Forest Fire NRT Emissions

Transcription

1 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 1 Forest Fire NRT Emissions A GMES service provided by MACC Johannes W. Kaiser, A. Heil, M.G. Schultz, G. R. van der Werf, M.J. Wooster, W. Xu, and more MACC partners Russia, August 21

2 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 2 Modelled AOD of Greek Fire Plumes, August 27 observed FRP modelled AOD Emissions calculated from Fire Radiative Power observed by SEVIRI on Meteosat. Emission factors from Andreae & Merlet 21 and Ichoku & Kaufman 25. Run at 25km global resolution, which is typical for regional models. 26 August 1: MODIS :5

3 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 3 Fire Detection from Satellite/Aircraft smoke burnt area: change in albedo at about pixel resolution active fire/hot spot: saturation of traditional IR channel by sub pixel fire fire radiative power (FRP): signal in 3.9 μ and 11 μ channels by sub pixel fire dedicated low gain 3.9 μ channel (MODIS, VIIRS, SLSTR): detection threshold 1 MW large pixel size (MSG, GOES E/W, MTSAT, FY2C): detection threshold 5 MW Fuller et al (2) Satellite remote sensing of biomass burning with optical & thermal sensors, Progress in Phys.Geog., 23,

4 Bottom-Up Estimation of Fire Emissions GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 4 promising best accuracy: MACC real time sat. obs. E i = FRE x CF x EF i (Wooster et al. 24) E i = BA x AFL x CC x EF i (Seiler & Crutzen 198) burnt biomass (dynamic) vegetation model most established, in particular GFED (van der Werf et al. 21): MACC retrospective 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) dt ~ const. CF = conversion factor [kg(biomass) / W(FRE)] land cover map key uncertainty (e.g. Reid et al. 29) graphics by M. Wooster

5 Large Uncertainty in Biomass Burning Emissions of CO GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 5 [Stroppiana et al. ACP 21] (23) GFASv

6 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 6 Observational FRP Coverage average number of observations damped for large VA of any area in.5 deg grid cell during 1 day Terra MODIS # observations on 1 Oct GOES West Imager GOES East Imager Aqua MODIS Meteosat 9 SEVIRI

7 MACC Global Fire Assimilation System (GFASv1.) GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 7 Alberta, Canada, May 21 (edmontonjournal.com)

8 NRT production of daily FRP maps GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 8 GFASv up to 16 May 211 MODIS & SEVIRI FRP observations ~125 km resolution GFASv1. since 17 May 211 MODIS FRP assimilation ~5 km resolution Sep 21 Aug atmosphere.eu/fire

9 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 9 GFED3 DM (Tg DM month-1) SA 1 AG 5 TF EF FRP conversion factor analysis against GFEDv SAOM AGOM PE EFOM MODIS-FRE (PJ month-1) SA SAOM AG AGOM DF PEAT Conversion factor depends on dominant fire type! (adapted from Heil et al., ECMWF TM628, 21) EF EFOM SA: Savanna fires SAOM: SA with potential OM burning AG: Agricultural fires AGOM: AG with potential OM burning DF: Tropical fires PEAT: peat burning EF: Extratropical fires EFOM: EF with potential burning]

10 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 1 Land cover specific conversion as combined approach consistent with GFED3 inventory (within its accuracy) advantages quantitative information real time availability low detection threshold MODIS burnt area based C emissions MODIS FRP based C emissions (Kaiser et al. BGD 211)

11 GFED3.1 Non Detection Areas Areas where GFAS1. shows Burning but GFED3.1 not GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 11 GFAS1. GFAS1. Frequent Fires in Croplands or Savannah FRE [kj m -2 year -1 ] N monthly fire observations [months] 33% of grid cells with GFAS1. fire observations not detected by GFED3.1: - typically low fuel consumption (< 2.7 kj m -2 year -1 ) - repeated small fires in savanna, deforestation and agricultural areas - contribute < 3% to global FRE 1% 5% Only.1% of grid cells with GFED3.1 fire observations not detected by GFAS1..5% 36% EF DF 39% SA 9% 1% AG

12 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 12 global Northern America Central America Southern Hem. America [Kaiser et al. BGD 211] Europe Northern Hem. Africa

13 Validation of Aerosol Emissions: AOD(OM) + AOD(BC) GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 13 assimilation of MODIS AOD active: analyses passive: model average of 15 Jul 31 Dec 21 [Kaiser et al. BGD 211] analyses (observation) model AOD (OM+BC) low by factor 3.4 similar to NASA (GFED2.2) NRL (Reid et al. 29) aerosol inversion by N. Huneeus inconsistent with INPE/CPTEC (Freitas et al. 25) GFED2/3 (van der Werf et al. 26/1) published emission factors (e.g. Andreae & Merlet 21) recommendations: correct by factor 3.4 do multi parameter analysis model * 3.4

