Articles | Volume 21, issue 5
https://doi.org/10.5194/bg-21-1301-2024
© Author(s) 2024. 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-21-1301-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Mar 2024
Research article |
| 18 Mar 2024
| 18 Mar 2024
Resolving heterogeneous fluxes from tundra halves the growing season carbon budget
Sarah M. Ludwig
CORRESPONDING AUTHOR
Department of Earth and Environmental Science, Columbia University, New York, NY, United States of America
Lamont-Doherty Earth Observatory, Palisades, NY, United States of America
Luke Schiferl
Lamont-Doherty Earth Observatory, Palisades, NY, United States of America
Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, United States of America
Jacqueline Hung
Woodwell Climate Research Center, Woods Hole, MA, United States of America
Susan M. Natali
Woodwell Climate Research Center, Woods Hole, MA, United States of America
Roisin Commane
Department of Earth and Environmental Science, Columbia University, New York, NY, United States of America
Lamont-Doherty Earth Observatory, Palisades, NY, United States of America
Related authors
No articles found.
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
Preprint under review for ESSD
Short summary
Short summary
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.
Elchin E. Jafarov, Hélène Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev., 18, 3857–3875, https://doi.org/10.5194/gmd-18-3857-2025, https://doi.org/10.5194/gmd-18-3857-2025, 2025
Short summary
Short summary
This study improves how we tune ecosystem models to reflect carbon and nitrogen storage in Arctic soils. By comparing model outputs with data from a black spruce forest in Alaska, we developed a clearer, more efficient method of matching observations. This is a key step towards understanding how Arctic ecosystems may respond to warming and release carbon, helping make future climate predictions more reliable.
Anna C. Talucci, Michael M. Loranty, Jean E. Holloway, Brendan M. Rogers, Heather D. Alexander, Natalie Baillargeon, Jennifer L. Baltzer, Logan T. Berner, Amy Breen, Leya Brodt, Brian Buma, Jacqueline Dean, Clement J. F. Delcourt, Lucas R. Diaz, Catherine M. Dieleman, Thomas A. Douglas, Gerald V. Frost, Benjamin V. Gaglioti, Rebecca E. Hewitt, Teresa Hollingsworth, M. Torre Jorgenson, Mark J. Lara, Rachel A. Loehman, Michelle C. Mack, Kristen L. Manies, Christina Minions, Susan M. Natali, Jonathan A. O'Donnell, David Olefeldt, Alison K. Paulson, Adrian V. Rocha, Lisa B. Saperstein, Tatiana A. Shestakova, Seeta Sistla, Oleg Sizov, Andrey Soromotin, Merritt R. Turetsky, Sander Veraverbeke, and Michelle A. Walvoord
Earth Syst. Sci. Data, 17, 2887–2909, https://doi.org/10.5194/essd-17-2887-2025, https://doi.org/10.5194/essd-17-2887-2025, 2025
Short summary
Short summary
Wildfires have the potential to accelerate permafrost thaw and the associated feedbacks to climate change. We assembled a dataset of permafrost thaw depth measurements from burned and unburned sites contributed by researchers from across the northern high-latitude region. We estimated maximum thaw depth for each measurement, which addresses a key challenge: the ability to assess impacts of wildfire on maximum thaw depth when measurement timing varies.
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
Short summary
Short summary
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.
Valeria Briones, Hélène Genet, Elchin E. Jafarov, Brendan M. Rogers, Jennifer D. Watts, Anna-Maria Virkkala, Annett Bartsch, Benjamin C. Maglio, Joshua Rady, and Susan M. Natali
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-226, https://doi.org/10.5194/essd-2025-226, 2025
Manuscript not accepted for further review
Short summary
Short summary
Arctic warming is causing permafrost to thaw, affecting ecosystems and climate. Since land cover, especially vegetation, shapes how permafrost responds, accurate maps are crucial. Using machine learning, we combined existing global and regional datasets to create a hybrid detailed 1-km map of Arctic-Boreal land cover, improving the representation of forests, shrubs, and wetlands across the circumpolar.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-1477, https://doi.org/10.5194/egusphere-2025-1477, 2025
Short summary
Short summary
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.
Luke D. Schiferl, Andrew Hallward-Driemeier, Yuwei Zhao, Ricardo Toledo-Crow, and Róisín Commane
EGUsphere, https://doi.org/10.5194/egusphere-2025-345, https://doi.org/10.5194/egusphere-2025-345, 2025
Short summary
Short summary
Accurate quantification and identification of methane sources to the atmosphere are key to meeting climate warming mitigation targets. Using rooftop observations, we find the methane emissions from New York City are larger and more variable across seasons and throughout the day than expected. The methane emissions are well correlated with emissions of carbon monoxide, which points to inefficient combustion from stationary sources as a potential missing source of methane emissions in this region.
Cynthia D. Nevison, Qing Liang, Paul A. Newman, Britton B. Stephens, Geoff Dutton, Xin Lan, Roisin Commane, Yenny Gonzalez, and Eric Kort
Atmos. Chem. Phys., 24, 10513–10529, https://doi.org/10.5194/acp-24-10513-2024, https://doi.org/10.5194/acp-24-10513-2024, 2024
Short summary
Short summary
This study examines the drivers of interannual variability in tropospheric N2O. New insights are obtained from aircraft data and a chemistry–climate model that explicitly simulates stratospheric N2O. The stratosphere is found to be the dominant driver of N2O variability in the Northern Hemisphere, while both the stratosphere and El Niño cycles are important in the Southern Hemisphere. These results are consistent with known atmospheric dynamics and differences between the hemispheres.
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
Short summary
Short summary
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.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan T. Berner, Amy L. Breen, Abhishek Chatterjee, Scott J. Davidson, Gerald V. Frost, Teresa N. Hollingsworth, Go Iwahana, Randi R. Jandt, Anja N. Kade, Tatiana V. Loboda, Matt J. Macander, Michelle Mack, Charles E. Miller, Eric A. Miller, Susan M. Natali, Martha K. Raynolds, Adrian V. Rocha, Shiro Tsuyuzaki, Craig E. Tweedie, Donald A. Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data, 16, 3687–3703, https://doi.org/10.5194/essd-16-3687-2024, https://doi.org/10.5194/essd-16-3687-2024, 2024
Short summary
Short summary
The Arctic tundra is experiencing widespread physical and biological changes, largely in response to warming, yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and studies compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which brings together field datasets and includes vegetation, active-layer, and fire properties.
