Chemical Transport Models and DA in the NCEO Atmospheric Chemistry Theme

1. Chemical Transport Models and DA in the NCEO Atmospheric Chemistry Theme • Summary of NCEO Atmospheric Composition Theme • Obs: RAL (Kerridge), Oxford (Grainger), Leicester (Remedios), York (Bernath) • Mod: Leeds (Chipperfield), Edinburgh (Palmer), Cambridge (Pyle), Reading • CTMs in AC Theme 3 (TOMCAT, GEOS-Chem) • Other related (non-NCEO-funded) DA/IM CTM work: • Leeds • Edinburgh ?

2. Theme 3: Atmospheric Composition Sub Themes • ST-1 Observation Interface • Integrated approach to sounding tropospheric composition (limb/nadir, nadir-shortwave/thermal, spectrometer/imager) • ST-2 Quantification of trace gas and aerosol distributions and emissions • Short-lived gases • Methane • Aerosol • ST-3 Quantification of climate-composition interaction • Testing of UK chemistry-climate model with satellite data (Hadley Centre, NCAS) • ST-4 New Applications • Chemistry/aerosol module and assimilation into global NWP model • Links to AQ modelling • ECMWF, Met Office, DEFRA

3. ST2 ST4 ST3 ST4 NCEO Obs. Relationship of NCEO Atmospheric Models 3D Off-line Chemical Transport Models (CTMs) TOMCAT/SLIMCAT GEOS-CHEM Chemistry/aerosol modules Constituent data assimilation Inverse Modelling Biosphere Model JULES MO NCEO NCAS NCAS NWP Model ECMWF IFS Chemistry/aerosol modules Constitutent data assimilation Coupled Earth System Climate Model UKCA Atmosphere/Ocean/Biosphere.. Chemistry/aerosol modules ECMWF NCAS/MO Regional AQ Model NAME-III UMAQ Output Code Coupling MO/DEFRA

4. CTM Surface Model CTM DA IM DA/IM inc. fluxes Constituent DA CTM CTM Observations ST-1 Models in AC S-T 2 Step Tools + Development  Results Observation operators Model/data consistency 1 CH4, CO, NOx, O3 NMHC, OVOC, aerosol Analysed constituent fields DA Scheme 2 CH4, CO, NO2, Estimated surface fluxes Fluxes as CV 3 CH4, CO, CO2 4 Coupling of SM Adjoint of SM Derived surface parameters

5. Science Objectives AC ST-2 Addressing key areas in tropospheric composition: • Improve quantitative understanding of the composition of the upper troposphere. New satellite data will yield observations of organic species in the mid-upper troposphere. Through detailed modelling studies this will lead to improved estimates of the oxidising capacity of the troposphere. • Long-range transport of surface air pollutants. Satellite data and models will be used to quantify the role of regional/intercontinental transport of primary pollutants and precursors in production of secondary trace gas and aerosol pollutants, complementing existing aircraft and ground-based data. • Source attribution and quantification of primary emissions. Determine (on scales accessible only to satellite observations) biogenic, pyrogenic and anthropogenic emission sources.

6. National Capability AC S-T 2 • Development of 3D modelling tools. We will develop modular tools for data assimilation, inverse modelling, coupling to surface modelling and model sampling (observation operators). These general tools can be applied to a range of future scientific studies.

7. CTMs in NCEO Atmospheric Composition • Two state-of-the-art offline chemical transport models: • TOMCAT/SLIMCAT • GEOS-Chem • Models widely used by NCEO groups for other studies, and by other groups in UK and worldwide.

8. TOMCAT/SLIMCAT 3D CTM • Off-line 3-D chemical transport model • Vertical coordinate (-p - TOMCAT, - - SLIMCAT). Variable resolution. • Horizontal winds and temperatures specified from analyses (e.g. ECMWF, UKMO). • Vertical winds from analysed divergence or diagnosed heating rates (in stratosphere). • Advection: Prather [1986] second-order moments, ‘slopes’ or • semi-Lagrangian. • Trajectories (4th order Runge Kutta embedded in model) • Physics: Tiedtke [1989] convection scheme. • Holtslag and Boville [1993] or Louis [1979] PBL schemes. • Chemistry: • Stratosphere: Ox, NOy, HOx, Cly, Bry, CHOx, source gases. Aerosols/PSCs.. • Troposphere: Ox, NOy, HOx C1-C3. C5H8, Bry Wet/dry deposition. Emissions etc… • Chemical Data Assimilation: Sub-optimal Kalman Filter • Aerosols: • Troposphere: Sulphate, sea-salt, (SOA),… (GLOMAP-bin, GLOMAP-mode) • Stratosphere: Denitrification microphysical model (DLAPSE)

