Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-841-2022
© Author(s) 2022. 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-15-841-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
31 Jan 2022
Development and technical paper |
| 31 Jan 2022
| 31 Jan 2022
The importance of turbulent ocean–sea ice nutrient exchanges for simulation of ice algal biomass and production with CICE6.1 and Icepack 1.2
Norwegian Polar Institute, Fram Centre, Tromsø, Norway
Philipp Assmy
Norwegian Polar Institute, Fram Centre, Tromsø, Norway
Karley Campbell
Department of Arctic and Marine Biology, UiT The Arctic University
of Norway, Tromsø, Norway
Bristol Glaciology Centre, University of Bristol, Bristol, UK
Arild Sundfjord
Norwegian Polar Institute, Fram Centre, Tromsø, Norway
Related authors
Letizia Tedesco, Giulia Castellani, Pedro Duarte, Meibing Jin, Sebastien Moreau, Eric Mortenson, Benjamin Tobey Saenz, Nadja Steiner, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-1107, https://doi.org/10.5194/egusphere-2025-1107, 2025
Short summary
Short summary
Sea ice is home to tiny algae that support polar marine life, but understanding how they grow and interact with their environment remains challenging. We compared six computer models that simulate these algae and nutrients in sea ice, testing them against real-world data from Arctic sea ice. Our results show that while models can capture algal growth, they struggle to represent nutrient changes. Improving these models will help in understanding how climate change affects polar marine ecosystems.
Pedro Duarte, Jostein Brændshøi, Dmitry Shcherbin, Pauline Barras, Jon Albretsen, Yvonne Gusdal, Nicholas Szapiro, Andreas Martinsen, Annette Samuelsen, Keguang Wang, and Jens Boldingh Debernard
Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, https://doi.org/10.5194/gmd-15-4373-2022, 2022
Short summary
Short summary
Sea ice models are often implemented for very large domains beyond the regions of sea ice formation, such as the whole Arctic or all of Antarctica. In this study, we implement changes in the Los Alamos Sea Ice Model, allowing it to be implemented for relatively small regions within the Arctic or Antarctica and yet considering the presence and influence of sea ice outside the represented areas. Such regional implementations are important when spatially detailed results are required.
Letizia Tedesco, Giulia Castellani, Pedro Duarte, Meibing Jin, Sebastien Moreau, Eric Mortenson, Benjamin Tobey Saenz, Nadja Steiner, and Martin Vancoppenolle
EGUsphere, https://doi.org/10.5194/egusphere-2025-1107, https://doi.org/10.5194/egusphere-2025-1107, 2025
Short summary
Short summary
Sea ice is home to tiny algae that support polar marine life, but understanding how they grow and interact with their environment remains challenging. We compared six computer models that simulate these algae and nutrients in sea ice, testing them against real-world data from Arctic sea ice. Our results show that while models can capture algal growth, they struggle to represent nutrient changes. Improving these models will help in understanding how climate change affects polar marine ecosystems.
Marta Santos-Garcia, Raja S. Ganeshram, Robyn E. Tuerena, Margot C. F. Debyser, Katrine Husum, Philipp Assmy, and Haakon Hop
Biogeosciences, 19, 5973–6002, https://doi.org/10.5194/bg-19-5973-2022, https://doi.org/10.5194/bg-19-5973-2022, 2022
Short summary
Short summary
Terrestrial sources of nitrate are important contributors to the nutrient pool in the fjords of Kongsfjorden and Rijpfjorden in Svalbard during the summer, and they sustain most of the fjord primary productivity. Ongoing tidewater glacier retreat is postulated to favour light limitation and less dynamic circulation in fjords. This is suggested to encourage the export of nutrients to the middle and outer part of the fjord system, which may enhance primary production within and in offshore areas.
