Articles | Volume 18, issue 15
https://doi.org/10.5194/bg-18-4549-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-4549-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Aug 2021
Research article |
| 09 Aug 2021
| 09 Aug 2021
Variability of North Atlantic CO2 fluxes for the 2000–2017 period estimated from atmospheric inverse analyses
School of Environmental Sciences, University of East Anglia, Norwich,
NR4 7TJ, UK
Parvadha Suntharalingam
School of Environmental Sciences, University of East Anglia, Norwich,
NR4 7TJ, UK
Andrew J. Watson
College of Life and Environmental Sciences, University of Exeter,
Exeter, EX4 4RJ, UK
Ute Schuster
College of Life and Environmental Sciences, University of Exeter,
Exeter, EX4 4RJ, UK
Jiang Zhu
International Center for Climate and Environment Sciences, Institute
of Atmospheric Physics, Chinese Academy of Science, Beijing, 10029, China
Ning Zeng
Department of Atmospheric and Oceanic Science and Earth System
Science Interdisciplinary Center, University of Maryland, College Park,
Maryland, 20742, USA
Related authors
No articles found.
Hyelim Yoo, Li-Qing Jiang, Chris Langdon, Alex Kozyr, Simone R. Alin, Jose Martin Hernandez Ayon, Kumiko Azetsu-Scott, Leticia Barbero, Nicole Besemer, Albert Boyd, Jean R. Brodeur, Wei-Jun Cai, Brendan R. Carter, Cecilia Chapa, Eugene Colbourne, Jessica N. Cross, Ian Enochs, Leticia Espinosa-Carreon, Richard A. Feely, Karina E. Giesbrecht, Helen Gurney-Smith, Burke Hales, Naomi Harada, James Hooper, Christopher W. Hunt, Debby Ianson, Mike Jankulak, Terrence M. Joyce, Esther G. Kennedy, Brian A. King, Arne Körtzinger, Matthias Lankhorst, Gary Maillet, Derek Manzello, Anna K. McLaskey, Viviane V. Menezes, Nicolas Metzl, Christine Michel, Natalie M. Monacci, Julio M. Morell, Akihiko Murata, Jan Newton, Emily O'Grady, Ana M. Palacio-Castro, Denis Pierrot, Marc Ringuette, Christopher Sabine, Joseph E. Salisbury, Akash R. Sastri, Ute Schuster, Uwe Send, Ryan Smith, Michel Starr, James H. Swift, Lynne D. Talley, Helmuth Thomas, Douglas W. R. Wallace, Zhaohui Aleck Wang, Rik Wanninkhof, Chi S. Wong, Patricia L. Yager, and Igor Yashayaev
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-516, https://doi.org/10.5194/essd-2026-516, 2026
Preprint under review for ESSD
Short summary
Short summary
The coastal Ocean Data Analysis Product in North America (CODAP-NA, Version 2026) substantially increased both the spatial and temporal coverage of coastal biogeochemical measurements across North American continental shelves. This version comprises 446 cruises, 31,864 hydrographic profiles, and 195,489 discrete data records covering continental shelf environments from Alaska to Mexico in the west and from Canada to the Caribbean in the east from 1981 to 2024.
Kai Cao, Qizhong Wu, Xiao Tang, Jinxi Li, Xueshun Chen, Huansheng Chen, Wending Wang, Huangjian Wu, Lei Kong, Jie Li, Jiang Zhu, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2026-3368, https://doi.org/10.5194/egusphere-2026-3368, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study achieves significant acceleration by developing an optimized advection module for Emission and atmospheric Processes Integrated and Coupled Community Model on Graphics Processing Unit. Through implementing thread-block indexing, minimizing Central Processing Unit and Graphics Processing Unit communication, and an parallel framework, we demonstrate speedups: 556.5× faster offline performance for the Heterogeneous Interface advection solver and 20.5× acceleration in coupled simulations.
Jialu Liu, Pengfei Han, Huiling Ouyang, Ning Zeng, Zhenfeng Wang, Jian Wang, Weiwei Fu, Honggang Lv, Wenhao Lin, Zheng Xia, and Bo Yao
Atmos. Meas. Tech., 19, 2837–2854, https://doi.org/10.5194/amt-19-2837-2026, https://doi.org/10.5194/amt-19-2837-2026, 2026
Short summary
Short summary
This study deployed a buoy equipped with three low-cost sensors in the northern South China Sea to continuously monitor atmospheric CO2 at the sea surface. After correcting for environmental factors and sensor drift, the measurement accuracy improved. CO2 levels were generally stable with small daily changes, while local fluctuations reflected air from land. This affordable approach enables reliable ocean CO2 monitoring and helps study the ocean’s role in the global carbon cycle.
