Articles | Volume 22, issue 13
https://doi.org/10.5194/acp-22-8897-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-8897-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Interannual variability in the Australian carbon cycle over 2015–2019, based on assimilation of Orbiting Carbon Observatory-2 (OCO-2) satellite data
Yohanna Villalobos
CORRESPONDING AUTHOR
School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Australia
ARC Centre of Excellence for Climate Extremes, Sydney, Australia
CSIRO Oceans and Atmosphere, Canberra, Australia
Peter J. Rayner
School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Australia
ARC Centre of Excellence for Climate Extremes, Sydney, Australia
Climate & Energy College, University of Melbourne, Melbourne, Australia
Jeremy D. Silver
School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Australia
School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
Steven Thomas
School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Australia
Vanessa Haverd
CSIRO Oceans and Atmosphere, Canberra, Australia
deceased, 19 January 2021
Jürgen Knauer
Hawkesbury Institute for the Environment, Western Sydney University, Penrith, Australia
CSIRO Oceans and Atmosphere, Canberra, Australia
Zoë M. Loh
CSIRO Oceans and Atmosphere, Aspendale, Australia
Nicholas M. Deutscher
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, Australia
David W. T. Griffith
Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, Australia
David F. Pollard
National Institute of Water and Atmospheric Research (NIWA), Lauder, New Zealand
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EGUsphere, https://doi.org/10.5194/egusphere-2025-1733, https://doi.org/10.5194/egusphere-2025-1733, 2025
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Tobias D. Schmitt, Jonas Kuhn, Ralph Kleinschek, Benedikt A. Löw, Stefan Schmitt, William Cranton, Martina Schmidt, Sanam N. Vardag, Frank Hase, David W. T. Griffith, and André Butz
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Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
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Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
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Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, https://doi.org/10.5194/esd-14-767-2023, 2023
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Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Yifan Guan, Gretchen Keppel-Aleks, Scott C. Doney, Christof Petri, Dave Pollard, Debra Wunch, Frank Hase, Hirofumi Ohyama, Isamu Morino, Justus Notholt, Kei Shiomi, Kim Strong, Rigel Kivi, Matthias Buschmann, Nicholas Deutscher, Paul Wennberg, Ralf Sussmann, Voltaire A. Velazco, and Yao Té
Atmos. Chem. Phys., 23, 5355–5372, https://doi.org/10.5194/acp-23-5355-2023, https://doi.org/10.5194/acp-23-5355-2023, 2023
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We characterize spatial–temporal patterns of interannual variability (IAV) in atmospheric CO2 based on NASA’s Orbiting Carbon Observatory-2 (OCO-2). CO2 variation is strongly impacted by climate events, with higher anomalies during El Nino years. We show high correlation in IAV between space-based and ground-based CO2 from long-term sites. Because OCO-2 has near-global coverage, our paper provides a roadmap to study IAV where in situ observation is sparse, such as open oceans and remote lands.
Yu Someya, Yukio Yoshida, Hirofumi Ohyama, Shohei Nomura, Akihide Kamei, Isamu Morino, Hitoshi Mukai, Tsuneo Matsunaga, Joshua L. Laughner, Voltaire A. Velazco, Benedikt Herkommer, Yao Té, Mahesh Kumar Sha, Rigel Kivi, Minqiang Zhou, Young Suk Oh, Nicholas M. Deutscher, and David W. T. Griffith
Atmos. Meas. Tech., 16, 1477–1501, https://doi.org/10.5194/amt-16-1477-2023, https://doi.org/10.5194/amt-16-1477-2023, 2023
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The updated retrieval algorithm for the Greenhouse gases Observing SATellite level 2 product is presented. The main changes in the algorithm from the previous one are the treatment of cirrus clouds, the degradation model of the sensor, solar irradiance, and gas absorption coefficient tables. The retrieval results showed improvements in fitting accuracy and an increase in the data amount over land. On the other hand, there are still large biases of XCO2 which should be corrected over the ocean.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
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
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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.
David F. Pollard, Frank Hase, Mahesh Kumar Sha, Darko Dubravica, Carlos Alberti, and Dan Smale
Earth Syst. Sci. Data, 14, 5427–5437, https://doi.org/10.5194/essd-14-5427-2022, https://doi.org/10.5194/essd-14-5427-2022, 2022
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We describe measurements made in Antarctica using an EM27/SUN, a near-infrared, portable, low-resolution spectrometer from which we can retrieve the average atmospheric concentration of several greenhouse gases. We show that these measurements are reliable and comparable to other, similar ground-based measurements. Comparisons to the ESA's Sentinel-5 precursor (S5P) satellite demonstrate the usefulness of these data for satellite validation.
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
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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.
Asher P. Mouat, Clare Paton-Walsh, Jack B. Simmons, Jhonathan Ramirez-Gamboa, David W. T. Griffith, and Jennifer Kaiser
Atmos. Chem. Phys., 22, 11033–11047, https://doi.org/10.5194/acp-22-11033-2022, https://doi.org/10.5194/acp-22-11033-2022, 2022
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We examine emissions of volatile organic compounds from 2020 wildfires in forested regions of Australia (AU). We find that biomass burning in temperate regions of the US and AU emit similar species in similar proportion, both in natural and lab settings. This suggests studies of wildfires in one region may be used to help improve air quality models in other parts of the world. We observe time series of ozone and nitrogen dioxide. Last, we look at which compounds contribute most to OH reactivity.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
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We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Mei Bai, Zoe Loh, David W. T. Griffith, Debra Turner, Richard Eckard, Robert Edis, Owen T. Denmead, Glenn W. Bryant, Clare Paton-Walsh, Matthew Tonini, Sean M. McGinn, and Deli Chen
Atmos. Meas. Tech., 15, 3593–3610, https://doi.org/10.5194/amt-15-3593-2022, https://doi.org/10.5194/amt-15-3593-2022, 2022
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The open-path laser (OPL) and open-path Fourier transform infrared (OP-FTIR) are used in agricultural research, but their error in emissions research has not been the focus of studies. We conducted trace gas release trials and herd and paddock emission studies to compare their applicability and performance. The OP-FTIR has better stability in stable conditions than OPL. The CH4 OPL accurately detects the low background level of CH4, but the NH3 OPL only detects background values >10 ppbv.
Stefan Noël, Maximilian Reuter, Michael Buchwitz, Jakob Borchardt, Michael Hilker, Oliver Schneising, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Robert J. Parker, Hiroshi Suto, Yukio Yoshida, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Cheng Liu, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Markus Rettinger, Coleen Roehl, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, and Thorsten Warneke
Atmos. Meas. Tech., 15, 3401–3437, https://doi.org/10.5194/amt-15-3401-2022, https://doi.org/10.5194/amt-15-3401-2022, 2022
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We present a new version (v3) of the GOSAT and GOSAT-2 FOCAL products.