Hanna M. Kauko, Philipp Assmy, Ilka Peeken, Magdalena Różańska-Pluta, Józef M. Wiktor, Gunnar Bratbak, Asmita Singh, Thomas J. Ryan-Keogh, and Sebastien Moreau
Biogeosciences, 19, 5449–5482, https://doi.org/10.5194/bg-19-5449-2022, https://doi.org/10.5194/bg-19-5449-2022, 2022
Short summary
Short summary
This article studies phytoplankton (microscopic
plantsin the ocean capable of photosynthesis) in Kong Håkon VII Hav in the Southern Ocean. Different species play different roles in the ecosystem, and it is therefore important to assess the species composition. We observed that phytoplankton blooms in this area are formed by large diatoms with strong silica armors, which can lead to high silica (and sometimes carbon) export to depth and be important prey for krill.
Øyvind Lundesgaard, Arild Sundfjord, Sigrid Lind, Frank Nilsen, and Angelika H. H. Renner
Ocean Sci., 18, 1389–1418, https://doi.org/10.5194/os-18-1389-2022, https://doi.org/10.5194/os-18-1389-2022, 2022
Short summary
Short summary
In this study, 2-year mooring observations show the evolution of temperature, salinity, and currents in the northern Barents Sea. Inflow of Atlantic Water from the north in autumn and winter was the main driver of the seasonal cycle in the ocean. Winds modulated the inflow on shorter timescales. The upper-ocean state reflected how much sea ice had previously melted in the area. The import of ocean water and sea ice from adjacent regions plays a key role in the complex air–ice–ocean interplay.
Pedro Duarte, Jostein Brændshøi, Dmitry Shcherbin, Pauline Barras, Jon Albretsen, Yvonne Gusdal, Nicholas Szapiro, Andreas Martinsen, Annette Samuelsen, Keguang Wang, and Jens Boldingh Debernard
Geosci. Model Dev., 15, 4373–4392, https://doi.org/10.5194/gmd-15-4373-2022, https://doi.org/10.5194/gmd-15-4373-2022, 2022
Short summary
Short summary
Sea ice models are often implemented for very large domains beyond the regions of sea ice formation, such as the whole Arctic or all of Antarctica. In this study, we implement changes in the Los Alamos Sea Ice Model, allowing it to be implemented for relatively small regions within the Arctic or Antarctica and yet considering the presence and influence of sea ice outside the represented areas. Such regional implementations are important when spatially detailed results are required.
Andrey V. Pnyushkov, Igor V. Polyakov, Robert Rember, Vladimir V. Ivanov, Matthew B. Alkire, Igor M. Ashik, Till M. Baumann, Genrikh V. Alekseev, and Arild Sundfjord
Ocean Sci., 14, 1349–1371, https://doi.org/10.5194/os-14-1349-2018, https://doi.org/10.5194/os-14-1349-2018, 2018
Short summary
Short summary
This study describes along-slope volume, heat, and salt transports derived from observations collected between 2013 and 2015 in the eastern Eurasian Basin of the Arctic Ocean using a cross-slope array of six moorings. Inferred transport estimates may have wide implications and should be considered when assessing high-latitude ocean dynamics.
Achim Randelhoff and Arild Sundfjord
Ocean Sci., 14, 293–300, https://doi.org/10.5194/os-14-293-2018, https://doi.org/10.5194/os-14-293-2018, 2018
Short summary
Short summary
The future of Arctic marine ecosystems has received increasing attention in recent years as the extent of the sea ice cover is dwindling. Regional differences in the hydrography, bathymetry and atmospheric forcing of nutrient fluxes essential for phytoplankton growth mean that wind-driven mixing, advection and upwelling will influence the polar ecosystem in differing magnitudes in different regions of the Arctic Ocean, with particular effects likely being restricted to very specific areas.
