Accurately quantifying carbon sinks and sources in coastal regions is essential for global carbon budgeting, but it can be a challenging task. This is especially true for rapidly changing sub-Arctic and Arctic seas where baseline observations of seawater CO2 partial pressure (pCO2) are limited. Hudson Bay, Canada, is a prime example of an area with sparse data geographically and temporally. To bridge this gap, we utilized a novel approach by integrating predictor variables from satellite imagery and reanalysis data with advanced machine learning algorithms to provide more precise regional estimates of pCO2. In addition, we examined the ocean's carbon uptake and spatiotemporal fluctuations over different periods by incorporating wind speed and atmospheric CO2 data. Our study not only reveals insights into the dynamics of Hudson Bay CO2 sources and sinks but also demonstrates the potential of machine learning in extrapolating ship observations over space and time.

The global ocean takes up nearly a quarter of anthropogenic CO2 emissions annually, but the variability in this uptake at regional scales remains poorly understood. Here we use a neural network approach to interpolate sparse observations, creating a monthly gridded seawater partial pressure of CO2 (pCO2) data product from January 1998 to December 2019, at 1/12∘ × 1/12∘ spatial resolution, in the northeast Pacific open ocean, a net sink region. The data product (ANN-NEP; NCEI Accession 0277836) was created from pCO2 observations within the 2021 version of the Surface Ocean CO2 Atlas (SOCAT) and a range of predictor variables acting as proxies for processes affecting pCO2 to create nonlinear relationships to interpolate observations at a spatial resolution 4 times greater than leading global products and with better overall performance. In moving to a higher resolution, we show that the internal division of training data is the most important parameter for reducing overfitting. Using our pCO2 product, wind speed, and atmospheric CO2, we evaluate air–sea CO2 flux variability. On sub-decadal to decadal timescales, we find that the upwelling strength of the subpolar Alaskan Gyre, driven by large-scale atmospheric forcing, acts as the primary control on air–sea CO2 flux variability (r2=0.93, p<0.01). In the northern part of our study region, divergence from atmospheric CO2 is enhanced by increased local wind stress curl, enhancing upwelling and entrainment of naturally CO2-rich subsurface waters, leading to decade-long intervals of strong winter outgassing. During recent Pacific marine heat waves from 2013 on, we find enhanced atmospheric CO2 uptake (by as much as 45 %) due to limited wintertime entrainment. Our product estimates long-term surface ocean pCO2 increase at a rate below the atmospheric trend (1.4 ± 0.1 µatm yr−1) with the slowest increase in the center of the subpolar gyre where there is strong interaction with subsurface waters. This mismatch suggests the northeast Pacific Ocean sink for atmospheric CO2 may be increasing.

Warming of the Arctic due to climate change means the Arctic Ocean is now free from ice for longer, as sea ice melts earlier and refreezes later. Yet, it remains unclear how this extended ice-free period will impact carbon dioxide (CO2) fluxes due to scarcity of surface ocean CO2 measurements. Baseline measurements are urgently needed to understand spatial and temporal air–sea CO2 flux variability in the changing Arctic Ocean. There is also uncertainty as to whether the previous basin-wide surveys are representative of the many smaller bays and inlets that make up the Canadian Arctic Archipelago (CAA). By using a research vessel that is based in the remote Inuit community of Ikaluqtuutiak (Cambridge Bay, Nunavut), we have been able to reliably survey pCO2 shortly after ice melt and access previously unsampled bays and inlets in the nearby region. Here we present 4 years of consecutive summertime pCO2 measurements collected in the Kitikmeot Sea in the southern CAA. Overall, we found that this region is a sink for atmospheric CO2 in August (average of all calculated fluxes over the four cruises was −4.64 mmol m−2 d−1), but the magnitude of this sink varies substantially between years and locations (average calculated fluxes of +3.58, −2.96, −16.79 and −0.57 mmol m−2 d−1 during the 2016, 2017, 2018 and 2019 cruises, respectively). Surface ocean pCO2 varied by up to 156 µatm between years, highlighting the importance of repeat observations in this region, as this high interannual variability would not have been captured by sparse and infrequent measurements. We find that the surface ocean pCO2 value at the time of ice melt is extremely important in constraining the magnitude of the air–sea CO2 flux throughout the ice-free season. However, further constraining the air–sea CO2 flux in the Kitikmeot Sea will require a better understanding of how pCO2 changes outside of the summer season. Surface ocean pCO2 measurements made in small bays and inlets of the Kitikmeot Sea were ∼ 20–40 µatm lower than in the main channels. Surface ocean pCO2 measurements made close in time to ice breakup (i.e. within 2 weeks) were ∼ 50 µatm lower than measurements made > 4 weeks after breakup. As previous basin-wide surveys of the CAA have focused on the deep shipping channels and rarely measure close to the ice breakup date, we hypothesize that there may be an observational bias in previous studies, leading to an underestimate of the CO2 sink in the CAA. These high-resolution measurements constitute an important new baseline for gaining a better understanding of the role this region plays in the uptake of atmospheric CO2.

