scholarly journals MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies

2013 ◽  
Vol 6 (5) ◽  
pp. 1767-1811 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Carbon Cycle ◽  
Ocean Acidification ◽  
Inorganic Carbon ◽  
Ecosystem Dynamics ◽  
Anthropogenic Activity ◽  
Full Description ◽  
Biogeochemical Model ◽  
Diverse Range ◽  
Intermediate Complexity ◽  
Nutrient Utilisation

Abstract. MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification) was developed as an "intermediate complexity" plankton ecosystem model to study the biogeochemical response, and especially that of the so-called "biological pump", to anthropogenically driven change in the World Ocean (Yool et al., 2011). The base currency in this model was nitrogen from which fluxes of organic carbon, including export to the deep ocean, were calculated by invoking fixed C:N ratios in phytoplankton, zooplankton and detritus. However, due to anthropogenic activity, the atmospheric concentration of carbon dioxide (CO2) has significantly increased above its natural, inter-glacial background. As such, simulating and predicting the carbon cycle in the ocean in its entirety, including ventilation of CO2 with the atmosphere and the resulting impact of ocean acidification on marine ecosystems, requires that both organic and inorganic carbon be afforded a more complete representation in the model specification. Here, we introduce MEDUSA-2.0, an expanded successor model which includes additional state variables for dissolved inorganic carbon, alkalinity, dissolved oxygen and detritus carbon (permitting variable C:N in exported organic matter), as well as a simple benthic formulation and extended parameterizations of phytoplankton growth, calcification and detritus remineralisation. A full description of MEDUSA-2.0, including its additional functionality, is provided and a multi-decadal spin-up simulation (1860–2005) is performed. The biogeochemical performance of the model is evaluated using a diverse range of observational data, and MEDUSA-2.0 is assessed relative to comparable models using output from the Coupled Model Intercomparison Project (CMIP5).

scholarly journals MEDUSA-2.0: an intermediate complexity biogeochemical model of the marine carbon cycle for climate change and ocean acidification studies

2013 ◽  
Vol 6 (1) ◽  
pp. 1259-1365 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Carbon Cycle ◽  
Ocean Acidification ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
World Ocean ◽  
Ecosystem Dynamics ◽  
Full Description ◽  
Biogeochemical Model ◽  
Intermediate Complexity ◽  
Nutrient Utilisation

Abstract. MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification) was developed as an "intermediate complexity" plankton ecosystem model to study the biogeochemical response, and especially that of the so-called "biological pump", to anthropogenically-driven change in the World Ocean (Yool et al., 2011). The base currency in this model was nitrogen from which fluxes of organic carbon, including export to the deep ocean, were calculated by invoking fixed C:N ratios in phytoplankton, zooplankton and detritus. Since the beginning of the industrial era, the atmospheric concentration of carbon dioxide (CO2) has significantly increased above its natural, inter-glacial background concentration. Simulating and predicting the carbon cycle in the ocean in its entirety, including ventilation of CO2 with the atmosphere and the resulting impact of ocean acidification on marine ecosystems, therefore requires that both organic and inorganic carbon be afforded a full representation in the model specification. Here, we introduce MEDUSA-2.0, an expanded successor model which includes additional state variables for dissolved inorganic carbon, alkalinity, dissolved oxygen and detritus carbon (permitting variable C:N in exported organic matter), as well as a simple benthic formulation and extended parameterisations of phytoplankton growth, calcification and detritus remineralisation. A full description of MEDUSA-2.0, including its additional functionality, is provided and a multi-decadal hindcast simulation described (1860–2005), to evaluate the biogeochemical performance of the model.

scholarly journals <i>MEDUSA</i>: a new intermediate complexity plankton ecosystem model for the global domain

2010 ◽  
Vol 3 (4) ◽  
pp. 1939-2019 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Deep Ocean ◽  
Ecosystem Model ◽  
Global Scale ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Full Description ◽  
State Variables ◽  
Intermediate Complexity ◽  
Nutrient Utilisation ◽  
Parameter Values

