Articles | Volume 15, issue 12
https://doi.org/10.5194/tc-15-5281-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-5281-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Nov 2021
Research article |
| 30 Nov 2021
| 30 Nov 2021
Improved ELMv1-ECA simulations of zero-curtain periods and cold-season CH4 and CO2 emissions at Alaskan Arctic tundra sites
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, USA
Department of Civil and Environmental Engineering, University of
Washington, Seattle, WA, 98195, USA
Qing Zhu
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, USA
William J. Riley
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, CA, 94720, USA
Rebecca B. Neumann
Department of Civil and Environmental Engineering, University of
Washington, Seattle, WA, 98195, USA
Related authors
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3517, https://doi.org/10.5194/egusphere-2025-3517, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We studied how well Earth System Models simulate soil moisture and its connection to plant growth and water use. Using a model evaluation tool and real-world data, we found that models generally perform well at the surface but struggle deeper in the soil. These issues vary by region, especially in colder regions. Our results can help improve future model development and support better predictions of how ecosystems respond to a changing environment.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-3517, https://doi.org/10.5194/egusphere-2025-3517, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We studied how well Earth System Models simulate soil moisture and its connection to plant growth and water use. Using a model evaluation tool and real-world data, we found that models generally perform well at the surface but struggle deeper in the soil. These issues vary by region, especially in colder regions. Our results can help improve future model development and support better predictions of how ecosystems respond to a changing environment.
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
Short summary
Short summary
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.
Elsa Abs, Christoph Keuschnig, Pierre Amato, Chris Bowler, Eric Capo, Alexander Chase, Luciana Chavez Rodriguez, Abraham Dabengwa, Thomas Dussarrat, Thomas Guzman, Linnea Honeker, Jenni Hultman, Kirsten Küsel, Zhen Li, Anna Mankowski, William Riley, Scott Saleska, and Lisa Wingate
EGUsphere, https://doi.org/10.5194/egusphere-2025-1716, https://doi.org/10.5194/egusphere-2025-1716, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Ashley Brereton, Zelalem Mekonnen, Bhavna Arora, William Riley, Kunxiaojia Yuan, Yi Xu, Yu Zhang, Qing Zhu, Tyler Anthony, and Adina Paytan
EGUsphere, https://doi.org/10.5194/egusphere-2025-361, https://doi.org/10.5194/egusphere-2025-361, 2025
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
Short summary
Short summary
Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
Robinson I. Negrón-Juárez, Jennifer A. Holm, Boris Faybishenko, Daniel Magnabosco-Marra, Rosie A. Fisher, Jacquelyn K. Shuman, Alessandro C. de Araujo, William J. Riley, and Jeffrey Q. Chambers
Biogeosciences, 17, 6185–6205, https://doi.org/10.5194/bg-17-6185-2020, https://doi.org/10.5194/bg-17-6185-2020, 2020
Short summary
Short summary
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, and Scott R. Saleska
Biogeosciences, 17, 5849–5860, https://doi.org/10.5194/bg-17-5849-2020, https://doi.org/10.5194/bg-17-5849-2020, 2020
Short summary
Short summary
Methane (CH4) is a strong greenhouse gas that can accelerate climate change and offset mitigation efforts. A key assumption embedded in many large-scale climate models is that ecosystem CH4 emissions can be estimated by fixed temperature relations. Here, we demonstrate that CH4 emissions cannot be parameterized by emergent temperature response alone due to variability driven by microbial and abiotic interactions. We also provide mechanistic understanding for observed CH4 emission hysteresis.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
Short summary
Short summary
It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
Cited articles
Anthony, K. M. W., Anthony, P., Grosse, G., and Chanton, J.: Geologic
methane seeps along boundaries of Arctic permafrost thaw and melting
glaciers, Nat. Geosci., 5, 419–426, 2012.
Arndt, K. A., Oechel, W. C., Goodrich, J. P., Bailey, B. A., Kalhori, A.,
Hashemi, J., Sweeney, C., and Zona, D.: Sensitivity of Methane Emissions to
Later Soil Freezing in Arctic Tundra Ecosystems, J. Geophys. Res.-Biogeo., 124,
2595–2609, https://doi.org/10.1029/2019JG005242, 2019.
Belshe, E. F., Schuur, E. A. G., and Bolker, B. M.: Tundra ecosystems
observed to be CO2 sources due to differential amplification of the carbon
cycle, Ecol. Lett., 16, 1307–1315, https://doi.org/10.1111/ele.12164, 2013.
Bhanja, S. N. and Wang, J. Y.: Estimating influences of environmental
drivers on soil heterotrophic respiration in the Athabasca River Basin,
Canada, Environ. Pollut., 257, https://doi.org/10.1016/j.envpol.2019.113630, 2020.
Bisht, G., Riley, W. J., Wainwright, H. M., Dafflon, B., Yuan, F., and Romanovsky, V. E.: Impacts of microtopographic snow redistribution and lateral subsurface processes on hydrologic and thermal states in an Arctic polygonal ground ecosystem: a case study using ELM-3D v1.0, Geosci. Model Dev., 11, 61–76, https://doi.org/10.5194/gmd-11-61-2018, 2018.
