Articles | Volume 19, issue 9
https://doi.org/10.5194/bg-19-2507-2022
© 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/bg-19-2507-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
13 May 2022
Research article | Highlight paper |
| 13 May 2022
| 13 May 2022
Gaps in network infrastructure limit our understanding of biogenic methane emissions for the United States
Sparkle L. Malone
CORRESPONDING AUTHOR
Institute of the Environment & Department of Biological
Sciences, Florida International University, 11200 S.W. 8th Street, Miami, FL
33199, USA
Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, CO 80309, USA
Kyle A. Arndt
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, 8 College Rd, Durham, NH
03824, USA
George Burba
LI-COR Biosciences, 4421 Superior St., Lincoln, NE 68504, USA
The Robert B. Daugherty Water for Food Global Institute and School
of Natural Resources, University of Nebraska, Lincoln, NE 68583, USA
Roisin Commane
Department of Earth & Environmental Sciences, Lamont-Doherty
Earth Observatory, Columbia University, Palisades, NY 10964, USA
Alexandra R. Contosta
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, 8 College Rd, Durham, NH
03824, USA
Jordan P. Goodrich
School of Science, University of Waikato, Gate 1 Knighton Rd,
Hillcrest 3240, Hamilton, New Zealand
Henry W. Loescher
Battelle, National Ecological Observatory Network (NEON), Boulder, CO
80301, USA
Institute of Alpine and Arctic Research, University of Colorado,
Boulder, CO 80301, USA
Gregory Starr
Department of Biological Sciences, University of Alabama,
Tuscaloosa, AL 35487, USA
Ruth K. Varner
Earth Systems Research Center, Institute for the Study of Earth,
Oceans, and Space, University of New Hampshire, 8 College Rd, Durham, NH
03824, USA
Department of Earth Sciences, University of New Hampshire, 56
College Rd, Durham, NH 03824, USA
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EGUsphere, https://doi.org/10.5194/egusphere-2026-3153, https://doi.org/10.5194/egusphere-2026-3153, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We evaluate methane emissions and their trends in New York State based on observations of methane from 29 sites, including four new long-term sites. These observations are most sensitive to emissions in southeastern New York State, including New York City. We find regional trends and biases in some locations, including underestimated emissions in the New York City area. Our results also demonstrate that policy-relevant validation of methane emissions needs a combination of observation platforms.
Andrew Hallward-Driemeier, Raghav Dhall, Yuwei Zhao, and Róisín Commane
EGUsphere, https://doi.org/10.5194/egusphere-2026-3919, https://doi.org/10.5194/egusphere-2026-3919, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Nitrous oxide is the third most important greenhouse gas and an important ozone depleting substance. We tested three nitrous oxide analyzers and found all three were suitable for urban tower and mobile measurements. We observed large plumes of nitrous oxide associated with wastewater treatment, rather than traffic. We estimated that nitrous oxide emissions for the New York City region were three times larger than inventory estimates, which emphasizes the importance of urban measurements.
Sparkle L. Malone, Jaclyn H. Matthes, Cove S. Sturtevant, Angel Chen, Roisin Commane, Kyle B. Delwiche, Ankur R. Desai, Christopher R. Florian, Jonathan D. Gewirtzman, Samuel A. Jurado, David E. Reed, Jinshu Chi, Hiroki Iwata, Erik J. Lundin, Ivan Mammarella, Matthias Peichl, Masahito Ueyama, and Camilo Rey-Sanchez
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-413, https://doi.org/10.5194/essd-2026-413, 2026
Preprint under review for ESSD
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We created the first continent-wide dataset to test the gradient flux method, which estimates how gases move between ecosystems and the atmosphere. This approach can provide reliable results when sensors are placed under favorable conditions, especially for shorter, less complex vegetation. The dataset will help improve future monitoring and support a better understanding of ecosystem responses to environmental change.
Kelcy Kent, Kyle Arndt, Danielle Trangmoe, Patrick Murphy, Sigrid Dengel, Margaret Torn, Oriana Chafe, Marco Montemayor, and Susan Natali
EGUsphere, https://doi.org/10.5194/egusphere-2026-3328, https://doi.org/10.5194/egusphere-2026-3328, 2026
This preprint is open for discussion and under review for The Cryosphere (TC).
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With Arctic warming, thawing frozen ground releases greenhouse gases that further accelerate climate change. We analyzed a six-year record of carbon dioxide and methane gas exchange between the ecosystem and atmosphere at an Arctic tundra site, finding the site shifted between absorbing and releasing carbon depending on temperature, soil moisture, and landscape features, highlighting the importance of accounting for landscape variation when tracking and predicting evolving Arctic carbon stocks.
Yuwei Zhao, Andrew Hallward-Driemeier, Luke Schiferl, Trey Maddaleno, Michael Vermeuel, Dylan Millet, Delphine Farmer, and Roisin Commane
EGUsphere, https://doi.org/10.5194/egusphere-2026-1742, https://doi.org/10.5194/egusphere-2026-1742, 2026
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Reducing methane emissions offers a near-term climate mitigation opportunity. Cities contain large and often underestimated methane sources. In New York City, methane emissions peak during heating and cooling seasons, with natural gas as a dominant source. Emissions track gas consumption and show significant incomplete combustion signatures. We estimate a 1.7 % loss rate, ~$300M USD/year in wasted gas, highlighting mechanisms of inefficient natural gas combustion as a future mitigation focus.
Tiia Määttä, Samantha Bosman, Jeffrey Chanton, Patrick Crill, Suzanne Hodgkins, Jalisha Theanutti Kallingal, Rachel Wilson, Ruth Varner, and Avni Malhotra
EGUsphere, https://doi.org/10.5194/egusphere-2026-467, https://doi.org/10.5194/egusphere-2026-467, 2026
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Permafrost thaw can lead to vegetation shifts and higher methane (CH4) emissions in peatlands. However, plant belowground controls of these emissions are uncertain. We investigated how plant roots and rhizomes contribute to CH4 emissions along a peatland permafrost thaw gradient. We found that low herbaceous plant root tissue density and high rhizome surface area were associated with higher CH4 emissions. This indicated increased plant-mediated CH4 transport with thaw.
Luke D. Schiferl, Andrew Hallward-Driemeier, Yuwei Zhao, Ricardo Toledo-Crow, and Róisín Commane
Atmos. Chem. Phys., 25, 15683–15700, https://doi.org/10.5194/acp-25-15683-2025, https://doi.org/10.5194/acp-25-15683-2025, 2025
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Identifying and quantifying methane emissions from urban sources is important for mitigating climate change. Using 6 years of winter and spring observations, we found that methane emissions from New York City are highly variable and underestimated compared to inventories, especially in winter, and correlate well with emissions of carbon monoxide (CO). Our results suggest a common urban incomplete combustion source for both methane and CO currently missing from emissions inventories.
Linda Ort, Andrea Pozzer, Peter Hoor, Florian Obersteiner, Andreas Zahn, Thomas B. Ryerson, Chelsea R. Thompson, Jeff Peischl, Róisín Commane, Bruce Daube, Ilann Bourgeois, Jos Lelieveld, and Horst Fischer
Atmos. Chem. Phys., 25, 14987–15007, https://doi.org/10.5194/acp-25-14987-2025, https://doi.org/10.5194/acp-25-14987-2025, 2025
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This study investigates the role of lightning emissions on the O3–CO ratio in the northern subtropics. We used in situ observations and a global circulation model to show an effect of up to 40 % onto the subtropical O3–CO ratio by tropical air masses transported via the Hadley cell. This influence of lightning emissions and its photochemistry has a global effect on trace and greenhouse gases and needs to be considered for global chemical distributions.
