Articles | Volume 21, issue 18
https://doi.org/10.5194/bg-21-4195-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-4195-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Sep 2024
Research article |
| 26 Sep 2024
| 26 Sep 2024
Future projections of Siberian wildfire and aerosol emissions
Reza Kusuma Nurrohman
Graduate School of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan
Department of Agricultural Engineering, University of Mataram, Mataram, Nusa Tenggara Barat, 83126, Indonesia
Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan
Hideki Ninomiya
Graduate School of Global Food Resources, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan
Lea Végh
Biodiversity Assessment and Projection Section, National Institute for Environmental Studies, Tsukuba, Ibaraki, 305-8506, Japan
Nicolas Delbart
Laboratoire Interdisciplinaire des Energies de Demain, UMR 8236 CNRS, Université de Paris, 75013, Paris, France
Tatsuya Miyauchi
Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan
Hisashi Sato
Research Institute for Global Change (RIGC), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa, 236-0001, Japan
Tomohiro Shiraishi
School of Engineering, Nippon Bunri University, Oita, Oita, 870-0397, Japan
Ryuichi Hirata
Earth System Division, National Institute for Environmental Studies, Tsukuba, Ibaraki, 305-8506, Japan
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Earth Syst. Sci. Data, 17, 3807–3833, https://doi.org/10.5194/essd-17-3807-2025, https://doi.org/10.5194/essd-17-3807-2025, 2025
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The JapanFlux2024 dataset, created through collaboration across Japan and East Asia, includes eddy covariance data from 83 sites spanning 683 site-years (1990–2023). This comprehensive dataset offers valuable insights into energy, water, and CO2 fluxes, supporting research on land–atmosphere interactions and process models; fosters global collaboration; and advances research in environmental science and regional climate dynamics.
Marin Nagata, Astrid Yusara, Tomomichi Kato, and Yuji Masutomi
EGUsphere, https://doi.org/10.5194/egusphere-2025-1885, https://doi.org/10.5194/egusphere-2025-1885, 2025
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We developed maize version of process-based crop model coupled with a land surface model (MATCRO). It extends the original MATCRO-Rice by incorporating C4 photosynthesis and maize-specific parameters. The model was validated using field data from four sites and global yield data from FAOSTAT. MATCRO-Maize captured the interannual yield variability in global and county-level yield data, demonstrating its potential for climate impact assessments on maize production.
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Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Astrid Yusara, Tomomichi Kato, Elizabeth A. Ainsworth, Rafael Battisti, Etsushi Kumagai, Satoshi Nakano, Yushan Wu, Yutaka Tsusumi-Morita, Kazuhiko Kobayashi, and Yuji Masutomi
EGUsphere, https://doi.org/10.5194/egusphere-2025-453, https://doi.org/10.5194/egusphere-2025-453, 2025
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We developed a soybean model, an ecosystem model for crop yield (namely MATCRO-Soy), integrating crop response toward climate variable. It offers a detailed yield estimation. Parameter tuning in the model used literature and field experiments. The model shows a moderate correlation with observed yields at the global, national, and grid levels. Development of MATCRO-Soy enhances crop modeling diversity approaches, particularly in climate change impact studies.
Hisashi Sato
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-106, https://doi.org/10.5194/bg-2023-106, 2024
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Modelling potential natural biome distribution is one of the most classical issues in biogeoscience. This study shows how accurate models can be constructed without simplifying climate data by employing machine-learning techniques. While extreme climate data enhance predictions, their inclusion can significantly reduce model reliability. With the convolutional neural network algorithm emerging as the preferred choice, this research paves the way for more robust global-scale biome predictions.
