Continuous assimilation of simulated Geosat altimetric sea level into an eddy-resolving numerical ocean model: 1. Sea level differences

1990 ◽  
Vol 95 (C3) ◽  
pp. 3219 ◽  
Author(s):  
Warren B. White ◽  
Chang-Kou Tai ◽  
William R. Holland
2013 ◽  
Vol 71 (4) ◽  
pp. 957-969 ◽  
Author(s):  
Mari S. Myksvoll ◽  
Kyung-Mi Jung ◽  
Jon Albretsen ◽  
Svein Sundby

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.


2001 ◽  
Vol 24 (1) ◽  
pp. 53-63 ◽  
Author(s):  
S. K. Singh ◽  
Sujit Basu ◽  
Raj Kumar ◽  
Vijay K. Agarwal

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

2022 ◽  
pp. 1-31

Abstract Projections of relative sea-level change (RSLC) are commonly reported at an annual mean basis. The seasonality of RSLC is often not considered, even though it may modulate the impacts of annual mean RSLC. Here, we study seasonal differences in 21st-century ocean dynamic sea-level change (DSLC, 2081-2100 minus 1995-2014) on the Northwestern European Shelf (NWES) and their drivers, using an ensemble of 33 CMIP6 models complemented with experiments performed with a regional ocean model. For the high-end emissions scenario SSP5-8.5, we find substantial seasonal differences in ensemble mean DSLC, especially in the southeastern North Sea. For example, at Esbjerg (Denmark), winter mean DSLC is on average 8.4 cm higher than summer mean DSLC. Along all coasts on the NWES, DSLC is higher in winter and spring than in summer and autumn. For the low-end emissions scenario SSP1-2.6, these seasonal differences are smaller. Our experiments indicate that the changes in winter and summer sea-level anomalies are mainly driven by regional changes in wind-stress anomalies, which are generally southwesterly and east-northeasterly over the NWES, respectively. In spring and autumn, regional wind-stress changes play a smaller role. We also show that CMIP6 models not resolving currents through the English Channel cannot accurately simulate the effect of seasonal wind-stress changes on he NWES. Our results imply that using projections of annual mean RSLC may underestimate the projected changes in extreme coastal sea levels in spring and winter. Additionally, changes in the seasonal sea-level cycle may affect groundwater dynamics and the inundation characteristics of intertidal ecosystems.


Author(s):  
Zhenchang Zhang ◽  
Libin Gao ◽  
Minquan Guo ◽  
Riqing Chen

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%.


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