Net primary production (NPP) fueled by nitrate is often equated with carbon export, providing a metric for CO2 removal to the deep ocean. This “new production paradigm” assumes that nitrification, the oxidation of regenerated ammonium to nitrate, is negligible in the sunlit upper ocean. While surface layer nitrification has been measured in other oceanic regions, very few data exist for the Southern Ocean. We measured NPP, nitrogen (N) uptake, and nitrification in the upper 200 m across the Atlantic Southern Ocean in winter and summer. Rates of winter mixed-layer nitrate uptake were low, while ammonium uptake was surprisingly high. NPP was also low, such that NPP and total N (nitrate+ammonium) uptake were decoupled; we attribute this to ammonium consumption by heterotrophic bacteria. By contrast, NPP and total N uptake were strongly coupled in summer except at two stations where an additional regenerated N source, likely dissolved organic N, apparently supported 30–45% of NPP. Summertime nitrate uptake rates were fairly high and nitrate fueled >50% of NPP, indicating the potential for significant carbon export. Nitrification supplied <10% of the nitrate consumed in summertime surface waters, while in winter, mixed-layer nitrification was on average 16 times higher than nitrate uptake. Despite the near-zero nitrification rates measured in the summer mixed layer, the classically defined f ratio does not well-represent Southern Ocean carbon export potential annually. This is because some fraction of the nitrate regenerated in the winter mixed layer is likely supplied to phytoplankton in summer; its consumption cannot, therefore, be equated with export.
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Specific uptake rates of carbon (VC) versus nitrogen (VN_total = VNO3 + VNH4) for (a) summer and (b) winter. Colored symbols show the rates at the individual incubation depths, and the corresponding black symbols represent the 0–200 m‐weighted average rates at each station. Integrating to the base of the mixed layer or euphotic zone rather than 200 m makes little difference to the averages because the specific rates at 200 m are very low. The solid
black line represents VC:VN_total = 1:1, which is expected for balanced phytoplankton growth assuming that the only N
forms being assimilated are NO3− and NH4+.
Optical particle measurements are emerging as an important technique for understanding the ocean carbon cycle, including contributions to estimates of their downward flux, which sequesters carbon dioxide (CO2) in the deep sea. Optical instruments can be used from ships or installed on autonomous platforms, delivering much greater spatial and temporal coverage of particles in the mesopelagic zone of the ocean than traditional techniques, such as sediment traps. Technologies to image particles have advanced greatly over the last two decades, but the quantitative translation of these immense datasets into biogeochemical properties remains a challenge. In particular, advances are needed to enable the optimal translation of imaged objects into carbon content and sinking velocities. In addition, different devices often measure different optical properties, leading to difficulties in comparing results. Here we provide a practical overview of the challenges and potential of using these instruments, as a step toward improvement and expansion of their applications.
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Working steps to derive small and large particles from single photodetectors. A median filter is fitted and assumed to be representative of small particles. Spikes are caused by large particles passing through the sampling frame.
The Southern Ocean (SO) is highly sensitive to climate change. Therefore, an accurate estimate of phytoplankton biomass is key to being able to predict the climate trajectory of the 21st century. In this study, MODerate resolution Imaging Spectroradiometer (MODIS), on board EOS Aqua spacecraft, Level 2 (nominal 1 km 1 km resolution) chlorophyll-a (CSat) and Particulate Organic Carbon (POCsat) products are evaluated by comparison with an in situ dataset from 11 research cruises (2008–2017) to the SO, across multiple seasons, which includes measurements of POC and chlorophyll-a (Cin situ) from both High Performance Liquid Chromatography (CHPLC) and fluorometry (CFluo). Contrary to a number of previous studies, results highlighted good performance of the algorithm in the SO when comparing estimations with HPLC measurements. Using a time window of 12 h and a mean satellite chlorophyll from a 5 5 pixel box centered on the in situ location, the median CSat:Cin situ ratios were 0.89 (N = 46) and 0.49 (N = 73) for CHPLC and CFluo respectively. Differences between CHPLC and CFluo were associated with the presence of diatoms containing chlorophyll-c pigments, which induced an overestimation of chlorophyll-a when measured fluorometrically due to a potential overlap of the chlorophyll-a and chlorophyll-c emission spectra. An underestimation of 0.13 mg m-3 was observed for the global POC algorithm. This error was likely due to an overestimate of in situ POCin situ measurements from the impact of dissolved organic carbon not accounted for in the blank correction. These results highlight the important implications of different in situ methodologies when validating ocean colour products.