Li-Qing Jiang, Amanda Fay, Jens Daniel Müller, Luke Gregor, Alizée Roobaert, Lydia Keppler, Dustin Carroll, Siv K. Lauvset, Tim DeVries, Judith Hauck, Christian Rödenbeck, Nicolas Metzl, Andrea J. Fassbender, Jean-Pierre Gattuso, Peter Landschützer, Rik Wanninkhof, Christopher Sabine, Simone R. Alin, Mario Hoppema, Are Olsen, Matthew P. Humphreys, Kunal Chakraborty, Ana C. Franco, Kumiko Azetsu-Scott, Dorothee C. E. Bakker, Leticia Barbero, Nicholas R. Bates, Nicole Besemer, Henry C. Bittig, Albert E. Boyd, Daniel Broullón, Wei-Jun Cai, Brendan R. Carter, Thi-Tuyet-Trang Chau, Chen-Tung Arthur Chen, Frédéric Cyr, John E. Dore, Ian Enochs, Richard A. Feely, Hernan E. Garcia, Marion Gehlen, Prasanna Kanti Ghoshal, Lucas Gloege, Melchor González-Dávila, Nicolas Gruber, Debby Ianson, Yosuke Iida, Masao Ishii, Apurva Padamnabh Joshi, Esther Kennedy, Alex Kozyr, Nico Lange, Claire Lo Monaco, Derek P. Manzello, Galen A. McKinley, Natalie M. Monacci, Xose A. Padin, Ana M. Palacio-Castro, Fiz F. Pérez, J. Magdalena Santana-Casiano, Jonathan Sharp, Adrienne Sutton, Jim Swift, Toste Tanhua, Maciej Telszewski, Jens Terhaar, Ruben van Hooidonk, Anton Velo, Andrew J. Watson, Angelicque E. White, Zelun Wu, Liang Xue, Hyelim Yoo, Jiye Zeng, and Guorong Zhong
Earth Syst. Sci. Data, 18, 1405–1462, https://doi.org/10.5194/essd-18-1405-2026, https://doi.org/10.5194/essd-18-1405-2026, 2026
Short summary
Short summary
This review article provides an overview of 68 existing ocean carbonate chemistry data products and data product sets, encompassing a broad range of types, including compilations of cruise datasets, gap-filled observational products, model simulations, and more. It is designed to help researchers identify and access the data products that best support their scientific objectives, thereby facilitating progress in understanding the ocean's changing carbonate chemistry.
Qixiang Cai, Ning Zeng, Xiaoyu Yang, Chi Xu, Zhaojun Wang, and Pengfei Han
Atmos. Meas. Tech., 18, 4871–4884, https://doi.org/10.5194/amt-18-4871-2025, https://doi.org/10.5194/amt-18-4871-2025, 2025
Short summary
Short summary
Mid- and low-cost CO2 sensors are attractive in carbon monitoring and atmospheric inversions. They are useful in both fixed stations and mobile monitoring. Yet the performance faces great challenges due to environmental impacts and long-term drifts. Here, we conducted 30 months of co-located observations using such sensors with a reference instrument. After corrections of environmental impacts and drifts, the accuracy reached 1–3 ppm. We recommend standard gas calibration within 3–6 months.
Kai Cao, Qizhong Wu, Xiao Tang, Jinxi Li, Xueshun Chen, Huansheng Chen, Wending Wang, Huangjian Wu, Lei Kong, Jie Li, Jiang Zhu, and Zifa Wang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2918, https://doi.org/10.5194/egusphere-2025-2918, 2025
Preprint archived
Short summary
Short summary
This study achieves significant acceleration by developing an optimized advection module for Emission and atmospheric Processes Integrated and Coupled Community Model on GPU-like accelerators. Through implementing thread-block coordinated indexing, minimizing CPU-GPU communication, and an hybrid parallelization framework, we demonstrate prominent speedups: 556.5× faster offline performance for the Heterogeneous Interface PPM solver and 20.5× acceleration in coupled simulations.
Pengfei Han, Ning Zeng, Bo Yao, Wen Zhang, Weijun Quan, Pucai Wang, Ting Wang, Minqiang Zhou, Qixiang Cai, Yuzhong Zhang, Ruosi Liang, Wanqi Sun, and Shengxiang Liu
Atmos. Chem. Phys., 25, 4965–4988, https://doi.org/10.5194/acp-25-4965-2025, https://doi.org/10.5194/acp-25-4965-2025, 2025
Short summary
Short summary
Methane (CH4) is a potent greenhouse gas. Northern China contributes a large proportion of CH4 emissions, yet large observation gaps exist. Here we compiled a comprehensive dataset, which is publicly available, that includes ground-based, satellite-based, inventory, and modeling results to show the CH4 concentrations, enhancements, and spatial–temporal variations. The data can benefit the research community and policy-makers for future observations, atmospheric inversions, and policy-making.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
Short summary
Short summary
The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Jie Li, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data, 16, 4351–4387, https://doi.org/10.5194/essd-16-4351-2024, https://doi.org/10.5194/essd-16-4351-2024, 2024
Short summary
Short summary
A new long-term inversed emission inventory for Chinese air quality (CAQIEI) is developed in this study, which contains constrained monthly emissions of NOx, SO2, CO, PM2.5, PM10, and NMVOCs in China from 2013 to 2020 with a horizontal resolution of 15 km. Emissions of different air pollutants and their changes during 2013–2020 were investigated and compared with previous emission inventories, which sheds new light on the complex variations of air pollutant emissions in China.
