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.
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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Guocheng Wang, Zhongkui Luo, Yao Huang, Wenjuan Sun, Yurong Wei, Liujun Xiao, Xi Deng, Jinhuan Zhu, Tingting Li, and Wen Zhang
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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
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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
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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
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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
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
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
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCO v4-Hg: the role of surfactants and waves
BOATSv2: New ecological and economic features improve simulations of High Seas catch and effort
Lambda-PFLOTRAN 1.0: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling
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
A dynamical process-based model AMmonia–CLIMate v1.0 (AMCLIM v1.0) for quantifying global agricultural ammonia emissions – Part 1: Land module for simulating emissions from synthetic fertilizer use
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
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
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
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
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 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
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the 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 responses 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.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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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 to 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.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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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. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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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. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
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
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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.
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
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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
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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
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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.
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
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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.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie Fisher, and Harrie-Jan Hendricks Franssen
EGUsphere, https://doi.org/10.5194/egusphere-2024-978, https://doi.org/10.5194/egusphere-2024-978, 2024
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land Surface Models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes and variability of carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research on these processes.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-81, https://doi.org/10.5194/gmd-2024-81, 2024
Revised manuscript accepted for GMD
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The estimation of Hg0 fluxes is of great uncertainty due to neglecting wave breaking and sea surfactant. Integrating these factors into MITgcm significantly rise Hg0 transfer velocity. The updated model shows increased fluxes in high wind and wave regions and vice versa, enhancing the spatial heterogeneity. It shows a stronger correlation between Hg0 transfer velocity and wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-26, https://doi.org/10.5194/gmd-2024-26, 2024
Revised manuscript accepted for GMD
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Numerical models that capture key features of the global dynamics of fish communities play a crucial role in addressing the impacts of climate change and industrial fishing on ecosystems and societies. Here, we detail an update of the BiOeconomic marine Trophic Size-spectrum model that corrects the model representation of the dynamic of fisheries in the High Seas. This update also allows a better representation of biodiversity to improve future global and regional fisheries studies.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-34, https://doi.org/10.5194/gmd-2024-34, 2024
Revised manuscript accepted for GMD
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The newly developed Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate respiration and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow via a Jupyter Notebook interface, that digests raw organic matter chemistry data via FTICR-MS, develops the representative reaction network, and completes a biogeochemical simulation with the open source, parallel reactive flow and transport code PFLOTRAN.
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
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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 > 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
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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
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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 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
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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.
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
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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 by forest inventory data, and climatic effects are challenging to capture.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2024-962, https://doi.org/10.5194/egusphere-2024-962, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use, whilst taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers were lost due to NH3 emissions. Hot and dry conditions and regions with high pH soils can expect higher NH3 emissions.
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
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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
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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.
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
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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
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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.
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
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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
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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.
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
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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.
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
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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.
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.
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.
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.
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.
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.
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.
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...