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(a) Comparison between measured (Cin situ) and retrieved (CSat) chlorophyll-a for box with a time window of 12 h. Blue and orange dots indicate samples measured from HPLC (CHPLC) and fluorometry (CFluo) respectively, while the dashed line shows the 1:1 relationship. (b) Probability density function of the logarithm base 10 of the ratio between CSat and Cin situ from fluorometry (blue line) and HPLC (orange line).
The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i) improve Argo’s global coverage and value beyond the original design, (ii) extend Argo to span the full ocean depth, (iii) add biogeochemical sensors for improved understanding of oceanic cycles of carbon, nutrients, and ecosystems, and (iv) consider experimental sensors that might be included in the future, for example to document the spatial and temporal patterns of ocean mixing. For Core Argo and each of these enhancements, the past, present, and future progression along a path from experimental deployments to regional pilot arrays to global implementation is described. The objective is to create a fully global, top-to-bottom, dynamically complete, and multidisciplinary Argo Program that will integrate seamlessly with satellite and with other in situ elements of the Global Ocean Observing System (Legler et al., 2015). The integrated system will deliver operational reanalysis and forecasting capability, and assessment of the state and variability of the climate system with respect to physical, biogeochemical, and ecosystems parameters. It will enable basic research of unprecedented breadth and magnitude, and a wealth of ocean-education and outreach opportunities.
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The three main models of BGC-Argo floats presently in use include (A) Navis, (B) APEX, and (C) PROVOR.
Underwater gliders have become widely used in the last decade. This has led to a proliferation of data and the concomitant development of tools to process the data. These tools are focused primarily on converting the data from its raw form to more accessible formats and often rely on proprietary programming languages. This has left a gap in the processing of glider data for academics, who often need to perform secondary quality control (QC), calibrate, correct, interpolate and visualize data. Here, we present GliderTools, an open-source Python package that addresses these needs of the glider user community. The tool is designed to change the focus from the processing to the data. GliderTools does not aim to replace existing software that converts raw data and performs automatic first-order QC. In this paper, we present a set of tools, that includes secondary cleaning and calibration, calibration procedures for bottle samples, fluorescence quenching correction, photosynthetically available radiation (PAR) corrections and data interpolation in the vertical and horizontal dimensions. Many of these processes have been described in several other studies, but do not exist in a collated package designed for underwater glider data. Importantly, we provide potential users with guidelines on how these tools are used so that they can be easily and rapidly accessible to a wide range of users that span the student to the experienced researcher. We recognize that this package may not be all-encompassing for every user and we thus welcome community contributions and promote GliderTools as a community-driven project for scientists.
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The GliderTools “cheat sheet,” a quick reference guide to using and implementing the tool. The boxes represent the various modules within the package.
Subseasonal surface wind variability strongly impacts the annual mean and subseasonal turbulent atmospheric surface fluxes. However, the impacts of subseasonal wind variability on the ocean are not fully understood. Here, we quantify the sensitivity of the ocean surface stress (τ), buoyancy flux (B) and mixed‐layer depth (MLD) to subseasonal wind variability in both a one‐dimensional (1D) vertical column model and a three‐dimensional (3D) global mesoscale‐resolving ocean/sea‐ice model. The winds are smoothed by time‐filtering the pseudo‐stresses, so the mean stress is approximately unchanged and some important surface flux feedbacks are retained. The 1D results quantify the sensitivities to wind variability at different timescales from 120 days to 1 day at a few sites. The 3D results quantify the sensitivities to wind variability shorter than 60 days at all locations, and comparisons between 1D and 3D results highlight the importance of 3D ocean dynamics. Globally, subseasonal winds explain virtually all of subseasonal τ variance, about half of subseasonal B variance, but only a quarter of subseasonal MLD variance. Subseasonal winds also explain about a fifth of the annual mean MLD and a similar and spatially‐correlated fraction of the mean friction velocity, 
where ρsw is the density of seawater. Hence, the subseasonal MLD variance is relatively insensitive to subseasonal winds despite their strong impact on local B and τ variability, but the mean MLD is relatively sensitive to subseasonal winds to the extent that they modify the mean u*, and both of these sensitivities are modified by 3D ocean dynamics.
