Satellite Chl Research Articles

In situ estimates of net primary production in the open‐ocean Gulf of Mexico

AbstractNet primary production (NPP) measurements in the open‐ocean Gulf of Mexico are scarce and mostly limited to satellite‐derived surface estimates. Profiling floats can obtain depth‐resolved chlorophyll (Chl) and backscattering (bbp) data for NPP estimates, but historical float data in this region lacked field calibrations. We adjusted the float data with satellite Chl, generating a consistent dataset for deriving the first year‐round depth‐resolved NPP estimates in this region. The results revealed distinct seasonal patterns in surface NPP and depth‐integrated (0–100 m) total NPP, with the peak values in the winter and summer, respectively. The results showed that for most of the year subsurface (20–100 m) contributes more to the total (0–100 m) NPP than the surface layer (0–20 m), and the major controls of NPP in these two layers were light level and nutrient supply, respectively.

Red Sea MODIS Estimates of Chlorophyll a and Phytoplankton Biomass Risks to Saudi Arabian Coastal Desalination Plants

Harmful algal blooms (HABs) and the high biomass associated with them have afflicted marine desalination plants along coastal regions around the world. Few studies of HABs have been conducted in the Red Sea, where desalination plants along the Saudi Arabian Red Sea coast provide drinking water for millions of people. This study was conducted along the Saudi Arabian Red Sea coast from 2014 to 2015 to assess the potential for using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing of chlorophyll a (Chl a) or fluorescence line height (FLH) to identify risks for biofouling at these desalination plants. Ship-based surveys of phytoplankton were conducted along the Saudi Arabian coastline offshore of desalination plants at Jeddah, Al Shoaibah and Al Qunfudhuh to assess the density of phytoplankton populations and identify any potential HAB species. Ship-based surveys showed low to moderate concentrations of phytoplankton, averaging from 1800–10,000 cells L−1 at Jeddah, 2000–11,000 cells L−1 at Al Shoaibah and 1000–20,500 cells L−1 at Al Qunfudhuh. Sixteen different species of potentially toxigenic HABs were identified through these surveys. There was a good relationship between ship-based total phytoplankton counts and monthly averaged coastal MODIS Chl a (R2 = 0.49, root mean square error (RMSE) = 0.27 mg m−3) or FLH (R2 = 0.47, RMSE = 0.04 mW m−2 µm−1 sr−1) values. Monthly average near shore Chl a concentrations obtained using MODIS satellite imagery were much higher in the Red Sea coastal areas at Al Qunfudhuh (maximum of about 1.3 mg m−3) than at Jeddah or Al Shoaibah (maximum of about 0.4 and 0.5 mg m−3, respectively). Chlorophyll a concentrations were generally highest from the months of December to March, producing higher risks of biofouling desalination plants than in other months. Concentrations decreased significantly, on average, from April to September. Long-term (2005–2016) monthly averaged MODIS Chl a values were used to delineate four statistically distinct zones of differing HAB biomass across the entire Red Sea. Sinusoidal functions representing monthly variability were fit to satellite Chl a values in each zone (RMSE values from 0.691 to 0.07 mg m−3, from Zone 1 to 4). December to January mean values and annual amplitudes for Chl a in these four sinusoidal functions decreased from Zones 1–4. In general, the greatest risk of HABs to desalination occurs during winter months in Zone 1 (Southern Red Sea), while HAB risks to desalination plants in winter months are low to moderate in Zone 2 (South Central Red Sea), and negligible in Zones 3 (North Central) and 4 (Northern).

Reconsideration of the phytoplankton seasonality in the open Black Sea

AbstractChlorophyll a (Chl a) observations from satellites exhibit a unimodal seasonal variation in the open Black Sea that peaks during winter, which has led to the hypothesis that phytoplankton production is sustained by convective mixing of nitrate from deeper layers. We compiled in situ carbon‐to‐Chl a ratios for the entire year, displaying an expected seasonal pattern ranging from 46 in February to 195 mg C mg Chl−1 in September. We combined monthly ratios with satellite Chl a to obtain a comprehensive proxy data set for phytoplankton carbon biomass, and used this to examine the seasonal variation in phytoplankton biomass and bloom occurrences. Contrary to current understanding, our results showed that phytoplankton accumulation is predominantly taking place from July to September, when biomass increased threefold despite nitrate transport from below being negligible. We hypothesize that nitrate harvesting at depths and endosymbiont nitrogen fixation by large diatoms could be important, albeit unexplored mechanisms.

Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP, thanks to its ability to capture complex non-linear relationships, outperforms the SVR to capture satellite Chl spatial patterns (correlation of 0.75 vs. 0.65 on a global scale, respectively) along with its interannual variability and trend, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.

Integrating In Situ and Ocean Color Data to Evaluate Ecological Quality under the Water Framework Directive

The Water Framework Directive (WFD) aims at evaluating the ecological status of European coastal water bodies (CWBs). This is a rather complex task and first requires the use of long-term databases to assess the effect of anthropogenic pressure on biological communities. An in situ dataset was assembled using concomitant biological, i.e., chlorophyll a (Chl a) and environmental data, covering the years from 1995 to 2014, to enable a comprehensive assessment of eutrophication in the Western Iberia Coast (WIC). Given the temporal gaps in the dataset, especially in terms of Chl a, satellite observations were used to complement it. Positive relationships between Chl a 90th percentile and nitrogen concentrations were obtained. The Land-Uses Simplified Index (LUSI), as a pressure indicator, showed no relationship with Chl a, except in Galicia, but it highlighted a higher continental pressure in the Portuguese CWBs in comparison with Galician waters. In general terms, the trophic index (TRIX) showed that none of the CWBs were in degraded conditions. Nevertheless, the relatively high TRIX and LUSI values obtained for the water body in front of Tagus estuary suggest that this area should be subject to continued monitoring. Results highlighted the usefulness of satellite data in water quality assessments and set the background levels for the implementation of operational monitoring based on satellite Chl a. In the future, low uncertainty and harmonized satellite products across countries should be provided. Moreover, the assessment of satellite-based eutrophication indicators should also include metrics on phytoplankton phenology and community structure.

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 (↓).

Impact of physical and biogeochemical forcing on particle export in the South China Sea

This study examines temporal variations of 234Th-based particle export based on time-series observations conducted from 2004 to 2014 at the South-East Asian Time-series Study (SEATS) site in the basin area of the Northern South China Sea. It shows significant variability in particle export at both seasonal and interannual time scales. A significant feature of our data as observed by prior studies is that particle export was high during the NE monsoon in winter, but low during the inter-monsoon and SW monsoon. Particle flux increased with increasing mixed layer depth (MLD), primarily in winter due to the presence of a strong NE monsoon, in addition to influence by mesoscale features quantified by eddy kinetic energy values. Particle export was generally elevated when phytoplankton biomass was abundant, as shown by the significant correlation between particle fluxes and concentrations of chlorophyll (Chl) a and/or particulate organic carbon (POC) between 0 and 100 m. This is also supported by the negative correlation between 234Th residence time and Chl a concentration, suggesting that increased phytoplankton biomass accelerated downward particle sinking. The correlation disappeared however at Chl a values < 0.11 mg m−3, implicating other factors (e.g., phytoplankton community composition) as playing a more important role when biomass is relatively low. Satellite Chl a-based POC export estimated using various established algorithms agreed well with our measured fluxes when the export horizon was defined as the bottom of the mixed layer, but differed compared to estimated fluxes at 100 m depth. This is likely due to the fact that satellite-derived Chl a measurements only reflect surface values, suggesting that reliable algorithms must be used after considering the biological processes in subsurface waters.

Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32-years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979–2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability.

Estimation of primary production in the northwestern part of the Sea of Japan by ship- and satellite-based observations

In this paper, we analyzed data of POI FAB RAS cruise (No. 33) held on «Akademik M.A. Lavrentyev» vessel in the northwestern part of the Japan Sea in May 2004 and compared them with satellite derived data from Climate Change Initiative Ocean Colour (CCI-OC) and Ocean productivity databases. The following in situ data were used for the analysis: CTD-data, assimilation number, nutrients (P, N, Si) and chlorophyll a (Chl). Ship assessments of Chl in the first optical layer were in average 0.5±0.58 mg/m3, while satellite Chl were nearly twice more – 0.95±0.36 mg/m3. Ship assessments of primary production (PP) were 1870±900 mg C/m2day that is 1.5 times larger than satellite derived data - 1226±432. Vertical profiles of nutrients, temperature and Chl showed that main mass of Chl was concentrated in a layer from 20 to 45 m, where the main production is formed. According to values of assimilation number, main production formed in a layer of 0-55 m in the south of the region and 0-30 m in the north. Weak correlation between ship and satellite PP values found in this paper may be explained by low accuracy of satellite derived estimation.

