Articles | Volume 19, issue 2
https://doi.org/10.5194/gmd-19-867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-867-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
26 Jan 2026
Model experiment description paper |
| 26 Jan 2026
| 26 Jan 2026
Implementing riverine biogeochemical inputs in ECCO-Darwin: a sensitivity analysis of terrestrial fluxes in a data-assimilative global ocean biogeochemistry model
Raphaël Savelli
CORRESPONDING AUTHOR
Moss Landing Marine Laboratories, San José State University, Moss Landing, CA, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Dustin Carroll
Moss Landing Marine Laboratories, San José State University, Moss Landing, CA, USA
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Dimitris Menemenlis
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Jonathan M. Lauderdale
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Clément Bertin
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Stephanie Dutkiewicz
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Center for Global Change Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
Manfredi Manizza
Geosciences Research Division, Scripps Institution of Oceanography, University of California – San Diego, La Jolla, CA, USA
now at: National Institute of Oceanography and Applied Geophysics – OGS, Trieste, Italy
A. Anthony Bloom
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Karel Castro-Morales
Deutscher Wetterdienst, Climate and Environment, Offenbach, Germany
Charles E. Miller
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Marc Simard
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Kevin W. Bowman
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Hong Zhang
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Related authors
Hinne F. van der Zant, Olivier Sulpis, Jack J. Middelburg, Matthew P. Humphreys, Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Kay Sušelj, and Vincent Le Fouest
Geosci. Model Dev., 19, 1965–1989, https://doi.org/10.5194/gmd-19-1965-2026, https://doi.org/10.5194/gmd-19-1965-2026, 2026
Short summary
Short summary
We developed a model to simulate seafloor biogeochemical processes across a wide range of marine environments, from shallow coastal zones to deep-sea sediments. From this model, we derived a set of simple equations that predict how carbon, oxygen, and alkalinity are exchanged between sediments and overlying waters. These equations provide an efficient way to improve how ocean models represent seafloor interactions, which are often missing or overly simplified.
Hinne F. van der Zant, Olivier Sulpis, Jack J. Middelburg, Matthew P. Humphreys, Raphaël Savelli, Dustin Carroll, Dimitris Menemenlis, Kay Sušelj, and Vincent Le Fouest
Geosci. Model Dev., 19, 1965–1989, https://doi.org/10.5194/gmd-19-1965-2026, https://doi.org/10.5194/gmd-19-1965-2026, 2026
Short summary
Short summary
We developed a model to simulate seafloor biogeochemical processes across a wide range of marine environments, from shallow coastal zones to deep-sea sediments. From this model, we derived a set of simple equations that predict how carbon, oxygen, and alkalinity are exchanged between sediments and overlying waters. These equations provide an efficient way to improve how ocean models represent seafloor interactions, which are often missing or overly simplified.
John Worden, Sudhanshu Pandey, Hannah Nesser, Kevin Bowman, Colin Harkins, Congmeng Lyu, Joannes D. Maasakkers, Deborah Gordon, Daniel Jacob, Lucas Estrada, Daniel J. Varon, James D. East, Lauren Schmeisser, and Zhen Qu
EGUsphere, https://doi.org/10.5194/egusphere-2026-313, https://doi.org/10.5194/egusphere-2026-313, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
To support methane reduction, we compared three widely used maps of methane emissions in the United States with estimates derived from a methane-measuring satellite from 2012 to 2020. The satellite indicates higher emissions from oil and gas production and from livestock in several regions, while other sources were harder to pin down. Neither approach shows a clear change over time. The results point to where better measurements can most improve emissions reporting and guide mitigation.
Lucas A. Estrada, Daniel J. Jacob, Megan He, James D. East, Daniel J. Varon, Nicholas Balasus, Sarah E. Hancock, Melissa Sulprizio, Kevin W. Bowman, John R. Worden, Emily Reidy, and Benjamin R. K. Runkle
EGUsphere, https://doi.org/10.5194/egusphere-2026-655, https://doi.org/10.5194/egusphere-2026-655, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We use satellite observations to track U.S. methane emissions from 2019–2024 at national, state, and oil and gas field scales. While total emissions remain stable, oil and gas emissions decline even as production rises, indicating progress in emissions management for the oil and gas sector.
Pascal Matte, Xavier Chartrand, David Purnell, Marc Simard, Alexandra Christensen, Francis Fortin-Legault, Jonathan Morin, Yann Côté-Nadeau, Élaine Desrosiers, Anthony Ouellet, Cédric Chavanne, François Anctil, Gabriela Siles, Scott Bedart, Silvia Innocenti, Mohammed Dabboor, Daniel L. Peters, and Mélanie Trudel
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-30, https://doi.org/10.5194/essd-2026-30, 2026
Preprint under review for ESSD
Short summary
Short summary
We present a new public collection of water level and wave measurements from the St. Lawrence Estuary and Saguenay Fjord in Canada, gathered using state-of-the-art instruments and the largest network ever deployed in this region. Collected to assess the accuracy of the Surface Water and Ocean Topography satellite, these data will help scientists, decision-makers, and communities better study floods and coastal changes in estuaries, especially as the climate shifts and water levels rise.
Li-Qing Jiang, Amanda Fay, Jens Daniel Müller, Luke Gregor, Alizée Roobaert, Lydia Keppler, Dustin Carroll, Siv K. Lauvset, Tim DeVries, Judith Hauck, Christian Rödenbeck, Nicolas Metzl, Andrea J. Fassbender, Jean-Pierre Gattuso, Peter Landschützer, Rik Wanninkhof, Christopher Sabine, Simone R. Alin, Mario Hoppema, Are Olsen, Matthew P. Humphreys, Kunal Chakraborty, Ana C. Franco, Kumiko Azetsu-Scott, Dorothee C. E. Bakker, Leticia Barbero, Nicholas R. Bates, Nicole Besemer, Henry C. Bittig, Albert E. Boyd, Daniel Broullón, Wei-Jun Cai, Brendan R. Carter, Thi-Tuyet-Trang Chau, Chen-Tung Arthur Chen, Frédéric Cyr, John E. Dore, Ian Enochs, Richard A. Feely, Hernan E. Garcia, Marion Gehlen, Prasanna Kanti Ghoshal, Lucas Gloege, Melchor González-Dávila, Nicolas Gruber, Debby Ianson, Yosuke Iida, Masao Ishii, Apurva Padamnabh Joshi, Esther Kennedy, Alex Kozyr, Nico Lange, Claire Lo Monaco, Derek P. Manzello, Galen A. McKinley, Natalie M. Monacci, Xose A. Padin, Ana M. Palacio-Castro, Fiz F. Pérez, J. Magdalena Santana-Casiano, Jonathan Sharp, Adrienne Sutton, Jim Swift, Toste Tanhua, Maciej Telszewski, Jens Terhaar, Ruben van Hooidonk, Anton Velo, Andrew J. Watson, Angelicque E. White, Zelun Wu, Liang Xue, Hyelim Yoo, Jiye Zeng, and Guorong Zhong
Earth Syst. Sci. Data, 18, 1405–1462, https://doi.org/10.5194/essd-18-1405-2026, https://doi.org/10.5194/essd-18-1405-2026, 2026
Short summary
Short summary
This review article provides an overview of 68 existing ocean carbonate chemistry data products and data product sets, encompassing a broad range of types, including compilations of cruise datasets, gap-filled observational products, model simulations, and more. It is designed to help researchers identify and access the data products that best support their scientific objectives, thereby facilitating progress in understanding the ocean's changing carbonate chemistry.
Yoshihiro Nakayama, Shuntaro Hyogo, Yichen Lin, Taewook Park, Jinho Lee, Juistine Caillet, Gobishankar Mohan, Mattia Poinelli, Pierre Dutrieux, Kazuki Nakata, Hong Zhang, Brice Loose, and Lauren Kowalski
EGUsphere, https://doi.org/10.5194/egusphere-2025-5958, https://doi.org/10.5194/egusphere-2025-5958, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We develop a regional ocean model of the Amundsen and Bellingshausen Seas in Antarctica. Differences in model setups and parameter choices often limit usability and broader scientific application, especially for non-ocean modellers. We carefully evaluate the model outputs and establish a common control experiment that can be shared and applied across studies with tracer and particle applications. This effort aims to support wide community use and improve understanding of ice–ocean interactions.
Nicholas Balasus, Daniel J. Jacob, A. Anthony Bloom, James D. East, Lucas A. Estrada, Sarah E. Hancock, Megan He, Todd A. Mooring, Alexander J. Turner, and John R. Worden
EGUsphere, https://doi.org/10.5194/egusphere-2025-6251, https://doi.org/10.5194/egusphere-2025-6251, 2026
Short summary
Short summary
We use satellite observations of methane to determine the amount of methane coming from Africa and relate this to the physical processes responsible, with relevance for climate change given methane's potency as a greenhouse gas. We find that the amount of methane coming from Africa has increased by a third across 2019–2024, driven primarily by steady increases in emissions from livestock, in addition to irregular surges from wetlands.
Abdullah A. Fahad, Andrea Molod, Krzysztof Wargan, Dimitris Menemenlis, Patrick Heimbach, Atanas Trayanov, Ehud Strobach, and Lawrence Coy
Atmos. Chem. Phys., 26, 647–663, https://doi.org/10.5194/acp-26-647-2026, https://doi.org/10.5194/acp-26-647-2026, 2026
Short summary
Short summary
This study used reanalysis datasets and a 1-degree coupled General Circulation Model to analyze the Northern Hemisphere stratospheric temperature response in a decadal simulation. Results show that the polar stratospheric temperature increased from 1992 to 2000, contrary to the expectation of stratospheric cooling due to rising CO2. The study concluded that changes in ozone and CO2 drive the meridional eddy heat transport, dictating polar stratospheric temperature behavior.
Jean-Francois Lamarque, Pierre Friedlingstein, Brian Osias, Steve Strongin, Venkatramani Balaji, Kevin W. Bowman, Josep G. Canadell, Philippe Ciais, Heidi Cullen, Kenneth J. Davis, Scott C. Doney, Kevin R. Gurney, Alicia R. Karspeck, Charles D. Koven, Galen McKinley, Glen P. Peters, Julia Pongratz, Britt Stephens, and Colm Sweeney
EGUsphere, https://doi.org/10.5194/egusphere-2025-6457, https://doi.org/10.5194/egusphere-2025-6457, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
This Perspective highlights requirements to scale the carbon credit market and enable the growth in climate solutions funded through such market. The requirements are on the understanding of the value of the proposed carbon credit projects, and on the availability of a verification system. This verification becomes particularly relevant as the carbon credit market scales to significant impacts on CO2 (or other greenhouse gases), such that attribution to collective actions can be identified.