In addition to an increased number of XCO2 data, v3 also includes products for XCH4 (full-physics and proxy), XH2O and the relative ratio of HDO to H2O (δD). For GOSAT-2, we also present first XCO and XN2O results. All FOCAL data products show reasonable spatial distribution and temporal variations and agree well with TCCON. Global XN2O maps show a gradient from the tropics to higher latitudes on the order of 15 ppb.
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
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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.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
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Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Edward Malina, Ben Veihelmann, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, and Isamu Morino
Atmos. Meas. Tech., 15, 2377–2406, https://doi.org/10.5194/amt-15-2377-2022, https://doi.org/10.5194/amt-15-2377-2022, 2022
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Methane retrievals from remote sensing instruments are fundamentally based on spectroscopic parameters, which indicate spectral-line positions, and their characteristics. These parameters are stored in several databases that vary in their make-up. Here we assess how concentrations of methane isotopologues measured from the same Total Carbon Column Observing Network (TCCON) instruments vary across a range of spectral windows using different spectroscopic databases and comment on the implications.
Zhenyi Chen, Robyn Schofield, Melita Keywood, Sam Cleland, Alastair G. Williams, Alan Griffiths, Stephen Wilson, Peter Rayner, and Xiaowen Shu
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-104, https://doi.org/10.5194/acp-2022-104, 2022
Revised manuscript not accepted
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This study studied the marine boundary layer (MBL) process and aerosol properties in the Southern Ocean using miniMPL, ceilometer and sodar. Compared to the gradient method, the Image Edge Detection Algorithm provides more reliable boundary layer height estimations, especially when a convective MBL with stratification existed. The diurnal characteristic of BLH with the veering of the wind vector was also observed. Under the continental sources, the MBL maintained a well-mixed layer of 0.3 km.
Thomas E. Taylor, Christopher W. O'Dell, David Crisp, Akhiko Kuze, Hannakaisa Lindqvist, Paul O. Wennberg, Abhishek Chatterjee, Michael Gunson, Annmarie Eldering, Brendan Fisher, Matthäus Kiel, Robert R. Nelson, Aronne Merrelli, Greg Osterman, Frédéric Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Cheng Liu, Martine De Mazière, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Matthias Schneider, Coleen M. Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, and Debra Wunch
Earth Syst. Sci. Data, 14, 325–360, https://doi.org/10.5194/essd-14-325-2022, https://doi.org/10.5194/essd-14-325-2022, 2022
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We provide an analysis of an 11-year record of atmospheric carbon dioxide (CO2) concentrations derived using an optimal estimation retrieval algorithm on measurements made by the GOSAT satellite. The new product (version 9) shows improvement over the previous version (v7.3) as evaluated against independent estimates of CO2 from ground-based sensors and atmospheric inversion systems. We also compare the new GOSAT CO2 values to collocated estimates from NASA's Orbiting Carbon Observatory-2.
Yohanna Villalobos, Peter J. Rayner, Jeremy D. Silver, Steven Thomas, Vanessa Haverd, Jürgen Knauer, Zoë M. Loh, Nicholas M. Deutscher, David W. T. Griffith, and David F. Pollard
Atmos. Chem. Phys., 21, 17453–17494, https://doi.org/10.5194/acp-21-17453-2021, https://doi.org/10.5194/acp-21-17453-2021, 2021
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Semi-arid ecosystems such as those in Australia are evolving and might play an essential role in the future of climate change. We use carbon dioxide concentrations derived from the OCO-2 satellite instrument and a regional transport model to understand if Australia was a carbon sink or source of CO2 in 2015. Our research's main findings suggest that Australia acted as a carbon sink of about −0.41 ± 0.08 petagrams of carbon in 2015, driven primarily by savanna and sparsely vegetated ecosystems.
Mahesh Kumar Sha, Bavo Langerock, Jean-François L. Blavier, Thomas Blumenstock, Tobias Borsdorff, Matthias Buschmann, Angelika Dehn, Martine De Mazière, Nicholas M. Deutscher, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Michel Grutter, James W. Hannigan, Frank Hase, Pauli Heikkinen, Christian Hermans, Laura T. Iraci, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Nicolas Kumps, Jochen Landgraf, Alba Lorente, Emmanuel Mahieu, Maria V. Makarova, Johan Mellqvist, Jean-Marc Metzger, Isamu Morino, Tomoo Nagahama, Justus Notholt, Hirofumi Ohyama, Ivan Ortega, Mathias Palm, Christof Petri, David F. Pollard, Markus Rettinger, John Robinson, Sébastien Roche, Coleen M. Roehl, Amelie N. Röhling, Constantina Rousogenous, Matthias Schneider, Kei Shiomi, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Osamu Uchino, Voltaire A. Velazco, Corinne Vigouroux, Mihalis Vrekoussis, Pucai Wang, Thorsten Warneke, Tyler Wizenberg, Debra Wunch, Shoma Yamanouchi, Yang Yang, and Minqiang Zhou
Atmos. Meas. Tech., 14, 6249–6304, https://doi.org/10.5194/amt-14-6249-2021, https://doi.org/10.5194/amt-14-6249-2021, 2021
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This paper presents, for the first time, Sentinel-5 Precursor methane and carbon monoxide validation results covering a period from November 2017 to September 2020. For this study, we used global TCCON and NDACC-IRWG network data covering a wide range of atmospheric and surface conditions across different terrains. We also show the influence of a priori alignment, smoothing uncertainties and the sensitivity of the validation results towards the application of advanced co-location criteria.
Matthias M. Frey, Frank Hase, Thomas Blumenstock, Darko Dubravica, Jochen Groß, Frank Göttsche, Martin Handjaba, Petrus Amadhila, Roland Mushi, Isamu Morino, Kei Shiomi, Mahesh Kumar Sha, Martine de Mazière, and David F. Pollard
Atmos. Meas. Tech., 14, 5887–5911, https://doi.org/10.5194/amt-14-5887-2021, https://doi.org/10.5194/amt-14-5887-2021, 2021
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In this study, we present measurements of carbon dioxide, methane and carbon monoxide from a recently established site in Gobabeb, Namibia. Gobabeb is the first site observing these gases on the African mainland and improves the global coverage of measurement sites. Gobabeb is a hyperarid desert site, offering unique characteristics. Measurements started 2015 as part of the COllaborative Carbon Column Observing Network. We compare our results with other datasets and find a good agreement.
