Articles | Volume 22, issue 24
https://doi.org/10.5194/acp-22-15817-2022
© 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-22-15817-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2022
Research article |
| 16 Dec 2022
| 16 Dec 2022
Optimizing 4 years of CO2 biospheric fluxes from OCO-2 and in situ data in TM5: fire emissions from GFED and inferred from MOPITT CO data
Hélène Peiro
CORRESPONDING AUTHOR
GeoCarb Mission Collaboration, University of Oklahoma, Norman, OK, USA
Sean Crowell
GeoCarb Mission Collaboration, University of Oklahoma, Norman, OK, USA
Berrien Moore III
GeoCarb Mission Collaboration, University of Oklahoma, Norman, OK, USA
Related authors
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Russell Doughty, Michael C. Wimberly, Dan Wanyama, Helene Peiro, Nicholas Parazoo, Sean Crowell, and Moses Azong Cho
Biogeosciences, 22, 1985–2004, https://doi.org/10.5194/bg-22-1985-2025, https://doi.org/10.5194/bg-22-1985-2025, 2025
Short summary
Short summary
We find West African solar-induced fluorescence (SIF) increases during the dry season and peaks before precipitation, similar to the Amazon. In central Africa, a continental-scale bimodal SIF seasonality appears; its minimum aligns with precipitation, but its maximum seems less environmentally driven. Notably, differences between SIF and vegetation index (VI) seasonality indicate VI-based photosynthesis estimates may be inaccurate.
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.
Hélène Peiro, Sean Crowell, Andrew Schuh, David F. Baker, Chris O'Dell, Andrew R. Jacobson, Frédéric Chevallier, Junjie Liu, Annmarie Eldering, David Crisp, Feng Deng, Brad Weir, Sourish Basu, Matthew S. Johnson, Sajeev Philip, and Ian Baker
Atmos. Chem. Phys., 22, 1097–1130, https://doi.org/10.5194/acp-22-1097-2022, https://doi.org/10.5194/acp-22-1097-2022, 2022
Short summary
Short summary
Satellite CO2 observations are constantly improved. We study an ensemble of different atmospheric models (inversions) from 2015 to 2018 using separate ground-based data or two versions of the OCO-2 satellite. Our study aims to determine if different satellite data corrections can yield different estimates of carbon cycle flux. A difference in the carbon budget between the two versions is found over tropical Africa, which seems to show the impact of corrections applied in satellite data.
Hélène Peiro, Emanuele Emili, Daniel Cariolle, Brice Barret, and Eric Le Flochmoën
Atmos. Chem. Phys., 18, 6939–6958, https://doi.org/10.5194/acp-18-6939-2018, https://doi.org/10.5194/acp-18-6939-2018, 2018
Short summary
Short summary
Tropospheric ozone (O3) is the third most important greenhouse gas. Several sensors on satellites measure O3 concentration like the infrared sounding IASI of MetOp satellites with a spatiotemporal coverage never reached so far. The assimilation of tropospheric IASI data within the chemistry transport model MOCAGE gives a good representation of O3 variability over the tropics linked with ENSO and the Walker circulation. IASI assimilation can contribute to the long-term monitoring of O3.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Russell Doughty, Michael C. Wimberly, Dan Wanyama, Helene Peiro, Nicholas Parazoo, Sean Crowell, and Moses Azong Cho
Biogeosciences, 22, 1985–2004, https://doi.org/10.5194/bg-22-1985-2025, https://doi.org/10.5194/bg-22-1985-2025, 2025
Short summary
Short summary
We find West African solar-induced fluorescence (SIF) increases during the dry season and peaks before precipitation, similar to the Amazon. In central Africa, a continental-scale bimodal SIF seasonality appears; its minimum aligns with precipitation, but its maximum seems less environmentally driven. Notably, differences between SIF and vegetation index (VI) seasonality indicate VI-based photosynthesis estimates may be inaccurate.
Peter Somkuti, Greg M. McGarragh, Christopher O'Dell, Antonio Di Noia, Leif Vogel, Sean Crowell, Lesley E. Ott, and Hartmut Bösch
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-145, https://doi.org/10.5194/amt-2024-145, 2025
Revised manuscript accepted for AMT
Short summary
Short summary
In space-based estimates of atmospheric methane concentrations, one can often observe biases that look like imprints of surface features. We performed realistic simulation experiments and find the root cause to be unaccounted aerosols. Since good knowledge of aerosols is difficult to achieve for operational science data processing, we conclude that a comprehensive surface bias correction scheme is highly important for missions utilizing the 2.3 µm spectral band for methane retrievals.
Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1, https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
Preprint archived
Short summary
Short summary
Here we present a novel model of global photosynthesis, ChloFluo, which uses spaceborne chlorophyll fluorescence to estimate the amount of photosynthetically active radiation absorbed by chlorophyll. Potential uses of our model are to advance our understanding of the timing and magnitude of photosynthesis, its effect on atmospheric carbon dioxide fluxes, and vegetation response to climate events and change.
Gregory R. McGarragh, Christopher W. O'Dell, Sean M. R. Crowell, Peter Somkuti, Eric B. Burgh, and Berrien Moore III
Atmos. Meas. Tech., 17, 1091–1121, https://doi.org/10.5194/amt-17-1091-2024, https://doi.org/10.5194/amt-17-1091-2024, 2024
Short summary
Short summary
Carbon dioxide and methane are greenhouse gases that have been rapidly increasing due to human activity since the industrial revolution, leading to global warming and subsequently negative affects on the climate. It is important to measure the concentrations of these gases in order to make climate predictions that drive policy changes to mitigate climate change. GeoCarb aims to measure the concentrations of these gases from space over the Americas at unprecedented spatial and temporal scales.
Yuanwei Qin, Xiangming Xiao, Hao Tang, Ralph Dubayah, Russell Doughty, Diyou Liu, Fang Liu, Yosio Shimabukuro, Egidio Arai, Xinxin Wang, and Berrien Moore III
Earth Syst. Sci. Data, 16, 321–336, https://doi.org/10.5194/essd-16-321-2024, https://doi.org/10.5194/essd-16-321-2024, 2024
Short summary
Short summary
Forest definition has two major biophysical parameters, i.e., canopy height and canopy coverage. However, few studies have assessed forest cover maps in terms of these two parameters at a large scale. Here, we assessed the annual forest cover maps in the Brazilian Amazon using 1.1 million footprints of canopy height and canopy coverage. Over 93 % of our forest cover maps are consistent with the FAO forest definition, showing the high accuracy of these forest cover maps in the Brazilian Amazon.
William R. Keely, Steffen Mauceri, Sean Crowell, and Christopher W. O'Dell
Atmos. Meas. Tech., 16, 5725–5748, https://doi.org/10.5194/amt-16-5725-2023, https://doi.org/10.5194/amt-16-5725-2023, 2023
Short summary
Short summary
Measurement errors in satellite observations of CO2 attributed to co-estimated atmospheric variables are corrected using a linear regression on quality-filtered data. We propose a nonlinear method that improves correction against a set of ground truth proxies and allows for high throughput of well-corrected data.
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.
Sean Crowell, Tobias Haist, Michael Tscherpel, Jérôme Caron, Eric Burgh, and Berrien Moore III
Atmos. Meas. Tech., 16, 195–208, https://doi.org/10.5194/amt-16-195-2023, https://doi.org/10.5194/amt-16-195-2023, 2023
Short summary
Short summary
Variations in brightness in radiance measurements cause errors that can be mitigated with hardware that scrambles the pattern of the incoming light. GeoCarb took this route to minimize this source of errors, but lab testing determined that the solution chosen was too sensitive to the the polarization of the incoming light. Modeling found that this was a predictable result of using gold coatings in the design, which is typical of spaceflight optical instruments.
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
Short summary
Short summary
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.
Hélène Peiro, Sean Crowell, Andrew Schuh, David F. Baker, Chris O'Dell, Andrew R. Jacobson, Frédéric Chevallier, Junjie Liu, Annmarie Eldering, David Crisp, Feng Deng, Brad Weir, Sourish Basu, Matthew S. Johnson, Sajeev Philip, and Ian Baker
Atmos. Chem. Phys., 22, 1097–1130, https://doi.org/10.5194/acp-22-1097-2022, https://doi.org/10.5194/acp-22-1097-2022, 2022
Short summary
Short summary
Satellite CO2 observations are constantly improved. We study an ensemble of different atmospheric models (inversions) from 2015 to 2018 using separate ground-based data or two versions of the OCO-2 satellite. Our study aims to determine if different satellite data corrections can yield different estimates of carbon cycle flux. A difference in the carbon budget between the two versions is found over tropical Africa, which seems to show the impact of corrections applied in satellite data.
Susan S. Kulawik, Sean Crowell, David Baker, Junjie Liu, Kathryn McKain, Colm Sweeney, Sebastien C. Biraud, Steve Wofsy, Christopher W. O'Dell, Paul O. Wennberg, Debra Wunch, Coleen M. Roehl, Nicholas M. Deutscher, Matthäus Kiel, David W. T. Griffith, Voltaire A. Velazco, Justus Notholt, Thorsten Warneke, Christof Petri, Martine De Mazière, Mahesh K. Sha, Ralf Sussmann, Markus Rettinger, Dave F. Pollard, Isamu Morino, Osamu Uchino, Frank Hase, Dietrich G. Feist, Sébastien Roche, Kimberly Strong, Rigel Kivi, Laura Iraci, Kei Shiomi, Manvendra K. Dubey, Eliezer Sepulveda, Omaira Elena Garcia Rodriguez, Yao Té, Pascal Jeseck, Pauli Heikkinen, Edward J. Dlugokencky, Michael R. Gunson, Annmarie Eldering, David Crisp, Brendan Fisher, and Gregory B. Osterman
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-257, https://doi.org/10.5194/amt-2019-257, 2019
Publication in AMT not foreseen
Short summary
Short summary
This paper provides a benchmark of OCO-2 v8 and ACOS-GOSAT v7.3 XCO2 and lowermost tropospheric (LMT) errors. The paper focuses on the systematic errors and subtracts out validation, co-location, and random errors, looks at the correlation scale-length (spatially and temporally) of systematic errors, finding that the scale lengths are similar to bias correction scale-lengths. The assimilates of the bias correction term is used to place an error on fluxes estimates.
Sean Crowell, David Baker, Andrew Schuh, Sourish Basu, Andrew R. Jacobson, Frederic Chevallier, Junjie Liu, Feng Deng, Liang Feng, Kathryn McKain, Abhishek Chatterjee, John B. Miller, Britton B. Stephens, Annmarie Eldering, David Crisp, David Schimel, Ray Nassar, Christopher W. O'Dell, Tomohiro Oda, Colm Sweeney, Paul I. Palmer, and Dylan B. A. Jones
Atmos. Chem. Phys., 19, 9797–9831, https://doi.org/10.5194/acp-19-9797-2019, https://doi.org/10.5194/acp-19-9797-2019, 2019
Short summary
Short summary
Space-based retrievals of carbon dioxide offer the potential to provide dense data in regions that are sparsely observed by the surface network. We find that flux estimates that are informed by the Orbiting Carbon Observatory-2 (OCO-2) show different character from that inferred using surface measurements in tropical land regions, particularly in Africa, with a much larger total emission and larger amplitude seasonal cycle.
Jeffrey Nivitanont, Sean M. R. Crowell, and Berrien Moore III
Atmos. Meas. Tech., 12, 3317–3334, https://doi.org/10.5194/amt-12-3317-2019, https://doi.org/10.5194/amt-12-3317-2019, 2019
Short summary
Short summary
A scanning strategy is proposed for the upcoming GeoCarb instrument that minimizes predicted retrieval errors of CO2 by optimizing signal-to-noise ratio with respect to air mass factor and solar zenith angle. The strategy is generated using a modified greedy algorithm that optimizes over these stationary processes while also considering operational constraints. This method increases the number of soundings with predicted CO2 retrieval error less than 2 ppm by 18–41 %.
Christopher W. O'Dell, Annmarie Eldering, Paul O. Wennberg, David Crisp, Michael R. Gunson, Brendan Fisher, Christian Frankenberg, Matthäus Kiel, Hannakaisa Lindqvist, Lukas Mandrake, Aronne Merrelli, Vijay Natraj, Robert R. Nelson, Gregory B. Osterman, Vivienne H. Payne, Thomas E. Taylor, Debra Wunch, Brian J. Drouin, Fabiano Oyafuso, Albert Chang, James McDuffie, Michael Smyth, David F. Baker, Sourish Basu, Frédéric Chevallier, Sean M. R. Crowell, Liang Feng, Paul I. Palmer, Mavendra Dubey, Omaira E. García, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Isamu Morino, Justus Notholt, Hirofumi Ohyama, Christof Petri, Coleen M. Roehl, Mahesh K. Sha, Kimberly Strong, Ralf Sussmann, Yao Te, Osamu Uchino, and Voltaire A. Velazco
Atmos. Meas. Tech., 11, 6539–6576, https://doi.org/10.5194/amt-11-6539-2018, https://doi.org/10.5194/amt-11-6539-2018, 2018
Hélène Peiro, Emanuele Emili, Daniel Cariolle, Brice Barret, and Eric Le Flochmoën
Atmos. Chem. Phys., 18, 6939–6958, https://doi.org/10.5194/acp-18-6939-2018, https://doi.org/10.5194/acp-18-6939-2018, 2018
Short summary
Short summary
Tropospheric ozone (O3) is the third most important greenhouse gas. Several sensors on satellites measure O3 concentration like the infrared sounding IASI of MetOp satellites with a spatiotemporal coverage never reached so far. The assimilation of tropospheric IASI data within the chemistry transport model MOCAGE gives a good representation of O3 variability over the tropics linked with ENSO and the Walker circulation. IASI assimilation can contribute to the long-term monitoring of O3.
S. Crowell, P. Rayner, S. Zaccheo, and B. Moore
Atmos. Meas. Tech., 8, 2685–2697, https://doi.org/10.5194/amt-8-2685-2015, https://doi.org/10.5194/amt-8-2685-2015, 2015
Short summary
Short summary
We derive a yes/no requirement for the usefulness of an O2 lidar as part of the ASCENDS mission that incorporates errors due to atmospheric state misspecification as well as instrumental noise. We find that the larger the CO2 instrument's sensitivity to surface pressure errors, the lower the precision requirement for the O2 instrument to be useful. In particular, the 2um CO2 instrument would benefit the most from the inclusion of an O2 lidar with high precision retrievals of surface pressure.
