The Canadian ADAGIO Project for Mapping Total Atmospheric Deposition

Transcription

1 The Canadian ADAGIO Project for Mapping Total Atmospheric Deposition Amanda S. Cole Environment & Climate Change Canada (ECCC) MMF-GTAD Workshop Geneva, Switzerland February 28, 2017

2 ADAGIO team Amanda Cole Alain Robichaud Vincent Fortin Alexandru Lupu Mike Moran Guy Roy Mike Shaw Robert Vet Marc Beauchemin

3 ADAGIO Atmospheric Deposition Analysis Generated by optimal Interpolation from Observations Goal: produce annual wet, dry and total deposition for sulphur and nitrogen for N. America Developing methodology using 2010 pilot year Working towards operational (routine) product

4 Methodology overview Measured Modelled (GEM-MACH) Measured air Modelled air (GEM-MACH) Optimal interpolation Optimal interpolation Precipitation concentration Air concentration Wet deposition Total deposition Dry deposition Modelled dry deposition of unmeasured species Precipitation depth map Measured depth Optimal interpolation (CaPA) Modelled depth (GEM) Modelled dry deposition velocities (GEM-MACH)

5 Measurements: 2010 air (gas and particle) NO x SO 2 NH 3 HNO 3 p-no 3 - p-nh 4 + p-so 4 2-

6 Measurements: 2010 wet deposition NO 3 - NH 4 + nss-so 4 2-

7 Measurements: 2010 depth

8 Measurements: analysis and quality control Seasonal mean (or PWM) Seasons aligned with Tuesdays to minimize artifacts from weekly samples: Mar. 2, Jun. 1, Aug. 31, Nov. 30 Gas and particle : >75% of season has valid concentration data Wet : >90% of season has valid amount >70% of collected has valid concentration data Precipitation depth: Maximum wind speed for snow collectors All: remove duplicates

9 Methodology overview Measured Modelled (GEM-MACH) Measured air Modelled air (GEM-MACH) Optimal interpolation Optimal interpolation Precipitation concentration Air concentration Wet deposition Total deposition Dry deposition Modelled dry deposition of unmeasured species Precipitation depth map Measured depth Optimal interpolation (CaPA) Modelled depth (GEM) Modelled dry deposition velocities (GEM-MACH)

10 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 GEM-MACH Grid GEM-LAM10 Grid 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)

11 Model: GEM-MACH Full representation of oxidant and aerosol chemistry processes: gas, aqueous & heterogeneous chemistry mechanisms aerosol dynamics dry and wet deposition (including inand below-cloud scavenging) 2-bin sectional representation of PM size distribution (i.e., and µm) with 8 chemical components 2010 simulation completed, hourly surface and wet and dry deposition fluxes archived Surface SO 2 predicted by GEM- MACH v2 for January 2010 V2 is now operational AQ model

12 Methodology overview Measured Modelled (GEM-MACH) Measured air Modelled air (GEM-MACH) Optimal interpolation Optimal interpolation Precipitation concentration Air concentration Wet deposition Total deposition Dry deposition Modelled dry deposition of unmeasured species Precipitation depth map Measured depth Optimal interpolation (CaPA) Modelled depth (GEM) Modelled dry deposition velocities (GEM-MACH)

13 Optimal interpolation Analysis increment x a = x b + K z ( ) H ( x b ) Final analysis (fusion) values Model values (background) Observations Weight matrix Observation operator Concentrations (wet and dry) of each species and season Daily

14 analysis observations Spring 2010 p-nh 4 + analysis increment model

15 analysis observations Summer 2010 SO 4 2- analysis increment model

16 Summer 2010 depth Model Analysis (CaPA = Canadian Precipitation Analysis) Sum of daily analysis products

17 Methodology overview Measured Modelled (GEM-MACH) Measured air Modelled air (GEM-MACH) Optimal interpolation Optimal interpolation Precipitation concentration Air concentration Wet deposition Total deposition Dry deposition Modelled dry deposition of unmeasured species Precipitation depth map Measured depth Optimal interpolation (CaPA) Modelled depth (GEM) Modelled dry deposition velocities (GEM-MACH)

18 Deposition calculation Wet flux= PWM concentration OI Precipitation (OI) Dry flux=mean concentration (OI) V V =! "# $ % (!&$)! '&('()"*)+&( (!&$) =,*( "# $ % (!&$),*( '&('()"*)+&( (!&$) Sum/mean values are over entire season Calculating the mean deposition velocity directly can cause biases due to covariance between hourly and deposition velocities

19 Dry deposition of SO 2 model Analysis domain mean annual flux 10% lower than model, but maximum value 17% higher ADAGIO (V d eff )

20 Dry deposition of SO 2 : average V d model No correction for cross-correlation: 29% lower than analysis with V d eff 36% lower than model ADAGIO (V d avg )

21 Dry deposition of reduced N (kg/ha) model ADAGIO (V d eff )

22 Dry deposition of oxidized N (kg/ha) model ADAGIO (V d eff )

23 Wet deposition of S (kg/ha) model ADAGIO

24 S deposition: old and new Kriging of wet deposition Former measurement-based wet deposition only limited by station density Wet Dry Total

25 ADAGIO: Future directions OI testing and validation Quantitative comparison with TDEP in U.S. domain Incorporate satellite measurements of SO 2 and NO 2 Additional species, e.g. Hg, O 3 Next model evaluation run: 2014 Routine annual deposition using QC d measurements and archived values from operational GEM-MACH runs

26 CaPA (Canadian Precipitation Analysis) for depth Optimal interpolation combines GEM model forecast with ground weather station data CaPA 2010 annual totals Operational version now includes radar data (not available for 2010 pilot) Precipitation amounts from chemistry sites now included

27 Canadian Precipitation Analysis (CaPA) and derived products Network of networks Real-time integrated monitoring of Operational product covers all of North America 6h accumulation UT Atmospheric model Great Lakes domain shown Mapping of accumulated over time RADAR Total for July 2012, Eastern Canada

28 Measurement-model fusion concept model Spatially and temporally continuous Biases compared to measurements Gaps: compounds, sources, sinks analysis observations Superior precision and accuracy at measurement sites Miss hotspots between stations Gaps: compounds, time periods