Neill Mackay, Taimoor Sohail, Jan David Zika, Richard G. Williams, Oliver Andrews, and Andrew James Watson
Geosci. Model Dev., 17, 5987–6005, https://doi.org/10.5194/gmd-17-5987-2024, https://doi.org/10.5194/gmd-17-5987-2024, 2024
Short summary
Short summary
The ocean absorbs carbon dioxide from the atmosphere, mitigating climate change, but estimates of the uptake do not always agree. There is a need to reconcile these differing estimates and to improve our understanding of ocean carbon uptake. We present a new method for estimating ocean carbon uptake and test it with model data. The method effectively diagnoses the ocean carbon uptake from limited data and therefore shows promise for reconciling different observational estimates.
Lijing Cheng, Yuying Pan, Zhetao Tan, Huayi Zheng, Yujing Zhu, Wangxu Wei, Juan Du, Huifeng Yuan, Guancheng Li, Hanlin Ye, Viktor Gouretski, Yuanlong Li, Kevin E. Trenberth, John Abraham, Yuchun Jin, Franco Reseghetti, Xiaopei Lin, Bin Zhang, Gengxin Chen, Michael E. Mann, and Jiang Zhu
Earth Syst. Sci. Data, 16, 3517–3546, https://doi.org/10.5194/essd-16-3517-2024, https://doi.org/10.5194/essd-16-3517-2024, 2024
Short summary
Short summary
Observational gridded products are essential for understanding the ocean, the atmosphere, and climate change; they support policy decisions and socioeconomic developments. This study provides an update of an ocean subsurface temperature and ocean heat content gridded product, named the IAPv4 data product, which is available for the upper 6000 m (119 levels) since 1940 (more reliable after ~1955) for monthly and 1° × 1° temporal and spatial resolutions.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
Short summary
Short summary
Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Michael P. Cartwright, Richard J. Pope, Jeremy J. Harrison, Martyn P. Chipperfield, Chris Wilson, Wuhu Feng, David P. Moore, and Parvadha Suntharalingam
Atmos. Chem. Phys., 23, 10035–10056, https://doi.org/10.5194/acp-23-10035-2023, https://doi.org/10.5194/acp-23-10035-2023, 2023
Short summary
Short summary
A 3-D chemical transport model, TOMCAT, is used to simulate global atmospheric carbonyl sulfide (OCS) distribution. Modelled OCS compares well with satellite observations of OCS from limb-sounding satellite observations. Model simulations also compare adequately with surface and atmospheric observations and suitably capture the seasonality of OCS and background concentrations.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Qixiang Cai, and Pengfei Han
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-15, https://doi.org/10.5194/gmd-2023-15, 2023
Revised manuscript not accepted
Short summary
Short summary
We introduced a novel algorithm that assimilates a better a priori knowledge to improve the estimation of global surface carbon flux. The algorithm aims at separating the first-order systematic biases in the a priori "bottom-up" flux estimations out of the inversion framework from a comprehensive data assimilation perspective.
Tian Tian, Lijing Cheng, Gongjie Wang, John Abraham, Wangxu Wei, Shihe Ren, Jiang Zhu, Junqiang Song, and Hongze Leng
Earth Syst. Sci. Data, 14, 5037–5060, https://doi.org/10.5194/essd-14-5037-2022, https://doi.org/10.5194/essd-14-5037-2022, 2022
Short summary
Short summary
A high-resolution gridded dataset is crucial for understanding ocean processes at various spatiotemporal scales. Here we used a machine learning approach and successfully reconstructed a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 (monthly) by merging in situ salinity profile observations with high-resolution satellite remote-sensing data. This new product could be useful in various applications in ocean and climate fields.
Zhiqiang Liu, Ning Zeng, Yun Liu, Eugenia Kalnay, Ghassem Asrar, Bo Wu, Qixiang Cai, Di Liu, and Pengfei Han
Geosci. Model Dev., 15, 5511–5528, https://doi.org/10.5194/gmd-15-5511-2022, https://doi.org/10.5194/gmd-15-5511-2022, 2022
Short summary
Short summary
We described the application of a constrained ensemble Kalman filter (CEnKF) in a joint CO2 and surface carbon fluxes estimation study. By assimilating the pseudo-surface and OCO-2 observations, the annual global flux estimation is significantly biased without mass conservation. With the additional CEnKF process, the CO2 mass is strictly constrained, and the estimation of annual fluxes is significantly improved.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
Short summary
Short summary
The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Ruqi Yang, Jun Wang, Ning Zeng, Stephen Sitch, Wenhan Tang, Matthew Joseph McGrath, Qixiang Cai, Di Liu, Danica Lombardozzi, Hanqin Tian, Atul K. Jain, and Pengfei Han
Earth Syst. Dynam., 13, 833–849, https://doi.org/10.5194/esd-13-833-2022, https://doi.org/10.5194/esd-13-833-2022, 2022
Short summary
Short summary
We comprehensively investigate historical GPP trends based on five kinds of GPP datasets and analyze the causes for any discrepancies among them. Results show contrasting behaviors between modeled and satellite-based GPP trends, and their inconsistencies are likely caused by the contrasting performance between satellite-derived and modeled leaf area index (LAI). Thus, the uncertainty in satellite-based GPP induced by LAI undermines its role in assessing the performance of DGVM simulations.
