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A station of AErosol RObotic NETwork Ocean Color (AERONET-OC) has been set on the Ariake Observation Tower of Saga University on April, 2018, for verification of the Second generation Global Imager (SGLI)/Global Change Observation Mission-Climate (GCOM-C). Remote sensing reflectance (Rrs) observed by the AERONET-OC was used for verification of SGLI. SGLI Version 1 data underestimated the shortwave Rrs and Rrs (380) and Rrs(412) were mostly negative, while the estimation was improved by Version 2 with the correction of Rrs(412) to be positive. It was indicated that absorptive aerosol was influenced to SGLI atmospheric correction and caused the underestimation of Rrs. Simple linear correction method to improve shortwave Rrs also worked well for specifically Version 1 data. Water constituents, chlorophyll-a (Chl-a), total suspended matter (TSM) and colored dissolved organic matter (CDOM) of the SGLI were also verified by the ship observation data. All constituents were improved from Version 1 to 2 with the correction of Rrs, although Version 2 underestimated Chl-a and CDOM. Simple regression algorithms were also examined with in situ as well as SGLI Rrs data, and it indicated that more sophisticated algorithms may be required. Time series of water constituents derived from AERONET-OC and SGLI data were compared to river discharge and spring–neap tidal cycle. The results indicated that the behavior, such as the increase of Chl-a after river discharge and interaction of Chl-a and TSM with the spring–neap tidal cycles were observed, although estimations of water constituents were not completely separated by the algorithms.

Seasonal and interannual variations of chlorophyll-a (chl-a) in the upper Gulf of Thailand (uGoT) were obtained using new regionally tuned algorithms applied to Moderate Resolution Imaging Spectroradiometer-Aqua. This long time-series (2003–2017) data were analyzed in the context of variations in environmental conditions associated with the Southeast Asian Monsoon. Chl-a distribution patterns were distinct for the non-monsoon (NOM), southwest-monsoon (SWM), and northeast-monsoon (NEM) seasons. During the SWM/NEM, high/low chl-a concentrations were associated with high/low precipitation and river discharge. During the NOM chl-a concentrations were generally low, because of low precipitation. In general, chl-a variability was tightly coupled to discharge from the Chao Phraya and Tha Chin rivers. Chl-a concentrations were generally higher in the north, but chl-a accumulation in the east/west of the uGoT could be linked to piling of freshwater to the east/west during the SWM/NEM caused by changes in wind direction and the reversal of currents. Interannual changes in chl-a were attributed to El Niño-Southern Oscillation (ENSO) rather than Indian Ocean Dipole (IOD) driven changes in precipitation, river discharge, and wind patterns. During the SWM, positive/negative chl-a anomalies coincided with high/low precipitation and river discharge during La Niña/El Niño. During the NEM, positive/negative chl-a anomaly coincided with high/low river discharge and strong/weak wind during La Niña/El Niño. Meanwhile, during NOM, positive chl-a anomaly could be attributed to anomalous high wind speed and precipitation during El Niño.

The 13th Symposium on Polar Science/Interdisciplinary sessions [IA] Arctic research, Thu. 17 Nov.

Eutrophication is an emerging global issue associated with increasing anthropogenic nutrient loading. The impacts and extent of eutrophication are often limited to regions with dedicated monitoring programmes. Here we introduce the first global and Google Earth Engine-based interactive assessment tool of coastal eutrophication potential (CEP). The tool evaluates trends in satellite-derived chlorophyll-a (CHL) to devise a global map of CEP. Our analyses suggest that, globally, coastal waters (depth ≤200 m) covering ∼1.15 million km2 are eutrophic potential. Also, waters associated with CHL increasing trends—eutrophication potential—are twofold higher than those showing signs of recovery. The tool effectively identified areas of known eutrophication with severe symptoms, like dead zones, as well as those with limited to no information of the eutrophication. Our tool introduces the prospect for a consistent global assessment of eutrophication trends with major implications for monitoring Sustainable Development Goals (SDGs) and the application of Earth Observations in support of SDGs., Satellite-derived chlorophyll data and Google Earth Engine (GEE) are used to introduce the first global map of coastal eutrophication potential as a GEE app. The prospects of the app being used as a global framework for eutrophication screening/monitoring are discussed.

