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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

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slide1

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

?

slide2

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
slide3

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

slide4

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

slide5

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.
slide6

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.
slide7

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.
slide8

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)
slide9

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.

slide10

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

slide11

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
slide12

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.
slide13

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.
slide15

May include priors in formulation

  • Minimization using conjugate gradients
  • Gradients to be calculated using forward followed by
  • backward run using adjoint transport model
slide16

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.
slide17

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
slide20

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

slide21

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’

slide22

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

slide24

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

slide25

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

slide26

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

slide27

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
slide28

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