Abstract

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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pco2_ensemble_time_series_biomes

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.

Abstract

The Southern Ocean accounts for 40 % of oceanic CO2 uptake, but the estimates are bound by large uncertainties due to a paucity in observations. Gap-filling empirical methods have been used to good effect to approximate pCO2 from satellite observable variables in other parts of the ocean, but many of these methods are not in agreement in the Southern Ocean. In this study we propose two additional methods that perform well in the Southern Ocean: support vector regression (SVR) and random forest regression (RFR). The methods are used to estimate ΔpCO2 in the Southern Ocean based on SOCAT v3, achieving similar trends to the SOM-FFN method by Landschützer et al. (2014). Results show that the SOM-FFN and RFR approaches have RMSEs of similar magnitude (14.84 and 16.45 µatm, where 1 atm = 101 325 Pa) where the SVR method has a larger RMSE (24.40 µatm). However, the larger errors for SVR and RFR are, in part, due to an increase in coastal observations from SOCAT v2 to v3, where the SOM-FFN method used v2 data. The success of both SOM-FFN and RFR depends on the ability to adapt to different modes of variability. The SOM-FFN achieves this by having independent regression models for each cluster, while this flexibility is intrinsic to the RFR method. Analyses of the estimates shows that the SVR and RFR’s respective sensitivity and robustness to outliers define the outcome significantly. Further analyses on the methods were performed by using a synthetic dataset to assess the following: which method (RFR or SVR) has the best performance? What is the effect of using time, latitude and longitude as proxy variables on ΔpCO2? What is the impact of the sampling bias in the SOCAT v3 dataset on the estimates? We find that while RFR is indeed better than SVR, the ensemble of the two methods outperforms either one, due to complementary strengths and weaknesses of the methods. Results also show that for the RFR and SVR implementations, it is better to include coordinates as proxy variables as RMSE scores are lowered and the phasing of the seasonal cycle is more accurate. Lastly, we show that there is only a weak bias due to undersampling. The synthetic data provide a useful framework to test methods in regions of sparse data coverage and show potential as a useful tool to evaluate methods in future studies.

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ml_esetimates_of_deltapCO2

Time series of ∆pCO2 estimates for the three Southern Ocean biomes as defined by Fay and McKinley (2014): SAZ, PFZ and MIZ. The y-axis grid lines represent the same scale for figures (a) through (c). The SOM-FFN estimates are only available until 2011 as it is trained with SOCAT v2, while the SVR and RFR are trained with SOCAT v3. The ∆pCO2 normalized to sea ice cover is shown by dashed lines in the AZ.

Dorothee Bakker, Benjamin Pfeil, Camilla S. Landa, ..., Gregor L, Monteiro P.M.S., ...
Abstract

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID.

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socatv3_new_old

Global distribution of (a) all and (b) newly added surface water fCO2 values (µatm) and (c) the timing of the newly added data sets in SOCAT version 3 with data set flags of A to E. Version 3 has data sets from 1957 to 2014.

Thomalla S.J., Gilbert Ogunkoya, Vichi M., Swart S.
Abstract

One approach to deriving phytoplankton carbon biomass estimates (Cphyto) at appropriate scales is through optical products. This study uses a high-resolution glider data set in the Sub-Antarctic Zone (SAZ) of the Southern Ocean to compare four different methods of deriving Cphyto from particulate backscattering and fluorescence-derived chlorophyll (chl-a). A comparison of the methods showed that at low (<0.5 mg m−3) chlorophyll concentrations (e.g., early spring and at depth), all four methods produced similar estimates of Cphyto, whereas when chlorophyll concentrations were elevated one method derived higher concentrations of Cphyto than the others. The use of methods derived from particulate backscattering rather than fluorescence can account for cellular adjustments in chl-a:Cphytothat are not driven by biomass alone. A comparison of the glider chl-a:Cphyto ratios from the different optical methods with ratios from laboratory cultures and cruise data found that some optical methods of deriving Cphyto performed better in the SAZ than others and that regionally derived methods may be unsuitable for application to the Southern Ocean. A comparison of the glider chl-a:Cphyto ratios with output from a complex biogeochemical model shows that although a ratio of 0.02 mg chl-a mg C−1 is an acceptable mean for SAZ phytoplankton (in spring-summer), the model misrepresents the seasonal cycle (with decreasing ratios from spring to summer and low sub-seasonal variability). As such, it is recommended that models expand their allowance for variable chl-a:Cphyto ratios that not only account for phytoplankton acclimation to low light conditions in spring but also to higher optimal chl-a:Cphyto ratios with increasing growth rates in summer.

