Articles | Volume 13, issue 8
https://doi.org/10.5194/gmd-13-3769-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-3769-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
26 Aug 2020
Model evaluation paper |
| 26 Aug 2020
| 26 Aug 2020
Evaluation of CH4MODwetland and Terrestrial Ecosystem Model (TEM) used to estimate global CH4 emissions from natural wetlands
Tingting Li
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
Yanyu Lu
CORRESPONDING AUTHOR
Anhui Institute of Meteorological Sciences, Key Laboratory of
Atmospheric Sciences and Remote Sensing of Anhui Province, Hefei 230031, China
Lingfei Yu
CORRESPONDING AUTHOR
Institute of Botany, Chinese Academy of Sciences, Beijing 100049,
China
Wenjuan Sun
Institute of Botany, Chinese Academy of Sciences, Beijing 100049,
China
Qing Zhang
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Wen Zhang
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Guocheng Wang
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Zhangcai Qin
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China
School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou
510245, China
Lijun Yu
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Hailing Li
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
College of Earth and Planetary Science, University of Chinese Academy of Sciences, Beijing 100049, China
Ran Zhang
CCRC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Related authors
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 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, Xi Yi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1584, https://doi.org/10.5194/egusphere-2024-1584, 2024
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 per year 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.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, Pierre Regnier, Joseph 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, Akihito Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul 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, Joel Thanwerdas, Hanquin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido van der Werf, Doug E. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-115, https://doi.org/10.5194/essd-2024-115, 2024
Preprint under review for ESSD
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 synthesize and update the budget of the sources and sinks of CH4. This edition benefits from important progresses 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.
Yao Gao, Eleanor J. Burke, Sarah E. Chadburn, Maarit Raivonen, Mika Aurela, Lawrence B. Flanagan, Krzysztof Fortuniak, Elyn Humphreys, Annalea Lohila, Tingting Li, Tiina Markkanen, Olli Nevalainen, Mats B. Nilsson, Włodzimierz Pawlak, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-229, https://doi.org/10.5194/bg-2022-229, 2022
Manuscript not accepted for further review
Short summary
Short summary
We coupled a process-based peatland CH4 emission model HIMMELI with a state-of-art land surface model JULES. The performance of the coupled model was evaluated at six northern wetland sites. The coupled model is considered to be more appropriate in simulating wetland CH4 emission. In order to improve the simulated CH4 emission, the model requires better representation of the peat soil carbon and hydrologic processes in JULES and the methane production and transportation processes in HIMMELI.
Guocheng Wang, Zhongkui Luo, Yao Huang, Wenjuan Sun, Yurong Wei, Liujun Xiao, Xi Deng, Jinhuan Zhu, Tingting Li, and Wen Zhang
Atmos. Chem. Phys., 21, 3059–3071, https://doi.org/10.5194/acp-21-3059-2021, https://doi.org/10.5194/acp-21-3059-2021, 2021
Short summary
Short summary
We simulate the spatiotemporal dynamics of aboveground biomass (AGB) in Inner Mongolian grasslands using a machine-learning-based approach. Under climate change, on average, compared with the historical AGB (average of 1981–2019), the AGB at the end of this century (average of 2080–2100) would decrease by 14 % under RCP4.5 and 28 % under RCP8.5. The decrease in AGB might be mitigated or even reversed by positive carbon dioxide enrichment effects on plant growth.
Guocheng Wang, Wen Zhang, Wenjuan Sun, Tingting Li, and Pengfei Han
Atmos. Chem. Phys., 17, 11849–11859, https://doi.org/10.5194/acp-17-11849-2017, https://doi.org/10.5194/acp-17-11849-2017, 2017
Short summary
Short summary
Cropland soil carbon sequestration contribute to not only climate change mitigation but also to sustainable agricultural production. This paper investigates soil carbon dynamics across the global main cereal cropping systems at a fine spatial resolution, using a modeling approach based on state-of-the-art databases of soil and climate. The key environmental controls on soil carbon changes were also identified.
Wen Zhang, Wenjuan Sun, and Tingting Li
Biogeosciences, 14, 163–176, https://doi.org/10.5194/bg-14-163-2017, https://doi.org/10.5194/bg-14-163-2017, 2017
Short summary
Short summary
Regional estimated uncertainties originate from methodological failures, errors, and supporting data insufficiency. A case study showed that the fallacy of the CH4MOD contributed 56.6 % to the uncertainty of a national inventory, with the remaining 43.4 % attributed to the scarcity of model inputs. We also revealed a dilemma between model performance and data availability: a model with better performance may reduce uncertainty from model fallacy but increases the uncertainty from data scarcity.
T. Li, W. Zhang, Q. Zhang, Y. Lu, G. Wang, Z. Niu, M. Raivonen, and T. Vesala
Biogeosciences, 12, 6853–6868, https://doi.org/10.5194/bg-12-6853-2015, https://doi.org/10.5194/bg-12-6853-2015, 2015
Short summary
Short summary
Natural wetlands in China have experienced extensive conversion and climate warming, which makes the estimation of methane emission from wetlands highly uncertain. In this paper, we simulated an increase of 25.5% in national CH4 fluxes from 1950 to 2010, which was mainly induced by climate warming. Although climate warming has accelerated CH4 fluxes, the total amount of national CH4 emissions decreased by approximately 2.35 Tg (1.91－2.81 Tg), due to a large wetland loss of 17.0 million ha.
W. Zhang, Q. Zhang, Y. Huang, T. T. Li, J. Y. Bian, and P. F. Han
Geosci. Model Dev., 7, 1211–1224, https://doi.org/10.5194/gmd-7-1211-2014, https://doi.org/10.5194/gmd-7-1211-2014, 2014
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 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, Xi Yi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1584, https://doi.org/10.5194/egusphere-2024-1584, 2024
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 per year 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.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter Raymond, Pierre Regnier, Joseph 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, Akihito Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul 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, Joel Thanwerdas, Hanquin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido van der Werf, Doug E. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-115, https://doi.org/10.5194/essd-2024-115, 2024
Preprint under review for ESSD
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 synthesize and update the budget of the sources and sinks of CH4. This edition benefits from important progresses 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.
Daju Wang, Peiyang Ren, Xiaosheng Xia, Lei Fan, Zhangcai Qin, Xiuzhi Chen, and Wenping Yuan
Earth Syst. Sci. Data, 16, 2465–2481, https://doi.org/10.5194/essd-16-2465-2024, https://doi.org/10.5194/essd-16-2465-2024, 2024
Short summary
Short summary
This study generated a high-precision dataset, locating forest harvested carbon and quantifying post-harvest wood emissions for various uses. It enhances our understanding of forest harvesting and post-harvest carbon dynamics in China, providing essential data for estimating the forest ecosystem carbon budget and emphasizing wood utilization's impact on carbon emissions.
Yao Gao, Eleanor J. Burke, Sarah E. Chadburn, Maarit Raivonen, Mika Aurela, Lawrence B. Flanagan, Krzysztof Fortuniak, Elyn Humphreys, Annalea Lohila, Tingting Li, Tiina Markkanen, Olli Nevalainen, Mats B. Nilsson, Włodzimierz Pawlak, Aki Tsuruta, Huiyi Yang, and Tuula Aalto
Biogeosciences Discuss., https://doi.org/10.5194/bg-2022-229, https://doi.org/10.5194/bg-2022-229, 2022
Manuscript not accepted for further review
Short summary
Short summary
We coupled a process-based peatland CH4 emission model HIMMELI with a state-of-art land surface model JULES. The performance of the coupled model was evaluated at six northern wetland sites. The coupled model is considered to be more appropriate in simulating wetland CH4 emission. In order to improve the simulated CH4 emission, the model requires better representation of the peat soil carbon and hydrologic processes in JULES and the methane production and transportation processes in HIMMELI.
