Articles | Volume 12, issue 3
https://doi.org/10.5194/gmd-12-1119-2019
© Author(s) 2019. 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-12-1119-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
25 Mar 2019
Development and technical paper |
| 25 Mar 2019
| 25 Mar 2019
Realized ecological forecast through an interactive Ecological Platform for Assimilating Data (EcoPAD, v1.0) into models
Yuanyuan Huang
CORRESPONDING AUTHOR
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, Oklahoma, USA
Laboratoire des Sciences du Climat et de l'Environnement, 91191
Gif-sur-Yvette, France
Mark Stacy
University of Oklahoma Information Technology, Norman, Oklahoma, USA
Jiang Jiang
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, Oklahoma, USA
Key Laboratory of Soil and Water Conservation and Ecological Restoration
in Jiangsu Province, Collaborative Innovation Center of Sustainable Forestry
in Southern China of Jiangsu Province, Nanjing Forestry University, Nanjing,
Jiangsu, China
Nilutpal Sundi
Department of Computer Science, University of Oklahoma, Norman, Oklahoma,
USA
Shuang Ma
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, Oklahoma, USA
Center for Ecosystem Science and Society, Northern Arizona University,
Flagstaff, Arizona, USA
Volodymyr Saruta
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, Oklahoma, USA
Center for Ecosystem Science and Society, Northern Arizona University,
Flagstaff, Arizona, USA
Chang Gyo Jung
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, Oklahoma, USA
Center for Ecosystem Science and Society, Northern Arizona University,
Flagstaff, Arizona, USA
Zheng Shi
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, Oklahoma, USA
Jianyang Xia
Tiantong National Forest Ecosystem Observation and Research Station,
School of Ecological and Environmental Sciences, East China Normal
University, Shanghai 200062, China
Research Center for Global Change and Ecological Forecasting, East China
Normal University, Shanghai 200062, China
Paul J. Hanson
Environmental Sciences Division and Climate Change Science Institute, Oak
Ridge National Laboratory, Oak Ridge, Tennessee, USA
Daniel Ricciuto
Environmental Sciences Division and Climate Change Science Institute, Oak
Ridge National Laboratory, Oak Ridge, Tennessee, USA
Department of Microbiology and Plant Biology, University of Oklahoma,
Norman, Oklahoma, USA
Center for Ecosystem Science and Society, Northern Arizona University,
Flagstaff, Arizona, USA
Department of Earth System Science, Tsinghua University, Beijing
100084,
China
Related authors
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, and Enqing Hou
Biogeosciences, 20, 4147–4163, https://doi.org/10.5194/bg-20-4147-2023, https://doi.org/10.5194/bg-20-4147-2023, 2023
Short summary
Short summary
We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. We predicted soil P pools’ distributions in natural systems, which can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
Short summary
Short summary
In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
Short summary
Short summary
Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
Short summary
Short summary
Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Elisa Bruni, Bertrand Guenet, Yuanyuan Huang, Hugues Clivot, Iñigo Virto, Roberta Farina, Thomas Kätterer, Philippe Ciais, Manuel Martin, and Claire Chenu
Biogeosciences, 18, 3981–4004, https://doi.org/10.5194/bg-18-3981-2021, https://doi.org/10.5194/bg-18-3981-2021, 2021
Short summary
Short summary
Increasing soil organic carbon (SOC) stocks is beneficial for climate change mitigation and food security. One way to enhance SOC stocks is to increase carbon input to the soil. We estimate the amount of carbon input required to reach a 4 % annual increase in SOC stocks in 14 long-term agricultural experiments around Europe. We found that annual carbon input should increase by 43 % under current temperature conditions, by 54 % for a 1 °C warming scenario and by 120 % for a 5 °C warming scenario.
Yan Sun, Daniel S. Goll, Jinfeng Chang, Philippe Ciais, Betrand Guenet, Julian Helfenstein, Yuanyuan Huang, Ronny Lauerwald, Fabienne Maignan, Victoria Naipal, Yilong Wang, Hui Yang, and Haicheng Zhang
Geosci. Model Dev., 14, 1987–2010, https://doi.org/10.5194/gmd-14-1987-2021, https://doi.org/10.5194/gmd-14-1987-2021, 2021
Short summary
Short summary
We evaluated the performance of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 against remote sensing, ground-based measurement networks and ecological databases. The simulated carbon, nitrogen and phosphorus fluxes among different spatial scales are generally in good agreement with data-driven estimates. However, the recent carbon sink in the Northern Hemisphere is substantially underestimated. Potential causes and model development priorities are discussed.
Jing Wang, Jianyang Xia, Xuhui Zhou, Kun Huang, Jian Zhou, Yuanyuan Huang, Lifen Jiang, Xia Xu, Junyi Liang, Ying-Ping Wang, Xiaoli Cheng, and Yiqi Luo
Biogeosciences, 16, 917–926, https://doi.org/10.5194/bg-16-917-2019, https://doi.org/10.5194/bg-16-917-2019, 2019
Short summary
Short summary
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Haicheng Zhang, Daniel S. Goll, Stefano Manzoni, Philippe Ciais, Bertrand Guenet, and Yuanyuan Huang
Geosci. Model Dev., 11, 4779–4796, https://doi.org/10.5194/gmd-11-4779-2018, https://doi.org/10.5194/gmd-11-4779-2018, 2018
Short summary
Short summary
Carbon use efficiency (CUE) of decomposers depends strongly on the organic matter quality (C : N ratio) and soil nutrient availability rather than a fixed value. A soil biogeochemical model with flexible CUE can better capture the differences in respiration rate of litter with contrasting C : N ratios and under different levels of mineral N availability than the model with fixed CUE, and well represent the effect of varying litter quality (N content) on SOM formation across temporal scales.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
Short summary
Short summary
We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Ye Huang, Bertrand Guenet, Philippe Ciais, Ivan A. Janssens, Jennifer L. Soong, Yilong Wang, Daniel Goll, Evgenia Blagodatskaya, and Yuanyuan Huang
Geosci. Model Dev., 11, 2111–2138, https://doi.org/10.5194/gmd-11-2111-2018, https://doi.org/10.5194/gmd-11-2111-2018, 2018
Short summary
Short summary
ORCHIMIC is a modeling effort trying to improve the representation of SOC dynamics in Earth system models (ESM). It has a structure that can be easily incorporated into CENTURY-based ESMs. In ORCHIMIC, key microbial dynamics (i.e., enzyme production, enzymatic decomposition and microbial dormancy) are included. The ORCHIMIC model can also reproduce the observed temporal dynamics of respiration and priming effects; thus it is an improved tool for climate projections and SOC response predictions.
Y. Huang and S. Gerber
Biogeosciences, 12, 6405–6427, https://doi.org/10.5194/bg-12-6405-2015, https://doi.org/10.5194/bg-12-6405-2015, 2015
Short summary
Short summary
The prediction of the greenhouse gas N2O from natural soils globally is sensitive to the representation of soil water. Factors that regulate nitrogen retention and nitrogen limitation, including fire and biological nitrogen fixation are further influencing the N2O gas production. Responses to warming and CO2 increase are strongly controlled by tropical soils. Therefore extrapolation of mostly extra-tropical field studies the globe warrants caution.
Hongkai Gao, Markus Hrachowitz, Lan Wang-Erlandsson, Fabrizio Fenicia, Qiaojuan Xi, Jianyang Xia, Wei Shao, Ge Sun, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 4477–4499, https://doi.org/10.5194/hess-28-4477-2024, https://doi.org/10.5194/hess-28-4477-2024, 2024
Short summary
Short summary
The concept of the root zone is widely used but lacks a precise definition. Its importance in Earth system science is not well elaborated upon. Here, we clarified its definition with several similar terms to bridge the multi-disciplinary gap. We underscore the key role of the root zone in the Earth system, which links the biosphere, hydrosphere, lithosphere, atmosphere, and anthroposphere. To better represent the root zone, we advocate for a paradigm shift towards ecosystem-centred modelling.
