Detecting and characterizing upwelling filaments in a numerical ocean model

2019 ◽  
Vol 122 ◽  
pp. 25-34 ◽  
Author(s):  
Osvaldo Artal ◽  
Héctor H. Sepúlveda ◽  
Domingo Mery ◽  
Christian Pieringer
Keyword(s):  
Ocean Model ◽  
Numerical Ocean Model

scholarly journals Modelling dispersal of eggs and quantifying connectivity among Norwegian coastal cod subpopulations

2013 ◽  
Vol 71 (4) ◽  
pp. 957-969 ◽  
Author(s):  
Mari S. Myksvoll ◽  
Kyung-Mi Jung ◽  
Jon Albretsen ◽  
Svein Sundby
Keyword(s):  
Ocean Model ◽  
Small Scale ◽  
Coastal Current ◽  
Life Stages ◽  
Early Life Stages ◽  
Dispersal Patterns ◽  
Norwegian Coast ◽  
Spawning Areas ◽  
Numerical Ocean Model ◽  
Norwegian Coastal Current

Abstract The Norwegian coast is populated by two cod populations: Northeast Arctic cod and Norwegian Coastal cod. In this paper, we use a further division based on life history: oceanic cod, coastal cod, and fjord cod. A numerical ocean model was implemented for the northern Norwegian coast where all these populations have spawning areas. The model results were used to simulate connectivity and retention of cod eggs from the different subpopulations. The model reproduced the observed variability and mesoscale activity in the Norwegian Coastal Current. Eggs released at an oceanic spawning area were transported northwards along the coastline. Coastal cod eggs had intermediate connectivity with each other and fjord cod eggs had high local retention. Although the high retention of eggs in fjord areas is mainly caused by a subsurface distribution of eggs, the intermediate retention of eggs from coastal spawning areas is caused by small-scale eddies in-between many small islands. The high-resolution ocean model made it possible to reveal these specific dispersal patterns. The high retention of early life stages in fjords combined with strong homing to spawning areas indicates that fjord subpopulations may be described as a metapopulation.

Application of HY-2 Satellite SST Data in 4D Variational Assimilation Ocean Forecast Model

2017 ◽  
Vol 8 (2) ◽  
pp. 15-26
Author(s):  
Zhenchang Zhang ◽  
Libin Gao ◽  
Minquan Guo ◽  
Riqing Chen
Keyword(s):  
Time Window ◽  
Forecast Model ◽  
Ocean Model ◽  
Satellite Sst ◽  
Regional Ocean Model System ◽  
Model Research ◽  
Numerical Ocean Model ◽  
Regional Ocean Model ◽  
Taiwan Straits ◽  
Marine Environmental

The 4D variational (4DVAR) assimilation numerical ocean model research is proposed. This model for Taiwan Straits (TWS) is based on Regional Ocean Model System (ROMS). The background of the 4DVAR method is introduced and the development process of assimilation system is presented. In the present research, the model assimilated with Sea Surface Temperature (SST) data of HY-2 satellite (Qi, 2012; Xu, 2013) which is the first marine environmental monitoring satellite of China. In this paper, the model processes from Feb. 1 to Feb. 7, 2014 with one-day assimilation time window and root mean square error (RMSE) reduces averagely by 14.7%.

scholarly journals A regional numerical ocean model of the circulation in the Bay of Biscay

2007 ◽  
Vol 112 (C9) ◽  
Author(s):  
Y. Friocourt ◽  
B. Levier ◽  
S. Speich ◽  
B. Blanke ◽  
S. S. Drijfhout
Keyword(s):  
Ocean Model ◽  
Bay Of Biscay ◽  
Numerical Ocean Model

Mean dynamic topography: intercomparisons and errors

2006 ◽  
Vol 364 (1841) ◽  
pp. 903-916 ◽  
Author(s):  
Rory J Bingham ◽  
Keith Haines
Keyword(s):  
North Atlantic ◽  
Ocean Model ◽  
Alternative Methods ◽  
Surface Currents ◽  
Dynamic Topography ◽  
Mean Dynamic Topography ◽  
Novel Approach ◽  
Numerical Ocean Model ◽  
Error Field ◽  
Earth’S Climate

Knowledge of the ocean dynamic topography, defined as the height of the sea surface above its rest-state (the geoid), would allow oceanographers to study the absolute circulation of the ocean and determine the associated geostrophic surface currents that help to regulate the Earth's climate. Here a novel approach to computing a mean dynamic topography (MDT), together with an error field, is presented for the northern North Atlantic. The method uses an ensemble of MDTs, each of which has been produced by the assimilation of hydrographic data into a numerical ocean model, to form a composite MDT, and uses the spread within the ensemble as a measure of the error on this MDT. The r.m.s. error for the composite MDT is 3.2 cm, and for the associated geostrophic currents the r.m.s. error is 2.5 cm s −1 . Taylor diagrams are used to compare the composite MDT with several MDTs produced by a variety of alternative methods. Of these, the composite MDT is found to agree remarkably well with an MDT based on the GRACE geoid GGM01C. It is shown how the composite MDT and its error field are useful validation products against which other MDTs and their error fields can be compared.

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