Decadal increase in Arctic dimethylsulfide emission.

Dimethylsulfide (DMS), a gas produced by marine microbial food webs, promotes aerosol formation in pristine atmospheres, altering cloud radiative forcing and precipitation. Recent studies suggest that DMS controls aerosol formation in the summertime Arctic atmosphere and call for an assessment of pan-Arctic DMS emission (EDMS) in a context of dramatic ecosystem changes. Using a remote sensing algorithm, we show that summertime EDMS from ice-free waters increased at a mean rate of 13.3 ± 6.7 Gg S decade-1 (∼33% decade-1) north of 70°N between 1998 and 2016. This trend, mostly explained by the reduction in sea-ice extent, is consistent with independent atmospheric measurements showing an increasing trend of methane sulfonic acid, a DMS oxidation product. Extrapolation to an ice-free Arctic summer could imply a 2.4-fold (±1.2) increase in EDMS compared to present emission. However, unexpected regime shifts in Arctic geo- and ecosystems could result in future EDMS departure from the predicted range. Superimposed on the positive trend, EDMS shows substantial interannual changes and nonmonotonic multiyear trends, reflecting the interplay between physical forcing, ice retreat patterns, and phytoplankton productivity. Our results provide key constraints to determine whether increasing marine sulfur emissions, and resulting aerosol-cloud interactions, will moderate or accelerate Arctic warming in the context of sea-ice retreat and increasing low-level cloud cover.

A simple and effective method for monitoring floating green macroalgae blooms: a case study in the Yellow Sea.

Several algorithms have been proposed to detect floating macroalgae blooms in the global ocean. However, some of them are difficult or even impossible to routinely apply by non-experts because of performing a sophisticated atmospheric correction scheme or due to the mismatch in spectral bands from one sensor to another. Here, a generic, simple and effective method, referred to as the Floating Green Tide Index (FGTI), was proposed to detect floating green macroalgae blooms (GMB). The FGTI was defined as the difference between greenness and wetness features extracted from digital number (DN) observation through Tasseled Cap Transformation analysis, providing the advantage of bypassing the atmospheric correction procedure. Through cross-index and cross-sensor comparisons, the FGTI showed similar performance to the existing VB-FAH (Virtual-Baseline Floating macroAlgae Height) and FAI (Floating Algae Index) algorithms but proved more robust than the traditional NDVI (Normalized Difference Vegetation Index) in terms of response to perturbations by environmental conditions, viewing geometry, sun glint, and thin cloud contamination. Given the requirement for spectral bands in the current and planned satellite sensors, the FGTI design can easily be extended to any satellite sensor, and therefore provide an excellent data resource for studying GMB in any part of the global ocean.

Estimation of phytoplankton taxonomic groups in the Arctic Ocean using phytoplankton absorption properties: implication for ocean-color remote sensing.

Knowledge on the phenology and distribution of phytoplankton taxonomic groups (PTGs) represent valuable information when studying marine ecosystem, especially in the Arctic Ocean where rapid warming has drastic effects on sea-ice dynamics, which affect the marine food web. Taxonomic groups of phytoplankton can be discriminated based on their pigment signatures, which, in turn, impact their absorption spectra, given that different pigments have different absorption windows in the visible. Using concurrent measurements of phytoplankton diagnostic pigments and absorption spectra (aph) collected in the Bering and Chukchi Seas, a novel and direct approach was designed for simultaneously estimating the biomass concentrations of several PTGs (Ci) as well as their specific absorption coefficient. The chemotaxonomic tool CHEMTAX was applied to twelve diagnostic pigments measured by high-performance liquid chromatography (HPLC). Their results revealed that the phytoplankton community composition was made of nine groups, from which six dominant were identified: diatoms, dinoflagellates, c3-flagellate, haptophytes type 7, two types of prasinophytes. Out of 117 samples, twenty pairs of Ci derived by CHEMTAX and measured aph were randomly selected and used in a linear unmixing model to extract the specific absorption spectral of each group. This step was repeated 1000 times to provide the mean specific absorption of a given phytoplankton group. These specific absorption spectra were used to reconstruct total aph, which was consistent with the measured aph (R2 from 0.8 to 0.95) at all visible wavelengths (400-700 nm). The derived specific absorption spectra were further used with the measured aph(λ) at ten Moderate Resolution Imaging Spectroradiometer (MODIS) wavebands in a linear unmixing model to test the ability to retrieve the concentrations of PTGs from satellite remote sensing. A comparison between estimated and measured Ci showed that the approach used in this study performed best when retrieving five groups (i.e., dinoflagellates, c3-flagellate, haptophytes, two types of prasinophytes) from the nine initially identified using CHEMTAX with a mean absolute percentage error (MAPE) <35%, except for diatoms with a MAPE value of about 45%. Our approach provides a practical basis for estimation of PTGs using aph(λ) derived from satellite observations and field measurements.

