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 SpeciesOH
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
Ozone CO
SCIA
SBUV/2 NOAA-17
SBUV/2 NOAA-18
OMI
MLS
MOPITT
IASI
, 0z
, 0z
NO2
SO2
OMI
GOME-2
OMI
SCIA
GOME-2
, 12z
, 0z

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”