In Mikawa Bay, where hypoxia occurs in the bottom layer during summer, six shipboard observations were conducted from the mouth to the head of the bay from May to August 2014 to investigate the spatiotemporal variation in the bottom layer oxygen consumption rate (OCR). The OCR was determined from the dark incubation of sample waters using an optical oxygen sensor, which showed a range of 5.7-38.3 mmol m

This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system in which the local ensemble transform Kalman filter (LETKF) is implemented with version 1.0 of the Stony Brook Parallel Ocean Model (sbPOM) to assimilate satellite and in situ observations at a daily frequency. A series of sensitivity experiments are performed with various settings of the incremental analysis update (IAU) and covariance inflation methods, for which the relaxation-to-prior perturbations and spread (RTPP and RTPS, respectively) and multiplicative inflation (MULT) are considered. We evaluate the geostrophic balance and the analysis accuracy compared with the control experiment in which the IAU and covariance inflation are not applied. The results show that the IAU improves the geostrophic balance, degrades the accuracy, and reduces the ensemble spread, and that the RTPP and RTPS have the opposite effect. The experiment using a combination of the IAU and RTPP results in a significant improvement for both balance and analysis accuracy when the RTPP parameter is 0.8–0.9. The combination of the IAU and RTPS improves the balance when the RTPS parameter is ≤0.8 and increases the analysis accuracy for parameter values between 1.0 and 1.1, but the balance and analysis accuracy are not improved significantly at the same time. The experiments with MULT inflating the forecast ensemble spread by 5 % do not demonstrate sufficient skill in maintaining the balance and reproducing the surface flow field regardless of whether the IAU is applied or not. The 11 d ensemble forecast experiments show consistent results. Therefore, the combination of the IAU and RTPP with a parameter value of 0.8–0.9 is found to be the best setting for the EnKF-based ocean data assimilation system.

Despite the importance of the primary production in coastal Large Marine Ecosystems, the complex bio-optical properties in some coastal waters make the estimation of primary productivity using satellite data quite challenging. Here we critically reviewed the accuracy of parametrization of three core variables, i.e. chlorophyll-a, chlorophyll-specific photosynthetic rate, and euphotic depth, in the Yellow Sea, East China Sea, and Sea of Japan/East Sea Large Marine Ecosystems. We also compared the annual primary productivity estimates of the three Large Marine Ecosystems by a depth-integrated model with those from two other global assessments, which used different primary productivity models: an absorption-based model, and a time and depth-resolved model. Our assessment suggests that 1) accurate parametrization of the core variables is more important than choosing a primary productivity model, and 2) the previous global Large Marine Ecosystem assessments might have overestimated the annual primary productivity in the Yellow Sea by a factor of 2 or so.

There is data for the journal of "Detection and tracking of Chattonella spp. and Skeletonema spp.blooms using Geostationary Ocean Color Imager (GOCI) in Ariake Sea, JapanDetection and tracing of Chattonella spp. and diatom blooms using Geostationary Ocean Color Imager (GOCI) in Ariake Sea, Japan" Excel data for method developement and method verification was uploaded

We investigated the spatio-temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) associated with spring&ndash, neap tidal cycles in the Ariake Sea, Japan. Our study relied on significantly improved, regionally-tuned datasets derived from the ocean color sensor Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua over a 16-year period (2002&ndash, 2017). The results revealed that spring&ndash, neap tidal variations in Chl-a and TSM within this macrotidal embayment (the Ariake Sea) are clearly different regionally and seasonally. Generally, the spring&ndash, neap tidal variability of Chl-a in the inner part of the Ariake Sea was controlled by TSM for seasons other than summer, whereas it was controlled by river discharge for summer. On the other hand, the contribution of TSM to the variability of Chl-a was not large for two areas in the middle of Ariake Sea where TSM was not abundant. This study demonstrates that ocean color satellite observations of Chl-a and TSM in the macrotidal embayment offer strong advantages for understanding the variations during the spring&ndash, neap tidal cycle.

