Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3641-2023
© 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/essd-15-3641-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
15 Aug 2023
Data description paper |
| 15 Aug 2023
| 15 Aug 2023
A monthly 1° resolution dataset of daytime cloud fraction over the Arctic during 2000–2020 based on multiple satellite products
Xinyan Liu
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
now at: Aerospace Information Research Institute, Henan Academy of Sciences, Henan 450046, China
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Shunlin Liang
Department of Geography, The University of Hong Kong, Hong Kong 999077, China
Ruibo Li
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Xiongxin Xiao
Institute of Geography and Oeschger Center for Climate Change Research, University of Bern, Bern 3012, Switzerland
Rui Ma
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Yichuan Ma
Hubei Key Laboratory of Quantitative Remote Sensing of Land and Atmosphere, School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-16, https://doi.org/10.5194/essd-2024-16, 2024
Preprint under review for ESSD
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This study describes 1-km all-weather instantaneous, and daily mean LST datasets for global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long time-series, global scale. The generated datasets were evaluated by the observations in-situ stations and other LST datasets, and the evaluation indicated the dataset is sufficiently reliable.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
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Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
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Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
Sinan Li, Li Zhang, Jingfeng Xiao, Rui Ma, Xiangjun Tian, and Min Yan
Hydrol. Earth Syst. Sci., 26, 6311–6337, https://doi.org/10.5194/hess-26-6311-2022, https://doi.org/10.5194/hess-26-6311-2022, 2022
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Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
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The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
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Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
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Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
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Numerical experiments with a coupled Earth system model show that large-scale nighttime artificial lighting in tropical forests will significantly increase carbon sink, local temperature, and precipitation, and it requires less energy than direct air carbon capture for capturing 1 t of carbon, suggesting that it could be a powerful climate mitigation option. Side effects include CO2 outgassing after the termination of the nighttime lighting and impacts on local wildlife.
Xiaona Chen, Shunlin Liang, Lian He, Yaping Yang, and Cong Yin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-279, https://doi.org/10.5194/essd-2021-279, 2021
Preprint withdrawn
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The present study developed a 39 year consistent 8-day 0.05 degree gap-free SCE dataset over the NH for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite based on the NOAA AVHRR-SR CDR and several contributory datasets. Compared with published SCE datasets, GLASS SCE has several advantages in snow cover studies, including long time series, finer spatial resolution (especially for years before 2000), and complete spatial coverage.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, https://doi.org/10.5194/essd-13-5087-2021, 2021
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Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
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This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-53, https://doi.org/10.5194/essd-2021-53, 2021
Preprint withdrawn
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The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
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Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
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Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
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Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Han Liu, Peng Gong, Jie Wang, Nicholas Clinton, Yuqi Bai, and Shunlin Liang
Earth Syst. Sci. Data, 12, 1217–1243, https://doi.org/10.5194/essd-12-1217-2020, https://doi.org/10.5194/essd-12-1217-2020, 2020
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We built the first set of 5 km resolution CDRs to record the annual dynamics of global land cover (GLASS-GLC) from 1982 to 2015. The average overall accuracy is 82 %. By conducting long-term change analysis, significant land cover changes and spatiotemporal patterns at various scales were found, which can improve our understanding of global environmental change and help achieve sustainable development goals. This will be further applied in Earth system modeling to facilitate relevant studies.
Aolin Jia, Shunlin Liang, Dongdong Wang, Bo Jiang, and Xiaotong Zhang
Atmos. Chem. Phys., 20, 881–899, https://doi.org/10.5194/acp-20-881-2020, https://doi.org/10.5194/acp-20-881-2020, 2020
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The Tibetan Plateau (TP) plays a vital role in regional and global climate change due to its location and orography. After generating a long-term surface radiation (SR) dataset, we characterized the SR spatiotemporal variation along with temperature. Evidence from multiple data sources indicated that the TP dimming was primarily driven by increased aerosols from human activities, and the cooling effect of aerosol loading offsets TP surface warming, revealing the human impact on regional warming.
Xiongxin Xiao, Tingjun Zhang, Xinyue Zhong, Xiaodong Li, and Yuxing Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-300, https://doi.org/10.5194/tc-2019-300, 2019
Manuscript not accepted for further review
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Seasonal snow cover is an important component of the climate system and global water cycle that stores large amounts of freshwater. Our research attempts to develop a long-term Northern Hemisphere daily snow depth and snow water equivalent product data using a new algorithm applying in historical passive microwave dataset from 1992 to 2016. Our further analysis showed that snow cover has a significant declining trend across the Northern Hemisphere, especially beginning in the new century.
Xiongxin Xiao, Tingjun Zhang, Xinyue Zhong, Xiaodong Li, and Yuxing Li
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-33, https://doi.org/10.5194/tc-2019-33, 2019
Revised manuscript not accepted
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Seasonal snow cover is an important component of the climate system and global water cycle that stores large amounts of freshwater. Our research attempts to develop a long-term Northern Hemisphere daily snow depth and snow water equivalent products using a new algorithm applying in historical passive microwave data sets from 1992 to 2016. Our further analysis showed the snow cover has a significant declining trend across the Northern Hemisphere, especially beginning at the new century.
Tanguang Gao, Jie Liu, Tingjun Zhang, Yuantao Hu, Jianguo Shang, Shufa Wang, Xiongxin Xiao, Chuankun Liu, Shichang Kang, Mika Sillanpää, and Yulan Zhang
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-176, https://doi.org/10.5194/tc-2017-176, 2017
Preprint retracted
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Understanding the interactions between groundwater and surface water in permafrost regions is essential to the understanding of flood frequencies and river water quality of high latitude/altitude basins. Thus, we analyzed the interaction between surface water and groundwater in a permafrost region in the northern Tibetan Plateau by using heat tracing methods.
X. Xie, S. Meng, S. Liang, and Y. Yao
Hydrol. Earth Syst. Sci., 18, 3923–3936, https://doi.org/10.5194/hess-18-3923-2014, https://doi.org/10.5194/hess-18-3923-2014, 2014
Q. Shi and S. Liang
Atmos. Chem. Phys., 14, 5659–5677, https://doi.org/10.5194/acp-14-5659-2014, https://doi.org/10.5194/acp-14-5659-2014, 2014
N. F. Liu, Q. Liu, L. Z. Wang, S. L. Liang, J. G. Wen, Y. Qu, and S. H. Liu
Hydrol. Earth Syst. Sci., 17, 2121–2129, https://doi.org/10.5194/hess-17-2121-2013, https://doi.org/10.5194/hess-17-2121-2013, 2013
T. R. Xu, S. M. Liu, Z. W. Xu, S. Liang, and L. Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-3927-2013, https://doi.org/10.5194/hessd-10-3927-2013, 2013
Preprint withdrawn
Related subject area
Domain: ESSD – Atmosphere | Subject: Atmospheric chemistry and physics
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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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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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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.
