Articles | Volume 13, issue 7
https://doi.org/10.5194/gmd-13-3119-2020
© 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-13-3119-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
10 Jul 2020
Model evaluation paper |
| 10 Jul 2020
| 10 Jul 2020
Marine biogeochemical cycling and oceanic CO2 uptake simulated by the NUIST Earth System Model version 3 (NESM v3)
Yifei Dai
Earth System Modeling Center, and Key Laboratory of Meteorological
Disaster of Ministry of Education, Nanjing University of Information Science
and Technology, Nanjing 210044, China
China–US joint Atmosphere–Ocean Research Center and International
Pacific Research Center, University of Hawaii at Manoa, Honolulu, HI 96822,
USA
Long Cao
CORRESPONDING AUTHOR
Key Laboratory of GeoScience Big Data and Deep Resource of Zhejiang
Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027,
China
Bin Wang
Earth System Modeling Center, and Key Laboratory of Meteorological
Disaster of Ministry of Education, Nanjing University of Information Science
and Technology, Nanjing 210044, China
China–US joint Atmosphere–Ocean Research Center and International
Pacific Research Center, University of Hawaii at Manoa, Honolulu, HI 96822,
USA
Related authors
No articles found.
Abdul Malik, Peer J. Nowack, Joanna D. Haigh, Long Cao, Luqman Atique, and Yves Plancherel
Atmos. Chem. Phys., 20, 15461–15485, https://doi.org/10.5194/acp-20-15461-2020, https://doi.org/10.5194/acp-20-15461-2020, 2020
Short summary
Short summary
Solar geoengineering has been introduced to mitigate human-caused global warming by reflecting sunlight back into space. This research investigates the impact of solar geoengineering on the tropical Pacific climate. We find that solar geoengineering can compensate some of the greenhouse-induced changes in the tropical Pacific but not all. In particular, solar geoengineering will result in significant changes in rainfall, sea surface temperatures, and increased frequency of extreme ENSO events.
Krishna-Pillai Sukumara-Pillai Krishnamohan, Govindasamy Bala, Long Cao, Lei Duan, and Ken Caldeira
Earth Syst. Dynam., 10, 885–900, https://doi.org/10.5194/esd-10-885-2019, https://doi.org/10.5194/esd-10-885-2019, 2019
Short summary
Short summary
We find that sulfate aerosols are more effective in cooling the climate system when they reside higher in the stratosphere. We explain this sensitivity in terms of radiative forcing at the top of the atmosphere. Sulfate aerosols heat the stratospheric layers, causing an increase in stratospheric water vapor content and a reduction in high clouds. These changes are larger when aerosols are prescribed near the tropopause, offsetting part of the aerosol-induced negative radiative forcing/cooling.
Mi Yan, Bin Wang, Jian Liu, Axing Zhu, Liang Ning, and Jian Cao
Clim. Past, 14, 2037–2052, https://doi.org/10.5194/cp-14-2037-2018, https://doi.org/10.5194/cp-14-2037-2018, 2018
Jian Cao, Bin Wang, Young-Min Yang, Libin Ma, Juan Li, Bo Sun, Yan Bao, Jie He, Xiao Zhou, and Liguang Wu
Geosci. Model Dev., 11, 2975–2993, https://doi.org/10.5194/gmd-11-2975-2018, https://doi.org/10.5194/gmd-11-2975-2018, 2018
Short summary
Short summary
The development of version 3 of the Nanjing University of Information Science and Technology (NUIST) Earth System Model (NESM v3) aims at building up a comprehensive numerical modeling laboratory for multidisciplinary studies of the climate system and Earth system. The model evaluation shows the model obtained stable long-term integrations and reasonable global mean states under preindustrial (PI) forcing and simulated reasonable climate responses to transient and abrupt CO2 forcing.
Yifei Dai, Long Cao, and Bin Wang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-68, https://doi.org/10.5194/gmd-2018-68, 2018
Revised manuscript not accepted
Short summary
Short summary
NESM-2.0.1 is one of the few models from China that present the ocean carbon cycle simulations. Our results demonstrate that NESM-2.0.1 does a reasonable job in simulating current-day marine ecosystems and oceanic CO2 uptake. The model also can be used as a useful tool in the investigation of feedback interactions between the ocean carbon cycle, atmospheric CO2, and climate change.
Related subject area
Biogeosciences
Ocean biogeochemistry in the coupled ocean–sea ice–biogeochemistry model FESOM2.1–REcoM3
Forcing the Global Fire Emissions Database burned-area dataset into the Community Land Model version 5.0: impacts on carbon and water fluxes at high latitudes
Modeling of non-structural carbohydrate dynamics by the spatially explicit individual-based dynamic global vegetation model SEIB-DGVM (SEIB-DGVM-NSC version 1.0)
MEDFATE 2.9.3: a trait-enabled model to simulate Mediterranean forest function and dynamics at regional scales
Modelling the role of livestock grazing in C and N cycling in grasslands with LPJmL5.0-grazing
Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage
The Permafrost and Organic LayEr module for Forest Models (POLE-FM) 1.0
CompLaB v1.0: a scalable pore-scale model for flow, biogeochemistry, microbial metabolism, and biofilm dynamics
Validation of a new spatially explicit process-based model (HETEROFOR) to simulate structurally and compositionally complex forest stands in eastern North America
Improving nitrogen cycling in a land surface model (CLM5) to quantify soil N2O, NO and NH3 emissions from enhanced rock weathering with croplands
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)
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model
ForamEcoGEnIE 2.0: incorporating symbiosis and spine traits into a trait-based global planktic foraminiferal model
FABM-NflexPD 2.0: testing an instantaneous acclimation approach for modeling the implications of phytoplankton eco-physiology for the carbon and nutrient cycles
SedTrace 1.0: a Julia-based framework for generating and running reactive-transport models of marine sediment diagenesis specializing in trace elements and isotopes
Evaluating the vegetation–atmosphere coupling strength of ORCHIDEE land surface model (v7266)
Non-Redfieldian carbon model for the Baltic Sea (ERGOM version 1.2) – implementation and budget estimates
Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1
Simulating long-term responses of soil organic matter turnover to substrate stoichiometry by abstracting fast and small-scale microbial processes: the Soil Enzyme Steady Allocation Model (SESAM; v3.0)
Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)
Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment
Matrix representation of lateral soil movements: scaling and calibrating CE-DYNAM (v2) at a continental level
CANOPS-GRB v1.0: a new Earth system model for simulating the evolution of ocean–atmosphere chemistry over geologic timescales
Low sensitivity of three terrestrial biosphere models to soil texture over the South American tropics
FESDIA (v1.0): exploring temporal variations of sediment biogeochemistry under the influence of flood events using numerical modelling
Impact of changes in climate and CO2 on the carbon storage potential of vegetation under limited water availability using SEIB-DGVM version 3.02
FORCCHN V2.0: an individual-based model for predicting multiscale forest carbon dynamics
Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)
Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon
SurEau-Ecos v2.0: a trait-based plant hydraulics model for simulations of plant water status and drought-induced mortality at the ecosystem level
Improved representation of plant physiology in the JULES-vn5.6 land surface model: photosynthesis, stomatal conductance and thermal acclimation
Representation of the phosphorus cycle in the Joint UK Land Environment Simulator (vn5.5_JULES-CNP)
CLM5-FruitTree: a new sub-model for deciduous fruit trees in the Community Land Model (CLM5)
The impact of hurricane disturbances on a tropical forest: implementing a palm plant functional type and hurricane disturbance module in ED2-HuDi V1.0
A validation standard for area of habitat maps for terrestrial birds and mammals
Soil Cycles of Elements simulator for Predicting TERrestrial regulation of greenhouse gases: SCEPTER v0.9
Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)
A map of global peatland extent created using machine learning (Peat-ML)
Implementation and evaluation of the unified stomatal optimization approach in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)
ECOSMO II(CHL): a marine biogeochemical model for the North Atlantic and the Arctic
Water Ecosystems Tool (WET) 1.0 – a new generation of flexible aquatic ecosystem model
Development of an open-source regional data assimilation system in PEcAn v. 1.7.2: application to carbon cycle reanalysis across the contiguous US using SIPNET
Predicting global terrestrial biomes with the LeNet convolutional neural network
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
Assessing methane emissions for northern peatlands in ORCHIDEE-PEAT revision 7020
A dynamic local-scale vegetation model for lycopsids (LYCOm v1.0)
Soil-related developments of the Biome-BGCMuSo v6.2 terrestrial ecosystem model
Global evaluation of the Ecosystem Demography model (ED v3.0)
A new snow module improves predictions of the isotope-enabled MAIDENiso forest growth model
Calibrating the soil organic carbon model Yasso20 with multiple datasets
Özgür Gürses, Laurent Oziel, Onur Karakuş, Dmitry Sidorenko, Christoph Völker, Ying Ye, Moritz Zeising, Martin Butzin, and Judith Hauck
Geosci. Model Dev., 16, 4883–4936, https://doi.org/10.5194/gmd-16-4883-2023, https://doi.org/10.5194/gmd-16-4883-2023, 2023
Short summary
Short summary
This paper assesses the biogeochemical model REcoM3 coupled to the ocean–sea ice model FESOM2.1. The model can be used to simulate the carbon uptake or release of the ocean on timescales of several hundred years. A detailed analysis of the nutrients, ocean productivity, and ecosystem is followed by the carbon cycle. The main conclusion is that the model performs well when simulating the observed mean biogeochemical state and variability and is comparable to other ocean–biogeochemical models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 16, 4699–4713, https://doi.org/10.5194/gmd-16-4699-2023, https://doi.org/10.5194/gmd-16-4699-2023, 2023
Short summary
Short summary
Wildfire is a crucial factor in carbon and water fluxes on the Earth system. About 2.1 Pg of carbon is released into the atmosphere by wildfires annually. Because the fire processes are still limitedly represented in land surface models, we forced the daily GFED4 burned area into the land surface model over Alaska and Siberia. The results with the GFED4 burned area significantly improved the simulated carbon emissions and net ecosystem exchange compared to the default simulation.