Annual mean (top) and seasonal cycle amplitude (bottom) of the magnitude of the ocean mixed-layer depth from the control run (CTL, left) and the fractional difference between CTL and a low-pass run with smoothed winds (CTL-LP, right; see section 2.5 for more on the metrics). In (B) and (D), points are blanked if zero is included in the 95% confidence interval, which is derived non-parametrically using 1000 bootstrap samples at each point. In (B) and (D), red means the metric is greater in CTL, whereas blue means the metric is greater in LP.
Seasonal progression of dissolved iron (DFe) concentrations in the upper water column were examined during four occupations in the Atlantic sector of the Southern Ocean. DFe inventories from euphotic and aphotic reservoirs decreased progressively from July to February, while dissolved inorganic nitrogen decreased from July to January with no significant change between January and February. Results suggest that between July and January, DFe loss from both euphotic and aphotic reservoirs was predominantly in support of phytoplankton growth (iron‐to‐carbon uptake ratio of 16 ± 3 μmol/mol), highlighting the importance of the “winter DFe reservoir” for biological uptake. During January to February, excess loss of DFe relative to dissolved inorganic nitrogen (iron‐to‐carbon uptake ratio of 44 ± 8 μmol/mol and aphotic DFe loss rate of 0.34 ± 0.06 μmol·m−2·day−1) suggests that scavenging is the dominant removal mechanism of DFe from the aphotic, while continued production is likely supported by recycled nutrients.
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Vertical profiles of (a) dissolved iron (DFe, nM) with ±standard deviation, (b) insert of upper water column DFe concentrations, (c) vertical profiles of dissolved inorganic nitrogen (DIN, μM), and (d) insert of upper water column DIN concentrations. MLD = mixed layer depth.
The OceanGliders program started in 2016 to support active coordination and enhancement of global glider activity. OceanGliders contributes to the international efforts of the Global Ocean Observation System (GOOS) for Climate, Ocean Health, and Operational Services. It brings together marine scientists and engineers operating gliders around the world: (1) to observe the long-term physical, biogeochemical, and biological ocean processes and phenomena that are relevant for societal applications; and, (2) to contribute to the GOOS through real-time and delayed mode data dissemination. The OceanGliders program is distributed across national and regional observing systems and significantly contributes to integrated, multi-scale and multi-platform sampling strategies. OceanGliders shares best practices, requirements, and scientific knowledge needed for glider operations, data collection and analysis. It also monitors global glider activity and supports the dissemination of glider data through regional and global databases, in real-time and delayed modes, facilitating data access to the wider community. OceanGliders currently supports national, regional and global initiatives to maintain and expand the capabilities and application of gliders to meet key global challenges such as improved measurement of ocean boundary currents, water transformation and storm forecast.
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Gliders tracks of past deployments until October 2018 (OceanObs’19 submissions), as can be viewed using google-earth from different locations.
A multi-excitation fluorometer (MFL, JFE Advantech Co., Ltd.), originally designed to discriminate between phytoplankton species present within a population, has been redirected for use in fluorescence quantum yield (FQY) determination. While this calibration for apparent FQY requires no modification of the MFL, it is necessary to have an independent measurement of the spectral absorption coefficient of the subject fluid. Two different approaches to calibration were implemented. The primary method made use of reference fluorescent dye solutions of known quantum yield. The second method made use of acrylic fluorescent plaques and films. The two methods yielded consistent results, except in the 570 and 590 nm LED channels of the MFL. Application of the MFL in FQY determination is illustrated with an in situ Southern Ocean sample.