Jianzhong Xu, Xinghua Zhang, Wenhui Zhao, Lixiang Zhai, Miao Zhao, Jinsen Shi, Junying Sun, Yanmei Liu, Conghui Xie, Yulong Tan, Kemei Li, Xinlei Ge, Qi Zhang, and Shichang Kang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-506, https://doi.org/10.5194/essd-2023-506, 2023
Revised manuscript accepted for ESSD
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A comprehensive aerosol observation project was carried out in the Tibetan Plateau (TP) and its surrounding 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 researches in atmospheric, cryospheric, and environmental sciences in this unique region.
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.
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 Discuss., https://doi.org/10.5194/essd-2023-414, https://doi.org/10.5194/essd-2023-414, 2023
Revised manuscript accepted for ESSD
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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 HCDF has been demonstrated to outperform the official dataset.
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.
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 Smith
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-306, https://doi.org/10.5194/essd-2023-306, 2023
Revised manuscript accepted for ESSD
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Anthropogenic emissions are the result of transportation, power generation, industrial, residential and commercial activities, waste treatment and agriculture practices. This paper 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.
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 Discuss., https://doi.org/10.5194/essd-2023-331, https://doi.org/10.5194/essd-2023-331, 2023
Revised manuscript accepted for ESSD
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This paper describes a new version, called GGG2020, of a dataset 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 towards site-to-site consistency. This dataset plays a key role in validating space-based greenhouse gas observations and in understanding the carbon cycle.
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.
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.
Robert Pincus, Paul A. Hubanks, Steven Platnick, Kerry Meyer, Robert E. Holz, Denis Botambekov, and Casey J. Wall
Earth Syst. Sci. Data, 15, 2483–2497, https://doi.org/10.5194/essd-15-2483-2023, https://doi.org/10.5194/essd-15-2483-2023, 2023
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This paper describes a new global dataset of cloud properties observed by a specific satellite program created to facilitate comparison with a matching observational proxy used in climate models. Statistics are accumulated over daily and monthly timescales on an equal-angle grid. Statistics include cloud detection, cloud-top pressure, and cloud optical properties. Joint histograms of several variable pairs are also available.
Emma L. Yates, Laura T. Iraci, Susan S. Kulawik, Ju-Mee Ryoo, Josette E. Marrero, Caroline L. Parworth, Jason M. St. Clair, Thomas F. Hanisco, Thao Paul V. Bui, Cecilia S. Chang, and Jonathan M. Dean-Day
Earth Syst. Sci. Data, 15, 2375–2389, https://doi.org/10.5194/essd-15-2375-2023, https://doi.org/10.5194/essd-15-2375-2023, 2023
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The Alpha Jet Atmospheric eXperiment (AJAX) flew scientific flights between 2011 and 2018 providing measurements of carbon dioxide, methane, ozone, formaldehyde, water vapor and meteorological parameters over California and Nevada, USA. AJAX was a multi-year, multi-objective, multi-instrument program with a variety of sampling strategies resulting in an extensive dataset of interest to a wide variety of users. AJAX measurements have been published at https://asdc.larc.nasa.gov/project/AJAX.
Leïla Simon, Valérie Gros, Jean-Eudes Petit, François Truong, Roland Sarda-Estève, Carmen Kalalian, Alexia Baudic, Caroline Marchand, and Olivier Favez
Earth Syst. Sci. Data, 15, 1947–1968, https://doi.org/10.5194/essd-15-1947-2023, https://doi.org/10.5194/essd-15-1947-2023, 2023
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Long-term measurements of volatile organic compounds (VOCs) have been set up to better characterize the atmospheric chemistry at the SIRTA national facility (Paris area, France). Results obtained from the first 2 years (2020–2021) confirm the importance of local sources for short-lived compounds and the role played by meteorology and air mass origins in the long-term analysis of VOCs. They also point to a substantial influence of anthropogenic on the monoterpene loadings.
Ka Lok Chan, Pieter Valks, Klaus-Peter Heue, Ronny Lutz, Pascal Hedelt, Diego Loyola, Gaia Pinardi, Michel Van Roozendael, François Hendrick, Thomas Wagner, Vinod Kumar, Alkis Bais, Ankie Piters, Hitoshi Irie, Hisahiro Takashima, Yugo Kanaya, Yongjoo Choi, Kihong Park, Jihyo Chong, Alexander Cede, Udo Frieß, Andreas Richter, Jianzhong Ma, Nuria Benavent, Robert Holla, Oleg Postylyakov, Claudia Rivera Cárdenas, and Mark Wenig
Earth Syst. Sci. Data, 15, 1831–1870, https://doi.org/10.5194/essd-15-1831-2023, https://doi.org/10.5194/essd-15-1831-2023, 2023
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This paper presents the theoretical basis as well as verification and validation of the Global Ozone Monitoring Experiment-2 (GOME-2) daily and monthly level-3 products.
Kristina Collins, John Gibbons, Nathaniel Frissell, Aidan Montare, David Kazdan, Darren Kalmbach, David Swartz, Robert Benedict, Veronica Romanek, Rachel Boedicker, William Liles, William Engelke, David G. McGaw, James Farmer, Gary Mikitin, Joseph Hobart, George Kavanagh, and Shibaji Chakraborty
Earth Syst. Sci. Data, 15, 1403–1418, https://doi.org/10.5194/essd-15-1403-2023, https://doi.org/10.5194/essd-15-1403-2023, 2023
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This paper summarizes radio data collected by citizen scientists, which can be used to analyze the charged part of Earth's upper atmosphere. The data are collected from several independent stations. We show ways to look at the data from one station or multiple stations over different periods of time and how it can be combined with data from other sources as well. The code provided to make these visualizations will still work if some data are missing or when more data are added in the future.
Melisa Diaz Resquin, Pablo Lichtig, Diego Alessandrello, Marcelo De Oto, Darío Gómez, Cristina Rössler, Paula Castesana, and Laura Dawidowski
Earth Syst. Sci. Data, 15, 189–209, https://doi.org/10.5194/essd-15-189-2023, https://doi.org/10.5194/essd-15-189-2023, 2023
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We explored the performance of the random forest algorithm to predict CO, NOx, PM10, SO2, and O3 air quality concentrations and comparatively assessed the monitored and modeled concentrations during the COVID-19 lockdown phases. We provide the first long-term O3 and SO2 observational dataset for an urban–residential area of Buenos Aires in more than a decade and study the responses of O3 to the reduction in the emissions of its precursors because of its relevance regarding emission control.