Ziqi Lin, Yongjiu Dai, Umakant Mishra, Guocheng Wang, Wei Shangguan, Wen Zhang, and Zhangcai Qin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-232, https://doi.org/10.5194/essd-2022-232, 2022
Manuscript not accepted for further review
Short summary
Short summary
Spatial soil organic carbon (SOC) data is critical for predictions in carbon climate feedbacks and future climate trends, but no conclusion has yet been reached on which dataset to be used for specific purposes. We evaluated the SOC estimates from five widely used global soil datasets and a regional permafrost dataset, and identify uncertainties of SOC estimates by region, biome, and data sources, hoping to help improve SOC/soil data in the future.
Chen Yang, Yue Shi, Wenjuan Sun, Jiangling Zhu, Chengjun Ji, Yuhao Feng, Suhui Ma, Zhaodi Guo, and Jingyun Fang
Biogeosciences, 19, 2989–2999, https://doi.org/10.5194/bg-19-2989-2022, https://doi.org/10.5194/bg-19-2989-2022, 2022
Short summary
Short summary
Quantifying China's forest biomass C pool is important in understanding C cycling in forests. However, most of studies on forest biomass C pool were limited to the period of 2004–2008. Here, we used a biomass expansion factor method to estimate C pool from 1977 to 2018. The results suggest that afforestation practices, forest growth, and environmental changes were the main drivers of increased C sink. Thus, this study provided an essential basis for achieving China's C neutrality target.
Zhiping Tian, Dabang Jiang, Ran Zhang, and Baohuang Su
Geosci. Model Dev., 15, 4469–4487, https://doi.org/10.5194/gmd-15-4469-2022, https://doi.org/10.5194/gmd-15-4469-2022, 2022
Short summary
Short summary
We present an experimental design for a new set of transient experiments for the Holocene from 11.5 ka to the preindustrial period (1850) with a relatively high-resolution Earth system model. Model boundary conditions include time-varying full and single forcing of orbital parameters, greenhouse gases, and ice sheets. The simulations will help to study the mean climate trend and abrupt climate changes through the Holocene in response to both full and single external forcings.
Xiaohui Lin, Wen Zhang, Monica Crippa, Shushi Peng, Pengfei Han, Ning Zeng, Lijun Yu, and Guocheng Wang
Earth Syst. Sci. Data, 13, 1073–1088, https://doi.org/10.5194/essd-13-1073-2021, https://doi.org/10.5194/essd-13-1073-2021, 2021
Short summary
Short summary
CH4 is a potent greenhouse gas, and China’s anthropogenic CH4 emissions account for a large proportion of global total emissions. However, the existing estimates either focus on a specific sector or lag behind real time by several years. We collected and analyzed 12 datasets and compared them to reveal the spatiotemporal changes and their uncertainties. We further estimated the emissions from 1990–2019, and the estimates showed a robust trend in recent years when compared to top-down results.
Guocheng Wang, Zhongkui Luo, Yao Huang, Wenjuan Sun, Yurong Wei, Liujun Xiao, Xi Deng, Jinhuan Zhu, Tingting Li, and Wen Zhang
Atmos. Chem. Phys., 21, 3059–3071, https://doi.org/10.5194/acp-21-3059-2021, https://doi.org/10.5194/acp-21-3059-2021, 2021
Short summary
Short summary
We simulate the spatiotemporal dynamics of aboveground biomass (AGB) in Inner Mongolian grasslands using a machine-learning-based approach. Under climate change, on average, compared with the historical AGB (average of 1981–2019), the AGB at the end of this century (average of 2080–2100) would decrease by 14 % under RCP4.5 and 28 % under RCP8.5. The decrease in AGB might be mitigated or even reversed by positive carbon dioxide enrichment effects on plant growth.
Zhongshi Zhang, Qing Yan, Ran Zhang, Florence Colleoni, Gilles Ramstein, Gaowen Dai, Martin Jakobsson, Matt O'Regan, Stefan Liess, Denis-Didier Rousseau, Naiqing Wu, Elizabeth J. Farmer, Camille Contoux, Chuncheng Guo, Ning Tan, and Zhengtang Guo
Clim. Past Discuss., https://doi.org/10.5194/cp-2020-38, https://doi.org/10.5194/cp-2020-38, 2020
Manuscript not accepted for further review
Short summary
Short summary
Whether an ice sheet once grew over Northeast Siberia-Beringia has been debated for decades. By comparing climate modelling with paleoclimate and glacial records from around the North Pacific, this study shows that the Laurentide-Eurasia-only ice sheet configuration fails in explaining these records, while a scenario involving the ice sheet over Northeast Siberia-Beringia succeeds. It highlights the complexity in glacial climates and urges new investigations across Northeast Siberia-Beringia.
Xiangyu Li, Chuncheng Guo, Zhongshi Zhang, Odd Helge Otterå, and Ran Zhang
Clim. Past, 16, 183–197, https://doi.org/10.5194/cp-16-183-2020, https://doi.org/10.5194/cp-16-183-2020, 2020
Short summary
Short summary
Here we report the PlioMIP2 simulations by two versions of the Norwegian Earth System Model (NorESM) with updated boundary conditions derived from Pliocene Research, Interpretation and Synoptic Mapping version 4. The two NorESM versions both produce warmer and wetter Pliocene climate with deeper and stronger Atlantic meridional overturning circulation. Compared to PlioMIP1, PlioMIP2 simulates lower Pliocene warming with NorESM-L, likely due to the closure of seaways at northern high latitudes.
Yating Lin, Gilles Ramstein, Haibin Wu, Raj Rani, Pascale Braconnot, Masa Kageyama, Qin Li, Yunli Luo, Ran Zhang, and Zhengtang Guo
Clim. Past, 15, 1223–1249, https://doi.org/10.5194/cp-15-1223-2019, https://doi.org/10.5194/cp-15-1223-2019, 2019
Short summary
Short summary
The mid-Holocene has been an excellent target for comparing models and data. This work shows that, over China, all the ocean–atmosphere general circulation models involved in PMIP3 show a very large discrepancy with pollen data reconstruction when comparing annual and seasonal temperature. It demonstrates that to reconcile models and data and to capture the signature of seasonal thermal response, it is necessary to integrate non-linear processes, particularly those related to vegetation changes.
Zhongshi Zhang, Qing Yan, Elizabeth J. Farmer, Camille Li, Gilles Ramstein, Terence Hughes, Martin Jakobsson, Matt O'Regan, Ran Zhang, Ning Tan, Camille Contoux, Christophe Dumas, and Chuncheng Guo
Clim. Past Discuss., https://doi.org/10.5194/cp-2018-79, https://doi.org/10.5194/cp-2018-79, 2018
Revised manuscript not accepted
Short summary
Short summary
Our study challenges the widely accepted idea that the Laurentide-Eurasian ice sheets gradually extended across North America and Northwest Eurasia, and suggests the growth of the NH ice sheets is much more complicated. We find climate feedbacks regulate the distribution of the NH ice sheets, producing swings between two distinct ice sheet configurations: the Laurentide-Eurasian and a circum-Arctic configuration, where large ice sheets existed over Northeast Siberia and the Canadian Rockies.
Baohuang Su, Dabang Jiang, Ran Zhang, Pierre Sepulchre, and Gilles Ramstein
Clim. Past, 14, 751–762, https://doi.org/10.5194/cp-14-751-2018, https://doi.org/10.5194/cp-14-751-2018, 2018
Short summary
Short summary
The present numerical experiments undertaken by a coupled atmosphere–ocean model indicate that the uplift of the Tibetan Plateau alone could have been a potential driver for the reorganization of Pacific and Atlantic meridional overturning circulations between the late Eocene and early Oligocene. In other words, the Tibetan Plateau could play an important role in maintaining the current large-scale overturning circulation in the Atlantic and Pacific.
