Modelling the Canadian Arctic and Northern Air Quality Using GEM-MACH Wanmin Gong 1, Stephen R. Beagley 1,4, Sophie Cousineau 2, Jack Chen 2,3, Mourad Sassi 2 1 Air Quality Research Division, STB, Environment Canada 2 National Prediction Operations, MSC, Environment Canada 3 Now at Marine and Ice Service, MSC, Environment Canada 4 Interchange from ESSE, York University WWOSC 2014 Montreal, August 17, 2014

Page 2 – July 2, 2015 Talk Outline Motivation and objectives of the project The base model (GEM-MACH), challenges, and development approach Results: simulations of 2010 shipping season o Model evaluation o Impact of sea ice and dry deposition o Impact of long range transport to the Arctic o NA wildfire emission and injection methodology o Impact of marine (shipping) emission Summary and future work

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Page 4 – July 2, 2015 Motivation and objectives The Arctic is recognized as one of the key areas of the globe, both in terms of its sensitivity to climate change, and by the increasing economic activity associated with the opening up of Arctic areas in a warming climate:  decreases in ice are leading to increased navigability  increased navigability could encourage large increase in resource development, thereby increasing marine activity  this increased marine activity has potential to harm the environment, hurt Aboriginals’ way of life, and disturb fragile ecosystems  due to the current low levels of shipping activity in the Canadian Arctic, any increase in activity will represent a significant change [Arctic Council, 2012] Short term objective: Develop and test Environment Canada’s air quality modelling capacity for assessing the impact of current and future Arctic marine/shipping activities on Northern air quality and environment. In a longer term: A credible modelling tool to meet scientific research and policy needs for protecting/managing Canadian Arctic and Northern air quality and ecosystem.

Page 5 – July 2, 2015 Base model: GEM-MACH (EC’s on-line AQ forecast model) GEM: Global Environmental Multiscale – Environment Canada’s numerical weather forecast model (global, NA regional, and hi-res configurations) with an extensive physics library. MACH: Modelling Air quality and CHemistry – chemistry and aerosol microphysics. Dynamic core (including tracer advection) Physics processor (radiation, PBL, vertical diffusion of momentum and thermal variables, cloud microphysics, etc.) Chemistry processor (emissions, vertical diffusion of chemical tracers, gas phase chemistry, SOA formation, aerosol microphysics, aqueous phase and heterogeneous chemistry, wet and dry deposition) GEM MACH Gas-Phase Chemistry: 42 species and 114 reactions Aerosol representation: standard 2-bin ( µm, 2.5 – 10 µm) and experimental 12-bin ( µm); 9 chemical species (SO 4, NO 3, NH 4, EC, pOC, sOC, CM, SS, H 2 O)

Page 6 – July 2, 2015 Modelling Canadian Arctic and challenges Domain (15-km resolution) Regional, limited area, covering all of Canada and as much as the Arctic (within the regional GEM, or RDPS, domain) Challenges (a partial list)  Long-range transport into the Arctic domain: chemical boundary conditions  Arctic ocean and sea ice: impact on removal, role of oceanic DMS on aerosol formation  Emission information and processing for the North and regions outside NA  Lack of observational data for model evaluation (limited monitoring sites; past field campaigns conducted mostly during O 3 depletion period) RAQPS domain GEM-MACH Arctic domain

Page 7 – July 2, 2015 Staged model development and evaluation Adapting forecast model for long-term simulation: “jump-back” start to allow meteorology-only spin-up while keeping chemistry continuous; Introducing dynamic sea ice in dry deposition module and revised dry deposition velocities over ice/snow (following Helmig et al., 2007 ACP); Enhancing chemical boundary condition (CBC) to address long-range transport (from outside of the model domain): from constant profiles (as in RAQPS) to (1) a quarterly averaged “climatology” based on a 1-year (2010) global GEM-MACH simulation and (2) MACC MOZART-IFS global reanalysis (Inness et al., 2013) for 2010 (daily averages from 3-hly data); southern CBC are constructed from operational GEM-MACH (RAQPS) archives; Incorporating NA wildfire emission and testing injection algorithms; Incorporating marine shipping emission (within Canadian waters; see Sophie Cousineau’s poster on processing marine emission, SCI-POM1100) and assessing its impact.

