Search

This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by - 9.7 % for CH 4 , - 21 % for CO, and - 18 % for O 3. Results for CH 4 are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O 3 has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons. [ABSTRACT FROM AUTHOR]

Both measurements and modelling of air pollution in the Arctic are difficult. Yet with the Arctic warming at nearly four times the global average rate, and changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. This study examines the simulations of atmospheric concentrations of methane, carbon monoxide and ozone in the Arctic by 11 models. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). Mixing ratios of trace gases are modelled at three-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1 and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by -9.6 % for CH4, -21 % for CO and -18 % for O3. Results for CH4 are relatively consistent across the four years, whereas CO has a maximum negative bias in the spring and minimum in the summer, and O3 has a maximum difference centred around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model-location comparisons. [ABSTRACT FROM AUTHOR]

Nineteen years of atmospheric composition measurements made at the University of Toronto Atmospheric Observatory (TAO, 43.66°N, 79.40°W, 174 m.a.s.l.) are presented. These are retrieved from Fourier Transform InfraRed (FTIR) solar absorption spectra recorded with an ABB Bomem DA8 spectrometer from May 2002 to December 2020. The retrievals have been optimized for fourteen species: O3, HCl, HF, HNO3, CH4, C2H6, CO, HCN, N2O, C2H2, H2CO, CH3OH, HCOOH and NH3 using the SFIT4 algorithm. The measurements have been archived in the Network for Detection of Atmospheric Composition Change (NDACC) data repository in Hierarchical Data Format version 4 (HDF4) files following the Generic Earth Observation Metadata Standard (GEOMS) and are also publicly available on Borealis, the Canadian Dataverse Repository. In this paper, we describe the instrumentation, the retrieval strategy, the vertical sensitivity of the retrievals, the quality assurance process, and error analysis of the TAO FTIR measurements, and present the current version of the time series. [ABSTRACT FROM AUTHOR]

As the third most important greenhouse gas (GHG) after CO2 and methane, tropospheric ozone (O3) is also an air pollutant causing damage to human health and ecosystems. This study brings together recent research on observations and modeling of tropospheric O3 in the Arctic, a rapidly warming and sensitive environment. At different locations in the Arctic, the observed surface O3 seasonal cycles are quite different. Coastal Arctic locations, for example, have a minimum in the springtime due to O3 depletion events resulting from surface bromine chemistry. In contrast, other Arctic locations have a maximum in the spring. The 12 state-of-the-art models used in this study lack the surface halogen chemistry needed to simulate coastal Arctic surface O3 depletion in the springtime, however, the multi-model median (MMM) has accurate seasonal cycles at non-coastal Arctic locations. There is a large amount of variability among models, which has been reported previously, and we show that there continues to be no convergence among models, nor improved accuracy in simulating tropospheric O3 and its precursor species. The MMM underestimates Arctic surface O3 by 5% to 15% depending on the location. The vertical distribution of tropospheric O3 is studied from recent ozonesonde measurements and the models. The models are highly variable, simulating free-tropospheric O3 within a range of +/- 50% depending on the model and the altitude. The MMM performs best, within +/- 8% at most locations and seasons. However, nearly all models overestimate O3 near the tropopause (~300 hPa or ~8 km), likely due to ongoing issues with underestimating the altitude of the tropopause and excessive downward transport of stratospheric O3 at high latitudes. For example, the MMM is biased high by about 20% at Eureka. Observed and simulated O3 precursors (CO, NOx and reservoir PAN) are evaluated throughout the troposphere. Models underestimate wintertime CO everywhere, likely due to a combination of underestimating CO emissions and possibly overestimating OH. Throughout the vertical profile (compared to aircraft measurements), the MMM underestimates both CO and NOx but overestimates PAN. Perhaps as a result of competing deficiencies, the MMM O3 matches the observed O3 reasonably well. Our findings suggest that despite model updates over the last decade, model results are as highly variable as ever, and have not increased in accuracy for representing Arctic tropospheric. [ABSTRACT FROM AUTHOR]

The Arctic atmosphere is warming rapidly and its relatively pristine environment is sensitive to the long-range transport of atmospheric pollutants. While carbon dioxide is the main cause for global warming, short-lived climate forcers (SLCFs) such as methane, ozone, and particles also play a role in Arctic climate on near-term time scales. Atmospheric modelling is critical for understanding the abundance and distribution of SLCFs throughout the Arctic atmosphere, and is used as a tool towards determining 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, 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 four years (2008-2009 and 2014-2015) conducted for the 2021 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship and aircraft-based observations. 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 was able to represent the general features of SLCFs in the Arctic, though vertical mixing, long-range transport, deposition, and wildfire emissions remain highly uncertain processes. These need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. [ABSTRACT FROM AUTHOR]

The Arctic is warming two to three times faster than the global average, partly due to changes in short-lived climate forcers (SLCFs) including aerosols. In order to study the effects of atmospheric aerosols in this warming, recent past (1990-2014) and future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model to study the aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), using anthropogenic emissions from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Surface aerosol levels, in particular black carbon (BC) and sulfate (SO42-), have been significantly underestimated by more than 50%, with the smallest biases calculated for the nudged atmosphere-only simulations. CMIP6 simulations performed slightly better in simulating both surface concentrations of aerosols and climate parameters, compared to the Eclipse simulations. In addition, fully-coupled simulations had slightly smaller biases in aerosol levels compared to atmosphere only simulations without nudging. Arctic BC, organic carbon (OC) and SO42- burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m-2, of which - 0.24±0.01 W m-2 were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m-2 for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m-2), of which the anthropogenic aerosols accounted for -0.26 W m-2. Finally, all simulations showed an 46 increase in the Arctic surface air temperatures both throughout the simulation period. In 2050, surface air temperatures are projected to increase by 2.4 °C to 2.6 °C in the Eclipse ensemble and 1.9 °C to 2.6 °C in the CMIP6 ensemble, compared to the 1990-2010 mean. Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss. [ABSTRACT FROM AUTHOR]