Spatial and temporal variation in chlorophyll a concentration in the Eastern China Seas based on a locally modified satellite dataset

Satellite chlorophyll a (Chl) is an essential parameter for large-scale studies on changes in the marine ecosystem and biogeochemistry. However, in the eastern China seas (ECSs), the coexistence and interaction of turbid and clear water pose challenges to the application of standard satellite Chl products to studies on phytoplankton dynamics. Based on the empirical modification of the OC-CCI (Ocean Colour Climate Change Initiative) standard products, we herein provide a locally modified chlorophyll a (LMC) dataset and compare the large-scale Chl distributions with satellite and in-situ Chl datasets. Compared with standard Chl, LMC has a higher correlation with in-situ data (R2 = 0.58) and lower Bias (0.08) and is more consistent with the ship-measured Chl distributions. Using the LMC dataset, we analyzed the climatological Chl distributions in the ECSs and classified the Chl annual cycles into four types. Our results suggest that high Chl patterns not only rely on nutrient supply, but also relate to light conditions controlled by vertical mixing and suspended matter. The different Chl annual cycles are mainly caused by differences in the duration and range of nutrients and light limitation, and thus their distributions tend to vary with water depth. We believe that this work presents a more accurate dataset in the ECSs and can improve the understanding of local biogeochemical cycles.

Summer diatom blooms in the eastern North Pacific gyre investigated with a long-endurance autonomous surface vehicle.

Satellite chlorophyll a (chl a) observations have repeatedly noted summertime phytoplankton blooms in the North Pacific subtropical gyre (NPSG), a region of open ocean that is far removed from any land-derived or Ekman upwelling nutrient sources. These blooms are dominated by N2-fixing diatom-cyanobacteria associations of the diatom genera Rhizosolenia Brightwell and Hemiaulus Ehrenberg. Their nitrogen fixing endosymbiont, Richelia intracellularis J.A. Schmidt, is hypothesized to be critical to the development of blooms in this nitrogen limited region. However, due to the remote location and unpredictable duration of the summer blooms, prolonged in situ observations are rare outside of the Station ALOHA time-series off of Hawai’i. In summer, 2015, a proof-of-concept mission using the autonomous vehicle, Honey Badger (Wave Glider SV2; Liquid Robotics, a Boeing company, Sunnyvale, CA, USA), collected near-surface (<20 m) observations in the NPSG using hydrographic, meteorological, optical, and imaging sensors designed to focus on phytoplankton abundance, distribution, and physiology of this bloom-forming region. Hemiaulus and Rhizosolenia cell abundance was determined using digital holography for the entire June–November mission. Honey Badger was not able to reach the 30°N subtropical front region where most of the satellite chl a blooms have been observed, but near-real time navigational control allowed it to transect two blooms near 25°N. The two taxa did not co-occur in large numbers, rather the blooms were dominated by either Hemiaulus or Rhizosolenia. The August 2–4, 2015 bloom was comprised of 96% Hemiaulus and the second bloom, August 15–17, 2015, was dominated by Rhizosolenia (75%). The holograms also imaged undisturbed, fragile Hemiaulus aggregates throughout the sampled area at ∼10 L−1. Aggregated Hemiaulus represented the entire observed population at times and had a widespread distribution independent of the summer export pulse, a dominant annual event suggested to be mediated by aggregate fluxes. Aggregate occurrence was not consistent with a density dependent formation mechanism and may represent a natural growth form in undisturbed conditions. The photosynthetic potential index (Fv:Fm) increased from ∼0.4 to ∼0.6 during both blooms indicating a robust, active phytoplankton community in the blooms. The diel pattern of Fv:Fm (nocturnal maximum; diurnal minimum) was consistent with macronutrient limitation throughout the mission with no evidence of Fe-limitation despite the presence of nitrogen fixing diatom-diazotroph assemblages. During the 5-month mission, Honey Badger covered ∼5,690 km (3,070 nautical miles), acquired 9,336 holograms, and reliably transmitted data onshore in near real-time. Software issues developed with the active fluorescence sensor that terminated measurements in early September. Although images were still useful at the end of the mission, fouling of the LISST-Holo optics was considerable, and appeared to be the most significant issue facing deployments of this duration.

