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Atmospheric dimethyl sulfide, DMS(g), is a climatically important sulfur compound and is the main source of biogenic sulfate aerosol in the Arctic atmosphere. DMS(g) production and emission to the atmosphere increase during the summer due to the greater ice-free sea surface and higher biological activity. We implemented DMS(g) in the Environment and Climate Change Canada’s (ECCC) online air quality forecast model, GEM-MACH (Global Environmental Multiscale–Modelling Air quality and CHemistry), and compared model simulations with DMS(g) measurements made in Baffin Bay and the Canadian Arctic Archipelago in July and August 2014. Two seawater DMS(aq) datasets were used as input for the simulations: (1) a DMS(aq) climatology dataset based on seawater concentration measurements (Lana et al., 2011) and (2) a DMS(aq) dataset based on satellite detection (Galí et al., 2018). In general, GEM-MACH simulations under-predict DMS(g) measurements, which is likely due to the negative biases in both DMS(aq) datasets. However, a higher correlation and smaller bias were obtained with the satellite dataset. Agreement with the observations improved when climatological values were replaced by DMS(aq) in situ values that were measured concurrently with atmospheric observations over Baffin Bay and the Lancaster Sound area in July 2014. The addition of DMS(g) to the GEM-MACH model resulted in a significant increase in atmospheric SO2 for some regions of the Canadian Arctic (up to 100 %). Analysis of the size-segregated sulfate aerosol in the model shows that a significant increase in sulfate mass occurs for particles with a diameter smaller than 200 nm due to the formation and growth of biogenic aerosol at high latitudes (>70∘ N). The enhancement in sulfate particles is most significant in the size range from 50 to 100 nm; however, this enhancement is stronger in the 200–1000 nm size range at lower latitudes (∘ N). These results emphasize the important role of DMS(g) in the formation and growth of fine and ultrafine sulfate-containing particles in the Arctic during the summertime.

Dimethylsulfide (DMS), outgassed from ocean waters, plays an important role in the climate system, as it oxidizes to methane sulfonic acid (MSA) and sulfur dioxide (SO2), which can lead to the formation of sulfate aerosol. Newly formed sulfate aerosol resulting from DMS oxidation may grow by condensation of gases, in-cloud oxidation, and coagulation to sizes where they may act as cloud condensation nuclei (CCN) and influence cloud properties. Under future global warming conditions, sea ice in the Arctic region is expected to decline significantly, which may lead to increased emissions of DMS from the open ocean and changes in cloud regimes. In this study we evaluate impacts of DMS on Arctic sulfate aerosol budget, changes in cloud droplet number concentration (CDNC), and cloud radiative forcing in the Arctic region under current and future sea ice conditions using an atmospheric global climate model. Given that future DMS concentrations are highly uncertain, several simulations with different surface seawater DMS concentrations and spatial distributions in the Arctic were performed in order to determine the sensitivity of sulfate aerosol budgets, CDNC, and cloud radiative forcing to Arctic surface seawater DMS concentrations. For any given amount and distribution of Arctic surface seawater DMS, similar amounts of sulfate are produced by oxidation of DMS in 2000 and 2050 despite large increases in DMS emission in the latter period due to sea ice retreat in the simulations. This relatively low sensitivity of sulfate burden is related to enhanced sulfate wet removal by precipitation in 2050. However simulated aerosol nucleation rates are higher in 2050, which results in an overall increase in CDNC and substantially more negative cloud radiative forcing. Thus potential future reductions in sea ice extent may cause cloud albedos to increase, resulting in a negative climate feedback on radiative forcing in the Arctic associated with ocean DMS emissions.