Guocheng Wang, Wen Zhang, Wenjuan Sun, Tingting Li, and Pengfei Han
Atmos. Chem. Phys., 17, 11849–11859, https://doi.org/10.5194/acp-17-11849-2017, https://doi.org/10.5194/acp-17-11849-2017, 2017
Short summary
Short summary
Cropland soil carbon sequestration contribute to not only climate change mitigation but also to sustainable agricultural production. This paper investigates soil carbon dynamics across the global main cereal cropping systems at a fine spatial resolution, using a modeling approach based on state-of-the-art databases of soil and climate. The key environmental controls on soil carbon changes were also identified.
Wen Zhang, Wenjuan Sun, and Tingting Li
Biogeosciences, 14, 163–176, https://doi.org/10.5194/bg-14-163-2017, https://doi.org/10.5194/bg-14-163-2017, 2017
Short summary
Short summary
Regional estimated uncertainties originate from methodological failures, errors, and supporting data insufficiency. A case study showed that the fallacy of the CH4MOD contributed 56.6 % to the uncertainty of a national inventory, with the remaining 43.4 % attributed to the scarcity of model inputs. We also revealed a dilemma between model performance and data availability: a model with better performance may reduce uncertainty from model fallacy but increases the uncertainty from data scarcity.
T. Li, W. Zhang, Q. Zhang, Y. Lu, G. Wang, Z. Niu, M. Raivonen, and T. Vesala
Biogeosciences, 12, 6853–6868, https://doi.org/10.5194/bg-12-6853-2015, https://doi.org/10.5194/bg-12-6853-2015, 2015
Short summary
Short summary
Natural wetlands in China have experienced extensive conversion and climate warming, which makes the estimation of methane emission from wetlands highly uncertain. In this paper, we simulated an increase of 25.5% in national CH4 fluxes from 1950 to 2010, which was mainly induced by climate warming. Although climate warming has accelerated CH4 fluxes, the total amount of national CH4 emissions decreased by approximately 2.35 Tg (1.91－2.81 Tg), due to a large wetland loss of 17.0 million ha.
W. Zhang, Q. Zhang, Y. Huang, T. T. Li, J. Y. Bian, and P. F. Han
Geosci. Model Dev., 7, 1211–1224, https://doi.org/10.5194/gmd-7-1211-2014, https://doi.org/10.5194/gmd-7-1211-2014, 2014
R. Zhang, Q. Yan, Z. S. Zhang, D. Jiang, B. L. Otto-Bliesner, A. M. Haywood, D. J. Hill, A. M. Dolan, C. Stepanek, G. Lohmann, C. Contoux, F. Bragg, W.-L. Chan, M. A. Chandler, A. Jost, Y. Kamae, A. Abe-Ouchi, G. Ramstein, N. A. Rosenbloom, L. Sohl, and H. Ueda
Clim. Past, 9, 2085–2099, https://doi.org/10.5194/cp-9-2085-2013, https://doi.org/10.5194/cp-9-2085-2013, 2013
Related subject area
Biogeosciences
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: A new parameterization scheme for photosynthetic and stomatal responses
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Exploring the Potential of History Matching for Land Surface Model Calibration
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
EAT v0.9.6: a 1D testbed for physical-biogeochemical data assimilation in natural waters
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Assimilation of Carbonyl Sulfide (COS) fluxes within the adjoint-based data assimilation system–Nanjing University Carbon Assimilation System (NUCAS v1.0)
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling boreal forest’s mineral soil and peat C stock dynamics with Yasso07 model coupled with updated moisture modifier
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-6, https://doi.org/10.5194/gmd-2024-6, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study introduces a new scheme to simulate surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal response to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2996, https://doi.org/10.5194/egusphere-2023-2996, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. Here we test a technique called “History matching” against more common approaches. This method adapts well to these models, helping better understand how they work and therefore how to make them more realistic.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-230, https://doi.org/10.5194/gmd-2023-230, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study uses a deep learning method to enhance the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model for more accurate predictions of climate shifts. The method may be critical in developing new fully-continuous methods that are able to translate over a moving continental surface in deep time with high-resolution at a reasonable computational expense.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-242, https://doi.org/10.5194/gmd-2023-242, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems as the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-238, https://doi.org/10.5194/gmd-2023-238, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through “data assimilation”, which blends numerical models and observations. Here, we present EAT, a flexible and efficient testbed that allows any scientist to explore and further develop the state-of-the-art in data assimilation.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1955, https://doi.org/10.5194/egusphere-2023-1955, 2023
Short summary
Short summary
In this work, Nanjing University Carbon Assimilation System (NUCAS) was developed. Data assimilation experiments were conducted to demonstrate the robustness and to investigate the feasibility and applicability of NUCAS. The assimilation of COS significantly improved the model performance in gross primary productivity, sensible heat, latent heat and even soil moisture, demonstrating that COS can provide strong constraints on parameters relevant to water, energy and carbon processes.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Stefano Manzoni, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
EGUsphere, https://doi.org/10.5194/egusphere-2023-1523, https://doi.org/10.5194/egusphere-2023-1523, 2023
Short summary
Short summary
Improving representation of peatlands in Earth system models (ESMs) (e.g., in Yasso soil C model coupled with JSBACH model) is rare. We updated Yasso07 moisture dependency for applications on both the mineral soils and peatlands of boreal forest. Our moisture function improved the simulated peatland C stocks and CO2 dynamics. However, due to nature of the measurements used for Bayesian MCMC data assimilation, it contrasts with functions in other ESMs (see graphical abstract).
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
Short summary
Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
Short summary
Short summary
Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
Short summary
Short summary
Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
Short summary
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
Short summary
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Cited articles
Allen, O. B. and Raktoe, B. L.: Accuracy analysis with special reference to
the prediction of grassland yield, Biom. J., 23, 371–388, 1981.
Alvalá, P. C. and Kirchhoff, V. W. J. H.: Methane fluxes from the
Pantanal floodplain in Brazil: seasonal variation, in: Non-CO2
greenhouse gases: Scientific understanding, control and implementation:
Proceedings of the Second International Symposium, 8–10 September 1999, Noordwijkerhout, the Netherlands, edited by: van Ham, J., Baede, A. P. M.,
Meyer, L. A., and Ybema, R., Springer Netherlands, Dordrecht, the Netherlands, 95–99,
2000
Antonov, J. I., Seidov, D., Boyer, T. P., Locarnini, R. A., Mishonov,
A. V., Garcia, H. E., Baranova, O. K., Zweng, M. M., and Johnson, D. R.,
World Ocean Atlas 2009, Volume 2: Salinity, in: NOAA Atlas NESDIS 69, edited
by: Levitus, S., U.S. Government Printing Office, USA, 2010.
Aubinet, M., Vesala, T., and Papale, D.: Eddy covariance: a practical guide
to measurement and data analysis, Springer Science & Business Media, Dordrecht, the Netherlands,
2012.
Aurela, M., Laurila, T., and Tuovinen, J. P.: Annual CO2 balance of a
subarctic fen in northern Europe: importance of the wintertime efflux, J.
Geophys. Res.-Atmos., 107, 4607, https://doi.org/10.1029/2002JD002055, 2002.
Bartlett, K. B., Harriss, R., and Sebacher, D.: Methane flux from coastal salt
marshes, J. Geophys. Res.-Atmos., 90, 5710–5720, 1985.
Bartlett, K. B., Bartlett, D. S., Harriss, R. C., and Sebacher, D. I.:
Methane emissions along a salt marsh salinity gradient, Biogeochemistry, 4,
183–202, https://doi.org/10.1007/bf02187365, 1987.
Bartlett, K. B., Crill, P., Sass, R., Harriss, R., and Dise, N.: Methane
emissions from tundra environments in the Yukon-Kuskokwim Delta, Alaska, J.
