Articles | Volume 13, issue 2
https://doi.org/10.5194/gmd-13-597-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-597-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Feb 2020
Model description paper |
| 17 Feb 2020
| 17 Feb 2020
A coupled pelagic–benthic–sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model
Kelly Kearney
CORRESPONDING AUTHOR
University of Washington, Joint Institute for the Study of the Atmosphere and Oceans (JISAO), Seattle, WA, USA
NOAA Alaska Fisheries Science Center, Seattle, WA, USA
Albert Hermann
University of Washington, Joint Institute for the Study of the Atmosphere and Oceans (JISAO), Seattle, WA, USA
NOAA Pacific Marine Environmental Laboratory, Seattle, WA, USA
Wei Cheng
University of Washington, Joint Institute for the Study of the Atmosphere and Oceans (JISAO), Seattle, WA, USA
NOAA Pacific Marine Environmental Laboratory, Seattle, WA, USA
Ivonne Ortiz
University of Washington, Joint Institute for the Study of the Atmosphere and Oceans (JISAO), Seattle, WA, USA
NOAA Alaska Fisheries Science Center, Seattle, WA, USA
Kerim Aydin
NOAA Alaska Fisheries Science Center, Seattle, WA, USA
Related authors
Darren Pilcher, Jessica Cross, Natalie Monacci, Linquan Mu, Kelly Kearney, Albert Hermann, and Wei Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2024-1096, https://doi.org/10.5194/egusphere-2024-1096, 2024
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The Bering Sea shelf is a highly productive marine ecosystem that is vulnerable to ocean acidification. We use a computational model to simulate the carbon cycle and acidification rates from 1970–2022. The results suggest that bottom water acidification rates are more than twice as great as surface rates. Bottom waters are also naturally more acidic, thus these waters will pass key thresholds known to negatively impact marine organisms, such as red king crab, much sooner than surface waters.
Darren Pilcher, Jessica Cross, Natalie Monacci, Linquan Mu, Kelly Kearney, Albert Hermann, and Wei Cheng
EGUsphere, https://doi.org/10.5194/egusphere-2024-1096, https://doi.org/10.5194/egusphere-2024-1096, 2024
Short summary
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The Bering Sea shelf is a highly productive marine ecosystem that is vulnerable to ocean acidification. We use a computational model to simulate the carbon cycle and acidification rates from 1970–2022. The results suggest that bottom water acidification rates are more than twice as great as surface rates. Bottom waters are also naturally more acidic, thus these waters will pass key thresholds known to negatively impact marine organisms, such as red king crab, much sooner than surface waters.
Related subject area
Biogeosciences
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCO v4-Hg: the role of surfactants and waves
BOATSv2: New ecological and economic features improve simulations of High Seas catch and effort
Lambda-PFLOTRAN 1.0: Workflow for Incorporating Organic Matter Chemistry Informed by Ultra High Resolution Mass Spectrometry into Biogeochemical Modeling
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
A dynamical process-based model AMmonia–CLIMate v1.0 (AMCLIM v1.0) for quantifying global agricultural ammonia emissions – Part 1: Land module for simulating emissions from synthetic fertilizer use
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
A model of the within-population variability of budburst in forest trees
Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models
The community-centered freshwater biogeochemistry model unified RIVE v1.0: a unified version for water column
Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)
The statistical emulators of GGCMI phase 2: responses of year-to-year variation of crop yield to CO2, temperature, water, and nitrogen perturbations
A novel Eulerian model based on central moments to simulate age and reactivity continua interacting with mixing processes
AdaScape 1.0: a coupled modelling tool to investigate the links between tectonics, climate, and biodiversity
An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas
Peatland-VU-NUCOM (PVN 1.0): using dynamic plant functional types to model peatland vegetation, CH4, and CO2 emissions
Quantification of hydraulic trait control on plant hydrodynamics and risk of hydraulic failure within a demographic structured vegetation model in a tropical forest (FATES–HYDRO V1.0)
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
A high-resolution marine mercury model MITgcm-ECCO2-Hg with online biogeochemistry
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO, and NH3 emissions from enhanced rock weathering with croplands
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
Simulating Bark Beetle Outbreak Dynamics and their Influence on Carbon Balance Estimates with ORCHIDEE r7791
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
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
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We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-81, https://doi.org/10.5194/gmd-2024-81, 2024
Revised manuscript accepted for GMD
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The estimation of Hg0 fluxes is of great uncertainty due to neglecting wave breaking and sea surfactant. Integrating these factors into MITgcm significantly rise Hg0 transfer velocity. The updated model shows increased fluxes in high wind and wave regions and vice versa, enhancing the spatial heterogeneity. It shows a stronger correlation between Hg0 transfer velocity and wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-26, https://doi.org/10.5194/gmd-2024-26, 2024
Revised manuscript accepted for GMD
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Numerical models that capture key features of the global dynamics of fish communities play a crucial role in addressing the impacts of climate change and industrial fishing on ecosystems and societies. Here, we detail an update of the BiOeconomic marine Trophic Size-spectrum model that corrects the model representation of the dynamic of fisheries in the High Seas. This update also allows a better representation of biodiversity to improve future global and regional fisheries studies.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-34, https://doi.org/10.5194/gmd-2024-34, 2024
Revised manuscript accepted for GMD
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The newly developed Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate respiration and the resulting biogeochemistry. Lambda-PFLOTRAN is a python-based workflow via a Jupyter Notebook interface, that digests raw organic matter chemistry data via FTICR-MS, develops the representative reaction network, and completes a biogeochemical simulation with the open source, parallel reactive flow and transport code PFLOTRAN.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
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We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
EGUsphere, https://doi.org/10.5194/egusphere-2024-962, https://doi.org/10.5194/egusphere-2024-962, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use, whilst taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers were lost due to NH3 emissions. Hot and dry conditions and regions with high pH soils can expect higher NH3 emissions.
