Wave induced turbulence effect on oceanic biogeochemistry and study of ocean color response to changing wave climate

2021 ◽  
Author(s):  
Chinglen Meetei Tensubam ◽  
Alexander V. Babanin
Keyword(s):  
Net Primary Productivity ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Ocean Color ◽  
Ocean Waves ◽  
Wave Climate ◽  
Upper Ocean ◽  
P Value ◽  
Chl A ◽  
Wave Induced

<p>The role of surface ocean waves becomes substantial in the upper ocean layer mixing. Due to turbulence induced by the surface waves (both broken and unbroken waves), the upper ocean mixing is enhanced, and important upper ocean parameters are affected such as lowering of sea surface temperature (SST), deepening of mixed layer depth (MLD) and most interestingly, the changes in oceanic biogeochemistry. The main objective of this study is to analyze the effect of wave induced turbulence on oceanic biogeochemistry such as the supply and distribution of nutrients to tiny plants in the ocean called phytoplanktons, and how it affects their concentrations. Marine phytoplanktons formed the basis of marine ecosystem which accounts for about 45 percent of global net primary productivity and play an important part in global carbon cycle. The population of phytoplanktons depends mainly on nutrients (both micro and macro), availability of sunlight and grazing organisms. For this study, we use global coupled ocean-sea ice model ACCESS-OM2 with biogeochemical module called WOMBAT to estimate the effect of wave induced turbulence and study the difference between ‘with waves’ and ‘without waves’ effect on oceanic biogeochemistry. The same effect of wave induced turbulence on oceanic biogeochemistry are also studied by incorporating the change in wave climate such as increase in significant wave height and wind speed. From the investigation of merged satellite ocean color data from ESA’s GlobColour project for the period of 23 years between 1997 and 2019, it was found that chlorophyll-a (Chl-a, an index of phytoplankton biomass) concentration showed increasing trend of 0.015 mg/m3 globally and 0.062 mg/m3 in the Southern Ocean (SO) for the study period with p-value less than 0.01. It was also found that most of the increasing trends are shown spatially in the open ocean and decreasing trend in the coastal regions during the study period.</p>

scholarly journals Delineation of marine ecosystem zones in the northern Arabian Sea during winter

2018 ◽  
Vol 15 (5) ◽  
pp. 1395-1414 ◽  
Author(s):  
Saleem Shalin ◽  
Annette Samuelsen ◽  
Anton Korosov ◽  
Nandini Menon ◽  
Björn C. Backeberg ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Temporal Variability ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Chlorophyll Concentration ◽  
Spatial And Temporal Variability ◽  
Chl A ◽  
Northern Arabian Sea ◽  
Cross Correlation Analysis

Abstract. The spatial and temporal variability of marine autotrophic abundance, expressed as chlorophyll concentration, is monitored from space and used to delineate the surface signature of marine ecosystem zones with distinct optical characteristics. An objective zoning method is presented and applied to satellite-derived Chlorophyll a (Chl a) data from the northern Arabian Sea (50–75∘ E and 15–30∘ N) during the winter months (November–March). Principal component analysis (PCA) and cluster analysis (CA) were used to statistically delineate the Chl a into zones with similar surface distribution patterns and temporal variability. The PCA identifies principal components of variability and the CA splits these into zones based on similar characteristics. Based on the temporal variability of the Chl a pattern within the study area, the statistical clustering revealed six distinct ecological zones. The obtained zones are related to the Longhurst provinces to evaluate how these compared to established ecological provinces. The Chl a variability within each zone was then compared with the variability of oceanic and atmospheric properties viz. mixed-layer depth (MLD), wind speed, sea-surface temperature (SST), photosynthetically active radiation (PAR), nitrate and dust optical thickness (DOT) as an indication of atmospheric input of iron to the ocean. The analysis showed that in all zones, peak values of Chl a coincided with low SST and deep MLD. The rate of decrease in SST and the deepening of MLD are observed to trigger the algae bloom events in the first four zones. Lagged cross-correlation analysis shows that peak Chl a follows peak MLD and SST minima. The MLD time lag is shorter than the SST lag by 8 days, indicating that the cool surface conditions might have enhanced mixing, leading to increased primary production in the study area. An analysis of monthly climatological nitrate values showed increased concentrations associated with the deepening of the mixed layer. The input of iron seems to be important in both the open-ocean and coastal areas of the northern and north-western parts of the northern Arabian Sea, where the seasonal variability of the Chl a pattern closely follows the variability of iron deposition.

scholarly journals Delineation of marine ecosystem zones in the northern Arabian Sea using an objective method