Jozef Skákala, Shubha Sathyendranath, Yuri Artioli, Deep S. Banerjee, Heather Bouman, Robert J. W. Brewin, Momme Butenschön, Stefano Ciavatta, Stephanie Dutkiewicz, Yanna Fidai, David Ford, Grinson George, Karen Guihou, Bror Jönsson, Marija Bačeković Koloper, Žarko Kovač, Lekshmi Krishnakumary, Gemma Kulk, Charlotte Laufkötter, Gennadi Lessin, Jann Paul Mattern, Angélique Melet, Alexandre Mignot, David Moffat, Fanny Monteiro, Mayra Rodriguez Bennadji, Cécile Rousseaux, Ranjini Swaminathan, Osvaldo Ulloa, and Jerry Tjiputra
EGUsphere, https://doi.org/10.5194/egusphere-2025-6256, https://doi.org/10.5194/egusphere-2025-6256, 2025
Short summary
Short summary
Marine primary production (PP) is a key component of the Earth's climate system, but its current estimates and future projections are highly uncertain. We review the PP uncertainties and discuss their sources both across the ecosystem and satellite models. We propose to reduce the PP uncertainties by better addressing the PP model structures and parametrizations. We also argue that for many models it is desirable to consider spatial and temporal variability in the model parameter values.
Yuming Jin, Britton B. Stephens, Eric J. Morgan, Manfredi Manizza, and Jonathan Sharp
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-738, https://doi.org/10.5194/essd-2025-738, 2025
Preprint under review for ESSD
Short summary
Short summary
We produce monthly air-sea O2 flux estimates (2004–2024) on a 1°×1° grid using machine learning-based dissolved oxygen fields (GOBAI-O2, Sharp et al., 2023). Fluxes are calculated with multiple gas exchange schemes and wind products, then constrained to match independent regional to global annual mean fluxes estimates. We evaluate flux seasonal cycles and annual patterns against atmospheric O2 observations and quantify flux uncertainties from multiple sources.
Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblueh, Clarissa A. Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
Geosci. Model Dev., 18, 9385–9415, https://doi.org/10.5194/gmd-18-9385-2025, https://doi.org/10.5194/gmd-18-9385-2025, 2025
Short summary
Short summary
We provide a new Earth System model configuration framed into the ICON architecture, which provides the baseline for the next generation of climate predictions and projections (hereafter ICON XPP). Two resolutions of ICON XPP are presented that show high runtime performances making it suitable to run long integrations and large-ensemble experiments. ICON XPP similarly perform to CMIP6-class of climate models making it a good basis for climate forecasts and projections, and climate research.
Hannah Nesser, Kevin W. Bowman, Matthew D. Thill, Daniel J. Varon, Cynthia A. Randles, Ashutosh Tewari, Felipe J. Cardoso-Saldaña, Emily Reidy, Joannes D. Maasakkers, and Daniel J. Jacob
Geosci. Model Dev., 18, 9279–9291, https://doi.org/10.5194/gmd-18-9279-2025, https://doi.org/10.5194/gmd-18-9279-2025, 2025
Short summary
Short summary
Regional analyses of atmospheric trace gases can improve knowledge of fluxes at high resolution but rely on specified boundary conditions (BCs) at the domain edges. Biases in the often-uncertain BCs propagate to the inferred fluxes. We develop a framework to explain how errors in the BCs influence the optimized fluxes, derive two metrics to estimate this influence, and compare two methods to correct for the biases. We demonstrate correcting BCs directly is more effective at reducing bias.
Clément Bertin, Vincent Le Fouest, Dustin Carroll, Stephanie Dutkiewicz, Dimitris Menemenlis, Atsushi Matsuoka, Manfredi Manizza, and Charles E. Miller
Biogeosciences, 22, 6607–6629, https://doi.org/10.5194/bg-22-6607-2025, https://doi.org/10.5194/bg-22-6607-2025, 2025
Short summary
Short summary
We adjusted a model of the Mackenzie River region to account for the riverine export of organic matter that affects light in the water. We show that such export causes a delay in the phytoplankton growth by two weeks and raises the water surface temperature by 1.7 °C. We found that temperature increase turns this coastal region from a sink of carbon dioxide to an emitter. Our findings suggest that rising exports of organic matter can significantly affect the carbon cycle in Arctic coastal areas.
Gavin A. Schmidt, Kenneth D. Mankoff, Jonathan L. Bamber, Clara Burgard, Dustin Carroll, David M. Chandler, Violaine Coulon, Benjamin J. Davison, Matthew H. England, Paul R. Holland, Nicolas C. Jourdain, Qian Li, Juliana M. Marson, Pierre Mathiot, Clive R. McMahon, Twila A. Moon, Ruth Mottram, Sophie Nowicki, Anna Olivé Abelló, Andrew G. Pauling, Thomas Rackow, and Damien Ringeisen
Geosci. Model Dev., 18, 8333–8361, https://doi.org/10.5194/gmd-18-8333-2025, https://doi.org/10.5194/gmd-18-8333-2025, 2025
Short summary
Short summary
The impact of increasing mass loss from the Greenland and Antarctic ice sheets has not so far been included in historical climate model simulations. This paper describes the protocols and data available for modeling groups to add this anomalous freshwater to their ocean modules to better represent the impacts of these fluxes on ocean circulation, sea ice, salinity and sea level.
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.
Frank Werner, Kevin W. Bowman, Seungwon Lee, Joshua L. Laughner, Vivienne H. Payne, and James L. McDuffie
EGUsphere, https://doi.org/10.5194/egusphere-2025-4864, https://doi.org/10.5194/egusphere-2025-4864, 2025
Short summary
Short summary
We developed a hybrid machine learning-optimal estimation retrieval system that efficiently and accurately mimics operational retrieval results. Crucially, this algorithm also predicts critical diagnostic variables including observation operators needed for comparison with independent data and ingestion into downstream chemical data assimilation models.
Anne Boynard, Catherine Wespes, Juliette Hadji-Lazaro, Selviga Sinnathamby, Daniel Hurtmans, Pierre-François Coheur, Marie Doutriaux-Boucher, Jacobus Onderwaater, Wolfgang Steinbrecht, Elyse A. Pennington, Kevin Bowman, and Cathy Clerbaux
Atmos. Chem. Phys., 25, 11719–11755, https://doi.org/10.5194/acp-25-11719-2025, https://doi.org/10.5194/acp-25-11719-2025, 2025
Short summary
Short summary
This study analyzes 16 years of global ozone data to assess its impact on air quality and climate. Using satellite measurements, we observed a global decrease in tropospheric ozone, particularly in tropical and European regions. The drop became more noticeable around 2020, partly due to reduced emissions during the pandemic. The study highlights the need for better data processing and integration to more accurately monitor ozone changes over time.
Michael Dominik Tyka, Mengyang Zhou, Elizabeth Yankovsky, and Dustin Carroll
EGUsphere, https://doi.org/10.5194/egusphere-2025-3713, https://doi.org/10.5194/egusphere-2025-3713, 2025
Short summary
Short summary
Quantification of the kinetics of the induced ocean CO2 uptake following application of marine carbon dioxide removal technologies (mCDR) is crucial for such technologies to gain scientific and social acceptance. Here, we compare two circulation models commonly used for this purpose and find substantial differences in their predictions. We analyze which physical aspects of the models contribute the most to the inter-model discrepancies, and thus require future research.
Aman KC, Ellyn M. Enderlin, Dominik Fahrner, Twila Moon, and Dustin Carroll
The Cryosphere, 19, 3089–3106, https://doi.org/10.5194/tc-19-3089-2025, https://doi.org/10.5194/tc-19-3089-2025, 2025
Short summary
Short summary
The sum of ice flowing towards a glacier’s terminus and changes in the position of the terminus over time collectively makes up terminus ablation. We found that terminus ablation has more seasonal variability than previously concluded from flux-based estimates of ice discharge. The findings are of importance in understanding the timing and location of the freshwater input to the fjords and surrounding ocean basins affecting local and regional ecosystems and ocean properties.
Elyse A. Pennington, Gregory B. Osterman, Vivienne H. Payne, Kazuyuki Miyazaki, Kevin W. Bowman, and Jessica L. Neu
Atmos. Chem. Phys., 25, 8533–8552, https://doi.org/10.5194/acp-25-8533-2025, https://doi.org/10.5194/acp-25-8533-2025, 2025
Short summary
Short summary
Tropospheric ozone is a harmful pollutant and powerful greenhouse gas. For satellite products to accurately quantify trends in tropospheric ozone, they must have a low bias compared to a reliable source of data. This study compares three NASA satellite products to ozonesonde data. They have low global measurement bias and thus can be used to detect global tropospheric ozone trends, but the measurement bias should be considered in certain regions and time periods.