Hideki Ninomiya, Tomomichi Kato, Lea Végh, and Lan Wu
Geosci. Model Dev., 16, 4155–4170, https://doi.org/10.5194/gmd-16-4155-2023, https://doi.org/10.5194/gmd-16-4155-2023, 2023
Short summary
Short summary
Non-structural carbohydrates (NSCs) play a crucial role in plants to counteract the effects of climate change. We added a new NSC module into the SEIB-DGVM, an individual-based ecosystem model. The simulated NSC levels and their seasonal patterns show a strong agreement with observed NSC data at both point and global scales. The model can be used to simulate the biotic effects resulting from insufficient NSCs, which are otherwise difficult to measure in terrestrial ecosystems globally.
Miquel De Cáceres, Roberto Molowny-Horas, Antoine Cabon, Jordi Martínez-Vilalta, Maurizio Mencuccini, Raúl García-Valdés, Daniel Nadal-Sala, Santiago Sabaté, Nicolas Martin-StPaul, Xavier Morin, Francesco D'Adamo, Enric Batllori, and Aitor Améztegui
Geosci. Model Dev., 16, 3165–3201, https://doi.org/10.5194/gmd-16-3165-2023, https://doi.org/10.5194/gmd-16-3165-2023, 2023
Short summary
Short summary
Regional-level applications of dynamic vegetation models are challenging because they need to accommodate the variation in plant functional diversity. This can be done by estimating parameters from available plant trait databases while adopting alternative solutions for missing data. Here we present the design, parameterization and evaluation of MEDFATE (version 2.9.3), a novel model of forest dynamics for its application over a region in the western Mediterranean Basin.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Yimian Ma, Xu Yue, Stephen Sitch, Nadine Unger, Johan Uddling, Lina M. Mercado, Cheng Gong, Zhaozhong Feng, Huiyi Yang, Hao Zhou, Chenguang Tian, Yang Cao, Yadong Lei, Alexander W. Cheesman, Yansen Xu, and Maria Carolina Duran Rojas
Geosci. Model Dev., 16, 2261–2276, https://doi.org/10.5194/gmd-16-2261-2023, https://doi.org/10.5194/gmd-16-2261-2023, 2023
Short summary
Short summary
Plants have been found to respond differently to O3, but the variations in the sensitivities have rarely been explained nor fully implemented in large-scale assessment. This study proposes a new O3 damage scheme with leaf mass per area to unify varied sensitivities for all plant species. Our assessment reveals an O3-induced reduction of 4.8 % in global GPP, with the highest reduction of >10 % for cropland, suggesting an emerging risk of crop yield loss under the threat of O3 pollution.
Winslow D. Hansen, Adrianna Foster, Benjamin Gaglioti, Rupert Seidl, and Werner Rammer
Geosci. Model Dev., 16, 2011–2036, https://doi.org/10.5194/gmd-16-2011-2023, https://doi.org/10.5194/gmd-16-2011-2023, 2023
Short summary
Short summary
Permafrost and the thick soil-surface organic layers that insulate permafrost are important controls of boreal forest dynamics and carbon cycling. However, both are rarely included in process-based vegetation models used to simulate future ecosystem trajectories. To address this challenge, we developed a computationally efficient permafrost and soil organic layer module that operates at fine spatial (1 ha) and temporal (daily) resolutions.
Heewon Jung, Hyun-Seob Song, and Christof Meile
Geosci. Model Dev., 16, 1683–1696, https://doi.org/10.5194/gmd-16-1683-2023, https://doi.org/10.5194/gmd-16-1683-2023, 2023
Short summary
Short summary
Microbial activity responsible for many chemical transformations depends on environmental conditions. These can vary locally, e.g., between poorly connected pores in porous media. We present a modeling framework that resolves such small spatial scales explicitly, accounts for feedback between transport and biogeochemical conditions, and can integrate state-of-the-art representations of microbes in a computationally efficient way, making it broadly applicable in science and engineering use cases.
Arthur Guignabert, Quentin Ponette, Frédéric André, Christian Messier, Philippe Nolet, and Mathieu Jonard
Geosci. Model Dev., 16, 1661–1682, https://doi.org/10.5194/gmd-16-1661-2023, https://doi.org/10.5194/gmd-16-1661-2023, 2023
Short summary
Short summary
Spatially explicit and process-based models are useful to test innovative forestry practices under changing and uncertain conditions. However, their larger use is often limited by the restricted range of species and stand structures they can reliably account for. We therefore calibrated and evaluated such a model, HETEROFOR, for 23 species across southern Québec. Our results showed that the model is robust and can predict accurately both individual tree growth and stand dynamics in this region.
Maria Val Martin, Elena Blanc-Betes, Ka Ming Fung, Euripides P. Kantzas, Ilsa B. Kantola, Isabella Chiaravalloti, Lyla T. 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. Discuss., https://doi.org/10.5194/gmd-2023-47, https://doi.org/10.5194/gmd-2023-47, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Enhanced rock weathering (ERW) is a CO2 removal strategy that involves applying crushed rocks (eg, basalt) to agricultural soils. However, unintended processes within the N cycle due to soil pH changes may affect the climate benefits from C sequestration. ERW could drive changes in the soil emissions of non-CO2 GHGs (N2O), and trace gases (NO & 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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2023-278, https://doi.org/10.5194/egusphere-2023-278, 2023
Short summary
Short summary
This paper introduces a plant hydrodynamic model for the DOE-sponsored dynamic vegetation model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). To better understand this new model system and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We have identified the key parameters that affect the simulated plant hydrodynamics to guide both modeling and field campaign studies.
Maureen Beaudor, Nicolas Vuichard, Juliette Lathière, Nikolaos Evangeliou, Martin Van Damme, Lieven Clarisse, and Didier Hauglustaine
Geosci. Model Dev., 16, 1053–1081, https://doi.org/10.5194/gmd-16-1053-2023, https://doi.org/10.5194/gmd-16-1053-2023, 2023
Short summary
Short summary
Ammonia mainly comes from the agricultural sector, and its volatilization relies on environmental variables. Our approach aims at benefiting from an Earth system model framework to estimate it. By doing so, we represent a consistent spatial distribution of the emissions' response to environmental changes.
We greatly improved the seasonal cycle of emissions compared with previous work. In addition, our model includes natural soil emissions (that are rarely represented in modeling approaches).
Rui Ying, Fanny M. Monteiro, Jamie D. Wilson, and Daniela N. Schmidt
Geosci. Model Dev., 16, 813–832, https://doi.org/10.5194/gmd-16-813-2023, https://doi.org/10.5194/gmd-16-813-2023, 2023
Short summary
Short summary
Planktic foraminifera are marine-calcifying zooplankton; their shells are widely used to measure past temperature and productivity. We developed ForamEcoGEnIE 2.0 to simulate the four subgroups of this organism. We found that the relative abundance distribution agrees with marine sediment core-top data and that carbon export and biomass are close to sediment trap and plankton net observations respectively. This model provides the opportunity to study foraminiferal ecology in any geological era.