The raphidophyte Chattonella spp. and diatom Skeletonema spp. are the dominant harmful algal species of summer blooms in Ariake Sea, Japan. A new bio-optical algorithm based on backscattering features has been developed to differentiate harmful raphidophyte blooms from diatom blooms using MODIS imagery. Bloom waters were first discriminated from other water types based on the distinct spectral shape of the remote sensing reflectance R r s (λ) data derived from MODIS. Specifically, bloom waters were discriminated by the positive value of Spectral Shape, SS (645), which arises from the R r s (λ) shoulder at 645 nm in bloom waters. Next, the higher cellular-specific backscattering coefficient, estimated from MODIS data and quasi-analytical algorithm (QAA) of raphidophyte, Chattonella spp., was utilized to discriminate it from blooms of the diatom, Skeletonema spp. A new index b b p − i n d e x ( 555 ) was calculated based on a semi-analytical bio-optical model to discriminate the two algal groups. This index, combined with a supplemental Red Band Ratio (RBR) index, effectively differentiated the two bloom types. Validation of the method was undertaken using MODIS satellite data coincident with confirmed bloom observations from local field surveys, which showed that the newly developed method, based on backscattering features, could successfully discriminate the raphidophyte Chattonella spp. from the diatom Skeletonema spp. and thus provide reliable information on the spatial distribution of harmful blooms in Ariake Sea.

The surface distribution of the phytoplankton community was investigated in July 2009, 2010, 2011, and 2013 on the mid-shelf of the East China Sea (ECS), which is under the influence of Changjiang River Diluted Water (CDW) and the Kuroshio Current. This study, based on a CHEMTAX analysis of phytoplankton pigments, revealed a predominance of cyanobacteria and prochlorophytes in the eastern ECS, which is perennially under the influence of oligotrophic Kuroshio Surface Water. Towards the west, on the mid-shelf of the ECS, the composition of the phytoplankton community varied from year to year. Diatoms dominated in 2009 and 2013, when dissolved inorganic phosphate (DIP) concentrations were higher than during 2010 and 2011. During the latter two years, characterized as high-nitrate years, a mixed population of cyanobacteria, chlorophytes, and other groups was observed. Cluster analysis based on the phytoplankton community composition together with a PCA of shipboard hydrographic and nutrient data for all four years helped to confirm that the summer phytoplankton community structure of the ECS was regulated by the mixing of water masses and the variability of nutrient ratios within the CDW as it moved offshore. Our results show that elevated DIP concentrations in the CDW favor the growth of diatoms and dinoflagellates. The primary pathway for DIP inputs appears to be the upwelling of high-phosphate subsurface waters along the coast of China.

To confirm the usefulness of optical oxygen sensors in hypoxia-related research in coastal regions, a contactless optical sensor (COS) (FireStingO2, Pyroscience) was used to measure dissolved oxygen concentrations, and the values obtained were compared with those obtained using the traditional Winkler method. During spring and summer seasons, bottom water samples from the Mikawa Bay, where hypoxia frequently occurs, were analyzed. The correlation between the oxygen concentrations obtained using the COS and Winkler methods had a slope of 1.01, an intercept of −6.17 μmol kg−1, and a coefficient of determination of 0.97. Further, when the oxygen consumption rates in the bottom water (OCRbw) were measured using these two methods, the slope, intercept, and coefficient of determination of the correlation between these values were 1.05, −0.08 μmol kg−1 d−1, and 0.63, respectively. These findings indicate that the COS method is suitable for obtaining accurate oxygen concentrations as well as OCRbw values for coastal waters, but it is still difficult to adapt it to small OCRbw values in the open ocean. The highest OCRbw (38.3 μmol kg−1 d−1) was observed at the inner part of the Bay in the end of August. Furthermore, the COS method also allowed the measurement of oxygen concentrations in the water overlaying the sediment core (OCRs + w), which in the Mikawa Bay were 3–5-fold higher than the corresponding OCRbw values. Observations made using the COS method in the Upper Gulf of Thailand (UGoT), which is also characterized by a highly hypoxic bottom layer, showed high OCRbw (>30 μmol kg−1 d−1) and OCRs + w (>40 μmol kg−1 d−1) near the estuarine of large rivers not in the rainy season but also in the dry season. The COS method has potential for use in the collection of OCR data that can significantly contribute to the accuracy of hypoxia prediction models.