Abstract. The ongoing, anthropogenically-driven changes to the global ocean are expected to have significant consequences for plankton ecosystems in the future. Because of the role that plankton play in the ocean's "biological pump", changes in abundance, distribution and productivity will likely have additional consequences for the wider carbon cycle. Just as in the terrestrial biosphere, marine ecosystems exhibit marked diversity in species and functional types of organisms. Predicting potential change in plankton ecosystems therefore requires the use of models that are suited to this diversity, but whose parameterisation also permits robust and realistic functional behaviour. In the past decade, advances in model sophistication have attempted to address diversity, but have been criticised for doing so inaccurately or ahead of a requisite understanding of underlying processes. Here we introduce MEDUSA (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification), a new "intermediate complexity" plankton ecosystem model that expands on traditional nutrient-phytoplankton-zooplankton-detritus (NPZD) models, and remains amenable to global-scale evaluation. MEDUSA includes the biogeochemical cycles of nitrogen, silicon and iron, broadly structured into "small" and "large" plankton size classes, of which the "large" phytoplankton class is representative of a key phytoplankton group, the diatoms. A full description of MEDUSA's state variables, differential equations, functional forms and parameter values is included, with particular attention focused on the submodel describing the export of organic carbon from the surface to the deep ocean. MEDUSA is used here in a multi-decadal hindcast simulation, and its biogeochemical performance evaluated at the global scale.

scholarly journals Medusa-1.0: a new intermediate complexity plankton ecosystem model for the global domain

2011 ◽  
Vol 4 (2) ◽  
pp. 381-417 ◽  
Author(s):  
A. Yool ◽  
E. E. Popova ◽  
T. R. Anderson
Keyword(s):  
Deep Ocean ◽  
Ecosystem Model ◽  
Global Scale ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Full Description ◽  
State Variables ◽  
Intermediate Complexity ◽  
Nutrient Utilisation ◽  
Parameter Values

Abstract. The ongoing, anthropogenically-driven changes to the global ocean are expected to have significant consequences for plankton ecosystems in the future. Because of the role that plankton play in the ocean's "biological pump", changes in abundance, distribution and productivity will likely have additional consequences for the wider carbon cycle. Just as in the terrestrial biosphere, marine ecosystems exhibit marked diversity in species and functional types of organisms. Predicting potential change in plankton ecosystems therefore requires the use of models that are suited to this diversity, but whose parameterisation also permits robust and realistic functional behaviour. In the past decade, advances in model sophistication have attempted to address diversity, but have been criticised for doing so inaccurately or ahead of a requisite understanding of underlying processes. Here we introduce MEDUSA-1.0 (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification), a new "intermediate complexity" plankton ecosystem model that expands on traditional nutrient-phytoplankton-zooplankton-detritus (NPZD) models, and remains amenable to global-scale evaluation. MEDUSA-1.0 includes the biogeochemical cycles of nitrogen, silicon and iron, broadly structured into "small" and "large" plankton size classes, of which the "large" phytoplankton class is representative of a key phytoplankton group, the diatoms. A full description of MEDUSA-1.0's state variables, differential equations, functional forms and parameter values is included, with particular attention focused on the submodel describing the export of organic carbon from the surface to the deep ocean. MEDUSA-1.0 is used here in a multi-decadal hindcast simulation, and its biogeochemical performance evaluated at the global scale.

Using new observations and Machine Learning to improve organic sinking processes in the PlankTOM global ocean biogeochemical model 

2021 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Corinne Le Quéré ◽  
Erik Buitenhuis ◽  
Lionel Guidi ◽  
Jean-Olivier Irisson
Keyword(s):  
Organic Carbon ◽  
Carbon Cycle ◽  
Deep Ocean ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Biogeochemical Model ◽  
Carbon Export ◽  
Mixing Depth ◽  
Chl A ◽  
Biogeochemical Models