Onur Kerimoglu, Markus Pahlow, Prima Anugerahanti, and Sherwood Lan Smith
Geosci. Model Dev., 16, 95–108, https://doi.org/10.5194/gmd-16-95-2023, https://doi.org/10.5194/gmd-16-95-2023, 2023
Short summary
Short summary
In classical models that track the changes in the elemental composition of phytoplankton, additional state variables are required for each element resolved. In this study, we show how the behavior of such an explicit model can be approximated using an
instantaneous acclimationapproach, in which the elemental composition of the phytoplankton is assumed to adjust to an optimal value instantaneously. Through rigorous tests, we evaluate the consistency of this scheme.
Jianghui Du
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-281, https://doi.org/10.5194/gmd-2022-281, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Trace elements and isotopes (TEIs) are important tools to study the changes of 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.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
Short summary
Short summary
There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Thomas Neumann, Hagen Radtke, Bronwyn Cahill, Martin Schmidt, and Gregor Rehder
Geosci. Model Dev., 15, 8473–8540, https://doi.org/10.5194/gmd-15-8473-2022, https://doi.org/10.5194/gmd-15-8473-2022, 2022
Short summary
Short summary
Marine ecosystem models are usually constrained by the elements nitrogen and phosphorus and consider carbon in organic matter in a fixed ratio. Recent observations show a substantial deviation from the simulated carbon cycle variables. In this study, we present a marine ecosystem model for the Baltic Sea which allows for a flexible uptake ratio for carbon, nitrogen, and phosphorus. With this extension, the model reflects much more reasonable variables of the marine carbon cycle.
Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet
Geosci. Model Dev., 15, 8453–8471, https://doi.org/10.5194/gmd-15-8453-2022, https://doi.org/10.5194/gmd-15-8453-2022, 2022
Short summary
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.
Thomas Wutzler, Lin Yu, Marion Schrumpf, and Sönke Zaehle
Geosci. Model Dev., 15, 8377–8393, https://doi.org/10.5194/gmd-15-8377-2022, https://doi.org/10.5194/gmd-15-8377-2022, 2022
Short summary
Short summary
Soil microbes process soil organic matter and affect carbon storage and plant nutrition at the ecosystem scale. We hypothesized that decadal dynamics is constrained by the ratios of elements in litter inputs, microbes, and matter and that microbial community optimizes growth. This allowed the SESAM model to descibe decadal-term carbon sequestration in soils and other biogeochemical processes explicitly accounting for microbial processes but without its problematic fine-scale parameterization.
Ensheng Weng, Igor Aleinov, Ram Singh, Michael J. Puma, Sonali S. McDermid, Nancy Y. Kiang, Maxwell Kelley, Kevin Wilcox, Ray Dybzinski, Caroline E. Farrior, Stephen W. Pacala, and Benjamin I. Cook
Geosci. Model Dev., 15, 8153–8180, https://doi.org/10.5194/gmd-15-8153-2022, https://doi.org/10.5194/gmd-15-8153-2022, 2022
Short summary
Short summary
We develop a demographic vegetation model to improve the representation of terrestrial vegetation dynamics and ecosystem biogeochemical cycles in the Goddard Institute for Space Studies ModelE. The individual-based competition for light and soil resources makes the modeling of eco-evolutionary optimality possible. This model will enable ModelE to simulate long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems at decadal to centurial temporal scales.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
Short summary
Short summary
To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Arthur Nicolaus Fendrich, Philippe Ciais, Emanuele Lugato, Marco Carozzi, Bertrand Guenet, Pasquale Borrelli, Victoria Naipal, Matthew McGrath, Philippe Martin, and Panos Panagos
Geosci. Model Dev., 15, 7835–7857, https://doi.org/10.5194/gmd-15-7835-2022, https://doi.org/10.5194/gmd-15-7835-2022, 2022
Short summary
Short summary
Currently, spatially explicit models for soil carbon stock can simulate the impacts of several changes. However, they do not incorporate the erosion, lateral transport, and deposition (ETD) of soil material. The present work developed ETD formulation, illustrated model calibration and validation for Europe, and presented the results for a depositional site. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models.
Kazumi Ozaki, Devon B. Cole, Christopher T. Reinhard, and Eiichi Tajika
Geosci. Model Dev., 15, 7593–7639, https://doi.org/10.5194/gmd-15-7593-2022, https://doi.org/10.5194/gmd-15-7593-2022, 2022
Short summary
Short summary
A new biogeochemical model (CANOPS-GRB v1.0) for assessing the redox stability and dynamics of the ocean–atmosphere system on geologic timescales has been developed. In this paper, we present a full description of the model and its performance. CANOPS-GRB is a useful tool for understanding the factors regulating atmospheric O2 level and has the potential to greatly refine our current understanding of Earth's oxygenation history.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
Short summary
Short summary
Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Stanley I. Nmor, Eric Viollier, Lucie Pastor, Bruno Lansard, Christophe Rabouille, and Karline Soetaert
Geosci. Model Dev., 15, 7325–7351, https://doi.org/10.5194/gmd-15-7325-2022, https://doi.org/10.5194/gmd-15-7325-2022, 2022
Short summary
Short summary
The coastal marine environment serves as a transition zone in the land–ocean continuum and is susceptible to episodic phenomena such as flash floods, which cause massive organic matter deposition. Here, we present a model of sediment early diagenesis that explicitly describes this type of deposition while also incorporating unique flood deposit characteristics. This model can be used to investigate the temporal evolution of marine sediments following abrupt changes in environmental conditions.
Shanlin Tong, Weiguang Wang, Jie Chen, Chong-Yu Xu, Hisashi Sato, and Guoqing Wang
Geosci. Model Dev., 15, 7075–7098, https://doi.org/10.5194/gmd-15-7075-2022, https://doi.org/10.5194/gmd-15-7075-2022, 2022
Short summary
Short summary
Plant carbon storage potential is central to moderate atmospheric CO2 concentration buildup and mitigation of climate change. There is an ongoing debate about the main driver of carbon storage. To reconcile this discrepancy, we use SEIB-DGVM to investigate the trend and response mechanism of carbon stock fractions among water limitation regions. Results show that the impact of CO2 and temperature on carbon stock depends on water limitation, offering a new perspective on carbon–water coupling.
Jing Fang, Herman H. Shugart, Feng Liu, Xiaodong Yan, Yunkun Song, and Fucheng Lv
Geosci. Model Dev., 15, 6863–6872, https://doi.org/10.5194/gmd-15-6863-2022, https://doi.org/10.5194/gmd-15-6863-2022, 2022
Short summary
Short summary
Our study provided a detailed description and a package of an individual tree-based carbon model, FORCCHN2. This model used non-structural carbohydrate (NSC) pools to couple tree growth and phenology. The model could reproduce daily carbon fluxes across Northern Hemisphere forests. Given the potential importance of the application of this model, there is substantial scope for using FORCCHN2 in fields as diverse as forest ecology, climate change, and carbon estimation.
Johannes Oberpriller, Christine Herschlein, Peter Anthoni, Almut Arneth, Andreas Krause, Anja Rammig, Mats Lindeskog, Stefan Olin, and Florian Hartig
Geosci. Model Dev., 15, 6495–6519, https://doi.org/10.5194/gmd-15-6495-2022, https://doi.org/10.5194/gmd-15-6495-2022, 2022
Short summary
Short summary
Understanding uncertainties of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyzed these across European forests. We find that uncertainties are dominantly induced by parameters related to water, mortality, and climate, with an increasing importance of climate from north to south. These results highlight that climate not only contributes uncertainty but also modifies uncertainties in other ecosystem processes.
Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí
Geosci. Model Dev., 15, 5713–5737, https://doi.org/10.5194/gmd-15-5713-2022, https://doi.org/10.5194/gmd-15-5713-2022, 2022
Short summary
Short summary
This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
Julien Ruffault, François Pimont, Hervé Cochard, Jean-Luc Dupuy, and Nicolas Martin-StPaul
Geosci. Model Dev., 15, 5593–5626, https://doi.org/10.5194/gmd-15-5593-2022, https://doi.org/10.5194/gmd-15-5593-2022, 2022
Short summary
Short summary
A widespread increase in tree mortality has been observed around the globe, and this trend is likely to continue because of ongoing climate change. Here we present SurEau-Ecos, a trait-based plant hydraulic model to predict tree desiccation and mortality. SurEau-Ecos can help determine the areas and ecosystems that are most vulnerable to drying conditions.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
Short summary
Short summary
We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Mahdi André Nakhavali, Lina M. Mercado, Iain P. Hartley, Stephen Sitch, Fernanda V. Cunha, Raffaello di Ponzio, Laynara F. Lugli, Carlos A. Quesada, Kelly M. Andersen, Sarah E. Chadburn, Andy J. Wiltshire, Douglas B. Clark, Gyovanni Ribeiro, Lara Siebert, Anna C. M. Moraes, Jéssica Schmeisk Rosa, Rafael Assis, and José L. Camargo
Geosci. Model Dev., 15, 5241–5269, https://doi.org/10.5194/gmd-15-5241-2022, https://doi.org/10.5194/gmd-15-5241-2022, 2022
Short summary
Short summary
In tropical ecosystems, the availability of rock-derived elements such as P can be very low. Thus, without a representation of P cycling, tropical forest responses to rising atmospheric CO2 conditions in areas such as Amazonia remain highly uncertain. We introduced P dynamics and its interactions with the N and P cycles into the JULES model. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon forest with low-fertility soils.