Vitali E. Fioletov, Chris A. McLinden, Debora Griffin, Ihab Abboud, Nickolay Krotkov, Peter J. T. Leonard, Can Li, Joanna Joiner, Nicolas Theys, and Simon Carn
Earth Syst. Sci. Data, 15, 75–93, https://doi.org/10.5194/essd-15-75-2023, https://doi.org/10.5194/essd-15-75-2023, 2023
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Sulfur dioxide (SO2) measurements from three satellite instruments were used to update and extend the previously developed global catalogue of large SO2 emission sources. This version 2 of the global catalogue covers the period of 2005–2021 and includes a total of 759 continuously emitting point sources. The catalogue data show an approximate 50 % decline in global SO2 emissions between 2005 and 2021, although emissions were relatively stable during the last 3 years.
Jed O. Kaplan and Katie Hong-Kiu Lau
Earth Syst. Sci. Data, 14, 5665–5670, https://doi.org/10.5194/essd-14-5665-2022, https://doi.org/10.5194/essd-14-5665-2022, 2022
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Global lightning strokes are recorded continuously by a network of ground-based stations. We consolidated these point observations into a map form and provide these as electronic datasets for research purposes. Here we extend our dataset to include lightning observations from 2021.
Haris Rahadianto, Hirokazu Tatano, Masato Iguchi, Hiroshi L. Tanaka, Tetsuya Takemi, and Sudip Roy
Earth Syst. Sci. Data, 14, 5309–5332, https://doi.org/10.5194/essd-14-5309-2022, https://doi.org/10.5194/essd-14-5309-2022, 2022
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We simulated the Taisho (1914) eruption of Sakurajima volcano under various weather conditions to show how a similar eruption would affect contemporary Japan in a worst-case scenario. We provide the dataset of projected airborne ash concentration and deposit over all of Japan to support risk assessment and planning for disaster management. Our work extends previous analyses of local risks to cover distal locations in Japan where a large population could be exposed to devastating impacts.
Xiangyue Chen, Hongchao Zuo, Zipeng Zhang, Xiaoyi Cao, Jikai Duan, Chuanmei Zhu, Zhe Zhang, and Jingzhe Wang
Earth Syst. Sci. Data, 14, 5233–5252, https://doi.org/10.5194/essd-14-5233-2022, https://doi.org/10.5194/essd-14-5233-2022, 2022
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Arid and semi-arid areas are data-scarce aerosol areas. We provide path-breaking, high-resolution, full coverage, and long time series AOD datasets (FEC AOD) to support the atmosphere and related studies in northwestern China. The FEC AOD effectively compensates for the deficiency and constraints of in situ observations and satellite AOD products. Meanwhile, FEC AOD products demonstrate a reliable accuracy and ability to capture long-term change information.
Natalie Kaifler, Bernd Kaifler, Markus Rapp, and David C. Fritts
Earth Syst. Sci. Data, 14, 4923–4934, https://doi.org/10.5194/essd-14-4923-2022, https://doi.org/10.5194/essd-14-4923-2022, 2022
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We measured polar mesospheric clouds (PMCs), our Earth’s highest clouds at the edge of space, with a Rayleigh lidar from a stratospheric balloon. We describe how we derive the cloud’s brightness and discuss the stability of the gondola pointing and the sensitivity of our measurements. We present our high-resolution PMC dataset that is used to study dynamical processes in the upper mesosphere, e.g. regarding gravity waves, mesospheric bores, vortex rings, and Kelvin–Helmholtz instabilities.
Yi Cheng, Shaofei Kong, Liquan Yao, Huang Zheng, Jian Wu, Qin Yan, Shurui Zheng, Yao Hu, Zhenzhen Niu, Yingying Yan, Zhenxing Shen, Guofeng Shen, Dantong Liu, Shuxiao Wang, and Shihua Qi
Earth Syst. Sci. Data, 14, 4757–4775, https://doi.org/10.5194/essd-14-4757-2022, https://doi.org/10.5194/essd-14-4757-2022, 2022
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This work establishes the first emission inventory of carbonaceous aerosols from cooking, fireworks, sacrificial incense, joss paper burning, and barbecue, using multi-source datasets and tested emission factors. These emissions were concentrated in specific periods and areas. Positive and negative correlations between income and emissions were revealed in urban and rural regions. The dataset will be helpful for improving modeling studies and modifying corresponding emission control policies.
Qiang Cui, Yilin Lei, and Bin Chen
Earth Syst. Sci. Data, 14, 4419–4433, https://doi.org/10.5194/essd-14-4419-2022, https://doi.org/10.5194/essd-14-4419-2022, 2022
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This paper calculates the emissions of six kinds of emissions from China’s foreign routes from 2014 to 2019, enriching the existing database. This paper applies the improved BFFM2-FOA-FPM method and ICAO method to calculate the emissions, which can combine CO2 and non-CO2 emissions calculations and calculate the aircraft types' emission intensity.
Mengze Li, Andrea Pozzer, Jos Lelieveld, and Jonathan Williams
Earth Syst. Sci. Data, 14, 4351–4364, https://doi.org/10.5194/essd-14-4351-2022, https://doi.org/10.5194/essd-14-4351-2022, 2022
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We present a northern hemispheric airborne measurement dataset of atmospheric ethane, propane and methane and temporal trends for the time period 2006–2016 in the upper troposphere and lower stratosphere. The growth rates of ethane, methane, and propane in the upper troposphere are -2.24, 0.33, and -0.78 % yr-1, respectively, and in the lower stratosphere they are -3.27, 0.26, and -4.91 % yr-1, respectively, in 2006–2016.
Malika Menoud, Carina van der Veen, Dave Lowry, Julianne M. Fernandez, Semra Bakkaloglu, James L. France, Rebecca E. Fisher, Hossein Maazallahi, Mila Stanisavljević, Jarosław Nęcki, Katarina Vinkovic, Patryk Łakomiec, Janne Rinne, Piotr Korbeń, Martina Schmidt, Sara Defratyka, Camille Yver-Kwok, Truls Andersen, Huilin Chen, and Thomas Röckmann
Earth Syst. Sci. Data, 14, 4365–4386, https://doi.org/10.5194/essd-14-4365-2022, https://doi.org/10.5194/essd-14-4365-2022, 2022
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Emission sources of methane (CH4) can be distinguished with measurements of CH4 stable isotopes. We present new measurements of isotope signatures of various CH4 sources in Europe, mainly anthropogenic, sampled from 2017 to 2020. The present database also contains the most recent update of the global signature dataset from the literature. The dataset improves CH4 source attribution and the understanding of the global CH4 budget.
