1. Modelling and Assimilation of Atmospheric Chemistry

2. Overview Motivation Basic concepts of atmospheric chemistry modelling
Data assimilation of trace gases
Observations
Chemical data assimilation at ECMWF (GEMS & MACC)
Examples for O3 and SO2
Summary

3. Why Atmospheric Chemistry at NWP centres ? - or in a NWP Training Course?
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 (condensation nuclei)‏
Satellite data retrievals improved with information on aerosol
Hydrocarbon (Methane) oxidation is water vapour source

4. Example: Impact of Aerosol Climatology on SW Radiation
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 ):

5. Surface Sensible heat flux differences
Example: Impact of Aerosol Climatology on SW Radiation
Surface Sensible heat flux differences
old
20 W m-2 ~ 20-30%
new
New-old
Boundary layer height increases >1km

6. 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 signicantly 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)‏

7. An other motivation …

8. Modelling atmospheric composition Gas phase
Transport
Source an Sinks
Chemical conversion
Emissions
Deposition

9. Atmospheric Composition
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

10. Atmospheric Chemistry
Chemical
Reactions
Photolysis
Transport
Transport
catalytical
Cycles
Diese Folie gibt die wesentlichen Komponenten des Systems Atmosphärenchemie wieder: SPurengase werden aus Quellen freigesetzt und mit den Winden transportiert, sie reagieren miteinander (unterschiedlich schnell und abhängig von Temperatur, Druck, und Licht), werden weiter transportiert und schliesslich abgebaut (entweder durch chemische Reaktionen in der Atmosphäre oder „Deposition“ am Boden.
Zwei wichtige Begriffe für die Atmosphärenchemie sind:
(1) katalytische Zyklen, d.h. Reaktionsketten, in denen ein Molekül nicht verbraucht sondern immer wieder neu generiert wird
(2) Reservoirspezies, d.h. Moleküle werden in eine Form umgewandelt, in der sie länger haltbar sind und dadurch über weite Strecken transportiert werden können. Durch erneutes Freisetzen der Moleküle können sie dann weit von ihren Quellen entfernt wirken. Beispiel NO2 und PAN (Peroxyacetylnitrat).
Emissions
wet & dry
Deposition
Atmospheric
Reservoir
Dr. Martin Schultz - Max-Planck-Institut für Meteorologie, Hamburg

11. Modelling atmospheric composition
Mass balance equation for chemical species ( up to 150 in state-of-the-art Chemical Transport Models)
Transport
Source and Sinks

12. Examples of Global Mass Budget
Global transport contribution is zero if model conserves mass

13. 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

14. Some very general remarks about gas phase chemistry in the Atmosphere …
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 O3 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, O3 and Hydrocarbons
We need to quantify the concentration change due to chemistry

15. Chemical Kinetics (I)‏
Gas-phase reactions
A + B  C + D
A + B  C
C  A + B
A + B + M  C + D + M
Photolytic reactions if  <  limit
A + h  B + C
heterogeneous (aerosol-, liquid-phase) reactions surface reactions
often A+B  C

16. Chemical Kinetics (II)‏
Reaction speed (= concentration change per time) is proportional to product of concentration of reacting species
Example: A B  C
Example: Photolysis A + h  B + C
Chemical loss of A is proportional to concentration of A

17. Detour … Sub-grid scale chemistry parameterisation??
The non-linear nature of the chemical Kineticis leads to a potential influence of the spatially unresolved sub-scale variability on the model resolved scale (“turbulent” reynolds averaging)
There is no solution for sub-grid scale variability of chemistry yet
No solution to this problem yet
Well mixed – reaction occurs
Not mixed – no reaction

18. Chemical loss and production - lifetime
Chemistry is budget of loss (L) and production (P) rate
A’s loss rate is always proportional (l ) to concentration of A
Usually mag(L) similar mag(P)‏
Loss rate coefficient l is often proportional to [OH] in troposphere
Chemical lifetime τ determines transport scale
Only a subset of species is transported ( lifetime > 10 min)‏

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

20. Ozone cycle in Stratosphere (Chapman, 1930)

21. Ozone Production in Stratosphere (Chapman 1932)‏
Chemical Equations
Kinetics equations
Assumptions
The shape of the ozone profile can be qualitatively explained by the derived ozone production
(a) is most efficient in upper atmosphere (more UV input)‏
(b) is most efficient in lower atmosphere (higher density)‏