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024, https://doi.org/10.5194/gmd-17-2299-2024, 2024
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By unlocking the mysteries of CH4 emissions from wetlands, our work improved the accuracy of the LPJ-GUESS vegetation model using Bayesian statistics. Via assimilation of long-term real data from a wetland, we significantly enhanced CH4 emission predictions. This advancement helps us better understand wetland contributions to atmospheric CH4, which are crucial for addressing climate change. Our method offers a promising tool for refining global climate models and guiding conservation efforts
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024, https://doi.org/10.5194/gmd-17-1175-2024, 2024
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Creating computational models of how phytoplankton grows in the ocean is a technical challenge. We developed a new tool set (Xarray-simlab-ODE) for building such models using the programming language Python. We demonstrate the tool set in a library of plankton models (Phydra). Our goal was to allow scientists to develop models quickly, while also allowing the model structures to be changed easily. This allows us to test many different structures of our models to find the most appropriate one.
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024, https://doi.org/10.5194/gmd-17-997-2024, 2024
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Quantification of carbon fluxes of crops is an essential building block for the construction of a monitoring, reporting, and verification approach. We developed an end-to-end platform (AgriCarbon-EO) that assimilates, through a Bayesian approach, high-resolution (10 m) optical remote sensing data into radiative transfer and crop modelling at regional scale (100 x 100 km). Large-scale estimates of carbon flux are validated against in situ flux towers and yield maps and analysed at regional scale.
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024, https://doi.org/10.5194/gmd-17-931-2024, 2024
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To manage soil organic matter (SOM) sustainably, we need a better understanding of the role that soil microbes play in aggregate protection. Here, we propose the SAMM model, which connects soil aggregate formation to microbial growth. We tested it against data from a tropical long-term experiment and show that SAMM effectively represents the microbial growth, SOM, and aggregate dynamics and that it can be used to explore the importance of aggregate formation in SOM stabilization.
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024, https://doi.org/10.5194/gmd-17-865-2024, 2024
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Currently, the high variability of budburst between individual trees is overlooked. The consequences of this neglect when projecting the dynamics and functioning of tree communities are unknown. Here we develop the first process-oriented model to describe the difference in budburst dates between individual trees in plant populations. Beyond budburst, the model framework provides a basis for studying the dynamics of phenological traits under climate change, from the individual to the community.
Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington
Geosci. Model Dev., 17, 621–649, https://doi.org/10.5194/gmd-17-621-2024, https://doi.org/10.5194/gmd-17-621-2024, 2024
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Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Shuaitao Wang, Vincent Thieu, Gilles Billen, Josette Garnier, Marie Silvestre, Audrey Marescaux, Xingcheng Yan, and Nicolas Flipo
Geosci. Model Dev., 17, 449–476, https://doi.org/10.5194/gmd-17-449-2024, https://doi.org/10.5194/gmd-17-449-2024, 2024
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This paper presents unified RIVE v1.0, a unified version of the freshwater biogeochemistry model RIVE. It harmonizes different RIVE implementations, providing the referenced formalisms for microorganism activities to describe full biogeochemical cycles in the water column (e.g., carbon, nutrients, oxygen). Implemented as open-source projects in Python 3 (pyRIVE 1.0) and ANSI C (C-RIVE 0.32), unified RIVE v1.0 promotes and enhances collaboration among research teams and public services.
Sam S. Rabin, William J. Sacks, Danica L. Lombardozzi, Lili Xia, and Alan Robock
Geosci. Model Dev., 16, 7253–7273, https://doi.org/10.5194/gmd-16-7253-2023, https://doi.org/10.5194/gmd-16-7253-2023, 2023
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Climate models can help us simulate how the agricultural system will be affected by and respond to environmental change, but to be trustworthy they must realistically reproduce historical patterns. When farmers plant their crops and what varieties they choose will be important aspects of future adaptation. Here, we improve the crop component of a global model to better simulate observed growing seasons and examine the impacts on simulated crop yields and irrigation demand.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
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We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Jurjen Rooze, Heewon Jung, and Hagen Radtke
Geosci. Model Dev., 16, 7107–7121, https://doi.org/10.5194/gmd-16-7107-2023, https://doi.org/10.5194/gmd-16-7107-2023, 2023
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Chemical particles in nature have properties such as age or reactivity. Distributions can describe the properties of chemical concentrations. In nature, they are affected by mixing processes, such as chemical diffusion, burrowing animals, and bottom trawling. We derive equations for simulating the effect of mixing on central moments that describe the distributions. We then demonstrate applications in which these equations are used to model continua in disturbed natural environments.
Esteban Acevedo-Trejos, Jean Braun, Katherine Kravitz, N. Alexia Raharinirina, and Benoît Bovy
Geosci. Model Dev., 16, 6921–6941, https://doi.org/10.5194/gmd-16-6921-2023, https://doi.org/10.5194/gmd-16-6921-2023, 2023
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The interplay of tectonics and climate influences the evolution of life and the patterns of biodiversity we observe on earth's surface. Here we present an adaptive speciation component coupled with a landscape evolution model that captures the essential earth-surface, ecological, and evolutionary processes that lead to the diversification of taxa. We can illustrate with our tool how life and landforms co-evolve to produce distinct biodiversity patterns on geological timescales.
Veli Çağlar Yumruktepe, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen
Geosci. Model Dev., 16, 6875–6897, https://doi.org/10.5194/gmd-16-6875-2023, https://doi.org/10.5194/gmd-16-6875-2023, 2023
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We present an along BGC-Argo track 1D modelling framework. The model physics is constrained by the BGC-Argo temperature and salinity profiles to reduce the uncertainties related to mixed layer dynamics, allowing the evaluation of the biogeochemical formulation and parameterization. We objectively analyse the model with BGC-Argo and satellite data and improve the model biogeochemical dynamics. We present the framework, example cases and routines for model improvement and implementations.