Patrick Hupe, Lars Ceranna, Alexis Le Pichon, Robin S. Matoza, and Pierrick Mialle
Earth Syst. Sci. Data, 14, 4201–4230, https://doi.org/10.5194/essd-14-4201-2022, https://doi.org/10.5194/essd-14-4201-2022, 2022
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Sound waves with frequencies below the human hearing threshold can travel long distances through the atmosphere. A global network of sensors records such infrasound to detect clandestine nuclear tests in the atmosphere. These data are generally not public. This study provides four data products based on global infrasound signal detections to make infrasound data available to a broad community. This will advance the use of infrasound observations for scientific studies and civilian applications.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
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Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Xiaoli Sun, Paul T. Kolbeck, James B. Abshire, Stephan R. Kawa, and Jianping Mao
Earth Syst. Sci. Data, 14, 3821–3833, https://doi.org/10.5194/essd-14-3821-2022, https://doi.org/10.5194/essd-14-3821-2022, 2022
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We describe the measurement and data processing of the atmospheric backscatter profile data by our CO2 Sounder lidar from the 2017 ASCENDS/ABoVE airborne campaign. It is an additional data set from the column average CO2 mixing ratio measurements from laser sounding. It not only helps to interpret the CO2 mixing ratio measurement but also give a standalone data set for atmosphere backscattering study at 1572 nm wavelength.
Lei Li, Yevgeny Derimian, Cheng Chen, Xindan Zhang, Huizheng Che, Gregory L. Schuster, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Christian Matar, Fabrice Ducos, Yana Karol, Benjamin Torres, Ke Gui, Yu Zheng, Yuanxin Liang, Yadong Lei, Jibiao Zhu, Lei Zhang, Junting Zhong, Xiaoye Zhang, and Oleg Dubovik
Earth Syst. Sci. Data, 14, 3439–3469, https://doi.org/10.5194/essd-14-3439-2022, https://doi.org/10.5194/essd-14-3439-2022, 2022
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A climatology of aerosol composition concentration derived from POLDER-3 observations using GRASP/Component is presented. The conceptual specifics of the GRASP/Component approach are in the direct retrieval of aerosol speciation without intermediate retrievals of aerosol optical characteristics. The dataset of satellite-derived components represents scarce but imperative information for validation and potential adjustment of chemical transport models.
Junting Zhong, Xiaoye Zhang, Ke Gui, Jie Liao, Ye Fei, Lipeng Jiang, Lifeng Guo, Liangke Liu, Huizheng Che, Yaqiang Wang, Deying Wang, and Zijiang Zhou
Earth Syst. Sci. Data, 14, 3197–3211, https://doi.org/10.5194/essd-14-3197-2022, https://doi.org/10.5194/essd-14-3197-2022, 2022
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Historical long-term PM2.5 records with high temporal resolution are essential but lacking for research and environmental management. Here, we reconstruct site-based and gridded PM2.5 datasets at 6-hour intervals from 1960 to 2020 that combine visibility, meteorological data, and emissions based on a machine learning model with extracted spatial features. These two PM2.5 datasets will lay the foundation of research studies associated with air pollution, climate change, and aerosol reanalysis.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
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A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Cited articles
Ackerman, S. A., Holz, R. E., Frey, R., Eloranta, E. W., Maddux, B. C., and
McGill, M.: Cloud detection with MODIS. Part II: Validation, J.
Atmos. Ocean. Tech., 25, 1073–1086, https://doi.org/10.1175/2007jtecha1053.1,
2008.
Beckerman, B. S., Jerrett, M., Serre, M., Martin, R. V., Lee, S.-J., van
Donkelaar, A., Ross, Z., Su, J., and Burnett, R. T.: A Hybrid Approach to
Estimating National Scale Spatiotemporal Variability of PM2.5 in the
Contiguous United States, Environ. Sci. Technol., 47,
7233–7241, https://doi.org/10.1021/es400039u, 2013.
Bogaert, P., Christakos, G., Jerrett, M., and Yu, H. L.: Spatiotemporal
modelling of ozone distribution in the State of California, Atmos.
Environ., 43, 2471–2480, https://doi.org/10.1016/j.atmosenv.2009.01.049, 2009.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and
Zemp, M.: The concept of essential climate variables in support of climate
research, applications, and policy, B. Am. Meteorol. Soc., 95, 1431–1443, https://doi.org/10.1175/bams-d-13-00047.1, 2014.
Brocca, L., Hasenauer, S., Lacava, T., Melone, F., Moramarco, T., Wagner,
W., Dorigo, W., Matgen, P., Martínez-Fernández, J., Llorens, P.,
Latron, J., Martin, C., and Bittelli, M.: Soil moisture estimation through
ASCAT and AMSR-E sensors: An intercomparison and validation study across
Europe, Remote Sens. Environ., 115, 3390–3408, https://doi.org/10.1016/j.rse.2011.08.003, 2011.
Chatterjee, A., Michalak, A. M., Kahn, R. A., Paradise, S. R., Braverman, A.
J., and Miller, C. E.: A geostatistical data fusion technique for merging
remote sensing and ground-based observations of aerosol optical thickness,
J. Geophys. Res.-Atmos., 115, D20207, https://doi.org/10.1029/2009jd013765,
2010.
Christakos, G.: Modern Spatiotemporal Geostatistics, New York, NY: Oxford University Press, 2000.
Christakos, G.: Integrative problem-solving in a time of decadence, Springer
Science & Business Media, https://doi.org/10.1007/978-90-481-9890-0, 2010.
Christakos, G. and Serre, M. L.: BME analysis of spatiotemporal particulate matter
distributions in North Carolina, Atmos. Environ., 34, 3393–3406, 2000.
Christakos, G., Kolovos, A., Serre, M. L., and Vukovich, F.: Total ozone
mapping by integrating databases from remote sensing instruments and
empirical models, IEEE T. Geosci. Remote, 42,
991–1008, https://doi.org/10.1109/Tgrs.2003.822751, 2004.
Claudia, S., William, R., and Stefan, K.: Assessment of Global Cloud Data Sets
from Satellites A Project of the World Climate Research Programme Global
Energy and Water Cycle Experiment (GEWEX) Radiation Panel, World Climate
Research Program Proport, http://climate.org/documents/GEWEX_Cloud_Assessment_2012.pdf (last access: 2019), 2012.
Cressie, N.: Statistics for spatial data, John Wiley & Sons, https://doi.org/10.1002/9781119115151, 2015.
Danso, D. K., Anquetin, S., Diedhiou, A., Kouadio, K., and Kobea, A. T.: Daytime low-level clouds in West Africa – occurrence, associated drivers, and shortwave radiation attenuation, Earth Syst. Dynam., 11, 1133–1152, https://doi.org/10.5194/esd-11-1133-2020, 2020.
Doelling, D. R., Sun, M., Nguyen, L. T., Nordeen, M. L., Haney, C. O.,
Keyes, D. F., and Mlynczak, P. E.: Advances in Geostationary-Derived
Longwave Fluxes for the CERES Synoptic (SYN1deg) Product, J.
Atmos. Ocean. Tech., 33, 503–521, https://doi.org/10.1175/Jtech-D-15-0147.1,
2016.
Drusch, M.: Observation operators for the direct assimilation of TRMM
microwave imager retrieved soil moisture, Geophys. Res. Lett., 32, L15403, https://doi.org/10.1029/2005gl023623, 2005.