Kazuyuki Miyazaki, Yuliya Marchetti, James Montgomery, Steven Lu, and Kevin Bowman
Atmos. Chem. Phys., 25, 8507–8532, https://doi.org/10.5194/acp-25-8507-2025, https://doi.org/10.5194/acp-25-8507-2025, 2025
Short summary
Short summary
This study employs explainable machine learning to analyze the causes of significant biases in surface ozone estimates from chemical reanalysis. By analyzing global observations and chemical reanalysis outputs, key bias drivers, such as meteorological conditions and precursor emissions, were identified. This provides actionable insights to improve chemical transport models, observation systems, and emissions inventories, ultimately enhancing ozone reanalysis for better air pollution management.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
Short summary
Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Edward Malina, Jure Brence, Jennifer Adams, Jovan Tanevski, Sašo Džeroski, Valentin Kantchev, and Kevin W. Bowman
Atmos. Meas. Tech., 18, 1689–1715, https://doi.org/10.5194/amt-18-1689-2025, https://doi.org/10.5194/amt-18-1689-2025, 2025
Short summary
Short summary
The large fleet of Earth observation satellites in orbit currently generate huge volumes of data, requiring significant computational resources to process these data in a timely manner. We present a method for predicting poor-quality measurements using machine learning. We find that machine learning methods can accurately predict poor-quality measurements and remove them from the processing chain, saving time and computational resources.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
Short summary
Short summary
We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
Jana Krause, Dustin Carroll, Juan Höfer, Jeremy Donaire, Eric P. Achterberg, Emilio Alarcón, Te Liu, Lorenz Meire, Kechen Zhu, and Mark J. Hopwood
The Cryosphere, 18, 5735–5752, https://doi.org/10.5194/tc-18-5735-2024, https://doi.org/10.5194/tc-18-5735-2024, 2024
Short summary
Short summary
Here we analysed calved ice samples from both the Arctic and Antarctic to assess the variability in the composition of iceberg meltwater. Our results suggest that low concentrations of nitrate and phosphate in ice are primarily from the ice matrix, whereas sediment-rich layers impart a low concentration of silica and modest concentrations of iron and manganese. At a global scale, there are very limited differences in the nutrient composition of ice.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Yafei Nie, Ian Fenty, Matthew Mazloff, Armin Köhl, and Dimitris Menemenlis
Geosci. Model Dev., 17, 8613–8638, https://doi.org/10.5194/gmd-17-8613-2024, https://doi.org/10.5194/gmd-17-8613-2024, 2024
Short summary
Short summary
Global- and basin-scale ocean reanalyses are becoming easily accessible. However, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluation. We conduct intercomparison analyses of Massachusetts Institute of Technology General Circulation Model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open-ocean temporal variability and Antarctic continental shelves require improvements.
Edward Malina, Kevin W. Bowman, Valentin Kantchev, Le Kuai, Thomas P. Kurosu, Kazuyuki Miyazaki, Vijay Natraj, Gregory B. Osterman, Fabiano Oyafuso, and Matthew D. Thill
Atmos. Meas. Tech., 17, 5341–5371, https://doi.org/10.5194/amt-17-5341-2024, https://doi.org/10.5194/amt-17-5341-2024, 2024
Short summary
Short summary
Characterizing the distribution of ozone in the atmosphere is a challenging problem, with current Earth observation satellites using either thermal infrared (TIR) or ultraviolet (UV) instruments, sensitive to different portions of the atmosphere, making it difficult to gain a full picture. In this work, we combine measurements from the TIR and UV instruments Suomi NPP CrIS and Sentinel-5P/TROPOMI to improve sensitivity through the whole atmosphere and improve knowledge of ozone distribution.
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.
Charles E. Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy J. Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data, 16, 2605–2624, https://doi.org/10.5194/essd-16-2605-2024, https://doi.org/10.5194/essd-16-2605-2024, 2024
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 120 000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. This paper summarizes those results and provides links to details on ~ 80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Sandra Raab, Karel Castro-Morales, Anke Hildebrandt, Martin Heimann, Jorien Elisabeth Vonk, Nikita Zimov, and Mathias Goeckede
Biogeosciences, 21, 2571–2597, https://doi.org/10.5194/bg-21-2571-2024, https://doi.org/10.5194/bg-21-2571-2024, 2024
Short summary
Short summary
Water status is an important control factor on sustainability of Arctic permafrost soils, including production and transport of carbon. We compared a drained permafrost ecosystem with a natural control area, investigating water levels, thaw depths, and lateral water flows. We found that shifts in water levels following drainage affected soil water availability and that lateral transport patterns were of major relevance. Understanding these shifts is crucial for future carbon budget studies.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, https://doi.org/10.5194/acp-24-5069-2024, 2024
Short summary
Short summary
We quantify 2019 methane emissions in the contiguous US (CONUS) at a ≈ 25 km × 25 km resolution using satellite methane observations. We find a 13 % upward correction to the 2023 US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) for 2019, with large corrections to individual states, urban areas, and landfills. This may present a challenge for US climate policies and goals, many of which target significant reductions in methane emissions.
Luca Cortese, Carmine Donatelli, Xiaohe Zhang, Justin A. Nghiem, Marc Simard, Cathleen E. Jones, Michael Denbina, Cédric G. Fichot, Joshua P. Harringmeyer, and Sergio Fagherazzi
Biogeosciences, 21, 241–260, https://doi.org/10.5194/bg-21-241-2024, https://doi.org/10.5194/bg-21-241-2024, 2024
Short summary
Short summary
This study shows that numerical models in coastal areas can greatly benefit from the spatial information provided by remote sensing. Three Delft3D numerical models in coastal Louisiana are calibrated using airborne SAR and hyperspectral remote sensing products from the recent NASA Delta-X mission. The comparison with the remote sensing allows areas where the models perform better to be spatially verified and yields more representative parameters for the entire area.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
Short summary
Short summary
This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Daniel H. Cusworth, Andrew K. Thorpe, Charles E. Miller, Alana K. Ayasse, Ralph Jiorle, Riley M. Duren, Ray Nassar, Jon-Paul Mastrogiacomo, and Robert R. Nelson
Atmos. Chem. Phys., 23, 14577–14591, https://doi.org/10.5194/acp-23-14577-2023, https://doi.org/10.5194/acp-23-14577-2023, 2023
Short summary
Short summary
Carbon dioxide (CO2) emissions from combustion sources are uncertain in many places across the globe. Satellites have the ability to detect and quantify emissions from large CO2 point sources, including coal-fired power plants. In this study, we tasked two satellites to routinely observe CO2 emissions at 30 coal-fired power plants between 2021 and 2022. These results present the largest dataset of space-based CO2 emission estimates to date.
Jinsol Kim, John B. Miller, Charles E. Miller, Scott J. Lehman, Sylvia E. Michel, Vineet Yadav, Nick E. Rollins, and William M. Berelson
Atmos. Chem. Phys., 23, 14425–14436, https://doi.org/10.5194/acp-23-14425-2023, https://doi.org/10.5194/acp-23-14425-2023, 2023
Short summary
Short summary
In this study, we present the partitioning of CO2 signals from biogenic, petroleum and natural gas sources by combining CO, 13CO2 and 14CO2 measurements. Using measurements from flask air samples at three sites in the greater Los Angeles region, we find larger and positive contributions of biogenic signals in winter and smaller and negative contributions in summer. The largest contribution of natural gas combustion generally occurs in summer.
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023, https://doi.org/10.5194/gmd-16-5219-2023, 2023
Short summary
Short summary
Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael L. Forget, Christian Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, and Shubha Sathyendranath
Geosci. Model Dev., 16, 4639–4657, https://doi.org/10.5194/gmd-16-4639-2023, https://doi.org/10.5194/gmd-16-4639-2023, 2023
Short summary
Short summary
While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
Short summary
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Xueying Yu, Dylan B. Millet, Daven K. Henze, Alexander J. Turner, Alba Lorente Delgado, A. Anthony Bloom, and Jianxiong Sheng
Atmos. Chem. Phys., 23, 3325–3346, https://doi.org/10.5194/acp-23-3325-2023, https://doi.org/10.5194/acp-23-3325-2023, 2023
Short summary
Short summary
We combine satellite measurements with a novel downscaling method to map global methane emissions at 0.1°×0.1° resolution. These fine-scale emission estimates reveal unreported emission hotspots and shed light on the roles of agriculture, wetlands, and fossil fuels for regional methane budgets. The satellite-derived emissions point in particular to missing fossil fuel emissions in the Middle East and to a large emission underestimate in South Asia that appears to be tied to monsoon rainfall.
Brendan Byrne, David F. Baker, Sourish Basu, Michael Bertolacci, Kevin W. Bowman, Dustin Carroll, Abhishek Chatterjee, Frédéric Chevallier, Philippe Ciais, Noel Cressie, David Crisp, Sean Crowell, Feng Deng, Zhu Deng, Nicholas M. Deutscher, Manvendra K. Dubey, Sha Feng, Omaira E. García, David W. T. Griffith, Benedikt Herkommer, Lei Hu, Andrew R. Jacobson, Rajesh Janardanan, Sujong Jeong, Matthew S. Johnson, Dylan B. A. Jones, Rigel Kivi, Junjie Liu, Zhiqiang Liu, Shamil Maksyutov, John B. Miller, Scot M. Miller, Isamu Morino, Justus Notholt, Tomohiro Oda, Christopher W. O'Dell, Young-Suk Oh, Hirofumi Ohyama, Prabir K. Patra, Hélène Peiro, Christof Petri, Sajeev Philip, David F. Pollard, Benjamin Poulter, Marine Remaud, Andrew Schuh, Mahesh K. Sha, Kei Shiomi, Kimberly Strong, Colm Sweeney, Yao Té, Hanqin Tian, Voltaire A. Velazco, Mihalis Vrekoussis, Thorsten Warneke, John R. Worden, Debra Wunch, Yuanzhi Yao, Jeongmin Yun, Andrew Zammit-Mangion, and Ning Zeng
Earth Syst. Sci. Data, 15, 963–1004, https://doi.org/10.5194/essd-15-963-2023, https://doi.org/10.5194/essd-15-963-2023, 2023
Short summary
Short summary
Changes in the carbon stocks of terrestrial ecosystems result in emissions and removals of CO2. These can be driven by anthropogenic activities (e.g., deforestation), natural processes (e.g., fires) or in response to rising CO2 (e.g., CO2 fertilization). This paper describes a dataset of CO2 emissions and removals derived from atmospheric CO2 observations. This pilot dataset informs current capabilities and future developments towards top-down monitoring and verification systems.
Robert J. Parker, Chris Wilson, Edward Comyn-Platt, Garry Hayman, Toby R. Marthews, A. Anthony Bloom, Mark F. Lunt, Nicola Gedney, Simon J. Dadson, Joe McNorton, Neil Humpage, Hartmut Boesch, Martyn P. Chipperfield, Paul I. Palmer, and Dai Yamazaki
Biogeosciences, 19, 5779–5805, https://doi.org/10.5194/bg-19-5779-2022, https://doi.org/10.5194/bg-19-5779-2022, 2022
Short summary
Short summary
Wetlands are the largest natural source of methane, one of the most important climate gases. The JULES land surface model simulates these emissions. We use satellite data to evaluate how well JULES reproduces the methane seasonal cycle over different tropical wetlands. It performs well for most regions; however, it struggles for some African wetlands influenced heavily by river flooding. We explain the reasons for these deficiencies and highlight how future development will improve these areas.
