Articles | Volume 23, issue 6
https://doi.org/10.5194/acp-23-3493-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-3493-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimating enhancement ratios of nitrogen dioxide, carbon monoxide and carbon dioxide using satellite observations
Cameron G. MacDonald
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada
Atmospheric and Oceanic Sciences, Princeton University, Princeton, New Jersey, USA
Jon-Paul Mastrogiacomo
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Joshua L. Laughner
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Jacob K. Hedelius
Department of Physics, University of Toronto, Toronto, Ontario, Canada
Space Dynamics Laboratory, Utah State University, North Logan, Utah, USA
Ray Nassar
Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
Debra Wunch
CORRESPONDING AUTHOR
Department of Physics, University of Toronto, Toronto, Ontario, Canada
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Constantina Rousogenous, Christof Petri, Pierre-Yves Quehe, Thomas Laemmel, Joshua L. Laughner, Maximilien Desservettaz, Michael Pikridas, Michel Ramonet, Efstratios Bourtsoukidis, Matthias Buschmann, Justus Notholt, Thorsten Warneke, Jean-Daniel Paris, Jean Sciare, and Mihalis Vrekoussis
EGUsphere, https://doi.org/10.5194/egusphere-2025-1442, https://doi.org/10.5194/egusphere-2025-1442, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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The Eastern Mediterranean and Middle East is a greenhouse gas emission hotspot but lacks atmospheric monitoring. Our study introduces the first Total Carbon Column Observing Network site in this region, in Cyprus, providing high-precision columnar measurement of key greenhouse gases. This new dataset enhances global climate monitoring efforts, supports the validation of satellites, will help assess regional emission trends, filling a critical observational gap in this climate-sensitive region.
Carley D. Fredrickson, Scott J. Janz, Lok N. Lamsal, Ursula A. Jongebloed, Joshua L. Laughner, and Joel A. Thornton
Atmos. Meas. Tech., 18, 3669–3689, https://doi.org/10.5194/amt-18-3669-2025, https://doi.org/10.5194/amt-18-3669-2025, 2025
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We present an analysis of high-resolution remote sensing measurements of nitrogen-containing trace gases emitted by wildfires. The measurements were made using an instrument on the NASA ER-2 aircraft in the summer of 2019. We find that time-resolved fire intensity is critical to quantify trace gas emissions over a fire's entire lifespan. These findings have implications for improving air pollution forecasts downwind of wildfires using computer models of atmospheric chemistry and meteorology.
Joshua L. Laughner, Susan S. Kulawik, and Vivienne H. Payne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2293, https://doi.org/10.5194/egusphere-2025-2293, 2025
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We developed an algorithm to infer peroxyacyl nitrates from an instrument that has been in space for over 20 years. These nitrates can transport pollution significant distances downwind, thus having a long term record of their concentrations can help understand how transport of pollution changed over time. We were able to develop a product for this instrument that produces results compatible with a more recent instrument, allowing them to work together in future analyses.
Sina Voshtani, Dylan B. A. Jones, Debra Wunch, Drew C. Pendergrass, Paul O. Wennberg, David F. Pollard, Isamu Morino, Hirofumi Ohyama, Nicholas M. Deutscher, Frank Hase, Ralf Sussmann, Damien Weidmann, Rigel Kivi, Omaira García, Yao Té, Jack Chen, Kerry Anderson, Robin Stevens, Shobha Kondragunta, Aihua Zhu, Douglas Worthy, Senen Racki, Kathryn McKain, Maria V. Makarova, Nicholas Jones, Emmanuel Mahieu, Andrea Cadena-Caicedo, Paolo Cristofanelli, Casper Labuschagne, Elena Kozlova, Thomas Seitz, Martin Steinbacher, Reza Mahdi, and Isao Murata
EGUsphere, https://doi.org/10.5194/egusphere-2025-858, https://doi.org/10.5194/egusphere-2025-858, 2025
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We assess the complementarity of the greater temporal coverage provided by ground-based remote sensing data with the spatial coverage of satellite observations when these data are used together to quantify CO emissions from extreme wildfires in 2023. Our results reveal that the commonly used biomass burning emission inventories significantly underestimate the fire emissions and emphasize the importance of the ground-based remote sensing data in reducing uncertainties in the estimated emissions.
Jonas Hachmeister, Debra Wunch, Erin McGee, Kimberly Strong, Rigel Kivi, Justus Notholt, Thorsten Warneke, and Matthias Buschmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-4055, https://doi.org/10.5194/egusphere-2024-4055, 2025
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Methane measurements from the Total Carbon Column Observing Network (TCCON) are important for climate research, especially in the Arctic where few measurements are available. We show that during early spring systematic errors are present in these data that are correlated to the presence of the polar vortex. These errors occur due to the usage of wrong methane prior shapes in the retrieval and can be alleviated by modifying the prior shape accordingly.
Christopher Chan Miller, Sébastien Roche, Jonas S. Wilzewski, Xiong Liu, Kelly Chance, Amir H. Souri, Eamon Conway, Bingkun Luo, Jenna Samra, Jacob Hawthorne, Kang Sun, Carly Staebell, Apisada Chulakadabba, Maryann Sargent, Joshua S. Benmergui, Jonathan E. Franklin, Bruce C. Daube, Yang Li, Joshua L. Laughner, Bianca C. Baier, Ritesh Gautam, Mark Omara, and Steven C. Wofsy
Atmos. Meas. Tech., 17, 5429–5454, https://doi.org/10.5194/amt-17-5429-2024, https://doi.org/10.5194/amt-17-5429-2024, 2024
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MethaneSAT is an upcoming satellite mission designed to monitor methane emissions from the oil and gas (O&G) industry globally. Here, we present observations from the first flight campaign of MethaneAIR, a MethaneSAT-like instrument mounted on an aircraft. MethaneAIR can map methane with high precision and accuracy over a typically sized oil and gas basin (~200 km2) in a single flight. This paper demonstrates the capability of the upcoming satellite to routinely track global O&G emissions.
Benedikt Herkommer, Carlos Alberti, Paolo Castracane, Jia Chen, Angelika Dehn, Florian Dietrich, Nicholas M. Deutscher, Matthias Max Frey, Jochen Groß, Lawson Gillespie, Frank Hase, Isamu Morino, Nasrin Mostafavi Pak, Brittany Walker, and Debra Wunch
Atmos. Meas. Tech., 17, 3467–3494, https://doi.org/10.5194/amt-17-3467-2024, https://doi.org/10.5194/amt-17-3467-2024, 2024
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The Total Carbon Column Observing Network is a network of ground-based Fourier transform infrared (FTIR) spectrometers used mainly for satellite validation. To ensure the highest-quality validation data, the network needs to be highly consistent. This is a major challenge, which so far is solved by site comparisons with airborne in situ measurements. In this work, we describe the use of a portable FTIR spectrometer as a travel standard for evaluating the consistency of TCCON sites.
