Articles | Volume 15, issue 8
https://doi.org/10.5194/essd-15-3597-2023
© 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-15-3597-2023
© 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
| 
10 Aug 2023
Data description paper |
| 10 Aug 2023
| 10 Aug 2023
Seamless mapping of long-term (2010–2020) daily global XCO2 and XCH4 from the Greenhouse Gases Observing Satellite (GOSAT), Orbiting Carbon Observatory 2 (OCO-2), and CAMS global greenhouse gas reanalysis (CAMS-EGG4) with a spatiotemporally self-supervised fusion method
Yuan Wang
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
Tongwen Li
School of Geospatial Engineering and Science, Sun Yat-sen University,
Guangzhou, Guangdong, 519082, China
Yuanjian Yang
School of Atmospheric Physics, Nanjing University of Information Science & Technology, Nanjing, Jiangsu, 210044, China
Siqin Zhou
School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei, 430079, China
Liangpei Zhang
The State Key Laboratory of Information Engineering in Surveying,
Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, 430079, China
Related authors
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
Short summary
Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Tao Shi, Yuanjian Yang, Ping Qi, and Simone Lolli
EGUsphere, https://doi.org/10.5194/egusphere-2024-1200, https://doi.org/10.5194/egusphere-2024-1200, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
In the background of global warming and the rapid urbanization, heat wave have emerged as increasingly frequent occurrences. Despite this, the specific roles played by local circulation patterns and urban morphology in the synergistic interaction between HW and CUHI remain elusive. To address this gap, this paper used automatic weather stations data and meachine learning model to delve into the spatiotemporal patterns governing the intricate interactions between HW and CUHI.
Chaman Gul, Shichang Kang, Yuanjian Yang, Xinlei Ge, and Dong Guo
EGUsphere, https://doi.org/10.5194/egusphere-2024-1144, https://doi.org/10.5194/egusphere-2024-1144, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Long-term variations in upper atmospheric temperature and water vapor in the selected domains of time and space are presented. The temperature during the past two decades showed a cooling trend and water vapor showed an increasing trend and had an inverse relation with temperature in selected domains of space and time. Seasonal temperature variations are distinct, with a summer minimum and a winter maximum. Our results can be an early warning indication for future climate change.
Tao Shi, Yuanjian Yang, Gaopeng Lu, Zuofang Zheng, Yucheng Zi, Ye Tian, Lei Liu, and Simone Lolli
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2024-3, https://doi.org/10.5194/acp-2024-3, 2024
Preprint under review for ACP
Short summary
Short summary
This study found that CG lightning tends to cluster around the outer boundaries of large cities, but gathers within small cities. The urban underlying surface can contribute to the separation of cold pools, weakening vertical airflow, and triggering thunderstorm bifurcation. The density of buildings also influences the barrier effect. This research provides a foundation for predicting and assessing urban CG lightning risks.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
Short summary
Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Yilin Chen, Yuanjian Yang, and Meng Gao
Atmos. Meas. Tech., 16, 1279–1294, https://doi.org/10.5194/amt-16-1279-2023, https://doi.org/10.5194/amt-16-1279-2023, 2023
Short summary
Short summary
The Guangdong–Hong Kong–Macao Greater Bay Area suffers from summertime air pollution events related to typhoons. The present study leverages machine learning to predict typhoon-associated air quality over the area. The model evaluation shows that the model performs excellently. Moreover, the change in meteorological drivers of air quality on typhoon days and non-typhoon days suggests that air pollution control strategies should have different focuses on typhoon days and non-typhoon days.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
Short summary
Short summary
We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Fan Wang, Gregory R. Carmichael, Jing Wang, Bin Chen, Bo Huang, Yuguo Li, Yuanjian Yang, and Meng Gao
Atmos. Chem. Phys., 22, 13341–13353, https://doi.org/10.5194/acp-22-13341-2022, https://doi.org/10.5194/acp-22-13341-2022, 2022
Short summary
Short summary
Unprecedented urbanization in China has led to serious urban heat island (UHI) issues, exerting intense heat stress on urban residents. We find diverse influences of aerosol pollution on urban heat island intensity (UHII) under different circulations. Our results also highlight the role of black carbon in aggravating UHI, especially during nighttime. It could thus be targeted for cooperative management of heat islands and aerosol pollution.
Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun
Earth Syst. Sci. Data, 14, 4473–4488, https://doi.org/10.5194/essd-14-4473-2022, https://doi.org/10.5194/essd-14-4473-2022, 2022
Short summary
Short summary
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
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
Short summary
Short summary
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.
Lian Zong, Yuanjian Yang, Haiyun Xia, Meng Gao, Zhaobin Sun, Zuofang Zheng, Xianxiang Li, Guicai Ning, Yubin Li, and Simone Lolli
Atmos. Chem. Phys., 22, 6523–6538, https://doi.org/10.5194/acp-22-6523-2022, https://doi.org/10.5194/acp-22-6523-2022, 2022
Short summary
Short summary
Heatwaves (HWs) paired with higher ozone (O3) concentration at surface level pose a serious threat to human health. Taking Beijing as an example, three unfavorable synoptic weather patterns were identified to dominate the compound HW and O3 pollution events. Under the synergistic stress of HWs and O3 pollution, public mortality risk increased, and synoptic patterns and urbanization enhanced the compound risk of events in Beijing by 33.09 % and 18.95 %, respectively.
Shaohui Zhou, Yuanjian Yang, Zhiqiu Gao, Xingya Xi, Zexia Duan, and Yubin Li
Atmos. Meas. Tech., 15, 757–773, https://doi.org/10.5194/amt-15-757-2022, https://doi.org/10.5194/amt-15-757-2022, 2022
Short summary
Short summary
Our research has determined the possible relationship between Weibull natural wind mesoscale parameter c and shape factor k with height under the conditions of a desert steppe terrain in northern China, which has great potential in wind power generation. We have gained an enhanced understanding of the seasonal changes in the surface roughness of the desert grassland and the changes in the incoming wind direction.
Shihan Chen, Yuanjian Yang, Fei Deng, Yanhao Zhang, Duanyang Liu, Chao Liu, and Zhiqiu Gao
Atmos. Meas. Tech., 15, 735–756, https://doi.org/10.5194/amt-15-735-2022, https://doi.org/10.5194/amt-15-735-2022, 2022
Short summary
Short summary
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.
