Predicting water-column phytoplankton biomass from near-surface measurements is a common approach in biological oceanography, particularly since the advent of satellite remote sensing of ocean color (OC). In the Arctic Ocean, deep subsurface chlorophyll maxima (SCMs) that significantly contribute to primary production (PP) are often observed. These are neither detected by ocean color sensors nor accounted for in the primary production models applied to the Arctic Ocean. Here, we assemble a large database of pan-Arctic observations (i.e., 5206 stations) and develop an empirical model to estimate vertical chlorophyll a (Chl a) according to (1) the shelf–offshore gradient delimited by the 50 m isobath, (2) seasonal variability along pre-bloom, post-bloom, and winter periods, and (3) regional differences across ten sub-Arctic and Arctic seas. Our detailed analysis of the dataset shows that, for the pre-bloom and winter periods, as well as for high surface Chl a concentration (Chl a_surf; 0.7–30 mg m⁻³) throughout the open water period, the Chl a maximum is mainly located at or near the surface. Deep SCMs occur chiefly during the post-bloom period when Chl a_surf is low (0–0.5 mg m⁻³). By applying our empirical model to annual Chl a_surf time series, instead of the conventional method assuming vertically homogenous Chl a, we produce novel pan-Arctic PP estimates and associated uncertainties. Our results show that vertical variations in Chl a have a limited impact on annual depth-integrated PP. Small overestimates found when SCMs are shallow (i.e., pre-bloom, post-bloom > 0.7 mg m⁻³, and the winter period) somehow compensate for the underestimates found when SCMs are deep (i.e., post-bloom < 0.5 mg m⁻³). SCMs are, however, important seasonal features with a substantial impact on depth-integrated PP estimates, especially when surface nitrate is exhausted in the Arctic Ocean and where highly stratified and oligotrophic conditions prevail.

A series of papers have suggested that freshwater discharge, including a large amount of dissolved organic matter (DOM), has increased since the middle of the 20th century. In this study, a semi-analytical algorithm for estimating light absorption coefficients of the colored fraction of DOM (CDOM) was developed for southern Beaufort Sea waters using remote sensing reflectance at six wavelengths in the visible spectral domain corresponding to MODIS ocean color sensor. This algorithm allows the separation of colored detrital matter (CDM) into CDOM and non-algal particles (NAP) through the determination of NAP absorption using an empirical relationship between NAP absorption and particle backscattering coefficients. Evaluation using independent datasets, which were not used for developing the algorithm, showed that CDOM absorption can be estimated accurately to within an uncertainty of 35% and 50% for oceanic and coastal waters, respectively. A previous paper (Matsuoka et al., 2012) showed that dissolved organic carbon (DOC) concentrations were tightly correlated with CDOM absorption in our study area (r² = 0.97). By combining the CDOM absorption algorithm together with the DOC versus CDOM relationship, it is now possible to estimate DOC concentrations in the near-surface layer of the southern Beaufort Sea using satellite ocean color data. DOC concentrations in the surface waters were estimated using MODIS ocean color data, and the estimates showed reasonable values compared to in situ measurements. We propose a routine and near real-time method for deriving DOC concentrations from space, which may open the way to an estimate of DOC budgets for Arctic coastal waters.

River water is the main source of dissolved organic carbon (DOC) in the Arctic Ocean. DOC plays an important role in the Arctic carbon cycle, and its export from land to sea is expected to increase as ongoing climate change accelerates permafrost thaw. However, transport pathways and transformation of DOC in the land-to-ocean transition are mostly unknown. We collected DOC and aCDOM(λ) samples from 11 expeditions to river, coastal and offshore waters and present a new DOC–aCDOM(λ) model for the fluvial–marine transition zone in the Laptev Sea. The aCDOM(λ) characteristics revealed that the dissolved organic matter (DOM) in samples of this dataset are primarily of terrigenous origin. Observed changes in aCDOM(443) and its spectral slopes indicate that DOM is modified by microbial and photo-degradation. Ocean colour remote sensing (OCRS) provides the absorption coefficient of coloured dissolved organic matter (aCDOM(λ)sat) at λ=440 or 443 nm, which can be used to estimate DOC concentration at high temporal and spatial resolution over large regions. We tested the statistical performance of five OCRS algorithms and evaluated the plausibility of the spatial distribution of derived aCDOM(λ)sat. The OLCI (Sentinel-3 Ocean and Land Colour Instrument) neural network swarm (ONNS) algorithm showed the best performance compared to in situ aCDOM(440) (r2=0.72). Additionally, we found ONNS-derived aCDOM(440), in contrast to other algorithms, to be partly independent of sediment concentration, making ONNS the most suitable aCDOM(λ)sat algorithm for the Laptev Sea region. The DOC–aCDOM(λ) model was applied to ONNS-derived aCDOM(440), and retrieved DOC concentration maps showed moderate agreement to in situ data (r2=0.53). The in situ and satellite-retrieved data were offset by up to several days, which may partly explain the weak correlation for this dynamic region. Satellite-derived surface water DOC concentration maps from Medium Resolution Imaging Spectrometer (MERIS) satellite data demonstrate rapid removal of DOC within short time periods in coastal waters of the Laptev Sea, which is likely caused by physical mixing and different types of degradation processes. Using samples from all occurring water types leads to a more robust DOC–aCDOM(λ) model for the retrievals of DOC in Arctic shelf and river waters.

