Articles | Volume 18, issue 21
https://doi.org/10.5194/gmd-18-8157-2025
© Author(s) 2025. 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-18-8157-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Nov 2025
Development and technical paper |
| 05 Nov 2025
| 05 Nov 2025
Development of a model framework for terrestrial carbon flux prediction: the Regional Carbon and Climate Analytics Tool (RCCAT) applied to non-tidal wetlands
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Department of Earth and Planetary Sciences, University of California at Santa Cruz, Santa Cruz, CA, USA
Zelalem A. Mekonnen
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Bhavna Arora
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
William J. Riley
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Kunxiaojia Yuan
Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA
Yi Xu
Department of Earth and Planetary Sciences, University of California at Santa Cruz, Santa Cruz, CA, USA
Yu Zhang
Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA
Qing Zhu
Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Tyler L. Anthony
California Department of Water Resources, Sacramento, CA, USA
Adina Paytan
Department of Earth and Planetary Sciences, University of California at Santa Cruz, Santa Cruz, CA, USA
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Li Xu, Qi Tang, Qing Zhu, William J. Riley, Guido R. van der Werf, Yang Chen, Michael J. Prather, and James T. Randerson
EGUsphere, https://doi.org/10.5194/egusphere-2026-4288, https://doi.org/10.5194/egusphere-2026-4288, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Using the E3SM version 3 model with interactive chemistry, this study shows that wildfires substantially perturb global and regional tropospheric chemistry and modify radiative effects through aerosol and ozone interactions, with particularly strong impacts in tropical fire regions. By connecting wildfire emissions with changes in atmospheric composition, radiation, and climate, this study provides important insights on how the atmosphere responds to changing fire activities.
Eyal Rahav and Adina Paytan
Ocean Sci., 22, 2711–2723, https://doi.org/10.5194/os-22-2711-2026, https://doi.org/10.5194/os-22-2711-2026, 2026
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To understand how ocean warming may alter carbon storage, we examined seasonal carbon cycling in the northern Red Sea over five months. As summer warming intensified water-column stratification and reduced nutrient supply, carbon fixation by microscopic algae declined, while a larger fraction of newly produced carbon was released as dissolved material and rapidly respired by bacteria. This near-surface recycling may reduce carbon transfer to depth and weaken ocean carbon storage.
Elsa Abs, Christoph Keuschnig, Pierre Amato, Chris Bowler, Eric Capo, Alexander B. Chase, Luciana Chavez Rodriguez, Abraham N. Dabengwa, Thomas Dussarrat, Thomas Guzman, Linnea K. Hernandez, Jenni Hultman, Kirsten Küsel, Zhen Li, Anna Mankowski, William J. Riley, Scott R. Saleska, and Lisa Wingate
Biogeosciences, 23, 5205–5237, https://doi.org/10.5194/bg-23-5205-2026, https://doi.org/10.5194/bg-23-5205-2026, 2026
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Meta-omics technologies offer new tools to understand how microbial and plant functional diversity shape biogeochemical cycles across ecosystems. This perspective explores how integrating omics data with ecological and modeling approaches can improve our understanding of greenhouse gas fluxes and nutrient dynamics, from soils to clouds, and from the past to the future. We highlight challenges and opportunities for scaling omics insights from local processes to Earth system models.
Nikola Besic, Philippe Ciais, Fa Li, Robert Jackson, Kunxiaojia Yuan, Shushi Peng, Benjamin Poulter, Zhen Zhang, and Qing Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2026-2604, https://doi.org/10.5194/egusphere-2026-2604, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Wetlands are the largest natural source of methane, an important greenhouse gas, but current estimates of their emissions remain highly uncertain because models often disagree. We combined field measurements from wetlands around the world with advanced statistical methods to identify where different models perform best. Our results show that accounting for regional differences in model skill can substantially change local methane estimates and potentially improve future climate assessments.