Olga Dombrowski, Cosimo Brogi, Harrie-Jan Hendricks Franssen, Damiano Zanotelli, and Heye Bogena
Geosci. Model Dev., 15, 5167–5193, https://doi.org/10.5194/gmd-15-5167-2022, https://doi.org/10.5194/gmd-15-5167-2022, 2022
Short summary
Short summary
Soil carbon storage and food production of fruit orchards will be influenced by climate change. However, they lack representation in models that study such processes. We developed and tested a new sub-model, CLM5-FruitTree, that describes growth, biomass distribution, and management practices in orchards. The model satisfactorily predicted yield and exchange of carbon, energy, and water in an apple orchard and can be used to study land surface processes in fruit orchards at different scales.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
Short summary
Short summary
We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Prabhat Raj Dahal, Maria Lumbierres, Stuart H. M. Butchart, Paul F. Donald, and Carlo Rondinini
Geosci. Model Dev., 15, 5093–5105, https://doi.org/10.5194/gmd-15-5093-2022, https://doi.org/10.5194/gmd-15-5093-2022, 2022
Short summary
Short summary
This paper describes the validation of area of habitat (AOH) maps produced for terrestrial birds and mammals. The main objective was to assess the accuracy of the maps based on independent data. We used open access data from repositories, such as ebird and gbif to check if our maps were a better reflection of species' distribution than random. When points were not available we used logistic models to validate the AOH maps. The majority of AOH maps were found to have a high accuracy.
Yoshiki Kanzaki, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 15, 4959–4990, https://doi.org/10.5194/gmd-15-4959-2022, https://doi.org/10.5194/gmd-15-4959-2022, 2022
Short summary
Short summary
Increasing carbon dioxide in the atmosphere is an urgent issue in the coming century. Enhanced rock weathering in soils can be one of the most efficient C capture strategies. On the basis as a weathering simulator, the newly developed SCEPTER model implements bio-mixing by fauna/humans and enables organic matter and crushed rocks/minerals at the soil surface with an option to track their particle size distributions. Those features can be useful for evaluating the carbon capture efficiency.
Félicien Meunier, Sruthi M. Krishna Moorthy, Marc Peaucelle, Kim Calders, Louise Terryn, Wim Verbruggen, Chang Liu, Ninni Saarinen, Niall Origo, Joanne Nightingale, Mathias Disney, Yadvinder Malhi, and Hans Verbeeck
Geosci. Model Dev., 15, 4783–4803, https://doi.org/10.5194/gmd-15-4783-2022, https://doi.org/10.5194/gmd-15-4783-2022, 2022
Short summary
Short summary
We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
Joe R. Melton, Ed Chan, Koreen Millard, Matthew Fortier, R. Scott Winton, Javier M. Martín-López, Hinsby Cadillo-Quiroz, Darren Kidd, and Louis V. Verchot
Geosci. Model Dev., 15, 4709–4738, https://doi.org/10.5194/gmd-15-4709-2022, https://doi.org/10.5194/gmd-15-4709-2022, 2022
Short summary
Short summary
Peat-ML is a high-resolution global peatland extent map generated using machine learning techniques. Peatlands are important in the global carbon and water cycles, but their extent is poorly known. We generated Peat-ML using drivers of peatland formation including climate, soil, geomorphology, and vegetation data, and we train the model with regional peatland maps. Our accuracy estimation approaches suggest Peat-ML is of similar or higher quality than other available peatland mapping products.
Qianyu Li, Shawn P. Serbin, Julien Lamour, Kenneth J. Davidson, Kim S. Ely, and Alistair Rogers
Geosci. Model Dev., 15, 4313–4329, https://doi.org/10.5194/gmd-15-4313-2022, https://doi.org/10.5194/gmd-15-4313-2022, 2022
Short summary
Short summary
Stomatal conductance is the rate of water release from leaves’ pores. We implemented an optimal stomatal conductance model in a vegetation model. We then tested and compared it with the existing empirical model in terms of model responses to key environmental variables. We also evaluated the model with measurements at a tropical forest site. Our study suggests that the parameterization of conductance models and current model response to drought are the critical areas for improving models.
Veli Çağlar Yumruktepe, Annette Samuelsen, and Ute Daewel
Geosci. Model Dev., 15, 3901–3921, https://doi.org/10.5194/gmd-15-3901-2022, https://doi.org/10.5194/gmd-15-3901-2022, 2022
Short summary
Short summary
We describe the coupled bio-physical model ECOSMO II(CHL), which is used for regional configurations for the North Atlantic and the Arctic hind-casting and operational purposes. The model is consistent with the large-scale climatological nutrient settings and is capable of representing regional and seasonal changes, and model primary production agrees with previous measurements. For the users of this model, this paper provides the underlying science, model evaluation and its development.
Nicolas Azaña Schnedler-Meyer, Tobias Kuhlmann Andersen, Fenjuan Rose Schmidt Hu, Karsten Bolding, Anders Nielsen, and Dennis Trolle
Geosci. Model Dev., 15, 3861–3878, https://doi.org/10.5194/gmd-15-3861-2022, https://doi.org/10.5194/gmd-15-3861-2022, 2022
Short summary
Short summary
We present the Water Ecosystems Tool (WET) – a new modular aquatic ecosystem model configurable to a wide array of physical setups, ecosystems and research questions based on the popular FABM–PCLake model. We aim for the model to become a community staple, thus helping to consolidate the state of the art under a few flexible models, with the aim of improving comparability across studies and preventing the
re-inventions of the wheelthat are common to our scientific modeling community.
Hamze Dokoohaki, Bailey D. Morrison, Ann Raiho, Shawn P. Serbin, Katie Zarada, Luke Dramko, and Michael Dietze
Geosci. Model Dev., 15, 3233–3252, https://doi.org/10.5194/gmd-15-3233-2022, https://doi.org/10.5194/gmd-15-3233-2022, 2022
Short summary
Short summary
We present a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon
reanalysisproduct that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. Here, we build on a decade of work on uncertainty propagation to generate the most complete and robust uncertainty accounting available to date.
Hisashi Sato and Takeshi Ise
Geosci. Model Dev., 15, 3121–3132, https://doi.org/10.5194/gmd-15-3121-2022, https://doi.org/10.5194/gmd-15-3121-2022, 2022
Short summary
Short summary
Accurately predicting global coverage of terrestrial biome is one of the earliest ecological concerns, and many empirical schemes have been proposed to characterize their relationship. Here, we demonstrate an accurate and practical method to construct empirical models for operational biome mapping via a convolutional neural network (CNN) approach.
Licheng Liu, Shaoming Xu, Jinyun Tang, Kaiyu Guan, Timothy J. Griffis, Matthew D. Erickson, Alexander L. Frie, Xiaowei Jia, Taegon Kim, Lee T. Miller, Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, Vipin Kumar, and Zhenong Jin
Geosci. Model Dev., 15, 2839–2858, https://doi.org/10.5194/gmd-15-2839-2022, https://doi.org/10.5194/gmd-15-2839-2022, 2022
Short summary
Short summary
By incorporating the domain knowledge into a machine learning model, KGML-ag overcomes the well-known limitations of process-based models due to insufficient representations and constraints, and unlocks the “black box” of machine learning models. Therefore, KGML-ag can outperform existing approaches on capturing the hot moment and complex dynamics of N2O flux. This study will be a critical reference for the new generation of modeling paradigm for biogeochemistry and other geoscience processes.
