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
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Dalei Hao, Ghassem R. Asrar, Yelu Zeng, Qing Zhu, Jianguang Wen, Qing Xiao, and Min Chen
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Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
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Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Han Qiu, Dongwei Gui, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-246, https://doi.org/10.5194/hess-2019-246, 2019
Manuscript not accepted for further review
Jing Tao, Randal D. Koster, Rolf H. Reichle, Barton A. Forman, Yuan Xue, Richard H. Chen, and Mahta Moghaddam
The Cryosphere, 13, 2087–2110, https://doi.org/10.5194/tc-13-2087-2019, https://doi.org/10.5194/tc-13-2087-2019, 2019
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Fushan Wang, Guangheng Ni, William J. Riley, Jinyun Tang, Dejun Zhu, and Ting Sun
Geosci. Model Dev., 12, 2119–2138, https://doi.org/10.5194/gmd-12-2119-2019, https://doi.org/10.5194/gmd-12-2119-2019, 2019
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The current lake model in the Weather Research and Forecasting system was reported to be insufficient in simulating deep lakes and reservoirs. We thus revised the lake model by improving its spatial discretization scheme, surface property parameterization, diffusivity parameterization, and convection scheme. The revised model was evaluated at a deep reservoir in southwestern China and the results were in good agreement with measurements.
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, Virginia I. Rich, and Scott R. Saleska
The Cryosphere, 13, 647–663, https://doi.org/10.5194/tc-13-647-2019, https://doi.org/10.5194/tc-13-647-2019, 2019
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Gautam Bisht, William J. Riley, Glenn E. Hammond, and David M. Lorenzetti
Geosci. Model Dev., 11, 4085–4102, https://doi.org/10.5194/gmd-11-4085-2018, https://doi.org/10.5194/gmd-11-4085-2018, 2018
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Xiyan Xu, William J. Riley, Charles D. Koven, and Gensuo Jia
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-257, https://doi.org/10.5194/bg-2018-257, 2018
Preprint withdrawn
Lara E. Pracht, Malak M. Tfaily, Robert J. Ardissono, and Rebecca B. Neumann
Biogeosciences, 15, 1733–1747, https://doi.org/10.5194/bg-15-1733-2018, https://doi.org/10.5194/bg-15-1733-2018, 2018
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Organic carbon in aquifer recharge waters and sediments can fuel microbial reactions that affect groundwater quality. We used high-resolution mass spectrometry to molecularly characterize organic carbon mobilized off sediment collected from a Bangladeshi aquifer, to reference its composition against dissolved organic carbon in aquifer recharge water, to track compositional changes during incubation, and to advance understanding of microbial processing of organic carbon in anaerobic environments.
Gautam Bisht, William J. Riley, Haruko M. Wainwright, Baptiste Dafflon, Fengming Yuan, and Vladimir E. Romanovsky
Geosci. Model Dev., 11, 61–76, https://doi.org/10.5194/gmd-11-61-2018, https://doi.org/10.5194/gmd-11-61-2018, 2018
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The land model integrated into the Energy Exascale Earth System Model was extended to include snow redistribution (SR) and lateral subsurface hydrologic and thermal processes. Simulation results at a polygonal tundra site near Barrow, Alaska, showed that inclusion of SR resulted in a better agreement with observations. Excluding lateral subsurface processes had a small impact on mean states but caused a large overestimation of spatial variability in soil moisture and temperature.
Gautam Bisht, Maoyi Huang, Tian Zhou, Xingyuan Chen, Heng Dai, Glenn E. Hammond, William J. Riley, Janelle L. Downs, Ying Liu, and John M. Zachara
Geosci. Model Dev., 10, 4539–4562, https://doi.org/10.5194/gmd-10-4539-2017, https://doi.org/10.5194/gmd-10-4539-2017, 2017
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A fully coupled three-dimensional surface and subsurface land model, CP v1.0, was developed to simulate three-way interactions among river water, groundwater, and land surface processes. The coupled model can be used for improving mechanistic understanding of ecosystem functioning and biogeochemical cycling along river corridors under historical and future hydroclimatic changes. The dataset presented in this study can also serve as a good benchmarking case for testing other integrated models.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
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Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Jin-Yun Tang and William J. Riley
Geosci. Model Dev., 10, 3277–3295, https://doi.org/10.5194/gmd-10-3277-2017, https://doi.org/10.5194/gmd-10-3277-2017, 2017
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We proposed the SUPECA kinetics to scale from single biogeochemical reactions to a network of mixed substrates and consumers. The framework for the first time represents single-substrate reactions, two-substrate reactions, and mineral surface sorption reactions in a scaling consistent manner. This new theory is theoretically solid and outperforms existing theories, particularly for substrate-limiting systems. The test with aerobic soil respiration showed its strengths for pragmatic application.