Motivated by the need to predict how the Arctic atmosphere will change in a warming world, this article summarizes recent advances made by the research consortium NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments) that contribute to our fundamental understanding of Arctic aerosol particles as they relate to climate forcing. The overall goal of NETCARE research has been to use an interdisciplinary approach encompassing extensive field observations and a range of chemical transport, earth system, and biogeochemical models. Several major findings and advances have emerged from NETCARE since its formation in 2013. (1) Unexpectedly high summertime dimethyl sulfide (DMS) levels were identified in ocean water (up to 75 nM) and the overlying atmosphere (up to 1 ppbv) in the Canadian Arctic Archipelago (CAA). Furthermore, melt ponds, which are widely prevalent, were identified as an important DMS source (with DMS concentrations of up to 6 nM and a potential contribution to atmospheric DMS of 20 % in the study area). (2) Evidence of widespread particle nucleation and growth in the marine boundary layer was found in the CAA in the summertime, with these events observed on 41 % of days in a 2016 cruise. As well, at Alert, Nunavut, particles that are newly formed and grown under conditions of minimal anthropogenic influence during the months of July and August are estimated to contribute 20 % to 80 % of the 30–50 nm particle number density. DMS-oxidation-driven nucleation is facilitated by the presence of atmospheric ammonia arising from seabird-colony emissions, and potentially also from coastal regions, tundra, and biomass burning. Via accumulation of secondary organic aerosol (SOA), a significant fraction of the new particles grow to sizes that are active in cloud droplet formation. Although the gaseous precursors to Arctic marine SOA remain poorly defined, the measured levels of common continental SOA precursors (isoprene and monoterpenes) were low, whereas elevated mixing ratios of oxygenated volatile organic compounds (OVOCs) were inferred to arise via processes involving the sea surface microlayer. (3) The variability in the vertical distribution of black carbon (BC) under both springtime Arctic haze and more pristine summertime aerosol conditions was observed. Measured particle size distributions and mixing states were used to constrain, for the first time, calculations of aerosol–climate interactions under Arctic conditions. Aircraft- and ground-based measurements were used to better establish the BC source regions that supply the Arctic via long-range transport mechanisms, with evidence for a dominant springtime contribution from eastern and southern Asia to the middle troposphere, and a major contribution from northern Asia to the surface. (4) Measurements of ice nucleating particles (INPs) in the Arctic indicate that a major source of these particles is mineral dust, likely derived from local sources in the summer and long-range transport in the spring. In addition, INPs are abundant in the sea surface microlayer in the Arctic, and possibly play a role in ice nucleation in the atmosphere when mineral dust concentrations are low. (5) Amongst multiple aerosol components, BC was observed to have the smallest effective deposition velocities to high Arctic snow (0.03 cm s−1).

Concentrations and δ34S values for SO2 and size-segregated sulfate aerosols were determined for air monitoring station 13 (AMS 13) at Fort MacKay in the Athabasca oil sands region, northeastern Alberta, Canada as part of the Joint Canada-Alberta Implementation Plan for Oil Sands Monitoring (JOSM) campaign from 13 August to 5 September 2013. Sulfate aerosols and SO2 were collected on filters using a high-volume sampler, with 12 or 24 h time intervals. Sulfur dioxide (SO2) enriched in 34S was exhausted by a chemical ionization mass spectrometer (CIMS) operated at the measurement site and affected isotope samples for a portion of the sampling period. It was realized that this could be a useful tracer and samples collected were divided into two sets. The first set includes periods when the CIMS was not running (CIMS-OFF) and no 34SO2 was emitted. The second set is for periods when the CIMS was running (CIMS-ON) and 34SO2 was expected to affect SO2 and sulfate high-volume filter samples. δ34S values for sulfate aerosols with diameter D > 0.49 µm during CIMS-OFF periods (no tracer 34SO2 present) indicate the sulfur isotope characteristics of secondary sulfate in the region. Such aerosols had δ34S values that were isotopically lighter (down to −5.3 ‰) than what was expected according to potential sulfur sources in the Athabasca oil sands region (+3.9 to +11.5 ‰). Lighter δ34S values for larger aerosol size fractions are contrary to expectations for primary unrefined sulfur from untreated oil sands (+6.4 ‰) mixed with secondary sulfate from SO2 oxidation and accompanied by isotope fractionation in gas phase reactions with OH or the aqueous phase by H2O2 or O3. Furthermore, analysis of 34S enhancements of sulfate and SO2 during CIMS-ON periods indicated rapid oxidation of SO2 from this local source at ground level on the surface of aerosols before reaching the high-volume sampler or on the collected aerosols on the filters in the high-volume sampler. Anti-correlations between δ34S values of dominantly secondary sulfate aerosols with D r = −0.80 and r = −0.76, respectively) were observed, suggesting that SO2 was oxidized by a transition metal ion (TMI) catalyzed pathway involving O2 and Fe3+ and/or Mn2+, an oxidation pathway known to favor lighter sulfur isotopes. Correlations between SO2 to sulfate conversion ratio (F(s)) and the concentrations of α-pinene (r = 0.85), β-pinene (r = 0.87), and limonene (r = 0.82) during daytime suggests that SO2 oxidation by Criegee biradicals may be a potential oxidation pathway in the study region.