Xinyan Li, Yuanjian Yang, Jiaqin Mi, Xueyan Bi, You Zhao, Zehao Huang, Chao Liu, Lian Zong, and Wanju Li
Atmos. Meas. Tech., 14, 7007–7023, https://doi.org/10.5194/amt-14-7007-2021, https://doi.org/10.5194/amt-14-7007-2021, 2021
Short summary
Short summary
A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.
Xiaobin Guan, Huanfeng Shen, Yuchen Wang, Dong Chu, Xinghua Li, Linwei Yue, Xinxin Liu, and Liangpei Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-156, https://doi.org/10.5194/essd-2021-156, 2021
Preprint withdrawn
Short summary
Short summary
This study generated the first global 1-km continuous NDVI product (STFLNDVI) for 4-decades by fusing multi-source satellite products. Simulated and real-data assessments confirmed the satisfactory and stable accuracy of STFLNDVI regarding spatial details and temporal variations. STFLNDVI is an ideal solution to the trade-off between spatial resolution and time coverage in current NDVI products, which of great significance for long-term regional and global vegetation and climate change studies.
Lian Zong, Yuanjian Yang, Meng Gao, Hong Wang, Peng Wang, Hongliang Zhang, Linlin Wang, Guicai Ning, Chao Liu, Yubin Li, and Zhiqiu Gao
Atmos. Chem. Phys., 21, 9105–9124, https://doi.org/10.5194/acp-21-9105-2021, https://doi.org/10.5194/acp-21-9105-2021, 2021
Short summary
Short summary
In recent years, summer O3 pollution over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 pollution can co-occur. However, the synoptic weather pattern (SWP) related to this compound pollution remains unclear. Regional PM2.5 and O3 compound pollution is characterized by various SWPs with different dominant factors. Our findings provide insights into the regional co-occurring high PM2.5 and O3 levels via the effects of certain meteorological factors.
Qiang Zhang, Qiangqiang Yuan, Jie Li, Yuan Wang, Fujun Sun, and Liangpei Zhang
Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, https://doi.org/10.5194/essd-13-1385-2021, 2021
Short summary
Short summary
Acquired daily soil moisture products are always incomplete globally (just about 30 %–80 % coverage ratio) due to the satellite orbit coverage and the limitations of soil moisture retrieval algorithms. To solve this inevitable problem, we generate long-term seamless global daily (SGD) AMSR2 soil moisture productions from 2013 to 2019. These productions are significant for full-coverage global daily hydrologic monitoring, rather than averaging as the monthly–quarter–yearly results.
Yuan Wang, Qiangqiang Yuan, Tongwen Li, Siyu Tan, and Liangpei Zhang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1004, https://doi.org/10.5194/acp-2020-1004, 2020
Revised manuscript not accepted
Short summary
Short summary
Estimating ambient PM2.5 and PM10 considering their precursors and chemical compositions instead of AOD products; Both remote sensing (Sentinel-5P) and assimilated data (GEOS-FP) are adopted; Sample-based Cross-Validation R2s and RMSEs are 0.93 (0.9) and 8.982 (17.604) μg/m3 for PM2.5 (PM10), respectively; Achieving better performance compared to the baseline (AOD-based) in different cases (e.g., overall and seasonal).
C. Zhou, J. Li, H. Shen, and Q. Yuan
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2020, 101–107, https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020, https://doi.org/10.5194/isprs-annals-V-5-2020-101-2020, 2020
Ziqiang Ma, Jintao Xu, Siyu Zhu, Jun Yang, Guoqiang Tang, Yuanjian Yang, Zhou Shi, and Yang Hong
Earth Syst. Sci. Data, 12, 1525–1544, https://doi.org/10.5194/essd-12-1525-2020, https://doi.org/10.5194/essd-12-1525-2020, 2020
Short summary
Short summary
Focusing on the potential drawbacks in generating the state-of-the-art IMERG data in both the TRMM and GPM era, a new daily calibration algorithm on IMERG was proposed, as well as a new AIMERG precipitation dataset (0.1°/half-hourly, 2000–2015, Asia) with better quality than IMERG for Asian scientific research and applications. The proposed daily calibration algorithm for GPM is promising and applicable in generating the future IMERG in either an operational scheme or a retrospective manner.
Linlin Wang, Junkai Liu, Zhiqiu Gao, Yubin Li, Meng Huang, Sihui Fan, Xiaoye Zhang, Yuanjian Yang, Shiguang Miao, Han Zou, Yele Sun, Yong Chen, and Ting Yang
Atmos. Chem. Phys., 19, 6949–6967, https://doi.org/10.5194/acp-19-6949-2019, https://doi.org/10.5194/acp-19-6949-2019, 2019
Short summary
Short summary
Urban boundary layer (UBL) affects the physical and chemical processes of the pollutants, and UBL structure can also be altered by pollutants. This paper presents the interactions between air pollution and the UBL structure by using the field data mainly collected from a 325 m meteorology tower, as well as from a Doppler wind lidar, during a severe heavy pollution event that occurred during 1–4 December 2016 in Beijing.
Xinghua Li, Yinghong Jing, Huanfeng Shen, and Liangpei Zhang
Hydrol. Earth Syst. Sci., 23, 2401–2416, https://doi.org/10.5194/hess-23-2401-2019, https://doi.org/10.5194/hess-23-2401-2019, 2019
Short summary
Short summary
This paper is a review article on the cloud removal methods of MODIS snow cover products.