Richard P. Sims, Michael Bedington, Ute Schuster, Andrew J. Watson, Vassilis Kitidis, Ricardo Torres, Helen S. Findlay, James R. Fishwick, Ian Brown, and Thomas G. Bell
Biogeosciences, 19, 1657–1674, https://doi.org/10.5194/bg-19-1657-2022, https://doi.org/10.5194/bg-19-1657-2022, 2022
Short summary
Short summary
The amount of carbon dioxide (CO2) being absorbed by the ocean is relevant to the earth's climate. CO2 values in the coastal ocean and estuaries are not well known because of the instrumentation used. We used a new approach to measure CO2 across the coastal and estuarine zone. We found that CO2 and salinity were linked to the state of the tide. We used our CO2 measurements and model salinity to predict CO2. Previous studies overestimate how much CO2 the coastal ocean draws down at our site.
Cited articles
Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S., Nojiri, Y., Schuster, U., Steinhoff, T., Sweeney, C., Takahashi, T., Tilbrook, B., Wada, C., Wanninkhof, R., Alin, S. R., Balestrini, C. F., Barbero, L., Bates, N. R., Bianchi, A. A., Bonou, F., Boutin, J., Bozec, Y., Burger, E. F., Cai, W.-J., Castle, R. D., Chen, L., Chierici, M., Currie, K., Evans, W., Featherstone, C., Feely, R. A., Fransson, A., Goyet, C., Greenwood, N., Gregor, L., Hankin, S., Hardman-Mountford, N. J., Harlay, J., Hauck, J., Hoppema, M., Humphreys, M. P., Hunt, C. W., Huss, B., Ibánhez, J. S. P., Johannessen, T., Keeling, R., Kitidis, V., Körtzinger, A., Kozyr, A., Krasakopoulou, E., Kuwata, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lo Monaco, C., Manke, A., Mathis, J. T., Merlivat, L., Millero, F. J., Monteiro, P. M. S., Munro, D. R., Murata, A., Newberger, T., Omar, A. M., Ono, T., Paterson, K., Pearce, D., Pierrot, D., Robbins, L. L., Saito, S., Salisbury, J., Schlitzer, R., Schneider, B., Schweitzer, R., Sieger, R., Skjelvan, I., Sullivan, K. F., Sutherland, S. C., Sutton, A. J., Tadokoro, K., Telszewski, M., Tuma, M., van Heuven, S. M. A. C., Vandemark, D., Ward, B., Watson, A. J., and Xu, S.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383–413, https://doi.org/10.5194/essd-8-383-2016, 2016.
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013.
Buitenhuis, E. T., Rivkin, R. B., Sailley, S., and Le Quéré, C.:
Biogeochemical fluxes through microzooplankton, Glob. Biogeochem. Cy., 24, GB4015, https://doi.org/10.1029/2009GB003601, 2010.
Buitenhuis, E. T., Hashioka, T., and Le Quéré, C.: Combined constraints on global ocean primary production using observations and models, Global Biogeochem. Cy., 27, 847–858, https://doi.org/10.1002/gbc.20074, 2013.
Carouge, C., Bousquet, P., Peylin, P., Rayner, P. J., and Ciais, P.: What can we learn from European continuous atmospheric CO2 measurements to quantify regional fluxes – Part 1: Potential of the 2001 network, Atmos. Chem. Phys., 10, 3107–3117, https://doi.org/10.5194/acp-10-3107-2010, 2010.
Chatterjee, A., Engelen, R. J., Kawa, S. R., Sweeney, C., and Michalak, A.
M.: Background error covariance estimation for atmospheric CO2 data
assimilation, J. Geophys. Res.-Atmos., 118,
10140–10154, https://doi.org/10.1002/jgrd.50564, 2013.
Chau, T. T., Gehlen, M., and Chevallier, F.: Global Ocean Surface Carbon
Product MULTIOBS_textunderscore GLO_BIO_CARBON_SURFACE_REP_015_008, E.U. Copernicus Marine Service Information,
available at: https://resources.marine.copernicus.eu/?option=com_cswandview=detailsandproduct_id=MULTIOBS_G, last access: 16 November 2020.
Chevallier, F., Ciais, P., Conway, T., Aalto, T., Anderson, B., Bousquet,
P., Brunke, E., Ciattaglia, L., Esaki, Y., and Fröhlich, M.: CO2
surface fluxes at grid point scale estimated from a global 21 year
reanalysis of atmospheric measurements, J. Geophys. Res.-Atmos., 115, D21307, https://doi.org/10.1029/2010JD013887, 2010.
Chevallier, F., Palmer, P. I., Feng, L., Boesch, H., O'Dell, C. W., and
Bousquet, P.: Toward robust and consistent regional CO2 flux estimates
from in situ and spaceborne measurements of atmospheric CO2,
Geophys. Res. Lett., 41, 1065–1070,
https://doi.org/10.1002/2013GL058772, 2014.