P. J. Rayner, S. R. Utembe, and S. Crowell
Atmos. Meas. Tech., 7, 3285–3293, https://doi.org/10.5194/amt-7-3285-2014, https://doi.org/10.5194/amt-7-3285-2014, 2014
Related subject area
Subject: Gases | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Distinct structures of interannual variations in stratosphere-to-troposphere ozone transport induced by the Tibetan Plateau thermal forcing
The importance of moist thermodynamics on neutral buoyancy height for plumes from anthropogenic sources
Partitioning anthropogenic and natural methane emissions in Finland during 2000–2021 by combining bottom-up and top-down estimates
A Diagnostic Intercomparison of Modeled Ozone Dry Deposition Over North America and Europe Using AQMEII4 Regional-Scale Simulations
Global CH4 Fluxes Derived from JAXA/GOSAT Lower Tropospheric Partial Column Data and the CTE-CH4 Atmospheric Inverse Model
The role of OCO-3 XCO2 retrievals in estimating global terrestrial net ecosystem exchanges
Inverse modelling of New Zealand's carbon dioxide balance estimates a larger than expected carbon sink
The improved Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST): update, validation and applications
Constraining elemental mercury air–sea exchange using long-term ground-based observations
Quantifying transboundary transport flux of CO over the Tibetan Plateau: variabilities and drivers
Climate Forcing due to Future Ozone Changes: An intercomparison of metrics and methods
Tracing the origins of stratospheric ozone intrusions: direct vs. indirect pathways and their impacts on Central and Eastern China in spring–summer 2019
Flow-dependent observation errors for greenhouse gas inversions in an ensemble Kalman smoother
A WRF-Chem study of the greenhouse gas column and in situ surface concentrations observed at Xianghe, China. Part 1: Methane (CH4)
A Better Understanding of an Extremely High Ozone Episode with Ensemble Simulation
Evaluation and updates to the oxidized reactive nitrogen trace gas dry deposition parameterization from the GEOS-Chem CTM, including a pathway for ground surface NO2 hydrolysis
Observational and model evidence for a prominent stratospheric influence on variability in tropospheric nitrous oxide
Estimation of Canada's methane emissions: inverse modelling analysis using the Environment and Climate Change Canada (ECCC) measurement network
Spatiotemporal source apportionment of ozone pollution over the Greater Bay Area
Potential of 14C-based vs. ΔCO-based ΔffCO2 observations to estimate urban fossil fuel CO2 (ffCO2) emissions
Combined CO2 measurement record indicates decreased Amazon forest carbon uptake, offset by Savannah carbon release
On the uncertainty of anthropogenic aromatic volatile organic compound emissions: model evaluation and sensitivity analysis
A mechanism of stratospheric O3 intrusion into the atmospheric environment: a case study of the North China Plain
Influence of atmospheric circulation on the interannual variability of transport from global and regional emissions into the Arctic
Surface networks in the Arctic may miss a future methane bomb
Potential of using CO2 observations over India in a regional carbon budget estimation by improving the modelling system
A bottom-up emission estimate for the 2022 Nord Stream gas leak: derivation, simulations, and evaluation
Can we gain knowledge on COS anthropogenic and biogenic emissions from a single atmospheric mixing ratios measurement site?
European CH4 inversions with ICON-ART coupled to the CarbonTracker Data Assimilation Shell
Extreme weather exacerbates ozone pollution in the Pearl River Delta, China: role of natural processes
Multidecadal ozone trends in China and implications for human health and crop yields: a hybrid approach combining a chemical transport model and machine learning
On the influence of vertical mixing, boundary layer schemes, and temporal emission profiles on tropospheric NO2 in WRF-Chem – comparisons to in situ, satellite, and MAX-DOAS observations
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling
The sensitivity of Southern Ocean atmospheric dimethyl sulfide (DMS) to modeled oceanic DMS concentrations and emissions
Impacts of maritime shipping on air pollution along the US East Coast
Understanding greenhouse gas (GHG) column concentrations in Munich using the Weather Research and Forecasting (WRF) model
Impact of transport model resolution and a priori assumptions on inverse modeling of Swiss F-gas emissions
Estimation of power plant SO2 emissions using the HYSPLIT dispersion model and airborne observations with plume rise ensemble runs
Can we use atmospheric CO2 measurements to verify emission trends reported by cities? Lessons from a 6-year atmospheric inversion over Paris
A new steady-state gas–particle partitioning model of polycyclic aromatic hydrocarbons: implication for the influence of the particulate proportion in emissions
An analysis of CMAQ gas-phase dry deposition over North America through grid-scale and land-use-specific diagnostics in the context of AQMEII4
Rethinking the role of transport and photochemistry in regional ozone pollution: insights from ozone concentration and mass budgets
Decreasing seasonal cycle amplitude of methane in the northern high latitudes being driven by lower-latitude changes in emissions and transport
The effect of anthropogenic emission, meteorological factors, and carbon dioxide on the surface ozone increase in China from 2008 to 2018 during the East Asia summer monsoon season
Development of a CMAQ–PMF-based composite index for prescribing an effective ozone abatement strategy: a case study of sensitivity of surface ozone to precursor volatile organic compound species in southern Taiwan
Comment on “Climate consequences of hydrogen emissions” by Ocko and Hamburg (2022)
Constraining emissions of volatile organic compounds from western US wildfires with WE-CAN and FIREX-AQ airborne observations
Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action
Coupled mesoscale–microscale modeling of air quality in a polluted city using WRF-LES-Chem
Impact of aerosol optics on vertical distribution of ozone in autumn over Yangtze River Delta
Qingjian Yang, Tiangliang Zhao, Yongqing Bai, Kai Meng, Yuehan Luo, Zhijie Tian, Xiaoyun Sun, Weikang Fu, Kai Yang, and Jun Hu
EGUsphere, https://doi.org/10.5194/egusphere-2025-737, https://doi.org/10.5194/egusphere-2025-737, 2025
Short summary
Short summary
This study reveals a unique driver of the Tibetan Plateau (TP) thermal forcing on the interannual variations in stratosphere-to-troposphere transport (STT) of ozone with diverse structures. Anomalous strong TP thermal forcing induces anticyclonic anomalies in the upper troposphere over the TP, which strengthens and attenuates the northern and southern branches of the westerly jet, intensifying and weakening the westerly trough for more (less) tropopause folds of ozone STT over East Asian region.
Sepehr Fathi, Paul Makar, Wanmin Gong, Junhua Zhang, Katherine Hayden, and Mark Gordon
Atmos. Chem. Phys., 25, 2385–2405, https://doi.org/10.5194/acp-25-2385-2025, https://doi.org/10.5194/acp-25-2385-2025, 2025
Short summary
Short summary
Our study explores the influence of water phase changes in plumes from industrial sources on atmospheric dispersion of emitted pollutants and air quality. Employing PRISM (Plume-Rise-Iterative-Stratified-Moist), a new method, we found that considering these effects significantly improves predictions of pollutant dispersion. This insight enhances our understanding of environmental impacts, enabling more accurate air quality modelling and fostering more effective pollution management strategies.
Maria K. Tenkanen, Aki Tsuruta, Hugo Denier van der Gon, Lena Höglund-Isaksson, Antti Leppänen, Tiina Markkanen, Ana Maria Roxana Petrescu, Maarit Raivonen, Hermanni Aaltonen, and Tuula Aalto
Atmos. Chem. Phys., 25, 2181–2206, https://doi.org/10.5194/acp-25-2181-2025, https://doi.org/10.5194/acp-25-2181-2025, 2025
Short summary
Short summary
Accurate national methane (CH4) emission estimates are essential for tracking progress towards climate goals. This study compares estimates from Finland, which use different methods and scales, and shows how well a global model estimates emissions within a country. The bottom-up estimates vary a lot, but constraining them with atmospheric CH4 measurements brought the estimates closer together. We also highlight the importance of quantifying natural emissions alongside anthropogenic emissions.
Christian Hogrefe, Stefano Galmarini, Paul A. Makar, Ioannis Kioutsioukis, Olivia E. Clifton, Ummugulsum Alyuz, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Tim Butler, Philip Cheung, Alma Hodzic, Richard Kranenburg, Aurelia Lupascu, Kester Momoh, Juan Luis Perez-Camanyo, Jonathan E. Pleim, Young-Hee Ryu, Roberto San Jose, Martijn Schaap, Donna B. Schwede, and Ranjeet Sokhi
EGUsphere, https://doi.org/10.5194/egusphere-2025-225, https://doi.org/10.5194/egusphere-2025-225, 2025
Short summary
Short summary
Performed under the umbrella of the fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4), this study applies AQMEII4 diagnostic tools to better characterize how dry deposition removes pollutants from the atmosphere in regional-scale models. The results also strongly suggest that improvement and harmonization of the representation of land use in these models would serve the community in their future development efforts.
Aki Tsuruta, Akihiko Kuze, Kei Shiomi, Fumie Kataoka, Nobuhiro Kikuchi, Tuula Aalto, Leif Backman, Ella Kivimäki, Maria K. Tenkanen, Kathryn McKain, Omaira E. García, Frank Hase, Rigel Kivi, Isamu Morino, Hirofumi Ohyama, David F. Pollard, Mahesh K. Sha, Kimberly Strong, Ralf Sussmann, Yao Te, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, Minqiang Zhou, and Hiroshi Suto
EGUsphere, https://doi.org/10.5194/egusphere-2025-159, https://doi.org/10.5194/egusphere-2025-159, 2025
Short summary
Short summary
Satellite data bring invaluable information about greenhouse gas emissions globally. We found that a new type of data from the Greenhouse Gas Observing Satellite (GOSAT), which contains information about methane in the lowest layer of Earth's atmosphere, could provide reliable estimates of recent methane emissions when combined with atmospheric modelling. Therefore, the use of such data is encouraged to improve emission quantification methods and advance our understanding of methane cycles.
Xingyu Wang, Fei Jiang, Hengmao Wang, Zhengqi Zhang, Mousong Wu, Jun Wang, Wei He, Weimin Ju, and Jing M. Chen
Atmos. Chem. Phys., 25, 867–880, https://doi.org/10.5194/acp-25-867-2025, https://doi.org/10.5194/acp-25-867-2025, 2025
Short summary
Short summary
The role of OCO-3 XCO2 retrievals in estimating global terrestrial carbon fluxes is unclear. We investigate this by assimilating OCO-3 XCO2 retrievals alone and in combination with OCO-2 XCO2. The assimilation of OCO-3 XCO2 alone underestimates global land sinks, mainly at high latitudes, due to the lack of observations beyond 52° S and 52° N, large variations in the number of data, and varying observation times, while the joint assimilation of OCO-2 and OCO-3 XCO2 has the best performance.
Beata Bukosa, Sara Mikaloff-Fletcher, Gordon Brailsford, Dan Smale, Elizabeth D. Keller, W. Troy Baisden, Miko U. F. Kirschbaum, Donna L. Giltrap, Lìyı̌n Liáng, Stuart Moore, Rowena Moss, Sylvia Nichol, Jocelyn Turnbull, Alex Geddes, Daemon Kennett, Dora Hidy, Zoltán Barcza, Louis A. Schipper, Aaron M. Wall, Shin-Ichiro Nakaoka, Hitoshi Mukai, and Andrea Brandon
EGUsphere, https://doi.org/10.5194/egusphere-2024-3866, https://doi.org/10.5194/egusphere-2024-3866, 2025
Short summary
Short summary
We used atmospheric measurements and inverse modeling to estimate New Zealand's carbon dioxide (CO₂) emissions and removals from 2011 to 2020. Our study reveals that New Zealand's land absorbs more CO₂ than previously estimated, particularly in areas dominated by indigenous forests. Our results highlight gaps in current national CO₂ estimates and methods, suggesting a need for further research to improve emissions reporting and refine approaches to track progress toward climate mitigation goals.
Zhou Zang, Jane Liu, David Tarasick, Omid Moeini, Jianchun Bian, Jinqiang Zhang, Anne M. Thompson, Roeland Van Malderen, Herman G. J. Smit, Ryan M. Stauffer, Bryan J. Johnson, and Debra E. Kollonige
Atmos. Chem. Phys., 24, 13889–13912, https://doi.org/10.5194/acp-24-13889-2024, https://doi.org/10.5194/acp-24-13889-2024, 2024
Short summary
Short summary
The Trajectory-mapped Ozonesonde dataset for the Stratosphere and Troposphere (TOST) provides a global-scale, long-term ozone climatology that is horizontally and vertically resolved. In this study, we improved, updated and validated TOST from 1970 to 2021. Based on this TOST dataset, we characterized global ozone variations spatially in both the troposphere and stratosphere and temporally by season and decade. We also showed a stagnant lower stratospheric ozone variation since the late 1990s.
Koketso Michelle Molepo, Johannes Bieser, Alkuin Maximilian Koenig, Ian Michael Hedgecock, Ralf Ebinghaus, Aurélien Dommergue, Olivier Magand, Hélène Angot, Oleg Travnikov, Lynwill Martin, Casper Labuschagne, Katie Read, and Yann Bertrand
EGUsphere, https://doi.org/10.5194/egusphere-2024-3722, https://doi.org/10.5194/egusphere-2024-3722, 2024
Short summary
Short summary
Mercury exchange between the ocean and atmosphere is poorly understood due to limited in situ data. Here, using atmospheric mercury observations from ground-based monitoring stations along with air mass trajectories, we found that atmospheric Hg levels increase with air mass ocean exposure time, matching predictions for ocean mercury emissions. This finding indicates that ocean emissions directly influence atmospheric mercury levels and enables us to estimate these emissions on a global scale.
Zhenda Sun, Hao Yin, Zhongfeng Pan, Chongyang Li, Xiao Lu, Youwen Sun, and Cheng Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3252, https://doi.org/10.5194/egusphere-2024-3252, 2024
Short summary
Short summary
This study investigates the variability and driving force of transboundary transport flux of carbon monoxide (CO) over the Tibetan Plateau from May 2018 to April 2024. We discovered that CO levels peak in late autumn and winter, driven mainly by external influx from South Asia, while internal emissions have decreased slightly. The findings reveal a growing trend in CO concentrations in the region, emphasizing the Tibetan Plateau's role as a significant receptor for transboundary pollutants.
