Modelling the Canadian Arctic and
Northern Air Quality Using GEM-MACH
Wanmin Gong1, Stephen R. Beagley1,4,
Sophie Cousineau2, Jack Chen2,3, Mourad Sassi2
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
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
Page 2 – March 23, 2016
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Page 3 – March 23, 2016
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 4 – March 23, 2016
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.
Physics processor
(radiation, PBL, vertical
diffusion of momentum
and thermal variables,
cloud microphysics,
etc.)
Dynamic core
(including
tracer
advection)
Chemistry processor
(emissions, vertical diffusion of
chemical tracers, gas phase
chemistry, SOA formation, aerosol
microphysics, aqueous phase and
heterogeneous chemistry, wet and
dry deposition)
MACH
GEM
Gas-Phase Chemistry: 42 species and 114 reactions
Aerosol representation: standard 2-bin (0 -2.5 µm, 2.5 – 10 µm) and experimental 12-bin
(0.01-40.96 µm); 9 chemical species (SO4, NO3, NH4, EC, pOC, sOC, CM, SS, H2O)
Page 5 – March 23, 2016
Modelling Canadian Arctic and challenges
GEM-MACH Arctic domain
RAQPS domain
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 O3 depletion period)
Page 6 – March 23, 2016
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 7 – March 23, 2016
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 8 – March 23, 2016
Nn. Surface Observation sites
(NAPS, AIRS and WDC).
Page 9 – March 23, 2016
Surface O3 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 10 – March 23, 2016
Grouped ‘Nn. sites’ Statistics: PM2.5
(with and without wildfire emissions)
Obs.
Exp A: without wildfire emis.
Exp B: with wildfire emis.
ug/m3
Page 11 – March 23, 2016
Group average time series PM2.5
Obs.
Exp A: without wildfire emis.
Exp B: with wildfire emis.
Page 12 – March 23, 2016
Grouped ‘Nn. sites’ statistics and averaged time series: O3
(with and without wildfire emissions)
Obs.
Exp A: without wildfire emis.
Exp B: with wildfire emis.
Page 13 – March 23, 2016
Impact of dry deposition over ice and snow
Ice fraction (over water)
Difference in averaged O3 conc.
(New dry dep. – old)
(averaged over May 2010)
• 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.
Page 14 – March 23, 2016
Addressing long range transport
(Impact of chemical boundary conditions)
Averaged surface O3 concentration for May 2010
reanalysis
CBC
Global
GEM-MACH
“Climate”
CBC
• There
are
significant
differences
the
surface
between
the global
Global
GEM-MACH
“Climate”
CBCin O3 close toMACC-IFS
MACC-IFS
reanalysis
CBC
GEM-MACH “climatology” and the MACC-IFS reanalysis
Both with enhanced southern chemical boundary conditions
• The impact on the GEM-MACH Arctic simulation from the use of different CBC
(from operational archives)
is most significant close to the eastern and western boundaries, decreasing
inward.
Page 15 – March 23, 2016
Impact of NA wild fire emissions: black carbon
BC2.5, July 2010 average (with fire)
“with fire” – “without fire” (in ug/m3)
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/m3
level (e.g., Shama et al., 2004)
Page 16 – March 23, 2016
Impact of fire injection algorithm: PM2.5, BC dep.
(Land-use based vs. PBL mixed)
Relative difference in July averaged PM2.5
Relative difference in July accum. BC dep.
(PBL-LU)/[0.5*(PBL+LU)]*100.)
(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 17 – March 23, 2016
Impact of marine shipping emissions
(with vs. without shipping emissions over Canadian waters)
Rel. difference in O3 (%)
Rel. difference in PM2.5 (%)
July 2010
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]:
O3 – 16.2-18.1% (Cape Dorset) and 2.9-4.8% (Resolute)
PM2.5 – 19.5-31.7% (Cape Dorset) and 6.5-7.2% (Resolute)
Page 18 – March 23, 2016
Comparison with ozonesonde observations
Temperature (C)
-60
-40
-20
Temperature (C)
0
20
-60
Temperature (C)
0
20
-60
8
6
4
2
Alert, NU
Resolute
23Z, 2010-07-21
8
6
4
2
Resolute, NU
0
0
40
80
120
160
200
-4
-2
0
0
-6
4
40
80
120
1.2
0.8
0.4
20
22
24
26
Ozone (ppbv)
Observed T
Modelled O3
6
Modelled T
4
2
Churchill, MB
40
80
-4
-2
28
30
120
160
200
240
Ozone (ppbv)
Temperature (C)
0
4
2
8
12
16
20
24
2
1.6
1.2
0.8
0.4
1.6
1.2
0.8
0.4
0
0
0
40
Observed O3
0
Geopotential height (km)
Geopotential height (km)
1.6
20
8
160
2
2
0
Churchill
23Z, 2010-07-21
Ozone (ppbv)
Temperature (C)
2
-20
0
Ozone (ppbv)
Temperature (C)
-6
-40
10
Geopotential height (km)
Alert
23Z, 2010-07-21
0
Geopotential height (km)
-20
10
Geopotential height (km)
Geopotential height (km)
10
-40
15
20 19 –
25March
30 23, 2016
35
Page
Ozone (ppbv)
40
10
20
30
Ozone (ppbv)
40
50
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.
Page 20 – March 23, 2016
20
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 PM2.5 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.
Page 21 – March 23, 2016
21
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 22 – March 23, 2016
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 23 – March 23, 2016
Thank you for your attention!
Page 24 – March 23, 2016
Supplementary slides
Page 25 – March 23, 2016
Supplementary slides
Page 26 – March 23, 2016
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 27 – March 23, 2016
Consideration for plume injection
Val Martin et al. (2010)
ACP 10, 1491-1510
Kahn et al. (2008) GRL vol. 35
3000
1
20100701
20100301
20100301
20100701
fraction of occurance
fire plume top (m)
0.8
2000
1000
0.6
0.4
0.2
0
0
0
1000
2000
PBL height (m)
Page3000
28 – March 23, 2016
-4
-2
0
2
plume top height - PBL height (km)
4
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 29 – March 23, 2016
Fire plume heights from the new landuse-based plume injection
(Examples: 20100701 on GEM-MACH-Arctic domain)
3000
0.2
20100701_landuse
fraction of occurance
fire plume top (m)
0.16
2000
1000
0.12
0.08
0.04
0
0
0
1000
2000
3000
-2
PBL height (m)
-1
0
1
plume top height - PBL height (km)
Page 30 – March 23, 2016
2
Results: % Contribution of Shipping to Total Pollution
(Aliabadi and Staebler, 2014)
% Shipping Cont. to Pollution:
F=100 Sship/(Sship+Sother)
% Shipping Cont. to O3 Titration:
FT=100 STship/(STship+STother)
% Shipping Cont. to O3 Enhancement: FE=100 SEship/(SEship+SEother)
Page 31 – March 23, 2016
31