chemical transport models and da in the nceo atmospheric chemistry theme

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


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


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

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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


• 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


• 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


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


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)

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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


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


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


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


Chevallier-type 4DVar Scheme

Minimise

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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


• 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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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


• 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


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Additional Slides

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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

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• 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


Rectification

Daytime summer

Atm

osph

eric

mix

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yer

Atm

osph

eric

mix

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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


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)


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

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• 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


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


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

UNIVERSITY OF LEEDS


chemical transport models and da in the nceo atmospheric chemistry theme

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