Joshua L. Laughner, Geoffrey C. Toon, Joseph Mendonca, Christof Petri, Sébastien Roche, Debra Wunch, Jean-Francois Blavier, David W. T. Griffith, Pauli Heikkinen, Ralph F. Keeling, Matthäus Kiel, Rigel Kivi, Coleen M. Roehl, Britton B. Stephens, Bianca C. Baier, Huilin Chen, Yonghoon Choi, Nicholas M. Deutscher, Joshua P. DiGangi, Jochen Gross, Benedikt Herkommer, Pascal Jeseck, Thomas Laemmel, Xin Lan, Erin McGee, Kathryn McKain, John Miller, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Haris Riris, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Steven C. Wofsy, Minqiang Zhou, and Paul O. Wennberg
Earth Syst. Sci. Data, 16, 2197–2260, https://doi.org/10.5194/essd-16-2197-2024, https://doi.org/10.5194/essd-16-2197-2024, 2024
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This paper describes a new version, called GGG2020, of a data set containing column-integrated observations of greenhouse and related gases (including CO2, CH4, CO, and N2O) made by ground stations located around the world. Compared to the previous version (GGG2014), improvements have been made toward site-to-site consistency. This data set plays a key role in validating space-based greenhouse gas observations and in understanding the carbon cycle.
Daniel H. Cusworth, Andrew K. Thorpe, Charles E. Miller, Alana K. Ayasse, Ralph Jiorle, Riley M. Duren, Ray Nassar, Jon-Paul Mastrogiacomo, and Robert R. Nelson
Atmos. Chem. Phys., 23, 14577–14591, https://doi.org/10.5194/acp-23-14577-2023, https://doi.org/10.5194/acp-23-14577-2023, 2023
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Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we tasked two satellites to routinely observe CO2 emissions at 30 coal-fired power plants between 2021 and 2022. These results present the largest dataset of space-based CO2 emission estimates to date.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
Geosci. Model Dev., 16, 6161–6185, https://doi.org/10.5194/gmd-16-6161-2023, https://doi.org/10.5194/gmd-16-6161-2023, 2023
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To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and assess its performance against TROPOMI v2 over power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind directions and prior emissions.
Lawson David Gillespie, Sébastien Ars, James Phillip Williams, Louise Klotz, Tianjie Feng, Stephanie Gu, Mishaal Kandapath, Amy Mann, Michael Raczkowski, Mary Kang, Felix Vogel, and Debra Wunch
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-193, https://doi.org/10.5194/amt-2023-193, 2023
Preprint withdrawn
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We investigate techniques for calculating emissions from mobile in situ gas concentrations recorded during downwind plume transects. We find that using the enhancement area to estimate emissions is the most consistent method when comparing different setups and instruments. Observations from a multi year urban methane survey and controlled release experiment are analyzed, and emissions rates for combined sewage overflow basins and a large wastewater treatment plant in Toronto are calculated.
Rafaella Chiarella, Matthias Buschmann, Joshua Laughner, Isamu Morino, Justus Notholt, Christof Petri, Geoffrey Toon, Voltaire A. Velazco, and Thorsten Warneke
Atmos. Meas. Tech., 16, 3987–4007, https://doi.org/10.5194/amt-16-3987-2023, https://doi.org/10.5194/amt-16-3987-2023, 2023
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The goal is to establish a window and strategy for xCO2 retrieval from ground-based Fourier transform spectrometers for NDACC. In the study we describe the spectroscopy of the region, the locations and instruments used, and the methods of calculating the retrieved xCO2. We performed tests to assess the sensitivity to diverse factors and sources of errors while comparing the retrieval to a well-established xCO2 retrieval from TCCON.
Thomas E. Taylor, Christopher W. O'Dell, David Baker, Carol Bruegge, Albert Chang, Lars Chapsky, Abhishek Chatterjee, Cecilia Cheng, Frédéric Chevallier, David Crisp, Lan Dang, Brian Drouin, Annmarie Eldering, Liang Feng, Brendan Fisher, Dejian Fu, Michael Gunson, Vance Haemmerle, Graziela R. Keller, Matthäus Kiel, Le Kuai, Thomas Kurosu, Alyn Lambert, Joshua Laughner, Richard Lee, Junjie Liu, Lucas Mandrake, Yuliya Marchetti, Gregory McGarragh, Aronne Merrelli, Robert R. Nelson, Greg Osterman, Fabiano Oyafuso, Paul I. Palmer, Vivienne H. Payne, Robert Rosenberg, Peter Somkuti, Gary Spiers, Cathy To, Brad Weir, Paul O. Wennberg, Shanshan Yu, and Jia Zong
Atmos. Meas. Tech., 16, 3173–3209, https://doi.org/10.5194/amt-16-3173-2023, https://doi.org/10.5194/amt-16-3173-2023, 2023
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NASA's Orbiting Carbon Observatory 2 and 3 (OCO-2 and OCO-3, respectively) provide complementary spatiotemporal coverage from a sun-synchronous and precession orbit, respectively. Estimates of total column carbon dioxide (XCO2) derived from the two sensors using the same retrieval algorithm show broad consistency over a 2.5-year overlapping time record. This suggests that data from the two satellites may be used together for scientific analysis.
Harrison A. Parker, Joshua L. Laughner, Geoffrey C. Toon, Debra Wunch, Coleen M. Roehl, Laura T. Iraci, James R. Podolske, Kathryn McKain, Bianca C. Baier, and Paul O. Wennberg
Atmos. Meas. Tech., 16, 2601–2625, https://doi.org/10.5194/amt-16-2601-2023, https://doi.org/10.5194/amt-16-2601-2023, 2023
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We describe a retrieval algorithm for determining limited information about the vertical distribution of carbon monoxide (CO) and carbon dioxide (CO2) from total column observations from ground-based observations. Our retrieved partial column values compare well with integrated in situ data. The average error for our retrieval is 1.51 ppb (~ 2 %) for CO and 5.09 ppm (~ 1.25 %) for CO2. We anticipate that this approach will find broad application for use in carbon cycle science.
Yifan Guan, Gretchen Keppel-Aleks, Scott C. Doney, Christof Petri, Dave Pollard, Debra Wunch, Frank Hase, Hirofumi Ohyama, Isamu Morino, Justus Notholt, Kei Shiomi, Kim Strong, Rigel Kivi, Matthias Buschmann, Nicholas Deutscher, Paul Wennberg, Ralf Sussmann, Voltaire A. Velazco, and Yao Té
Atmos. Chem. Phys., 23, 5355–5372, https://doi.org/10.5194/acp-23-5355-2023, https://doi.org/10.5194/acp-23-5355-2023, 2023
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We characterize spatial–temporal patterns of interannual variability (IAV) in atmospheric CO2 based on NASA’s Orbiting Carbon Observatory-2 (OCO-2). CO2 variation is strongly impacted by climate events, with higher anomalies during El Nino years. We show high correlation in IAV between space-based and ground-based CO2 from long-term sites. Because OCO-2 has near-global coverage, our paper provides a roadmap to study IAV where in situ observation is sparse, such as open oceans and remote lands.
