Biogeochemical and physical properties influencing the nutrient reservoirs of subsurface water in the changing Canada Basin

The Canada Basin is the largest basin in the Arctic Ocean.Its unique physical features have the highest concentration of nutrients being found in the subsurface layer,referred to as the subsurface nutrient maximum layer(SNM).Under climate change in the Arctic,the SNM is an essential material base for primary productivity.However,long-term trends of nutrient variations and dominant factors related to nutrient levels in the SNM are still unclear.In this study,we analyzed the SNM variations and main influencing factors of the Canada Basin based on the Global Ocean Data Analysis Project Version 2 between 1990 and 2015 and the Chinese Arctic Research Expedition between 2010 and 2016.We found that the nutrient concentrations in the SNM were relatively stable for decades[average concentrations of nitrate,phosphate,and silicate were(13.6±2.4)μmol/L,(1.8±0.2)μmol/L,and(31.5±5.7)μmol/L,respectively].Nutrient reservoirs were dominated by physical processes.Inflow and outflow water of the SNM contributed about 60.4%and-50.2%to the nutrient stocks,respectively,while particle deposition and remineralization in the Canada Basin contributed approximately one-third to the nutrient stocks.Nitrogen fixation and denitrification in the Canada Basin had no substantial impact on nutrient stocks.The overall stabilization of the SNM over the past few decades implied that the SNM would not substantially affect short term primary productivity.Understanding the long-term trends and dominant factors of reservoirs in the SNM will provide useful insights into the changing Canada Basin ecosystem.

Vertical characteristics revealed by biweekly and synoptic variability of upper sea temperature in the northern South China Sea

On the basis of the analysis of the sea temperature data that are observed from the three automatic temperature line acquisition sysem mooring buoys deployed in the central South China Sea (SCS) during South China Sea monsoon experiment, vertical features of bi- weekly and synoptic variability are discussed. There are five vertical modes, that is, subsurface temperature variability is in phase with, out of phase with, leads to, lags the surface temperature variability, and at depths within the subsurface layer the upper and lower tem- perature variations are out of phase. The formation of these vertical modes is related to the property of low-level atmospheric forcing and to the background in atmosphere and ocean. Wind stress curl is the main driving factor in forming Modes 1 and 3, and wind stress- es in forming Modes 2 and 4.

Vertical variations in the bio-optical properties of seawater in the northern South China Sea during summer 2008

Vertical variability in the bio-optical properties of seawater in the northern South China Sea(NSCS)including inherent optical properties(IOPs)and chlorophyll a concentration(Chl)were studied on the basis of in situ data collected in summer 2008 using an absorption/attenuation spectrophotometer.An empirical model was developed to estimate Chl profiles based on the absorption line height at long wavelengths,with a relative root mean square error of 37.03%.Bio-optical properties exhibited large horizontal and vertical spatial variability.As influenced by coastal upwelling and the Zhujiang River(Pearl River)discharge,both IOPs and Chl exhibited high values in the surface waters of the inner shelf,which tended to decrease with distance offshore.Subsurface maximum layers of IOPs and Chl were observed in the middle and outer shelf regions,along with significantly higher values of attenuation coefficients beneath this layer that rapidly increased towards the bottom.In the open ocean,both IOPs and Chl exhibited consistent variability,with the subsurface maximum layer typically located at34–84 m.Phytoplankton were found to be one of the major components in determining the vertical variability of bio-optical properties,with their vertical dynamics influenced by both physical forcing and light attenuation effects.The depth of the subsurface maximum layer was found to be closely related to the fluctuation of the oceanic thermocline and the depth of the euphotic zone,which also affected the total integrated biomass of the upper ocean.Typically high values of attenuation coefficients observed in the bottom waters of the continental shelf reflected the transport of particulate matter over the bottom boundary layer.Our results reveal large spatial differences in bio-optical profiles in response to complex marine ecodynamics in the NSCS.From the perspective of marine research,high-resolution optical measurements are clearly advantageous over conventional bottle sampling.

Verification of an operational ocean circulation-surface wave coupled forecasting system for the China＇s seas

An operational ocean circulation-surface wave coupled forecasting system for the seas off China and adjacent areas（OCFS-C） is developed based on parallelized circulation and wave models. It has been in operation since November 1, 2007. In this paper we comprehensively present the simulation and verification of the system, whose distinguishing feature is that the wave-induced mixing is coupled in the circulation model. In particular, with nested technique the resolution in the China＇s seas has been updated to（1/24）° from the global model with（1/2）°resolution. Besides, daily remote sensing sea surface temperature（SST） data have been assimilated into the model to generate a hot restart field for OCFS-C. Moreover, inter-comparisons between forecasting and independent observational data are performed to evaluate the effectiveness of OCFS-C in upper-ocean quantities predictions, including SST, mixed layer depth（MLD） and subsurface temperature. Except in conventional statistical metrics, non-dimensional skill scores（SS） is also used to evaluate forecast skill. Observations from buoys and Argo profiles are used for lead time and real time validations, which give a large SS value（more than 0.90）. Besides, prediction skill for the seasonal variation of SST is confirmed. Comparisons of subsurface temperatures with Argo profiles data indicate that OCFS-C has low skill in predicting subsurface temperatures between 100 m and 150 m. Nevertheless, inter-comparisons of MLD reveal that the MLD from model is shallower than that from Argo profiles by about 12 m, i.e., OCFS-C is successful and steady in MLD predictions. Validation of 1-d, 2-d and 3-d forecasting SST shows that our operational ocean circulation-surface wave coupled forecasting model has reasonable accuracy in the upper ocean.