Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry [email protected]

Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming

Modelling and Assimilation of Atmospheric Chemistry

[email protected]

contributions from Antje Innes, Johannes Kaiser, Jean-Jacques Morcrette, Vincent

Huijnen (KNMI) & Martin Schulz (FZJ)


Overview

Motivation / MACC

Basic concepts of atmospheric chemistry modelling

Chemistry

Emissions

Emissions vs. forecast initialisation (Data assimilation)

Russian Fires 2010

SO2 from volcanic eruptions


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


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


MACC Daily (NRT) Service Provision

Air quality

Global Pollution

Aerosol UV index Fires


Flux Inversions

MACC Service Provision (retrospective)

Reanalysis

2003-2010

Ozone records


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


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


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.


Atmospheric Composition

-Observation from space-Modelling


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


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


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


A. Richter, Optical Remote Sensing WS 2004/2005 - 14 -

SCIAMACHY and GOME-2: Target Species

OH


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


Satellite observations of atmospheric composition are getting better in terms of accuracy and spatial resolution.

Total ozone observations


Sentinel-5 Precursor

Sentinel-5

Sentinel-4

Expected primary satellite provision for measuring atmospheric composition – Reactive gases


Modelling of Atmospheric CompositionTransport, Emissions, Deposition Chemical conversion


Emissions

ChemicalReactions

AtmosphericReservoir

wet & dryDeposition

Transport TransportcatalyticCycles

Dr. Martin Schultz - Max-Planck-Institut für Meteorologie, Hamburg

Processes on Atmospheric Composition

Photolysis


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


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


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


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


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


◄ into stratosphere

No transport modelled

Chemical Lifetime vs. Spatial Scale


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)


Example emissions inventory after gridding

CO emissions from anthropogenic sources


Emissions variability

Anthropogenic COMACCity

Biomass burning CGFEDV3 and GFASv1.0South America

Western EuropeC. Granier J. Kaiser


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


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


ii EFFAE area burnt

combustionefficiency

fuelload

emissionfactor J. Hoelzemann

Emissions CO

Burnt Areafrom Satellite

Biomass amount

Global Wildfire Emission ModellingFire Radiative Power


CO biomass burning emissions – variability


Improving Forecast:Emissions modelling/observations

vs.Initialisation with Analyses (Data

Assimilation)

Russian Fires

Volcanic Erruptions


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


Russian Fires 2010

Moscow

Source: wikipedia


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


Russian Fires 2010


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


Russian Fires 2010

MOPITT OBS CNT (Climatological emissions)

FRP fire emissions

GFAS + Assimiliation


Russion Fires: Forecast CO vs observations

Total Column

Surface


Volcanic eruption - Forecast


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


SO2 satellite retrievals from GOME-2, OMI and SCIAMACHY


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


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


24 H Forecast with EMI and INI


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)


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”

Documents

Modelling and Assimilation of Atmospheric Chemistry – Johannes Flemming Modelling and Assimilation of Atmospheric Chemistry [email protected]