22. Ozone profile and life time predicted with Chapmann Cycle
theory
observed
Catalytic reactions with NOx, HOx,
ClOx and BrOx

23. Data assimilation of atmospheric composition
Observations
Assimilation System
Examples

24. Special Characteristics of Atmospheric Chemistry data (vs NWP) assimilation
Quality of NWP depends predominantly on Initial conditions whereas Atmospheric Chemistry modelling depends on initial state (lifetime) and emissions
Emissions data are uncertain and difficult to measure and biased
Chemical atmospheric fields have strong horizontal and vertical gradients for atmospheric composition
small scale emission variability
Heterogonous reactions on surface
Observation
Limited representativeness
Sparse
Poor near real time availability

25. Air quality observations at surface (… biased toward polluted areas )‏
NO2 annual mean in Berlin
Regional model (25 km) vs.
Air quality observations
Large variability of observations
Within GRID box
Is the model result “good” ?
Could data assimilation improve
model result?

26. Profile Observations ( … far to few)‏
Ozone sondes- GAW stations
MOZAIC flight observations

27. Satellite observations Assimilation of retrievals vs. radiances
analyses

28. NO2 retrievals from satellite observations
Retrieval of trop. NO2 from SCIAMACHY measurements
A priori information needed:
aerosol loading and profile
vertical profile of the observed species
surface reflectance at time of measurement
Obtained from climatologies or Models
Problems:
spatial resolution
loss of independence of measurement and model
clouds
DOAS analysis
Total Slant Column
Tropospheric Slant Column
SCIATRAN RTM (airmass factor)‏
Tropospheric Vertical Column
Joana Leitao. Uni Bremen

29. Special Characteristics of Atmospheric Chemistry satellite observations
Total or partial column retrieved from radiation measurements
No or only low vertical resolution
Ozone (and NO2) dominated by stratosphere
Weak or no signal from planetary boundary layer
Global coverage in a couple of days (LEO)‏
Limited to cloud free conditions
Fixed overpass time (LEO) – no daily maximum
Retrieval algorithms are ongoing research

30. MOPITT CO (TC) Data count Example April 2003
Very few observations in tropical regions (clouds)
Only Land Points were assimilated
Data have been thinned to 1°x1° grid

31. CO total column retrievals from different instruments/ retrievals
MOPITT- retrieval
Different retrievals tend to differ …
IASI – retrieval B
IASI - retrieval A

32. Trace gas assimilation system at ECMWF
Stratospheric Ozone with linearized ozone chemistry since 1999
GEMS-project ( )‏ / MACC-project ( )‏
Ozone in troposphere and stratosphere
CO, SO2, Formaldehyde, NOx, Aerosol and CO and CH4
Full Chemistry (CTM MOZART-3)

33. GEMS / MACC Global Production

34. The IFS is coupled to a CTM for data assimilation of atmospheric composition
Initial Conditions
Tracer Concentrations
Concentration
feedback
Production/Loss
Meteorology
Tracer Concentrations

35. ECMWF 4D-VAR Data Assimilation Scheme Assimilation of Reactive Gases and Aerosol
transport +
“chemistry”
advection
only
transport +
“chemistry”

36. Reactive gases assimilation – approach in GEMS and MACC
Include NOx, SO2, O3, CO and HCHO species in IFS (Transport and Assimilation)
Introduce source and sinks by coupling with Chemical Transport Models
MOZART (MPI-Hamburg), TM5 (KNMI), Mocage (Meteo France)‏
Assimilate species in IFS with existing 4D-VAR implementation developed for meteorological fields
Apply coupled system in out loops (forward trajectory run) only
NMC method (i.e. differences to different meteorological forecasts) to obtain background error statistic
Implement and test feedback mechanism to coupled CTM
Implement diagnostic NO2 (fast chemistry, observed) to NOX (slow chemistry, modelled) observation operator

37. Assimilation of Ozone Dominated by the stratosphere
Assimilated Ozone retrievals
Total Columns from UB-VIS instruments (OMI, SCHIMACHY and SBUV)
Low-resolution stratospheric profiles from Microwave-Limb-Sounder (MLS)

38. Ozone Total Columns – Inter-annual variability in GEMS re-analysis

39. Ozone Hole Development
Winter – no sun light:
Cold stable Vortex -> formation of Polar Stratospheric Clouds
Accumulation of Chlorine/Bromine compounds on PSC surface
Spring – gradually more sun-light
Rapid release of CLO on PSC surfaces
Quick catalytic destruction of ozone –> ozone hole
Vortex becomes more permeable – closure of ozone hole