Geophys. Res., 97, 16645–16660, https://doi.org/10.1029/91JD00610, 1992.
Belger, L., Forsberg, B. R., and Melack, J. M.: Carbon dioxide and methane
emissions from interfluvial wetlands in the upper Negro River basin, Brazil,
Biogeochemistry, 105, 171–183, https://doi.org/10.1007/s10533-010-9536-0, 2011.
Belward, A. S., Estes, J. E., and Kline, K. D.: The IGBP-DIS global 1-km
land-cover data set DISCover: A project overview, Photogramm. Eng. Remote
Sens., 65, 1013–1020, 1999.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton,
S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P.,
Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B.
D., and Andreassian, V.: Characterising performance of environmental models,
Environ. Model. Softw., 40, 1–20,
https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Beven, K., and Kirkby, M. J.: A physically based, variable contributing area
model of basin hydrology, Hydrol. Sci. B., 24, 43–69, 1979.
Bhullar, G. S., Iravani, M., Edwards, P. J., and Venterink, H. O.: Methane
transport and emissions from soil as affected by water table and vascular
plants, BMC Ecol., 13, 32, https://doi.org/10.1186/1472-6785-13-32, 2013.
Bohn, T., Lettenmaier, D., Sathulur, K., Bowling, L., Podest, E., McDonald,
K., and Friborg, T.: Methane emissions from western Siberian wetlands:
heterogeneity and sensitivity to climate change, Environ. Res. Lett., 2,
045015, https://doi.org/10.1088/1748-9326/2/4/045015, 2007.
Boucher, O., Friedlingstein, P., Collins, B., and Shine, K. P.: The indirect
global warming potential and global temperature change potential due to
methane oxidation, Environ. Res. Lett., 4, 044007, https://doi.org/10.1088/1748-9326/4/4/044007, 2009.
Bousquet, P., Ciais, P., Miller, J., Dlugokencky, E., Hauglustaine, D.,
Prigent, C., Van der Werf, G., Peylin, P., Brunke, E.-G., and Carouge, C.:
Contribution of anthropogenic and natural sources to atmospheric methane
variability, Nature, 443, 439–443, 2006.
Bridgham, S., Updegraff, K., and Pastor, J.: Carbon, Nitrogen, and
Phosphorus Mineralization in Northern Wetlands, Ecology, 79, 1545–1561,
1998.
Bridgham, S. D., Cadillo-Quiroz, H., Keller, J. K., and Zhuang, Q.: Methane
emissions from wetlands: biogeochemical, microbial, and modeling
perspectives from local to global scales, Glob. Change Biol., 19,
1325–1346, 2013.
Bruhwiler, L., Dlugokencky, E., Masarie, K., Ishizawa, M., Andrews, A., Miller, J., Sweeney, C., Tans, P., and Worthy, D.: CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane, Atmos. Chem. Phys., 14, 8269–8293, https://doi.org/10.5194/acp-14-8269-2014, 2014.
Cao, M., Marshall, S., and Gregson, K.: Global carbon exchange and methane
emissions from natural wetlands: Application of a process-based model, J.
Geophys. Res.-Atmos., 101, 14399–14414, 1996.
Carter, A. J. and Scholes, R. J.: Spatial global database of soil
properties., IGBP Global Soil Data Task CD-ROM, International
Geosphere-Biosphere Programme Data Information Systems, Toulouse, France, 2000.
Chaichana, N., Bellingrath-Kimura, S., Komiya, S., Fujii, Y., Noborio, K.,
Dietrich, O., and Pakoktom, T.: Comparison of closed chamber and eddy
covariance methods to improve the understanding of methane fluxes from rice
paddy fields in Japan, Atmosphere, 9, 356, https://doi.org/10.3390/atmos9090356, 2018.
Chanton, J. P.: The effect of gas transport on the isotope signature of
methane in wetlands, Org. Geochem., 36, 753–768, 2005.
Christensen, T. R.: Methane emission from Arctic tundra, Biogeochemistry,
21, 117–139, https://doi.org/10.1007/BF00000874, 1993.
Christensen, T. R., Friborg, T., Sommerkorn, M., Kaplan, J., Illeris, L.,
Soegaard, H., Nordstroem, C., and Jonasson, S.: Trace gas exchange in a
high-Arctic valley: 1. Variationsin CO2 and CH4 flux between
tundra vegetation types, Global Biogeochem. Cy., 14, 701–713, 2000.
Crill, P. M., Bartlett, K. B., Wilson, J. O., Sebacher, D. I., Harriss, R.
C., Melack, J. M., MacIntyre, S., Lesack, L., and Smith-Morrill, L.:
Tropospheric methane from an Amazonian floodplain lake, J. Geophys. Res.-Atmos., 93, 1564–1570, https://doi.org/10.1029/JD093iD02p01564, 1988.
Dalsøren, S. B., Myhre, C. L., Myhre, G., Gomez-Pelaez, A. J., Søvde, O. A., Isaksen, I. S. A., Weiss, R. F., and Harth, C. M.: Atmospheric methane evolution the last 40 years, Atmos. Chem. Phys., 16, 3099–3126, https://doi.org/10.5194/acp-16-3099-2016, 2016.
Deemer, B. R., Harrison, J. A., Li, S., Beaulieu, J. J., DelSontro, T.,
Barros, N., Bezerra-Neto, J. F., Powers, S. M., dos Santos, M. A., and Vonk,
J. A.: Greenhouse gas emissions from reservoir water surfaces: A new global
synthesis, BioScience, 66, 949–964, https://doi.org/10.1093/biosci/biw117, 2016.
Delaune, R. D., Smith, C. J., and Patrick Jr., W. H.: Methane release from Gulf
coast wetlands, Tellus B, 35B, 8–15, https://doi.org/10.1111/j.1600-0889.1983.tb00002.x,
1983.
Devol, A. H., Richey, J. E., Clark, W. A., King, S. L., and Martinelli, L.
A.: Methane emissions to the troposphere from the Amazon floodplain, J.
Geophys. Res.-Atmos., 93, 1583–1592, https://doi.org/10.1029/JD093iD02p01583, 1988.
Dlugokencky, E. J.: NOAA/ESRL, available at: http://www.esrl.noaa.gov/gmd/ccgg/trends_ch4/, last access: 18 July
2016.
Dlugokencky, E. J., Bruhwiler, L., White, J., Emmons, L., Novelli, P. C.,
Montzka, S. A., Masarie, K. A., Lang, P. M., Crotwell, A., and Miller, J.
B.: Observational constraints on recent increases in the atmospheric
CH4 burden, Geophys. Res. Lett., 36, L18803, https://doi.org/10.1029/2009GL039780, 2009.
Duan, X., Wang, X., Mu, Y., and Ouyang, Z.: Seasonal and diurnal variations
in methane emissions from Wuliangsu Lake in arid regions of China, Atmos.
Environ., 39, 4479–4487, 2005.
Fan, S. M., Wofsy, S. C., Bakwin, P. S., Jacob, D. J., Anderson, S. M.,
Kebabian, P. L., McManus, J. B., Kolb, C. E., and Fitzjarrald, D. R.:
Micrometeorological measurements of CH4 and CO2 exchange between
the atmosphere and subarctic tundra, J. Geophys. Res.-Atmos., 97,
16627–16643, https://doi.org/10.1029/91jd02531, 1992.
Fan, Y. and van den Dool, H.: Climate Prediction Center global monthly soil
moisture data set at 0.5 resolution for 1948 to present, J. Geophys. Res.-Atmos., 109, D10102, https://doi.org/10.1029/2003JD004345, 2004.
FAO/IIASA/ISRIC/ISS-CAS/JRC: Harmonized World Soil Database, version 1.0,
FAO, Rome, Italy and IIASA, Laxenburg, Austria, 42 pp., 2008.
