Evaluating Optical Proxies of Particulate Organic Carbon across the Surface Atlantic Ocean

Empirical relationships between particulate organic carbon (POC) and inherent optical properties (IOPs) are required for estimating POC from ocean-colour remote sensing and autonomous platforms. The main relationships studied are those between POC and particulate attenuation (cp) and backscattering (bbp) coefficients. The parameters of these relationships can however differ considerably due to differences in the methodologies applied for measuring IOPs and POC as well as variations in particle characteristics. Therefore it is important to assess existing relationships and explore new optical proxies of POC. In this study, we evaluated empirical relationships between surface POC and IOPs (cp, bbp and the particulate absorption coefficient, ap) using an extensive dataset collected during two Atlantic Meridional Transect (AMT 19 and 22) cruises spanning a wide range of oceanographic regimes. IOPs and POC were measured during the two cruises using consistent methodologies. To independently assess the accuracy of the POC-IOPs relationships, we predicted surface POC for AMT-22 using relationships developed based on independent data from AMT-19. We found typical biases in predicting POC ranging between 2-3%, 4-9%, and 6-13% for cp, bbp and ap, respectively, and typical random uncertainties of 20-30%. We conclude that 1) accurate POC-cp and POC-bbp relationships were obtained due to the consistent methodologies used to estimate POC and IOPs and 2) ap could be considered as an alternative optical proxy for POC in open-ocean waters, only if all physiological variability in the POC:chl ratio could be modeled and used to correct ap

Surplus Photosynthetic Antennae Complexes Underlie Diagnostics of Iron Limitation in a Cyanobacterium

Chlorophyll fluorescence from phytoplankton provides a tool to assess iron limitation in the oceans, but the physiological mechanism underlying the fluorescence response is not understood. We examined fluorescence properties of the model cyanobacterium Synechocystis PCC6803 and a ΔisiA knock-out mutant of the same species grown under three culture conditions which simulate nutrient conditions found in the open ocean: (1) nitrate and iron replete, (2) limiting-iron and high-nitrate, representative of natural high-nitrate, low-chlorophyll regions, and (3) iron and nitrogen co-limiting. We show that low variable fluorescence, a key diagnostic of iron limitation, results from synthesis of antennae complexes far in excess of what can be accommodated by the iron-restricted pool of photosynthetic reaction centers. Under iron and nitrogen co-limiting conditions, there are no excess antennae complexes and variable fluorescence is high. These results help to explain the well-established fluorescence characteristics of phytoplankton in high-nutrient, low-chlorophyll ocean regions, while also accounting for the lack of these properties in low-iron, low-nitrogen regions. Importantly, our results complete the link between unique molecular consequences of iron stress in phytoplankton and global detection of iron stress in natural populations from space

Predictability of Seawater DMS During the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES)

This work presents an overview of a unique set of surface ocean dimethylsulfide (DMS) measurements from four shipboard field campaigns conducted during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) project. Variations in surface seawater DMS are discussed in relation to biological and physical observations. Results are considered at a range of timescales (seasons to days) and spatial scales (regional to sub-mesoscale). Elevated DMS concentrations are generally associated with greater biological productivity, although chlorophyll a (Chl) only explains a small fraction of the DMS variability (15%). Physical factors that determine the location of oceanic temperature fronts and depth of vertical mixing have an important influence on seawater DMS concentrations during all seasons. The interplay of biomass and physics influences DMS concentrations at regional/seasonal scales and at smaller spatial and shorter temporal scales. Seawater DMS measurements are compared with the global seawater DMS climatology and predictions made using a recently published algorithm and by a neural network model. The climatology is successful at capturing the seasonal progression in average seawater DMS, but does not reproduce the shorter spatial/temporal scale variability. The input terms common to the algorithm and neural network approaches are biological (Chl) and physical (mixed layer depth, photosynthetically active radiation, seawater temperature). Both models predict the seasonal North Atlantic average seawater DMS trends better than the climatology. However, DMS concentrations tend to be under-predicted and the episodic occurrence of higher DMS concentrations is poorly predicted. The choice of climatological seawater DMS product makes a substantial impact on the estimated DMS flux into the North Atlantic atmosphere. These results suggest that additional input terms are needed to improve the predictive capability of current state-of-the-art approaches to estimating seawater DMS

Oceanography : plankton in a warmer world

Author Posting. © Nature Publishing Group, 2006. This is the author's version of the work. It is posted here by permission of Nature Publishing Group for personal use, not for redistribution. The definitive version was published in Nature 444 (2006): 695-696, doi:10.1038/444695a.Satellite data show that phytoplankton biomass and growth generally decline as the oceans’ surface waters warm up. Is this trend, seen over the past decade, a harbinger of the future for marine ecosystems

Mining a Sea of Data: Deducing the Environmental Controls of Ocean Chlorophyll

Chlorophyll biomass in the surface ocean is regulated by a complex interaction of physiological, oceanographic, and ecological factors and in turn regulates the rates of primary production and export of organic carbon to the deep ocean. Mechanistic models of phytoplankton responses to climate change require the parameterization of many processes of which we have limited knowledge. We develop a statistical approach to estimate the response of remote-sensed ocean chlorophyll to a variety of physical and chemical variables. Irradiance over the mixed layer depth, surface nitrate, sea-surface temperature, and latitude and longitude together can predict 83% of the variation in log chlorophyll in the North Atlantic. Light and nitrate regulate biomass through an empirically determined minimum function explaining nearly 50% of the variation in log chlorophyll by themselves and confirming that either light or macronutrients are often limiting and that much of the variation in chlorophyll concentration is determined by bottom-up mechanisms. Assuming the dynamics of the future ocean are governed by the same processes at work today, we should be able to apply these response functions to future climate change scenarios, with changes in temperature, nutrient distributions, irradiance, and ocean physics