William J. Collins, Fiona M. O'Connor, Connor R. Barker, Rachael E. Byrom, Sebastian D. Eastham, Øivind Hodnebrog, Patrick Jöckel, Eloise A. Marais, Mariano Mertens, Gunnar Myhre, Matthias Nützel, Dirk Olivié, Ragnhild Bieltvedt Skeie, Laura Stecher, Larry W. Horowitz, Vaishali Naik, Gregory Faluvegi, Ulas Im, Lee T. Murray, Drew Shindell, Kostas Tsigaridis, Nathan Luke Abraham, and James Keeble
EGUsphere, https://doi.org/10.5194/egusphere-2024-3698, https://doi.org/10.5194/egusphere-2024-3698, 2024
Short summary
Short summary
If reductions aren’t implemented to limit emissions of pollutants that produce ozone then we calculate that this will cause a warming of climate. We assess how the future warming from ozone is affected by changing meteorological variables such as clouds and atmospheric temperatures. We find that reductions in high cloud cover tend to slightly reduce the warming from ozone.
Kai Meng, Tianliang Zhao, Yongqing Bai, Ming Wu, Le Cao, Xuewei Hou, Yuehan Luo, and Yongcheng Jiang
Atmos. Chem. Phys., 24, 12623–12642, https://doi.org/10.5194/acp-24-12623-2024, https://doi.org/10.5194/acp-24-12623-2024, 2024
Short summary
Short summary
We studied the impact of stratospheric intrusions (SIs) on tropospheric and near-surface ozone in Central and Eastern China from a stratospheric source tracing perspective. SIs contribute the most in the eastern plains, with a contribution exceeding 15 %, and have a small contribution to the west and south. Western Siberia and Mongolia are the most critical source areas for indirect and direct SIs, with the Rossby wave and northeast cold vortex being important driving circulation systems.
Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner
Atmos. Chem. Phys., 24, 12447–12463, https://doi.org/10.5194/acp-24-12447-2024, https://doi.org/10.5194/acp-24-12447-2024, 2024
Short summary
Short summary
Atmospheric greenhouse gas inversions have great potential to independently check reported bottom-up emissions; however they are subject to large uncertainties. It is paramount to address and reduce the largest source of uncertainty, which stems from the representation of atmospheric transport in the models. In this study, we show that the use of a temporally varying flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation in an idealized experiment.
Sieglinde Callewaert, Minqiang Zhou, Bavo Langerock, Pucai Wang, Ting Wang, Emmanuel Mahieu, and Martine De Mazière
EGUsphere, https://doi.org/10.5194/egusphere-2024-3228, https://doi.org/10.5194/egusphere-2024-3228, 2024
Short summary
Short summary
We used an atmospheric transport model and satellite data to study CH4 observations at Xianghe, China. Our study shows the key source sectors that influence the concentrations and their respective importance. Furthermore, meteorological factors such as wind direction are discussed. This research highlights the challenges in accurately simulating these kind of measurements and helps us to better understand CH4 variability in the region.
Jinhui Gao and Hui Xiao
EGUsphere, https://doi.org/10.5194/egusphere-2024-3070, https://doi.org/10.5194/egusphere-2024-3070, 2024
Short summary
Short summary
The ensemble simulation provides a solution to better reproduce and understand the extremely high ozone episode occurred in the GBA when Typhoon NIDA approached South China in summer 2016. Elevated temperatures and weak winds contributed to the increase in ozone precursors, resulting in enhanced ozone production. Large amounts of ozone aloft also contributed to the increase in surface ozone via vertical transport. Notably, ozone from Southeast Asia contributed through this mechanism.
Brian L. Boys, Randall V. Martin, and Trevor C. VandenBoer
EGUsphere, https://doi.org/10.5194/egusphere-2024-2994, https://doi.org/10.5194/egusphere-2024-2994, 2024
Short summary
Short summary
A widely used dry deposition parameterization for NO2 is updated by including a well-known heterogeneous hydrolysis reaction on deposition surfaces. This mechanistic update eliminates a large low bias of -80 % in simulated NO2 nocturnal deposition velocities evaluated against long-term eddy covariance flux observations over Harvard Forest. We highlight the importance of canopy surface area effects as well as soil NO emission in formulating and evaluating NO2 dry deposition parameterizations.
Cynthia D. Nevison, Qing Liang, Paul A. Newman, Britton B. Stephens, Geoff Dutton, Xin Lan, Roisin Commane, Yenny Gonzalez, and Eric Kort
Atmos. Chem. Phys., 24, 10513–10529, https://doi.org/10.5194/acp-24-10513-2024, https://doi.org/10.5194/acp-24-10513-2024, 2024
Short summary
Short summary
This study examines the drivers of interannual variability in tropospheric N2O. New insights are obtained from aircraft data and a chemistry–climate model that explicitly simulates stratospheric N2O. The stratosphere is found to be the dominant driver of N2O variability in the Northern Hemisphere, while both the stratosphere and El Niño cycles are important in the Southern Hemisphere. These results are consistent with known atmospheric dynamics and differences between the hemispheres.
Misa Ishizawa, Douglas Chan, Doug Worthy, Elton Chan, Felix Vogel, Joe R. Melton, and Vivek K. Arora
Atmos. Chem. Phys., 24, 10013–10038, https://doi.org/10.5194/acp-24-10013-2024, https://doi.org/10.5194/acp-24-10013-2024, 2024
Short summary
Short summary
Methane (CH4) emissions in Canada for 2007–2017 were estimated using Canada’s surface greenhouse gas measurements. The estimated emissions show no significant trend, but emission uncertainty was reduced as more measurement sites became available. Notably for climate change, we find the wetland CH4 emissions show a positive correlation with surface air temperature in summer. Canada’s measurement network could monitor future CH4 emission changes and compliance with climate change mitigation goals.
Yiang Chen, Xingcheng Lu, and Jimmy C. H. Fung
Atmos. Chem. Phys., 24, 8847–8864, https://doi.org/10.5194/acp-24-8847-2024, https://doi.org/10.5194/acp-24-8847-2024, 2024
Short summary
Short summary
This study investigates the contribution of pollutants from different emitting periods to ozone episodes over the Greater Bay Area. The analysis reveals the variation in major spatiotemporal contributors to the O3 pollution under the influence of typhoons and subtropical high pressure. Through temporal contribution analysis, our work offers a new perspective on the evolution of O3 pollution and can aid in developing effective and timely control policies under unfavorable weather conditions.
Fabian Maier, Christian Rödenbeck, Ingeborg Levin, Christoph Gerbig, Maksym Gachkivskyi, and Samuel Hammer
Atmos. Chem. Phys., 24, 8183–8203, https://doi.org/10.5194/acp-24-8183-2024, https://doi.org/10.5194/acp-24-8183-2024, 2024
Short summary
Short summary
We investigate the usage of discrete radiocarbon (14C)-based fossil fuel carbon dioxide (ffCO2) concentration estimates vs. continuous carbon monoxide (CO)-based ffCO2 estimates to evaluate the seasonal cycle of ffCO2 emissions in an urban region with an inverse modeling framework. We find that the CO-based ffCO2 estimates allow us to reconstruct robust seasonal cycles, which show the distinct COVID-19 drawdown in 2020 and can be used to validate emission inventories.
Santiago Botía, Saqr Munassar, Thomas Koch, Danilo Custodio, Luana S. Basso, Shujiro Komiya, Jost V. Lavric, David Walter, Manuel Gloor, Giordane Martins, Stijn Naus, Gerbrand Koren, Ingrid Luijkx, Stijn Hantson, John B. Miller, Wouter Peters, Christian Rödenbeck, and Christoph Gerbig
EGUsphere, https://doi.org/10.5194/egusphere-2024-1735, https://doi.org/10.5194/egusphere-2024-1735, 2024
Short summary
Short summary
This study uses CO2 data from the Amazon Tall Tower Observatory and airborne profiles to estimate net carbon exchange. We found that the biogeographic Amazon is a net carbon sink, while the Cerrado and Caatinga biomes are net carbon sources, resulting in an overall neutral balance. To further reduce the uncertainty in our estimates we call for an expansion of the monitoring capacity, especially in the Amazon-Andes foothills.
Kevin Oliveira, Marc Guevara, Oriol Jorba, Hervé Petetin, Dene Bowdalo, Carles Tena, Gilbert Montané Pinto, Franco López, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 24, 7137–7177, https://doi.org/10.5194/acp-24-7137-2024, https://doi.org/10.5194/acp-24-7137-2024, 2024
Short summary
Short summary
In this work, we assess and evaluate benzene, toluene, and xylene primary emissions and air quality levels in Spain by combining observations, emission inventories, and air quality modelling techniques. The comparison between modelled and observed levels allows identifying uncertainty sources within the emission input. This contributes to improving air quality models' performance when simulating these compounds, leading to better support for the design of effective pollution control strategies.
Yuehan Luo, Tianliang Zhao, Kai Meng, Jun Hu, Qingjian Yang, Yongqing Bai, Kai Yang, Weikang Fu, Chenghao Tan, Yifan Zhang, Yanzhe Zhang, and Zhikuan Li
Atmos. Chem. Phys., 24, 7013–7026, https://doi.org/10.5194/acp-24-7013-2024, https://doi.org/10.5194/acp-24-7013-2024, 2024
Short summary
Short summary
We reveal a significant mechanism of stratospheric O3 intrusion (SI) into the atmospheric environment induced by an extratropical cyclone system. This system facilitates the downward transport of stratospheric O3 to the near-surface layer by vertical coupling, involving the upper westerly trough, the middle northeast cold vortex, and the lower extratropical cyclone in the troposphere. On average, stratospheric O3 contributed 26.77 % to near-surface O3 levels over the North China Plain.
Cheng Zheng, Yutian Wu, Mingfang Ting, and Clara Orbe
Atmos. Chem. Phys., 24, 6965–6985, https://doi.org/10.5194/acp-24-6965-2024, https://doi.org/10.5194/acp-24-6965-2024, 2024
Short summary
Short summary
Trace gases and aerosols in the Arctic, which typically originate from midlatitude and tropical emission regions, modulate the Arctic climate via their radiative and chemistry impacts. Thus, long-range transport of these substances is important for understanding the current and the future change of Arctic climate. By employing chemistry–climate models, we explore how year-to-year variations in the atmospheric circulation modulate atmospheric long-range transport into the Arctic.
Sophie Wittig, Antoine Berchet, Isabelle Pison, Marielle Saunois, and Jean-Daniel Paris
Atmos. Chem. Phys., 24, 6359–6373, https://doi.org/10.5194/acp-24-6359-2024, https://doi.org/10.5194/acp-24-6359-2024, 2024
Short summary
Short summary
The aim of this work is to analyse how accurately a methane bomb event could be detected with the current and a hypothetically extended stationary observation network in the Arctic. For this, we incorporate synthetically modelled possible future CH4 concentrations based on plausible emission scenarios into an inverse modelling framework. We analyse how well the increase is detected in different Arctic regions and evaluate the impact of additional observation sites in this respect.
Vishnu Thilakan, Dhanyalekshmi Pillai, Jithin Sukumaran, Christoph Gerbig, Haseeb Hakkim, Vinayak Sinha, Yukio Terao, Manish Naja, and Monish Vijay Deshpande
Atmos. Chem. Phys., 24, 5315–5335, https://doi.org/10.5194/acp-24-5315-2024, https://doi.org/10.5194/acp-24-5315-2024, 2024
Short summary
Short summary
This study investigates the usability of CO2 mixing ratio observations over India to infer regional carbon sources and sinks. We demonstrate that a high-resolution modelling system can represent the observed CO2 variations reasonably well by improving the transport and flux variations at a fine scale. Future carbon data assimilation systems can thus benefit from these recently available CO2 observations when fine-scale variations are adequately represented in the models.
Rostislav Kouznetsov, Risto Hänninen, Andreas Uppstu, Evgeny Kadantsev, Yalda Fatahi, Marje Prank, Dmitrii Kouznetsov, Steffen Manfred Noe, Heikki Junninen, and Mikhail Sofiev
Atmos. Chem. Phys., 24, 4675–4691, https://doi.org/10.5194/acp-24-4675-2024, https://doi.org/10.5194/acp-24-4675-2024, 2024
Short summary
Short summary
By relying solely on publicly available media reports, we were able to infer the temporal evolution and the injection height for the Nord Stream gas leaks in September 2022. The inventory specifies locations, vertical distributions, and temporal evolution of the methane sources. The inventory can be used to simulate the event with atmospheric transport models. The inventory is supplemented with a set of observational data tailored to evaluate the results of the simulated atmospheric dispersion.
Antoine Berchet, Isabelle Pison, Camille Huselstein, Clément Narbaud, Marine Remaud, Sauveur Belviso, Camille Abadie, and Fabienne Maignan
EGUsphere, https://doi.org/10.5194/egusphere-2024-549, https://doi.org/10.5194/egusphere-2024-549, 2024
Short summary
Short summary
We use the measurements of atmospheric carbonyl sulfide (COS) concentrations at the monitoring site of Gif-sur-Yvette (in the Paris area) from August 2014 to December 2019, combined with existing knowledge on COS fluxes in the atmosphere and and transport model to gain insight on COS fluxes, either natural such as the oceanic emissions or the vegetation and soil fluxes, or anthropogenic, from industrial activities and power generation.
Michael Steiner, Wouter Peters, Ingrid Luijkx, Stephan Henne, Huilin Chen, Samuel Hammer, and Dominik Brunner
Atmos. Chem. Phys., 24, 2759–2782, https://doi.org/10.5194/acp-24-2759-2024, https://doi.org/10.5194/acp-24-2759-2024, 2024
Short summary
Short summary
The Paris Agreement increased interest in estimating greenhouse gas (GHG) emissions of individual countries, but top-down emission estimation is not yet considered policy-relevant. It is therefore paramount to reduce large errors and to build systems that are based on the newest atmospheric transport models. In this study, we present the first application of ICON-ART in the inverse modeling of GHG fluxes with an ensemble Kalman filter and present our results for European CH4 emissions.
Nan Wang, Hongyue Wang, Xin Huang, Xi Chen, Yu Zou, Tao Deng, Tingyuan Li, Xiaopu Lyu, and Fumo Yang
Atmos. Chem. Phys., 24, 1559–1570, https://doi.org/10.5194/acp-24-1559-2024, https://doi.org/10.5194/acp-24-1559-2024, 2024
Short summary
Short summary
This study explores the influence of extreme-weather-induced natural processes on ozone pollution, which is often overlooked. By analyzing meteorological factors, natural emissions, chemistry pathways and atmospheric transport, we discovered that these natural processes could substantially exacerbate ozone pollution. The findings contribute to a deeper understanding of ozone pollution and offer valuable insights for controlling ozone pollution in the context of global warming.
