.org/10.5194/acp-12-8037-2012, 2012.
Hu, B., Zhang, X., Sun, R., and Zhu, X.: Retrieval of Horizontal Visibility Using MODIS Data: A Deep Learning Approach, Atmosphere-Basel, 10, 740, https://doi.org/10.3390/atmos10120740, 2019.
Hu, K., Kumar, K. R., Kang, N., Boiyo, R., and Wu, J.: Spatiotemporal characteristics of aerosols and their trends over mainland China with the recent Collection 6 MODIS and OMI satellite datasets, Environ. Sci. Pollut. R., 25, 6909–6927, https://doi.org/10.1007/s11356-017-0715-6, 2018.
Husar, R. B., Husar, J. D., and Martin, L.: Distribution of continental surface aerosol extinction based on visual range data, Atmos. Environ., 34, 5067–5078, https://doi.org/10.1016/s1352-2310(00)00324-1, 2000.
IPCC: Climate Change 2021: The Physical Science Basis, Cambridge University Press, New York, 2021.
Ivanova, G., Ivanov, V., Kukavskaya, E., and Soja, A.: The frequency of forest fires in Scots pine stands of Tuva, Russia, Environ. Res. Lett., 5, 015002, https://doi.org/10.1088/1748-9326/5/1/015002, 2010.
Kang, Y., Choi, H., Im, J., Park, S., Shin, M., Song, C.-K., and Kim, S.: Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia, Environ. Pollut., 288, 117711, https://doi.org/10.1016/j.envpol.2021.117711, 2021.
Kang, Y., Kim, M., Kang, E., Cho, D., and Im, J.: Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia, ISPRS J. Photogramm., 183, 253–268, https://doi.org/10.1016/j.isprsjprs.2021.11.016, 2022.
Kaufman, Y. J. and Boucher, O.: A satellite view of aerosols in the climate system, Nature, 419, 215–215, https://doi.org/10.1038/nature01091, 2002.
Kim, D. H., Sohn, B. J., Nakajima, T., Takamura, T., Takemura, T., Choi, B. C., and Yoon, S. C.: Aerosol optical properties over east Asia determined from ground-based sky radiation measurements, J. Geophys. Res.-Atmos., 109, D13211, https://doi.org/10.1029/2003jd003387, 2004.
Klett, J. D.: Lidar inversion with variable backscatter/extinction ratios, Appl. Optics, 24, 1638–1643, https://doi.org/10.1364/AO.24.001638, 1985.
Koelemeijer, R., Homan, C., and Matthijsen, J.: Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe, Atmos. Environ., 40, 5304–5315, https://doi.org/10.1016/j.atmosenv.2006.04.044, 2006.
Koschmieder, H.: Theorie der horizontalen Sichtweite, Beitrage zur Physik der freien Atmosphare, 12, 33–55, 1924.
Krylov, A., McCarty, J. L., Potapov, P., Loboda, T., Tyukavina, A., Turubanova, S., and Hansen, M. C.: Remote sensing estimates of stand-replacement fires in Russia, 2002–2011, Environ. Res. Lett., 9, 105007, https://doi.org/10.1088/1748-9326/9/10/105007, 2014.
Kulmala, M., Vehkamäki, H., Petäjä, T., Dal Maso, M., Lauri, A., Kerminen, V. M., Birmili, W., and McMurry, P. H.: Formation and growth rates of ultrafine atmospheric particles: A review of observations, J. Aerosol Sci., 35, 143–176, https://doi.org/10.1016/j.jaerosci.2003.10.003, 2004.
Kummu, M., De Moel, H., Salvucci, G., Viviroli, D., Ward, P. J., and Varis, O.: Over the hills and further away from coast: global geospatial patterns of human and environment over the 20th–21st centuries, Environ. Res. Lett., 11, 034010, https://doi.org/10.1088/1748-9326/11/3/034010, 2016.
Lee, L. A., Reddington, C. L., and Carslaw, K. S.: On the relationship between aerosol model uncertainty and radiative forcing uncertainty, P. Natl. Acad. Sci. USA, 113, 5820–5827, https://doi.org/10.1073/pnas.1507050113, 2016.
Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.: Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, J. Geophys. Res.-Atmos., 112, D13211, https://doi.org/10.1029/2006JD007811, 2007.
Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R., and Eck, T. F.: Global evaluation of the Collection 5 MODIS dark-target aerosol products over land, Atmos. Chem. Phys., 10, 10399–10420, https://doi.org/10.5194/acp-10-10399-2010, 2010.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Levy, R. C., Mattoo, S., Sawyer, V., Shi, Y., Colarco, P. R., Lyapustin, A. I., Wang, Y., and Remer, L. A.: Exploring systematic offsets between aerosol products from the two MODIS sensors, Atmos. Meas. Tech., 11, 4073–4092, https://doi.org/10.5194/amt-11-4073-2018, 2018.