Yu Someya, Yukio Yoshida, Hirofumi Ohyama, Shohei Nomura, Akihide Kamei, Isamu Morino, Hitoshi Mukai, Tsuneo Matsunaga, Joshua L. Laughner, Voltaire A. Velazco, Benedikt Herkommer, Yao Té, Mahesh Kumar Sha, Rigel Kivi, Minqiang Zhou, Young Suk Oh, Nicholas M. Deutscher, and David W. T. Griffith
Atmos. Meas. Tech., 16, 1477–1501, https://doi.org/10.5194/amt-16-1477-2023, https://doi.org/10.5194/amt-16-1477-2023, 2023
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The updated retrieval algorithm for the Greenhouse gases Observing SATellite level 2 product is presented. The main changes in the algorithm from the previous one are the treatment of cirrus clouds, the degradation model of the sensor, solar irradiance, and gas absorption coefficient tables. The retrieval results showed improvements in fitting accuracy and an increase in the data amount over land. On the other hand, there are still large biases of XCO2 which should be corrected over the ocean.
Nasrin Mostafavi Pak, Jacob K. Hedelius, Sébastien Roche, Liz Cunningham, Bianca Baier, Colm Sweeney, Coleen Roehl, Joshua Laughner, Geoffrey Toon, Paul Wennberg, Harrison Parker, Colin Arrowsmith, Joseph Mendonca, Pierre Fogal, Tyler Wizenberg, Beatriz Herrera, Kimberly Strong, Kaley A. Walker, Felix Vogel, and Debra Wunch
Atmos. Meas. Tech., 16, 1239–1261, https://doi.org/10.5194/amt-16-1239-2023, https://doi.org/10.5194/amt-16-1239-2023, 2023
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Ground-based remote sensing instruments in the Total Carbon Column Observing Network (TCCON) measure greenhouse gases in the atmosphere. Consistency between TCCON measurements is crucial to accurately infer changes in atmospheric composition. We use portable remote sensing instruments (EM27/SUN) to evaluate biases between TCCON stations in North America. We also improve the retrievals of EM27/SUN instruments and evaluate the previous (GGG2014) and newest (GGG2020) retrieval algorithms.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
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Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Joshua L. Laughner, Sébastien Roche, Matthäus Kiel, Geoffrey C. Toon, Debra Wunch, Bianca C. Baier, Sébastien Biraud, Huilin Chen, Rigel Kivi, Thomas Laemmel, Kathryn McKain, Pierre-Yves Quéhé, Constantina Rousogenous, Britton B. Stephens, Kaley Walker, and Paul O. Wennberg
Atmos. Meas. Tech., 16, 1121–1146, https://doi.org/10.5194/amt-16-1121-2023, https://doi.org/10.5194/amt-16-1121-2023, 2023
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Observations using sunlight to measure surface-to-space total column of greenhouse gases in the atmosphere need an initial guess of the vertical distribution of those gases to start from. We have developed an approach to provide those initial guess profiles that uses readily available meteorological data as input. This lets us make these guesses without simulating them with a global model. The profiles generated this way match independent observations well.
Ali Jalali, Kaley A. Walker, Kimberly Strong, Rebecca R. Buchholz, Merritt N. Deeter, Debra Wunch, Sébastien Roche, Tyler Wizenberg, Erik Lutsch, Erin McGee, Helen M. Worden, Pierre Fogal, and James R. Drummond
Atmos. Meas. Tech., 15, 6837–6863, https://doi.org/10.5194/amt-15-6837-2022, https://doi.org/10.5194/amt-15-6837-2022, 2022
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This study validates MOPITT version 8 carbon monoxide measurements over the Canadian high Arctic for the period 2006 to 2019. The MOPITT products from different detector pixels and channels are compared with ground-based measurements from the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut, Canada. These results show good consistency between the satellite and ground-based measurements and provide guidance on the usage of these MOPITT data at high latitudes.
Matthias Schneider, Benjamin Ertl, Qiansi Tu, Christopher J. Diekmann, Farahnaz Khosrawi, Amelie N. Röhling, Frank Hase, Darko Dubravica, Omaira E. García, Eliezer Sepúlveda, Tobias Borsdorff, Jochen Landgraf, Alba Lorente, André Butz, Huilin Chen, Rigel Kivi, Thomas Laemmel, Michel Ramonet, Cyril Crevoisier, Jérome Pernin, Martin Steinbacher, Frank Meinhardt, Kimberly Strong, Debra Wunch, Thorsten Warneke, Coleen Roehl, Paul O. Wennberg, Isamu Morino, Laura T. Iraci, Kei Shiomi, Nicholas M. Deutscher, David W. T. Griffith, Voltaire A. Velazco, and David F. Pollard
Atmos. Meas. Tech., 15, 4339–4371, https://doi.org/10.5194/amt-15-4339-2022, https://doi.org/10.5194/amt-15-4339-2022, 2022
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We present a computationally very efficient method for the synergetic use of level 2 remote-sensing data products. We apply the method to IASI vertical profile and TROPOMI total column space-borne methane observations and thus gain sensitivity for the tropospheric methane partial columns, which is not achievable by the individual use of TROPOMI and IASI. These synergetic effects are evaluated theoretically and empirically by inter-comparisons to independent references of TCCON, AirCore, and GAW.
Carlos Alberti, Frank Hase, Matthias Frey, Darko Dubravica, Thomas Blumenstock, Angelika Dehn, Paolo Castracane, Gregor Surawicz, Roland Harig, Bianca C. Baier, Caroline Bès, Jianrong Bi, Hartmut Boesch, André Butz, Zhaonan Cai, Jia Chen, Sean M. Crowell, Nicholas M. Deutscher, Dragos Ene, Jonathan E. Franklin, Omaira García, David Griffith, Bruno Grouiez, Michel Grutter, Abdelhamid Hamdouni, Sander Houweling, Neil Humpage, Nicole Jacobs, Sujong Jeong, Lilian Joly, Nicholas B. Jones, Denis Jouglet, Rigel Kivi, Ralph Kleinschek, Morgan Lopez, Diogo J. Medeiros, Isamu Morino, Nasrin Mostafavipak, Astrid Müller, Hirofumi Ohyama, Paul I. Palmer, Mahesh Pathakoti, David F. Pollard, Uwe Raffalski, Michel Ramonet, Robbie Ramsay, Mahesh Kumar Sha, Kei Shiomi, William Simpson, Wolfgang Stremme, Youwen Sun, Hiroshi Tanimoto, Yao Té, Gizaw Mengistu Tsidu, Voltaire A. Velazco, Felix Vogel, Masataka Watanabe, Chong Wei, Debra Wunch, Marcia Yamasoe, Lu Zhang, and Johannes Orphal
Atmos. Meas. Tech., 15, 2433–2463, https://doi.org/10.5194/amt-15-2433-2022, https://doi.org/10.5194/amt-15-2433-2022, 2022
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Space-borne greenhouse gas missions require ground-based validation networks capable of providing fiducial reference measurements. Here, considerable refinements of the calibration procedures for the COllaborative Carbon Column Observing Network (COCCON) are presented. Laboratory and solar side-by-side procedures for the characterization of the spectrometers have been refined and extended. Revised calibration factors for XCO2, XCO and XCH4 are provided, incorporating 47 new spectrometers.