Elodie Salmon, Fabrice Jégou, Bertrand Guenet, Line Jourdain, Chunjing Qiu, Vladislav Bastrikov, Christophe Guimbaud, Dan Zhu, Philippe Ciais, Philippe Peylin, Sébastien Gogo, Fatima Laggoun-Défarge, Mika Aurela, M. Syndonia Bret-Harte, Jiquan Chen, Bogdan H. Chojnicki, Housen Chu, Colin W. Edgar, Eugenie S. Euskirchen, Lawrence B. Flanagan, Krzysztof Fortuniak, David Holl, Janina Klatt, Olaf Kolle, Natalia Kowalska, Lars Kutzbach, Annalea Lohila, Lutz Merbold, Włodzimierz Pawlak, Torsten Sachs, and Klaudia Ziemblińska
Geosci. Model Dev., 15, 2813–2838, https://doi.org/10.5194/gmd-15-2813-2022, https://doi.org/10.5194/gmd-15-2813-2022, 2022
Short summary
Short summary
A methane model that features methane production and transport by plants, the ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model, which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4, was calibrated and evaluated on 14 peatland sites. Results show that the model is sensitive to temperature and substrate availability over the top 75 cm of soil depth.
Suman Halder, Susanne K. M. Arens, Kai Jensen, Tais W. Dahl, and Philipp Porada
Geosci. Model Dev., 15, 2325–2343, https://doi.org/10.5194/gmd-15-2325-2022, https://doi.org/10.5194/gmd-15-2325-2022, 2022
Short summary
Short summary
A dynamic vegetation model, designed to estimate potential impacts of early vascular vegetation, namely, lycopsids, on the biogeochemical cycle at a local scale. Lycopsid Model (LYCOm) estimates the productivity and physiological properties of lycopsids across a broad climatic range along with natural selection, which is then utilized to adjudge their weathering potential. It lays the foundation for estimation of their impacts during their long evolutionary history starting from the Ordovician.
Dóra Hidy, Zoltán Barcza, Roland Hollós, Laura Dobor, Tamás Ács, Dóra Zacháry, Tibor Filep, László Pásztor, Dóra Incze, Márton Dencső, Eszter Tóth, Katarína Merganičová, Peter Thornton, Steven Running, and Nándor Fodor
Geosci. Model Dev., 15, 2157–2181, https://doi.org/10.5194/gmd-15-2157-2022, https://doi.org/10.5194/gmd-15-2157-2022, 2022
Short summary
Short summary
Biogeochemical models used by the scientific community can support society in the quantification of the expected environmental impacts caused by global climate change. The Biome-BGCMuSo v6.2 biogeochemical model has been created by implementing a lot of developments related to soil hydrology as well as the soil carbon and nitrogen cycle and by integrating crop model components. Detailed descriptions of developments with case studies are presented in this paper.
Lei Ma, George Hurtt, Lesley Ott, Ritvik Sahajpal, Justin Fisk, Rachel Lamb, Hao Tang, Steve Flanagan, Louise Chini, Abhishek Chatterjee, and Joseph Sullivan
Geosci. Model Dev., 15, 1971–1994, https://doi.org/10.5194/gmd-15-1971-2022, https://doi.org/10.5194/gmd-15-1971-2022, 2022
Short summary
Short summary
We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking datasets demonstrated the model’s capability to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has the potential to be linked with remote sensing observations to address key scientific questions.
Ignacio Hermoso de Mendoza, Etienne Boucher, Fabio Gennaretti, Aliénor Lavergne, Robert Field, and Laia Andreu-Hayles
Geosci. Model Dev., 15, 1931–1952, https://doi.org/10.5194/gmd-15-1931-2022, https://doi.org/10.5194/gmd-15-1931-2022, 2022
Short summary
Short summary
We modify the numerical model of forest growth MAIDENiso by explicitly simulating snow. This allows us to use the model in boreal environments, where snow is dominant. We tested the performance of the model before and after adding snow, using it at two Canadian sites to simulate tree-ring isotopes and comparing with local observations. We found that modelling snow improves significantly the simulation of the hydrological cycle, the plausibility of the model and the simulated isotopes.
Toni Viskari, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski
Geosci. Model Dev., 15, 1735–1752, https://doi.org/10.5194/gmd-15-1735-2022, https://doi.org/10.5194/gmd-15-1735-2022, 2022
Short summary
Short summary
We wanted to examine how the chosen measurement data and calibration process affect soil organic carbon model calibration. In our results we found that there is a benefit in using data from multiple litter-bag decomposition experiments simultaneously, even with the required assumptions. Additionally, due to the amount of noise and uncertainties in the system, more advanced calibration methods should be used to parameterize the models.
Cited articles
Anav, A., Friedlingstein, P., Kidston, M., Boop, L., Ciais, P., Cox, P.,
Jones, C., Jung, M., Myneni, R., and Zhu, Z.: Evaluating the Land and Ocean
Components of the Global Carbon Cycle in the CMIP5 Earth System Models,
J. Climate, 26, 6801–6843, 2013.
Antoine, D., André, J. M., and Morel, A.: Oceanic primary production: 2.
Estimation at global scale from satellite (Coastal Zone Color Scanner)
chlorophyll, Global Biogeochem. Cy., 10, 57–69, 1996.
Arora, V. K., Boer, G. J., Friedlingstein, P., Eby, M., Jones, C. D.,
Christian, J. R., Bonan, G., Bopp, L., Brovkin, V., Cadule, P., Hajima, T.,
Ilyina, T., Lindsay, K., Tjiputra, J. F., and Wu, T.: Carbon–concentration
and carbon–climate feedbacks in CMIP5 Earth system models, J.
Climate, 26, 5289-5314, 2013.
Aumont, O., Belviso, S., and Monfray, P.: Dimethylsulfoniopropionate (DMSP)
and dimethylsulfide (DMS) sea surface distributions simulated from a global
three-dimensional ocean carbon cycle model, J. Geophys. Res.-Oceans, 107, 4.1–4.19, https://doi.org/10.1029/1999JC000111, 2002.
Aumont, O., Maier-Reimer, E., Blain, S., and Monfray, P.: An ecosystem model
of the global ocean including Fe, Si, P colimitations, Global Biogeochem.
Cy., 17, 1060, https://doi.org/10.1029/2001GB001745, 2003.
Aumont, O., Ethé, C., Tagliabue, A., Bopp, L., and Gehlen, M.: PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies, Geosci. Model Dev., 8, 2465–2513, https://doi.org/10.5194/gmd-8-2465-2015, 2015.
Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P., and White, J. W. C.:
Increase in observed net carbon dioxide uptake by land and oceans during the
past 50 years, Nature, 488, 70–72, 2012.
Behrenfeld, M. J. and Falkowski, P. G.: Photosynthetic rates derived from
satellite-based chlorophyll concentration,. Limnol. Oceanogr.,
42, 1–20, 1997a.
Behrenfeld, M. J. and Falkowski, P. G.: A consumer's guide to phytoplankton
primary productivity models, Limnol. Oceanogr., 42, 1479–1491,
1997b.
Behrenfeld, M. J., Boss, E., Siegel, D. A., and Shea, D. M.: Carbon-based ocean
productivity and phytoplankton physiology from space, Global Biogeochem.
Cy., 19, GB1006, https://doi.org/10.1029/2004GB002299, 2005.
Boer, G. J. and Arora, V.: Temperature and concentration feedbacks in the
carbon cycle, Geophys. Res. Lett., 36, L02704, https://doi.org/10.1029/2008GL036220, 2009.
Bopp, L., Aumont, O., Cadule, P., Alvain, S., and Gehlen, M.: Response of
diatoms distribution to global warming and potential implications: A global
model study, Geophys. Res. Lett., 32, L19606, https://doi.org/10.1029/2005GL023653, 2005.
Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., and Vichi, M.: Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models, Biogeosciences, 10, 6225–6245, https://doi.org/10.5194/bg-10-6225-2013, 2013.
Buitenhuis, E. T., Hashioka, T., and Quéré, C. L.: Combined constraints
on global ocean primary production using observations and models, Global
Biogeochem. Cy., 27, 847–858, 2013.
Cao, J., Wang, B., Xiang, B., Li, J., Wu, T., Fu, X., Wu, L., and Min, J.:
Major modes of short-term climate variability in the newly developed NUIST
Earth System Model (NESM), Adv. Atmos. Sci., 32,
585–600, https://doi.org/10.1007/s00376-014-4200-6, 2015.
Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., Bao, Y., He, J., Zhou, X., and Wu, L.: The NUIST Earth System Model (NESM) version 3: description and preliminary evaluation, Geosci. Model Dev., 11, 2975–2993, https://doi.org/10.5194/gmd-11-2975-2018, 2018.
Cao, L. and Zhang, H.: The role of biological rates in the simulated
warming effect on oceanic CO2 uptake, J. Geophys. Res.-Biogeo., 122, 1098–1106, 2017.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J.,
Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le
Quéré, C., Myneni, R. B., Piao, S., and Thornton, P.: Carbon and
Other Biogeochemical Cycles, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D.,
Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA, 2013.