Box, J. E., Colgan, W. T., Christensen, T. R., Schmidt, N. M., Lund, M.,
Parmentier, F. J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S.,
Romanovsky, V. E., Walsh, J. E., Overland, J. E., Wang, M. Y., Corell, R.
W., Meier, W. N., Wouters, B., Mernild, S., Mard, J., Pawlak, J., and Olsen,
M. S.: Key indicators of Arctic climate change: 1971–2017, Environ.
Res. Lett., 14, 045010, https://doi.org/10.1088/1748-9326/aafc1b, 2019.
Burrows, S. M., Maltrud, M., Yang, X., Zhu, Q., Jeffery, N., Shi, X.,
Ricciuto, D., Wang, S., Bisht, G., Tang, J., Wolfe, J., Harrop, B. E.,
Singh, B., Brent, L., Baldwin, S., Zhou, T., Cameron-Smith, P., Keen, N.,
Collier, N., Xu, M., Hunke, E. C., Elliott, S. M., Turner, A. K., Li, H.,
Wang, H., Golaz, J. C., Bond-Lamberty, B., Hoffman, F. M., Riley, W. J.,
Thornton, P. E., Calvin, K., and Leung, L. R.: The DOE E3SM v1.1
Biogeochemistry Configuration: Description and Simulated Ecosystem-Climate
Responses to Historical Changes in Forcing, J. Adv. Model. Earth. Sy., 12, e2019MS001766, https://doi.org/10.1029/2019MS001766, 2020.
Cary, J. W. and Mayland, H. F.: Salt and Water Movement in Unsaturated
Frozen Soil, Soil. Sci. Soc. Am. Pro., 36, 549–555, 1972.
Chadburn, S. E., Aalto, T., Aurela, M., Baldocchi, D., Biasi, C., Boike, J.,
Burke, E. J., Comyn-Platt, E., Dolman, A. J., Duran-Rojas, C., Fan, Y. C.,
Friborg, T., Gao, Y., Gedney, N., Gockede, M., Hayman, G. D., Holl, D.,
Hugelius, G., Kutzbach, L., Lee, H., Lohila, A., Parmentier, F. J. W.,
Sachs, T., Shurpali, N. J., and Westermann, S.: Modeled Microbial Dynamics
Explain the Apparent Temperature Sensitivity of Wetland Methane Emissions,
Global Biogeochem. Cy., 34, e2020GB006678, https://doi.org/10.1029/2020GB006678, 2020.
Chang, K.-Y., Riley, W. J., Crill, P. M., Grant, R. F., Rich, V. I., and Saleska, S. R.: Large carbon cycle sensitivities to climate across a permafrost thaw gradient in subarctic Sweden, The Cryosphere, 13, 647–663, https://doi.org/10.5194/tc-13-647-2019, 2019.
Chang, K.-Y., Riley, W. J., Crill, P. M., Grant, R. F., and Saleska, S. R.: Hysteretic temperature sensitivity of wetland CH4 fluxes explained by substrate availability and microbial activity, Biogeosciences, 17, 5849–5860, https://doi.org/10.5194/bg-17-5849-2020, 2020.
Chang, K. Y., Riley, W. J., Knox, S. H., et al.:
Substantial hysteresis in emergent temperature sensitivity of global wetland
CH4 emissions, Nat. Commun., 12, 2266, https://doi.org/10.1038/s41467-021-22452-1, 2021.
Clapp, R. B. and Hornberger, G. M.: Empirical equations for some soil
hydraulic properties, Water Resour. Res., 14, 601–604, 1978.
Commane, R., Lindaas, J., Benmergui, J., Luus, K. A., Chang, R. Y. W.,
Daube, B. C., Euskirchen, E. S., Henderson, J. M., Karion, A., Miller, J.
B., Miller, S. M., Parazoo, N. C., Randerson, J. T., Sweeney, C., Tans, P.,
Thoning, K., Veraverbeke, S., Miller, C. E., and Wofsy, S. C.: Carbon
dioxide sources from Alaska driven by increasing early winter respiration
from Arctic tundra, P. Natl. Acad. Sci. USA, 114, 5361–5366, 2017.
Dankers, R., Burke, E. J., and Price, J.: Simulation of permafrost and seasonal thaw depth in the JULES land surface scheme, The Cryosphere, 5, 773–790, https://doi.org/10.5194/tc-5-773-2011, 2011.
Davidson, E. A. and Janssens, I. A.: Temperature sensitivity of soil carbon
decomposition and feedbacks to climate change, Nature, 440, 165–173, 2006.
Davidson, S. J. and Zona, D.: Arctic Vegetation Plots in Flux Tower
Footprints, North Slope, Alaska, 2014, ORNL DAAC, Oak Ridge, Tennessee, USA,
https://doi.org/10.3334/ORNLDAAC/1546, 2018.