Anna-Maria Virkkala, Isabel Wargowsky, Judith Vogt, McKenzie A. Kuhn, Simran Madaan, Richard O'Keefe, Tiffany Windholz, Kyle A. Arndt, Brendan M. Rogers, Jennifer D. Watts, Kelcy Kent, Mathias Göckede, David Olefeldt, Gerard Rocher-Ros, Edward A. G. Schuur, David Bastviken, Kristoffer Aalstad, Kelly Aho, Joonatan Ala-Könni, Haley Alcock, Inge Althuizen, Christopher D. Arp, Jun Asanuma, Katrin Attermeyer, Mika Aurela, Sivakiruthika Balathandayuthabani, Alan Barr, Maialen Barret, Ochirbat Batkhishig, Christina Biasi, Mats P. Björkman, Andrew Black, Elena Blanc-Betes, Pascal Bodmer, Julia Boike, Abdullah Bolek, Frédéric Bouchard, Ingeborg Bussmann, Lea Cabrol, Eleonora Canfora, Sean Carey, Karel Castro-Morales, Namyi Chae, Andres Christen, Torben R. Christensen, Casper T. Christiansen, Housen Chu, Graham Clark, Francois Clayer, Patrick Crill, Christopher Cunada, Scott J. Davidson, Joshua F. Dean, Sigrid Dengel, Matteo Detto, Catherine Dieleman, Florent Domine, Egor Dyukarev, Colin Edgar, Bo Elberling, Craig A. Emmerton, Eugenie Euskirchen, Grant Falvo, Thomas Friborg, Michelle Garneau, Mariasilvia Giamberini, Mikhail V. Glagolev, Miquel A. Gonzalez-Meler, Gustaf Granath, Jón Guðmundsson, Konsta Happonen, Yoshinobu Harazono, Lorna Harris, Josh Hashemi, Nicholas Hasson, Janna Heerah, Liam Heffernan, Manuel Helbig, Warren Helgason, Michal Heliasz, Greg Henry, Geert Hensgens, Tetsuya Hiyama, Macall Hock, David Holl, Beth Holmes, Jutta Holst, Thomas Holst, Gabriel Hould-Gosselin, Elyn Humphreys, Jacqueline Hung, Jussi Huotari, Hiroki Ikawa, Danil V. Ilyasov, Mamoru Ishikawa, Go Iwahana, Hiroki Iwata, Marcin Antoni Jackowicz-Korczynski, Joachim Jansen, Järvi Järveoja, Vincent E. J. Jassey, Rasmus Jensen, Katharina Jentzsch, Robert G. Jespersen, Carl-Fredrik Johannesson, Chersity P. Jones, Anders Jonsson, Ji Young Jung, Sari Juutinen, Evan Kane, Jan Karlsson, Sergey Karsanaev, Kuno Kasak, Julia Kelly, Kasha Kempton, Marcus Klaus, George W. Kling, Natacha Kljun, Jacqueline Knutson, Hideki Kobayashi, John Kochendorfer, Kukka-Maaria Kohonen, Pasi Kolari, Mika Korkiakoski, Aino Korrensalo, Pirkko Kortelainen, Egle Koster, Kajar Koster, Ayumi Kotani, Praveena Krishnan, Juliya Kurbatova, Lars Kutzbach, Min Jung Kwon, Ethan D. Kyzivat, Jessica Lagroix, Theodore Langhorst, Elena Lapshina, Tuula Larmola, Klaus S. Larsen, Isabelle Laurion, Justin Ledman, Hanna Lee, A. Joshua Leffler, Lance Lesack, Anders Lindroth, David Lipson, Annalea Lohila, Efrén López-Blanco, Vincent L. St. Louis, Erik Lundin, Misha Luoto, Takashi Machimura, Marta Magnani, Avni Malhotra, Marja Maljanen, Ivan Mammarella, Elisa Männistö, Luca Belelli Marchesini, Phil Marsh, Pertti J. Martkainen, Maija E. Marushchak, Mikhail Mastepanov, Alex Mavrovic, Trofim Maximov, Christina Minions, Marco Montemayor, Tomoaki Morishita, Patrick Murphy, Daniel F. Nadeau, Erin Nicholls, Mats B. Nilsson, Anastasia Niyazova, Jenni Nordén, Koffi Dodji Noumonvi, Hannu Nykanen, Walter Oechel, Anne Ojala, Tomohiro Okadera, Sujan Pal, Alexey V. Panov, Tim Papakyriakou, Dario Papale, Sang-Jong Park, Frans-Jan W. Parmentier, Gilberto Pastorello, Mike Peacock, Matthias Peichl, Roman Petrov, Kyra St. Pierre, Norbert Pirk, Jessica Plein, Vilmantas Preskienis, Anatoly Prokushkin, Jukka Pumpanen, Hilary A. Rains, Niklas Rakos, Aleski Räsänen, Helena Rautakoski, Riika Rinnan, Janne Rinne, Adrian Rocha, Nigel Roulet, Alexandre Roy, Anna Rutgersson, Aleksandr F. Sabrekov, Torsten Sachs, Erik Sahlée, Alejandro Salazar, Henrique Oliveira Sawakuchi, Christopher Schulze, Roger Seco, Armando Sepulveda-Jauregui, Svetlana Serikova, Abbey Serrone, Hanna M. Silvennoinen, Sofie Sjogersten, June Skeeter, Jo Snöälv, Sebastian Sobek, Oliver Sonnentag, Emily H. Stanley, Maria Strack, Lena Strom, Patrick Sullivan, Ryan Sullivan, Anna Sytiuk, Torbern Tagesson, Pierre Taillardat, Julie Talbot, Suzanne E. Tank, Mario Tenuta, Irina Terenteva, Frederic Thalasso, Antoine Thiboult, Halldor Thorgeirsson, Fenix Garcia Tigreros, Margaret Torn, Amy Townsend-Small, Claire Treat, Alain Tremblay, Carlo Trotta, Eeva-Stiina Tuittila, Merritt Turetsky, Masahito Ueyama, Muhammad Umair, Aki Vähä, Lona van Delden, Maarten van Hardenbroek, Andrej Varlagin, Ruth K. Varner, Elena Veretennikova, Timo Vesala, Tarmo Virtanen, Carolina Voigt, Jorien E. Vonk, Robert Wagner, Katey Walter Anthony, Qinxue Wang, Masataka Watanabe, Hailey Webb, Jeffrey M. Welker, Andreas Westergaard-Nielsen, Sebastian Westermann, Jeffrey R. White, Christian Wille, Scott N. Williamson, Scott Zolkos, Donatella Zona, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-585, https://doi.org/10.5194/essd-2025-585, 2025
Revised manuscript accepted for ESSD
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This dataset includes monthly measurements of carbon dioxide and methane exchange between land, water, and the atmosphere from over 1,000 sites in Arctic and boreal regions. It combines measurements from a variety of ecosystems, including wetlands, forests, tundra, lakes, and rivers, gathered by over 260 researchers from 1984–2024. This dataset can be used to improve and reduce uncertainty in carbon budgets in order to strengthen our understanding of climate feedbacks in a warming world.
Maxime Thomas, Thomas Moenaert, Julien Radoux, Baptiste Delhez, Eléonore du Bois d'Aische, Maëlle Villani, Catherine Hirst, Erik Lundin, François Jonard, Sébastien Lambot, Kristof Van Oost, Veerle Vanacker, Matthias B. Siewert, Carl-Magnus Mörth, Michael W. Palace, Ruth K. Varner, Franklin B. Sullivan, Christina Herrick, and Sophie Opfergelt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3788, https://doi.org/10.5194/egusphere-2025-3788, 2025
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This study examines the rate of permafrost degradation, in the form of the transition from intact well-drained palsa to fully thawed and inundated fen at the Stordalen mire, Abisko, Sweden. Across the 14 hectares of the palsa mire, we demonstrate a 5-fold acceleration of the degradation in 2019–2021 compared to previous periods (1970–2014) which might lead to a pool of 12 metric tons of organic carbon exposed annually for the topsoil (23 cm depth), and an increase of ~1.3%/year of GHG emissions.
Judith Vogt, Martijn M. T. A. Pallandt, Luana S. Basso, Abdullah Bolek, Kseniia Ivanova, Mark Schlutow, Gerardo Celis, McKenzie Kuhn, Marguerite Mauritz, Edward A. G. Schuur, Kyle Arndt, Anna-Maria Virkkala, Isabel Wargowsky, and Mathias Göckede
Earth Syst. Sci. Data, 17, 2553–2573, https://doi.org/10.5194/essd-17-2553-2025, https://doi.org/10.5194/essd-17-2553-2025, 2025
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We present a meta-dataset of greenhouse gas observations in the Arctic and boreal regions, including information on sites where greenhouse gases have been measured using different measurement techniques. We provide a novel repository of metadata to facilitate synthesis efforts for regions undergoing rapid environmental change. The meta-dataset shows where measurements are missing and will be updated as new measurements are published.
Qing Ying, Benjamin Poulter, Jennifer D. Watts, Kyle A. Arndt, Anna-Maria Virkkala, Lori Bruhwiler, Youmi Oh, Brendan M. Rogers, Susan M. Natali, Hilary Sullivan, Amanda Armstrong, Eric J. Ward, Luke D. Schiferl, Clayton D. Elder, Olli Peltola, Annett Bartsch, Ankur R. Desai, Eugénie Euskirchen, Mathias Göckede, Bernhard Lehner, Mats B. Nilsson, Matthias Peichl, Oliver Sonnentag, Eeva-Stiina Tuittila, Torsten Sachs, Aram Kalhori, Masahito Ueyama, and Zhen Zhang
Earth Syst. Sci. Data, 17, 2507–2534, https://doi.org/10.5194/essd-17-2507-2025, https://doi.org/10.5194/essd-17-2507-2025, 2025
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We present daily methane (CH4) fluxes of northern wetlands at 10 km resolution during 2016–2022 (WetCH4) derived from a novel machine learning framework. We estimated an average annual CH4 emission of 22.8 ± 2.4 Tg CH4 yr−1 (15.7–51.6 Tg CH4 yr−1). Emissions were intensified in 2016, 2020, and 2022, with the largest interannual variation coming from Western Siberia. Continued, all-season tower observations and improved soil moisture products are needed for future improvement of CH4 upscaling.
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
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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.