Jia Mao, Amos P. K. Tai, David H. Y. Yung, Tiangang Yuan, Kong T. Chau, and Zhaozhong Feng
Atmos. Chem. Phys., 24, 345–366, https://doi.org/10.5194/acp-24-345-2024, https://doi.org/10.5194/acp-24-345-2024, 2024
Short summary
Short summary
Surface ozone (O3) is well-known for posing great threats to both human health and agriculture worldwide. However, a multidecadal assessment of the impacts of O3 on public health and agriculture in China is lacking without sufficient O3 observations. We used a hybrid approach combining a chemical transport model and machine learning to provide a robust dataset of O3 concentrations over the past 4 decades in China, thereby filling the gap in the long-term O3 trend and impact assessment in China.
Leon Kuhn, Steffen Beirle, Vinod Kumar, Sergey Osipov, Andrea Pozzer, Tim Bösch, Rajesh Kumar, and Thomas Wagner
Atmos. Chem. Phys., 24, 185–217, https://doi.org/10.5194/acp-24-185-2024, https://doi.org/10.5194/acp-24-185-2024, 2024
Short summary
Short summary
NO₂ is an important air pollutant. It was observed that the WRF-Chem model shows significant deviations in NO₂ abundance when compared to measurements. We use a 1-month simulation over central Europe to show that these deviations can be mostly resolved by reparameterization of the vertical mixing routine. In order to validate our results, they are compared to in situ, satellite, and MAX-DOAS measurements.
Ondřej Tichý, Sabine Eckhardt, Yves Balkanski, Didier Hauglustaine, and Nikolaos Evangeliou
Atmos. Chem. Phys., 23, 15235–15252, https://doi.org/10.5194/acp-23-15235-2023, https://doi.org/10.5194/acp-23-15235-2023, 2023
Short summary
Short summary
We show declining trends in NH3 emissions over Europe for 2013–2020 using advanced dispersion and inverse modelling and satellite measurements from CrIS. Emissions decreased by −26% since 2013, showing that the abatement strategies adopted by the European Union have been very efficient. Ammonia emissions are low in winter and peak in summer due to temperature-dependent soil volatilization. The largest decreases were observed in central and western Europe in countries with high emissions.
Yusuf A. Bhatti, Laura E. Revell, Alex J. Schuddeboom, Adrian J. McDonald, Alex T. Archibald, Jonny Williams, Abhijith U. Venugopal, Catherine Hardacre, and Erik Behrens
Atmos. Chem. Phys., 23, 15181–15196, https://doi.org/10.5194/acp-23-15181-2023, https://doi.org/10.5194/acp-23-15181-2023, 2023
Short summary
Short summary
Aerosols are a large source of uncertainty over the Southern Ocean. A dominant source of sulfate aerosol in this region is dimethyl sulfide (DMS), which is poorly simulated by climate models. We show the sensitivity of simulated atmospheric DMS to the choice of oceanic DMS data set and emission scheme. We show that oceanic DMS has twice the influence on atmospheric DMS than the emission scheme. Simulating DMS more accurately in climate models will help to constrain aerosol uncertainty.
Maryam Golbazi and Cristina Archer
Atmos. Chem. Phys., 23, 15057–15075, https://doi.org/10.5194/acp-23-15057-2023, https://doi.org/10.5194/acp-23-15057-2023, 2023
Short summary
Short summary
We use scientific models to study the impact of ship emissions on air quality along the US East Coast. We find an increase in three major pollutants (PM2.5, NO2, and SO2) in coastal regions. However, we detect a reduction in ozone (O3) levels in major coastal cities. This reduction is linked to the significant emissions of nitrogen oxides (NOx) from ships, which scavenged O3, especially in highly polluted urban areas experiencing an NOx-limited regime.
Xinxu Zhao, Jia Chen, Julia Marshall, Michal Gałkowski, Stephan Hachinger, Florian Dietrich, Ankit Shekhar, Johannes Gensheimer, Adrian Wenzel, and Christoph Gerbig
Atmos. Chem. Phys., 23, 14325–14347, https://doi.org/10.5194/acp-23-14325-2023, https://doi.org/10.5194/acp-23-14325-2023, 2023
Short summary
Short summary
We develop a modeling framework using the Weather Research and Forecasting model at a high spatial resolution (up to 400 m) to simulate atmospheric transport of greenhouse gases and interpret column observations. Output is validated against weather stations and column measurements in August 2018. The differential column method is applied, aided by air-mass transport tracing with the Stochastic Time-Inverted Lagrangian Transport (STILT) model, also for an exploratory measurement interpretation.
Ioannis Katharopoulos, Dominique Rust, Martin K. Vollmer, Dominik Brunner, Stefan Reimann, Simon J. O'Doherty, Dickon Young, Kieran M. Stanley, Tanja Schuck, Jgor Arduini, Lukas Emmenegger, and Stephan Henne
Atmos. Chem. Phys., 23, 14159–14186, https://doi.org/10.5194/acp-23-14159-2023, https://doi.org/10.5194/acp-23-14159-2023, 2023
Short summary
Short summary
The effectiveness of climate change mitigation needs to be scrutinized by monitoring greenhouse gas (GHG) emissions. Countries report their emissions to the UN in a bottom-up manner. By combining atmospheric observations and transport models someone can independently validate emission estimates in a top-down fashion. We report Swiss emissions of synthetic GHGs based on kilometer-scale transport and inverse modeling, highlighting the role of appropriate resolution in complex terrain.
Tianfeng Chai, Xinrong Ren, Fong Ngan, Mark Cohen, and Alice Crawford
Atmos. Chem. Phys., 23, 12907–12933, https://doi.org/10.5194/acp-23-12907-2023, https://doi.org/10.5194/acp-23-12907-2023, 2023
Short summary
Short summary
The SO2 emissions of three power plants are estimated using aircraft observations and an ensemble of HYSPLIT dispersion simulations with different plume rise parameters. The emission estimates using the runs with the lowest root mean square errors (RMSEs) and the runs with the best correlation coefficients between the predicted and observed mixing ratios both agree well with the Continuous Emissions Monitoring Systems (CEMS) data. The RMSE-based plume rise appears to be more reasonable.
Jinghui Lian, Thomas Lauvaux, Hervé Utard, François-Marie Bréon, Grégoire Broquet, Michel Ramonet, Olivier Laurent, Ivonne Albarus, Mali Chariot, Simone Kotthaus, Martial Haeffelin, Olivier Sanchez, Olivier Perrussel, Hugo Anne Denier van der Gon, Stijn Nicolaas Camiel Dellaert, and Philippe Ciais
Atmos. Chem. Phys., 23, 8823–8835, https://doi.org/10.5194/acp-23-8823-2023, https://doi.org/10.5194/acp-23-8823-2023, 2023
Short summary
Short summary
This study quantifies urban CO2 emissions via an atmospheric inversion for the Paris metropolitan area over a 6-year period from 2016 to 2021. Results show a long-term decreasing trend of about 2 % ± 0.6 % per year in the annual CO2 emissions over Paris. We conclude that our current capacity can deliver near-real-time CO2 emission estimates at the city scale in under a month, and the results agree within 10 % with independent estimates from multiple city-scale inventories.
Fu-Jie Zhu, Peng-Tuan Hu, and Wan-Li Ma
Atmos. Chem. Phys., 23, 8583–8590, https://doi.org/10.5194/acp-23-8583-2023, https://doi.org/10.5194/acp-23-8583-2023, 2023
Short summary
Short summary
A new steady-state gas–particle partitioning model of polycyclic aromatic hydrocarbons was established based on the level-III multimedia fugacity model, which proved that the particulate proportion of PAHs in emissions was a crucial factor for G–P partitioning of PAHs. In addition, gaseous and particulate interference was also derived in the new steady-state model determined by the particulate proportion in emission that could derivate the G–P partitioning quotients from the equilibrium state.
Christian Hogrefe, Jesse O. Bash, Jonathan E. Pleim, Donna B. Schwede, Robert C. Gilliam, Kristen M. Foley, K. Wyat Appel, and Rohit Mathur
Atmos. Chem. Phys., 23, 8119–8147, https://doi.org/10.5194/acp-23-8119-2023, https://doi.org/10.5194/acp-23-8119-2023, 2023
Short summary
Short summary
Under the umbrella of the fourth phase of the Air Quality Model Evaluation International Initiative (AQMEII4), this study applies AQMEII4 diagnostic tools to better characterize how dry deposition removes pollutants from the atmosphere in the widely used CMAQ model. The results illustrate how these tools can provide insights into similarities and differences between the two CMAQ dry deposition options that affect simulated pollutant budgets and ecosystem impacts from atmospheric pollution.
Kun Qu, Xuesong Wang, Xuhui Cai, Yu Yan, Xipeng Jin, Mihalis Vrekoussis, Maria Kanakidou, Guy P. Brasseur, Jin Shen, Teng Xiao, Limin Zeng, and Yuanhang Zhang
Atmos. Chem. Phys., 23, 7653–7671, https://doi.org/10.5194/acp-23-7653-2023, https://doi.org/10.5194/acp-23-7653-2023, 2023
Short summary
Short summary
Basic understandings of ozone processes, especially transport and chemistry, are essential to support ozone pollution control, but studies often have different views on their relative importance. We developed a method to quantify their contributions in the ozone mass and concentration budgets based on the WRF-CMAQ model. Results in a polluted region highlight the differences between two budgets. For future studies, two budgets are both needed to fully understand the effects of ozone processes.
Emily Dowd, Chris Wilson, Martyn P. Chipperfield, Emanuel Gloor, Alistair Manning, and Ruth Doherty
Atmos. Chem. Phys., 23, 7363–7382, https://doi.org/10.5194/acp-23-7363-2023, https://doi.org/10.5194/acp-23-7363-2023, 2023
Short summary
Short summary
Surface observations of methane show that the seasonal cycle amplitude (SCA) of methane is decreasing in the northern high latitudes (NHLs) but increased globally (1995–2020). The NHL decrease is counterintuitive, as we expect the SCA to increase with increasing concentrations. We use a chemical transport model to investigate changes in SCA in the NHLs. We find well-mixed methane and changes in emissions from Canada, the Middle East, and Europe are the largest contributors to the SCA in NHLs.
Danyang Ma, Tijian Wang, Hao Wu, Yawei Qu, Jian Liu, Jane Liu, Shu Li, Bingliang Zhuang, Mengmeng Li, and Min Xie
Atmos. Chem. Phys., 23, 6525–6544, https://doi.org/10.5194/acp-23-6525-2023, https://doi.org/10.5194/acp-23-6525-2023, 2023
Short summary
Short summary
Increasing surface ozone (O3) concentrations have long been a significant environmental issue in China, despite the Clean Air Action Plan launched in 2013. Most previous research ignores the contributions of CO2 variations. Our study comprehensively analyzed O3 variation across China from various perspectives and highlighted the importance of considering CO2 variations when designing long-term O3 control policies, especially in high-vegetation-coverage areas.
Jackson Hian-Wui Chang, Stephen M. Griffith, Steven Soon-Kai Kong, Ming-Tung Chuang, and Neng-Huei Lin
Atmos. Chem. Phys., 23, 6357–6382, https://doi.org/10.5194/acp-23-6357-2023, https://doi.org/10.5194/acp-23-6357-2023, 2023
Short summary
Short summary
A novel CMAQ–PMF-based composite index is developed to identify the key VOC source species for an effective ozone abatement strategy. The index provides information as to which VOC species are key to ozone formation and where to reduce sources of these VOC species. Using the composite index, we recommended the VOC control measures in southern Taiwan should prioritize solvent usage, vehicle emissions, and the petrochemical industry.
Lei Duan and Ken Caldeira
Atmos. Chem. Phys., 23, 6011–6020, https://doi.org/10.5194/acp-23-6011-2023, https://doi.org/10.5194/acp-23-6011-2023, 2023
Short summary
Short summary
Ocko and Hamburg (2022) emphasize the short-term climate impact of hydrogen, and we present an analysis that places greater focus on long-term outcomes. We have derived equations that describe the time-evolving impact of hydrogen and show that higher methane leakage is primarily responsible for the warming potential of blue hydrogen, while hydrogen leakage plays a less critical role. Fossil fuels show more prominent longer-term climate impacts than clean hydrogen under all emission scenarios.
Lixu Jin, Wade Permar, Vanessa Selimovic, Damien Ketcherside, Robert J. Yokelson, Rebecca S. Hornbrook, Eric C. Apel, I-Ting Ku, Jeffrey L. Collett Jr., Amy P. Sullivan, Daniel A. Jaffe, Jeffrey R. Pierce, Alan Fried, Matthew M. Coggon, Georgios I. Gkatzelis, Carsten Warneke, Emily V. Fischer, and Lu Hu
Atmos. Chem. Phys., 23, 5969–5991, https://doi.org/10.5194/acp-23-5969-2023, https://doi.org/10.5194/acp-23-5969-2023, 2023
Short summary
Short summary
Air quality in the USA has been improving since 1970 due to anthropogenic emission reduction. Those gains have been partly offset by increased wildfire pollution in the western USA in the past 20 years. Still, we do not understand wildfire emissions well due to limited measurements. Here, we used a global transport model to evaluate and constrain current knowledge of wildfire emissions with recent observational constraints, showing the underestimation of wildfire emissions in the western USA.
Zichong Chen, Daniel J. Jacob, Ritesh Gautam, Mark Omara, Robert N. Stavins, Robert C. Stowe, Hannah Nesser, Melissa P. Sulprizio, Alba Lorente, Daniel J. Varon, Xiao Lu, Lu Shen, Zhen Qu, Drew C. Pendergrass, and Sarah Hancock
Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, https://doi.org/10.5194/acp-23-5945-2023, 2023
Short summary
Short summary
We quantify methane emissions from individual countries in the Middle East and North Africa by inverse analysis of 2019 TROPOMI satellite observations of atmospheric methane. We show that the ability to simply relate oil/gas emissions to activity metrics is compromised by stochastic nature of local infrastructure and management practices. We find that the industry target for oil/gas methane intensity is achievable through associated gas capture, modern infrastructure, and centralized operations.
Yuting Wang, Yong-Feng Ma, Domingo Muñoz-Esparza, Jianing Dai, Cathy Wing Yi Li, Pablo Lichtig, Roy Chun-Wang Tsang, Chun-Ho Liu, Tao Wang, and Guy Pierre Brasseur
Atmos. Chem. Phys., 23, 5905–5927, https://doi.org/10.5194/acp-23-5905-2023, https://doi.org/10.5194/acp-23-5905-2023, 2023
Short summary
Short summary
Air quality in urban areas is difficult to simulate in coarse-resolution models. This work exploits the WRF (Weather Research and Forecasting) model coupled with a large-eddy simulation (LES) component and online chemistry to perform high-resolution (33.3 m) simulations of air quality in a large city. The evaluation of the simulations with observations shows that increased model resolution improves the representation of the chemical species near the pollution sources.
