Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-521-2021
© Author(s) 2021. 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-14-521-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Numerical model to simulate long-term soil organic carbon and ground ice budget with permafrost and ice sheets (SOC-ICE-v1.0)
Research Center for Environmental Modeling and Application, JAMSTEC, Yokohama, 236-0001, Japan
Hirokazu Machiya
CORRESPONDING AUTHOR
Research Center for Environmental Modeling and Application, JAMSTEC, Yokohama, 236-0001, Japan
Go Iwahana
International Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
Tokuta Yokohata
National Institute for Environment Studies, Tsukuba, 305-0053, Japan
Hiroshi Ohno
Kitami Institute of Technology, Kitami, 090-8507, Japan
Related authors
Kazuyuki Saito, Go Iwahana, Hiroki Ikawa, Hirohiko Nagano, and Robert C. Busey
Geosci. Instrum. Method. Data Syst., 7, 223–234, https://doi.org/10.5194/gi-7-223-2018, https://doi.org/10.5194/gi-7-223-2018, 2018
Short summary
Short summary
A DTS system, using fibre-optic cables as a temperature sensor, measured surface and subsurface temperatures at a boreal forest underlain by permafrost in the interior of Alaska for 2 years every 30 min at 0.5-metre intervals along 2.7 km to monitor the daily and seasonal temperature changes, whose temperature ranges between −40 ºC in winter and 30 ºC in summer. This instrumentation illustrated characteristics of temperature variations and snow pack dynamics under different land cover types.
Kazuyuki Saito, Amy Hendricks, John Walsh, and Nancy Bigelow
Clim. Past Discuss., https://doi.org/10.5194/cp-2018-29, https://doi.org/10.5194/cp-2018-29, 2018
Preprint withdrawn
Short summary
Short summary
Vegetation in Beringia, between Alaska and Eastern Russia, were simulated for a glacial (LGM) and warm past (mid-Holocene), the modern, and the end of this century. Modern and mid-Holocene biomes were simulated consistent with observations. Pollens indicate cold tundras covered the Bering Land Bridge almost entirely at the LGM, but the simulations show large variations, with the majority producing northern forests at southeastern. The future results show a general northward shift of biomes.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
Short summary
Short summary
We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
K. Saito, T. Sueyoshi, S. Marchenko, V. Romanovsky, B. Otto-Bliesner, J. Walsh, N. Bigelow, A. Hendricks, and K. Yoshikawa
Clim. Past, 9, 1697–1714, https://doi.org/10.5194/cp-9-1697-2013, https://doi.org/10.5194/cp-9-1697-2013, 2013
Tomotaka Saruya, Atsushi Miyamoto, Shuji Fujita, Kumiko Goto-Azuma, Motohiro Hirabayashi, Akira Hori, Makoto Igarashi, Yoshinori Iizuka, Takao Kameda, Hiroshi Ohno, Wataru Shigeyama, and Shun Tsutaki
EGUsphere, https://doi.org/10.5194/egusphere-2023-3146, https://doi.org/10.5194/egusphere-2023-3146, 2024
Short summary
Short summary
Crystal orientation fabrics (COF) and microstructures in the deep sections of the Dome Fuji ice core were investigated using innovative methods with unprecedentedly high statistical significance and dense depth coverage. Together with our previous studies, we have obtained a whole layer profile of the COF and physical properties of the Dome Fuji ice core. COF profile and its fluctuation were found to be highly dependent on impurities concentrations and recrystallization processes.
Charles Miller, Peter C. Griffith, Elizabeth Hoy, Naiara S. Pinto, Yunling Lou, Scott Hensley, Bruce D. Chapman, Jennifer Baltzer, Kazem Bakian-Dogaheh, W. Robert Bolton, Laura Bourgeau-Chavez, Richard H. Chen, Byung-Hun Choe, Leah K. Clayton, Thomas A. Douglas, Nancy French, Jean E. Holloway, Gang Hong, Lingcao Huang, Go Iwahana, Liza Jenkins, John S. Kimball, Tatiana Loboda, Michelle Mack, Philip Marsh, Roger J. Michaelides, Mahta Moghaddam, Andrew Parsekian, Kevin Schaefer, Paul R. Siqueira, Debjani Singh, Alireza Tabatabaeenejad, Merritt Turetsky, Ridha Touzi, Elizabeth Wig, Cathy Wilson, Paul Wilson, Stan D. Wullschleger, Yonghong Yi, Howard A. Zebker, Yu Zhang, Yuhuan Zhao, and Scott J. Goetz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-172, https://doi.org/10.5194/essd-2021-172, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne synthetic aperture radar (SAR) surveys of over 4 million km2 in Alaska and northwestern Canada during 2017, 2018, and 2019. This paper summarizes those results and gives details on ~80 individual flight lines. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.
Xiaoran Zhu, Dong Chen, Maruko Kogure, Elizabeth Hoy, Logan Berner, Amy Breen, Abhishek Chatterjee, Scott Davidson, Gerald Frost, Teresa Hollingsworth, Go Iwahana, Randi Jandt, Anja Kade, Tatiana Loboda, Matt Macander, Michelle Mack, Charles Miller, Eric Miller, Susan Natali, Martha Raynolds, Adrian Rocha, Shiro Tsuyuzaki, Craig Tweedie, Donald Walker, Mathew Williams, Xin Xu, Yingtong Zhang, Nancy French, and Scott Goetz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-222, https://doi.org/10.5194/essd-2023-222, 2023
Revised manuscript under review for ESSD
Short summary
Short summary
The Arctic tundra is experiencing widespread physical and biological changes largely in response to warming. Yet scientific understanding of tundra ecology and change remains limited due to relatively limited accessibility and study compared to other terrestrial biomes. To support synthesis research and inform future studies, we created the Synthesized Alaskan Tundra Field Dataset (SATFiD), which pulls together field datasets and includes vegetation, active layer, and fire-related properties.
Ikumi Oyabu, Kenji Kawamura, Shuji Fujita, Ryo Inoue, Hideaki Motoyama, Kotaro Fukui, Motohiro Hirabayashi, Yu Hoshina, Naoyuki Kurita, Fumio Nakazawa, Hiroshi Ohno, Konosuke Sugiura, Toshitaka Suzuki, Shun Tsutaki, Ayako Abe-Ouchi, Masashi Niwano, Frédéric Parrenin, Fuyuki Saito, and Masakazu Yoshimori
Clim. Past, 19, 293–321, https://doi.org/10.5194/cp-19-293-2023, https://doi.org/10.5194/cp-19-293-2023, 2023
Short summary
Short summary
We reconstructed accumulation rate around Dome Fuji, Antarctica, over the last 5000 years from 15 shallow ice cores and seven snow pits. We found a long-term decreasing trend in the preindustrial period, which may be associated with secular surface cooling and sea ice expansion. Centennial-scale variations were also found, which may partly be related to combinations of volcanic, solar and greenhouse gas forcings. The most rapid and intense increases of accumulation rate occurred since 1850 CE.