Luke D. Schiferl, Cong Cao, Bronte Dalton, Andrew Hallward-Driemeier, Ricardo Toledo-Crow, and Róisín Commane
Atmos. Chem. Phys., 24, 10129–10142, https://doi.org/10.5194/acp-24-10129-2024, https://doi.org/10.5194/acp-24-10129-2024, 2024
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Carbon monoxide (CO) is an air pollutant and an important indicator of the incomplete combustion of fossil fuels in cities. Using 4 years of winter and spring observations in New York City, we found that both the magnitude and variability of CO from the metropolitan area are greater than expected. Transportation emissions cannot explain the missing and variable CO, which points to energy from buildings as a likely underappreciated source of urban air pollution and greenhouse gas emissions.
Sarah M. Ludwig, Luke Schiferl, Jacqueline Hung, Susan M. Natali, and Roisin Commane
Biogeosciences, 21, 1301–1321, https://doi.org/10.5194/bg-21-1301-2024, https://doi.org/10.5194/bg-21-1301-2024, 2024
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Landscapes are often assumed to be homogeneous when using eddy covariance fluxes, which can lead to biases when calculating carbon budgets. In this study we report eddy covariance carbon fluxes from heterogeneous tundra. We used the footprints of each flux observation to unmix the fluxes coming from components of the landscape. We identified and quantified hot spots of carbon emissions in the landscape. Accurately scaling with landscape heterogeneity yielded half as much regional carbon uptake.
Josué Delgado-Balbuena, Henry W. Loescher, Carlos A. Aguirre-Gutiérrez, Teresa Alfaro-Reyna, Luis F. Pineda-Martínez, Rodrigo Vargas, and Tulio Arredondo
Biogeosciences, 20, 2369–2385, https://doi.org/10.5194/bg-20-2369-2023, https://doi.org/10.5194/bg-20-2369-2023, 2023
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In the semiarid grassland, an increase in soil moisture at shallow depths instantly enhances carbon release through respiration. In contrast, deeper soil water controls plant carbon uptake but with a delay of several days. Previous soil conditions, biological activity, and the size and timing of precipitation are factors that determine the amount of carbon released into the atmosphere. Thus, future changes in precipitation patterns could convert ecosystems from carbon sinks to carbon sources.
Róisín Commane, Andrew Hallward-Driemeier, and Lee T. Murray
Atmos. Meas. Tech., 16, 1431–1441, https://doi.org/10.5194/amt-16-1431-2023, https://doi.org/10.5194/amt-16-1431-2023, 2023
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Methane / ethane ratios can be used to identify and partition the different sources of methane, especially in areas with natural gas mixed with biogenic methane emissions, such as cities. We tested three commercially available laser-based analyzers for sensitivity, precision, size, power requirement, ease of use on mobile platforms, and expertise needed to operate the instrument, and we make recommendations for use in various situations.
Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven C. Wofsy
Atmos. Chem. Phys., 23, 99–117, https://doi.org/10.5194/acp-23-99-2023, https://doi.org/10.5194/acp-23-99-2023, 2023
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We have prepared a unique and unusual result from the recent ATom aircraft mission: a measurement-based derivation of the production and loss rates of ozone and methane over the ocean basins. These are the key products of chemistry models used in assessments but have thus far lacked observational metrics. It also shows the scales of variability of atmospheric chemical rates and provides a major challenge to the atmospheric models.
Luke D. Schiferl, Jennifer D. Watts, Erik J. L. Larson, Kyle A. Arndt, Sébastien C. Biraud, Eugénie S. Euskirchen, Jordan P. Goodrich, John M. Henderson, Aram Kalhori, Kathryn McKain, Marikate E. Mountain, J. William Munger, Walter C. Oechel, Colm Sweeney, Yonghong Yi, Donatella Zona, and Róisín Commane
Biogeosciences, 19, 5953–5972, https://doi.org/10.5194/bg-19-5953-2022, https://doi.org/10.5194/bg-19-5953-2022, 2022
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As the Arctic rapidly warms, vast stores of thawing permafrost could release carbon dioxide (CO2) into the atmosphere. We combined observations of atmospheric CO2 concentrations from aircraft and a tower with observed CO2 fluxes from tundra ecosystems and found that the Alaskan North Slope in not a consistent source nor sink of CO2. Our study shows the importance of using both site-level and atmospheric measurements to constrain regional net CO2 fluxes and improve biogenic processes in models.
Peeyush Khare, Jordan E. Krechmer, Jo E. Machesky, Tori Hass-Mitchell, Cong Cao, Junqi Wang, Francesca Majluf, Felipe Lopez-Hilfiker, Sonja Malek, Will Wang, Karl Seltzer, Havala O. T. Pye, Roisin Commane, Brian C. McDonald, Ricardo Toledo-Crow, John E. Mak, and Drew R. Gentner
Atmos. Chem. Phys., 22, 14377–14399, https://doi.org/10.5194/acp-22-14377-2022, https://doi.org/10.5194/acp-22-14377-2022, 2022
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Ammonium adduct chemical ionization is used to examine the atmospheric abundances of oxygenated volatile organic compounds associated with emissions from volatile chemical products, which are now key contributors of reactive precursors to ozone and secondary organic aerosols in urban areas. The application of this valuable measurement approach in densely populated New York City enables the evaluation of emissions inventories and thus the role these oxygenated compounds play in urban air quality.
Helen M. Worden, Gene L. Francis, Susan S. Kulawik, Kevin W. Bowman, Karen Cady-Pereira, Dejian Fu, Jennifer D. Hegarty, Valentin Kantchev, Ming Luo, Vivienne H. Payne, John R. Worden, Róisín Commane, and Kathryn McKain
Atmos. Meas. Tech., 15, 5383–5398, https://doi.org/10.5194/amt-15-5383-2022, https://doi.org/10.5194/amt-15-5383-2022, 2022
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Satellite observations of global carbon monoxide (CO) are essential for understanding atmospheric chemistry and pollution sources. This paper describes a new data product using radiance measurements from the Cross-track Infrared Sounder (CrIS) instrument on the Suomi National Polar-orbiting Partnership (SNPP) satellite that provides vertical profiles of CO from single-field-of-view observations. We show how these satellite CO profiles compare to aircraft observations and evaluate their biases.
Jessica Plein, Rulon W. Clark, Kyle A. Arndt, Walter C. Oechel, Douglas Stow, and Donatella Zona
Biogeosciences, 19, 2779–2794, https://doi.org/10.5194/bg-19-2779-2022, https://doi.org/10.5194/bg-19-2779-2022, 2022
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Tundra vegetation and the carbon balance of Arctic ecosystems can be substantially impacted by herbivory. We tested how herbivory by brown lemmings in individual enclosure plots have impacted carbon exchange of tundra ecosystems via altering carbon dioxide (CO2) and methane (CH4) fluxes. Lemmings significantly decreased net CO2 uptake while not affecting CH4 emissions. There was no significant difference in the subsequent growing season due to recovery of the vegetation.
Merritt Deeter, Gene Francis, John Gille, Debbie Mao, Sara Martínez-Alonso, Helen Worden, Dan Ziskin, James Drummond, Róisín Commane, Glenn Diskin, and Kathryn McKain
Atmos. Meas. Tech., 15, 2325–2344, https://doi.org/10.5194/amt-15-2325-2022, https://doi.org/10.5194/amt-15-2325-2022, 2022
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The MOPITT (Measurements of Pollution in the Troposphere) satellite instrument uses remote sensing to obtain retrievals (measurements) of carbon monoxide (CO) in the atmosphere. This paper describes the latest MOPITT data product, Version 9. Globally, the number of daytime MOPITT retrievals over land has increased by 30 %–40 % compared to the previous product. The reported improvements in the MOPITT product should benefit a wide variety of applications including studies of pollution sources.
Maria Tzortziou, Charlotte F. Kwong, Daniel Goldberg, Luke Schiferl, Róisín Commane, Nader Abuhassan, James J. Szykman, and Lukas C. Valin
Atmos. Chem. Phys., 22, 2399–2417, https://doi.org/10.5194/acp-22-2399-2022, https://doi.org/10.5194/acp-22-2399-2022, 2022
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The COVID-19 pandemic created an extreme natural experiment in which sudden changes in human behavior significantly impacted urban air quality. Using a combination of model, satellite, and ground-based data, we examine the impact of multiple waves and phases of the pandemic on atmospheric nitrogen pollution in the New York metropolitan area, and address the role of weather as a key driver of high pollution episodes observed even during – and despite – the stringent early lockdowns.