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T.,
Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G.,
Shongwe, M., Tebaldi, C., Weaver, A. J., and Wehner, M.: Long-term Climate
Change: Projections, Commitments and Irreversibility, in: Climate Change
2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D.,
Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 2013.
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.:
Acceleration of global warming due to carbon-cycle feedbacks in a coupled
climate model, Nature, 408, 184–187, 2000.
Cheng, W., Chiang, J. C., and Zhang, D.: Atlantic meridional overturning
circulation (AMOC) in CMIP5 models: RCP and historical simulations, J. Climate, 26, 7187–7197, 2013.
Dai, Y.: NUIST-ESM v3 [Data set], Zenodo, https://doi.org/10.5281/zenodo.3524938, 2019.
de Baar, H. J., Buma, A. G., Nolting, R. F., Cadée, G. C., Jacques, G., and
Tréguer, P. J.: On iron limitation of the Southern Ocean: experimental
observations in the Weddell and Scotia Seas, Marine Ecol. Prog. Ser.,
65, 105–122, 1990.
Denman, K. L., Brasseur, G., Chidthaisong, A., Ciais, P., Cox, P. M.,
Dickinson, R. E., Hauglustaine, D., Heinze, C., Holland, E., Jacob, D., Lohmann, U., Ramachandran, S., da Silva Dias, P. L., Wofsy, S. C., and Zhang, X.: Couplings
Between Changes in the Climate System and Biogeochemistry, in: Climate
Change 2007: The Physical Science Basis. Contribution of Working Group I to
the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B.,
Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge,
United Kingdom and New York, NY, USA, 2007.
Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide,
National Oceanic & Atmospheric Administration, Earth System Research
Laboratory (NOAA/ESRL), available at:
https://www.esrl.noaa.gov/gmd/ccgg/trends/gl_data.html, last access: 8 July 2020.
Doney, S. C., Lindsay, K., Caldeira, K., Campin, J. M., Drange, H., Dutay,
J. C., Follows, M., Gao, Y., Gnanadesikan, A., Gruber, N., Ishida, A., Joos,
F., Madec, G., Maier-Reimer, E., Marshall, J. C., Matear, R. J., Monfray, P.,
Mouchet, A., Naijar, R., Orr, J. C., Plattner, G.-K., Sarmiento, J.,
Schlitzer, R., Slater, R., Totterdell, I. J., Weirig, M.-F., Yamanaka, Y.,
and Yool, A.: Evaluating global ocean carbon models: The importance of
realistic physics, Global Biogeochem. Cy., 18, GB3017, https://doi.org/10.1029/2003GB002150, 2004.
Droop, M. R.: 25 years of algal growth kinetics, Bot. Mar., 26, 99–112,
1983.
Dunne, J. P., Sarmiento, J. L., and Gnanadesikan, A.: A synthesis of global
particle export from the surface ocean and cycling through the ocean
interior and on the seafloor, Global Biogeochem. Cy., 21, GB4006, https://doi.org/10.1029/2006GB002907,
2007.
Duteil, O., Koeve, W., Oschlies, A., Aumont, O., Bianchi, D., Bopp, L., Galbraith, E., Matear, R., Moore, J. K., Sarmiento, J. L., and Segschneider, J.: Preformed and regenerated phosphate in ocean general circulation models: can right total concentrations be wrong?, Biogeosciences, 9, 1797–1807, https://doi.org/10.5194/bg-9-1797-2012, 2012.
Dutkiewicz, S., Follows, M. J., and Parekh, P.: Interactions of the iron and
phosphorus cycles: A three-dimensional model study, Global Biogeochem.
Cy., 19, GB1021, https://doi.org/10.1029/2004GB002342, 2005.
Eppley, R. W.: Temperature and phytoplankton growth in the sea, Fish. Bull.,
70, 1063–1085, 1972.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
Friedlingstein, P., Cox, P., Betts, R., Bopp, L., von Bloh, W., Brovkin, V.,
Cadule, P., Doney, S., Eby, M., Fung, I., Bala, G., John, J., Jones, C.,
Joos, F., Kato, T., Kawamiya, M., Knorr, W., Lindsay, K., Matthews, H. D.,
Raddatz, T., Rayner, P., Reick, C., Roeckner, E., Schnitzler, K.-G., Schnur,
R., Strassmann, K., Weaver, A. J., Yoshikawa, C., and Zeng, N.:
Climate–carbon cycle feedback analysis: results from the C4MIP model
intercomparison, J. Climate, 19, 3337–3353, 2006.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 Climate Projections
due to Carbon Cycle Feedbacks, J. Climate, 27, 511–526, 2014.
Garcia, H., Locarnini, R., Boyer, T., Antonov, J., Zweng, M., Baranova, O.,
and Johnson, D.: World Ocean Atlas 2009, vol. 4, Nutrients (Phosphate,
Nitrate, Silicate), edited by: Levitus, S., NOAA Atlas NESDIS, US Gov.
Printing Office, Wash., DC, 2010.
Garcia, H. E., Weathers, K., Paver, C. R.,Smolyar, I., Boyer, T. P.,
Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Baranova, O. K., Seidov, D., and
Reagan, J. R.: World Ocean Atlas 2018, Volume 4: Dissolved Inorganic Nutrients
(phosphate, nitrate and nitrate+nitrite, silicate), edited by: Mishonov, A., NOAA Atlas NESDIS 84, 35 pp., 2018.
Geider, R. J., MacIntyre, H. L., and Kana, T. M.: Dynamic model of phytoplankton
growth and acclimation: responses of the balanced growth rate and the
chlorophyll a: carbon ratio to light, nutrient-limitation and temperature,
Mar. Ecol. Prog. Ser., 148, 187–200, 1997.
Giorgetta, M. A., Roeckner, E., Mauritsen, T., Bader, J., Crueger, T., Esch,
M., Rast, S., Kornblueh, L., Schmidt, H., Kinne, S., Hohenegger, C.,
Möbis, B., Krismer, T., Wieners, K.-H., and Stevens, B.: The Atmospheric
General Circulation Model ECHAM6: Model Description, Tech. rep., Max Planck
Institute for Meteorology, Hamburg, Germany, 2013.
Goris, N., Tjiputra, J., Schwinger, J., and Heinze, C.: Responses of carbon
uptake and oceanic pCO2 to climate change in the North Atlantic: A
model study with the Bergen Earth System Model, Global Biogeochem. Cy.,
29, 1567–1583, 2015.
Gregory, J. M., Dixon, K. W., Stouffer, R. J., Weaver, A. J., Driesschaert, E.,
Eby, M., Fichefet, T., Hasumi, H., Hu, A., Jungclaus, J. H., Kamenkovich,
I. V., Levermann, A., Montoya, M., Murakami, S., Nawrath, S., Oka, A.,
Sokolov, A. P., and Thorpe, R. B.: A model intercomparison of changes in the
Atlantic thermohaline circulation in response to increasing atmospheric
CO2 concentration, Geophys. Res. Lett., 32, L12703, https://doi.org/10.1029/2005GL023209, 2005.
Gregory, J. M., Jones, C. D., Cadule, P., and Friedlingstein, P.: Quantifying
carbon cycle feedbacks, J. Climate, 22, 5232–5250, 2009.
Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow, R., Suzuki, K., Isada, T., Howell, E., Hashioka, T., Noguchi-Aita, M., and Yamanaka, Y.: Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types, Biogeosciences, 8, 311–327, https://doi.org/10.5194/bg-8-311-2011, 2011.
Hood, R. R., Kohler, K. E., McCreary, J. P., and Smith, S. L.: A four-dimensional
validation of a coupled physical–biological model of the Arabian Sea, Deep-Sea Res. Pt. II, 50, 2917–2945,
2003.
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.:
CICE: the Los Alamos Sea Ice Model Documentation and Software User's Manual
Version 4.1 LA-CC-06-012. T-3 Fluid Dynamics Group, Los Alamos National
Laboratory, Los Alamos N.M, 2010.
Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E.,
Li, H., and Nunez-Riboni, I.: Global ocean biogeochemistry model
HAMOCC: Model architecture and performance as component of the MPI-Earth
system model in different CMIP5 experimental realizations, J.
Adv. Model. Earth Sy., 5, 287–315, 2013.
Jochum, M.: Impact of latitudinal variations in vertical diffusivity on
climate simulations, J. Geophys. Res.-Oceans, 114, C01010, https://doi.org/10.1029/2008JC005030,
2009.
Jones, C., Robertson, E., Arora, V., Friedlingstein, P., Shevliakova, E.,
Bopp, L., Brovkin, V., Hajima, T., Kato, E., Kawamiya, M. Liddicoat, S.,
Lindsay, K., Reick, C. H., Roelandt, C., Segschneider, J., and Tjiputra, J.:
Twenty-first-century compatible CO2 emissions and airborne fraction
simulated by CMIP5 earth system models under four representative
concentration pathways, J. Climate, 26, 4398–4413, 2013.