Shun Tsutaki, Shuji Fujita, Kenji Kawamura, Ayako Abe-Ouchi, Kotaro Fukui, Hideaki Motoyama, Yu Hoshina, Fumio Nakazawa, Takashi Obase, Hiroshi Ohno, Ikumi Oyabu, Fuyuki Saito, Konosuke Sugiura, and Toshitaka Suzuki
The Cryosphere, 16, 2967–2983, https://doi.org/10.5194/tc-16-2967-2022, https://doi.org/10.5194/tc-16-2967-2022, 2022
Short summary
Short summary
We constructed an ice thickness map across the Dome Fuji region, East Antarctica, from improved radar data and previous data that had been collected since the late 1980s. The data acquired using the improved radar systems allowed basal topography to be identified with higher accuracy. The new ice thickness data show the bedrock topography, particularly the complex terrain of subglacial valleys and highlands south of Dome Fuji, with substantially high detail.
Tomotaka Saruya, Shuji Fujita, Yoshinori Iizuka, Atsushi Miyamoto, Hiroshi Ohno, Akira Hori, Wataru Shigeyama, Motohiro Hirabayashi, and Kumiko Goto-Azuma
The Cryosphere, 16, 2985–3003, https://doi.org/10.5194/tc-16-2985-2022, https://doi.org/10.5194/tc-16-2985-2022, 2022
Short summary
Short summary
Crystal orientation fabrics (COF) of the Dome Fuji ice core were investigated with an innovative method with unprecedentedly high statistical significance and dense depth coverage. The COF profile and its fluctuation were found to be highly dependent on concentrations of chloride ion and dust. The data suggest deformation of ice at the deepest zone is highly influenced by COF fluctuations that progressively develop from the near-surface firn toward the deepest zone within ice sheets.
Irina Melnikova, Olivier Boucher, Patricia Cadule, Katsumasa Tanaka, Thomas Gasser, Tomohiro Hajima, Yann Quilcaille, Hideo Shiogama, Roland Séférian, Kaoru Tachiiri, Nicolas Vuichard, Tokuta Yokohata, and Philippe Ciais
Earth Syst. Dynam., 13, 779–794, https://doi.org/10.5194/esd-13-779-2022, https://doi.org/10.5194/esd-13-779-2022, 2022
Short summary
Short summary
The deployment of bioenergy crops for capturing carbon from the atmosphere facilitates global warming mitigation via generating negative CO2 emissions. Here, we explored the consequences of large-scale energy crops deployment on the land carbon cycle. The land-use change for energy crops leads to carbon emissions and loss of future potential increase in carbon uptake by natural ecosystems. This impact should be taken into account by the modeling teams and accounted for in mitigation policies.
Tokuta Yokohata, Tsuguki Kinoshita, Gen Sakurai, Yadu Pokhrel, Akihiko Ito, Masashi Okada, Yusuke Satoh, Etsushi Kato, Tomoko Nitta, Shinichiro Fujimori, Farshid Felfelani, Yoshimitsu Masaki, Toshichika Iizumi, Motoki Nishimori, Naota Hanasaki, Kiyoshi Takahashi, Yoshiki Yamagata, and Seita Emori
Geosci. Model Dev., 13, 4713–4747, https://doi.org/10.5194/gmd-13-4713-2020, https://doi.org/10.5194/gmd-13-4713-2020, 2020
Short summary
Short summary
The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balances, human water management, and crop growth incorporates a land-use decision-making model based on economic activities. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand.
Ji-Woong Yang, Jinho Ahn, Go Iwahana, Sangyoung Han, Kyungmin Kim, and Alexander Fedorov
The Cryosphere, 14, 1311–1324, https://doi.org/10.5194/tc-14-1311-2020, https://doi.org/10.5194/tc-14-1311-2020, 2020
Short summary
Short summary
Thawing permafrost may lead to decomposition of soil carbon and nitrogen and emission of greenhouse gases. Thus, methane and nitrous oxide compositions in ground ice may provide information on their production mechanisms in permafrost. We test conventional wet and dry extraction methods. We find that both methods extract gas from the easily extractable parts of the ice and yield similar results for mixing ratios. However, both techniques are unable to fully extract gas from the ice.
Ryo Shingubara, Atsuko Sugimoto, Jun Murase, Go Iwahana, Shunsuke Tei, Maochang Liang, Shinya Takano, Tomoki Morozumi, and Trofim C. Maximov
Biogeosciences, 16, 755–768, https://doi.org/10.5194/bg-16-755-2019, https://doi.org/10.5194/bg-16-755-2019, 2019
Short summary
Short summary
(1) Wetting event with extreme precipitation increased methane emission from wetland, especially two summers later, despite the decline in water level after the wetting. (2) Isotopic compositions of methane in soil pore water suggested enhancement of production and less significance of oxidation in the following two summers after the wetting event. (3) Duration of water saturation in the active layer may be important for predicting methane emission after a wetting event in permafrost ecosystems.
Kazuyuki Saito, Go Iwahana, Hiroki Ikawa, Hirohiko Nagano, and Robert C. Busey
Geosci. Instrum. Method. Data Syst., 7, 223–234, https://doi.org/10.5194/gi-7-223-2018, https://doi.org/10.5194/gi-7-223-2018, 2018
Short summary
Short summary
A DTS system, using fibre-optic cables as a temperature sensor, measured surface and subsurface temperatures at a boreal forest underlain by permafrost in the interior of Alaska for 2 years every 30 min at 0.5-metre intervals along 2.7 km to monitor the daily and seasonal temperature changes, whose temperature ranges between −40 ºC in winter and 30 ºC in summer. This instrumentation illustrated characteristics of temperature variations and snow pack dynamics under different land cover types.
Kazuyuki Saito, Amy Hendricks, John Walsh, and Nancy Bigelow
Clim. Past Discuss., https://doi.org/10.5194/cp-2018-29, https://doi.org/10.5194/cp-2018-29, 2018
Preprint withdrawn
Short summary
Short summary
Vegetation in Beringia, between Alaska and Eastern Russia, were simulated for a glacial (LGM) and warm past (mid-Holocene), the modern, and the end of this century. Modern and mid-Holocene biomes were simulated consistent with observations. Pollens indicate cold tundras covered the Bering Land Bridge almost entirely at the LGM, but the simulations show large variations, with the majority producing northern forests at southeastern. The future results show a general northward shift of biomes.
