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).

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH₄, O₃, BC, and SO${}_{\mathrm{4}}^{\mathrm{2}-}$ ), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for CH₄ and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

The representations of cloud processing of gases and aerosols in some of the current state-of-the-art regional air quality models in North America and Europe are reviewed. Key processes reviewed include aerosol activation (or nucleation scavenging of aerosols), aqueous-phase chemistry, and wet deposition/removal of atmospheric tracers. It was found that models vary considerably in the parameterizations or algorithms used in representing these processes. As an emerging area of research, the current understanding of the uptake of water soluble organics by cloud droplets and the potential aqueous-phase reaction pathways leading to the atmospheric secondary organic aerosol (SOA) formation is also reviewed. Sensitivity tests using the AURAMS model have been conducted in order to assess the impact on modeled regional particulate matter (PM) from: (1) the different aerosol activation schemes, (2) the different below-cloud particle scavenging algorithms, and (3) the inclusion of cloud processing of water soluble organics as a potential pathway for the formation of atmospheric SOA. It was found that the modeled droplet number concentrations and ambient PM size distributions were strongly affected by the use of different aerosol activation schemes. The impact on the modeled average ambient PM mass concentration was found to be limited in terms of averaged PM_2.5 concentration (~a few percents) but more significant in terms of PM_1.0 (up to 10 percents). The modeled ambient PM was found to be moderately sensitive to the below-cloud particle scavenging algorithms, with relative differences up to 10% and 20% in terms of PM_2.5 and PM₁₀, respectively, when using the two different algorithms for the scavenging coefficient (Λ) corresponding to the lower and upper bounds in the parameterization for Λ. The model simulation with the additional cloud uptake and processing of water-soluble organic gases was shown to improve the evaluation statistics for modeled PM_2.5 OA compared to the IMPROVE network data, and it was demonstrated that the cloud processing of water-soluble organics can indeed be an important mechanism in addition to the traditional secondary organic gas uptake to the particle organic phase.

Plume rise parameterizations calculate the rise of pollutant plumes due to effluent buoyancy and exit momentum. Some form of these parameterizations is used by most air quality models. In this paper, the performance of the commonly used Briggs plume rise algorithm was extensively evaluated, through a comparison of the algorithm's results when driven by meteorological observations with direct observations of plume heights in the Athabasca oil sands region. The observations were carried out as part of the Canada-Alberta Joint Oil Sands Monitoring Plan in August and September of 2013. Wind and temperature data used to drive the algorithm were measured in the region of emissions from various platforms, including two meteorological towers, a radio-acoustic profiler, and a research aircraft. Other meteorological variables used to drive the algorithm include friction velocity, boundary-layer height, and the Obukhov length. Stack emissions and flow parameter information reported by continuous emissions monitoring systems (CEMSs) were used to drive the plume rise algorithm. The calculated plume heights were then compared to interpolated aircraft SO ₂ measurements, in order to evaluate the algorithm's prediction for plume rise. We demonstrate that the Briggs algorithm, when driven by ambient observations, significantly underestimated plume rise for these sources, with more than 50 % of the predicted plume heights falling below half the observed values from this analysis. With the inclusion of the effects of effluent momentum, the choice of different forms of parameterizations, and the use of different stability classification systems, this essential finding remains unchanged. In all cases, approximately 50 % or more of the predicted plume heights fall below half the observed values. These results are in contrast to numerous plume rise measurement studies published between 1968 and 1993. We note that the observations used to drive the algorithms imply the potential presence of significant spatial heterogeneity in meteorological conditions; we examine the potential impact of this heterogeneity in our companion paper (Akingunola et al., 2018). It is suggested that further study using long-term in situ measurements with currently available technologies is warranted to investigate this discrepancy, and that wherever possible, meteorological input variables are observed in the immediate vicinity of the emitting stacks.

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 (<70 ^∘ 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.

A first regional assessment of the impact of shipping emissions on air pollution in the Canadian Arctic and northern regions was conducted in this study. Model simulations were carried out on a limited-area domain (at 15 km horizontal resolution) centred over the Canadian Arctic, using the Environment and Climate Change Canada's on-line air quality forecast model, GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and CHemistry), to investigate the contribution from the marine shipping emissions over the Canadian Arctic waters (at both present and projected future levels) to ambient concentrations of criteria pollutants (O ₃ , PM _2.5 , NO ₂ , and SO ₂ ), atmospheric deposition of sulfur (S) and nitrogen (N), and atmospheric loading and deposition of black carbon (BC) in the Arctic. Several model upgrades were introduced for this study, including the treatment of sea ice in the dry deposition parameterization, chemical lateral boundary conditions, and the inclusion of North American wildfire emissions. The model is shown to have similar skills in predicting ambient O ₃ and PM _2.5 concentrations in the Canadian Arctic and northern regions, as the current operational air quality forecast models in North America and Europe. In particular, the model is able to simulate the observed O ₃ and PM components well at the Canadian high Arctic site, Alert. The model assessment shows that, at the current (2010) level, Arctic shipping emissions contribute to less than 1 % of ambient O ₃ concentration over the eastern Canadian Arctic and between 1 and 5 % of ambient PM _2.5 concentration over the shipping channels. Arctic shipping emissions make a much greater contributions to the ambient NO ₂ and SO ₂ concentrations, at 10 %–50 % and 20 %–100 %, respectively. At the projected 2030 business-as-usual (BAU) level, the impact of Arctic shipping emissions is predicted to increase to up to 5 % in ambient O ₃ concentration over a broad region of the Canadian Arctic and to 5 %–20 % in ambient PM _2.5 concentration over the shipping channels. In contrast, if emission controls such as the ones implemented in the current North American Emission Control Area (NA ECA) are to be put in place over the Canadian Arctic waters, the impact of shipping to ambient criteria pollutants would be significantly reduced. For example, with NA-ECA-like controls, the shipping contributions to the population-weighted concentrations of SO ₂ and PM _2.5 would be brought down to below the current level. The contribution of Canadian Arctic shipping to the atmospheric deposition of sulfur and nitrogen is small at the current level, < 5 %, but is expected to increase to up to 20 % for sulfur and 50 % for nitrogen under the 2030 BAU scenario. At the current level, Canadian Arctic shipping also makes only small contributions to BC column loading and BC deposition, with < 0.1 % on average and up to 2 % locally over the eastern Canadian Arctic for the former, and between 0.1 % and 0.5 % over the shipping channels for the latter. The impacts are again predicted to increase at the projected 2030 BAU level, particularly over the Baffin Island and Baffin Bay area in response to the projected increase in ship traffic there, e.g., up to 15 % on BC column loading and locally exceeding 30 % on BC deposition. Overall, the study indicates that shipping-induced changes in atmospheric composition and deposition are at regional to local scales (particularly in the Arctic). Climate feedbacks are thus likely to act at these scales, so climate impact assessments will require modelling undertaken at much finer resolutions than those used in the existing radiative forcing and climate impact assessments.