Tongwen Li, Chengyue Zhang, Huanfeng Shen, Qiangqiang Yuan, and Liangpei Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 143–147, https://doi.org/10.5194/isprs-annals-IV-3-143-2018, https://doi.org/10.5194/isprs-annals-IV-3-143-2018, 2018
Zhiwei Li, Huanfeng Shen, Yancong Wei, Qing Cheng, and Qiangqiang Yuan
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 149–152, https://doi.org/10.5194/isprs-annals-IV-3-149-2018, https://doi.org/10.5194/isprs-annals-IV-3-149-2018, 2018
X. Meng, H. Shen, Q. Yuan, H. Li, and L. Zhang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 831–835, https://doi.org/10.5194/isprs-archives-XLII-2-W7-831-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-831-2017, 2017
Hongyan Zhang, Han Zhai, Wenzhi Liao, Liqin Cao, Liangpei Zhang, and Aleksandra Pižurica
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 945–948, https://doi.org/10.5194/isprs-archives-XLI-B3-945-2016, https://doi.org/10.5194/isprs-archives-XLI-B3-945-2016, 2016
Tianzhu Xiang, Gui-Song Xia, and Liangpei Zhang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-3, 287–294, https://doi.org/10.5194/isprs-annals-III-3-287-2016, https://doi.org/10.5194/isprs-annals-III-3-287-2016, 2016
Related subject area
Domain: ESSD – Global | Subject: Atmospheric chemistry and physics
Four decades of global surface albedo estimates in the third edition of the CM SAF cLoud, Albedo and surface Radiation (CLARA) climate data record
Spatially coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: the NASA ACTIVATE dataset
An investigation of the global uptake of CO2 by lime from 1930 to 2020
Isotopic measurements in water vapor, precipitation, and seawater during EUREC4A
Global Carbon Budget 2022
Aku Riihelä, Emmihenna Jääskeläinen, and Viivi Kallio-Myers
Earth Syst. Sci. Data, 16, 1007–1028, https://doi.org/10.5194/essd-16-1007-2024, https://doi.org/10.5194/essd-16-1007-2024, 2024
Short summary
Short summary
We describe a new climate data record describing the surface albedo, or reflectivitity, of Earth's surface (called CLARA-A3 SAL). The climate data record spans over 4 decades of satellite observations, beginning in 1979. We conduct a quality assessment of the generated data, comparing them against other satellite data and albedo observations made on the ground. We find that the new data record in general matches surface observations well and is stable through time.
Armin Sorooshian, Mikhail D. Alexandrov, Adam D. Bell, Ryan Bennett, Grace Betito, Sharon P. Burton, Megan E. Buzanowicz, Brian Cairns, Eduard V. Chemyakin, Gao Chen, Yonghoon Choi, Brian L. Collister, Anthony L. Cook, Andrea F. Corral, Ewan C. Crosbie, Bastiaan van Diedenhoven, Joshua P. DiGangi, Glenn S. Diskin, Sanja Dmitrovic, Eva-Lou Edwards, Marta A. Fenn, Richard A. Ferrare, David van Gilst, Johnathan W. Hair, David B. Harper, Miguel Ricardo A. Hilario, Chris A. Hostetler, Nathan Jester, Michael Jones, Simon Kirschler, Mary M. Kleb, John M. Kusterer, Sean Leavor, Joseph W. Lee, Hongyu Liu, Kayla McCauley, Richard H. Moore, Joseph Nied, Anthony Notari, John B. Nowak, David Painemal, Kasey E. Phillips, Claire E. Robinson, Amy Jo Scarino, Joseph S. Schlosser, Shane T. Seaman, Chellappan Seethala, Taylor J. Shingler, Michael A. Shook, Kenneth A. Sinclair, William L. Smith Jr., Douglas A. Spangenberg, Snorre A. Stamnes, Kenneth L. Thornhill, Christiane Voigt, Holger Vömel, Andrzej P. Wasilewski, Hailong Wang, Edward L. Winstead, Kira Zeider, Xubin Zeng, Bo Zhang, Luke D. Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data, 15, 3419–3472, https://doi.org/10.5194/essd-15-3419-2023, https://doi.org/10.5194/essd-15-3419-2023, 2023
Short summary
Short summary
The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions. HU-25 Falcon and King Air aircraft conducted systematic and spatially coordinated flights over the northwest Atlantic Ocean. This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes.
Longfei Bing, Mingjing Ma, Lili Liu, Jiaoyue Wang, Le Niu, and Fengming Xi
Earth Syst. Sci. Data, 15, 2431–2444, https://doi.org/10.5194/essd-15-2431-2023, https://doi.org/10.5194/essd-15-2431-2023, 2023
Short summary
Short summary
We provided CO2 uptake inventory for global lime materials from 1930–2020, The majority of CO2 uptake was from the lime in China.
Our dataset and the accounting mathematical model may serve as a set of tools to improve the CO2 emission inventories and provide data support for policymakers to formulate scientific and reasonable policies under
carbon neutraltarget.
Adriana Bailey, Franziska Aemisegger, Leonie Villiger, Sebastian A. Los, Gilles Reverdin, Estefanía Quiñones Meléndez, Claudia Acquistapace, Dariusz B. Baranowski, Tobias Böck, Sandrine Bony, Tobias Bordsdorff, Derek Coffman, Simon P. de Szoeke, Christopher J. Diekmann, Marina Dütsch, Benjamin Ertl, Joseph Galewsky, Dean Henze, Przemyslaw Makuch, David Noone, Patricia K. Quinn, Michael Rösch, Andreas Schneider, Matthias Schneider, Sabrina Speich, Bjorn Stevens, and Elizabeth J. Thompson
Earth Syst. Sci. Data, 15, 465–495, https://doi.org/10.5194/essd-15-465-2023, https://doi.org/10.5194/essd-15-465-2023, 2023
Short summary
Short summary
One of the novel ways EUREC4A set out to investigate trade wind clouds and their coupling to the large-scale circulation was through an extensive network of isotopic measurements in water vapor, precipitation, and seawater. Samples were taken from the island of Barbados, from aboard two aircraft, and from aboard four ships. This paper describes the full collection of EUREC4A isotopic in situ data and guides readers to complementary remotely sensed water vapor isotope ratios.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Cited articles
Agustí-Panareda, A., Barré, J., Massart, S., Inness, A., Aben, I., Ades, M., Baier, B. C., Balsamo, G., Borsdorff, T., Bousserez, N., Boussetta, S., Buchwitz, M., Cantarello, L., Crevoisier, C., Engelen, R., Eskes, H., Flemming, J., Garrigues, S., Hasekamp, O., Huijnen, V., Jones, L., Kipling, Z., Langerock, B., McNorton, J., Meilhac, N., Noël, S., Parrington, M., Peuch, V.-H., Ramonet, M., Razinger, M., Reuter, M., Ribas, R., Suttie, M., Sweeney, C., Tarniewicz, J., and Wu, L.: Technical note: The CAMS greenhouse gas reanalysis from 2003 to 2020, Atmos. Chem. Phys., 23, 3829–3859, https://doi.org/10.5194/acp-23-3829-2023, 2023.