Tomoo Ogura, Hideo Shiogama, Masahiro Watanabe, Masakazu Yoshimori, Tokuta Yokohata, James D. Annan, Julia C. Hargreaves, Naoto Ushigami, Kazuya Hirota, Yu Someya, Youichi Kamae, Hiroaki Tatebe, and Masahide Kimoto
Geosci. Model Dev., 10, 4647–4664, https://doi.org/10.5194/gmd-10-4647-2017, https://doi.org/10.5194/gmd-10-4647-2017, 2017
Short summary
Short summary
Present-day climate simulated by coupled ocean atmosphere models exhibits significant biases in top-of-atmosphere radiation and clouds. This study shows that only limited part of the biases can be removed by parameter tuning in a climate model. The results underline the importance of improving parameterizations in climate models based on cloud process studies. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
Short summary
Short summary
We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
K. Nishina, A. Ito, P. Falloon, A. D. Friend, D. J. Beerling, P. Ciais, D. B. Clark, R. Kahana, E. Kato, W. Lucht, M. Lomas, R. Pavlick, S. Schaphoff, L. Warszawaski, and T. Yokohata
Earth Syst. Dynam., 6, 435–445, https://doi.org/10.5194/esd-6-435-2015, https://doi.org/10.5194/esd-6-435-2015, 2015
Short summary
Short summary
Our study focused on uncertainties in terrestrial C cycling under newly developed scenarios with CMIP5. This study presents first results for examining relative uncertainties of projected terrestrial C cycling in multiple projection components. Only using our new model inter-comparison project data sets enables us to evaluate various uncertainty sources in projection periods. The information on relative uncertainties is useful for climate science and climate change impact evaluation.
K. Nishina, A. Ito, D. J. Beerling, P. Cadule, P. Ciais, D. B. Clark, P. Falloon, A. D. Friend, R. Kahana, E. Kato, R. Keribin, W. Lucht, M. Lomas, T. T. Rademacher, R. Pavlick, S. Schaphoff, N. Vuichard, L. Warszawaski, and T. Yokohata
Earth Syst. Dynam., 5, 197–209, https://doi.org/10.5194/esd-5-197-2014, https://doi.org/10.5194/esd-5-197-2014, 2014
M. Yoshida, J. M. Haywood, T. Yokohata, H. Murakami, and T. Nakajima
Atmos. Chem. Phys., 13, 10827–10845, https://doi.org/10.5194/acp-13-10827-2013, https://doi.org/10.5194/acp-13-10827-2013, 2013
K. Saito, T. Sueyoshi, S. Marchenko, V. Romanovsky, B. Otto-Bliesner, J. Walsh, N. Bigelow, A. Hendricks, and K. Yoshikawa
Clim. Past, 9, 1697–1714, https://doi.org/10.5194/cp-9-1697-2013, https://doi.org/10.5194/cp-9-1697-2013, 2013
Related subject area
Biogeosciences
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
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
Dynamic ecosystem assembly and escaping the “fire-trap” in the tropics: Insights from FATES_15.0.0
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
A global behavioural model of human fire use and management: WHAM! v1.0
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)
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): calibration-free calculations of water and energy fluxes
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
In-silico calculation of soil pH by SCEPTER v1.0
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)
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: Simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
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.
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.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles D. Koven, Ryan G. Knox, Lara M. Kueppers, and Chonggang Xu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-191, https://doi.org/10.5194/gmd-2023-191, 2023
Revised manuscript accepted for GMD
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.
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.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James Millington
EGUsphere, https://doi.org/10.5194/egusphere-2023-2162, https://doi.org/10.5194/egusphere-2023-2162, 2023
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.
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.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
EGUsphere, https://doi.org/10.5194/egusphere-2023-1626, https://doi.org/10.5194/egusphere-2023-1626, 2023
Short summary
Short summary
Numerous estimations of water and energy balances heavily depend on empirical equations that necessitate site-specific calibration. This equifinality poses the risk of obtaining 'right answers for wrong reasons.' In this paper, we introduce novel formulations based on first-principles to calculate calibration-free quantities, such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent.
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.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-137, https://doi.org/10.5194/gmd-2023-137, 2023
Revised manuscript accepted for GMD
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 pH and bulk soil pH is thus crucial to accurate evaluation of alkalinity required to counteract soil acidification and resulting capture of anthropogenic carbon dioxide through the Enhanced Rock Weathering technique. This has been enabled by the updated reactive-transport SCEPTER code and newly developed framework to simulate soil pH.
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.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
EGUsphere, https://doi.org/10.5194/egusphere-2023-1287, https://doi.org/10.5194/egusphere-2023-1287, 2023
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predict their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that important questions related to plant-atmosphere interactions can be addressed, such as the effects of rising CO2 and ozone pollution on carbon uptake of the biosphere. The model has been well validated with both ground and satellite observations.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
Short summary
Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
Short summary
Short summary
Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
Short summary
Short summary
Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
Short summary
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
Short summary
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
Short summary
Short summary
We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
Short summary
Short summary
A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
Short summary
Short summary
Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
Short summary
Short summary
The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Cited articles
Alley, R. A., Brook, E. J., and Anandakrishnan, S.: A northern lead in the orbital band: north–south phasing of Ice-Age events, Quaternary Sci. Rev., 21, 431–441, 2002.
AMAP:
Snow, Water, Ice and Permafrost in the Arctic (SWIPA): Climate Change and the Cryosphere,
Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, 2011.
AMAP:
Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017,
Arctic Monitoring and Assessment Programme (AMAP), Oslo, Norway, 2017.
Argus, D. F., Peltier, W. R., Drummond, R., and Moore, A. W.:
The Antarctica component of postglacial rebound model ICE-6G_C (VM5a) based upon GPS positioning, exposure age dating of ice thicknesses, and relative sea level histories,
Geophys. J. Int.,
198, 537–563, https://doi.org/10.1093/gji/ggu140, 2014.
Beilman, D. W., MacDonald, G. M., Smith, L. C., and Reimer, P. J.:
Carbon accumulation in peatlands of West Siberia over the last 2000 years,
Global Biogeochem. Cy.,
23, GB1012, https://doi.org/10.1029/2007GB003112, 2009.
Belyea, L. R. and Baird, A. J.:
Beyond “The limits to peat bog growth”: Cross-scale feedback in peatland development,
Ecol. Monogr.,
76, 299–322, 2006.
Berrisford, P., Dee, D. P., Poli, P., Brugge, R., Fielding, M., Fuentes, M., Kållberg, P. W., Kobayashi, S., Uppala, S., and Simmons, A.: The ERA-Interim archive Version 2.0, https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-interim, last access: 25 January 2021.
Biasi, C., Rusalimova, O., Meyer, H., Kaiser, C., Wanek, W., Barsukov, P., Junger, H., and Richter, A.:
Temperature-dependent shift from labile to recalcitrant carbon sources of arctic heterotrophs,
Rapid Commun. Mass Sp.,
19, 1401–1408, https://doi.org/10.1002/rcm.1911, 2005.