Delwiche, K. B., Knox, S. H., Malhotra, A., Fluet-Chouinard, E., McNicol, G., Feron, S., Ouyang, Z., Papale, D., Trotta, C., Canfora, E., Cheah, Y.-W., Christianson, D., Alberto, Ma. C. R., Alekseychik, P., Aurela, M., Baldocchi, D., Bansal, S., Billesbach, D. P., Bohrer, G., Bracho, R., Buchmann, N., Campbell, D. I., Celis, G., Chen, J., Chen, W., Chu, H., Dalmagro, H. J., Dengel, S., Desai, A. R., Detto, M., Dolman, H., Eichelmann, E., Euskirchen, E., Famulari, D., Fuchs, K., Goeckede, M., Gogo, S., Gondwe, M. J., Goodrich, J. P., Gottschalk, P., Graham, S. L., Heimann, M., Helbig, M., Helfter, C., Hemes, K. S., Hirano, T., Hollinger, D., Hörtnagl, L., Iwata, H., Jacotot, A., Jurasinski, G., Kang, M., Kasak, K., King, J., Klatt, J., Koebsch, F., Krauss, K. W., Lai, D. Y. F., Lohila, A., Mammarella, I., Belelli Marchesini, L., Manca, G., Matthes, J. H., Maximov, T., Merbold, L., Mitra, B., Morin, T. H., Nemitz, E., Nilsson, M. B., Niu, S., Oechel, W. C., Oikawa, P. Y., Ono, K., Peichl, M., Peltola, O., Reba, M. L., Richardson, A. D., Riley, W., Runkle, B. R. K., Ryu, Y., Sachs, T., Sakabe, A., Sanchez, C. R., Schuur, E. A., Schäfer, K. V. R., Sonnentag, O., Sparks, J. P., Stuart-Haëntjens, E., Sturtevant, C., Sullivan, R. C., Szutu, D. J., Thom, J. E., Torn, M. S., Tuittila, E.-S., Turner, J., Ueyama, M., Valach, A. C., Vargas, R., Varlagin, A., Vazquez-Lule, A., Verfaillie, J. G., Vesala, T., Vourlitis, G. L., Ward, E. J., Wille, C., Wohlfahrt, G., Wong, G. X., Zhang, Z., Zona, D., Windham-Myers, L., Poulter, B., and Jackson, R. B.: FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands, Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, 2021.
Etiope, G. and Klusman, R. W.: Microseepage in drylands: Flux and
implications in the global atmospheric source/sink budget of methane, Global Planet. Change, 72, 265–274, 2010.
Fahnestock, J. T., Jones, M. H., Brooks, P. D., Walker, D. A., and Welker,
J. M.: Winter and early spring CO2 efflux from tundra communities of
northern Alaska, J. Geophys. Res.-Atmos., 103,
29023–29027, https://doi.org/10.1029/98jd00805, 1998.
Fuchs, M., Campbell, G., and Papendick, R.: An analysis of sensible and
latent heat flow in a partially frozen unsaturated soil, Soil Sci. Soc. Am. J.,
42, 379–385, 1978.
Golaz, J. C., Caldwell, P. M., Van Roekel, L. P., Petersen, M. R., Tang, Q.,
Wolfe, J. D., Abeshu, G., Anantharaj, V., Asay-Davis, X. S., Bader, D. C.,
Baldwin, S. A., Bisht, G., Bogenschutz, P. A., Branstetter, M., Brunke, M.
A., Brus, S. R., Burrows, S. M., Cameron-Smith, P. J., Donahue, A. S.,
Deakin, M., Easter, R. C., Evans, K. J., Feng, Y., Flanner, M., Foucar, J.
G., Fyke, J. G., Griffin, B. M., Hannay, C., Harrop, B. E., Hoffman, M. J.,
Hunke, E. C., Jacob, R. L., Jacobsen, D. W., Jeffery, N., Jones, P. W.,
Keen, N. D., Klein, S. A., Larson, V. E., Leung, L. R., Li, H. Y., Lin, W.
Y., Lipscomb, W. H., Ma, P. L., Mahajan, S., Maltrud, M. E., Mametjanov, A.,
McClean, J. L., McCoy, R. B., Neale, R. B., Price, S. F., Qian, Y., Rasch,
P. J., Eyre, J. E. J. R., Riley, W. J., Ringler, T. D., Roberts, A. F.,
Roesler, E. L., Salinger, A. G., Shaheen, Z., Shi, X. Y., Singh, B., Tang,
J. Y., Taylor, M. A., Thornton, P. E., Turner, A. K., Veneziani, M., Wan,
H., Wang, H. L., Wang, S. L., Williams, D. N., Wolfram, P. J., Worley, P.
H., Xie, S. C., Yang, Y., Yoon, J. H., Zelinka, M. D., Zender, C. S., Zeng,
X. B., Zhang, C. Z., Zhang, K., Zhang, Y., Zheng, X., Zhou, T., and Zhu, Q.:
The DOE E3SM Coupled Model Version 1: Overview and Evaluation at Standard
Resolution, J. Adv. Model. Earth Sy., 11, 2089–2129, 2019.
Graf, A., Weihermuller, L., Huisman, J. A., Herbst, M., and Vereecken, H.:
Comment on “Global Convergence in the Temperature Sensitivity of Respiration
at Ecosystem Level”, Science, 331, 1265, https://doi.org/10.1126/science.1196948, 2011.
Grant, R. F., Mekonnen, Z. A., Riley, W. J., Arora, B., and Torn, M. S.:
Mathematical Modelling of Arctic Polygonal Tundra with Ecosys: 2.
Microtopography Determines How CO2 and CH4 Exchange Responds to Changes in
Temperature and Precipitation, J. Geophys. Res.-Biogeo., 122, 3174–3187, 2017a.
