scholarly journals A NEMO-based model of <i>Sargassum</i> distribution in the tropical Atlantic: description of the model and sensitivity analysis (NEMO-Sarg1.0)

2021 ◽  
Vol 14 (6) ◽  
pp. 4069-4086
Author(s):  
Julien Jouanno ◽  
Rachid Benshila ◽  
Léo Berline ◽  
Antonin Soulié ◽  
Marie-Hélène Radenac ◽  
...  
Keyword(s):  
Large Scale ◽  
Ocean Model ◽  
Model Parameters ◽  
Tropical Atlantic ◽  
Nutrient Inputs ◽  
Modeling Framework ◽  
Socioeconomic Impacts ◽  
Scale Modeling ◽  
Basin Scale ◽  
And Performance

Abstract. The tropical Atlantic has been facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. The development of large-scale modeling of Sargassum transport and physiology is essential to clarify the link between Sargassum distribution and environmental conditions, and to lay the groundwork for a seasonal forecast at the scale of the tropical Atlantic basin. We developed a modeling framework based on the Nucleus for European Modelling of the Ocean (NEMO) ocean model, which integrates transport by currents and waves, and physiology of Sargassum with varying internal nutrients quota, and considers stranding at the coast. The model is initialized from basin-scale satellite observations, and performance was assessed over the year 2017. Model parameters are calibrated through the analysis of a large ensemble of simulations, and the sensitivity to forcing fields like riverine nutrient inputs, atmospheric deposition, and waves is discussed. Overall, results demonstrate the ability of the model to reproduce and forecast the seasonal cycle and large-scale distribution of Sargassum biomass.

scholarly journals A NEMO-based model of <i>Sargassum</i> distribution in the Tropical Atlantic: description of the model and sensitivity analysis (NEMO-Sarg1.0)

2020 ◽  
Author(s):  
Julien Jouanno ◽  
Rachid Benshila ◽  
Léo Berline ◽  
Antonin Soulié ◽  
Marie-Hélène Radenac ◽  
...  
Keyword(s):  
Seasonal Cycle ◽  
Large Scale ◽  
Seasonal Forecast ◽  
Ocean Model ◽  
Model Parameters ◽  
Tropical Atlantic ◽  
Socioeconomic Impacts ◽  
Modelling Framework ◽  
Basin Scale ◽  
And Performance

Abstract. The Tropical Atlantic is facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. The development of Sargassum modelling is essential to clarify the link between Sargassum distribution and environmental conditions, and to lay the groundwork for a seasonal forecast on the scale of the Tropical Atlantic basin. We developed a modelling framework based on the NEMO ocean model, which integrates transport by currents and waves, physiology of Sargassum with varying internal nutrients quota, and considers stranding at the coast. The model is initialized from basin scale satellite observations and performance was assessed over the Sargassum year 2017. Model parameters are calibrated through the analysis of a large ensemble of simulations, and the sensitivity to forcing fields like riverine nutrients inputs, atmospheric deposition, and waves is discussed. Overall, results demonstrate the ability of the model to reproduce and forecast the seasonal cycle and large-scale distribution of Sargassum biomass.

A NEMO-based model of Sargassum distribution in the Tropical Atlantic 

2021 ◽  
Author(s):  
Rachid Benshila ◽  
Julien Jouanno ◽  
Léo Berline ◽  
Antonin Soulié ◽  
Marie-Hélène Marie-Hélène ◽  
...  
Keyword(s):  
Large Scale ◽  
Seasonal Forecast ◽  
Ocean Model ◽  
Model Parameters ◽  
Tropical Atlantic ◽  
Socioeconomic Impacts ◽  
Modelling Framework ◽  
Basin Scale ◽  
Large Ensembles ◽  
And Performance

&lt;p&gt;&lt;strong&gt;The Tropical Atlantic is facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. The development of Sargassum modelling is essential to clarify the link between Sargassum distribution and environmental conditions, and to lay the groundwork for a seasonal forecast on the scale of the Tropical Atlantic basin. We present here a modelling framework based on the NEMO ocean model which integrates transport by currents and waves, stranding at the coast, and physiology of Sargassum with varying internal nutrients quota. The model is initialized from basin scale satellite observations and performance was assessed over the Sargassum year 2017. Model parameters are calibrated through the analysis of large ensembles of simulations, and the sensitivity to forcing fields like riverine nutrients inputs, atmospheric deposition, and waves is investigated. Overall, results demonstrate the ability of the model to reproduce the seasonal cycle and large-scale distribution of Sargassum biomass.&lt;/strong&gt;&lt;/p&gt;

