Satellite Ocean Color Observations Research Articles

Ocean Variability in the Costa Rica Thermal Dome Region from 2012 to 2021

Satellite ocean color and sea surface temperature (SST) observations from 2012 to 2021 and sea surface salinity (SSS) measurements from the Soil Moisture Active Passive (SMAP) mission from 2015 to 2021 are used to characterize and quantify the seasonal and interannual variability in the physical, optical, and biological sea surface features in the Costa Rica Thermal Dome (CRTD) region. High-resolution climatology and the seasonal variability in SST, SSS, and ocean color properties are produced. Chlorophyll-a (Chl-a) concentration, SST, and SSS show these three properties are linked with similar spatial patterns and seasonal variations, i.e., elevated Chl-a concentrations, match the depressed SST and increased SSS and vice versa. This reflects that the physical driving force is the same for these three ocean properties and implies that nutrient supply associated with the physical processes is the major driver for the seasonal biological variability. The interannual changes in Chl-a, SST, and SSS also show that these three ocean properties are consistent among themselves. The positive (negative) Chl-a anomaly generally occurs with negative (positive) SST anomaly and enhanced (reduced) SSS. The in situ measurements evidently show that the subsurface ocean dynamics in the upper 100 m controls the sea surface variability for Chl-a, SST, and SSS. We report that no significant enhancement of Chl-a is observed in the CRTD region during the central Pacific (CP)-type 2020–2021 La Niña event, while Chl-a changes are significant in the other three of four ENSO events between 2012 and 2021. Furthermore, the difference in Chl-a variability driven by the CP-type ENSO and eastern Pacific (EP)-type ENSO is further discussed.

Particle Size Distribution Slope Changes along the Yellow River Delta Observed from Sentinel 3A/B OLCI Images

Quantitative estimates of particle size in estuaries and shelf areas are important to understand ocean ecology and biogeochemistry. Particle size can be characterized qualitatively from satellite observations of ocean color. As a typical marginal sea, the Yellow River Delta (YRD) with the Bohai Sea experiences a complex hydrodynamic environment. Here, we attempt to quantify the particle size distribution (PSD) slope (ξ) based on its relationship with the particle backscattering exponent from Sentinel-3A/B OLCI. The PSD slope, ξ displays temporal and spatial variability in the YRD with the Bohai Sea. Its value varies between 3 and 4, and typically exceeds 5 in offshore areas. The lowest value of ξ occurs in the winter, indicating the presence of fine inorganic particles in the water, while high values are attained in the spring, when phytoplankton blooms increase the particle size. ξ decreases near the river mouth because of the large sediment-laden discharge debouching into the sea. We detected a slight increase in ξ when turbid waters were present in the period 2016–2022. Environmental factors, such as sea surface temperature, sea surface wave height, and wind, may control particle size and ξ in the long term. Inorganic suspended particle matter is derived along the YRD using the magnitude of ξ. The mean inorganic suspended particle matter area in winter approaches 23,900 km2 when ξ < 4.6. This study thoroughly characterizes variations in ξ in the YRD with the Bohai Sea and clarifies the contributions of driving factors from human activities and climate change.

CoastFLOOD: A High-Resolution Model for the Simulation of Coastal Inundation Due to Storm Surges