Grant, R. F., Mekonnen, Z. A., Riley, W. J., Wainwright, H. M., Graham, D.,
and Torn, M. S.: Mathematical Modelling of Arctic Polygonal Tundra with
Ecosys: 1. Microtopography Determines How Active Layer Depths Respond to
Changes in Temperature and Precipitation, J. Geophys. Res.-Biogeo., 122,
3161–3173, 2017b.
Hansson, K., Simunek, J., Mizoguchi, M., Lundin, L. C., and van Genuchten, M. T.: Water flow and heat transport in frozen soil: Numerical solution and freeze-thaw applications, Vadose Zone J., 3, 693–704, 2004.
Harmon, M. and Domingo, J.: A users guide to STANDCARB version 2.0: a model
to simulate the carbon stores in forest stands, Dep. of For. Sci., Oreg.
State Univ., Corvallis, OR, USA, 2001.
Harris, I.: CRU JRA v1. 1: A forcings dataset of gridded land surface blend
of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; January
1901–December 2017, University of East Anglia Climatic Research
Unit, Centre for Environmental Data Analysis, 2905, Norwich NR4 7TJ, United Kingdom, https://doi.org/10.5285/13f3635174794bb98cf8ac4b0ee8f4ed, 2019.
Jones, M. H., Fahnestock, J. T., and Welker, J. M.: Early and late winter
CO2 efflux from arctic tundra in the Kuparuk River watershed, Alaska, USA,
Arct. Antarct. Alp. Res., 31, 187–190, https://doi.org/10.2307/1552607, 1999.
Kelly, R., Parton, W., Hartman, M., Stretch, L., Ojima, D., and Schimel, D.:
Intra-annual and interannual variability of ecosystem processes in
shortgrass steppe, J. Geophys. Res.-Atmos., 105,
20093–20100, 2000.
Kim, D., Lee, M. I., and Seo, E.: Improvement of Soil Respiration
Parameterization in a Dynamic Global Vegetation Model and Its Impact on the
Simulation of Terrestrial Carbon Fluxes, J. Climate, 32, 127–143,
2019.
Kim, Y., Ueyama, M., Nakagawa, F., Tsunogai, U., Harazono, Y., and Tanaka,
N.: Assessment of winter fluxes of CO2 and CH4 in boreal forest soils of
central Alaska estimated by the profile method and the chamber method: a
diagnosis of methane emission and implications for the regional carbon
budget, Tellus B, 59, 223–233, 2007.
Kittler, F., Heimann, M., Kolle, O., Zimov, N., Zimov, S., and Gockede, M.:
Long-Term Drainage Reduces CO2 Uptake and CH4 Emissions in a Siberian
Permafrost Ecosystem, Global Biogeochem. Cy., 31, 1704–1717, 2017.
Knox, S. H., Jackson, R. B., Poulter, B., McNicol, G., Fluet-Chouinard, E.,
Zhang, Z., Hugelius, G., Bousquet, P., Canadell, J. G., Saunois, M., Papale,
D., Chu, H., Keenan, T. F., Baldocchi, D., Torn, M. S., Mammarella, I.,
Trotta, C., Aurela, M., Bohrer, G., Campbell, D. I., Cescatti, A.,
Chamberlain, S., Chen, J., Chen, W., Dengel, S., Desai, A. R., Euskirchen,
E., Friborg, T., Gasbarra, D., Goded, I., Goeckede, M., Heimann, M., Helbig,
M., Hirano, T., Hollinger, D. Y., Iwata, H., Kang, M., Klatt, J., Krauss, K.
W., Kutzbach, L., Lohila, A., Mitra, B., Morin, T. H., Nilsson, M. B., Niu,
S., Noormets, A., Oechel, W. C., Peichl, M., Peltola, O., Reba, M. L.,
Richardson, A. D., Runkle, B. R. K., Ryu, Y., Sachs, T., Schafer, K. V. R.,
Schmid, H. P., Shurpali, N., Sonnentag, O., Tang, A. C. I., Ueyama, M.,
Vargas, R., Vesala, T., Ward, E. J., Windham-Myers, L., Wohlfahrt, G., and
Zona, D.: FLUXNET-CH4 Synthesis Activity: Objectives, Observations, and
Future Directions, B. Am. Meteorol. Soc., 100,
2607–2632, https://doi.org/10.1175/Bams-D-18-0268.1, 2019.
Koven, C. D., Ringeval, B., Friedlingstein, P., Ciais, P., Cadule, P.,
Khvorostyanov, D., Krinner, G., and Tarnocai, C.: Permafrost carbon-climate
feedbacks accelerate global warming, P. Natl. Acad. Sci. USA, 108, 14769–14774,
2011.
Koven, C. D., Riley, W. J., and Stern, A.: Analysis of Permafrost Thermal
Dynamics and Response to Climate Change in the CMIP5 Earth System Models,
J. Climate, 26, 1877–1900, https://doi.org/10.1175/Jcli-D-12-00228.1, 2013a.
Koven, C. D., Riley, W. J., Subin, Z. M., Tang, J. Y., Torn, M. S., Collins, W. D., Bonan, G. B., Lawrence, D. M., and Swenson, S. C.: The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4, Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, 2013b.