Hector S. Torres, Patrice Klein, Jinbo Wang, Alexander Wineteer, Bo Qiu, Andrew F. Thompson, Lionel Renault, Ernesto Rodriguez, Dimitris Menemenlis, Andrea Molod, Christopher N. Hill, Ehud Strobach, Hong Zhang, Mar Flexas, and Dragana Perkovic-Martin
Geosci. Model Dev., 15, 8041–8058, https://doi.org/10.5194/gmd-15-8041-2022, https://doi.org/10.5194/gmd-15-8041-2022, 2022
Short summary
Short summary
Wind work at the air-sea interface is the scalar product of winds and currents and is the transfer of kinetic energy between the ocean and the atmosphere. Using a new global coupled ocean-atmosphere simulation performed at kilometer resolution, we show that all scales of winds and currents impact the ocean dynamics at spatial and temporal scales. The consequential interplay of surface winds and currents in the numerical simulation motivates the need for a winds and currents satellite mission.
Karel Castro-Morales, Anna Canning, Sophie Arzberger, Will A. Overholt, Kirsten Küsel, Olaf Kolle, Mathias Göckede, Nikita Zimov, and Arne Körtzinger
Biogeosciences, 19, 5059–5077, https://doi.org/10.5194/bg-19-5059-2022, https://doi.org/10.5194/bg-19-5059-2022, 2022
Short summary
Short summary
Permafrost thaw releases methane that can be emitted into the atmosphere or transported by Arctic rivers. Methane measurements are lacking in large Arctic river regions. In the Kolyma River (northeast Siberia), we measured dissolved methane to map its distribution with great spatial detail. The river’s edge and river junctions had the highest methane concentrations compared to other river areas. Microbial communities in the river showed that the river’s methane likely is from the adjacent land.
Tai-Long He, Dylan B. A. Jones, Kazuyuki Miyazaki, Kevin W. Bowman, Zhe Jiang, Xiaokang Chen, Rui Li, Yuxiang Zhang, and Kunna Li
Atmos. Chem. Phys., 22, 14059–14074, https://doi.org/10.5194/acp-22-14059-2022, https://doi.org/10.5194/acp-22-14059-2022, 2022
Short summary
Short summary
We use a deep-learning (DL) model to estimate Chinese NOx emissions by combining satellite analysis and in situ measurements. Our results are consistent with conventional analyses of Chinese NOx emissions. Comparison with mobility data shows that the DL model has a better capability to capture changes in NOx. We analyse Chinese NOx emissions during the COVID-19 pandemic lockdown period. Our results illustrate the potential use of DL as a complementary tool for conventional air quality studies.
Broghan M. Erland, Cristen Adams, Andrea Darlington, Mackenzie L. Smith, Andrew K. Thorpe, Gregory R. Wentworth, Steve Conley, John Liggio, Shao-Meng Li, Charles E. Miller, and John A. Gamon
Atmos. Meas. Tech., 15, 5841–5859, https://doi.org/10.5194/amt-15-5841-2022, https://doi.org/10.5194/amt-15-5841-2022, 2022
Short summary
Short summary
Accurately estimating greenhouse gas (GHG) emissions is essential to reaching net-zero goals to combat the climate crisis. Airborne box-flights are ideal for assessing regional GHG emissions, as they can attain small error. We compare two box-flight algorithms and found they produce similar results, but daily variability must be considered when deriving emissions inventories. Increasing the consistency and agreement between airborne methods moves us closer to achieving more accurate estimates.
Brendan Byrne, Junjie Liu, Yonghong Yi, Abhishek Chatterjee, Sourish Basu, Rui Cheng, Russell Doughty, Frédéric Chevallier, Kevin W. Bowman, Nicholas C. Parazoo, David Crisp, Xing Li, Jingfeng Xiao, Stephen Sitch, Bertrand Guenet, Feng Deng, Matthew S. Johnson, Sajeev Philip, Patrick C. McGuire, and Charles E. Miller
Biogeosciences, 19, 4779–4799, https://doi.org/10.5194/bg-19-4779-2022, https://doi.org/10.5194/bg-19-4779-2022, 2022
Short summary
Short summary
Plants draw CO2 from the atmosphere during the growing season, while respiration releases CO2 to the atmosphere throughout the year, driving seasonal variations in atmospheric CO2 that can be observed by satellites, such as the Orbiting Carbon Observatory 2 (OCO-2). Using OCO-2 XCO2 data and space-based constraints on plant growth, we show that permafrost-rich northeast Eurasia has a strong seasonal release of CO2 during the autumn, hinting at an unexpectedly large respiration signal from soils.
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.
Vivienne H. Payne, Susan S. Kulawik, Emily V. Fischer, Jared F. Brewer, L. Gregory Huey, Kazuyuki Miyazaki, John R. Worden, Kevin W. Bowman, Eric J. Hintsa, Fred Moore, James W. Elkins, and Julieta Juncosa Calahorrano
Atmos. Meas. Tech., 15, 3497–3511, https://doi.org/10.5194/amt-15-3497-2022, https://doi.org/10.5194/amt-15-3497-2022, 2022
Short summary
Short summary
We compare new satellite measurements of peroxyacetyl nitrate (PAN) with reference aircraft measurements from two different instruments flown on the same platform. While there is a systematic difference between the two aircraft datasets, both show the same large-scale distribution of PAN and the discrepancy between aircraft datasets is small compared to the satellite uncertainties. The satellite measurements show skill in capturing large-scale variations in PAN.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
Short summary
Short summary
This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
Short summary
Short summary
This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
Short summary
Short summary
The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Olivier Sulpis, Matthew P. Humphreys, Monica M. Wilhelmus, Dustin Carroll, William M. Berelson, Dimitris Menemenlis, Jack J. Middelburg, and Jess F. Adkins
Geosci. Model Dev., 15, 2105–2131, https://doi.org/10.5194/gmd-15-2105-2022, https://doi.org/10.5194/gmd-15-2105-2022, 2022
Short summary
Short summary
A quarter of the surface of the Earth is covered by marine sediments rich in calcium carbonates, and their dissolution acts as a giant antacid tablet protecting the ocean against human-made acidification caused by massive CO2 emissions. Here, we present a new model of sediment chemistry that incorporates the latest experimental findings on calcium carbonate dissolution kinetics. This model can be used to predict how marine sediments evolve through time in response to environmental perturbations.
Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi
Geosci. Model Dev., 15, 1789–1802, https://doi.org/10.5194/gmd-15-1789-2022, https://doi.org/10.5194/gmd-15-1789-2022, 2022
Short summary
Short summary
Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM, we have utilized the data assimilation capability and designed the benchmarking framework by using field observations in modeling. Results show that data assimilation improves model performance in different aspects.
Marion Kersalé, Denis L. Volkov, Kandaga Pujiana, and Hong Zhang
Ocean Sci., 18, 193–212, https://doi.org/10.5194/os-18-193-2022, https://doi.org/10.5194/os-18-193-2022, 2022
Short summary
Short summary
The southern Indian Ocean is one of the major basins for regional heat accumulation and sea level rise. The year-to-year changes of regional sea level are influenced by water exchange with the Pacific Ocean via the Indonesian Throughflow. Using a general circulation model, we show that the spatiotemporal pattern of these changes is primarily set by local wind forcing modulated by El Niño–Southern Oscillation, while oceanic signals originating in the Pacific can amplify locally forced signals.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
Short summary
Short summary
Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold-temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
Short summary
Short summary
We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Zhen Qu, Daniel J. Jacob, Lu Shen, Xiao Lu, Yuzhong Zhang, Tia R. Scarpelli, Hannah Nesser, Melissa P. Sulprizio, Joannes D. Maasakkers, A. Anthony Bloom, John R. Worden, Robert J. Parker, and Alba L. Delgado
Atmos. Chem. Phys., 21, 14159–14175, https://doi.org/10.5194/acp-21-14159-2021, https://doi.org/10.5194/acp-21-14159-2021, 2021
Short summary
Short summary
The recent launch of TROPOMI offers an unprecedented opportunity to quantify the methane budget from a top-down perspective. We use TROPOMI and the more mature GOSAT methane observations to estimate methane emissions and get consistent global budgets. However, TROPOMI shows biases over regions where surface albedo is small and provides less information for the coarse-resolution inversion due to the larger error correlations and spatial variations in the number of observations.
Yi Yin, Frederic Chevallier, Philippe Ciais, Philippe Bousquet, Marielle Saunois, Bo Zheng, John Worden, A. Anthony Bloom, Robert J. Parker, Daniel J. Jacob, Edward J. Dlugokencky, and Christian Frankenberg
Atmos. Chem. Phys., 21, 12631–12647, https://doi.org/10.5194/acp-21-12631-2021, https://doi.org/10.5194/acp-21-12631-2021, 2021
Short summary
Short summary
The growth of methane, the second-most important anthropogenic greenhouse gas after carbon dioxide, has been accelerating in recent years. Using an ensemble of multi-tracer atmospheric inversions constrained by surface or satellite observations, we show that global methane emissions increased by nearly 1 % per year from 2010–2017, with leading contributions from the tropics and East Asia.
Yoshihiro Nakayama, Dimitris Menemenlis, Ou Wang, Hong Zhang, Ian Fenty, and An T. Nguyen
Geosci. Model Dev., 14, 4909–4924, https://doi.org/10.5194/gmd-14-4909-2021, https://doi.org/10.5194/gmd-14-4909-2021, 2021
Short summary
Short summary
High ice shelf melting in the Amundsen Sea has attracted many observational campaigns in the past decade. One method to combine observations with numerical models is the adjoint method. After 20 iterations, the cost function, defined as a sum of the weighted model–data difference, is reduced by 65 % by adjusting initial conditions, atmospheric forcing, and vertical diffusivity. This study demonstrates adjoint-method optimization with explicit representation of ice shelf cavity circulation.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
Short summary
Short summary
This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
Zhe Jiang, Hongrong Shi, Bin Zhao, Yu Gu, Yifang Zhu, Kazuyuki Miyazaki, Xin Lu, Yuqiang Zhang, Kevin W. Bowman, Takashi Sekiya, and Kuo-Nan Liou
Atmos. Chem. Phys., 21, 8693–8708, https://doi.org/10.5194/acp-21-8693-2021, https://doi.org/10.5194/acp-21-8693-2021, 2021
Short summary
Short summary
We use the COVID-19 pandemic as a unique natural experiment to obtain a more robust understanding of the effectiveness of emission reductions toward air quality improvement by combining chemical transport simulations and observations. Our findings imply a shift from current control policies in California: a strengthened control on primary PM2.5 emissions and a well-balanced control on NOx and volatile organic compounds are needed to effectively and sustainably alleviate PM2.5 and O3 pollution.