Shuqi Yan, Bin Zhu, Shuangshuang Shi, Wen Lu, Jinhui Gao, Hanqing Kang, and Duanyang Liu
Atmos. Chem. Phys., 23, 5177–5190, https://doi.org/10.5194/acp-23-5177-2023, https://doi.org/10.5194/acp-23-5177-2023, 2023
Short summary
Short summary
We analyze ozone response to aerosol mixing states in the vertical direction by WRF-Chem simulations. Aerosols generally lead to turbulent suppression, precursor accumulation, low-level photolysis reduction, and upper-level photolysis enhancement under different underlying surface and pollution conditions. Thus, ozone decreases within the entire boundary layer during the daytime, and the decrease is the least in aerosol external mixing states compared to internal and core shell mixing states.
Cited articles
Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S.,
Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and
domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, https://doi.org/10.5194/acp-11-4039-2011, 2011. a, b, c
Andreae, M. O.: Emission of trace gases and aerosols from biomass burning – an updated assessment, Atmos. Chem. Phys., 19, 8523–8546, https://doi.org/10.5194/acp-19-8523-2019, 2019. a
Arellano, A. F., Kasibhatla, P. S., Giglio, L., van der Werf, G. R., and
Randerson, J. T.: Top-down estimates of global CO sources using MOPITT
measurements, Geophys. Res. Lett., 31, 1–5, https://doi.org/10.1029/2003GL018609, 2004. a
Atkinson, R.: Atmospheric chemistry of VOCs and NOx, Atmos. Environ., 34, 2063–2101, 2000. a
Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E.,
McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D. H., Strow, L. L., and Susskind, J.: AIRS/AMSU/HSB on the aqua mission:
design, science objectives, data products, and processing systems, IEEE T. Geosci. Remote, 41, 253–264, https://doi.org/10.1109/TGRS.2002.808356, 2003. a
Baker, D. F., Doney, S. C., and Schimel, D. S.: Variational data assimilation for atmospheric CO2, Tellus B, 58, 359–365, https://doi.org/10.1111/j.1600-0889.2006.00218.x, 2006. a
Baker, D. F., Bösch, H., Doney, S. C., O'Brien, D., and Schimel, D. S.:
Carbon source/sink information provided by column CO2 me–4165, https://doi.org/10.5194/acp-10-4145-2010, 2010. a
Bakwin, P. S., Hurst, D. F., Tans, P. P., and Elkins, J. W.: Anthropogenic sources of halocarbons, sulfur hexafluoride, carbon monoxide, and methane in the southeastern United States, J. Geophys. Res., 102, 915–925, 1997. a
Baldassarre, G., Pozzoli, L., Schmidt, C. C., Unal, A., Kindap, T., Menzel,
W. P., Whitburn, S., Coheur, P. F., Kavgaci, A., and Kaiser, J. W.: Using
SEVIRI fire observations to drive smoke plumes in the CMAQ air quality model:
A case study over Antalya in 2008, Atmos. Chem. Phys., 15, 8539–8558, https://doi.org/10.5194/acp-15-8539-2015, 2015. a
Ballantyne, A. P., Alden, C. B., Miller, J. B., Trans, P. P., and White, J. W.: A Newtonian approach to extraordinarily strong negative refraction, Nature, 488, 70–73, https://doi.org/10.1038/nature11299, 2012. a
Bastos, A., Friedlingstein, P., Sitch, S., Chen, C., Mialon, A., Wigneron,
J. P., Arora, V. K., Briggs, P. R., Canadell, J. G., Ciais, P., Chevallier,
F., Cheng, L., Delire, C., Haverd, V., Jain, A. K., Joos, F., Kato, E.,
Lienert, S., Lombardozzi, D., Melton, J. R., Myneni, R., Nabel, J. E.,
Pongratz, J., Poulter, B., Rödenbeck, C., Séférian, R., Tian, H., Van Eck, C., Viovy, N., Vuichard, N., Walker, A. P., Wiltshire, A., Yang, J., Zaehle, S., Zeng, N., and Zhu, D.: Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches, Philos. T. Roy. Soc. B, 373, 20170304, https://doi.org/10.1098/rstb.2017.0304, 2018. 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, b
Basu, S., Krol, M., Butz, A., Clerbaux, C., Sawa, Y., Machida, T., Matsueda,
H., Frankenberg, C., Hasekamp, O. P., and Aben, I.: The seasonal variation
of the CO2 flux over Tropical Asia estimated from GOSAT, CONTRAIL, and IASI, Geophys. Res. Lett., 41, 1809–1815, https://doi.org/10.1002/2013GL059105,
2014. a, b
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
Beer, R., Glavich, T. A., and Rider, D. M.: Tropospheric emission spectrometer for the Earth Observing System's Aura satellite, Appl. Optics, 40, 2356–2367, 2001. a
Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., Aardenne, J. V., Monni, S., Vermeulen, A. T., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R. E., Doherty, S O., and Dlugokencky, E. J.: Inverse modeling of European CH4 emissions 2001–2006, J. Geophys. Res.-Atmos., 115, 1–18, https://doi.org/10.1029/2010JD014180, 2010. a
Blumenstock, T., Hase, F., Schneider, M., García, O. E., and Sepúlveda, E.: TCCON data from Izana (ES), Release GGG2014.R1,
Caltech data by Caltech library, https://doi.org/10.14291/TCCON.GGG2014.IZANA01.R1, 2017. a
Burling, I. R., Yokelson, R. J., Akagi, S. K., Urbanski, S. P., Wold, C. E.,
Griffith, D. W., Johnson, T. J., Reardon, J., and Weise, D. R.: Airborne and
ground-based measurements of the trace gases and particles emitted by
prescribed fires in the United States, Atmos. Chem. Phys. 11, 12197–12216, https://doi.org/10.5194/acp-11-12197-2011, 2011. a
Byrne, B., Jones, D. B., Strong, K., Zeng, Z. C., Deng, F., and Liu, J.:
Sensitivity of CO2 surface flux constraints to observational coverage, J. Geophys. Res., 122, 6672–6694, https://doi.org/10.1002/2016JD026164, 2017. a, b
Chevallier, F., Engelen, R. J., and Peylin, P.: The contribution of AIRS data
to the estimation of CO2 sources and sinks, Geophys. Res. Lett., 32, 1–4, https://doi.org/10.1029/2005GL024229, 2005. a
Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal
infrared IASI/MetOp sounder, Atmos. Chem. Phys., 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009. a
Cooperative Global Atmospheric Data Integration Project: Multi-laboratory
compilation of atmospheric carbon dioxide data for the period 1957–2018;
obspack_co2_1_GLOBALVIEWplus_v5.0_2019_08_12, NOAA, https://doi.org/10.25925/20190812, 2019. a
Crisp, D. and Johnson, C.: The orbiting carbon observatory mission, Acta
Astronaut., 56, 193–197, https://doi.org/10.1016/j.actaastro.2004.09.032, 2005. a
Crisp, D., Fisher, B. M., O'Dell, C., Frankenberg, C., Basilio, R., Bösch, H., Brown, L. R., Castano, R., Connor, B., Deutscher, N. M.,
Eldering, A., Griffith, D., Gunson, M., Kuze, A., Mandrake, L., McDuffie, J.,
Messerschmidt, J., Miller, C. E., Morino, I., Natraj, V., Notholt, J., O'Brien, D. M., Oyafuso, F., Polonsky, I., Robinson, J., Salawitch, R.,
Sherlock, V., Smyth, M., Suto, H., Taylor, T. E., Thompson, D. R., Wennberg,
P. O., Wunch, D., and Yung, Y. L.: The ACOS CO2 retrieval algorithm – Part II: Global XCO2 data characterization, Atmos. Meas. Tech., 5, 687–707, https://doi.org/10.5194/amt-5-687-2012, 2012. a
Crisp, D., Pollock, H., Rosenberg, R., Chapsky, L., Lee, R., Oyafuso, F.,
Frankenberg, C., Dell, C., Bruegge, C., Doran, G., Eldering, A., Fisher, B.,
Fu, D., Gunson, M., Mandrake, L., Osterman, G., Schwandner, F., Sun, K.,
Taylor, T., Wennberg, P., and Wunch, D.: The on-orbit performance of the
Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically
calibrated products, Atmos. Meas. Tech., 10, 59–81, https://doi.org/10.5194/amt-10-59-2017, 2017. 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, d, e, f, g, h, i, j
Crowell, S. M., Randolph Kawa, S., Browell, E. V., Hammerling, D. M., Moore,
B., Schaefer, K., and Doney, S. C.: On the Ability of Space-Based Passive
and Active Remote Sensing Observations of CO2 to Detect Flux Perturbations to the Carbon Cycle, J. Geophys. Res.-Atmos., 123, 1460–1477, https://doi.org/10.1002/2017JD027836, 2018. a, b
Darmenov, A. S. and Silva, A.: The Quick Fire Emissions Dataset (QFED):
Documentation of versions 2.1, 2.2 and 2.4, NASA Technical Report Series on
Global Modeling and Data Assimilation, NASA, NASA/TM-2015-104606, 38 pp., https://www.semanticscholar.org/paper/The-Quick-Fire-Emissions-Dataset-(QFED):-of-2.1,on-Koster-Darmenov/f18b64dce3bc0f3384d0d8377b409597c
(last access: 12 December 2022), 2015. a
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., 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
Deeter, M. N., Emmons, L. K., Francis, G. L., Edwards, D. P., Gille, J. C.,
Warner, J. X., Khattatov, B., Ziskin, D., Lamarque, J. F., Ho, S. P., Yudin,
V., Attié, J. L., Packman, D., Chen, J., Mao, D., and Drummond, J. R.:
Operational carbon monoxide retrieval algorithm and selected results for the
MOPITT instrument, J. Geophys. Res.-Atmos., 108, 1–11, https://doi.org/10.1029/2002jd003186, 2003. a
Deeter, M. N., Edwards, D. P., Gille, J. C., and Drummond, J. R.: Sensitivity
of MOPITT observations to carbon monoxide in the lower troposphere, J. Geophys. Res.-Atmos., 112, 1–9, https://doi.org/10.1029/2007JD008929, 2007. a
Deeter, M. N., Edwards, D. P., Gille, J. C., and Drummond, J. R.: CO retrievals based on MOPITT near-infrared observations, J. Geophys. Res.-Atmos., 114, 1–9, https://doi.org/10.1029/2008JD010872,
2009. a
Deeter, M. N., Martínez-Alonso, S., Edwards, D. P., Emmons, L. K., Gille,
J. C., Worden, H. M., Pittman, J. V., Daube, B. C., and Wofsy, S. C.:
Validation of MOPITT Version 5 thermal-infrared, near-infrared, and multispectral carbon monoxide profile retrievals for 2000–2011, J. Geophys. Res.-Atmos., 118, 6710–6725, https://doi.org/10.1002/jgrd.50272, 2013. a
Deeter, M. N., Edwards, D. P., Gille, J. C., and Worden, H. M.: Information
content of MOPITT CO profile retrievals: Temporal and geographical variability, J. Geophys. Res., 120, 12723–12738, https://doi.org/10.1002/2015JD024024, 2015. a
Deeter, M. N., Edwards, D. P., Francis, G. L., Gille, J. C., Mao, D., Martínez-Alonso, S., Worden, H. M., Ziskin, D., and Andreae, M. O.: Radiance-based retrieval bias mitigation for the MOPITT instrument: the version 8 product, Atmos. Meas. Tech., 12, 4561–4580, https://doi.org/10.5194/amt-12-4561-2019, 2019. a, b, c, d
De Mazière, M., Sha, M. K., Desmet, F., Hermans, C., Scolas, F., Kumps,
N., Metzger, J.-M., Duflot, V., and Cammas, J.-P.: TCCON data from Réunion Island (RE), Release GGG2014.R1, Caltech data by Caltech Library, https://doi.org/10.14291/TCCON.GGG2014.REUNION01.R1, 2017. a
Deutscher, N. M., Notholt, J., Messerschmidt, J., Weinzierl, C., Warneke, T.,
Petri, C., and Grupe, P.: TCCON data from Bialystok (PL), Release
GGG2014.R2, Caltech data by Caltech Library, https://doi.org/10.14291/TCCON.GGG2014.BIALYSTOK01.R2, 2019. a
Drummond, J. R.: Measurements of pollution in the tropopshere (MOPITT)
instrument, Sensor Syst. Early Earth Obs. Syst. Platforms, 1939, 126–136, https://doi.org/10.1117/12.152839, 1993. a
Drummond, J. R., Zou, J., Nichitiu, F., Kar, J., Deschambaut, R., and Hackett, J.: A review of 9-year performance and operation of the MOPITT instrument, Adv. Space Res., 45, 760–774, https://doi.org/10.1016/j.asr.2009.11.019,
2010. a
Drummond, J. R., Hackett, J., and Caldwell, D.: Measurements ofpollution in
the troposphere (MOPITT), in: Optical Payloads for Space Missions, edited by: Qian, S.-E., Wiley and Sons, West Sussex, UK, 639–652, ISBN 978-1-118-94514-8, 2016. a
Edwards, D. P., Halvorson, C. M., and Gille, J. C.: Radiative transfer
modeling for the EOS Terra satellite Measurement of Pollution in the
Troposphere (MOPITT) instrument, J. Geophys. Res.-Atmos., 104, 16755–16775, https://doi.org/10.1029/1999JD900167, 1999. a
Eldering, A., O'Dell, C. W., Wennberg, P. O., Crisp, D., Gunson, M. R., Viatte, C., Avis, C., Braverman, A., Castano, R., Chang, A., Chapsky, L., Cheng, C., Connor, B., Dang, L., Doran, G., Fisher, B., Frankenberg, C., Fu, D., Granat, R., Hobbs, J., Lee, R. A., Mandrake, L., McDuffie, J., Miller, C. E., Myers, V., Natraj, V., O'Brien, D., Osterman, G. B., Oyafuso, F., Payne, V. H., Pollock, H. R., Polonsky, I., Roehl, C. M., Rosenberg, R., Schwandner, F., Smyth, M., Tang, V., Taylor, T. E., To, C., Wunch, D., and Yoshimizu, J.:
The Orbiting Carbon Observatory-2: First 18 months of science data products, Atmos. Meas. Tech., 10, 549–563, https://doi.org/10.5194/amt-10-549-2017, 2017. a, b, c, d
Feist, D. G., Arnold, S. G., John, N., and Geibel, M. C.: TCCON data from
Ascension Island (SH), Release GGG2014R0, TCCON data archive, hosted by
CaltechDATA, https://doi.org/10.14291/tccon.ggg2014.ascension01.R0/1149285, 2014. a
Fortems-Cheiney, A., Chevallier, F., Pison, I., Bousquet, P., Szopa, S.,
