scholarly journals BFM17 v1.0: Reduced-Order Biogeochemical Flux Model for Upper Ocean Biophysical Simulations

2020 ◽  
Author(s):  
Katherine M. Smith ◽  
Skyler Kern ◽  
Peter E. Hamlington ◽  
Marco Zavatarelli ◽  
Nadia Pinardi ◽  
...  
Keyword(s):  
Organic Matter ◽  
Observational Data ◽  
Functional Group ◽  
Computational Cost ◽  
Open Ocean ◽  
Ecosystem Dynamics ◽  
Nutrient Ratios ◽  
Group Approach ◽  
Reduced Order ◽  
Flux Model

Abstract. We present a newly developed reduced-order biogeochemical flux model that is complex and flexible enough to capture open-ocean ecosystem dynamics, but reduced enough to incorporate into highly resolved numerical simulations with limited additional computational cost. The reduced-order model, which is derived from the full 56 state variable Biogeochemical Flux Model (BFM56; Vichi et al. (2007)), follows a biological and chemical functional group approach and allows for the development of critical non-Redfield nutrient ratios. Matter is expressed in units of carbon, nitrogen, and phosphate, following techniques used in more complex models. To reduce the overall computational cost and to focus on open-ocean conditions, the reduced model eliminates certain processes, such as benthic, silicate, and iron influences, and parameterizes others, such as the bacterial loop. The model explicitly tracks 17 state variables, divided into phytoplankton, zooplankton, dissolved organic matter, particulate organic matter, and nutrient groups. It is correspondingly called the Biogeochemical Flux Model 17 (BFM17). After providing a detailed description of BFM17, we couple it with the one-dimensional Princeton Ocean Model (POM) for validation using observational data from the Sargasso Sea. Results show good agreement with the observational data and with corresponding results from BFM56, including the ability to capture the subsurface chlorophyll maximum and bloom intensity. In comparison to previous reduced-order models of similar size, BFM17 provides improved correlations between model output and field data, indicating that significant improvements in the reproduction of in situ data can be achieved with a low number of variables, while maintaining the functional group approach.

Detecting Climate Fingerprints in Southern Ocean Carbon using a Biogeochemical Ocean Model

2021 ◽  
Author(s):  
Rebecca Wright ◽  
Corinne Le Quéré ◽  
Erik Buitenhuis ◽  
Dorothee Bakker
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Southern Ocean ◽  
Observational Data ◽  
Ecosystem Dynamics ◽  
Global Ocean ◽  
Subsurface Waters ◽  
Climatic Drivers ◽  
Ocean Carbon ◽  
And Storage

<p>The Southern Ocean plays an important role in the uptake, transport and storage of carbon by the global oceans. These properties are dominated by the response to the rise in anthropogenic CO<sub>2</sub> in the atmosphere, but they are modulated by climate variability and climate change. Here we explore the effect of climate variability and climate change on ocean carbon uptake and storage in the Southern Ocean. We assess the extent to which climate change may be distinguishable from the anthropogenic CO<sub>2</sub> signal and from the natural background variability. We use a combination of biogeochemical ocean modelling and observations from the GLODAPv2020 database to detect climate fingerprints in dissolved inorganic carbon (DIC).</p><p>We conduct an ensemble of hindcast model simulations of the period 1920-2019, using a global ocean biogeochemical model which incorporates plankton ecosystem dynamics based on twelve plankton functional types. We use the model ensemble to isolate the changes in DIC due to rising anthropogenic CO<sub>2</sub> alone and the changes due to climatic drivers (both climate variability and climate change), to determine their relative roles in the emerging total DIC trends and patterns. We analyse these DIC trends for a climate fingerprint over the past four decades, across spatial scales from the Southern Ocean, to basin level and down to regional ship transects. Highly sampled ship transects were extracted from GLODAPv2020 to obtain locations with the maximum spatiotemporal coverage, to reduce the inherent biases in patchy observational data. Model results were sampled to the ship transects to compare the climate fingerprints directly to the observational data.</p><p>Model results show a substantial change in DIC over a 35-year period, with a range of more than +/- 30 µmol/L. In the surface ocean, both anthropogenic CO<sub>2</sub> and climatic drivers act to increase DIC concentration, with the influence of anthropogenic CO<sub>2</sub> dominating at lower latitudes and the influence of climatic drivers dominating at higher latitudes. In the deep ocean, the anthropogenic CO<sub>2</sub> generally acts to increase DIC except in the subsurface waters at lower latitudes, while climatic drivers act to decrease DIC concentration. The combined fingerprint of anthropogenic CO<sub>2</sub> and climatic drivers on DIC concentration is for an increasing trend at the surface and decreasing trends in low latitude subsurface waters. Preliminary comparison of the model fingerprints to observational ship transects will also be presented.</p>