FAO/IIASA/ISRIC/ISS-CAS/JRC: Harmonized World Soil Database, version 1.2,
FAO and IIASA, Rome, Italy and Laxenburg, Austria, 43 pp., 2012.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005rg000183, 2007.
Fraser, A., Palmer, P. I., Feng, L., Boesch, H., Cogan, A., Parker, R., Dlugokencky, E. J., Fraser, P. J., Krummel, P. B., Langenfelds, R. L., O'Doherty, S., Prinn, R. G., Steele, L. P., van der Schoot, M., and Weiss, R. F.: Estimating regional methane surface fluxes: the relative importance of surface and GOSAT mole fraction measurements, Atmos. Chem. Phys., 13, 5697–5713, https://doi.org/10.5194/acp-13-5697-2013, 2013.
Friborg, T., Christensen, T., and Søgaard, H.: Rapid response of
greenhouse gas emission to early spring thaw in a subarctic mire as shown by
micrometeorological techniques, Geophys. Res. Lett., 24, 3061–3064, https://doi.org/10.1029/97GL03024, 1997.
Galand, P. E., Yrjälä, K., and Conrad, R.: Stable carbon isotope fractionation during methanogenesis in three boreal peatland ecosystems, Biogeosciences, 7, 3893–3900, https://doi.org/10.5194/bg-7-3893-2010, 2010.
Gedney, N., Cox, P., and Huntingford, C.: Climate feedback from wetland
methane emissions, Geophys. Res. Lett., 31, L20503, https://doi.org/10.1029/2004GL020919, 2004.
Ghosh, A., Patra, P. K., Ishijima, K., Umezawa, T., Ito, A., Etheridge, D. M., Sugawara, S., Kawamura, K., Miller, J. B., Dlugokencky, E. J., Krummel, P. B., Fraser, P. J., Steele, L. P., Langenfelds, R. L., Trudinger, C. M., White, J. W. C., Vaughn, B., Saeki, T., Aoki, S., and Nakazawa, T.: Variations in global methane sources and sinks during 1910–2010, Atmos. Chem. Phys., 15, 2595–2612, https://doi.org/10.5194/acp-15-2595-2015, 2015.
Hanis, K. L., Tenuta, M., Amiro, B. D., and Papakyriakou, T. N.: Seasonal dynamics of methane emissions from a subarctic fen in the Hudson Bay Lowlands, Biogeosciences, 10, 4465–4479, https://doi.org/10.5194/bg-10-4465-2013, 2013.
Hao, Q. J.: Effect of land-use change on greenhouse gases emissions in
freshwater marshes in the Sanjiang Plain, PhD Dissertation, Institute of
Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, 2006.
Harazono, Y., Mano, M., Miyata, A., Yoshimoto, M., Zulueta, R., Vourlitis,
G., Kwon, H., and Oechel, W.: Temporal and spatial differences of methane
flux at arctic tundra in Alaska, Mem. Natl. Inst. Polar Res., 59, 79–95,
2006.
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated high-resolution
grids of monthly climatic observations – the CRU TS3. 10 Dataset, Int. J.
Climatol., 34, 623–642, 2014.
Hatala, J. A., Detto, M., Sonnentag, O., Deverel, S. J., Verfaillie, J., and
Baldocchi, D. D.: Greenhouse gas (CO2, CH4, H2O) fluxes from
drained and flooded agricultural peatlands in the Sacramento-San Joaquin
Delta, Agr., Ecosyst. Environ., 150, 1–18, 2012.
Hirota, M., Tang, Y., Hu, Q., Hirata, S., Kato, T., Mo, W., Cao, G., and
Mariko, S.: Methane emissions from different vegetation zones in a
Qinghai-Tibetan Plateau wetland, Soil Biol. Biochem., 36, 737–748, 2004.
Huang, G., Li, X., Hu, Y., Shi, Y., and Xiao, D.: Methane (CH4)
emission from a natural wetland of northern China, J. Environ. Sci. Health,
40, 1227–1238, 2005.
Huang, P. Y., Yu, H. X., Chai, L. H., Chai, F. Y., and Zhang, W. F.: Methane
emission flux of Zhalong Phragmites Australis wetlands in growth season,
Chin. J. Appl. Ecol., 22, 1219–1224, 2011.
Huang, Y. A. O., Sass, R., and Fisher, F.: Methane emission from Texas rice
paddy soils. 1. Quantitative multi-year dependence of CH4 emission on
soil, cultivar and grain yield, Glob. Change Biol., 3, 479–489, 1997.
Huttunen, J. T., Alm, J., Saarijärvi, E., Lappalainen, K. M., Silvola,
J., and Martikainen, P. J.: Contribution of winter to the annual CH4
emission from a eutrophied boreal lake, Chemosphere, 50, 247–250, 2003.
IPCC: Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories:
Reference Manual, Paris, France, 1996.
Jauhiainen, J., Takahashi, H., Heikkinen, J. E., Martikainen, P. J., and
Vasander, H.: Carbon fluxes from a tropical peat swamp forest floor, Glob.
Change Biol., 11, 1788–1797, 2005.
Jitka, V., Jiří, D., Stanislav, S., Lenka, M., and Hana, Č.:
Effect of hummock-forming vegetation on methane emissions from a temperate
sedge-grass marsh, Wetlands, 37, 675–686, https://doi.org/10.1007/s13157-017-0898-0, 2017.
Joabsson, A. and Christensen, T. R.: Methane emissions from wetlands and
their relationship with vascular plants: an Arctic example, Glob. Change
Biol., 7, 919–932, https://doi.org/10.1046/j.1354-1013.2001.00044.x, 2001.
Kang, W. X., Zhao, Z. H., Tian, D. L., He, J. N., and Deng, X. W.: CO2
exchanges between mangrove- and shoal wetland ecosystems and atmosphere in
Guangzhou, Chin. J. Appl. Ecol., 19, 2605–2610, 2008.
Keddy, P. A.: Wetland ecology: principles and conservation, Cambridge
University Press, Cambridge, UK, 2010.
Keller, J. and Bridgham, S.: Pathways of Anaerobic Carbon Cycling Across an
Ombrotrophic–Minerotrophic Peatland Gradient, Limnol. Oceanogr., 52,
96–107, https://doi.org/10.4319/lo.2007.52.1.0096, 2007.
King, J., Reeburgh, W., Thieler, K., Kling, G., Loya, W., Johnson, L., and
Nadelhoffer, K.: Pulse-labeling studies of carbon cycling in Arctic tundra
ecosystems: The contribution of photosynthates to methane emission, Global
Biogeochem. Cy., 16, 1062, https://doi.org/10.1029/2001GB001456, 2002.
Kingsford, R. T., Basset, A., and Jackson, L.: Wetlands: conservation's poor
cousins, Aquat. Conserv., 26,
892–916, https://doi.org/10.1002/aqc.2709, 2016.
Kirschke, S., Bousquet, P., Ciais, P., Saunois, M., Canadell, J. G.,
Dlugokencky, E. J., Bergamaschi, P., Bergmann, D., Blake, D. R., and
Bruhwiler, L.: Three decades of global methane sources and sinks, Nat.
Geosci., 6, 813–823, 2013.
Koh, H. S., Ochs, C., and Yu, K.: Hydrologic gradient and vegetation
controls on CH4 and CO2 fluxes in a spring-fed forested wetland,
Hydrobiologia, 630, 271–286, https://doi.org/10.1007/s10750-009-9821-x, 2009.
Kramer, K., Leinonen, I., Bartelink, H., Berbigier, P., Borghetti, M.,
Bernhofer, C., Cienciala, E., Dolman, A., Froer, O., and Gracia, C.:
Evaluation of six process-based forest growth models using eddy-covariance
measurements of CO2 and H2O fluxes at six forest sites in Europe,
Glob. Change Biol., 8, 213–230, 2002.
