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
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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
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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
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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
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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.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles D. Koven, Ryan G. Knox, Lara M. Kueppers, and Chonggang Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-191, https://doi.org/10.5194/gmd-2023-191, 2023
Preprint under review for GMD
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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.
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
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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
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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.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James Millington
EGUsphere, https://doi.org/10.5194/egusphere-2023-2162, https://doi.org/10.5194/egusphere-2023-2162, 2023
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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.
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
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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
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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.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
EGUsphere, https://doi.org/10.5194/egusphere-2023-1626, https://doi.org/10.5194/egusphere-2023-1626, 2023
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Numerous estimations of water and energy balances heavily depend on empirical equations that necessitate site-specific calibration. This equifinality poses the risk of obtaining 'right answers for wrong reasons.' In this paper, we introduce novel formulations based on first-principles to calculate calibration-free quantities, such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
EGUsphere, https://doi.org/10.5194/egusphere-2023-2503, https://doi.org/10.5194/egusphere-2023-2503, 2023
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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 >25 % of the potential pre-industrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of land, water, and fertilizer use, as well as climate change.
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
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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
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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
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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.
Elin Ristorp Aas, Heleen A. de Wit, and Terje Koren Berntsen
EGUsphere, https://doi.org/10.5194/egusphere-2023-2069, https://doi.org/10.5194/egusphere-2023-2069, 2023
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By including microbial processes in soil models, we learn about how the soil system interacts with its environment, and responds to climate change. We present a soil process model, MIMICS+, able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also found that when adding nitrogen, the relationship between soil microbes changed notably. Coupling the model to a vegetation model, will allow further investigation of these mechanisms.
Yannek Käber, Florian Hartig, and Harald Bugmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2114, https://doi.org/10.5194/egusphere-2023-2114, 2023
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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 with forest inventory data, and climatic effects are challenging to capture.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
EGUsphere, https://doi.org/10.5194/egusphere-2023-2003, https://doi.org/10.5194/egusphere-2023-2003, 2023
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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.
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
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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
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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.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
EGUsphere, https://doi.org/10.5194/egusphere-2023-1492, https://doi.org/10.5194/egusphere-2023-1492, 2023
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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 behavior at low microbial biomass led us formulate the constrained enzyme hypothesis. We now can better describe how microbial mediated linkages of elemental fluxes adapt across decades.
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
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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.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-1287, https://doi.org/10.5194/egusphere-2023-1287, 2023
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predict their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that important questions related to plant-atmosphere interactions can be addressed, such as the effects of rising CO2 and ozone pollution on carbon uptake of the biosphere. The model has been well validated with both ground and satellite observations.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
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We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
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Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
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A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
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Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
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The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
Short summary
Short summary
Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
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...