Modeled Chl:C ratio and derived estimates of phytoplankton carbon biomass and its contribution to total particulate organic carbon in the global surface ocean

AbstractChlorophyll (Chl) is a distinctive component of autotrophic organisms, often used as an indicator of phytoplankton biomass in the ocean. However, assessment of phytoplankton biomass from Chl relies on the accurate estimation of the Chl:carbon(C) ratio. Here we present global patterns of Chl:C ratios in the surface ocean obtained from a phytoplankton growth model that accounts for the optimal acclimation of phytoplankton to ambient nutrient, light, and temperature conditions. The model agrees largely with observed/expected global patterns of Chl:C. Combining our Chl:C estimates with satellite Chl and particulate organic carbon (POC), we infer phytoplankton C concentration in the surface ocean and its contribution to the total POC pool. Our results suggest that the portion of POC corresponding to living phytoplankton is higher in subtropical latitudes and less productive regions (∼30–70%) and decreases to ∼10–30% toward high latitudes and productive regions. An important caveat of our model is the lack of iron limiting effects on phytoplankton physiology. Comparison of our predicted phytoplankton biomass with an independent estimate of total POC reveals a positive correlation between nitrate concentrations and nonphotosynthetic POC in the surface ocean. This correlation disappears when a constant Chl:C is applied. Our analysis is not constrained by assumptions of constant Chl:C or phytoplankton:POC ratio, providing a novel independent analysis of phytoplankton biomass in the surface ocean. These results highlight the importance of accounting for the variability in Chl:C and its application in distinguishing the autotrophic and heterotrophic components in the assemblage of the marine plankton ecosystem.

Regional chlorophyll a algorithms in the Arctic Ocean and their effect on satellite-derived primary production estimates

The Arctic is warming at approximately twice the global rate in response to anthropogenic climate change, resulting in disappearing sea ice, increased open water area, and a longer growing season (IPCC, 2013). This loss of sea ice has resulted in a 30% increase in annual net primary production (NPP) by Arctic Ocean phytoplankton between 1998 and 2012 (Arrigo and van Dijken, 2015). To quantify NPP, many algorithms require input of chlorophyll a (Chl a) concentration, which serves as a biomass proxy for phytoplankton. While satellites provide temporally and spatially extensive data, including Chl a, the standard global ocean color algorithms are prone to errors in Arctic Ocean waters due to higher than average phytoplankton pigment-packaging and chromophoric dissolved organic matter (CDOM) concentrations. Here, we evaluate retrievals of Chl a using existing ocean color algorithms, test and develop a new empirical ocean color algorithm for use in the Chukchi Sea, and evaluate the effect of using different satellite Chl a products as input to an NPP algorithm. Our results show that in the Chukchi Sea, Chl a was overestimated by the global algorithm (MODIS OC3Mv6) at concentrations lower than 0.9mgm−3 because of contamination by CDOM absorption, but underestimated at higher concentrations because of pigment packaging. Only within the in situ Chl a range of 0.6–2mgam−3 was the satellite retrieval error by the OC3Mv6 algorithm below the ocean color community goal of <35%. Using coincident in situ Chl a concentrations and optical data, a new linear empirical algorithm is developed (OC3L) that yields the lowest statistical error when estimating Chl a in the Chukchi Sea, compared to existing ocean color algorithms (OC3Mv6, OC4L, OC4P). When we estimated regional NPP using different Chl a satellite products as input, three distinct bio-optical provinces within the Arctic Ocean emerged. These provinces correspond to the inflow shelves, interior shelves, and outflow shelves+deep basin as defined by Carmack et al. (2006). Eleven sub-regions within the Arctic Ocean were grouped into each of these three provinces based on their mean value for R, the ratio of blue to green remote sensing reflectance (RRS). Our results suggest that three algorithms tuned to each of the three bio-optical provinces may be sufficient to capture the bio-optical heterogeneity within the Arctic Ocean. Currently, only within the inflow shelf province do we feel confident that Chl a and NPP can be accurately estimated by satellite using the OC3L algorithm. The interior and outflow shelf+basin provinces require development of ocean color algorithms specific to their respective bio-optical conditions.