14 Black Carbon Cross validation GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 14 GFASv1. (with aerosol enhancement) compares well with NASA s QFEDv2.2. Regional differences exist, though (courtesy A. da Silva)

15 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 15 Atmospheric Validation: Russian Fires in 21 MACC global model runs with aerosol (Kaiser, Morcrette, Benedetti) TM5 reactive gas chemistry (Huijnen, Flemming, Inness) good agreement of emitted amounts aerosols and CO with independent observations TM5 reactive gas chemistry (V. Huijnen & J. Flemming) Version Atm. Assimilation Fire Emissions CNT/REF no GFED2 climatology Assim AOD, CO, O 3, NO 2 GFED2 climatology GFAS no GFASv1. Assim GFAS AOD, CO, O 3, NO 2 GFASv1. (graphics by V. Huijnen)

16 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 16 Analysis/Forecast Benefit: Russian Fires in 21 daily max surface concentrations Emissions dominate over data assimilation. Version Atm. Assimilation Fire Emissions REF no GFED2 climatology Assim AOD, CO, O 3, NO 2 GFED2 climatology GFAS no GFASv1. Assim GFAS AOD, CO, O 3, NO 2 GFASv1. Realistic emissions reduce bias of CO forecast. (graphics by V. Huijnen)

17 Russian Fires in 21 : MACC AQ forecasts for Finland GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, PM1 (μg/m 3 ) Virolahti, Finland PM1 (μg/m 3 ) Virolahti, Finland 12 Observed Observed h-24h forecasts h-48h forecasts August August 12 1 PM1 (μg/m 3 ) Virolahti, Finland Observed 12 1 PM1 (μg/m 3 ) Virolahti, Finland Observed h-72h forecasts h-96h forecasts August August

18 Other Applications / Collaborations GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 18 fire climate monitoring negative anomalies in 21 global: 9% Northern Asia: 51% NH Africa: 22% Tropical Asia: 79% Australia: 59% positive anomalies in 21 SH America: +2% SH Africa: +12% Europe & Mediterranean: +85% fire danger index development (with JRC & F. Pappenberger) emission estimation for species not covered in MACC atmospheric systems, e.g. PAHs monitoring of gas flares (needs observations over sea!) (volcanoes) 21 Anomaly Climate [Kaiser et al., In: State of the Climate in 21, BAMS, 211]

19 Summary GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 19 MACC produces global biomass burning emissions in real time atmospheric smoke plume forecasting improved fire (climate) monitored 4+ species & extendable resolution: 1 day,.5 deg soon 1 hour,.1 deg fruitful interaction of emission estimation AQ modelling some overlap with inventories MACC updates the retrospective GFED inventory complementary information to JRC EFFIS, Geoland2 & SAFER Grib, NetCDF, gif & KML products are public, see atmosphere.eu/fire

20 Complementarity with JRC EFFIS GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 2 JRC EFFIS provides NRT fire danger NRT hot spots lagged burnt area retrospective integration with forestry reports MACC adds NRT combustion and emissions rates global coverage

21 Complementarity with Geoland2 & SAFER GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 21 [ ] none of the fire products can be replaced by any other. J.W. Kaiser (ECMWF, UK), R. Lacaze (HYGEOS, FR), A. de Santis (INSA, IT), Overview of GMES Services Related to Biomass Burning in Open Fires, 21 April 21

22 Major Non Biomass Burning FRP Signals GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 22 Source identification based on the analysis of the stationarity and/or magnitude of the.1 deg FRP signal, visual inspection using GoogleEarth/Bing Maps, MODIS CMG Land Cover Data, NOAA DMSP-OLS Gas Flaring Inventory Frequency [N grids] 1,, 1, 1, 1, World s Top1 grid cells by FRE. Range:.1 to 1.48 W m 2 Top1 FRP: Source Categories Contribution to total dry matter burned equivalent (Sum Top1 FRP grid cells: 172 Tg) 8% 5% 13% 44% 3% VOLCANO GASFLARE INDUSTRY UNCLEAR FIRES MODIS-FRP [W m-2] Histogram of time-averaged MODIS-FRP grids (in.1 Wm -2 bins) (r36x18 6,48, grid cells) 1.41 ~ 1.3% of global total FRE

23 Average Annual Emissions [Tg] GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 23

24 GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 24 global Northern America Central America [Kaiser et al. BGD 211] Europe Southern Hem. America Northern Hem. Africa

25 21 Fires in Russia: Burning throughout the Night! GMES Emissions WS, Copenhagen, 11 Oct 211 Kaiser, 25 Jul Aug according to FRP, probably smouldering sub surface fires Conversion and emission factors need to be adapted dynamically. 8% peat fires 2% forest fires explain the CO and NO2 observations.