Thomas E. Taylor, Christopher W. O'Dell, David Crisp, Akhiko Kuze, Hannakaisa Lindqvist, Paul O. Wennberg, Abhishek Chatterjee, Michael Gunson, Annmarie Eldering, Brendan Fisher, Matthäus Kiel, Robert R. Nelson, Aronne Merrelli, Greg Osterman, Frédéric Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Manvendra K. Dubey, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Frank Hase, Laura T. Iraci, Rigel Kivi, Cheng Liu, Martine De Mazière, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Matthias Schneider, Coleen M. Roehl, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, and Debra Wunch
Earth Syst. Sci. Data, 14, 325–360, https://doi.org/10.5194/essd-14-325-2022, https://doi.org/10.5194/essd-14-325-2022, 2022
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We provide an analysis of an 11-year record of atmospheric carbon dioxide (CO2) concentrations derived using an optimal estimation retrieval algorithm on measurements made by the GOSAT satellite. The new product (version 9) shows improvement over the previous version (v7.3) as evaluated against independent estimates of CO2 from ground-based sensors and atmospheric inversion systems. We also compare the new GOSAT CO2 values to collocated estimates from NASA's Orbiting Carbon Observatory-2.
Joseph Mendonca, Ray Nassar, Christopher W. O'Dell, Rigel Kivi, Isamu Morino, Justus Notholt, Christof Petri, Kimberly Strong, and Debra Wunch
Atmos. Meas. Tech., 14, 7511–7524, https://doi.org/10.5194/amt-14-7511-2021, https://doi.org/10.5194/amt-14-7511-2021, 2021
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Machine learning has become an important tool for pattern recognition in many applications. In this study, we used a neural network to improve the data quality of OCO-2 measurements made at northern high latitudes. The neural network was trained and used as a binary classifier to filter out bad OCO-2 measurements in order to increase the accuracy and precision of OCO-2 XCO2 measurements in the Boreal and Arctic regions.
Nicole Jacobs, William R. Simpson, Kelly A. Graham, Christopher Holmes, Frank Hase, Thomas Blumenstock, Qiansi Tu, Matthias Frey, Manvendra K. Dubey, Harrison A. Parker, Debra Wunch, Rigel Kivi, Pauli Heikkinen, Justus Notholt, Christof Petri, and Thorsten Warneke
Atmos. Chem. Phys., 21, 16661–16687, https://doi.org/10.5194/acp-21-16661-2021, https://doi.org/10.5194/acp-21-16661-2021, 2021
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Spatial patterns of carbon dioxide seasonal cycle amplitude and summer drawdown timing derived from the OCO-2 satellite over northern high latitudes agree well with corresponding estimates from two models. The Asian boreal forest is anomalous with the largest amplitude and earliest seasonal drawdown. Modeled land contact tracers suggest that accumulated CO2 exchanges during atmospheric transport play a major role in shaping carbon dioxide seasonality in northern high-latitude regions.
Mahesh Kumar Sha, Bavo Langerock, Jean-François L. Blavier, Thomas Blumenstock, Tobias Borsdorff, Matthias Buschmann, Angelika Dehn, Martine De Mazière, Nicholas M. Deutscher, Dietrich G. Feist, Omaira E. García, David W. T. Griffith, Michel Grutter, James W. Hannigan, Frank Hase, Pauli Heikkinen, Christian Hermans, Laura T. Iraci, Pascal Jeseck, Nicholas Jones, Rigel Kivi, Nicolas Kumps, Jochen Landgraf, Alba Lorente, Emmanuel Mahieu, Maria V. Makarova, Johan Mellqvist, Jean-Marc Metzger, Isamu Morino, Tomoo Nagahama, Justus Notholt, Hirofumi Ohyama, Ivan Ortega, Mathias Palm, Christof Petri, David F. Pollard, Markus Rettinger, John Robinson, Sébastien Roche, Coleen M. Roehl, Amelie N. Röhling, Constantina Rousogenous, Matthias Schneider, Kei Shiomi, Dan Smale, Wolfgang Stremme, Kimberly Strong, Ralf Sussmann, Yao Té, Osamu Uchino, Voltaire A. Velazco, Corinne Vigouroux, Mihalis Vrekoussis, Pucai Wang, Thorsten Warneke, Tyler Wizenberg, Debra Wunch, Shoma Yamanouchi, Yang Yang, and Minqiang Zhou
Atmos. Meas. Tech., 14, 6249–6304, https://doi.org/10.5194/amt-14-6249-2021, https://doi.org/10.5194/amt-14-6249-2021, 2021
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This paper presents, for the first time, Sentinel-5 Precursor methane and carbon monoxide validation results covering a period from November 2017 to September 2020. For this study, we used global TCCON and NDACC-IRWG network data covering a wide range of atmospheric and surface conditions across different terrains. We also show the influence of a priori alignment, smoothing uncertainties and the sensitivity of the validation results towards the application of advanced co-location criteria.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Martin Keller, Daven K. Henze, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Atmos. Chem. Phys., 21, 9545–9572, https://doi.org/10.5194/acp-21-9545-2021, https://doi.org/10.5194/acp-21-9545-2021, 2021
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We explore the utility of a weak-constraint (WC) four-dimensional variational (4D-Var) data assimilation scheme for mitigating systematic errors in methane simulation in the GEOS-Chem model. We use data from the Greenhouse Gases Observing Satellite (GOSAT) and show that, compared to the traditional 4D-Var approach, the WC scheme improves the agreement between the model and independent observations. We find that the WC corrections to the model provide insight into the source of the errors.
Astrid Müller, Hiroshi Tanimoto, Takafumi Sugita, Toshinobu Machida, Shin-ichiro Nakaoka, Prabir K. Patra, Joshua Laughner, and David Crisp
Atmos. Chem. Phys., 21, 8255–8271, https://doi.org/10.5194/acp-21-8255-2021, https://doi.org/10.5194/acp-21-8255-2021, 2021
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Over oceans, high uncertainties in satellite CO2 retrievals exist due to limited reference data. We combine commercial ship and aircraft observations and, with the aid of model calculations, obtain column-averaged mixing ratios of CO2 (XCO2) data over the Pacific Ocean. This new dataset has great potential as a robust reference for XCO2 measured from space and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.
Sébastien Roche, Kimberly Strong, Debra Wunch, Joseph Mendonca, Colm Sweeney, Bianca Baier, Sébastien C. Biraud, Joshua L. Laughner, Geoffrey C. Toon, and Brian J. Connor
Atmos. Meas. Tech., 14, 3087–3118, https://doi.org/10.5194/amt-14-3087-2021, https://doi.org/10.5194/amt-14-3087-2021, 2021
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We evaluate CO2 profile retrievals from ground-based near-infrared solar absorption spectra after implementing several improvements to the GFIT2 retrieval algorithm. Realistic errors in the a priori temperature profile (~ 2 °C in the lower troposphere) are found to be the leading source of differences between the retrieved and true CO2 profiles, differences that are larger than typical CO2 variability. A temperature retrieval or correction is critical to improve CO2 profile retrieval results.
