Source inversions and prospects for CO2 modelling with SILAM CTM

M.Sofiev, R.Kouznetsov, J.Vira

Outline• SILAM overview (AQ and GHG)

• Emission inversion examples

• Fire emission system IS4FIRES

• CO2 modelling experiment

• Summary

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

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

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

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

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

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

8

Comparison with MODIS

9

Mean AOD for 2008: MODIS Model a priori

Comparison with MODIS

10

Mean AOD for 2008: MODIS Model a posteriori

Model vs MODIS

11

A priori A posteriori

Independent data: AATSR 555 nm AOD, 2008

12

AATSR SILAM a-posteriori

SILAM a-priori

MODIS

13

Asia:PM, SO2 emissionsa priori + posteriori

primary OC+BC

SO2

Kurokawa et al, REAS

PM emissions, monthly, kg

14

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

Fire data for emission: IS4FIRES

is4fires.fmi.fi

IS4FIRES v.2.0: PM emission 2000-2015

CO2 modelling experiment: Europe

CO2 in Central Europe, SILAM prediction, 5 Jul 2012

Background Background + Background + emission +natural surface exchange natural surface exchange

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