UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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
?
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
3D Off-line Chemical Transport Models (CTMs)TOMCAT/SLIMCAT GEOS-CHEMChemistry/aerosol modulesConstituent data assimilationInverse Modelling
Relationship of NCEO Atmospheric Models
Coupled Earth System Climate ModelUKCAAtmosphere/Ocean/Biosphere..Chemistry/aerosol modules
NWP ModelECMWF IFSChemistry/aerosol modulesConstitutent data assimilation
Regional AQ ModelNAME-III UMAQ
Biosphere ModelJULES
CouplingOutput Code
NCAS/MO
MO/DEFRA
ECMWF
NCEO
MO
NC
AS
NCEO Obs.
ST2
ST4
ST3
ST4
NC
AS
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
Models in AC S-T 2
Step
CTM
Development Tools Results
ObservationsST-1
2
3
4
Model/data consistency
Analysed constituent fields
Estimated surface fluxes
Derived surface parameters
CTM
CTM
CTM
Constituent DA
DA/IMinc. fluxes
DAIM
Observation operators
DA Scheme
Fluxes as CV
Coupling of SMAdjoint of SM
1CH4, CO, NOx, O3NMHC, OVOC, aerosol
CH4, CO, CO2
CH4, CO, NO2,
Surface Model
+
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
• 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.
Science Objectives AC ST-2
Addressing key areas in tropospheric composition:
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
• 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.
National Capability AC S-T 2
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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.
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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)
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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.
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
No Assim.
Assimilation
31/1/1992
TOMCAT/SLIMCAT 3D CTM Assimilation Scheme
Assimilation of HALOE CH4 profiles
ATMOS Profiles 27-31 March 1992
CH4N2O H2O HCl O3
17 N
9 N
8 S
39 S
47 S
52 S
CH4 better in mid-lat LS
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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.
Other Ongoing CTM DA/IM Work
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT UNIVERSITY OF LEEDS
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.
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
Chevallier-type 4DVar Scheme
Minimise
J( f ) J0 i
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( f ) with
J0 i( f ) (X i
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f fluxes, e.g. monthly time and model grid spatial resolution
X mixing ratios
H observation operator
T0 i Transport from time t 0 to time ti of ith observation
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
• May include priors in formulation• Minimization using conjugate gradients• Gradients to be calculated using forward followed by backward run using adjoint transport model
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then
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Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT UNIVERSITY OF LEEDS
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.
SCHOOL OF GEOGRAPHY
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
• 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
An Ensemble Kalman Filter for Assimilating CO2 Column Measurements:
Liang Feng, Paul Palmer, Sarah Dance, Hartmut Bösch
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
Additional Slides
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
• 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’
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
Rectification
Daytime summer
Atm
osph
eric
mix
ed la
yer
Atm
osph
eric
mix
ed la
yer
Nighttime summer
Photosynthesis Respiration
Assume C flux due to photosynthesis equal due to respiration then mean CO2 concentration near to the ground will not be zero
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENTStephens et al., Science, 2007
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
1. Limb/nadir Accurately characterise stratosphere & upper trop– Derive lower trop (e.g. O3, HNO3, NO2 & CH4)
2. Nadir-shortwave/thermal Discriminate near-surface layer (eg. O3, CH4 & NO2 )
3. 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
Integrated approach to sounding tropospheric composition
Observation Interface (ST-1)
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT UNIVERSITY OF LEEDS
ESA(ERS-2, Envisat)
Eumetsat(EPS-MetOp)
ATSR/AATSRDual View
VIS/IR Imager
MIPASIR Limb
SCIASWIR Nadir
GOME 2UV/VIS Nadir
IASIIR Nadir
AVHRR/3VIS/IR Imager
IntegratedScheme
Integrated Scheme - Limb / Nadir - Spectrometer / Imager - Shortwave / Thermal
Sub-pixel Cloud,Aerosol &
Surface Properties
Self-consistent trace gas& aerosol fields
(+ cloud & surface properties)
Scientific Exploitationin “Climate Theme”
Scientific Exploitationin Sub-Themes 2&3
Assimilation Trials in Sub-Theme 4
ACE &AURA
Self-consistent cloud,aerosol & surface properties
1995 - present
Observation Interface – Sub-Theme 1
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
• 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
Quantification of trace gas and aerosol distributions and emissions (ST-2)
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
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
UNIVERSITY OF LEEDS
Institute for Atmospheric ScienceSCHOOL OF EARTH AND ENVIRONMENT
Manuel GloorTwo 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