Alba Lorente, Tobias Borsdorff, Andre Butz, Otto Hasekamp, Joost aan de Brugh, Andreas Schneider, Lianghai Wu, Frank Hase, Rigel Kivi, Debra Wunch, David F. Pollard, Kei Shiomi, Nicholas M. Deutscher, Voltaire A. Velazco, Coleen M. Roehl, Paul O. Wennberg, Thorsten Warneke, and Jochen Landgraf
Atmos. Meas. Tech., 14, 665–684, https://doi.org/10.5194/amt-14-665-2021, https://doi.org/10.5194/amt-14-665-2021, 2021
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TROPOMI aboard Sentinel-5P satellite provides methane (CH4) measurements with exceptional temporal and spatial resolution. The study describes a series of improvements developed to retrieve CH4 from TROPOMI. The updated CH4 product features (among others) a more accurate a posteriori correction derived independently of any reference data. The validation of the improved data product shows good agreement with ground-based and satellite measurements, which highlights the quality of the TROPOMI CH4.
Robert J. Parker, Alex Webb, Hartmut Boesch, Peter Somkuti, Rocio Barrio Guillo, Antonio Di Noia, Nikoleta Kalaitzi, Jasdeep S. Anand, Peter Bergamaschi, Frederic Chevallier, Paul I. Palmer, Liang Feng, Nicholas M. Deutscher, Dietrich G. Feist, David W. T. Griffith, Frank Hase, Rigel Kivi, Isamu Morino, Justus Notholt, Young-Suk Oh, Hirofumi Ohyama, Christof Petri, David F. Pollard, Coleen Roehl, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Thorsten Warneke, Paul O. Wennberg, and Debra Wunch
Earth Syst. Sci. Data, 12, 3383–3412, https://doi.org/10.5194/essd-12-3383-2020, https://doi.org/10.5194/essd-12-3383-2020, 2020
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This work presents the latest release of the University of Leicester GOSAT methane data and acts as the definitive description of this dataset. We detail the processing, validation and evaluation involved in producing these data and highlight its many applications. With now over a decade of global atmospheric methane observations, this dataset has helped, and will continue to help, us better understand the global methane budget and investigate how it may respond to a future changing climate.
Nicole Jacobs, William R. Simpson, Debra Wunch, Christopher W. O'Dell, Gregory B. Osterman, Frank Hase, Thomas Blumenstock, Qiansi Tu, Matthias Frey, Manvendra K. Dubey, Harrison A. Parker, Rigel Kivi, and Pauli Heikkinen
Atmos. Meas. Tech., 13, 5033–5063, https://doi.org/10.5194/amt-13-5033-2020, https://doi.org/10.5194/amt-13-5033-2020, 2020
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The boreal forest is the largest seasonally varying biospheric CO2-exchange region on Earth. This region is also undergoing amplified climate warming, leading to concerns about the potential for altered regional carbon exchange. Satellite missions, such as the Orbiting Carbon Observatory-2 (OCO-2) project, can measure CO2 abundance over the boreal forest but need validation for the assurance of accuracy. Therefore, we carried out a ground-based validation of OCO-2 CO2 data at three locations.
Ilya Stanevich, Dylan B. A. Jones, Kimberly Strong, Robert J. Parker, Hartmut Boesch, Debra Wunch, Justus Notholt, Christof Petri, Thorsten Warneke, Ralf Sussmann, Matthias Schneider, Frank Hase, Rigel Kivi, Nicholas M. Deutscher, Voltaire A. Velazco, Kaley A. Walker, and Feng Deng
Geosci. Model Dev., 13, 3839–3862, https://doi.org/10.5194/gmd-13-3839-2020, https://doi.org/10.5194/gmd-13-3839-2020, 2020
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Systematic errors in atmospheric models pose a challenge for inverse modeling studies of methane (CH4) emissions. We evaluated the CH4 simulation in the GEOS-Chem model at the horizontal resolutions of 4° × 5° and 2° × 2.5°. Our analysis identified resolution-dependent biases in the model, which we attributed to discrepancies between the two model resolutions in vertical transport in the troposphere and in stratosphere–troposphere exchange.
Cited articles
Altman, N. S.: An introduction to kernel and nearest-neighbor nonparametric
regression, Am. Stat., 46, 175–185, 1992. a
Asefi-Najafabady, S., Rayner, P., Gurney, K., McRobert, A., Song, Y., Coltin,
K., Huang, J., Elvidge, C., and Baugh, K.: A multiyear, global gridded fossil
fuel CO2 emission data product: Evaluation and analysis of results, J. Geophys. Res.-Atmos., 119, 10213–10231, https://doi.org/10.1002/2013JD021296, 2014 (data available at: http://ffdas.rc.nau.edu/Data.html, last access: 10 March 2023). a, b
Borsdorff, T., aan de Brugh, J., Hu, H., Hasekamp, O., Sussmann, R., Rettinger, M., Hase, F., Gross, J., Schneider, M., Garcia, O., Stremme, W., Grutter, M., Feist, D. G., Arnold, S. G., De Mazière, M., Kumar Sha, M., Pollard, D. F., Kiel, M., Roehl, C., Wennberg, P. O., Toon, G. C., and Landgraf, J.: Mapping carbon monoxide pollution from space down to city scales with daily global coverage, Atmos. Meas. Tech., 11, 5507–5518, https://doi.org/10.5194/amt-11-5507-2018, 2018. a, b
Borsdorff, T., aan de Brugh, J., Schneider, A., Lorente, A., Birk, M., Wagner, G., Kivi, R., Hase, F., Feist, D. G., Sussmann, R., Rettinger, M., Wunch, D., Warneke, T., and Landgraf, J.: Improving the TROPOMI CO data product: update of the spectroscopic database and destriping of single orbits, Atmos. Meas. Tech., 12, 5443–5455, https://doi.org/10.5194/amt-12-5443-2019, 2019. a
Connor, B. J., Boesch, H., Toon, G., Sen, B., Miller, C., and Crisp, D.:
Orbiting Carbon Observatory: Inverse method and prospective error
analysis, J. Geophys. Res.-Atmos., 113, D05305,
https://doi.org/10.1029/2006JD008336, 2008. a
Corbane, C., Florczyk, A., Pesaresi, M., Politis, P., and Syrris, V.: GHS-BUILT R2018A – GHS built-up grid, derived from Landsat, multitemporal (1975–1990–2000–2014) – OBSOLETE RELEASE, European Commission, Joint Research Centre (JRC) [data set], https://doi.org/10.2905/jrc-ghsl-10007, 2018. a, b
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M., Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution temporal profiles in the Emissions Database for Global Atmospheric Research (EDGAR), Nature Scientific Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2, 2019a (data available at: https://data.jrc.ec.europa.eu/collection/EDGAR, last access: 10 March 2023). a
Crippa, M., Oreggioni, G., Guizzardi, D., Muntean, M., Schaaf, E., Lo Vullo, E., Solazzo, E., Monforti-Ferrario, F., Olivier, J., and Vignati, E.: Fossil CO2 and GHG emissions of all world countries, EUR 29849 EN, Publications Office of the European Union, Luxembourg, https://doi.org/10.2760/687800, 2019b. a