A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity

2018 ◽  
Vol 19 (1) ◽  
pp. 201-225 ◽  
Author(s):  
Wahid Palash ◽  
Yudan Jiang ◽  
Ali S. Akanda ◽  
David L. Small ◽  
Amin Nozari ◽  
...  
Keyword(s):  
Water Level ◽  
Lead Time ◽  
Large Scale ◽  
Predictive Ability ◽  
Large River ◽  
Modeling Framework ◽  
Forecasting Accuracy ◽  
Weather Model ◽  
And Performance ◽  
The Ganges

A forecasting lead time of 5–10 days is desired to increase the flood response and preparedness for large river basins. Large uncertainty in observed and forecasted rainfall appears to be a key bottleneck in providing reliable flood forecasting. Significant efforts continue to be devoted to developing mechanistic hydrological models and statistical and satellite-driven methods to increase the forecasting lead time without exploring the functional utility of these complicated methods. This paper examines the utility of a data-based modeling framework with requisite simplicity that identifies key variables and processes and develops ways to track their evolution and performance. Findings suggest that models with requisite simplicity—relying on flow persistence, aggregated upstream rainfall, and travel time—can provide reliable flood forecasts comparable to relatively more complicated methods for up to 10 days lead time for the Ganges, Brahmaputra, and upper Meghna (GBM) gauging locations inside Bangladesh. Forecasting accuracy improves further by including weather-model-generated forecasted rainfall into the forecasting scheme. The use of water level in the model provides equally good forecasting accuracy for these rivers. The findings of the study also suggest that large-scale rainfall patterns captured by the satellites or weather models and their “predictive ability” of future rainfall are useful in a data-driven model to obtain skillful flood forecasts up to 10 days for the GBM basins. Ease of operationalization and reliable forecasting accuracy of the proposed framework is of particular importance for large rivers, where access to upstream gauge-measured rainfall and flow data are limited, and detailed modeling approaches are operationally prohibitive and functionally ineffective.

scholarly journals Tropical Atlantic Biases in CCSM4

2012 ◽  
Vol 25 (11) ◽  
pp. 3684-3701 ◽  
Author(s):  
Semyon A. Grodsky ◽  
James A. Carton ◽  
Sumant Nigam ◽  
Yuko M. Okumura
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Tropical Atlantic ◽  
Climate System Model ◽  
Stratocumulus Clouds ◽  
Subtropical Highs ◽  
The Tropics

This paper focuses on diagnosing biases in the seasonal climate of the tropical Atlantic in the twentieth-century simulation of the Community Climate System Model, version 4 (CCSM4). The biases appear in both atmospheric and oceanic components. Mean sea level pressure is erroneously high by a few millibars in the subtropical highs and erroneously low in the polar lows (similar to CCSM3). As a result, surface winds in the tropics are ~1 m s−1 too strong. Excess winds cause excess cooling and depressed SSTs north of the equator. However, south of the equator SST is erroneously high due to the presence of additional warming effects. The region of highest SST bias is close to southern Africa near the mean latitude of the Angola–Benguela Front (ABF). Comparison of CCSM4 to ocean simulations of various resolutions suggests that insufficient horizontal resolution leads to the insufficient northward transport of cool water along this coast and an erroneous southward stretching of the ABF. A similar problem arises in the coupled model if the atmospheric component produces alongshore winds that are too weak. Erroneously warm coastal SSTs spread westward through a combination of advection and positive air–sea feedback involving marine stratocumulus clouds. This study thus highlights three aspects to improve to reduce bias in coupled simulations of the tropical Atlantic: 1) large-scale atmospheric pressure fields; 2) the parameterization of stratocumulus clouds; and 3) the processes, including winds and ocean model resolution, that lead to errors in seasonal SST along southwestern Africa. Improvements of the latter require horizontal resolution much finer than the 1° currently used in many climate models.

scholarly journals Oceanic CO<sub>2</sub> outgassing and biological production hotspots induced by pre-industrial river loads of nutrients and carbon in a global modelling approach