Beata Bukosa, Jenny Fisher, Nicholas Deutscher, and Dylan Jones
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-173, https://doi.org/10.5194/gmd-2021-173, 2021
Revised manuscript not accepted
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Human activities led to rising levels of greenhouse gases (carbon dioxide (CO2), methane (CH4), carbon monoxide (CO)) in the atmosphere, threatening our future. We use models and measurements to predict and understand the climatological impact of these gases. Here, we describe a new simulation in the GEOS-Chem model that uses a more accurate method to simulate CO2, CH4 and CO, through their chemical dependence. Relative to the original simulations our results agree better with measurements.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Martin Keller, Daven K. Henze, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Atmos. Chem. Phys., 21, 9545–9572, https://doi.org/10.5194/acp-21-9545-2021, https://doi.org/10.5194/acp-21-9545-2021, 2021
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We explore the utility of a weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation scheme for mitigating systematic errors in methane simulation in the GEOS-Chem model. We use data from the Greenhouse Gases Observing Satellite (GOSAT) and show that, compared to the traditional 4D-Var approach, the WC scheme improves the agreement between the model and independent observations. We find that the WC corrections to the model provide insight into the source of the errors.
Matthieu Dogniaux, Cyril Crevoisier, Raymond Armante, Virginie Capelle, Thibault Delahaye, Vincent Cassé, Martine De Mazière, Nicholas M. Deutscher, Dietrich G. Feist, Omaira E. Garcia, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Isamu Morino, Justus Notholt, David F. Pollard, Coleen M. Roehl, Kei Shiomi, Kimberly Strong, Yao Té, Voltaire A. Velazco, and Thorsten Warneke
Atmos. Meas. Tech., 14, 4689–4706, https://doi.org/10.5194/amt-14-4689-2021, https://doi.org/10.5194/amt-14-4689-2021, 2021
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We present the Adaptable 4A Inversion (5AI), an implementation of the optimal estimation (OE) algorithm, relying on the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer model, that enables the retrieval of greenhouse gas atmospheric weighted columns from infrared measurements. It is tested on a sample of Orbiting Carbon Observatory-2 observations, and its results satisfactorily compare to several reference products, thus showing the reliability of 5AI OE implementation.
Stefan Noël, Maximilian Reuter, Michael Buchwitz, Jakob Borchardt, Michael Hilker, Heinrich Bovensmann, John P. Burrows, Antonio Di Noia, Hiroshi Suto, Yukio Yoshida, Matthias Buschmann, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Isamu Morino, Justus Notholt, Hirofumi Ohyama, Christof Petri, James R. Podolske, David F. Pollard, Mahesh Kumar Sha, Kei Shiomi, Ralf Sussmann, Yao Té, Voltaire A. Velazco, and Thorsten Warneke
Atmos. Meas. Tech., 14, 3837–3869, https://doi.org/10.5194/amt-14-3837-2021, https://doi.org/10.5194/amt-14-3837-2021, 2021
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We present the first GOSAT and GOSAT-2 XCO2 data derived with the FOCAL retrieval algorithm. Comparisons of the GOSAT-FOCAL product with other data reveal long-term agreement within about 1 ppm over 1 decade, differences in seasonal variations of about 0.5 ppm, and a mean regional bias to ground-based TCCON data of 0.56 ppm with a mean scatter of 1.89 ppm. GOSAT-2-FOCAL data are preliminary only, but first comparisons show that they compare well with the GOSAT-FOCAL results and TCCON.
Nicholas M. Deutscher, Travis A. Naylor, Christopher G. R. Caldow, Hamish L. McDougall, Alex G. Carter, and David W. T. Griffith
Atmos. Meas. Tech., 14, 3119–3130, https://doi.org/10.5194/amt-14-3119-2021, https://doi.org/10.5194/amt-14-3119-2021, 2021
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This work describes the performance of an open-path measurement system for greenhouse gases in an extended field trial. The instrument obtained measurement repeatability of 0.1 % or better for CO2 and CH4 measurements over a 1.55 km one-way pathway. Comparison to co-located in situ measurements allows characterisation of biases relative to global reference scales. The research was done to show the applicability of the technique and its ability to detect atmospheric-relevant sources and sinks.
David F. Pollard, John Robinson, Hisako Shiona, and Dan Smale
Atmos. Meas. Tech., 14, 1501–1510, https://doi.org/10.5194/amt-14-1501-2021, https://doi.org/10.5194/amt-14-1501-2021, 2021
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This work describes the steps taken to ensure a continuous, high-quality dataset of column-averaged greenhouse gas retrievals from the Total Carbon Column Observing Network (TCCON) site at Lauder, New Zealand, following a change in the Fourier transform spectrometer used to make the measurements from which the retrievals are made.
Thomas Blumenstock, Frank Hase, Axel Keens, Denis Czurlok, Orfeo Colebatch, Omaira Garcia, David W. T. Griffith, Michel Grutter, James W. Hannigan, Pauli Heikkinen, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Erik Lutsch, Maria Makarova, Hamud K. Imhasin, Johan Mellqvist, Isamu Morino, Tomoo Nagahama, Justus Notholt, Ivan Ortega, Mathias Palm, Uwe Raffalski, Markus Rettinger, John Robinson, Matthias Schneider, Christian Servais, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, and Voltaire A. Velazco
Atmos. Meas. Tech., 14, 1239–1252, https://doi.org/10.5194/amt-14-1239-2021, https://doi.org/10.5194/amt-14-1239-2021, 2021
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This study investigates the level of channeling (optical resonances) of each FTIR spectrometer within the Network for the Detection of Atmospheric Composition Change (NDACC). Since the air gap of the beam splitter is a significant source of channeling, we propose new beam splitters with an increased wedge of the air gap. This study shows the potential for reducing channeling in the FTIR spectrometers operated by the NDACC, thereby increasing the quality of recorded spectra across the network.
Alba Lorente, Tobias Borsdorff, Andre Butz, Otto Hasekamp, Joost aan de Brugh, Andreas Schneider, Lianghai Wu, Frank Hase, Rigel Kivi, Debra Wunch, David F. Pollard, Kei Shiomi, Nicholas M. Deutscher, Voltaire A. Velazco, Coleen M. Roehl, Paul O. Wennberg, Thorsten Warneke, and Jochen Landgraf
Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, https://doi.org/10.5194/amt-14-665-2021, 2021
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TROPOMI aboard Sentinel-5P satellite provides methane (CH4) measurements with exceptional temporal and spatial resolution. The study describes a series of improvements developed to retrieve CH4 from TROPOMI. The updated CH4 product features (among others) a more accurate a posteriori correction derived independently of any reference data. The validation of the improved data product shows good agreement with ground-based and satellite measurements, which highlights the quality of the TROPOMI CH4.