Storm surges due to severe weather events threaten low-land littoral areas by increasing the risk of seawater inundation of coastal floodplains. In this paper, we present recent developments of a numerical modelling system for coastal inundation induced by sea level elevation due to storm surges enhanced by astronomical tides. The proposed numerical code (CoastFLOOD) performs high-resolution (5 m × 5 m) raster-based, storage-cell modelling of coastal inundation by Manning-type equations in decoupled 2-D formulation at local-scale (20 km × 20 km) lowland littoral floodplains. It is fed either by outputs of either regional-scale storm surge simulations or satellite altimetry data for the sea level anomaly. The presented case studies refer to model applications at 10 selected coastal sites of the Ionian Sea (east-central Mediterranean Sea). The implemented regular Cartesian grids (up to 5 m) are based on Digital Elevation/Surface Models (DEM/DSM) of the Hellenic Cadastre. New updated features of the model are discussed herein concerning the detailed surveying of terrain roughness and bottom friction, the expansion of Dirichlet boundary conditions for coastal currents (besides sea level), and the enhancement of wet/dry cell techniques for flood front propagation over steep water slopes. Verification of the model is performed by comparisons against satellite ocean color observations (Sentinel-2 images) and estimated flooded areas by the Normalized Difference Water Index (NDWI). The qualitative comparisons are acceptable, i.e., the modelled flooded areas contain all wet area estimations by NDWI. CoastFLOOD results are also compared to a simplified, static level, “bathtub” inundation approach with hydraulic connectivity revealing very good agreement (goodness-of-fit > 0.95). Furthermore, we show that proper treatment of bottom roughness referring to realistic Land Cover datasets provides more realistic estimations of the maximum flood extent timeframe.

Satellite‐Observed Interannual Variations in Sea Surface Chlorophyll‐a Concentration in the Yellow Sea Over the Past Two Decades

AbstractOcean chlorophyll‐a concentration is a reliable indicator of phytoplankton biomass that plays an important role in controlling the marine ecosystem. Here, we investigated the interannual variations in sea surface chlorophyll‐a concentration (SSC) in the Yellow Sea and underlying mechanisms with 22 yr (1998–2019) of satellite ocean color observations. Results indicated that SSC showed a positive trend (0.039 mg m−3 yr−1 on average) during 1998–2010 and a negative trend (−0.058 mg m−3 yr−1 on average) during 2011–2019. Similar SSC trends occurred in the four seasons. These trends had larger magnitudes in inshore waters than in offshore waters. SSC variations were primarily driven by rainfall and anthropogenic nutrient emissions with a possibly secondary contribution from sea surface temperature (SST). During 1998–2010, increased rainfall and anthropogenic nutrient emissions largely enhanced the terrestrial nutrient inputs into the Bohai Sea and Yellow Sea. Coincidentally, decreased SST might have led to a weakened stratification in summer, favoring upward turbulent diffusion of deeper waters rich in nutrients and chlorophyll‐a. These processes resulted in significant increases of surface nutrient concentrations and SSC. During 2011–2019, the opposite processes occurred to generate decreased SSC. The current study highlights the change of SSC trends in the Yellow Sea in the first two decades of the 21st century and physical processes at work, enriching our understanding of ocean chlorophyll‐a dynamics in the global shelf seas.

Performance assessment and validation of ocean color sensor-specific algorithms for estimating the concentration of particulate organic carbon in oceanic surface waters from satellite observations

Global observations of ocean color from space provide a unique capability to conduct multi-decadal studies of biogeochemical constituents and processes in the upper ocean layers at the regional, basin, and global scales. The concentration of particulate organic carbon (POC) in the surface ocean waters is one of the biogeochemically important data products derivable from satellite ocean color observations. The data record of standard global POC product is available from NASA and has been obtained from the empirical power-function relationship between POC and a single blue-to-green band ratio of ocean remote-sensing reflectance, Rrs(λ), which is here referred to as the BR-PF algorithm. Recently, we formulated a new suite of satellite ocean-color sensor-specific global POC algorithms that are referred to as hybrid algorithms because they contain two components, one based on the maximum band-ratio (MBR) and the other on the band ratio difference index (BRDI) of reflectance (Stramski et al., 2022). The present study describes the validation analysis and provides performance assessment of hybrid algorithms for SeaWiFS, MODIS, and VIIRS-SNPP satellite ocean color sensors. For comparison, the standard global (BR-PF) algorithms and the color-index (CI) algorithm from the literature were also analyzed. The analyses were made with an in situ validation dataset containing concurrent measurements of POC and Rrs(λ) as well as three satellite validation datasets containing data matchups of field measurements of POC and satellite-derived Rrs(λ) from the SeaWIFS, MODIS-Aqua, and VIIRS-SNPP missions. The hybrid algorithms showed an overall improvement of performance compared to other algorithms, in particular in waters with relatively high POC compared to the standard BR-PF algorithms. The aggregate bias of hybrid algorithm-derived POC is small or negligible (<10%) for the validation datasets, and a measure of median percentage difference is about 20% for the in situ validation dataset and ranges between about 20% and 30% for the satellite validation datasets. In addition, comparisons of POC retrievals from the three sensor-specific hybrid algorithms show good inter-sensor consistency. The validation results of this study indicate that hybrid algorithms have high potential to provide improved POC retrievals within a broader range of POC than the predecessor global algorithms and the capability for generating a consistent long-term POC data record from multiple satellite missions.