Li, J., Garshick, E., Hart, J. E., Li, L., Shi, L., Al-Hemoud, A., Huang, S., and Koutrakis, P.: Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing, Environ. Int., 151, 106445, https://doi.org/10.1016/j.envint.2021.106445, 2021.
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., Dubovik, O., Ding, A., Lacis, A. A., Zhang, L., and Dong, Y.: Scattering and absorbing aerosols in the climate system, Nat. Rev. Earth. Environ., 3, 363–379, https://doi.org/10.1038/s43017-022-00296-7, 2022.
Li, S., Chen, L., Huang, G., Lin, J., Yan, Y., Ni, R., Huo, Y., Wang, J., Liu, M., and Weng, H.: Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations, Atmos. Environ., 222, 117121, https://doi.org/10.1016/j.atmosenv.2019.117121, 2020.
Li, Z., Lau, W. M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M., Liu, J., Qian, Y., Li, J., and Zhou, T.: Aerosol and monsoon climate interactions over Asia, Rev. Geophys., 54, 866–929, https://doi.org/10.1002/2015RG000500, 2016.
Liao, H., Chang, W., and Yang, Y.: Climatic Effects of Air Pollutants over China: A Review, Adv. Atmos. Sci., 32, 115–139, https://doi.org/10.1007/s00376-014-0013-x, 2015.
Lin, J. T., van Donkelaar, A., Xin, J. Y., Che, H. Z., and Wang, Y. S.: Clear-sky aerosol optical depth over East China estimated from visibility measurements and chemical transport modeling, Atmos. Environ., 95, 258–267, https://doi.org/10.1016/j.atmosenv.2014.06.044, 2014.
Liu, B., Ma, X., Ma, Y., Li, H., Jin, S., Fan, R., and Gong, W.: The relationship between atmospheric boundary layer and temperature inversion layer and their aerosol capture capabilities, Atmos. Res., 271, 106121, https://doi.org/10.1016/j.atmosres.2022.106121, 2022.
Mahowald, N. M., Ballantine, J. A., Feddema, J., and Ramankutty, N.: Global trends in visibility: implications for dust sources, Atmos. Chem. Phys., 7, 3309–3339, https://doi.org/10.5194/acp-7-3309-2007, 2007.
McNeill, V. F.: Atmospheric Aerosols: Clouds, Chemistry, and Climate, Annu. Rev. Chem. Biomol., 8, 427–444, https://doi.org/10.1146/annurev-chembioeng-060816-101538, 2017.
Mehta, M., Singh, R., Singh, A., and Singh, N.: Recent global aerosol optical depth variations and trends—A comparative study using MODIS and MISR level 3 datasets, Remote Sens. Environ., 181, 137–150, https://doi.org/10.1016/j.rse.2016.04.004, 2016.
Mitra, R., Bajpai, A., and Biswas, K.: ADASYN-assisted machine learning for phase prediction of high entropy carbides, Comp. Mater. Sci., 223, https://doi.org/10.1016/j.commatsci.2023.112142, 2023.
Mortier, A., Gliß, J., Schulz, M., Aas, W., Andrews, E., Bian, H., Chin, M., Ginoux, P., Hand, J., Holben, B., Zhang, H., Kipling, Z., Kirkevåg, A., Laj, P., Lurton, T., Myhre, G., Neubauer, D., Olivié, D., von Salzen, K., Skeie, R. B., Takemura, T., and Tilmes, S.: Evaluation of climate model aerosol trends with ground-based observations over the last 2 decades – an AeroCom and CMIP6 analysis, Atmos. Chem. Phys., 20, 13355–13378, https://doi.org/10.5194/acp-20-13355-2020, 2020.
Mukkavilli, S., Prasad, A., Taylor, R., Huang, J., Mitchell, R., Troccoli, A., and Kay, M.: Assessment of atmospheric aerosols from two reanalysis products over Australia, Atmos. Res., 215, 149–164, https://doi.org/10.1016/j.atmosres.2018.08.026, 2019.
Nagaraja Rao, C., Stowe, L., and McClain, E.: Remote sensing of aerosols over the oceans using AVHRR data Theory, practice and applications, Int. J. Remote Sens., 10, 743–749, https://doi.org/10.1080/01431168908903915, 1989.
Nakajima, T., Campanelli, M., Che, H., Estellés, V., Irie, H., Kim, S.-W., Kim, J., Liu, D., Nishizawa, T., Pandithurai, G., Soni, V. K., Thana, B., Tugjsurn, N.-U., Aoki, K., Go, S., Hashimoto, M., Higurashi, A., Kazadzis, S., Khatri, P., Kouremeti, N., Kudo, R., Marenco, F., Momoi, M., Ningombam, S. S., Ryder, C. L., Uchiyama, A., and Yamazaki, A.: An overview of and issues with sky radiometer technology and SKYNET, Atmos. Meas. Tech., 13, 4195–4218, https://doi.org/10.5194/amt-13-4195-2020, 2020.