Koven, C. D., Lawrence, D. M., and Riley, W. J.: Permafrost carbon-climate
feedback is sensitive to deep soil carbon decomposability but not deep soil
nitrogen dynamics, P. Natl. Acad. Sci. USA, 112, 3752–3757,
https://doi.org/10.1073/pnas.1415123112, 2015.
Koven, C. D., Hugelius, G., Lawrence, D. M., and Wieder, W. R.: Higher
climatological temperature sensitivity of soil carbon in cold than warm
climates, Nat. Clim. Change, 7, 817–822, https://doi.org/10.1038/Nclimate3421, 2017.
Kuhn, M. A., Varner, R. K., Bastviken, D., Crill, P., MacIntyre, S., Turetsky, M., Walter Anthony, K., McGuire, A. D., and Olefeldt, D.: BAWLD-CH4: a comprehensive dataset of methane fluxes from boreal and arctic ecosystems, Earth Syst. Sci. Data, 13, 5151–5189, https://doi.org/10.5194/essd-13-5151-2021, 2021.
Kurylyk, B. L. and Watanabe, K.: The mathematical representation of freezing and thawing processes in variably-saturated, non-deformable soils, Adv. Water Resour., 60, 160–177, https://doi.org/10.1016/j.advwatres.2013.07.016, 2013.
Kurylyk, B. L. and Hayashi, M.: Improved Stefan Equation Correction Factors
to Accommodate Sensible Heat Storage during Soil Freezing or Thawing,
Permafrost Periglac., 27, 189–203, 2016.
Lawrence, D. M., Thornton, P. E., Oleson, K. W., and Bonan, G. B.: The
partitioning of evapotranspiration into transpiration, soil evaporation, and
canopy evaporation in a GCM: Impacts on land-atmosphere interaction, J.
Hydrometeorol., 8, 862–880, 2007.
Lawrence, D. M. and Slater, A. G.: Incorporating organic soil into a global
climate model, Clim. Dynam., 30, 145–160, https://doi.org/10.1007/s00382-007-0278-1, 2008.
Lawrence, D. M. and Slater, A. G.: The contribution of snow condition
trends to future ground climate, Clim. Dynam., 34, 969–981, 2010.
Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J., and Slater, A.
G.: Permafrost thaw and resulting soil moisture changes regulate projected
high-latitude CO2 and CH4 emissions, Environ. Res. Lett., 10, 094011, https://doi.org/10.1088/1748-9326/10/9/094011, 2015.
Le Moigne, P., Boone, A., Belamari, S., Brun, E., Calvet, J., Decharme, B.,
Faroux, S., Gibelin, A., Giordani, H., Lafont, S., Lebeaupin, C., Le Moigne,
P., Mahfouf, J., Martin, E., Masson, V., Mironov, D., Morin, S., Noilhan,
J., Tulet, P., Van den Hurk, B., and Vionnet, V.: SURFEX Scientific
Documentation, Note de centre (CNRM/GMME), Météo-France, Toulouse,
France, 2012.
Liu, Y. N., Bisht, G., Subin, Z. M., Riley, W. J., and Pau, G. S. H.: A
Hybrid Reduced-Order Model of Fine-Resolution Hydrologic Simulations at a
Polygonal Tundra Site, Vadose Zone. J., 15, vzj2015.05.0068, https://doi.org/10.2136/vzj2015.05.0068, 2016.
Lyman, S. N., Tran, H. N., Mansfield, M. L., Bowers, R., and Smith, A.:
Strong temporal variability in methane fluxes from natural gas well pad
soils, Atmo. Poll. Res., 11, 1386–1395, https://doi.org/10.1016/j.apr.2020.05.011, 2020.
Mahecha, M. D., Reichstein, M., Carvalhais, N., Lasslop, G., Lange, H.,
Seneviratne, S. I., Vargas, R., Ammann, C., Arain, M. A., Cescatti, A.,
Janssens, I. A., Migliavacca, M., Montagnani, L., and Richardson, A. D.:
Global Convergence in the Temperature Sensitivity of Respiration at
Ecosystem Level, Science, 329, 838–840, 2010.
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013.
Mekonnen, Z. A., Riley, W. J., Grant, R. F., and Romanovsky, V.: Changes in
precipitation and air temperature contribute comparably to permafrost
degradation in a warmer climate, Environ. Res. Lett., 16, 024008, https://doi.org/10.1088/1748-9326/abc444, 2020.
Meyer, N., Welp, G., and Amelung, W.: The Temperature Sensitivity (Q10) of
Soil Respiration: Controlling Factors and Spatial Prediction at Regional
Scale Based on Environmental Soil Classes, Global Biogeochem. Cy., 32,
306–323, 2018.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R.
D., and Veith, T. L.: Model evaluation guidelines for systematic
quantification of accuracy in watershed simulations, T. Asabe, 50, 885–900, 2007.