Deeter, M. N., and Clerbaux, C.: Ten years of CO emissions as seen from
Measurements of Pollution in the Troposphere (MOPITT), J. Geophys. Res.-Atmos., 116, 1–17, https://doi.org/10.1029/2010JD014416, 2011. a
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 climate projections
due to carbon cycle feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/JCLI-D-12-00579.1, 2014. 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
Giglio, L., Csiszar, I., and Justice, C. O.: Global distribution and
seasonality of active fires as observed with the Terra and Aqua Moderate
Resolution Imaging Spectroradiometer (MODIS) sensors, J. Geophys. Res.-Biogeo., 111, 1–12, https://doi.org/10.1029/2005JG000142, 2006. a, b, c
Giglio, L., Randerson, J. T., van der Werf, G. R., Kasibhatla, P. S., Collatz, G. J., Morton, D. C., and DeFries, R. S.: Assessing variability and long-term trends in burned area by merging multiple satellite fire products, Biogeosciences, 7, 1171–1186, https://doi.org/10.5194/bg-7-1171-2010, 2010. a
Giglio, L., Randerson, J. T., and Van Der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth-generation global fire
emissions database (GFED4), J. Geophys. Res.-Biogeo., 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013. a, b
Gilbert, J. C. and Lemaréchal, C.: Browining1968VAD.pdf, Math. Program., 45, 407–435, https://doi.org/10.1127/0941-2948/2007/0220, 1989. a
Global Forest Watch: We Lost a Football Pitch of Primary Rainforest Every 6 Seconds in 2019,
https://www.globalforestwatch.org/blog/data-and-research/global-tree-cover-loss-data-2019/
(last access: 12 December 2022), 2020. a
Goo, T.-Y., Oh, Y.-S., and Velazco, V. A.: TCCON data from Anmeyondo (KR),
Release GGG2014R0, TCCON data archive, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.anmeyondo01.R0, 2014. a
Granier, C., Bessagnet, B., Bond, T., D'Angiola, A., van der Gon, H. D., Frost, G. J., Heil, A., Kaiser, J. W., Kinne, S., Klimont, Z., Kloster, S.,
Lamarque, J. F., Liousse, C., Masui, T., Meleux, F., Mieville, A., Ohara, T.,
Raut, J. ., Riahi, K., Schultz, M. G., Smith, S. J., Thompson, A., van Aardenne, J., van der Werf, G. R., and van Vuuren, D.P.: Evolution of
anthropogenic and biomass burning emissions of air pollutants at global and
regional scales during the 1980–2010 period, Climatic Change, 109, 163–190,
https://doi.org/10.1007/s10584-011-0154-1, 2011. a
Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Wennberg, P. O., Yavin,
Y., Aleks, G. K., Washenfelder, R. A., Toon, G. C., Blavier, J.-F., Murphy,
C., Jones, N., Kettlewell, G., Connor, B. J., Macatangay, R., Roehl, C.,
Ryczek, M., Glowacki, J., Culgan, T., and Bryant, G.: TCCON data from Darwin (AU), Release GGG2014R0, TCCON data archive, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.darwin01.R0/1149290, 2014a. a
Griffith, D. W. T., Velazco, V. A., Deutscher, N. M., Murphy, C., Jones, N.,
Wilson, S., Macatangay, R., Kettlewell, G., Buchholz, R. R., and Riggenbach,
M.: TCCON data from Wollongong (AU), Release GGG2014R0, TCCON data archive,
CaltechDATA, https://doi.org/10.14291/tccon.ggg2014.wollongong01.R0/1149, 2014b. a
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T.,
Emmons, L. K., and Wang, X.: The model of emissions of gases and aerosols
from nature version 2.1 (MEGAN2.1): An extended and updated framework for
modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492,
https://doi.org/10.5194/gmd-5-1471-2012, 2012. a
Gurney, K. R., Law, R. M., 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 Yuen, C. W.: Towards robust regional estimates of annual mean CO2 sources and sinks, Nature, 415, 626–630, 2002. a
Gurney, K. R., Law, R. M., 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., Kowalczyk, E., Maki, T., Maksyutov, S., Peylin, P., Prather, M., Pak, B. C., Sarmiento, J., Taguchi, S., Takahashi, T., and Yuen, C. W.: 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/10.1034/j.1600-0889.2003.00049.x, 2003. a
Guyon, P., Frank, G. P., Welling, M., Chand, D., Artaxo, P., Rizzo, L.,
Nishioka, G., Kolle, O., Fritsch, H., Silva Dias, M. A., Gatti, L. V.,
Cordova, A. M., and Andreae, M. O.: Airborne measurements of trace gas and
aerosol particle emissions from biomass burning in Amazonia, Atmos. Chem. Phys. 5, 2989–3002, https://doi.org/10.5194/acp-5-2989-2005, 2005. a
Hase, F., Blumenstock, T., Dohe, S., Groß, J., and Kiel, M.: TCCON data
from Karlsruhe (DE), Release GGG2014.R1, Caltech Data by Caltech Library, https://doi.org/10.14291/TCCON.GGG2014.KARLSRUHE01., 2015. a
Hooghiemstra, P. B., Krol, M. C., Meirink, J. F., Bergamaschi, P., Van Der
Werf, G. R., Novelli, P. C., Aben, I., and Röckmann, T.: Optimizing global CO emission estimates using a four-dimensional variational data
assimilation system and surface network observations, Atmos. Chem. Phys., 11, 4705–4723, https://doi.org/10.5194/acp-11-4705-2011, 2011. a, b, c
Hooghiemstra, P. B., Krol, M. C., Bergamaschi, P., De Laat, A. T. J., Van Der Werf, G. R., Novelli, P. C., Deeter, M. N., Aben, I., and Rckmann, T.: Comparing optimized CO emission estimates using MOPITT or NOAA surface network observations, J. Geophys. Res.-Atmos., 117, 1–23, https://doi.org/10.1029/2011JD017043, 2012. a
Hooijer, A. and Vernimmen, R.: Quick Assessment and Nationwide Screening (QANS) of Peat and Lowland Resources and Action Planning for the
Implementation of a National Lowland Strategy Technical Guidance for Peatland
Policy Development, Partners for Water Programme, Ede, the Netherlands, https://www.deltares.nl/app/uploads/2015/03/QANS-Peat-mapping-report-final-with-cover.pdf
(last access: 12 December 2022), 2013. a
Houweling, S., Baker, D., Basu, S., Boesch, H., Butz, A., Chevallier, F., Deng, F., Dlugokencky, E. J., Feng, L., Ganshin, A., Hasekamp, O., Jones, D.,
Maksyutov, S., Marshall, J., Oda, T., O'Dell, C. W., Oshchepkov, S., Palmer,
P. I., Peylin, P., Poussi, Z., Reum, F., Takagi, H., Yoshida, Y., and
Zhuravlev, R.: An intercomparison of inversemodels for estimating sources
and sinks of CO2 using GOSAT measurements, J. Geophys. Res., 120, 5253–5266, https://doi.org/10.1002/2014JD022962, 2015. a
Huijnen, V., Williams, J., Van Weele, M., Van Noije, T., Krol, M., Dentener, F., Segers, A., Houweling, S., Peters, W., De Laat, J., Boersma, F., Bergamaschi, P., Van Velthoven, P., Le Sager, P., Eskes, H., Alkemade, F., Scheele, R., Nédélec, P., and Pätz, H. W.: The global chemistry transport model TM5: Description and evaluation of the tropospheric chemistry version 3.0, Geosci. Model Dev., 3, 445–473, https://doi.org/10.5194/gmd-3-445-2010, 2010. a, b
Huijnen, V., Wooster, M. J., Kaiser, J. W., Gaveau, D. L., Flemming, J.,
Parrington, M., Inness, A., Murdiyarso, D., Main, B., and Van Weele, M.:
Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997, Scient. Rep., 6, 1–8, https://doi.org/10.1038/srep26886, 2016. a, b
IPCC: Climate Change 2014 Synthesis Report. Contribution of Working Groups I, II and III to the fifth Assessement Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/CBO9781107415324, 2014. a
Iraci, L. T., Podolske, J., Hillyard, P. W., Roehl, C., Wennberg, P. O.,
Blavier, J.-F., Allen, N., Wunch, D., Osterman, G. B., and Albertson, R.:
TCCON data from Edwards (US), Release GGG2014R1, TCCON data archive, CaltechDATA, https://doi.org/10.14291/tccon.ggg2014.edwards01.R1/1255068, 2016. a
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones,
L., Morcrette, J. J., Razinger, M., Schultz, M. G., Suttie, M., and Van Der
Werf, G. R.: Biomass burning emissions estimated with a global fire
assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012. a
Kawakami, S., Ohyama, H., Arai, K., Okumura, H., Taura, C., Fukamachi, T., and Sakashita, M.: TCCON data from Saga (JP), Release GGG2014R0, TCCON data
archive, CaltechDATA, https://doi.org/10.14291/tccon.ggg2014.saga01.R0/1149283, 2014. a
Khatiwala, S., Primeau, F., and Hall, T.: Reconstruction of the history of
anthropogenic CO2 concentrations in the ocean, Nature, 462, 346–349, https://doi.org/10.1038/nature08526, 2009. a
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. 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 X
Kivi, R., Heikkinen, P., and Kyrö, E.: TCCON data from Sodankyla (FI),
Release GGG2014R0, TCCON data archive, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.sodankyla01.R0/11492, 2014. a
Konovalov, I. B., Berezin, E. V., Ciais, P., Broquet, G., Beekmann, M.,
Hadji-Lazaro, J., Clerbaux, C., Andreae, M. O., Kaiser, J. W., and Schulze, E. D.: Constraining CO2 emissions from open biomass burning by satellite observations of co-emitted species: A method and its application to wildfires in Siberia, Atmos. Chem. Phys., 14, 10383–10410,
https://doi.org/10.5194/acp-14-10383-2014, 2014. a
Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way nested global chemistry-transport zoom model TM5: algorithm and applications, Atmos. Chem. Phys., 5, 417–432, https://doi.org/10.5194/acp-5-417-2005, 2005. a
Kulawik, S., Wunch, D., O'Dell, C., Frankenberg, C., Reuter, M., Oda, T.,
Chevallier, F., Sherlock, V., Buchwitz, M., Osterman, G., Miller, C. E.,
Wennberg, P. O., Griffith, D., Morino, I., Dubey, M. K., Deutscher, N. M.,
Notholt, J., Hase, F., Warneke, T., Sussmann, R., Robinson, J., Strong, K.,
Schneider, M., De Mazière, M., Shiomi, K., Feist, D. G., Iraci, L. T., and Wolf, J.: Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON, Atmos. Meas. Tech., 9, 683–709, https://doi.org/10.5194/amt-9-683-2016, 2016. a
Kuze, A., Suto, H., Nakajima, M., and Hamazaki, T.: Initial Onboard
Performance of TANSO-FTS on GOSAT, in: Advances in Imaging, Optical Society of America, FTuC2, https://doi.org/10.1364/FTS.2009.FTuC2, 2009. a
Landgraf, J., Aan De Brugh, J., Scheepmaker, R., Borsdorff, T., Hu, H.,
Houweling, S., Butz, A., Aben, I., and Hasekamp, O.: Carbon monoxide total
column retrievals from TROPOMI shortwave infrared measurements, Atmos. Meas. Tech., 9, 4955–4975, https://doi.org/10.5194/amt-9-4955-2016, 2016. a
Lauvaux, T., Schuh, A. E., Bocquet, M., Wu, L., Richardson, S., Miles, N., and Davis, K.J.: Network design for mesoscale inversions of CO2 sources and sinks, Tellus B: 64, 1–12,
https://doi.org/10.3402/tellusb.v64i0.17980, 2012a. a
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, 2012b. a
Le Quéré, C., Raupach, M. R., Canadell, J. G., Marland, G., Bopp,
L., Ciais, P., Conway, T. J., Doney, S. C., Feely, R. A., Foster, P.,
Friedlingstein, P., Gurney, K., Houghton, R. A., House, J. I., Huntingford,
C., Levy, P. E., Lomas, M. R., Majkut, J., Metzl, N., Ometto, J. P., Peters,
G. P., Prentice, I. C., Randerson, J. T., Running, S. W., Sarmiento, J. L.,
Schuster, U., Sitch, S., Takahashi, T., Viovy, N., Van Der Werf, G. R., and
Woodward, F. I.: Trends in the sources and sinks of carbon dioxide, Nat.
Geosci., 2, 831–836, https://doi.org/10.1038/ngeo689, 2009. a
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck, J., Pongratz, J., Pickers, P. A., Korsbakken, J. I., Peters, G. P., Canadell, J. G., Arneth, A., Arora, V. K., Barbero, L., Bastos, A., Bopp, L., Chevallier, F., Chini, L. P., Ciais, P., Doney, S. C., Gkritzalis, T., Goll, D. S., Harris, I., Haverd, V., Hoffman, F. M., Hoppema, M., Houghton, R. A., Hurtt, G., Ilyina, T., Jain, A. K., Johannessen, T., Jones, C. D., Kato, E., Keeling, R. F., Goldewijk, K. K., Landschützer, P., Lefèvre, N., Lienert, S., Liu, Z., Lombardozzi, D., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., Neill, C., Olsen, A., Ono, T., Patra, P., Peregon, A., Peters, W., Peylin, P., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rocher, M., Rödenbeck, C., Schuster, U., Schwinger, J., Séféérian, R., Skjelvan, I., Steinhoff, T., Sutton, A., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., Wright, R., Zaehle, S., and Zheng, B.: Global Carbon Budget 2018, Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, 2018. a, b, c
Lin, X., Ciais, P., Bousquet, P., Ramonet, M., Yin, Y., Cozic, A., Delmotte,
M., Evangeliou, N., Lin, X., Ciais, P., Bousquet, P., Ramonet, M., Yin, Y.,
Ch, S., Lin, X., Ciais, P., Bousquet, P., Ramonet, M., Yin, Y., Balkanski, Y., and Cozic, A.: Simulating CH4 and CO2 over South and East Asia using the zoomed chemistry transport model LMDz-INCA, Atmos. Chem.
