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Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry

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Presentation on theme: "Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry"— Presentation transcript:

1 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry contributions from Antje Innes, Johannes Kaiser, Jean-Jacques Morcrette, Vincent Huijnen (KNMI) & Martin Schulz (FZJ)

2 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Overview Motivation / MACC Basic concepts of atmospheric chemistry modelling Chemistry Emissions Emissions vs. forecast initialisation (Data assimilation) Russian Fires 2010 SO 2 from volcanic eruptions

3 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Why Atmospheric Composition at NWP centres? Environmental concern Air pollution Ozone hole Climate change Expertise in data assimilation of satellite, profile and surface obs. Best meteorological data for chemical transport modelling Interaction between trace gases & aerosol and NWP radiation triggered heating and cooling precipitation and clouds (condensation nuclei, lifetime …) Satellite data retrievals improved with information on aerosol Hydrocarbon (Methane) oxidation is water vapour source

4 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Atmospheric Composition at ECMWF Operational NWP Climatologies for aerosol, green house gases ozone + methane Ozone with linearized stratospheric chemistry and assimilation of ozone (TC) GMES Atmospheric Service development (GEMS / MACC/ MACC II ) 2005 – 2014 … (Atmospheric Composition division at ECMWF since 2012 !!) aerosol and global-reactive-gases modules in IFS Data assimilation of AOD and trace gases (ozone, CO, SO 2, NO 2, HCHO, CO 2 CH 4 ) retrievals (TC) with IFS 4D-VAR Near-real-time Forecast and re-analysis of GRG, GHG and Aerosol

5 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming MACC Daily (NRT) Service Provision Air quality Global Pollution Aerosol UV indexFires

6 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Flux Inversions MACC Service Provision (retrospective) Reanalysis Ozone records

7 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Change in Aerosol Optical Thickness Climatologies New: reduction in Saharan sand dust & increased sand dust over Horn of Africa Old aerosol dominated by Saharan sand dust 26r3: New aerosol (June) Tegen et. al ): 26r1: Old aerosol (Tanre et al. 84 annually fixed) Thickness at 550nm Impact of Aerosol Climatology on NWP J.-J. Morcrette A. Tompkins

8 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Surface Sensible heat flux differences 20 W m -2 ~ 20-30% Boundary layer height increases >1km Impact of Aerosol Climatology on NWP old new New-old

9 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Improved Predictability with improved Aerosol Climatology Published in Quart. J. Roy. Meteorol. Soc., 134, (2008) Rodwell and Jung Figure 3: Average anomaly correlation coefficients (see main text for details) for forecasts of meridional wind variations at 700 hPa with the `old' (solid) and the `new' (dashed) aerosol climatology for (a) the African easterly jet region (15oW.35oE, 5oN.20oN) and (b) the eastern tropical Atlantic (40oW.15oW, 5oN.20oN). Forecast lead-times for which the score with the `new' aerosol is significantly better (at the 5% level) are marked with circles. Results are based on the weather forecasts (see main text for details) started at 12 UTC on each day between 26 June to 26 July 2004.

10 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Atmospheric Composition -Observation from space -Modelling

11 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming N2N2 O2O2 H 2 O Argon 20% 78% 1% N2ON2O 310 H2H2 CO Ozone ppb 1:10 9 CO 2 CH 4 (1.8) ppm 1: Ne 18 He (5) HCHO 300 Ethane SO 2 NO x ppt 1:10 12 NH CH 3 OOH 700 H2O2H2O2 500 HNO3 300 others Atmospheric Composition – global average The small concentrations do matter because chemical conversion is non-linear small concentrations could mean high turn-over, i.e. high reactivity

12 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Spectral ranges Remote sensing of trace gases Radiation absorbed or emitted from trace gases and aerosols are measured by satellites instruments: The radiance information has to be converted into concentrations / total burdens in a process call retrieval (More in Angelas lecture on observations operatorestomorrow)

13 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming A. Richter, Optical Remote Sensing WS 2004/ Wavelength Ranges in Remote Sensing UV:gas absorptions + profile information aerosols vis:surface information (vegetation) gas absorptions aerosol information IR:temperature information cloud information water / ice distinction many absorptions / emissions + profile information MW:no problems with clouds ice / water contrast surfaces some emissions + profile information