Eyal Rahav, Scott D. Wankel, and Adina Paytan
Biogeosciences, 23, 4171–4185, https://doi.org/10.5194/bg-23-4171-2026, https://doi.org/10.5194/bg-23-4171-2026, 2026
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Microbes in the sunlit waters of the nitrogen-poor northern Red Sea transform ammonia into other forms of nitrogen, a process once thought to be almost absent in surface waters. We found that this activity does occur, but at very low rates. Additionally, these microbes contribute only a small share to carbon production, yet they recycle nitrogen and influence how scientists estimate ocean productivity in a warming world.
Jinyun Tang, William J. Riley, Gianna L. Marschmann, and Eoin L. Brodie
Biogeosciences, 23, 3995–4010, https://doi.org/10.5194/bg-23-3995-2026, https://doi.org/10.5194/bg-23-3995-2026, 2026
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Carbon Use Efficiency (CUE) measures how biological organisms use carbon to synthesize new biomass, inferred to first increase and then decrease with specific growth rate. Our analysis of six biological growth models reveals that source-driven models fail to capture this relationship, while sink-driven models, using a reserve biomass pool, succeed. Existing biogeochemical models often depict a deterministic CUE-controlling factor relationship, which we find should be modeled dynamically instead.
Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David J. Beerling, Dmitry A. Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison M. Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Yi Xi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 18, 3507–3524, https://doi.org/10.5194/essd-18-3507-2026, https://doi.org/10.5194/essd-18-3507-2026, 2026
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We proposed a framework that combines machine-learning and climate data to predict global natural vegetated wetland methane emissions for 2000–2025. We found that although total global emissions remained stable in the post-2020s, Northern Hemisphere emissions surged whilst tropical emissions fell. This approach allows us to rapidly monitor emissions and provides early warnings for climate impacts.
Elias C. Massoud, Nathan Collier, Yaoping Wang, Jiafu Mao, Adrian Harpold, Steven A. Kannenberg, Gerbrand Koren, Mukesh Kumar, Pushpendra Raghav, Pallav Ray, Mingjie Shi, Jing Tao, Sreedevi P. Vasu, Huiqi Wang, Qing Zhu, and Forrest M. Hoffman
Geosci. Model Dev., 19, 3427–3453, https://doi.org/10.5194/gmd-19-3427-2026, https://doi.org/10.5194/gmd-19-3427-2026, 2026
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Soil moisture helps control how plants grow and how land exchanges water and energy with the atmosphere. We evaluated how well 16 Earth System Models represent soil moisture and its links to plant activity using a benchmarking system and observational data. Many models simulate surface soil moisture well but show larger errors deeper in the soil and in colder regions. Models that represent plant–soil water interactions more directly better capture how ecosystems respond to water availability.
Jeffrey Beem-Miller, William J. Riley, Peter B. Reich, Michael W. I. Schmidt, Yuxuan Bai, Raimundo Bermudez Villanueva, Zach Brown, Abad Chabbi, Susan E. Crow, Wenxu Dong, Serita D. Frey, Paul J. Hanson, Kai Jensen, Melissa A. Knorr, Emma Lathrop, Avni Malhotra, Patrick Megonigal, Adrienne Nicotra, Andrew Nottingham, Genevieve L. Noyce, Roy L. Rich, Heidi Rodenhizer, Agustín Sarquis, Andreas Schindlbacher, Edward A. G. Schuur, Zheng Shi, Artur Stefanski, Viktoria Unger, Tana E. Wood, Yuanhe Yang, Zhijie Yang, Jizhong Zhou, Biao Zhu, and Margaret S. Torn
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-23, https://doi.org/10.5194/essd-2026-23, 2026
Revised manuscript accepted for ESSD
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The Soil Warming to Depth Data Integration Effort (SWEDDIE) synthesizes data from deep soil warming experiments around the world (n = 23), offering new insight into warming responses of both surface and subsoils. We demonstrate that variation in soil warming with depth is driven largely by warming methodology, while soil moisture changes due to warming differ by ecosystem. This work serves a foundation for future syntheses with SWEDDIE.