2017 ◽  
Author(s):  
Saleem Shalin ◽  
Annette Samuelsen ◽  
Anton Korosov ◽  
Nandini Menon ◽  
Björn C. Backeberg ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Temporal Variability ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Chlorophyll Concentration ◽  
Spatial And Temporal Variability ◽  
Chl A ◽  
Northern Arabian Sea ◽  
Cross Correlation Analysis

Abstract. The spatial and temporal variability of marine autotrophic abundance, expressed as chlorophyll concentration, is monitored from space and used to delineate the surface signature of marine ecosystem zones with distinct optical characteristics. An objective zoning method is presented and applied to satellite-derived Chlorophyll a (Chl-a) data from the northern Arabian Sea (50°–75° E and 15°–30° N) during the winter months (November–March). Principal Component Analysis (PCA) and Cluster Analysis (CA) were used to statistically delineate the Chl-a into zones with similar surface distribution patterns and temporal variability. The PCA identifies principal components of variability and the CA splits these into zones based on similar characteristics. Based on the temporal variability of Chl-a pattern within the study area, the statistical clustering revealed six distinct ecological zones. The obtained zones are related to the Longhurst provinces to evaluate how these compared to established ecological provinces. The Chl-a variability within each zone was then compared with the variability of oceanic and atmospheric properties viz. mixed-layer depth (MLD), wind speed, sea-surface temperature (SST), Photosynthetically Active Radiation (PAR), nitrate and Dust Optical Thickness (DOT) as an indication of atmospheric input of iron to the ocean. The analysis showed that in all zones, peak values of Chl-a coincided with low SST and deep MLD. Rate of decrease in SST and deepening of MLD are observed to trigger the intensity of the algae bloom events in the first four zones. Lagged cross-correlation analysis shows that peak Chl-a follows peak MLD and SST minima. The MLD time-lag is shorter than the SST lag by eight days, indicating that the cool surface conditions might have enhanced mixing, leading to increased primary production in the study area. An analysis of monthly climatological nitrate values showed increased concentrations associated with the deepening of the mixed-layer. The input of iron seems to be important in both the open ocean and coastal areas of the northern and north-western part of the Northern Arabian Sea, where the seasonal variability of the Chl-a pattern closely follows the variability of iron deposition.

scholarly journals Satellite detection of dinoflagellate blooms off California by UV reflectance ratios

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Mati Kahru ◽  
Clarissa Anderson ◽  
Andrew D. Barton ◽  
Melissa L. Carter ◽  
Dylan Catlett ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Marine Ecosystem ◽  
Ocean Color ◽  
Optical Data ◽  
Blue Band ◽  
Chl A ◽  
Color Sensor ◽  
Uv Reflectance

As harmful algae blooms are increasing in frequency and magnitude, one goal of a new generation of higher spectral resolution satellite missions is to improve the potential of satellite optical data to monitor these events. A satellite-based algorithm proposed over two decades ago was used for the first time to monitor the extent and temporal evolution of a massive bloom of the dinoflagellate Lingulodinium polyedra off Southern California during April and May 2020. The algorithm uses ultraviolet (UV) data that have only recently become available from the single ocean color sensor on the Japanese GCOM-C satellite. Dinoflagellates contain high concentrations of mycosporine-like amino acids and release colored dissolved organic matter, both of which absorb strongly in the UV part of the spectrum. Ratios <1 of remote sensing reflectance of the UV band at 380 nm to that of the blue band at 443 nm were used as an indicator of the dinoflagellate bloom. The satellite data indicated that an observed, long, and narrow nearshore band of elevated chlorophyll-a (Chl-a) concentrations, extending from northern Baja to Santa Monica Bay, was dominated by L. polyedra. In other high Chl-a regions, the ratios were >1, consistent with historical observations showing a sharp transition from dinoflagellate- to diatom-dominated waters in these areas. UV bands are thus potentially useful in the remote sensing of phytoplankton blooms but are currently available only from a single ocean color sensor. As several new satellites such as the NASA Plankton, Aerosol, Cloud, and marine Ecosystem mission will include UV bands, new algorithms using these bands are needed to enable better monitoring of blooms, especially potentially harmful algal blooms, across large spatiotemporal scales.

scholarly journals Satellite-Derived Protein Concentration of Phytoplankton in the Southwestern East/Japan Sea

2021 ◽  
Vol 9 (2) ◽  
pp. 189
Author(s):  
Hyeonji Bae ◽  
Dabin Lee ◽  
Jae Joong Kang ◽  
Jae Hyung Lee ◽  
Naeun Jo ◽  
...  
Keyword(s):  
Protein Concentration ◽  
Japan Sea ◽  
Biological Properties ◽  
Measured Data ◽  
Trophic Levels ◽  
Coefficient Of Determination ◽  
P Value ◽  
Chl A ◽  
Major Factors