Related subject area
Biogeosciences
Alquimia v1.0: a generic interface to biogeochemical codes – a tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
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
Sergi Molins, Benjamin J. Andre, Jeffrey N. Johnson, Glenn E. Hammond, Benjamin N. Sulman, Konstantin Lipnikov, Marcus S. Day, James J. Beisman, Daniil Svyatsky, Hang Deng, Peter C. Lichtner, Carl I. Steefel, and J. David Moulton
Geosci. Model Dev., 18, 3241–3263, https://doi.org/10.5194/gmd-18-3241-2025, https://doi.org/10.5194/gmd-18-3241-2025, 2025
Short summary
Short summary
Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
Short summary
Short summary
Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
Short summary
Short summary
Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
Short summary
Short summary
We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
Short summary
Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
Short summary
Short summary
The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
Short summary
Short summary
Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
Short summary
Short summary
Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
Short summary
Short summary
We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
Short summary
Short summary
Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
Short summary
Short summary
Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
Short summary
Short summary
Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
Short summary
Short summary
The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
Short summary
Short summary
The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
Short summary
Short summary
In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
Short summary
Short summary
The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
Short summary
Short summary
Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
Short summary
Short summary
This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
Short summary
Short summary
Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
Short summary
Short summary
Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
Short summary
Short summary
We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
Short summary
Short summary
We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
Short summary
Short summary
We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
Short summary
Short summary
Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
Short summary
Short summary
The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
Short summary
Short summary
Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
Short summary
Short summary
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
Short summary
Short summary
A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
Short summary
Short summary
A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
Short summary
Short summary
We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
Short summary
Short summary
To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
Short summary
Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
Short summary
Short summary
Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
Short summary
Short summary
We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
Short summary
Short summary
Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
Short summary
Short summary
Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
Short summary
We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
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.
Cited articles
Arrigo, K. R., Kremer, J. N., and Sullivan, C. W.: A Simulated Antarctic
Fast Ice Ecosystem, J. Geophys. Res, 98, 6929–6946, 1993.
Assmy, P., Duarte, P., Dujardin, J., Fernández-Méndez, M., Fransson, A., Hodgson, R., Kauko, H., Kristiansen, S., Mundy, C. J., Olsen, L. M., Peeken, I., Sandbu, M., Wallenschus, J., and Wold, A.: N-ICE2015 water column biogeochemistry, Norwegian Polar Institute [data set], https://doi.org/10.21334/npolar.2016.3ebb7f64, 2016.
Assmy, P., Dodd, P. A., Duarte, P., Dujardin, J., Elliott, A.,
Fernández-Méndez, M., Fransson, A., Granskog, M. A., Hendry, K.,
Hodgson, R., Kauko, H., Kristiansen, S., Leng, M. J., Meyer, A., Mundy, C.
J., Olsen, L. M., Peeken, I., Sandbu, M., Wallenschus, J., and Wold, A.:
N-ICE2015 sea ice biogeochemistry, Norwegian Polar Institute [data set],
https://doi.org/10.21334/npolar.2017.d3e93b31, 2017.
Brzezinski, M. A.: The Si-C-N Ratio of Marine Diatoms – Interspecific
Variability and the Effect of Some Environmental Variables, J. Phycol., 21,
347–357, 1985.
Campbell, K., Mundy, C. J., Barber, D. G., and Gosselin, M.: Characterizing
the sea ice algae chlorophyll a–snow depth relationship over Arctic spring
melt using transmitted irradiance, J. Mar. Sys., 147, 76–84, https://doi.org/10.1016/j.jmarsys.2014.01.008, 2015.
Campbell, K., Mundy, C. J., Landy, J. C., Delaforge, A., Michel, C., and
Rysgaard, S.: Community dynamics of bottom-ice algae in Dease Strait of the
Canadian Arctic, Prog. Oceanogr., 149, 27–39, https://doi.org/10.1016/j.pocean.2016.10.005, 2016.
Carmack, E.: Circulation and Mixing in Ice-Covered Waters, in: The
Geophysics of Sea Ice. NATO ASI Series (Series B: Physics), edited by:
Untersteiner N. Springer, Boston, MA, 641–712,
https://doi.org/10.1007/978-1-4899-5352-0_11, 1986.