Evaluation of sea-surface photosynthetically available radiation algorithms under various sky conditions and solar elevations.

In this study, we report on the performance of satellite-based photosynthetically available radiation (PAR) algorithms used in published oceanic primary production models. The performance of these algorithms was evaluated using buoy observations under clear and cloudy skies, and for the particular case of low sun angles typically encountered at high latitudes or at moderate latitudes in winter. The PAR models consisted of (i) the standard one from the NASA-Ocean Biology Processing Group (OBPG), (ii) the Gregg and Carder (GC) semi-analytical clear-sky model, and (iii) look-up-tables based on the Santa Barbara DISORT atmospheric radiative transfer (SBDART) model. Various combinations of atmospheric inputs, empirical cloud corrections, and semi-analytical irradiance models yielded a total of 13 (11 + 2 developed in this study) different PAR products, which were compared with in situ measurements collected at high frequency (15 min) at a buoy site in the Mediterranean Sea (the "BOUée pour l'acquiSition d'une Série Optique à Long termE," or, "BOUSSOLE" site). An objective ranking method applied to the algorithm results indicated that seven PAR products out of 13 were well in agreement with the in situ measurements. Specifically, the OBPG method showed the best overall performance with a root mean square difference (RMSD) (bias) of 19.7% (6.6%) and 10% (6.3%) followed by the look-up-table method with a RMSD (bias) of 25.5% (6.8%) and 9.6% (2.6%) at daily and monthly scales, respectively. Among the four methods based on clear-sky PAR empirically corrected for cloud cover, the Dobson and Smith method consistently underestimated daily PAR while the Budyko formulation overestimated daily PAR. Empirically cloud-corrected methods using cloud fraction (CF) performed better under quasi-clear skies (CF<0.3) with an RMSD (bias) of 9.7%-14.8% (3.6%-11.3%) than under partially clear to cloudy skies (0.30.7), however, all methods showed larger RMSD differences (biases) ranging between 32% and 80.6% (-54.5%-8.7%). Finally, three methods tested for low sun elevations revealed systematic overestimation, and one method showed a systematic underestimation of daily PAR, with relative RMSDs as large as 50% under all sky conditions. Under partially clear to overcast conditions all the methods underestimated PAR. Model uncertainties predominantly depend on which cloud products were used.

CDOM Sources and Photobleaching Control Quantum Yields for Oceanic DMS Photolysis.

Photolysis is a major removal pathway for the biogenic gas dimethylsulfide (DMS) in the surface ocean. Here we tested the hypothesis that apparent quantum yields (AQY) for DMS photolysis varied according to the quantity and quality of its photosensitizers, chiefly chromophoric dissolved organic matter (CDOM) and nitrate. AQY compiled from the literature and unpublished studies ranged across 3 orders of magnitude at the 330 nm reference wavelength. The smallest AQY(330) were observed in coastal waters receiving major riverine inputs of terrestrial CDOM (0.06-0.5 m3 (mol quanta)-1). In open-ocean waters, AQY(330) generally ranged between 1 and 10 m3 (mol quanta)-1. The largest AQY(330), up to 34 m3 (mol quanta)-1), were seen in the Southern Ocean potentially associated with upwelling. Despite the large AQY variability, daily photolysis rate constants at the sea surface spanned a smaller range (0.04-3.7 d-1), mainly because of the inverse relationship between CDOM absorption and AQY. Comparison of AQY(330) with CDOM spectral signatures suggests there is an interplay between CDOM origin (terrestrial versus marine) and photobleaching that controls variations in AQYs, with a secondary role for nitrate. Our results can be used for regional or large-scale assessment of DMS photolysis rates in future studies.