The cellular macromolecular contents and energy value of phytoplankton as primary food source determine the growth of higher trophic levels, affecting the balance and sustainability of oceanic food webs. Especially, proteins are more directly linked with basic functions of phytoplankton biosynthesis and cell division and transferred through the food chains. In recent years, the East/Japan Sea (EJS) has been changed dramatically in environmental conditions, such as physical and chemical characteristics, as well as biological properties. Therefore, developing an algorithm to estimate the protein concentration of phytoplankton and monitor their spatiotemporal variations on a broad scale would be invaluable. To derive the protein concentration of phytoplankton in EJS, the new regional algorithm was developed by using multiple linear regression analyses based on field-measured data which were obtained from 2012 to 2018 in the southwestern EJS. The major factors for the protein concentration were identified as chlorophyll-a (Chl-a) and sea surface nitrate (SSN) in the southwestern EJS. The coefficient of determination (r2) between field-measured and algorithm-derived protein concentrations was 0.55, which is rather low but reliable. The satellite-derived estimation generally follows the 1:1 line with the field-measured data, with Pearson’s correlation coefficient, which was 0.40 (p-value < 0.01, n = 135). No remarkable trend in the long-term annual protein concentration of phytoplankton was found in the study area during our observation period. However, some seasonal difference was observed in winter protein concentration between the 2003–2005 and 2017–2019 periods. The algorithm is developed for the regional East/Japan Sea (EJS) and could contribute to long-term monitoring for climate-associated ecosystem changes. For a better understanding of spatiotemporal variation in the protein concentration of phytoplankton in the EJS, this algorithm should be further improved with continuous field surveys.

scholarly journals Summertime increases in upper-ocean stratification and mixed-layer depth

Nature ◽  
2021 ◽  
Vol 591 (7851) ◽  
pp. 592-598
Author(s):  
Jean-Baptiste Sallée ◽  
Violaine Pellichero ◽  
Camille Akhoudas ◽  
Etienne Pauthenet ◽  
Lucie Vignes ◽  
...  
Keyword(s):  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Upper Ocean ◽  
Layer Depth ◽  
Ocean Stratification

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

scholarly journals Assessing Phytoplankton Bloom Phenology in Upwelling-Influenced Regions Using Ocean Color Remote Sensing

2021 ◽  
Vol 13 (4) ◽  
pp. 675
Author(s):  
Afonso Ferreira ◽  
Vanda Brotas ◽  
Carla Palma ◽  
Carlos Borges ◽  
Ana C. Brito
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Phytoplankton Bloom ◽  
Ocean Color ◽  
Coastal Region ◽  
Coastal Areas ◽  
Ocean Color Remote Sensing ◽  
Chl A ◽  
Peak Biomass ◽  
Phytoplankton Communities

Phytoplankton bloom phenology studies are fundamental for the understanding of marine ecosystems. Mismatches between fish spawning and plankton peak biomass will become more frequent with climate change, highlighting the need for thorough phenology studies in coastal areas. This study was the first to assess phytoplankton bloom phenology in the Western Iberian Coast (WIC), a complex coastal region in SW Europe, using a multisensor long-term ocean color remote sensing dataset with daily resolution. Using surface chlorophyll a (chl-a) and biogeophysical datasets, five phenoregions (i.e., areas with coherent phenology patterns) were defined. Oceanic phytoplankton communities were seen to form long, low-biomass spring blooms, mainly influenced by atmospheric phenomena and water column conditions. Blooms in northern waters are more akin to the classical spring bloom, while blooms in southern waters typically initiate in late autumn and terminate in late spring. Coastal phytoplankton are characterized by short, high-biomass, highly heterogeneous blooms, as nutrients, sea surface height, and horizontal water transport are essential in shaping phenology. Wind-driven upwelling and riverine input were major factors influencing bloom phenology in the coastal areas. This work is expected to contribute to the management of the WIC and other upwelling systems, particularly under the threat of climate change.

scholarly journals Remote Sensing Supported Sea Surface pCO2 Estimation and Variable Analysis in the Baltic Sea

2021 ◽  
Vol 13 (2) ◽  
pp. 259
Author(s):  
Shuping Zhang ◽  
Anna Rutgersson ◽  
Petra Philipson ◽  
Marcus B. Wallin
Keyword(s):  
Remote Sensing ◽  
Baltic Sea ◽  
Mean Squared Error ◽  
Numerical Models ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
The Baltic Sea ◽  
Chl A ◽  
Marginal Seas ◽  
The Baltic

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.

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