Arora, V. K. and Melton, J. R.: Reduction in global area burned and wildfire
emissions since 1930s enhances carbon uptake by land, Nat. Commun., 9, 1326,
https://doi.org/10.1038/s41467-018-03838-0, 2018.
August, T., Klaes, D., Schlüssel, P., Hultberg, T., Crapeau, M.,
Arriaga, A., O'Carroll, A., Coppens, D., Munro, R., and Calbet, X.: IASI on
Metop-A: Operational Level 2 retrievals after five years in orbit, J.
Quant. Spectrosc. Ra., 113, 1340–1371,
https://doi.org/10.1016/j.jqsrt.2012.02.028, 2012.
Battin, T. J., Luyssaert, S., Kaplan, L. A., Aufdenkampe, A. K., Richter,
A., and Tranvik, L. J.: The boundless carbon cycle, Nat. Geosci., 2,
598–600, https://doi.org/10.1038/ngeo618, 2009.
Beirle, S., Lampel, J., Wang, Y., Mies, K., Dörner, S., Grossi, M., Loyola, D., Dehn, A., Danielczok, A., Schröder, M., and Wagner, T.: The ESA GOME-Evolution “Climate” water vapor product: a homogenized time series of H2O columns from GOME, SCIAMACHY, and GOME-2, Earth Syst. Sci. Data, 10, 449–468, https://doi.org/10.5194/essd-10-449-2018, 2018.
Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C.,
Scheepmaker, R. A., Dlugokencky, E., Wofsy, S. C., Kort, E. A., Sweeney, C.,
Schuck, T., Brenninkmeijer, C., Chen, H., Beck, V., and Gerbig, C.:
Atmospheric CH4 in the first decade of the 21st century: Inverse modeling
analysis using SCIAMACHY satellite retrievals and NOAA surface measurements,
J. Geophys. Res.-Atmos., 118, 7350–7369,
https://doi.org/10.1002/jgrd.50480, 2013.
Bhattacharjee, S., Mitra, P., and Ghosh, S. K.: Spatial Interpolation to
Predict Missing Attributes in GIS Using Semantic Kriging, IEEE T.
Geosci. Remote, 52, 4771–4780,
https://doi.org/10.1109/TGRS.2013.2284489, 2014.
Buchwitz, M., Reuter, M., Schneising, O., Boesch, H., Guerlet, S., Dils, B.,
Aben, I., Armante, R., Bergamaschi, P., Blumenstock, T., Bovensmann, H.,
Brunner, D., Buchmann, B., Burrows, J. P., Butz, A., Chédin, A.,
Chevallier, F., Crevoisier, C. D., Deutscher, N. M., Frankenberg, C., Hase,
F., Hasekamp, O. P., Heymann, J., Kaminski, T., Laeng, A., Lichtenberg, G.,
De Mazière, M., Noël, S., Notholt, J., Orphal, J., Popp, C., Parker,
R., Scholze, M., Sussmann, R., Stiller, G. P., Warneke, T., Zehner, C.,
Bril, A., Crisp, D., Griffith, D. W. T., Kuze, A., O'Dell, C., Oshchepkov,
S., Sherlock, V., Suto, H., Wennberg, P., Wunch, D., Yokota, T., and
Yoshida, Y.: The Greenhouse Gas Climate Change Initiative (GHG-CCI):
Comparison and quality assessment of near-surface-sensitive
satellite-derived CO2 and CH4 global data sets, Remote Sens.
Environ., 162, 344–362, https://doi.org/10.1016/j.rse.2013.04.024, 2015.
Burrows, J. P., Hölzle, E., Goede, A. P. H., Visser, H., and Fricke, W.:
SCIAMACHY–scanning imaging absorption spectrometer for atmospheric
chartography, Acta Astronaut., 35, 445–451,
https://doi.org/10.1016/0094-5765(94)00278-T, 1995.
Chazdon, R. L., Broadbent, E. N., Rozendaal, D. M. A., Bongers, F.,
Zambrano, A. M. A., Aide, T. M., Balvanera, P., Becknell, J. M., Boukili,
V., Brancalion, P. H. S., Craven, D., Almeida-Cortez, J. S., Cabral, G. A.
L., de Jong, B., Denslow, J. S., Dent, D. H., DeWalt, S. J., Dupuy, J. M.,
Durán, S. M., Espírito-Santo, M. M., Fandino, M. C., César, R.
G., Hall, J. S., Hernández-Stefanoni, J. L., Jakovac, C. C., Junqueira,
A. B., Kennard, D., Letcher, S. G., Lohbeck, M., Martínez-Ramos, M.,
Massoca, P., Meave, J. A., Mesquita, R., Mora, F., Muñoz, R.,
Muscarella, R., Nunes, Y. R. F., Ochoa-Gaona, S., Orihuela-Belmonte, E.,
Peña-Claros, M., Pérez-García, E. A., Piotto, D., Powers, J.
S., Rodríguez-Velazquez, J., Romero-Pérez, I. E., Ruíz, J.,
Saldarriaga, J. G., Sanchez-Azofeifa, A., Schwartz, N. B., Steininger, M.
K., Swenson, N. G., Uriarte, M., van Breugel, M., van der Wal, H., Veloso,
M. D. M., Vester, H., Vieira, I. C. G., Bentos, T. V., Williamson, G. B.,
and Poorter, L.: Carbon sequestration potential of second-growth forest
regeneration in the Latin American tropics, Sci. Adv., 2, e1501639,
https://doi.org/10.1126/sciadv.1501639, 2016.
Chen, H., Xu, X., Fang, C., Li, B., and Nie, M.: Differences in the
temperature dependence of wetland CO2 and CH4 emissions vary with water
table depth, Nat. Clim. Change, 11, 766–771,
https://doi.org/10.1038/s41558-021-01108-4, 2021.
Choulga, M., Janssens-Maenhout, G., Super, I., Solazzo, E., Agusti-Panareda, A., Balsamo, G., Bousserez, N., Crippa, M., Denier van der Gon, H., Engelen, R., Guizzardi, D., Kuenen, J., McNorton, J., Oreggioni, G., and Visschedijk, A.: Global anthropogenic CO2 emissions and uncertainties as a prior for Earth system modelling and data assimilation, Earth Syst. Sci. Data, 13, 5311–5335, https://doi.org/10.5194/essd-13-5311-2021, 2021.