Bindshadler, R. A., Nowicki, S., Abe-Ouchi, A., Aschwanden, A., Choi, H., Fastook, J., Granzow, G., Greve, R., Gutowski, G., Herzfeld, U., Jackson, C., Johnson, J., Khroulev, C., Levermann, A., Lipscomp, W. H., Martin, M. A., Morlighem, M., Parizek, B. R., Pollard, D., Price, S. F., Ren, D., Saito, F., Sato, T., Seddik, H., Seroussi, H., Takahashi, K., Walker, R., and Wang, W. L.:
Ice-sheet model sensitivities to environmental forcing and their use in projecting future sea level (the SeaRISE project),
J. Glaciol.,
59, https://doi.org/10.3189/2013JoG12J125, 2013.
Boudreau, B. P. and Ruddick, B. R.:
On a reactive continuum representation of organic matter diagenesis,
Am. J. Sci.,
291, 507–538, 1991.
Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J., Masson-Delmotte, V., Abe-Ouchi, A., Otto-Bliesner, B., and Zhao, Y.:
Evaluation of climate models using palaeoclimatic data,
Nat. Clim. Change,
2, 417–424, https://doi.org/10.1038/NCLIMATE1456, 2012.
Brouchkov, A. and Fukuda, M.:
Preliminary Measurements on Methane Content in Permafrost, Central Yakutia, and some Experimental Data,
Permafrost Periglac.,
13, 187–197, 2002.
Brovkin, V. Boysen, L., Raddatz, T., Gayler, V., Loew, A., and Claussen, M.: Evaluation of vegetation cover and land-surface albedo in MPI-ESM CMIP5 simulations, J. Adv. Model. Earth Syst., 5, 48–57, https://doi.org/10.1029/2012MS000169, 2013.
Brown, J., Ferrians, O. J., Heginbottom, J. A., and Melnikov, E. S.:
Circum-arctic map of permafrost and ground ice conditions,
Digital media,
National Snow and Ice Data Center, Boulder, CO, 1998 (revised 2002).
Charman, D. J., Amesbury, M. J., Hinchliffe, W., Hughes, P. D. M., Mallon, G., Blake, W. H., Daley, T. J., Gallego-Sala, A. V., and Mauquoy, D.:
Drivers of Holocene peatland carbon accumulation across a climate gradient in northeastern North America,
Quaternary Sci. Rev.,
121, 110–119, 2015.
Clymo, R. S.:
The limits to peat bog growth,
Philos. T. R. Soc. Lon. B,
303, 605–654, 1984.
Clymo, R. S.:
Models of peat growth,
Suo,
43, 127–136, 1992.
Conant, R., Ryan, M., Ågren, G. I., Birgé, H., Davidson, E., Eliasson, P., Evans, S., Frey, S., Giardina, Ch., Hopkins, F., Hyvönen, R., Kirschbaum, M., Lavallee, J., Leifeld, J., Parton, W., Steinweg, J. M., Wallenstein, M., Wetterstedt, M., and Bradford, M.:
Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward,
Glob. Change Biol.,
17, 3392–3404, https://doi.org/10.1111/j.1365-2486.2011.02496.x, 2011.
Cook, E. R., Esper, J., and D'Arrigo, R. D.: Extra-tropical Northern Hemisphere land temperature variability over the past 1000 years, Quaternary Sci. Rev., 23, 2063–2074, 2004.
Dean, J. F., Middelburg, J. J., Röckmann, T., Aerts, R., Blauw, L. G., Egger, M., Jetten, M. S. M., de Jong, A. E. E., Meisel, O. H., Rasigraf, O., Slomp, C. P., in't Zandt, M. H., and Dolman, A. J.:
Methane Feedbacks to the Global Climate System in a Warmer World,
Rev. Geophys.,
56, 207–250, https://doi.org/10.1002/2017RG000559, 2018a.
Dean, J. F., van derVelde, Y., Garnett, M. H., Dinsmore, K. J., Baxter, R., Lessels, J. S., Smith, P., and Street, L. E.:
Abundant pre-industrial carbon detected in Canadian Arctic headwaters: implications for the permafrost carbon feedback,
Environ. Res. Lett.,
13, 034024, 2018b.
De Deyn, G. B., Cornelissen, J. H. C., and Bardgett, R. D.:
Plant functional traits and soil carbon sequestrationin contrasting biomes,
Ecol. Lett.,
11, 516–531, https://doi.org/10.1111/j.1461-0248.2008.01164.x, 2008.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F.:
The ERA-Interim reanalysis: configuration and performance of the data assimilation system,
Q. J. Roy. Meteor. Soc.,
137, 553–597, 2011.
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O. Balkanski, Y. Bekki, S. Bellenger, H., Benshila, R., Bony, S., Bopp, L. Braconnot, P., Brockmann, P., Cadule, P., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., de Noblet, N., Duvel, J.-P., Ethé, C., Fairhead, L., Fichefet, T., Flavoni, S., Friedlingstein, P., Grandpeix, J.-Y., Guez, L., Guilyardi, E., Hauglustaine, D., Hourdin, F., Idelkadi, A., Ghattas, J., Joussaume, S., Kageyama, M., Krinner, G., Labetoulle, S., Lahellec, A., Lefebvre, M.-P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S. Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Talandier, C., Terray, P., Viovy, N., and Vuichard, N.:
Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5,
Clim. Dynam.,
40, 2123, https://doi.org/10.1007/s00382-012-1636-1, 2013.
Dyke, A. S.:
Late Quaternary Vegetation History of Northern North America Based on Pollen Macrofossil, and Faunal Remains,
Geogr. Phys. Quatern.,
59, 211–262, 2005.
Dyke, L. D. and Sladen, W. E.: Permafrost and Peatland Evolution in the Northern Hudson Bay Lowland, Manitoba, Arctic, 63, 429–441, 2010.
Esper, J., Cook, E. R., and Schweingruber, F. H.: Low-Frequency Signals in Long Tree-Ring Chronologies for Reconstructing Past Temperature Variability, Science, 295, 2250–2253, 2002.
French, H. M.:
The periglacial environment,
John Wiley & Sons Ltd., Chichester, England, pp. 458, 2007.
Frenzel, B., Pécsi, M., and Velichko, A. A. (Eds.):
Atlas of Paleoclimates and Paleoenvironments of the Northern Hemisphere,
Geographical Research Institute, Hungarian Academy of Sciences, Budapest, 153 pp., Gustav Fischer Verlag, Stuttgart, 1992.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M., Worley, P. H., Yang, Z., and Zhang, M.:
The Community Climate System Model Version 4,
J. Climate,
24, 4973–4991, 2011.
Gorham, E.:
Northern Peatlands: Role in the carbon cycle and probable responses to climatic warming,
Ecol. Appl.,
1, 182–195, 1991.