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.
Caroline A. Famiglietti, T. Luke Smallman, Paul A. Levine, Sophie Flack-Prain, Gregory R. Quetin, Victoria Meyer, Nicholas C. Parazoo, Stephanie G. Stettz, Yan Yang, Damien Bonal, A. Anthony Bloom, Mathew Williams, and Alexandra G. Konings
Biogeosciences, 18, 2727–2754, https://doi.org/10.5194/bg-18-2727-2021, https://doi.org/10.5194/bg-18-2727-2021, 2021
Short summary
Short summary
Model uncertainty dominates the spread in terrestrial carbon cycle predictions. Efforts to reduce it typically involve adding processes, thereby increasing model complexity. However, if and how model performance scales with complexity is unclear. Using a suite of 16 structurally distinct carbon cycle models, we find that increased complexity only improves skill if parameters are adequately informed. Otherwise, it can degrade skill, and an intermediate-complexity model is optimal.
Yang Feng, Dimitris Menemenlis, Huijie Xue, Hong Zhang, Dustin Carroll, Yan Du, and Hui Wu
Geosci. Model Dev., 14, 1801–1819, https://doi.org/10.5194/gmd-14-1801-2021, https://doi.org/10.5194/gmd-14-1801-2021, 2021
Short summary
Short summary
Simulation of coastal plume regions was improved in global ECCOv4 with a series of sensitivity tests. We find modeled SSS is closer to SMAP when using daily point-source runoff as well as increasing the resolution from coarse to intermediate. The plume characteristics, freshwater transport, and critical water properties are modified greatly. But this may not happen with a further increase to high resolution. The study will advance the seamless modeling of land–ocean–atmosphere feedback in ESMs.
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, and Shuang Ma
Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, https://doi.org/10.5194/acp-21-4637-2021, 2021
Short summary
Short summary
We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
Joannes D. Maasakkers, Daniel J. Jacob, Melissa P. Sulprizio, Tia R. Scarpelli, Hannah Nesser, Jianxiong Sheng, Yuzhong Zhang, Xiao Lu, A. Anthony Bloom, Kevin W. Bowman, John R. Worden, and Robert J. Parker
Atmos. Chem. Phys., 21, 4339–4356, https://doi.org/10.5194/acp-21-4339-2021, https://doi.org/10.5194/acp-21-4339-2021, 2021
Short summary
Short summary
We use 2010–2015 GOSAT satellite observations of atmospheric methane over North America in a high-resolution inversion to estimate methane emissions. We find general consistency with the gridded EPA inventory but higher oil and gas production emissions, with oil production emissions twice as large as in the latest EPA Greenhouse Gas Inventory. We find lower wetland emissions than predicted by WetCHARTs and a small increasing trend in the eastern US, apparently related to unconventional oil/gas.
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, https://doi.org/10.5194/acp-21-3643-2021, 2021
Short summary
Short summary
We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical and boreal wetlands and tropical livestock (South Asia, Africa, Brazil), with an insignificant positive trend in emissions from the fossil fuel sector. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
Cited articles
Amiotte Suchet, P., Probst, J.-L., and Ludwig, W.: Worldwide distribution of continental rock lithology: Implications for the atmospheric/soil CO2 uptake by continental weathering and alkalinity river transport to the oceans, Global Biogeochemical Cycles, 17, https://doi.org/10.1029/2002GB0018, 2003. a, b
Andrews, A. E., Kofler, J. D., Trudeau, M. E., Williams, J. C., Neff, D. H., Masarie, K. A., Chao, D. Y., Kitzis, D. R., Novelli, P. C., Zhao, C. L., Dlugokencky, E. J., Lang, P. M., Crotwell, M. J., Fischer, M. L., Parker, M. J., Lee, J. T., Baumann, D. D., Desai, A. R., Stanier, C. O., De Wekker, S. F. J., Wolfe, D. E., Munger, J. W., and Tans, P. P.: CO2, CO, and CH4 measurements from tall towers in the NOAA Earth System Research Laboratory's Global Greenhouse Gas Reference Network: instrumentation, uncertainty analysis, and recommendations for future high-accuracy greenhouse gas monitoring efforts, Atmospheric Measurement Techniques, 7, 647–687, https://doi.org/10.5194/amt-7-647-2014, 2014. a
Araujo, M., Noriega, C., and Lefèvre, N.: Nutrients and carbon fluxes in the estuaries of major rivers flowing into the tropical Atlantic, Frontiers in Marine Science, 1, https://doi.org/10.3389/fmars.2014.00010, 2014. a, b
Ardyna, M. and Arrigo, K. R.: Phytoplankton dynamics in a changing Arctic Ocean, Nature Climate Change, 10, 892–903, 2020. a
Bakker, D. C. E., Pfeil, B., Landa, C. S., Metzl, N., O'Brien, K. M., Olsen, A., Smith, K., Cosca, C., Harasawa, S., Jones, S. D., Nakaoka, S., Nojiri, Y., Schuster, U., Steinhoff, T., Sweeney, C., Takahashi, T., Tilbrook, B., Wada, C., Wanninkhof, R., Alin, S. R., Balestrini, C. F., Barbero, L., Bates, N. R., Bianchi, A. A., Bonou, F., Boutin, J., Bozec, Y., Burger, E. F., Cai, W.-J., Castle, R. D., Chen, L., Chierici, M., Currie, K., Evans, W., Featherstone, C., Feely, R. A., Fransson, A., Goyet, C., Greenwood, N., Gregor, L., Hankin, S., Hardman-Mountford, N. J., Harlay, J., Hauck, J., Hoppema, M., Humphreys, M. P., Hunt, C. W., Huss, B., Ibánhez, J. S. P., Johannessen, T., Keeling, R., Kitidis, V., Körtzinger, A., Kozyr, A., Krasakopoulou, E., Kuwata, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lo Monaco, C., Manke, A., Mathis, J. T., Merlivat, L., Millero, F. J., Monteiro, P. M. S., Munro, D. R., Murata, A., Newberger, T., Omar, A. M., Ono, T., Paterson, K., Pearce, D., Pierrot, D., Robbins, L. L., Saito, S., Salisbury, J., Schlitzer, R., Schneider, B., Schweitzer, R., Sieger, R., Skjelvan, I., Sullivan, K. F., Sutherland, S. C., Sutton, A. J., Tadokoro, K., Telszewski, M., Tuma, M., van Heuven, S. M. A. C., Vandemark, D., Ward, B., Watson, A. J., and Xu, S.: A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT), Earth Syst. Sci. Data, 8, 383–413, https://doi.org/10.5194/essd-8-383-2016, 2016. a, b, c
Battin, T. J., Lauerwald, R., Bernhardt, E. S., Bertuzzo, E., Gener, L. G., Hall, R. O., Jr, Hotchkiss, E. R., Maavara, T., Pavelsky, T. M., Ran, L., Raymond, P., Rosentreter, J. A., and Regnier, P.: River ecosystem metabolism and carbon biogeochemistry in a changing world. Nature, 613, 449–459, https://doi.org/10.1038/s41586-022-05500-8, 2023. a, b
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnology and Oceanography, 42, 1–20, https://doi.org/10.4319/lo.1997.42.1.0001, 1997. a
Behrenfeld, M. J., Boss, E., Siegel, D. A., and Shea, D. M.: Carbon-based ocean productivity and phytoplankton physiology from space, Global Biogeochemical Cycles, 19, https://doi.org/10.1029/2004GB002299, 2005. a
Bendtsen, J., Vives, C. R., and Richardson, K.: Primary production in the North Atlantic estimated from in situ water column data observed by Argo floats and remote sensing, Frontiers in Marine Science, 10, 1062413, https://doi.org/10.3389/fmars.2023.1062413, 2023. a
Bertin, C., Carroll, D., Menemenlis, D., Dutkiewicz, S., Zhang, H., Matsuoka, A., Tank, S., Manizza, M., Miller, C. E., Babin, M., Mangin, A., and Le Fouest, V.: Biogeochemical river runoff drives intense coastal Arctic Ocean CO2 outgassing, Geophysical Research Letters, 50, e2022GL102377, https://doi.org/10.1029/2022GL102377, 2023. a, b, c, d
Bertin, C., Carroll, D., Menemenlis, D., Dutkiewicz, S., Zhang, H., Matsuoka, A., Schwab, M., Savelli, R., Matsuoka, A., Manizza, M., Miller, C. E., Bowring, S., Guenet, B., and Le Fouest, V.: Paving the way for improved representation of coupled Physical and biogeochemical processes in Arctic River Plumes – A case study of the Mackenzie shelf, Permafrost and Periglacial Processes, https://doi.org/10.1002/ppp.2271, 2025. a
Bloom, A. A., Bowman, K. W., Liu, J., Konings, A. G., Worden, J. R., Parazoo, N. C., Meyer, V., Reager, J. T., Worden, H. M., Jiang, Z., Quetin, G. R., Smallman, T. L., Exbrayat, J.-F., Yin, Y., Saatchi, S. S., Williams, M., and Schimel, D. S.: Lagged effects regulate the inter-annual variability of the tropical carbon balance, Biogeosciences, 17, 6393–6422, https://doi.org/10.5194/bg-17-6393-2020, 2020. a
Cai, W.-J., Hu, X., Huang, W.-J., Jiang, L.-Q., Wang, Y., Peng, T.-H., and Zhang, X.: Alkalinity distribution in the western North Atlantic Ocean margins, Journal of Geophysical Research: Oceans, 115, https://doi.org/10.1029/2009JC005482, 2010. a