Cota, G. F. and Horne, E. P. W.: Physical Control of Arctic Ice Algal
Production, Mar. Ecol. Prog. Ser., 52, 111–121, https://doi.org/10.3354/meps052111,
1989.
Cota, G. F. and Sullivan, C. W.: Photoadaptation, Growth and Production of
Bottom Ice Algae in the Antarctic, J. Phycol., 26, 399–411, https://doi.org/10.1111/j.0022-3646.1990.00399.x, 1990.
Cota, G. F., Prinsenberg, S. J., Bennett, E. B., Loder, J. W., Lewis, M. R.,
Anning, J. L., Watson, N. H. F., and Harris, L. R.: Nutrient Fluxes during
Extended Blooms of Arctic Ice Algae, J. Geophys. Res.-Oceans, 92, 1951–1962,
https://doi.org/10.1029/Jc092ic02p01951, 1987.
Dalman, L. A., Else, B. G. T., Barber, D., Carmack, E., Williams, W. J.,
Campbell, K. , Duke, P. J., Kirillov, S., and Mundy, C. J.: Enhanced
bottom-ice algal biomass across a tidal strait in the Kitikmeot Sea of the
Canadian Arctic, Elem. Sci. Anth., 7, 1–16, https://doi.org/10.1525/elementa.361, 2019.
Duarte, P.: CICE-Consortium/Icepack: Icepack with bottom drag, heat and
nutrient turbulent diffusion (Version 1.1), Zenodo [code], https://doi.org/10.5281/zenodo.4675021, 2021a.
Duarte, P.: CICE-Consortium/CICE: CICE with bottom drag, heat and nutrient
turbulent diffusion (Version 1.1), Zenodo [code], https://doi.org/10.5281/zenodo.4675097, 2021b.
Duarte, P.: The importance of turbulent ocean-sea ice nutrient exchanges for
simulation of ice algal biomass and production with CICE6.1 and Icepack 1.2
– CICE forcing files (Version v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4672176, 2021c.
Duarte, P.: The importance of turbulent ocean-sea ice nutrient exchanges for
simulation of ice algal biomass and production with CICE6.1 and Icepack 1.2
– model simulations (Version v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4672210, 2021d.
Duarte, P., Meyer, A., Olsen, L. M., Kauko, H. M., Assmy, P., Rosel, A.,
Itkin, P., Hudson, S. R., Granskog, M. A., Gerland, S., Sundfjord, A.,
Steen, H., Hop, H., Cohen, L., Peterson, A. K., Jeffery, N., Elliott, S. M.,
Hunke, E. C., and Turner, A. K.: Sea ice thermohaline dynamics and
biogeochemistry in the Arctic Ocean: Empirical and model results, J.
Geophys. Res.-Biogeo., 122, 1632–1654, https://doi.org/10.1002/2016JG003660,
2017.
Gerland, S., Granskog, M. A., King, J., and Rösel, A.: N-ICE2015 Ice core
physics: temperature, salinity and density, Norwegian Polar
Institute [data set], https://doi.org/10.21334/npolar.2017.c3db82e3, 2017.
Gosselin, M., Legendre, L., Demers, S., and Ingram, R. G.: Responses of
Sea-Ice Microalgae to Climatic and Fortnightly Tidal Energy Inputs
(Manitounuk Sound, Hudson-Bay), Can. J. Fish. Aquat. Sci., 42, 999–1006,
https://doi.org/10.1139/f85-125, 1985.
Graham, R. M., Rinke, A., Cohen, L., Hudson, S. R., Walden, V. P., Granskog,
M. A., Dorn, W., Kayser, M., and Maturilli, M.: A comparison of the two
Arctic atmospheric winter states observed during N-ICE2015 and SHEBA, J.