Anna-Maria Virkkala, Susan M. Natali, Brendan M. Rogers, Jennifer D. Watts, Kathleen Savage, Sara June Connon, Marguerite Mauritz, Edward A. G. Schuur, Darcy Peter, Christina Minions, Julia Nojeim, Roisin Commane, Craig A. Emmerton, Mathias Goeckede, Manuel Helbig, David Holl, Hiroki Iwata, Hideki Kobayashi, Pasi Kolari, Efrén López-Blanco, Maija E. Marushchak, Mikhail Mastepanov, Lutz Merbold, Frans-Jan W. Parmentier, Matthias Peichl, Torsten Sachs, Oliver Sonnentag, Masahito Ueyama, Carolina Voigt, Mika Aurela, Julia Boike, Gerardo Celis, Namyi Chae, Torben R. Christensen, M. Syndonia Bret-Harte, Sigrid Dengel, Han Dolman, Colin W. Edgar, Bo Elberling, Eugenie Euskirchen, Achim Grelle, Juha Hatakka, Elyn Humphreys, Järvi Järveoja, Ayumi Kotani, Lars Kutzbach, Tuomas Laurila, Annalea Lohila, Ivan Mammarella, Yojiro Matsuura, Gesa Meyer, Mats B. Nilsson, Steven F. Oberbauer, Sang-Jong Park, Roman Petrov, Anatoly S. Prokushkin, Christopher Schulze, Vincent L. St. Louis, Eeva-Stiina Tuittila, Juha-Pekka Tuovinen, William Quinton, Andrej Varlagin, Donatella Zona, and Viacheslav I. Zyryanov
Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, https://doi.org/10.5194/essd-14-179-2022, 2022
Short summary
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The effects of climate warming on carbon cycling across the Arctic–boreal zone (ABZ) remain poorly understood due to the relatively limited distribution of ABZ flux sites. Fortunately, this flux network is constantly increasing, but new measurements are published in various platforms, making it challenging to understand the ABZ carbon cycle as a whole. Here, we compiled a new database of Arctic–boreal CO2 fluxes to help facilitate large-scale assessments of the ABZ carbon cycle.
Jennifer D. Hegarty, Karen E. Cady-Pereira, Vivienne H. Payne, Susan S. Kulawik, John R. Worden, Valentin Kantchev, Helen M. Worden, Kathryn McKain, Jasna V. Pittman, Róisín Commane, Bruce C. Daube Jr., and Eric A. Kort
Atmos. Meas. Tech., 15, 205–223, https://doi.org/10.5194/amt-15-205-2022, https://doi.org/10.5194/amt-15-205-2022, 2022
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Carbon monoxide (CO) is produced by combustion of substances such as fossil fuels and plays an important role in atmospheric pollution and climate. We evaluated estimates of atmospheric CO derived from outgoing radiation measurements of the Atmospheric Infrared Sounder (AIRS) on a satellite orbiting the Earth against CO measurements from aircraft to show that these satellite measurements are reliable for continuous global monitoring of atmospheric CO concentrations.
Linda M. J. Kooijmans, Ara Cho, Jin Ma, Aleya Kaushik, Katherine D. Haynes, Ian Baker, Ingrid T. Luijkx, Mathijs Groenink, Wouter Peters, John B. Miller, Joseph A. Berry, Jerome Ogée, Laura K. Meredith, Wu Sun, Kukka-Maaria Kohonen, Timo Vesala, Ivan Mammarella, Huilin Chen, Felix M. Spielmann, Georg Wohlfahrt, Max Berkelhammer, Mary E. Whelan, Kadmiel Maseyk, Ulli Seibt, Roisin Commane, Richard Wehr, and Maarten Krol
Biogeosciences, 18, 6547–6565, https://doi.org/10.5194/bg-18-6547-2021, https://doi.org/10.5194/bg-18-6547-2021, 2021
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The gas carbonyl sulfide (COS) can be used to estimate photosynthesis. To adopt this approach on regional and global scales, we need biosphere models that can simulate COS exchange. So far, such models have not been evaluated against observations. We evaluate the COS biosphere exchange of the SiB4 model against COS flux observations. We find that the model is capable of simulating key processes in COS biosphere exchange. Still, we give recommendations for further improvement of the model.
McKenzie A. Kuhn, Ruth K. Varner, David Bastviken, Patrick Crill, Sally MacIntyre, Merritt Turetsky, Katey Walter Anthony, Anthony D. McGuire, and David Olefeldt
Earth Syst. Sci. Data, 13, 5151–5189, https://doi.org/10.5194/essd-13-5151-2021, https://doi.org/10.5194/essd-13-5151-2021, 2021
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Methane (CH4) emissions from the boreal–Arctic region are globally significant, but the current magnitude of annual emissions is not well defined. Here we present a dataset of surface CH4 fluxes from northern wetlands, lakes, and uplands that was built alongside a compatible land cover dataset, sharing the same classifications. We show CH4 fluxes can be split by broad land cover characteristics. The dataset is useful for comparison against new field data and model parameterization or validation.
David Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, David Bastviken, Theodore J. Bohn, John Connolly, Patrick Crill, Eugénie S. Euskirchen, Sarah A. Finkelstein, Hélène Genet, Guido Grosse, Lorna I. Harris, Liam Heffernan, Manuel Helbig, Gustaf Hugelius, Ryan Hutchins, Sari Juutinen, Mark J. Lara, Avni Malhotra, Kristen Manies, A. David McGuire, Susan M. Natali, Jonathan A. O'Donnell, Frans-Jan W. Parmentier, Aleksi Räsänen, Christina Schädel, Oliver Sonnentag, Maria Strack, Suzanne E. Tank, Claire Treat, Ruth K. Varner, Tarmo Virtanen, Rebecca K. Warren, and Jennifer D. Watts
Earth Syst. Sci. Data, 13, 5127–5149, https://doi.org/10.5194/essd-13-5127-2021, https://doi.org/10.5194/essd-13-5127-2021, 2021
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Wetlands, lakes, and rivers are important sources of the greenhouse gas methane to the atmosphere. To understand current and future methane emissions from northern regions, we need maps that show the extent and distribution of specific types of wetlands, lakes, and rivers. The Boreal–Arctic Wetland and Lake Dataset (BAWLD) provides maps of five wetland types, seven lake types, and three river types for northern regions and will improve our ability to predict future methane emissions.
Eric J. Hintsa, Fred L. Moore, Dale F. Hurst, Geoff S. Dutton, Bradley D. Hall, J. David Nance, Ben R. Miller, Stephen A. Montzka, Laura P. Wolton, Audra McClure-Begley, James W. Elkins, Emrys G. Hall, Allen F. Jordan, Andrew W. Rollins, Troy D. Thornberry, Laurel A. Watts, Chelsea R. Thompson, Jeff Peischl, Ilann Bourgeois, Thomas B. Ryerson, Bruce C. Daube, Yenny Gonzalez Ramos, Roisin Commane, Gregory W. Santoni, Jasna V. Pittman, Steven C. Wofsy, Eric Kort, Glenn S. Diskin, and T. Paul Bui
Atmos. Meas. Tech., 14, 6795–6819, https://doi.org/10.5194/amt-14-6795-2021, https://doi.org/10.5194/amt-14-6795-2021, 2021
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We built UCATS to study atmospheric chemistry and transport. It has measured trace gases including CFCs, N2O, SF6, CH4, CO, and H2 with gas chromatography, as well as ozone and water vapor. UCATS has been part of missions to study the tropical tropopause; transport of air into the stratosphere; greenhouse gases, transport, and chemistry in the troposphere; and ozone chemistry, on both piloted and unmanned aircraft. Its design, capabilities, and some results are shown and described here.
Charles A. Brock, Karl D. Froyd, Maximilian Dollner, Christina J. Williamson, Gregory Schill, Daniel M. Murphy, Nicholas J. Wagner, Agnieszka Kupc, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jason C. Schroder, Douglas A. Day, Derek J. Price, Bernadett Weinzierl, Joshua P. Schwarz, Joseph M. Katich, Siyuan Wang, Linghan Zeng, Rodney Weber, Jack Dibb, Eric Scheuer, Glenn S. Diskin, Joshua P. DiGangi, ThaoPaul Bui, Jonathan M. Dean-Day, Chelsea R. Thompson, Jeff Peischl, Thomas B. Ryerson, Ilann Bourgeois, Bruce C. Daube, Róisín Commane, and Steven C. Wofsy
Atmos. Chem. Phys., 21, 15023–15063, https://doi.org/10.5194/acp-21-15023-2021, https://doi.org/10.5194/acp-21-15023-2021, 2021
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The Atmospheric Tomography Mission was an airborne study that mapped the chemical composition of the remote atmosphere. From this, we developed a comprehensive description of aerosol properties that provides a unique, global-scale dataset against which models can be compared. The data show the polluted nature of the remote atmosphere in the Northern Hemisphere and quantify the contributions of sea salt, dust, soot, biomass burning particles, and pollution particles to the haziness of the sky.