A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns

Abstract Dimethylsulfoniopropionate (DMSP) is a ubiquitous phytoplankton metabolite and the main precursor of the climate-active gas dimethylsulfide (DMS) in the oceans' surface. Here we use total DMSP (DMSPt) and ancillary measurements from a global database to develop a remote sensing algorithm for DMSPt in the upper mixed layer (UML). Over 55% of total DMSPt variability (log10 scale) is explained by in situ chlorophyll a (Chl) after dividing the database into two subsets, according to “stratified” and “mixed” water column criteria, based on the ratio between euphotic layer depth (Zeu) and mixed layer depth (MLD). Up to 70% of the variability is explained when adding sea surface temperature (SST) and log10(Zeu/MLD) as predictors for the stratified and mixed subsets, respectively. Independent validation on satellite Chl match-ups indicates that the algorithm predicts DMSPt across three orders of magnitude with a root-mean-squared error spanning from 0.20 to 0.26 (log10 space) and mean absolute error typically around 45% (linear space). An additional submodel based on remotely sensed particulate inorganic carbon (PIC) is used to predict DMSPt in coccolithophore blooms if satellite Chl is not available. We use the algorithm to produce a monthly global DMSPt climatology, and estimate that DMSP synthesis amounts to 5–9% of oceanic phytoplankton gross carbon production. Our algorithm provides a new remote sensing tool for resolving temporal and spatial variations in DMSPt concentration, and represents a step forward toward improved diagnosis of contemporary DMS emission based on satellite Earth observation.

Validation of standard and alternative satellite ocean-color chlorophyll products off Western Iberia

Abstract Chlorophyll a concentration (Chl) product validation off the Western Iberian coast is here undertaken by directly comparing remote sensing data with in situ surface reference values. Both standard and recently developed alternative algorithms are considered for match-up data analysis. The investigated standard products are those produced by the MERIS (algal 1 and algal 2) and MODIS (OC3M) algorithms. The alternative data products include those generated within the CoastColour Project and Ocean Color Climate Change Initiative (OC-CCI) funded by ESA, as well as a neural net model trained with field measurements collected in the Atlantic off Portugal (MLP ATLP ). Statistical analyses showed that satellite Chl estimates tend to be larger than in situ reference values. The study also revealed that a non-uniform Chl distribution in the water column can be a concurring factor to the documented overestimation tendency when considering larger optical depth match-up stations. Among standard remote sensing products, MODIS OC3M and MERIS algal 2 yield the best agreement with in situ data. The performance of MLP ATLP highlights the capability of regional solutions to further improve Chl retrieval by accounting for environmental specificities. Results also demonstrate the relevance of oceanographic regions such as the Nazare area to evaluate how complex hydrodynamic conditions can influence the quality of Chl products.

Bromocarbons in the tropical coastal and open ocean atmosphere during the 2009 Prime Expedition Scientific Cruise (PESC-09)