An absorption‐based approach to improved estimates of phytoplankton biomass and net primary production

The abundance and productivity of phytoplankton constrain energy transfer through marine food webs and the export of organic carbon to the deep ocean. Bio-optical measurements correlate well with phytoplankton carbon (Cphyto), but the effect of taxonomic variability on this relationship is still uncertain. Here, we explore how changes in phytoplankton community composition influence the relationship between the particulate backscatter coefficient (bbp) and Cphyto and present a new approach to estimate phytoplankton biomass more accurately using bbp. We found that using a fixed scaling factor for the conversion of bbp to Cphyto could lead to the underestimation or overestimation of biomass, depending on the dominant taxonomic group in the phytoplankton community. In addition, we demonstrate how a simple ratio of absorption at two wavelengths can be used to provide a coarse approximation of phytoplankton community composition when scaling bbp to Cphyto, thereby improving the estimation of net primary production

Informing ocean color inversion products by seeding with ancillary observations

Ocean reflectance inversion algorithms provide many products used in ecological and biogeochemical models. While a number of different inversion approaches exist, they all use only spectral remote-sensing reflectances (Rrs(λ)) as input to derive inherent optical properties (IOPs) in optically deep oceanic waters. However, information content in Rrs(λ) is limited, so spectral inversion algorithms may benefit from additional inputs. Here, we test the simplest possible case of ingesting optical data (‘seeding’) within an inversion scheme (the Generalized Inherent Optical Property algorithm framework default configuration (GIOP-DC)) with both simulated and satellite datasets of an independently known or estimated IOP, the particulate backscattering coefficient at 532 nm (bbp(532)). We find that the seeded-inversion absorption products are substantially different and more accurate than those generated by the standard implementation. On global scales, seasonal patterns in seeded-inversion absorption products vary by more than 50% compared to absorption from the GIOP-DC. This study proposes one framework in which to consider the next generation of ocean color inversion schemes by highlighting the possibility of adding information collected with an independent sensor

Predicting the Electron Requirement for Carbon Fixation in Seas and Oceans

Marine phytoplankton account for about 50% of all global net primary productivity (NPP). Active fluorometry, mainly Fast Repetition Rate fluorometry (FRRf), has been advocated as means of providing high resolution estimates of NPP. However, not measuring CO2-fixation directly, FRRf instead provides photosynthetic quantum efficiency estimates from which electron transfer rates (ETR) and ultimately CO2-fixation rates can be derived. Consequently, conversions of ETRs to CO2-fixation requires knowledge of the electron requirement for carbon fixation (Φe,C, ETR/CO2 uptake rate) and its dependence on environmental gradients. Such knowledge is critical for large scale implementation of active fluorescence to better characterise CO2-uptake. Here we examine the variability of experimentally determined Φe,C values in relation to key environmental variables with the aim of developing new working algorithms for the calculation of Φe,C from environmental variables. Coincident FRRf and 14C-uptake and environmental data from 14 studies covering 12 marine regions were analysed via a meta-analytical, non-parametric, multivariate approach. Combining all studies, Φe,C varied between 1.15 and 54.2 mol e- (mol C)-1 with a mean of 10.9±6.91 mol e- mol C)-1. Although variability of Φe,C was related to environmental gradients at global scales, region-specific analyses provided far improved predictive capability. However, use of regional Φe,C algorithms requires objective means of defining regions of interest, which remains challenging. Considering individual studies and specific small-scale regions, temperature, nutrient and light availability were correlated with Φe,C albeit to varying degrees and depending on the study/region and the composition of the extant phytoplankton community. At the level of large biogeographic regions and distinct water masses, Φe,C was related to nutrient availability, chlorophyll, as well as temperature and/or salinity in most regions, while light availability was also important in Baltic Sea and shelf waters. The novel Φe,C algorithms provide a major step forward for widespread fluorometry-based NPP estimates and highlight the need for further studying the natural variability of Φe,C to verify and develop algorithms with improved accuracy. © 2013 Lawrenz et al

Substantial Seasonal Contribution of Observed Biogenic Sulfate Particles to Cloud Condensation Nuclei

Biogenic sources contribute to cloud condensation nuclei (CCN) in the clean marine atmosphere, but few measurements exist to constrain climate model simulations of their importance. The chemical composition of individual atmospheric aerosol particles showed two types of sulfate-containing particles in clean marine air masses in addition to mass-based Estimated Salt particles. Both types of sulfate particles lack combustion tracers and correlate, for some conditions, to atmospheric or seawater dimethyl sulfide (DMS) concentrations, which means their source was largely biogenic. The first type is identified as New Sulfate because their large sulfate mass fraction (63% sulfate) and association with entrainment conditions means they could have formed by nucleation in the free troposphere. The second type is Added Sulfate particles (38% sulfate), because they are preexisting particles onto which additional sulfate condensed. New Sulfate particles accounted for 31% (7 cm−3) and 33% (36 cm−3) CCN at 0.1% supersaturation in late-autumn and late-spring, respectively, whereas sea spray provided 55% (13 cm−3) in late-autumn but only 4% (4 cm−3) in late-spring. Our results show a clear seasonal difference in the marine CCN budget, which illustrates how important phytoplankton-produced DMS emissions are for CCN in the North Atlantic