Hao Guo, Clare M. Flynn, Michael J. Prather, Sarah A. Strode, Stephen D. Steenrod, Louisa Emmons, Forrest Lacey, Jean-Francois Lamarque, Arlene M. Fiore, Gus Correa, Lee T. Murray, Glenn M. Wolfe, Jason M. St. Clair, Michelle Kim, John Crounse, Glenn Diskin, Joshua DiGangi, Bruce C. Daube, Roisin Commane, Kathryn McKain, Jeff Peischl, Thomas B. Ryerson, Chelsea Thompson, Thomas F. Hanisco, Donald Blake, Nicola J. Blake, Eric C. Apel, Rebecca S. Hornbrook, James W. Elkins, Eric J. Hintsa, Fred L. Moore, and Steven Wofsy
Atmos. Chem. Phys., 21, 13729–13746, https://doi.org/10.5194/acp-21-13729-2021, https://doi.org/10.5194/acp-21-13729-2021, 2021
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The NASA Atmospheric Tomography (ATom) mission built a climatology of the chemical composition of tropospheric air parcels throughout the middle of the Pacific and Atlantic oceans. The level of detail allows us to reconstruct the photochemical budgets of O3 and CH4 over these vast, remote regions. We find that most of the chemical heterogeneity is captured at the resolution used in current global chemistry models and that the majority of reactivity occurs in the
hottest20 % of parcels.
Cited articles
Ahmad, A. and Dey, L.: A k-mean clustering algorithm for mixed numeric and
categorical data, Data Knowl. Eng., 63, 503–527,
https://doi.org/10.1016/j.datak.2007.03.016, 2007.
Arndt, K. A., Oechel, W. C., Goodrich, J. P., Bailey, B. A., Kalhori, A.,
Hashemi, J., Sweeney, C., and Zona, D.: Sensitivity of methane emissions to
later soil freezing in arctic tundra ecosystems, J. Geophys. Res.-Biogeosci., 124, 2595–2609, https://doi.org/10.1029/2019jg005242, 2019.
Baldocchi, D.: Measuring fluxes of trace gases and energy between ecosystems
and the atmosphere – the state and future of the eddy covariance method,
Glob. Change Biol., 20, 3600–3609, https://doi.org/10.1111/gcb.12649, 2014.
Baldocchi, D., Reichstein, M., and Papale, D.: The role of trace gas flux
networks in the biogeosciences, Eos Trans. Am. Geophys. Union, https://doi.org/10.1029/2012EO230001, 2012.
Balijepally, V., Mangalaraj, G., and Iyengar, K.: Are We Wielding this
Hammer Correctly? A Reflective Review of the Application of Cluster Analysis
in Information Systems Research, J. Assoc. Inf.
Syst., 12, 375–413, https://doi.org/10.17705/1jais.00266, 2011.
Barkley, Z. R., Lauvaux, T., Davis, K. J., Deng, A., Miles, N. L., Richardson, S. J., Cao, Y., Sweeney, C., Karion, A., Smith, M., Kort, E. A., Schwietzke, S., Murphy, T., Cervone, G., Martins, D., and Maasakkers, J. D.: Quantifying methane emissions from natural gas production in north-eastern Pennsylvania, Atmos. Chem. Phys., 17, 13941–13966, https://doi.org/10.5194/acp-17-13941-2017, 2017.
Bartlett, K. B. and Harriss, R. C.: Review and assessment of methane
emissions from wetlands, Chemosphere, 26, 261–320,
https://doi.org/10.1016/0045-6535(93)90427-7, 1993.
Bessembinder, J., Overbeek, B., and Siegmund, P.: Climate normals and climate change: how to communicate these together?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4032, https://doi.org/10.5194/egusphere-egu21-4032, 2021.
Billesbach, D. P., Kim, J., Clement, R. J., Verma, S. B., and Ullman, F. G.: An Intercomparison of Two Tunable Diode Laser Spectrometers Used for Eddy Correlation Measurements of Methane Flux in a Prairie Wetland, J. Atmos. Ocean. Technol., 15, 197–206, https://doi.org/10.1175/1520-0426(1998)015<0197:aiottd>2.0.co;2, 1998.
Boryan, C., Yang, Z., Mueller, R., and Craig, M.: Monitoring US agriculture:
the US Department of Agriculture, National Agricultural Statistics Service,
Cropland Data Layer Program, Geocarto Int., 26, 341–358,
https://doi.org/10.1080/10106049.2011.562309, 2011.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001.
Bruhwiler, L., Parmentier, F.-J. W., Crill, P., Leonard, M., and Palmer, P.
I.: The Arctic Carbon Cycle and Its Response to Changing Climate, 7, 14–34,
https://doi.org/10.1007/s40641-020-00169-5, 2021.
Burba, G.: 9 – Atmospheric flux measurements, in: Advances in Spectroscopic
Monitoring of the Atmosphere, edited by: Chen, W., Venables, D. S., and
Sigrist, M. W., Elsevier, 443–520,
https://doi.org/10.1016/B978-0-12-815014-6.00004-X, 2021.
Burba, G., Anderson, T., and Komissarov, A.: Accounting for spectroscopic
effects in laser-based open-path eddy covariance flux measurements, Glob.
Change Biol., 25, 2189–2202, https://doi.org/10.1111/gcb.14614, 2019.
Chadburn, S. E., Burke, E. J., Cox, P. M., Friedlingstein, P., Hugelius, G.,
and Westermann, S.: An observation-based constraint on permafrost loss as a
function of global warming, Nat. Clim. Change, 7, 340–344,
https://doi.org/10.1038/nclimate3262, 2017.
Chang, R. Y.-W., Miller, C. E., Dinardo, S. J., Karion, A., Sweeney, C.,
Daube, B. C., Henderson, J. M., Mountain, M. E., Eluszkiewicz, J., Miller,
J. B., Bruhwiler, L. M. P., and Wofsy, S. C.: Methane emissions from Alaska
in 2012 from CARVE airborne observations, P. Natl. Acad. Sci. USA,
111, 16694–16699, https://doi.org/10.1073/pnas.1412953111, 2014.
Chen, B., Coops, N. C., Fu, D., Margolis, H. A., Amiro, B. D., Barr, A. G.,
Black, T. A., Arain, M. A., Bourque, C. P.-A., Flanagan, L. B., Lafleur, P.
M., McCaughey, J. H., and Wofsy, S. C.: Assessing eddy-covariance flux tower
location bias across the Fluxnet-Canada Research Network based on remote
sensing and footprint modelling, Agr. Forest Meteorol., 151, 87–100,
https://doi.org/10.1016/j.agrformet.2010.09.005, 2011.
Chu, H., Luo, X., Ouyang, Z., Chan, W. S., Dengel, S., Biraud, S. C., Torn,
M. S., Metzger, S., Kumar, J., Arain, M. A., Arkebauer, T. J., Baldocchi,
D., Bernacchi, C., Billesbach, D., Black, T. A., Blanken, P. D., Bohrer, G.,
Bracho, R., Brown, S., Brunsell, N. A., Chen, J., Chen, X., Clark, K.,
Desai, A. R., Duman, T., Durden, D., Fares, S., Forbrich, I., Gamon, J. A.,
Gough, C. M., Griffis, T., Helbig, M., Hollinger, D., Humphreys, E., Ikawa,
H., Iwata, H., Ju, Y., Knowles, J. F., Knox, S. H., Kobayashi, H., Kolb, T.,
Law, B., Lee, X., Litvak, M., Liu, H., Munger, J. W., Noormets, A., Novick,
K., Oberbauer, S. F., Oechel, W., Oikawa, P., Papuga, S. A., Pendall, E.,
Prajapati, P., Prueger, J., Quinton, W. L., Richardson, A. D., Russell, E.
S., Scott, R. L., Starr, G., Staebler, R., Stoy, P. C.,
Stuart-Haëntjens, E., Sonnentag, O., Sullivan, R. C., Suyker, A.,
Ueyama, M., Vargas, R., Wood, J. D., and Zona, D.: Representativeness of
Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites,
Agr. Forest Meteorol., 301/302, 108350,
https://doi.org/10.1016/j.agrformet.2021.108350, 2021.
Ciais, P., Dolman, A. J., Bombelli, A., Duren, R., Peregon, A., Rayner, P.
J., Miller, C., Gobron, N., Kinderman, G., Marland, G., Gruber, N.,
Chevallier, F., Andres, R. J., Balsamo, G., Bopp, L., Bréon, F.-M.,
Broquet, G., Dargaville, R., Battin, T. J., Borges, A., Bovensmann, H.,
Buchwitz, M., Butler, J., Canadell, J. G., Cook, R. B., DeFries, R.,
Engelen, R., Gurney, K. R., Heinze, C., Heimann, M., Held, A., Henry, M.,
Law, B., Luyssaert, S., Miller, J., Moriyama, T., Moulin, C., Myneni, R. B.,
Nussli, C., Obersteiner, M., Ojima, D., Pan, Y., Paris, J.-D., Piao, S. L.,
Poulter, B., Plummer, S., Quegan, S., Raymond, P., Reichstein, M., Rivier,
L., Sabine, C., Schimel, D., Tarasova, O., Valentini, R., Wang, R., van der
Werf, G., Wickland, D., Williams, M., and Zehner, C.: Current systematic
carbon-cycle observations and the need for implementing a policy-relevant
carbon observing system, Biogeosciences, 11, 3547–3602,
https://doi.org/10.5194/bg-11-3547-2014, 2014.
Cook, T. D. and Campbell, D. T.: Quasi-experimentation: Design and Analysis
Issues for Field Settings, Rand McNally College, 405 pp., ISBN 9780528686948, 1979.