Crippa, M., Solazzo, E., Huang, G., Guizzardi, D., Koffi, E., Muntean, M.,
Schieberle, C., Friedrich, R., and Janssens-Maenhout, G.: High resolution
temporal profiles in the Emissions Database for Global Atmospheric Research,
Scientific Data, 7, 121, https://doi.org/10.1038/s41597-020-0462-2, 2020. a
Crisp, D., Atlas, R. M., Breon, F.-M., Brown, L. R., Burrows, J. P., Ciais, P., Connor, B. J., Doney, S. C., Fung, I. Y., Jacob, D. J., Miller, C. E., O'Brien, D., Pawson, S., Randerson, J. T., Rayner, P., Salawitch, R. J., Sander, S. P., Sen, B., Stephens, G. L., Tans, P. P., Toon, G. C., Wennberg, P. O., Wofsy, S. C., Yung, Y. L., Kuang, Z., Chudasama, B., Sprague, G., Weiss, B., Pollock, R., Kenyon, D., and Schroll, S.: The orbiting carbon observatory
(OCO) mission, Adv. Space Res., 34, 700–709, https://doi.org/10.1016/j.asr.2003.08.062, 2004. a, b
Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M., Oyafuso, F. A., Frankenberg, C., O'Dell, C. W., Bruegge, C. J., Doran, G. B., Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman, G. B., Schwandner, F. M., Sun, K., Taylor, T. E., Wennberg, P. O., and Wunch, D.: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products, Atmos. Meas. Tech., 10, 59–81, https://doi.org/10.5194/amt-10-59-2017, 2017. a, b
d'Angiola, A., Mieville, A., Heil, A., and Lamarque, J.-F.: MACCity (MACC/CityZEN EU projects) emissions dataset, GEIA-ACCENT emission data portal [data set], http://accent.aero.jussieu.fr/MACC_metadata.php (last access: 24 March 2020), 2010. a
De Smedt, I., Theys, N., Yu, H., Danckaert, T., Lerot, C., Compernolle, S., Van Roozendael, M., Richter, A., Hilboll, A., Peters, E., Pedergnana, M., Loyola, D., Beirle, S., Wagner, T., Eskes, H., van Geffen, J., Boersma, K. F., and Veefkind, P.: Algorithm theoretical baseline for formaldehyde retrievals from S5P TROPOMI and from the QA4ECV project, Atmos. Meas. Tech., 11, 2395–2426, https://doi.org/10.5194/amt-11-2395-2018, 2018. a
Duren, R. M. and Miller, C. E.: Measuring the carbon emissions of megacities,
Nat. Clim. Change, 2, 560–562, 2012. a
Efron, B. and Gong, G.: A leisurely look at the bootstrap, the jackknife, and
cross-validation, Am. Stat., 37, 36–48, 1983. a
Eldering, A., Taylor, T. E., O'Dell, C. W., and Pavlick, R.: The OCO-3 mission: measurement objectives and expected performance based on 1 year of simulated data, Atmos. Meas. Tech., 12, 2341–2370, https://doi.org/10.5194/amt-12-2341-2019, 2019. a, b, c, d
ESA: TROPOMI/S5P Total Ozone: Algorithm Theoretical Basis Document,
S5P-L2-DLR-ATBD-400A,
https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-5p/products-algorithms,
last access: 13 December 2022. a
ESA and Koninklijk Nederlands Meteorologisch Instituut (KNMI): Sentinel-5P TROPOMI Tropospheric NO2 1-Orbit L2 7 km × 3.5 km, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5270/S5P-s4ljg54, 2018 (data available at: https://disc.gsfc.nasa.gov/datasets/S5P_L2__NO2____1/summary, last access: 2 June 2020). a
ESA and Koninklijk Nederlands Meteorologisch Instituut (KNMI): Sentinel-5P TROPOMI Tropospheric NO2 1-Orbit L2 5.5 km × 3.5 km, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5270/S5P-s4ljg54, 2019 (data available at: https://disc.gsfc.nasa.gov/datasets/S5P_L2__NO2____HiR_1/summary, last access: 2 June 2020). a
ESA, Koninklijk Nederlands Meteorologisch Instituut (KNMI)/Netherlands Institute for Space Research (SRON): Sentinel-5P TROPOMI Carbon Monoxide CO Column 1-Orbit L2 7 km × 7 km, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5270/S5P-1hkp7rp, 2018 (data available at: https://disc.gsfc.nasa.gov/datasets/S5P_L2__CO_____1/summary, last access: 9 June 2020). a
ESA, Koninklijk Nederlands Meteorologisch Instituut (KNMI)/Netherlands Institute for Space Research (SRON): Sentinel-5P TROPOMI Carbon Monoxide CO Column 1-Orbit L2 5.5 km × 7 km, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5270/S5P-1hkp7rp, 2019 (data available at: https://disc.gsfc.nasa.gov/datasets/S5P_L2__CO_____HiR_1/summary, last access: 9 June 2020). a
Eskes, H., van Geffen, J., Boersma, F., Eichmann, K., Apituley, A., Pedergnana,
M., Sneep, M., Veefkind, J., and Loyola, D.: Sentinel-5 precursor/TROPOMI
Level 2 Product User Manual Nitrogendioxide, Ministry of Infrastructure and
Water Management, S5P-KNMI-L2-0021-MA, 2019. a
European Commission, Joint Research Centre, Corbane, C., Freire, S., Maffenini, M., Tommasi, P., Airaghi, D., Schiavina, M., Zanchetta, L., Politis, P., Ehrlich, D., Pesaresi, M., Kemper, T., Sabo, S., Melchiorri, M., and Florczyk, A.: Description of the GHS Urban Centre Database 2015: public release 2019: version 1.0, Publications Office [data set], https://doi.org/10.2760/037310, 2019. a, b
Global Modeling and Assimilation Office (GMAO): inst3_3d_asm_Cp: MERRA-2 3D IAU State, Meteorology Instantaneous 3-hourly (p-coord, 0.625x0.5L42), version 5.12.4, Greenbelt, MD, USA: Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC) [data set], https://doi.org/10.5067/VJAFPLI1CSIV, 2015. 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.-C.,
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, https://doi.org/10.1007/s10584-011-0154-1, 2011. a
Gurney, K. R., Razlivanov, I., Song, Y., Zhou, Y., Benes, B., and Abdul-Massih,
M.: Quantification of fossil fuel CO2 emissions on the building/street scale
for a large US city, Environ. Sci. Technol., 46,
12194–12202, 2012. a
Gurney, K., Romero-Lankao, P., Pincetl, S., Betsill, M., Chester, M., Creutzig,
F., and Schäfer, K.: Chapter 4: Understanding urban carbon fluxes, in: Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report, edited by: Cavallaro, N., Shrestha, G., Birdsey, R., Mayes, M. A., Najjar, R. G., Reed, S. C., Romero-Lankao, P., and Zhu, Z., U.S. Global Change Research Program, Washington, DC, USA, 189–228, https://doi.org/10.7930/SOCCR2.2018.Ch4, 2018a. a
Gurney, K. R., Liang, J., Yuyu, Z., Bedrich, B., and Patarasuk, R.: Hestia
Fossil Fuel Carbon Dioxide Emissions Inventory for Urban Regions, NIST [data set],
https://doi.org/10.18434/T4/1502499, 2018b. a, b
Gurney, K. R., Liang, J., O'Keeffe, D., Patarasuk, R., Hutchins, M., Huang, J.,
Rao, P., and Song, Y.: Comparison of Global Downscaled Versus Bottom-Up
Fossil Fuel CO2 Emissions at the Urban Scale in Four U.S. Urban Areas,
J. Geophys. Res.-Atmos., 124, 2823–2840,
https://doi.org/10.1029/2018JD028859, 2019. a
Hakkarainen, J., Szeląg, M. E., Ialongo, I., Retscher, C., Oda, T., and
Crisp, D.: Analyzing nitrogen oxides to carbon dioxide emission ratios from
space: A case study of Matimba Power Station in South Africa, Atmos.