Tanya J. R. Lippmann, Ype van der Velde, Monique M. P. D. Heijmans, Han Dolman, Dimmie M. D. Hendriks, and Ko van Huissteden
Geosci. Model Dev., 16, 6773–6804, https://doi.org/10.5194/gmd-16-6773-2023, https://doi.org/10.5194/gmd-16-6773-2023, 2023
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Vegetation is a critical component of carbon storage in peatlands but an often-overlooked concept in many peatland models. We developed a new model capable of simulating the response of vegetation to changing environments and management regimes. We evaluated the model against observed chamber data collected at two peatland sites. We found that daily air temperature, water level, harvest frequency and height, and vegetation composition drive methane and carbon dioxide emissions.
Chonggang Xu, Bradley Christoffersen, Zachary Robbins, Ryan Knox, Rosie A. Fisher, Rutuja Chitra-Tarak, Martijn Slot, Kurt Solander, Lara Kueppers, Charles Koven, and Nate McDowell
Geosci. Model Dev., 16, 6267–6283, https://doi.org/10.5194/gmd-16-6267-2023, https://doi.org/10.5194/gmd-16-6267-2023, 2023
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We introduce a plant hydrodynamic model for the U.S. Department of Energy (DOE)-sponsored model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest ecosystems, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Jianghui Du
Geosci. Model Dev., 16, 5865–5894, https://doi.org/10.5194/gmd-16-5865-2023, https://doi.org/10.5194/gmd-16-5865-2023, 2023
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Trace elements and isotopes (TEIs) are important tools to study the changes in the ocean environment both today and in the past. However, the behaviors of TEIs in marine sediments are poorly known, limiting our ability to use them in oceanography. Here we present a modeling framework that can be used to generate and run models of the sedimentary cycling of TEIs assisted with advanced numerical tools in the Julia language, lowering the coding barrier for the general user to study marine TEIs.
Siyu Zhu, Peipei Wu, Siyi Zhang, Oliver Jahn, Shu Li, and Yanxu Zhang
Geosci. Model Dev., 16, 5915–5929, https://doi.org/10.5194/gmd-16-5915-2023, https://doi.org/10.5194/gmd-16-5915-2023, 2023
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In this study, we estimate the global biogeochemical cycling of Hg in a state-of-the-art physical-ecosystem ocean model (high-resolution-MITgcm/Hg), providing a more accurate portrayal of surface Hg concentrations in estuarine and coastal areas, strong western boundary flow and upwelling areas, and concentration diffusion as vortex shapes. The high-resolution model can help us better predict the transport and fate of Hg in the ocean and its impact on the global Hg cycle.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla L. Taylor, Louisa K. Emmons, William R. Wieder, Noah J. Planavsky, Michael D. Masters, Evan H. DeLucia, Amos P. K. Tai, and David J. Beerling
Geosci. Model Dev., 16, 5783–5801, https://doi.org/10.5194/gmd-16-5783-2023, https://doi.org/10.5194/gmd-16-5783-2023, 2023
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Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (e.g., basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits of C sequestration. ERW could drive changes in soil emissions of non-CO2 GHGs (N2O) and trace gases (NO and NH3) that may affect air quality. We present a new improved N cycling scheme for the land model (CLM5) to evaluate ERW effects on soil gas N emissions.
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
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This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
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Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
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Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne-Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
EGUsphere, https://doi.org/10.5194/egusphere-2023-1216, https://doi.org/10.5194/egusphere-2023-1216, 2023
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This research looks at how climate change influences forests, particularly how altered wind and insect activities could make forests emit, instead of absorb, carbon. We've updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, like insect outbreaks, can dramatically affect carbon storage, offering crucial insights for tackling climate change.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
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Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
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
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We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Cited articles
Aguilar-Islas, A. M., Hurst, M. P., Buck, K. N., Sohst, B., Smith, G. J.,
Lohan, M. C., and Bruland, K. W.: Micro- and macronutrients in the southeastern Bering Sea: Insight into iron-replete and iron-depleted regimes, Prog. Oceanogr., 73, 99–126, https://doi.org/10.1016/j.pocean.2006.12.002, 2007. a, b, c, d, e
Ambrose, W. G., Von Quillfeldt, C., Clough, L. M., Tilney, P. V., and Tucker,
T.: The sub-ice algal community in the Chukchi sea: Large- and small-scale
patterns of abundance based on images from a remotely operated vehicle, Polar Biol., 28, 784–795, https://doi.org/10.1007/s00300-005-0002-8, 2005. a
Arhonditsis, G. B. and Brett, M. T.: Eutrophication model for Lake Washington (USA): Part I. Model description and sensitivity analysis, Ecol.