Page 8 – July 2, 2015 Results: model evaluation and sensitivity Simulation period: March – October 2010 (shipping season) Emission data (anthropogenic): current – NA regional emissions from 2006 Canadian inventory and 2005 US inventory projected to 2011, supplemented by existing global GEM-MACH emission data (old); in preparation – NA regional emissions from 2010 Canadian inventory and 2011 US inventory, supplemented by 2010 HTAP global emissions (0.1 x 0.1 deg.) Model evaluation:  Staged, based on latest adaptations, compare with surface based (monitoring network) and profile (ozonesonde) observational data.  Compare latest changes and assess impact.  From entire domain view to more targeted regional, and process specific aspects to identify and understand the signals and data seen in the latest prototype simulations.  Use varied simple statistics to identify underlying physical, numerical and chemical issues/features of the model and thus point the way forward for further development and analysis.

Page 9 – July 2, 2015 Nn. Surface Observation sites (NAPS, AIRS and WDC).

Page 10 – July 2, 2015 Surface O 3 at ‘selected’ High Arctic sites (different chemical LBC: “climatology” vs. MACC reanalysis) Obs. Exp A: “clim.” CBC Exp B: MACC reanal. CBC R = 0.39; SL = 0.30 R = 0.47; SL = 0.38

Page 11 – July 2, 2015 Grouped ‘Nn. sites’ Statistics: PM 2.5 (with and without wildfire emissions) ug/m3 Obs. Exp A: without wildfire emis. Exp B: with wildfire emis.

Page 12 – July 2, 2015 Group average time series PM 2.5 Obs. Exp A: without wildfire emis. Exp B: with wildfire emis.

Page 13 – July 2, 2015 Grouped ‘Nn. sites’ statistics and averaged time series: O 3 (with and without wildfire emissions) Obs. Exp A: without wildfire emis. Exp B: with wildfire emis.

Page 14 – July 2, 2015 Impact of dry deposition over ice and snow Difference in averaged O 3 conc. (New dry dep. – old) Significant impact, mainly over the ocean but also (to a lesser degree) over coastal and inland areas The impact should be less significant later in summer season as the sea ice recedes. Ice fraction (over water) (averaged over May 2010)

Page 15 – July 2, 2015 Addressing long range transport (Impact of chemical boundary conditions) Averaged surface O 3 concentration for May 2010 Global GEM-MACH “Climate” CBC MACC-IFS reanalysis CBC There are significant differences in O 3 close to the surface between the global GEM-MACH “climatology” and the MACC-IFS reanalysis The impact on the GEM-MACH Arctic simulation from the use of different CBC is most significant close to the eastern and western boundaries, decreasing inward. Both with enhanced southern chemical boundary conditions (from operational archives) Global GEM-MACH “Climate” CBCMACC-IFS reanalysis CBC

Page 16 – July 2, 2015 Impact of NA wild fire emissions: black carbon BC 2.5, July 2010 average (with fire)“with fire” – “without fire” (in ug/m 3 ) Impact from the NA wild fire emissions to the Arctic is significant, considering the typical black carbon concentrations observed at Alert at around a few ng/m 3 level (e.g., Shama et al., 2004)

Page 17 – July 2, 2015 Impact of fire injection algorithm: PM 2.5, BC dep. (Land-use based vs. PBL mixed) Relative difference in July averaged PM 2.5 (PBL-LU)/[0.5*(PBL+LU)]*100.) Relative difference in July accum. BC dep. (PBL-LU)/[0.5*(PBL+LU)]*100.) The PBL mixed algorithm distributes fire emissions evenly within PBL, while the land-use (or biomass) based algorithm allows crown fire emissions to be released at higher levels (can be above PBL) resulting in transport to farther distance downwind.