Vertical distributions of atmospheric dimethyl sulfide (DMS(g)) were sampled aboard the research aircraft Polar 6 near Lancaster Sound, Nunavut, Canada, in July 2014 and on pan-Arctic flights in April 2015 that started from Longyearbyen, Spitzbergen, and passed through Alert and Eureka, Nunavut, and Inuvik, Northwest Territories. Larger mean DMS(g) mixing ratios were present during April 2015 (campaign mean of 116 ± 8 pptv) compared to July 2014 (campaign mean of 20 ± 6 pptv). During July 2014, the largest mixing ratios were found near the surface over the ice edge and open water. DMS(g) mixing ratios decreased with altitude up to about 3 km. During April 2015, profiles of DMS(g) were more uniform with height and some profiles showed an increase with altitude. DMS reached as high as 100 pptv near 2500 m. Relative to the observation averages, GEOS-Chem (www.geos-chem.org) chemical transport model simulations were higher during July and lower during April. Based on the simulations, more than 90 % of the July DMS(g) below 2 km and more than 90 % of the April DMS(g) originated from Arctic seawater (north of 66° N). During April, 60 % of the DMS(g), between 500 and 3000 m originated from Arctic seawater. During July 2014, FLEXPART (FLEXible PARTicle dispersion model) simulations locate the sampled air mass over Baffin Bay and the Canadian Arctic Archipelago 4 days back from the observations. During April 2015, the locations of the air masses 4 days back from sampling were varied: Baffin Bay/Canadian Archipelago, the Arctic Ocean, Greenland and the Pacific Ocean. Our results highlight the role of open water below the flight as the source of DMS(g) during July 2014 and the influence of long-range transport (LRT) of DMS(g) from further afield in the Arctic above 2500 m during April 2015.

Size-segregated aerosol sulfate concentrations were measured on board the Canadian Coast Guard Ship (CCGS) Amundsen in the Arctic during July 2014. The objective of this study was to utilize the isotopic composition of sulfate to address the contribution of anthropogenic and biogenic sources of aerosols to the growth of the different aerosol size fractions in the Arctic atmosphere. Non-sea-salt sulfate is divided into biogenic and anthropogenic sulfate using stable isotope apportionment techniques. A considerable amount of the average sulfate concentration in the fine aerosols with a diameter 63 %), which is higher than in previous Arctic studies measuring above the ocean during fall ( 30 %) (Norman et al., 1999). The anthropogenic sulfate concentration was less than that of biogenic sulfate, with potential sources being long-range transport and, more locally, the Amundsen's emissions. Despite attempts to minimize the influence of ship stack emissions, evidence from larger-sized particles demonstrates a contribution from local pollution. A comparison of δ34S values for SO2 and fine aerosols was used to show that gas-to-particle conversion likely occurred during most sampling periods. δ34S values for SO2 and fine aerosols were similar, suggesting the same source for SO2 and aerosol sulfate, except for two samples with a relatively high anthropogenic fraction in particles 2 emphasize the role of marine organisms (e.g., phytoplankton, algae, bacteria) in the formation of fine particles above the Arctic Ocean during the productive summer months.

Maintaining consistent traceability of high-precision measurements of CO2 isotopes is critical in order to obtain accurate atmospheric trends of δ13C and δ18O (in CO2). Although a number of laboratories/organizations around the world have been conducting baseline measurements of atmospheric CO2 isotopes for several decades, reports on the traceability and maintenance are rare. In this paper, a principle and an approach for maintaining consistent traceability in high-precision isotope measurements (δ13C and δ18O) of atmospheric CO2 are described. The concept of Big Delta is introduced and its role in maintaining traceability of the isotope measurements is described and discussed extensively. The uncertainties of the traceability have been estimated based on annual calibration records over the last 10 yr. The overall uncertainties of CO2 isotope measurements for individual ambient samples analyzed by the program at Environment Canada have been estimated (excluding these associated with the sampling). The values are 0.02 and 0.05‰ in δ13C and δ18O, respectively, which are close to the World Meteorological Organization (WMO) targets for data compatibility. The annual rates of change in δ13C and δ18O of the primary anchor (which links the flask measurements back to the VPDB-CO2 scale) are close to zero (−0.0016 ± 0.0012‰, and −0.006 ± 0.003‰ per year, respectively) over a period of 10 yr (2001–2011). The average annual changes of δ13C and δ18O in air CO2 at Alert GAW station over the period from 1999 to 2010 have been evaluated and confirmed; they are −0.025 ± 0.003‰ and 0.000 ± 0.010‰, respectively. The results are consistent with a continuous contribution of fossil fuel CO2 to the atmosphere, having a trend toward more negative in δ13C, whereas the lack of change in δ18O likely reflects the influence from the global hydrologic cycle. The total change of δ13C and δ18O during this period is ~0.27‰ and ~0.00‰, respectively. Finally, the challenges and recommendations as strategies to maintain a consistent traceability are described.

