Surface ocean carbon dioxide during the Atlantic Meridional Transect (1995–2013); evidence of ocean acidification

Here we present more than 21,000 observations of carbon dioxide fugacity in air and seawater (fCO2) along the Atlantic Meridional Transect (AMT) programme for the period 1995–2013. Our dataset consists of 11 southbound and 2 northbound cruises in boreal autumn and spring respectively. Our paper is primarily focused on change in the surface-ocean carbonate system during southbound cruises. We used observed fCO2 and total alkalinity (TA), derived from salinity and temperature, to estimate dissolved inorganic carbon (DIC) and pH (total scale). Using this approach, estimated pH was consistent with spectrophotometric measurements carried out on 3 of our cruises. The AMT cruises transect a range of biogeographic provinces where surface Chlorophyll-a spans two orders of magnitude (mesotrophic high latitudes to oligotrophic subtropical gyres). We found that surface Chlorophyll-a was negatively correlated with fCO2, but that the deep chlorophyll maximum was not a controlling variable for fCO2. Our data show clear evidence of ocean acidification across 100� of latitude in the Atlantic Ocean. Over the period 1995–2013 we estimated annual rates of change in: (a) sea surface temperature of 0.01 ± 0.05 �C, (b) seawater fCO2 of 1.44 ± 0.84 latm, (c) DIC of 0.87 ± 1.02 lmol per kg and (d) pH of �0.0013 ± 0.0009 units. Monte Carlo simulations propagating the respective analytical uncertainties showed that the latter were < 5% of the observed trends. Seawater fCO2 increased at the same rate as atmospheric CO2

Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient

The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (&lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt;. Application of this method to spectral &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in &lt;i&gt;c&lt;/i&gt;&lt;sub&gt;p&lt;/sub&gt; can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m&lt;sup&gt;−3&lt;/sup&gt; and accounted for approximately 10% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity

Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient

The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (cp) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in cp. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in cp. Application of this method to spectral cp measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in cp can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6-1.5 1/4μm. The inferred carbon biomass of these cells was about 5.2-6.0 mg mg -\u273 and accounted for approximately 10% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity

Synoptic relationships between surface Chlorophyll-<i>a</i> and diagnostic pigments specific to phytoplankton functional types

Error-quantified, synoptic-scale relationships between chlorophyll-<i>a</i> (Chl-<i>a</i>) and phytoplankton pigment groups at the sea surface are presented. A total of ten pigment groups were considered to represent three Phytoplankton Size Classes (PSCs, micro-, nano- and picoplankton) and seven Phytoplankton Functional Types (PFTs, i.e. diatoms, dinoflagellates, green algae, prymnesiophytes (haptophytes), pico-eukaryotes, prokaryotes and <i>Prochlorococcus</i> sp.). The observed relationships between Chl-<i>a</i> and PSCs/PFTs were well-defined at the global scale to show that a community shift of phytoplankton at the basin and global scales is reflected by a change in Chl-<i>a</i> of the total community. Thus, Chl-<i>a</i> of the total community can be used as an index of not only phytoplankton biomass but also of their community structure. Within these relationships, we also found non-monotonic variations with Chl-<i>a</i> for certain pico-sized phytoplankton (pico-eukaryotes, Prokaryotes and <i>Prochlorococcus</i> sp.) and nano-sized phytoplankton (Green algae, prymnesiophytes). The relationships were quantified with a least-square fitting approach in order to enable an estimation of the PFTs from Chl-<i>a</i> where PFTs are expressed as a percentage of the total Chl-<i>a</i>. The estimated uncertainty of the relationships depends on both PFT and Chl-<i>a</i> concentration. Maximum uncertainty of 31.8% was found for diatoms at Chl-<i>a</i> = 0.49 mg m⁻³. However, the mean uncertainty of the relationships over all PFTs was 5.9% over the entire Chl-<i>a</i> range observed in situ (0.02 &lt; Chl-<i>a</i> &lt; 4.26 mg m^&minus;3). The relationships were applied to SeaWiFS satellite Chl-<i>a</i> data from 1998 to 2009 to show the global climatological fields of the surface distribution of PFTs. Results show that microplankton are present in the mid and high latitudes, constituting only ~10.9% of the entire phytoplankton community in the mean field for 1998–2009, in which diatoms explain ~7.5%. Nanoplankton are ubiquitous throughout the global surface oceans, except the subtropical gyres, constituting ~45.5%, of which prymnesiophytes (haptophytes) are the major group explaining ~31.7% while green algae contribute ~13.9%. Picoplankton are dominant in the subtropical gyres, but constitute ~43.6% globally, of which prokaryotes are the major group explaining ~26.5% (<i>Prochlorococcus</i> sp. explaining 22.8%), while pico-eukaryotes explain ~17.2% and are relatively abundant in the South Pacific. These results may be of use to evaluate global marine ecosystem models

