scholarly journals Impact of the reemergence of North Pacific subtropical mode water on the multi-year modulation of marine heatwaves in the North Pacific Ocean during winter and early spring

Author(s):  
Yong-Jin Tak ◽  
Hajoon Song ◽  
Yang-Ki Cho
2021 ◽  
Author(s):  
Michio Aoyama ◽  
Yayoi Inomata ◽  
Daisuke Tsumune ◽  
Takaki Tsubono

<p>One of the greatest results obtained by analyzing seawater samples from the North Pacific Ocean was the estimation of the total amount of 137Cs in the North Pacific to be 15-18 PBq (Aoyama et al., 2016). This estimation has been validated by two methods described by Tsubono et al. (2016) and Inomata et al. (2016). Coastal modeling results gave the amount of 137Cs direct discharge from the FDNPP to coastal waters to be (3.5 ± 0.7) PBq (Tsumune et al., 2012) which was the first and the most accurate result. Since the amount of direct discharge was accurately determined, the amount of 137Cs released into the atmosphere was also properly determined by the mass balance consideration as discussed in Aoyama et al. (2016a). </p><p>For the calculation of the final mass balance, we did not include several results as they did not cover the whole region, or they included the amount of atmospheric fallout as part of the direct discharge. The total amount of radiocesium released to the atmosphere was estimated to be from 8.1 PBq (Yumimoto et al., 2016) to 36 PBq (Stohl et al., 2O12). Based on mass balance consideration we conclude that (15.2-20.4) PBq of the FDNPP-derived 137Cs might be a reasonable value for the total atmospheric release (supported by Aoyama et al., 2016a; Katata et al., 2015; Mathieu et al., 2012; Saunier et al., 2013; Winiarek et al., 2014). The estimated land deposition is (3.4–6.2) PBq (Aoyama et al., 2016). The estimated 137Cs inventories in the North Pacific are in the range (15.2–18.3) PBq, as obtained by Tsubono et al. (2016) and  Inomata et al. (2016), while only (3–6) PBq was the contribution from the direct discharge (consensus value, Aoyama et al., 2016), although our previous estimate was more precise, (3.5 ± 0.7) PBq. For atmospheric deposition to the North Pacific, the estimated values are in the range (11.7–14.8) PBq (Aoyama et al., 2016; Inomata et al., 2016; Tsubono et al., 2016).</p><p>The radiocesium inventories in the interior domains of the North Pacific Ocean have been estimated. The radiocesium inventory in the STMW (Subtropical Mode Water) is (4.2 ± 1.1) PBq (Kaeriyama et al., 2016), and (7.9 ± 1.4) PBq in the surface layer (Inomata et al., 2018b). In the CMW (Central Mode Water), the radiocesium inventory is (2.5 ± 0.9) PBq (Inomata et al., 2018b). The radiocesium sediment inventory is (0.20 ± 0.06) PBq (Otosaka, 2017). The inventory in marine biota might be less than 200 GBq (Aoyama et al., 2019).</p>


Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li

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.


2021 ◽  
Author(s):  
R. J. David Wells ◽  
Veronica A. Quesnell ◽  
Robert L. Humphreys ◽  
Heidi Dewar ◽  
Jay R. Rooker ◽  
...  

2010 ◽  
Vol 37 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Robert H. Byrne ◽  
Sabine Mecking ◽  
Richard A. Feely ◽  
Xuewu Liu

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