Kwon, M. J., Beulig, F., Ilie, I., Wildner, M., Küsel, K., Merbold, L.,
Mahecha, M. D., Zimov, N., Zimov, S. A., Heimann, M., Schuur, E. A. G.,
Kostka, J. E., Kolle, O., Hilke, I., and Göckede, M.: Plants,
microorganisms, and soil temperatures contribute to a decrease in methane
fluxes on a drained Arctic floodplain, Glob. Change Biol., 23, 2396–2412, https://doi.org/10.1111/gcb.13558, 2017.
Lehner, B. and Döll, P.: Development and validation of a global
database of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22,
2004.
Li, T., Huang, Y., Zhang, W., and Song, C.: CH4MODwetland: A
biogeophysical model for simulating methane emissions from natural wetlands,
Ecol. Model., 221, 666–680, 2010.
Li, T., Zhang, W., Zhang, Q., Lu, Y., Wang, G., Niu, Z., Raivonen, M., and Vesala, T.: Impacts of climate and reclamation on temporal variations in CH4 emissions from different wetlands in China: from 1950 to 2010, Biogeosciences, 12, 6853–6868, https://doi.org/10.5194/bg-12-6853-2015, 2015.
Li, T., Xie, B., Wang, G., Zhang, W., Zhang, Q., Vesala, T., and Raivonen,
M.: Field-scale simulation of methane emissions from coastal wetlands in
China using an improved version of CH4MODwetland, Sci. Total Environ.,
559, 256–267, https://doi.org/10.1016/j.scitotenv.2016.03.186, 2016.
Li, T., Zhang, Q., Cheng, Z., Wang, G., Yu, L., and Zhang, W.: Performance
of CH4MODwetland for the case study of different regions of natural
Chinese wetland, J. Environ. Sci., 57, 356–369, 2017.
Li, T., Li, H., Zhang, Q., Ma, Z., Yu, L., Lu, Y., Niu, Z., Sun, W., and
Liu, J.: Prediction of CH4 emissions from potential natural wetlands on
the Tibetan Plateau during the 21st century, Sci. Total Environ., 657,
498–508, https://doi.org/10.1016/j.scitotenv.2018.11.275, 2019a.
Li, T., Lu, Y., Yu, L., Sun, W., Zhang, Q., Zhang, W., Wang, G., Qin, Z., Yu, L., Li, H., and Zhang, R.: valuation of two process-based models used to estimate global CH4 emissions from natural wetlands, Zenodo, https://doi.org/10.5281/zenodo.3537621, 2019b.
Li, Y. J., Cheng, Z. L., Wang, D. Q., Hu, H., and Wang, C.: Methane emission
in the process of wetland and vegetation succession in salt marsh of Yangtze
River estuary, Acta Sci. Circumst., 34, 2035–2402, 2014.
Long, K. D., Flanagan, L. B., and Cai, T.: Diurnal and seasonal variation in
methane emissions in a northern Canadian peatland measured by eddy
covariance, Glob. Change Biol., 16, 2420–2435, https://doi.org/10.1111/j.1365-2486.2009.02083.x, 2010.
Loveland, T., Reed, B., Brown, J., Ohlen, D., Zhu, Z., Yang, L., and
Merchant, J.: Development of a global land cover characteristics database
and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330,
2000.
Marthews, T. R., Dadson, S. J., Lehner, B., Abele, S., and Gedney, N.: High-resolution global topographic index values for use in large-scale hydrological modelling, Hydrol. Earth Syst. Sci., 19, 91–104, https://doi.org/10.5194/hess-19-91-2015, 2015.
Mastepanov, M., Sigsgaard, C., Dlugokencky, E. J., Houweling, S., Ström,
L., Tamstorf, M. P., and Christensen, T. R.: Large tundra methane burst
during onset of freezing, Nature, 456, 628–630, 2008.
McEwing, K. R., Fisher, J. P., and Zona, D.: Environmental and vegetation
controls on the spatial variability of CH4 emission from wet-sedge and
tussock tundra ecosystems in the Arctic, Plant Soil, 388, 37–52, https://doi.org/10.1007/s11104-014-2377-1, 2015.
Meirink, J. F., Bergamaschi, P., and Krol, M. C.: Four-dimensional variational data assimilation for inverse modelling of atmospheric methane emissions: method and comparison with synthesis inversion, Atmos. Chem. Phys., 8, 6341–6353, https://doi.org/10.5194/acp-8-6341-2008, 2008.
Melack, J. M., Hess, L. L., Gastil, M., Forsberg, B. R., Hamilton, S. K.,
Lima, I. B. T., and Novo, E. M. L. M.: Regionalization of methane emissions
in the Amazon Basin with microwave remote sensing, Glob. Change Biol., 10,
530–544, https://doi.org/10.1111/j.1365-2486.2004.00763.x, 2004.
Melillo, J. M., McGuire, A. D., Kicklighter, D. W., Moore, B., Vorosmarty,
C. J., and Schloss, A. L.: Global climate change and terrestrial net primary
production, Nature, 363, 234–240, 1993.
Melling, L., Hatanoa, R., and Gohc, K. J.: Methane fluxes from three
ecosystems in tropical peatland of Sarawak, Malaysia, Soil Biol. Biochem.,
37, 1445–1453, 2005.
Melton, J. R., Wania, R., Hodson, E. L., Poulter, B., Ringeval, B., Spahni, R., Bohn, T., Avis, C. A., Beerling, D. J., Chen, G., Eliseev, A. V., Denisov, S. N., Hopcroft, P. O., Lettenmaier, D. P., Riley, W. J., Singarayer, J. S., Subin, Z. M., Tian, H., Zürcher, S., Brovkin, V., van Bodegom, P. M., Kleinen, T., Yu, Z. C., and Kaplan, J. O.: Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP), Biogeosciences, 10, 753–788, https://doi.org/10.5194/bg-10-753-2013, 2013.
Meng, L., Hess, P. G. M., Mahowald, N. M., Yavitt, J. B., Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Jauhiainen, J., and Fuka, D. R.: Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations, Biogeosciences, 9, 2793–2819, https://doi.org/10.5194/bg-9-2793-2012, 2012.
Moore, T., Roulet, N., and Knowles, R.: Spatial and temporal variations of
methane flux from subarctic/northern Boreal fens, Global Biogeochem. Cy.,
4, 29–46, https://doi.org/10.1029/GB004i001p00029, 1990.
Moore, T., Heyes, A., and Roulet, N.: Methane emissions from wetlands,
southern Hudson Bay Lowland, J. Geophys. Res., 99, 1455–1467, https://doi.org/10.1029/93JD02457, 1994.
Moore, T., Young, A., Bubier, J., Humphreys, E., Lafleur, P., and Roulet,
N.: A multi-year record of methane flux at the Mer Bleue Bog, Southern
Canada, Ecosystems, 14, 646–657, https://doi.org/10.1007/s10021-011-9435-9, 2011.
Morse, J. L., Ardón, M., and Bernhardt, E. S.: Greenhouse gas fluxes in
southeastern U.S. coastal plain wetlands under contrasting land uses, Ecol.
Appl., 22, 264–280, https://doi.org/10.1890/11-0527.1, 2012.
Myhre, G., Shindell, D., Bréon, F. M., Collins, W., Fuglestvedt, J.,
Huang, J., Koch, D., Lamarque, J. F., Lee, D., Mendoza, B., Nakajima, T.,
Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and
Natural Radiative Forcing, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Inter-governmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A.,
Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge,
UK and New York, NY, USA, 2013.