Geophys. Res.-Atmos., 122, 5716–5737, https://doi.org/10.1002/2016JD025475, 2017.
Graham, R. M., Itkin, P., Meyer, A., Sundfjord, A., Spreen, G., Smedsrud, L.
H., Liston, G. E., Cheng, B., Cohen, L., Divine, D., Fer, I., Fransson, A.,
Gerland, S., Haapala, J., Hudson, S. R., Johansson, A. M., King, J.,
Merkouriadi, I., Peterson, A. K., Provost, C., Randelhoff, A., Rinke, A.,
Rosel, A., Sennechael, N., Walden, V., Duarte, P., Assmy, P., Steen, H., and
Granskog, M. A.: Winter storms accelerate the demise of sea ice in the
Atlantic sector of the Arctic Ocean, Sci. Rep.-UK, 9, 9222, https://doi.org/10.1038/S41598-019-45574-5, 2019.
Granskog, M. A., Fer, I., Rinke, A., and Steen, H.:
Atmosphere-Ice-Ocean-Ecosystem Processes in a Thinner Arctic Sea Ice Regime:
The Norwegian Young Sea ICE (N-ICE2015) Expedition, J. Geophys. Res.-Oceans,
123, 1586–1594, https://doi.org/10.1002/2017jc013328, 2018.
Hegseth, E. N.: Sub-Ice Algal Assemblages of the Barents Sea – Species
Composition, Chemical-Composition, and Growth-Rates, Polar. Biol., 12,
485–496, 1992.
Hudson, S. R., Cohen, L., and Walden, V.: N-ICE2015 surface meteorology, Norwegian Polar Institute [data
set], https://doi.org/10.21334/npolar.2015.056a61d1, 2015.
Hudson, S. R., Cohen, L., and Walden, V.: N-ICE2015 surface broadband radiation
data, Norwegian Polar Institute [data set], https://doi.org/10.21334/npolar.2016.a89cb766, 2016.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliot, S.: CICE:
the Los Alamos Sea Ice Model. Documentation and User's Manual Version 5.1,
Los Alamos National Laboratory, USA, LA-CC-06-012, 2015.
Ingram, R. G., Osler, J. C., and Legendre, L.: Influence of Internal
Wave-Induced Vertical Mixing on Ice Algal Production in a Highly Stratified
Sound, Estuar. Coast. Shelf. S., 29, 435–446, https://doi.org/10.1016/0272-7714(89)90078-4, 1989.
Jeffery, N., Hunke, E. C., and Elliott, S. M.: Modeling the transport of
passive tracers in sea ice, J. Geophys. Res.-Oceans, 116, C07020,
https://doi.org/10.1029/2010jc006527, 2011.
Jeffery, N., Elliott, S., Hunke, E. C., Lipscomb, W. H., and Turner, A. K.:
Biogeochemistry of CICE: The Los Alamos Sea Ice Model, Documentation and
User's Manual,Zbgc_colpkg modifications to Version 5, Los
Alamos National Laboratory, Los Alamos, NM, 2016.
Jin, M., Deal, C. J., Wang, J., Shin, K. H., Tanaka, N., Whitledge, T. E.,
Lee, S. H., and Gradinger, R. R.: Controls of the landfast ice–ocean
ecosystem offshore Barrow, Alaska, Ann. Glaciol., 44, 63–72, 2006.
Jin, M., Deal, C., and Jia, W.: A coupled ice-ocean ecosystem model for I-D
and 3-D applications in the Bering and Chukchi Seas, Chinese Journal of
Polar Science, 19, 218–229, 2008.
Krause, J. W., Duarte, C. M., Marquez, I. A., Assmy, P., Fernández-Méndez, M., Wiedmann, I., Wassmann, P., Kristiansen, S., and Agustí, S.: Biogenic silica production and diatom dynamics in the Svalbard region during spring, Biogeosciences, 15, 6503–6517, https://doi.org/10.5194/bg-15-6503-2018, 2018.