Phys., 18, 9475–9497, https://doi.org/10.5194/acp-18-9475-2018, 2018. a
Liu, J., Rodriguez, J. M., Steenrod, S. D., Douglass, A. R., Logan, J. A.,
Olsen, M. A., Wargan, K., and Ziemke, J. R.: Causes of interannual variability over the southern hemispheric tropospheric ozone maximum, Atmos. Chem. Phys., 17, 3279–3299, https://doi.org/10.5194/acp-17-3279-2017, 2017a. a, b
Liu, J. C., Wilson, A., Mickley, L. J., Dominici, F., Ebisu, K., Wang, Y.,
Sulprizio, M. P., Peng, R. D., Yue, X., Son, J. Y., Anderson, G. B., and Bell, M. L.: Wildfire-specific fine particulate matter and risk of hospital
admissions in urban and rural counties, Epidemiology, 28, 77–85,
https://doi.org/10.1097/EDE.0000000000000556, 2017b. a
Liu, T., Mickley, L. J., Marlier, M. E., DeFries, R. S., Khan, M. F., Latif,
M. T., and Karambelas, A.: Diagnosing spatial biases and uncertainties in
global fire emissions inventories: Indonesia as regional case study, Remote
Sens. Environ., 237, 111557, https://doi.org/10.1016/j.rse.2019.111557, 2020. a
Logan, J. A., Prather, M. J., Wofsy, S. C., and Mcelroy, M. B.: Tropospheric
chemistry: A global perspective, J. Geophys. Res., 86, 7210–7254, 1981. a
Lohberger, S., Stangel, M., Atwood, E., and Siegert, F.: Spatial evaluation of Indonesia's 2015 fire affected area and estimated carbon emissions using
Sentinel-1, Global Change Biol. 24, 644–654, https://doi.org/10.1111/gcb.13841, 2017. a
Masarie, K. A., Peters, W., Jacobson, A. R., and Tans, P. P.: ObsPack: A
framework for the preparation, delivery, and attribution of atmospheric
greenhouse gas measurements, Earth Syst. Sci. Data, 6, 375–384,
https://doi.org/10.5194/essd-6-375-2014, 2014. a
Meirink, J. F., Bergamaschi, P., Frankenberg, C., D'Amelio, M. T., Dlugokencky, E. J., Gatti, L. V., Houweling, S., Miller, J. B., Röckmann, T., Villani, M. G., and Krol, M. C.: Four-dimensional variational data
assimilation for inverse modeling of atmospheric methane emissions: Analysis
of SCIAMACHY observations, J. Geophys. Res.-Atmos., 113, 1–20, https://doi.org/10.1029/2007JD009740, 2008. a, b, c
Moore, B., Crowell, S. M., Rayner, P. J., Kumer, J., O'Dell, C. W., O'Brien,
D., Utembe, S., Polonsky, I., Schimel, D., and Lemen, J.: The Potential of
the Geostationary Carbon Cycle Observatory (GeoCarb) to provide multi-scale
constraints on the carbon cycle in the Americas, Front. Environ. Sci., 6, 1–13, https://doi.org/10.3389/fenvs.2018.00109, 2018. a
Morino, I., Matsuzaki, T., and Horikawa, M.: TCCON data from Tsukuba (JP),
125HR, Release GGG2014.R2, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.TSUKUBA02.R2, 2018a. a
Morino, I., Yokozeki, N., Matsuzaki, T., and Horikawa, M.: TCCON data from
Rikubetsu (JP), Release GGG2014.R2, CaltechDATA,
https://doi.org/10.14291/TCCON.GGG2014.RIKUBETSU01.R2, 2018b. a
Nara, H., Tanimoto, H., Tohjima, Y., Mukai, H., Nojiri, Y., and Machida, T.:
Emission factors of CO2, CO and CH4 from Sumatran peatland fires in 2013 based on shipboard measurements, Tellus B, 69, 1399047, https://doi.org/10.1080/16000889.2017.1399047, 2017. a
NASA: MOPITT Data, https://terra.nasa.gov/data/mopitt-data, last access: 14 December 2022. 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
Nechita-Banda, N., Krol, M., van der Werf, G. R., Kaiser, J. W., Pandey, S.,
Huijnen, V., Clerbaux, C., Coheur, P., Deeter, M. N., and Röckmann, T.:
Monitoring emissions from the 2015 Indonesian fires using CO satellite data, Philos. T. Roy. Soc. B, 373, 20170307, https://doi.org/10.1098/rstb.2017.0307, 2018. a, b, c, d, e, f, g, h, i, j, k, l, m, n
NOAA Carbon Cycle Group ObsPack Team: INPE atmospheric carbon dioxide data
for the period 2015–2017; obspack_co2_1_INPE_RESTRICTED_v2.0_2018-11-13, NOAA [data set],
https://doi.org/10.25925/20181030, 2018. a, b
NOAA Carbon Cycle Group ObsPack Team: Multi-laboratory compilation of
atmospheric carbon dioxide data for the years 2018–2019;
obspack_co2_1_NRT_v5.0_2019-08-13, NOAA, https://doi.org/10.25925/20190813, 2019. a
NOAA ESRL/GML: OCO-2 v9 MIP Download, https://gml.noaa.gov/ccgg/OCO2_v9mip/download.php, last access: 14 December 2022. a
Notholt, J., Petri, C., Warneke, T., Deutscher, N. M., Buschmann, M.,
Weinzierl, C., Macatangay, R., and Grupe, P.: TCCON data from Bremen (DE),
Release GGG2014R0 TCCON data archive, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.bremen01.R0/1149275, 2014a. a
Notholt, J., Warneke, T., Petri, C., Deutscher, N. M., Weinzierl, C., Palm, M., and Buschmann, M.: TCCON data from Ny Ålesund, Spitsbergen (NO), Release GGG2014.R0, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.NYALESUND01., 2014b. a
Oda, T. and Maksyutov, S.: A very high-resolution (1 km × 1 km) global fossil fuel CO2 emission inventory derived using a point source database and satellite observations of nighttime lights, Atmos. Chem.
Phys., 11, 543–556, https://doi.org/10.5194/acp-11-543-2011, 2011. a
O'Dell, C. W., Connor, B., Bösch, H., O'Brien, D., Frankenberg, C.,
Castano, R., Christi, M., Eldering, D., Fisher, B., Gunson, M., McDuffie, J.,
Miller, C. E., Natraj, V., Oyafuso, F., Polonsky, I., Smyth, M., Taylor, T.,
Toon, G. C., Wennberg, P. O., and Wunch, D.: The ACOS CO2 retrieval
algorithm – Part 1: Description and validation against synthetic observations, Atmos. Meas. Tech., 5, 99–121, https://doi.org/10.5194/amt-5-99-2012,
2012. 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., Feng, L., Palmer, D. P. I., Dubey, M., García, O. E., Griffith, D. W., 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, c, d, e
Ott, L.: GEOS-Carb CASA-GFED 3-hourly Ecosystem Exchange Fluxes 0.5 degree × 0.625 degree V3, Goddard Earth Sciences Data and
Information Services Center (GES DISC), Greenbelt, MD, USA, https://doi.org/10.5067/VQPRALE26L20, 2020. a, b, c
Palmer, P. I., Feng, L., Baker, D., Chevallier, F., Bösch, H., and
Somkuti, P.: Net carbon emissions from African biosphere dominate pan-tropical atmospheric CO2 signal, Nat. Commun., 10, 1–10,
https://doi.org/10.1038/s41467-019-11097-w, 2019. a, b, c
Patra, P. K., Crisp, D., Kaiser, J. W., Wunch, D., Saeki, T., Ichii, K.,
Sekiya, T., Wennberg, P. O., Feist, D. G., Pollard, D. F., Griffith, D. W.,
Velazco, V. A., De Maziere, M., Sha, M. K., Roehl, C., Chatterjee, A., and
Ishijima, K.: The Orbiting Carbon Observatory (OCO-2) tracks 2–3 peta-gram
increase in carbon release to the atmosphere during the 2014–2016 El Niño, Scient. Rep., 7, 1–12, https://doi.org/10.1038/s41598-017-13459-0, 2017. a
Pechony, O., Shindell, D. T., and Faluvegi, G.: Direct top-down estimates of
biomass burning CO emissions using TES and MOPITT versus bottom-up GFED
inventory, J. Geophys. Res.-Atmos., 118, 8054–8066, https://doi.org/10.1002/jgrd.50624, 2013. a
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, g, h, i, j
Peters, W., Jacobson, A. R., Sweeney, C., Andrews, A. E., Conway, T. J.,
Masarie, K., Miller, J. B., Bruhwiler, L. M., Pétron, G., Hirsch, A. I., Worthy, D. E., Van Der Werf, G. R., Randerson, J. T., Wennberg, P. O., Krol, M. C., and Tans, P. P.: An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker, P. Natl. Acad. Sci. USA, 104, 18925–18930,
https://doi.org/10.1073/pnas.0708986104, 2007. a
Pétron, G., Granier, C., Khattatov, B., Lamarque, J. F., Yudin, V.,
Müller, J. F., and Gille, J.: Inverse modeling of carbon monoxide
surface emissions using Climate Monitoring and Diagnostics Laboratory network
observations, J. Geophys. Res.-Atmos., 107, 1–23,
https://doi.org/10.1029/2001JD001305, 2002. a
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, b
Pfister, G. G., Emmons, L. K., Hess, P. G., Lamarque, J. F., Orlando, J. J.,
Walters, S., Guenther, A., Palmer, P. I., and Lawrence, P. J.: Contribution
of isoprene to chemical budgets: A model tracer study with the NCAR CTM
MOZART-4, J. Geophys. Res.-Atmos., 113, D05308, https://doi.org/10.1029/2007JD008948, 2008. a
Philip, S., Johnson, M. S., Potter, C., Genovesse, V., Baker, D. F., Haynes,
K. D., Henze, D. K., Liu, J., and Poulter, B.: Prior biosphere model impact
on global terrestrial CO2 fluxes estimated from OCO-2 retrievals,
Atmos. Chem. Phys., 19, 13267–13287, https://doi.org/10.5194/acp-19-13267-2019, 2019. a, b, c
Potosnak, M. J., Wofsy, C., Denning, A. S., Munger, J. W., and Barnes, D. H.:
Influence of biotic exchange and combustion sources on atmospheric CO2 concentrations in New England from observations at a forest flux tower, J. Geophys. Res., 104, 9561–9569, 1999. a
Quilcaille, Y., Gasser, T., Ciais, P., Lecocq, F., Janssens-Maenhout, G., and
Mohr, S.: Uncertainty in projected climate change arising from uncertain
fossil-fuel emission factors, Environ. Res. Lett., 13, 044017, https://doi.org/10.1088/1748-9326/aab304, 2018. a
Ramo, R., Roteta, E., Bistinas, I., van Wees, D., Bastarrika, A., Chuvieco, E., and van der Werf, G. R.: African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data, P. Natl. Acad. Sci. USA, 118, 1–7, https://doi.org/10.1073/pnas.2011160118, 2021. a, b
Randerson, J. T., Chen, Y., Van Der Werf, G. R., Rogers, B. M., and Morton, D. C.: Global burned area and biomass burning emissions from small fires,
J. Geophys. Res.-Biogeo., 117, G04012, https://doi.org/10.1029/2012JG002128, 2012. a, b
Remaud, M., Chevallier, F., Cozic, A., Lin, X., and Bousquet, P.: On the
impact of recent developments of the LMDz atmospheric general circulation
model on the simulation of CO2 transport, Geosci. Model Dev., 11, 4489–4513, https://doi.org/10.5194/gmd-11-4489-2018, 2018. a
Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Schneising, O., Pillai, D., Bovensmann, H., Burrows, J. P., Bösch, H., Parker, R., Butz, A., Hasekamp, O., O'Dell, C. W., Yoshida, Y., Gerbig, C., Nehrkorn, T., Deutscher, N. M., Warneke, T., Notholt, J., Hase, F., Kivi, R., Sussmann, R., Machida, T., Matsueda, H., and Sawa, Y.: Satellite-inferred European carbon sink larger than expected, Atmos. Chem. Phys., 14, 13739–13753, https://doi.org/10.5194/acp-14-13739-2014, 2014. a, b
Reuter, M., Buchwitz, M., Hilker, M., Heymann, J., Bovensmann, H., Burr Ows, J. P., Houweling, S., Liu, Y. Y., Nassar, R., Chevallier, F., Ciais, P.,
Marshall, J., and Reichstein, M.: How much CO2 is taken up by the European terrestrial biosphere?, B. Am. Meteorol. Soc., 98, 665–671, https://doi.org/10.1175/BAMS-D-15-00310.1, 2017. a
Rodgers, C.: Inverse Methods for Atmospheric Sounding – Theory and Practice,
World Scientific Publishing, 2, https://doi.org/10.1142/9789812813718, 2000. a
Rothman, L. S., Gordon, I. E., Babikov, Y., Barbe, A., Chris Benner, D.,
Bernath, P. F., Birk, M., Bizzocchi, L., Boudon, V., Brown, L. R., Campargue,
A., Chance, K., Cohen, E. A., Coudert, L. H., Devi, V. M., Drouin, B. J.,
Fayt, A., Flaud, J. M., Gamache, R. R., Harrison, J. J., Hartmann, J. M.,
Hill, C., Hodges, J. T., Jacquemart, D., Jolly, A., Lamouroux, J., Le Roy,
R. J., Li, G., Long, D. A., Lyulin, O. M., Mackie, C. J., Massie, S. T.,
Mikhailenko, S., Müller, H. S., Naumenko, O. V., Nikitin, A. V., Orphal, J., Perevalov, V., Perrin, A., Polovtseva, E. R., Richard, C., Smith, M. A., Starikova, E., Sung, K., Tashkun, S., Tennyson, J., Toon, G. C., Tyuterev, V. G., and Wagner, G.: The HITRAN2012 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 130, 4–50, https://doi.org/10.1016/j.jqsrt.2013.07.002, 2013. 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, b
Sherlock, V., Connor, B. J., Robinson, J., Shiona, H., Smale, D., and Pollard, D.: TCCON data from Lauder (NZ), 125HR, Release GGG2014.R0, TCCON data archive, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.LAUDER02.R0/, 2014. a
Shi, Y., Matsunaga, T., Saito, M., Yamaguchi, Y., and Chen, X.: Comparison of global inventories of CO2 emissions from biomass burning during