Nakano, T., Kuniyoshi, S., and Fukuda, M.: Temporal variation in methane
emission from tundra wetlands in a permafrost area, northeastern Siberia,
Atmos. Environ., 34, 1205–1213, https://doi.org/10.1016/S1352-2310(99)00373-8, 2000.
Nisbet, E., Manning, M., Dlugokencky, E., Fisher, R., Lowry, D., Michel, S.,
Lund Myhre, C., Platt, S., Allen, G., Bousquet, P., Brownlow, R., Cain, M.,
France, J., Hermansen, O., Hossaini, R., Jones, A., Levin, I., Manning, A.,
Myhre, G., and White, J.: Very strong atmospheric methane growth in the four
years 2014–2017: Implications for the Paris Agreement, Global Biogeochem. Cy., 33, 318–342, https://doi.org/10.1029/2018GB006009, 2019.
Olefeldt, D., Roulet, N. T., Bergeron, O., Crill, P., Bäckstrand, K.,
and Christensen, T. R.: Net carbon accumulation of a high-latitude
permafrost palsa mire similar to permafrost-free peatlands, Geophys. Res.
Lett., 39, L03501, https://doi.org/10.1029/2011GL050355, 2012.
Olson, D., Griffis, T., Noormets, A., Kolka, R., and Chen, J.: Interannual,
seasonal, and retrospective analysis of the methane and carbon dioxide
budgets of a temperate peatland, J. Geophys. Res.-Biogeo., 118, 226–238, https://doi.org/10.1002/jgrg.20031, 2013.
Page, S., Rieley, J., Shotyk, W., and Weiss, D.: Interdependence of peat and
vegetation in a tropical peat swamp forest, Philos. T. Roy. Soc. Lond. B, 354, 1885–1897, https://doi.org/10.1098/rstb.1999.0529, 1999.
Parmentier, F. J. W., van Huissteden, J., van der Molen, M. K.,
Schaepman-Strub, G., Karsanaev, S. A., Maximov, T. C., and Dolman, A. J.:
Spatial and temporal dynamics in eddy covariance observations of methane
fluxes at a tundra site in northeastern Siberia, J. Geophys. Res.-Biogeo., 116, G03016, https://doi.org/10.1029/2010jg001637, 2011.
Poffenbarger, H. J., Needelman, B. A., and Megonigal, J. P.: Salinity
influence on methane emissions from tidal marshes, Wetlands, 31, 831–842, https://doi.org/10.1007/s13157-011-0197-0, 2011.
Potter, C. S.: An ecosystem simulation model for methane production and emission from wetlands, Global Biogeochem. Cy., 11, 495–506, 1997.
Poulter, B., Bousquet, P., Canadell, J. G., Ciais, P., Peregon, A., Saunois,
M., Arora, V. K., Beerling, D. J., Brovkin, V., and Jones, C. D.: Global
wetland contribution to 2000–2012 atmospheric methane growth rate dynamics,
Environ. Res. Lett., 12, 094013, https://doi.org/10.1088/1748-9326/aa8391, 2017.
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.
Rykiel, E. J.: Testing ecological models: the meaning of validation, Ecol.
Model., 90, 229–244, https://doi.org/10.1016/0304-3800(95)00152-2, 1996.
Sachs, T., Giebels, M., Boike, J., and Kutzbach, L.: Environmental controls
on CH4 emission from polygonal tundra on the microsite scale in the
Lena river delta, Siberia, Glob. Change Biol., 16, 3096–3110, 2010.
Schimel, J., Nadelhoffer, K., Shaver, G., Giblin, A., and Rastetter, E.: Methane
and carbon dioxide emissions were monitored in control, greenhouse, and
nitrogen and phosphorus fertilized plots of three different plant
communities Arctic LTER experimental plots, Toolik Field Station, 1992,
Environmental Data Initiative, https://doi.org/10.6073/pasta/3e2ae7928b00f7546338086d0dc3bd55, 1994.
Schimel, J., Nadelhoffer, K., Shaver, G., Giblin, A., Rastetter, E.: Methane
and carbon dioxide emissions were monitored in control, greenhouse, and
nitrogen and phosphorus fertilized plots of three different plant
communities, Toolik Field Station, North Slope Alaska, Arctic LTER 1993,
Environmental Data Initiative, https://doi.org/10.6073/pasta/64c4ad25b7efb6f98acc22301dd1802a, 1995.
Sebacher, D., Harriss, R., Bartlett, K., Sebacher, S., and Grice, S.:
Atmospheric methane sources: Alaskan tundra bogs, an alpine fen, and a
subarctic boreal marsh, Tellus B, 38B, 1–10, https://doi.org/10.1111/j.1600-0889.1986.tb00083.x, 1986.
Sellers, P. J., Hall, F. G., Kelly, R. D., Black, A., Baldocchi, D., Berry,
J., Ryan, M., Ranson, K. J., Crill, P. M., and Lettenmaier, D. P.: BOREAS in
1997: Experiment overview, scientific results, and future directions, J.
Geophys. Res.-Atmos., 102, 28731–28769, 1997.
Shannon, R. D., White, J. R., Lawson, J. E., and Gilmour, B. S.: Methane
efflux from emergent vegetation in peatlands, J. Ecol., 84, 239–246, 1996.
Shindell, D., Kuylenstierna, J. C. I., Vignati, E., van Dingenen, R., Amann,
M., Klimont, Z., Anenberg, S. C., Muller, N., Janssens-Maenhout, G., Raes,
F., Schwartz, J., Faluvegi, G., Pozzoli, L., Kupiainen, K.,
Höglund-Isaksson, L., Emberson, L., Streets, D., Ramanathan, V., Hicks,
K., Oanh, N. T. K., Milly, G., Williams, M., Demkine, V., and Fowler, D.:
Simultaneously Mitigating Near-Term Climate Change and Improving Human
Health and Food Security, Science, 335, 183–189, https://doi.org/10.1126/science.1210026, 2012.
Song, C., Zhang, J., Wang, Y., Wang, Y., and Zhao, Z.: Emission of CO2, CH4
and N2O from freshwater marsh in northeast of China, J. Environ. Manag., 88,
428–436, https://doi.org/10.1016/j.jenvman.2007.03.030, 2008.
Spiers, A. G.: Review of international/continental wetland resources, in:
Global review of wetland resources and priorities for wetland inventory,
edited by: Finlayson, C. M., and Spiers, A. G., Supervising Scientist Report
144, Supervising Scientist, Canberra, Australia, 63–104, 1999.
Stanley, K. M., Heppell, C. M., Belyea, L. R., Baird, A. J., and Field, R.
H.: The Importance of CH4 Ebullition in Floodplain Fens, J. Geophys.
Res.-Biogeo., 124, 1750–1763, https://doi.org/10.1029/2018jg004902, 2019.
Suyker, A. E., Verma, S. B., Clement, R. J., and Billesbach, D. P.: Methane
flux in a boreal fen: Season-long measurement by eddy correlation, J.
Geophys. Res.-Atmos., 101, 28637–28647, https://doi.org/10.1029/96JD02751, 1996.
Svensson, B., and Rosswall, T.: In situ methane production
from acid peat in plant communities with different moisture regimes in a
subarctic mire, Oikos, 43, 341–350, https://doi.org/10.2307/3544151, 1984.
Tathy, J., Cros, B., Delmas, R., Marenco, A., Servant, J., and Labat, M.:
CH4 emission from flooded forest in Central Africa, J. Geophys. Res.,
97, 6159–6168, https://doi.org/10.1029/90JD02555, 1992.
Tian, H., Chen, G., Lu, C., Xu, X., Ren, W., Zhang, B., Banger, K., Tao, B.,
Pan, S., and Liu, M.: Global methane and nitrous oxide emissions from
terrestrial ecosystems due to multiple environmental changes, Ecosystem
Health and Sustainability, 1, 1–20, https://doi.org/10.1890/EHS14-0015.1,
2015.