Sina Muster, Kurt Roth, Moritz Langer, Stephan Lange, Fabio Cresto Aleina, Annett Bartsch, Anne Morgenstern, Guido Grosse, Benjamin Jones, A. Britta K. Sannel, Ylva Sjöberg, Frank Günther, Christian Andresen, Alexandra Veremeeva, Prajna R. Lindgren, Frédéric Bouchard, Mark J. Lara, Daniel Fortier, Simon Charbonneau, Tarmo A. Virtanen, Gustaf Hugelius, Juri Palmtag, Matthias B. Siewert, William J. Riley, Charles D. Koven, and Julia Boike
Earth Syst. Sci. Data, 9, 317–348, https://doi.org/10.5194/essd-9-317-2017, https://doi.org/10.5194/essd-9-317-2017, 2017
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Waterbodies are abundant in Arctic permafrost lowlands. Most waterbodies are ponds with a surface area smaller than 100 x 100 m. The Permafrost Region Pond and Lake Database (PeRL) for the first time maps ponds as small as 10 x 10 m. PeRL maps can be used to document changes both by comparing them to historical and future imagery. The distribution of waterbodies in the Arctic is important to know in order to manage resources in the Arctic and to improve climate predictions in the Arctic.
Kathrin M. Keller, Sebastian Lienert, Anil Bozbiyik, Thomas F. Stocker, Olga V. Churakova (Sidorova), David C. Frank, Stefan Klesse, Charles D. Koven, Markus Leuenberger, William J. Riley, Matthias Saurer, Rolf Siegwolf, Rosemarie B. Weigt, and Fortunat Joos
Biogeosciences, 14, 2641–2673, https://doi.org/10.5194/bg-14-2641-2017, https://doi.org/10.5194/bg-14-2641-2017, 2017
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
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An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Xiyan Xu, William J. Riley, Charles D. Koven, Dave P. Billesbach, Rachel Y.-W. Chang, Róisín Commane, Eugénie S. Euskirchen, Sean Hartery, Yoshinobu Harazono, Hiroki Iwata, Kyle C. McDonald, Charles E. Miller, Walter C. Oechel, Benjamin Poulter, Naama Raz-Yaseef, Colm Sweeney, Margaret Torn, Steven C. Wofsy, Zhen Zhang, and Donatella Zona
Biogeosciences, 13, 5043–5056, https://doi.org/10.5194/bg-13-5043-2016, https://doi.org/10.5194/bg-13-5043-2016, 2016
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Wetlands are the largest global natural methane source. Peat-rich bogs and fens lying between 50°N and 70°N contribute 10–30% to this source. The predictive capability of the seasonal methane cycle can directly affect the estimation of global methane budget. We present multiscale methane seasonal emission by observations and modeling and find that the uncertainties in predicting the seasonal methane emissions are from the wetland extent, cold-season CH4 production and CH4 transport processes.
Xiaofeng Xu, Fengming Yuan, Paul J. Hanson, Stan D. Wullschleger, Peter E. Thornton, William J. Riley, Xia Song, David E. Graham, Changchun Song, and Hanqin Tian
Biogeosciences, 13, 3735–3755, https://doi.org/10.5194/bg-13-3735-2016, https://doi.org/10.5194/bg-13-3735-2016, 2016
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Accurately projecting future climate change requires a good methane modeling. However, how good the current models are and what are the key improvements needed remain unclear. This paper reviews the 40 published methane models to characterize the strengths and weakness of current methane models and further lay out the roadmap for future model improvements.