Sea surface salinity (SSS) in coastal oceans is a direct indicator of riverine plumes and provides essential information about the ocean environment and ecosystem, which affects coastal fisheries, aquaculture, and marine harvests. However, to accurately capture SSS patterns in coastal oceans, high temporal and spatial resolutions are required. This paper introduces a methodology to produce high-resolution (~ 500 m) SSS maps for analysis of river plumes in coastal oceans based on hourly chromophoric dissolved organic matter data collected by the Geostationary Ocean Color Imager. Osaka Bay, located in the eastern Seto Inland Sea, was selected as a pilot region. A comparison between the initial estimates and calibrated SSS data showed a substantial decrease in estimation error, by up to 71%, over a wide range of salinity (20–34) using in situ SSS data collected through an automated observation system. Calculating the salinity anomaly based on the SSS map to identify plume areas, we evaluated the impact of a large runoff event induced by a super typhoon on the river plumes. After the plume formed in the estuary, it extended southward to the bay mouth along the southeastern coast. The plume area during the post-typhoon period covered half of the bay, approximately 1.5 times the area during the pre-typhoon period. The post-typhoon, low-SSS period continued for approximately 2 weeks. Our approach can be of practical use for analyzing the dynamics of river plumes in coastal oceans, leading to the development of coastal ocean prediction models related to operational oceanography., ファイル公開：2019-06-01

Intensive surveys with repeated CTD and microstructure turbulent observations, water and sediments sampling as well as onboard incubation and sediment trap experiments were conducted to reveal the nitrogen budget in the center of the East China Sea (ECS) during July 2010 and 2011. Low salinity water (Changjiang Diluted Water, CDW) covered the study area in 2010, but not in 2011. Higher chlorophyll a (chl. a ) concentration, primary productivity, and downward particle flux in the upper layer were observed in 2010 than those in 2011. Existence of the CDW resulted in a steep pycnocline and an associated subsurface chl. a maximum (SCM) layer directly beneath the CDW. From chemical analyses of particulate carbon and nitrogen contents and isotope ratios, it became apparent that the particles sunk out the euphotic zone in 2010 was primarily originated in the CDW layer and secondly in the SCM layer. Whereas, in 2011, sinking particles were originated in the surface layer but a part of them were decomposed in the bottom of pycnocline. Our findings indicate that the CDW would supply particles into the deep layer and contribute to the downward transport of materials and the efficiency of biological pump in the ECS.

Quantifying the diffuse attenuation coefficient of the photosynthetically available radiation (Kpar) can improve our knowledge of euphotic depth (Zeu) and biomass heating effects in the upper layers of oceans. An algorithm to semi-analytically derive Kpar from remote sensing reflectance (Rrs) is developed for the global open oceans. This algorithm includes the following two portions: (1) a neural network model for deriving the diffuse attention coefficients (Kd) that considers the residual error in satellite Rrs, and (2) a three band depth-dependent Kpar algorithm (TDKA) for describing the spectral selective attenuation mechanism of underwater solar radiation in the open oceans. This algorithm is evaluated with both in situ PAR profile data and satellite images, and the results show that it can produce acceptable PAR profile estimations while clearly removing the impacts of satellite residual errors on Kpar estimations. Furthermore, the performance of the TDKA algorithm is evaluated by its applicability in Zeu derivation and mean temperature within a mixed layer depth (TML) simulation, and the results show that it can significantly decrease the uncertainty in both compared with the classical chlorophyll-a concentration-based Kpar algorithm. Finally, the TDKA algorithm is applied in simulating biomass heating effects in the Sargasso Sea near the Bermuda, with new Kpar data it is found that the biomass heating effects can lead to a 3.4°C maximum warming in the upper layers but could result in a 0.67°C maximum cooling in the deep layers.