Model., 187, 140–178, https://doi.org/10.1016/j.ecolmodel.2005.01.040, 2005. a, b
Arrigo, K. R. and Sullivan, C. W.: The influence of salinity and temperature
covariation on the photophysiological characteristics of Antarctic sea ice
microalgae, J. Phycol., 28, 746–756, 1992. a
Arrigo, K. R., Kremer, J. N., and Sullivan, C. W.: A simulated Antarctic fast
ice ecosystem, J. Geophys. Res., 98, 6929–6946, https://doi.org/10.1029/93JC00141, 1993. a, b
Aumont, O. and Bopp, L.: Globalizing results from ocean in situ iron
fertilization studies, Global Biogeochem. Cy., 20, GB2017, https://doi.org/10.1029/2005GB002591, 2006. a
Banas, N. S., Zhang, J., Campbell, R. G., Sambrotto, R. N., Lomas, M. W.,
Sherr, E., Sherr, B., Ashjian, C., Stoecker, D., and Lessard, E. J.: Spring
plankton dynamics in the Eastern Bering Sea, 1971–2050: Mechanisms of
interannual variability diagnosed with a numericalmodel, J. Geophys. Res.-Oceans, 121, 3372–3380, https://doi.org/10.1002/2015JC011449, 2016. a
Bond, N. A., Cronin, M. F., Freeland, H., and Mantua, N.: Causes and impacts
of the 2014 warm anomaly in the NE Pacific, Geophys. Res. Lett., 42, 3414–3420, https://doi.org/10.1002/2015GL063306, 2015. a
Buckley, T. W., Greig, A., and Boldt, J. L.: Describing summer pelagic habitat over the continental shelf in the eastern Bering Sea, 1982–2006, NOAA Technical Memorandum, p. 49, available at: http://www.afsc.noaa.gov/Publications/AFSC-TM/NOAA-TM-AFSC-196.pdf
(last access: 22 July 2019), 2009. a
Butenschön, M., Clark, J., Aldridge, J. N., Icarus Allen, J., Artioli, Y., Blackford, J., Bruggeman, J., Cazenave, P., Ciavatta, S., Kay, S., Lessin, G., Van Leeuwen, S., Van Der Molen, J., De Mora, L., Polimene, L.,
Sailley, S., Stephens, N., and Torres, R.: ERSEM 15.06: A generic model for
marine biogeochemistry and the ecosystem dynamics of the lower trophic
levels, Geosci. Model Dev., 9, 1293–1339, https://doi.org/10.5194/gmd-9-1293-2016, 2016. a
Campbell, R. G., Ashjian, C. J., Sherr, E. B., Sherr, B. F., Lomas, M. W.,
Ross, C., Alatalo, P., Gelfman, C., and Keuren, D. V.: Mesozooplankton
grazing during spring sea-ice conditions in the eastern Bering Sea, Deep-Sea
Res. Pt. II, 134, 157–172, https://doi.org/10.1016/j.dsr2.2015.11.003, 2016. a
Carton, J. A. and Giese, B. S.: A Reanalysis of Ocean Climate Using Simple
Ocean Data Assimilation (SODA), Mon. Weather Rev., 136, 2999–3017,
https://doi.org/10.1175/2007MWR1978.1, 2008. a
Cavalieri, D., Parkinson, C., Gloersen, P., and Zwally, H. J.: Sea Ice
Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Passive Microwave
Data, https://doi.org/10.5067/8GQ8LZQVL0VL, 1996. a
Cheng, W., Curchitser, E., Ladd, C., Stabeno, P., and Wang, M.: Influences of
sea ice on the Eastern Bering Sea: NCAR CESM simulations and comparison with
observations, Deep-Sea Res. Pt. II, 109, 27–38, https://doi.org/10.1016/j.dsr2.2014.03.002, 2014. a
Coachman, L. K.: Circulation, water masses, and fluxes on the southeastern Bering Sea shelf, Cont. Shelf Res., 5, 23–108, https://doi.org/10.1016/0278-4343(86)90011-7, 1986. a, b
Coachman, L. K. and Charnell, R. L.: On lateral water mass interaction – a
case study, Bristol Bay, Alaska, J. Phys. Oceanogr., 9, 278–297, 1978. a
Coyle, K. O., Pinchuk, A. I., Eisner, L. B., and Napp, J. M.: Zooplankton
species composition, abundance and biomass on the eastern Bering Sea shelf
during summer: The potential role of water-column stability and nutrients in
structuring the zooplankton community, Deep-Sea Res. Pt. II, 55, 1775–1791, https://doi.org/10.1016/j.dsr2.2008.04.029, 2008. a
Danielson, S., Aagaard, K., Weingartner, T., Martin, S., Winsor, P.,
Gawarkiewicz, G., and Quadfasel, D.: The St. Lawrence polynya and the Bering
shelf circulation: New observations and a model comparison, J. Geophys. Res.-Oceans, 111, 1–18, https://doi.org/10.1029/2005JC003268, 2006. a
Danielson, S., Curchitser, E., Hedstrom, K., Weingartner, T., and Stabeno, P.: On ocean and sea ice modes of variability in the Bering Sea, J. Geophys. Res.-Oceans, 116, 1–24, https://doi.org/10.1029/2011JC007389, 2011. a, b, c, d
D'Errico, J.: Surface fitting with gridfit, available at:
https://www.mathworks.com/matlabcentral/fileexchange/8998-surface-fitting-using-gridfit, last access: 10 March 2016. a
Duffy-Anderson, J. T., Barbeaux, S. J., Farley, E., Heintz, R., Horne, J. K.,
Parker-Stetter, S. L., Petrik, C., Siddon, E. C., and Smart, T. I.: The
critical first year of life of walleye pollock (Gadus chalcogrammus) in the
eastern Bering Sea: Implications for recruitment and future research, Deep-Sea Res. Pt. II, 134, 283–301, https://doi.org/10.1016/j.dsr2.2015.02.001, 2016. a
Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J. P.,
Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., Cooke, W.,
Dunne, K. A., Griffies, S. M., Hallberg, R. W., Harrison, M. J., Levy, H.,
Wittenberg, A. T., Phillips, P. J., and Zadeh, N.: GFDL's ESM2 global
coupled climate-carbon Earth System Models Part II: Carbon system formulation
and baseline simulation characteristics, J. Climate, 26, 2247–2266, https://doi.org/10.1175/JCLI-D-12-00150.1, 2012. a
Durski, S. M. and Kurapov, A. L.: A high-resolution coupled ice-ocean model of winter circulation on the bering sea shelf. Part I: Ice model refinements and skill assessments, Ocean Model., 133, 145–161,
https://doi.org/10.1016/j.ocemod.2018.11.004, 2019. a
Ebenhöh, W., Kohlmeier, C., and Radford, P. J.: The benthic biological
submodel in the European regional seas ecosystem model, Neth. J. Sea Res., 33, 423–452, https://doi.org/10.1016/0077-7579(95)90056-X, 1995. a
Fairall, C. W., Bradley, E. F., Rogers, D. P., Edson, J. B., and Young, G. S.: Bulk parameterization of air-sea fluxes for Tropical Ocean-Global Atmosphere Coupled-Ocean Atmosphere Response Experiment, J. Geophys. Res., 101, 3747–3764, https://doi.org/10.1029/95JC03205, 1996. a