2019 ◽  
Author(s):  
Fabrice Lacroix ◽  
Tatiana Ilyina ◽  
Jens Hartmann
Keyword(s):  
Net Primary Production ◽  
Co2 Flux ◽  
Ocean Model ◽  
Global Ocean ◽  
Nutrient Concentrations ◽  
Strong Increase ◽  
Tropical Atlantic ◽  
Nutrient Inputs ◽  
Dissolved Carbon

Abstract. Rivers are a major source of nutrients, carbon and alkalinity for the global ocean, where the delivered compounds strongly impact biogeochemical processes. In this study, we firstly estimate pre-industrial riverine fluxes of nutrients, carbon and alkalinity based on a hierarchy of weathering and land-ocean export models, while identifying regional hotspots of the land-ocean exports. Secondly, we implement the riverine loads into a global biogeochemical ocean model and describe their implications for oceanic nutrient concentrations, the net primary production (NPP) and CO2 fluxes globally, as well as in a regional shelf analysis. Thirdly, we quantify the terrestrial origins and the long-term oceanic fate of riverine carbon in the framework, while assessing the potential implementation of riverine carbon fluxes in a fully coupled land-atmosphere-ocean model. Our approach leads to annual pre-industrial riverine exports of 3.7 Tg P, 27 Tg N, 158 Tg Si and 603 Tg C, which were derived from weathering and non-weathering sources and were fractionated into organic and inorganic compounds. We thereby identify the tropical Atlantic catchments (20 % of global C), Arctic rivers (9 % of total C) and Southeast Asian rivers (15 % of total C) as dominant providers of carbon to the ocean. The riverine exports lead to a global oceanic source of CO2 to the atmosphere (231 Tg C yr−1), which is largely a result of a source from inorganic riverine carbon loads (183 Tg C yr−1), and from organic riverine carbon inputs (128 Tg C yr−1). Additionally, a sink of 80 Tg C yr−1 is caused by the enhancement of the biological carbon uptake by dissolved inorganic nutrient inputs, resulting alkalinity production and a slight model drift. While large outgassing fluxes are mostly found in proximity to major river mouths, substantial outgassing fluxes can also be observed further offshore, most prominently in the tropical Atlantic. Furthermore, we find evidence for the interhemispheric transfer of carbon in the model; we detect a stronger relative outgassing flux (49 % of global river induced outgassing) in the southern hemisphere in comparison to the hemisphere's relative riverine inputs (33 % of global river inputs), as well as an outgassing flux of 17 Tg C yr-1 in the Southern Ocean. Riverine exports lead to a strong increase in NPP in the tropical West Atlantic, Bay of Bengal and the East China Sea (166 %, 377 % and 71 % respectively). While the NPP is not strongly sensitive to riverine loads on the light limited Arctic shelves, the CO2 flux is strongly altered due to substantial dissolved carbon supplies to the region. While our study confirms that the ocean circulation is the main driver for open ocean biogeochemical distributions, it reveals the necessity to consider riverine exports for the representation of heterogeneous features of the coastal ocean, to represent riverine-induced carbon outgassing, as well as to consider the long-term volcanic CO2 flux to close the atmospheric carbon budget in a coupled land-ocean-atmosphere setting.

scholarly journals The analysis of large-scale turbulence characteristics in the Indonesian seas derived from a regional model based on the Princeton Ocean Model

Ocean Science ◽  
2012 ◽  
Vol 8 (4) ◽  
pp. 615-631
Author(s):  
K. O'Driscoll ◽  
V. Kamenkovich
Keyword(s):  
Large Scale ◽  
Regional Model ◽  
Ocean Model ◽  
Velocity Shear ◽  
Princeton Ocean Model ◽  
Turbulence Characteristics ◽  
Indonesian Seas ◽  
The North ◽  
Basin Scale ◽  
Mixing Coefficients