Hannah Nesser, Daniel J. Jacob, Joannes D. Maasakkers, Alba Lorente, Zichong Chen, Xiao Lu, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Margaux Winter, Shuang Ma, A. Anthony Bloom, John R. Worden, Robert N. Stavins, and Cynthia A. Randles
Atmos. Chem. Phys., 24, 5069–5091, https://doi.org/10.5194/acp-24-5069-2024, https://doi.org/10.5194/acp-24-5069-2024, 2024
Short summary
Short summary
We quantify 2019 methane emissions in the contiguous US (CONUS) at a ≈ 25 km × 25 km resolution using satellite methane observations. We find a 13 % upward correction to the 2023 US Environmental Protection Agency (EPA) Greenhouse Gas Emissions Inventory (GHGI) for 2019, with large corrections to individual states, urban areas, and landfills. This may present a challenge for US climate policies and goals, many of which target significant reductions in methane emissions.
Russell Doughty, Yujie Wang, Jennifer Johnson, Nicholas Parazoo, Troy Magney, Zoe Pierrat, Xiangming Xiao, Luis Guanter, Philipp Köhler, Christian Frankenberg, Peter Somkuti, Shuang Ma, Yuanwei Qin, Sean Crowell, and Berrien Moore III
EGUsphere, https://doi.org/10.22541/essoar.168167172.20799710/v1, https://doi.org/10.22541/essoar.168167172.20799710/v1, 2024
Short summary
Short summary
Here we present a novel model of global photosynthesis, ChloFluo, which uses spaceborne chlorophyll fluorescence to estimate the amount of photosynthetically active radiation absorbed by chlorophyll. Potential uses of our model are to advance our understanding of the timing and magnitude of photosynthesis, its effect on atmospheric carbon dioxide fluxes, and vegetation response to climate events and change.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Ying-Ping Wang, Julian Helfenstein, Yuanyuan Huang, and Enqing Hou
Biogeosciences, 20, 4147–4163, https://doi.org/10.5194/bg-20-4147-2023, https://doi.org/10.5194/bg-20-4147-2023, 2023
Short summary
Short summary
We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. We predicted soil P pools’ distributions in natural systems, which can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
Short summary
Short summary
We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Kevin R. Wilcox, Scott L. Collins, Alan K. Knapp, William Pockman, Zheng Shi, Melinda D. Smith, and Yiqi Luo
Biogeosciences, 20, 2707–2725, https://doi.org/10.5194/bg-20-2707-2023, https://doi.org/10.5194/bg-20-2707-2023, 2023
Short summary
Short summary
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
Short summary
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
Short summary
Short summary
Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
John R. Worden, Daniel H. Cusworth, Zhen Qu, Yi Yin, Yuzhong Zhang, A. Anthony Bloom, Shuang Ma, Brendan K. Byrne, Tia Scarpelli, Joannes D. Maasakkers, David Crisp, Riley Duren, and Daniel J. Jacob
Atmos. Chem. Phys., 22, 6811–6841, https://doi.org/10.5194/acp-22-6811-2022, https://doi.org/10.5194/acp-22-6811-2022, 2022
Short summary
Short summary
This paper is intended to accomplish two goals: 1) describe a new algorithm by which remotely sensed measurements of methane or other tracers can be used to not just quantify methane fluxes, but also attribute these fluxes to specific sources and regions and characterize their uncertainties, and 2) use this new algorithm to provide methane emissions by sector and country in support of the global stock take.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
Short summary
Short summary
The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
Short summary
Short summary
In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi
Geosci. Model Dev., 15, 1789–1802, https://doi.org/10.5194/gmd-15-1789-2022, https://doi.org/10.5194/gmd-15-1789-2022, 2022
Short summary
Short summary
Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM, we have utilized the data assimilation capability and designed the benchmarking framework by using field observations in modeling. Results show that data assimilation improves model performance in different aspects.
Xiao Lu, Daniel J. Jacob, Haolin Wang, Joannes D. Maasakkers, Yuzhong Zhang, Tia R. Scarpelli, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Hannah Nesser, A. Anthony Bloom, Shuang Ma, John R. Worden, Shaojia Fan, Robert J. Parker, Hartmut Boesch, Ritesh Gautam, Deborah Gordon, Michael D. Moran, Frances Reuland, Claudia A. Octaviano Villasana, and Arlyn Andrews
Atmos. Chem. Phys., 22, 395–418, https://doi.org/10.5194/acp-22-395-2022, https://doi.org/10.5194/acp-22-395-2022, 2022
Short summary
Short summary
We evaluate methane emissions and trends for 2010–2017 in the gridded national emission inventories for the United States, Canada, and Mexico by inversion of in situ and satellite methane observations. We find that anthropogenic methane emissions for all three countries are underestimated in the national inventories, largely driven by oil emissions. Anthropogenic methane emissions in the US peak in 2014, in contrast to the report of a steadily decreasing trend over 2010–2017 from the US EPA.
Xianjin He, Laurent Augusto, Daniel S. Goll, Bruno Ringeval, Yingping Wang, Julian Helfenstein, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou
Earth Syst. Sci. Data, 13, 5831–5846, https://doi.org/10.5194/essd-13-5831-2021, https://doi.org/10.5194/essd-13-5831-2021, 2021
Short summary
Short summary
Our database of globally distributed natural soil total P (STP) concentration showed concentration ranged from 1.4 to 9630.0 (mean 570.0) mg kg−1. Global predictions of STP concentration increased with latitude. Global STP stocks (excluding Antarctica) were estimated to be 26.8 and 62.2 Pg in the topsoil and subsoil, respectively. Our global map of STP concentration can be used to constrain Earth system models representing the P cycle and to inform quantification of global soil P availability.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
Short summary
Short summary
Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
Preprint withdrawn
Short summary
Short summary
Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
Daniel M. Ricciuto, Xiaojuan Yang, Dali Wang, and Peter E. Thornton
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-163, https://doi.org/10.5194/bg-2021-163, 2021
Publication in BG not foreseen
Short summary
Short summary
This paper uses a novel approach to quantify the impacts of the choice of decomposition model on carbon and nitrogen cycling. We compare the models to experimental data that examined litter decomposition over five different biomes. Despite widely differing assumptions, the models produce similar patterns of decomposition when nutrients are limiting. This differs from past analyses that did not consider the impacts of changing environmental conditions or nutrients.
Elisa Bruni, Bertrand Guenet, Yuanyuan Huang, Hugues Clivot, Iñigo Virto, Roberta Farina, Thomas Kätterer, Philippe Ciais, Manuel Martin, and Claire Chenu
Biogeosciences, 18, 3981–4004, https://doi.org/10.5194/bg-18-3981-2021, https://doi.org/10.5194/bg-18-3981-2021, 2021
Short summary
Short summary
Increasing soil organic carbon (SOC) stocks is beneficial for climate change mitigation and food security. One way to enhance SOC stocks is to increase carbon input to the soil. We estimate the amount of carbon input required to reach a 4 % annual increase in SOC stocks in 14 long-term agricultural experiments around Europe. We found that annual carbon input should increase by 43 % under current temperature conditions, by 54 % for a 1 °C warming scenario and by 120 % for a 5 °C warming scenario.
Rafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, and Jordi Martínez-Vilalta
Earth Syst. Sci. Data, 13, 2607–2649, https://doi.org/10.5194/essd-13-2607-2021, https://doi.org/10.5194/essd-13-2607-2021, 2021
Short summary
Short summary
Transpiration is a key component of global water balance, but it is poorly constrained from available observations. We present SAPFLUXNET, the first global database of tree-level transpiration from sap flow measurements, containing 202 datasets and covering a wide range of ecological conditions. SAPFLUXNET and its accompanying R software package
sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
Yan Sun, Daniel S. Goll, Jinfeng Chang, Philippe Ciais, Betrand Guenet, Julian Helfenstein, Yuanyuan Huang, Ronny Lauerwald, Fabienne Maignan, Victoria Naipal, Yilong Wang, Hui Yang, and Haicheng Zhang
Geosci. Model Dev., 14, 1987–2010, https://doi.org/10.5194/gmd-14-1987-2021, https://doi.org/10.5194/gmd-14-1987-2021, 2021
Short summary
Short summary
We evaluated the performance of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 against remote sensing, ground-based measurement networks and ecological databases. The simulated carbon, nitrogen and phosphorus fluxes among different spatial scales are generally in good agreement with data-driven estimates. However, the recent carbon sink in the Northern Hemisphere is substantially underestimated. Potential causes and model development priorities are discussed.