14 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming A. Richter, Optical Remote Sensing WS 2004/ SCIAMACHY and GOME-2: Target Species OH

15 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming SO 2, GOME-2, SACS, BIRA/DLR/EUMETSAT NO 2, OMI, KNMI/NASA Aerosol Optical Depth, MODIS, NASA SO 2, IASI, Univ. of Brussels/ EUMETSAT Exciting satellite observations

16 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Satellite observations of atmospheric composition are getting better in terms of accuracy and spatial resolution. Total ozone observations

17 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming , 0z OMI SBUV/2 NOAA-17 SBUV/2 NOAA-18 MLS SCIA MOPITTIASI GOME-2OMI , 0z , 0z Ozone CO NO2 GOME-2OMI , 12z SO2 SCIA Satellite Observations / Retrievals

18 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Sentinel-5 Precursor Sentinel-5 Sentinel-4 Expected primary satellite provision for measuring atmospheric composition – Reactive gases

19 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Modelling of Atmospheric Composition Transport, Emissions, Deposition Chemical conversion

20 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Atmospheric Composition in the Earth - system

21 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Emissions Chemical Reactions Atmospheric Reservoir wet & dry Deposition Transport catalytic Cycles Dr. Martin Schultz - Max-Planck-Institut für Meteorologie, Hamburg Processes on Atmospheric Composition Photolysis

22 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Modelling of Atmospheric Composition Mass balance equation for chemical species ( up to 150 in state-of-the- art Chemical Transport Models) Source and Sinks - not included in NWP Transport

23 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Integration of chemistry & aerosol modules in ECMWFs integrated forecast system (IFS ) Coupled System IFS- MOZART3 / TM5 C-IFS On-line Integration of Chemistry in IFS Developed in GEMS Used in MACC Developed in MACC 10 x more efficient than Coupled System Flemming et al. 2009

24 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Nitrogen Oxides - sources and sinks Total Columns Concentrations Surface Emissions Chemical Production and Loss & Lightning Vincent Huijnen, KNMI MOZART-3 CTM Note: High Loss is related to high concentrations

25 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Tropospheric Ozone - sinks and sources Total Columns Concentration Chemical Production and Loss TM5 Chemical transport model Vincent Huijnen, KNMI Note: Strong night/day differences in chemical activity No ozone emissions

26 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Atmospheric Chemistry Under atmospheric conditions (p and T) but no sunlight atmospheric chemistry of the gas phase would be slow Sun radiation (UV) splits (photolysis) even very stable molecules such as O 2 (but also ozone or NO 2 ) in to very reactive molecules These fast reacting molecules are called radicals and the most prominent examples are O mainly in stratosphere and above, but also in troposphere OH (Hydroxyl radical) and HO 2 (peroxy radical) in troposphere Reaction with OH is the most important loss mechanism in the troposphere for very common species such as CO, NO 2, ozone and hydrocarbons Chemical Mechanisms typically contain species and chemical reactions

27 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming into stratosphere No transport modelled Chemical Lifetime vs. Spatial Scale

28 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Emission Types Combustion related (CO, NO x, SO 2, VOC): fossil fuel combustion biofuel combustion vegetation fires (man-made and wild fires) volcanic emissions Release without combustion (VOC, Methan): biogenic emissions (plants and soils) agricultural emissions (incl. fertilisation) Wind blown dust and sea salt (from spray)

29 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Example emissions inventory after gridding CO emissions from anthropogenic sources

30 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Emissions variability Anthropogenic CO MACCity Biomass burning C GFEDV3 and GFASv1.0 South America Western Europe C. Granier J. Kaiser

31 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Emission estimates, modelling and obs Emissions are one of the major uncertainties in modeling The compilation of emissions inventories is a labor -intensive task based on a wide variety of socio-economic and land use data Some emissions can be modeled based on wind (sea salt aerosol) or temperature (biogenic emissions) Some emissions can be observed indirectly in near real time from satellites instruments (Fire radiative power, burnt area, volcanic plumes) Several attempts have been made to correct emission estimates based on observations and using inverse methods also used in data assimilation – in particular for long lived gases such as CO2 and Methane