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MFL LED normalized photon spectra, plotted on a log scale with ATTO-TEC Dye and Perspex 4T56 relative spectral absorptivity.
Active fluorescence measurements can provide rapid, non-intrusive estimates of phytoplankton primary production at high spatial and temporal resolution, but there is uncertainty in converting from electrons to ecologically relevant rates of CO2 assimilation. In this study, we examine the light-dependent rates of photosynthetic electron transport and 13C-uptake in the Atlantic sector of the Southern Ocean to derive a conversion factor for both winter (July 2015–August 2015) and summer (December 2015–February 2016). The results revealed significant seasonal differences in the light-saturated chlorophyll specific rate of 13C-uptake, (PBmax), with mean summer values 2.3 times higher than mean winter values, and the light limited chlorophyll specific efficiency, (αB), with mean values 2.7 times higher in summer than in winter. Similar patterns were observed in the light-saturated photosynthetic electron transport rates (ETRRCIImax, 1.5 times higher in summer) and light limited photosynthetic electron transport efficiency (αRCII, 1.3 times higher in summer). The conversion factor between carbon and electrons (Φe:C (mol e– mol C-1)) was derived utilizing in situ measurements of the chlorophyll-normalized number of reaction centers (nRCII), resulting in a mean summer Φe:C which was 3 times lower than the mean winter Φe:C. Empirical relationships were established between Φe:C, light and NPQ, however they were not consistent across locations or seasons. The seasonal decoupling of Φe:C is the result of differences in Ek-dependent and Ek-independent variability, which require new modelling approaches and improvements to bio-optical techniques to account for these inter-seasonal differences in both taxonomy and environmental mean conditions.
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(a) Correlation of non photochemical quenching, NPQNSV, and Φe:C/nRCII (mol e– mol C-1 mol Chl a-1 mol RCII-1); (b) correlation of NPQNSV and Φe:C (mol e– mol C-1). Values of NPQNSV and Φe:C were derived from light response curves of 13C-uptake and FLC measurements. Linear regressions calculated for summer (excluding points on far left from ICE station—see Supporting Information Fig. S12): all p<0.001.
°The seasonal and sub-seasonal dynamics of iron availability within the sub-Antarctic zone (SAZ; 40–45°S) play an important role in the distribution, biomass and productivity of the phytoplankton community. The variability in iron availability is due to an interplay between winter entrainment, diapycnal diffusion, storm-driven entrainment, atmospheric deposition, iron scavenging and iron recycling processes. Biological observations utilizing grow-out iron addition incubation experiments were performed at different stages of the seasonal cycle within the SAZ to determine whether iron availability at the time of sampling was sufficient to meet biological demands at different times of the growing season. Here we demonstrate that at the beginning of the growing season, there is sufficient iron to meet the demands of the phytoplankton community, but that as the growing season develops the mean iron concentrations in the mixed layer decrease and are insufficient to meet biological demand. Phytoplankton increase their photosynthetic efficiency and net growth rates following iron addition from midsummer to late summer, with no differences determined during early summer, suggestive of seasonal iron depletion and an insufficient resupply of iron to meet biological demand. The result of this is residual macronutrients at the end of the growing season and the prevalence of the high-nutrient low-chlorophyll (HNLC) condition. We conclude that despite the prolonged growing season characteristic of the SAZ, which can extend into late summer/early autumn, results nonetheless suggest that iron supply mechanisms are insufficient to maintain potential maximal growth and productivity throughout the season.
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Fv/Fm (a, c, e) and chlorophyll a (Chl a) responses (mg m3) (b, d, f), from the control and Fe addition treatments of experiments initiated in the sub-Antarctic zone over early summer (a, b), midsummer (c, d) and late summer (e, f). Displayed here are averages with standard deviations (n = 3-5 for all time points, except the end time point where n = 6-12; see Table S1 for exact sample numbers). Please note the different scales in panels (a) and (b).