9. TOMCAT/SLIMCAT 3D CTM Assimilation Scheme • Based on code of Khattatov et al., J. Geophys. Res , 105, 29,135, 2000 • See Chipperfield, J. Geophys. Res., 2002 • Sequential method • Sub-optimal Kalman filter with estimate of analysis errors • Assimilation needs: • Observational error • Model error (tunable parameter for error growth) • Representativeness error (tunable parameter) Tunable parameters chosen based on OmF and 2 diagnostics.

10. TOMCAT/SLIMCAT 3D CTM Assimilation Scheme Assimilation of HALOE CH4 profiles N2O CH4 H2O HCl O3 No Assim. 17 N 9 N 8 S CH4 better in mid-lat LS Assimilation 39 S 47 S 52 S 31/1/1992 ATMOS Profiles 27-31 March 1992

11. GEOS-Chem community model • Development HQ at Harvard but now has developers in many countries around the world (>100 active users) • Free to download and easy to use (extensive current documentation) • Uses assimilated meteorology from NASA GMAO (native 1x1 degree). New version of meteorology will be higher resolution. • Extensively evaluated using different measurement platforms • Current simulations: • OX-NOX-VOC-aerosol chemistry (bread and butter code) • Tagged CO, CO2, CH4, mercury, hydrogen, CH3Cl • Capability of using ECMWF within NCEO TBC

12. Other Ongoing CTM DA/IM Work • Non-NCEO funded, but related: • Leeds: • - IM for surface fluxes with ‘4D-var’-type sheme • - Kalman Smoother • Edinburgh: • - Development of Ensemble Kalman Filter (EnKF) DA.

13. Estimation of CO2, CO, CH4 fluxes from atmospheric concentrations (in situ flask-based and satellite retrievals) • Chris Wilson, Manuel Gloor, Martyn Chipperfield • DARC PhD Studentship (Chris Wilson), started Oct. 2007. • Will develop IM capability within TOMCAT. • ‘4D-Var type’, similar to Chevallier et al. JGR 2005. • - Minimization of cost function using conjugate gradients. • - Gradients to be calculated with adjoint. • Inclusion of CH4 and CO (fixed OH fields from full-chemistry TOMCAT run) – help attribution of sources. • Also includes SF6 – as test model model transport. • Adjoint will be developed in collaboration with F. Chevallier.

14. Chevallier-type 4DVar Scheme Minimise

15. May include priors in formulation • Minimization using conjugate gradients • Gradients to be calculated using forward followed by • backward run using adjoint transport model

16. SCHOOL OF GEOGRAPHY Flux Estimation with Kalman Smoother Manuel Gloor • Kalman smoother: differs from Kalman filter in that several - not only one - time steps backwards in time are updated thus there will be several subsequent estimates for the same quantity. • Up to the last one these are used as ‘first guesses’. • Alternative to 4D-var type scheme • Kalman Smoother (Bruehweiler et al. ACP 2005) in MOZART 2.4. • Similar to Bousquet et al. 2007 (?) - iterative via linearization. • OH fields from MOZART / TOMCAT. • Sensitivities dX/df simulated using forward pulses. • 70 Regions with bi-weekly flux resolution. • Here 6 months backwards in time used.