Lake, R. A. and Lewis, E. L.: Salt rejection by sea ice during growth,
J. Geophys. Res., 75, 583–597, 1970.
Lannuzel, D., Tedesco, T., van Leeuwe, M., Campbell, K., Flores, H.,
Delille, B., Miller, L., Stefels, J., Assmy, P., Bowman, J., Brown, K.,
Castellani, G., Chierici, M., Crabeck, O., Damm, E., Else, B., Fransson, A.,
Fripiat, F., Geilfus, N. X., Jacques, C., Jones, E., Kaartokallio, H.,
Kotovitch, M., Meiners, K., Moreau, S., Nomura, D., Peeken, I., Rintala, J.
M., Steiner, N., Tison, J. L., Vancoppenolle, M., Van der Linden, F., Vichi,
M., and Wongpan, P.: The future of Arctic sea-ice biogeochemistry and
ice-associated ecosystems, Nat. Clim. Change, 10, 983–992, https://doi.org/10.1038/s41558-020-00940-4, 2020.
Lavoie, D., Denman, K., and Michel, C.: Modeling ice algal growth and
decline in a seasonally ice-covered region of the Arctic (Resolute Passage,
Canadian Archipelago), J. Geophys. Res.-Oceans, 110, C11009, https://doi.org/10.1029/2005jc002922, 2005.
Leu, E., Mundy, C. J., Assmy, P., Campbell, K., Gabrielsen, T. M., Gosselin,
M., Juul-Pedersen, T., and Gradinger, R.: Arctic spring awakening – Steering
principles behind the phenology of vernal ice algal blooms, Progr.
Oceanogr., 139, 151–170, https://doi.org/10.1016/j.pocean.2015.07.012, 2015.
Lim, S. M., Moreau, S., Vancoppenolle, M., Deman, F., Roukaerts, A.,
Meiners, K. M., Janssens, J., and Lannuzel, D.: Field Observations and
Physical-Biogeochemical Modeling Suggest Low Silicon Affinity for Antarctic
Fast Ice Diatoms, J. Geophys. Res.-Oceans, 124, 7837–7853,
https://doi.org/10.1029/2018jc014458, 2019.
Mann, K. H. and Lazier, J. R. N.: Dynamics of Marine Ecosystems, Third Edition,
Blackwell Publishing Ltd., Carlton, Victoria 3053, Australia, 503 pp.,
https://doi.org/10.1002/9781118687901, 2005.
McPhee, M.: Air-ice-ocean interaction: Turbulent ocean boundary layer
exchange processes. Springer-Verlag, New York, 216 pp., https://doi.org/10.1007/978-0-387-78335-2, 2008.
McPhee, M. G., Morison, J. H., and Nilsen, F.: Revisiting heat and salt
exchange at the ice-ocean interface: Ocean flux and modeling considerations,
J. Geophys. Res.-Oceans, 113, C06014, https://doi.org/10.1029/2007jc004383, 2008.
Mortenson, E., Hayashida, H., Steiner, N., Monahan, A., Blais, M., Gale, M.
A., Galindo, V., Gosselin, M., Hu, X. M., Lavoie, D., and Mundy, C. J.: A
model-based analysis of physical and biological controls on ice algal and
pelagic primary production in Resolute Passage, Elem. Sci. Anth., 5,
39, https://doi.org/10.1525/Elementa.229, 2017.
Nelson, D. M. and Treguer, P.: Role of Silicon as a Limiting Nutrient to
Antarctic Diatoms – Evidence from Kinetic-Studies in the Ross Sea Ice-Edge
Zone, Mar. Ecol. Prog. Ser., 80, 255–264, https://doi.org/10.3354/meps080255, 1992.