&lt;p&gt;A lot of effort has been put in the representation of surface ecosystem processes in global carbon cycle models, in particular through the grouping of organisms into Plankton Functional Types (PFTs) which have specific influences on the carbon cycle. In contrast, the transfer of ecosystem dynamics into carbon export to the deep ocean has received much less attention, so that changes in the representation of the PFTs do not necessarily translate into changes in sinking of particulate matter. Models constrain the air-sea CO&lt;sub&gt;2&lt;/sub&gt; flux by drawing down carbon into the ocean interior. This export flux is five times as large as the CO&lt;sub&gt;2&lt;/sub&gt; emitted to the atmosphere by human activities. When carbon is transported from the surface to intermediate and deep ocean, more CO&lt;sub&gt;2 &lt;/sub&gt;can be absorbed at the surface. Therefore, even small variability in sinking organic carbon fluxes can have a large impact on air-sea CO&lt;sub&gt;2&lt;/sub&gt; fluxes, and on the amount of CO&lt;sub&gt;2&lt;/sub&gt; emissions that remain in the atmosphere.&lt;/p&gt;&lt;p&gt;In this work we focus on the representation of organic matter sinking in global biogeochemical models, using the PlankTOM model in its latest version representing 12 PFTs. We develop and test a methodology that will enable the systematic use of new observations to constrain sinking processes in the model. The approach is based on a Neural Network (NN) and is applied to the PlankTOM model output to test its ability to reconstruction small and large particulate organic carbon with a limited number of observations. We test the information content of geographical variables (location, depth, time of year), physical conditions (temperature, mixing depth, nutrients), and ecosystem information (CHL a, PFTs). These predictors are used in the NN to test their influence on the model-generation of organic particles and the robustness of the results. We show preliminary results using the NN approach with real plankton and particle size distribution observations from the Underwater Vision Profiler (UVP) and plankton diversity data from Tara Oceans expeditions and discuss limitations.&lt;/p&gt;

scholarly journals A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations

2018 ◽  
Vol 15 (21) ◽  
pp. 6685-6711 ◽  
Author(s):  
Prima Anugerahanti ◽  
Shovonlal Roy ◽  
Keith Haines
Keyword(s):  
Peak Height ◽  
Ecosystem Dynamics ◽  
Abyssal Plain ◽  
Biogeochemical Model ◽  
Structural Sensitivity ◽  
Statistical Measures ◽  
Biogeochemical Models ◽  
Nutrient Utilisation ◽  
The Impact

Abstract. The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivity. We assessed the impact of structural sensitivity in a biogeochemical model of intermediate complexity by modelling the chlorophyll and dissolved inorganic nitrogen (DIN) concentrations. The model is run at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain, over a 10-year timescale to observe the effect in different regions. A 1-D Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification (MEDUSA) ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe some of the key biogeochemical processes, namely nutrient uptake, zooplankton grazing, and plankton mortalities. The impact is quantified using phytoplankton phenology (initiation, bloom time, peak height, duration, and termination of phytoplankton blooms) and statistical measures such as RMSE (root-mean-squared error), mean, and range for chlorophyll and nutrients. The spread of the ensemble as a measure of uncertainty is assessed against observations using the normalized RMSE ratio (NRR). We found that even small perturbations in model structure can produce large ensemble spreads. The range of 10-year mean surface chlorophyll concentration in the ensemble is between 0.14 and 3.69 mg m−3 at coastal stations, 0.43 and 1.11 mg m−3 on the abyssal plain, and 0.004 and 0.16 mg m−3 at the oligotrophic stations. Changing both phytoplankton and zooplankton mortalities and the grazing functions has the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range. The RMSEs between in situ observations and the ensemble mean and median are mostly reduced compared to the default model output. The NRRs for monthly variability suggest that the ensemble spread is generally narrow (NRR 1.21–1.39 for DIN and 1.19–1.39 for chlorophyll profiles, 1.07–1.40 for surface chlorophyll, and 1.01–1.40 for depth-integrated chlorophyll). Among the five stations, the most reliable ensembles are obtained for the oligotrophic station ALOHA (for the surface and integrated chlorophyll and bloom peak height), for coastal station L4 (for inter-annual mean), and for the abyssal plain station PAP (for bloom peak height). Overall our study provides a novel way to generate a realistic ensemble of a biogeochemical model by perturbing the model equations and parameterizations, which will be helpful for the probabilistic predictions.