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Time-evolution of chl-a:Cphyto ratios at the surface (10 m) derived from the 30%POC method (solid lighter green top line), the B05 method (red line), the M13 method (blue line), and the S09 method (pink line). In addition, Chl-a:Cphyto ratios were calculated using the chl-a:POC ratio from the cruise data (which implies that all POC is phytoplankton specific) and is presented as 100%POC (darker green bottom dashed line). Included for comparision are (1) the chl-a:Cphyto ratios derived from the original equation from Sathyendranath et al. (2009) presented as S09original (purple line), (2) the satellite range of ratios from Behrenfeld et al. (2005) (black dotted lines) and (3) the ratios derived from the PELAGOS025 model (McKiver et al., 2015, extracted from the model for the same geographical co-ordinates as the glider transect in time but for a year 2011 simulation, solid black line). The inset shows a detail of the daily signal for the B05 method.

Time-evolution of chl-a:Cphyto ratios at the surface (10 m) derived from the 30%POC method (solid lighter green top line), the B05 method (red line), the M13 method (blue line), and the S09 method (pink line). In addition, Chl-a:Cphyto ratios were calculated using the chl-a:POC ratio from the cruise data (which implies that all POC is phytoplankton specific) and is presented as 100%POC (darker green bottom dashed line). Included for comparision are (1) the chl-a:Cphyto ratios derived from the original equation from Sathyendranath et al. (2009) presented as S09original (purple line), (2) the satellite range of ratios from Behrenfeld et al. (2005) (black dotted lines) and (3) the ratios derived from the PELAGOS025 model (McKiver et al., 2015, extracted from the model for the same geographical co-ordinates as the glider transect in time but for a year 2011 simulation, solid black line). The inset shows a detail of the daily signal for the B05 method.

Abstract

The Southern Ocean (SO) contributes most of the uncertainty in contemporary estimates of the mean annual flux of carbon dioxide CO2 between the ocean and the atmosphere. Attempts to reduce this uncertainty have aimed at resolving the seasonal cycle of the fugacity of CO2 (fCO2). We use hourly CO2 flux and driver observations collected by the combined deployment of ocean gliders to show that resolving the seasonal cycle is not sufficient to reduce the uncertainty of the flux of CO2 to below the threshold required to reveal climatic trends in CO2 fluxes. This was done by iteratively subsampling the hourly CO2 data set at various time intervals. We show that because of storm-linked intraseasonal variability in the spring-late summer, sampling intervals longer than 2 days alias the seasonal mean flux estimate above the required threshold. Moreover, the regional nature and long-term trends in storm characteristics may be an important influence in the future role of the SO in the carbon-climate system.

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The spatial variability of the FCO2 uncertainties, which arise from a uniform 10 day sampling period choice. The Southern Ocean is characterized with uncertainties of 10–25% (10–25 μmol m2 h1) at this sampling period.

The spatial variability of the FCO2 uncertainties, which arise from a uniform 10 day sampling period choice. The Southern Ocean is characterized with uncertainties of 10–25% (10–25 μmol m2 h1) at this sampling period.

Meredith, M., Swart S., Monteiro P.M.S., et al.
Abstract

The Southern Ocean exerts a disproportionately strong influence on global climate, so determining its changing state is of key importance in understanding the planetary-scale system. This is a consequence of the connectedness of the Southern Ocean, which links the other major ocean basins and is a site of strong lateral fluxes of climatically important tracers. It is also a consequence of processes occurring within the Southern Ocean, including the vigorous overturning circulation that leads to the formation of new water masses, and to the strong exchange of carbon, heat, and other climatically relevant properties at the ocean surface. However, determining the state of the Southern Ocean in a given year is even more problematic than for other ocean basins, due to the paucity of observations. Nonetheless, using the limited data available, some key aspects of the state of the Southern Ocean in 2014 can be ascertained.