Carr, M.-E., Friedrichs, M. A., Schmeltz, M., Noguchi Aita, M., Antoine, D., Arrigo, K. R., Asanuma, I., Aumont, O., Barber, R., Behrenfeld, M., Bidigare, R., Buitenhuis, E. T., Campbell, J., Ciotti, A., Dierssen, H., Dowell, M., Dunne, J., Esaias, W., Gentili, B., Gregg, W., Groom, S., Hoepffner, N., Ishizaka, J., Kameda, T., Le Quéré, C., Lohrenz, S., Marra, J., Mélin, F., Moore, K., Morel, A., Reddy, T. E., Ryan, J., Scardi, M., Smyth, T., Turpie, K., Tilstone, G., Waters, K., and Yamanaka, Y.: A comparison of global estimates of marine primary production from ocean color, Deep Sea Research Part II: Topical Studies in Oceanography, 53, 741–770, https://doi.org/10.1016/j.dsr2.2006.01.028, 2006. a
Carroll, D., Menemenlis, D., Adkins, J., Bowman, K., Brix, H., Dutkiewicz, S., Fenty, I., Gierach, M., Hill, C., Jahn, O., Landschützer, P., Lauderdale, J. M., Liu, J., Manizza, M., Naviaux, J. D., Rödenbeck, C., Schimel, D. S., Van der Stocken, T., and Zhang, H.: The ECCO-Darwin data-assimilative global ocean biogeochemistry model: Estimates of seasonal to multidecadal surface ocean pCO2 and air-sea CO2 flux, Journal of Advances in Modeling Earth Systems, 12, e2019MS001888, https://doi.org/10.1029/2019MS001888, 2020. a, b, c, d, e, f, g
Carroll, D., Menemenlis, D., Dutkiewicz, S., Lauderdale, J. M., Adkins, J. F., Bowman, K. W., Brix, H., Fenty, I., Gierach, M. M., Hill, C., Jahn, O., Landschützer, P., Manizza, M., Mazloff, M. R., Miller, C. E., Schimel, D. S., Verdy, A., Whitt, D. B., and Zhang, H.: Attribution of space-time variability in global-ocean dissolved inorganic carbon, Global Biogeochemical Cycles, 36, e2021GB007162, https://doi.org/10.1029/2021GB007162, 2022. a, b, c
Carroll, D., Menemenlis, D., Zhang, H., Mazloff, M., McKinley, G., Fay, A., Dutkiewicz, S., Lauderdale, J., and Fenty, I.: Evaluation of the ECCO-DarwinOcean Biogeochemistry State Estimates. In-situ Observations, Zenodo, https://doi.org/10.5281/zenodo.10627664, 2024. a
Carroll, D., Menemenlis, D., Bertin, C., Hong, Z., Jahn, O., and Savelli, R.: rsavelli1617/ecco_darwin: ECCO-Darwin BGC runoff Savelli et al., 2026 GMD (Savelli1.0), Zenodo, [code], https://doi.org/10.5281/zenodo.18319710, 2026. a
Chau, T. T. T., Gehlen, M., and Chevallier, F.: A seamless ensemble-based reconstruction of surface ocean pCO2 and air–sea CO2 fluxes over the global coastal and open oceans, Biogeosciences, 19, 1087–1109, https://doi.org/10.5194/bg-19-1087-2022, 2022. a, b
Chen, F., Bai, X., Luo, G., Zhang, G., Ran, C., and Luo, X.: Assessing the global flux of organic carbon transported from terrestrial surfaces to oceans by rivers, Carbon Balance and Management, 20, https://doi.org/10.1186/s13021-025-00318-z, 2025. a
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Al., E., and House, J. I.: Carbon and other biogeochemical cycles, in: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, 465–570, 2014. a
Compton, J., Mallinson, D., Glenn, C. R., Filippelli, G., Föllmi, K., Shields, G., and Zanin, Y.: Variations in the global phosphorus cycle, SEPM (Society for Sedimentary Geology), 21–33, https://doi.org/10.2110/pec.00.66.0021, 2000. a
Cotrim da Cunha, L., Buitenhuis, E. T., Le Quéré, C., Giraud, X., and Ludwig, W.: Potential impact of changes in river nutrient supply on global ocean biogeochemistry, Global Biogeochemical Cycles, 21, https://doi.org/10.1029/2006GB002718, 2007. a
Dai, M., Su, J., Zhao, Y., Hofmann, E. E., Cao, Z., Cai, W.-J., Gan, J., Lacroix, F., Laruelle, G. G., Meng, F., Müller, J. D., Regnier, P. A., Wang, G., and Wang, Z.: Carbon Fluxes in the Coastal Ocean: Synthesis, Boundary Processes, and Future Trends, Annual Review of Earth and Planetary Sciences, 50, 593–626, https://doi.org/10.1146/annurev-earth-032320-090746, 2022. a
de Verneil, A., Lachkar, Z., Smith, S., and Lévy, M.: Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers, Biogeosciences, 19, 907–929, https://doi.org/10.5194/bg-19-907-2022, 2022. a
Doney, S. C., Mitchell, K. A., Henson, S. A., Cavan, E., DeVries, T., Gruber, N., Hauck, J., Mouw, C. B., Müller, J. D., and Primeau, F. W.: Observational and numerical modeling constraints on the global ocean biological carbon pump, Global Biogeochemical Cycles, 38, e2024GB008156, https://doi.org/10.1029/2024GB008156, 2024. a, b
Dubois, K. D., Lee, D., and Veizer, J.: Isotopic constraints on alkalinity, dissolved organic carbon, and atmospheric carbon dioxide fluxes in the Mississippi River, Journal of Geophysical Research: Biogeosciences, 115, https://doi.org/10.1029/2009JG001102, 2010. a
Dutkiewicz, S., Hickman, A. E., Jahn, O., Henson, S., Beaulieu, C., and Monier, E.: Ocean colour signature of climate change, Nature Communications, 10, 578, https://doi.org/10.1038/s41467-019-08457-x, 2019. a
ECCO, C., Fenty, I., Fukumori, I., and Wang, O.: Synopsis of the ECCO V4r5 Global Ocean and Sea-Ice State Estimate, Zenodo, https://doi.org/10.5281/zenodo.13926714, 2024. a
Fekete, B. M., Vörösmarty, C. J., and Grabs, W.: High-resolution fields of global runoff combining observed river discharge and simulated water balances, Global Biogeochemical Cycles, 16, 15–1–15–10, 2002. a
Feng, Y., Menemenlis, D., Xue, H., Zhang, H., Carroll, D., Du, Y., and Wu, H.: Improved representation of river runoff in Estimating the Circulation and Climate of the Ocean Version 4 (ECCOv4) simulations: implementation, evaluation, and impacts to coastal plume regions, Geosci. Model Dev., 14, 1801–1819, https://doi.org/10.5194/gmd-14-1801-2021, 2021. a, b
Follows, M. J., Ito, T., and Dutkiewicz, S.: On the solution of the carbonate chemistry system in ocean biogeochemistry models, Ocean Modelling, 12, 290–301, 2006. a
Forget, G., Campin, J.-M., Heimbach, P., Hill, C. N., Ponte, R. M., and Wunsch, C.: ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation, Geosci. Model Dev., 8, 3071–3104, https://doi.org/10.5194/gmd-8-3071-2015, 2015. a
Frings, P. J., Clymans, W., Fontorbe, G., Christina, L., and Conley, D. J.: The continental Si cycle and its impact on the ocean Si isotope budget, Chemical Geology, 425, 12–36, 2016. a
Gao, S., Schwinger, J., Tjiputra, J., Bethke, I., Hartmann, J., Mayorga, E., and Heinze, C.: Riverine impact on future projections of marine primary production and carbon uptake, Biogeosciences, 20, 93–119, https://doi.org/10.5194/bg-20-93-2023, 2023. a, b
Gao, Y., Jia, J., Lu, Y., Sun, K., Wang, J., and Wang, S.: Carbon transportation, transformation, and sedimentation processes at the land-river-estuary continuum, Fundamental Research, 4, 1594–1602, https://doi.org/10.1016/j.fmre.2022.07.007, 2024. a, b
Gaspar, P., Grégoris, Y., and Lefevre, J.-M.: A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: Tests at station Papa and long-term upper ocean study site, Journal of Geophysical Research: Oceans, 95, 16179–16193, 1990. a
Ghosh, J., Chakraborty, K., Chanda, A., Akhand, A., Bhattacharya, T., Das, S., Das, I., Hazra, S., Choudhury, S., and Wells, M.: Outwelling of total alkalinity and dissolved inorganic carbon from the Hooghly River to the adjacent coastal Bay of Bengal, Environmental Monitoring and Assessment, 193, 415, https://doi.org/10.1007/s10661-021-09191-y, 2021. a
Gibson, G. A., Elliot, S., Clement Kinney, J., Piliouras, A., and Jeffery, N.: Assessing the Potential Impact of River Chemistry on Arctic Coastal Production, Frontiers in Marine Science, 9, 738363, https://doi.org/10.3389/fmars.2022.738363, 2022. a
Gomez, F. A., Lee, S.-K., Stock, C. A., Ross, A. C., Resplandy, L., Siedlecki, S. A., Tagklis, F., and Salisbury, J. E.: RC4USCoast: a river chemistry dataset for regional ocean model applications in the US East Coast, Gulf of Mexico, and US West Coast, Earth Syst. Sci. Data, 15, 2223–2234, https://doi.org/10.5194/essd-15-2223-2023, 2023. a
Hagemann, S. and Dümenil, L.: A parametrization of the lateral waterflow for the global scale, Climate Dynamics, 14, 17–31, 1997. a
Hagemann, S. and Gates, L. D.: Improving a subgrid runoff parameterization scheme for climate models by the use of high resolution data derived from satellite observations, Climate Dynamics, 21, 349–359, 2003. a
Hansell, D. A., Kadko, D., and Bates, N. R.: Degradation of terrigenous dissolved organic carbon in the western Arctic Ocean, Science, 304, 858–861, 2004. a
Hartmann, J., Jansen, N., Dürr, H. H., Kempe, S., and Köhler, P.: Global CO2-consumption by chemical weathering: What is the contribution of highly active weathering regions?, Global and Planetary Change, 69, 185–194, 2009. a
Holmes, R. M., McClelland, J. W., Raymond, P. A., Frazer, B. B., Peterson, B. J., and Stieglitz, M.: Lability of DOC transported by Alaskan rivers to the Arctic Ocean, Geophysical Research Letters, 35, https://doi.org/10.1029/2007GL032837, 2008. a, b, c