In the Southern Ocean, increasing evidence from recent studies is highlighting the need for high-resolution sampling at fine spatial (meso- to sub-mesoscale) and temporal scales (intra-seasonal) in order to understand longer-term variability of phytoplankton and the controlling physical and biogeochemical processes. Here, high-resolution glider data (3 hourly, 2 km horizontal resolution) and satellite ocean colour data (2-4 km) from the Sub-Antarctic zone (SAZ) were used to 1) quantify the dominant scales of variability of the glider time series, 2) determine the minimum sampling frequency required to adequately characterise the glider time series and 3) discriminate how much of the variability measured with a glider is the result of temporal variations versus spatial patchiness. Results highlight the important role of signals shorter than 10 days in characterising surface chlorophyll (chl-a) variability , particularly in spring (97%) and to a lesser degree in summer (27%). These small scales of variability were also evident in the physical indices of SST, wind stress and mixed layer depth. Further analysis revealed that sampling at high frequencies (
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Box-and-whisker plots for the mean and standard deviation for subsampling at periodic frequencies (blue boxes) and the corresponding random extraction (red boxes) for the summer surface chl-a time series.
Availability of dissolved iron and light are both regulating factors for primary productivity in high (macro)nutrient, low chlorophyll regions of the Southern Ocean. Here, using on-board iron/light incubation experiments conducted in 2015 in the Atlantic sector of the Southern Ocean, we show that irradiance limited significant phytoplankton growth (in chlorophyll-a and particulate organic carbon) north of the Polar Front (46 °S 08 °E), while iron addition resulted in growth stimulation even at low light levels in the Antarctic zone (65 °S 0 °E). The phytoplankton community in the Polar Frontal Zone showed a greater functional diversity than the one in the Antarctic Zone. The community structure changed over the course of the incubations in response to increased iron and light. The observed increase in chlorophyll-a under high light in the Polar Frontal Zone was driven predominantly by an increase in pico(0.2-2 µm) and large (>5 µm) nanophytoplankton. Pigment fingerprinting indicated an increase in the contribution of diatoms and Phaeocystis over the course of the incubation. In contrast, in the Antarctic Zone, the increase in chlorophyll-a after iron enrichment was predominantly due to an increase in the contribution of diatoms and large nanophytoplankton. The photochemical efficiency (Fv/Fm) was low at both sites at the beginning of the incubations, but increased upon iron fertilization in both water masses, indicating stress relief. However, the acclimation strategies fundamentally differed between the two communities. The ratio of photoprotective versus light-harvesting pigments increased under high light in the Polar Frontal Zone independent of iron enrichment, whereas this ratio declined upon iron enrichment in the Antarctic Zone even under high light. At the same time, the functional cross section of photosystem II (σPSII) decreased upon iron enrichment in the Antarctic Zone, but not in the Polar Frontal Zone. Our experiments support the need to take biogeographical differences between Southern Ocean water masses into account when interpreting ecosystem dynamics.
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Phytoplankton composition at the end of each treatment for (a) incubation experiment S54-46 in the PFZ and (b) incubation experiment S54–65 in the AAZ. Initial = Initial community with no treatment; L1 = low light; L2 = high light; +Fe = iron enrichment.