Abstract. Turbulence characteristics in the Indonesian seas on the horizontal scale of order of 100 km were calculated with a regional model of the Indonesian seas circulation in the area based on the Princeton Ocean Model (POM). As is well known, the POM incorporates the Mellor–Yamada turbulence closure scheme. The calculated characteristics are: twice the turbulence kinetic energy per unit mass, q2; the turbulence master scale, &amp;ell;; mixing coefficients of momentum, KM; and temperature and salinity, KH; etc. The analyzed turbulence has been generated essentially by the shear of large-scale ocean currents and by the large-scale wind turbulence. We focused on the analysis of turbulence around important topographic features, such as the Lifamatola Sill, the North Sangihe Ridge, the Dewakang Sill, and the North and South Halmahera Sea Sills. In general, the structure of turbulence characteristics in these regions turned out to be similar. For this reason, we have carried out a detailed analysis of the Lifamatola Sill region because dynamically this region is very important and some estimates of mixing coefficients in this area are available. Briefly, the main results are as follows. The distribution of q2 is quite adequately reproduced by the model. To the north of the Lifamatola Sill (in the Maluku Sea) and to the south of the Sill (in the Seram Sea), large values of q2 occur in the deep layer extending several hundred meters above the bottom. The observed increase of q2 near the very bottom is probably due to the increase of velocity shear and the corresponding shear production of q2 very close to the bottom. The turbulence master scale, &amp;ell;, was found to be constant in the main depth of the ocean, while &amp;ell; rapidly decreases close to the bottom, as one would expect. However, in deep profiles away from the sill, the effect of topography results in the &amp;ell; structure being unreasonably complicated as one moves towards the bottom. Values of 15 to 20 × 10−4 m2 s−1 were obtained for KM and KH in deep water in the vicinity of the Lifamatola Sill. These estimates agree well with basin-scale averaged values of 13.3 × 10−4 m2 s−1 found diagnostically for KH in the deep Banda and Seram Seas (Gordon et al., 2003) and a value of 9.0 × 10−4 m2 s−1 found diagnostically for KH for the deep Banda Sea system (van Aken et al., 1988). The somewhat higher simulated values can be explained by the presence of steep topography around the sill.

scholarly journals Job assignment in large-scale service systems with affinity relations

2019 ◽  
Vol 93 (3-4) ◽  
pp. 227-268 ◽  
Author(s):  
Ellen Cardinaels ◽  
Sem C. Borst ◽  
Johan S. H. van Leeuwaarden
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Data Locality ◽  
Service Systems ◽  
Model Parameters ◽  
Symmetric Model ◽  
Performance Bounds ◽  
Fluid Limit ◽  
Network Topologies ◽  
And Performance

Abstract We consider load balancing in service systems with affinity relations between jobs and servers. Specifically, an arriving job can be assigned to a fast, primary server from a particular selection associated with this job or to a secondary server to be processed at a slower rate. Such job–server affinity relations can model network topologies based on geographical proximity, or data locality in cloud scenarios. We introduce load balancing schemes that assign jobs to primary servers if available, and otherwise to secondary servers. A novel coupling construction is developed to obtain stability conditions and performance bounds. We also conduct a fluid limit analysis for symmetric model instances, which reveals a delicate interplay between the model parameters and load balancing performance.

scholarly journals Social Collective Attack Model and Procedures for Large-Scale Cyber-Physical Systems

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 991
Author(s):  
Peidong Zhu ◽  
Peng Xun ◽  
Yifan Hu ◽  
Yinqiao Xiong
Keyword(s):  
Smart Grid ◽  
Collective Behavior ◽  
Physical System ◽  
Large Scale ◽  
Public Utility ◽  
Modeling Framework ◽  
Vast Number ◽  
Attack Model ◽  
The Social ◽  
Social Collective

A large-scale Cyber-Physical System (CPS) such as a smart grid usually provides service to a vast number of users as a public utility. Security is one of the most vital aspects in such critical infrastructures. The existing CPS security usually considers the attack from the information domain to the physical domain, such as injecting false data to damage sensing. Social Collective Attack on CPS (SCAC) is proposed as a new kind of attack that intrudes into the social domain and manipulates the collective behavior of social users to disrupt the physical subsystem. To provide a systematic description framework for such threats, we extend MITRE ATT&CK, the most used cyber adversary behavior modeling framework, to cover social, cyber, and physical domains. We discuss how the disinformation may be constructed and eventually leads to physical system malfunction through the social-cyber-physical interfaces, and we analyze how the adversaries launch disinformation attacks to better manipulate collective behavior. Finally, simulation analysis of SCAC in a smart grid is provided to demonstrate the possibility of such an attack.

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