scholarly journals Enhancing Ocean Biogeochemical Models With Phytoplankton Variable Composition

2021 ◽  
Vol 8 ◽  
Author(s):  
Prima Anugerahanti ◽  
Onur Kerimoglu ◽  
S. Lan Smith
Keyword(s):  
Large Scale ◽  
C:N Ratio ◽  
Model Performance ◽  
Variable Composition ◽  
State Variables ◽  
Allocation Model ◽  
Biogeochemical Models ◽  
Model Variant ◽  
Parameter Perturbations ◽  
Optimal Resource

Chlorophyll (Chl) is widely taken as a proxy for phytoplankton biomass, despite well-known variations in Chl:C:biomass ratios as an acclimative response to changing environmental conditions. For the sake of simplicity and computational efficiency, many large scale biogeochemical models ignore this flexibility, compromising their ability to capture phytoplankton dynamics. Here we evaluate modelling approaches of differing complexity for phytoplankton growth response: fixed stoichiometry, fixed stoichiometry with photoacclimation, classical variable-composition with photoacclimation, and Instantaneous Acclimation with optimal resource allocation. Model performance is evaluated against biogeochemical observations from time-series sites BATS and ALOHA, where phytoplankton composition varies substantially. We analyse the sensitivity of each model variant to the affinity parameters for light and nutrient, respectively. Models with fixed stoichiometry are more sensitive to parameter perturbations, but the inclusion of photoacclimation in the fixed-stoichiometry model generally captures Chl observations better than other variants when individually tuned for each site and when using similar parameter sets for both sites. Compared to the fixed stoichiometry model including photoacclimation, models with variable C:N ratio perform better in cross-validation experiments using model-specific parameter sets tuned for the other site; i.e., they are more portable. Compared to typical variable composition approaches, instantaneous acclimation, which requires fewer state variables, generally yields better performance but somewhat lower portability than the fully dynamic variant. Further assessments using objective optimisation and more contrasting stations are suggested.

scholarly journals FABM-NflexPD 1.0: assessing an instantaneous acclimation approach for modeling phytoplankton growth

2021 ◽  
Vol 14 (10) ◽  
pp. 6025-6047
Author(s):  
Onur Kerimoglu ◽  
Prima Anugerahanti ◽  
Sherwood Lan Smith
Keyword(s):  
Primary Production ◽  
Large Scale ◽  
Grid Cell ◽  
Cost Effective ◽  
State Variables ◽  
Time Step ◽  
Ocean Turbulence ◽  
State Variable ◽  
Biogeochemical Models ◽  
Additional State

Abstract. Coupled physical–biogeochemical models can generally reproduce large-scale patterns of primary production and biogeochemistry, but they often underestimate observed variability and gradients. This is partially caused by insufficient representation of systematic variations in the elemental composition and pigment density of phytoplankton. Although progress has been made through approaches accounting for the dynamics of phytoplankton composition with additional state variables, formidable computational challenges arise when these are applied in spatially explicit setups. The instantaneous acclimation (IA) approach addresses these challenges by assuming that Chl:C:nutrient ratios are instantly optimized locally (within each modeled grid cell, at each time step), such that they can be resolved as diagnostic variables. Here, we present the first tests of IA in an idealized 1-D setup: we implemented the IA in the Framework for Aquatic Biogeochemical Models (FABM) and coupled it with the General Ocean Turbulence Model (GOTM) to simulate the spatiotemporal dynamics in a 1-D water column. We compare the IA model against a fully dynamic, otherwise equivalently acclimative (dynamic acclimation; DA) variant with an additional state variable and a third, non-acclimative and fixed-stoichiometry (FS) variant. We find that the IA and DA variants, which require the same parameter set, behave similarly in many respects, although some differences do emerge especially during the winter–spring and autumn–winter transitions. These differences however are relatively small in comparison to the differences between the DA and FS variants, suggesting that the IA approach can be used as a cost-effective improvement over a fixed-stoichiometry approach. Our analysis provides insights into the roles of acclimative flexibilities in simulated primary production and nutrient drawdown rates, seasonal and vertical distribution of phytoplankton biomass, formation of thin chlorophyll layers and stoichiometry of detrital material.