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BAMS Sate of the Climate 2014 cover

BAMS Sate of the Climate 2014 cover

Abstract

In the Southern Ocean there is increasing evidence that seasonal to sub-seasonal temporal scales, meso- and submesoscales play an important role in understanding the sensitivity of ocean primary productivity to climate change. In this study, high-resolution glider data (3 hourly, 2km horizontal resolution), from ~6 months of sampling (spring through summer) in the Sub-Antarctic Zone, is used to assess 1) the different forcing mechanisms driving variability in upper ocean physics and 2) how these may characterize the seasonal cycle of phytoplankton production. Results highlight the important role meso- to submesoscale features have in driving vertical stratification and early phytoplankton bloom initiations in spring by increasing light exposure. In summer, the combined role of solar heat flux, mesoscale features and subseasonal storms on the extent of the mixed layer is proposed to regulate both light and iron to the upper ocean at appropriate time scales for phytoplankton growth, thereby sustaining the bloom for an extended period through to late summer. This study highlights the need for climate models to resolve both meso- to submesoscale and subseasonal processes in order to accurately reflect the phenology of the phytoplankton community and understand the sensitivity of ocean primary productivity to climate change.

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Glider sections of (a) temperature (°C), (b) stratification and (c) chlorophyll-a concentration (mg m-3) during the 'spring bloom initiation phase' of SOSCEx. The MLD is depicted using a white curve.

Glider sections of (a) temperature (°C), (b) stratification and (c) chlorophyll-a concentration (mg m-3) during the ‘spring bloom initiation phase’ of SOSCEx. The MLD is depicted using a white curve.

Swart S., Liu, J., Bhaskar, P., Newman, L., Finney, K., Meredith, M., Schofield, O.
Abstract

The first Southern Ocean Observing System (SOOS) Asian Workshop was successfully held in Shanghai, China in May 2013, attracting over 40 participants from six Asian nations and widening exposure to the objectives and plans of SOOS. The workshop was organized to clarify Asian research activities currently taking place in the Southern Ocean and to discuss, amongst other items, the potential for collaborative efforts with and between Asian countries in SOOS-related activities. The workshop was an important mechanism to initiate discussion, understanding and collaborative avenues in the Asian domain of SOOS beyond current established efforts. Here we present some of the major outcomes of the workshop covering the principle themes of SOOS and attempt to provide a way forward to achieve a more integrated research community, enhance data collection and quality, and guide scientific strategy in the Southern Ocean.

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Map of the Southern Ocean and approximate location of regular shipping transects maintained by Asian nations.

Map of the Southern Ocean and approximate location of regular shipping transects maintained by Asian nations.

Liu, J., Swart S., Bhaskar, P., Newman, L., Meredith, M., Schofield, O., Jianfeng, HE.
Abstract

SOOS must be a fully integrated and coordinated international system with infrastructure, resources and investment from all nations involved in the Southern Ocean research and observations. This was the motivation behind the organization of the SOOS Asian workshop. The objective of the SOOS Asian Workshop was to highlight the activities of Asian countries currently engaged in Southern Ocean research and observations relevant to the SOOS science strategy, and to stimulate discussion and foster further involvement from Asian countries in the SOOS activities.

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The Southern Ocean Observing System

The Southern Ocean Observing System

Abstract

One of the important gaps in the reliable prediction of the response of the Southern Ocean carbon cycle to climate change is its sensitivity to seasonal, subseasonal forcings (in time) and mesoscales (in space). The Southern Ocean Carbon and Climate Observatory (SOCCO), a CSIR-led consortium, is planning the Southern Ocean Seasonal Cycle Experiment (SOSCEx), which will be a new type of large-scale experiment. SOSCEx reflects a shift from the historical focus on ship-based descriptive Southern Ocean oceanography and living resource conservation, to system-scale dynamics studies spanning much greater time and space scales. The experiment provides a new and unprecedented opportunity to gain a better understanding of the links between climate drivers and ecosystem productivity and climate feedbacks in the Southern Ocean. This combined high-resolution approach to both observations and modelling experiments will permit us, for the first time, to address some key questions relating to the physical nature of the Southern Ocean and its carbon cycle.

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A space–time plot showing relative scale magnitudes of a number of platforms (ships, instrumented moorings and gliders), the seasonal cycle and climate projections. This graphical representation emphasises that, even with both ships and moorings observational platforms, it is not possible to address questions on the seasonal cycle sensitivity of climate projections without using autonomous platforms. Ocean gliders are uniquely poised to bridge the spatial and temporal gap between ships and moorings – a bridge which critically covers the seasonal 'window' in the Southern Ocean Seasonal Cycle Experiment.