Benzene and polycyclic aromatic hydrocarbons (PAHs) are toxic air pollutants that have long been associated with motor vehicle emissions, though the importance of such emissions has never been quantified over an extended domain using a chemical transport model. Herein we present the first application of such a model (GEM-MACH-PAH) to examine the contribution of motor vehicles to benzene and PAHs in ambient air. We have applied the model over a region that is centered on Toronto, Canada, and includes much of southern Ontario and the northeastern United States. The resolution (2.5 km) was the highest ever employed by a model for these compounds in North America, and the model domain was the largest at this resolution in the world to date. Using paired model simulations that were run with vehicle emissions turned on and off (while all other emissions were left on), we estimated the absolute and relative contributions of motor vehicles to ambient pollutant concentrations. Our results provide estimates of motor vehicle contributions that are realistic as a result of the inclusion of atmospheric processing, whereas assessing changes in benzene and PAH emissions alone would neglect effects caused by shifts in atmospheric oxidation and particle/gas partitioning. A secondary benefit of our scenario approach is in its utility in representing a fleet of zero emission vehicles (ZEV), whose adoption is being encouraged in a variety of jurisdictions. Our simulations predicted domain-average on-road vehicle contributions to benzene and PAH concentrations of 4-21% and 14-24% in the spring-summer and fall-winter periods, respectively, depending on the aromatic compound. Contributions to PAH concentrations up to 50% were predicted for the Greater Toronto Area, with a domain maximum of 91%. Such contributions are substantially higher than those reported in national emissions inventories, and they also differ from inventory estimates at the sub-national scale because those do not account for the physico-chemical processing that alters pollutant concentrations in the atmosphere. The removal of on-road vehicle emissions generally led to decreases in benzene and PAH concentrations during both periods that were studied, though atmospheric processing (such as chemical reactions and changes to gas/particle partitioning) contributed to non-linear behaviour at some locations or times of year. Such results demonstrate the added value associated with regional air quality modelling relative to examinations of emissions inventories alone. We also found that removing on-road vehicle emissions reduced spring-summertime surface O3 volume mixing ratios and fall-wintertime PM10 concentrations each by ~ 10% in the model domain, providing further air quality benefits. Toxic equivalents contributed by vehicle emissions of PAHs were found to be substantial (20-60% depending on location), and this finding is particularly relevant to the study of public health in the urban areas of our model area where human population, ambient concentrations, and traffic volumes tend to be high. [ABSTRACT FROM AUTHOR]

Environment and Climate Change Canada's online air quality forecasting model, GEMMACH, was extended to simulate atmospheric concentrations of benzene and seven polycyclic aromatic hydrocarbons (PAHs): phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, and benzo(a)pyrene (BaP). In the expanded model, benzene and PAHs are emitted from major point, area, and mobile sources, with emissions based on recent emission factors. Modelled PAHs undergo gas-particle partitioning (whereas benzene is only in the gas phase), atmospheric transport, oxidation, cloud processing, and dry and wet deposition. To represent PAH gas-particle partitioning, the Dachs-Eisenreich scheme was used, and we have improved gas-particle partitioning parameters based on an empirical analysis to get significantly better gas-particle partitioning results than the previous North American PAH model, AURAMS-PAH. Other added process parameterizations include the particle phase benzo(a)pyrene reaction with ozone via the Kwamena scheme and gas-phase scavenging of PAHs by snow via vapor sorption to the snow surface. The resulting GEM-MACH-PAH model was used to generate the first online model simulations of PAH emissions, transport, chemical transformation and deposition for a high resolution domain (2.5-km grid cell spacing) in North America, centered on the PAH-data-rich region of southern Ontario, Canada and the north-eastern United States. Model output for two seasons was compared to measurements from three monitoring networks spanning Canada and the U.S. Average summertime model results were found to be statistically indistinguishable from measurements of benzene and all seven PAHs. The same was true for the winter seasonal mean, except for BaP, which had a statistically significant positive bias. We present evidence that the benzo(a)pyrene results may be ameliorated via further improvements to PM and oxidant processes and transport. Our analysis focused on four key components to the prediction of atmospheric PAH levels: spatial variability; sensitivity to mobile emissions; gas-particle partitioning; and wet deposition. Spatial variability of PAHs/PM2.5 at 2.5-km resolution was found to be comparable to measurements. Predicted ambient surface concentrations of benzene and the PAHs were found to be critically dependent on mobile emission factors, indicating the mobile emissions sector has a significant influence on ambient PAH levels in the study region. PAH wet deposition was overestimated due to additive precipitation biases in the model and the measurements. Our overall performance evaluation suggests that GEM-MACHPAH can provide seasonal estimates for benzene and PAHs and be suitable for emissions scenario simulations. [ABSTRACT FROM AUTHOR]