On the seasonal changes in the depth of the mixed layer in the north Pacific Ocean

1972 ◽  
Vol 77 (36) ◽  
pp. 7138-7150 ◽  
Author(s):  
Karl H. Bathen
Keyword(s):  
Pacific Ocean ◽  
Mixed Layer ◽  
North Pacific ◽  
Seasonal Changes ◽  
North Pacific Ocean ◽  
The North ◽  
The North Pacific

scholarly journals Methylmercury production below the mixed layer in the North Pacific Ocean

2013 ◽  
Vol 6 (10) ◽  
pp. 879-884 ◽  
Author(s):  
Joel D. Blum ◽  
Brian N. Popp ◽  
Jeffrey C. Drazen ◽  
C. Anela Choy ◽  
Marcus W. Johnson
Keyword(s):  
Pacific Ocean ◽  
Mixed Layer ◽  
North Pacific ◽  
North Pacific Ocean ◽  
The North ◽  
Methylmercury Production ◽  
The North Pacific

scholarly journals Response of the ocean mixed layer depth to global warming and its impact on primary production: a case for the North Pacific Ocean

2011 ◽  
Vol 68 (6) ◽  
pp. 996-1007 ◽  
Author(s):  
Chan Joo Jang ◽  
Jisoo Park ◽  
Taewook Park ◽  
Sinjae Yoo
Keyword(s):  
Global Warming ◽  
Pacific Ocean ◽  
Primary Production ◽  
Mixed Layer ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Mixed Layer Depth ◽  
Layer Depth ◽  
The North ◽  
The North Pacific

Abstract Jang, C. J., Park, J., Park, T., and Yoo, S. 2011. Response of the ocean mixed layer depth to global warming and its impact on primary production: a case for the North Pacific Ocean. – ICES Journal of Marine Science, 68: 996–1007. This study investigates changes in the mixed layer depth (MLD) in the North Pacific Ocean in response to global warming and their impact on primary production by comparing outputs from 11 models of the coupled model intercomparison projects phase 3. The MLD in the 21st century decreases in most regions of the North Pacific, whereas the spatial pattern of the MLD is nearly unchanged. The overall shoaling results in part from intensified upper-ocean stratification caused by both surface warming and freshening. A significant MLD decrease (>30 m) is found in the Kuroshio extension (KE), which is predominantly driven by reduced surface cooling, caused by weakening of wind. Associated with the mixed layer shoaling in the KE, the primary production component resulting from seasonal vertical mixing will be reduced by 10.7–40.3% (ranges of medians from 11 models) via decreased nitrate fluxes from below it. Spring blooms in most models are projected to initiate earlier in the KE by 0–13 d (ranges of medians from 11 models). Despite the overall trends, the magnitude of changes in primary production and timing of spring blooms are quite different depending on models and 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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