Decadal trends in the release of terrigenous organic carbon to the Mackenzie Delta (Canadian Arctic) using satellite ocean color data (1998–2019)

Arctic rivers operate as integrators of northern high latitude regions, where large stocks of soil organic carbon (OC) are currently experiencing rapid warming. Here we show that tracking total OC in the Mackenzie Delta whose upstream catchment is underlain by permafrost soils is now possible using polar-orbiting satellite ocean color observations. A non-parametric trend analysis that is valid for hydrological data shows a significant increase in dissolved OC (DOC) as well as particulate OC (POC) concentrations in late summer (0.019 and 0.069 g m−3 year−1; p < 0.05 for both). Uncertainties of the satellite estimates of DOC and POC do not influence our results. These concentration increases are not related to changes in river discharge. Parallel increases of independent long-term (1979–2018) in situ measurements of thaw depth of the active layer, as well as meteorological and hydrological patterns suggest that these late summer increases can likely be explained by increasing inputs of permafrost OC. This study shows great promise for remote, large-scale detection of catchment-scale thaw impacts from space.

Biological dipole mode indices: New parameters to characterize the physical and biological processes of the Indian Ocean Dipole event

The Indian Ocean Dipole (IOD) is a basin-wide ocean–atmosphere phenomenon that has profound impacts on the global climate, land, and ocean. The Dipole Mode Index (DMI), which is defined as the difference of the sea surface temperature (SST) anomaly in the east IOD zone and the west IOD zone has long been used to characterize and quantify the strength of IOD events. In this study, we propose biological dipole mode indices (BDMIs) based on the dipolar observations of chlorophyll-a (Chl-a) anomalies (difference and relative difference) in the east and west IOD zones during the IOD event. The two BDMIs, which are based on Chl-a difference and relative difference, not only represent the dipolar biological activities in the Equatorial Indian Ocean, but also reflect the thermocline dynamics in the east IOD zone and west IOD zone. The in situ measurements in the east and west IOD zones show the clear linkage between the BDMIs and the dynamics of the 20°C isothermal depth. This linkage is attributed to the changes of the nutrient supplies driven by the various ocean physical processes in the IOD event, thus the BDMIs could also act as the surrogate for the thermocline dynamics in the two IOD zones. The BDMIs from satellite ocean color observations show that they can identify and characterize all the major IOD events in the last two and half decades. The SST-based DMI and Chl-a-based BDMIs may depict some different aspects of the IOD events (e.g., surface versus subsurface properties). The performance comparison between the two BDMIs and DMI also shows that the BDMIs and traditional DMI can effectively detect IOD signal for the major IOD events. Indeed, the BDMIs and DMI are complementary for characterizing the IOD events, and the combination of these indices can provide a better understanding of the atmosphere and ocean processes for both surface and subsurface, as well as biological processes in the Equatorial Indian Ocean.