Xiao Lu, Daniel J. Jacob, Yuzhong Zhang, Joannes D. Maasakkers, Melissa P. Sulprizio, Lu Shen, Zhen Qu, Tia R. Scarpelli, Hannah Nesser, Robert M. Yantosca, Jianxiong Sheng, Arlyn Andrews, Robert J. Parker, Hartmut Boesch, A. Anthony Bloom, and Shuang Ma
Atmos. Chem. Phys., 21, 4637–4657, https://doi.org/10.5194/acp-21-4637-2021, https://doi.org/10.5194/acp-21-4637-2021, 2021
Short summary
Short summary
We use an analytical solution to the Bayesian inverse problem to quantitatively compare and combine the information from satellite and in situ observations, and to estimate global methane budget and their trends over the 2010–2017 period. We find that satellite and in situ observations are to a large extent complementary in the inversion for estimating global methane budget, and reveal consistent corrections of regional anthropogenic and wetland methane emissions relative to the prior inventory.
Yuzhong Zhang, Daniel J. Jacob, Xiao Lu, Joannes D. Maasakkers, Tia R. Scarpelli, Jian-Xiong Sheng, Lu Shen, Zhen Qu, Melissa P. Sulprizio, Jinfeng Chang, A. Anthony Bloom, Shuang Ma, John Worden, Robert J. Parker, and Hartmut Boesch
Atmos. Chem. Phys., 21, 3643–3666, https://doi.org/10.5194/acp-21-3643-2021, https://doi.org/10.5194/acp-21-3643-2021, 2021
Short summary
Short summary
We use 2010–2018 satellite observations of atmospheric methane to interpret the factors controlling atmospheric methane and its accelerating increase during the period. The 2010–2018 increase in global methane emissions is driven by tropical and boreal wetlands and tropical livestock (South Asia, Africa, Brazil), with an insignificant positive trend in emissions from the fossil fuel sector. The peak methane growth rates in 2014–2015 are also contributed by low OH and high fire emissions.
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486, https://doi.org/10.5194/bg-18-467-2021, https://doi.org/10.5194/bg-18-467-2021, 2021
Short summary
Short summary
The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
Erqian Cui, Chenyu Bian, Yiqi Luo, Shuli Niu, Yingping Wang, and Jianyang Xia
Biogeosciences, 17, 6237–6246, https://doi.org/10.5194/bg-17-6237-2020, https://doi.org/10.5194/bg-17-6237-2020, 2020
Short summary
Short summary
Mean annual net ecosystem productivity (NEP) is related to the magnitude of the carbon sink of a specific ecosystem, while its inter-annual variation (IAVNEP) characterizes the stability of such a carbon sink. Thus, a better understanding of the co-varying NEP and IAVNEP is critical for locating the major and stable carbon sinks on land. Based on daily NEP observations from eddy-covariance sites, we found local indicators for the spatially varying NEP and IAVNEP, respectively.
Jian Zhou, Jianyang Xia, Ning Wei, Yufu Liu, Chenyu Bian, Yuqi Bai, and Yiqi Luo
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-76, https://doi.org/10.5194/gmd-2020-76, 2020
Revised manuscript not accepted
Short summary
Short summary
The increase of model complexity and data volume challenges the evaluation of Earth system models (ESMs), which mainly stems from the untraceable, unautomatic, and high computational costs. Here, we built up an online Traceability analysis system for Model Evaluation (TraceME), which is traceable, automatic and shareable. The TraceME (v1.0) can trace the structural uncertainty of simulated carbon (C) storage in ESMs and provide some new implications for the next generation of model evaluation.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, https://doi.org/10.5194/gmd-12-4133-2019, 2019
Short summary
Short summary
We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
Dan Lu and Daniel Ricciuto
Geosci. Model Dev., 12, 1791–1807, https://doi.org/10.5194/gmd-12-1791-2019, https://doi.org/10.5194/gmd-12-1791-2019, 2019
Short summary
Short summary
This work uses machine-learning techniques to advance the predictive understanding of large-scale Earth systems.
Junyi Liang, Gangsheng Wang, Daniel M. Ricciuto, Lianhong Gu, Paul J. Hanson, Jeffrey D. Wood, and Melanie A. Mayes
Geosci. Model Dev., 12, 1601–1612, https://doi.org/10.5194/gmd-12-1601-2019, https://doi.org/10.5194/gmd-12-1601-2019, 2019
Short summary
Short summary
Soil respiration, the second largest carbon fluxes between the atmosphere and land, is not well represented in global land models. In this study, using long-term observations at a temperate forest, we identified a solution for using better soil water potential simulations to improve predictions of soil respiration in the E3SM land model. In addition, parameter calibration further improved model performance.
Jing Wang, Jianyang Xia, Xuhui Zhou, Kun Huang, Jian Zhou, Yuanyuan Huang, Lifen Jiang, Xia Xu, Junyi Liang, Ying-Ping Wang, Xiaoli Cheng, and Yiqi Luo
Biogeosciences, 16, 917–926, https://doi.org/10.5194/bg-16-917-2019, https://doi.org/10.5194/bg-16-917-2019, 2019
Short summary
Short summary
Soil is critical in mitigating climate change mainly because soil carbon turns over much slower in soils than vegetation and the atmosphere. However, Earth system models (ESMs) have large uncertainty in simulating carbon dynamics due to their biased estimation of soil carbon transit time (τsoil). Here, the τsoil estimates from 12 ESMs that participated in CMIP5 were evaluated by a database of measured τsoil. We detected a large spatial variation in measured τsoil across the globe.
Haicheng Zhang, Daniel S. Goll, Stefano Manzoni, Philippe Ciais, Bertrand Guenet, and Yuanyuan Huang
Geosci. Model Dev., 11, 4779–4796, https://doi.org/10.5194/gmd-11-4779-2018, https://doi.org/10.5194/gmd-11-4779-2018, 2018
Short summary
Short summary
Carbon use efficiency (CUE) of decomposers depends strongly on the organic matter quality (C : N ratio) and soil nutrient availability rather than a fixed value. A soil biogeochemical model with flexible CUE can better capture the differences in respiration rate of litter with contrasting C : N ratios and under different levels of mineral N availability than the model with fixed CUE, and well represent the effect of varying litter quality (N content) on SOM formation across temporal scales.
Qianyu Li, Xingjie Lu, Yingping Wang, Xin Huang, Peter M. Cox, and Yiqi Luo
Biogeosciences, 15, 6909–6925, https://doi.org/10.5194/bg-15-6909-2018, https://doi.org/10.5194/bg-15-6909-2018, 2018
Short summary
Short summary
Land-surface models have been widely used to predict the responses of terrestrial ecosystems to climate change. A better understanding of model mechanisms that govern terrestrial ecosystem responses to rising atmosphere [CO2] is needed. Our study for the first time shows that the expansion of leaf area under rising [CO2] is the most important response for the stimulation of land carbon accumulation by a land-surface model: CABLE. Processes related to leaf area should be better calibrated.
Xingjie Lu, Ying-Ping Wang, Yiqi Luo, and Lifen Jiang
Biogeosciences, 15, 6559–6572, https://doi.org/10.5194/bg-15-6559-2018, https://doi.org/10.5194/bg-15-6559-2018, 2018
Short summary
Short summary
How long does C cycle through terrestrial ecosystems is a critical question for understanding land C sequestration capacity under future rising atmosphere [CO2] and climate warming. Under climate change, previous conventional concepts with a steady-state assumption will no longer be suitable for a non-steady state. Our results using the new concept, C transit time, suggest more significant responses in terrestrial C cycle under rising [CO2] and climate warming.