Page 18 – July 2, 2015 Impact of marine shipping emissions (with vs. without shipping emissions over Canadian waters) Rel. difference in O 3 (%)Rel. difference in PM 2.5 (%) Rel. difference in S deposition (%) Observational evidence Estimated percentage contribution of shipping to total pollution (cumulative), from an analysis based on measurements at two Arctic sites [Aliabadi and Staebler, 2014]: O 3 – % (Cape Dorset) and % (Resolute) PM 2.5 – % (Cape Dorset) and % (Resolute) July 2010

Page 19 – July 2, 2015 Comparison with ozonesonde observations Alert, NUResolute, NUChurchill, MB

Page 20 – July 2, 2015 Summary (1/2) A GEM-MACH based Arctic air quality modelling framework has been developed, and a new version of the model platform (prototype) for simulating the base year 2010 has been delivered to MSC/AQMAS. Several issues were addressed in this prototype, including the representation of sea ice and its impact on dry deposition, global emission, long-range transport (through chemical boundary conditions), and incorporating NA wildfire emissions. Model evaluation through comparison with observations from the existing monitoring network has been conducted continuously with staged tests during the model development; Analysis has been carried out trying to understand model performance issues, identify possible causes and areas for improvement. 20

Page 21 – July 2, 2015 Summary (2/2) The modelled surface ozone is improving with each incremental model development. Model has difficulty in capturing vertical structure in the Arctic region (e.g., free troposphere ozone, thermal structure close to the surface) based on comparison with ozonesonde observations. The modelled PM 2.5 was found to be biased low and investigations on modelled biogenic sources are under consideration. The incorporation of wild fire emission resulted in significant improvement during fire events. It is shown that the impact from the North American wildfire emissions (particularly in the Canadian boreal region) does extend to far north into the Arctic region; different injection algorithms are seen to have an impact on long range transport and are being evaluated. Preliminary tests on the impact of marine shipping emission are being conducted, and results are still being analysed. 21

Page 22 – July 2, 2015 Future work Short term:  Testing and evaluation of 12-bin configuration with new emission inputs (based on 2010 Canadian, 2011 US, and 2010 HTAP inventories)  Continued model evaluation and analysis; possible use of satellite data. Longer term:  Improve on other science modules (e.g., new wet deposition scheme, microphysics and cloud processing).  Collaboration within NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments): incorporating new results (e.g., a recent Polar 6 campaign); collaboration with UQAM on aerosol feedback through ice nucleation.  Nesting in global GEM-MACH (e.g., HTAP modelling activities)

Page 23 – July 2, 2015 Acknowledgement TD/ESB for leading the overall project and funding (partial support for Stephen); support of AQRD management The MACC-II project and Xiaobo Yang (ECMWF) for processing the MACC-MOZART reanalysis data Junhua Zhang (EC) for assisting with preparation of emission files The EC GEM-MACH development team NATChem for providing data from NA air quality monitoring networks WOUDC (and D. Tarasick) for ozonesonde data

Page 24 – July 2, 2015 Thank you for your attention!

Page 25 – July 2, 2015 Supplementary slides

Page 26 – July 2, 2015 Supplementary slides

Page 27 – July 2, 2015 Inclusion of wild fire emission 2010 NA fire emission was processed by AQMAS (Jack Chen, Mourad Sassi) based on hotspot data from the Canadian Wildland Fire Information System and the NOAA Office of Satellite Data Processing and Distribution. Currently treated as major point sources using Briggs plume-rise calculation as for anthropogenic point sources “Stack” parameters for all fires are set at 3 m for stack height, 773 K (or 500 C) for exit temperature, and 1 m/s for exit velocity Same as FireWork-GEMMACH to be run in parallel at CMC this summer

Page 28 – July 2, 2015 Kahn et al. (2008) GRL vol. 35 Val Martin et al. (2010) ACP 10, Consideration for plume injection

Page 29 – July 2, 2015 Landuse (vegetation type) based plume injection Use of plume statistics based on 5-year satellite observation (Val Martin et al., 2010). 4 vegetation/forest categories (boreal, temperate, shrubs/savannah, crops/grassland). Flaming vs. smoldering: –Gaussian plume distribution for flaming portion (with plume height and depth determined according to landuse/vegetation type); –Uniformly mixed within the PBL for smoldering portion. Consideration of PBL stability. Main objective is to test the impact of allowing fire plumes to be injected above boundary layer.

Page 30 – July 2, 2015 Fire plume heights from the new landuse-based plume injection (Examples: on GEM-MACH-Arctic domain)

Page 31 – July 2, 2015 Results: % Contribution of Shipping to Total Pollution (Aliabadi and Staebler, 2014) % Shipping Cont. to Pollution: F=100 S ship /(S ship +S other ) % Shipping Cont. to O3 Titration: FT=100 ST ship /(ST ship +ST other ) % Shipping Cont. to O3 Enhancement: FE=100 SE ship /(SE ship +SE other ) 31