Holmes, R. M., McClelland, J. W., Peterson, B. J., Tank, S. E., Bulygina, E., Eglinton, T.I., Gordeev, V. V., Gurtovaya, T. Y., Raymond, P. A., Repeta, D. J., Staples, R., Striegl, R. G., Zhulidov, A. V., and Zimov, S. A.: Seasonal and annual fluxes of nutrients and organic matter from large rivers to the Arctic Ocean and surrounding seas, Estuaries and Coasts, 35, 369–382, 2012. a, b, c, d, e, f
Jersild, A., Landschützer, P., Gruber, N., Bakker, D. C. E.: An observation-based global monthly gridded sea surface pCO2 and air-sea CO2 flux product from 1982 onward and its monthly climatology (NCEI Accession 0160558). NOAA National Centers for Environmental Information [Data set], https://doi.org/10.7289/v5z899n6, 2023. a, b, c
Jones, M. W., Andrew, R. M., Peters, G. P., Janssens-Maenhout, G., De-Gol, A. J., Ciais, P., Patra, P. K., Chevallier, F., and Le Quéré, C.: Gridded fossil CO2 emissions and related O2 combustion consistent with national inventories 1959–2018, Scientific Data, 8, https://doi.org/10.1038/s41597-020-00779-6, 2021. a
Kaiser, K., Benner, R., and Amon, R.: The fate of terrigenous dissolved organic carbon on the E urasian shelves and export to the North Atlantic, Journal of Geophysical Research: Oceans, 122, 4–22, 2017. a
Kartadikaria, A. R., Watanabe, A., Nadaoka, K., Adi, N. S., Prayitno, H. B., Soemorumekso, S., Muchtar, M., Triyulianti, I., Setiawan, A., Suratno, S., and Khasanah, E. N.: CO2 sink/source characteristics in the tropical Indonesian seas, Journal of Geophysical Research: Oceans, 120, 7842–7856, 2015. a
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochemical Cycles, 19, https://doi.org/10.1029/2003GB002199, 2005. a
Lacroix, F., Ilyina, T., Laruelle, G. G., and Regnier, P.: Reconstructing the Preindustrial Coastal Carbon Cycle Through a Global Ocean Circulation Model: Was the Global Continental Shelf Already Both Autotrophic and a CO2 Sink?, Global Biogeochemical Cycles, 35, e2020GB006603, https://doi.org/10.1029/2020GB006603, 2021a. a
Le Fouest, V., Babin, M., and Tremblay, J.-É.: The fate of riverine nutrients on Arctic shelves, Biogeosciences, 10, 3661–3677, https://doi.org/10.5194/bg-10-3661-2013, 2013. a, b
Le Fouest, V., Manizza, M., Tremblay, B., and Babin, M.: Modelling the impact of riverine DON removal by marine bacterioplankton on primary production in the Arctic Ocean, Biogeosciences, 12, 3385–3402, https://doi.org/10.5194/bg-12-3385-2015, 2015. a
Liu, M., Raymond, P. A., Lauerwald, R., Zhang, Q., Trapp-Müller, G., Davis, K. L., Moosdorf, N., Xiao, C., Middelburg, J. J., Bouwman, A. F., Beusen, A. H. W., Peng, C., Lacroix, F., Tian, H., Wang, J., Li, M., Zhu, Q., Cohen, S., van Hoek, W. J., Li, Y., Li, Y., Yao, Y., and Regnier, P.: Global riverine land-to-ocean carbon export constrained by observations and multi-model assessment, Nature Geoscience, https://doi.org/10.1038/s41561-024-01524-z, 2024. a, b, c, d
Liu, X., Dunne, J. P., Stock, C. A., Harrison, M. J., Adcroft, A., and Resplandy, L.: Simulating Water Residence Time in the Coastal Ocean: A Global Perspective, Geophysical Research Letters, 46, 13910–13919, https://doi.org/10.1029/2019GL085097, 2019. a
Lønborg, C., Carreira, C., Jickells, T., and Álvarez-Salgado, X. A.: Impacts of Global Change on Ocean Dissolved Organic Carbon (DOC) Cycling, Frontiers in Marine Science, 7, https://doi.org/10.3389/fmars.2020.00466, 2020. a
Ma, M., Zhang, H., Lauerwald, R., Ciais, P., and Regnier, P.: Estimating lateral nitrogen transfers over the last century through the global river network using a land surface model, Earth Syst. Dynam., 16, 841–867, https://doi.org/10.5194/esd-16-841-2025, 2025. a, b
Mahowald, N. M., Engelstaedter, S., Luo, C., Sealy, A., Artaxo, P., Benitez-Nelson, C., Bonnet, S., Chen, Y., Chuang, P. Y., Cohen, D. D., Dulac, F., Herut, B., Johansen, A. M., Kubilay, N., Losno, R., Maenhaut, W., Paytan, A., Prospero, J. M., Shank, L. M., and Siefert, R. L.: Atmospheric iron deposition: global distribution, variability, and human perturbations, Annual Review of Marine Science, 1, 245–278, 2009. a
Manizza, M., Follows, M. J., Dutkiewicz, S., Menemenlis, D., McClelland, J. W., Hill, C. N., Peterson, B. J., and Key, R. M.: A model of the Arctic Ocean carbon cycle, Journal of Geophysical Research: Oceans, 116, https://doi.org/10.1029/2011JC006998, 2011. a, b
Marra, J., Ho, C., and Trees, C.: An alternative algorithm for the calculation of primary productivity from remote sensing data, Lamont Doherty Earth Observatory Technical Report (LDEO-2003-1), https://doi.org/10.7916/d8-cbv5-jh05, 2003. a
Marshall, J., Adcroft, A., Hill, C. N., Perelman, L., and Heisey, C.: A finite‐volume, incompressible Navier Stokes model for studies of the ocean on parallel computers, Journal of Geophysical Research: Oceans, 102, 5753–5766, https://doi.org/10.1029/96JC02775, 1997a. a
Marshall, J., Hill, C., Perelman, L., and Adcroft, A.: Hydrostatic, quasi‐hydrostatic, and nonhydrostatic ocean modeling, Journal of Geophysical Research: Oceans, 102, 5733–5752, https://doi.org/10.1029/96JC02776, 1997b. a
Mathis, M., Logemann, K., Maerz, J., Lacroix, F., Hagemann, S., Chegini, F., Ramme, L., Ilyina, T., Korn, P., and Schrum, C.: Seamless integration of the coastal ocean in global marine carbon cycle modeling, Journal of Advances in Modeling Earth Systems, 14, e2021MS002789, https://doi.org/10.1029/2021MS002789, 2022. a
Mathis, M., Lacroix, F., Hagemann, S., Nielsen, D. M., Ilyina, T., and Schrum, C.: Enhanced CO2 uptake of the coastal ocean is dominated by biological carbon fixation, Nature Climate Change, https://doi.org/10.1038/s41558-024-01956-w, 2024. a
Mayer, B., Hagemann, S., Zhou, Y., Chen, Y., Ang, S. B. H., Pätsch, J., and Martin, P.: Modeling Terrestrial Dissolved Organic Carbon and Its Effect on the Carbonate System in the Sunda Shelf Seas, Southeast Asia, Global Biogeochemical Cycles, 39, e2024GB008433, https://doi.org/10.1029/2024GB008433, 2025. a
Medeiros, P. M., Seidel, M., Ward, N. D., Carpenter, E. J., Gomes, H. R., Niggemann, J., Krusche, A. V., Richey, J. E., Yager, P. L., and Dittmar, T.: Fate of the Amazon River dissolved organic matter in the tropical Atlantic Ocean, Global Biogeochemical Cycles, 29, 677–690, 2015. a
Menemenlis, D., Fukumori, I., and Lee, T.: Using Green's functions to calibrate an ocean general circulation model, Monthly Weather Review, 133, 1224–1240, 2005. a
Mol, J., Thomas, H., Myers, P. G., Hu, X., and Mucci, A.: Inorganic carbon fluxes on the Mackenzie Shelf of the Beaufort Sea, Biogeosciences, 15, 1011–1027, https://doi.org/10.5194/bg-15-1011-2018, 2018. a
Resplandy, L., Keeling, R., Rödenbeck, C., Stephens, B., Khatiwala, S., Rodgers, K., Long, M., Bopp, L., and Tans, P.: Revision of global carbon fluxes based on a reassessment of oceanic and riverine carbon transport, Nature Geoscience, 11, 504–509, 2018. a
Resplandy, L., Hogikyan, A., Müller, J. D., Najjar, R. G., Bange, H. W., Bianchi, D., Weber, T., Cai, W.-J., Doney, S. C., Fennel, K., Gehlen, M., Hauck, J., Lacroix, F., Landschützer, P., Le Quéré, C., Roobaert, A., Schwinger, J., Berthet, S., Bopp, L., Chau, T. T. T., Dai, M., Gruber, N., Ilyina, T., Kock, A., Manizza, M., Lachkar, Z., Laruelle, G. G., Liao, E., Lima, I. D., Nissen, C., Rödenbeck, C., Séférian, R., Toyama, K., Tsujino, H., and Regnier, P.: A Synthesis of Global Coastal Ocean Greenhouse Gas Fluxes, Global Biogeochemical Cycles, 38, e2023GB007803, https://doi.org/10.1029/2023GB007803, 2024. a, b
Rödenbeck, C.: Estimating CO2 sources and sinks from atmospheric mixing ratio measurements using a global inversion of atmospheric transport, Technical reports, ISSN 1615-7400, 2005. a
Rödenbeck, C., Keeling, R. F., Bakker, D. C. E., Metzl, N., Olsen, A., Sabine, C., and Heimann, M.: Global surface-ocean pCO2 and sea–air CO2 flux variability from an observation-driven ocean mixed-layer scheme, Ocean Sci., 9, 193–216, https://doi.org/10.5194/os-9-193-2013, 2013. a, b