scholarly journals Coastal hypoxia and sediment biogeochemistry

2009 ◽  
Vol 6 (7) ◽  
pp. 1273-1293 ◽  
Author(s):  
J. J. Middelburg ◽  
L. A. Levin
Keyword(s):  
Organic Matter ◽  
Bottom Water ◽  
Water Exchange ◽  
Transport Processes ◽  
Ecosystem Dynamics ◽  
Ecosystem Functions ◽  
Oxygen Levels ◽  
Concise Review ◽  
Water Oxygen ◽  
Sediment Biogeochemistry

Abstract. The intensity, duration and frequency of coastal hypoxia (oxygen concentration <63 μM) are increasing due to human alteration of coastal ecosystems and changes in oceanographic conditions due to global warming. Here we provide a concise review of the consequences of coastal hypoxia for sediment biogeochemistry. Changes in bottom-water oxygen levels have consequences for early diagenetic pathways (more anaerobic at expense of aerobic pathways), the efficiency of re-oxidation of reduced metabolites and the nature, direction and magnitude of sediment-water exchange fluxes. Hypoxia may also lead to more organic matter accumulation and burial and the organic matter eventually buried is also of higher quality, i.e. less degraded. Bottom-water oxygen levels also affect the organisms involved in organic matter processing with the contribution of metazoans decreasing as oxygen levels drop. Hypoxia has a significant effect on benthic animals with the consequences that ecosystem functions related to macrofauna such as bio-irrigation and bioturbation are significantly affected by hypoxia as well. Since many microbes and microbial-mediated biogeochemical processes depend on animal-induced transport processes (e.g. re-oxidation of particulate reduced sulphur and denitrification), there are indirect hypoxia effects on biogeochemistry via the benthos. Severe long-lasting hypoxia and anoxia may result in the accumulation of reduced compounds in sediments and elimination of macrobenthic communities with the consequences that biogeochemical properties during trajectories of decreasing and increasing oxygen may be different (hysteresis) with consequences for coastal ecosystem dynamics.

A functional group approach reveals important fish recruitments driven by flood pulses in floodplain ecosystem

2019 ◽  
Vol 99 ◽  
pp. 130-139 ◽  
Author(s):  
Chao Wang ◽  
Zhongguan Jiang ◽  
Lizhi Zhou ◽  
Bingguo Dai ◽  
Zhuoyan Song
Keyword(s):  
Functional Group ◽  
Group Approach ◽  
Flood Pulses ◽  
Important Fish

scholarly journals An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques

2020 ◽  
Author(s):  
Nicola Demo ◽  
Giulio Ortali ◽  
Gianluca Gustin ◽  
Gianluigi Rozza ◽  
Gianpiero Lavini
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Gaussian Process Regression ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Free Form ◽  
Dynamic Mode ◽  
Two Phase ◽  
Initial Shape ◽  
Reduced Order

Abstract This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive—especially dealing with complex industrial geometries—we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.