Fast repetition rate fluorometry (FRRF) provides a means to examine primary productivity at high resolution across broad scales, but must be coupled with independent knowledge of the electron requirement for carbon uptake (KC) to convert FRRF-measured electron transfer rate (ETR) to an inorganic carbon (C) uptake rate. Previous studies have demonstrated that variability of KC can be explained by key environmental factors (e.g. light, nutrients, temperature). However, how such reconciliation of KC reflects changes of phytoplankton physiological status versus that of community composition has not been well resolved. Therefore, using a dataset of coupled FRRF and C uptake measurements, we examined how the environmental dependency of KC potentially varied with parallel changes in phytoplankton community structure. Data were combined from 14 campaigns conducted during the summer season throughout 2007 to 2014 in the East China Sea (ECS) and Tsushima Strait (TS). KC varied considerably, but this variability was best explained by a linear relationship with light availability (R^2 = 0.66). Co-variability between KC and light availability was slightly improved by considering data as 2 clusters of physico-chemical conditions (R^2 = 0.74), but was best improved as 2 taxonomic clusters: samples dominated by micro-phytoplankton (>20 µm) versus small phytoplankton (nano + pico, ファイル公開：2018-09-29

In summer, Changjiang Diluted Water (CDW) expands over the shelf region of the northern East China Sea. Dilution of the low salinity water could be caused by vertical mixing through the halocline. Vertical mixing through the pycnocline can transport not only saline water, but also high nutrient water from deeper layers to the surface euphotic zone. It is therefore very important to quantitatively evaluate the vertical mixing to understand the process of primary production in the CDW region. We conducted extensive measurements in the region during the period 2009–2011. Detailed investigations of the relative relationship between the subsurface chlorophyll maximum (SCM) and the nitracline suggested that there were two patterns relating to the N/P ratio. Comparing the depths of the nitracline and SCM, it was found that the SCM was usually located from 20 to 40 m and just above the nitracline, where the N/P ratio within the nitracline was below 15, whereas it was located from 10 to 30 m and within the nitracline, where the N/P ratio was above 20. The large value of the N/P ratio in the latter case suggests the influence of CDW. Turbulence measurements showed that the vertical flux of nutrients with vertical mixing was large (small) where the N/P ratio was small (large). A comparison with a time series of primary production revealed a consistency with the pattern of snapshot measurements, suggesting that the nutrient supply from the lower layer contributes considerably to the maintenance of SCM.

GCOM-C/SGLI is a multi-wavelength optical radiometer launched on December 23, 2017. The data provision has started from December 20, 2018. In this research, we briefly introduce standard Level 2 products and their validation results based on in situ data. Mean absolute percentage differences are 16.3 - 69.6% for NLW between 380 - 670 nm, 39 and 64.5% for AOT at 670 nm and 865 nm, 27.9% for CHL and 44.2% for aCDOM. Although the number of in situ validation data are still scare for a few ocean color products, the accuracy of GCOM-C/SGLI data will be improved by our future efforts of calibration/validation activities.