Cox, M. A. A. and Cox, T. F.: Multidimensional Scaling, in: Handbook of Data
Visualization, edited by: Chen, C.-H., Härdle, W., and Unwin, A.,
Springer Berlin Heidelberg, Berlin, Heidelberg, 315–347,
https://doi.org/10.1007/978-3-540-33037-0_14, 2008.
Dalmaijer, E. S., Nord, C. L., and Astle, D. E.: Statistical power for
cluster analysis, arXiv [stat.ML], arXiv, https://doi.org/10.48550/arXiv.2003.00381, 2020.
Delwiche, K. B., Knox, S. H., Malhotra, A., Fluet-Chouinard, E., McNicol, G., Feron, S., Ouyang, Z., Papale, D., Trotta, C., Canfora, E., Cheah, Y.-W., Christianson, D., Alberto, Ma. C. R., Alekseychik, P., Aurela, M., Baldocchi, D., Bansal, S., Billesbach, D. P., Bohrer, G., Bracho, R., Buchmann, N., Campbell, D. I., Celis, G., Chen, J., Chen, W., Chu, H., Dalmagro, H. J., Dengel, S., Desai, A. R., Detto, M., Dolman, H., Eichelmann, E., Euskirchen, E., Famulari, D., Fuchs, K., Goeckede, M., Gogo, S., Gondwe, M. J., Goodrich, J. P., Gottschalk, P., Graham, S. L., Heimann, M., Helbig, M., Helfter, C., Hemes, K. S., Hirano, T., Hollinger, D., Hörtnagl, L., Iwata, H., Jacotot, A., Jurasinski, G., Kang, M., Kasak, K., King, J., Klatt, J., Koebsch, F., Krauss, K. W., Lai, D. Y. F., Lohila, A., Mammarella, I., Belelli Marchesini, L., Manca, G., Matthes, J. H., Maximov, T., Merbold, L., Mitra, B., Morin, T. H., Nemitz, E., Nilsson, M. B., Niu, S., Oechel, W. C., Oikawa, P. Y., Ono, K., Peichl, M., Peltola, O., Reba, M. L., Richardson, A. D., Riley, W., Runkle, B. R. K., Ryu, Y., Sachs, T., Sakabe, A., Sanchez, C. R., Schuur, E. A., Schäfer, K. V. R., Sonnentag, O., Sparks, J. P., Stuart-Haëntjens, E., Sturtevant, C., Sullivan, R. C., Szutu, D. J., Thom, J. E., Torn, M. S., Tuittila, E.-S., Turner, J., Ueyama, M., Valach, A. C., Vargas, R., Varlagin, A., Vazquez-Lule, A., Verfaillie, J. G., Vesala, T., Vourlitis, G. L., Ward, E. J., Wille, C., Wohlfahrt, G., Wong, G. X., Zhang, Z., Zona, D., Windham-Myers, L., Poulter, B., and Jackson, R. B.: FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands, Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, 2021.
Desai, A. R.: Climatic and phenological controls on coherent regional
interannual variability of carbon dioxide flux in a heterogeneous landscape,
J. Geophys. Res., 115, G00J02, https://doi.org/10.1029/2010jg001423, 2010.
Desai, A. R., Xu, K., Tian, H., Weishampel, P., Thom, J., Baumann, D.,
Andrews, A. E., Cook, B. D., King, J. Y., and Kolka, R.: Landscape-level
terrestrial methane flux observed from a very tall tower, Agr. Forest
Meteorol., 201, 61–75, https://doi.org/10.1016/j.agrformet.2014.10.017,
2015.
Dlugokencky, E.: Trends in Atmospheric Methane Global CH4 Monthly Means,
NOAA, https://gml.noaa.gov/ccgg/trends_ch4/ (last access: 5 January 2022), 2021.
Dlugokencky, E. J., Nisbet, E. G., Fisher, R., and Lowry, D.: Global
atmospheric methane: budget, changes and dangers, Philos. Trans. A Math.
Phys. Eng. Sci., 369, 2058–2072, https://doi.org/10.1098/rsta.2010.0341,
2011.
Elder, C. D., Thompson, D. R., Thorpe, A. K., Hanke, P., Walter Anthony, K.
M., and Miller, C. E.: Airborne mapping reveals emergent power law of arctic
methane emissions, Geophys. Res. Lett., 47, e2019GL085707,
https://doi.org/10.1029/2019gl085707, 2020.
Gower, J. C.: A General Coefficient of Similarity and Some of Its
Properties, Biometrics, 27, 857–871, https://doi.org/10.2307/2528823, 1971.
Groffman, P. M., Hardy, J. P., Driscoll, C. T., and Fahey, T. J.: Snow
depth, soil freezing, and fluxes of carbon dioxide, nitrous oxide and
methane in a northern hardwood forest, Glob. Change Biol., 12, 1748–1760,
https://doi.org/10.1111/j.1365-2486.2006.01194.x, 2006.
Hargrove, W. W. and Hoffman, F. M.: New analysis reveals representativeness
of the AmeriFlux network, Eos Trans. Amer. Geophys. Union, 84, 529–544, 2003.
Harikumar, S. and Pv, S.: K-Medoid Clustering for Heterogeneous DataSets,
Procedia Comput. Sci., 70, 226–237,
https://doi.org/10.1016/j.procs.2015.10.077, 2015.
He, H., Zhang, L., Gao, Y., Ren, X., Zhang, L., Yu, G., and Wang, S.:
Regional representativeness assessment and improvement of eddy flux
observations in China, Sci. Total Environ., 502, 688–698,
https://doi.org/10.1016/j.scitotenv.2014.09.073, 2015.
Hijmans, R. J.: Geographic Data Analysis and Modeling [R package raster
version 3.4-13], Comprehensive R Archive Network (CRAN) http://cran.stat.unipd.it/web/packages/raster/, last access: 12 August 2021.
Hoffman, F. M., Kumar, J., Mills, R. T., and Hargrove, W. W.:
Representativeness-based sampling network design for the State of Alaska, 28, 1567–1586,
https://doi.org/10.1007/s10980-013-9902-0, 2013.
Huang, Z.: Clustering large data sets with mixed numeric and categorical values, in: Proceedings of the 1st pacific-asia conference on knowledge discovery and data mining (PAKDD), PAKDD, Singapore, 21–34, 23–24 February, https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.9984&rep=rep1&type=pdf (last access: 9 May 2022), 1997.
Hutchins, D. A., Jansson, J. K., Remais, J. V., Rich, V. I., Singh, B. K.,
and Trivedi, P.: Climate change microbiology - problems and perspectives,
Nat. Rev. Microbiol., 17, 391–396,
https://doi.org/10.1038/s41579-019-0178-5, 2019.
IPCC: The physical science basis, Contribution of working group I to the
fifth assessment report of the intergovernmental panel on climate change,
USA, Cambridge University Press, 1535 pp., https://www.ipcc.ch/report/ar5/wg1/ (last access: 9 May 2022), 2013.
IPCC: Climate Change 2021: The Physical Science Basis, Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, https://doi.org/10.1017/9781009157896.002, 2021.
Ippoliti, C., Candeloro, L., Gilbert, M., Goffredo, M., Mancini, G., Curci,
G., Falasca, S., Tora, S., Di Lorenzo, A., Quaglia, M., and Conte, A.:
Defining ecological regions in Italy based on a multivariate clustering
approach: A first step towards a targeted vector borne disease surveillance,
PLoS One, 14, e0219072, https://doi.org/10.1371/journal.pone.0219072, 2019.
Jin, S., Homer, C., Yang, L., Danielson, P., Dewitz, J., Li, C., Zhu, Z.,
Xian, G., and Howard, D.: Overall Methodology Design for the United States
National Land Cover Database 2016 Products, Remote Sens., 11, 2971,
https://doi.org/10.3390/rs11242971, 2019.
Jongman, R. H. G., Skidmore, A. K., Mücher, C. A. S., Bunce, R. G. H.,
and Metzger, M. J.: Global terrestrial ecosystem observations: why, where,
what and how?, in: The GEO handbook on biodiversity observation networks,
Springer, Cham, 19–38, ISBN 978-3-319-27288-7, 2017.
Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D.,
Altaf Arain, M., Arneth, A., Bernhofer, C., Bonal, D., Chen, J., Gianelle,
D., Gobron, N., Kiely, G., Kutsch, W., Lasslop, G., Law, B. E., Lindroth,
A., Merbold, L., Montagnani, L., Moors, E. J., Papale, D., Sottocornola, M.,
Vaccari, F., and Williams, C.: Global patterns of land-atmosphere fluxes of
carbon dioxide, latent heat, and sensible heat derived from eddy covariance,
satellite, and meteorological observations, J. Geophys. Res., 116, G00J07,
https://doi.org/10.1029/2010jg001566, 2011.
Kaufman, L. and Rousseeuw, P. J.: Finding Groups in Data: An Introduction to
Cluster Analysis, John Wiley & Sons, 342 pp., https://doi.org/10.1002/9780470316801, 2009.