Xiang Huang, Yu Zhang, Bo Gao, Charles J. Abolt, Ryan L. Crumley, Cansu Demir, Richard P. Fiorella, Bob Busey, Bob Bolton, Scott L. Painter, and Katrina E. Bennett
Geosci. Model Dev., 19, 1193–1211, https://doi.org/10.5194/gmd-19-1193-2026, https://doi.org/10.5194/gmd-19-1193-2026, 2026
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Predicting hydrological runoff in Arctic permafrost regions is difficult due to limited observations and complex terrain. We used a detailed physics-based model simulations to improve runoff estimates in a Earth system land model. Our method improved runoff accuracy and worked well across two different Arctic regions. This helps make runoff parameterization schemes more reliable for understanding water flow in permafrost areas under a changing climate.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
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Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, P. Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankararaman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Johann Engelbrecht, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbigniew Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gómez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal L. Weagle, and Xi Zhao
Atmos. Chem. Phys., 25, 4665–4702, https://doi.org/10.5194/acp-25-4665-2025, https://doi.org/10.5194/acp-25-4665-2025, 2025
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Aerosol particles are an important part of the Earth system, but their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Here, we present a new compilation of PM2.5 and PM10 aerosol observations, focusing on the spatial variability across different observational stations, including composition, and demonstrate a method for comparing the data sets to model output.
Jinyun Tang and William J. Riley
Biogeosciences, 22, 1809–1819, https://doi.org/10.5194/bg-22-1809-2025, https://doi.org/10.5194/bg-22-1809-2025, 2025
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A new mathematical formulation of the dynamic energy budget model is presented for the growth of biological organisms. This new formulation combines mass conservation law and chemical kinetics theory and is computationally faster than the standard formulation of dynamic energy budget models. In simulating the growth of Thalassiosira weissflogii in a nitrogen-limiting chemostat, the new model is as good as the standard dynamic energy budget model using almost the same parameter values.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
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This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Kamal Nyaupane, Umakant Mishra, Feng Tao, Kyongmin Yeo, William J. Riley, Forrest M. Hoffman, and Sagar Gautam
Biogeosciences, 21, 5173–5183, https://doi.org/10.5194/bg-21-5173-2024, https://doi.org/10.5194/bg-21-5173-2024, 2024
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Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Using machine learning, we develop and compare predictive relationships in observations (Obs) and ESMs. We find different relationships between environmental factors and SOC stocks in Obs and ESMs. SOC prediction in ESMs may be improved by representing the functional relationships of environmental controllers in a way consistent with observations.
Hanqin Tian, Naiqing Pan, Rona L. Thompson, Josep G. Canadell, Parvadha Suntharalingam, Pierre Regnier, Eric A. Davidson, Michael Prather, Philippe Ciais, Marilena Muntean, Shufen Pan, Wilfried Winiwarter, Sönke Zaehle, Feng Zhou, Robert B. Jackson, Hermann W. Bange, Sarah Berthet, Zihao Bian, Daniele Bianchi, Alexander F. Bouwman, Erik T. Buitenhuis, Geoffrey Dutton, Minpeng Hu, Akihiko Ito, Atul K. Jain, Aurich Jeltsch-Thömmes, Fortunat Joos, Sian Kou-Giesbrecht, Paul B. Krummel, Xin Lan, Angela Landolfi, Ronny Lauerwald, Ya Li, Chaoqun Lu, Taylor Maavara, Manfredi Manizza, Dylan B. Millet, Jens Mühle, Prabir K. Patra, Glen P. Peters, Xiaoyu Qin, Peter Raymond, Laure Resplandy, Judith A. Rosentreter, Hao Shi, Qing Sun, Daniele Tonina, Francesco N. Tubiello, Guido R. van der Werf, Nicolas Vuichard, Junjie Wang, Kelley C. Wells, Luke M. Western, Chris Wilson, Jia Yang, Yuanzhi Yao, Yongfa You, and Qing Zhu
Earth Syst. Sci. Data, 16, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024, https://doi.org/10.5194/essd-16-2543-2024, 2024
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Atmospheric concentrations of nitrous oxide (N2O), a greenhouse gas 273 times more potent than carbon dioxide, have increased by 25 % since the preindustrial period, with the highest observed growth rate in 2020 and 2021. This rapid growth rate has primarily been due to a 40 % increase in anthropogenic emissions since 1980. Observed atmospheric N2O concentrations in recent years have exceeded the worst-case climate scenario, underscoring the importance of reducing anthropogenic N2O emissions.