Moyano, F. E., Manzoni, S., and Chenu, C.: Responses of soil heterotrophic
respiration to moisture availability: An exploration of processes and
models, Soil Biol. Biochem., 59, 72–85, 2013.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Natali, S. M., Watts, J. D., Rogers, B. M., Potter, S., Ludwig, S. M.,
Selbmann, A.-K., Sullivan, P. F., Abbott, B. W., Arndt, K. A., Birch, L.,
Björkman, M. P., Bloom, A. A., Celis, G., Christensen, T. R.,
Christiansen, C. T., Commane, R., Cooper, E. J., Crill, P., Czimczik, C.,
Davydov, S., Du, J., Egan, J. E., Elberling, B., Euskirchen, E. S., Friborg,
T., Genet, H., Göckede, M., Goodrich, J. P., Grogan, P., Helbig, M.,
Jafarov, E. E., Jastrow, J. D., Kalhori, A. A. M., Kim, Y., Kimball, J. S.,
Kutzbach, L., Lara, M. J., Larsen, K. S., Lee, B.-Y., Liu, Z., Loranty, M.
M., Lund, M., Lupascu, M., Madani, N., Malhotra, A., Matamala, R.,
McFarland, J., McGuire, A. D., Michelsen, A., Minions, C., Oechel, W. C.,
Olefeldt, D., Parmentier, F.-J. W., Pirk, N., Poulter, B., Quinton, W.,
Rezanezhad, F., Risk, D., Sachs, T., Schaefer, K., Schmidt, N. M., Schuur,
E. A. G., Semenchuk, P. R., Shaver, G., Sonnentag, O., Starr, G., Treat, C.
C., Waldrop, M. P., Wang, Y., Welker, J., Wille, C., Xu, X., Zhang, Z.,
Zhuang, Q., and Zona, D.: Large loss of CO2 in winter observed across the
northern permafrost region, Nat. Clim. Change, 9, 852–857, https://doi.org/10.1038/s41558-019-0592-8,
2019.
Neumann, R. B., Moorberg, C. J., Lundquist, J. D., Turner, J. C., Waldrop,
M. P., McFarland, J. W., Euskirchen, E. S., Edgar, C. W., and Turetsky, M.
R.: Warming effects of spring rainfall increase methane emissions from
thawing permafrost, Geophys. Res. Lett., 46, 1393–1401, https://doi.org/10.1029/2018GL081274, 2019.
Nicolsky, D. J., Romanovsky, V. E., Alexeev, V. A., and Lawrence, D. M.:
Improved modeling of permafrost dynamics in a GCM land-surface scheme,
Geophys. Res. Lett., 34, L08501, https://doi.org/10.1029/2007GL029525, 2007.
Niu, G. Y., and Yang, Z. L.: Effects of frozen soil on snowmelt runoff and
soil water storage at a continental scale, J. Hydrometeorol., 7, 937–952,
2006.
Oechel, W. C., Vourlitis, G. L., Hastings, S. J., Zulueta, R. C., Hinzman,
L., and Kane, D.: Acclimation of ecosystem CO2 exchange in the Alaskan
Arctic in response to decadal climate warming, Nature, 406, 978–981, 2000.
Oechel, W. C., Laskowski, C. A., Burba, G., Gioli, B., and Kalhori, A. A.
M.: Annual patterns and budget of CO2 flux in an Arctic tussock tundra
ecosystem, J. Geophys. Res.-Biogeo., 119, 323–339, 2014.
Oechel, W. C. and Kalhori, A.: ABoVE: CO2 and CH4 Fluxes and Meteorology at
Flux Tower Sites, Alaska, 2015–2017, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ornldaac/1562,
2018.
Oleson, K. W., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C.,
Levis, S., Li, F., Riley, W., and Subin, Z.: Technical Description of
version 4.5 of the Community Land Model (CLM)(NCAR Technical Note No.
NCAR/TN-503+ STR), Citeseer, National Center for Atmospheric Research, PO
Box, 3000, Boulder, Colorado, USA, 2013.
Outcalt, S. I., Nelson, F. E., and Hinkel, K. M.: The Zero-Curtain Effect –
Heat and Mass-Transfer across an Isothermal Region in Freezing Soil, Water Resour. Res., 26, 1509–1516, 1990.
Permafrost Laboratory: The UAF observations, available at: http://permafrost.gi.alaska.edu, last access: 19 November 2021.
Pau, G. S. H., Shen, C. P., Riley, W. J., and Liu, Y. N.: Accurate and
efficient prediction of fine-resolution hydrologic and carbon dynamic
simulations from coarse-resolution models, Water Resour. Res., 52,
791–812, 2016.
Peltola, O., Vesala, T., Gao, Y., Räty, O., Alekseychik, P., Aurela, M., Chojnicki, B., Desai, A. R., Dolman, A. J., Euskirchen, E. S., Friborg, T., Göckede, M., Helbig, M., Humphreys, E., Jackson, R. B., Jocher, G., Joos, F., Klatt, J., Knox, S. H., Kowalska, N., Kutzbach, L., Lienert, S., Lohila, A., Mammarella, I., Nadeau, D. F., Nilsson, M. B., Oechel, W. C., Peichl, M., Pypker, T., Quinton, W., Rinne, J., Sachs, T., Samson, M., Schmid, H. P., Sonnentag, O., Wille, C., Zona, D., and Aalto, T.: Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations, Earth Syst. Sci. Data, 11, 1263–1289, https://doi.org/10.5194/essd-11-1263-2019, 2019.