32 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Biomass Burning (vegetation fires) Accounts for ~ 30% of total CO and NO x emissions, ~10% CH4 Vegetation fires occur episodically and exhibit a large inter-annual variability. Classic climatological approach: use forest fire statistic Emission data based on satellite observation New approach: Use satellite observations of burned areas size Newer approach: satellite observation (SEVIRI) of Fire Radiative Power to account for area burnt * fuel load Increased variability Still high uncertainty for estimates of burnt fuel and related emissions

33 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming area burnt combustion efficiency fuel load emission factor J. Hoelzemann Emissions CO Burnt Area from Satellite Biomass amount Global Wildfire Emission Modelling Fire Radiative Power

34 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming CO biomass burning emissions – variability

35 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Improving Forecast: Emissions modelling/observations vs. Initialisation with Analyses (Data Assimilation) Russian Fires Volcanic Erruptions

36 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Atmospheric Composition data assimilation vs. Numerical Weather Prediction assimilation Quality of NWP depends predominantly on initial state AC modelling depends on initial state (lifetime) and surface fluxes (Emissions) CTM have large biases than NWP models Only a few species (out of 100+) can be observed AC Satellite retrievals Little or no vertical information from satellite observations Fixed overpass times and day light conditions only (UV-VIS) Retrievals errors can be large AC in-situ observations Sparse (in particular profiles) limited or unknown spatial representativeness

37 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Russian Fires 2010 Moscow Source: wikipedia

38 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming NRT Fires emissions Fire emissions is inferred from MODIS and SEVIRI Fire Radiative Power (FRP) FRP allows NRT estimate of fire emissions NRT fire emission improve AQ forecast

39 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Russian Fires 2010

40 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Russian Fires – Model Simulation Model run with climatological emissions – no assimilation (CNT) Model run with observed emissions (FRP) - no assimilation (GFAS) Model run initialised with analyses – climatological emissions (ASSIM) Model run initialised with analyses and observed emissions (ASSIM-GFAS) Huijnen et al, 2011

41 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Russian Fires 2010 MOPITT OBSCNT (Climatological emissions) FRP fire emissions GFAS + Assimiliation

42 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Russion Fires: Forecast CO vs observations Total Column Surface

43 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Volcanic eruption - Forecast

44 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Grimsvoetn eruption 2011 – SO2 forecasts SO2 has shown to be a good proxi for volcanic ash (Thomas and Prata, 2011) Estimates of SO2 source strength and emission height based on UV-VIS observations Assimilation of GOME-2 SO2 retrievals for inialisiation The forecasts: EMI (only with emission estimate) INI (only with initialisation) INI&EMI (initialisation and

45 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming SO2 satellite retrievals from GOME-2, OMI and SCIAMACHY

46 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Analysis of TCSO2 using a log-normal and a normal background error covariance model Volcanic eruptions plumes are rare and extreme events. It is therefore difficult to correctly prescribe the background error statistics. Special screening is needed to correctly identify the plume from erroneous pixels. Plume height information was needed to determine the vertical structure of the back-ground error covariance (BGEC

47 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Plume strength and height information 1. Release test tracer at different levels – find best match in position 2. Scale emissions of test tracer to observation to get emission estimate

48 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming 24 H Forecast with EMI and INI

49 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Plume forecast evaluation Check plume position and strength with thresholds (5 DU) hit rate false alarm rate Check plume extend and strength without considering overlap 99-Percentile Plume size (> 5 DU)

50 Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Summary Atmospheric composition and weather interact Sound modelling of atmospheric chemistry needs to include many species with concentrations varying over several orders of magnitude Atmospheric Composition forecast benefit from realistic initial conditions (data assimilation) but likewise from improved emissions MACC system produces useful forecast and analyses of atmospheric composition Showed Russian Fire Example and SO 2 Volcanos NRT forecast and Re-analysis of Ozone, CO and Aerosol ( ) are available at More on AC Data assimilation of AC in Antjes talk Environmental Monitoring and Angelas talks Observation Operators


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