Environ. X, 10, 100110,
https://doi.org/10.1016/j.aeaoa.2021.100110, 2021. a
Hedelius, J. K., Liu, J., Oda, T., Maksyutov, S., Roehl, C. M., Iraci, L. T., Podolske, J. R., Hillyard, P. W., Liang, J., Gurney, K. R., Wunch, D., and Wennberg, P. O.: Southern California megacity CO2, CH4, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model, Atmos. Chem. Phys., 18, 16271–16291, https://doi.org/10.5194/acp-18-16271-2018, 2018. a, b
Hu, H., Hasekamp, O., Butz, A., Galli, A., Landgraf, J., Aan de Brugh, J., Borsdorff, T., Scheepmaker, R., and Aben, I.: The operational methane retrieval algorithm for TROPOMI, Atmos. Meas. Tech., 9, 5423–5440, https://doi.org/10.5194/amt-9-5423-2016, 2016. a
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., Bergamaschi, P., Pagliari, V., Olivier, J. G. J., Peters, J. A. H. W., van Aardenne, J. A., Monni, S., Doering, U., Petrescu, A. M. R., Solazzo, E., and Oreggioni, G. D.: EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970–2012, Earth Syst. Sci. Data, 11, 959–1002, https://doi.org/10.5194/essd-11-959-2019, 2019. a
Kiel, M., O'Dell, C. W., Fisher, B., Eldering, A., Nassar, R., MacDonald, C. G., and Wennberg, P. O.: How bias correction goes wrong: measurement of XCO2 affected by erroneous surface pressure estimates, Atmos. Meas. Tech., 12, 2241–2259, https://doi.org/10.5194/amt-12-2241-2019, 2019. a
Kiel, M., Eldering, A., Roten, D. D., Lin, J. C., Feng, S., Lei, R., Lauvaux,
T., Oda, T., Roehl, C. M., Blavier, J.-F., and Iraci, L. T.: Urban-focused
satellite CO2 observations from the Orbiting Carbon Observatory-3: A first
look at the Los Angeles megacity, Remote Sens. Environ., 258,
112314, https://doi.org/10.1016/j.rse.2021.112314, 2021. a
Kort, E. A., Frankenberg, C., Miller, C. E., and Oda, T.: Space-based
observations of megacity carbon dioxide, Geophys. Res. Lett., 39, L17806, https://doi.org/10.1029/2012GL052738,
2012. a
Krings, T., Gerilowski, K., Buchwitz, M., Reuter, M., Tretner, A., Erzinger, J., Heinze, D., Pflüger, U., Burrows, J. P., and Bovensmann, H.: MAMAP – a new spectrometer system for column-averaged methane and carbon dioxide observations from aircraft: retrieval algorithm and first inversions for point source emission rates, Atmos. Meas. Tech., 4, 1735–1758, https://doi.org/10.5194/amt-4-1735-2011, 2011. a, b, c
Lama, S., Houweling, S., Boersma, K. F., Eskes, H., Aben, I., Denier van der Gon, H. A. C., Krol, M. C., Dolman, H., Borsdorff, T., and Lorente, A.: Quantifying burning efficiency in megacities using the ratio from the Tropospheric Monitoring Instrument (TROPOMI), Atmos. Chem. Phys., 20, 10295–10310, https://doi.org/10.5194/acp-20-10295-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l
Lamarque, J.-F., Bond, T. C., Eyring, V., Granier, C., Heil, A., Klimont, Z., Lee, D., Liousse, C., Mieville, A., Owen, B., Schultz, M. G., Shindell, D., Smith, S. J., Stehfest, E., Van Aardenne, J., Cooper, O. R., Kainuma, M., Mahowald, N., McConnell, J. R., Naik, V., Riahi, K., and van Vuuren, D. P.: Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: methodology and application, Atmos. Chem. Phys., 10, 7017–7039, https://doi.org/10.5194/acp-10-7017-2010, 2010. 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
Lucchesi, R.: File Specification for GEOS-5 FP-IT (forward processing for
instrument teams), Tech. rep., NASA Goddard Space Flight Center, Greenbelt,
MD, USA, https://gmao.gsfc.nasa.gov/pubs/docs/Lucchesi865.pdf (last access:
13 October 2020), 2015. a
Lutsch, E., Strong, K., Jones, D. B. A., Blumenstock, T., Conway, S., Fisher, J. A., Hannigan, J. W., Hase, F., Kasai, Y., Mahieu, E., Makarova, M., Morino, I., Nagahama, T., Notholt, J., Ortega, I., Palm, M., Poberovskii, A. V., Sussmann, R., and Warneke, T.: Detection and attribution of wildfire pollution in the Arctic and northern midlatitudes using a network of Fourier-transform infrared spectrometers and GEOS-Chem, Atmos. Chem. Phys., 20, 12813–12851, https://doi.org/10.5194/acp-20-12813-2020, 2020. a
Martin, D. O.: Comment On “The Change of Concentration Standard Deviations with
Distance”, JAPCA J. Air Waste Ma., 26, 145–147,
https://doi.org/10.1080/00022470.1976.10470238, 1976. a
McKain, K., Wofsy, S. C., Nehrkorn, T., Eluszkiewicz, J., Ehleringer, J. R.,
and Stephens, B. B.: Assessment of ground-based atmospheric observations for
verification of greenhouse gas emissions from an urban region, P.
Natl. Acad. Sci. USA, 109, 8423–8428, 2012. a
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015. 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 (data available at: https://cdiac.ess-dive.lbl.gov/ftp/Nassar_Emissions_Scale_Factors/, last access: 10 March 2023). a, b
Nassar, R., Mastrogiacomo, J.-P., Bateman-Hemphill, W., McCracken, C.,
MacDonald, C. G., Hill, T., O'Dell, C. W., Kiel, M., and Crisp, D.: Advances
in quantifying power plant CO2 emissions with OCO-2, Remote Sens.
Environ., 264, 112579, https://doi.org/10.1016/j.rse.2021.112579,
2021. a, b, c, d
OCO-2 Science Team, Gunson, M., and Eldering, A.: OCO-2 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing V9r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/W8QGIYNKS3JC, 2018. a
OCO-2/OCO-3 Science Team, Chatterjee, A., and Payne, V.: OCO-3 Level 2 bias-corrected XCO2 and other select fields from the full-physics retrieval aggregated as daily files, Retrospective processing v10.4r, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/970BCC4DHH24, 2022. 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
Oda, T. and Maksyutov, S.: ODIAC Fossil Fuel CO2 Emissions Dataset, Center for Global Environmental Research, National Institute for Environmental Studies [data set], https://doi.org/10.17595/20170411.001, 2018. a
Pandis, S. N. and Seinfeld, J. H.: Atmospheric chemistry and physics: From air
pollution to climate change, Wiley, ISBN 978-1-118-94740-1, 2006. a
Pasquill, F.: The estimation of the dispersion of windborne material, Meteorol.