Predicting conversion of photosynthetic electron transport to inorganic carbon uptake rates (the so-called electron requirement for carbon fixation, KC) is central to the broad scale deployment of Fast Repetition Rate fluorometry (FRRf) for primary productivity studies. However, reconciling variability of KC over space and time to produce robust algorithms remains challenging, given the large number of factors that influence KC. We have previously shown that light appears to be a proximal driver of Kc in several ocean regions and we therefore examined whether and how light similarly regulated KC variability in the Arctic Ocean, during a summer cruise in 2016. Sampling transited ice-free and ice-covered waters, with temperature, salinity and Chl-a concentrations all higher for the ice-free than ice covered surface waters. Micro- and pico-phytoplankton generally dominated the ice-free and ice-covered waters, respectively. Values of KC, determined from parallel measures of daily integrated electron transport rates and 14C-uptake, were overall lower for the ice-covered vs. ice-free stations. As in our previous studies, KC was strongly linearly correlated to daily PAR (r = 0.68, n = 46, p < 0.001) and this relationship could be further improved (r = 0.84, n = 46, p < 0.001) by separating samples into ice-free (micro-phytoplankton dominated) vs. ice-covered (Nano- and Pico-phytoplankton dominated water. We subsequently contrasted the PAR-KC relationship form the Arctic waters with the previous relationships from the Ariake Bay and East China Sea and revealed that these various PAR-KC relationships can be systematically explained across regions by phytoplankton community size structures. Specifically, the value of the linear slope describing PAR-KC decreases as water bodies have an increasing fraction of larger phytoplankton. We propose that this synoptic trend reflects how phytoplankton community structure integrates past and immediate environmental histories and hence may be a better broad-scale predictor of KC than specific environmental factors such as temperature and nutrients. We provide a novel algorithm that may enable broad-scale retrieval of CO2 uptake from FRRf with knowledge of light and phytoplankton community size information.

A study of the use of a visualization technique for the distributions of dissolved organic matter (DOM) in coastal seas was conducted to assist environmental water management. In this study, the absorption coefficient of colored dissolved organic matter (aCDOM) obtained from a satellite equipped with an ocean color sensor as an index for visualizing the DOM distributions. In 2010, the first geostationary satellite having the sensor (COMS/GOCI) was launched, enabling hourly high-resolution data (~500m) on aCDOM to be obtained. The spatial and temporal resolutions of the data derived from this satellite are the most appropriate for investigating the water quality in coastal seas, which changes dynamically depending on weather conditions. A surface water sampling was undertaken in 2015 in Osaka Bay, Japan. The concentrations of dissolved organic carbon and nitrogen, the main components of DOM, were highly correlated with field-obtained aCDOM, (R2 = 0.91, 0.90 (n = 39), respectively). The field-obtained aCDOM was significantly correlated with the satellite-derived aCDOM (R2 = 0.60,(n = 54)). These results indicate the potential to use satellite ocean color sensors (GOCI) to visualize and assess the distribution of DOM in coastal seas at high temporal resolution.

Estimation of chlorophyll concentration in the marine biosphere has been the central topic of ocean color remote sensing since its advent. While various algorithms were proposed in the literature so far and tested for oceanic waters of diverse constituent composition, an independent algorithm evaluation is needed for local ocean waters that have dynamic variation in optically active water constituents such as colored dissolved organic matters (CDOM) and suspended particulate matter (SPM). This paper evaluates the performance of chlorophyll algorithms for Geostationary Ocean Color Imager (GOCI) radiometric data, using in situ measurements collected at 491 stations around Korea Peninsula during 2010–2014 from which there were 130 match-ups with GOCI data. For the evaluation in areas with high variation in SPM, water samples were first classified into three levels of SPM, and then the coefficients of candidate algorithms were newly derived for the turbidity cases using the in situ and GOCI remote sensing reflectance ( R rs ) data. Functional forms of traditional band ratio algorithms ( e.g. OC algorithms (O′Reilly et al., 1998) and Tassan's algorithm (Tassan, 1994)), fluorescence line height algorithm, and near-infrared-to-red band ratio approach were tested. The evaluation results for the coincident in situ pairs of R rs and chlorophyll measurements showed that the mean uncertainty was