Abstract. Atmospheric concentrations of very short-lived species (VSLS) bromocarbons, including CHBr3, CH2Br2, CHCl2Br, CHClBr2, and CH2BrCl, were measured in the Strait of Malacca and the South China and Sulu–Sulawesi seas during a two-month research cruise in June–July 2009. The highest bromocarbon concentrations were found in the Strait of Malacca, with smaller enhancements in coastal regions of northern Borneo. CHBr3 was the most abundant bromocarbon, ranging from 5.2 pmol mol−1 in the Strait of Malacca to 0.94 pmol mol−1 over the open ocean. Other bromocarbons showed lower concentrations, in the range of 0.8–1.3 pmol mol−1 for CH2Br2, 0.1–0.5 pmol mol−1 for CHCl2Br, and 0.1–0.4 pmol mol−1 for CHClBr2. There was no significant correlation between bromocarbons and in situ chlorophyll a, but positive correlations with both MODIS and SeaWiFS satellite chlorophyll a. Together, the short-lived bromocarbons contribute an average of 8.9 pmol mol−1 (range 5.2–21.4 pmol mol−1) to tropospheric bromine loading, which is similar to that found in previous studies from global sampling networks (Montzka et al., 2011). Statistical tests showed strong Spearman correlations between brominated compounds, suggesting a common source. Log–log plots of CHBr3/CH2Br2 versus CHBr2Cl/CH2Br2 show that both chemical reactions and dilution into the background atmosphere contribute to the composition of these halocarbons at each sampling point. We have used the correlation to make a crude estimate of the regional emissions of CHBr3 and to derive a value of 32 Gg yr−1 for the Southeast (SE) Asian region (10° N–20° S, 90–150° E). Finally, we note that satellite-derived chlorophyll a (chl a) products do not always agree well with in situ measurements, particularly in coastal regions of high turbidity, meaning that satellite chl a may not always be a good proxy for marine productivity.

Patterns of the Kara Sea primary production in autumn: Biotic and abiotic forcing of subsurface layer

Primary production and fundamental environmental factors were measured during September–October 1993, 2007 and 2011 in the Kara Sea. Relationships between the depth-integrated primary production (PPint), the surface chlorophyll a (Chl0) concentration and the maximum chlorophyll specific carbon rate within water column (Pbopt) had shown that only 12% of PPint variability were determined by Chl0 and there were strong correlations between PPint and Pbopt (R2=0.64). Thus, in the autumn PPint values were largely influenced by the phytoplankton assimilation activity. At the end of a vegetative season high (close or above 1mgm−3) values of Chl0 were not an index of phytoplankton productivity within photosynthetic layer where the organic matter synthesis rate was low which testifies the lack of correlation between Chl0 and PPint (R2=0.12) and between Chl0 and depth-integrated chlorophyll a (chl a) (R2=0.22). This conclusion is based on the low assimilation activity within water column and small thickness of the photosynthetic layer. The latter corresponds to low insolation and water transparency. The lack of correlation between surface and depth-integrated productivity parameters is the challenge for satellite PPint estimations. In turn PPint and Pbopt depended mainly on photosynthetically available radiation (PAR) and weakly related to the nitrogen and phosphorus concentrations. At the end of a vegetative season PAR level, apparently, should be considered as the main factor for primary production in the Kara Sea. Comparison between the integrated primary production from shipboard in September and the simulated evaluations of productivity (PP models) suggests that PP models overestimate the in situ PPint by a factor of 3–7 in the different Kara Sea regions. Improving of Kara Sea primary production estimations implies the development of regional satellite chl a algorithm and local primary production model considering specific features of PP in this Arctic Ocean region.

Distribution and biogeographic trends of decapod assemblages from Galicia Bank (NE Atlantic) at depths between 700 and 1800 m, with connexions to regional water masses