Jinyun Tang and William J. Riley
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-233, https://doi.org/10.5194/bg-2016-233, 2016
Preprint retracted
J. Y. Tang and W. J. Riley
Biogeosciences, 13, 723–735, https://doi.org/10.5194/bg-13-723-2016, https://doi.org/10.5194/bg-13-723-2016, 2016
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We present a generic flux-limiting approach to simultaneously handle the availability limitation from many substrates, a problem common in all biogeochemical models. Our approach does not have the ordering problem like a few existing ad hoc approaches, and is straightforward to implement. Our results imply that significant uncertainties could have occurred in many biogeochemical models because of the improper handling of the substrate co-limitation problem.
Q. Zhu, W. J. Riley, J. Tang, and C. D. Koven
Biogeosciences, 13, 341–363, https://doi.org/10.5194/bg-13-341-2016, https://doi.org/10.5194/bg-13-341-2016, 2016
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Here we develop, calibrate, and test a nutrient competition model that accounts for multiple soil nutrients interacting with multiple biotic and abiotic consumers based on enzyme kinetics theory. Our model provides an ecologically consistent representation of nutrient competition appropriate for land biogeochemical models integrated in Earth system models.
C. D. Koven, J. Q. Chambers, K. Georgiou, R. Knox, R. Negron-Juarez, W. J. Riley, V. K. Arora, V. Brovkin, P. Friedlingstein, and C. D. Jones
Biogeosciences, 12, 5211–5228, https://doi.org/10.5194/bg-12-5211-2015, https://doi.org/10.5194/bg-12-5211-2015, 2015
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Terrestrial carbon feedbacks are a large uncertainty in climate change. We separate modeled feedback responses into those governed by changed carbon inputs (productivity) and changed outputs (turnover). The disaggregated responses show that both are important in controlling inter-model uncertainty. Interactions between productivity and turnover are also important, and research must focus on these interactions for more accurate projections of carbon cycle feedbacks.
U. Mishra and W. J. Riley
Biogeosciences, 12, 3993–4004, https://doi.org/10.5194/bg-12-3993-2015, https://doi.org/10.5194/bg-12-3993-2015, 2015
T. J. Bohn, J. R. Melton, A. Ito, T. Kleinen, R. Spahni, B. D. Stocker, B. Zhang, X. Zhu, R. Schroeder, M. V. Glagolev, S. Maksyutov, V. Brovkin, G. Chen, S. N. Denisov, A. V. Eliseev, A. Gallego-Sala, K. C. McDonald, M.A. Rawlins, W. J. Riley, Z. M. Subin, H. Tian, Q. Zhuang, and J. O. Kaplan
Biogeosciences, 12, 3321–3349, https://doi.org/10.5194/bg-12-3321-2015, https://doi.org/10.5194/bg-12-3321-2015, 2015
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We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
N. J. Bouskill, W. J. Riley, and J. Y. Tang
Biogeosciences, 11, 6969–6983, https://doi.org/10.5194/bg-11-6969-2014, https://doi.org/10.5194/bg-11-6969-2014, 2014
G. Bisht and W. J. Riley
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-11-12833-2014, https://doi.org/10.5194/hessd-11-12833-2014, 2014
Revised manuscript has not been submitted
G. S. H. Pau, G. Bisht, and W. J. Riley
Geosci. Model Dev., 7, 2091–2105, https://doi.org/10.5194/gmd-7-2091-2014, https://doi.org/10.5194/gmd-7-2091-2014, 2014
J. Y. Tang and W. J. Riley
Biogeosciences, 11, 3721–3728, https://doi.org/10.5194/bg-11-3721-2014, https://doi.org/10.5194/bg-11-3721-2014, 2014
W. J. Riley, F. Maggi, M. Kleber, M. S. Torn, J. Y. Tang, D. Dwivedi, and N. Guerry
Geosci. Model Dev., 7, 1335–1355, https://doi.org/10.5194/gmd-7-1335-2014, https://doi.org/10.5194/gmd-7-1335-2014, 2014
W. J. Riley and C. Shen
Hydrol. Earth Syst. Sci., 18, 2463–2483, https://doi.org/10.5194/hess-18-2463-2014, https://doi.org/10.5194/hess-18-2463-2014, 2014
I. N. Williams, W. J. Riley, M. S. Torn, S. C. Biraud, and M. L. Fischer
Atmos. Chem. Phys., 14, 1571–1585, https://doi.org/10.5194/acp-14-1571-2014, https://doi.org/10.5194/acp-14-1571-2014, 2014
J. Y. Tang and W. J. Riley