Chevallier, F., Remaud, M., O'Dell, C. W., Baker, D., Peylin, P., and Cozic, A.: Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions [data set], Atmos. Chem. Phys., 19, 14233–14251, https://doi.org/10.5194/acp-19-14233-2019, 2019.
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics, Geosci. Model Dev., 4, 701–722, https://doi.org/10.5194/gmd-4-701-2011, 2011.
Crippa, M., Janssens-Maenhout, G., Dentener, F., Guizzardi, D., Sindelarova, K., Muntean, M., Van Dingenen, R., and Granier, C.: Forty years of improvements in European air quality: regional policy-industry interactions with global impacts, Atmos. Chem. Phys., 16, 3825–3841, https://doi.org/10.5194/acp-16-3825-2016, 2016.
Denvil-Sommer, A., Gehlen, M., Vrac, M., and Mejia, C.: LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean pCO2 over the global ocean [data set], Geosci. Model Dev., 12, 2091–2105, https://doi.org/10.5194/gmd-12-2091-2019, 2019.
DeVries, T., Le Quéré, C., Andrews, O., Berthet, S., Hauck, J.,
Ilyina, T., Landschützer, P., Lenton, A., Lima, I. D., and Nowicki, M.:
Decadal trends in the ocean carbon sink, P. Natl. Acad. Sci. USA, 116, 11646–11651, https://doi.org/10.1073/pnas.1900371116,
2019.
Doney, S. C., Lima, I., Feely, R. A., Glover, D. M., Lindsay, K., Mahowald,
N., Moore, J. K., and Wanninkhof, R.: Mechanisms governing interannual
variability in upper-ocean inorganic carbon system and air–sea CO2
fluxes: Physical climate and atmospheric dust, Deep Sea Res. Pt. II, 56, 640–655,
https://doi.org/10.1016/j.dsr2.2008.12.006, 2009.
Feng, L., Palmer, P. I., Parker, R. J., Deutscher, N. M., Feist, D. G., Kivi, R., Morino, I., and Sussmann, R.: Estimates of European uptake of CO2 inferred from GOSAT X
Gaubert, B., Stephens, B. B., Basu, S., Chevallier, F., Deng, F., Kort, E. A., Patra, P. K., Peters, W., Rödenbeck, C., Saeki, T., Schimel, D., Van der Laan-Luijkx, I., Wofsy, S., and Yin, Y.: Global atmospheric CO2 inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate, Biogeosciences, 16, 117–134, https://doi.org/10.5194/bg-16-117-2019, 2019.
Gregor, L., Lebehot, A. D., Kok, S., and Scheel Monteiro, P. M.: A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?, Geosci. Model Dev., 12, 5113–5136, https://doi.org/10.5194/gmd-12-5113-2019, 2019.
Gruber, N., Gloor, M., Mikaloff Fletcher, S. E., Doney, S. C., Dutkiewicz,
S., Follows, M. J., Gerber, M., Jacobson, A. R., Joos, F., and Lindsay, K.:
Oceanic sources, sinks, and transport of atmospheric CO2, Glob. Biogeochem. Cy., 23, GB1005, https://doi.org/10.1029/2008GB003349,
2009.
Gruber, N., Landschützer, P., and Lovenduski, N. S.: The variable
Southern Ocean carbon sink, Annu. Rev. Mar. Sci., 11, 159–186,
https://doi.org/10.1146/annurev-marine-121916-063407, 2019.
Gurney, K. R., Law, R., Denning, A. S., Rayner, P. J., Baker, D., Bousquet, P.,
Bruhwiler, L., Chen, Y. H., Ciais, P., Fan, S., Fung, I. Y., Gloor, M.,
Heimann, M., Higuchi, K., John, J., Maki, T., Maksyutov, S., Masarie, K.,
Peylin, P., Prather, M., Pak, B. C., Randerson, J., Sarmiento, J., Taguchi,
S., Takahashi, T., and Yuan, C.: Towards robust regional estimates of
CO2 sources and sinks using atmospheric transport models, Nature,
415, 626–630, https://doi.org/10.1038/415626a, 2002.
Gurney, K. R., Law, R. M., Denning, A. S., Rayner, P. J., Baker, D.,
Bousquet, P., Bruhwiler, L., Chen, Y. H., Ciais, P., and Fan, S.: TransCom 3
CO2 inversion intercomparison: 1. Annual mean control results and
sensitivity to transport and prior flux information, Tellus B, 55, 555–579,
https://doi.org/10.3402/tellusb.v55i2.16728, 2003.
Hauck, J., Köhler, P., Wolf-Gladrow, D., and Völker, C.: Iron
fertilisation and century-scale effects of open ocean dissolution of olivine
in a simulated CO2 removal experiment, Environ. Res. Lett.,
11, 024007, https://doi.org/10.1088/1748-9326/11/2/024007, 2016.