Stefano Potter, Sol Cooperdock, Sander Veraverbeke, Xanthe Walker, Michelle C. Mack, Scott J. Goetz, Jennifer Baltzer, Laura Bourgeau-Chavez, Arden Burrell, Catherine Dieleman, Nancy French, Stijn Hantson, Elizabeth E. Hoy, Liza Jenkins, Jill F. Johnstone, Evan S. Kane, Susan M. Natali, James T. Randerson, Merritt R. Turetsky, Ellen Whitman, Elizabeth Wiggins, and Brendan M. Rogers
Biogeosciences, 20, 2785–2804, https://doi.org/10.5194/bg-20-2785-2023, https://doi.org/10.5194/bg-20-2785-2023, 2023
Short summary
Short summary
Here we developed a new burned-area detection algorithm between 2001–2019 across Alaska and Canada at 500 m resolution. We estimate 2.37 Mha burned annually between 2001–2019 over the domain, emitting 79.3 Tg C per year, with a mean combustion rate of 3.13 kg C m−2. We found larger-fire years were generally associated with greater mean combustion. The burned-area and combustion datasets described here can be used for local- to continental-scale applications of boreal fire science.
Michael Moubarak, Seeta Sistla, Stefano Potter, Susan M. Natali, and Brendan M. Rogers
Biogeosciences, 20, 1537–1557, https://doi.org/10.5194/bg-20-1537-2023, https://doi.org/10.5194/bg-20-1537-2023, 2023
Short summary
Short summary
Tundra wildfires are increasing in frequency and severity with climate change. We show using a combination of field measurements and computational modeling that tundra wildfires result in a positive feedback to climate change by emitting significant amounts of long-lived greenhouse gasses. With these effects, attention to tundra fires is necessary for mitigating climate change.
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
Short summary
Short summary
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.
Peter Stimmler, Mathias Goeckede, Bo Elberling, Susan Natali, Peter Kuhry, Nia Perron, Fabrice Lacroix, Gustaf Hugelius, Oliver Sonnentag, Jens Strauss, Christina Minions, Michael Sommer, and Jörg Schaller
Earth Syst. Sci. Data, 15, 1059–1075, https://doi.org/10.5194/essd-15-1059-2023, https://doi.org/10.5194/essd-15-1059-2023, 2023
Short summary
Short summary
Arctic soils store large amounts of carbon and nutrients. The availability of nutrients, such as silicon, calcium, iron, aluminum, phosphorus, and amorphous silica, is crucial to understand future carbon fluxes in the Arctic. Here, we provide, for the first time, a unique dataset of the availability of the abovementioned nutrients for the different soil layers, including the currently frozen permafrost layer. We relate these data to several geographical and geological parameters.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Colm Sweeney, Abhishek Chatterjee, Sonja Wolter, Kathryn McKain, Robert Bogue, Stephen Conley, Tim Newberger, Lei Hu, Lesley Ott, Benjamin Poulter, Luke Schiferl, Brad Weir, Zhen Zhang, and Charles E. Miller
Atmos. Chem. Phys., 22, 6347–6364, https://doi.org/10.5194/acp-22-6347-2022, https://doi.org/10.5194/acp-22-6347-2022, 2022
Short summary
Short summary
The Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of aircraft profiles for independent evaluation of model-derived emissions and uptake of atmospheric CO2, CH4, and CO from land and ocean. Comparison with the Goddard Earth Observing System (GEOS) modeling system suggests that fluxes of CO2 are very consistent with observations, while those of CH4 have some regional and seasonal biases, and that CO comparison is complicated by transport errors.
Sparkle L. Malone, Youmi Oh, Kyle A. Arndt, George Burba, Roisin Commane, Alexandra R. Contosta, Jordan P. Goodrich, Henry W. Loescher, Gregory Starr, and Ruth K. Varner
Biogeosciences, 19, 2507–2522, https://doi.org/10.5194/bg-19-2507-2022, https://doi.org/10.5194/bg-19-2507-2022, 2022
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Yenny Gonzalez, Róisín Commane, Ethan Manninen, Bruce C. Daube, Luke D. Schiferl, J. Barry McManus, Kathryn McKain, Eric J. Hintsa, James W. Elkins, Stephen A. Montzka, Colm Sweeney, Fred Moore, Jose L. Jimenez, Pedro Campuzano Jost, Thomas B. Ryerson, Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, Eric Ray, Paul O. Wennberg, John Crounse, Michelle Kim, Hannah M. Allen, Paul A. Newman, Britton B. Stephens, Eric C. Apel, Rebecca S. Hornbrook, Benjamin A. Nault, Eric Morgan, and Steven C. Wofsy
Atmos. Chem. Phys., 21, 11113–11132, https://doi.org/10.5194/acp-21-11113-2021, https://doi.org/10.5194/acp-21-11113-2021, 2021
Short summary
Short summary
Vertical profiles of N2O and a variety of chemical species and aerosols were collected nearly from pole to pole over the oceans during the NASA Atmospheric Tomography mission. We observed that tropospheric N2O variability is strongly driven by the influence of stratospheric air depleted in N2O, especially at middle and high latitudes. We also traced the origins of biomass burning and industrial emissions and investigated their impact on the variability of tropospheric N2O.
Elizabeth B. Wiggins, Arlyn Andrews, Colm Sweeney, John B. Miller, Charles E. Miller, Sander Veraverbeke, Roisin Commane, Steven Wofsy, John M. Henderson, and James T. Randerson
Atmos. Chem. Phys., 21, 8557–8574, https://doi.org/10.5194/acp-21-8557-2021, https://doi.org/10.5194/acp-21-8557-2021, 2021
Short summary
Short summary
We analyzed high-resolution trace gas measurements collected from a tower in Alaska during a very active fire season to improve our understanding of trace gas emissions from boreal forest fires. Our results suggest previous studies may have underestimated emissions from smoldering combustion in boreal forest fires.
Leah Birch, Christopher R. Schwalm, Sue Natali, Danica Lombardozzi, Gretchen Keppel-Aleks, Jennifer Watts, Xin Lin, Donatella Zona, Walter Oechel, Torsten Sachs, Thomas Andrew Black, and Brendan M. Rogers
Geosci. Model Dev., 14, 3361–3382, https://doi.org/10.5194/gmd-14-3361-2021, https://doi.org/10.5194/gmd-14-3361-2021, 2021
Short summary
Short summary
The high-latitude landscape or Arctic–boreal zone has been warming rapidly, impacting the carbon balance both regionally and globally. Given the possible global effects of climate change, it is important to have accurate climate model simulations. We assess the simulation of the Arctic–boreal carbon cycle in the Community Land Model (CLM 5.0). We find biases in both the timing and magnitude photosynthesis. We then use observational data to improve the simulation of the carbon cycle.
