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One of the most important breakthroughs in oceanography in the last 30 years was the discovery that iron (Fe) controls biological production as a micronutrient, and our understanding of Fe and nutrient biogeochemical dynamics in the ocean has significantly advanced. In this review, we looked back both previous and updated knowledge of the natural Fe supply processes and nutrient dynamics in the subarctic Pacific and its impact on biological production. Although atmospheric dust has been considered to be the most important source of Fe affecting biological production in the subarctic Pacific, other oceanic sources of Fe have been discovered. We propose a coherent explanation for the biological response in subarctic Pacific high nutrient low chlorophyll (HNLC) waters that incorporates knowledge of both the atmospheric Fe supplies and the oceanic Fe supplies. Finally, we extract future directions for Fe oceanographic research in the subarctic Pacific and summarize the uncertain issues identified thus far.

Iron (Fe) is well known as a limiting factor to control primary productivity especially in high-nutrient and low chlorophyll area such as the subarctic Pacific. The solubility of Fe is believed to be controlled by its complexation with natural organic ligands, while the distribution of organic ligands is poorly understood. Here, we report that dissolved (

Aerosols and clouds play important roles in the Arctic climate. Conversely, aerosol emissions and cloud formation are affected by changes in the Arctic climate. This paper reviews studies of aerosols and clouds performed during the Arctic Challenge for Sustainability (ArCS) project carried out by the National Institute of Polar Research (NIPR) in Japan and collaborating institutions. The ArCS project included intensive studies of black carbon aerosols (BC). We installed Continuous Soot Monitoring System (COSMOS) instruments to measure atmospheric BC at four locations in the Arctic, establishing the Arctic BC COSMOS Measurement Network (ABCM-net). We also measured BC concentrations in snowpack in extensive areas of the Arctic and showed that previous studies have greatly overestimated BC in snowpack. We developed and improved new aerosol models that achieved better agreements with measurements of BC in the Arctic atmosphere, snowpack, and falling snow. We made new estimates of radiative forcing of BC in the Arctic atmosphere and snow/ice surfaces that lower their albedo. In addition to these researches on BC, we made accurate measurements of ice nucleating particles (INPs) at Ny-Alesund, Svalbard, showing that their concentrations increased in summer as a result of dust particle emissions from glacial outwash sediments. This high ice nucleating ability was likely due to the presence of organic substances mixed with the dust particles. We also made continuous cloud radar measurements and the first continuous in-situ measurements of cloud microphysical properties in the Arctic at Ny-Alesund. Results from these cloud measurements and their relationship with aerosols are described.

The toxicity and bioavailability of Cu in seawater were estimated using its chemical speciation. In this study, the concentrations and conditional stability constants (K'CuL) of Cu complexes with Cu-binding organic ligands (L) in seawater were determined using the reverse titration–competitive ligand exchange–adsorptive cathodic stripping voltammetry method. The concentrations of strong and weak ligands (L1 and L2, respectively) were determined using reverse titration via the addition of Cu to seawater samples to achieve ligand saturation. Our results revealed that the reverse titration method can be successfully used for the detection of high concentrations of L2. Using the reverse titration method, we detected L2 concentrations that exceeded 80 and 120 nM in seawater samples from the subtropical western North Pacific Ocean and Otsuchi Bay Japan, respectively. The K′CuL1 and K′CuL2 values obtained using the reverse titration method were comparable with those obtained using the forward titration method. Therefore, the reverse titration method can be used to determine the chemical speciation of Cu in the ocean and coastal regions as supplement to the more conventional forward titration method.

Distributions of trace metals (Mn, Fe, Ni, Zn and Cd) in the western Arctic Ocean (Chukchi Sea and Canada Basin) in September 2012 were investigated to elucidate the mechanisms behind the transport of these metals from the Chukchi Shelf to the Canada Basin. Filtered (, Deep Sea Research Part I: Oceanographic Research Papers, 116, pp.236-252; 2016

