CAMS Policy Support Uptake of emissions inventories including condensable in CAMS products The CAMS Air Control Tool (ACT ) Training session ...
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CAMS Policy Support
Uptake of emissions inventories including
condensable in CAMS products
The CAMS Air Control Tool (ACT ) Training session
Augustin Colette (INERIS, FR)
Atmosphere Monitoring
June 29th, 2021
The issue
Atmosphere
Monitoring
• The emission factors for PM emissions
resulting from combustion processes
can include:
– Only the filterable fraction (A)
– All the consensable fraction (B)
– In between (C)
• The range of uncertainty is especially Simpson, D., et al., 2020, How should condensables be included
large for residential heating (wood in PM emission inventories reported to EMEP/CLRTAP? MSC-W
Technical Report 4/2020
burning) https://emep.int/publ/reports/2020/emep_mscw_technical_r
eport_4_2020.pdf
The issue
Atmosphere • The rate of PM emission per unit of wood differs:
Monitoring – Between countries
– Between reporting years for a given country
– Compared to TNO « science based » consistent inventory (REF2)
• Denier van der Gon, et al., ACP, 2015
D. Simpson, TFMM 2021 Meeting
Consequences for modellers
Atmosphere
Monitoring
PM2.5 : EC wood burning / PM2.5:
• systematic low bias • sharp country discrepancies
• No outstanding country effect
Eurodelta-Carb
Atmosphere
Monitoring • Joint EMEP-TFMM/CAMS multi-
model intercomparison
• Based on EMEP/ACTRIS/COLOSSAL
Intensive Measurement Period : Dec
2017 - Feb 2018
• 11 Participating models:
– EMEP/MSC-W, Lotos-Euros, Eurad-
IM, IFS, MINNI, DEHM, MONARCH,
MATCH, CHIMERE, SILAM, WRF-
CHEM
Eurodelta-Carb
Atmosphere
Monitoring • Increase in PM2.5 concentrations in several countries (ex: FR, PL, AT, DE), when
including condensables
PM2.5 in REF1 (official) PM2.5 in REF2 (TNO)
Eurodelta-Carb
Atmosphere
Monitoring • Improvement of the negative PM2.5 negative bias for all models
On average over all
European countries the
PM10 bias is reduced by 22%
Eurodelta-Carb
Atmosphere
Monitoring
EC wood burning / PM2.5 in REF1: EC wood burning / PM2.5 in REF2:
sharp country discrepancies country discrepancies vanish
J. Tokaya, TNO
Eurodelta-Carb
Atmosphere
Monitoring • Comparison with EMEP/ACTRIS/COLOSSAL field campaing
– Share of wood burning from Aethalometer PMF (S. Platt, NILU)
– The systematic overestimation of EC_wb is reduced when including (OC) condensables in REF2
Wrap-up
Atmosphere
Monitoring • Consistent condensable from wood burning emissions should be included in
CAMS Regional Production:
– Evidence-based emission improvement
• Strong collaboration with CAMS_81
– Performances of the CAMS_50 Regional Ensemble:
• Improvement of model KPI: 15% for RMSE
– Evaluation:
• Comparison with in situ source apportionment limits the risk of compensation of errors
• Nice example of synergy with EMEP/ACTRIS/COLOSSAL, also supported by CAMS_61
– Consistency with modelling supporting Policy
• Joint modelling exercise with EMEP/TFMM
• REF2 also used by CAMS_71 (Policy Service) and EMEP/LRTAP MSC-W products
REF2 entered in CAMS_50 operational production June 8th 2021AC T: Ai r Co nt rol To o l b ox
Atmosphere
Monitoring
• Web-based fast response scenario forecasting
– Applies to every-day forecast
– Accounting for the complexity of atmospheric
chemistry & long range transport
– Allow a user to explore « on the fly » any
mitigation scenario
• Operational since June 2018
– policy.atmosphere.copernicus.eu
=> Air Control Toolbox
– Documentation
https://gmd.copernicus.org/preprints/gmd-2020-433/Linearity of the AQ response to emissions
Atmosphere
Monitoring • PM10 sensitivity: December 1st, 2016
– Air quality response to emission change can be very simple: linear or 2nd
order polynomial fits well uniform sectoral emission reductions
– BUT: this relationship needs to be updated at each grid point and every
day to full CTM simulations => automated machine learningHow does it work?
Atmosphere
• Main steps:
Monitoring
– Training data set: Atmospheric Chemisty
• 12 Chimere scenarios run on High Performance Computer
• Reference + reductions : 60% Traffic, 100% Traffic, 60% Agri,
100% Agri, 100%Agri and 100 Traffic, etc…
– Automated daily machine learning: Surrogate Model
• Fit a 2nd order, 4 dimension polynomial regression model
• At each grid point
• For each forecast day
• For each species
– Upload the statistical regression on a fast web-tool User flexibilitySurrogate AQ models
Atmosphere
Monitoring
Structure Species Temporal Emission resolution Concentration
resolution
EMEP SRM Linear SOMO35, PM25, Annual Country Grid (~20km)
Deposition
SHERPA Linear PM2.5, NO2 Annual 7km 7km
ACT Non-linear O3max, PM10, PM2.5, Daily Europe Grid (~20km)
NO2
• The main structural difference between ACT and SHERPA or EMEP SRM is the non-
linearity, which allows capturing any change in emissions between 0 and 100%
• This design is made possible by :
– Assuming homogeneous reductions in Europe
– Relying on operational production of a dozen daily scenarios: automated learningCase Study: Ozone June 18th, 2021
Atmosphere
Monitoring
EEA UTD Viewer ACT O3 daily max
https://maps.eea.europa.eu/UTDViewer/Case Study: Ozone June 18th, 2021
Atmosphere
Monitoring
ACT O3 daily max ACT O3 daily max
including natural/background excluding natural/backgroundCase Study: Ozone June 18th, 2021
Atmosphere
Monitoring
-50% Trafic & Industry
-50% Trafic
-50% IndustryCase Study: Ozone June 18th, 2021
Atmosphere
Monitoring ACT Sector allocation
ACT Country Scenarios
EMEP Country allocationCase Study: Ozone June 18th, 2021 Atmosphere Monitoring • Chemical regimes To appear soon! London: strong titration effect Berlin: no titration effect
Case Study: Ozone June 18th, 2021
Atmosphere
Monitoring • Difference relative to total (left), or anthrop alone (right) To appear soon!
– Example of reducing 50% TRA&IND
Difference, relative to total concentration (including Difference, relative to anthropogenic contribution alone
natural/background)Wra p - u p : CAM S _ AC T: Ai r Co nt rol To o l b ox
Atmosphere
Monitoring
• Web-based fast response episode scenario forecasting
– Accounting for the complexity of atmospheric
chemistry & long range transport for the current
forecasting situation
– Allow a user to explore « on the fly » any mitigation
scenario
• Now fully operational in every-day forecast
• CAMS_71 keeps developing new features
=> policy.atmosphere.copernicus.eu
=> https://gmd.copernicus.org/preprints/gmd-2020-433/Atmosphere
Monitoring
THANK YOU FOR YOUR AT TENTION
Augustin.Colette@ineris.frYou can also read