Ocean Color Remote Sensing of Suspended Sediments along a Continuum from Rivers to River Plumes: Concentration, Transport, Fluxes and Dynamics

This study investigates the capability of high and medium spatial resolution ocean color satellite data to monitor the transport of suspended particulate matter (SPM) along a continuum from river to river mouth to river plume. An existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over the very wide range (from 1 to more than 1000 g.m−3) encountered over such a continuum. The method was applied to time series of OLI, MSI, and MODIS satellite data. Satisfactory validation results were obtained even at the river gauging station. The river liquid discharge is not only related to the SPM concentration at the gauging station and at the river mouth, but also to the turbid plume area and SPM mass estimated within the surface of the plume. The overall results highlight the potential of combined field and ocean color satellite observations to monitor the transport and fluxes of SPM discharged by rivers into the coastal ocean.

Interannual-decadal variation in satellite-derived surface chlorophyll-a concentration in the Bohai Sea over the past 16 years

Chlorophyll-a (Chl-a) concentration is an important indicator of the state of marine ecosystems. In the current study, the interannual-decadal variation in the Bohai Sea's surface Chl-a concentration and the underlying dynamics are investigated based on 16 years (August 2002–February 2019) of satellite ocean colour observations. The Chl-a concentration shows a spatially coherent increasing trend from 2003 to 2011 and a decreasing trend from 2012 to 2018 in both the Bohai and North Yellow Seas. Both decadal trends are clearer in the Bohai Sea than in the Yellow Sea and in coastal waters than in offshore waters. The decreasing trend is more obvious than the increasing trend. The low-frequency variations are mainly controlled by sea surface temperature (SST) and rainfall variations and eventually related to the Pacific Decadal Oscillation (PDO). During positive PDO phases, warm SST anomalies exist in the Bohai and Yellow Seas and negative rainfall anomalies exist over the sea and surrounding lands. These anomalies would decrease the surface layer dissolved inorganic nitrogen concentration through suppressing the upward turbulent nutrient diffusion and reducing land-sourced nutrient fluxes into the ocean, leading to negative Chl-a concentration anomalies. During negative PDO phases, the opposite situation occurs. Human activities may make a secondary contribution through enhancing/reducing the anthropogenic nitrogen emission before/after 2011. This study emphasizes the natural climate variability in modulating low-frequency variations in surface Chl-a concentrations in the Bohai and Yellow Seas in recent decades and is useful for marine resource planning and marine ecological management in the Bohai Sea in the future. • Surface chlorophyll-a concentration in the Bohai Sea has interannual-decadal variation. • Spatially coherent upward trend over 2003–2011 and downward trend over 2012–2018. • Downward trends are more obvious than upward trends. • Chlorophyll-a concentration variation is related to the Pacific Decadal Oscillation.

Application of satellite observations to study the changes of hypoxic conditions in near-bottom water in the western part of Peter the Great Bay (the Sea of Japan)