Total dissolved zinc (Zn) and its speciation was investigated by cathodic stripping voltammetry (CSV) in the western North Pacific and its marginal seas, the Sea of Okhotsk and the Sea of Japan (East Sea). In these high latitude areas, Zn complexing ligands in most samples were saturated with zinc because of the high dissolved Zn concentration. Relatively high total ligand concentrations (C L ) and relatively low conditional stability constants ( K Z n L , Z n 2 + ′ ) have been obtained in the surface waters of the Sea of Okhotsk (C L = 2.6 nM, log K Z n L , Z n 2 + ′ = 9.5) and the Sea of Japan (East Sea) (C L = 1.3 nM, log K Z n L , Z n 2 + ′ = 9.1) compared with those in the open ocean (subtropical and subarctic North Pacific, C L = 0.2–1.2 nM, log K ' Z n L , Z n 2 + = 9.1–10.8). The fraction of Zn labile in the deep waters of the Sea of Okhotsk was relatively small (34–38%, > 1479 m), which might indicate the presence of high concentrations of Zn complexing ligands in deep waters of the Sea of Okhotsk. In the western North Pacific, positive relationships were obtained between total ligand and chlorophyll a concentrations, suggesting that Zn complexing ligands in the surface water might be derived from bacteria and phytoplankton in the western North Pacific. However, relatively high concentrations of the ligands exist in low chlorophyll- a surface water in the Sea of Okhotsk and the Sea of Japan (East Sea), implying that Zn complexed ligands in those marginal seas have a different origin.

With the common goal of more accurately and consistently quantifying ambient concentrations of free metal ions and natural organic ligands in aquatic ecosystems, researchers from 15 laboratories that routinely analyze trace metal speciation participated in an intercomparison of statistical methods used to model the most widely-obtained type of experimental dataset, the complexometric titration. All were asked to apply statistical techniques that they felt comfortable using to model synthetic titration curves that are typical of those obtained by applying state-of-the-art electrochemical methods – anodic stripping voltammetry (ASV) and competitive ligand equilibration/adsorptive cathodic stripping voltammetry (CLE-ACSV) – to the analysis of natural waters. Herein, we compare our estimates for parameters describing the natural ligands, examine the accuracy of inferred ambient free metal ion concentrations ([Mf]), and evaluate the influence of the various methods and assumptions used in analyzing the data on these results. The ASV- type titrations were designed to test each participants’ ability to correctly describe the natural ligands present in a sample when provided with data free of measurement error, i.e., random noise. For the three virtual samples containing just one natural ligand, all participants were able to correctly identify the number of ligand classes present and accurately estimate their parameter values. For the four virtual samples containing two or three ligand classes, a few participants detected too few or too many classes and consequently reported inaccurate “measurements” of ambient [Mf]. Since the problematic results arose from human error rather than any specific method of analyzing the data, we recommend that analysts should make a practice of using one’s parameter estimates to generate simulated (back-calculated) titration curves for comparison to the original data. The root-mean squared difference between the fitted observations and simulated curves should be comparable to the expected error of the analytical method and upon visual inspection the distribution of residuals should be unskewed. Modeling the synthetic, CLE-ACSV-type titration dataset proved to be more challenging. The participants were provided with five distinct titration curves generated at different levels of competing ligand added (analytical windows) to the virtual sample. Random measurement error was also incorporated. Comparison of the submitted results was complicated by the differing interpretations of our task. Most adopted the provided “true” instrumental sensitivity in modeling the CLE- ACSV curves, but several estimated sensitivities using internal calibration, exactly as is required for actual samples. Since the fitted sensitivities were biased low, systematic biases in inferred ambient [Mf] and in estimated weak ligand (L2) concentrations resulted from their use. The main distinction between the mathematical approaches taken by participants lies in their choice of the speciation model equation/function, with its implicit definition of independent and dependent variables. In “direct modeling”, the dependent variable is the measured [Mf] (or Ip) and the total metal concentration ([M]T) considered independent. In other, much more widely used methods of analyzing titration data – classical linearization, best known as van den Berg/Ružić, and isotherm fitting by nonlinear regression, best known as the Langmuir or Gerringa methods – [Mf] is defined as independent and the dependent variable derived from a calculation that involves both [M]T and [Mf]. Close inspection of the biases and variability in the estimates of ligand parameters and in predictions of ambient [Mf] revealed that the best results were obtained by the first approach. Linear regression of transformed data yielded the largest bias and greatest variability, while non-linear isotherm fitting generated results with mean bias comparable to direct modeling, but also with greater variability. Participants that performed a unified analysis of ACSV titration curves at multiple detection windows for a sample improved their results regardless of the basic mathematical approach taken. Overall, the three most accurate sets of results were obtained using automated-unified analysis while the single most accurate set of results combined simultaneous calibration and parameter estimation. We therefore recommend that where sample volume and time permit, titration experiments for all natural water samples be designed to include two or more detection windows, especially for coastal and estuarine waters. It is vital that even more practical experimental designs for multi-window titrations be developed. In addition, while nearly every mathematical approach can prove to be adequate for some datasets, matrix-based equilibrium models are most naturally suited to the task for all datasets and can most easily handle the challenges encountered in this work, i.e., the cases where the added ligand in ACSV became titrated. The ProMCC program (Omanović et al., this issue) as well as the Excel Add-in based KINETEQL Multiwindow Solver spreadsheet (Hudson, 2014) have this capability and have been made available for public use as a result of this intercalibration exercise.