Niedrauer, T. M. and Martin, S.: Experimental-Study of Brine Drainage and
Convection in Young Sea Ice, J. Geophys. Res.-Oceans, 84, 1176–1186, https://doi.org/10.1029/JC084iC03p01176, 1979.
Notz, D. and Worster, M. G.: Desalination processes of sea ice revisited, J.
Geophys. Res.-Oceans, 114, C05006, https://doi.org/10.1029/2008jc004885, 2009.
Olsen, L. M., Laney, S. R., Duarte, P., Kauko, H. M.,
Fernández-Méndez, M., Mundy, C. J., Rösel, A., Meyer, A., Itkin,
P., Cohen, L., Peeken, I., Tatarek, A., Róźańska, M., Wiktor,
J., Taskjelle, T., Pavlov, A. K., Hudson, S. R., Granskog, M. A., Hop, H.,
and Assmy, P.: The seeding of ice-algal blooms in Arctic pack ice: the
multiyear ice seed repository hypothesis, J. Geophys. Res.-Biogeo.,
122, 1529–1548, https://doi.org/10.1002/2016jg003668, 2017.
Olsen, L. M., Duarte, P., Peralta-Ferriz, C., Kauko, H. M., Johansson, M.,
Peeken, I., Różańska-Pluta, M., Tatarek, A., Wiktor, J.,
Fernández-Méndez, M., Wagner, P. M., Pavlov, A. K., Hop, H., and
Assmy, P.: A red tide in the pack ice of the Arctic Ocean, Sci. Rep.-UK, 9, 9536,
https://doi.org/10.1038/s41598-019-45935-0, 2019.
Peterson, A. K., Fer, I., Randelhoff, A., Meyer, A., Håvik, L.,
Smedsrud, L. H., Onarheim, L., Muilwijk, M., Sundfjord, A., and McPhee, M. G.:
N-ICE2015 Ocean turbulent fluxes from under-ice turbulence cluster (TIC), Norwegian Polar Institute
[data set], https://doi.org/10.21334/npolar.2016.ab29f1e2, 2016.
Reeburgh, W. S.: Fluxes Associated with Brine Motion in Growing Sea Ice,
Polar Biol., 3, 29–33, https://doi.org/10.1007/Bf00265564, 1984.
Rinke, A., Maturilli, M., Graham, R. M., Matthes, H., Handorf, D., Cohen,
L., Hudson, S. R., and Moore, J. C.: Extreme cyclone events in the Arctic:
Wintertime variability and trends, Environ. Res. Lett., 12, 094006,
https://doi.org/10.1088/1748-9326/Aa7def, 2017.
Smith, R. E. H., Cavaletto, J. F., Eadie, B. J., and Gardner, W. S.: Growth
and Lipid-Composition of High Arctic Ice Algae during the Spring Bloom at
Resolute, Northwest-Territories, Canada, Mar. Ecol. Prog. Ser., 97, 19–29,
https://doi.org/10.3354/meps097019, 1993.
Takeda, S.: Influence of iron availability on nutrient consumption ratio of
diatoms in oceanic waters, Nature, 393, 774–777, https://doi.org/10.1038/31674, 1998.
Tedesco, L. and Vichi, M.: BFM-SI: a new implementation of the Biogeochemical
Flux Model in sea ice. in: CMCC Research Papers, available at:
http://hdl.handle.net/2122/5956 (last access: 27 January 2022), 2010.
Tedesco, L., Vichi, M., and Scoccimarro, E.: Sea-ice algal phenology in a
warmer Arctic, Sci. Adv., 5, eaav4830, https://doi.org/10.1126/sciadv.aav4830,
2019.
Thomas, M., Vancoppenolle, M., France, J. L., Sturges, W. T., Bakker, D. C.
E., Kaiser, J., and von Glasow, R.: Tracer Measurements in Growing Sea Ice
Support Convective Gravity Drainage Parameterizations, J. Geophys. Res.-Oceans,
125, e2019JC015791, https://doi.org/10.1029/2019JC015791, 2020.