Robert J. Parker, Alex Webb, Hartmut Boesch, Peter Somkuti, Rocio Barrio Guillo, Antonio Di Noia, Nikoleta Kalaitzi, Jasdeep S. Anand, Peter Bergamaschi, Frederic Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Coleen Roehl, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Thorsten Warneke, Paul O. Wennberg, and Debra Wunch
Earth Syst. Sci. Data, 12, 3383–3412, https://doi.org/10.5194/essd-12-3383-2020, https://doi.org/10.5194/essd-12-3383-2020, 2020
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This work presents the latest release of the University of Leicester GOSAT methane data and acts as the definitive description of this dataset. We detail the processing, validation and evaluation involved in producing these data and highlight its many applications. With now over a decade of global atmospheric methane observations, this dataset has helped, and will continue to help, us better understand the global methane budget and investigate how it may respond to a future changing climate.
Ashok K. Luhar, David M. Etheridge, Zoë M. Loh, Julie Noonan, Darren Spencer, Lisa Smith, and Cindy Ong
Atmos. Chem. Phys., 20, 15487–15511, https://doi.org/10.5194/acp-20-15487-2020, https://doi.org/10.5194/acp-20-15487-2020, 2020
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With the sharp rise in coal seam gas (CSG) production in Queensland’s Surat Basin, there is much interest in quantifying methane emissions from this area and from unconventional gas production in general. We develop and apply a regional Bayesian inverse model that uses hourly methane concentration data from two sites and modelled backward dispersion to quantify emissions. The model requires a narrow prior and suggests that the emissions from the CSG areas are 33% larger than bottom-up estimates.
Robert G. Ryan, Jeremy D. Silver, Richard Querel, Dan Smale, Steve Rhodes, Matt Tully, Nicholas Jones, and Robyn Schofield
Atmos. Meas. Tech., 13, 6501–6519, https://doi.org/10.5194/amt-13-6501-2020, https://doi.org/10.5194/amt-13-6501-2020, 2020
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Models have identified Australasia as a formaldehyde (HCHO) hotspot from vegetation sources, but few measurement studies exist to verify this. We compare, and find good agreement between, HCHO measurements using three – two ground-based and one satellite-based – different spectroscopic techniques in Australia and New Zealand. This gives confidence in using satellite observations to study HCHO and associated air chemistry and pollution problems in this under-studied part of the world.
John Robinson, Dan Smale, David Pollard, and Hisako Shiona
Atmos. Meas. Tech., 13, 5855–5871, https://doi.org/10.5194/amt-13-5855-2020, https://doi.org/10.5194/amt-13-5855-2020, 2020
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Solar trackers are used by spectrometers to measure atmospheric trace gas concentrations using direct-sun spectroscopy. The ideal tracker should be sufficiently accurate, highly reliable, and with a longevity that exceeds the lifetime of the spectrometer which it serves. It should also be affordable, easy to use, and not too complex should maintenance be required. We present a design that fulfils these requirements using some simple innovations.
Hirofumi Ohyama, Isamu Morino, Voltaire A. Velazco, Theresa Klausner, Gerry Bagtasa, Matthäus Kiel, Matthias Frey, Akihiro Hori, Osamu Uchino, Tsuneo Matsunaga, Nicholas M. Deutscher, Joshua P. DiGangi, Yonghoon Choi, Glenn S. Diskin, Sally E. Pusede, Alina Fiehn, Anke Roiger, Michael Lichtenstern, Hans Schlager, Pao K. Wang, Charles C.-K. Chou, Maria Dolores Andrés-Hernández, and John P. Burrows
Atmos. Meas. Tech., 13, 5149–5163, https://doi.org/10.5194/amt-13-5149-2020, https://doi.org/10.5194/amt-13-5149-2020, 2020
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Column-averaged dry-air mole fractions of CO2 and CH4 measured by a solar viewing portable Fourier transform spectrometer (EM27/SUN) were validated with in situ profile data obtained during the transfer flights of two aircraft campaigns. Atmospheric dynamical properties based on ERA5 and WRF-Chem were used as criteria for selecting the best aircraft profiles for the validation. The resulting air-mass-independent correction factors for the EM27/SUN data were 0.9878 for CO2 and 0.9829 for CH4.
Mahesh Kumar Sha, Martine De Mazière, Justus Notholt, Thomas Blumenstock, Huilin Chen, Angelika Dehn, David W. T. Griffith, Frank Hase, Pauli Heikkinen, Christian Hermans, Alex Hoffmann, Marko Huebner, Nicholas Jones, Rigel Kivi, Bavo Langerock, Christof Petri, Francis Scolas, Qiansi Tu, and Damien Weidmann
Atmos. Meas. Tech., 13, 4791–4839, https://doi.org/10.5194/amt-13-4791-2020, https://doi.org/10.5194/amt-13-4791-2020, 2020
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We present the results of the 2017 FRM4GHG campaign at the Sodankylä TCCON site aimed at characterising the assessment of several low-cost portable instruments for precise solar absorption measurements of column-averaged dry-air mole fractions of CO2, CH4, and CO. The test instruments provided stable and precise measurements of these gases with quantified small biases. This qualifies the instruments to complement TCCON and expand the global coverage of ground-based measurements of these gases.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Geosci. Model Dev., 13, 3839–3862, https://doi.org/10.5194/gmd-13-3839-2020, https://doi.org/10.5194/gmd-13-3839-2020, 2020
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Systematic errors in atmospheric models pose a challenge for inverse modeling studies of methane (CH4) emissions. We evaluated the CH4 simulation in the GEOS-Chem model at the horizontal resolutions of 4° × 5° and 2° × 2.5°. Our analysis identified resolution-dependent biases in the model, which we attributed to discrepancies between the two model resolutions in vertical transport in the troposphere and in stratosphere–troposphere exchange.
Cited articles
Annual climate statement, Bureau of Meteorology: Annual climate statement
2019, Bureau of Meteorology, The Bureau of Meteorology, Australia,
http://www.bom.gov.au/climate/current/annual/aus/2019/ (last access: 8 September 2021), 2019. a
Archibald, S. A., Kirton, A., van der Merwe, M. R., Scholes, R. J., Williams,
C. A., and Hanan, N.: Drivers of inter-annual variability in Net Ecosystem
Exchange in a semi-arid savanna ecosystem, South Africa, Biogeosciences, 6,
251–266, https://doi.org/10.5194/bg-6-251-2009, 2009. a
Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., Krummel, P., Steele, P., Langenfelds, R., Torn, M., Biraud, S., Stephens, B., Andrews, A., and Worthy, D.: Global CO2 fluxes estimated from GOSAT retrievals of total column CO2, Atmos. Chem. Phys., 13, 8695–8717, https://doi.org/10.5194/acp-13-8695-2013, 2013. a
Basu, S., Baker, D. F., Chevallier, F., Patra, P. K., Liu, J., and Miller,
J. B.: The impact of transport model differences on CO2 surface flux
estimates from OCO-2 retrievals of column average CO2, Atmos.