Mag., 90, 33–49, 1961. a
Plant, G., Kort, E. A., Murray, L. T., Maasakkers, J. D., and Aben, I.:
Evaluating urban methane emissions from space using TROPOMI methane and
carbon monoxide observations, Remote Sens. Environ., 268, 112756,
https://doi.org/10.1016/j.rse.2021.112756, 2022a. a
Plant, G., Kort, E. A., Murray, L. T., Maasakkers, J. D., and Aben, I.:
Evaluating urban methane emissions from space using TROPOMI methane and
carbon monoxide observations, Remote Sens. Environ., 268, 112756,
https://doi.org/10.1016/j.rse.2021.112756, 2022b. a
Reuter, M., Buchwitz, M., Schneising, O., Krautwurst, S., O'Dell, C. W., Richter, A., Bovensmann, H., and Burrows, J. P.: Towards monitoring localized CO2 emissions from space: co-located regional CO2 and NO2 enhancements observed by the OCO-2 and S5P satellites, Atmos. Chem. Phys., 19, 9371–9383, https://doi.org/10.5194/acp-19-9371-2019, 2019. a, b, c
Rodgers, C. D. and Connor, B. J.: Intercomparison of remote sounding
instruments, J. Geophys. Res.-Atmos., 108, 4116, https://doi.org/10.1029/2002JD002299, 2003. a, b
Silva, S. J. and Arellano, A.: Characterizing regional-scale combustion using
satellite retrievals of CO, NO2 and CO2, Remote Sensing, 9, 744, https://doi.org/10.3390/rs9070744, 2017. a
Theys, N., De Smedt, I., Yu, H., Danckaert, T., van Gent, J., Hörmann, C., Wagner, T., Hedelt, P., Bauer, H., Romahn, F., Pedergnana, M., Loyola, D., and Van Roozendael, M.: Sulfur dioxide retrievals from TROPOMI onboard Sentinel-5 Precursor: algorithm theoretical basis, Atmos. Meas. Tech., 10, 119–153, https://doi.org/10.5194/amt-10-119-2017, 2017. a
van Geffen, J., Boersma, K. F., Eskes, H., Sneep, M., ter Linden, M., Zara, M., and Veefkind, J. P.: S5P TROPOMI NO2 slant column retrieval: method, stability, uncertainties and comparisons with OMI, Atmos. Meas. Tech., 13, 1315–1335, https://doi.org/10.5194/amt-13-1315-2020, 2020. a, b
Veefkind, J., Aben, I., McMullan, K., Förster, H., De Vries, J., Otter, G.,
Claas, J., Eskes, H., De Haan, J., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingmann, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.: TROPOMI on the ESA
Sentinel-5 Precursor: A GMES mission for global observations of the
atmospheric composition for climate, air quality and ozone layer
applications, Remote Sens. Environ., 120, 70–83, https://doi.org/10.1016/j.rse.2011.09.027, 2012. a, b
Williams, J. E., Boersma, K. F., Le Sager, P., and Verstraeten, W. W.: The high-resolution version of TM5-MP for optimized satellite retrievals: description and validation, Geosci. Model Dev., 10, 721–750, https://doi.org/10.5194/gmd-10-721-2017, 2017 (data available at: https://s5phub.copernicus.eu/dhus/, last access: 10 March 2023).
a, b
Wu, D., Lin, J. C., Fasoli, B., Oda, T., Ye, X., Lauvaux, T., Yang, E. G., and Kort, E. A.: A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”), Geosci. Model Dev., 11, 4843–4871, https://doi.org/10.5194/gmd-11-4843-2018, 2018. a, b
Wu, D., Liu, J., Wennberg, P. O., Palmer, P. I., Nelson, R. R., Kiel, M., and Eldering, A.: Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO, Atmos. Chem. Phys., 22, 14547–14570, https://doi.org/10.5194/acp-22-14547-2022, 2022. a
Wunch, D., Wennberg, P., Toon, G., Keppel-Aleks, G., and Yavin, Y.: Emissions
of greenhouse gases from a North American megacity, Geophys. Res.
Lett., 36, L15810, https://doi.org/10.1029/2009GL039825, 2009. a, b, c, d
Wunch, D., Toon, G. C., Hedelius, J. K., Vizenor, N., Roehl, C. M., Saad, K. M., Blavier, J.-F. L., Blake, D. R., and Wennberg, P. O.: Quantifying the loss of processed natural gas within California's South Coast Air Basin using long-term measurements of ethane and methane, Atmos. Chem. Phys., 16, 14091–14105, https://doi.org/10.5194/acp-16-14091-2016, 2016. a, b
Wunch, D., Wennberg, P. O., Osterman, G., Fisher, B., Naylor, B., Roehl, C. M., O'Dell, C., Mandrake, L., Viatte, C., Kiel, M., Griffith, D. W. T., Deutscher, N. M., Velazco, V. A., Notholt, J., Warneke, T., Petri, C., De Maziere, M., Sha, M. K., Sussmann, R., Rettinger, M., Pollard, D., Robinson, J., Morino, I., Uchino, O., Hase, F., Blumenstock, T., Feist, D. G., Arnold, S. G., Strong, K., Mendonca, J., Kivi, R., Heikkinen, P., Iraci, L., Podolske, J., Hillyard, P. W., Kawakami, S., Dubey, M. K., Parker, H. A., Sepulveda, E., García, O. E., Te, Y., Jeseck, P., Gunson, M. R., Crisp, D., and Eldering, A.: Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON, Atmos. Meas. Tech., 10, 2209–2238, https://doi.org/10.5194/amt-10-2209-2017, 2017. a
York, D., Evensen, N. M., Martınez, M. L., and De Basabe Delgado, J.:
Unified equations for the slope, intercept, and standard errors of the best
straight line, Am. J. Phys., 72, 367–375, 2004. a
Zhao, X., Griffin, D., Fioletov, V., McLinden, C., Cede, A., Tiefengraber, M., Müller, M., Bognar, K., Strong, K., Boersma, F., Eskes, H., Davies, J., Ogyu, A., and Lee, S. C.: Assessment of the quality of TROPOMI high-spatial-resolution NO2 data products in the Greater Toronto Area, Atmos. Meas. Tech., 13, 2131–2159, https://doi.org/10.5194/amt-13-2131-2020, 2020. a
Short summary
We use three satellites measuring carbon dioxide (CO2), carbon monoxide (CO) and nitrogen dioxide (NO2) to calculate atmospheric enhancements of these gases from 27 urban areas. We calculate enhancement ratios between the species and compare those to ratios derived from four globally gridded anthropogenic emission inventories. We find that the global inventories generally underestimate CO emissions in many North American and European cities relative to our observed enhancement ratios.
We use three satellites measuring carbon dioxide (CO2), carbon monoxide (CO) and nitrogen...
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