First measurements of labile dissolved copper (LdCu) and dissolved zinc (dZn) and nickel (dNi) in the Atlantic sector of the Southern Ocean in winter are compared with summer data at reoccupied stations in order to better understand the winter reset state and supply of these trace metals to support productivity. In summer, vertical profiles of zinc behaved similarly to silicate (Si) and increased from sub-nanomolar surface concentrations to 8 nmol kg−1 in bottom waters. Copper profiles also resembled Si and were typically 1 nmol kg−1 in surface waters and increased to 3 nmol kg−1 at depth. First summer nickel data reported from this transect displayed comparatively higher surface concentrations of ~4.6 nmol kg−1 increasing more rapidly to local intermediate depth maximums between 6.5 and 7.0 nmol kg−1, similar to phosphate (PO4). Trace metal seasonality was most apparent in the mixed layer where the average of winter concentrations within the mixed layer exceeded summer values by approximately 0.2 nmol kg−1 for LdCu, 1.2 nmol kg−1 for dZn and 0.3 nmol kg−1 for dNi owing to low utilization under unfavourable growth conditions for phytoplankton. In an effort to estimate the winter reserve, two scenarios were considered. Scenario 1 accounted for in-situ mixed layer depths (MLD) where the winter reserve inventory was calculated by subtracting the summer depth integrated metal inventory (surface to summer MLD) from the winter equivalent (surface to winter MLD). Scenario 2 assumed a constant mixed layer (taken as the depth of the maximum winter mixed layer) where summer depth integrated metal inventories (surface to maximum winter MLD) were subtracted from the winter equivalents (surface to maximum winter MLD). Results for scenario 1 were predominantly dependent on the mixed layer depth which varied spatially and seasonally. Scenario 2 showed a southwards increase in the winter reserve inventory suggesting a greater role for entrainment at higher latitudes in this region. This is however heavily dependent on other physical processes controlling vertical trace metal supply e.g. diapycnal diffusion, Ekman upwelling/downwelling. Zinc (R2 > 0.75) and copper (R2 > 0.73) were strongly correlated with Si throughout the study implicating diatoms as strong controllers of their biogeochemical cycling. Nickel was more strongly correlated with PO4 in the upper water column (R2 > 0.75), as compared to the whole water column (R2 > 0.52), while in the deep ocean nickel appears to correlate with Si although more deep ocean data is needed to confirm this. Trace metal to major nutrient ratios were higher in winter suggesting reduced micronutrient requirement relative to macronutrients under stressed but low productivity conditions.
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Summer distributions of a) dissolved labile copper (LdCu), b) dissolved zinc (dZn) and c) dissolved nickel (dNi). Figures generated in Ocean Data View (ODV) (Schlitzer, 2017) using weighted average gridding method based on 6 sampling stations between 46°S and 68°S.
Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO2 to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO2. In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Landschützer et al. (2016). The interpolated estimates of ΔpCO2 are separated into nine regions in the Southern Ocean defined by basin (Indian, Pacific, and Atlantic) and biomes (as defined by Fay and McKinley, 2014a). The regional approach shows that, while there is good agreement in the overall trend of the products, there are periods and regions where the confidence in estimated ΔpCO2 is low due to disagreement between the products. The regional breakdown of the data highlighted the seasonal decoupling of the modes for summer and winter interannual variability. Winter interannual variability had a longer mode of variability compared to summer, which varied on a 4–6-year timescale. We separate the analysis of the ΔpCO2 and its drivers into summer and winter. We find that understanding the variability of ΔpCO2 and its drivers on shorter timescales is critical to resolving the long-term variability of ΔpCO2. Results show that ΔpCO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of ΔpCO2 variability with mixed layer depth. Summer pCO2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower pCO2 concentrations. In regions of low chlorophyll a concentrations, wind stress and sea surface temperature emerged as stronger drivers of ΔpCO2. In summary we propose that sub-decadal variability is explained by summer drivers, while winter variability contributes to the long-term changes associated with the SAM. This approach is a useful framework to assess the drivers of ΔpCO2 but would greatly benefit from improved estimates of ΔpCO2 and a longer time series.
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Panels (a–f) show the ensemble mean of ∆pCO2 (black) plotted by biome (rows) and basin (columns). Biomes are defined by Fay and McKinley (2014a). The blue line shows the maximum for each year (winter outgassing) and the dashed blue line shows the same line less the average seasonal amplitude (diff) – this is the expected amplitude. The orange line shows the minimum ∆pCO2 for each summer season. The shaded regions around the seasonal maxima and minima show the standard deviation of the three products. Eb is the average between-method error and ∆pCO2 is the average for the entire time series. Light grey shading in (a–f) shows the periods used in Figs. used for comparisons.