Long-term variation in dissolved inorganic carbon and ocean acidification indices in the Northwest Pacific from 1993 to 2018: A study of a biogeochemical model with an operational ocean model product and observational evidence

2021 ◽  
Author(s):  
Miho Ishizu ◽  
Yasumasa Miyazawa ◽  
Xinyu Guo
Keyword(s):  
Ocean Acidification ◽  
Inorganic Carbon ◽  
Dissolved Inorganic Carbon ◽  
Kuroshio Extension ◽  
Vertical Mixing ◽  
Ocean Model ◽  
Biogeochemical Model ◽  
Northwest Pacific ◽  
Subarctic Region ◽  
Surface Ocean

Abstract The multi-decadal variation in ocean acidification indices in the Northwest Pacific was examined using a biogeochemical model with an operational ocean model product for the period 1993–2018. We found that ocean acidification varied regionally in the Northwest Pacific. The surface ocean (above 100 m depth) underwent acidification that progressed more quickly in the subtropical region and the Kuroshio extension than in the subarctic region due to vertical mixing of the dissolved inorganic carbon (DIC) supply exceeding DIC release by air–sea exchange. Below 100 m depth, acidification and alkalinization occurred in the subtropical and subarctic regions, respectively. We attribute these regional differences in acidification and alkalinization to spatially variable biological processes in the upper layer and physical redistribution of DIC, both horizontally and vertically.

scholarly journals Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems

2017 ◽  
Vol 14 (14) ◽  
pp. 3401-3429 ◽  
Author(s):  
Marko Scholze ◽  
Michael Buchwitz ◽  
Wouter Dorigo ◽  
Luis Guanter ◽  
Shaun Quegan
Keyword(s):  
Data Assimilation ◽  
Carbon Cycle ◽  
Global Carbon Cycle ◽  
Ecosystem Dynamics ◽  
Full Description ◽  
Terrestrial Carbon ◽  
Carbon Cycle Model ◽  
Terrestrial Carbon Cycle ◽  
Uncertainty Estimates ◽  
Global Carbon

Abstract. The global carbon cycle is an important component of the Earth system and it interacts with the hydrology, energy and nutrient cycles as well as ecosystem dynamics. A better understanding of the global carbon cycle is required for improved projections of climate change including corresponding changes in water and food resources and for the verification of measures to reduce anthropogenic greenhouse gas emissions. An improved understanding of the carbon cycle can be achieved by data assimilation systems, which integrate observations relevant to the carbon cycle into coupled carbon, water, energy and nutrient models. Hence, the ingredients for such systems are a carbon cycle model, an algorithm for the assimilation and systematic and well error-characterised observations relevant to the carbon cycle. Relevant observations for assimilation include various in situ measurements in the atmosphere (e.g. concentrations of CO2 and other gases) and on land (e.g. fluxes of carbon water and energy, carbon stocks) as well as remote sensing observations (e.g. atmospheric composition, vegetation and surface properties).We briefly review the different existing data assimilation techniques and contrast them to model benchmarking and evaluation efforts (which also rely on observations). A common requirement for all assimilation techniques is a full description of the observational data properties. Uncertainty estimates of the observations are as important as the observations themselves because they similarly determine the outcome of such assimilation systems. Hence, this article reviews the requirements of data assimilation systems on observations and provides a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation. We report on progress since the review of model-data synthesis in terrestrial carbon observations by Raupach et al.(2005), emphasising the rapid advance in relevant space-based observations.

scholarly journals A regional hindcast model simulating ecosystem dynamics, inorganic carbon chemistry and ocean acidification in the Gulf of Alaska

2020 ◽  
Author(s):  
Claudine Hauri ◽  
Cristina Schultz ◽  
Katherine Hedstrom ◽  
Seth Danielson ◽  
Brita Irving ◽  
...  
Keyword(s):  
Ocean Acidification ◽  
Seasonal Cycle ◽  
Bottom Water ◽  
Inorganic Carbon ◽  
Gulf Of Alaska ◽  
Ecosystem Dynamics ◽  
Saturation State ◽  
Aragonite Saturation ◽  
Carbon Chemistry ◽  
Seward Line