Chem. Phys., 18, 7189–7215, https://doi.org/10.5194/acp-18-7189-2018, 2018. a, b
Byrd, R., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm for
Bound Constrained Optimization, SIAM J. Sci. Comput., 16,
1190–1208, https://doi.org/10.1137/0916069, 1995. a
Chevallier, F., Ciais, P., Conway, T. J., Aalto, T., Anderson, B. E., Bousquet,
P., Brunke, E. G., Ciattaglia, L., Esaki, Y., Fröhlich, M., Gomez, A.,
Gomez-Pelaez, A. J., Haszpra, L., Krummel, P. B., Langenfelds, R. L.,
Leuenberger, M., Machida, T., Maignan, F., Matsueda, H., Morguí, J. A.,
Mukai, H., Nakazawa, T., Peylin, P., Ramonet, M., Rivier, L., Sawa, Y.,
Schmidt, M., Steele, L. P., Vay, S. A., Vermeulen, A. T., Wofsy, S., and
Worthy, D.: CO2 surface fluxes at grid point scale estimated from a
global 21 year reanalysis of atmospheric measurements, J.
Geophys. Res., 115, D21307, https://doi.org/10.1029/2010JD013887, 2010. a, b
Churkina, G. and Running, S. W.: Contrasting climatic controls on the estimated
productivity of global terrestrial biomes, Ecosystems, 1, 206–215, 1998. a
Connor, B. J., Boesch, H., Toon, G., Sen, B., Miller, C., and Crisp, D.:
Orbiting Carbon Observatory: Inverse method and prospective error analysis,
J. Geophys. Res.-Atmos., 113, 1–14,
https://doi.org/10.1029/2006JD008336, 2008. a
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M.,
Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution
temporal profiles in the Emissions Database for Global Atmospheric Research,
Sci. Data, 7, 121 pp., https://doi.org/10.1038/s41597-020-0462-2, 2020. a
Crowell, S., Baker, D., Schuh, A., Basu, S., Jacobson, A. R., Chevallier, F.,
Liu, J., Deng, F., Feng, L., McKain, K., Chatterjee, A., Miller, J. B.,
Stephens, B. B., Eldering, A., Crisp, D., Schimel, D., Nassar, R., O'Dell,
C. W., Oda, T., Sweeney, C., Palmer, P. I., and Jones, D. B. A.: The
2015–2016 carbon cycle as seen from OCO-2 and the global in situ network,
Atmos. Chem. Phys., 19, 9797–9831,
https://doi.org/10.5194/acp-19-9797-2019, 2019. a, b, c
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc.,
137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Detmers, R. G., Hasekamp, O., Aben, I., Houweling, S., van Leeuwen, T. T.,
Butz, A., Landgraf, J., Köhler, P., Guanter, L., and Poulter, B.: Anomalous
carbon uptake in Australia as seen by GOSAT, Geophys. Res. Lett.,
42, 8177–8184, https://doi.org/10.1002/2015GL065161, 2015. a
Deutscher, N. M., Griffith, D. W. T., Bryant, G. W., Wennberg, P. O., Toon,
G. C., Washenfelder, R. A., Keppel-Aleks, G., Wunch, D., Yavin, Y., Allen,
N. T., Blavier, J.-F., Jiménez, R., Daube, B. C., Bright, A. V., Matross,
D. M., Wofsy, S. C., and Park, S.: Total column CO2 measurements at
Darwin, Australia − site description and calibration against in situ
aircraft profiles, Atmos. Meas. Tech., 3, 947–958,
https://doi.org/10.5194/amt-3-947-2010, 2010. a
Deutscher, N. M., Sherlock, V., Mikaloff Fletcher, S. E., Griffith, D. W. T.,
Notholt, J., Macatangay, R., Connor, B. J., Robinson, J., Shiona, H.,
Velazco, V. A., Wang, Y., Wennberg, P. O., and Wunch, D.: Drivers of
column-average CO2 variability at Southern Hemispheric Total Carbon
Column Observing Network sites, Atmos. Chem. Phys., 14,
9883–9901, https://doi.org/10.5194/acp-14-9883-2014, 2014. a
Didan, K.: MOD13C1 MODIS/Terra Vegetation Indices 16-Day L3 Global 0.05 Deg
CMG V006 [Data set], https://doi.org/10.5067/MODIS/MOD13C1.006, 2014. a
Donohue, R. J., Hume, I. H., Roderick, M. L., McVicar, T. R., Beringer, J.,
Hutley, L. B., Gallant, J. C., Austin, J. M., van Gorsel, E., Cleverly,
J. R., Meyer, W. S., and Arndt, S. K.: Evaluation of the
remote-sensing-based DIFFUSE model for estimating photosynthesis of
vegetation, Remote Sens. Environ., 155, 349–365,
https://doi.org/10.1016/j.rse.2014.09.007, 2014. a
Eldering, A.: OCO-2 Lite product V9r,
https://oco2.gesdisc.eosdis.nasa.gov/data/s4pa/OCO2_DATA/OCO2_L2_Lite_FP.9r/2015 (last access: January 2020), 2018. a
Eldering, A., Taylor, T. E., O'Dell, C. W., and Pavlick, R.: The OCO-3 mission:
measurement objectives and expected performance based on 1 year of simulated
data, Atmos. Meas. Tech., 12, 2341–2370,
https://doi.org/10.5194/amt-12-2341-2019, 2019. a
Etheridge, D., Loh, Z., Schroder, I., Berko, H., Kuske, T., Allison, C.,
Gregory, R., Spencer, D., Langenfelds, R., Zegelin, S., Hibberd, M., and
Feitz, A.: Metadata report: Arcturus atmospheric greenhouse gas monitoring,
Tech. rep., Geoscience Australia, Canberra, CSIRO's Gas Industry Social and Environmental Research Alliance (GISERA),
https://doi.org/10.11636/Record.2014.037, 2014. a
Etheridge, D. M., Day, S., M. F. Hibberd, A. L., Spencer, D. A., Loh, Z. M.,
Zegelin, S., Krummel, P. B., van Gorsel, E., Thornton, D. P., Gregory, R. L.,
Ong, C., and Barrett, D.: Characterisation of Regional Fluxes of Methane in
the Surat Basin, Queensland – Milestone 3.1 GISERA Greenhouse Gas Research
– Phase 3, Tech. rep., CSIRO, Australia, CSIRO's Gas Industry Social and Environmental Research Alliance (GISERA),
ISBN 978-1-4863-0830-9,
2016. a
FIRM: The Fire Information for Resource Management System (FIRMS),
https://firms.modaps.eosdis.nasa.gov/map/, last access: 18 July
2020. a
Friedlingstein, P., O'Sullivan, M., Jones, M. W., Andrew, R. M., Hauck, J.,
Olsen, A., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré,
C., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S., Aragão, L. E.