A space–time plot showing relative scale magnitudes of a number of platforms (ships, instrumented moorings and gliders), the seasonal cycle and climate projections. This graphical representation emphasises that, even with both ships and moorings observational platforms, it is not possible to address questions on the seasonal cycle sensitivity of climate projections without using autonomous platforms. Ocean gliders are uniquely poised to bridge the spatial and temporal gap between ships and moorings – a bridge which critically covers the seasonal ‘window’ in the Southern Ocean Seasonal Cycle Experiment.

Goni, G., Roemmich, D. , Swart S., et al.
Abstract

The Ship Of Opportunity Program (SOOP) is an international World Meteorological Organization (WMO)-Intergovernmental  Oceanographic Commission (IOC) program that addresses both scientific and operational goals to contribute to building a sustained ocean observing system. The SOOP main mission is the collection of upper ocean temperature profiles using eXpendable  BathyThermographs (XBTs), mostly from volunteer vessels. The XBT deployments are designated by their spatial and temporal sampling goals or modes of deployment (Low Density, Frequently Repeated, and High Density) and sample along well-observed transects, on either large or small spatial scales, or at special locations such as boundary currents and chokepoints, all of which are complementary to the Argo global broad scale array.

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XBT observations transmitted in (red) real- and (blue) delayed- and real-time in 2008

XBT observations transmitted in (red) real- and (blue) delayed- and real-time in 2008

Abstract

In this study we use the southern Benguela upwelling system to investigate the role of nutrient and carbon stoichiometry on carbonate dynamics in eastern boundary upwelling systems. Six months in 2010 were sampled along a cross-shelf transect. Data were classified into summer, autumn, and winter. Nitrate, phosphate, dissolved inorganic carbon, and total alkalinity ratios were used in a stoichiometric reconstruction model to determine the contribution of biogeochemical processes on a parcel of water as it upwelled. Deviations from the Redfield ratio were dominated by denitrification and sulfate reduction in the subsurface waters. The N:P ratio was lowest (7.2) during autumn once anoxic waters had formed. Total alkalinity (TA) generation by anaerobic remineralization decreased pCO2 by 227 μatm. Ventilation during summer and winter resulted in elevated N:P ratios (12.3). We propose that anaerobic production of TA has an important regional effect in mitigating naturally high CO2 and making upwelled waters less corrosive.

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Marine Carbonate fluxes in the southern Benguela: A schematic showing the magnitude of processes contributing to DIC and TA fluxes. Black numbers represent DIC and gray TA. The solid/dashed/dotted lines represent the thermocline and its intensity. Increases in DIC throughout all seasons were largely due to aerobic remineralization (RM). Large TA gains in autumn were due to benthic processes: denitrification (DN), sulfate reduction (SR), and calcite dissolution (CD). Strong primary production (PP) in summer reduced the surface DIC, while calcification (CL) in autumn resulted in decreased TA.

Marine Carbonate fluxes in the southern Benguela: A schematic showing the magnitude of processes contributing to DIC and TA fluxes. Black numbers represent DIC and gray TA. The solid/dashed/dotted lines represent the thermocline and its intensity. Increases in DIC throughout all seasons were largely due to aerobic remineralization (RM). Large TA gains in autumn were due to benthic processes: denitrification (DN), sulfate reduction (SR), and calcite dissolution (CD). Strong primary production (PP) in summer reduced the surface DIC, while calcification (CL) in autumn resulted in decreased TA.

Abstract

In the Ocean, the seasonal cycle is the mode that couples climate forcing to ecosystem response in production, diversity and carbon export. A better characterisation of the ecosystem’s seasonal cycle therefore addresses an important gap in our ability to estimate the sensitivity of the biological pump to climate change. In this study, the regional characteristics of the seasonal cycle of phytoplankton biomass in the Southern Ocean are examined in terms of the timing of the bloom initiation, its amplitude, regional scale variability and the importance of the climatological seasonal cycle in explaining the overall variance. The seasonal cycle was consequently defined into four broad zonal regions; the subtropical zone (STZ), the transition zone (TZ), the Antarctic circumpolar zone (ACZ) and the marginal ice zone (MIZ). Defining the Southern Ocean according to the characteristics of its seasonal cycle provides a more dynamic understanding of ocean productivity based on underlying physical drivers rather than climatological biomass. The response of the biology to the underlying physics of the different seasonal zones resulted in an additional classification of four regions based on the extent of inter-annual seasonal phase locking and the magnitude of the integrated seasonal biomass. This regionalisation contributes towards an improved understanding of the regional differences in the sensitivity of the Southern Oceans ecosystem to climate forcing, potentially allowing more robust predictions of the effects of long term climate trends.