Jones, C. D., Arora, V., Friedlingstein, P., Bopp, L., Brovkin, V., Dunne, J., Graven, H., Hoffman, F., Ilyina, T., John, J. G., Jung, M., Kawamiya, M., Koven, C., Pongratz, J., Raddatz, T., Randerson, J. T., and Zaehle, S.: C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6, Geosci. Model Dev., 9, 2853–2880, https://doi.org/10.5194/gmd-9-2853-2016, 2016.
Joos, F. and Spahni, R.: Rates of change in natural and anthropogenic
radiative forcing over the past 20,000 years, P. Natl. Acad. Sci. USA, 105, 1425–1430, 2008.
Kahru, M. and Mitchell, B. G.: Blending of ocean colour algorithms applied
to the Southern Ocean, Remote Sens. Lett., 1, 119–124, 2010.
Key, R. M., Kozyr, A., Sabine, C. L., Lee, K., Wanninkhof, R., Bullister, J.
L., Feely, R. A., Millero, F. J., Mordy, C., and Peng, T.-H.: A global ocean
carbon climatology: Results from Global Data Analysis Project (GLODAP),
Global Biogeochem. Cy., 18, 357–370, 2004.
Key, R. M., Olsen, A., van Heuven, S., Lauvset, S. K., Velo, A., Lin, X.,
Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S.,
Steinfeldt, R., Jeansson, E., Ishi, M., Perez, F. F., and Suzuki, T.: Global Ocean
Data Analysis Project, Version 2 (GLODAPv2), ORNL/CDIAC-162, NDP-P093.
Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory,
US Department of Energy, Oak Ridge, Tennessee, 2015.
Knutti, R. and Sedláček, J.: Robustness and uncertainties in the
new CMIP5 climate model projections, Nat. Clim. Change, 3, 369–373,
2013.
Koné, V., Aumont, O., Lévy, M., and Resplandy, L.: Physical and
biogeochemical controls of the phytoplankton seasonal cycle in the Indian
Ocean: A modeling study, Indian Ocean Biogeochemical Processes and
Ecological Variability, 185, 147–166, 2009.
Körtzinger, A., Hedges, J. I., and Quay, P. D.: Redfield ratios revisited:
Removing the biasing effect of anthropogenic CO2, Limnol.
Oceanogr., 46, 964–970, 2001.
Lauvset, S. K., Key, R. M., Olsen, A., van Heuven, S., Velo, A., Lin, X., Schirnick, C., Kozyr, A., Tanhua, T., Hoppema, M., Jutterström, S., Steinfeldt, R., Jeansson, E., Ishii, M., Perez, F. F., Suzuki, T., and Watelet, S.: A new global interior ocean mapped climatology: the
Lee, C. M., Jones, B. H., Brink, K. H., and Fischer, A. S.: The upper-ocean
response to monsoonal forcing in the Arabian Sea: seasonal and spatial
variability, Deep-Sea Res. Pt. II,
47, 1177–1226, 2000.
Lee, K., Tong, L. T., Millero, F. J., Sabine, C. L., Dickson, A. G., Goyet, C.,
Park, G. H., Wanninkhof, R., Feely, R. A., and Key, R. M.: Global relationships
of total alkalinity with salinity and temperature in surface waters of the
world's oceans, Geophys. Res. Lett., 33, L19605, https://doi.org/10.1029/2006GL027207, 2006.
Lengaigne, M., Madec, G., Bopp, L., Menkes, C., Aumont, O., and Cadule, P.:
Bio-physical feedbacks in the Arctic Ocean using an Earth system model,
Geophys. Res. Lett., 36, L21602, https://doi.org/10.1029/2009GL040145, 2009.
Levin, I. and Hesshaimer, V.: Radiocarbon–a unique tracer of global carbon
cycle dynamics, Radiocarbon, 42, 69–80, 2000.
Lévy, M., Estublier, A., and Madec, G.: Choice of an advection scheme
for biogeochemical models, Geophys. Res. Lett., 28, 3725–3728,
2001a.
Lévy, M., Klein, P., and Treguier, A. M.: Impact of sub-mesoscale physics
on production and subduction of phytoplankton in an oligotrophic regime,
J. Marine Res., 59, 535–565, 2001b.
Lewandowska, A. M., Boyce, D. G., Hofmann, M., Matthiessen, B., Sommer, U., and
Worm, B.: Effects of sea surface warming on marine plankton, Ecol.
Lett., 17, 614–623, 2014.
Li, J., Yang, Y. M., and Wang, B.: Evaluation of NESMv3 and CMIP5 Models'
Performance on Simulation of Asian-Australian Monsoon, Atmosphere, 9, 327, https://doi.org/10.3390/atmos9090327,
2018.
Lin, H., Kuzminov, F. I., Park, J., Lee, S., Falkowski, P. G., and Gorbunov,
M. Y.: The fate of photons absorbed by phytoplankton in the global ocean,
Science, 351, 264–267, 2016.
Longhurst, A., Sathyendranath, S., Platt, T., and Caverhill, C.: An estimate
of global primary production in the ocean from satellite radiometer data,
J. Plankton Res., 17, 1245–1271, 1995.
Madec, G.: NEMO ocean engine. Note du pôle de modélisation, No. 27,
Institut Pierre-Simon Laplace (IPSL), France, 2012.
Maritorena, S., d'Andon, O. H. F., Mangin, A., and Siegel, D. A.: Merged
satellite ocean color data products using a bio-optical model:
Characteristics, benefits and issues, Remote Sens. Environ., 114,
1791–1804, 2010.
McCarthy, J. J.: The kinetics of nutrient utilization, Can. B. Fish. Aquat.
Sci., 210, 211–233, 1980.
McGillicuddy Jr., D. J., Robinson, A. R., Siegel, D. A., Jannasch, H. W., Johnson, R.,
Dickey, T. D., Mcneil, J., Michaels, A. F., and Knap, A. H.: Influence of
mesoscale eddies on new production in the Sargasso Sea, Nature, 394,
263–266, 1998.
Moore, J. K., Lindsay, K., Doney, S. C., Long, M. C., and Misumi, K.: Marine
Ecosystem Dynamics and Biogeochemical Cycling in the Community Earth System
Model [CESM1(BGC)]: Comparison of the 1990s with the 2090s under the RCP4.5
and RCP8.5 Scenarios, J. Climate, 26, 9291–9312, 2013.
Morel, A: Light and marine photosynthesis: a spectral model with geochemical
and climatological implications, Prog. Oceanogr., 26, 263–306,
1991.
NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology
Processing Group: Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Ocean
Color Data, NASA OB.DAAC, 2014.
Olonscheck, D., Hofmann, M., Worm, B., and Schellnhuber, H. J.: Decomposing
the effects of ocean warming on chlorophyll a concentrations into physically
and biologically driven contributions, Environ. Res. Lett., 8, 014043, https://doi.org/10.1088/1748-9326/8/1/014043,
2013.
Pierce, D. W., Gleckler, P. J., Barnett, T. P., Santer, B. D., and Durack, P. J.:
The fingerprint of human-induced changes in the ocean's salinity and
temperature fields, Geophys. Res. Lett., 39, L21704, https://doi.org/10.1029/2012GL053389,
2012.
Resplandy, L., Lévy, M., Bopp, L., Echevin, V., Pous, S., Sarma, V. V. S. S., and Kumar, D.: Controlling factors of the oxygen balance in the Arabian Sea's OMZ, Biogeosciences, 9, 5095–5109, https://doi.org/10.5194/bg-9-5095-2012, 2012.
Rhein, M., Rintoul, S. R., Aoki, S., Campos, E., Chambers, D., Feely, R. A.,
Gulev, S., Johnson, G. C., Josey, S.A., Kostianoy, A., Mauritzen, C.,
Roemmich, D., Talley, L.D., and Wang, F.: Observations: Ocean. In: Climate
Change 2013: The Physical Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University
Press, Cambridge, United Kingdom and New York, NY, USA, 2013.
Roy, T., Bopp, L., Gehlen, M., Schneider, B., Cadule, P., Frölicher,
T. L., Segschneider, J., Tjiputra, J., Heinze, C., and Joos, F.: Regional
impacts of climate change and atmospheric CO2 on future ocean carbon
uptake: A multimodel linear feedback analysis, J. Climate, 24, 2300–2318,
2011.