Sabine, C. L., Hankin, S., Koyuk, H., Bakker, D. C. E., Pfeil, B., Olsen, A., Metzl, N., Kozyr, A., Fassbender, A., Manke, A., Malczyk, J., Akl, J., Alin, S. R., Bellerby, R. G. J., Borges, A., Boutin, J., Brown, P. J., Cai, W.-J., Chavez, F. P., Chen, A., Cosca, C., Feely, R. A., González-Dávila, M., Goyet, C., Hardman-Mountford, N., Heinze, C., Hoppema, M., Hunt, C. W., Hydes, D., Ishii, M., Johannessen, T., Key, R. M., Körtzinger, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lourantou, A., Merlivat, L., Midorikawa, T., Mintrop, L., Miyazaki, C., Murata, A., Nakadate, A., Nakano, Y., Nakaoka, S., Nojiri, Y., Omar, A. M., Padin, X. A., Park, G.-H., Paterson, K., Perez, F. F., Pierrot, D., Poisson, A., Ríos, A. F., Salisbury, J., Santana-Casiano, J. M., Sarma, V. V. S. S., Schlitzer, R., Schneider, B., Schuster, U., Sieger, R., Skjelvan, I., Steinhoff, T., Suzuki, T., Takahashi, T., Tedesco, K., Telszewski, M., Thomas, H., Tilbrook, B., Vandemark, D., Veness, T., Watson, A. J., Weiss, R., Wong, C. S., and Yoshikawa-Inoue, H.: Surface Ocean CO2 Atlas (SOCAT) gridded data products, Earth Syst. Sci. Data, 5, 145–153, https://doi.org/10.5194/essd-5-145-2013, 2013. a, b, c
Savelli, R.: Implementing Riverine Biogeochemical Inputs in ECCO-Darwin: A Sensitivity Analysis of Terrestrial Fluxes in a Data-Assimilative Global Ocean Biogeochemistry Model, Zenodo [data set], https://doi.org/10.5281/zenodo.17317011, 2025. a
Séférian, R., Berthet, S., Yool, A., Palmiéri, J., Bopp, L., Tagliabue, A., Kwiatkowski, L., Aumont, O., Christian, J., Dunne, J., Gehlen, M., Ilyina, T., John, J. G., Li, H., Long, M. C., Luo, J. Y., Nakano, H., Romanou, A., Schwinger, J., Stock, C., Santana-Falcón, Y., Takano, Y., Tjiputra, J., Tsujino, H., Watanabe, M., Wu, T., Wu, F., and Yamamoto, A.: Tracking improvement in simulated marine biogeochemistry between CMIP5 and CMIP6, Current Climate Change Reports, 6, 95–119, 2020. a
Seitzinger, S. P., Mayorga, E., Bouwman, A. F., Kroeze, C., Beusen, A. H., Billen, G., Van Drecht, G., Dumont, E., Fekete, B., Garnier, J., and Harrison, J. A.: Global river nutrient export: A scenario analysis of past and future trends, Global Biogeochemical Cycles, 24, https://doi.org/10.1029/2009GB003587, 2010. a
Shen, Y., Fichot, C. G., and Benner, R.: Dissolved organic matter composition and bioavailability reflect ecosystem productivity in the Western Arctic Ocean, Biogeosciences, 9, 4993–5005, https://doi.org/10.5194/bg-9-4993-2012, 2012. a, b
Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J., and Westberry, T. K.: The CAFE model: A net production model for global ocean phytoplankton, Global Biogeochemical Cycles, 30, 1756–1777, https://doi.org/10.1002/2016GB005521, 2016. a
Spencer, R. G. M., Mann, P. J., Dittmar, T., Eglinton, T. I., McIntyre, C., Holmes, R. M., Zimov, N., and Stubbins, A.: Detecting the signature of permafrost thaw in Arctic rivers, Geophysical Research Letters, 42, 2830–2835, https://doi.org/10.1002/2015GL063498, 2015. a
Suchet, P. A. and Probst, J.-L.: A global model for present-day atmospheric/soil CO2 consumption by chemical erosion of continental rocks (GEM-CO2), Tellus B, 47, 273–280, 1995. a
Sulpis, O., Humphreys, M. P., Wilhelmus, M. M., Carroll, D., Berelson, W. M., Menemenlis, D., Middelburg, J. J., and Adkins, J. F.: RADIv1: a non-steady-state early diagenetic model for ocean sediments in Julia and MATLAB/GNU Octave, Geosci. Model Dev., 15, 2105–2131, https://doi.org/10.5194/gmd-15-2105-2022, 2022. a
Suzuki, T., Yamazaki, D., Tsujino, H., Komuro, Y., Nakano, H., and Urakawa, S.: A dataset of continental river discharge based on JRA-55 for use in a global ocean circulation model, Journal of Oceanography, 74, 421–429, 2018. a
Tank, S. E., Raymond, P. A., Striegl, R. G., McClelland, J. W., Holmes, R. M., Fiske, G. J., and Peterson, B. J.: A land-to-ocean perspective on the magnitude, source and implication of DIC flux from major Arctic rivers to the Arctic Ocean, Global Biogeochemical Cycles, 26, https://doi.org/10.1029/2011GB004192, 2012. a, b
Tank, S. E., McClelland, J. W., Spencer, R. G., Shiklomanov, A. I., Suslova, A., Moatar, F., Amon, R. M., Cooper, L. W., Elias, G., Gordeev, V. V., Guay, C., Gurtovaya, T. Y., Kosmenko, L. S., Mutter, E. A., Peterson, B. J., Peucker-Ehrenbrink, B., Raymond, P. A., Schuster, P. F., Scott, L., Staples, R., Striegl, R. G., Tretiakov, M., Zhulidov, A. V., Zimov, N., Zimov, S., and Holmes, R. M.: Recent trends in the chemistry of major northern rivers signal widespread Arctic change, Nature Geoscience, 16, 789–796, 2023. a
Terhaar, J., Lauerwald, R., Regnier, P., Gruber, N., and Bopp, L.: Around one third of current Arctic Ocean primary production sustained by rivers and coastal erosion, Nature Communications, 12, https://doi.org/10.1038/s41467-020-20470-z, 2021. a, b
Tian, H., Yao, Y., Li, Y., Shi, H., Pan, S., Najjar, R. G., Pan, N., Bian, Z., Ciais, P., Cai, W.-J., Dai, M., Friedrichs, M. A. M., Li, H.-Y., Lohrenz, S., and Leung, L. R.: Increased Terrestrial Carbon Export and CO2 Evasion From Global Inland Waters Since the Preindustrial Era, Global Biogeochemical Cycles, 37, e2023GB007776, https://doi.org/10.1029/2023GB007776, 2023. a, b
Tivig, M., Keller, D. P., and Oschlies, A.: Riverine nitrogen supply to the global ocean and its limited impact on global marine primary production: a feedback study using an Earth system model, Biogeosciences, 18, 5327–5350, https://doi.org/10.5194/bg-18-5327-2021, 2021. a, b, c, d
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S. G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C. W., Bozec, A., Chassignet, E. P., Curchitser, E., Boeira Dias, F., Durack, P. J., Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C., Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J., Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.: JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do), Ocean Modelling, 130, 79–139, 2018. a
Vörösmarty, C., Fekete, B. M., Meybeck, M., and Lammers, R. B.: Global system of rivers: Its role in organizing continental land mass and defining land-to-ocean linkages, Global Biogeochemical Cycles, 14, 599–621, 2000. a
Wanninkhof, R.: Relationship between wind speed and gas exchange over the ocean revisited, Limnology and Oceanography: Methods, 12, 351–362, 2014. a
Ward, N. D., Megonigal, J. P., Bond-Lamberty, B., Bailey, V. L., Butman, D., Canuel, E. A., Diefenderfer, H., Ganju, N. K., Goñi, M. A., Graham, E. B., Hopkinson, C. S., Khangaonkar, T., Adam Langley, J., McDowell, N. G., Myers-Pigg, A. N., Neumann, R. B., Osburn, C. L., Price, R. M., Rowland, J., Sengupta, A., Simard, M., Thornton, P. E., Tzortziou, M., Vargas, R., Weisenhorn, P. B., and Windham-Myers, L.: Representing the function and sensitivity of coastal interfaces in Earth system models, Nature communications, 11, 2458, https://doi.org/10.1038/s41467-020-16236-2, 2020. a
Wickland, K. P., Aiken, G. R., Butler, K., Dornblaser, M. M., Spencer, R., and Striegl, R. G.: Biodegradability of dissolved organic carbon in the Yukon River and its tributaries: Seasonality and importance of inorganic nitrogen, Global Biogeochemical Cycles, 26, https://doi.org/10.1029/2012GB004342, 2012. a, b
Wunsch, C. and Heimbach, P.: Dynamically and kinematically consistent global ocean circulation and ice state estimates, in: International geophysics, Elsevier, 103, 553–579, https://doi.org/10.1016/B978-0-12-391851-2.00021-0, 2013. a
Wunsch, C., Heimbach, P., Ponte, R. M., Fukumori, I., and the ECCO-GODAE consortium members: The global general circulation of the ocean estimated by the ECCO-Consortium, Oceanography, 22, 88–103, 2009. a
Yasunaka, S., Manizza, M., Terhaar, J., Olsen, A., Yamaguchi, R., Landschützer, P., Watanabe, E., Carroll, D., Adiwira, H., Müller, J. D., and Hauck, J.: An Assessment of CO2 Uptake in the Arctic Ocean From 1985 to 2018, Global Biogeochemical Cycles, 37, e2023GB007806, https://doi.org/10.1029/2023GB007806, 2023. a
Zhang, H., Menemenlis, D., and Fenty, I.: ECCO LLC270 Ocean-Ice State Estimate, Tech. rep., Jet Propulsion Laboratory, California Institute of Technology, http://hdl.handle.net/1721.1/119821 (last access: 20 January 2026), 2018. a
Zhong, G., Li, X., Song, J., Qu, B., Wang, F., Wang, Y., Zhang, B., Tian, D., Ma, J., Yuan, H., Duan, L., Li, N., Wang, Q., and Xing, J.: The increasing big gap of carbon sink between the western and eastern Pacific in the last three decades, Frontiers in Marine Science, 9, https://doi.org/10.3389/fmars.2022.1088181, 2022. a
Short summary
Accounting for carbon and nutrients in rivers is essential for resolving carbon dioxide (CO2) exchanges between the ocean and the atmosphere. In this study, we add the effect of present-day rivers to a pioneering global-ocean biogeochemistry model. This study highlights the challenge for global ocean numerical models to cover the complexity of the flow of water and carbon across the Land-to-Ocean Aquatic Continuum.
Accounting for carbon and nutrients in rivers is essential for resolving carbon dioxide (CO2)...