Kerlinger, F. N.: Foundations of Behavioral Research, Holt, Rinehart
Winston, New York, NY, ISBN 9780030417610, 1986.
Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., Bruhwiler,
L., Cameron-Smith, P., Castaldi, S., Chevallier, F., Feng, L., Fraser, A.,
Heimann, M., Hodson, E. L., Houweling, S., Josse, B., Fraser, P. J.,
Krummel, P. B., Lamarque, J.-F., Langenfelds, R. L., Le Quéré, C.,
Naik, V., O'Doherty, S., Palmer, P. I., Pison, I., Plummer, D., Poulter, B.,
Prinn, R. G., Rigby, M., Ringeval, B., Santini, M., Schmidt, M., Shindell,
D. T., Simpson, I. J., Spahni, R., Steele, L. P., Strode, S. A., Sudo, K.,
Szopa, S., van der Werf, G. R., Voulgarakis, A., van Weele, M., Weiss, R.
F., Williams, J. E., and Zeng, G.: Three decades of global methane sources
and sinks, Nat. Geosci., 6, 813–823, https://doi.org/10.1038/ngeo1955, 2013.
Knox, S. H., Jackson, R. B., Poulter, B., McNicol, G., Fluet-Chouinard, E., Zhang, Z., Hugelius, G., Bousquet, P., Canadell, J. G., Saunois, M., Papale, D., Chu, H., Keenan, T. F., Baldocchi, D., Torn, M. S., Mammarella, I., Trotta, C., Aurela, M., Bohrer, G., Campbell, D. I., Cescatti, A., Chamberlain, S., Chen, J., Chen, W., Dengel, S., Desai, A. R., Euskirchen, E., Friborg, T., Gasbarra, D., Goded, I., Goeckede, M., Heimann, M., Helbig, M., Hirano, T., Hollinger, D. Y., Iwata, H., Kang, M., Klatt, J., Krauss, K. W., Kutzbach, L., Lohila, A., Mitra, B., Morin, T. H., Nilsson, M. B., Niu, S., Noormets, A., Oechel, W. C., Peichl, M., Peltola, O., Reba, M. L., Richardson, A. D., Runkle, B. R. K., Ryu, Y., Sachs, T., Schäfer, K. V. R., Schmid, H. P., Shurpali, N., Sonnentag, O., Tang, A. C. I., Ueyama, M., Vargas, R., Vesala, T., Ward, E. J., Windham-Myers, L., Wohlfahrt, G., and Zona, D: FLUXNET-CH 4 Synthesis Activity: Objectives, Observations, and
Future Directions, Bull. Am. Meteorol. Soc., 100, 2607–2632, 2019.
Kumar, J., Hoffman, F. M., Hargrove, W. W., and Collier, N.: Understanding the representativeness of FLUXNET for upscaling carbon flux from eddy covariance measurements, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2016-36, 2016.
Lassey, K. R.: Livestock methane emission and its perspective in the global
methane cycle, Aust. J. Exp. Agr., 48, 114–118,
https://doi.org/10.1071/EA07220, 2008.
Le Quéré, C., Peters, G. P., Friedlingstein, P., Andrew, R. M.,
Canadell, J. G., Davis, S. J., Jackson, R. B., and Jones, M. W.: Fossil CO2
emissions in the post-COVID-19 era, Nat. Clim. Change, 11, 197–199,
https://doi.org/10.1038/s41558-021-01001-0, 2021.
Liaw, A. and Wiener, M.: Classification and regression by randomForest, R news, 2, 18–22,
2002.
Lovett, G. M., Burns, D. A., Driscoll, C. T., Jenkins, J. C., Mitchell, M.
J., Rustad, L., Shanley, J. B., Likens, G. E., and Haeuber, R.: Who needs
environmental monitoring?, Front. Ecol. Environ., 5, 253–260,
https://doi.org/10.1890/1540-9295(2007)5[253:WNEM]2.0.CO;2, 2007.
Lu, X., Zhou, Y., Zhuang, Q., Prigent, C., Liu, Y., and Teuling, A.:
Increasing methane emissions from natural land ecosystems due to sea-level
rise, J. Geophys. Res.-Biogeo., 123, 1756–1768,
https://doi.org/10.1029/2017jg004273, 2018.
Mahecha, M. D., Gans, F., Sippel, S., Donges, J. F., Kaminski, T., Metzger,
S., Migliavacca, M., Papale, D., Rammig, A., and Zscheischler, J.: Detecting
impacts of extreme events with ecological in situ monitoring networks,
Biogeosciences, 14, 4255–4277, https://doi.org/10.5194/bg-14-4255-2017,
2017.
Malone, S.: Gaps in Network Infrastructure limit our understanding of biogenic methane emissions in the United States, knb [data set], https://doi.org/10.5063/F1FF3QS3, 2021.
Matthews, E. and Fung, I.: Methane emission from natural wetlands: Global
distribution, area, and environmental characteristics of sources, Global
Biogeochem. Cy., 1, 61–86, https://doi.org/10.1029/GB001i001p00061,
1987.
McDermitt, D., Burba, G., Xu, L., Anderson, T., Komissarov, A., Riensche,
B., Schedlbauer, J., Starr, G., Zona, D., Oechel, W., Oberbauer, S., and
Hastings, S.: A new low-power, open-path instrument for measuring methane
flux by eddy covariance, Appl. Phys. B, 102, 391–405,
https://doi.org/10.1007/s00340-010-4307-0, 2011.
Michalak, A. M., Jackson, R., Marland, G., and Sabine, C.: A U.S. Carbon Cycle Science Plan:, First Meeting of the Carbon Cycle Science Working Group, Eos Transactions American Geophysical Union, Washington, D. C, 102–103, https://doi.org/10.1029/2009eo120003, 2009.
Nemitz, E., Mammarella, I., Ibrom, A., Aurela, M., Burba, G. G., Dengel, S.,
Gielen, B., Grelle, A., Heinesch, B., Herbst, M., Hörtnagl, L.,
Klemedtsson, L., Lindroth, A., Lohila, A., McDermitt, D. K., Meier, P.,
Merbold, L., Nelson, D., Nicolini, G., Nilsson, M. B., Peltola, O., Rinne,
J., and Zahniser, M.: Standardisation of eddy-covariance flux measurements
of methane and nitrous oxide, Int. Agrophys., 32, 517–549,
https://doi.org/10.1515/intag-2017-0042, 2018.
Ni, X. and Groffman, P. M.: Declines in methane uptake in forest soils,
P. Natl. Acad. Sci. USA, 115, 8587–8590,
https://doi.org/10.1073/pnas.1807377115, 2018.
Nisbet, E. G., Dlugokencky, E. J., and Bousquet, P.: Methane on the
Rise – Again, Science, 343, 493–495,
https://doi.org/10.1126/science.1247828, 2014.
Nisbet, E. G., Manning, M. R., Dlugokencky, E. J., Fisher, R. E., Lowry, D.,
Michel, S. E., Myhre, C. L., Platt, S. M., Allen, G., Bousquet, P.,
Brownlow, R., Cain, M., France, J. L., Hermansen, O., Hossaini, R., Jones,
A. E., Levin, I., Manning, A. C., Myhre, G., Pyle, J. A., Vaughn, B. H.,
Warwick, N. J., and White, J. W. C.: Very strong atmospheric methane growth
in the 4 years 2014–2017: Implications for the Paris agreement, Global
Biogeochem. Cy., 33, 318–342, https://doi.org/10.1029/2018gb006009,
2019.
Novick, K. A., Biederman, J. A., Desai, A. R., Litvak, M. E., Moore, D. J.
P., Scott, R. L., and Torn, M. S.: The AmeriFlux network: A coalition of the
willing, Agr. Forest Meteorol., 249, 444–456,
https://doi.org/10.1016/j.agrformet.2017.10.009, 2018.
Oh, Y., Zhuang, Q., Liu, L., Welp, L. R., Lau, M. C. Y., Onstott, T. C.,
Medvigy, D., Bruhwiler, L., Dlugokencky, E. J., Hugelius, G., D'Imperio, L.,
and Elberling, B.: Reduced net methane emissions due to microbial methane
oxidation in a warmer Arctic, Nat. Clim. Change, 10, 317–321,
https://doi.org/10.1038/s41558-020-0734-z, 2020.
Papale, D., Black, T. A., Carvalhais, N., Cescatti, A., Chen, J., Jung, M.,
Kiely, G., Lasslop, G., Mahecha, M. D., Margolis, H., Merbold, L.,
Montagnani, L., Moors, E., Olesen, J. E., Reichstein, M., Tramontana, G.,
Gorsel, E., Wohlfahrt, G., and Ráduly, B.: Effect of spatial sampling
from European flux towers for estimating carbon and water fluxes with
artificial neural networks, J. Geophys. Res.-Biogeo., 120, 1941–1957,
https://doi.org/10.1002/2015jg002997, 2015.
Peltola, O., Mammarella, I., Haapanala, S., Burba, G., and Vesala, T.: Field intercomparison of four methane gas analyzers suitable for eddy covariance flux measurements, Biogeosciences, 10, 3749–3765, https://doi.org/10.5194/bg-10-3749-2013, 2013.
Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations, Earth Syst. Sci. Data, 11, 1263–1289, https://doi.org/10.5194/essd-11-1263-2019, 2019.
Podani, J.: Extending Gower's general coefficient of similarity to ordinal
characters, Taxon, 48, 331–340, https://doi.org/10.2307/1224438, 1999.
Punj, G. and Stewart, D. W.: Cluster Analysis in Marketing Research: Review
and Suggestions for Application, J. Mark. Res., 20, 134–148,
https://doi.org/10.1177/002224378302000204, 1983.
R Core Team: R: A language and environment for statistical computing, Version 4.0.4, R Foundation for Statistical Computing, https://www.R-project.org/ (last access: 9 May 2022), 2021.
Reynolds, A. P., Richards, G., de la Iglesia, B., and Rayward-Smith, V. J.:
Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering
Algorithms, J. Math. Model. Algor., 5, 475–504,
https://doi.org/10.1007/s10852-005-9022-1, 2006.
Rinne, J., Riutta, T., Pihlatie, M., Aurela, M., Haapanala, S., Tuovinen,
J.-P., Tuittila, E.-S., and Vesala, T.: Annual cycle of methane emission
from a boreal fen measured by the eddy covariance technique, Tellus B, 59, 449–457,
https://doi.org/10.1111/j.1600-0889.2007.00261.x, 2007.
Ripley, B. D.: Pattern Recognition and Neural Networks, Cambridge University
Press, 403 pp., ISBN 9780521717700, 2007.
Robertson, G. P., Paul, E. A., and Harwood, R. R.: Greenhouse gases in
intensive agriculture: contributions of individual gases to the radiative
forcing of the atmosphere, Science, 289, 1922–1925,
https://doi.org/10.1126/science.289.5486.1922, 2000.
Rosentreter, J. A., Borges, A. V., Deemer, B. R., Holgerson, M. A., Liu, S.,
Song, C., Melack, J., Raymond, P. A., Duarte, C. M., Allen, G. H., Olefeldt,
D., Poulter, B., Battin, T. I., and Eyre, B. D.: Half of global methane
emissions come from highly variable aquatic ecosystem sources, Nat. Geosci.,
14, 225–230, https://doi.org/10.1038/s41561-021-00715-2, 2021.
Schaefer, H.: On the Causes and Consequences of Recent Trends in Atmospheric
Methane, , Current Climate Change Reports, 5, 259–274, https://doi.org/10.1007/s40641-019-00140-z, 2019.
Schimel, D. and Keller, M.: Big questions, big science: meeting the
challenges of global ecology, Oecologia, 177, 925–934,
https://doi.org/10.1007/s00442-015-3236-3, 2015.
Schubert, E. and Rousseeuw, P. J.: Faster k-Medoids Clustering: Improving
the PAM, CLARA, and CLARANS Algorithms, V1, 171–187,
https://doi.org/10.1007/978-3-030-32047-8_16, 2019.
Schubert, E. and Rousseeuw, P. J.: Fast and eager k-medoids clustering: O(k)
runtime improvement of the PAM, CLARA, and CLARANS algorithms, Inf. Syst.,
101, 101804, https://doi.org/10.1016/j.is.2021.101804, 2021.
Serreze, M. C. and Barry, R. G.: Processes and impacts of Arctic
amplification: A research synthesis, Glob. Planet. Change, 77, 85–96,
https://doi.org/10.1016/j.gloplacha.2011.03.004, 2011.
Sherwood, O. A., Schwietzke, S., Arling, V. A., and Etiope, G.: Global
inventory of gas geochemistry data from fossil fuel, microbial and burning
sources, version 2017, Earth Syst. Sci. Data, 9, 639–656,
https://doi.org/10.5194/essd-9-639-2017, 2017.
Shurpali, N. J., Verma, S. B., Clement, R. J., and Billesbach, D. P.:
Seasonal distribution of methane flux in a Minnesota peatland measured by
eddy correlation, J. Geophys. Res., 98, 20649,
https://doi.org/10.1029/93jd02181, 1993.
Sulkava, M., Luyssaert, S., Zaehle, S., and Papale, D.: Assessing and
improving the representativeness of monitoring networks: The European flux
tower network example, J. Geophys. Res., 116,
https://doi.org/10.1029/2010jg001562, 2011.
Sulla-Menashe, D. and Friedl, M. A.: User guide to collection 6 MODIS land
cover (MCD12Q1 and MCD12C1) product, NASA, 2018.
Thornton, B. F., Wik, M., and Crill, P. M.: Double-counting challenges the
accuracy of high-latitude methane inventories, Geophys. Res. Lett., 43,
12569–12577, https://doi.org/10.1002/2016gl071772, 2016.
Thornton, M. M., Thornton, P. E., Wei, Y., Vose, R. S., and Boyer, A. G.:
Daymet: Station-level inputs and model predicted values for North America,
Version 3, 2017.
Torgerson, W. S.: Theory and methods of scaling, Wiley, Oxford, England, ISBN 195907320000, 1958.
Vaughan, H., Brydges, T., Fenech, A., and Lumb, A.: Monitoring long-term
ecological changes through the Ecological Monitoring and Assessment Network:
science-based and policy relevant, Environ. Monit. Assess., 67, 3–28,
https://doi.org/10.1023/a:1006423432114, 2001.
Venables, W. N. and Ripley, B. D.: Modern applied statistics with S, Springer, New York, NY, https://doi.org/10.1007/978-0-387-21706-2, 2002.
Villarreal, S., Guevara, M., Alcaraz-Segura, D., Brunsell, N. A., Hayes, D.,
Loescher, H. W., and Vargas, R.: Ecosystem functional diversity and the
representativeness of environmental networks across the conterminous United
States, Agr. Forest Meteorol., 262, 423–433,
https://doi.org/10.1016/j.agrformet.2018.07.016, 2018.
Wik, M., Thornton, B. F., Bastviken, D., Uhlbäck, J., and Crill, P. M.:
Biased sampling of methane release from northern lakes: A problem for
extrapolation, Geophys. Res. Lett., 43, 1256–1262,
https://doi.org/10.1002/2015gl066501, 2016.
Wilen, B. O. and Bates, M. K.: The US fish and wildlife service's national
wetlands inventory project, in: Classification and Inventory of the World's
Wetlands, Springer Netherlands, Dordrecht, 153–169,
https://doi.org/10.1007/978-94-011-0427-2_13, 1995.
Xiao, J., Chen, J., Davis, K. J., and Reichstein, M.: Advances in upscaling
of eddy covariance measurements of carbon and water fluxes, J. Geophys.
Res., 117, G00J01, https://doi.org/10.1029/2011jg001889, 2012.
Yang, F., Zhu, A.-X., Ichii, K., White, M. A., Hashimoto, H., and Nemani, R.
R.: Assessing the representativeness of the AmeriFlux network using MODIS
and GOES data, J. Geophys. Res., 113, G04036, https://doi.org/10.1029/2007jg000627,
2008.
Yumashev, D., Hope, C., Schaefer, K., Riemann-Campe, K., Iglesias-Suarez,
F., Jafarov, E., Burke, E. J., Young, P. J., Elshorbany, Y., and Whiteman,
G.: Climate policy implications of nonlinear decline of Arctic land
permafrost and other cryosphere elements, Nat. Commun., 10, 1900,
https://doi.org/10.1038/s41467-019-09863-x, 2019.
Zhang, Z., Zimmermann, N. E., Stenke, A., Li, X., Hodson, E. L., Zhu, G.,
Huang, C., and Poulter, B.: Emerging role of wetland methane emissions in
driving 21st century climate change, P. Natl. Acad. Sci. USA, 114,
9647–9652, https://doi.org/10.1073/pnas.1618765114, 2017.
Zhou, X., Zhang, M., Krause, S. M. B., Bu, X., Gu, X., Guo, Z., Jia, Z.,
Zhou, X., Wang, X., Chen, X., and Wang, Y.: Soil aeration rather than
methanotrophic community drives methane uptake under drought in a
subtropical forest, Sci. Total Environ., 792, 148292,
https://doi.org/10.1016/j.scitotenv.2021.148292, 2021.
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E.,
Dinardo, S. J., Dengel, S., Sweeney, C., Karion, A., Chang, R. Y.-W.,
Henderson, J. M., Murphy, P. C., Goodrich, J. P., Moreaux, V., Liljedahl,
A., Watts, J. D., Kimball, J. S., Lipson, D. A., and Oechel, W. C.: Cold
season emissions dominate the Arctic tundra methane budget, P. Natl.
Acad. Sci. USA, 113, 40–45, https://doi.org/10.1073/pnas.1516017113,
2016.
Short summary
To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United States of America, a multi-scale CH4 observation network focused on CH4 flux rates, processes, and scaling methods is required. This can be achieved with a network of ground-based observations that are distributed based on climatic regions and land cover.
To understand the CH4 flux potential of natural ecosystems and agricultural lands in the United...
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