Fabienne Maignan, Camille Abadie, Marine Remaud, Linda M. J. Kooijmans, Kukka-Maaria Kohonen, Róisín Commane, Richard Wehr, J. Elliott Campbell, Sauveur Belviso, Stephen A. Montzka, Nina Raoult, Ulli Seibt, Yoichi P. Shiga, Nicolas Vuichard, Mary E. Whelan, and Philippe Peylin
Biogeosciences, 18, 2917–2955, https://doi.org/10.5194/bg-18-2917-2021, https://doi.org/10.5194/bg-18-2917-2021, 2021
Short summary
Short summary
The assimilation of carbonyl sulfide (COS) by continental vegetation has been proposed as a proxy for gross primary production (GPP). Using a land surface and a transport model, we compare a mechanistic representation of the plant COS uptake (Berry et al., 2013) to the classical leaf relative uptake (LRU) approach linking GPP and vegetation COS fluxes. We show that at high temporal resolutions a mechanistic approach is mandatory, but at large scales the LRU approach compares similarly.
Junjie Liu, Latha Baskaran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nicholas C. Parazoo, Tomohiro Oda, Dustin Carroll, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney, and Steven Wofsy
Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, https://doi.org/10.5194/essd-13-299-2021, 2021
Short summary
Short summary
On average, the terrestrial biosphere carbon sink is equivalent to ~ 20 % of fossil fuel emissions. Understanding where and why the terrestrial biosphere absorbs carbon from the atmosphere is pivotal to any mitigation policy. Here we present a regionally resolved satellite-constrained net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: CMS-Flux NBE 2020. The dataset provides a unique perspective on monitoring regional contributions to the CO2 growth rate.
Cited articles
Amiro, B.: Footprint climatologies for evapotranspiration in a boreal catchment, Agr. Forest Meteorol., 90, 195–201, https://doi.org/10.1016/S0168-1923(97)00096-8, 1998. a
Baillargeon, N., Pold, G., Natali, S. M., and Sistla, S. A.: Lowland tundra plant stoichiometry is somewhat resilient decades following fire despite substantial and sustained shifts in community structure, Arct. Antarct. Alp. Res., 54, 525–536, https://doi.org/10.1080/15230430.2022.2121246, 2022. a
Baker, J. M. and Griffis, T. J.: Examining strategies to improve the carbon balance of corn/soybean agriculture using eddy covariance and mass balance techniques, Agr. Forest Meteorol., 128, 163–177, https://doi.org/10.1016/j.agrformet.2004.11.005, 2005. a
Baldocchi, D. D.: Assessing the eddy covariance technique for evaluating carbon dioxide exchange rates of ecosystems: past, present and future, Glob. Change Biol., 9, 479–492, https://doi.org/10.1046/j.1365-2486.2003.00629.x, 2003. a
Baldocchi, D. D., Hincks, B. B., and Meyers, T. P.: Measuring Biosphere-Atmosphere Exchanges of Biologically Related Gases with Micrometeorological Methods, Ecology, 69, 1331–1340, https://doi.org/10.2307/1941631, 1988. a
Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M., and Enrich-prast, A.: Freshwater Methane Emissions Offset the Continental Carbon Sink, Science, 331, 50, https://doi.org/10.1126/science.1196808, 2011. a
Beaulieu, J. J., Waldo, S., Balz, D. A., Barnett, W., Hall, A., Platz, M. C., and White, K. M.: Methane and Carbon Dioxide Emissions From Reservoirs: Controls and Upscaling, J. Geophys. Res.-Biogeo., 125, e2019JG005474, https://doi.org/10.1029/2019JG005474, 2020. a
Beckebanze, L., Rehder, Z., Holl, D., Wille, C., Mirbach, C., and Kutzbach, L.: Ignoring carbon emissions from thermokarst ponds results in overestimation of tundra net carbon uptake, Biogeosciences, 19, 1225–1244, https://doi.org/10.5194/bg-19-1225-2022, 2022. a
Bowden, W. B., Gooseff, M. N., Balser, A., Green, A., Peterson, B. J., and Bradford, J.: Sediment and nutrient delivery from thermokarst features in the foothills of the North Slope, Alaska: Potential impacts on headwater stream ecosystems, J. Geophys. Res., 113, G02026, https://doi.org/10.1029/2007JG000470, 2008. a
Brooks, S. P. and Gelman, A.: General Methods for Monitoring Convergence of Iterative Simulations, J. Comput. Graph. Stat., 7, 434–455, https://doi.org/10.1080/10618600.1998.10474787, 1998. a
Budishchev, A., Mi, Y., van Huissteden, J., Belelli-Marchesini, L., Schaepman-Strub, G., Parmentier, F. J. W., Fratini, G., Gallagher, A., Maximov, T. C., and Dolman, A. J.: Evaluation of a plot-scale methane emission model using eddy covariance observations and footprint modelling, Biogeosciences, 11, 4651–4664, https://doi.org/10.5194/bg-11-4651-2014, 2014. a
Chen, L., Dirmeyer, P. A., Guo, Z., and Schultz, N. M.: Pairing FLUXNET sites to validate model representations of land-use/land-cover change, Hydrol. Earth Syst. Sci., 22, 111–125, https://doi.org/10.5194/hess-22-111-2018, 2018. a
Chevallier, F., Wang, T., Ciais, P., Maignan, F., Bocquet, M., Altaf Arain, M., Cescatti, A., Chen, J., Dolman, A. J., Law, B. E., Margolis, H. A., Montagnani, L., and Moors, E. J.: What eddy-covariance measurements tell us about prior land flux errors in CO2-flux inversion schemes, Global Biogeochem. Cy., 26, GB1021, https://doi.org/10.1029/2010GB003974, 2012. a
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. a, b
Coates, T. W., Alam, M., Flesch, T. K., and Hernandez-Ramirez, G.: Field testing two flux footprint models, Atmos. Meas. Tech., 14, 7147–7152, https://doi.org/10.5194/amt-14-7147-2021, 2021. a
Davidson, S., Santos, M., Sloan, V., Reuss-Schmidt, K., Phoenix, G., Oechel, W., and Zona, D.: Upscaling CH4 Fluxes Using High-Resolution Imagery in Arctic Tundra Ecosystems, Remote Sens.-Basel, 9, 1227, https://doi.org/10.3390/rs9121227, 2017. a