Abstract. The coastal ecosystem of the Gulf of Alaska (GOA) is especially vulnerable to the effects of ocean acidification and climate change that can only be understood within the context of the natural variability of physical and chemical conditions. Controlled by its complex bathymetry, iron enriched freshwater discharge, and wind and solar radiation, the GOA is a highly dynamic system that exhibits large inorganic carbon variability from subseasonal to interannual timescales. This variability is poorly understood due to the lack of observations in this expansive and remote region. To improve our conceptual understanding of the system, we developed a new model set-up for the GOA that couples the three-dimensional Regional Oceanic Model System (ROMS), the Carbon, Ocean Biogeochemistry and Lower Trophic (COBALT) ecosystem model, and a high resolution terrestrial hydrological model. Here, we evaluate the model on seasonal to interannual timescales using the best available inorganic carbon observations. The model was particularly successful in reproducing observed aragonite oversaturation and undersaturation of near-bottom water in May and September, respectively. The largest deficiency of the model is perhaps its inability to adequately simulate spring time surface inorganic carbon chemistry, as it overestimates surface dissolved inorganic carbon, which translates into an underestimation of the surface aragonite saturation state at this time. We also use the model to describe the seasonal cycle and drivers of inorganic carbon parameters along the Seward Line transect in under-sampled months. As such, model output suggests that a majority of the near-bottom water along the Seward Line is seasonally under-saturated with regard to aragonite between June and January, as a result of upwelling and remineralization. Such an extensive period of reoccurring aragonite undersaturation may be harmful to CO2 sensitive organisms. Furthermore, the influence of freshwater not only decreases aragonite saturation state in coastal surface waters in summer and fall, but simultaneously also decreases surface pCO2, thereby decoupling the aragonite saturation state from pCO2. The full seasonal cycle and geographic extent of the GOA region is undersampled, and our model results give new and important insights for months of the year and areas that lack in situ inorganic carbon observations.

scholarly journals Development of the UKESM-TOPAZ Earth System Model (Version 1.0) and Preliminary Evaluation of its Biogeochemical Simulations

2021 ◽  
Author(s):  
Hyomee Lee ◽  
Byung-Kwon Moon ◽  
Hyun-Chae Jung ◽  
Jong-Yeon Park ◽  
Sungbo Shim ◽  
...  
Keyword(s):  
Dissolved Inorganic Carbon ◽  
Initial Conditions ◽  
Southern Oscillation ◽  
Chlorophyll Concentration ◽  
Ecosystem Dynamics ◽  
Biogeochemical Model ◽  
Preliminary Evaluation ◽  
Earth System ◽  
Simulation Performance ◽  
Nutrient Utilisation

AbstractEarth system models (ESMs) comprise various Earth system components and simulate the interactions between these components. ESMs can be used to understand climate feedbacks between physical, chemical, and biological processes and predict future climate. We developed a new ESM, UKESM-TOPAZ, by coupling the UK ESM (UKESM1) and the Tracers of Phytoplankton with Allometric Zooplankton (TOPAZ) biogeochemical module. We then compared the preliminary simulated biogeochemical variables, which were conducted over a period of 70 years, using observational and existing UKESM1 model data. Similar to UKESM1, the newly developed UKESM-TOPAZ closely simulated the relationship between the El Niño-Southern Oscillation and chlorophyll concentration anomalies during the boreal winter. However, there were differences in the chlorophyll distributions in the eastern equatorial Pacific between the two models, which were due to dissolved iron, as this value was higher in UKESM-TOPAZ than in UKESM1. In a mean field analysis, the distributions of the major marine biogeochemical variables in UKESM-TOPAZ (i.e., nitrate, silicate, dissolved oxygen, dissolved inorganic carbon, and alkalinity) were not significantly different from those of UKESM1, likely because the models share the same initial conditions. Our results indicate that TOPAZ has a simulation performance that does not lag behind UKESM1’s basic biogeochemical model (Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification; MEDUSA). The UKESM-TOPAZ model can simulate the variability of the observed Niño 3.4 and 4 indices more closely than UKESM1. Thus, the UKESM-TOPAZ model can be used to deepen our understanding of the Earth system and to estimate ESM uncertainty.

Sign in / Sign up

Export Citation Format

Share Document