Modelling and Assimilation of Atmospheric Chemistry Johannes.Flemming@ecmwf.int

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

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

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 1997 997):

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

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, 1479.1497 (2008)‏

An other motivation …

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

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

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

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

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

Nitrogen Oxides - sources and sinks MOZART-3 CTM 2003070500 Surface Emissions Total Columns Concentrations Note: High Loss is related to high concentrations Vincent Huijnen, KNMI Chemical Production and Loss & Lightning

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

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

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

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

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)‏

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

Ozone cycle in Stratosphere (Chapman, 1930)

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)‏

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

Data assimilation of atmospheric composition Observations Assimilation System Examples

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

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?

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

Satellite observations Assimilation of retrievals vs. radiances analyses

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

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

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

CO total column retrievals from different instruments/ retrievals MOPITT- retrieval Different retrievals tend to differ … 27.-31.08.2008 IASI – retrieval B IASI - retrieval A

Trace gas assimilation system at ECMWF Stratospheric Ozone with linearized ozone chemistry since 1999 GEMS-project (2004-2009)‏ / MACC-project (2009-2011)‏ Ozone in troposphere and stratosphere CO, SO2, Formaldehyde, NOx, Aerosol and CO and CH4 Full Chemistry (CTM MOZART-3)

GEMS / MACC Global Production 2003 -2008

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

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

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

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)

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

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

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

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

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

Total Columns vs. Ozone Sondes Without assimilation With assimilation

Ozone hole size with different CTMs and assimilation No assimilation Assimilation Forecast initialised by analyses every 15 days

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

Assimilation of Volcanic SO2

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

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

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

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 http://so2.umbc.edu/omi/

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

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)‏

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 (2003-2008) and NRT forecast up to present are available at http://www.gmes-atmosphere.eu/

Thank You !

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

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