Denwood, M. J.: runjags : An R Package Providing Interface Utilities, Model Templates, Parallel Computing Methods and Additional Distributions for MCMC Models in JAGS, J. Stat. Softw., 71, https://doi.org/10.18637/jss.v071.i09, 2016. a
Detto, M., Montaldo, N., Albertson, J. D., Mancini, M., and Katul, G.: Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy, Water Resour. Res., 42, W08419, https://doi.org/10.1029/2005WR004693, 2006. a
Euskirchen, E., Bret-Harte, M., and Scott, G.: Seasonal patterns of carbon dioxide and water fluxes in three representative tundra ecosystems in northern Alaska, Ecosphere, 3, 4, https://doi.org/10.1890/ES11-00202.1, 2012. a
Euskirchen, E. S., Edgar, C. W., Turetsky, M. R., Waldrop, M. P., and Harden, J. W.: Differential response of carbon fluxes to climate in three peatland ecosystems that vary in the presence and stability of permafrost, J. Geophys. Res.-Biogeo., 119, 1576–1595, https://doi.org/10.1002/2014JG002683, 2014. a
Forbrich, I., Kutzbach, L., Wille, C., Becker, T., Wu, J., and Wilmking, M.: Cross-evaluation of measurements of peatland methane emissions on microform and ecosystem scales using high-resolution landcover classification and source weight modelling, Agr. Forest Meteorol., 151, 864–874, https://doi.org/10.1016/j.agrformet.2011.02.006, 2011. a
Fox, A. M., Huntley, B., Lloyd, C. R., Williams, M., and Baxter, R.: Net ecosystem exchange over heterogeneous Arctic tundra: Scaling between chamber and eddy covariance measurements, Global Biogeochem. Cy., 22, GB2027, https://doi.org/10.1029/2007GB003027, 2008. a
Fratini, G. and Mauder, M.: Towards a consistent eddy-covariance processing: an intercomparison of EddyPro and TK3, Atmos. Meas. Tech., 7, 2273–2281, https://doi.org/10.5194/amt-7-2273-2014, 2014. a
Friend, A. D., Arneth, A., Kiang, N. Y., Lomas, M., Ogée, J., Rödenbeck, C., Running, S. W., Santaren, J.-D., Sitch, S., Viovy, N., Ian Woodward, F., and Zaehle, S.: FLUXNET and modelling the global carbon cycle, Glob. Change Biol., 13, 610–633, https://doi.org/10.1111/j.1365-2486.2006.01223.x, 2007. a
Frost, G. V., Loehman, R. A., Saperstein, L. B., Macander, M. J., Nelson, P. R., Paradis, D. P., and Natali, S. M.: Multi-decadal patterns of vegetation succession after tundra fire on the Yukon–Kuskokwim Delta, Alaska, Environ. Res. Lett., 15, 025003, https://doi.org/10.1088/1748-9326/ab5f49, 2020. a
Gelman, A. and Rubin, D. B.: Inference from Iterative Simulation Using Multiple Sequences, Stat. Sci., 7, 457–472, https://doi.org/10.1214/ss/1177011136, 1992. a
Gelman, A., Meng, X.-L., and Stern, H.: Posterior Predictive Assesment of Model Fitness via Realized Discrepancies, Stat. Sinica, 6, 733–760, http://www.jstor.org/stable/24306036 (last access: 26 October 2022), 1996. a
Giannico, V., Chen, J., Shao, C., Ouyang, Z., John, R., and Lafortezza, R.: Contributions of landscape heterogeneity within the footprint of eddy-covariance towers to flux measurements, Agr. Forest Meteorol., 260–261, 144–153, https://doi.org/10.1016/j.agrformet.2018.06.004, 2018. a, b
Goodrich, J., Oechel, W., Gioli, B., Moreaux, V., Murphy, P., Burba, G., and Zona, D.: Impact of different eddy covariance sensors, site set-up, and maintenance on the annual balance of CO2 and CH4 in the harsh Arctic environment, Agr. Forest Meteorol., 228–229, 239–251, https://doi.org/10.1016/j.agrformet.2016.07.008, 2016. a
Griebel, A., Bennett, L. T., Metzen, D., Cleverly, J., Burba, G., and Arndt, S. K.: Effects of inhomogeneities within the flux footprint on the interpretation of seasonal, annual, and interannual ecosystem carbon exchange, Agr. Forest Meteorol., 221, 50–60, https://doi.org/10.1016/j.agrformet.2016.02.002, 2016. a, b
Hannun, R. A., Wolfe, G. M., Kawa, S. R., Hanisco, T. F., Newman, P. A., Alfieri, J. G., Barrick, J., Clark, K. L., DiGangi, J. P., Diskin, G. S., King, J., Kustas, W. P., Mitra, B., Noormets, A., Nowak, J. B., Thornhill, K. L., and Vargas, R.: Spatial heterogeneity in CO2, CH4, and energy fluxes: insights from airborne eddy covariance measurements over the Mid-Atlantic region, Environ. Res. Lett., 15, 035008, https://doi.org/10.1088/1748-9326/ab7391, 2020. a
Hobbs, N. T. and Hooten, M. B.: Bayesian Models: A Statistical Primer for Ecologists, Princeton University Press, https://doi.org/10.1515/9781400866557, 2015. a
Howard, D., Agnan, Y., Helmig, D., Yang, Y., and Obrist, D.: Environmental controls on ecosystem-scale cold-season methane and carbon dioxide fluxes in an Arctic tundra ecosystem, Biogeosciences, 17, 4025–4042, https://doi.org/10.5194/bg-17-4025-2020, 2020. a
Hsieh, C.-I., Katul, G., and Chi, T.-w.: An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows, Adv. Water Resour., 23, 765–772, https://doi.org/10.1016/S0309-1708(99)00042-1, 2000. a
Jammet, M., Dengel, S., Kettner, E., Parmentier, F.-J. W., Wik, M., Crill, P., and Friborg, T.: Year-round CH4 and CO2 flux dynamics in two contrasting freshwater ecosystems of the subarctic, Biogeosciences, 14, 5189–5216, https://doi.org/10.5194/bg-14-5189-2017, 2017. a
Jones, M. and Ludwig, S. M.: arctic-carbon/eddy-footprint: v0.2.2 (v0.2.2), Zenodo [code], https://doi.org/10.5281/zenodo.10578685, 2024. a
Jung, M., Reichstein, M., and Bondeau, A.: Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model, Biogeosciences, 6, 2001–2013, https://doi.org/10.5194/bg-6-2001-2009, 2009. a
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020. a