Cintra, R. J. and Bayer, F. M.: A DCT Approximation for Image Compression,
IEEE Signal Proc. Let., 18, 579–582,
https://doi.org/10.1109/LSP.2011.2163394, 2011.
Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M., Oyafuso, F. A., Frankenberg, C., O'Dell, C. W., Bruegge, C. J., Doran, G. B., Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman, G. B., Schwandner, F. M., Sun, K., Taylor, T. E., Wennberg, P. O., and Wunch, D.: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products, Atmos. Meas. Tech., 10, 59–81, https://doi.org/10.5194/amt-10-59-2017, 2017.
Crosswell, J. R., Anderson, I. C., Stanhope, J. W., Van Dam, B., Brush, M.
J., Ensign, S., Piehler, M. F., McKee, B., Bost, M., and Paerl, H. W.:
Carbon budget of a shallow, lagoonal estuary: Transformations and
source-sink dynamics along the river-estuary-ocean continuum, Limnol.
Oceanogr., 62, S29–S45, https://doi.org/10.1002/lno.10631, 2017.
Deng, F., Jones, D. B. A., Henze, D. K., Bousserez, N., Bowman, K. W., Fisher, J. B., Nassar, R., O'Dell, C., Wunch, D., Wennberg, P. O., Kort, E. A., Wofsy, S. C., Blumenstock, T., Deutscher, N. M., Griffith, D. W. T., Hase, F., Heikkinen, P., Sherlock, V., Strong, K., Sussmann, R., and Warneke, T.: Inferring regional sources and sinks of atmospheric CO2 from GOSAT XCO2 data, Atmos. Chem. Phys., 14, 3703–3727, https://doi.org/10.5194/acp-14-3703-2014, 2014.
Doughty, R., Kurosu, T. P., Parazoo, N., Köhler, P., Wang, Y., Sun, Y., and Frankenberg, C.: Global GOSAT, OCO-2, and OCO-3 solar-induced chlorophyll fluorescence datasets, Earth Syst. Sci. Data, 14, 1513–1529, https://doi.org/10.5194/essd-14-1513-2022, 2022.
El-Mahallawy, M. S. and Hashim, M.: Material Classification of Underground
Utilities From GPR Images Using DCT-Based SVM Approach,
IEEE Geosci. Remote S., 10, 1542–1546,
https://doi.org/10.1109/LGRS.2013.2261796, 2013.
Fraser, A., Palmer, P. I., Feng, L., Boesch, H., Cogan, A., Parker, R., Dlugokencky, E. J., Fraser, P. J., Krummel, P. B., Langenfelds, R. L., O'Doherty, S., Prinn, R. G., Steele, L. P., van der Schoot, M., and Weiss, R. F.: Estimating regional methane surface fluxes: the relative importance of surface and GOSAT mole fraction measurements, Atmos. Chem. Phys., 13, 5697–5713, https://doi.org/10.5194/acp-13-5697-2013, 2013.
Fredj, E., Roarty, H., Kohut, J., Smith, M., and Glenn, S.: Gap Filling of
the Coastal Ocean Surface Currents from HFR Data: Application to the
Mid-Atlantic Bight HFR Network, J. Atmos. Ocean.
Tech., 33, 1097–1111, https://doi.org/10.1175/JTECH-D-15-0056.1, 2016.
Garcia, D.: Robust smoothing of gridded data in one and higher dimensions
with missing values, Comput. Stat. Data An., 54,
1167–1178, https://doi.org/10.1016/j.csda.2009.09.020, 2010.
Hakkarainen, J., Ialongo, I., and Tamminen, J.: Direct space-based
observations of anthropogenic CO2 emission areas from OCO-2, Geophys.
Rese. Lett., 43, 11400–11406, https://doi.org/10.1002/2016GL070885,
2016.
Hamazaki, T., Kaneko, Y., Kuze, A., and Kondo, K.: Fourier transform
spectrometer for Greenhouse Gases Observing Satellite (GOSAT), Enabling
Sensor and Platform Technologies for Spaceborne Remote Sensing, 73–80,
https://doi.org/10.1117/12.581198, 2005.
He, C., Ji, M., Grieneisen, M. L., and Zhan, Y.: A review of datasets and
methods for deriving spatiotemporal distributions of atmospheric CO2,
J. Environ. Manage., 322, 116101,
https://doi.org/10.1016/j.jenvman.2022.116101, 2022a.
He, C., Ji, M., Li, T., Liu, X., Tang, D., Zhang, S., Luo, Y., Grieneisen,
M. L., Zhou, Z., and Zhan, Y.: Deriving Full-Coverage and Fine-Scale XCO2
Across China Based on OCO-2 Satellite Retrievals and CarbonTracker Output,
Geophys. Res. Lett., 49, e2022GL098435,
https://doi.org/10.1029/2022GL098435, 2022b.
He, J., Yuan, Q., Li, J., and Zhang, L.: PoNet: A universal physical
optimization-based spectral super-resolution network for arbitrary
multispectral images, Inform. Fusion, 80, 205–225,
https://doi.org/10.1016/j.inffus.2021.10.016, 2022.
He, J., Yuan, Q., Li, J., Xiao, Y., Liu, D., Shen, H., and Zhang, L.:
Spectral super-resolution meets deep learning: achievements and challenges,
Inform. Fusion, 97, 101812,
https://doi.org/10.1016/j.inffus.2023.101812, 2023.
He, Z., Lei, L., Zhang, Y., Sheng, M., Wu, C., Li, L., Zeng, Z.-C., and
Welp, L. R.: Spatio-Temporal Mapping of Multi-Satellite Observed Column
Atmospheric CO2 Using Precision-Weighted Kriging Method, Remote Sensing, 12,
576, https://doi.org/10.3390/rs12030576, 2020.
Hong, X., Zhang, P., Bi, Y., Liu, C., Sun, Y., Wang, W., Chen, Z., Yin, H.,
Zhang, C., Tian, Y., and Liu, J.: Retrieval of Global Carbon Dioxide From
TanSat Satellite and Comprehensive Validation With TCCON Measurements and
Satellite Observations, IEEE T. Geosci. Remote,
60, 1–16, https://doi.org/10.1109/TGRS.2021.3066623, 2022.