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A schematic summarising the response of phytoplankton biomass to the underlying physics of the different seasonal regimes. Regions in blue represent regions of low ( 0.4) (Region A, light blue) or low seasonal cycle reproducibility (R2  0.25 mgm−3) with either high seasonal cycle reproducibility (Region C, dark green) or low seasonal cycle reproducibility (Region D, light green). Mean (1998–2007) frontal positions are shown for the STF (red), the SAF (black), the PF (orange) and the SACCF (blue).

A schematic summarising the response of phytoplankton biomass to the underlying physics of the different seasonal regimes. Regions in blue represent regions of low (< 0.25 mgm−3) chlorophyll concentration with either high seasonal cycle reproducibility (R2 > 0.4) (Region A, light blue) or low seasonal cycle reproducibility (R2 < 0.4) (Region B, dark blue). Regions in green represent regions of high chlorophyll concentration ( > 0.25 mgm−3) with either high seasonal cycle reproducibility (Region C, dark green) or low seasonal cycle reproducibility (Region D, light green). Mean (1998–2007) frontal positions are shown for the STF (red), the SAF (black), the PF (orange) and the SACCF (blue).

Martins, R.S., Roberts, M.J., Chang N., Verley, P., Moloney C.L., Vidal, E.A.G.
Abstract

Specific gravity is an important parameter in the dispersal of marine zooplankton, because the velocity of currents, and therefore the speed of transport, is usually greatest near the surface. For the South African chokka squid (Loligo reynaudii), recruitment is thought to be influenced by the successful transport of paralarvae from the spawning grounds to a food-rich feature known as the cold ridge some 100–200 km away. The role of paralarval specific gravity on such transport is investigated. Specific gravity ranged from 1.0373 to 1.0734 g cm−3 during the yolk-utilization phase, implying that paralarvae are always negatively buoyant, regardless of yolk content. The data were incorporated into a coupled individual-based model (IBM)—Regional Ocean Modelling System model. The output showed that dispersal was dominantly westward towards the cold ridge. Also, modelled paralarval vertical distribution suggested that hydrodynamic turbulence was an important factor in dispersal. The negative buoyancy of early chokka squid paralarvae may reduce the risk of paralarvae being advected off the eastern Agulhas Bank and into the open ocean, where food is less abundant, so specific gravity may be important in enhancing the survival and recruitment of chokka squid.

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caption

caption

Sanders, R., Morris, P. J. , Stinchcombe, M. C. , Charalampopoulou, A., Lucas M., Thomalla S.J.
Abstract

The oceanic biological carbon pump (BCP), a large (10 GT C yr−1) component of the global carbon cycle, is dominated by the sinking (export) of particulate organic carbon (POC) from surface waters. In the deep ocean, strong correlations between downward fluxes of biominerals and POC (the so-called ‘ballast effect’) suggest a potential causal relationship, the nature of which remains uncertain. We show that similar correlations occur in the upper ocean with high rates of export only occurring when biominerals are also exported. Exported particles are generally biomineral rich relative to the upper ocean standing stock, due either to: (1) exported material being formed from the aggregation of a biomineral rich subset of upper ocean particles; or (2) the unfractionated aggregation of the upper ocean particulate pool with respiration then selectively removing POC relative to biominerals until particles are dense enough to sink.

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Figure 4 caption: POC (a) calcite, (b) opal ratios in exported and upper ocean particulate pools at 18 sites in the subpolar, subtropical and tropical Atlantic Ocean. Full symbols are from the AMT study [Thomalla et al., 2008]. Empty symbols are new observations reported here from the Iceland Basin in 2007 (auxiliary material). Note the broken axis required to include all data points.

Figure 4 caption: POC (a) calcite, (b) opal ratios in exported and upper ocean particulate pools at 18 sites in the subpolar, subtropical and tropical Atlantic Ocean. Full symbols are from the AMT study [Thomalla
et al., 2008]. Empty symbols are new observations reported here from the Iceland Basin in 2007 (auxiliary
material). Note the broken axis required to include all data points.

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