Greve, R., Saito, F., and Abe-Ouchi, A.: Initial results of the SeaRISE numerical experiments with the models SICOPOLIS and IcIES for the Greenland ice sheet, Ann. Glaciol., 52, 23–30, https://doi.org/10.3189/172756411797252068, 2011.
Harden, J. W., Sundquist, E. T., Stallard, R. F., and Mark, R. K.:
Dynamics of Soil Carbon During Deglaciation of the Laurentide Ice Sheet,
Science,
258, 5090, 1921–1924, 1992.
Harris, S. A.: Climatic Relationships of Permafrost Zones in Areas of Low Winter Snow-Cover, Arctic, 34, 64–70, 1981.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.:
SoilGrids250m: Global gridded soil information based on machine learning,
PLoS ONE,
12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hilbert, D. W., Roulet, N., and Moore, T.:
Modelling and analysis of peatlands as dynamical systems,
J. Ecol.,
88, 230–242, 2000.
Hu, F., Philip, S., Higuera, E., Duffy, P., Chipman, M. L., Rocha, A. V., Young, A. M., Kelly, R., and Dietze, M. C.:
Arctic tundra fires: natural variability and responses to climate change,
Front. Ecol. Environ.,
13, 369–377, https://doi.org/10.1890/150063, 2015.
Hugelius, G., Strauss, J., Zubrzycki, S., Harden, J. W., Schuur, E. A. G., Ping, C.-L., Schirrmeister, L., Grosse, G., Michaelson, G. J., Koven, C. D., O'Donnell, J. A., Elberling, B., Mishra, U., Camill, P., Yu, Z., Palmtag, J., and Kuhry, P.: Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps, Biogeosciences, 11, 6573–6593, https://doi.org/10.5194/bg-11-6573-2014, 2014.
Hugelius, G., Loisel, J., Chadburn, S., Jackson, R. B., Jones, M., MacDonald, G., Marushchak, M., Olefeldt, D., Packalen, M., Siewert, M. B., Treat, C., Turetsky, M., Voigt, C., and Yu, Z.:
Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw,
P. Natl. Acad. Sci. USA,
117, 20438–20446, 2020.
Ingram, H. A. P.:
Soil layers in mires: function and terminology,
J. Soil Sci.,
29, 224–227, 1978.
IPCC:
Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013.
Ito, A.:
Methane emission from pan-Arctic natural wetlands estimated using a process-based model, 1901–2016,
Polar Sci.,
21, 26–36, https://doi.org/10.1016/j.polar.2018.12.001, 2019.
Iwahana, G., Uchida, M., Liu, L., Gong, W., Meyer, F., Guritz, R., Yamanokuchi, T., and Hinzman, L.:
InSAR Detection and Field Evidence for Thermokarst after a Tundra Wildfire, Using ALOS-PALSAR,
Remote Sens.-Basel,
8, 218, https://doi.org/10.3390/rs8030218, 2016.
Jassey, V. E. and Signarbieux, J. C.:
Effects of climate warming on Sphagnum photosynthesis in peatlands depend on peat moisture and species-specific anatomical traits,
Glob. Change Biol.,
25, 3859–3870, https://doi.org/10.1111/gcb.14788, 2019.
Jenny, H., Gessel, S. P., and Bingham, F. T.:
Comparative study of decomposition rates of organic matter in temperate and tropical regions,
Soil Sci.,
68, 419–432, 1949.
Johnsen, S. J., Clausen, H. B., Dansgaard, W., Fuhrer, K., Gundestrup, N., Hammer, C. U., Iversen, P., Jouzel, J., Stauffer, B., and Steffensen, J. P.:
Irregular glacial interstadials recorded in a new Greenland ice core,
Nature,
359, 311–313, https://doi.org/10.1038/359311a0, 1992.
Johnsen, S. J., Clausen, H. B., Dansgaard, W., Gundestrup, N. S., Hammer, C. U., Andersen, U., Andersen, K. K., Hvidberg, C. S, Dahl-Jensen, D., Steffensen, J. P., Shoji, H., Sveinbjörnsdóttir, Á. E., White, J., Jouzel, J., and Fisher, D.:
The δ18O record along the Greenland Ice Core Project deep ice core and the problem of possible Eemian climatic instability,
J. Geophys. Res.,
102, 26397–26410, https://doi.org/10.1029/97JC00167, 1997.
Jones, B. M., Kolden, C. A., Jandt, R., Abatzoglou, J. T., Urban, F., and Arp, C. D.:
Fire behavior, weather, and burn severity of the 2007 Anaktuvuk River tundra fire, North Slope, Alaska,
Arct. Antarct. Alp. Res.,
41, 309–316, 2009.
Jones, M. C. and Yu, Z.:
Rapid deglacial and early Holocene expansion of peatlands in Alaska,
P. Natl. Acad. Sci. USA,
107, 7347–7352, https://doi.org/10.1073/pnas.0911387107, 2010.
Jorgenson, M. T., Kanevskiy, M., Shur, Y., Moskalenko, N., Brown, D. R. N., Wickland, K., Striegl, R., and Koch, J.:
Role of ground ice dynamics and ecological feedbacks in recent ice wedge degradation and stabilization,
J. Geophys. Res. Earth,
120, 2280–2297, https://doi.org/10.1002/2015JF003602, 2015.
Kanevskiy, M., Shur, Y., Fortier, D., Jorgenson, M. T., and Stephani, E.:
Cryostratigraphy of late Pleistocene syngenetic permafrost (yedoma) in northern Alaska, Itkillik River exposure,
Quaternary Res.,
75, 584–596, https://doi.org/10.1016/j.yqres.2010.12.003, 2011.
Kanevskiy, M., Shur, Y., Jorgenson, M. T., Ping, C.-L., Michaelson, G. J., Fortier, D., Stephani, E., Dillon, M., and Tumskoy, V.:
Ground ice in the upper permafrost of the Beaufort Sea coast of Alaska,
Cold Reg. Sci. Technol.,
85, 56–70, https://doi.org/10.1016/j.coldregions.2012.08.002, 2013.
Klein, E. S., Booth, R. K., Yu, Z., Mark, B. G., and Stansell, N. D.:
Hydrology-mediated differential response of carbon accumulation to late Holocene climate change at two peatlands in Southcentral Alaska,
Quaternary Sci. Rev.,
64, 61–75, 2013.
Koven, C. D., Schuur, E. A. G., Schädel, C., Bohn, T. J., Burke, E. J., Chen, G., Chen, X,. Ciais, P., Grosse, G., Harden, J. W., Hayes, D. J., Hugelius, G., Jafarov, E. E., Krinner, G., Kuhry, P., Lawrence, D. M., MacDougall, A. H., Marchenko, S. S., McGuire, A. D., Natali, S. M., Nicolsky, D. J., Olefeldt, D., Peng, S., Romanovsky, V. E., Schaefer, K. M., Strauss, J., Treat, C. C., and Turetsky, M.:
A simplified, data-constrained approach to estimate the permafrost carbon–climate feedback,
Philos. T. R. Soc. A,
373, 20140423, 2015.