Juutinen, S., Aurela, M., Tuovinen, J.-P., Ivakhov, V., Linkosalmi, M., Räsänen, A., Virtanen, T., Mikola, J., Nyman, J., Vähä, E., Loskutova, M., Makshtas, A., and Laurila, T.: Variation in CO2 and CH4 fluxes among land cover types in heterogeneous Arctic tundra in northeastern Siberia, Biogeosciences, 19, 3151–3167, https://doi.org/10.5194/bg-19-3151-2022, 2022. a
Kade, A., Bret-Harte, M. S., Euskirchen, E. S., Edgar, C., and Fulweber, R. A.: Upscaling of CO2 fluxes from heterogeneous tundra plant communities in Arctic Alaska, J. Geophys. Res.-Biogeo., 117, G04007, https://doi.org/10.1029/2012JG002065, 2012. a
Kljun, N., Calanca, P., Rotach, M. W., and Schmid, H. P.: A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP), Geosci. Model Dev., 8, 3695–3713, https://doi.org/10.5194/gmd-8-3695-2015, 2015. a
Klosterhalfen, A., Chi, J., Kljun, N., Lindroth, A., Laudon, H., Nilsson, M. B., and Peichl, M.: Two-level eddy covariance measurements reduce bias in land–atmosphere exchange estimates over a heterogeneous boreal forest landscape, Agr. Forest Meteorol., 339, 109523, https://doi.org/10.1016/j.agrformet.2023.109523, 2023. a
Knauer, J., Zaehle, S., Medlyn, B. E., Reichstein, M., Williams, C. A., Migliavacca, M., De Kauwe, M. G., Werner, C., Keitel, C., Kolari, P., Limousin, J.-M., and Linderson, M.-L.: Towards physiologically meaningful water-use efficiency estimates from eddy covariance data, Glob. Change Biol., 24, 694–710, https://doi.org/10.1111/gcb.13893, 2018. a
Kormann, R. and Meixner, F. X.: An Analytical Footprint Model For Non-Neutral Stratification, Bound.-Lay. Meteorol., 99, 207–224, https://doi.org/10.1023/A:1018991015119, 2001. a
Kruschke, J.: Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Academic Press, ISBN 9780124059160, 2014. a
Kuhn, M., Lundin, E. J., Giesler, R., Johansson, M., and Karlsson, J.: Emissions from thaw ponds largely offset the carbon sink of northern permafrost wetlands, Sci. Rep.-UK, 8, 9535, https://doi.org/10.1038/s41598-018-27770-x, 2018. a, b
Kuhn, M. A., Varner, R. K., Bastviken, D., Crill, P., MacIntyre, S., Turetsky, M., Walter Anthony, K., McGuire, A. D., and Olefeldt, D.:
Lara, M. J., McGuire, A. D., Euskirchen, E. S., Genet, H., Yi, S., Rutter, R., Iversen, C., Sloan, V., and Wullschleger, S. D.: Local-scale Arctic tundra heterogeneity affects regional-scale carbon dynamics, Nat. Commun., 11, 4925, https://doi.org/10.1038/s41467-020-18768-z, 2020. a
Lee, H., Schuur, E. a. G., Vogel, J. G., Lavoie, M., Bhadra, D., and Staudhammer, C. L.: A spatially explicit analysis to extrapolate carbon fluxes in upland tundra where permafrost is thawing, Glob. Change Biol., 17, 1379–1393, https://doi.org/10.1111/j.1365-2486.2010.02287.x, 2011. a
Loranty, M. M., Goetz, S. J., Rastetter, E. B., Rocha, A. V., Shaver, G. R., Humphreys, E. R., and Lafleur, P. M.: Scaling an Instantaneous Model of Tundra NEE to the Arctic Landscape, Ecosystems, 14, 76–93, https://doi.org/10.1007/s10021-010-9396-4, 2011. a
Ludwig, S. M.: arctic-carbon/heterogeneous_C_fluxes: v1.0.0 (v1.0.0), Zenodo [code, data set], https://doi.org/10.5281/zenodo.10578675, 2024. a
Ludwig, S., Holmes, R. M., Natali, S. M., Mann, P. J., and Schade, J. D.: Yukon–Kuskokwim Delta fire: aquatic data, Yukon–Kuskokwim Delta Alaska, Arctic Data Center [data set], https://doi.org/10.18739/A22804Z8M, 2018a. a
Ludwig, S., Holmes, R. M., Natali, S. M., Mann, P. J., Schade, J. D., Jardine, L., Melton, S., and Navarro-Perez, E.: Polaris Project 2017: Soil fluxes, carbon, and nitrogen, Yukon–Kuskokwim Delta, Alaska, Arctic Data Center [data set], https://doi.org/10.18739/A2Q23R08G, 2018b. a, b
Ludwig, S. M., Natali, S. M., Mann, P. J., Schade, J. D., Holmes, R. M., Powell, M., Fiske, G., and Commane, R.: Using Machine Learning to Predict Inland Aquatic CO2 and CH4 Concentrations and the Effects of Wildfires in the Yukon–Kuskokwim Delta, Alaska, Global Biogeochem. Cy., 36, e2021GB007146, https://doi.org/10.1029/2021GB007146, 2022. a, b, c
Ludwig, S. M., Natali, S. M., Schade, J. D., Powell, M., Fiske, G., Schiferl, L., and Commane, R.: CO2 and CH4 fluxes from waterbodies, and landcover map, YK Delta, Alaska, 2016–2019, ORNL-DAAC [data set], https://doi.org/10.3334/ORNLDAAC/2178, 2023a. a
Ludwig, S. M., Natali, S. M., Schade, J. D., Powell, M., Fiske, G., Schiferl, L. D., and Commane, R.: Scaling waterbody carbon dioxide and methane fluxes in the arctic using an integrated terrestrial-aquatic approach, Environ. Res. Lett., 18, 064019, https://doi.org/10.1088/1748-9326/acd467, 2023b. a, b, c
Luus, K. A. and Lin, J. C.: The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO2 exchange, Geosci. Model Dev., 8, 2655–2674, https://doi.org/10.5194/gmd-8-2655-2015, 2015. a
Luus, K. A., Commane, R., Parazoo, N. C., Benmergui, J., Euskirchen, E. S., Frankenberg, C., Joiner, J., Lindaas, J., Miller, C. E., Oechel, W. C., Zona, D., Wofsy, S., and Lin, J. C.: Tundra photosynthesis captured by satellite-observed solar-induced chlorophyll fluorescence, Geophys. Res. Lett., 44, 1564–1573, https://doi.org/10.1002/2016GL070842, 2017. a, b
Makowski, D., Ben-Shachar, M. S., and Lüdecke, D.: bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework, Journal of Open Source Software, 4, 1541, https://doi.org/10.21105/joss.01541, 2019. a
Matthes, J. H., Sturtevant, C., Verfaillie, J., Knox, S., and Baldocchi, D.: Parsing the variability in CH4 flux at a spatially heterogeneous wetland: Integrating multiple eddy covariance towers with high-resolution flux footprint analysis, J. Geophys. Res.-Biogeo., 119, 1322–1339, https://doi.org/10.1002/2014JG002642, 2014. a
Mauder, M., Foken, T., Clement, R., Elbers, J. A., Eugster, W., Grünwald, T., Heusinkveld, B., and Kolle, O.: Quality control of CarboEurope flux data – Part 2: Inter-comparison of eddy-covariance software, Biogeosciences, 5, 451–462, https://doi.org/10.5194/bg-5-451-2008, 2008. a