Phytoplankton are expected to respond to recent environmental changes of the Arctic Ocean. In terms of bottom-up control, modifying the phytoplankton distribution will ultimately affect the entire food web and carbon export. However, detecting and quantifying changes in phytoplankton communities in the Arctic Ocean remains difficult because of the lack of data and the inconsistent identification methods used. Based on pigment and microscopy data sampled in the Beaufort Sea during summer 2009, we optimized the chemotaxonomic tool CHEMTAX (CHEMical TAXonomy) for the assessment of phytoplankton community composition in an Arctic setting. The geographical distribution of the main phytoplankton groups was determined with clustering methods. Four phytoplankton assemblages were determined and related to bathymetry, nutrients and light availability. Surface waters across the whole survey region were dominated by prasinophytes and chlorophytes, whereas the subsurface chlorophyll maximum was dominated by the centric diatoms Chaetoceros socialis on the shelf and by two populations of nanoflagellates in the deep basin. Microscopic counts showed a high contribution of the heterotrophic dinoflagellates Gymnodinium and Gyrodinium spp. to total carbon biomass, suggesting high grazing activity at this time of the year. However, CHEMTAX was unable to detect these dinoflagellates because they lack peridinin. In heterotrophic dinoflagellates, the inclusion of the pigments of their prey potentially leads to incorrect group assignments and some misinterpretation of CHEMTAX. Thanks to the high reproducibility of pigment analysis, our results can serve as a baseline to assess change and spatial or temporal variability in several phytoplankton populations that are not affected by these misinterpretations.

Arctic warming alters land‐to‐sea fluxes of nutrients and organic matter, which impact air‐sea carbon exchange. Here we use an ocean‐biogeochemical model of the southeastern Beaufort Sea (SBS) to investigate the role of Mackenzie River biogeochemical discharge in modulating air‐sea CO₂ fluxes during 2000–2019. The contribution of six biogeochemical discharge constituents leads to a net CO₂ outgassing of 0.13 TgC yr⁻¹, with a decrease in the coastal SBS carbon sink of 0.23 and 0.4 TgC yr⁻¹ due to riverine dissolved organic and inorganic carbon, respectively. Years with high (low) discharge promote more CO₂ outgassing (uptake) from the river plume. These results demonstrate that the Mackenzie River modulates the capacity of the SBS to act as a sink or source of atmospheric CO₂. Our work suggests that accurate model representation of land‐to‐sea biogeochemical coupling can be critical for assessing present‐day Arctic coastal ocean response to the rapidly changing environment.

The Green Edge initiative was developed to investigate the processes controlling the primary productivity and fate of organic matter produced during the Arctic phytoplankton spring bloom (PSB) and to determine its role in the ecosystem. Two field campaigns were conducted in 2015 and 2016 at an ice camp located on landfast sea ice southeast of Qikiqtarjuaq Island in Baffin Bay (67.4797^∘ N, 63.7895^∘ W). During both expeditions, a large suite of physical, chemical and biological variables was measured beneath a consolidated sea-ice cover from the surface to the bottom (at 360 m depth) to better understand the factors driving the PSB. Key variables, such as conservative temperature, absolute salinity, radiance, irradiance, nutrient concentrations, chlorophyll a concentration, bacteria, phytoplankton and zooplankton abundance and taxonomy, and carbon stocks and fluxes were routinely measured at the ice camp. Meteorological and snow-relevant variables were also monitored. Here, we present the results of a joint effort to tidy and standardize the collected datasets, which will facilitate their reuse in other Arctic studies. The dataset is available at https://doi.org/10.17882/59892 (Massicotte et al., 2019 a).