Eastman, R. and Warren, S. G.: Arctic Cloud Changes from Surface and
Satellite Observations, J. Climate, 23, 4233–4242, https://doi.org/10.1175/2010jcli3544.1, 2010.
English, J. M., Kay, J. E., Gettelman, A., Liu, X. H., Wang, Y., Zhang, Y. Y., and Chepfer, H.: Contributions of clouds, surface albedos, and mixed phase ice nucleation schemes to Arctic radiation biases in CAM5, J. Climate, 27, 5174–5197, https://doi.org/10.1175/JCLI-D-13-00608.1, 2014.
Enriquez-Alonso, A., Sanchez-Lorenzo, A., Calbo, J., Gonzalez, J. A., and
Norris, J. R.: Cloud cover climatologies in the Mediterranean obtained from
satellites, surface observations, reanalyses, and CMIP5 simulations:
validation and future scenarios, Clim. Dynam., 47, 249–269, https://doi.org/10.1007/s00382-015-2834-4, 2016.
Forbes, R. M. and Ahlgrimm, M.: On the Representation of High-Latitude
Boundary Layer Mixed-Phase Cloud in the ECMWF Global Model, Mon.
Weather Rev., 142, 3425–3445, https://doi.org/10.1175/MWR-D-13-00325.1,
2014.
Freeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C.,
Angel, W. E., Berry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W.,
Ji, Z., Lawrimore, J., Rayner, N. A., Rosenhagen, G., and Smith, S. R.:
ICOADS Release 3.0: a major update to the historical marine climate record,
Int. J. Climatol., 37, 2211–2232, https://doi.org/10.1002/joc.4775, 2017.
Fuentes, M. and Raftery, A. E.: Model evaluation and spatial interpolation
by Bayesian combination of observations with outputs from numerical models,
Biometrics, 61, 36–45, https://doi.org/10.1111/j.0006-341X.2005.030821.x, 2005.
Gao, F., Masek, J., Schwaller, M., and Hall, F.:
On the blending of the Landsat and MODIS surface reflectance: Predicting
daily Landsat surface reflectance, IEEE T. Geosci. Remote, 44, 2207–2218, 2006.
Griffith, D. A.: Statistics for spatial data – CRESSIE, N, Geogr.
Anal., 25, 271–275, 1993.
Hakuba, M. Z., Folini, D., Wild, M., Long, C. N., Schaepman-Strub, G., and
Stephens, G. L.: Cloud effects on atmospheric solar absorption in light of
most recent surface and satellite measurements, in: Radiation Processes in the Atmosphere and Ocean (IRS2016), Proceedings of the International Radiation Symposium (IRC/IAMAS), International Radiation Symposium (IRC/IAMAS), IRS 2016, Auckland, New Zealand, 16–22 April 2016, American Institute of Physics, https://doi.org/10.1063/1.4975543, 2017.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci. Data,
7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
He, J. and Kolovos, A.: Bayesian maximum entropy approach and its
applications: a review, Stoch. Env. Re. Risk
A., 32, 859–877, https://doi.org/10.1007/s00477-017-1419-7, 2017.
Heidinger, A. K., Evan, A. T., Foster, M. J., and Walther, A.: A Naive
Bayesian Cloud-Detection Scheme Derived from CALIPSO and Applied within
PATMOS-x, J. Appl. Meteorol. Climatol., 51, 1129–1144, https://doi.org/10.1175/Jamc-D-11-02.1, 2012.
Heidinger, A. K., Foster, M. J., Walther, A., and Zhao, X. P.: The
Pathfinder Atmospheres-Extended Avhrr Climate Dataset, B.
Am. Meteorol. Soc., 95, 909–922, https://doi.org/10.1175/Bams-D-12-00246.1,
2014.
Hilker, T., Wulder, M. A., Coops, N. C., Linke, J., McDermid, G., Masek, J.
G., Gao, F., and White, J. C.: A new data fusion model for high spatial- and
temporal-resolution mapping of forest disturbance based on Landsat and
MODIS, Remote Sens. Environ., 113, 1613–1627, https://doi.org/10.1016/j.rse.2009.03.007, 2009.
Hollmann, R.: ESA Cloud_cci Product Validation and
Intercomparison Report (PVIR), https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002, 2018.
Hollmann, R., Merchant, C. J., Saunders, R., Downy, C., Buchwitz, M.,
Cazenave, A., Chuvieco, E., Defourny, P., de Leeuw, G., Forsberg, R.,
Holzer-Popp, T., Paul, F., Sandven, S., Sathyendranath, S., van Roozendael,
M., and Wagner, W.: The ESA Climate Change Initiative Satellite Data Records
for Essential Climate Variables, B. Am. Meteorol. Soc., 94, 1541–1552, https://doi.org/10.1175/bams-d-11-00254.1, 2013.
Hu, M. and Xue, M.: Implementation and evaluation of cloud
analysis with WSR-88D reflectivity data for GSI and WRF-ARW, Geophys. Res. Lett., 34, L07808, https://doi.org/10.1029/2006GL028847, 2007.
Huang, Y. Y., Dong, X. Q., Xi, B. K., Dolinar, E. K., Stanfield, R. E., and
Qiu, S. Y.: Quantifying the Uncertainties of Reanalyzed Arctic Cloud and
Radiation Properties Using Satellite Surface Observations, J.
Climate, 30, 8007–8029, https://doi.org/10.1175/Jcli-D-16-0722.1, 2017.
Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker, P. L.,
and Weimer, C.: CALIPSO Lidar Description and Performance Assessment,
J. Atmos. Ocean. Tech., 26, 1214–1228, https://doi.org/10.1175/2009jtecha1223.1, 2009.
Jaynes, E. T.: Information theory and statistical mechanics, Phys. Rev., 106, 620, https://doi.org/10.1103/PhysRev.106.620,
1957.
Jin, W., Fu, R.-D., Ye, M., and Li, J.-X.: Meteorological Cloud Image Fusion
Using Contourlet Transform and Compressed Sensing, International Conference
on Ecological Protection of Lakes-Wetlands-Watershed and Application of 3S
Technology (EPLWW3S 2011), Nanchang, Peoples R China,
25–26 June 2011, WOS:000391516000097, 413–416, 2011.
Karlsson, K.-G. and Devasthale, A.: Inter-Comparison and Evaluation of the
Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA
Cloud CCI V3, ISCCP-HGM, and PATMOS-x, Remote Sens.-Basel, 10, 1567, https://doi.org/10.3390/rs10101567, 2018.
Karlsson, K.-G. and Dybbroe, A.: Evaluation of Arctic cloud products from the EUMETSAT Climate Monitoring Satellite Application Facility based on CALIPSO-CALIOP observations, Atmos. Chem. Phys., 10, 1789–1807, https://doi.org/10.5194/acp-10-1789-2010, 2010.
Karlsson, K.-G. and Håkansson, N.: Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record, Atmos. Meas. Tech., 11, 633–649, https://doi.org/10.5194/amt-11-633-2018, 2018.