Hotchkiss, E. R., Hall Jr, R. O., Sponseller, R. A., Butman, D., Klaminder,
J., Laudon, H., Rosvall, M., and Karlsson, J.: Sources of and processes
controlling CO2 emissions change with the size of streams and rivers, Nat.
Geosci., 8, 696–699, https://doi.org/10.1038/ngeo2507, 2015.
Houweling, S., Baker, D., Basu, S., Boesch, H., Butz, A., Chevallier, F.,
Deng, F., Dlugokencky, E. J., Feng, L., Ganshin, A., Hasekamp, O., Jones,
D., Maksyutov, S., Marshall, J., Oda, T., O'Dell, C. W., Oshchepkov, S.,
Palmer, P. I., Peylin, P., Poussi, Z., Reum, F., Takagi, H., Yoshida, Y.,
and Zhuravlev, R.: An intercomparison of inverse models for estimating
sources and sinks of CO2 using GOSAT measurements, J. Geophys.
Res.-Atmos., 120, 5253–5266,
https://doi.org/10.1002/2014JD022962, 2015.
Jiang, F., Ju, W., He, W., Wu, M., Wang, H., Wang, J., Jia, M., Feng, S., Zhang, L., and Chen, J. M.: A 10-year global monthly averaged terrestrial net ecosystem exchange dataset inferred from the ACOS GOSAT v9 XCO2 retrievals (GCAS2021), Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, 2022.
Katzfuss, M. and Cressie, N.: Tutorial on fixed rank kriging (FRK) of CO2
data, Department of Statistics, The Ohio State University, Columbus, https://documents.uow.edu.au/content/groups/public/@web/@inf/@math/documents/mm/uow175999.pdf (last access: 23 November 2022), 2011.
Kenea, S. T., Lee, H., Patra, P. K., Li, S., Labzovskii, L. D., and Joo, S.:
Long-term changes in CH4 emissions: Comparing ÄCH4/ÄCO2 ratios
between observation and proved model in East Asia (2010–2020), Atmos.
Environ., 293, 119437, https://doi.org/10.1016/j.atmosenv.2022.119437,
2023.
Kiel, M., O'Dell, C. W., Fisher, B., Eldering, A., Nassar, R., MacDonald, C. G., and Wennberg, P. O.: How bias correction goes wrong: measurement of X
Laughner, J. L., Roche, S., Kiel, M., Toon, G. C., Wunch, D., Baier, B. C., Biraud, S., Chen, H., Kivi, R., Laemmel, T., McKain, K., Quéhé, P.-Y., Rousogenous, C., Stephens, B. B., Walker, K., and Wennberg, P. O.: A new algorithm to generate a priori trace gas profiles for the GGG2020 retrieval algorithm, Atmos. Meas. Tech., 16, 1121–1146, https://doi.org/10.5194/amt-16-1121-2023, 2023.
Le Quéré, C., Korsbakken, J. I., Wilson, C., Tosun, J., Andrew, R.,
Andres, R. J., Canadell, J. G., Jordan, A., Peters, G. P., and van Vuuren,
D. P.: Drivers of declining CO2 emissions in 18 developed economies, Nat.
Clim. Change, 9, 213–217, https://doi.org/10.1038/s41558-019-0419-7, 2019.
Li, L., Lei, L., Song, H., Zeng, Z., and He, Z.: Spatiotemporal
Geostatistical Analysis and Global Mapping of CH4 Columns from GOSAT
Observations, Remote Sensing, 14, 654, https://doi.org/10.3390/rs14030654,
2022.
Lin, X., Zhang, W., Crippa, M., Peng, S., Han, P., Zeng, N., Yu, L., and Wang, G.: A comparative study of anthropogenic CH4 emissions over China based on the ensembles of bottom-up inventories, Earth Syst. Sci. Data, 13, 1073–1088, https://doi.org/10.5194/essd-13-1073-2021, 2021.
Liu, J., Fung, I., Kalnay, E., and Kang, J.-S.: CO2 transport uncertainties
from the uncertainties in meteorological fields, Geophys. Res.
Lett., 38, L12808, https://doi.org/10.1029/2011GL047213, 2011.
Liu, L. and Greaver, T. L.: A review of nitrogen enrichment effects on three
biogenic GHGs: the CO2 sink may be largely offset by stimulated N2O and CH4
emission, Ecol. Lett., 12, 1103–1117,
https://doi.org/10.1111/j.1461-0248.2009.01351.x, 2009.
Liu, Y., Wang, J., Yao, L., Chen, X., Cai, Z., Yang, D., Yin, Z., Gu, S.,
Tian, L., Lu, N., and Lyu, D.: The TanSat mission: preliminary global
observations, Sci. Bull., 63, 1200–1207,
https://doi.org/10.1016/j.scib.2018.08.004, 2018.
Liu, Z., Liu, Z., Song, T., Gao, W., Wang, Y., Wang, L., Hu, B., Xin, J.,
and Wang, Y.: Long-term variation in CO2 emissions with implications for the
interannual trend in PM2.5 over the last decade in Beijing, China,
Environ. Pollut., 266, 115014,
https://doi.org/10.1016/j.envpol.2020.115014, 2020.
Meinshausen, M., Meinshausen, N., Hare, W., Raper, S. C. B., Frieler, K.,
Knutti, R., Frame, D. J., and Allen, M. R.: Greenhouse-gas emission targets
for limiting global warming to 2 ∘ C, Nature, 458, 1158–1162,
https://doi.org/10.1038/nature08017, 2009.
Montzka, S. A., Dlugokencky, E. J., and Butler, J. H.: Non-CO2 greenhouse
gases and climate change, Nature, 476, 43–50,
https://doi.org/10.1038/nature10322, 2011.
Moran, D., Pichler, P.-P., Zheng, H., Muri, H., Klenner, J., Kramel, D., Többen, J., Weisz, H., Wiedmann, T., Wyckmans, A., Strømman, A. H., and Gurney, K. R.: Estimating CO2 emissions for 108 000 European cities, Earth Syst. Sci. Data, 14, 845–864, https://doi.org/10.5194/essd-14-845-2022, 2022.
Mueller, T. G., Pusuluri, N. B., Mathias, K. K., Cornelius, P. L.,
Barnhisel, R. I., and Shearer, S. A.: Map quality for ordinary kriging and
inverse distance weighted interpolation, Soil Sci. Soc. Am.
J., 68, 2042–2047, 2004.
