Source inversions and prospects for CO2 modelling with ...SILAM v.5.5 • Modules Ø 9 chemical and...
Transcript of Source inversions and prospects for CO2 modelling with ...SILAM v.5.5 • Modules Ø 9 chemical and...
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Source inversions and prospects for CO2 modelling with SILAM CTM
M.Sofiev, R.Kouznetsov, J.Vira
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Outline• SILAM overview (AQ and GHG)
• Emission inversion examples
• Fire emission system IS4FIRES
• CO2 modelling experiment
• Summary
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GHG vs AQ modelling
Other differences are very minor (hopefully…)
GHG AQ
Final product Fluxes Concentrations(VMR)
Lifetime many years Minutes to weeks
Dynamic range +- 10 % Orders of magnitude
Sources and sinks are same, little chemistry
SourcesDepositionsChemistry
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SILAM v.5.5
• ModulesØ 9 chemical and physical
transformation modules (7 open for operational use),
Ø 8 source terms (all open), Ø 4 aerosol dynamics (1 open)Ø 3D-,4D- Var, EnKF
• Domains: from global to beta-meso scale (~1km resolution)
• Meteo input:Ø ECMWFØ HIRLAM, AROME, HIRHAM,
ECHAM, and any other who can write GRIB-1 or GRIB-2
Ø WRFØ ECHAM, NorESM, other GCM /
RCM
Wild fires
Desert dust
CB4 + stratosph.
SOx
Acid-basic
CB4
Pollen
CBM-4 - SOA
RadioactivePassive, timetag,
self-decayLong-lived multi-media
Transformations
Area
Point
Nuclear bomb
Source types
Map ofspeciesmasses
Emis
sion
Tran
sfor
mat
ion
Transport
Advectiondiffusion
Aerosol dynamics
Bio-VOC
Pollen
Sea salt
Simple
Basic
Transformation
Dry
Wet
DepositionInitialization,
3D-Var
Simulation controlforward adjoint 4D-Var EnKF
PSC
SOA VBS
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Physiography,land use
forest mapping
SILAM AQ assessment and forecasting platform
Active fires
EVALUATIONand
DATA ASSIMILATION3D-, 4D-VAR
Aerobiologicalobservations
Meteorological data pool
Global: ECMWFRegional: HIRLAM, WRFMeso-scale: HARMONIE,
AROME,WRFClimate scenarios:
ECHAM, RCA, G/RCM
Online AQmonitoring
Products at:
Emissioninventories
http://silam.fmi.fi
Satelliteobservations
Globalboundary cond.
+own simulations
STEAM emission model
SILAM CTMPhenologicalmodel
Fire emission model
IS4FIRES
Desert dustemission model
BioVOCemission model
Sea saltemission model
AIS ship data
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Operational AC/AQ-modelling
Global: 50km, troposphere + stratosphere
All forecasts: 4 days with 1hr step, SILAM v.5.xhttp://silam.fmi.fi
Asia: 10km, troposphere
Northern Europe: 2.5km, troposphere
Europe: 10km, troposphereboundaries: C-IFShindcast: 3D-Var
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SILAM in general• Chemistry-transport model interfaced to a multitude of
NWPs
• A wide variety of problems/featuresØ Global-to-meso-γ scales (up to 1km resolution)
Ø Troposphere and stratosphere (optional)Ø adjoint formalism, 3D- and 4D- variational and ensemble Kalman
filter DA– emission inversion via data assimilation
• Open-code and open-data system, installed in 7 countries, modules used in >10 other models
• Data supplied to CAMS, SDS-WAS, GEIA ECCAD, MarcoPolo-Panda, EAN, various authorities, open for download from http://silam.fmi.fi
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PM, SO2 emission inversion• A 4D-Var based optimization
Ø an emission distribution minimizing the observation-model discrepancy
• Included aerosol species:Ø primary OC, BC (MACCITY) or primary PM2.5/10 (TNO-MACC)Ø sulfates from SO2 oxidation
Ø nitrates (not adjusted)
Ø sea saltØ desert dust
Ø PM2.5 from wildfires
• Assimilated: MODIS 550 nm AOD
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Comparison with MODIS
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Mean AOD for 2008: MODIS Model a priori
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Comparison with MODIS
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Mean AOD for 2008: MODIS Model a posteriori
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Model vs MODIS
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A priori A posteriori
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Independent data: AATSR 555 nm AOD, 2008
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AATSR SILAM a-posteriori
SILAM a-priori
MODIS
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Asia:PM, SO2 emissionsa priori + posteriori
primary OC+BC
SO2
Kurokawa et al, REAS
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PM emissions, monthly, kg
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Africa, in PM1
Asia, in PM1
Europe, in PM2.5
Patterns for BC (Asia and Africa)similar but moremoderate
Seasonality in Europe and Asia affected by availability of MODIS data
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Fire data for emission: IS4FIRES
is4fires.fmi.fi
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IS4FIRES v.2.0: PM emission 2000-2015
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CO2 modelling experiment: Europe
CO2 in Central Europe, SILAM prediction, 5 Jul 2012
Background Background + Background + emission +natural surface exchange natural surface exchange
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Summary• SILAM global-to-meso-scale CTM is capable of emission
inversion via observations data assimilationØ 4D- varØ ensemble Kalman filter
• Assimilation & source inversion with satellite and/or in-situ(fires, volcanoes, pollen, anthropogenic sources)
• Estimation of Ø fluxes Ø emission model parameters
• IS4FIRES provides time- and space- resolved emission from vegetation fires, globally
• Experiment with CO2 dispersion in Europe: impact of surface exchange vs anthropogenic & natural emission
• Comparison to GHG inversions would be interesting