Karlsson, K.-G., Riihelä, A., Müller, R., Meirink, J. F., Sedlar, J., Stengel, M., Lockhoff, M., Trentmann, J., Kaspar, F., Hollmann, R., and Wolters, E.: CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data, Atmos. Chem. Phys., 13, 5351–5367, https://doi.org/10.5194/acp-13-5351-2013, 2013.
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017.
Kato, S., Loeb, N. G., Rutan, D. A., Rose, F. G., Sun-Mack, S., Miller, W.
F., and Chen, Y.: Uncertainty Estimate of Surface Irradiances Computed with
MODIS-, CALIPSO-, and CloudSat-Derived Cloud and Aerosol Properties, Surv.
Geophys., 33, 395-412, https://doi.org/10.1007/s10712-012-9179-x, 2012.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X., Smith, W. L., Su, W., and Ham, S.-H.: Surface Irradiances
of Edition 4.0 Clouds and the Earth's Radiant Energy System (CERES) Energy
Balanced and Filled (EBAF) Data Product, J. Climate, 31, 4501–4527, https://doi.org/10.1175/jcli-d-17-0523.1, 2018a.
Kato, S., Rose, F. G., Rutan, D. A., Thorsen, T. J., Loeb, N. G., Doelling,
D. R., Huang, X. L., Smith, W. L., Su, W. Y., and Ham, S. H.: Surface
Irradiances of Edition 4.0 Clouds and the Earth's Radiant Energy System
(CERES) Energy Balanced and Filled (EBAF) Data Product, J. Climate,
31, 4501–4527, https://doi.org/10.1175/Jcli-D-17-0523.1, 2018b.
Kato, S., Rose, F. G., Sun-Mack, S., Miller, W. F., Chen, Y., Rutan, D. A.,
Stephens, G. L., Loeb, N. G., Minnis, P., Wielicki, B. A., Winker, D. M.,
Charlock, T. P., Stackhouse, P. W., Xu, K.-M., and Collins, W. D.:
Improvements of top-of-atmosphere and surface irradiance computations with
CALIPSO-, CloudSat-, and MODIS-derived cloud and aerosol properties, J.
Geophys. Res., 116, D19209, https://doi.org/10.1029/2011jd016050, 2011.
Kennedy, A., Xi, B., Dong, X., and Zib, B. J.: Evaluation and
Intercomparison of Cloud Fraction and Radiative Fluxes in Recent Reanalyses
over the Arctic Using BSRN Surface Observations, J. Climate, 25, 2291–2305, https://doi.org/10.1175/jcli-d-11-00147.1, 2012.
Kenyon, J. S., Moninger, W. R., Smith, T. L., Peckham, S. E., Lin, H.,
Grell, G. A., Dowell, D. C., James, E. P., Olson, J. B., Smirnova, T. G.,
Alexander, C. R., Hu, M., Brown, J. M., Weygandt, S. S., Benjamin, S. G.,
and Manikin, G. S.: A North American Hourly Assimilation and Model Forecast
Cycle: The Rapid Refresh, Mon. Weather Rev., 144, 1669–1694, https://doi.org/10.1175/mwr-d-15-0242.1, 2016.
Kim, D. and Ramanathan, V.: Solar radiation budget and radiative forcing due
to aerosols and clouds, J. Geophys. Res., 113, D02203, https://doi.org/10.1029/2007jd008434, 2008.
Kotarba, A. Z.: Evaluation of ISCCP cloud amount with MODIS observations,
Atmos. Res., 153, 310–317, https://doi.org/10.1016/j.atmosres.2014.09.006, 2015.
Kotarba, A. Z.: Calibration of global MODIS cloud amount using CALIOP cloud profiles, Atmos. Meas. Tech., 13, 4995–5012, https://doi.org/10.5194/amt-13-4995-2020, 2020.
Li, A., Bo, Y., Zhu, Y., Guo, P., Bi, J., and He, Y.: Blending
multi-resolution satellite sea surface temperature (SST) products using
Bayesian maximum entropy method, Remote Sens. Environ., 135, 52–63, https://doi.org/10.1016/j.rse.2013.03.021, 2013.
Li, L., Shi, R., Zhang, L., Zhang, J., and Gao, W.: The data fusion of
aerosol optical thickness using universal kriging and stepwise regression in
East China, Conference on Remote Sensing and Modeling of Ecosystems for
Sustainability XI, San Diego, CA,
18–20 August 2014, WOS:000344548600027, https://doi.org/10.1117/12.2061764, 2014.
Li, S. and Yang, B.: Multifocus image fusion by combining curvelet and
wavelet transform, Pattern Recogn. Lett., 29, 1295–1301, https://doi.org/10.1016/j.patrec.2008.02.002, 2008.
Liu, X., He, T., Sun, L., Xiao, X., Liang, S., and Li, S.: Analysis of
Daytime Cloud Fraction Spatiotemporal Variation over the Arctic from 2000 to
2019 from Multiple Satellite Products, J. Climate, 35, 3995–4023, https://doi.org/10.1175/jcli-d-22-0007.1, 2022.
Liu, Y., Ackerman, S. A., Maddux, B. C., Key, J. R., and Frey, R. A.: Errors
in Cloud Detection over the Arctic Using a Satellite Imager and Implications
for Observing Feedback Mechanisms, J. Climate, 23, 1894–1907, https://doi.org/10.1175/2009jcli3386.1, 2010.
Liu, Y., Wu, W., Jensen, M. P., and Toto, T.: Relationship between cloud radiative forcing, cloud fraction and cloud albedo, and new surface-based approach for determining cloud albedo, Atmos. Chem. Phys., 11, 7155–7170, https://doi.org/10.5194/acp-11-7155-2011, 2011.
Liu, Y., Key, J. R., Liu, Z., Wang, X., and Vavrus, S. J.: A cloudier Arctic
expected with diminishing sea ice, Geophys. Res. Lett., 39,
https://doi.org/10.1029/2012gl051251, 2012.
Liu, Y., Liu, S., and Wang, Z.: A general framework for image fusion based
on multi-scale transform and sparse representation, Inform. Fusion, 24,
147–164, https://doi.org/10.1016/j.inffus.2014.09.004, 2015.
Liu, Y. H., Key, J. R., Ackerman, S. A., Mace, G. G., and Zhang, Q. Q.:
Arctic cloud macrophysical characteristics from CloudSat and CALIPSO, Remote
Sens. Environ., 124, 159–173, https://doi.org/10.1016/j.rse.2012.05.006, 2012.
Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.: Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425–436, https://doi.org/10.5194/hess-15-425-2011, 2011.
Loyola R, D. G., Thomas, W., Spurr, R., and Mayer, B.: Global patterns in
daytime cloud properties derived from GOME backscatter UV-VIS measurements,
Int. J. Remote Sens., 31, 4295–4318, https://doi.org/10.1080/01431160903246741, 2010.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Marchant, B., Platnick, S., Meyer, K., and Wind, G.: Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products, Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, 2020.