Zhenggang Du, Ensheng Weng, Lifen Jiang, Yiqi Luo, Jianyang Xia, and Xuhui Zhou
Geosci. Model Dev., 11, 4399–4416, https://doi.org/10.5194/gmd-11-4399-2018, https://doi.org/10.5194/gmd-11-4399-2018, 2018
Short summary
Short summary
In this study, based on a traceability analysis technique, we evaluated alternative representations of C–N interactions and their impacts on the C cycle using the TECO model framework. Our results showed that different representations of C–N coupling processes lead to divergent effects on plant production, C residence time, and thus the ecosystem C storage capacity. Identifying those effects can help us to improve the N limitation assumptions employed in terrestrial ecosystem models.
Misha B. Krassovski, Glen E. Lyon, Jeffery S. Riggs, and Paul J. Hanson
Geosci. Instrum. Method. Data Syst., 7, 289–295, https://doi.org/10.5194/gi-7-289-2018, https://doi.org/10.5194/gi-7-289-2018, 2018
Short summary
Short summary
Climate change studies are growing and related experiments are getting bigger and more complex. They are often conducted in remote areas where communications are limited. In cases like that the data can be transferred via a satellite connection, but these types of connections are slow. We found that by using the little known possibilities of LoggerNet software (the most popular data logger software in environmental science) it is possible to transfer quite a large amount of data.
Yilong Wang, Philippe Ciais, Daniel Goll, Yuanyuan Huang, Yiqi Luo, Ying-Ping Wang, A. Anthony Bloom, Grégoire Broquet, Jens Hartmann, Shushi Peng, Josep Penuelas, Shilong Piao, Jordi Sardans, Benjamin D. Stocker, Rong Wang, Sönke Zaehle, and Sophie Zechmeister-Boltenstern
Geosci. Model Dev., 11, 3903–3928, https://doi.org/10.5194/gmd-11-3903-2018, https://doi.org/10.5194/gmd-11-3903-2018, 2018
Short summary
Short summary
We present a new modeling framework called Global Observation-based Land-ecosystems Utilization Model of Carbon, Nitrogen and Phosphorus (GOLUM-CNP) that combines a data-constrained C-cycle analysis with data-driven estimates of N and P inputs and losses and with observed stoichiometric ratios. GOLUM-CNP provides a traceable tool, where a consistency between different datasets of global C, N, and P cycles has been achieved.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
Short summary
Short summary
Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Ye Huang, Bertrand Guenet, Philippe Ciais, Ivan A. Janssens, Jennifer L. Soong, Yilong Wang, Daniel Goll, Evgenia Blagodatskaya, and Yuanyuan Huang
Geosci. Model Dev., 11, 2111–2138, https://doi.org/10.5194/gmd-11-2111-2018, https://doi.org/10.5194/gmd-11-2111-2018, 2018
Short summary
Short summary
ORCHIMIC is a modeling effort trying to improve the representation of SOC dynamics in Earth system models (ESM). It has a structure that can be easily incorporated into CENTURY-based ESMs. In ORCHIMIC, key microbial dynamics (i.e., enzyme production, enzymatic decomposition and microbial dormancy) are included. The ORCHIMIC model can also reproduce the observed temporal dynamics of respiration and priming effects; thus it is an improved tool for climate projections and SOC response predictions.
Liming Yan, Xiaoni Xu, and Jianyang Xia
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-124, https://doi.org/10.5194/bg-2018-124, 2018
Manuscript not accepted for further review
Short summary
Short summary
The patterns of the ratio of atmospheric deposited ammonium- to nitrate-N shows an increasing trend with the total N load since the industrial revolution. As a key role of N in plant growth, it is important to know the general response patterns of plant growth to N forms. By the synthesized dataset and meta-analysis, we found a higher response of plant growth to NH4+-N than NO3−-N addition across all species. Our results suggest plant could more positively respond to N deposition in the future.
Henrique F. Duarte, Brett M. Raczka, Daniel M. Ricciuto, John C. Lin, Charles D. Koven, Peter E. Thornton, David R. Bowling, Chun-Ta Lai, Kenneth J. Bible, and James R. Ehleringer
Biogeosciences, 14, 4315–4340, https://doi.org/10.5194/bg-14-4315-2017, https://doi.org/10.5194/bg-14-4315-2017, 2017
Short summary
Short summary
We evaluate the Community Land Model (CLM4.5) against observations at an old-growth coniferous forest site that is subjected to water stress each summer. We found that, after calibration, CLM was able to reasonably simulate the observed fluxes of energy and carbon, carbon stocks, carbon isotope ratios, and ecosystem response to water stress. This study demonstrates that carbon isotopes can expose structural weaknesses in CLM and provide a key constraint that may guide future model development.
Dan Lu, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger
Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, https://doi.org/10.5194/bg-14-4295-2017, 2017
Short summary
Short summary
Calibration of terrestrial ecosystem models (TEMs) is important but challenging. This study applies an advanced sampling technique for parameter estimation of a TEM. The results improve the model fit and predictive performance.
Erik A. Hobbie, Janet Chen, Paul J. Hanson, Colleen M. Iversen, Karis J. McFarlane, Nathan R. Thorp, and Kirsten S. Hofmockel
Biogeosciences, 14, 2481–2494, https://doi.org/10.5194/bg-14-2481-2017, https://doi.org/10.5194/bg-14-2481-2017, 2017
Short summary
Short summary
We measured carbon and nitrogen isotope ratios (13C : 12C and 15N : 14N) in peat cores in a northern Minnesota bog to understand how climate, vegetation type, and decomposition affected C and N budgets over the last 9000 years. 13C : 12C patterns were primarily influenced by shifts in temperature, peatland vegetation and atmospheric CO2, whereas tree colonization and upland N influxes affected 15N : 14N ratios. Isotopic markers provided new insights into long-term patterns of CO2 and nitrogen losses.
Paul J. Hanson, Jeffery S. Riggs, W. Robert Nettles, Jana R. Phillips, Misha B. Krassovski, Leslie A. Hook, Lianhong Gu, Andrew D. Richardson, Donald M. Aubrecht, Daniel M. Ricciuto, Jeffrey M. Warren, and Charlotte Barbier
Biogeosciences, 14, 861–883, https://doi.org/10.5194/bg-14-861-2017, https://doi.org/10.5194/bg-14-861-2017, 2017
Short summary
Short summary
This paper describes operational methods to achieve whole-ecosystem warming (WEW) for tall-stature, high-carbon, boreal forest peatlands. The methods enable scientists to study immediate and longer-term (1 decade) responses of organisms (microbes to trees) and ecosystem functions (carbon, water and nutrient cycles). The WEW technology allows researchers to have a plausible glimpse of future environmental conditions for study that are not available in the current observational record.
Yiqi Luo, Zheng Shi, Xingjie Lu, Jianyang Xia, Junyi Liang, Jiang Jiang, Ying Wang, Matthew J. Smith, Lifen Jiang, Anders Ahlström, Benito Chen, Oleksandra Hararuk, Alan Hastings, Forrest Hoffman, Belinda Medlyn, Shuli Niu, Martin Rasmussen, Katherine Todd-Brown, and Ying-Ping Wang
Biogeosciences, 14, 145–161, https://doi.org/10.5194/bg-14-145-2017, https://doi.org/10.5194/bg-14-145-2017, 2017
Short summary
Short summary
Climate change is strongly regulated by land carbon cycle. However, we lack the ability to predict future land carbon sequestration. Here, we develop a novel framework for understanding what determines the direction and rate of future change in land carbon storage. The framework offers a suite of new approaches to revolutionize land carbon model evaluation and improvement.
Brett Raczka, Henrique F. Duarte, Charles D. Koven, Daniel Ricciuto, Peter E. Thornton, John C. Lin, and David R. Bowling
Biogeosciences, 13, 5183–5204, https://doi.org/10.5194/bg-13-5183-2016, https://doi.org/10.5194/bg-13-5183-2016, 2016
Short summary
Short summary
We use carbon isotopes of CO2 to improve the performance of a land surface model, a component with earth system climate models. We found that isotope observations can provide important information related to the exchange of carbon and water from vegetation driven by environmental stress from low atmospheric moisture and nitrogen limitation. It follows that isotopes have a unique potential to improve model performance and provide insight into land surface model development.