An Adaptive Scheduler Framework for Complex Workflow Jobs on Grid Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 63-79 ◽  
Author(s):  
G. M. Siddesh ◽  
K. G. Srinisas
Keyword(s):  
Large Scale ◽  
Job Scheduling ◽  
Scheduling Algorithm ◽  
Model Performance ◽  
Grid Resource ◽  
Distributed Resources ◽  
Allocation Model ◽  
Utilization Factor ◽  
First Come First Serve ◽  
Job Rank

Grid Computing provides sharing of geographically distributed resources among large scale complex applications. Due to dynamic nature of resources in grid, there is a need of highly efficient job scheduling and resource management policies in grid. A novel Grid Resource Scheduler (GRS) is proposed to effectively utilize the available resources in Grid. Proposed GRS contributes, an optimal job scheduling algorithm on Job Rank-Backfilling policy and a resource matching algorithm based on ranking of resources with best fit allocation model. Performance of GRS is measured by considering a web based BLAST algorithm – a bioinformatics application. GRS aims in reducing; Makespan of the job workflow, execution time of varied size jobs, response time of the submitted jobs and overhead of using the system. It also considers improving the utilization factor and throughput of the available heterogeneous resources in grid. The experimental results prove that proposed grid scheduler framework performs better when evaluated against widely used First Come First Serve (FCFS), Shortest Job First (SJF) and Minimum Time to Due Date (MTTD) scheduling strategies.

scholarly journals FABM-NflexPD 1.0: Assessing an Instantaneous Acclimation Approach for Modelling Phytoplankton Growth

2021 ◽  
Author(s):  
Onur Kerimoglu ◽  
Prima Anugerahanti ◽  
Sherwood Lan Smith
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Growth Dynamics ◽  
Grid Cell ◽  
Nutrient Ratios ◽  
State Variables ◽  
Ocean Turbulence ◽  
Biogeochemical Models ◽  
Additional State ◽  
Spatio Temporal

Abstract. Coupled physical-biogeochemical models can generally reproduce large-scale patterns of primary production and biogeochemistry, but they often underestimate observed variability and gradients. This is partially caused by insufficient representation of systematic variations in the elemental composition and pigment density of phytoplankton. Although progress has been made through approaches accounting for the dynamics of phytoplankton composition with additional state variables, formidable computational challenges arise when these are applied in spatially explicit setups. The Instantaneous Acclimation (IA) approach addresses these challenges by assuming that Chl : C : nutrient ratios are instantly optimized locally (within each modelled grid cell, at each timestep), such that they can be resolved as diagnostic variables. Here we present the first tests of IA in an idealized, 1D setup: we implemented the IA in the Framework for Aquatic Biogeochemical Models (FABM), and coupled it with the General Ocean Turbulence Model (GOTM) to simulate the spatio-temporal dynamics in a 1-D water column. We show that the IA model and a fully dynamic, otherwise equivalently acclimative (DA) variant with an additional state variable behave similarly, and both resolve nutrient and growth dynamics not captured by a third, non-acclimative and fixed-stoichiometry (FS) variant.

scholarly journals Process-Based Model Prediction of Coastal Dune Erosion through Parametric Calibration

2021 ◽  
Vol 9 (6) ◽  
pp. 635
Author(s):  
Hyeok Jin ◽  
Kideok Do ◽  
Sungwon Shin ◽  
Daniel Cox
Keyword(s):  
Large Scale ◽  
Numerical Models ◽  
Model Performance ◽  
Coastal Dunes ◽  
Wave Transformation ◽  
Storm Event ◽  
Face Model ◽  
Dune Erosion ◽  
Simulation Performance ◽  
Sand Bar

Coastal dunes are important morphological features for both ecosystems and coastal hazard mitigation. Because understanding and predicting dune erosion phenomena is very important, various numerical models have been developed to improve the accuracy. In the present study, a process-based model (XBeachX) was tested and calibrated to improve the accuracy of the simulation of dune erosion from a storm event by adjusting the coefficients in the model and comparing it with the large-scale experimental data. The breaker slope coefficient was calibrated to predict cross-shore wave transformation more accurately. To improve the prediction of the dune erosion profile, the coefficients related to skewness and asymmetry were adjusted. Moreover, the bermslope coefficient was calibrated to improve the simulation performance of the bermslope near the dune face. Model performance was assessed based on the model-data comparisons. The calibrated XBeachX successfully predicted wave transformation and dune erosion phenomena. In addition, the results obtained from other two similar experiments on dune erosion with the same calibrated set matched well with the observed wave and profile data. However, the prediction of underwater sand bar evolution remains a challenge.