Piao, S. L., Ciais, P., Friedlingstein, P., Peylin, P., Reichstein, M.,
Luyssaert, S., Margolis, H., Fang, J. Y., Barr, A., Chen, A. P., Grelle, A.,
Hollinger, D. Y., Laurila, T., Lindroth, A., Richardson, A. D., and Vesala,
T.: Net carbon dioxide losses of northern ecosystems in response to autumn
warming, Nature, 451, 49–52, 2008.
Rafique, R., Xia, J., Hararuk, O., Asrar, G. R., Leng, G., Wang, Y., and Luo, Y.: Divergent predictions of carbon storage between two global land models: attribution of the causes through traceability analysis, Earth Syst. Dynam., 7, 649–658, https://doi.org/10.5194/esd-7-649-2016, 2016.
Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Torn, M. S., Meng, L., Mahowald, N. M., and Hess, P.: Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM, Biogeosciences, 8, 1925–1953, https://doi.org/10.5194/bg-8-1925-2011, 2011.
Romanovsky, V. E., Kholodov, A. L., Cable, W. L., Cohen, L., Panda, S.,
Marchenko, S., Muskett, R. R., and Nicolsky, D.: Network of Permafrost
Observatories in North America and Russia, NSF Arctic Data Center, https://doi.org/10.18739/A2SH27, 2009.
Russell, S. J., Bohrer, G., Johnson, D. R., Villa, J. A., Heltzel, R.,
Rey-Sanchez, C., and Matthes, J. H.: Quantifying CH4 concentration spikes
above baseline and attributing CH4 sources to hydraulic fracturing
activities by continuous monitoring at an off-site tower, Atmos. Environ., 228, 117452, https://doi.org/10.1016/j.atmosenv.2020.117452, 2020.
Sapriza-Azuri, G., Gamazo, P., Razavi, S., and Wheater, H. S.: On the appropriate definition of soil profile configuration and initial conditions for land surface–hydrology models in cold regions, Hydrol. Earth Syst. Sci., 22, 3295–3309, https://doi.org/10.5194/hess-22-3295-2018, 2018.
Schimel, J. P.: Plant-Transport and Methane Production as Controls on
Methane Flux from Arctic Wet Meadow Tundra, Biogeochemistry, 28, 183–200,
1995.
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S., and Janssens,
I.: Sensitivity of decomposition rates of soil organic matter with respect
to simultaneous changes in temperature and moisture, J. Adv. Model Earth Sy,
7, 335–356, 2015.
Sierra, C. A., Malghani, S., and Loescher, H. W.: Interactions among temperature, moisture, and oxygen concentrations in controlling decomposition rates in a boreal forest soil, Biogeosciences, 14, 703–710, https://doi.org/10.5194/bg-14-703-2017, 2017.
Skopp, J., Jawson, M., and Doran, J.: Steady-state aerobic microbial
activity as a function of soil water content, Soil Sci. Soc. Am. J., 54,
1619–1625, 1990.
Slater, A. G., Lawrence, D. M., and Koven, C. D.: Process-level model evaluation: a snow and heat transfer metric, The Cryosphere, 11, 989–996, https://doi.org/10.5194/tc-11-989-2017, 2017.
Tang, J. Y. and Riley, W. J.: Weaker soil carbon-climate feedbacks
resulting from microbial and abiotic interactions, Nat. Clim. Change, 5,
56–60, https://doi.org/10.1038/Nclimate2438, 2015.
Tang, J. Y. and Riley, W. J.: A Theory of Effective Microbial Substrate
Affinity Parameters in Variably Saturated Soils and an Example Application
to Aerobic Soil Heterotrophic Respiration, J. Geophys. Res.-Biogeo., 124,
918–940, 2019.
Tao, J., Reichle, R. H., Koster, R. D., Forman, B. A., and Xue, Y.:
Evaluation and Enhancement of Permafrost Modeling With the NASA Catchment
Land Surface Model, J. Adv. Model. Earth. Sy., 9, 2771–2795, 2017.
Tao, J., Koster, R. D., Reichle, R. H., Forman, B. A., Xue, Y., Chen, R. H., and Moghaddam, M.: Permafrost variability over the Northern Hemisphere based on the MERRA-2 reanalysis, The Cryosphere, 13, 2087–2110, https://doi.org/10.5194/tc-13-2087-2019, 2019.
Tao, J., Zhu, Q., Riley, W. J., and Neumann, R. B.: Warm-season net CO2 uptake outweighs cold-season emissions over Alaskan North Slope tundra under current and RCP8.5 climate, Environ. Res. Lett., 16, 055012, https://doi.org/10.1088/1748-9326/abf6f5, 2021a.
Tao, J., Zhu, Q., Riley, W., and Neuman, R.: Updated ELMv1-ECA for improved simulations of soil zero-curtain periods and cold-season carbon emissions at tundra sites (ELMv1av1b), Zenodo [code], https://doi.org/10.5281/zenodo.5725525, 2021b.
Taylor, M. A., Celis, G., Ledman, J. D., Bracho, R., and Schuur, E. A. G.:
Methane Efflux Measured by Eddy Covariance in Alaskan Upland Tundra
Undergoing Permafrost Degradation, J. Geophys. Res.-Biogeo., 123, 2695–2710,
2018.