J. Mao, D. M. Ricciuto, P. E. Thornton, J. M. Warren, A. W. King, X. Shi, C. M. Iversen, and R. J. Norby
Biogeosciences, 13, 641–657, https://doi.org/10.5194/bg-13-641-2016, https://doi.org/10.5194/bg-13-641-2016, 2016
Short summary
Short summary
The aim of this study is to implement, calibrate and evaluate the CLM4 against carbon and hydrology observations from a shading and labeling experiment in a stand of young loblolly pines. We found a combination of parameters measured on-site and calibration targeting biomass, transpiration, and 13C discrimination gave good agreement with pretreatment measurements. We also used observations from the experiment to develop a conceptual model of short-term photosynthate storage and transport.
X. Shi, P. E. Thornton, D. M. Ricciuto, P. J. Hanson, J. Mao, S. D. Sebestyen, N. A. Griffiths, and G. Bisht
Biogeosciences, 12, 6463–6477, https://doi.org/10.5194/bg-12-6463-2015, https://doi.org/10.5194/bg-12-6463-2015, 2015
Y. Huang and S. Gerber
Biogeosciences, 12, 6405–6427, https://doi.org/10.5194/bg-12-6405-2015, https://doi.org/10.5194/bg-12-6405-2015, 2015
Short summary
Short summary
The prediction of the greenhouse gas N2O from natural soils globally is sensitive to the representation of soil water. Factors that regulate nitrogen retention and nitrogen limitation, including fire and biological nitrogen fixation are further influencing the N2O gas production. Responses to warming and CO2 increase are strongly controlled by tropical soils. Therefore extrapolation of mostly extra-tropical field studies the globe warrants caution.
C. Safta, D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams, and P. E. Thornton
Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, https://doi.org/10.5194/gmd-8-1899-2015, 2015
Short summary
Short summary
In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model and focus on the comparison between steady-state and transient
simulation setups. We study model parameters via global sensitivity analysis and employ a Bayesian approach to calibrate these parameters using NEE observations at the Harvard Forest site. The calibration results are then used to assess the predictive skill of the model via posterior predictive checks.
Y. Wei, S. Liu, D. N. Huntzinger, A. M. Michalak, N. Viovy, W. M. Post, C. R. Schwalm, K. Schaefer, A. R. Jacobson, C. Lu, H. Tian, D. M. Ricciuto, R. B. Cook, J. Mao, and X. Shi
Geosci. Model Dev., 7, 2875–2893, https://doi.org/10.5194/gmd-7-2875-2014, https://doi.org/10.5194/gmd-7-2875-2014, 2014
X. Yang, P. E. Thornton, D. M. Ricciuto, and W. M. Post
Biogeosciences, 11, 1667–1681, https://doi.org/10.5194/bg-11-1667-2014, https://doi.org/10.5194/bg-11-1667-2014, 2014
Z. Shi, M. L. Thomey, W. Mowll, M. Litvak, N. A. Brunsell, S. L. Collins, W. T. Pockman, M. D. Smith, A. K. Knapp, and Y. Luo
Biogeosciences, 11, 621–633, https://doi.org/10.5194/bg-11-621-2014, https://doi.org/10.5194/bg-11-621-2014, 2014
D. N. Huntzinger, C. Schwalm, A. M. Michalak, K. Schaefer, A. W. King, Y. Wei, A. Jacobson, S. Liu, R. B. Cook, W. M. Post, G. Berthier, D. Hayes, M. Huang, A. Ito, H. Lei, C. Lu, J. Mao, C. H. Peng, S. Peng, B. Poulter, D. Riccuito, X. Shi, H. Tian, W. Wang, N. Zeng, F. Zhao, and Q. Zhu
Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, https://doi.org/10.5194/gmd-6-2121-2013, 2013
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
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
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
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems 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
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. 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
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. 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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Short summary
By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Short summary
Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary
Short summary
Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
Short summary
Short summary
Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
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
Short summary
Short summary
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
Short summary
Short summary
By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
Short summary
Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
Short summary
Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
Short summary
To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary
Short summary
Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
Short summary
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
Short summary
Short summary
This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
Short summary
Short summary
Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
Short summary
Short summary
Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
Short summary
Short summary
The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
Short summary
Short summary
We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
Short summary
Short summary
Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
Short summary
Short summary
We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
Short summary
Short summary
In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Cited articles
Ahlstrom, A., Schurgers, G., Arneth, A., and Smith, B.: Robustness and
uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change
projections, Environ. Res. Lett., 7, 044008, https://doi.org/10.1088/1748-9326/7/4/044008,
2012.
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Avellano, A.: The data assimilation research testbed A Community Facility, B.
Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009bams2618.1, 2009.
Baldocchi, D., Falge, E., Gu, L. H., Olson, R., Hollinger, D., Running, S.,
Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A.,
Katul, G., Law, B., Lee, X. H., Malhi, Y., Meyers, T., Munger, W., Oechel,
W., U, K. T. P., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S.,
Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A new tool to study the
temporal and spatial variability of ecosystem-scale carbon dioxide, water
vapor, and energy flux densities, B. Am. Meteorol. Soc., 82, 2415–2434,
https://doi.org/10.1175/1520-0477(2001)082<2415:fantts>2.3.co;2, 2001.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal conductance
and its contribution to the control of photosynthesis under different
environmental conditions, in: Progress in Photosynthesis
Research, edited by: Biggens, J. Martinus Nijhoff Publishers,
The Netherlands, 221–224, 1987.
Bastiaanssen, W. G. M. and Ali, S.: A new crop yield forecasting model
based on satellite measurements applied across the Indus Basin, Pakistan,
Agr. Ecosyst. Environ., 94, 321–340, https://doi.org/10.1016/s0167-8809(02)00034-8,
2003.
Beckage, B., Gross, L. J., and Kauffman, S.: The limits to prediction in
ecological systems, Ecosphere, 2, UNSP 125, https://doi.org/10.1890/es11-00211.1,
2011.
Bloom, A. A., Exbrayat, J. F., van der Velde, I. R., Feng, L., and Williams,
M.: The decadal state of the terrestrial carbon cycle: Global retrievals of
terrestrial carbon allocation, pools, and residence times, P. Natl. Acad.
Sci. USA, 113, 1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016.
Botkin, D. B., Saxe, H., Araujo, M. B., Betts, R., Bradshaw, R. H. W.,
Cedhagen, T., Chesson, P., Dawson, T. P., Etterson, J. R., Faith, D. P.,
Ferrier, S., Guisan, A., Hansen, A. S., Hilbert, D. W., Loehle, C., Margules,
C., New, M., Sobel, M. J., and Stockwell, D. R. B.: Forecasting the effects
of global warming on biodiversity, Bioscience, 57, 227–236,
https://doi.org/10.1641/b570306, 2007.
Braswell, B. H., Sacks, W. J., Linder, E., and Schimel, D. S.: Estimating
diurnal to annual ecosystem parameters by synthesis of a carbon flux model
with eddy covariance net ecosystem exchange observations, Glob. Change Biol.,
11, 335–355, https://doi.org/10.1111/j.1365-2486.2005.00897.x, 2005.
Clark, J. S., Carpenter, S. R., Barber, M., Collins, S., Dobson, A., Foley,
J. A., Lodge, D. M., Pascual, M., Pielke, R., Pizer, W., Pringle, C., Reid,
W. V., Rose, K. A., Sala, O., Schlesinger, W. H., Wall, D. H., and Wear, D.:
Ecological forecasts: An emerging imperative, Science, 293, 657–660,
https://doi.org/10.1126/science.293.5530.657, 2001.
Clark, J. S., Lewis, M., McLachlan, J. S., and HilleRisLambers, J.:
Estimating population spread: What can we forecast and how well?, Ecology,
84, 1979–1988, https://doi.org/10.1890/01-0618, 2003.
Corbet, S. A., Saville, N. M., Fussell, M., PrysJones, O. E., and Unwin, D.
M.: The competition box: A graphical aid to forecasting pollinator
performance, J. Appl. Ecol., 32, 707–719, https://doi.org/10.2307/2404810, 1995.