Mauder, M., Cuntz, M., Drüe, C., Graf, A., Rebmann, C., Schmid, H. P., Schmidt, M., and Steinbrecher, R.: A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements, Agr. Forest Meteorol., 169, 122–135, https://doi.org/10.1016/j.agrformet.2012.09.006, 2013. a
McElreath, R.: Statistical Rethinking: A Bayesian Course with Examples in R and STAN, CRC Press, ISBN 9780429639142, 2015. a
McGuire, A. D., Genet, H., Lyu, Z., Pastick, N., Stackpoole, S., Birdsey, R., D'Amore, D., He, Y., Rupp, T. S., Striegl, R., Wylie, B. K., Zhou, X., Zhuang, Q., and Zhu, Z.: Assessing historical and projected carbon balance of Alaska: A synthesis of results and policy/management implications, Ecol. Appl., 28, 1396–1412, https://doi.org/10.1002/eap.1768, 2018. a
McGuire, D., Anderson, L., Christensen, T., Dallimore, S., Guo, L., Hayes, D. J., Heimann, M., Lorenson, T., Macdonald, R. W., and Roulet, N. T.: Sensitivity of the carbon cycle in the Arctic to climate change, Ecol. Monogr., 79, 523–555, 2009. a
Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, K., Trancón y Widemann, B., Neidl, F., Schäfer, K., Wieneke, S., Zheng, X. H., Schmid, H. P., and Foken, T.: Spatially explicit regionalization of airborne flux measurements using environmental response functions, Biogeosciences, 10, 2193–2217, https://doi.org/10.5194/bg-10-2193-2013, 2013. a
Moncrieff, J. B., Massheder, J. M., de Bruin, H., Elbers, J., Friborg, T., Heusinkveld, B., Kabat, P., Scott, S., Soegaard, H., and Verhoef, A.: A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide, J. Hydrol., 188–189, 589–611, https://doi.org/10.1016/S0022-1694(96)03194-0, 1997. a
Morin, T. H., Bohrer, G., Stefanik, K. C., Rey-Sanchez, A. C., Matheny, A. M., and Mitsch, W. J.: Combining eddy-covariance and chamber measurements to determine the methane budget from a small, heterogeneous urban floodplain wetland park, Agr. Forest Meteorol., 237–238, 160–170, https://doi.org/10.1016/j.agrformet.2017.01.022, 2017. a
Mullen, A. L., Watts, J. D., Rogers, B. M., Carroll, M. L., Elder, C. D., Noomah, J., Williams, Z., Caraballo-Vega, J. A., Bredder, A., Rickenbaugh, E., Levenson, E., Cooley, S. W., Hung, J. K. Y., Fiske, G., Potter, S., Yang, Y., Miller, C. E., Natali, S. M., Douglas, T. A., and Kyzivat, E. D.: Using High-Resolution Satellite Imagery and Deep Learning to Track Dynamic Seasonality in Small Water Bodies, Geophys. Res. Lett., 50, e2022GL102327, https://doi.org/10.1029/2022GL102327, 2023. a
Natali, S. M., Watts, J. D., Rogers, B. M., Potter, S., Ludwig, S. M., Selbmann, A.-K., Sullivan, P. F., Abbott, B. W., Arndt, K. A., Birch, L., Björkman, M. P., Bloom, A. A., Celis, G., Christensen, T. R., Christiansen, C. T., Commane, R., Cooper, E. J., Crill, P., Czimczik, C., Davydov, S., Du, J., Egan, J. E., Elberling, B., Euskirchen, E. S., Friborg, T., Genet, H., Göckede, M., Goodrich, J. P., Grogan, P., Helbig, M., Jafarov, E. E., Jastrow, J. D., Kalhori, A. A. M., Kim, Y., Kimball, J. S., Kutzbach, L., Lara, M. J., Larsen, K. S., Lee, B.-Y., Liu, Z., Loranty, M. M., Lund, M., Lupascu, M., Madani, N., Malhotra, A., Matamala, R., McFarland, J., McGuire, A. D., Michelsen, A., Minions, C., Oechel, W. C., Olefeldt, D., Parmentier, F.-J. W., Pirk, N., Poulter, B., Quinton, W., Rezanezhad, F., Risk, D., Sachs, T., Schaefer, K., Schmidt, N. M., Schuur, E. A. G., Semenchuk, P. R., Shaver, G., Sonnentag, O., Starr, G., Treat, C. C., Waldrop, M. P., Wang, Y., Welker, J., Wille, C., Xu, X., Zhang, Z., Zhuang, Q., and Zona, D.: Large loss of CO2 in winter observed across the northern permafrost region, Nat. Clim. Change, 9, 852–857, https://doi.org/10.1038/s41558-019-0592-8, 2019. a
Natali, S. M., Holdren, J. P., Rogers, B. M., Treharne, R., Duffy, P. B., Pomerance, R., and MacDonald, E.: Permafrost carbon feedbacks threaten global climate goals, P. Natl. Acad. Sci. USA, 118, e2100163118, https://doi.org/10.1073/pnas.2100163118, 2021. a
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. a
Pallandt, M. M. T. A., Kumar, J., Mauritz, M., Schuur, E. A. G., Virkkala, A.-M., Celis, G., Hoffman, F. M., and Göckede, M.: Representativeness assessment of the pan-Arctic eddy covariance site network and optimized future enhancements, Biogeosciences, 19, 559–583, https://doi.org/10.5194/bg-19-559-2022, 2022. a
Papale, D.: Ideas and perspectives: enhancing the impact of the FLUXNET network of eddy covariance sites, Biogeosciences, 17, 5587–5598, https://doi.org/10.5194/bg-17-5587-2020, 2020. a
Rantanen, M., Karpechko, A. Y., Lipponen, A., Nordling, K., Hyvärinen, O., Ruosteenoja, K., Vihma, T., and Laaksonen, A.: The Arctic has warmed nearly four times faster than the globe since 1979, Communications Earth and Environment, 3, 1–10, https://doi.org/10.1038/s43247-022-00498-3, 2022. a
Raynolds, M. and Walker, D.: Raster Circumpolar Arctic Vegetation Map, Mendeley Data [data set], https://doi.org/10.17632/c4xj5rv6kv.2, 2022. a
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grünwald, T., Havránková, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.-M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Change Biol., 11, 1424–1439, https://doi.org/10.1111/j.1365-2486.2005.001002.x, 2005. a, b
Reichstein, M., Papale, D., Valentini, R., Aubinet, M., Bernhofer, C., Knohl, A., Laurila, T., Lindroth, A., Moors, E., Pilegaard, K., and Seufert, G.: Determinants of terrestrial ecosystem carbon balance inferred from European eddy covariance flux sites, Geophys. Res. Lett., 34, L01402, https://doi.org/10.1029/2006GL027880, 2007. a