Hauck, J., Zeising, M., Le Quéré, C., Gruber, N., Bakker, D. C.,
Bopp, L., Chau, T. T., Gürses, Ö., Ilyina, T., Landschützer, P.,
Lenton, A., Resplandy, L., Rödenbeck, C., Schwinger, J., and
Séférian, R.: Consistency and challenges in the ocean carbon sink
estimate for the Global Carbon Budget, Front. Mar. Sci., 7, 852,
https://doi.org/10.3389/fmars.2020.571720, 2020.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica
D, 230, 112–126,
https://doi.org/10.1016/j.physd.2006.11.008, 2007.
Iida, Y., Kojima, A., Takatani, Y., Nakano, T. Midorikawa, T., and Ishii,
M.: Trends in pCO2 and sea-air CO2 flux over the global open oceans for the
last two decades [data set], J. Oceanogr., 71, 637–661,
https://doi.org/10.1007/s10872-015-0306-4, 2015.
Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E., Li, H., and
Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC: Model
architecture and performance as component of the MPI-Earth system model in
different CMIP5 experimental realizations, J. Adv. Model. Earth Sys., 5, 287–315, https://doi.org/10.1029/2012MS000178, 2013.
Jacobson, A. R., Mikaloff Fletcher, S. E., Gruber, N., Sarmiento, J. L., and
Gloor, M.: A joint atmosphere-ocean inversion for surface fluxes of carbon
dioxide: 1. Methods and global-scale fluxes, Glob. Biogeochem. Cy.,
21, GB1019, https://doi.org/10.1029/2005GB002556, 2007.
Jones, S. D., Le Quéré, C., and Rödenbeck, C.: Autocorrelation
characteristics of surface ocean pCO2 and air-sea CO2
fluxes, Global Biogeochem. Cy., 26, 2,
https://doi.org/10.1029/2010GB004017, 2012.
Kang, J. S., Kalnay, E., Liu, J., Fung, I., Miyoshi, T., and Ide, K.:
“Variable localization” in an ensemble Kalman filter: Application to the
carbon cycle data assimilation, J. Geophys. Res.-Atmos., 116, D09110,
https://doi.org/10.1029/2010JD014673, 2011.
Kang, J. S., Kalnay, E., Miyoshi, T., Liu, J., and Fung, I.: Estimation of
surface carbon fluxes with an advanced data assimilation methodology,
J. Geophys. Res.-Atmos., 117, D24101,
https://doi.org/10.1029/2012JD018259, 2012.
Khatiwala, S., Tanhua, T., Mikaloff Fletcher, S., Gerber, M., Doney, S. C., Graven, H. D., Gruber, N., McKinley, G. A., Murata, A., Ríos, A. F., and Sabine, C. L.: Global ocean storage of anthropogenic carbon, Biogeosciences, 10, 2169–2191, https://doi.org/10.5194/bg-10-2169-2013, 2013.
Landschützer, P., Gruber, N., Bakker, D. C., Schuster, U., Nakaoka, S.
I., Payne, M. R., Sasse, T. P., and Zeng, J.: A neural network-based
estimate of the seasonal to inter-annual variability of the Atlantic Ocean
carbon sink, Biogeosciences, 10, 7793–7815,
https://doi.org/10.5194/bg-10-7793-2013, 2013.
Landschuetzer, P., Gruber, N., and Bakker, D. C.: Decadal variations and
trends of the global ocean carbon sink [data set], Global Biogeochem. Cy., 30,
1396–1417, https://doi.org/10.1002/2015GB005359, 2016.
Landschützer, P., Ilyina, T., and Lovenduski, N. S.: Detecting Regional
Modes of Variability in Observation-Based Surface Ocean pCO2,
Geophys. Res. Lett., 46, 2670–2679,
https://doi.org/10.1029/2018GL081756, 2019.
Lebehot, A. D., Halloran, P. R., Watson, A. J., McNeall, D., Ford, D. A.,
Landschützer, P., Lauvset, S. K., and Schuster, U.: Reconciling
observation and model trends in North Atlantic surface CO2, Global Biogeochem. Cy., 33, 1204–1222, https://doi.org/10.1029/2019GB006186,
2019.
Liu, J., Bowman, K. W., and Lee, M.: Comparison between the Local Ensemble
Transform Kalman Filter (LETKF) and 4D-Var in atmospheric CO2 flux
inversion with the Goddard Earth Observing System-Chem model and the
observation impact diagnostics from the LETKF, J. Geophys. Res.-Atmos., 121, 13066–13087,
https://doi.org/10.1002/2016JD025100, 2016.
Liu, Y., Kalnay, E., Zeng, N., Asrar, G., Chen, Z., and Jia, B.: Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1, Geosci. Model Dev., 12, 2899–2914, https://doi.org/10.5194/gmd-12-2899-2019, 2019.
Macovei, V. A., Hartman, S. E., Schuster, U., Torres-Valdés, S., Moore,
C. M., and Sanders, R. J.: Impact of physical and biological processes on
temporal variations of the ocean carbon sink in the mid-latitude North
Atlantic (2002–2016), Prog. Oceanogr., 180, 102223,
https://doi.org/10.1016/j.pocean.2019.102223, 2020.