Miao, Q. and Wang, B.: A Novel Image Fusion Method Using Contourlet
Transform, International Conference on Communications, Communications, Circuits and Systems, Guilin, China, 548–552, https://doi.org/10.1109/ICCCAS.2006.284696, 2006.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen,
Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L., Ayers,
J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V., Takano, Y.,
Liou, K. N., Xie, Y., and Yang, P.: CERES Edition-2 Cloud Property
Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data-Part I: Algorithms,
IEEE T. Geosci. Remote, 49, 4374–4400, https://doi.org/10.1109/tgrs.2011.2144601, 2011.
Nazelle, A. D., Arunachalam, S., and Serre, M. L.: Bayesian maximum entropy
integration of ozone observations and model predictions: an application for
attainment demonstration in North Carolina, Environ. Sci. Technol., 44,
5707–5713, https://doi.org/10.1021/es100228w, 2010.
Nie, S., Wu, T., Luo, Y., Deng, X., Shi, X., Wang, Z., Liu, X., and Huang,
J.: A strategy for merging objective estimates of global daily precipitation
from gauge observations, satellite estimates, and numerical predictions,
Adv. Atmos. Sci., 33, 889–904, https://doi.org/10.1007/s00376-016-5223-y,
2016.
Paul, A. H.: Collection 6.1 Change Summary Document MODIS Atmosphere Level-3
Algorithm and Global Products, https://atmosphere-imager.gsfc.nasa.gov/sites/default/files/ModAtmo/L3_C61_Changes_v2.pdf (last access: 2021), 2017.
Philipp, D., Stengel, M., and Ahrens, B.: Analyzing the Arctic Feedback
Mechanism between Sea Ice and Low-Level Clouds Using 34 Years of Satellite
Observations, J. Climate, 33, 7479–7501, https://doi.org/10.1175/jcli-d-19-0895.1,
2020.
Poulsen, C. J., Tabor, C., and White, J.: Response to Comment on “Long-term
climate forcing by atmospheric oxygen concentrations”, Science, 353, 132, https://doi.org/10.1126/science.aad8550, 2016.
Qian, Y., Long, C. N., Wang, H., Comstock, J. M., McFarlane, S. A., and Xie, S.: Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations, Atmos. Chem. Phys., 12, 1785–1810, https://doi.org/10.5194/acp-12-1785-2012, 2012.
Ramanathan, V., Cess, R. D., Harrison, E. F., Minnis, P., Barkstrom, B. R.,
Ahmad, E., and Hartmann, D.: Cloud-Radiative Forcing and Climate – Results
from the Earth Radiation Budget Experiment, Science, 243, 57–63, https://doi.org/10.1126/science.243.4887.57, 1989.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:Aiucfi>2.0.Co;2, 1999.
Savelyeva, E., Utkin, S., Kazakov, S., and Demyanov, V.: Modeling Spatial
Uncertainty for Locally Uncertain Data, 7th International Conference on
Geostatistics for Environmental Applications, Southampton, England, WOS:000288481100026, https://doi.org/10.1007/978-90-481-2322-3_26, 2010.
Shupe, M. D., Turner, D. D., Walden, V. P., Bennartz, R., Cadeddu, M. P.,
Castellani, B. B., Cox, C. J., Hudak, D. R., Kulie, M. S., Miller, N. B.,
Neely, R. R., Neff, W. D., and Rowe, P. M.: HIGH AND DRY New Observations of
Tropospheric and Cloud Properties above the Greenland Ice Sheet, B. Am.
Meteorol. Soc., 94, 169–186, https://doi.org/10.1175/Bams-D-11-00249.1, 2013.
Sledd, A. and L'Ecuyer, T. S.: Emerging Trends in Arctic Solar Absorption,
Geophys. Res. Lett., 48, e2021GL095813, https://doi.org/10.1029/2021gl095813, 2021.
Spadavecchia, L. and Williams, M.: Can spatio-temporal geostatistical
methods improve high resolution regionalisation of meteorological
variables?, Agr. Forest Meteorol., 149, 1105–1117, https://doi.org/10.1016/j.agrformet.2009.01.008, 2009.
Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., Christensen, M., Carbajal Henken, C., Preusker, R., Fischer, J., Devasthale, A., Willén, U., Karlsson, K.-G., McGarragh, G. R., Proud, S., Povey, A. C., Grainger, R. G., Meirink, J. F., Feofilov, A., Bennartz, R., Bojanowski, J. S., and Hollmann, R.: Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project, Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, 2017.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen,
C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao,
G.: Assessment of Global Cloud Datasets from Satellites: Project and
Database Initiated by the GEWEX Radiation Panel, B. Am.
Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/bams-d-12-00117.1, 2013.
Sun, B. M., Free, M., Yoo, H. L., Foster, M. J., Heidinger, A., and
Karlsson, K. G.: Variability and Trends in U.S. Cloud Cover: ISCCP,
PATMOS-x, and CLARA-A1 Compared to Homogeneity-Adjusted Weather
Observations, J. Climate, 28, 4373–4389, https://doi.org/10.1175/jcli-d-14-00805.1,
2015.
Tang, Q., Bo, Y., and Zhu, Y.: Spatiotemporal fusion of multiple-satellite
aerosol optical depth (AOD) products using Bayesian maximum entropy method,
J. Geophys. Res.-Atmos., 121, 4034–4048, https://doi.org/10.1002/2015jd024571, 2016.
Tiedtke, M.: Representation of Clouds in Large-Scale Models, Mon. Weather
Rev., 121, 3040–3061, https://doi.org/10.1175/1520-0493(1993)121<3040:Rocils>2.0.Co;2, 1993.
Toll, V., Christensen, M., Quaas, J., and Bellouin, N.: Weak average
liquid-cloud-water response to anthropogenic aerosols, Nature, 572, 51–55, https://doi.org/10.1038/s41586-019-1423-9, 2019.
Trepte, Q. Z., Bedka, K. M., Chee, T. L., Minnis, P., Sun-Mack, S., Yost, C.
R., Chen, Y., Jin, Z., Hong, G., Chang, F.-L., and Smith, W. L.: Global
Cloud Detection for CERES Edition 4 Using Terra and Aqua MODIS Data, IEEE
T. Geosci. Remote, 57, 9410–9449, https://doi.org/10.1109/tgrs.2019.2926620, 2019.
Tzallas, V., Hatzianastassiou, N., Benas, N., Meirink, J. F., Matsoukas, C.,
Stackhouse, P., and Vardavas, I.: Evaluation of CLARA-A2 and ISCCP-H Cloud
Cover Climate Data Records over Europe with ECA&D Ground-Based
Measurements, Remote Sens.-Basel, 11, 212, https://doi.org/10.3390/rs11020212, 2019.