In this paper we explore the possibilities of applying satellite ocean colour (OC) observations and SST to study the changes in the conditions of hypoxia in the near-bottom water in the western part of Peter the Great Bay. Near-bottom water hypoxia occurs in water bodies with increased organic matter influx when the dissolved oxygen (DO) consumed at its oxidation is not restored. Consumption of most DO is usually attributed to the oxidation of organic matter formed as a result of increased algae growth during water eutrophication. Satellite data on indicators of phytoplankton (chlorophyll-a concentration (Chl) and fluorescence (FLH)) allow to analyze the spatial-temporal changes of this substation. Coloured dissolved organic matter (CDOM), non-algal particles (NAP) influence on satellite Chl estimates and also on near-bottom water hypoxia formation. This study analyzes daily, seasonal, and inter-annual changes in the distributions of indicators (Chl, FLH, the coefficients of light absorption by coloured detrital matter (aCDM) and light backscattering by suspended particles (bbp)), based on the instant satellite OC data from MODIS-Aqua. Data on the Chl, the sea surface temperature (SST) from the MODIS-Aqua, the precipitation from the TRMM satellite and the hydrometeorological stations (HMSs), the wind speed and direction from HMS “Vladivostok” are used to study the influence of hydrometeorological conditions on the Chl values. These distributions were compared with the literary information based on field observations of the hypoxia cases in the same area and with the changes in the vertical DO, Chl, temperature, salinity distribution obtained by coastal expeditions in October-November 2010 and February-March 2011. Significant interrelations within 95% confidence level between the satellite Chl, FLH values calculated at the MUMM atmospheric correction and in situ Chl values obtained in the autumn of 2010 were reached separately for the cases with winds of northern and southern directions with the correlation coefficients of 0.71, 0.48 and 0.49, 0.71, respectively. Significant dependences of Chl on SST and Chl on wind speed explained by the influence of continental runoff and water ventilation were obtained. Therefore, the changes of Chl reflect the changes of hypoxic conditions in the near-bottom water. In Amursky Bay the onset of hypoxia was at the Chl and SST values equal to 4 mg m−3 and 13 °C (↑ – at increasing SST); near Furugelm Island it was at 1.6 mg m−3 and 25 °C (↑), 1 mg m−3 and 21 °C (↓). The difference in the Chl values was reflected in the hypoxia onset timings that were the beginning of June (2011), August (2013), and September (2014), respectively. The water flow from the eastern coast of Amursky Bay in early August of 2013 recorded from the OC and SST satellite imagers appeared in an additional hypoxic zone. Decreased OC characteristics in the runoff of the Razdolnaya River in August-September of 2014 were a sign of hypoxia at its mouth. Near Furugelm Island the hypoxia destruction (increase in the DO level from 1 to 4.5 ml L−1) was observed at the Chl of 0.9 mg m−3 and SST = 18 °C (↓). At the autumn maximum of Chl equal to 1.7 mg m−3 and SST = 4 °C (↓) in mid-November the DO level here increased to 8 ml L−1. In Amursky Bay, short-term destructions/weakening of hypoxia manifested themselves in sharp increases of Chl. At that, the ratio between the Chl value and the approximation level was equal to 2 and higher for SST equal to 22–25 °C (↑), to 0.9 and higher for SST equal to 5–13 °C (↓). With the water stratification destruction in temperature and the noticeable weakening of the stratification in salinity (mid-November), the hypoxia destructed (the DO level increased from 2 to 6 ml L−1). In this case, Chl and SST were about 3 mg m−3 and 5 °C (↓).

Improving the Retrieval of Carbon-Based Phytoplankton Biomass from Satellite Ocean Colour Observations

Phytoplankton is at the base of the marine food web and plays a fundamental role in the global carbon cycle. Ongoing climate change significantly impacts phytoplankton distribution in the ocean. Monitoring phytoplankton is crucial for a full understanding of changes in the marine ecosystem. To observe phytoplankton from space, chlorophyll-a concentration (Chl) has been widely used as a proxy of algal biomass, although it can be impacted by physiology. Therefore, there has been an increasing focus towards estimating phytoplankton biomass in units of carbon (Cphyto). Here, we developed an algorithm to quantify Cphyto from space-based observations that accounts for the spatio-temporal variations of the backscattering coefficient associated with the fraction of detrital particles that do not covary with Chl. The main findings are: (i) a spatial and temporal variation of the detritus component must be accounted for in the Cphyto algorithm; (ii) the refined Cphyto algorithm performs better (relative bias of 23.7%) than any previously existing model; and (iii) our algorithm shows the lowest error in Cphyto across areas where picophytoplankton dominates (relative bias of 14%). In other areas, it is currently not possible to accurately assess the performance of the refined algorithm due to the paucity of in situ carbon data associated with nano- and micro-phytoplankton size classes.