O. C., Arneth, A., Arora, V., Bates, N. R., Becker, M., Benoit-Cattin, A.,
Bittig, H. C., Bopp, L., Bultan, S., Chandra, N., Chevallier, F., Chini,
L. P., Evans, W., Florentie, L., Forster, P. M., Gasser, T., Gehlen, M.,
Gilfillan, D., Gkritzalis, T., Gregor, L., Gruber, N., Harris, I., Hartung,
K., Haverd, V., Houghton, R. A., Ilyina, T., Jain, A. K., Joetzjer, E.,
Kadono, K., Kato, E., Kitidis, V., Korsbakken, J. I., Landschützer, P.,
Lefèvre, N., Lenton, A., Lienert, S., Liu, Z., Lombardozzi, D., Marland,
G., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y.,
O'Brien, K., Ono, T., Palmer, P. I., Pierrot, D., Poulter, B., Resplandy, L.,
Robertson, E., Rödenbeck, C., Schwinger, J., Séférian, R., Skjelvan,
I., Smith, A. J. P., Sutton, A. J., Tanhua, T., Tans, P. P., Tian, H.,
Tilbrook, B., van der Werf, G., Vuichard, N., Walker, A. P., Wanninkhof, R.,
Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, X., and Zaehle,
S.: Global Carbon Budget 2020, Earth Syst. Sci. Data, 12, 3269–3340,
https://doi.org/10.5194/essd-12-3269-2020, 2020. a
Gerbig, C., Körner, S., and Lin, J. C.: Vertical mixing in atmospheric tracer
transport models: error characterization and propagation, Atmos.
Chem. Phys., 8, 591–602, https://doi.org/10.5194/acp-8-591-2008, 2008. a
Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Wennberg, P. O., Yavin, Y., Keppel-Aleks, G., Washenfelder, R. A., Toon, G. C., Blavier, J.-F., Paton-Walsh, C., Jones, N. B., Kettlewell, G. C., Connor, B. J., Macatangay, R. C., Roehl, C., Ryczek, M., Glowacki, J., Culgan, T., and Bryant, G. W.: TCCON
data from Darwin, Australia, Release GGG2014R0, TCCON data archive, hosted by
Caltech-DATA, California Institute of Technology, Pasadena, CA, USA, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.darwin01.R0/1149290,
2017a. a
Griffith, D., Velazco, V., Deutscher, N., Murphy, C., Jones, N., Wilson, S.,
Macatangay, R., Kettlewell, G., Buchholz, R., and Riggenbach, M.: TCCON data
from Wollongong, Australia, Release GGG2014R0. TCCON data archive, hosted by
CaltechDATA, California Institute of Technology, Pasadena, CA, USA, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.wollongong01.R0/1149291,
2017b. a
Hakami, A., Henze, D. K., Seinfeld, J. H., Singh, K., Sandu, A., Kim, S., Byun,
and Li, Q.: The Adjoint of CMAQ, Environ. Sci. Technol., 41,
7807–7817, https://doi.org/10.1021/es070944p, pMID: 18075092, 2007. a
Haverd, V., Raupach, M. R., Briggs, P. R., Canadell., J. G., Davis, S. J., Law,
R. M., Meyer, C. P., Peters, G. P., Pickett-Heaps, C., and Sherman, B.: The
Australian terrestrial carbon budget, Biogeosciences, 10, 851–869,
https://doi.org/10.5194/bg-10-851-2013, 2013a. a
Haverd, V., Smith, B., Cook, G. D., Briggs, P. R., Nieradzik, L., Roxburgh,
S. H., Liedloff, A., Meyer, C. P., and Canadell, J. G.: A stand-alone tree
demography and landscape structure module for Earth system models,
Geophys. Res. Lett., 40, 5234–5239,
https://doi.org/10.1002/grl.50972, 2013b. a
Haverd, V., Smith, B., and Trudinger, C.: Process contributions of Australian
ecosystems to interannual variations in the carbon cycle, Environ.
Res. Lett., 11, 054013, https://doi.org/10.1088/1748-9326/11/5/054013, 2016. a, b
Haverd, V., Smith, B., Nieradzik, L., Briggs, P. R., Woodgate, W., Trudinger,
C. M., Canadell, J. G., and Cuntz, M.: A new version of the CABLE land
surface model (Subversion revision r4601) incorporating land use and land
cover change, woody vegetation demography, and a novel optimisation-based
approach to plant coordination of photosynthesis, Geosci. Model
Dev., 11, 2995–3026, https://doi.org/10.5194/gmd-11-2995-2018, 2018. a, b
Jones, D. A., Wang, W., and Fawcett, R.: High-quality spatial climate data-sets
for Australia, Austr. Meteorol. Oceanogr. J., 58,
233–248, 2009. a
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala,
S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F.,
Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu,
J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M.,
Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch,
S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon
fluxes from eddy covariance sites to globe: synthesis and evaluation of the
FLUXCOM approach, Biogeosciences, 17, 1343–1365,
https://doi.org/10.5194/bg-17-1343-2020, 2020. a
Kawasaki, M., Yoshioka, H., Jones, N. B., Macatangay, R., Griffith, D. W. T.,
Kawakami, S., Ohyama, H., Tanaka, T., Morino, I., Uchino, O., and Ibuki, T.:
Usability of optical spectrum analyzer in measuring atmospheric CO2 and
CH4 column densities: inspection with FTS and aircraft profiles in situ,
Atmos. Meas. Tech., 5, 2593–2600,
https://doi.org/10.5194/amt-5-2593-2012, 2012. a
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 XCO2 affected by erroneous surface pressure estimates, Atmos. Meas. Tech., 12, 2241–2259, https://doi.org/10.5194/amt-12-2241-2019, 2019. a
King, E. A., Paget, M. J., Briggs, P. R., Trudinger, C. M., and Raupach, M. R.: Operational Delivery of Hydro-Meteorological Monitoring and Modeling Over the Australian Continent,
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2, 241–249, https://doi.org/10.1109/JSTARS.2009.2031331, 2009.