The Galicia Bank (NE Atlantic, 42°67′N–11°74′W) is an isolated seamount, near NW Spain, a complex geomorphological and sedimentary structure that receives influences from contrasting water masses of both northern and southern origins. Within the project INDEMARES, three cruises were performed on the bank in 2009 (Ecomarg0709), 2010 (BanGal0810) and 2011 (BanGal0811) all in July–August. Decapods and other macrobenthic crustaceans (eucarids and peracarids) were collected with different sampling systems, mainly beam trawls (BT, 10mm of mesh size at codend) and a GOC73 otter trawl (20mm mesh size). Sixty-seven species of decapod crustaceans, 6 euphausiids, 19 peracarids and 1 ostracod were collected at depths between 744 and 1808m. We found two new species, one a member of the Chirostylidae, Uroptychus cartesi Baba & Macpherson, 2012, the other of the Petalophthalmidae (Mysida) Petalophthalmus sp. A, in addition to a number of new biogeographic species records for European or Iberian waters. An analysis of assemblages showed a generalized species renewal with depth, with different assemblages between 744 and ca. 1400m (the seamount top assemblage, STA) and between ca. 1500 and 1800m (the deep-slope assemblage over seamount flanks, DSA). These were respectively associated with Mediterranean outflow waters (MOW) and with Labrador Sea Water (LSW). Another significant factor separating different assemblages over the Galician Bank was the co-occurrence of corals (both colonies of hard corals such as Lophelia pertusa and Madrepora oculata and/or gorgonians) in hauls. Munidopsids (Munidopsis spp.), chirostylids (Uroptychus spp.), and the homolodromiid Dicranodromia mahieuxii formed a part of this coral-associated assemblage. Dominant species at the STA were the pandalid Plesionika martia (a shrimp of subtropical-southern distribution) and the crabs Bathynectes maravigna and Polybius henslowii, whereas dominant species in the DSA were of northern origin, the lithodid Neolithodes grimaldii and the crangonid Glyphocrangon longiristris, likely associated with LSW. The diversity (H and J) of small crustaceans (collected with BT) seemed to be controlled by the phytoplankton blooms (satellite Chl a data) over bank surface 3 months before the samplings, both at the top (Spearman r=0.57, p=0.03) and on the flanks (r=0.74, p=0.02) of Galicia Bank, while no significant relationships with Chl a were found for the larger decapods collected with GOC73, on average they feed at the higher trophic levels than those collected with BT.

Influence of atmospheric forcing and freshwater discharge on interannual variability of the vertical diffuse attenuation coefficient at 490 nm in the Baltic Sea

Each year, spatial patterns of ocean color in the Baltic Sea differ in temporal evolution and magnitude. We have investigated the interannual variability of the spatially averaged vertical diffuse attenuation coefficient at 490nm (Kd(490)) in response to atmospheric forcing and river discharge. Our results indicate that atmospheric forcing does not have a significant influence on interannual anomalies of Kd(490) in the Baltic Sea. This is dissimilar to the North Atlantic at the same latitudes, where interannual variability of phytoplankton blooms (ocean color chlorophyll concentration, Chl, and Kd) is to a large degree controlled by local weather. In contrast, in the Baltic Sea the interannual variability of Kd(490) is significantly influenced by river runoff. Higher values of Kd(490) are observed in years with larger inflow of water from rivers. Without access to more detailed information about the concentrations of various optically significant water components we can only speculate about the possible reasons for this relationship. Most likely a combination of several factors causes the observed positive correlation between Kd(490) and river discharge. These factors include development of more intense phytoplankton blooms in response to a larger supply of nutrients delivered by rivers. This conclusion is supported by satellite Chl data. However, an advection of optically important material with river water most likely also contributes to the bserved variability of Kd(490). The diffuse attenuation coefficient plays a critical role in many oceanographic processes. For example, Kd is essential for quantification of radiative heating of the ocean, in models of primary production and other photoprocesses, and in studies discussing water turbidity and water quality. Better understanding of the variability of Kd in the Baltic Sea can improve our knowledge of this marine environment.

Ocean color response to wind forcing in the Alboran Sea: A new forecasting method

This work proposes a new method to reconstruct and forecast surface Chlorophyll (Chl) from remotely sensed (SeaWiFS) data in the Alboran Sea, based on the correlation between zonal wind velocities and satellite Chl concentrations. First, the spatial and temporal variability of Chl and zonal wind have been characterized using standard statistics. Second, the annual cycle and trends are removed from the original time series and the residuals submitted to an EOF computation scheme. Then, the correlations between the amplitudes of the first temporal modes of Chl-wind couple have been quantified. Using the most highly correlated pair (Chl-zonal wind, with r=0.63), a simple linear relationship is proposed to reconstruct and forecast the Chl field. This forecasting method is more efficient than persistence for forecast horizons longer than 100days, with a mean correlation between original and predicted field of 0.73 as compared to 0.5 for persistence for all the year round. In partic\\ular, this new method gives a 0.95 mean correlation for periods from 100 to 290days (while persistence gives 0.5). However, for a constant 8-days prediction horizon the persistence performs marginally better than the proposed method (0.79 vs. 0.68), giving some insight into the temporal scales of the features studied. These results may have significant implications for both short-term operational applications and seasonal forecasts.