Reuss-Schmidt, K., Levy, P., Oechel, W., Tweedie, C., Wilson, C., and Zona, D.: Understanding spatial variability of methane fluxes in Arctic wetlands through footprint modelling, Environ. Res. Lett., 14, 125010, https://doi.org/10.1088/1748-9326/ab4d32, 2019. a, b, c
Rey-Sanchez, C., Arias-Ortiz, A., Kasak, K., Chu, H., Szutu, D., Verfaillie, J., and Baldocchi, D.: Detecting Hot Spots of Methane Flux Using Footprint-Weighted Flux Maps, J. Geophys. Res.-Biogeo., 127, e2022JG006977, https://doi.org/10.1029/2022JG006977, 2022. a, b, c
Rößger, N., Wille, C., Veh, G., Boike, J., and Kutzbach, L.: Scaling and balancing methane fluxes in a heterogeneous tundra ecosystem of the Lena River Delta, Agr. Forest Meteorol., 266–267, 243–255, https://doi.org/10.1016/j.agrformet.2018.06.026, 2019. a, b
Saunois, M., Stavert, A. R., Poulter, B., Bousquet, P., Canadell, J. G., Jackson, R. B., Raymond, P. A., Dlugokencky, E. J., Houweling, S., Patra, P. K., Ciais, P., Arora, V. K., Bastviken, D., Bergamaschi, P., Blake, D. R., Brailsford, G., Bruhwiler, L., Carlson, K. M., Carrol, M., Castaldi, S., Chandra, N., Crevoisier, C., Crill, P. M., Covey, K., Curry, C. L., Etiope, G., Frankenberg, C., Gedney, N., Hegglin, M. I., Höglund-Isaksson, L., Hugelius, G., Ishizawa, M., Ito, A., Janssens-Maenhout, G., Jensen, K. M., Joos, F., Kleinen, T., Krummel, P. B., Langenfelds, R. L., Laruelle, G. G., Liu, L., Machida, T., Maksyutov, S., McDonald, K. C., McNorton, J., Miller, P. A., Melton, J. R., Morino, I., Müller, J., Murguia-Flores, F., Naik, V., Niwa, Y., Noce, S., O'Doherty, S., Parker, R. J., Peng, C., Peng, S., Peters, G. P., Prigent, C., Prinn, R., Ramonet, M., Regnier, P., Riley, W. J., Rosentreter, J. A., Segers, A., Simpson, I. J., Shi, H., Smith, S. J., Steele, L. P., Thornton, B. F., Tian, H., Tohjima, Y., Tubiello, F. N., Tsuruta, A., Viovy, N., Voulgarakis, A., Weber, T. S., van Weele, M., van der Werf, G. R., Weiss, R. F., Worthy, D., Wunch, D., Yin, Y., Yoshida, Y., Zhang, W., Zhang, Z., Zhao, Y., Zheng, B., Zhu, Q., Zhu, Q., and Zhuang, Q.: The Global Methane Budget 2000–2017, Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, 2020. a, b
Schiferl, L. D., Watts, J. D., Larson, E. J. L., Arndt, K. A., Biraud, S. C., Euskirchen, E. S., Goodrich, J. P., Henderson, J. M., Kalhori, A., McKain, K., Mountain, M. E., Munger, J. W., Oechel, W. C., Sweeney, C., Yi, Y., Zona, D., and Commane, R.: Using atmospheric observations to quantify annual biogenic carbon dioxide fluxes on the Alaska North Slope, Biogeosciences, 19, 5953–5972, https://doi.org/10.5194/bg-19-5953-2022, 2022. a, b, c
Schmid, H. P.: Footprint modeling for vegetation atmosphere exchange studies: a review and perspective, Agr. Forest Meteorol., 113, 159–183, https://doi.org/10.1016/S0168-1923(02)00107-7, 2002. a, b
Schuur, E. A. G., Crummer, K. G., Vogel, J. G., and Mack, M. C.: Plant Species Composition and Productivity following Permafrost Thaw and Thermokarst in Alaskan Tundra, Ecosystems, 10, 280–292, https://doi.org/10.1007/s10021-007-9024-0, 2007. a
Schuur, E. A. G., McGuire, A. D., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Schädel, C., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Koven, C. D., Kuhry, P., Lawrence, D. M., Natali, S. M., Olefeldt, D., Romanovsky, V. E., Schaefer, K., Turetsky, M. R., Treat, C. C., and Vonk, J. E.: Climate change and the permafrost carbon feedback, Nature, 520, 171–179, https://doi.org/10.1038/nature14338, 2015. a
Shaver, G. R., Street, L. E., Rastetter, E. B., Van Wijk, M. T., and Williams, M.: Functional convergence in regulation of net CO2 flux in heterogeneous tundra landscapes in Alaska and Sweden, J. Ecol., 95, 802–817, https://doi.org/10.1111/j.1365-2745.2007.01259.x, 2007. a, b
Shaver, G. R., Rastetter, E. B., Salmon, V., Street, L. E., Weg, M. J. V. D., Rocha, A., Wijk, M. T. V., and Williams, M.: Pan-Arctic modelling of net ecosystem exchange of CO2, Philos. T. R. Soc. B, 368, 1–13, https://doi.org/10.1098/rstb.2012.0485, 2013. a
Skytt, T., Nielsen, S. N., and Jonsson, B.-G.: Global warming potential and absolute global temperature change potential from carbon dioxide and methane fluxes as indicators of regional sustainability – A case study of Jämtland, Sweden, Ecological Indicators, 110, 105831, https://doi.org/10.1016/j.ecolind.2019.105831, 2020. a
Stocker, T. (Ed.): Climate Change 2013: The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, ISBN 9781107057999, 2013. a
Stoy, P. C., Williams, M., Evans, J. G., Prieto-Blanco, A., Disney, M., Hill, T. C., Ward, H. C., Wade, T. J., and Street, L. E.: Upscaling Tundra CO2 Exchange from Chamber to Eddy Covariance Tower, Arct. Antarct. Alp. Res., 45, 275–284, https://doi.org/10.1657/1938-4246-45.2.275, 2013. a
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. a, b
Tian, D., Uieda, L., Leong, W. J., Schlitzer, W., Fröhlich, Y., Grund, M., Jones, M., Toney, L., Yao, J., Magen, Y., Tong, J.-H., Materna, K., Belem, A., Newton, T., Anant, A., Ziebarth, M., Quinn, J., and Wessel, P.: PyGMT: A Python interface for the Generic Mapping Tools, Zenodo [code], https://doi.org/10.5281/zenodo.10578540, 2024. a
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016. a
Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V., and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness, Biogeosciences, 16, 255–274, https://doi.org/10.5194/bg-16-255-2019, 2019. a, b, c