McKinley, G. A., Fay, A. R., Takahashi, T., and Metzl, N.: Convergence of
atmospheric and North Atlantic carbon dioxide trends on multidecadal
timescales, Nat. Geosci., 4, 606–610, https://doi.org/10.1038/ngeo1193,
2011.
Mikaloff Fletcher, S., Gruber, N., Jacobson, A. R., Doney, S., Dutkiewicz,
S., Gerber, M., Follows, M., Joos, F., Lindsay, K., and Menemenlis, D.:
Inverse estimates of anthropogenic CO2 uptake, transport, and storage
by the ocean, Global Biogeochem. Cy., 20, GB2002,
https://doi.org/10.1029/2005GB002530, 2006.
Miyoshi, T., Yamane, S., and Enomoto, T.: Localizing the error covariance by
physical distances within a local ensemble transform Kalman filter (LETKF),
SOLA, 3, 89–92, https://doi.org/10.2151/sola.2007-023, 2007.
Montgomery, D. C., Peck, E. A., and Vining, G. G.: Introduction to Linear
Regression Analysis,5th Edition, John Wiley & Sons, 645 pp., 2012.
Nassar, R., Jones, D. B. A., Suntharalingam, P., Chen, J. M., Andres, R. J., Wecht, K. J., Yantosca, R. M., Kulawik, S. S., Bowman, K. W., Worden, J. R., Machida, T., and Matsueda, H.: Modeling global atmospheric CO2 with improved emission inventories and CO2 production from the oxidation of other carbon species, Geosci. Model Dev., 3, 689–716, https://doi.org/10.5194/gmd-3-689-2010, 2010.
Nassar, R., Jones, D. B. A., Kulawik, S. S., Worden, J. R., Bowman, K. W., Andres, R. J., Suntharalingam, P., Chen, J. M., Brenninkmeijer, C. A. M., Schuck, T. J., Conway, T. J., and Worthy, D. E.: Inverse modeling of CO2 sources and sinks using satellite observations of CO2 from TES and surface flask measurements, Atmos. Chem. Phys., 11, 6029–6047, https://doi.org/10.5194/acp-11-6029-2011, 2011.
Peters, W., Miller, J., Whitaker, J., Denning, A., Hirsch, A., Krol, M.,
Zupanski, D., Bruhwiler, L., and Tans, P.: An ensemble data assimilation
system to estimate CO2 surface fluxes from atmospheric trace gas
observations, J. Geophys. Res.-Atmos., 110, D24304,
https://doi.org/10.1029/2005JD006157, 2005.
Peylin, P., Law, R. M., Gurney, K. R., Chevallier, F., Jacobson, A. R., Maki, T., Niwa, Y., Patra, P. K., Peters, W., Rayner, P. J., Rödenbeck, C., van der Laan-Luijkx, I. T., and Zhang, X.: Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions, Biogeosciences, 10, 6699–6720, https://doi.org/10.5194/bg-10-6699-2013, 2013.
Rienecker, M. M., Suarez, M., Todling, R., Bacmeister, J., Takacs, L., Liu,
H., Gu, W., Sienkiewicz, M., Koster, R., and Gelaro, R.: The GEOS-5 Data
Assimilation System: Documentation of Versions 5.0.1, 5.1.0, and 5.2.0,
NASA Technical Report Series on Global Modelling and Data
Assimilation,NASA/TM–2008–104606, 27, 2008.
Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: Time-dependent
atmospheric CO2 inversions based on interannually varying tracer
transport, Tellus B, 55, 488–497,
https://doi.org/10.3402/tellusb.v55i2.16707, 2003.
Rödenbeck, C., Keeling, R. F., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., and Heimann, M.: Global surface-ocean pCO2 and sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme [data set], Ocean Sci., 9, 193–216, https://doi.org/10.5194/os-9-193-2013, 2013.
Rödenbeck, C., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., Cassar, N., Reum, F., Keeling, R. F., and Heimann, M.: Interannual sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Biogeosciences, 11, 4599–4613, https://doi.org/10.5194/bg-11-4599-2014, 2014.
Rödenbeck, C., Bakker, D. C. E., Gruber, N., Iida, Y., Jacobson, A. R., Jones, S., Landschützer, P., Metzl, N., Nakaoka, S., Olsen, A., Park, G.-H., Peylin, P., Rodgers, K. B., Sasse, T. P., Schuster, U., Shutler, J. D., Valsala, V., Wanninkhof, R., and Zeng, J.: Data-based estimates of the ocean carbon sink variability – first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM), Biogeosciences, 12, 7251–7278, https://doi.org/10.5194/bg-12-7251-2015, 2015.
Sabine, C. L., Feely, R. A., Gruber, N., Key, R. M., Lee, K., Bullister, J.
L., Wanninkhof, R., Wong, C., Wallace, D. W., and Tilbrook, B.: The oceanic
sink for anthropogenic CO2, Science, 305, 367–371,
https://doi.org/10.1126/science.1097403, 2004.