We present a year‐round time series of dissolved methane (CH 4 ), along with targeted observations during ice melt of CH 4 and carbon dioxide (CO 2 ) in a river and estuary adjacent to Cambridge Bay, Nunavut, Canada. During the freshet, CH 4 concentrations in the river and ice‐covered estuary were up to 240,000% saturation and 19,000% saturation, respectively, but quickly dropped by >100‐fold following ice melt. Observations with a robotic kayak revealed that river‐derived CH 4 and CO 2 were transported to the estuary and rapidly ventilated to the atmosphere once ice cover retreated. We estimate that river discharge accounts for >95% of annual CH 4 sea‐to‐air emissions from the estuary. These results demonstrate the importance of resolving seasonal dynamics in order to estimate greenhouse gas emissions from polar systems.

The Arctic climate is changing rapidly, yet predicting how aquatic species will respond to these changes remains challenging given the lack of empirical data for most high-latitude taxa. Acoustic telemetry has recently emerged as an important methodology for understanding horizontal and vertical space-use patterns in fishes. Here, we used acoustic telemetry to document marine habitat use and depth/temperature preference of 26 anadromous Arctic char Salvelinus alpinus within the Kitikmeot Sea region of the Canadian central Arctic over 4 yr (20132016). Most detections (similar to 70%) were within the top 3 m of the water column, and most were in estuarine (72.6%) vs. marine (27.4%) habitats. Arctic char preferred deeper waters later in the summer, but the temperature they occupied remained relatively constant throughout the marine feeding season (similar to 5-8 degrees C). Most Arctic char exhibited some degree of repetitive diving behavior, with individuals diving to 35 m. Diving activity increased later in the summer marine feeding season and is likely a response to the seasonal transition of their preferred prey to deeper waters as the season progresses. Finally, Arctic char preferred deeper waters with less ice cover and during the day, the latter suggesting potential diel patterns to marine habitat use. Finally, year-to-year variation in Arctic char depth and temperature use was very modest despite differences in climatic and ice conditions. This result suggests that habitat use is relatively fixed and may reflect their thermal and osmoregulatory physiology, which has important implications for forecasting the impacts of a changing Arctic on this economically valuable species.

Snow thickness measurements over relatively smooth Arctic first-year sea ice, obtained near Cambridge Bay in the Canadian Arctic (2014, 2016 and 2017) and near Elson Lagoon in the Alaskan Arctic (2003 and 2006), are analyzed to quantify physical length-scales and their relevant scaling behaviors. We use the multi-fractal temporally weighted detrended fluctuation analysis method to detect two major physical length-scales from the two independent study locations. Our results suggest that physical processes underlying the formation of snow dunes are consistent and that the wind is the main process shaping the snow thickness variability and redistribution. One scale, around 10 m, appears to be related to the formation of the snow ‘dunes’, while the other scale, between 30 and 100 m, is likely associated with the various interactions of the snow dunes such as merging, calving and lateral linking. Results imply that snow on level sea ice shows self-organized characteristics.

Sea ice algae are an important contributor of primary production in the Arctic ecosystem. Within the bottom-ice environment, access to nutrients from the underlying ocean is a major factor controlling production, phenology, and taxonomic composition of ice algae. Previous studies have demonstrated that tides and currents play an important role in driving the flux of nutrients to bottom-ice algal communities when biological demand during the spring bloom is high. In this study we investigate how surface currents under landfast first-year ice influence nutrient supply based on stoichiometric composition, algal chlorophyll a biomass and species composition during spring 2016, in Dease Strait, Nunavut. Stronger water dynamics over a shoaled and constricted strait dominated by tidal currents (tidal strait) supported turbulent flow more than 85% of the deployment duration in comparison to outside the tidal strait in an embayment where turbulent flow was only evidenced a small percentage (<15%) of the time. The system appeared to be nitrate-depleted with surface water concentrations averaging 1.3 μmol L–1. Increased currents were correlated significantly with a decrease in ice thickness and an increase in ice algal chlorophyll a. Furthermore, pennate diatoms dominated the ice algal community abundance with greater contribution within the strait where currents were greatest. These observations all support the existence of a greater nutrient flux to the ice bottom where currents increased towards the center of the tidal strait, resulting in an increase of bottom ice chlorophyll a biomass by 5–7 times relative to that outside of the strait. Therefore, expanding beyond the long identified biological hotspots of open water polynyas, this paper presents the argument for newly identified hotspots in regions of strong sub-ice currents but persistent ice covers, so called “invisible polynyas”.

As ocean acidification (OA) sensor technology develops and improves, in situ deployment of such sensors is becoming more widespread. However, the scientific value of these data depends on the development and application of best practices for calibration, validation, and quality assurance as well as on further development and optimization of the measurement technologies themselves. Here, we summarize the results of a 2-day workshop on OA sensor best practices held in February 2018, in Victoria, British Columbia, Canada, drawing on the collective experience and perspectives of the participants. The workshop on in situ Sensors for OA Research was organized around three basic questions: 1) What are the factors limiting the precision, accuracy and reliability of sensor data? 2) What can we do to facilitate the quality assurance/quality control (QA/QC) process and optimize the utility of these data? and 3) What sort of data or metadata are needed for these data to be most useful to future users? A synthesis of the discussion of these questions among workshop participants and conclusions drawn is presented in this paper.