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Modelling and Assimilation of Atmospheric Chemistry

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

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

2 Overview Motivation / MACC
Basic concepts of atmospheric chemistry modelling Chemistry Emissions Emissions vs. forecast initialisation (Data assimilation) Russian Fires 2010 SO2 from volcanic eruptions

3 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 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, SO2, NO2, HCHO, CO2 CH4) retrievals (TC) with IFS 4D-VAR Near-real-time Forecast and re-analysis of GRG, GHG and Aerosol

5 MACC Daily (NRT) Service Provision
Air quality Global Pollution UV index Fires Aerosol

6 MACC Service Provision (retrospective)
Reanalysis Flux Inversions Ozone records

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

8 Surface Sensible heat flux differences
Impact of Aerosol Climatology on NWP Surface Sensible heat flux differences old 20 W m-2 ~ 20-30% new New-old Boundary layer height increases >1km

9 Published in Quart. J. Roy. Meteorol. Soc., 134, 1479.1497 (2008)‏
Improved Predictability with improved Aerosol Climatology 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. Rodwell and Jung Published in Quart. J. Roy. Meteorol. Soc., 134, (2008)‏

10 Atmospheric Composition -Observation from space -Modelling

11 Atmospheric Composition – global average
H2O Argon 20% 78% 1% CO2 CH4 (1.8)‏ ppm 1:106 380 Ne 18 He (5)‏ N2O 310 H2 CO Ozone 500 100 30 ppb 1:109 HCHO 300 Ethane SO2 NOx 500 200 100 ppt 1:1012 NH3 400 CH3OOH 700 H2O2 HNO3 others The small concentrations do matter because chemical conversion is non-linear small concentrations could mean high turn-over, i.e. high reactivity

12 Remote sensing of trace gases
Spectral ranges 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 Angela’s lecture on observations operatorestomorrow)

13 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 A. Richter, Optical Remote Sensing WS 2004/2005

14 SCIAMACHY and GOME-2: Target Species
OH A. Richter, Optical Remote Sensing WS 2004/2005

15 Exciting satellite observations
SO2, IASI, Univ. of Brussels/ EUMETSAT SO2, GOME-2, SACS, BIRA/DLR/EUMETSAT The last few decades satellites have provided many exciting observations of atmospheric composition. This has allowed us to view the ozone hole, pollution, dust plumes, and volcanic ash, among many other things. NO2, OMI, KNMI/NASA Aerosol Optical Depth, MODIS, NASA

16 Total ozone observations
Over the years instruments have improved. For example, OMI provides much higher spatial resolution than SCIAMACHY. Satellite observations of atmospheric composition are getting better in terms of accuracy and spatial resolution.

17 Satellite Observations / Retrievals

18 Expected primary satellite provision for measuring atmospheric composition – Reactive gases
Sentinel-5 Precursor Sentinel-5 Sentinel-4

19 Modelling of Atmospheric Composition Transport, Emissions, Deposition Chemical conversion

20 Atmospheric Composition in the Earth - system

21 Processes on Atmospheric Composition
Chemical Reactions Photolysis Transport Transport catalytic Cycles Emissions wet & dry Deposition Atmospheric Reservoir Dr. Martin Schultz - Max-Planck-Institut für Meteorologie, Hamburg

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

23 Integration of chemistry & aerosol modules in ECMWF’s integrated forecast system (IFS)
C-IFS On-line Integration of Chemistry in IFS Coupled System IFS- MOZART3 / TM5 Developed in GEMS Used in MACC Developed in MACC 10 x more efficient than Coupled System Flemming et al. 2009

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

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

26 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 O2 (but also ozone or NO2) 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 HO2 (peroxy radical) in troposphere Reaction with OH is the most important loss mechanism in the troposphere for very common species such as CO , NO2, ozone and hydrocarbons Chemical Mechanisms typically contain species and chemical reactions

27 Chemical Lifetime vs. Spatial Scale
◄ into stratosphere No transport modelled

28 Emission Types Combustion related (CO, NOx, SO2, 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 CO emissions from anthropogenic sources
Example emissions inventory after gridding

30 Emissions variability
Biomass burning C GFEDV3 and GFASv1.0 South America Anthropogenic CO MACCity Western Europe C. Granier J. Kaiser

31 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 Biomass Burning (vegetation fires)
Accounts for ~ 30% of total CO and NOx 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 Global Wildfire Emission Modelling
Fire Radiative Power area burnt combustion efficiency fuel load emission factor J. Hoelzemann Burnt Area from Satellite Biomass amount Emissions CO

34 CO biomass burning emissions – variability

35 Russian Fires Volcanic Erruptions
Improving Forecast: Emissions modelling/observations vs. Initialisation with Analyses (Data Assimilation) Russian Fires Volcanic Erruptions

36 Atmospheric Composition data assimilation vs
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 Russian Fires 2010 Source: wikipedia Moscow

38 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 Russian Fires 2010

40 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 Russian Fires 2010 MOPITT OBS CNT (Climatological emissions)
FRP fire emissions GFAS + Assimiliation

42 Russion Fires: Forecast CO vs observations
Total Column Surface

43 Volcanic eruption - Forecast

44 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 SO2 satellite retrievals from GOME-2, OMI and SCIAMACHY

46 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 Plume strength and height information
Release test tracer at different levels – find best match in position Scale emissions of test tracer to observation to get emission estimate

48 24 H Forecast with EMI and INI

49 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 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 SO2 Volcanos NRT forecast and Re-analysis of Ozone, CO and Aerosol ( ) are available at More on AC Data assimilation of AC in Antje’s talk “Environmental Monitoring” and Angela’s talks “Observation Operators”

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