Identifying Climate Modes Contributing to Sea Surface Salinity Decadal Variation in the North Pacific Ocean

2020 ◽  
Vol 125 (10) ◽  
Author(s):  
Jian Chen ◽  
Hailong Liu ◽  
Chengzu Bai ◽  
Hengqian Yan ◽  
Kaicheng Lu ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Decadal Variation ◽  
Surface Salinity ◽  
Climate Modes ◽  
The North ◽  
The North Pacific

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.

scholarly journals Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a self-organizing map neural network technique

2013 ◽  
Vol 10 (9) ◽  
pp. 6093-6106 ◽  
Author(s):  
S. Nakaoka ◽  
M. Telszewski ◽  
Y. Nojiri ◽  
S. Yasunaka ◽  
C. Miyazaki ◽  
...  
Keyword(s):  
Neural Network ◽  
North Pacific ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
The North ◽  
The North Pacific ◽  
Self Organizing

Abstract. This study uses a neural network technique to produce maps of the partial pressure of oceanic carbon dioxide (pCO2sea) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea distribution was computed using a self-organizing map (SOM) originally utilized to map the pCO2sea in the North Atlantic. Four proxy parameters – sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS) – are used during the training phase to enable the network to resolve the nonlinear relationships between the pCO2sea distribution and biogeochemistry of the basin. The observed pCO2sea data were obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies (NIES). The reconstructed pCO2sea values agreed well with the pCO2sea measurements, with the root-mean-square error ranging from 17.6 μatm (for the NIES dataset used in the SOM) to 20.2 μatm (for independent dataset). We confirmed that the pCO2sea estimates could be improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several time series locations in the North Pacific. The distributions of pCO2sea revealed by 7 yr averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology, allowing, however, for a more detailed analysis of biogeochemical conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

scholarly journals Estimating temporal and spatial variation of ocean surface <i>p</i>CO<sub>2</sub> in the North Pacific using a Self Organizing Map neural network technique

2013 ◽  
Vol 10 (3) ◽  
pp. 4575-4610 ◽  
Author(s):  
S. Nakaoka ◽  
M. Telszewski ◽  
Y. Nojiri ◽  
S. Yasunaka ◽  
C. Miyazaki ◽  
...  
Keyword(s):  
Neural Network ◽  
North Pacific ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Self Organizing Map ◽  
Surface Salinity ◽  
The North ◽  
The North Pacific ◽  
Self Organizing

Abstract. This study produced maps of the partial pressure of oceanic carbon dioxide (pCO2sea) in the North Pacific on a 0.25° latitude × 0.25° longitude grid from 2002 to 2008. The pCO2sea values were estimated by using a self-organizing map neural network technique to explain the non-linear relationships between observed pCO2sea data and four oceanic parameters: sea surface temperature (SST), mixed layer depth, chlorophyll a concentration, and sea surface salinity (SSS). The observed pCO2sea data was obtained from an extensive dataset generated by the volunteer observation ship program operated by the National Institute for Environmental Studies. The reconstructed pCO2sea values agreed rather well with the pCO2sea measurements, the root mean square error being 17.6 μatm. The pCO2sea estimates were improved by including SSS as one of the training parameters and by taking into account secular increases of pCO2sea that have tracked increases in atmospheric CO2. Estimated pCO2sea values accurately reproduced pCO2sea data at several stations in the North Pacific. The distributions of pCO2sea revealed by seven-year averaged monthly pCO2sea maps were similar to Lamont-Doherty Earth Observatory pCO2sea climatology and more precisely reflected oceanic conditions. The distributions of pCO2sea anomalies over the North Pacific during the winter clearly showed regional contrasts between El Niño and La Niña years related to changes of SST and vertical mixing.

scholarly journals Methods for the Reconstruction of Vertical Profiles from Surface Data: Multivariate Analyses, Residual GEM, and Variable Temporal Signals in the North Pacific Ocean

2005 ◽  
Vol 22 (11) ◽  
pp. 1762-1781 ◽  
Author(s):  
Bruno Buongiorno Nardelli ◽  
Rosalia Santoleri
Keyword(s):  
Time Series ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Vertical Profiles ◽  
Eof Analysis ◽  
Surface Salinity ◽  
The North ◽  
Surface Measurements ◽  
The North Pacific

Abstract Different methods for the extrapolation of vertical profiles from sea surface measurements have been tested on 14 yr of conductivity–temperature–depth (CTD) data collected within the Hawaii Ocean Time-series (HOT) program at A Long-Term Oligotrophic Habitat Assessment (ALOHA) station in the North Pacific Ocean. A new technique, called multivariate EOF reconstruction (mEOF-R), has been proposed. The mEOF-R technique is similar to the previously developed coupled pattern reconstruction (CPR) technique and relies on the availability of surface measurements and historical profiles of salinity, temperature, and steric heights. The method is based on the multivariate EOF analysis of the vertical profiles of the three parameters and on the assumption that only a few modes are needed to explain most of the variance/covariance of the fields. The performances of CPR, single EOF reconstruction (sEOF-R), and mEOF-R have been compared with the results of residual GEM techniques and with ad hoc climatologies, stressing the potential of each method in relation to the length of the time series used to train the models and to the accuracy expected from planned satellite missions for the measurement of surface salinity, sea level, and temperature. The mEOF-R method generally produces the most reliable estimates (in the worst cases comparable to the climatologies) and seems to be slightly less susceptible to errors in the surface input. Multivariate EOF analysis of HOT data also gave by itself interesting results, being able to discriminate the three major signals driving the temporal variability in the area.

scholarly journals Regional Dependence of Atmospheric Responses to Oceanic Eddies in the North Pacific Ocean

2020 ◽  
Vol 12 (7) ◽  
pp. 1161 ◽  
Author(s):  
Jinlin Ji ◽  
Jing Ma ◽  
Changming Dong ◽  
John Chiang ◽  
Dake Chen
Keyword(s):  
Wind Speed ◽  
Pacific Ocean ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Sea Surface ◽  
The North ◽  
Oceanic Eddies ◽  
The North Pacific

Based on sea surface height anomaly (SSHA) from satellite altimeter and microwave radiometer datasets, this study investigates atmospheric responses to oceanic eddies in four subdomains of the North Pacific Ocean with strongest eddy activity: Kuroshio Extension (KE), Subtropical Front (SF), California Coastal Current (CC) and Aleutian Islands (AI). Analyses show that anticyclonic eddies cause sea surface temperature, surface wind speed and precipitation rate to increase in all four subdomains, and vice versa. Through a further examination of the regional dependence of atmospheric responses to oceanic eddies, it is found that the strongest and the weakest surface wind speed responses (in winter and summer) are observed in the KE and AI region, respectively. For precipitation rate, seasonal variation of the atmospheric responses to oceanic eddies is strongest in winter and weakest in summer in the KE, CC and AI regions, but stronger in summer in the SF area. The reasons for such regional dependence and seasonality are the differences in the strength of SST anomalies, the vertical kinetic energy flux and atmospheric instability in the four subdomains.

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