scholarly journals A reduced order approach for probabilistic inversions of 3-D magnetotelluric data I: general formulation

2020 ◽  
Vol 223 (3) ◽  
pp. 1837-1863
Author(s):  
M C Manassero ◽  
J C Afonso ◽  
F Zyserman ◽  
S Zlotnik ◽  
I Fomin
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Forward Problem ◽  
Parallel Structure ◽  
Data Sets ◽  
Magnetotelluric Data ◽  
Reduced Order ◽  
Mcmc Algorithms ◽  
Simulation Based ◽  
And Performance

SUMMARY Simulation-based probabilistic inversions of 3-D magnetotelluric (MT) data are arguably the best option to deal with the nonlinearity and non-uniqueness of the MT problem. However, the computational cost associated with the modelling of 3-D MT data has so far precluded the community from adopting and/or pursuing full probabilistic inversions of large MT data sets. In this contribution, we present a novel and general inversion framework, driven by Markov Chain Monte Carlo (MCMC) algorithms, which combines (i) an efficient parallel-in-parallel structure to solve the 3-D forward problem, (ii) a reduced order technique to create fast and accurate surrogate models of the forward problem and (iii) adaptive strategies for both the MCMC algorithm and the surrogate model. In particular, and contrary to traditional implementations, the adaptation of the surrogate is integrated into the MCMC inversion. This circumvents the need of costly offline stages to build the surrogate and further increases the overall efficiency of the method. We demonstrate the feasibility and performance of our approach to invert for large-scale conductivity structures with two numerical examples using different parametrizations and dimensionalities. In both cases, we report staggering gains in computational efficiency compared to traditional MCMC implementations. Our method finally removes the main bottleneck of probabilistic inversions of 3-D MT data and opens up new opportunities for both stand-alone MT inversions and multi-observable joint inversions for the physical state of the Earth’s interior.

scholarly journals A Proper Generalized Decomposition (PGD) approach to crack propagation in brittle materials: with application to random field material properties

2019 ◽  
Vol 65 (2) ◽  
pp. 451-473 ◽  
Author(s):  
Hasini Garikapati ◽  
Sergio Zlotnik ◽  
Pedro Díez ◽  
Clemens V. Verhoosel ◽  
E. Harald van Brummelen
Keyword(s):  
Crack Propagation ◽  
Material Properties ◽  
Computational Cost ◽  
Brittle Materials ◽  
Energy Criterion ◽  
Reduced Order Modeling ◽  
Proper Generalized Decomposition ◽  
Modeling Framework ◽  
Global Energy ◽  
Reduced Order

Abstract Understanding the failure of brittle heterogeneous materials is essential in many applications. Heterogeneities in material properties are frequently modeled through random fields, which typically induces the need to solve finite element problems for a large number of realizations. In this context, we make use of reduced order modeling to solve these problems at an affordable computational cost. This paper proposes a reduced order modeling framework to predict crack propagation in brittle materials with random heterogeneities. The framework is based on a combination of the Proper Generalized Decomposition (PGD) method with Griffith’s global energy criterion. The PGD framework provides an explicit parametric solution for the physical response of the system. We illustrate that a non-intrusive sampling-based technique can be applied as a post-processing operation on the explicit solution provided by PGD. We first validate the framework using a global energy approach on a deterministic two-dimensional linear elastic fracture mechanics benchmark. Subsequently, we apply the reduced order modeling approach to a stochastic fracture propagation problem.

scholarly journals Photobleaching as a factor controlling spectral characteristics of chromophoric dissolved organic matter in open ocean

2013 ◽  
Vol 10 (11) ◽  
pp. 7207-7217 ◽  
Author(s):  
Y. Yamashita ◽  
Y. Nosaka ◽  
K. Suzuki ◽  
H. Ogawa ◽  
K. Takahashi ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Surface Waters ◽  
Spectral Characteristics ◽  
Open Ocean ◽  
Global Ocean ◽  
Chromophoric Dissolved Organic Matter ◽  
Spectral Slope ◽  
Subtropical Region ◽  
Subarctic Region