17. An Ensemble Kalman Filter for Assimilating CO2 Column Measurements: Liang Feng, Paul Palmer, Sarah Dance, Hartmut Bösch • Funded through NERC EO Mission Support Scheme (pre NCEO), started September 2007 • EnKF developed with OCO and GOSAT in mind • OCO instrument characteristics (nadir/glint; aerosol and cloud cover) are from Hartmut Bösch; GOSAT characteristics to follow • Preliminary OSSE calculations look good, currently designing additional experiments • Developed in F90 and python – flexible and modular • Poster presentation at the upcoming EGU meeting in April

19. Additional Slides

20. Main reason for choice want to be able to ingest large amounts of data from space Why all three C related constituents: helps process identification - e.g. biomass burning

21. Adjoint planned to be coded in Paris with help of F. Chevallier (line by line) who did the same for LMDZ model • We are currently testing tropospheric transport of TOMCAT using SF6, CO, APO Transport evaluation is important - see recent paper by Stephens et al. 2007 because of ‘rectification effects’

22. Rectification Nighttime summer Daytime summer Atmospheric mixed layer Atmospheric mixed layer Photosynthesis Respiration Assume C flux due to photosynthesis equal due to respiration then mean CO2 concentration near to the ground will not be zero

23. Stephens et al., Science, 2007

24. Observation Interface (ST-1) Integrated approach to sounding tropospheric composition • Limb/nadir • Accurately characterise stratosphere & upper trop • Derive lower trop (e.g. O3, HNO3, NO2 & CH4) • Nadir-shortwave/thermal • Discriminate near-surface layer (eg. O3, CH4 & NO2 ) • Spectrometer/imager • Sub-pixel cloud, aerosol & surface in RTM • O2 A-band (polarised) for near-surface aerosol • Consistent trace gases, aerosol (+ cloud & surface) from EPS-MetOp/Envisat Integrated OE approach also for consistent cloud, aerosol & surface properties from ATSR-2 /AATSR joint mission

25. Observation Interface – Sub-Theme 1 ESA (ERS-2, Envisat) Eumetsat (EPS-MetOp) ATSR/AATSR Dual View VIS/IR Imager MIPAS IR Limb SCIA SWIR Nadir GOME 2 UV/VIS Nadir IASI IR Nadir AVHRR/3 VIS/IR Imager Integrated Scheme Integrated Scheme - Limb / Nadir - Spectrometer / Imager - Shortwave / Thermal Sub-pixel Cloud, Aerosol & Surface Properties ACE & AURA Self-consistent trace gas & aerosol fields (+ cloud & surface properties) Scientific Exploitation in “Climate Theme” Scientific Exploitation in Sub-Themes 2&3 Assimilation Trials in Sub-Theme 4 Self-consistent cloud, aerosol & surface properties 1995 - present

26. Quantification of trace gas and aerosol distributions and emissions (ST-2) • Objectives and R&D common for CH4, shorter-lived gases & aerosol • Quantitative analysis of distributions, sources & sinks: • Requirements for accurate & height-resolved data from ST-1 • Observational errors: • Vertical correlations • Shortwave: correlations trace gas – aerosol – BRDF – T • Thermal: correlations trace gas – T – humidity • Residual cloud & surface inhomogeneity • Model background error cov matrix B • Coupled chemistry & aerosol scheme in global CTM • Univariate – multivariate assimilation • Evolution to 4D-Var • Comparison of net surface fluxes with independent estimates from eg. biosphere model (necessary precursor to coupling) • Integrated approach adopted for CH4, shorter-lived gases & aerosol Integrated Approach to Data Assimilation & Inverse Modelling

27. Climate – Composition Interaction (ST-3) • Assessment of UK chemistry climate model (UKCA) through comparisons with multi-year satellite time series from ST-1 • - Variances (pressure, lat/lon, season) • - Interannual variability e.g. ENSO cycle • Global height-resolved O3 (from 1995) • NO2 • CH4, CO, VOCs & HNO3 in UT (from 2002) • Apply observation operators to model O/P • Compare like-with-like • Collaboration with NCAS & Hadley Centre

28. Manuel Gloor • Two methods: • Kalman Smoother (Bruehweiler et al. ACP 2005) • MOZART 2.4 - Kalman Smoother • Inclusion of CH4 and CO: • Kalman Smoother: similar to Bousquet et al. 2007 (?) - iterative via linearization- OH fields from MOZART / TOMCAT Not much details about Kalman Smoother here other than being done pretty much the ‘dumb way’: • Sensitivities dX/df simulated using forward pulses • 70 Regions with bi-weekly flux resolution • Kalman smoother: differs from Kalman filter in that several - not only one - time steps backwards in time are updated thus there will be several subsequent estimates for the same quantity - up to the last one these are used as ‘first guesses’ • here 6 months backwards in time used