O'Reilly, J. E., Maritorena, S., Mitchell, B. G., Siegel, D. A., Carder, K. L., Garver, S. A., Kahru, M., and McClain, C.: Ocean color chlorophyll algorithms for SeaWiFS, J. Geophys. Res., 103, 24937–24953, https://doi.org/10.1029/98jc02160, 1998.
Qiu, J. and Lin, Y.: A parameterization model of aerosol optical depths in China, Acta Meteorol. Sin., 59, 368–372, https://doi.org/10.11676/qxxb2001.039, 2001.
Ramanathan, V., Crutzen, P. J., Kiehl, J., and Rosenfeld, D.: Aerosols, climate, and the hydrological cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001.
Remer, L. A., Kaufman, Y. J., Tanre, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R. R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F., Vermote, E., and Holben, B. N.: The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62, 947–973, https://doi.org/10.1175/jas3385.1, 2005.
Remer, L. A., Kleidman, R. G., Levy, R. C., Kaufman, Y. J., Tanre, D., Mattoo, S., Martins, J. V., Ichoku, C., Koren, I., Yu, H., and Holben, B. N.: Global aerosol climatology from the MODIS satellite sensors, J. Geophys. Res.-Atmos., 113, D14S07, https://doi.org/10.1029/2007jd009661, 2008.
Salomonson, V. V., Barnes, W. L., Maymon, P. W., Montgomery, H. E., and Ostrow, H.: MODIS: advanced facility instrument for studies of the Earth as a system, IEEE T. Geosci. Remote, 27, 145–153, https://doi.org/10.1109/36.20292, 1987.
Schutgens, N., Tsyro, S., Gryspeerdt, E., Goto, D., Weigum, N., Schulz, M., and Stier, P.: On the spatio-temporal representativeness of observations, Atmos. Chem. Phys., 17, 9761–9780, https://doi.org/10.5194/acp-17-9761-2017, 2017.
Singh, A., Mahata, K. S., Rupakheti, M., Junkermann, W., Panday, A. K., and Lawrence, M. G.: An overview of airborne measurement in Nepal – Part 1: Vertical profile of aerosol size, number, spectral absorption, and meteorology, Atmos. Chem. Phys., 19, 245–258, https://doi.org/10.5194/acp-19-245-2019, 2019.
Smirnov, A., Holben, B., Slutsker, I., Giles, D., McClain, C., Eck, T., Sakerin, S., Macke, A., Croot, P., and Zibordi, G.: Maritime aerosol network as a component of aerosol robotic network, J. Geophys. Res.-Atmos., 114, D06204, https://doi.org/10.1029/2008JD011257, 2009.
Streets, D. G., Yan, F., Chin, M., Diehl, T., Mahowald, N., Schultz, M., Wild, M., Wu, Y., and Yu, C.: Anthropogenic and natural contributions to regional trends in aerosol optical depth, 1980–2006, J. Geophys. Res.-Atmos., 114, D00D18, https://doi.org/10.1029/2008JD011624, 2009.
Sun, Y. and Zhao, C.: Influence of Saharan dust on the large-scale meteorological environment for development of tropical cyclone over North Atlantic Ocean Basin, J. Geophys. Res.-Atmos., 125, e2020JD033454, https://doi.org/10.1029/2020JD033454, 2020.
Teixeira, A.: Classification and regression tree, Rev. Mal. Respir., 21, 1174–1176, https://doi.org/10.1016/S0761-8425(04)71596-X, 2004.
Tian, X., Tang, C., Wu, X., Yang, J., Zhao, F., and Liu, D.: The global spatial-temporal distribution and EOF analysis of AOD based on MODIS data during 2003-2021, Atmos. Environ., 302, 119722, https://doi.org/10.1016/j.atmosenv.2023.119722, 2023.
Wang, K., Dickinson, R. E., and Liang, S.: Clear Sky Visibility Has Decreased over Land Globally from 1973 to 2007, Science, 323, 1468–1470, https://doi.org/10.1126/science.1167549, 2009.
Wang, K. C., Dickinson, R. E., Su, L., and Trenberth, K. E.: Contrasting trends of mass and optical properties of aerosols over the Northern Hemisphere from 1992 to 2011, Atmos. Chem. Phys., 12, 9387–9398, https://doi.org/10.5194/acp-12-9387-2012, 2012.
Wei, J., Li, Z., Peng, Y., and Sun, L.: MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison, Atmos. Environ., 201, 428–440, https://doi.org/10.1016/j.atmosenv.2018.12.004, 2019.