Fissel, B., Dalton, M., Garber-Yonts, B., Haynie, A., Kasperski, S., Lee, J.,
Lew, D., Lavoie, A., Seung, C., Sparks, K., and Wise, S.: Stock Assessment
and Fishery Evaluation Report for the Groundfish Fisheries of the Gulf of
Alaska and Bering Sea/Aleutian Islands Area: Economic status of the groundfish fisheries off Alaska, 2016, Stock Assessment and Fishery Evaluation Report, North Pacific Fishery Management Council, Anchorage, 2017. a
Friedrichs, M. A., Dusenberry, J. A., Anderson, L. A., Armstrong, R. A., Chai, F., Christian, J. R., Doney, S. C., Dunne, J., Fujii, M., Hood, R.,
McGillicuddy, D. J., Moore, J. K., Schartau, M., Spitz, Y. H., and Wiggert,
J. D.: Assessment of skill and portability in regional marine biogeochemical
models: Role of multiple planktonic groups, J. Geophys. Res.-Oceans, 112, 1–22, https://doi.org/10.1029/2006JC003852, 2007. a
Frost, B. W.: Grazing control of phytoplankton stock in the open subarctic
Pacific Ocean: a model assessing the role of mesozooplankton, particularly
the large calanoid copepods Neocalanus spp., Mar. Ecol. Prog. Ser., 39, 49–68, 1987. a
Frost, B. W. and Franzen, N. C.: Grazing and iron limitation in the control of phytoplankton stock and nutrient concentration: a chemostat analogue of the Pacific equatorial upwelling zone, Marine Ecol. Prog. Ser., 83, 291–303, https://doi.org/10.3354/meps083291, 1992. a, b
Gibson, G. A. and Spitz, Y. H.: Impacts of biological parameterization, initial conditions, and environmental forcing on parameter sensitivity and
uncertainty in a marine ecosystem model for the Bering Sea, J. Mar. Syst., 88, 214–231, https://doi.org/10.1016/j.jmarsys.2011.04.008, 2011. a, b, c, d, e, f, g, h, i
Glibert, P. M., Wilkerson, F. P., Dugdale, R. C., Raven, J. A., Dupont, C. L., Leavitt, P. R., Parker, A. E., Burkholder, J. M., and Kana, T. M.: Pluses
and minuses of ammonium and nitrate uptake and assimilation by phytoplankton
and implications for productivity and community composition, with emphasis on
nitrogen-enriched conditions, Limnol. Oceanogr., 61, 165–197, https://doi.org/10.1002/lno.10203, 2016. a
Haidvogel, D. B., Arango, H., Budgell, W. P., Cornuelle, B. D., Curchitser, E., Di Lorenzo, E., Fennel, K., Geyer, W. R., Hermann, A. J., Lanerolle, L.,
Levin, J., McWilliams, J. C., Miller, A. J., Moore, A. M., Powell, T. M.,
Shchepetkin, A. F., Sherwood, C. R., Signell, R. P., Warner, J. C., and
Wilkin, J.: Ocean forecasting in terrain-following coordinates: Formulation
and skill assessment of the Regional Ocean Modeling System, J. Comput. Phys., 227, 3595–3624, https://doi.org/10.1016/j.jcp.2007.06.016, 2008. a
Harvey, H. R., Pleuthner, R. L., Lessard, E. J., Bernhardt, M. J., and
Tracy Shaw, C.: Physical and biochemical properties of the euphausiids
Thysanoessa inermis, Thysanoessa raschii, and Thysanoessa longipes in the
eastern Bering Sea, Deep-Sea Res. Pt. II, 65–70, 173–183, https://doi.org/10.1016/j.dsr2.2012.02.007, 2012. a
Hermann, A. J., Gibson, G. A., Bond, N. A., Curchitser, E. N., Hedstrom, K.,
Cheng, W., Wang, M., Stabeno, P. J., Eisner, L., and Cieciel, K. D.: A
multivariate analysis of observed and modeled biophysical variability on the
Bering Sea shelf: Multidecadal hindcasts (1970–2009) and forecasts (2010–2040), Deep-Sea Res. Pt. II, 94, 121–139, https://doi.org/10.1016/j.dsr2.2013.04.007, 2013. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Hermann, A. J., Curchitser, E. N., Hedstrom, K., Cheng, W., Bond, N. A., Wang, M., Aydin, K., Stabeno, P. J., Cokelet, E. D., and Gibson, G. A.: Projected future biophysical states of the Bering Sea, Deep-Sea Res. Pt. II,
134, 30–47, https://doi.org/10.1016/j.dsr2.2015.11.001, 2016. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Hermann, A. J., Gibson, G. A., Cheng, W., Ortiz, I., Aydin, K., Wang, M.,
Hollowed, A. B., and Holsman, K. K.: Projected biophysical conditions of the
Bering Sea to 2100 under multiple emission scenarios, ICES J. Mar. Sci., 76, 1280–1304, https://doi.org/10.1093/icesjms/fsz043, 2019. a, b
Howell-Kübler, A. N., Lessard, E. J., and Napp, J. M.: Springtime
microprotozoan abundance and biomass in the southeastern Bering Sea and
Shelikof Strait, Alaska, J. Plank. Res., 18, 731–745, https://doi.org/10.1093/plankt/18.5.731, 1996. a
Hunt, G. L., Coyle, K. O., Eisner, L., Farley, E. V., Heintz, R., Mueter, F.,
Napp, J. M., Overland, J. E., Ressler, P. H., Sale, S., and Stabeno, P. J.:
Climate impacts on eastern Bering Sea food webs: A synthesis of new data and
an assessment of the Oscillating Control Hypothesis, ICES J. Mar. Sci., 68, 1230–1243, https://doi.org/10.1093/icesjms/fsr036, 2010. a, b, c
Hunt, G. L., Ressler, P. H., Gibson, G. A., De Robertis, A., Aydin, K., Sigler, M. F., Ortiz, I., Lessard, E. J., Williams, B. C., Pinchuk, A., and Buckley, T.: Euphausiids in the eastern Bering Sea: A synthesis of recent studies of euphausiid production, consumption and population control, Deep-Sea Res. Pt. II, 134, 204–222, https://doi.org/10.1016/j.dsr2.2015.10.007, 2016. a, b, c, d
Jassby, A D. and Platt, T.: Mathematical formulation of the relatioship
between photosynthesis and light for phytoplankton, Limnol. Oceanogr., 21, 540–547, 1976. a
Kachel, N. B., Hunt, G. L., Salo, S. A., Schumacher, J. D., Stabeno, P. J., and Whitledge, T. E.: Characteristics and variability of the inner front of the southeastern Bering Sea, Deep-Sea Res. Pt. II, 49, 5889–5909, https://doi.org/10.1016/S0967-0645(02)00324-7, 2002. a, b
Kawamiya, M., Kishi, M. J., and Suginohara, N.: An ecosystem model for the
North Pacific embedded in a general circulation model. Part I: Model
description and characteristics of spatial distributions of biological
variables, J. Mar. Syst., 25, 129–157, https://doi.org/10.1016/S0924-7963(00)00012-9, 2000. a
Kearney, K. A.: Freshwater Input to the Bering Sea , 1950–2017, Tech.