scholarly journals Influence of temperature on the larval development of the edible crab, Cancer pagurus

2009 ◽  
Vol 89 (4) ◽  
pp. 753-759 ◽  
Author(s):  
Monika Weiss ◽  
Sven Thatje ◽  
Olaf Heilmayer ◽  
Klaus Anger ◽  
Thomas Brey ◽  
...  
Keyword(s):  
Larval Development ◽  
Body Mass ◽  
Large Scale ◽  
Citrate Synthase ◽  
C:N Ratio ◽  
Intraspecific Variability ◽  
Larval Body ◽  
Influence Of Temperature ◽  
Cancer Pagurus ◽  
Influence Of Temperature On

The influence of temperature on larval survival and development was studied in the edible crab, Cancer pagurus, from a population off the island of Helgoland, North Sea. In rearing experiments conducted at six different temperatures (6°, 10°, 14°, 15°, 18° and 24°C), zoeal development was only completed at 14° and 15°C. Instar duration of the Zoea I was negatively correlated with temperature. A model relating larval body mass to temperature and developmental time suggests that successful larval development is possible within a narrow temperature range (14° ± 3°C) only. This temperature optimum coincides with the highest citrate synthase activity found at 14°C. A comparison for intraspecific variability among freshly hatched zoeae from different females (CW 13–17 cm, N = 8) revealed that both body mass and elemental composition varied significantly. Initial larval dry weight ranged from 12.1 to 17.9 μg/individual, the carbon content from 4.6 to 5.8 μg/individual, nitrogen from 1.1 to 1.3 μg/individual, and the C:N ratio from 4.1 to 4.4. A narrow larval temperature tolerance range of C. pagurus as well as the indication of intraspecific variability in female energy allocation into eggs may indicate a potential vulnerability of this species to climate change. Large-scale studies on the ecological and physiological resilience potential of this commercially fished predator are needed.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

A Case Study of a Ross Ice Shelf Airstream Event: A New Perspective*

2009 ◽  
Vol 137 (11) ◽  
pp. 4030-4046 ◽  
Author(s):  
Daniel F. Steinhoff ◽  
Saptarshi Chaudhuri ◽  
David H. Bromwich
Keyword(s):  
Large Scale ◽  
Model Performance ◽  
Thermal Infrared ◽  
Integrated Approach ◽  
Cloud Formation ◽  
Synoptic Scale ◽  
Ice Shelf ◽  
Low Level ◽  
Ross Ice Shelf

Abstract A case study illustrating cloud processes and other features associated with the Ross Ice Shelf airstream (RAS), in Antarctica, is presented. The RAS is a semipermanent low-level wind regime primarily over the western Ross Ice Shelf, linked to the midlatitude circulation and formed from terrain-induced and large-scale forcing effects. An integrated approach utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, automatic weather station (AWS) data, and Antarctic Mesoscale Prediction System (AMPS) forecast output to study the synoptic-scale and mesoscale phenomena involved in cloud formation over the Ross Ice Shelf during a RAS event. A synoptic-scale cyclone offshore of Marie Byrd Land draws moisture across West Antarctica to the southern base of the Ross Ice Shelf. Vertical lifting associated with flow around the Queen Maud Mountains leads to cloud formation that extends across the Ross Ice Shelf to the north. The low-level cloud has a warm signature in thermal infrared imagery, resembling a surface feature of turbulent katabatic flow typically ascribed to the RAS. Strategically placed AWS sites allow assessment of model performance within and outside of the RAS signature. AMPS provides realistic simulation of conditions aloft but experiences problems at low levels due to issues with the model PBL physics. Key meteorological features of this case study, within the context of previous studies on longer time scales, are inferred to be common occurrences. The assumption that warm thermal infrared signatures are surface features is found to be too restrictive.

scholarly journals Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease

2021 ◽  
Author(s):  
Elizabeth Levitis ◽  
Jacob W Vogel ◽  
Thomas Funck ◽  
Vladimir Halchinski ◽  
Serge Gauthier ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Spatial Pattern ◽  
Amyloid Beta ◽  
Large Scale ◽  
Autosomal Dominant ◽  
Model Performance ◽  
Sporadic Alzheimer’S Disease ◽  
Individual Level ◽  
Data Driven Approach