Vekuri, H., Tuovinen, J.-P., Kulmala, L., Papale, D., Kolari, P., Aurela, M., Laurila, T., Liski, J., and Lohila, A.: A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates, Sci. Rep.-UK, 13, 1720, https://doi.org/10.1038/s41598-023-28827-2, 2023. a
Virkkala, A.-M., Aalto, J., Rogers, B. M., Tagesson, T., Treat, C. C., Natali, S. M., Watts, J. D., Potter, S., Lehtonen, A., Mauritz, M., Schuur, E. A. G., Kochendorfer, J., Zona, D., Oechel, W., Kobayashi, H., Humphreys, E., Goeckede, M., Iwata, H., Lafleur, P. M., Euskirchen, E. S., Bokhorst, S., Marushchak, M., Martikainen, P. J., Elberling, B., Voigt, C., Biasi, C., Sonnentag, O., Parmentier, F.-J. W., Ueyama, M., Celis, G., St.Louis, V. L., Emmerton, C. A., Peichl, M., Chi, J., Järveoja, J., Nilsson, M. B., Oberbauer, S. F., Torn, M. S., Park, S.-J., Dolman, H., Mammarella, I., Chae, N., Poyatos, R., López-Blanco, E., Christensen, T. R., Kwon, M. J., Sachs, T., Holl, D., and Luoto, M.: Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties, Glob. Change Biol., 27, 4040–4059, https://doi.org/10.1111/gcb.15659, 2021. a
Virkkala, A.-M., Natali, S. M., Rogers, B. M., Watts, J. D., Savage, K., Connon, S. J., Mauritz, M., Schuur, E. A. G., Peter, D., Minions, C., Nojeim, J., Commane, R., Emmerton, C. A., Goeckede, M., Helbig, M., Holl, D., Iwata, H., Kobayashi, H., Kolari, P., López-Blanco, E., Marushchak, M. E., Mastepanov, M., Merbold, L., Parmentier, F.-J. W., Peichl, M., Sachs, T., Sonnentag, O., Ueyama, M., Voigt, C., Aurela, M., Boike, J., Celis, G., Chae, N., Christensen, T. R., Bret-Harte, M. S., Dengel, S., Dolman, H., Edgar, C. W., Elberling, B., Euskirchen, E., Grelle, A., Hatakka, J., Humphreys, E., Järveoja, J., Kotani, A., Kutzbach, L., Laurila, T., Lohila, A., Mammarella, I., Matsuura, Y., Meyer, G., Nilsson, M. B., Oberbauer, S. F., Park, S.-J., Petrov, R., Prokushkin, A. S., Schulze, C., St. Louis, V. L., Tuittila, E.-S., Tuovinen, J.-P., Quinton, W., Varlagin, A., Zona, D., and Zyryanov, V. I.: The ABCflux database: Arctic–boreal CO2 flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems, Earth Syst. Sci. Data, 14, 179–208, https://doi.org/10.5194/essd-14-179-2022, 2022. a
Virtanen, T. and Ek, M.: The fragmented nature of tundra landscape, Int. J. Appl. Earth Obs., 27, 4–12, https://doi.org/10.1016/j.jag.2013.05.010, 2014. a
Wang, Y. P., Baldocchi, D., Leuning, R., Falge, E., and Vesala, T.: Estimating parameters in a land-surface model by applying nonlinear inversion to eddy covariance flux measurements from eight FLUXNET sites, Glob. Change Biol., 13, 652–670, https://doi.org/10.1111/j.1365-2486.2006.01225.x, 2007. a
Watts, J., Natali, S. M., Minions, C., Risk, D., Arndt, K. A., Zona, D., Euskirchen, E. S., Rocha, A. V., Sonnentag, O., Helbig, M., Kalhori, A., Oechel, W. C., Ikawa, H., Ueyama, M., Suzuki, R., Kobayashi, H., Celis, G., Schuur, E. A., Humphreys, E. R., Kim, Y., Lee, B.-Y., Goetz, S. J., Madani, N., Schiferl, L., Commane, R., Kimball, J. S., Liu, Z., Torn, M. S., Potter, S., Wang, J. A., Jorgenson, T., Xiao, J., Li, X., and Edgar, C.: Soil respiration strongly offsets carbon uptake in Alaska and Northwest Canada, Environ. Res. Lett., 16, 084051, https://doi.org/10.1088/1748-9326/ac1222, 2021. a
Watts, J. D., Farina, M., Kimball, J. S., Schiferl, L. D., Liu, Z., Arndt, K. A., Zona, D., Ballantyne, A., Euskirchen, E. S., Parmentier, F.-J. W., Helbig, M., Sonnentag, O., Tagesson, T., Rinne, J., Ikawa, H., Ueyama, M., Kobayashi, H., Sachs, T., Nadeau, D. F., Kochendorfer, J., Jackowicz-Korczynski, M., Virkkala, A., Aurela, M., Commane, R., Byrne, B., Birch, L., Johnson, M. S., Madani, N., Rogers, B., Du, J., Endsley, A., Savage, K., Poulter, B., Zhang, Z., Bruhwiler, L. M., Miller, C. E., Goetz, S., and Oechel, W. C.: Carbon uptake in Eurasian boreal forests dominates the high-latitude net ecosystem carbon budget, Glob. Change Biol., 29, 1870–1889, https://doi.org/10.1111/gcb.16553, 2023. a
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of flux measurements for density effects due to heat and water vapour transfer, Q. J. Roy. Meteorol. Soc., 106, 85–100, https://doi.org/10.1002/qj.49710644707, 1980. a
Wessel, P., Luis, J. F., Uieda, L., Scharroo, R., Wobbe, F., Smith, W. H. F., and Tian, D.: The generic mapping tools version 6, Geochem. Geophys. Geosy., 20, 5556–5564, https://doi.org/10.1029/2019gc008515, 2019. a
Williams, M., Street, L. E., Van Wijk, M. T., and Shaver, G. R.: Identifying differences in carbon exchange among arctic ecosystem types, Ecosystems, 9, 288–304, https://doi.org/10.1007/s10021-005-0146-y, 2006. a
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009. a, b
Wutzler, T., Lucas-Moffat, A., Migliavacca, M., Knauer, J., Sickel, K., Šigut, L., Menzer, O., and Reichstein, M.: Basic and extensible post-processing of eddy covariance flux data with REddyProc, Biogeosciences, 15, 5015–5030, https://doi.org/10.5194/bg-15-5015-2018, 2018. a, b, c
Xu, K., Metzger, S., and Desai, A. R.: Upscaling tower-observed turbulent exchange at fine spatio-temporal resolution using environmental response functions, Agr. Forest Meteorol., 232, 10–22, https://doi.org/10.1016/j.agrformet.2016.07.019, 2017. a
Zolkos, S., MacDonald, E., Hung, J. K. Y., Schade, J. D., Ludwig, S., Mann, P. J., Treharne, R., and Natali, S.: Physiographic Controls and Wildfire Effects on Aquatic Biogeochemistry in Tundra of the Yukon–Kuskokwim Delta, Alaska, J. Geophys. Res.-Biogeo., 127, e2022JG006891, https://doi.org/10.1029/2022JG006891, 2022. a, b
Short summary
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.
Landscapes are often assumed to be homogeneous when using eddy covariance fluxes, which can lead...
Altmetrics
Final-revised paper
Preprint