Rep. NMFS-AFSC-388, NOAA Tech. Memo., US Department of Commerce, Seattle, WA, 2019. a
Kearney, K., Hermann, A., Pilcher, D., Ortiz, I., Aydin, K., and Gibson, G.: beringnpz/roms-bering-sea: the Bering Sea ROMS model (version 2019.08.23), https://doi.org/10.5281/zenodo.3376314, 2019. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air–sea flux data set, Clim. Dynam., 33, 341–364, 2009. a
Lauth, R. R., Dawson, E. J., and Conner, J.: Results of the 2010 Eastern and
Northern Bering Sea Continental Shelf Bottom Trawl Survey of Groundfish and
Invertebrate Fauna, National Marine Fisheries Service, Seattle, WA, 1–265, 2011. a
Leblanc, K., Hare, C. E., Boyd, P. W., Bruland, K. W., Sohst, B., Pickmere, S., Lohan, M. C., Buck, K., Ellwood, M., and Hutchins, D. A.: Fe and Zn effects on the Si cycle and diatom community structure in two contrasting high and low-silicate HNLC areas, Deep-Sea Res. Pt. I, 52, 1842–1864, https://doi.org/10.1016/j.dsr.2005.06.005, 2005. a
Livingston, P. A., Aydin, K., Buckley, T. W., Lang, G. M., Yang, M. S., and
Miller, B. S.: Quantifying food web interactions in the North Pacific – a
data-based approach, Environ. Biol. Fish., 100, 443–470,
https://doi.org/10.1007/s10641-017-0587-0, 2017. a
Lomas, M. W. and Glibert, P. M.: Interactions between
Marchesiello, P., McWilliams, J. C., and Shchepetkin, A.: Open boundary
conditions for long-term integration of regional oceanic models, Ocean Model., 3, 1–20, https://doi.org/10.1016/S1463-5003(00)00013-5, 2001. a
Mizobata, K. and Saitoh, S. I.: Variability of Bering Sea eddies and primary
productivity along the shelf edge during 1998–2000 using satellite
multisensor remote sensing, J. Mar. Syst., 50, 101–111,
https://doi.org/10.1016/j.jmarsys.2003.09.014, 2004. a
Moore, J. K., Doney, S. C., Kleypas, J. A., Glover, D. M., and Fung, I. Y.: An intermediate complexity marine ecosystem model for the global domain,
Deep-Sea Res. Pt. II, 49, 403–462, https://doi.org/10.1016/S0967-0645(01)00108-4, 2002. a
Morel, A.: Optical modeling of the upper ocean in relation to its biogenous
matter content (case I waters), J. Geophys. Res., 93, 10749, https://doi.org/10.1029/JC093iC09p10749, 1988. a, b, c
Morel, A., Gentili, B., Claustre, H., Babin, M., Bricaud, A., Ras, J., and
Tieche, F.: Optical properties of the clearest natural waters, Limnol.
Oceanogr., 52, 217–229, 2007. a
Mueter, F. J. and Litzow, M. A.: Sea ice retreat alters the biogeography of
the Bering Sea continental shelf, Ecol. Appl., 18, 309–320,
https://doi.org/10.1890/07-0564.1, 2008. a
Mueter, F. J., Bond, N. A., Ianelli, J. N., and Hollowed, A. B.: Expected
declines in recruitment of walleye pollock (Theragra chalcogramma) in the
eastern Bering Sea under future climate change, ICES J. Mar. Sci., 68, 1284–1296, https://doi.org/10.1093/icesjms/fsr022, 2011. a
NASA Ocean Biology Processing Group: Moderate-resolution Imaging
Spectroradiometer (MODIS) Aqua Chlorophyll Data; 2018 Reprocessing, NASA
OB.DAAC, Greenbelt, MD, USA, https://doi.org/10.5067/aqua/modis/l3m/chl/2018, 2017. a
National Marine Fisheries Service: Fisheries Economics of the United States,
2015, NOAA Tech. Memo., US Dept. of Commerce, Silver Spring, MD, 2017. a
Niebauer, H. J., Alexander, V., and Henrichs, S. M.: A time-series study of
the spring bloom at the Bering Sea ice edge I. Physical processes, chlorophyll and nutrient chemistry, Cont. Shelf Res., 15, 1859–1877, https://doi.org/10.1016/0278-4343(94)00097-7, 1995. a
Okkonen, S. R., Schmidt, G. M., Cokelet, E. D., and Stabeno, P. J.: Satellite
and hydrographic observations of the Bering Sea `Green Belt', Deep-Sea Res. Pt. II, 51, 1033–1051, https://doi.org/10.1016/j.dsr2.2003.08.005, 2004. a, b
Olson, M. B. and Strom, S. L.: Phytoplankton growth, microzooplankton
herbivory and community structure in the southeast Bering Sea: Insight into
the formation and temporal persistence of an Emiliania huxleyi bloom, Deep-Sea Res. Pt. II, 49, 5969–5990, https://doi.org/10.1016/S0967-0645(02)00329-6, 2002. a
Ortiz, I., Aydin, K., Hermann, A. J., Gibson, G. A., Punt, A. E., Wiese, F. K., Eisner, L. B., Ferm, N., Buckley, T. W., Moffitt, E. A., Ianelli, J. N.,
Murphy, J., Dalton, M., Cheng, W., Wang, M., Hedstrom, K., Bond, N. A.,
Curchitser, E. N., and Boyd, C.: Climate to fish: Synthesizing field work,
data and models in a 39-year retrospective analysis of seasonal processes on
the eastern Bering Sea shelf and slope, Deep-Sea Res. Pt. II, 134, 390–412, https://doi.org/10.1016/j.dsr2.2016.07.009, 2016. a, b, c, d, e