Biogeosciences, 10, 8329–8351, https://doi.org/10.5194/bg-10-8329-2013, https://doi.org/10.5194/bg-10-8329-2013, 2013
C. D. Koven, W. J. Riley, Z. M. Subin, J. Y. Tang, M. S. Torn, W. D. Collins, G. B. Bonan, D. M. Lawrence, and S. C. Swenson
Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, https://doi.org/10.5194/bg-10-7109-2013, 2013
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
J. H. Shim, H. H. Powers, C. W. Meyer, A. Knohl, T. E. Dawson, W. J. Riley, W. T. Pockman, and N. McDowell
Biogeosciences, 10, 4937–4956, https://doi.org/10.5194/bg-10-4937-2013, https://doi.org/10.5194/bg-10-4937-2013, 2013
R. Wania, J. R. Melton, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, G. Chen, A. V. Eliseev, P. O. Hopcroft, W. J. Riley, Z. M. Subin, H. Tian, P. M. van Bodegom, T. Kleinen, Z. C. Yu, J. S. Singarayer, S. Zürcher, D. P. Lettenmaier, D. J. Beerling, S. N. Denisov, C. Prigent, F. Papa, and J. O. Kaplan
Geosci. Model Dev., 6, 617–641, https://doi.org/10.5194/gmd-6-617-2013, https://doi.org/10.5194/gmd-6-617-2013, 2013
S. C. Biraud, M. S. Torn, J. R. Smith, C. Sweeney, W. J. Riley, and P. P. Tans
Atmos. Meas. Tech., 6, 751–763, https://doi.org/10.5194/amt-6-751-2013, https://doi.org/10.5194/amt-6-751-2013, 2013
W. J. Riley
Geosci. Model Dev., 6, 345–352, https://doi.org/10.5194/gmd-6-345-2013, https://doi.org/10.5194/gmd-6-345-2013, 2013
J. Y. Tang and W. J. Riley
Hydrol. Earth Syst. Sci., 17, 873–893, https://doi.org/10.5194/hess-17-873-2013, https://doi.org/10.5194/hess-17-873-2013, 2013
J. R. Melton, R. Wania, E. L. Hodson, B. Poulter, B. Ringeval, R. Spahni, T. Bohn, C. A. Avis, D. J. Beerling, G. Chen, A. V. Eliseev, S. N. Denisov, P. O. Hopcroft, D. P. Lettenmaier, W. J. Riley, J. S. Singarayer, Z. M. Subin, H. Tian, S. Zürcher, V. Brovkin, P. M. van Bodegom, T. Kleinen, Z. C. Yu, and J. O. Kaplan
Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, https://doi.org/10.5194/bg-10-753-2013, 2013
J. Y. Tang, W. J. Riley, C. D. Koven, and Z. M. Subin
Geosci. Model Dev., 6, 127–140, https://doi.org/10.5194/gmd-6-127-2013, https://doi.org/10.5194/gmd-6-127-2013, 2013
Related subject area
Discipline: Frozen ground | Subject: Biogeochemistry/Biology
Review article: Terrestrial dissolved organic carbon in northern permafrost
Environmental controls on observed spatial variability of soil pore water geochemistry in small headwater catchments underlain with permafrost
Responses of dissolved organic carbon to freeze–thaw cycles associated with the changes in microbial activity and soil structure
Molecular biomarkers in Batagay megaslump permafrost deposits reveal clear differences in organic matter preservation between glacial and interglacial periods
High nitrate variability on an Alaskan permafrost hillslope dominated by alder shrubs
The role of vadose zone physics in the ecohydrological response of a Tibetan meadow to freeze–thaw cycles
Permafrost thawing exhibits a greater influence on bacterial richness and community structure than permafrost age in Arctic permafrost soils
Large carbon cycle sensitivities to climate across a permafrost thaw gradient in subarctic Sweden
Carbonaceous material export from Siberian permafrost tracked across the Arctic Shelf using Raman spectroscopy
Consumption of atmospheric methane by the Qinghai–Tibet Plateau alpine steppe ecosystem
Landform partitioning and estimates of deep storage of soil organic matter in Zackenberg, Greenland
Liam Heffernan, Dolly N. Kothawala, and Lars J. Tranvik
The Cryosphere, 18, 1443–1465, https://doi.org/10.5194/tc-18-1443-2024, https://doi.org/10.5194/tc-18-1443-2024, 2024
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The northern permafrost region stores half the world's soil carbon. As the region warms, permafrost thaws and releases dissolved organic carbon, which leads to decomposition of this carbon pool or export into aquatic ecosystems. In this study we developed a new database of 2276 dissolved organic carbon concentrations in eight different ecosystems from 111 studies published over 22 years. This study highlights that coastal areas may play an important role in future high-latitude carbon cycling.