Inferring phytoplankton carbon and eco-physiological rates from diel cycles of spectral particulate beam-attenuation coefficient

The diurnal fluctuations in solar irradiance impose a fundamental frequency on ocean biogeochemistry. Observations of the ocean carbon cycle at these frequencies are rare, but could be considerably expanded by measuring and interpreting the inherent optical properties. A method is presented to analyze diel cycles in particulate beam-attenuation coefficient (c[subscript p]) measured at multiple wavelengths. The method is based on fitting observations with a size-structured population model coupled to an optical model to infer the particle size distribution and physiologically relevant parameters of the cells responsible for the measured diel cycle in c[subscript p]. Results show that the information related to size and contained in the spectral data can be exploited to independently estimate growth and loss rates during the day and night. In addition, the model can characterize the population of particles affecting the diel variability in c[subscript p]. Application of this method to spectral c[subscript p] measured at a station in the oligotrophic Mediterranean Sea suggests that most of the observed variations in c[subscript p] can be ascribed to a synchronized population of cells with an equivalent spherical diameter around 4.6±1.5 μm. The inferred carbon biomass of these cells was about 5.2–6.0 mg m⁻³ and accounted for approximately 10% of the total particulate organic carbon. If successfully validated, this method may improve our in situ estimates of primary productivity

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition

Phytoplankton functional types from Space.

The concept of phytoplankton functional types has emerged as a useful approach to classifying phytoplankton. It finds many applications in addressing some serious contemporary issues facing science and society. Its use is not without challenges, however. As noted earlier, there is no universally-accepted set of functional types, and the types used have to be carefully selected to suit the particular problem being addressed. It is important that the sum total of all functional types matches all phytoplankton under consideration. For example, if in a biogeochemical study, we classify phytoplankton as silicifiers, calcifiers, DMS-producers and nitrogen fix- ers, then there is danger that the study may neglect phytoplankton that do not contribute in any significant way to those functions, but may nevertheless be a significant contributor to, say primary production. Such considerations often lead to the adoption of a category of “other phytoplankton” in models, with no clear defining traits assigned them, but that are nevertheless necessary to close budgets on phytoplankton processes. Since this group is a collection of all phytoplankton that defy classification according to a set of traits, it is difficult to model their physi- ological processes. Our understanding of the diverse functions of phytoplankton is still growing, and as we recognize more functions, there will be a need to balance the desire to incorporate the increasing number of functional types in models against observational challenges of identifying and mapping them adequately. Modelling approaches to dealing with increasing functional diversity have been proposed, for example, using the complex adaptive systems theory and system of infinite diversity, as in the work of Bruggemann and Kooijman (2007). But it is unlikely that remote-sensing approaches might be able to deal with anything but a few prominent functional types. As long as these challenges are explicitly addressed, the functional- type concept should continue to fill a real need to capture, in an economic fashion, the diversity in phytoplankton, and remote sensing should continue to be a useful tool to map them. Remote sensing of phytoplankton functional types is an emerging field, whose potential is not fully realised, nor its limitations clearly established. In this report, we provide an overview of progress to date, examine the advantages and limitations of various methods, and outline suggestions for further development. The overview provided in this chapter is intended to set the stage for detailed considerations of remote-sensing applications in later chapters. In the next chapter, we examine various in situ methods that exist for observing phytoplankton functional types, and how they relate to remote-sensing techniques. In the subsequent chapters, we review the theoretical and empirical bases for the existing and emerging remote-sensing approaches; assess knowledge about the limitations, assumptions, and likely accuracy or predictive skill of the approaches; provide some preliminary comparative analyses; and look towards future prospects with respect to algorithm development, validation studies, and new satellite mis- sions