Abstract Camera trap surveys are a popular ecological monitoring tool that produce vast numbers of images making their annotation extremely time‐consuming. Advances in machine learning, in the form of convolutional neural networks, have demonstrated potential for automated image classification, reducing processing time. These networks often have a poor ability to generalise, however, which could impact assessments of species in habitats undergoing change. Here, we (i) compare the performance of three network architectures in identifying species in camera trap images taken from tropical forest of varying disturbance intensities; (ii) explore the impacts of training dataset configuration; (iii) use habitat disturbance categories to investigate network generalisability and (iv) test whether classification performance and generalisability improve when using images cropped to bounding boxes. Overall accuracy (72.8%) was improved by excluding the rarest species and by adding extra training images (76.3% and 82.8%, respectively). Generalisability to new camera locations within a disturbance level was poor (mean F1‐score: 0.32). Performance across unseen habitat disturbance levels was worse (mean F1‐score: 0.27). Training the network on multiple disturbance levels improved generalisability (mean F1‐score on unseen disturbance levels: 0.41). Cropping images to bounding boxes improved overall performance (F1‐score: 0.77 vs. 0.47) and generalisability (mean F1‐score on unseen disturbance levels: 0.73), but at a cost of losing images that contained animals which the detector failed to detect. These results suggest researchers should consider using an object detector before passing images to a classifier, and an improvement in classification might be seen if labelled images from other studies are added to their training data. Composition of training data was shown to be influential, but including rarer classes did not compromise performance on common classes, providing support for the inclusion of rare species to inform conservation efforts. These findings have important implications for use of these methods for long‐term monitoring of habitats undergoing change, as they highlight the potential for misclassifications due to poor generalisability to impact subsequent ecological analyses. These methods therefore need to be considered as dynamic, in that changes to the study site would need to be reflected in the updated training of the network.

We investigate the ab-initio formation of super-massive stars in a pristine atomic cooling halo. The halo is extracted from a larger self-consistent parent simulation. The halo remains metal-free and star formation is suppressed due to a combination of dynamical heating from mergers and a mild ($J_{\rm LW} \sim 2 - 10 \ J_{21}$(z)) Lyman-Werner (LW) background. We find that more than 20 very massive stars form with stellar masses greater than 1000 M$_{\odot}$. The most massive star has a stellar mass of over 6000 M$_{\odot}$. However, accretion onto all stars declines significantly after the first $\sim$ 100 kyr of evolution as the surrounding material is accreted and the turbulent nature of the gas causes the stars to move to lower density regions. We post-process the impact of ionising radiation from the stars and find that ionising radiation is not a limiting factor when considering SMS formation and growth. Rather the birth environments are highly turbulent and a steady accretion flow is not maintained within the timescale (2 Myr) of our simulations. As the massive stars end their lives as direct collapse black holes this will seed these embryonic haloes with a population of black holes with masses between approximately 300 M$_{\odot}$ and 10,000 M$_{\odot}$. Afterwards they may sink to the centre of the haloes, eventually coalescing to form larger intermediate mass black holes whose in-situ mergers will be detectable by LISA.

The formation of supermassive stars has generally been studied under the assumption of rapid accretion of pristine metal-free gas. Recently it was found, however, that gas enriched to metallicities up to $Z \sim 10^{-3}$ Z$_{\odot}$ can also facilitate supermassive star formation, as long as the total mass infall rate onto the protostar remains sufficiently high. We extend the analysis further by examining how the abundance of supermassive star candidate haloes would be affected if all haloes with super-critical infall rates, regardless of metallicity were included. We investigate this scenario by identifying all atomic cooling haloes in the Renaissance simulations with central mass infall rates exceeding a fixed threshold. We find that among these haloes with central mass infall rates above 0.1 M$_{\odot}$ yr$^{-1}$ approximately two-thirds of these haloes have metallicities of $Z > 10^{-3}$ Z$_{\odot}$. If metal mixing within these haloes is inefficient early in their assembly and pockets of metal-poor gas can remain then the number of haloes hosting supermassive stars can be increased by at least a factor of four. Additionally the centres of these high infall-rate haloes provide ideal environments in which to grow pre-existing black holes. Further research into the (supermassive) star formation dynamics of rapidly collapsing haloes, with inhomogeneous metal distributions, is required to gain more insight into both supermassive star formation in early galaxies as well as early black hole growth.

The representativeness heuristic suggests that similarity judgments provide a basis for judgments of likelihood. We use Tversky’s (1977) contrast model of similarity to design tests of this underlying mechanism. If similarity is used to judge likelihood, factors that are known to affect similarity should also affect judgments of likelihood. In two experiments, we manipulated two such factors described in the contrast model of similarity: the nature of the task and context effects. In a between-subject design, respondents assessed either similarity of fictive citizens of 15th century Florence, or the likelihood that they belonged to the same family. The factors that affected similarity also affected the likelihood judgments. These results support the assumption that similarity is an important contributor to judgments of likelihood.