Nathan Alec Conroy, Jeffrey M. Heikoop, Emma Lathrop, Dea Musa, Brent D. Newman, Chonggang Xu, Rachael E. McCaully, Carli A. Arendt, Verity G. Salmon, Amy Breen, Vladimir Romanovsky, Katrina E. Bennett, Cathy J. Wilson, and Stan D. Wullschleger
The Cryosphere, 17, 3987–4006, https://doi.org/10.5194/tc-17-3987-2023, https://doi.org/10.5194/tc-17-3987-2023, 2023
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This study combines field observations, non-parametric statistical analyses, and thermodynamic modeling to characterize the environmental causes of the spatial variability in soil pore water solute concentrations across two Arctic catchments with varying extents of permafrost. Vegetation type, soil moisture and redox conditions, weathering and hydrologic transport, and mineral solubility were all found to be the primary drivers of the existing spatial variability of some soil pore water solutes.
You Jin Kim, Jinhyun Kim, and Ji Young Jung
The Cryosphere, 17, 3101–3114, https://doi.org/10.5194/tc-17-3101-2023, https://doi.org/10.5194/tc-17-3101-2023, 2023
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This study demonstrated the response of organic soils in the Arctic tundra to freeze–thaw cycles (FTCs), focusing on the quantitative and qualitative characteristics of dissolved organic carbon (DOC). The highlights found in this study are as follows: (i) FTCs altered DOC properties without decreasing soil microbial activities, and (ii) soil aggregate distribution influenced by FTCs changed DOC characteristics by enhancing microbial activities and altering specific-sized soil pore proportion.
Loeka L. Jongejans, Kai Mangelsdorf, Cornelia Karger, Thomas Opel, Sebastian Wetterich, Jérémy Courtin, Hanno Meyer, Alexander I. Kizyakov, Guido Grosse, Andrei G. Shepelev, Igor I. Syromyatnikov, Alexander N. Fedorov, and Jens Strauss
The Cryosphere, 16, 3601–3617, https://doi.org/10.5194/tc-16-3601-2022, https://doi.org/10.5194/tc-16-3601-2022, 2022
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Large parts of Arctic Siberia are underlain by permafrost. Climate warming leads to permafrost thaw. At the Batagay megaslump, permafrost sediments up to ~ 650 kyr old are exposed. We took sediment samples and analysed the organic matter (e.g. plant remains). We found distinct differences in the biomarker distributions between the glacial and interglacial deposits with generally stronger microbial activity during interglacial periods. Further permafrost thaw enhances greenhouse gas emissions.
Rachael E. McCaully, Carli A. Arendt, Brent D. Newman, Verity G. Salmon, Jeffrey M. Heikoop, Cathy J. Wilson, Sanna Sevanto, Nathan A. Wales, George B. Perkins, Oana C. Marina, and Stan D. Wullschleger
The Cryosphere, 16, 1889–1901, https://doi.org/10.5194/tc-16-1889-2022, https://doi.org/10.5194/tc-16-1889-2022, 2022
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Degrading permafrost and shrub expansion are critically important to tundra biogeochemistry. We observed significant variability in soil pore water NO3-N in an alder-dominated permafrost hillslope in Alaska. Proximity to alder shrubs and the presence or absence of topographic gradients and precipitation events strongly influence NO3-N availability and mobility. The highly dynamic nature of labile N on small spatiotemporal scales has implications for nutrient responses to a warming Arctic.
