ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 1 Russia, August 2010 MACC Biomass Burning Emissions and Plumes Johannes W. Kaiser, A. Benedetti, R. Detmers, A. Heil, J.‐J. Morcrette, R. Paugam, M.G. Schultz, G.R. van der Werf, P. Viterbo, M.J. Wooster, W. Xu
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MACC Biomass Burning Emissions and Plumesicap.atmos.und.edu/AERP/MeetingPDFs/SourceSink... · Power observed by SEVIRI on Meteosat. Emission factors from Andreae & Merlet 2001 and
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ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 1
Russia, August 2010
MACC Biomass Burning Emissionsand Plumes
Johannes W. Kaiser, A. Benedetti, R. Detmers, A. Heil, J.‐J. Morcrette, R. Paugam, M.G. Schultz, G.R. van der Werf, P. Viterbo, M.J. Wooster, W. Xu
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 2
Introduction
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ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 3
burnt biomass
Bottom-Up Estimation of Fire Emissions
Ei = FRE x CF x EFi (Wooster et al. 2003/5)
Ei = BA x AFL x CC x EFi (Seiler & Crutzen 1980)
sat. obs.
(dynamic) vegetation model
Ei = emission of species i [kg(species i)]BA = burnt area [m2]AFL = available fuel load [kg(biomass) / m2]CC = combustion completeness [kg(burnt fuel) / kg
(available fuel)]EFi = emission factor for species i [kg(species i) /
kg(biomass)]FRP = fire radiative power [W]FRE = fire radiative energy [J] = ∫ FRP(t) dtCF = conversion factor [kg(biomass) / W(FRE)]
land cover map~ const.
“key uncertainty”(e.g. Reid et al. 2009)
promising best accuracy: MACC real time
most established, in particular GFED (van der Werf et al. 2010): MACC retrospective
graphics by M. W
ooster
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 4
Emissions calculated from Fire Radiative Power observed by SEVIRI on Meteosat.
Emission factors from Andreae & Merlet 2001 and Ichoku & Kaufman 2005.
Run at 25km global resolution, which is typical for regional models.
2007-08-25 12:05
obse
rved
FR
Pm
odel
led
AO
D
Modelled AOD of Greek Fire Plumes, August 2007
MODIS26 August 10:00
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phot
o: M
. And
reae
MACC’s GFAS:The Global Fire Assimilation System
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Conversion factor depends on dominant fire type!
FRP conversion factor analysis against GFEDv3
(adapted from Heil et al., ECMWF TM628, 2010)
SA: Savanna fires SAOM: SA with potential OM burningAG: Agricultural fires AGOM: AG with potential OM burningDF: Tropical fires PEAT: peat burningEF: Extratropical firesEFOM: EF with potential burning]
SA SAOM AG AGOM DF PEAT EF EFOM
SAOMSA
AGOMAG
PE TF
EFOMEF
MODIS-FRE (PJ month-1)
GFE
D3
DM
(Tg
DM
mon
th-1
)
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0 5 10 15 20 25 30
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ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 13
2000
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(Kaiser et al. 2012)
GFED3: MODIS burnt area‐based C emissions
GFASv1: MODIS FRP‐based C emissions
■ consistentwithGFED3inventory(withinitsaccuracy)
■ advantages● quantitativeinformation
● lowdetectionthreshold
● real‐timeavailability
Land‐cover specific conversion is a combined approach.
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 14
Monthly C emission up to September 2011
global Europe
AustraliaS. Hem. America
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 15
This document is the property of Noveltis, no part of it shall be reproduced or transmitted without the express prior written authorisation of Noveltis
IASI‐CO based Inversion in ESA ALANIS Smoke Plumes
2010 Fires in Russia: Burning throughout the Night!
2007‐2009
[Kaiser et al. 2011]
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 35
Are NO2 emission estimates too high?
■
[Inne
ss et a
l. 2012,
almost sub
mitted
]
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 36
Injection Heights: Plume Rise Model
Pyroconvection
PBL
entrainment
• Convective zoneAtmospheric interaction
•Vegetation• Combustion Zone
Wind Drag
Active Fire AreaFire Radiative Power
Plume Rise Model Freitas et al 2007R
Convective Flux
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 37
Conclusion and Perspective on Plume Rise
37
Literature shows a need for more robust parameterization So far, the plume rise model approach seems the most adequate Current Work:
comprehensive MISR validation / training data set new assumption in the model Optimization and Validation are still undergoing
Use of more MISR Observation: other vegetation type and geographical location Possible collection of more detailed data (SAMBBA) usable for validation Use of high resolution model? [Trentmann 2002]
Validation at both fire event and regional scale
Perspective:
Conclusions so far:
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 38
Plume Rise Model: Result
38
y=0.7x + 0.7r2 = 0.7
Single fire : 29 fires :
detrainment zone and time dependence?
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 39
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Summary
ICAP, Frascati, May 2012 Kaiser et al. MACC Biomass Burning 40
FIRE Product Overview
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EXPV Time Range TemporalResolution
SpatialResolution Type Species
f7i1 10/2008-present
1 day / 1 hour T159 FRP-NRT a (since 17Jun10 alsoNOx, NMHC)
f7i210/2008-present 1 day / 1 hour
T159, archived as0.1 deg FRP-NRT (GFASv0)
a (since 17Jun10 alsoNOx, NMHC)
f922 1/2003-1/2009 1 day / 1 hour 0.1 deg MODIS FRP FRP (W/m2)
The NOx emissions refer to NOx (as NO) as we use the NOx (as NO) emission factors from Andreae&Merlet(2001, last updated 2009) for the calculation.
The GRIB identification codes and units for all species are listed in the GRIB Parameter Database.They cover the paramId range 210080-210118,210231-210241. You may want to start off bymodifying our example MARS retrieval script, which can be executed on ecgate. We alsorecommend the web-based MARS catalogue for checking data availability and example batchrequests. If the previous sentences do not make sense to you, please contact our user support.
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