Assessing the value of biogeochemical Argo profiles versus ocean color observations for biogeochemical model optimization in the Gulf of Mexico

Abstract. Biogeochemical ocean models are useful tools but subject to uncertainties arising from simplifications, inaccurate parameterization of processes, and poorly known model parameters. Parameter optimization is a standard method for addressing the latter but typically cannot constrain all biogeochemical parameters because of insufficient observations. Here we assess the trade-offs between satellite observations of ocean color and biogeochemical (BGC) Argo profiles and the benefits of combining both observation types for optimizing biogeochemical parameters in a model of the Gulf of Mexico. A suite of optimization experiments is carried out using different combinations of satellite chlorophyll and profile measurements of chlorophyll, phytoplankton biomass, and particulate organic carbon (POC) from autonomous floats. As parameter optimization in 3D models is computationally expensive, we optimize the parameters in a 1D model version and then perform 3D simulations using these parameters. We show first that the use of optimal 1D parameters, with a few modifications, improves the skill of the 3D model. Parameters that are only optimized with respect to surface chlorophyll cannot reproduce subsurface distributions of biological fields. Adding profiles of chlorophyll in the parameter optimization yields significant improvements for surface and subsurface chlorophyll but does not accurately capture subsurface phytoplankton and POC distributions because the parameter for the maximum ratio of chlorophyll to phytoplankton carbon is not well constrained in that case. Using all available observations leads to significant improvements of both observed (chlorophyll, phytoplankton, and POC) and unobserved (e.g., primary production) variables. Our results highlight the significant benefits of BGC-Argo measurements for biogeochemical parameter optimization and model calibration.

Effects of Spring–Neap Tidal Cycle on Spatial and Temporal Variability of Satellite Chlorophyll-A in a Macrotidal Embayment, Ariake Sea, Japan

We investigated the spatio-temporal variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) associated with spring–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 (2002–2017). The results revealed that spring–neap tidal variations in Chl-a and TSM within this macrotidal embayment (the Ariake Sea) are clearly different regionally and seasonally. Generally, the spring–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–neap tidal cycle.

Contrasted Contribution of Intraseasonal Time Scales to Surface Chlorophyll Variations in a Bloom and an Oligotrophic Regime

AbstractUnderstanding long‐term variations of ocean ecosystems requires untangling the time scales involved in their natural fluctuations. We applied a temporal decomposition procedure to two decades of satellite ocean color observations to characterize the time variability of surface chlorophyll‐a (SChl) in the Mediterranean Sea. In order to assess the reliability of the satellite data at capturing intraseasonal, seasonal, and interannual variability, we first show that satellite SChl data compare well with field data of chlorophyll‐a fluorescence from the long‐term BOUSSOLE time series, at all three time scales. The decomposition procedure is then applied to satellite SChl and to mixing‐layer depth (MxLD) data from an ocean reanalysis, both at the scale of the entire Mediterranean Sea. Our results reveal similar amplitude for the seasonal and intraseasonal SChl variations in the northwestern bloom region, together explaining about 90% of the SChl variance. Coherent seasonal SChl variations occur at the scale of the bloom region (~400 km) and are tightly connected with seasonal MxLD changes. Intraseasonal SChl fluctuations occur at smaller spatial scales (~100 km), suggesting they would be generated by storms although they weakly correlate to variations of the MxLD reanalysis. Over the oligotrophic part of the Mediterranean Sea, about 80% of the variability in both SChl and MxLD are explained by basin‐scale (~1,000 km) seasonal variations. Intraseasonal variability occurs at much smaller spatial scales, typical of mesoscale activity (~30 km). These results support the hypothesis that seasonal SChl variations are driven by changes in MxLD, while mesoscale activity and storms drive the intraseasonal SChl fluctuations.

Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier

Abstract. We processed daily ocean-color satellite observations to construct a monthly climatology of phytoplankton pigment concentrations in the Senegalo–Mauritanian region. Our proposed new method primarily consists of associating, in well-identified clusters, similar pixels in terms of ocean-color parameters and in situ pigment concentrations taken from a global ocean database. The association is carried out using a new self-organizing map (2S-SOM). Its major advantage is allowing the specificity of the optical properties of the water to be taken into account by adding specific weights to the different ocean-color parameters and the in situ measurements. In the retrieval phase, the pigment concentration of a pixel is estimated by taking the pigment concentration values associated with the 2S-SOM cluster presenting the ocean-color satellite spectral measurements that are the closest to those of the pixel under study according to some distance. The method was validated by using a cross-validation procedure. We focused our study on the fucoxanthin concentration, which is related to the abundance of diatoms. We showed that the fucoxanthin starts to develop in December, presents its maximum intensity in March when the upwelling intensity is maximum, extends up to the coast of Guinea in April and begins to decrease in May. The results are in agreement with previous observations and recent in situ measurements. The method is very general and can be applied in every oceanic region.

An Ensemble-Based Probabilistic Score Approach to Compare Observation Scenarios: An Application to Biogeochemical-Argo Deployments

AbstractA cross-validation algorithm is developed to perform probabilistic observing system simulation experiments (OSSEs). The use of a probability distribution of “true” states is considered rather than a single “truth” using a cross-validation algorithm in which each member of an ensemble simulation is alternatively used as the “truth” and to simulate synthetic observation data that reflect the observing system to be evaluated. The other available members are used to produce an updated ensemble by assimilating the specific data, while a probabilistic evaluation of the observation impacts is obtained using a comprehensive set of verification skill scores. To showcase this new type of OSSE studies with tractable numerical costs, a simple biogeochemical application under the Horizon 2020 AtlantOS project is presented for a single assimilation time step, in order to investigate the value of adding biogeochemical (BGC)-Argo floats to the existing satellite ocean color observations. Further experiments must be performed in time as well for a rigorous and effective evaluation of the BGC-Argo network design, though some evidence from this preliminary work suggests that assimilating chlorophyll data from a BGC-Argo array of 1000 floats can provide additional error reduction at the surface, where the use of spatial ocean color data is limited (due to cloudy conditions), as well at depths ranging from 50 to 150 m.

Southern Ocean Phytoplankton Blooms Observed by Biogeochemical Floats

AbstractThe spring bloom in the Southern Ocean is the rapid‐growth phase of the seasonal cycle in phytoplankton. Many previous studies have characterized the spring bloom using chlorophyll estimates from satellite ocean color observations. Assumptions regarding the chlorophyll‐to‐carbon ratio within phytoplankton and vertical structure of biogeochemical variables lead to uncertainty in satellite‐based estimates of phytoplankton carbon biomass. Here, we revisit the characterizations of the bloom using optical backscatter from biogeochemical floats deployed by the Southern Ocean Carbon and Climate Observations and Modeling and Southern Ocean and Climate Field Studies with Innovative Tools projects. In particular, by providing a three‐dimensional view of the seasonal cycle, we are able to identify basin‐wide bloom characteristics corresponding to physical features; biomass is low in Ekman downwelling regions north of the Antarctic Circumpolar Current region and high within and south of the Antarctic Circumpolar Current.

The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic.

We present a model that estimates the spectral phytoplankton absorption coefficient () of four phytoplankton groups (picophytoplankton, nanophytoplankton, dinoflagellates, and diatoms) as a function of the total chlorophyll-a concentration (C) and sea surface temperature (SST). Concurrent data on (at 12 visible wavelengths), C and SST, from the surface layer (<20 m depth) of the North Atlantic Ocean, were partitioned into training and independent validation data, the validation data being matched with satellite ocean-colour observations. Model parameters (the chlorophyll-specific phytoplankton absorption coefficients of the four groups) were tuned using the training data and found to compare favourably (in magnitude and shape) with results of earlier studies. Using the independent validation data, the new model was found to retrieve total with a similar performance to two earlier models, using either in situ or satellite data as input. Although more complex, the new model has the advantage of being able to determine for four phytoplankton groups and of incorporating the influence of SST on the composition of the four groups. We integrate the new four-population absorption model into a simple model of ocean colour, to illustrate the influence of changes in SST on phytoplankton community structure, and consequently, the blue-to-green ratio of remote-sensing reflectance. We also present a method of propagating error through the model and illustrate the technique by mapping errors in group-specific using a satellite image. We envisage the model will be useful for ecosystem model validation and assimilation exercises and for investigating the influence of temperature change on ocean colour.

Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations

The Forel-Ule (FU) color comparator scale is the oldest set of optical water types (OWTs). This scale was originally developed for visual comparison and generated an immense amount of data, with hundreds of thousands of observations being gathered from the last 130 years. Since recently, the FU scale is also applicable to remote sensing data. This has been possible thanks to an optical characterization of the 21 FU colors in terms of the (x,y) CIE standards and new algorithms that convert remote-sensing reflectances (Rrs) from satellite-borne ocean color sensors to FU. Rrs-derived hue angle and FU have been recently applied with success in the assessment of color variability of lakes and specific shelf areas, but an evaluation over global oceanic waters is still missing. By clustering global climatological ESA-OC-CCI v2.0 Rrs with the derived FU, we obtain a set of Rrs to be used as optical water types (OWTs). Diffuse attenuation coefficient, Secchi disk depth and chlorophyll concentration are also associated to the FU classes. The angular distances of a given Rrs to the two nearest FU classes are proposed as simple and robust membership indexes, adding up to one. We also evaluate the advantages and limitations of FU and the hue angle as monitoring tools over the full marine range, from the most oligotrophic areas to the turbid and productive coastal zones. The first 7 FU indexes cover 99% of global surface waters. Unlike the hue angle, that resolves all spatio-temporal color variations, the FU scale is coarse as a monitoring tool for oligotrophic waters as all the subtropical gyres saturate to FU = 1, while the color of other seas varies across 2, 3 or even 4 FU classes. We illustrate the introduction of a new “zero” FU class that increases monitoring resolution at the blue end of the color range. Finally, we show how optical diversity varies across the color range and compare several sets of OWTs from a color perspective. Overall, we provide a valuable and self-consistent dataset that enhances the usefulness of the FU scale by converting it to useful information for the oceanographic community. This OWT scheme keeps the advantages of other datasets, like being useful to study ocean color product quality and characterize the uncertainties, but also allows to continue to monitor long-term change in optical diversity over the global ocean color. Integration into the optical modules of ecosystem models can help verify past simulations that predate the satellite age, through comparisons with in-situ FU data collected at the time.

A blended inherent optical property algorithm for global satellite ocean color observations

AbstractWater inherent optical properties (IOPs) can be derived from satellite‐measured normalized water‐leaving radiance (nLw(λ)) spectra. In this study, we evaluate the performance of the quasi‐analytical algorithm (QAA) and the near‐infrared (NIR)‐based IOP algorithm using a Hydrolight simulation data set covering a wide range of water types that span from clear open ocean to turbid coastal/inland waters. The NIR‐based algorithm produces significantly improved IOP retrievals over turbid coastal and inland waters, while the QAA algorithm performs well in the open ocean and less turbid coastal waters. Based on the advantages of the NIR‐based and QAA‐based algorithms, a combination of the NIR‐ and QAA‐based algorithm has been proposed using satellite‐measured nLw(745) as the threshold in order to produce accurate IOP products for both the open ocean and turbid coastal/inland waters. The new combined IOP algorithm can produce reasonably accurate IOP data for all water types, and can be easily implemented into the satellite ocean color data processing. The La Plata River Estuary region is used as an example to show the difference in performance of IOP retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) measurements between 2012 and 2017 with the NIR‐based, QAA‐based, and NIR‐QAA combined IOP algorithms. We also demonstrate that the NIR‐QAA combined algorithm can be applied to VIIRS global ocean color observations to derive good quality IOP products in China's east coastal region, the US east coastal region, and the region of Mississippi River Estuary and tributaries.