Coreau, A., Pinay, G., Thompson, J. D., Cheptou, P. O., and Mermet, L.: The
rise of research on futures in ecology: rebalancing scenarios and
predictions, Ecol. Lett., 12, 1277–1286,
https://doi.org/10.1111/j.1461-0248.2009.01392.x, 2009.
Coreau, A., Treyer, S., Cheptou, P. O., Thompson, J. D., and Mermet, L.:
Exploring the difficulties of studying futures in ecology: what do ecological
scientists think?, Oikos, 119, 1364–1376,
https://doi.org/10.1111/j.1600-0706.2010.18195.x, 2010.
Craft, C., Clough, J., Ehman, J., Joye, S., Park, R., Pennings, S., Guo, H.
Y., and Machmuller, M.: Forecasting the effects of accelerated sea-level rise
on tidal marsh ecosystem services, Front. Ecol. Environ., 7, 73–78,
https://doi.org/10.1890/070219, 2009.
Cressie, N., Calder, C. A., Clark, J. S., Hoef, J. M. V., and Wikle, C. K.:
Accounting for uncertainty in ecological analysis: the strengths and
limitations of hierarchical statistical modeling, Ecol. Appl., 19, 553–570,
https://doi.org/10.1890/07-0744.1, 2009.
Dietze, M. C., Lebauer, D. S., and Kooper, R.: On improving the
communication between models and data, Plant Cell Environ., 36, 1575–1585,
https://doi.org/10.1111/pce.12043, 2013.
Diez, J. M., Ibanez, I., Miller-Rushing, A. J., Mazer, S. J., Crimmins, T.
M., Crimmins, M. A., Bertelsen, C. D., and Inouye, D. W.: Forecasting
phenology: from species variability to community patterns, Ecol. Lett., 15,
545–553, https://doi.org/10.1111/j.1461-0248.2012.01765.x, 2012.
Ellison, A. M.: Repeatability and transparency in ecological research,
Ecology, 91, 2536–2539, https://doi.org/10.1890/09-0032.1, 2010.
Farquhar, G. D., Caemmerer, S. V., and Berry, J. A.: A biochemical-model of
photosynthetic CO2 assimilation in leaves of C3 species,
Planta, 149, 78–90, https://doi.org/10.1007/bf00386231, 1980.
Fordham, D. A., Akcakaya, H. R., Araujo, M. B., Elith, J., Keith, D. A.,
Pearson, R., Auld, T. D., Mellin, C., Morgan, J. W., Regan, T. J., Tozer, M.,
Watts, M. J., White, M., Wintle, B. A., Yates, C., and Brook, B. W.: Plant
extinction risk under climate change: are forecast range shifts alone a good
indicator of species vulnerability to global warming?, Glob. Change Biol.,
18, 1357–1371, https://doi.org/10.1111/j.1365-2486.2011.02614.x, 2012.
Gao, C., Wang, H., Weng, E. S., Lakshmivarahan, S., Zhang, Y. F., and Luo,
Y. Q.: Assimilation of multiple data sets with the ensemble Kalman filter to
improve forecasts of forest carbon dynamics, Ecol. Appl., 21, 1461–1473,
2011.
Hampton, S. E., Strasser, C. A., Tewksbury, J. J., Gram, W. K., Budden, A.
E., Batcheller, A. L., Duke, C. S., and Porter, J. H.: Big data and the
future of ecology, Front. Ecol. Environ., 11, 156–162, https://doi.org/10.1890/120103,
2013.
Hanson, P. J., Riggs, J. S., Nettles, W. R., Phillips, J. R., Krassovski, M.
B., Hook, L. A., Gu, L., Richardson, A. D., Aubrecht, D. M., Ricciuto, D. M.,
Warren, J. M., and Barbier, C.: Attaining whole-ecosystem warming using air
and deep-soil heating methods with an elevated CO2 atmosphere,
Biogeosciences, 14, 861–883, https://doi.org/10.5194/bg-14-861-2017, 2017.
Hare, J. A., Alexander, M. A., Fogarty, M. J., Williams, E. H., and Scott,
J. D.: Forecasting the dynamics of a coastal fishery species using a coupled
climate-population model, Ecol. Appl., 20, 452–464, https://doi.org/10.1890/08-1863.1,
2010.
Huang, Y., Jiang, J., Ma, S., Ricciuto, D., Hanson, P. J., and Luo, Y.: Soil
thermal dynamics, snow cover and frozen depth under five temperature
treatments in an ombrotrophic bog: Constrained forecast with data
assimilation, J. Geophys. Res.-Biogeo., 122, 2046–2063, https://doi.org/10.1002/2016JG003725,
2017.
Jiang, J., Huang, Y., Ma, S., Stacy, M., Shi, Z., Ricciuto, D. M., Hanson,
P. J., and Luo, Y.: Forecasting responses of a northern peatland carbon cycle
to elevated CO2 and a gradient of experimental warming, J. Geophys.
Res.-Biogeo., 123, 1057–1071, https://doi.org/10.1002/2017jg004040, 2018.
Jiang, J., Huang, Y., and Jung, C. G.: EcoPAD v1.0 at the SPRUCE site,
available at: https://ecolab.nau.edu/ecopad_portal/, last access:
January 2019a.
Jiang, J., Stacy, M., Huang, Y., Sundi, N., and Ma., S.: GitHub site of
EcoPAD v1.0 code, available at: https://github.com/ou-ecolab,
last access: January 2019b.
Johnson, B. R., Kampe, T. U., and Kuester, M.: Development of airborne
remote sensing instrumentations for NEON, SPIE Optical Engineering+
Applications, 78090I-78090I-78010, https://doi.org/10.1117/12.860182, August, 2010.
Kearney, M. R., Wintle, B. A., and Porter, W. P.: Correlative and
mechanistic models of species distribution provide congruent forecasts under
climate change, Conserv. Lett., 3, 203–213,
https://doi.org/10.1111/j.1755-263X.2010.00097.x, 2010.
Keenan, T. F., Davidson, E., Moffat, A. M., Munger, W., and Richardson, A.
D.: Using model-data fusion to interpret past trends, and quantify
uncertainties in future projections, of terrestrial ecosystem carbon cycling,
Glob. Change Biol., 18, 2555–2569, https://doi.org/10.1111/j.1365-2486.2012.02684.x,
2012.
Kobori, H., Dickinson, J. L., Washitani, I., Sakurai, R., Amano, T.,
Komatsu, N., Kitamura, W., Takagawa, S., Koyama, K., Ogawara, T., and
Miller-Rushing, A. J.: Citizen science: a new approach to advance ecology,
education, and conservation, Ecol. Res., 31, 1–19,
https://doi.org/10.1007/s11284-015-1314-y, 2016.
Kolka, R. K., Sebestyen, S. D., Verry, E. S., and Brooks, K. N.: Peatland
biogeochemistry and watershed hydrology at the Marcell Experimental Forest,
CRC Press, Boca Raton, 488 pp., 2011.
Lebauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M.
C.: Facilitating feedbacks between field measurements and ecosystem models,
Ecol. Monogr., 83, 133–154, https://doi.org/10.1890/12-0137.1, 2013.
Liang, J. Y., Li, D. J., Shi, Z., Tiedje, J. M., Zhou, J. Z., Schuur, E. A.
G., Konstantinidis, K. T., and Luo, Y. Q.: Methods for estimating temperature
sensitivity of soil organic matter based on incubation data: A comparative
evaluation, Soil Biol. Biochem., 80, 127–135,
https://doi.org/10.1016/j.soilbio.2014.10.005, 2015.
Ludascher, B., Altintas, I., Berkley, C., Higgins, D., Jaeger, E., Jones,
M., Lee, E. A., Tao, J., and Zhao, Y.: Scientific workflow management and the
Kepler system, Concurr. Comp.-Pract. E., 18, 1039–1065, https://doi.org/10.1002/cpe.994,
2006.
Luo, Y. Q. and Reynolds, J. F.: Validity of extrapolating field
CO2 experiments to predict carbon sequestration in natural
ecosystems, Ecology, 80, 1568–1583,
https://doi.org/10.1890/0012-9658(1999)080[1568:VOEFCE]2.0.CO;2, 1999.