Kukla, G. J., Bender, M. L., de Beaulieu, J.-L., Bond, G., Broecker, W. S., Cleveringa, P., Gavin, J. E., Herbert, T. D., Imbrie, J., Jouzel, J., Keigwin, L. D., Knudsen, K.-L., McManus, J. F., Merkt, J., Muhs, D. R., Müller, H., Poore, R. Z., Porter, S. C, Seret, G., Shackleton, N. J., Turner, C., Polychronis C., Tzedakis, C., and Winograd, I. J.:
Last Interglacial Climates,
Quaternary Res.,
58, 2–13, https://doi.org/10.1006/qres.2001.2316, 2002.
Li, B., Rodell, M., Kumar, S., Beaudoing, H., Getirana, A., Zaitchik, B. F., de Goncalves, L. G., Cossetin, C., Bhanja, S., Mukherjee, A., Tian, S., Tangdamrongsub, N., Long, D., Nanteza, J., Lee, J., Policelli, F., Goni, I. B., Daira, D., Bila, M., de Lannoy, G., Mocko, D., Steele-Dunne, S. C., Save, H., and Bettadpuret, S.:
Global GRACE data assimilation for groundwater and drought monitoring: Advances and challenges,
Water Resour. Res.,
55, 7564–7586, https://doi.org/10.1029/2018wr024618, 2019.
Li, B., Beaudoing, H., and Rodell, M.:
GLDAS Catchment Land Surface Model L4 daily 0.25 × 0.25 degree GRACE-DA1 V2.2,
Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, Maryland, USA, https://doi.org/10.5067/TXBMLX370XX8 (last access: 25 January 2021), 2020.
Loisel, J. and Yu, Z.:
Holocene peatland carbon dynamics in Patagonia,
Quaternary Sci. Rev.,
69, 125–141, 2013.
Loisel, J., van Bellen, S., Pelletier, L., Talbot, J., Hugelius, G., Karran, D., Yu, Z., Nichols, J., and Holmquist, J.:
Insights and issues with estimating northern peatland carbon stocks and fluxes since the Last Glacial Maximum,
Earth-Sci. Rev.,
165, 9–80, 2017.
Lunardini, V.:
Permafrost formation time, CRREL Report 95–8,
Cold Regions Research and Engineering Laboratory, US Army Corps of Engineers, Hanover, New Hampshire, 1995.
Lund, M., Lafleur, P. M., Roulet, N. T., Lindroth, A., Christensen, T. R., Aurela, M., Chojnicki, B. H., Flanagan, L. B., Humphreys, E. R., Laurila, T., Oechel, W. C., Olejnik, J., Rinne, J., Schubert, P., and Nilson, M. B.:
Variability in exchange of CO2 across 12 northern peatland and tundra sites,
Glob. Change Biol.,
16, 2436–2448, https://doi.org/10.1111/j.1365-2486.2009.02104.x, 2010.
Luo, Zh., Wang, G., and Wang, E.:
Global subsoil organic carbon turnover times dominantly controlled by soil properties rather than climate,
Nat. Commun.,
10, 3688, https://doi.org/10.1038/s41467-019-11597-9, 2019.
MacDonald, G. M., Beilman, D. W., Kremenetski, K. V., Sheng, Y., Smith, L. C., and Velichko, A. A.:
Rapid Early Development of Circumarctic Peatlands and Atmospheric CH4 and CO2 Variations,
Science,
314, 285–288, https://doi.org/10.1126/science.1131722, 2006.
MacDougall, A. H. and Knutti, R.: Projecting the release of carbon from permafrost soils using a perturbed parameter ensemble modelling approach, Biogeosciences, 13, 2123–2136, https://doi.org/10.5194/bg-13-2123-2016, 2016.
Mann, M. E., Zhang, Z., Hughes, M. K., Bradley, R. S., Miller, S. K., Rutherford, S.,
and Ni, F.: Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia, P. Natl. Acad. Sci. USA, 105, 13252–13257, 2008.
McGuire, A. D., Koven, C., Lawrence, D. M., Clein, J. S., Xia, J., Beer, C., Burke, E., Chen, G., Chen, X., Delire, C., Jafarov, E., MacDougall, A. H., Marchenko, S., Nicolsky, D., Peng, S., Rinke, A., Saito, K., Zhang, W., Alkama, R., Bohn, T. J., Ciais, P., Decharme, B., Ekici, A., Gouttevin, I., Hajima, T., Hayes, D. J., Ji, D., Krinner, G., Lettenmaier, D. P., Miller, P. A., Moore, J. C., Romanovsky, V., Schadel, C., Schaefer, K., Schuur, E. A. G., Smith, B., Sueyoshi, T., and Zhuang, Q.:
Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009,
Global Biogeochem. Cy.,
30, 1015–1037, 2016.
Miyazaki, S., Saito, K., Mori, J., Yamazaki, T., Ise, T., Arakida, H., Hajima, T., Iijima, Y., Machiya, H., Sueyoshi, T., Yabuki, H., Burke, E. J., Hosaka, M., Ichii, K., Ikawa, H., Ito, A., Kotani, A., Matsuura, Y., Niwano, M., Nitta, T., O'ishi, R., Ohta, T., Park, H., Sasai, T., Sato, A., Sato, H., Sugimoto, A., Suzuki, R., Tanaka, K., Yamaguchi, S., and Yoshimura, K.: The GRENE-TEA model intercomparison project (GTMIP): overview and experiment protocol for Stage 1, Geosci. Model Dev., 8, 2841–2856, https://doi.org/10.5194/gmd-8-2841-2015, 2015.
Morris, P. J., Swindles, G. T., Valdes, P. J., Ivanovic, R. F., Gregoire, L. J., Smith, M. W., Tarasov, L., Haywood, A. M., and Bacon, K. L.:
Global peatland initiation driven by regionally asynchronous warming,
P. Natl. Acad. Sci. USA,
115, 4851–4856, https://doi.org/10.1073/pnas.1717838115, 2018.
Murton, J. B., Goslar, T., Edwards, M. E., Bateman, M. D., Danilov, P. P., Savvinov, G. N., Gubin, S. V., Ghaleb, B., Haile, J., Kanevskiy, M., Lozhkin, A. V., Lupachev, A. V., Murton, D. K., Shur, Y., Tikhonov, A., Vasil'chuk, A. C., Vasil'chuk, Y. K., and Wolfe, S. A.:
Palaeoenvironmental interpretation of Yedoma silt (Ice Complex) deposition as cold-climate loess, Duvanny Yar, Northeast Siberia,
Permafrost Periglac.,
26, 208–288, https://doi.org/10.1002/ppp.1843, 2015.