Lianyu Yu, Simone Fatichi, Yijian Zeng, and Zhongbo Su
The Cryosphere, 14, 4653–4673, https://doi.org/10.5194/tc-14-4653-2020, https://doi.org/10.5194/tc-14-4653-2020, 2020
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The role of soil water and heat transfer physics in portraying the function of a cold region ecosystem was investigated. We found that explicitly considering the frozen soil physics and coupled water and heat transfer is important in mimicking soil hydrothermal dynamics. The presence of soil ice can alter the vegetation leaf onset date and deep leakage. Different complexity in representing vadose zone physics does not considerably affect interannual energy, water, and carbon fluxes.
Mukan Ji, Weidong Kong, Chao Liang, Tianqi Zhou, Hongzeng Jia, and Xiaobin Dong
The Cryosphere, 14, 3907–3916, https://doi.org/10.5194/tc-14-3907-2020, https://doi.org/10.5194/tc-14-3907-2020, 2020
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Old permafrost soil usually has more carbohydrates, while younger soil contains more aliphatic carbons, which substantially impacts soil bacterial communities. However, little is known about how permafrost age and thawing drive microbial communities. We found that permafrost thawing significantly increased bacterial richness in young permafrost and changed soil bacterial compositions at all ages. This suggests that thawing results in distinct bacterial species and alters soil carbon degradation.
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, Virginia I. Rich, and Scott R. Saleska
The Cryosphere, 13, 647–663, https://doi.org/10.5194/tc-13-647-2019, https://doi.org/10.5194/tc-13-647-2019, 2019
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Permafrost peatlands store large amounts of carbon potentially vulnerable to decomposition under changing climate. We estimated effects of climate forcing biases on carbon cycling at a thawing permafrost peatland in subarctic Sweden. Our results indicate that many climate reanalysis products are cold and wet biased in our study region, leading to erroneous active layer depth and carbon budget estimates. Future studies should recognize the effects of climate forcing uncertainty on carbon cycling.
Robert B. Sparkes, Melissa Maher, Jerome Blewett, Ayça Doğrul Selver, Örjan Gustafsson, Igor P. Semiletov, and Bart E. van Dongen
The Cryosphere, 12, 3293–3309, https://doi.org/10.5194/tc-12-3293-2018, https://doi.org/10.5194/tc-12-3293-2018, 2018
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Ongoing climate change in the Siberian Arctic region has the potential to release large amounts of carbon, currently stored in permafrost, to the Arctic Shelf. Degradation can release this to the atmosphere as greenhouse gas. We used Raman spectroscopy to analyse a fraction of this carbon, carbonaceous material, a group that includes coal, lignite and graphite. We were able to trace this carbon from the river mouths and coastal erosion sites across the Arctic shelf for hundreds of kilometres.
Hanbo Yun, Qingbai Wu, Qianlai Zhuang, Anping Chen, Tong Yu, Zhou Lyu, Yuzhong Yang, Huijun Jin, Guojun Liu, Yang Qu, and Licheng Liu
The Cryosphere, 12, 2803–2819, https://doi.org/10.5194/tc-12-2803-2018, https://doi.org/10.5194/tc-12-2803-2018, 2018
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Here we reported the QTP permafrost region was a CH4 sink of −0.86 ± 0.23 g CH4-C m−2 yr−1 over 2012–2016, soil temperature and soil water content were dominant factors controlling CH4 fluxes, and their correlations changed with soil depth due to cryoturbation dynamics. This region was a net CH4 sink in autumn, but a net source in spring, despite both seasons experiencing similar top soil thawing and freezing dynamics.
Juri Palmtag, Stefanie Cable, Hanne H. Christiansen, Gustaf Hugelius, and Peter Kuhry
The Cryosphere, 12, 1735–1744, https://doi.org/10.5194/tc-12-1735-2018, https://doi.org/10.5194/tc-12-1735-2018, 2018
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This study aims to improve the previous soil organic carbon and total nitrogen storage estimates for the Zackenberg area (NE Greenland) that were based on a land cover classification approach, by using geomorphological upscaling. The landform-based approach more correctly constrains the depositional areas in alluvial fans and deltas with high SOC and TN storage. This research emphasises the need to consider geomorphology when assessing SOC pools in mountain permafrost landscapes.
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...
