A three-layer ocean circulation model application to numerical studies on the North Pacific Ocean circulation

1994 ◽  
Vol 12 (3) ◽  
pp. 262-273
Author(s):  
Zhang Li-ping ◽  
Qin Zeng-hao
Keyword(s):  
Pacific Ocean ◽  
Ocean Circulation ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Circulation Model ◽  
Ocean Circulation Model ◽  
Model Application ◽  
The North ◽  
Numerical Studies ◽  
The North Pacific

scholarly journals A modelling study of the 1976 climate regime shift in the North Pacific Ocean

1999 ◽  
Vol 56 (12) ◽  
pp. 2450-2462 ◽  
Author(s):  
Julia Qiuying Wu ◽  
William W Hsieh
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
General Circulation ◽  
Regime Shift ◽  
North Pacific Ocean ◽  
Circulation Model ◽  
Climate Regime ◽  
Climate Regime Shift ◽  
The North ◽  
The North Pacific

Around 1976, the North Pacific Ocean underwent a climate regime shift, with significant biological consequences. To model the changes in the ocean, an ocean general circulation model was forced by the wind stress and sea surface temperature monthly climatology of the 1952-1975 period in one numerical experiment and the 1976-1988 period in another. Changes in the ocean model between the two experiments revealed how the ocean might have changed under the 1976 climate regime shift. In winter, the intensified post-1976 Aleutian Low spun up the subarctic gyre and the subtropical gyre, except in the Gulf of Alaska, where the circulation weakened. Upwelling was generally enhanced in the subarctic and downwelling enhanced in the subtropical region, with temperature changes down to 600 m. In the post-1976 period, the meridional heat transports were also enhanced: poleward in the low latitudes, equatorward in the midlatitudes, and poleward in the high latitudes.

scholarly journals Neural Network Approach to Retrieving Ocean Subsurface Temperatures from Surface Parameters Observed by Satellites

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li
Keyword(s):  
Neural Network ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Sea Surface ◽  
Satellite Measurements ◽  
Surface Parameters ◽  
The North ◽  
The North Pacific ◽  
Bpnn Model

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.

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