Nitrogen fixation in the widely distributed marine γ-proteobacterial diazotroph Candidatus Thalassolituus haligoni.

The high diversity and global distribution of heterotrophic bacterial diazotrophs (HBDs) in the ocean has recently become apparent. However, understanding the role these largely uncultured microorganisms play in marine N2 fixation poses a challenge due to their undefined growth requirements and the complex regulation of the nitrogenase enzyme. We isolated and characterized Candidatus Thalassolituus haligoni, a member of a widely distributed clade of HBD belonging to the Oceanospirillales. Analysis of its nifH gene via amplicon sequencing revealed the extensive distribution of Cand. T. haligoni across the Pacific, Atlantic, and Arctic Oceans. Pangenome analysis indicates that the isolate shares >99% identity with an uncultured metagenome-assembled genome called Arc-Gamma-03, recently recovered from the Arctic Ocean. Through combined genomic, proteomic, and physiological approaches, we confirmed that the isolate fixes N2 gas. However, the mechanisms governing nitrogenase regulation in Cand. T. haligoni remain unclear. We propose Cand. T. haligoni as a globally distributed, cultured HBD model species within this understudied clade of Oceanospirillales.

Generalized ocean color inversion model for retrieving marine inherent optical properties.

Ocean color measured from satellites provides daily, global estimates of marine inherent optical properties (IOPs). Semi-analytical algorithms (SAAs) provide one mechanism for inverting the color of the water observed by the satellite into IOPs. While numerous SAAs exist, most are similarly constructed and few are appropriately parameterized for all water masses for all seasons. To initiate community-wide discussion of these limitations, NASA organized two workshops that deconstructed SAAs to identify similarities and uniqueness and to progress toward consensus on a unified SAA. This effort resulted in the development of the generalized IOP (GIOP) model software that allows for the construction of different SAAs at runtime by selection from an assortment of model parameterizations. As such, GIOP permits isolation and evaluation of specific modeling assumptions, construction of SAAs, development of regionally tuned SAAs, and execution of ensemble inversion modeling. Working groups associated with the workshops proposed a preliminary default configuration for GIOP (GIOP-DC), with alternative model parameterizations and features defined for subsequent evaluation. In this paper, we: (1) describe the theoretical basis of GIOP; (2) present GIOP-DC and verify its comparable performance to other popular SAAs using both in situ and synthetic data sets; and, (3) quantify the sensitivities of their output to their parameterization. We use the latter to develop a hierarchical sensitivity of SAAs to various model parameterizations, to identify components of SAAs that merit focus in future research, and to provide material for discussion on algorithm uncertainties and future emsemble applications.

Model of phytoplankton absorption based on three size classes.

Using the phytoplankton size-class model of Brewin et al. [Ecol. Model.221, 1472 (2010)], the two-population absorption model of Sathyendranath et al. [Int. J. Remote. Sens.22, 249 (2001)] and Devred et al. [J. Geophys. Res.111, C03011 (2006)] is extended to three populations of phytoplankton, namely, picophytoplankton, nanophytoplankton, and microphytoplankton. The new model infers total and size-dependent phytoplankton absorption as a function of the total chlorophyll-a concentration. A main characteristic of the model is that all the parameters that describe it have biological or optical interpretation. The three-population model performs better than the two-population model at retrieving total phytoplankton absorption. Accounting for the contributions of picophytoplankton and nanophytoplankton, rather than the combination of both as in the two-population model, improved significantly the retrieval of phytoplankton absorption at low chlorophyll-a concentrations. Class-dependent specific absorption of phytoplankton derived using the model compares well with previously published models. However, the model presented in this paper provides the specific absorption of three size classes and is applicable to a continuum of chlorophyll-a concentrations. Absorption obtained from remotely sensed chlorophyll-a using our model compares well with in situ absorption measurements.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models.

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.