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024, https://doi.org/10.5194/bg-21-1061-2024, 2024
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A chemical kinetics theory is proposed to explain the non-monotonic relationship between temperature and biochemical rates. It incorporates the observed thermally reversible enzyme denaturation that is ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution and three well-established theories: (1) law of mass action, (2) diffusion-limited chemical reaction theory, and (3) transition state theory.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankarararman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Hannele Hakola, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbiginiw Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gomez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal Weagle, and Xi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-1, https://doi.org/10.5194/essd-2024-1, 2024
Preprint withdrawn
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Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. Here we present a new compilation of aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
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We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Daniel François, Adina Paytan, Olga Maria Oliveira de Araújo, Ricardo Tadeu Lopes, and Cátia Fernandes Barbosa
Biogeosciences, 19, 5269–5285, https://doi.org/10.5194/bg-19-5269-2022, https://doi.org/10.5194/bg-19-5269-2022, 2022
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Our analysis revealed that under the two most conservative acidification projections foraminifera assemblages did not display considerable changes. However, a significant decrease in species richness was observed when pH decreases to 7.7 pH units, indicating adverse effects under high-acidification scenarios. A micro-CT analysis revealed that calcified tests of Archaias angulatus were of lower density in low pH, suggesting no acclimation capacity for this species.
Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson
Geosci. Model Dev., 15, 1899–1911, https://doi.org/10.5194/gmd-15-1899-2022, https://doi.org/10.5194/gmd-15-1899-2022, 2022
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Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Jinyun Tang, William J. Riley, and Qing Zhu
Geosci. Model Dev., 15, 1619–1632, https://doi.org/10.5194/gmd-15-1619-2022, https://doi.org/10.5194/gmd-15-1619-2022, 2022
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We here describe version 2 of BeTR, a reactive transport model created to help ease the development of biogeochemical capability in Earth system models that are used for quantifying ecosystem–climate feedbacks. We then coupled BeTR-v2 to the Energy Exascale Earth System Model to quantify how different numerical couplings of plants and soils affect simulated ecosystem biogeochemistry. We found that different couplings lead to significant uncertainty that is not correctable by tuning parameters.
Jing Tao, Qing Zhu, William J. Riley, and Rebecca B. Neumann
The Cryosphere, 15, 5281–5307, https://doi.org/10.5194/tc-15-5281-2021, https://doi.org/10.5194/tc-15-5281-2021, 2021
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We improved the DOE's E3SM land model (ELMv1-ECA) simulations of soil temperature, zero-curtain period durations, cold-season CH4, and CO2 emissions at several Alaskan Arctic tundra sites. We demonstrated that simulated CH4 emissions during zero-curtain periods accounted for more than 50 % of total emissions throughout the entire cold season (Sep to May). We also found that cold-season CO2 emissions largely offset warm-season net uptake currently and showed increasing trends from 1950 to 2017.
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Short summary
Wetlands absorb carbon dioxide (CO2), helping slow climate change, but they also release methane, a potent warming gas. We developed a collection of AI-based models to estimate magnitudes of CO2 and methane exchanged between the land and the atmosphere, for wetlands on a regional scale. This approach helps to inform land-use planning, restoration, and greenhouse gas accounting, while also creating a foundation for future advancements in prediction accuracy.
Wetlands absorb carbon dioxide (CO2), helping slow climate change, but they also release...