Nakagawa, T., Kitagawa, H., Yasuda, Y., Tarasov, P. E., Nishida, K., Gotanda, K., Sawai, Y., and Yangtze River Civilization Program Members: Asynchronous Climate Changes in the North Atlantic and Japan
During the Last Termination, Science, 299, 688–691, 2003.
Narita, K., Harada, K., Saito, K., Sawada, Y., Fukuda, M., and Tsuyuzaki, Sh.:
Vegetation and permafrost thaw depth 10 years after a tundra fire in 2002, Seward Peninsula, Alaska,
Arct. Antarct. Alp. Res.,
47, 547–559, https://doi.org/10.1657/AAAR0013-031, 2015.
Nichols, J. E. and Peteet, D. M.:
Rapid expansion of northern peatlands and doubled estimate of carbon storage,
Nat. Geosci.,
12, 917–921, https://doi.org/10.1038/s41561-019-0454-z, 2019.
Olefeldt, D., Goswami, S., Grosse, G., Hayes, D., Hugelius, G., Kuhry, P., McGuire, A. D., Romanovsky, V. E., Sannel, A. B. K., Schuur, E. A. G., and Turetsky, M. R.:
Circumpolar distribution and carbon storage of thermokarst landscapes,
Nat. Commun.,
7, 13043, https://doi.org/10.1038/ncomms13043, 2016.
Park, H., Iijima, Y., Yabuki, H., Ohta, T., Walsh, J., Kodama, Y., and Ohata, T.:
The application of a coupled hydrological and biogeochemical model (CHANGE) for modeling of energy, water, and CO2 exchanges over a larch forest in eastern Siberia,
J. Geophys. Res.,
116, D15102, https://doi.org/10.1029/2010JD015386, 2011.
Peltier, W. R., Argus, D. F., and Drummond, R.:
Space geodesy constrains ice-age terminal deglaciation: The global ICE-6G_C (VM5a) model,
J. Geophys. Res. Sol. Ea.,
120, 450–487, https://doi.org/10.1002/2014JB011176, 2015.
Perruchoud, D., Joos, F., Fischlin, A., Hajdas, I., and Bonani, G.:
Evaluating timescales of carbon turnover in temperate forest soils with radiocarbon data,
Global Biogeochem. Cy.,
13, 555–573, 1999.
Plaza, C., Pegoraro, E., Bracho, R., Kathryn, G. C., Crummer, G., Hutchings, J. A., Hicks Pries, C. E., Mauritz, M., Natali, S. M., Salmon, V. G., Schädel, C., Webb, E. E., and Schuur, E. A. G.:
Nat. Geosci.,
12, 627–631, https://doi.org/10.1038/s41561-019-0387-6, 2019.
Pugh, T. A. M., Rademacher, T., Shafer, S. L., Steinkamp, J., Barichivich, J., Beckage, B., Haverd, V., Harper, A., Heinke, J., Nishina, K., Rammig, A., Sato, H., Arneth, A., Hantson, S., Hickler, T., Kautz, M., Quesada, B., Smith, B., and Thonicke, K.: Understanding the uncertainty in global forest carbon turnover, Biogeosciences, 17, 3961–3989, https://doi.org/10.5194/bg-17-3961-2020, 2020.
Rodell, M. and Beaudoing, H. K:
GLDAS Mosaic Land Surface Model L4 3 Hourly 1.0 × 1.0 degree Subsetted V001,
Goddard Earth Sciences Data and Information Services Center (GES DISC), Greenbelt, Maryland, USA, https://doi.org/10.5067/DLVU8VOPKN7L (last access: 25 January 2021), 2007.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P., Lohmann, D., and Toll, D.:
The Global Land Data Assimilation System,
B. Am. Meteorol. Soc.,
85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004.
Saito, K. and Machiya, H.: Conceptual Model to Simulate Long-term Soil Organic Carbon and Ground Ice Budget with Permafrost and Ice Sheets (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.3839222, 2020.
Saito, K., Marchenko, S., Romanovsky, V., Hendricks, A., Bigelow, N., Yoshikawa, K., and Walsh, J.:
Evaluation of LPM permafrost distribution in NE Asia reconstructed and downscaled from GCM simulations,
Boreas,
43, 733–749, https://doi.org/10.1111/bor.12038, 2014.
Saito, K., Trombotto Liaudat, D., Yoshikawa, K., Mori, J., Sone, T., Marchenko, S., Romanovsky, V., Walsh, J., Hendricks, A., and Bottegal, E.:
Late Quaternary Permafrost Distributions Downscaled for South America: Examinations of GCM-based Maps with Observations,
Permafrost Periglac.,
27, 43–55, 2016.
Saito, K., Machiya, H., Iwahana, G., Ohno, H., and Yokohata, T.: Mapping simulated circum-Arctic organic carbon, ground ice, and vulnerability of ice-rich permafrost to degradation, Prog. Earth Planet. Sci., 7, 31, https://doi.org/10.1186/s40645-020-00345-z, 2020.
Sannel, A. B. K. and Kuhry, P.:
Warming-induced destabilization of peat plateau/thermokarst lake complexes,
J. Geophys. Res.,
116, G03035, https://doi.org/10.1029/2010JG001635, 2011.
Sato, H., Kobayashi, H., Iwahana, G., and Ohta, T.:
Endurance of larch forest ecosystems in eastern Siberia under warming trends,
Ecol. Evol.,
6, 5690–5704, 2016.
Schaefer, K., Lantuit, H., Romanovsky, V. E., Schuur, E. A. G., and Witt, R.: The impact of the permafrost carbon feedback on global
climate, Environ. Res. Lett., 9, 085003, https://doi.org/10.1088/1748-9326/9/8/085003, 2014..
Schneider von Deimling, T., Grosse, G., Strauss, J., Schirrmeister, L., Morgenstern, A., Schaphoff, S., Meinshausen, M., and Boike, J.: Observation-based modelling of permafrost carbon fluxes with accounting for deep carbon deposits and thermokarst activity, Biogeosciences, 12, 3469–3488, https://doi.org/10.5194/bg-12-3469-2015, 2015.
Schuur, E. A. G., Abbott, B., and Permafrost Carbon Network:
Permafrost Carbon High risk of permafrost thaw,
Nature,
480, 32–33, 2011.
Smith, L. C., MacDonald, G. M., Velichko, A. A., Beilman, D. W. Borisova, O. K. Frey, K. E., Kremenetski, K. V., and Shen, Y.:
Siberian Peatlands a Net Carbon Sink and Global Methane Source Since the Early Holocene,
Science, 303, 353–356, 2004.
