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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)
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
SO2 from volcanic eruptions
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
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, SO2, NO2, HCHO, CO2 CH4) retrievals (TC) with IFS 4D-VAR
Near-real-time Forecast and re-analysis of GRG, GHG and Aerosol
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
MACC Daily (NRT) Service Provision
Air quality
Global Pollution
Aerosol UV index Fires
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Flux Inversions
MACC Service Provision (retrospective)
Reanalysis
2003-2010
Ozone records
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Change in Aerosol Optical Thickness
ClimatologiesNew: 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 1997 997):
26r1: Old aerosol (Tanre et al. 84 annually fixed)
Thickness at 550nm
Impact of Aerosol Climatology on NWP
J.-J. Morcrette A. Tompkins
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Improved Predictability with improved Aerosol Climatology
Published in Quart. J. Roy. Meteorol. Soc., 134, 1479.1497 (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 scorewith 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.
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Atmospheric Composition
-Observation from space-Modelling
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
N2
O2
H2OArgon
20%
78%
1%
N2O 310
H2
CO
Ozone
500
100
30
ppb1:109
CO2
CH4 (1.8)
ppm1:106
380
Ne
18He (5)
HCHO 300
Ethane
SO2
NOx
500
200100
ppt1:1012
NH3 400
CH3OOH 700
H2O2 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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Spectral rangesRemote 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 Angela’s lecture on observations operatorestomorrow)
Wavelength λ
I I i I I I I I I I I I I I 1km 100m 10m 1m 0.1m 10cm 1cm 1mm 0.1mm 10μm 1μm 0.1μm 10nm 1nm Radiowaves Microwaves thermal X-ray Infrared Visible Ultraviolet Interaction of electromagnetic Rotation Vibration Electron radiation with matter Transition
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
A. Richter, Optical Remote Sensing WS 2004/2005 - 13 -
Wavelength Ranges in Remote Sensing
UV: gas absorptions + profile information aerosols
vis: surface information (vegetation)gas absorptionsaerosol information
IR: temperature informationcloud informationwater / ice distinctionmany absorptions / emissions+ profile information
MW: no problems with cloudsice / water contrastsurfacessome emissions + profile information
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
A. Richter, Optical Remote Sensing WS 2004/2005 - 14 -
SCIAMACHY and GOME-2: Target Species
OH
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
SO2, GOME-2, SACS, BIRA/DLR/EUMETSAT
NO2, OMI, KNMI/NASA
Aerosol Optical Depth, MODIS, NASA
SO2, IASI, Univ. of Brussels/EUMETSAT
Exciting satellite observations
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
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Modelling of Atmospheric CompositionTransport, Emissions, Deposition Chemical conversion
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Emissions
ChemicalReactions
AtmosphericReservoir
wet & dryDeposition
Transport TransportcatalyticCycles
Dr. Martin Schultz - Max-Planck-Institut für Meteorologie, Hamburg
Processes on Atmospheric Composition
Photolysis
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)
,
.
concentration of species i
( ) ... Emission
( , , , ...) ... Chemical conversion
... Deposition
i ih h i c i Z
i
i i
i i j k m
i Dep i
c cc w c K E R D
t z z z
c
E f c
R f c c c c
D l c
V
Source and Sinks- not included in NWP
Transport
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Integration of chemistry & aerosol modules in ECMWF’s integrated forecast system (IFS)
Dynamics & Physics
Chemistry
ctm
Dynamics & Physics
Transport & Chemistry
oasis4
oasis4
oasis4
IFS IFS CTM
Feedback Flow
Coupled SystemFeedback: slowFlexibility: high
Integrated System Feedback: fast Flexibility: low
Coupled SystemIFS- MOZART3 / TM5
C-IFSOn-line Integration of Chemistry in IFS
Developed in GEMSUsed in MACCDeveloped
in MACC
10 x more efficient than Coupled System
Flemming et al. 2009
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 CTM2003070500
Note: High Loss is related to high concentrations
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Tropospheric Ozone - sinks and sources
Total Columns Concentration
Chemical Production and Loss
TM5 Chemical transport model2003070500
Vincent Huijnen, KNMI
Note: Strong night/day differences in chemical activity
No ozone emissions
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 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 50- 100 species and 2---300 chemical reactions
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
◄ into stratosphere
No transport modelled
Chemical Lifetime vs. Spatial Scale
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
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)
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Example emissions inventory after gridding
CO emissions from anthropogenic sources
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Emissions variability
Anthropogenic COMACCity
Biomass burning CGFEDV3 and GFASv1.0South America
Western EuropeC. Granier J. Kaiser
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
ii EFFAE area burnt
combustionefficiency
fuelload
emissionfactor J. Hoelzemann
Emissions CO
Burnt Areafrom Satellite
Biomass amount
Global Wildfire Emission ModellingFire Radiative Power
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
CO biomass burning emissions – variability
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Improving Forecast:Emissions modelling/observations
vs.Initialisation with Analyses (Data
Assimilation)
Russian Fires
Volcanic Erruptions
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Russian Fires 2010
Moscow
Source: wikipedia
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Russian Fires 2010
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Russian Fires 2010
MOPITT OBS CNT (Climatological emissions)
FRP fire emissions
GFAS + Assimiliation
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Russion Fires: Forecast CO vs observations
Total Column
Surface
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
Volcanic eruption - Forecast
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
SO2 satellite retrievals from GOME-2, OMI and SCIAMACHY
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
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
Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming
24 H Forecast with EMI and INI
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)
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 SO2 Volcanos
NRT forecast and Re-analysis of Ozone, CO and Aerosol (2003-2008) are available at http://www.gmes-atmosphere.eu/
More on AC Data assimilation of AC in Antje’s talk “Environmental Monitoring” and Angela’s talks “Observation Operators”