Van Tricht, K., Lhermitte, S., Lenaerts, J. T. M., Gorodetskaya, I. V.,
L'Ecuyer, T. S., Noel, B., van den Broeke, M. R., Turner, D. D., and van
Lipzig, N. P. M.: Clouds enhance Greenland ice sheet meltwater runoff,
Nat. Commun., 7, 10266,
https://doi.org/10.1038/ncomms10266, 2016.
Vaughan, M., Young, S., Winker, D., Powell, K., Omar, A., Liu, Z. Y., Hu, Y.
X., and Hostetler, C.: Fully automated analysis of space-based lidar data:
an overview of the CALIPSO retrieval algorithms and data products, Proc. SPIE,
5575, 16–30, https://doi.org/10.1117/12.572024, 2004.
Vaughan, M. A., Powell, K. A., Kuehn, R. E., Young, S. A., Winker, D. M.,
Hostetler, C. A., Hunt, W. H., Liu, Z. Y., McGill, M. J., and Getzewich, B.
J.: Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO
Lidar Measurements, J. Atmos. Ocean. Tech., 26,
2034–2050, https://doi.org/10.1175/2009jtecha1228.1, 2009.
Vignesh, P. P., Jiang, J. H., Kishore, P., Su, H., Smay, T., Brighton, N.,
and Velicogna, I.: Assessment of CMIP6 Cloud Fraction and Comparison with
Satellite Observations, Earth Space Sci., 7, e2019EA000975, https://doi.org/10.1029/2019ea000975,
2020.
Walsh, J. E., Chapman, W. L., and Portis, D. H.: Arctic Cloud Fraction and
Radiative Fluxes in Atmospheric Reanalyses, J. Climate, 22,
2316–2334, https://doi.org/10.1175/2008jcli2213.1, 2009.
Wang, D., Bi, S., Wang, B., and Yan, J.: Satellite cloud image fusion based
on regional feature with nonsubsampled contourlet transform, J.
Comput. Appl., 32, 2585–2587, 2012.
Winker, D. M., Hunt, W. H., and McGill, M. J.: Initial performance
assessment of CALIOP, Geophys. Res. Lett., 34, L19803,
https://doi.org/10.1029/2007gl030135, 2007.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y. X., Powell, K. A., Liu, Z.
Y., 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.
Woodruff, S. D., Diaz, H. F., Worley, S. J., Reynolds, R. W., and Lubker, S.
J.: Early ship observational data and ICOADS, Climatic Change, 73, 169–194, https://doi.org/10.1007/s10584-005-3456-3, 2005.
Wu, W., Liu, Y. G., Jensen, M. P., Toto, T., Foster, M. J., and Long, C. N.:
A comparison of multiscale variations of decade-long cloud fractions from
six different platforms over the Southern Great Plains in the United States,
J. Geophys. Res.-Atmos., 119, 3438–3459, https://doi.org/10.1002/2013jd019813, 2014.
Xia, X., Zhao, B., Zhang, T., Wang, L., Gu, Y., Liou, K.-N., Mao, F., Liu,
B., Bo, Y., Huang, Y., Dong, J., Gong, W., and Zhu, Z.: Satellite-Derived
Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing
Based on Bayesian Maximum Entropy, IEEE T. Geosci.
Remote, 60, 1–13, https://doi.org/10.1109/tgrs.2021.3051799, 2022.
Xie, S. C., McCoy, R. B., Klein, S. A., Cederwall, R. T., Wiscombe, W. J.,
Clothiaux, E. E., Gaustad, K. L., Golaz, J. C., Hall, S. D., Jensen, M. P.,
Johnson, K. L., Lin, Y. L., Long, C. N., Mather, J. H., McCord, R. A.,
McFarlane, S. A., Palanisamy, G., Shi, Y., and Turner, D. D. D.: ARM CLIMATE
MODELING BEST ESTIMATE DATA A New Data Product for Climate Studies, B.
Am. Meteorol. Soc., 91, 13–20, https://doi.org/10.1175/2009bams2891.1,
2010.
Xu, S. and Cheng, J.: A new land surface temperature fusion strategy based
on cumulative distribution function matching and multiresolution Kalman
filtering, Remote Sens. Environ., 254, 112256, https://doi.org/10.1016/j.rse.2020.112256,
2021.
Xu, S., Cheng, J., and Zhang, Q.: Reconstructing All-Weather Land Surface
Temperature Using the Bayesian Maximum Entropy Method Over the Tibetan
Plateau and Heihe River Basin, IEEE J. Sel. Top. Appl., 12, 3307–3316, https://doi.org/10.1109/jstars.2019.2921924, 2019.
Yang, J. and Hu, M.: Filling the missing data gaps of daily MODIS AOD using
spatiotemporal interpolation, Sci. Total Environ., 633,
677–683, https://doi.org/10.1016/j.scitotenv.2018.03.202, 2018.
Yeo, H., Kim, M.-H., Son, S.-W., Jeong, J.-H., Yoon, J.-H., Kim, B.-M., and
Kim, S.-W.: Arctic cloud properties and associated radiative effects in the
three newer reanalysis datasets (ERA5, MERRA-2, JRA-55): Discrepancies and
possible causes, Atmos. Res., 270, 106080, https://doi.org/10.1016/j.atmosres.2022.106080,
2022.
Young, A. H., Knapp, K. R., Inamdar, A., Hankins, W., and Rossow, W. B.: The International Satellite Cloud Climatology Project H-Series climate data record product, Earth Syst. Sci. Data, 10, 583–593, https://doi.org/10.5194/essd-10-583-2018, 2018.
Yu, H.-L. and Wang, C.-H.: Retrospective prediction of intraurban
spatiotemporal distribution of PM2.5 in Taipei, Atmos. Environ., 44,
3053–3065, https://doi.org/10.1016/j.atmosenv.2010.04.030, 2010.
Zhang, C.-J., Chen, Y., Duanmu, C., and Feng, H.-J.: Multi-channel satellite
cloud image fusion in the tetrolet transform domain, Int. J. Remote Sens., 35,
8138–8168, https://doi.org/10.1080/01431161.2014.980918, 2014.
Zhang, Q., Cheng, J., and Liang, S.: Deriving high-quality surface
emissivity spectra from atmospheric infrared sounder data using cumulative
distribution function matching and principal component analysis regression,
Remote Sens. Environ., 211, 388–399, https://doi.org/10.1016/j.rse.2018.04.033,
2018.
Zhu, X., Chen, J., Gao, F., Chen, X., and Masek, J. G.: An enhanced spatial
and temporal adaptive reflectance fusion model for complex heterogeneous
regions, Remote Sens. Environ., 114, 2610–2623, https://doi.org/10.1016/j.rse.2010.05.032, 2010.
Short summary
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.
We proposed a data fusion strategy that combines the complementary features of...
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