Parker, R. J., Webb, A., Boesch, H., Somkuti, P., Barrio Guillo, R., Di Noia, A., Kalaitzi, N., Anand, J. S., Bergamaschi, P., Chevallier, F., Palmer, P. I., Feng, L., Deutscher, N. M., Feist, D. G., Griffith, D. W. T., Hase, F., Kivi, R., Morino, I., Notholt, J., Oh, Y.-S., Ohyama, H., Petri, C., Pollard, D. F., Roehl, C., Sha, M. K., Shiomi, K., Strong, K., Sussmann, R., Té, Y., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: A decade of GOSAT Proxy satellite CH4 observations, Earth Syst. Sci. Data, 12, 3383–3412, https://doi.org/10.5194/essd-12-3383-2020, 2020.
Petrescu, A. M. R., Qiu, C., Ciais, P., Thompson, R. L., Peylin, P., McGrath, M. J., Solazzo, E., Janssens-Maenhout, G., Tubiello, F. N., Bergamaschi, P., Brunner, D., Peters, G. P., Höglund-Isaksson, L., Regnier, P., Lauerwald, R., Bastviken, D., Tsuruta, A., Winiwarter, W., Patra, P. K., Kuhnert, M., Oreggioni, G. D., Crippa, M., Saunois, M., Perugini, L., Markkanen, T., Aalto, T., Groot Zwaaftink, C. D., Tian, H., Yao, Y., Wilson, C., Conchedda, G., Günther, D., Leip, A., Smith, P., Haussaire, J.-M., Leppänen, A., Manning, A. J., McNorton, J., Brockmann, P., and Dolman, A. J.: The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2017, Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, 2021.
Pham, H. T., Kim, S., Marshall, L., and Johnson, F.: Using 3D robust
smoothing to fill land surface temperature gaps at the continental scale,
Int. J. Appl. Earth Obs., 82,
101879, https://doi.org/10.1016/j.jag.2019.05.012, 2019.
Rao, K. R. and Yip, P.: Discrete Cosine Transform: Algorithms, Advantages,
Applications, Academic Press, 517 pp., https://books.google.com.sg/books?id=fWviBQAAQBAJ (last access: 23 November 2022), 2014.
Reithmaier, G. M. S., Chen, X., Santos, I. R., Drexl, M. J., Holloway, C.,
Call, M., Álvarez, P. G., Euler, S., and Maher, D. T.: Rainfall drives
rapid shifts in carbon and nutrient source-sink dynamics of an urbanised,
mangrove-fringed estuary, Estuarine, Coast. Shelf Sci., 249, 107064,
https://doi.org/10.1016/j.ecss.2020.107064, 2021.
Shine, K. P., Fuglestvedt, J. S., Hailemariam, K., and Stuber, N.:
Alternatives to the Global Warming Potential for Comparing Climate Impacts
of Emissions of Greenhouse Gases, Climatic Change, 68, 281–302,
https://doi.org/10.1007/s10584-005-1146-9, 2005.
Siabi, Z., Falahatkar, S., and Alavi, S. J.: Spatial distribution of XCO2
using OCO-2 data in growing seasons, J. Environ. Manage.,
244, 110–118, https://doi.org/10.1016/j.jenvman.2019.05.049, 2019.
Sjögersten, S., Black, C. R., Evers, S., Hoyos-Santillan, J., Wright, E.
L., and Turner, B. L.: Tropical wetlands: A missing link in the global
carbon cycle?, Global Biogeochem. Cy., 28, 1371–1386,
https://doi.org/10.1002/2014GB004844, 2014.
Solomon, S., Daniel, J. S., Sanford, T. J., Murphy, D. M., Plattner, G.-K.,
Knutti, R., and Friedlingstein, P.: Persistence of climate changes due to a
range of greenhouse gases, P. Natl. Acad. Sci. USA,
107, 18354–18359, https://doi.org/10.1073/pnas.1006282107, 2010.
Taylor, T. E., O'Dell, C. W., Frankenberg, C., Partain, P. T., Cronk, H. Q., Savtchenko, A., Nelson, R. R., Rosenthal, E. J., Chang, A. Y., Fisher, B., Osterman, G. B., Pollock, R. H., Crisp, D., Eldering, A., and Gunson, M. R.: Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data, Atmos. Meas. Tech., 9, 973–989, https://doi.org/10.5194/amt-9-973-2016, 2016.
Taylor, T. E., O'Dell, C. W., Crisp, D., Kuze, A., Lindqvist, H., Wennberg, P. O., Chatterjee, A., Gunson, M., Eldering, A., Fisher, B., Kiel, M., Nelson, R. R., Merrelli, A., Osterman, G., Chevallier, F., Palmer, P. I., Feng, L., Deutscher, N. M., Dubey, M. K., Feist, D. G., García, O. E., Griffith, D. W. T., Hase, F., Iraci, L. T., Kivi, R., Liu, C., De Mazière, M., Morino, I., Notholt, J., Oh, Y.-S., Ohyama, H., Pollard, D. F., Rettinger, M., Schneider, M., Roehl, C. M., Sha, M. K., Shiomi, K., Strong, K., Sussmann, R., Té, Y., Velazco, V. A., Vrekoussis, M., Warneke, T., and Wunch, D.: An 11-year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm, Earth Syst. Sci. Data, 14, 325–360, https://doi.org/10.5194/essd-14-325-2022, 2022.
Turner, A. J., Jacob, D. J., Wecht, K. J., Maasakkers, J. D., Lundgren, E., Andrews, A. E., Biraud, S. C., Boesch, H., Bowman, K. W., Deutscher, N. M., Dubey, M. K., Griffith, D. W. T., Hase, F., Kuze, A., Notholt, J., Ohyama, H., Parker, R., Payne, V. H., Sussmann, R., Sweeney, C., Velazco, V. A., Warneke, T., Wennberg, P. O., and Wunch, D.: Estimating global and North American methane emissions with high spatial resolution using GOSAT satellite data, Atmos. Chem. Phys., 15, 7049–7069, https://doi.org/10.5194/acp-15-7049-2015, 2015.
Velazco, V. A., Deutscher, N. M., Morino, I., Uchino, O., Bukosa, B., Ajiro, M., Kamei, A., Jones, N. B., Paton-Walsh, C., and Griffith, D. W. T.: Satellite and ground-based measurements of XCO2 in a remote semiarid region of Australia, Earth Syst. Sci. Data, 11, 935–946, https://doi.org/10.5194/essd-11-935-2019, 2019.