2002–2011 derived from multiple satellite products, Environ. Pollut. 206, 479–487, https://doi.org/10.1016/j.envpol.2015.08.009, 2015. a
Sofiev, M., Vankevich, R., Ermakova, T., and Hakkarainen, J.: Global mapping
of maximum emission heights and resulting vertical profiles of wildfire
emissions, Atmos. Chem. Phys., 13, 7039–7052, https://doi.org/10.5194/acp-13-7039-2013, 2013. a
Spivakovsky, C. M., Logan, J. A., Montzka, S. A., Balkanski, Y. J.,
Foreman-Fowler, M., Jones, D. B., Horowitz, L. W., Fusco, A. C.,
Brenninkmeijer, C. A., Prather, M. J., Wofsy, S. C., and McElroy, M. B.:
Three-dimensional climatological distribution of tropospheric OH: Update and
evaluation, J. Geophys. Res.-Atmos., 105, 8931–8980,
https://doi.org/10.1029/1999JD901006, 2000. a
Strong, K., Roche, S., Franklin, J. E., Mendonca, J., Lutsch, E., Weaver, D.,
Fogal, P. F., Drummond, J. R., Batchelor, R., and Lindenmaier, R.: TCCON
data from Eureka (CA), Release GGG2014.R3, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.EUREKA01.R3, 2019. a
Sussmann, R. and Rettinger, M.: TCCON data from Garmisch (DE), Release
GGG2014.R2, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.GARMISCH01.R2, 2018. a
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson, A., Bakker, D. C., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii, M.,
Midorikawa, T., Nojiri, Y., Körtzinger, A., Steinhoff, T., Hoppema, M.,
Olafsson, J., Arnarson, T. S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R., Wong, C. S., Delille, B., Bates, N. R., and de Baar, H. J.:
Corrigendum to “Climatological mean and decadal change in surface ocean pCO2, and net sea–air CO2 flux over the global oceans” [Deep Sea Res. II 56 (2009) 554–577], Deep-Sea Res. Pt. I, 56,
2075–2076, https://doi.org/10.1016/j.dsr.2009.07.007, 2009. a
TCCON: Total Carbon Column Observing Network (TCCON), https://tccondata.org, last access: 14 December 2022. a
Té, Y., Jeseck, P., and Janssen, C.: TCCON data from Paris (FR), Release
GGG2014.R0, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.PARIS01.R0/1149279, 2014. a
TM5: Global chemistry transport model, https://tm5.sourceforge.net/, last access: 14 December 2022. a
Tohjima, Y., Mukai, H., Machida, T., Nojiri, Y., and Gloor, M.: First
measurements of the latitudinal atmospheric O2 and CO2 distributions across the western Pacific, Geophys. Res. Lett., 32, L17805, https://doi.org/10.1029/2005GL023311, 2005. a
Tsigaridis, K., Daskalakis, N., Kanakidou, M., Adams, P. J., Artaxo, P.,
Bahadur, R., Balkanski, Y., Bauer, S. E., Bellouin, N., Benedetti, A.,
Bergman, T., Berntsen, T. K., Beukes, J. P., Bian, H., Carslaw, K. S., Chin,
M., Curci, G., Diehl, T., Easter, R. C., Ghan, S. J., Gong, S. L., Hodzic,
A., Hoyle, C. R., Iversen, T., Jathar, S., Jimenez, J. L., Kaiser, J. W.,
Kirkevåg, A., Koch, D., Kokkola, H., Lee, Y. H., Lin, G., Liu, X., Luo, G., Ma, X., Mann, G. W., Mihalopoulos, N., Morcrette, J. J., Müller, J. F., Myhre, G., Myriokefalitakis, S., Ng, N. L., O'donnell, D., Penner, J. E., Pozzoli, L., Pringle, K. J., Russell, L. M., Schulz, M., Sciare, J., Seland, Shindell, D. T., Sillman, S., Skeie, R. B., Spracklen, D., Stavrakou, T., Steenrod, S. D., Takemura, T., Tiitta, P., Tilmes, S., Tost, H., Van Noije, T., Van Zyl, P. G., Von Salzen, K., Yu, F., Wang, Z., Wang, Z., Zaveri, R. A., Zhang, H., Zhang, K., Zhang, Q., and Zhang, X.: The AeroCom evaluation and intercomparison of organic aerosol in global models, Atmos. Chem. Phys., 14, 10845–10895, https://doi.org/10.5194/acp-14-10845-2014, 2014. a
Turnbull, J. C., Miller, J. B., Lehman, S. J., Tans, P. P., Sparks, R. J., and Southon, J.: Comparison of 14 CO2, CO, and SF6 as tracers for recently added fossil fuel CO2 in the atmosphere and implications for biological CO2 exchange, Geophys. Res. Lett., 33, 2–6, https://doi.org/10.1029/2005GL024213, 2006. a
Turquety, S., Clerbaux, C., Hauglustaine, D. A., Clough, S. A., Schlu, P., and Me, G.: Operational trace gas retrieval algorithm for the Infrared
Atmospheric Sounding Interferometer, J. Geophys. Res., 109, 1–19, https://doi.org/10.1029/2004JD004821, 2004. a
van der Laan-Luijkx, I. T., Krol, M. C., Gatti, L. V., Domingues, L. G.,
Correia, C. S. C., Miller, J. B., Gloor, M., Leeuwen, T. T., Kaiser, J. W.,
Wiedinmyer, C., Basu, S., Clerbaux, C., and Peters, W.: Response of the
Amazon carbon balance to the 2010 drought derived with CarbonTracker South
America, Global Biogeochem. Cy., 29, 1092–1108, https://doi.org/10.1002/2014GB005082, 2015. a, b, c, d
van der Werf, G. R., Randerson, J. T., Giglio, L., Collatz, G. J., Mu, M.,
Kasibhatla, P. S., Morton, D. C., Defries, R. S., Jin, Y., and Van Leeuwen,
T. T.: Global fire emissions and the contribution of deforestation, savanna,
forest, agricultural, and peat fires (1997–2009), Atmos. Chem. Phys., 10, 11707–11735, https://doi.org/10.5194/acp-10-11707-2010, 2010. a, b, c
van der Werf, G. R., Randerson, J. T., Giglio, L., Van Leeuwen, T. T., Chen,
Y., Rogers, B. M., Mu, M., Van Marle, M. J., Morton, D. C., Collatz, G. J.,
Yokelson, R. J., and Kasibhatla, P. S.: Global fire emissions estimates
during 1997–2016, Earth Syst. Sci. Data, 9, 697–720,
https://doi.org/10.5194/essd-9-697-2017, 2017. a, b, c, d, e, f, g, h
Van Leeuwen, T. T., Peters, W., Krol, M. C., and Van Der Werf, G. R.:
Dynamic biomass burning emission factors and their impact on atmospheric CO mixing ratios, J. Geophys. Res.-Atmos., 118, 6797–6815,
https://doi.org/10.1002/jgrd.50478, 2013. a
Venevsky, S., Le Page, Y., Pereira, J. M., and Wu, C.: Analysis fire patterns and drivers with a global SEVER-FIRE v1.0 model incorporated into dynamic global vegetation model and satellite and on-ground observations, Geosci. Model Dev., 12, 89–110, https://doi.org/10.5194/gmd-12-89-2019, 2019. a
Vermote, E., Ellicott, E., Dubovik, O., Lapyonok, T., Chin, M., Giglio, L., and Roberts, G. J.: An approach to estimate global biomass burning emissions of organic and black carbon from MODIS fire radiative power, J. Geophys. Res.-Atmos., 114, 1–22, https://doi.org/10.1029/2008JD011188, 2009. a
Wang, J. S., Kawa, S. R., Collatz, G. J., Sasakawa, M., Gatti, L. V., Machida, T., Liu, Y., and Manyin, M. E.: A global synthesis inversion analysis of recent variability in CO2 fluxes using GOSAT and in situ observations, Atmos. Chem. Phys., 18, 11097–11124, https://doi.org/10.5194/acp-18-11097-2018, 2018. a
Wang, Y., Munger, J. W., Xu, S., McElroy, M. B., Hao, J., Nielsen, C. P., and
Ma, H.: CO2 and its correlation with CO at a rural site near Beijing: Implications for combustion efficiency in China, Atmos. Chem. Phys., 10, 8881—8897, https://doi.org/10.5194/acp-10-8881-2010, 2010. a
Warneke, T., Messerschmidt, J., Notholt, J., Weinzierl, C., Deutscher, N. M.,
Petri, C., and Grupe, P.: TCCON data from Orléans (FR), Release GGG2014.R1, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.ORLEANS01.R1, 2019. a
Wennberg, P. O., Wunch, D., Roehl, C., Blavier, J.-F., Toon, G. C., and Allen, N.: TCCON data from Caltech (US), Release GGG2014R1, TCCON data archive, CaltechDATA, https://doi.org/10.14291/tccon.ggg2014.pasadena01.R1/1182415,
2014. a
Wennberg, P. O., Wunch, D., Roehl, C., Blavier, J.-F., Toon, G. C., Allen, N., Dowell, P., Teske, K., Martin, C., and Martin., J.: TCCON data from Lamont (US), Release GGG2014R1, TCCON data archive, CaltechDATA,
https://doi.org/10.14291/tccon.ggg2014.lamont01.R1/1255070, 2016. a
Wennberg, P. O., Roehl, C. M., Wunch, D., Toon, G. C., Blavier, J.-F.,
Washenfelder, R., Keppel-Aleks, G., Allen, N. T., and Ayers, J.: TCCON data
from Park Falls (US), Release GGG2014.R1, CaltechDATA, https://doi.org/10.14291/TCCON.GGG2014.PARKFALLS01.R1, 2017. a
Wiedinmyer, C. and Neff, J. C.: Estimates of CO2 from fires in the United States: Implications for carbon management, Carb. Balance Manage., 2, 1–12, https://doi.org/10.1186/1750-0680-2-10, 2007. a
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): A high resolution global model to estimate the emissions from open burning,
Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011. a, b
Worden, H. M., Deeter, M. N., Edwards, D. P., Gille, J. C., Drummond, J. R.,
and Nédélec, P.: Observations of near-surface carbon monoxide from space using MOPITT multispectral retrievals, J. Geophys. Res.-Atmos., 115, 1–12, https://doi.org/10.1029/2010JD014242, 2010. a, b
Wunch, D., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Stephens, B. B.,
Fischer, M. L., Uchino, O., Abshire, J. B., Bernath, P., Biraud, S. C.,
Blavier, J. F., Boone, C., Bowman, K. P., Browell, E. V., Campos, T., Connor,
B. J., Daube, B. C., Deutscher, N. M., Diao, M., Elkins, J. W., Gerbig, C.,
Gottlieb, E., Griffith, D. W., Hurst, D. F., Jiménez, R., Keppel-Aleks,
G., Kort, E. A., MacAtangay, R., MacHida, T., Matsueda, H., Moore, F., Morino, I., Park, S., Robinson, J., Roehl, C. M., Sawa, Y., Sherlock, V.,
Sweeney, C., Tanaka, T., and Zondlo, M. A.: Calibration of the total carbon
column observing network using aircraft profile data, Atmos. Meas. Tech., 3, 1351–1362, https://doi.org/10.5194/amt-3-1351-2010, 2010. a
Wunch, D., Toon, G. C., Blavier, J. F. L., Washenfelder, R. A., Notholt, J.,
Connor, B. J., Griffith, D. W., Sherlock, V., and Wennberg, P. O.: The total
carbon column observing network, Philos. T. Roy. Soc. A, 369, 2087–2112,
https://doi.org/10.1098/rsta.2010.0240, 2011. a, b
Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, M. C., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON, Atmos. Meas. Techn., 10, 2209–2238, https://doi.org/10.5194/amt-10-2209-2017, 2017. a, b
Yarwood, G., Rao, S., Yocke, M., and Whitten, G.: Updates to the carbon bond chemical mechanism: CB05, Final report to the US EPA, EPA Report no. RT-0400675, US EPA, https://www.camx.com/ (last access: 12 December 2022), 2005. a
Yin, Y., Chevallier, F., Ciais, P., Broquet, G., Fortems-Cheiney, A., Pison,
I., and Saunois, M.: Decadal trends in global CO emissions as seen by MOPITT, Atmos. Chem. Phys., 15, 13433–13451, https://doi.org/10.5194/acp-15-13433-2015, 2015. a
Yin, Y., Ciais, P., Chevallier, F., van der Werf, G. R., Fanin, T., Broquet,
G., Boesch, H., Cozic, A., Hauglustaine, D., Szopa, S., and Wang, Y.:
Variability of fire carbon emissions in equatorial Asia and its nonlinear
sensitivity to El Niño, Geophys. Res. Lett., 43, 10472–10479, https://doi.org/10.1002/2016GL070971, 2016. a
Yokelson, R. J., Andreae, M. O., and Akagi, S. K.: Pitfalls with the use of
enhancement ratios or normalized excess mixing ratios measured in plumes to
characterize pollution sources and aging, Atmos. Meas. Tech., 6, 2155–2158, https://doi.org/10.5194/amt-6-2155-2013, 2013. a
Zheng, B., Chevallier, F., Ciais, P., Yin, Y., Deeter, M. N., Worden, H. M.,
Wang, Y., Zhang, Q., and He, K.: Rapid decline in carbon monoxide emissions
and export from East Asia between years 2005 and 2016, Environ. Res. Lett., 13, 044007, https://doi.org/10.1088/1748-9326/aab2b3, 2018a. a
Zheng, B., Chevallier, F., Ciais, P., Yin, Y., and Wang, Y.: On the Role of
the Flaming to Smoldering Transition in the Seasonal Cycle of African Fire
Emissions, Geophys. Res. Lett., 45, 11998–12007, https://doi.org/10.1029/2018GL079092, 2018b. a
Short summary
CO data can provide a powerful constraint on fire fluxes, supporting more accurate estimation of biospheric CO2 fluxes. We converted CO fire flux into CO2 fire prior, which is then used to adjust CO2 respiration. We applied this to two other fire flux products. CO2 inversions constrained by satellites or in situ data are then performed. Results show larger variations among the data assimilated than across the priors, but tropical flux from in situ inversions is sensitive to priors.
CO data can provide a powerful constraint on fire fluxes, supporting more accurate estimation of...
Altmetrics
Final-revised paper
Preprint