Lauvaux, T., Schuh, A. E., Uliasz, M., Richardson, S., Miles, N., Andrews,
A. E., Sweeney, C., Diaz, L. I., Martins, D., Shepson, P. B., and Davis,
K. J.: Constraining the CO2 budget of the corn belt: exploring
uncertainties from the assumptions in a mesoscale inverse system,
Atmos. Chem. Phys., 12, 337–354,
https://doi.org/10.5194/acp-12-337-2012, 2012. a
Law, R. M., Peters, W., Rödenbeck, C., Aulagnier, C., Baker, I.,
Bergmann, D. J., Bousquet, P., Brandt, J., Bruhwiler, L., Cameron-Smith,
P. J., Christensen, J. H., Delage, F., Denning, A. S., Fan, S., Geels, C.,
Houweling, S., Imasu, R., Karstens, U., Kawa, S. R., Kleist, J., Krol, M. C.,
Lin, S.-J., Lokupitiya, R., Maki, T., Maksyutov, S., Niwa, Y., Onishi, R.,
Parazoo, N., Patra, P. K., Pieterse, G., Rivier, L., Satoh, M., Serrar, S.,
Taguchi, S., Takigawa, M., Vautard, R., Vermeulen, A. T., and Zhu, Z.:
TransCom model simulations of hourly atmospheric CO2 : Experimental
overview and diurnal cycle results for 2002, Global Biogeochem. Cy.,
22, 15, https://doi.org/10.1029/2007GB003050, 2008. a
Ma, X., Huete, A., Cleverly, J., Eamus, D., Chevallier, F., Joiner, J.,
Poulter, B., Zhang, Y., Guanter, L., Meyer, W., Xie, Z., and Ponce-Campos,
G.: Drought rapidly diminishes the large net CO2 uptake in 2011 over
semi-arid Australia, Sci. Rep., 6, 37747,
https://doi.org/10.1038/srep37747, 2016. a
Merbold, L., Ardö, J., Arneth, A., Scholes, R. J., Nouvellon, Y.,
de Grandcourt, A., Archibald, S., Bonnefond, J. M., Boulain, N., Brueggemann,
N., Bruemmer, C., Cappelaere, B., Ceschia, E., El-Khidir, H. A. M., El-Tahir,
B. A., Falk, U., Lloyd, J., Kergoat, L., Le Dantec, V., Mougin, E., Muchinda,
M., Mukelabai, M. M., Ramier, D., Roupsard, O., Timouk, F., Veenendaal,
E. M., and Kutsch, W. L.: Precipitation as driver of carbon fluxes in 11
African ecosystems, Biogeosciences, 6, 1027–1041,
https://doi.org/10.5194/bg-6-1027-2009, 2009. a
Miller, S. M. and Michalak, A. M.: The impact of improved satellite retrievals
on estimates of biospheric carbon balance, Atmos. Chem. Phys.,
20, 323–331, https://doi.org/10.5194/acp-20-323-2020, 2020. a
Monteil, G., Broquet, G., Scholze, M., Lang, M., Karstens, U., Gerbig, C.,
Koch, F.-T., Smith, N. E., Thompson, R. L., Luijkx, I. T., White, E.,
Meesters, A., Ciais, P., Ganesan, A. L., Manning, A., Mischurow, M., Peters,
W., Peylin, P., Tarniewicz, J., Rigby, M., Rödenbeck, C., Vermeulen, A.,
and Walton, E. M.: The regional European atmospheric transport inversion
comparison, EUROCOM: first results on European-wide terrestrial carbon fluxes
for the period 2006–2015, Atmos. Chem. Phys., 20,
12063–12091, https://doi.org/10.5194/acp-20-12063-2020, 2020. a
Nassar, R., Napier-Linton, L., Gurney, K. R., Andres, R. J., Oda, T., Vogel,
F. R., and Deng, F.: Improving the temporal and spatial distribution of
CO2 emissions from global fossil fuel emission data sets, J.
Geophys. Res.-Atmos., 118, 917–933, https://doi.org/10.1029/2012JD018196,
2013. a
OCO-2 Data Quality Statement: Orbiting Carbon Observatory-2 (OCO-2) Data
Quality Statement: Level 2 Forward Processing Data Release 10 (V10),
https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_DQ_Statement.V10.pdf (last access: 20 May 2022),
2020. a
Oda, T., Maksyutov, S., and Andres, R. J.: The Open-source Data Inventory for
Anthropogenic CO2, version 2016 (ODIAC2016): a global monthly fossil
fuel CO2 gridded emissions data product for tracer transport simulations
and surface flux inversions, Earth Syst. Sci. Data, 10, 87–107,
https://doi.org/10.5194/essd-10-87-2018, 2018. a
O'Dell, C. W., Eldering, A., Wennberg, P. O., Crisp, D., Gunson, M. R., Fisher,
B., Frankenberg, C., Kiel, M., Lindqvist, H., Mandrake, L., Merrelli, A.,
Natraj, V., Nelson, R. R., Osterman, G. B., Payne, V. H., Taylor, T. E.,
Wunch, D., Drouin, B. J., Oyafuso, F., Chang, A., McDuffie, J., Smyth, M.,
Baker, D. F., Basu, S., Chevallier, F., Crowell, S. M. R., Feng, L., Palmer,
P. I., Dubey, M., García, O. E., Griffith, D. W. T., Hase, F., Iraci,
L. T., Kivi, R., Morino, I., Notholt, J., Ohyama, H., Petri, C., Roehl,
C. M., Sha, M. K., Strong, K., Sussmann, R., Te, Y., Uchino, O., and Velazco,
V. A.: Improved retrievals of carbon dioxide from Orbiting Carbon
Observatory-2 with the version 8 ACOS algorithm, Atmos. Meas.