The distributions of dissolved iron (Fe), Fe(II) and Fe(III)-binding organic ligands were investigated in the upper 1000 m of the eastern tropical South Pacific from January to March 2010, during an El Nino event. Dissolved Fe concentrations were exceedingly low in surface waters, showed minima near the chlorophyll maxima, and increased below that depth. High rates of nitrogen fixation have been inferred for this region from models, but our data suggest that surface Fe is frequently too low to support diazotrophs. Dissolved Fe and organic Fe(III) ligand concentrations at mid-depth were elevated in the nearshore stations, where virtually all dissolved Fe(III) was bound to these ligands. Subsurface Fe(II) maxima were observed in the secondary nitrite maxima of the oxygen minimum zone (OMZ), comprising 8 to 68% of dissolved Fe. Dissolved Fe concentrations displayed local maxima coinciding with the maxima in Fe(II) and nitrite. We propose that this zone, the most reducing part of the OMZ, plays an important role in Fe transport in the upper 400 m of the water column.

The spatial distribution of dissolved organic carbon (DOC) concentrations and the optical properties of dissolved organic matter (DOM) determined by ultraviolet-visible absorbance and fluorescence spectroscopy were measured in surface waters of the southern Chukchi Sea, western Arctic Ocean, during the early summer of 2013. Neither the DOC concentration nor the optical parameters of the DOM correlated with salinity. Principal component analysis using the DOM optical parameters clearly separated the DOM sources. A significant linear relationship was evident between the DOC and the principal component score for specific water masses, indicating that a high DOC level was related to a terrigenous source, whereas a low DOC level was related to a marine source. Relationships between the DOC and the principal component scores of the surface waters of the southern Chukchi Sea implied that the major factor controlling the distribution of DOC concentrations was the mixing of plural water masses rather than local production and degradation.

Anthropogenic pollution and wildfires are main producers of carbon monoxide (CO) and black carbon (BC) in the Northern Hemisphere. High concentrations of these compounds are transported into the Arctic troposphere, influencing the ecosystem in high northern latitudes and the global climate. The global chemical transport model MOZART-4 is used to quantify the seasonal evolution of the contribution of CO and BC from different source regions in spring and summer 2008 by tagging their emissions. Aircraft observations from the POLARCAT experiments, in particular NASA ARCTAS, NOAA ARCPAC, POLARCAT-France, DLR GRACE and YAK-AEROSIB, as well as the NSF START08/preHIPPO experiments during Spring-Summer 2008 are combined to quantify the representation of simulated tracer characteristics in anthropogenic and fire plumes. In general, the model reproduces CO and BC well. Based on aircraft measurements and FLEXPART back-trajectories, the altitude contribution of emissions coming from different source regions is well captured in the model. Uncertainties of the MOZART-4 model are identified by comparing the data with model results on the flight tracks and using MOPITT satellite observations. Anthropogenic emissions are underestimated by about 10% in high northern latitudes in spring, and shortcomings exist in simulating fire plumes. The remote impact of East-Siberian fire emissions is underestimated for spring, whereas the impact of Southeast Asian fire emissions to mid-latitude CO values is overestimated by the model. In summer, mid-latitude CO values agree well between model and observations, whereas summer high latitude East-Siberian fire emissions in the model are overestimated by 20% in comparison to observations in the region. On the other hand, CO concentrations are underestimated by about 30% over Alaska and Canada at altitudes above 4 km. BC values are overestimated by the model at altitudes above 4 km in summer. Based on MOZART-4, with tagged CO and BC tracers, anthropogenic emissions of Asia, Europe and the US have the largest contribution to the CO and BC in mid- and high latitudes in spring and summer. Southeast Asian, Chinese and Indian fires have a large impact on CO pollution in spring in low latitudes with a maximum between 20° and 30°, whereas Siberian fires contribute largely to the pollution in high latitudes, up to 10% in spring and up to 30% in summer. The largest contributions to BC values in high latitudes are from anthropogenic emissions (about 70%). CO and BC have larger mass loadings in April than in July, as a result of photochemistry and dynamics.