Wang, G., Garcia, D., Liu, Y., de Jeu, R., and Johannes Dolman, A.: A
three-dimensional gap filling method for large geophysical datasets:
Application to global satellite soil moisture observations, Environ.
Modell. Softw., 30, 139–142,
https://doi.org/10.1016/j.envsoft.2011.10.015, 2012.
Wang, H., Jiang, F., Wang, J., Ju, W., and Chen, J. M.: Terrestrial ecosystem carbon flux estimated using GOSAT and OCO-2 XCO2 retrievals, Atmos. Chem. Phys., 19, 12067–12082, https://doi.org/10.5194/acp-19-12067-2019, 2019.
Wang, T., Yu, P., Wu, Z., Lu, W., Liu, X., Li, Q. P., and Huang, B.:
Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent
Luzon Strait With a New Gap-Filled Remote Sensing Data Set, IEEE
T. Geosci. Remote, 60, 1–11,
https://doi.org/10.1109/TGRS.2021.3067646, 2022.
Wang, Y., Yuan, Q., Li, T., Zhu, L., and Zhang, L.: Estimating daily
full-coverage near surface O3, CO, and NO2 concentrations at a high spatial
resolution over China based on S5P-TROPOMI and GEOS-FP,
ISPRS J. Photogramm., 175, 311–325, 2021.
Wang, Y., Yuan, Q., Li, T., and Zhang, L.: Global long-term (2010–2020)
daily seamless fused XCO2 and XCH4 from CAMS, OCO-2, and GOSAT, Zenodo [data set],
https://doi.org/10.5281/zenodo.7388893, 2022a.
Wang, Y., Yuan, Q., Li, T., and Zhu, L.: Global spatiotemporal estimation of
daily high-resolution surface carbon monoxide concentrations using Deep
Forest, J. Clean. Prod., 350, 131500, https://doi.org/10.1016/j.jclepro.2022.131500, 2022b.
Wu, L., Hasekamp, O., Hu, H., Landgraf, J., Butz, A., aan de Brugh, J., Aben, I., Pollard, D. F., Griffith, D. W. T., Feist, D. G., Koshelev, D., Hase, F., Toon, G. C., Ohyama, H., Morino, I., Notholt, J., Shiomi, K., Iraci, L., Schneider, M., de Mazière, M., Sussmann, R., Kivi, R., Warneke, T., Goo, T.-Y., and Té, Y.: Carbon dioxide retrieval from OCO-2 satellite observations using the RemoTeC algorithm and validation with TCCON measurements, Atmos. Meas. Tech., 11, 3111–3130, https://doi.org/10.5194/amt-11-3111-2018, 2018.
Wunch, D., Toon, G. C., Blavier, J.-F. L., Washenfelder, R. A., Notholt, J.,
Connor, B. J., Griffith, D. W. T., Sherlock, V., and Wennberg, P. O.: The
Total Carbon Column Observing Network, Philos. T.
Roy. Soc. A, 369,
2087–2112, https://doi.org/10.1098/rsta.2010.0240, 2011.
Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, C. M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P. W., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) X
Xiao, Y., Yuan, Q., He, J., Zhang, Q., Sun, J., Su, X., Wu, J., and Zhang,
L.: Space-time super-resolution for satellite video: A joint framework based
on multi-scale spatial-temporal transformer,
Int. J.
Appl. Earth Obs., 108, 102731,
https://doi.org/10.1016/j.jag.2022.102731, 2022.
Xiao, Y., Yuan, Q., Jiang, K., He, J., Wang, Y., and Zhang, L.: From degrade
to upgrade: Learning a self-supervised degradation guided adaptive network
for blind remote sensing image super-resolution, Inform. Fusion, 96,
297–311, https://doi.org/10.1016/j.inffus.2023.03.021, 2023.
Yoro, K. O. and Daramola, M. O.: Chapter 1 – CO2 emission sources,
greenhouse gases, and the global warming effect, in: Advances in Carbon
Capture, edited by: Rahimpour, M. R., Farsi, M., and Makarem, M. A.,
Woodhead Publishing, 3–28,
https://doi.org/10.1016/B978-0-12-819657-1.00001-3, 2020.
Yoshida, Y., Kikuchi, N., Morino, I., Uchino, O., Oshchepkov, S., Bril, A., Saeki, T., Schutgens, N., Toon, G. C., Wunch, D., Roehl, C. M., Wennberg, P. O., Griffith, D. W. T., Deutscher, N. M., Warneke, T., Notholt, J., Robinson, J., Sherlock, V., Connor, B., Rettinger, M., Sussmann, R., Ahonen, P., Heikkinen, P., Kyrö, E., Mendonca, J., Strong, K., Hase, F., Dohe, S., and Yokota, T.: Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data, Atmos. Meas. Tech., 6, 1533–1547, https://doi.org/10.5194/amt-6-1533-2013, 2013.
Zhang, L., Li, T., and Wu, J.: Deriving gapless CO2 concentrations using a
geographically weighted neural network: China, 2014–2020, Int.
J. Appl. Earth Obs., 114, 103063,
https://doi.org/10.1016/j.jag.2022.103063, 2022.
Zhang, M. and Liu, G.: Mapping contiguous XCO2 by machine learning and
analyzing the spatio-temporal variation in China from 2003 to 2019, Sci.
Total Environ., 858, 159588,
https://doi.org/10.1016/j.scitotenv.2022.159588, 2023.
Zhou, S., Wang, Y., Yuan, Q., Yue, L., and Zhang, L.: Spatiotemporal
estimation of 6-hour high-resolution precipitation across China based on
Himawari-8 using a stacking ensemble machine learning model, J.
Hydrol., 609, 127718, https://doi.org/https://doi.org/10.1016/j.jhydrol.2022.127718, 2022.
Short summary
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless global XCO2 and XCH4 products. Results show that the proposed method achieves a satisfactory accuracy that distinctly exceeds that of CAMS-EGG4 and is superior or close to those of GOSAT and OCO-2. In particular, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission for COVID-19.
We propose a novel spatiotemporally self-supervised fusion method to establish long-term daily...
Altmetrics
Final-revised paper
Preprint