Overland, J. E. and Pease, C. H.: Modeling ice dynamics of coastal seas, J. Geophys. Res.-Oceans, 93, 15619–15637, https://doi.org/10.1029/JC093iC12p15619, 1988. a
Paulson, C. A. and Simpson, J. J.: Irradiance Measurements in the Upper Ocean, J. Phys. Oceanogr., 7, 952–956,
https://doi.org/10.1175/1520-0485(1977)007<0952:IMITUO>2.0.CO;2, 1977. a, b, c, d
Pilcher, D. J., Naiman, D. M., Cross, J. N., Hermann, A. J., Siedlecki, S. A., Gibson, G. A., and Mathis, J. T.: Modeled Effect of Coastal Biogeochemical Processes, Climate Variability, and Ocean Acidification on Aragonite Saturation State in the Bering Sea, Front. Mar. Sci., 5, 1–18,
https://doi.org/10.3389/fmars.2018.00508, 2019. a
Rho, T. K. and Whitledge, T. E.: Characteristics of seasonal and spatial
variations of primary production over the southeastern Bering Sea shelf,
Cont. Shelf Res., 27, 2556–2569, https://doi.org/10.1016/j.csr.2007.07.006, 2007. a
Richar, J. I., Kruse, G. H., Curchitser, E., and Hermann, A. J.: Patterns in
connectivity and retention of simulated Tanner crab (Chionoecetes bairdi)
larvae in the eastern Bering Sea, Prog. Oceanogr., 138, 475–485,
https://doi.org/10.1016/j.pocean.2014.08.001, 2015. a
Ryabchenko, V. A., Fasham, M. J., Kagan, B. A., and Popova, E. E.: What causes short-term oscillations in ecosystem models of the ocean mixed layer?,
J. Mar. Syst., 13, 33–50, https://doi.org/10.1016/S0924-7963(96)00110-8, 1997. a
Saha, S., Moorthi, S., Pan, H. L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y. T., Chuang, H. Y., Juang, H. M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., Van Den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J. K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C. Z.,
Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., and
Goldberg, M.: The NCEP climate forecast system reanalysis, B. Am. Meteorol. Soc., 91, 1015–1057, https://doi.org/10.1175/2010BAMS3001.1, 2010. a
Sambrotto, R. N., Niebauer, H. J., Goering, J. J., and Iverson, R. L.:
Relationships among vertical mixing, nitrate uptake, and phytoplankton growth during the spring bloom in the southeast Bering Sea middle shelf, Cont. Shelf Res., 5, 161–198, https://doi.org/10.1016/0278-4343(86)90014-2, 1986. a, b, c
Schumacher, J. D., Kinder, T. H., Pashinski, D. J., and Charnell, R. L.: A
Structural Front Over the Continental Shelf of the Eastern Bering Sea, J. Phys. Oceanogr., 9, 79–87,
https://doi.org/10.1175/1520-0485(1979)009<0079:ASFOTC>2.0.CO;2, 1979. a
Shchepetkin, A. F.: A method for computing horizontal pressure-gradient force
in an oceanic model with a nonaligned vertical coordinate, J. Geophys. Res., 108, 1–34, https://doi.org/10.1029/2001jc001047, 2003. a
Shchepetkin, A. F. and McWilliams, J. C.: The regional oceanic modeling system (ROMS: A split-explicit, free-surface, topography-following-coordinate
oceanic model, Ocean Model., 9, 347–404, https://doi.org/10.1016/j.ocemod.2004.08.002, 2005. a
Siddon, E. and Zador, S.: Ecosystem Status Report 2018 Eastern Bering Sea,
North Pacific Fisheries Managment Council, Seattle, WA, available at: https://access.afsc.noaa.gov/reem/ecoweb/pdf/2018ecosysEBS-508.pdf (last access: June 2019), 2018. a
Sigler, M. F., Harvey, H. R., Ashjian, C. J., Lomas, M. W., Napp, J. M.,
Stabeno, P. J., and Van Pelt, T. I.: How Does Climate Change Affect the Bering Sea Ecosystem?, Eos Trans. AGU, 91, 457–458, 2010. a
Sigler, M. F., Stabeno, P. J., Eisner, L. B., Napp, J. M., and Mueter, F. J.:
Spring and fall phytoplankton blooms in a productive subarctic ecosystem,
the eastern Bering Sea, during 1995–2011, Deep-Sea Res. Pt. II, 109, 71–83, https://doi.org/10.1016/j.dsr2.2013.12.007, 2014. a
Sigler, M. F., Napp, J. M., Stabeno, P. J., Heintz, R. A., Lomas, M. W., and
Hunt, G. L.: Variation in annual production of copepods, euphausiids, and
juvenile walleye pollock in the southeastern Bering Sea, Deep-Sea Res. Pt. II, 134, 223–234, https://doi.org/10.1016/j.dsr2.2016.01.003, 2016. a
Simpson, J. H., Hughes, D. G., and Morris, N. C. G.: The relation of seasonal
stratification to tidal mixing on the continental shelf, in: A voyage of
discovery, George Deacon 70th anniversary volume, edited by: Angel, M.,
Pergamon Press, 327–340, ISBN-10: 0080213804, ISBN-13: 978-0080213804, 1977. a
Sloughter, T. M., Banas, N. S., and Sambrotto, R. N.: Seasonal variation in
light response of polar phytoplankton, J. Mar. Syst., 191, 64–75, https://doi.org/10.1016/j.jmarsys.2018.12.003, 2019. a
Springer, A. M., McRoy, C. P., and Flint, M. V.: The Bering Sea Green Belt:
shelf-edge processes and ecosystem production, Fish. Oceanogr., 5, 205–223, https://doi.org/10.1111/j.1365-2419.1996.tb00118.x, 1996. a, b, c, d