Abstract. Chromophoric dissolved organic matter (CDOM) ubiquitously occurs in marine environments and plays a significant role in the marine biogeochemical cycles. Basin scale distributions of CDOM have recently been surveyed in the global ocean and indicate that quantity and quality of oceanic CDOM are mainly controlled by in situ production and photobleaching. However, factors controlling the spectral parameters of CDOM in the UV region, i.e., spectral slope of CDOM determined at 275–295 nm (S275–295) and the ratio of two spectral slope parameters (SR); the ratio of S275–295 to S350–400, have not been well documented. To evaluate the factor controlling the spectral characteristics of CDOM in the UV region in the open ocean, we determined the quantitative and qualitative characteristics of CDOM in the subarctic and subtropical surface waters (5–300 m) of the western North Pacific. Absorption coefficients at 320 nm in the subarctic region were higher than those in the subtropical region throughout surface waters, suggesting that magnitudes of photobleaching were different between the two regions. The values of S275–295 and SR were also higher in the subtropical region than the subarctic region. The dark microbial incubation showed biodegradation of DOM little affected S275–295, but slightly decreased SR. On the other hand, increases in S275–295 and relative stableness of SR were observed during photo-irradiation incubations respectively. These experimental results indicated that photobleaching of CDOM mainly induced qualitative differences in CDOM at UV region between the subarctic and subtropical surface waters. The results of this study imply that S275–295 can be used as a tracer of photochemical history of CDOM in the open ocean.

Towards an acoustic-based coupled observation and modelling system for monitoring and predicting ecosystem dynamics of the open ocean

2012 ◽  
Vol 14 (4) ◽  
pp. 605-615 ◽  
Author(s):  
Nils Olav Handegard ◽  
Louis du Buisson ◽  
Patrice Brehmer ◽  
Stewart J Chalmers ◽  
Alex De Robertis ◽  
...  
Keyword(s):  
Open Ocean ◽  
Ecosystem Dynamics ◽  
Modelling System

An Energy Closure Criterion for Model Reduction of a Kicked Euler–Bernoulli Beam

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Suparno Bhattacharyya ◽  
Joseph P. Cusumano
Keyword(s):  
Model Reduction ◽  
Mode Selection ◽  
Computational Cost ◽  
Impulsive Loading ◽  
Bernoulli Beam ◽  
Full System ◽  
Fixed Amount ◽  
Reduced Order ◽  
Proper Orthogonal ◽  
Euler Bernoulli Beam

Abstract Reduced order models (ROMs) can be simulated with lower computational cost while being more amenable to theoretical analysis. Here, we examine the performance of the proper orthogonal decomposition (POD), a data-driven model reduction technique. We show that the accuracy of ROMs obtained using POD depends on the type of data used and, more crucially, on the criterion used to select the number of proper orthogonal modes (POMs) used for the model. Simulations of a simply supported Euler–Bernoulli beam subjected to periodic impulsive loads are used to generate ROMs via POD, which are then simulated for comparison with the full system. We assess the accuracy of ROMs obtained using steady-state displacement, velocity, and strain fields, tuning the spatiotemporal localization of applied impulses to control the number of excited modes in, and hence the dimensionality of, the system’s response. We show that conventional variance-based mode selection leads to inaccurate models for sufficiently impulsive loading and that this poor performance is explained by the energy imbalance on the reduced subspace. Specifically, the subspace of POMs capturing a fixed amount (say, 99.9%) of the total variance underestimates the energy input and dissipated in the ROM, yielding inaccurate reduced-order simulations. This problem becomes more acute as the loading becomes more spatio-temporally localized (more impulsive). Thus, energy closure analysis provides an improved method for generating ROMs with energetics that properly reflect that of the full system, resulting in simulations that accurately represent the system’s true behavior.

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