Sabine, C. L., Feely, R. A., Gruber, N., Key, R. M., Lee, K., Bullister, J. L.,
Wanninkhof, R., Wong, C. S. L., Wallace, D. W., Tilbrook, B., Millero, F. J.,
Peng, T.-H., Kozyr, A., Ono, T., and Rios, A. F.: The oceanic sink for
anthropogenic CO2. Science, 305, 367–371, 2004.
Sarmiento, J. L. and Gruber, N.: Ocean Biogeochemical Dynamics, Princeton
University Press, Princeton, NJ, USA, 2006.
Schneider, B., Bopp, L., Gehlen, M., Segschneider, J., Frölicher, T. L., Cadule, P., Friedlingstein, P., Doney, S. C., Behrenfeld, M. J., and Joos, F.: Climate-induced interannual variability of marine primary and export production in three global coupled climate carbon cycle models, Biogeosciences, 5, 597–614, https://doi.org/10.5194/bg-5-597-2008, 2008.
Schwinger, J. and Tjiputra, J.: Ocean carbon cycle feedbacks under negative
emissions, Geophys. Res. Lett., 45, 5062–5070, 2018.
Schwinger, J., Tjiputra, J. F., Heinze, C., Bopp, L., Christian, J. R.,
Gehlen, M., Ilyina, T., Jones, C. D., y Mélia, D. S., Segschneider, J.,
Séférian, R., and Totterdell, I.: Nonlinearity of ocean carbon cycle
feedbacks in CMIP5 Earth system models, J. Climate, 27,
3869–3888, 2014.
Séférian, R., Bopp, L., Gehlen, M., Orr, J. C.,
Ethé, C., Cadule, P., Aumont, O., Salas y
Mélia, D., Voldoire, A., and Madec, G.: Skill assessment of three
earth system models with common marine biogeochemistry, Clim. Dynam.,
40, 2549–2573, 2013.
Skinner, L. C., Primeau, F., Freeman, E., de la Fuente, M., Goodwin, P. A.,
Gottschalk, J., Huang, E., McCave, I. N., Noble, T. L., and Scrivner, A. E.:
Radiocarbon constraints on the glacial ocean circulation and its impact on
atmospheric CO2, Nat. Commun., 8, 16010, https://doi.org/10.1038/ncomms16010, 2017.
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J.,
Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S.,
Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S.,
Vertenstein, M., and Yeager, S.: The parallel ocean program (POP) reference
manual ocean component of the community climate system model (CCSM) and
community earth system model (CESM), Rep. LAUR-01853, 141, 1–140, 2010.
Steinacher, M., Joos, F., Frölicher, T. L., Bopp, L., Cadule, P., Cocco, V., Doney, S. C., Gehlen, M., Lindsay, K., Moore, J. K., Schneider, B., and Segschneider, J.: Projected 21st century decrease in marine productivity: a multi-model analysis, Biogeosciences, 7, 979–1005, https://doi.org/10.5194/bg-7-979-2010, 2010.
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S.,
Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I.,
Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and
Roeckner, E.: The atmospheric component of the MPI-M Earth System Model:
ECHAM6, J. Adv. Model. Earth Sy., 5, 46–172, 2012.
Takahashi, T., Broecker, W. S., and Langer, S.: Redfield ratio based on
chemical data from isopycnal surfaces, J. Geophys. Res.-Oceans, 90, 6907–6924, 1985.
Takahashi, T., Sutherland, S. C., Wanninkhof, R., Sweeney, C., Feely, R. A.,
Chipman, D. W., Hales, B., Friederich, G., Chavez, F., Sabine, C., Watson,
A., Bakker, D. C. E., Schuster, U., Metzl, N., Yoshikawa-Inoue, H., Ishii, M.,
Midorikawa, T., Nojiri, Y., Kortzinger, A., Steinhoff, T., Hoppema, M.,
Olafsson, J., Arnarson, T.S., Tilbrook, B., Johannessen, T., Olsen, A.,
Bellerby, R., Wong, C.S., Delille, B., Bates, N. R., and de Baar, H. J .W.:
Climatological mean and decadal change in surface ocean pCO2, and net
sea–air CO2 flux over the global oceans, Deep-Sea Res. Pt. II, 56, 554–577, 2009.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
2001.
Tjiputra, J. F., Assmann, K., Bentsen, M., Bethke, I., Otterå, O. H., Sturm, C., and Heinze, C.: Bergen Earth system model (BCM-C): model description and regional climate-carbon cycle feedbacks assessment, Geosci. Model Dev., 3, 123–141, https://doi.org/10.5194/gmd-3-123-2010, 2010.
Tjiputra, J. F., Roelandt, C., Bentsen, M., Lawrence, D. M., Lorentzen, T., Schwinger, J., Seland, Ø., and Heinze, C.: Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM), Geosci. Model Dev., 6, 301–325, https://doi.org/10.5194/gmd-6-301-2013, 2013.
Uitz, J., Claustre, H., Gentili, B., and Stramski, D.: Phytoplankton
class-specific primary production in the world's oceans: seasonal and
interannual variability from satellite observations, Global Biogeochem.
Cy., 24, GB3016, https://doi.org/10.1029/2009GB003680, 2010.
Wanninkhof, R.: Relationship between wind speed and gas exchange over the
ocean, J. Geophys. Res.-Oceans, 97, 7373–7382, 1992.
Wanninkhof, R., Park, G.-H., Takahashi, T., Sweeney, C., Feely, R., Nojiri, Y., Gruber, N., Doney, S. C., McKinley, G. A., Lenton, A., Le Quéré, C., Heinze, C., Schwinger, J., Graven, H., and Khatiwala, S.: Global ocean carbon uptake: magnitude, variability and trends, Biogeosciences, 10, 1983–2000, https://doi.org/10.5194/bg-10-1983-2013, 2013.
Weaver, A. J., Sedláček, J., Eby, M., Alexander, K., Crespin, E.,
Fichefet, T., Philippon-Berthier, G., Joos, F., Kawamiya, M., Matsumoto, K.,
Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., and Zickfeld, K.:
Stability of the Atlantic meridional overturning circulation: A model
intercomparison, Geophys. Res. Lett., 39, L20709, https://doi.org/10.1029/2012GL053763, 2012.
Weiss, R. F.: August. The solubility of nitrogen, oxygen and argon in water
and seawater, Deep Sea Research and Oceanographic Abstracts, 17,
721–735, 1970.
Westberry, T., Behrenfeld, M. J., Siegel, D. A., and Boss, E.: Carbon-based
primary productivity modeling with vertically resolved photoacclimation,
Global Biogeochem. Cy., 22, GB2024, https://doi.org/10.1029/2007GB003078, 2008.
Whitney, F. A.: Nutrient variability in the mixed layer of the subarctic
Pacific Ocean, 1987–2010, J. Oceanogr., 67, 481–492, 2011.
Yang, Y. M. and Wang, B.: Improving MJO simulation by enhancing the interaction between boundary layer convergence and lower tropospheric heating, Clim. Dynam., 52, 4671–4693, 2019.
Yang, Y. M., Wang, B., and Li, J.: Improving Seasonal Prediction of East Asian
Summer Rainfall Using NESM3. 0: Preliminary Results, Atmosphere, 9, 487, https://doi.org/10.3390/atmos9120487,
2018.
Yi, C., Gong, P., Xu, M., and Qi, Y.: The effects of buffer and temperature
feedback on the oceanic uptake of CO2, Geophys. Res. Lett.,
28, 751–754, 2001.
Yool, A., Popova, E. E., and Anderson, T. R.: Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain, Geosci. Model Dev., 4, 381–417, https://doi.org/10.5194/gmd-4-381-2011, 2011.
Zickfeld, K., Eby, M., and Weaver, A. J.: Carbon-cycle feedbacks of changes
in the Atlantic meridional overturning circulation under future atmospheric
CO2, Global Biogeochem. Cy., 22, GB3024, https://doi.org/10.1029/2007GB003118, 2008.
Zickfeld, K., Eby, M., Matthews, H. D., Schmittner, A., and Weaver, A. J.:
Nonlinearity of carbon cycle feedbacks, J. Climate, 24,
4255–4275, 2011.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(19196 KB) - Full-text XML
- Corrigendum
-
Supplement
(1548 KB) - BibTeX
- EndNote
Short summary
NESM v3 is one of the CMIP6 registered Earth system models. We evaluate its ocean carbon cycle component and present its present-day and future oceanic CO2 uptake based on the CMIP6 historical and SSP5–8.5 scenarios. We hope that this paper can serve as a documentation of the marine biogeochemical cycle in NESM v3. Also, the model defects found and their underlying causes analyzed in this paper could help users and further model development.
NESM v3 is one of the CMIP6 registered Earth system models. We evaluate its ocean carbon cycle...