Tech., 11, 6539–6576, https://doi.org/10.5194/amt-11-6539-2018, 2018. a, b
Peiro, H., Crowell, S., Schuh, A., Baker, D. F., O'Dell, C., Jacobson, A. R., Chevallier, F., Liu, J., Eldering, A., Crisp, D., Deng, F., Weir, B., Basu, S., Johnson, M. S., Philip, S., and Baker, I.: Four years of global carbon cycle observed from the Orbiting Carbon Observatory 2 (OCO-2) version 9 and in situ data and comparison to OCO-2 version 7, Atmos. Chem. Phys., 22, 1097–1130, https://doi.org/10.5194/acp-22-1097-2022, 2022. a, b, c, d, e, f
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. a
Pollard, D. F., Sherlock, V., Robinson, J., Deutscher, N. M., Connor, B., and
Shiona, H.: The Total Carbon Column Observing Network site description for
Lauder, New Zealand, Earth Syst. Sci. Data, 9, 977–992,
https://doi.org/10.5194/essd-9-977-2017, 2017. a
Pollard, D. F., Robinson, J., and Shiona, H.: TCCON data from Lauder, New
Zealand, 125HR, Release GGG2014R0.TCCON data archive, hosted by CaltechDATA,
California Institute of Technology, Pasadena, CA, USA, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.lauder03.R0, 2019. a
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J., Broquet,
G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W., Sitch, S.,
and van der Werf, G. R.: Contribution of semi-arid ecosystems to interannual
variability of the global carbon cycle, Nature, 509, 600–603,
https://doi.org/10.1038/nature13376, 2014. a, b
Rayner, P. J., Michalak, A. M., and Chevallier, F.: Fundamentals of data
assimilation applied to biogeochemistry, Atmos. Chem. Phys.,
19, 13911–13932, https://doi.org/10.5194/acp-19-13911-2019, 2019. a
Running, S., Mu, Q., and Zhao, M.: MOD17A2H MODIS/terra gross primary
productivity 8-day L4 global 500m SIN grid V006,
https://lpdaac.usgs.gov/products/mod17a2hv006 (last access:
9-10 September 2019), 2015. a
Schuh, A. E., Jacobson, A. R., Basu, S., Weir, B., Baker, D., Bowman, K.,
Chevallier, F., Crowell, S., Davis, K. J., Deng, F., Denning, S., Feng, L.,
Jones, D., Liu, J., and Palmer, P. I.: Quantifying the Impact of Atmospheric
Transport Uncertainty on CO2 Surface Flux Estimates, Global
Biogeochem. Cy., 33, 484–500,
https://doi.org/10.1029/2018GB006086, 2019. a
Sherlock, V., Connor, B., Robinson, J., Shiona, H., Smale, D., and Pollard, D.:
TCCON data from Lauder, New Zealand, 125HR, Release GGG2014R0. TCCON data
archive, hosted by CaltechDATA, California Institute of Technology, Pasadena,
CA, USA, CaltechDATA, https://doi.org/10.14291/tccon.ggg2014.lauder02.R0/1149298,
2017. a
Sitch, S., Friedlingstein, P., Gruber, N., Jones, S. D., Murray-Tortarolo, G.,
Ahlström, A., Doney, S. C., Graven, H., Heinze, C., Huntingford, C.,
Levis, S., Levy, P. E., Lomas, M., Poulter, B., Viovy, N., Zaehle, S., Zeng,
N., Arneth, A., Bonan, G., Bopp, L., Canadell, J. G., Chevallier, F., Ciais,
P., Ellis, R., Gloor, M., Peylin, P., Piao, S. L., Le Quéré,
C., Smith, B., Zhu, Z., and Myneni, R.: Recent trends and drivers of
regional sources and sinks of carbon dioxide, Biogeosciences, 12, 653–679,
https://doi.org/10.5194/bg-12-653-2015, 2015. a
Skamarock, W., Klemp, J., Dudhi, J., Gill, D., Barker, D., Duda, M., Huang,
X.-Y., Wang, W., and Powers, J.: A Description of the Advanced Research WRF
Version 3, Tech. Rep., p. 113, https://doi.org/10.5065/D6DZ069T, 2008. a
Tarantola, A.: Inverse Problem Theory: methods for data fitting and model
parameter estimation, Elsevier, https://doi.org/10.1137/1.9780898717921, 1987. a, b
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. a, b
Thomas, S.: Python 4-dimensional variational data assimilation tool, Apalache Licence 2.0,
Copyright 2016 University of Melbourne,
https://github.com/steven-thomas/py4dvar, last access: 12 July 2020. a
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly,
B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L.,
Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon
dioxide and energy fluxes across global FLUXNET sites with regression
algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016,
2016. a
Trudinger, C. M., Haverd, V., Briggs, P. R., and Canadell, J. G.: Interannual
variability in Australia's terrestrial carbon cycle constrained by multiple
observation types, Biogeosciences, 13, 6363–6383,
https://doi.org/10.5194/bg-13-6363-2016, 2016. a, b
Villalobos, Y., Rayner, P., Thomas, S., and Silver, J.: The potential of
Orbiting Carbon Observatory-2 data to reduce the uncertainties in
CO2 surface fluxes over Australia using a variational assimilation
scheme, Atmos. Chem. Phys., 20, 8473–8500,
https://doi.org/10.5194/acp-20-8473-2020, 2020. a, b, c, d, e, f
Villalobos, Y., Rayner, P. J., Silver, J. D., Thomas, S., Haverd, V., Knauer,
J., Loh, Z. M., Deutscher, N. M., Griffith, D. W. T., and Pollard, D. F.: Was
Australia a sink or source of CO2 in 2015? Data assimilation using OCO-2
satellite measurements, Atmos. Chem. Phys., 21,
17453–17494, https://doi.org/10.5194/acp-21-17453-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m
Villalobos, Y., Rayner, P. J., Silver, J. D., Thomas, S., Haverd, V., Knauer, J., Loh, Z. M., Deutscher, N. M., Griffith, D. W. T., and Pollard, D. F.: Data associated with the publication “Interannual variability in the Australian carbon cycle over 2015–2019, based on assimilation of OCO-2 satellite data”, Zenodo [data set], https://doi.org/10.5281/zenodo.6649768, 2022. a
Wang, Y. P., Law, R. M., and Pak, B.: A global model of carbon, nitrogen and
phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7,
2261–2282, https://doi.org/10.5194/bg-7-2261-2010, 2010. a
Williams, C. A., Hanan, N. P., Baker, I., Collatz, G. J., Berry, J., and
Denning, A. S.: Interannual variability of photosynthesis across Africa and
its attribution, J. Geophys. Res.-Biogeo., 113, 15, https://doi.org/10.1029/2008JG000718, 2008.
a
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.
R. Soc. A, 369,
2087–2112, https://doi.org/10.1098/rsta.2010.0240, 2011. a, b
Zhao, C. L. and Tans, P. P.: Estimating uncertainty of the WMO mole fraction
scale for carbon dioxide in air, J. Geophys. Res.-Atmos., 111, 10, https://doi.org/10.1029/2005JD006003, 2006. a
Short summary
We study the interannual variability in Australian carbon fluxes for 2015–2019 derived from OCO-2 satellite data. Our results suggest that Australia's semi-arid ecosystems are highly responsive to variations in climate drivers such as rainfall and temperature. We found that high rainfall and low temperatures recorded in 2016 led to an anomalous carbon sink over savanna and sparsely vegetated regions, while unprecedented dry and hot weather in 2019 led to anomalous carbon release.
We study the interannual variability in Australian carbon fluxes for 2015–2019 derived from...
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