Wei, J., Li, Z., Sun, L., Peng, Y., Liu, L., He, L., Qin, W., and Cribb, M.: MODIS Collection 6.1 3 km resolution aerosol optical depth product: Global evaluation and uncertainty analysis, Atmos. Environ., 240, 117768, https://doi.org/10.1016/j.atmosenv.2020.117768, 2020.
Welton, E. J., Campbell, J. R., Berkoff, T. A., Spinhirne, J. D., and Starr, D. O.: The micro-pulse lidar network (MPLNET), Frontiers in Optics, https://doi.org/10.1364/fio.2003.mk2, 2002.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009jtecha1281.1, 2009.
Winker, D. M., Tackett, J. L., Getzewich, B. J., Liu, Z., Vaughan, M. A., and Rogers, R. R.: The global 3-D distribution of tropospheric aerosols as characterized by CALIOP, Atmos. Chem. Phys., 13, 3345–3361, https://doi.org/10.5194/acp-13-3345-2013, 2013.
Wu, J., Luo, J., Zhang, L., Xia, L., Zhao, D., and Tang, J.: Improvement of aerosol optical depth retrieval using visibility data in China during the past 50 years, J. Geophys. Res.-Atmos., 119, 13370–13387, https://doi.org/10.1002/2014jd021550, 2014.
Xia, X., Che, H., Zhu, J., Chen, H., Cong, Z., Deng, X., Fan, X., Fu, Y., Goloub, P., and Jiang, H.: Ground-based remote sensing of aerosol climatology in China: Aerosol optical properties, direct radiative effect and its parameterization, Atmos. Environ., 124, 243–251, https://doi.org/10.1016/j.atmosenv.2015.05.071, 2016.
Yang, X., Zhao, C., Yang, Y., and Fan, H.: Long-term multi-source data analysis about the characteristics of aerosol optical properties and types over Australia, Atmos. Chem. Phys., 21, 3803–3825, https://doi.org/10.5194/acp-21-3803-2021, 2021a.
Yang, X., Zhao, C., Yang, Y., Yan, X., and Fan, H.: Statistical aerosol properties associated with fire events from 2002 to 2019 and a case analysis in 2019 over Australia, Atmos. Chem. Phys., 21, 3833–3853, https://doi.org/10.5194/acp-21-3833-2021, 2021b.
Yang, X., Wang, Y., Zhao, C., Fan, H., Yang, Y., Chi, Y., Shen, L., and Yan, X.: Health risk and disease burden attributable to long-term global fine-mode particles, Chemosphere, 287, 132435, https://doi.org/10.1016/j.chemosphere.2021.132435, 2022.
Yang, Y., Ge, B., Chen, X., Yang, W., Wang, Z., Chen, H., Xu, D., Wang, J., Tan, Q., and Wang, Z.: Impact of water vapor content on visibility: Fog-haze conversion and its implications to pollution control, Atmos. Res., 256, 105565, https://doi.org/10.1016/j.atmosres.2021.105565, 2021
Yoon, J., Burrows, J. P., Vountas, M., von Hoyningen-Huene, W., Chang, D. Y., Richter, A., and Hilboll, A.: Changes in atmospheric aerosol loading retrieved from space-based measurements during the past decade, Atmos. Chem. Phys., 14, 6881–6902, https://doi.org/10.5194/acp-14-6881-2014, 2014.
Yoon, J., Pozzer, A., Chang, D. Y., Lelieveld, J., Kim, J., Kim, M., Lee, Y., Koo, J.-H., Lee, J., and Moon, K.: Trend estimates of AERONET-observed and model-simulated AOTs between 1993 and 2013, Atmos. Environ., 125, 33–47, https://doi.org/10.1016/j.atmosenv.2015.10.058, 2016.
Zhang, S., Wu, J., Fan, W., Yang, Q., and Zhao, D.: Review of aerosol optical depth retrieval using visibility data, Earth-Sci. Rev., 200, 102986, https://doi.org/10.1016/j.earscirev.2019.102986, 2020.
Zhang, Z., Wu, W., Wei, J., Song, Y., Yan, X., Zhu, L., and Wang, Q.: Aerosol optical depth retrieval from visibility in China during 1973-2014, Atmos. Environ., 171, 38–48, https://doi.org/10.1016/j.atmosenv.2017.09.004, 2017.
Zhao, A. D., Stevenson, D. S., and Bollasina, M. A.: The role of anthropogenic aerosols in future precipitation extremes over the Asian Monsoon Region, Clim. Dynam., 52, 6257–6278, https://doi.org/10.1007/s00382-018-4514-7, 2019.
Short summary
In this study, we employed a machine learning technique to derive daily aerosol optical depth from hourly visibility observations collected at more than 5000 airports worldwide from 1959 to 2021 combined with reanalysis meteorological parameters.
In this study, we employed a machine learning technique to derive daily aerosol optical depth...
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