Luo, Y. Q., Wan, S. Q., Hui, D. F., and Wallace, L. L.: Acclimatization of
soil respiration to warming in a tall grass prairie, Nature, 413, 622–625,
https://doi.org/10.1038/35098065, 2001.
Luo, Y. Q., Melillo, J., Niu, S. L., Beier, C., Clark, J. S., Classen, A.
T., Davidson, E., Dukes, J. S., Evans, R. D., Field, C. B., Czimczik, C. I.,
Keller, M., Kimball, B. A., Kueppers, L. M., Norby, R. J., Pelini, S. L.,
Pendall, E., Rastetter, E., Six, J., Smith, M., Tjoelker, M. G., and Torn, M.
S.: Coordinated approaches to quantify long-term ecosystem dynamics in
response to global change, Glob. Change Biol., 17, 843–854,
https://doi.org/10.1111/j.1365-2486.2010.02265.x, 2011a.
Luo, Y. Q., Ogle, K., Tucker, C., Fei, S. F., Gao, C., LaDeau, S., Clark, J.
S., and Schimel, D. S.: Ecological forecasting and data assimilation in a
data-rich era, Ecol. Appl., 21, 1429–1442, 2011b.
Luo, Y. Q., Ahlstrom, A., Allison, S. D., Batjes, N. H., Brovkin, V.,
Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A. C.,
Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y. J., Hopkins, F.,
Jiang, L. F., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J.
Y., McGuire, A. D., Parton, W., Peng, C. H., Randerson, J. T., Salazar, A.,
Sierra, C. A., Smith, M. J., Tian, H. Q., Todd-Brown, K. E. O., Torn, M., van
Groenigen, K. J., Wang, Y. P., West, T. O., Wei, Y. X., Wieder, W. R., Xia,
J. Y., Xu, X., Xu, X. F., and Zhou, T.: Toward more realistic projections of
soil carbon dynamics by Earth system models, Global Biogeochem. Cy., 30,
40–56, https://doi.org/10.1002/2015gb005239, 2016.
Ma, S., Jiang, J., Huang, Y. Y., Shi, Z., Wilson, R. M., Ricciuto, D.,
Sebestyen, S. D., Hanson, P. J., and Luo, Y. Q.: Data-Constrained Projections
of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated
CO2 and Warming, J. Geophys. Res.-Biogeosci., 122,
2841–2861, https://doi.org/10.1002/2017jg003932, 2017.
Medlyn, B. E., Badeck, F. W., De Pury, D. G. G., Barton, C. V. M.,
Broadmeadow, M., Ceulemans, R., De Angelis, P., Forstreuter, M., Jach, M. E.,
Kellomaki, S., Laitat, E., Marek, M., Philippot, S., Rey, A., Strassemeyer,
J., Laitinen, K., Liozon, R., Portier, B., Roberntz, P., Wang, K., and
Jarvis, P. G.: Effects of elevated CO2 on photosynthesis in European
forest species: a meta-analysis of model parameters, Plant Cell Environ., 22,
1475–1495, https://doi.org/10.1046/j.1365-3040.1999.00523.x, 1999.
Melillo, J. M., Frey, S. D., DeAngelis, K. M., Werner, W. J., Bernard, M.
J., Bowles, F. P., Pold, G., Knorr, M. A., and Grandy, A. S.: Long-term
pattern and magnitude of soil carbon feedback to the climate system in a
warming world, Science, 358, 101–105, https://doi.org/10.1126/science.aan2874, 2017.
Michener, W. K. and Jones, M. B.: Ecoinformatics: supporting ecology as a
data-intensive science, Trends Ecol. Evol., 27, 85–93,
https://doi.org/10.1016/j.tree.2011.11.016, 2012.
Miller-Rushing, A., Primack, R., and Bonney, R.: The history of public
participation in ecological research, Front. Ecol. Environ., 10, 285–290,
https://doi.org/10.1890/110278, 2012.
Moorcroft, P. R.: How close are we to a predictive science of the
biosphere?, Trends Ecol. Evol., 21, 400–407, https://doi.org/10.1016/j.tree.2006.04.009,
2006.
Mouquet, N., Lagadeuc, Y., Devictor, V., Doyen, L., Duputie, A., Eveillard,
D., Faure, D., Garnier, E., Gimenez, O., Huneman, P., Jabot, F., Jarne, P.,
Joly, D., Julliard, R., Kefi, S., Kergoat, G. J., Lavorel, S., Le Gall, L.,
Meslin, L., Morand, S., Morin, X., Morlon, H., Pinay, G., Pradel, R., Schurr,
F. M., Thuiller, W., and Loreau, M.: REVIEW: Predictive ecology in a changing
world, J. Appl. Ecol., 52, 1293–1310, https://doi.org/10.1111/1365-2664.12482, 2015.
Niu, S. L., Luo, Y. Q., Dietze, M. C., Keenan, T. F., Shi, Z., Li, J. W.,
and Chapin, F. S.: The role of data assimilation in predictive ecology,
Ecosphere, 5, 65, https://doi.org/10.1890/es13-00273.1, 2014.
Ong, J. B. S., Chen, M. I. C., Cook, A. R., Lee, H. C., Lee, V. J., Lin, R.
T. P., Tambyah, P. A., and Goh, L. G.: Real-Time Epidemic Monitoring and
Forecasting of H1N1-2009 Using Influenza-Like Illness from General Practice
and Family Doctor Clinics in Singapore, Plos One, 5, e10036,
https://doi.org/10.1371/journal.pone.0010036, 2010.
Osterweil, L. J., Clarke, L. A., Ellison, A. M., Boose, E., Podorozhny, R.,
and Wise, A.: Clear and Precise Specification of Ecological Data Management
Processes and Dataset Provenance, IEEE T. Autom. Sci. Eng., 7, 189–195,
https://doi.org/10.1109/tase.2009.2021774, 2010.
Parton, W. J., Stewart, J. W. B., and Cole, C. V.: Dynamics of c, n, p and s
in grassland soils – a model, Biogeochemistry, 5, 109–131,
https://doi.org/10.1007/bf02180320, 1988.
Parton, W. J., Morgan, J. A., Wang, G. M., and Del Grosso, S.: Projected
ecosystem impact of the Prairie Heating and CO2 Enrichment
experiment, New Phytol., 174, 823–834, https://doi.org/10.1111/j.1469-8137.2007.02052.x,
2007.
Perretti, C. T., Munch, S. B., and Sugihara, G.: Model-free forecasting
outperforms the correct mechanistic model for simulated and experimental
data, P. Natl. Acad. Sci. USA, 110, 5253–5257, https://doi.org/10.1073/pnas.1216076110,
2013.
Peylin, P., Bacour, C., MacBean, N., Leonard, S., Rayner, P., Kuppel, S.,
Koffi, E., Kane, A., Maignan, F., Chevallier, F., Ciais, P., and Prunet, P.:
A new stepwise carbon cycle data assimilation system using multiple data
streams to constrain the simulated land surface carbon cycle, Geosci. Model
Dev., 9, 3321–3346, https://doi.org/10.5194/gmd-9-3321-2016, 2016.
Purves, D., Scharlemann, J., Harfoot, M., Newbold, T., Tittensor, D. P.,
Hutton, J., and Emmott, S.: Time to model all life on Earth, Nature, 493,
295–297, 2013.
Robertson, G. P., Collins, S. L., Foster, D. R., Brokaw, N., Ducklow, H. W.,
Gragson, T. L., Gries, C., Hamilton, S. K., McGuire, A. D., and Moore, J. C.:
Long-term ecological research in a human-dominated world, BioScience, 62,
342–353, 2012.
Schaefer, K., Schwalm, C. R., Williams, C., Arain, M. A., Barr, A., Chen, J.