Amyloid-beta (Aβ) deposition is one of the hallmark pathologies in both sporadic Alzheimer's disease (sAD) and autosomal dominant Alzheimer's disease (ADAD), the latter of which is caused by mutations in genes involved in Aβ processing. Despite Aβ deposition being a centerpiece to both sAD and ADAD, some differences between these AD subtypes have been observed with respect to the spatial pattern of Aβ. Previous work has shown that the spatial pattern of Aβ in individuals spanning the sAD spectrum can be reproduced with high accuracy using an epidemic spreading model (ESM), which simulates the diffusion of Aβ across neuronal connections and is constrained by individual rates of Aβ production and clearance. However, it has not been investigated whether Aβ deposition in the rarer ADAD can be modeled in the same way, and if so, how congruent the spreading patterns of Aβ across sAD and ADAD are. We leverage the ESM as a data-driven approach to probe individual-level variation in the spreading patterns of Aβ across three different large-scale imaging datasets (2 SAD, 1 ADAD). We applied the ESM separately to the Alzheimer's Disease Neuroimaging initiative (N=737), the Open Access Series of Imaging Studies (N=510), and the Dominantly Inherited Alzheimer's Network (N=249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately, and further identified the most likely epicenter of Aβ spread for each individual. Using epicenters defined in previous work in sAD, the ESM provided moderate prediction of the regional pattern of Aβ deposition across all three datasets. We further find that, while the most likely epicenter for most Aβ-positive subjects overlaps with the default mode network, 13% of ADAD individuals were best characterized by a striatal origin of Aβ spread. These subjects were also distinguished by being younger than ADAD subjects with a DMN Aβ origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most ADAD patients express Aβ spreading patters similar to those of sAD, but that there may be a subset of ADAD patients with a separate, striatal phenotype.

scholarly journals A Clonal Selection Optimization System for Multiparty Secure Computing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Minyu Shi ◽  
Yongting Zhang ◽  
Huanhuan Wang ◽  
Junfeng Hu ◽  
Xiang Wu
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Differential Privacy ◽  
Clonal Selection ◽  
Smart Cities ◽  
Model Performance ◽  
Processing System ◽  
Optimization System ◽  
Learning Scheme ◽  
Training Efficiency

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.

scholarly journals Aerosol-cloud-turbulence interactions in well-coupled Arctic boundary layers over open water

2021 ◽  
Author(s):  
Jan Chylik ◽  
Dmitry Chechin ◽  
Regis Dupuy ◽  
Birte S. Kulla ◽  
Christof Lüpkes ◽  
...  
Keyword(s):  
Large Scale ◽  
Convective Cloud ◽  
Open Water ◽  
Convective Transport ◽  
Cloud Layer ◽  
Mixed Phase ◽  
State Variables ◽  
Strongly Coupled ◽  
Main Research ◽  
Weather Model

Abstract. Late springtime Arctic mixed-phase convective clouds over open water in the Fram Strait as observed during the recent ACLOUD field campaign are simulated at turbulence-resolving resolutions. The main research objective is to gain more insight into the coupling of these cloud layers to the surface, and into the role played by interactions between aerosol, hydrometeors and turbulence in this process. A composite case is constructed based on data collected by two research aircraft on 18 June 2017. The boundary conditions and large-scale forcings are based on weather model analyses, yielding a simulation that freely equilibrates towards the observed thermodynamic state. The results are evaluated against a variety of independent aircraft measurements. The observed cloud macro- and microphysical structure is well reproduced, consisting of a stratiform cloud layer in mixed-phase fed by surface-driven convective transport in predominantly liquid phase. Comparison to noseboom turbulence measurements suggests that the simulated cloud-surface coupling is realistic. A joint-pdf analysis of relevant state variables is conducted, suggesting that locations where the mixed-phase cloud layer is strongly coupled to the surface by convective updrafts act as hot-spots for invigorated interactions between turbulence, clouds and aerosol. A mixing-line analysis reveals that the turbulent mixing is similar to warm convective cloud regimes, but is accompanied by hydrometeor transitions that are unique for mixed-phase cloud systems. Distinct fingerprints in the joint-pdf diagrams also explain i) the typical ring-like shape of ice mass in the outflow cloud deck, ii) its slightly elevated buoyancy, and iii) an associated local minimum in CCN.

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