Hirofumi Ohyama, Matthias M. Frey, Isamu Morino, Kei Shiomi, Masahide Nishihashi, Tatsuya Miyauchi, Hiroko Yamada, Makoto Saito, Masanobu Wakasa, Thomas Blumenstock, and Frank Hase
Atmos. Chem. Phys., 23, 15097–15119, https://doi.org/10.5194/acp-23-15097-2023, https://doi.org/10.5194/acp-23-15097-2023, 2023
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We conducted a field campaign for CO2 column measurements in the Tokyo metropolitan area with three ground-based Fourier transform spectrometers. The model simulations using prior CO2 fluxes were generally in good agreement with the observations. We developed an urban-scale inversion system in which spatially resolved CO2 fluxes and a scaling factor of large point source emissions were estimated. The posterior total CO2 emissions agreed with emission inventories within the posterior uncertainty.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
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Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Hisashi Sato and Takeshi Ise
Geosci. Model Dev., 15, 3121–3132, https://doi.org/10.5194/gmd-15-3121-2022, https://doi.org/10.5194/gmd-15-3121-2022, 2022
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Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.
Makoto Saito, Tomohiro Shiraishi, Ryuichi Hirata, Yosuke Niwa, Kazuyuki Saito, Martin Steinbacher, Doug Worthy, and Tsuneo Matsunaga
Biogeosciences, 19, 2059–2078, https://doi.org/10.5194/bg-19-2059-2022, https://doi.org/10.5194/bg-19-2059-2022, 2022
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This study tested combinations of two sources of AGB data and two sources of LCC data and used the same burned area satellite data to estimate BB CO emissions. Our analysis showed large discrepancies in annual mean CO emissions and explicit differences in the simulated CO concentrations among the BB emissions estimates. This study has confirmed that BB emissions estimates are sensitive to the land surface information on which they are based.
Yuan Zhang, Ana Bastos, Fabienne Maignan, Daniel Goll, Olivier Boucher, Laurent Li, Alessandro Cescatti, Nicolas Vuichard, Xiuzhi Chen, Christof Ammann, M. Altaf Arain, T. Andrew Black, Bogdan Chojnicki, Tomomichi Kato, Ivan Mammarella, Leonardo Montagnani, Olivier Roupsard, Maria J. Sanz, Lukas Siebicke, Marek Urbaniak, Francesco Primo Vaccari, Georg Wohlfahrt, Will Woodgate, and Philippe Ciais
Geosci. Model Dev., 13, 5401–5423, https://doi.org/10.5194/gmd-13-5401-2020, https://doi.org/10.5194/gmd-13-5401-2020, 2020
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We improved the ORCHIDEE LSM by distinguishing diffuse and direct light in canopy and evaluated the new model with observations from 159 sites. Compared with the old model, the new model has better sunny GPP and reproduced the diffuse light fertilization effect observed at flux sites. Our simulations also indicate different mechanisms causing the observed GPP enhancement under cloudy conditions at different times. The new model has the potential to study large-scale impacts of aerosol changes.
Yuma Sakai, Hideki Kobayashi, and Tomomichi Kato
Geosci. Model Dev., 13, 4041–4066, https://doi.org/10.5194/gmd-13-4041-2020, https://doi.org/10.5194/gmd-13-4041-2020, 2020
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Chlorophyll fluorescence is one of the energy release pathways of excess incident light in the photosynthetic process. The canopy-scale Sun-induced chlorophyll fluorescence (SIF), which potentially provides a direct pathway to link leaf-level photosynthesis to global GPP, can be observed from satellites. We develop the three-dimensional Monte Carlo plant canopy radiative transfer model to understand the biological and physical mechanisms behind SIF emission from complex forest canopies.
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Short summary
SPITFIRE (SPread and InTensity of FIRE) was integrated into a spatially explicit individual-based dynamic global vegetation model to improve the accuracy of depicting Siberian forest fire frequency, intensity, and extent. Fires showed increased greenhouse gas and aerosol emissions in 2006–2100 for Representative Concentration Pathways. This study contributes to understanding fire dynamics, land ecosystem–climate interactions, and global material cycles under the threat of escalating fires.
SPITFIRE (SPread and InTensity of FIRE) was integrated into a spatially explicit...
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