Spatial distribution of primary production off Sanriku, northwestern Pacific, during spring estimated by Ocean Color and Temperature Scanner (OCTS)

1998 ◽  
Vol 54 (5) ◽  
pp. 553-564 ◽  
Author(s):  
Joji Ishizaka
2020 ◽  
Vol 244 ◽  
pp. 106897
Author(s):  
Satoshi Asaoka ◽  
Satoshi Nakada ◽  
Akira Umehara ◽  
Joji Ishizaka ◽  
Wataru Nishijima

2015 ◽  
Vol 12 (17) ◽  
pp. 14941-14980 ◽  
Author(s):  
N. Mayot ◽  
F. D'Ortenzio ◽  
M. Ribera d'Alcalà ◽  
H. Lavigne ◽  
H. Claustre

Abstract. D'Ortenzio and Ribera d'Alcalà (2009, DR09 hereafter) divided the Mediterranean Sea into "bioregions" based on the climatological seasonality (phenology) of phytoplankton. Here we investigate the interannual variability of this bioregionalization. Using 16 years of available ocean color observations (i.e. SeaWiFS and MODIS), we analyzed the spatial distribution of the DR09 trophic regimes on an annual basis. Additionally, we identified new trophic regimes, with seasonal cycles of phytoplankton biomass different from the DR09 climatological description and named "Anomalous". Overall, the classification of the Mediterranean phytoplankton phenology proposed by DR09 (i.e. "No Bloom", "Intermittently", "Bloom" and "Coastal"), is confirmed to be representative of most of the Mediterranean phytoplankton phenologies. The mean spatial distribution of these trophic regimes (i.e. bioregions) over the 16 years studied is also similar to the one proposed by DR09. But at regional scale some annual differences, in their spatial distribution and in the emergence of "Anomalous" trophic regimes, were observed compared to the DR09 description. These dissimilarities with the DR09 study were related to interannual variability in the sub-basin forcing: winter deep convection events, frontal instabilities, inflow of Atlantic or Black Sea Waters and river run-off. The large assortment of phytoplankton phenologies identified in the Mediterranean Sea is thus verified at interannual level, confirming the "sentinel" role of this basin to detect the impact of climate changes on the pelagic environment.


2019 ◽  
Vol 59 (5) ◽  
pp. 755-770
Author(s):  
A. B. Demidov ◽  
V. I. Gagarin ◽  
E. G. Arashkevich ◽  
P. N. Makkaveev ◽  
I. V. Konyukhov ◽  
...  

Spatial distribution of phytoplankton primary production and chlorophyll was studied based on the data of three cruises carried out in AugustSeptember of 2015, 2017 and 2018. The average value of water column primary production (IPP) along the transect from Lena`s mouth to the continental slope was 2.8 fold higher than that one along the transect from Khatanga`s mouth, which was explained by the level of incident radiation and nutrients concentration. Along the cross-slope transects increasing of photosynthetically layer integrated chlorophyll (Chlph) occurred due to developing of deep maxima. IPP and Chlph increasing was registered in the vicinity of the continental slope. In AugustSeptember the averaged IPP value was 100 mgC m-2 d-1 that is the evidence of oligotrophy of the Laptev Sea at the end of summer and at the beginning of autumn.


2013 ◽  
Vol 10 (1) ◽  
pp. 1345-1399 ◽  
Author(s):  
M. Ardyna ◽  
M. Babin ◽  
M. Gosselin ◽  
E. Devred ◽  
S. Bélanger ◽  
...  

Abstract. Predicting water-column phytoplankton biomass from near-surface measurements is a common approach in biological oceanography, particularly since the advent of satellite remote sensing of ocean color (OC). In the Arctic Ocean, deep subsurface chlorophyll maxima (SCMs) that significantly contribute to primary production (PP) are often observed. These are neither detected by ocean color sensors nor accounted for the primary production models applied to the Arctic Ocean. Here, we assemble a large database of pan-Arctic observations (i.e. 5206 stations) and develop an empirical model to estimate vertical chlorophyll a (chl a) according to: (1) the shelf-offshore gradient delimited by the 50 m isobath, (2) seasonal variability along pre-bloom, post-bloom and winter periods, and (3) regional differences across ten sub-Arctic and Arctic seas. Our detailed analysis of the dataset shows that, for the pre-bloom and winter periods, as well as for high surface chl a concentration (chl asurf; 0.7–30 mg m−3) throughout the open water period, the chl a maximum is mainly located at or near the surface. Deep SCMs occur chiefly during the post-bloom period when chl asurf is low (0–0.5 mg m−3). By applying our empirical model to annual chl asurf time series, instead of the conventional method assuming vertically homogenous chl a, we produce novel pan-Arctic PP estimates and associated uncertainties. Our results show that vertical variations in chl a have a limited impact on annual depth-integrated PP. Small overestimates found when SCMs are shallow (i.e. pre-bloom, post-bloom > 0.05 mg m−3 and the winter period) somehow compensate for the underestimates found when SCMs are deep (i.e. post-bloom < 0.05 mg m−3). SCMs are, however, important seasonal features with a substantial impact on depth-integrated PP estimates, especially when surface nitrate is exhausted in the Arctic Ocean and where highly stratified and oligotrophic conditions prevail.


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