Virkkala, A.-M., Aalto, J. A., Tagesson, T., Treat, C. C., Lehtonen, A.,
Rogers, B. M., Natali, S., and Luoto, M.: High-latitude terrestrial regions
remain a CO2 sink over 1990–2015, AGUFM, 2019, B43E-01, 2019.
Virkkala, A. M., Aalto, J., Rogers, B. M., Tagesson, T., Treat, C. C.,
Natali, S. M., Watts, J. D., Potter, S., Lehtonen, A., and Mauritz, M.:
Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra
and boreal domain: regional patterns and uncertainties, Glob. Change Biol.,
2021.
Wang, Y. H., Yuan, F. M., Yuan, F. H., Gu, B. H., Hahn, M. S., Torn, M. S.,
Ricciuto, D. M., Kumar, J., He, L. Y., Zona, D., Lipson, D. A., Wagner, R.,
Oechel, W. C., Wullschleger, S. D., Thornton, P. E., and Xu, X. F.:
Mechanistic Modeling of Microtopographic Impacts on CO2 and CH4 Fluxes in an
Alaskan Tundra Ecosystem Using the CLM-Microbe Model, J. Adv. Model. Earth Sy.,
11, 4288–4304, 2019.
Wania, R., Ross, I., and Prentice, I. C.: Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHyMe v1.3.1, Geosci. Model Dev., 3, 565–584, https://doi.org/10.5194/gmd-3-565-2010, 2010.
Wilkman, E., Zona, D., Tang, Y. F., Gioli, B., Lipson, D. A., and Oechel,
W.: Temperature Response of Respiration Across the Heterogeneous Landscape
of the Alaskan Arctic Tundra, J. Geophys. Res.-Biogeo., 123, 2287–2302, 2018.
Xu, X., Riley, W. J., Koven, C. D., Billesbach, D. P., Chang, R. Y.-W., Commane, R., Euskirchen, E. S., Hartery, S., Harazono, Y., Iwata, H., McDonald, K. C., Miller, C. E., Oechel, W. C., Poulter, B., Raz-Yaseef, N., Sweeney, C., Torn, M., Wofsy, S. C., Zhang, Z., and Zona, D.: A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands, Biogeosciences, 13, 5043–5056, https://doi.org/10.5194/bg-13-5043-2016, 2016.
Yan, Z. F., Bond-Lamberty, B., Todd-Brown, K. E., Bailey, V. L., Li, S. L.,
Liu, C. Q., and Liu, C. X.: A moisture function of soil heterotrophic
respiration that incorporates microscale processes, Nat. Commun., 9, 2562, https://doi.org/10.1038/s41467-018-04971-6, 2018.
Yang, K., Wang, C. H., and Li, S. Y.: Improved Simulation of Frozen-Thawing
Process in Land Surface Model (CLM4.5), J. Geophys. Res.-Atmos., 123, 13238–13258, 2018a.
Yang, Q., Dan, L., Wu, J., Jiang, R., Dan, J., Li, W., Yang, F., Yang, X.,
and Xia, L.: The Improved Freeze-Thaw Process of a Climate-Vegetation Model:
Calibration and Validation Tests in the Source Region of the Yellow River,
J. Geophys. Res.-Atmos., 123, 13346–13367, 2018b.
Zeng, J. Y., Matsunaga, T., Tan, Z. H., Saigusa, N., Shirai, T., Tang, Y.
H., Peng, S. S., and Fukuda, Y.: Global terrestrial carbon fluxes of
1999–2019 estimated by upscaling eddy covariance data with a random forest,
Sci. Data, 7, 313, https://doi.org/10.1038/s41597-020-00653-5, 2020.
Zhou, T., Shi, P. J., Hui, D. F., and Luo, Y. Q.: Global pattern of
temperature sensitivity of soil heterotrophic respiration (Q(10)) and its
implications for carbon-climate feedback, J. Geophys. Res.-Biogeo., 114, G02016, https://doi.org/10.1029/2008JG000850, 2009.
Zhu, Q., Riley, W. J., Tang, J. Y., Collier, N., Hoffman, F. M., Yang, X.
J., and Bisht, G.: Representing Nitrogen, Phosphorus, and Carbon
Interactions in the E3SM Land Model: Development and Global Benchmarking, J
Adv Model Earth Sy, 11, 2238–2258, 2019.
Zhu, Q., Riley, W. J., Iversen, C. M., and Kattge, J.: Assessing Impacts of
Plant Stoichiometric Traits on Terrestrial Ecosystem Carbon Accumulation
Using the E3SM Land Model, J. Adv. Model. Earth. Sy., 12, e2019MS001841, https://doi.org/10.1029/2019MS001841, 2020.
Zona, D., Oechel, W., Miller, C. E., Dinardo, S. J., Commane, R., Lindaas, J. O. W., Chang, R. Y.-W., Wofsy, S. C., Sweeney, C., and Karion, A.: CARVE-ARCSS: Methane Loss From Arctic- Fluxes From the Alaskan North Slope, 2012–2014, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/1300, 2015.
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E.,
Dinardo, S. J., Dengel, S., Sweeney, C., and Karion, A.: Cold season
emissions dominate the Arctic tundra methane budget, P. Natl. Acad. Sci. USA, 113, 40–45, 2016.
Short summary
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.
We improved the DOE's E3SM land model (ELMv1-ECA) simulations of soil temperature, zero-curtain...