40. Temperature and O3 over South Pole – Sonde observations
Closure by transport
PSC Formation
Chlorine Activation
Ozone Hole

41. Antarctic Ozone Hole 2008
Different Modelling Schemes for the Chemistry
Operational ECMWF ozone with O3 chemistry parameterisation
NRT coupled-system IFS-MOZART with full stratospheric chemistry:
coupled-system IFS-TM5 with stratospheric O3 climatology
Each scheme was run with (AN) and without (FC) data assimilation of O3 satellite observations at 0 UTC to provide initial ozone conditions
Assimilated Observations
Total columns (OMI, SCHIAMACHY, SBUV)
Stratospheric partial Columns (MLS)
Questions:
Inter-instrument biases
Impact of different chemistry schemes

42. Instument - Biases over Antarctica
MLS can observe during polar night
Large differences due to different sampling
Actual Biases are small (2-3%)
MLS can observe during polar night
Biases small

43. Total Columns vs. Ozone Sondes
Without assimilation
With assimilation

44. Ozone hole size with different CTMs and assimilationNo
assimilation
Assimilation
Forecast
initialised
by analyses
every 15 days

45. Vertical Profiles at Neumayer Station
MOZART
Full Chemistry
No MLS
TM5
Climatology
IFS
Linear
Chemistry

46. Assimilation of Volcanic SO2

47. Volcanic Eruptions Volcanic eruptions are a major natural SO2 source
Volcanic eruptions can penetrate the tropopause
SO2 is a aerosol precursor (SO4)‏
SO2/SO4 is long-lived in stratosphere
Volcano ash emission are an aviation hazard

48. Volcanic SO2 assimilation experiment
Questions:
What is the Volcano SO2 / ash flux
What is injection height and plume height
Can assimilation of satellite data pick up plume or do we need to model the dispersion of the SO2 / ash emissions in the assimilating model
Case study of Nyamuragira eruption 27/11/06- 4/12/06
Observations:
SCIAMACHY SO2 total column (BIRA) - Assimilated
OMI SO2 total column (plots) for comparison – Not Assimilated

49. Iceland Volcano Plume Forecast Injection height 3, 5 and 10 km

50. SO2 total column observations
OMI plots to estimate Volcano flux and injection height
Increase in total column – loss = flux estimate
Test runs with variable injection height to match with observed plume
Background error profile constructed according to estimated plume height
SO2 background error stdv profile
28.11
6.12
1.12
3.12
SCIAMACHY SO2 columns 1.12
OMI SO2 columns [DU] from

51. SO2 forecast assimilation: Total column SO2
1 Dec
3 Dec
5 Dec
7 Dec
Control run – no assimilation)‏
Tracer run injection height 14 km – resembles OMI SO2
Dobson Units
SO2 tracer emission estimated from OMI SO2 day to day total column change

52. SO2 assimilation: Total column SO2
1 Dec, 0z
3 Dec, 12z
5 Dec, 12z
7 Dec, 12z
Assimilation SO2 (no source)‏
Assimilation with SO2 volcano emission
Dobson Units
Assimilation without SO2 volcano emissions fluxes provides reasonable plume forecast if injection height is correctly guess (Background error statistics)‏

53. Summary Atmospheric composition and weather interact
Chemical lifetimes determine to what extent species are distributed in the atmosphere
Emissions are often uncertain but greatly determine concentrations
Chemical data assimilation has to deal with very heterogeneous fields
Chemical data assimilation should also help to improve emissions
MACC forecast system produces useful forecast and robust data assimilation products
Re-analysis of Ozone, CO and Aerosol ( ) and NRT forecast up to present are available at

54. Thank You !

55. Data from Schneefernerhaus/Zugspitze 2650m a.s.l.
Data from Zugspitze, Schneefernerhaus, 2650 m a.s.l. SO2 mixing ratio (contact: paticle > 10nm (UBA measurements, (contact: )
SO2 2010
SO2 mean 2000 – 2007
particle > 10 nm
Data from Schneefernerhaus/Zugspitze 2650m a.s.l.
Source: Anja Werner-DWD

56. Improvements in the IFS stratosphere and mesosphere by using improved (assimilated) CO, CH4 and O3 climatology P.
Bechtold
Temperature Error
Zonal Wind error
old
new
New: Non-Orographic gravity wave scheme and GEMS climatology for Radiation