Tsamados, M., Feltham, D. L., Schroeder, D., Flocco, D., Farrell, S. L.,
Kurtz, N., Laxon, S. W., and Bacon, S.: Impact of Variable Atmospheric and
Oceanic Form Drag on Simulations of Arctic Sea Ice, J. Phys.
Oceanogr., 44, 1329–1353, https://doi.org/10.1175/Jpo-D-13-0215.1, 2014.
Turner, A. K., Hunke, E. C., and Bitz, C. M.: Two modes of sea-ice gravity
drainage: A parameterization for large-scale modeling, J. Geophys.
Res.-Oceans, 118, 2279–2294, https://doi.org/10.1002/jgrc.20171, 2013.
Urrego-Blanco, J. R., Urban, N. M., Hunke, E. C., Turner, A. K., and
Jeffery, N.: Uncertainty quantification and global sensitivity analysis of
the Los Alamos sea ice model, J. Geophys. Res.-Oceans, 121, 2709–2732, https://doi.org/10.1002/2015JC011558, 2016.
Vancoppenolle, M., Bitz, C. M., and Fichefet, T.: Summer landfast sea ice
desalination at Point Barrow, Alaska: Modeling and observations, J. Geophys.
Res.-Oceans, 112, C04022, https://doi.org/10.1029/2006jc003493, 2007.
Vancoppenolle, M., Goosse, H., de Montety, A., Fichefet, T., Tremblay, B.,
and Tison, J. L.: Modeling brine and nutrient dynamics in Antarctic sea ice:
The case of dissolved silica, J. Geophys. Res.-Oceans, 115, C02005, https://doi.org/10.1029/2009jc005369, 2010.
Vancoppenolle, M., Bopp, L., Madec, G., Dunne, J., Ilyina, T., Halloran, P.
R., and Steiner, N.: Future Arctic Ocean primary productivity from CMIP5
simulations: Uncertain outcome, but consistent mechanisms, Global Biogeochem.
Cy., 27, 605–619, https://doi.org/10.1002/gbc.20055, 2013.
van Leeuwe, M. A., Tedesco, L., Arrigo, K. R., Assmy, P., Campbell, K.,
Meiners, K. M., Rintala, J. M., Selz, V., Thomas, D. N., and Stefels, J.:
Microalgal community structure and primary production in Arctic and
Antarctic sea ice: A synthesis, Elem. Sci. Anth., 6, 4, https://doi.org/10.1525/elementa.267, 2018.
Wakatsuchi, M. and Ono, N.: Measurements of Salinity and Volume of Brine
Excluded from Growing Sea Ice, J. Geophys. Res.-Oceans, 88, 2943–2951, https://doi.org/10.1029/JC088iC05p02943, 1983.
Webster, M. A., Rigor, I. G., Nghiem, S. V., Kurtz, N. T., Farrell, S. L.,
Perovich, D. K., and Sturm, M.: Interdecadal changes in snow depth on Arctic
sea ice, J. Geophys. Res.-Oceans, 119, 5395–5406,
https://doi.org/10.1002/2014JC009985, 2014.
Wells, A. J., Wettlaufer, J. S., and Orszag, S. A.: Brine fluxes from
growing sea ice, Geophys. Res. Lett., 38, L04501, https://doi.org/10.1029/2010gl046288, 2011.
Short summary
Sea ice modeling is an important part of Earth system models (ESMs). The results of ESMs are used by the Intergovernmental Panel on Climate Change in their reports. In this study we present an improvement to calculate the exchange of nutrients between the ocean and the sea ice. This nutrient exchange is an essential process to keep the ice-associated ecosystem functioning. We found out that previous calculation methods may underestimate the primary production of the ice-associated ecosystem.
Sea ice modeling is an important part of Earth system models (ESMs). The results of ESMs are...