M., Davis, K. J., Dimitrov, D., Hilton, T. W., Hollinger, D. Y., Humphreys,
E., Poulter, B., Raczka, B. M., Richardson, A. D., Sahoo, A., Thornton, P.,
Vargas, R., Verbeeck, H., Anderson, R., Baker, I., Black, T. A., Bolstad, P.,
Chen, J. Q., Curtis, P. S., Desai, A. R., Dietze, M., Dragoni, D., Gough, C.,
Grant, R. F., Gu, L. H., Jain, A., Kucharik, C., Law, B., Liu, S. G.,
Lokipitiya, E., Margolis, H. A., Matamala, R., McCaughey, J. H., Monson, R.,
Munger, J. W., Oechel, W., Peng, C. H., Price, D. T., Ricciuto, D., Riley, W.
J., Roulet, N., Tian, H. Q., Tonitto, C., Torn, M., Weng, E. S., and Zhou, X.
L.: A model-data comparison of gross primary productivity: Results from the
North American Carbon Program site synthesis, J. Geophys. Res.-Biogeo., 117,
G03010, https://doi.org/10.1029/2012jg001960, 2012.
Schimel, D.: The era of continental-scale ecology, Front. Ecol.
Environ., 9, p. 311, 2011.
Schindler, D. E. and Hilborn, R.: Prediction, precaution, and policy under
global change, Science, 347, 953–954, https://doi.org/10.1126/science.1261824, 2015.
Scholze, M., Kaminski, T., Rayner, P., Knorr, W., and Giering, R.:
Propagating uncertainty through prognostic carbon cycle data assimilation
system simulations, J. Geophys. Res.-Atmos., 112, D17305, https://doi.org/10.1029/2007jd008642,
2007.
Shi, Z., Xu, X., Hararuk, O., Jiang, L. F., Xia, J. Y., Liang, J. Y., Li, D.
J., and Luo, Y. Q.: Experimental warming altered rates of carbon processes,
allocation, and carbon storage in a tallgrass prairie, Ecosphere, 6, 210,
https://doi.org/10.1890/es14-00335.1, 2015.
Shi, Z., Yang, Y. H., Zhou, X. H., Weng, E. S., Finzi, A. C., and Luo, Y.
Q.: Inverse analysis of coupled carbon-nitrogen cycles against multiple
datasets at ambient and elevated CO2, J. Plant Ecol., 9, 285–295,
https://doi.org/10.1093/jpe/rtv059, 2016.
Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L.,
Betts, R., Ciais, P., Cox, P., Friedlingstein, P., Jones, C. D., Prentice, I.
C., and Woodward, F. I.: Evaluation of the terrestrial carbon cycle, future
plant geography and climate-carbon cycle feedbacks using five Dynamic Global
Vegetation Models (DGVMs), Glob. Change Biol., 14, 2015–2039,
https://doi.org/10.1111/j.1365-2486.2008.01626.x, 2008.
Steppe, K., von der Crone, J. S., and Pauw, D. J. W.: TreeWatch.net: A Water
and Carbon Monitoring and Modeling Network to Assess Instant Tree Hydraulics
and Carbon Status, Front. Plant Sci., 7, 993, https://doi.org/10.3389/fpls.2016.00993,
2016.
Stumpf, R. P., Tomlinson, M. C., Calkins, J. A., Kirkpatrick, B., Fisher,
K., Nierenberg, K., Currier, R., and Wynne, T. T.: Skill assessment for an
operational algal bloom forecast system, J. Marine Syst., 76, 151–161,
https://doi.org/10.1016/j.jmarsys.2008.05.016, 2009.
Sugihara, G., May, R., Ye, H., Hsieh, C. H., Deyle, E., Fogarty, M., and
Munch, S.: Detecting Causality in Complex Ecosystems, Science, 338, 496–500,
https://doi.org/10.1126/science.1227079, 2012.
Thomas, R. Q., Brooks, E. B., Jersild, A. L., Ward, E. J., Wynne, R. H.,
Albaugh, T. J., Dinon-Aldridge, H., Burkhart, H. E., Domec, J.-C., Fox, T.
R., Gonzalez-Benecke, C. A., Martin, T. A., Noormets, A., Sampson, D. A., and
Teskey, R. O.: Leveraging 35 years of Pinus taeda research in the
southeastern US to constrain forest carbon cycle predictions: regional data
assimilation using ecosystem experiments, Biogeosciences, 14, 3525–3547,
https://doi.org/10.5194/bg-14-3525-2017, 2017.
Vitolo, C., Elkhatib, Y., Reusser, D., Macleod, C. J. A., and Buytaert, W.:
Web technologies for environmental Big Data, Environ. Modell.
Softw., 63, 185–198, https://doi.org/10.1016/j.envsoft.2014.10.007, 2015.
Walker, A. P., Hanson, P. J., De Kauwe, M. G., Medlyn, B. E., Zaehle, S.,
Asao, S., Dietze, M., Hickler, T., Huntingford, C., Iversen, C. M., Jain, A.,
Lomas, M., Luo, Y. Q., McCarthy, H., Parton, W. J., Prentice, I. C.,
Thornton, P. E., Wang, S. S., Wang, Y. P., Warlind, D., Weng, E. S., Warren,
J. M., Woodward, F. I., Oren, R., and Norby, R. J.: Comprehensive ecosystem
model-data synthesis using multiple data sets at two temperate forest
free-air CO2 enrichment experiments: Model performance at ambient
CO2 concentration, J. Geophys. Res.-Biogeo., 119, 937–964,
https://doi.org/10.1002/2013jg002553, 2014.
Wang, Y. P. and Leuning, R.: A two-leaf model for canopy conductance,
photosynthesis and partitioning of available energy I: Model description and
comparison with a multi-layered model, Agr. Forest Meteorol., 91, 89–111,
https://doi.org/10.1016/s0168-1923(98)00061-6, 1998.
Wang, Y. P., Law, R. M., and Pak, B.: A global model of carbon, nitrogen and
phosphorus cycles for the terrestrial biosphere, Biogeosciences, 7,
2261–2282, https://doi.org/10.5194/bg-7-2261-2010, 2010.
Ward, E. J., Holmes, E. E., Thorson, J. T., and Collen, B.: Complexity is
costly: a meta-analysis of parametric and non-parametric methods for
short-term population forecasting, Oikos, 123, 652–661,
https://doi.org/10.1111/j.1600-0706.2014.00916.x, 2014.
Weng, E. S. and Luo, Y. Q.: Soil hydrological properties regulate grassland
ecosystem responses to multifactor global change: A modeling analysis, J.
Geophys. Res.-Biogeo., 113, G03003, https://doi.org/10.1029/2007jg000539, 2008.
Weng, E. S. and Luo, Y. Q.: Relative information contributions of model vs.
data to short- and long-term forecasts of forest carbon dynamics, Ecol.
Appl., 21, 1490–1505, 2011.
Weng, E. S., Luo, Y. Q., Gao, C., and Oren, R.: Uncertainty analysis of
forest carbon sink forecast with varying measurement errors: a data
assimilation approach, J. Plant Ecol., 4, 178–191, https://doi.org/10.1093/jpe/rtr018,
2011.
Wieder, W. R., Bonan, G. B., and Allison, S. D.: Global soil carbon
projections are improved by modelling microbial processes, Nat. Clim. Change,
3, 909–912, https://doi.org/10.1038/nclimate1951, 2013.
Xu, T., White, L., Hui, D. F., and Luo, Y. Q.: Probabilistic inversion of a
terrestrial ecosystem model: Analysis of uncertainty in parameter estimation
and model prediction, Global Biogeochem. Cy., 20, GB2007, https://doi.org/10.1029/2005gb002468,
2006.
Zhou, T. and Luo, Y. Q.: Spatial patterns of ecosystem carbon residence
time and NPP-driven carbon uptake in the conterminous United States, Global
Biogeochem. Cy., 22, GB3032, https://doi.org/10.1029/2007gb002939, 2008.
Zhou, X. H., Zhou, T., and Luo, Y. Q.: Uncertainties in carbon residence
time and NPP-driven carbon uptake in terrestrial ecosystems of the
conterminous USA: a Bayesian approach, Tellus B, 64, 17223,
https://doi.org/10.3402/tellusb.v64i0.17223, 2012.
Short summary
Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD) into models. EcoPAD also has the potential to become an interactive tool for resource management, stimulate citizen science in ecology, and transform environmental education.
Predicting future changes in ecosystem services is not only highly desirable but is also...