Schuster, U., McKinley, G. A., Bates, N., Chevallier, F., Doney, S. C., Fay,
A., González-Dávila, M., Gruber, N., Jones, S., and Krijnen, J.: An
assessment of the Atlantic and Arctic sea-air CO2 fluxes, 1990–2009,
Biogeosciences, 10, 607–627, https://doi.org/10.5194/bg-10-607-2013, 2013.
Schwinger, J., Goris, N., Tjiputra, J. F., Kriest, I., Bentsen, M., Bethke, I., Ilicak, M., Assmann, K. M., and Heinze, C.: Evaluation of NorESM-OC (versions 1 and 1.2), the ocean carbon-cycle stand-alone configuration of the Norwegian Earth System Model (NorESM1), Geosci. Model Dev., 9, 2589–2622, https://doi.org/10.5194/gmd-9-2589-2016, 2016.
Séférian, R., Bopp, L., Gehlen, M., Orr, J. C., Ethé, C.,
Cadule, P., Aumont, O., Mélia, D. S., Voldoire, A., and Madec, G.:
Skill assessment of three earth system models with common marine
biogeochemistry, Clim. Dynam., 40, 2549–2573,
https://doi.org/10.1007/s00382-012-1362-8, 2013.
Suntharalingam, P., Randerson, J. T., Krakauer, N., Logan, J. A., and Jacob,
D. J.: Influence of reduced carbon emissions and oxidation on the
distribution of atmospheric CO2: Implications for inversion analyses,
Global Biogeochem. Cy., 19, GB4003,
https://doi.org/10.1029/2005GB002466, 2005.
Takahashi, T., Sutherland, S. C., Sweeney, C., Poisson, A., Metzl, N.,
Tilbrook, B., Bates, N., Wanninkhof, R., Feely, R. A., and Sabine, C.:
Global sea–air CO2 flux based on climatological surface ocean
pCO2, and seasonal biological and temperature effects, Deep Sea
Res. Pt. II, 49, 1601–1622,
https://doi.org/10.1016/S0967-0645(02)00003-6, 2002.
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., and Sabine, C.:
Climatological mean and decadal change in surface ocean pCO2, and net
sea–air CO2 flux over the global oceans [data set], Deep Sea Res. Pt. II, 56,
554–577, https://doi.org/10.1016/j.dsr2.2008.12.009, 2009.
van der Laan-Luijkx, I. T., van der Velde, I. R., van der Veen, E., Tsuruta, A., Stanislawska, K., Babenhauserheide, A., Zhang, H. F., Liu, Y., He, W., Chen, H., Masarie, K. A., Krol, M. C., and Peters, W.: The CarbonTracker Data Assimilation Shell (CTDAS) v1.0: implementation and global carbon balance 2001–2015 [data set], Geosci. Model Dev., 10, 2785–2800, https://doi.org/10.5194/gmd-10-2785-2017, 2017.
Wanninkhof, R., Pickers, P., Omar, A., Sutton, A., Murata, A., Olsen, A.,
Bb, S., Tilbrook, B., Munro, D., and Pierrot, D.: A surface ocean CO2
reference network, SOCONET and associated marine boundary layer CO2
measurements, Front. Mar. Sci., 6, 400,
https://doi.org/10.3389/fmars.2019.00400, 2019.
Watson, A. J., Schuster, U., Bakker, D. C., Bates, N. R., Corbière, A.,
González-Dávila, M., Friedrich, T., Hauck, J., Heinze, C., and
Johannessen, T.: Tracking the variable North Atlantic sink for atmospheric
CO2, Science, 326, 1391–1393, https://doi.org/10.1126/science.1177394,
2009.
Watson, A. J., Schuster, U., Shutler, J. D., Holding, T., Ashton, I. G. C.,
Landschützer, P., Woolf, D. K., and Goddijn-Murphy, L.: Revised
estimates of ocean-atmosphere CO2 flux are consistent with ocean carbon
inventory, Nat. Commun., 11, 1–6, https://doi.org/10.1038/s41467-020-18203-3, 2020.
Zeng, J., Nojiri, Y., Nakaoka, Shin-ichiro, Nakajima, H., and Shirai, T.:
Surface ocean CO2 in 1990–2011 modelled using a feed-forward neural
network [data set], Geosci. Data J., 2, 47–51, https://doi.org/10.1002/gdj3.26, 2015.
Ziehn, T., Lenton, A., Law, R. M., Matear, R. J., and Chamberlain, M. A.: The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 2: Historical simulations, Geosci. Model Dev., 10, 2591–2614, https://doi.org/10.5194/gmd-10-2591-2017, 2017.
Short summary
As the global temperature continues to increase, carbon dioxide (CO2) is a major driver of this global warming. The increased CO2 is mainly caused by emissions from fossil fuel use and land use. At the same time, the ocean is a significant sink in the carbon cycle. The North Atlantic is a critical ocean region in reducing CO2 concentration. We estimate the CO2 uptake in this region based on a carbon inverse system and atmospheric CO2 observations.
As the global temperature continues to increase, carbon dioxide (CO2) is a major driver of this...
Altmetrics
Final-revised paper
Preprint