Stabeno, P. J., Bond, N. A., Kachel, N. B., Salo, S. A., and Schumacher, J. D.: On the temporal variability of the physical environment over the
south-eastern Bering Sea, Fish. Oceanogr., 10, 81–98,
https://doi.org/10.1046/j.1365-2419.2001.00157.x, 2001. a, b, c, d
Stabeno, P. J., Kachel, N., Mordy, C., Righi, D., and Salo, S.: An examination of the physical variability around the Pribilof Islands in 2004, Deep-Sea Res. Pt. II, 55, 1701–1716, https://doi.org/10.1016/j.dsr2.2008.03.006, 2008. a
Stabeno, P. J., Farley, E. V., Kachel, N. B., Moore, S., Mordy, C. W., Napp,
J. M., Overland, J. E., Pinchuk, A. I., and Sigler, M. F.: A comparison of
the physics of the northern and southern shelves of the eastern Bering Sea
and some implications for the ecosystem, Deep-Sea Res. Pt. II, 65–70, 14–30, https://doi.org/10.1016/j.dsr2.2012.02.019, 2012. a, b, c
Stabeno, P. J., Danielson, S. L., Kachel, D. G., Kachel, N. B., and Mordy, C. W.: Currents and transport on the Eastern Bering Sea shelf: An integration of over 20 years of data, Deep-Sea Res. Pt. II, 134, 13–29, https://doi.org/10.1016/j.dsr2.2016.05.010, 2016. a
Stow, C. A., Jolliff, J., McGillicuddy, D. J., Doney, S. C., Allen, J. I.,
Friedrichs, M. A. M., Rose, K. A., and Wallhead, P.: Skill assessment for
coupled biological/physical models of marine systems, J. Mar. Syst., 76, 4–15, https://doi.org/10.1016/j.jmarsys.2008.03.011, 2009. a
Suzuki, K., Liu, H., Saino, T., Obata, H., Takano, M., Okamura, K., Sohrin, Y., and Fujishima, Y.: East-west gradients in the photosynthetic potential of
phytoplankton and iron concentration in the subarctic Pacific Ocean during
early summer, Limnol. Oceanogr., 47, 1581–1594, https://doi.org/10.4319/lo.2002.47.6.1581, 2002. a
Tedesco, L. and Vichi, M.: Sea ice biogeochemistry: A guide for modellers,
PLoS ONE, 9, e89217, https://doi.org/10.1371/journal.pone.0089217, 2014. a
Thimijan, R. W. and Heins, R. D.: Photometric, radiometric, and quantum light
units of measure: a review of procedures for interconversion, HortScience,
18, 818–822, 1983. a
Uye, S. and Shimauchi, H.: Population biomass, feeding, respiration and growth rates, and carbon budget of the scyphomedusa Aurelia aurita in the Inland Sea of Japan, J. Plankt. Res., 27, 237–248, https://doi.org/10.1093/plankt/fbh172, 2005. a
Vidal, J. and Smith, S. L.: Biomass, growth, and development of populations of herbivorous zooplankton in the southeastern Bering Sea during spring, Deep-Sea Res. Pt. A, 33, 523–556, https://doi.org/10.1016/0198-0149(86)90129-9, 1986.
a
Walsh, J. J. and McRoy, C. P.: Ecosystem analysis in the southeastern Bering
Sea, Cont. Shelf Res., 5, 259–288, 1986. a
Walsh, J. J., Rowe, G. T., Iverson, R. L., and McRoy, C. P.: Biological export of shelf carbon is a sink of the global CO2 cycle, Nature, 291, 197–201, 1981. a
Wang, S., Tedesco, M., Xu, M., and Alexander, P. M.: Mapping Ice Algal Blooms
in Southwest Greenland From Space, Geophys. Res. Lett., 45, 11779–11788, https://doi.org/10.1029/2018GL080455, 2018. a
Warner, J. C., Sherwood, C. R., Signell, R. P., Harris, C. K., and Arango, H. G.: Development of a three-dimensional, regional, coupled wave, current, and sediment-transport model, Comput. Geosci., 34, 1284–1306,
https://doi.org/10.1016/j.cageo.2008.02.012, 2008. a
Whitledge, T. E., Reeburgh, W. S., and Walsh, J. J.: Seasonal inorganic nitrogen distributions and dynamics in the southeastern Bering Sea, Cont. Shelf Res., 5, 109–132, https://doi.org/10.1016/0278-4343(86)90012-9, 1986. a, b
Wilderbuer, T., Duffy-Anderson, J. T., Stabeno, P., and Hermann, A.: Differential patterns of divergence in ocean drifters: Implications for
larval flatfish advection and recruitment, J. Sea Res., 111, 11–24, https://doi.org/10.1016/j.seares.2016.03.003, 2016. a
Woodgate, R. A. and Aagaard, K.: Revising the Bering Strait freshwater flux
into the Arctic Ocean, Geophys. Res. Lett., 32, 1–4, https://doi.org/10.1029/2004GL021747, 2005. a
Short summary
We describe an ecosystem model for the Bering Sea. Biological components in the Bering Sea can be found in the water column, on and within the bottom sediments, and within the porous lower layer of seasonal sea ice. This model simulates the exchange of material between nutrients and plankton within all three environments. Here, we thoroughly document the model and assess its skill in capturing key biophysical features across the Bering Sea.
We describe an ecosystem model for the Bering Sea. Biological components in the Bering Sea can...