Tsuruta, A., Aalto, T., Backman, L., Hakkarainen, J., van der Laan-Luijkx, I. T., Krol, M. C., Spahni, R., Houweling, S., Laine, M., Dlugokencky, E., Gomez-Pelaez, A. J., van der Schoot, M., Langenfelds, R., Ellul, R., Arduini, J., Apadula, F., Gerbig, C., Feist, D. G., Kivi, R., Yoshida, Y., and Peters, W.: Global methane emission estimates for 2000–2012 from CarbonTracker Europe-CH4 v1.0, Geosci. Model Dev., 10, 1261–1289, https://doi.org/10.5194/gmd-10-1261-2017, 2017.
Twine, T. E., Kustas, W., Norman, J., Cook, D., Houser, P., Meyers, T.,
Prueger, J., Starks, P., and Wesely, M.: Correcting eddy-covariance flux
underestimates over a grassland, Agr. Forest Meteorol., 103,
279–300, 2000.
Wagner, D., Kobabe, S., Pfeiffer, E. M., and Hubberten, H. W.: Microbial
controls on methane fluxes from a polygonal tundra of the Lena Delta,
Siberia, Permafrost Periglac., 14, 173–185, 2003.
Walter, B. P. and Heimann, M.: A process-based, climate-sensitive model to
derive methane emissions from natural wetlands: Application to five wetland
sites, sensitivity to model parameters, and climate, Global Biogeochem. Cy., 14, 745–765, 2000.
Walter, B. P., Heimann, M., Shannon, R. D., and White, J. R.: A process‐based model to derive methane emissions from natural wetlands, Geophys. Res. Lett., 23, 3731–3734, 1996.
Wang, D., Lv, X., Ding, W., Cai, Z., Gao, J., and Yang, F.: Methan emission
from narshes in Zoige Plateau, Adv. Earth Sci., 17, 877–880, 2002.
Wei, D. and Wang, X.: Uncertainty and dynamics of natural wetland CH4
release in China: Research status and priorities, Atmos. Environ., 154,
95–105, https://doi.org/10.1016/j.atmosenv.2017.01.038, 2017.
Werle, P. and Kormann, R.: Fast chemical sensor for eddy-correlation
measurements of methane emissions from rice paddy fields, Appl. Optics,
40, 846–858, 2001.
Whalen, S. C. and Reeburgh, W. S.: Interannual variations in tundra methane
emission: A 4-year time series at fixed sites, Global Biogeochem. Cy., 6,
139–159, 1992.
Wille, C., Kutzbach, L., Sachs, T., Wagner, D., and Pfeiffer, E. M.: Methane
emission from Siberian arctic polygonal tundra: eddy covariance measurements
and modeling, Glob. Change Biol., 14, 1395–1408, 2008.
Xu, X., Yuan, F., Hanson, P. J., Wullschleger, S. D., Thornton, P. E., Riley, W. J., Song, X., Graham, D. E., Song, C., and Tian, H.: Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems, Biogeosciences, 13, 3735–3755, https://doi.org/10.5194/bg-13-3735-2016, 2016.
Ye, Y., Lu, C., and Lin, P.: CH4 dynamics in sediments of Bruguiera
sexangula mangrove at Hegang Estuary, Soil Environ. Sci., 9,
91–95, 2000 (in Chinese).
Zhang, Q., Zhang, W., Li, T., Sun, W., Yu, Y., and Wang, G.: Projective
analysis of staple food crop productivity in adaptation to future climate
change in China, Int. J. Biometeorol., 61, 1445–1460, https://doi.org/10.1007/s00484-017-1322-4, 2017.
Zhang, Y., Li, C., Trettin, C. C., and Li, H.: An integrated model of soil,
hydrology, and vegetation for carbon dynamics in wetland ecosystems, Global Biogeochem. Cy., 16, 1061–1078, 2002.
Zhu, Q., Liu, J., Peng, C., Chen, H., Fang, X., Jiang, H., Yang, G., Zhu, D., Wang, W., and Zhou, X.: Modelling methane emissions from natural wetlands by development and application of the TRIPLEX-GHG model, Geosci. Model Dev., 7, 981–999, https://doi.org/10.5194/gmd-7-981-2014, 2014.
Zhu, Q., Peng, C. H., Chen, H., Fang, X. Q., Liu, J. X., Jiang, H., Yang, Y.
Z., and Yang, G.: Estimating global natural wetland methane emissions using
process modelling: spatio-temporal patterns and contributions to atmospheric
methane fluctuations, Global Ecol. Biogeogr., 24, 959–972, https://doi.org/10.1111/geb.12307, 2015.
Zhu, X., Zhuang, Q., Gao, X., Sokolov, A., and Schlosser, C. A.: Pan-Arctic
land–atmospheric fluxes of methane and carbon dioxide in response to
climate change over the 21st century, Environ. Res. Lett., 8, 045003,
https://doi.org/10.1088/1748-9326/8/4/045003, 2013.
Zhuang, Q., Melillo, J. M., Kicklighter, D. W., Prinn, R. G., McGuire, A.
D., Steudler, P. A., Felzer, B. S., and Hu, S.: Methane fluxes between
terrestrial ecosystems and the atmosphere at northern high latitudes during
the past century: A retrospective analysis with a process-based
biogeochemistry model, Global Biogeochem. Cy., 18, GB3010, https://doi.org/10.1029/2004gb002239, 2004.
Zhuang, Q., Melillo, J. M., Sarofim, M. C., Kicklighter, D. W., McGuire, A.
D., Felzer, B. S., Sokolov, A., Prinn, R. G., Steudler, P. A., and Hu, S.:
CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern
high latitudes over the 21st century, Geophys. Res. Lett., 33, L17403,
https://doi.org/10.1029/2006GL026972, 2006.
Zhuang, Q., Melillo, J., McGuire, A., Kicklighter, D., Prinn, R., Steudler,
P., Felzer, B., and Hu, S.: Net emissions of CH4 and CO2 in
Alaska: Implications for the region's greenhouse gas budget, Ecol. Appl.,
17, 203–212, 2007.
Zhuang, Q., Chen, M., Xu, K., Tang, J., Saikawa, E., Lu, Y., Melillo, J. M.,
Prinn, R. G., and McGuire, A. D.: Response of global soil consumption of
atmospheric methane to changes in atmospheric climate and nitrogen
deposition, Global Biogeochem. Cy., 27, 650–663, 2013.
Zona, D., Oechel, W., Kochendorfer, J., Paw U, K., Salyuk, A., Olivas, P.,
Oberbauer, S., and Lipson, D.: Methane fluxes during the initiation of a
large-scale water table manipulation experiment in the Alaskan Arctic
tundra, Global Biogeochem. Cy., 23, GB2013, https://doi.org/10.1029/2009GB003487, 2009.
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E.,
Dinardo, S. J., Dengel, S., Sweeney, C., Karion, A., Chang, R. Y.-W.,
Henderson, J. M., Murphy, P. C., Goodrich, J. P., Moreaux, V., Liljedahl,
A., Watts, J. D., Kimball, J. S., Lipson, D. A., and Oechel, W. C.: Cold
season emissions dominate the Arctic tundra methane budget, P.
Natl. Acad. Sci. USA, 113, 40–45, https://doi.org/10.1073/pnas.1516017113, 2016.
Short summary
Reliable models are required to estimate global wetland CH4 emissions, which are the largest and most uncertain source of atmospheric CH4. This paper evaluated CH4MODwetland and TEM models against CH4 measurements from different continents and wetland types. Based on best-model performance, we estimated 117–125 Tg yr−1 of global CH4 emissions from wetlands for the period 2000–2010. Efforts should be made to reduce estimate uncertainties for different wetland types and regions.
Reliable models are required to estimate global wetland CH4 emissions, which are the largest and...
