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The Canadian ADAGIO Project for Mapping Total Atmospheric

Deposition

Amanda S. ColeEnvironment & Climate Change Canada (ECCC)

MMF-GTAD WorkshopGeneva, Switzerland

February 28, 2017

ADAGIO team

Amanda ColeAlain RobichaudVincent FortinAlexandru LupuMike MoranGuy RoyMike ShawRobert Vet

Marc Beauchemin

ADAGIO

• Atmospheric Deposition Analysis Generated by optimal Interpolation from Observations

• Goal: produce annual wet, dry and total deposition maps for sulphur and nitrogen for N. America

• Developing methodology using 2010 pilot year

• Working towards operational (routine) product

Methodology overview

Precipitation concentration maps

Wet deposition maps

Modelled dry deposition velocities

(GEM-MACH)

Air concentration maps

Dry deposition maps

Total deposition

maps

Measured air concentrations

Modelled air concentrations (GEM-MACH)

Measured precipitation

depth

Modelled precipitation depth (GEM)


concentrations

Modelled precipitation

concentrations (GEM-MACH)

Precipitation depth map

Optimal interpolation Optimal interpolation

Optimal interpolation (CaPA)

Modelled dry deposition of unmeasured

species

NOx SO2

NH3

HNO3p-NO3

-

p-NH4+

p-SO42-

Measurements: 2010 air (gas and particle) concentrations

Measurements: 2010 wet deposition concentrations

NO3-

NH4+

nss-SO42-

Measurements: 2010 precipitation depth

Measurements: analysis and quality control

• Seasonal mean (or PWM) concentrations

• Seasons aligned with Tuesdays to minimize artifacts from weekly samples: Mar. 2, Jun. 1, Aug. 31, Nov. 30

• Gas and particle concentrations:

• >75% of season has valid concentration data

• Wet concentrations:

• >90% of season has valid precipitation amount

• >70% of precipitation collected has valid concentration data

• Precipitation depth:

• Maximum wind speed for snow collectors

• All: remove duplicates



Wet deposition maps


(GEM-MACH)


Dry deposition maps

Total deposition

maps




depth



concentrations







species

Model: GEM-MACH

• GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and CHemistry) model: Environment and Climate Change Canada’s in-line meteorology‒chemistry model

• Met-only GEM model supplies initial conditions and meteorological lateral boundary conditions for GEM-MACH

• 10-km horizontal grid spacing, 80 vertical levels to 0.1 hPa

• One-way coupling (meteorology affects chemistry)

GEM-MACH GridGEM-LAM10 Grid

Model: GEM-MACH

• Full representation of oxidant and aerosol chemistry processes:

• gas, aqueous & heterogeneous chemistry mechanisms

• aerosol dynamics• dry and wet deposition (including in-

and below-cloud scavenging)

• 2-bin sectional representation of PM size distribution (i.e., 0-2.5 and 2.5-10 µm) with 8 chemical components

• 2010 simulation completed, hourly surface concentrations and wet and dry deposition fluxes archived

• V2 is now operational AQ model

Surface SO2 predicted by GEM-MACH v2 for January 2010



Wet deposition maps


(GEM-MACH)


Dry deposition maps

Total deposition

maps




depth



concentrations







species

Optimal interpolation

• Concentrations (wet and dry) of each species and season

• Daily precipitation

( ))( bba H xzKxx −+=Final analysis (fusion) values

Model values (background)

Observations Observation operator

Weight matrix

Analysis increment

Sp

ring

2010 p-N

H4 +

modelanalysis increment

analysisobservations

Summer 2010 precipitation SO42-

mod

elan

alys

is in

crem

ent

analysisobservations

Summer 2010 precipitation depth

ModelAnalysis (CaPA = Canadian

Precipitation Analysis)

Sum of daily analysis products



Wet deposition maps


(GEM-MACH)


Dry deposition maps

Total deposition

maps




depth



concentrations







species

Deposition calculation

• Wetflux = PWMconcentration OI × Precipitation(OI)

• Dryflux = Meanconcentration(OI) × V��

• V�� =

� !�"#�$ %(!&��$)

� !'&('�()"*)+&((!&��$)=

,�*(�"#�$ %(!&��$)

,�*('&('�()"*)+&((!&��$)

– Sum/mean values are over entire season

– Calculating the mean deposition velocity directly can cause biases

due to covariance between hourly concentrations and deposition

velocities

ADAGIO (Vdeff)5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

Dry deposition of SO2

Analysis domain mean annual flux 10% lower than model, but maximum value 17% higher

model5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

ADAGIO (Vdavg)

Dry deposition of SO2: average Vd

No correction for cross-correlation: • 29% lower than analysis with Vd

eff

• 36% lower than model

model5.0

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

Dry deposition of reduced N (kg/ha)

ADAGIO (Vdeff)10

9

8

7

6

5

4

3

2

1

model10

9

8

7

6

5

4

3

2

1

Dry deposition of oxidized N (kg/ha)

ADAGIO (Vdeff)10

9

8

7

6

5

4

3

2

1

model10

9

8

7

6

5

4

3

2

1

Wet deposition of S (kg/ha)

ADAGIO20

18

16

14

12

10

8

6

4

2

model20

18

16

14

12

10

8

6

4

2

S deposition: old and new

Wet Dry Total

Kriging of wet deposition • Former measurement-based maps

• wet deposition only

• limited by station density

ADAGIO: Future directions

• OI testing and validation

• Quantitative comparison with TDEP in U.S. domain

• Incorporate satellite measurements of SO2 and NO2

• Additional species, e.g. Hg, O3

• Next model evaluation run: 2014

• Routine annual deposition maps using QC’dmeasurements and archived values from operational GEM-MACH runs

CaPA (Canadian Precipitation Analysis) for precipitation depth

• Optimal interpolation combines GEM model forecast with ground weather station data

• Operational version now includes radar data (not available for 2010 pilot)

• Precipitation amounts from precipitation chemistry sites now included

CaPA 2010 annual precipitation totals

Canadian Precipitation Analysis (CaPA) and derived products

6h accumulation - 2012-06-12 00-06 UT

Great Lakes domain shown

Networkof networks

Atmosphericmodel

RADAR

Real-time integratedmonitoring of precipitationOperational product covers

all of North America

Mapping of accumulated precipitation over time

Total for July 2012,Eastern Canada

• Spatially and temporally continuous

• Biases compared to measurements

• Gaps: compounds, sources, sinks

• Superior precision and accuracy at measurement sites

• Miss “hotspots” between stations

• Gaps: compounds, time periods

analysis

Measurement-model fusion concept

model

observations

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