Strauss, J., Schirrmeister, L., Grosse, G., Fortier, D., Hugelius, G., Knoblauch, C., Romanovsky, V., Schädel, C., Schneidervon Deimling, T., Schuur, E. A. G., Shmelev, D., Ulrich, M., and Veremeeva, A.:
Deep Yedoma permafrost: A synthesis of depositional characteristics and carbon vulnerability,
Earth-Sci. Rev.,
172, 75–86, https://doi.org/10.1016/j.earscirev.2017.07.007, 2017.
Sueyoshi, T., Saito, K., Miyazaki, S., Mori, J., Ise, T., Arakida, H., Suzuki, R., Sato, A., Iijima, Y., Yabuki, H., Ikawa, H., Ohta, T., Kotani, A., Hajima, T., Sato, H., Yamazaki, T., and Sugimoto, A.: The GRENE-TEA model intercomparison project (GTMIP) Stage 1 forcing data set, Earth Syst. Sci. Data, 8, 1–14, https://doi.org/10.5194/essd-8-1-2016, 2016.
Svenning, J.-C. and Sandel, B.:
Disequilibrium vegetation dynamics under future climate change,
Am. J. Bot.,
100, 1266–1286, https://doi.org/10.3732/ajb.1200469, 2013.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.:
An overview ofcmip5 and the experiment design,
B. Am. Meteorol. Soc.,
93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Thornton, P. E., Law, B. E., Gholz, H. L., Clark, K. L., Falge, E., Ellsworth, D. S., Goldstein, A. H., Monson, R. K., Hallinger, D., Falk, M., Chen, J., and Sparks, J. P.:
Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests,
Agr. Forest Meteorol.,
113, 185–222, 2002.
Turetsky, M. R., Abbott, B. W., Jones, M. C., Anthony, K. W., Olefeldt, D., Schuur, E. A. G., Grosse, G., Kuhry, P., Hugelius, G., Koven, C., Lawrence, D. M., Gibson, C., Sannel, A. B. K., and McGuire, A. D.:
Carbon release through abrupt permafrost thaw,
Nat. Geosci.,
13, 38–143, https://doi.org/10.1038/s41561-019-0526-0, 2020.
van Bellen, S., Garneau, M., and Booth, R. K.:
Holocene carbon accumulation rates from three ombrotrophic peatlands in boreal Quebec, Canada: impact of climate-driven ecohydrological change,
Holocene,
21, 1217e1231, 2011.
van Everdingen, R.: Multi-language glossary of permafrost and related ground-ice terms,The Arctic Institute of North America, The University of Calgary, Calgary, Alberta, Canada, 1998.
Vitt, D. H., Halsey, L. A., Bauer, I. E., and Campbell, C.:
Spatial and temporal trends in carbon storage of peatlands of continental Western Canada through the Holocene,
Can. J. Earth Sci.,
37, 683–693, 2000a.
Vitt, D. H., Halsey, L. A., and Zoltai, S. C.:
The changing landscape of Canada's western boreal forest: the current dynamics of permafrost,
Can. J. Forest Res.,
30, 283–287, 2000b.
Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S., Valcke, S., Beau, I., Alias, A., Chevallier, M., Déqué, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L., and Chauvin, F.:
The CNRM-CM5.1 global climate model: description and basic evaluation,
Clim. Dynam.,
40, 2091–2121, https://doi.org/10.1007/s00382-011-1259-y, 2011.
Walter Anthony, K. M., Schneider von Deimling, T., Nitze, I., Frolking, S., Emond, A., Daanen, R., Anthony, P., Lindgren, P., Jones, B., and Grosse, G.:
21st-century modeled permafrost carbon emissions accelerated by abrupt thaw beneath lakes,
Nat. Commun.,
9, 3262, https://doi.org/10.1038/s41467-018-05738-9, 2018.
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011.
Willeit, M. and Ganopolski, A.: Coupled Northern Hemisphere permafrost–ice-sheet evolution over the last glacial cycle, Clim. Past, 11, 1165–1180, https://doi.org/10.5194/cp-11-1165-2015, 2015.
Willmott, C. J. and Matsuura, K.:
Terrestrial Air Temperature and Precipitation: Monthly and Annual Time Series (1950–1999),
NOAA/OAR/ESRL PSD, available at: http://climate.geog.udel.edu/~climate/html_pages/README.ghcn_ts2.html (last access: 25 January 2021), 2001.
Willmott, C. J. and Matsuura, K.: Air Temperature & Precipitation, University of Delaware, available at: https://psl.noaa.gov/data/gridded/data.UDel_AirT_Precip.html (last access: 25 January 2021).
Xing, W., Bao, K., Gallego-Sala, A. V., Charman, D. J., Zhang, Z., Gao, C., Lu, X., and Wang, G.:
Climate controls on carbon accumulation in peatlands of Northeast China,
Quaternary Sci. Rev.,
115, 78–88, 2015.
Yokohata, T., Saito, K., Ito, A., Ohno, H., Tanaka, K., Hajima, T., and Iwahana, G.:
Future projection of climate change due to permafrost degradation with a simple numerical scheme,
Prog. Earth Planet. Sci.,
7, 56, https://doi.org/10.1186/s40645-020-00366-8, 2020.
Yu, Z., Vitt, D. H., Campbell, I. D., and Apps, M. J:
Understanding Holocene peat accumulation pattern of continental fens in western Canada,
Can. J. Botany,
81, 267–282, 2003.
Yu, Z., Beilman, D. W., and Jones, M. C.:
Sensitivity of Northern Peatland Carbon Dynamics to Holocene Climate Change,
Geoph. Monog. Series,
184, 55–69, https://doi.org/10.1029/2008GM000822, 2009.
Yu, Z., Loisel, J., Brosseau, D. P., Beilman, D. W., and Hunt, S. J.:
Global peatland dymanics since the Last Glacial Maximum,
Geophys. Res. Lett.,
37, L13402, https://doi.org/10.1029/2010GL043584, 2010.
Yu, Z. C.: Northern peatland carbon stocks and dynamics: a review, Biogeosciences, 9, 4071–4085, https://doi.org/10.5194/bg-9-4071-2012, 2012.
Yukimoto, S., Adachi, Y., Hosaka, M., Sakami, T., Yoshimura, H., Hirabara, M., Tanaka, T. Y., Shindo, E., Tsujino, H., Deushi, M., and Mizuta, R.:
A new global climate model of the Meteorological Research Institute: MRI-CGCM3 model description and basic performance,
J. Meteorol. Soc. Jpn.,
90A, 23–64, 2012.
Short summary
Soil organic carbon (SOC) and ground ice (ICE) are essential but under-documented information to assess the circum-Arctic permafrost degradation impacts. A simple numerical model of essential SOC and ICE dynamics, developed and integrated north of 50° N for 125,000 years since the last interglacial, reconstructed the history and 1° distribution of SOC and ICE consistent with current knowledge, together with successful demonstration of climatic and topographical controls on SOC evolution.
Soil organic carbon (SOC) and ground ice (ICE) are essential but under-documented information to...