Peter Cox (Met Office)Martin Heimann, Wolfgang Knorr (MPI-BGC)

Tony Hollingsworth, Richard Engelen (ECMWF)Philippe Ciais, Philippe Peylin (LSCE)

Alain Chedin (LMD)

GEMS – GREENHOUSE GASES

• Subproject objectives

• Parent Projects

• Subproject plan

• Outstanding issues

1. Map daily-to-seasonal variations of total column GHGs (CO2, CH4, N2O, CO), which will necessitate representations of source-sink terms in the assimilating model.

2. Validation of concentration fields using existing observational data.

3. Inversion systems to infer carbon sources and sinks.

GEMS-GHG Objectives

GEMS-GHG Parent Projects

• COCO FP5 Project (Dec 2001 – Dec 2004) [obj. 1]

• CarboEurope (FP5 cluster and FP6 IP) CarboEurope - AEROCARB (March 2000 – March 2003) [2,3] CarboEurope - CAMELS (Nov 2002 – Nov 2005) [3]

• New satellite obs OCO (NASA, 2007) GOSAT (NASDA, 2007?)

• EVERGREEN (non-CO2 retrievals) (2002-2005)

COCO Objectives

1. Develop algorithms for retrieving CO2 concentrations from

satellite measurements with the instruments IASI, AIRS, AMSU and SCIAMACHY, comparing 3 possible methods:

a) Standalone approach: exploiting the synergy of passive thermal IR and microwave measurements of AIRS and AMSU satellite obs

b) 4D Var: using ECMWF model’s wind fields consistent with the temperature and CO2 fields.

c) Differential absorption: of solar radiation in the near-infrared region of the spectrum, using SCIAMACHY observations.

2. Assess the utility of this new data in estimating surface fluxes.

CO2 is implemented a column variable in the 4D-Var assimilation system at ECMWF.

First results indicate that retrievals in the tropics should be accurate.

Validation is highly needed to prove this.

Especially, the accounting for all possible bias errors is a tough undertaking.

Next step is to include CO2 as a tracer in the forecast model, enabling a full 4D-Var CO2 analysis. This will allow a transport model constraint on the CO2 analysis that will probably reduce the horizontal scatter.

CO

2 D

ata

Assim

ilati

on

at

EC

MW

F

Current status of CO2 analysis at ECMWF

First half of June, tropical area: significant deviations from background, but are these realistic?

Model simulations show similar variability, but patterns are not everywhere the same.C

O2 D

ata

Assim

ilati

on

at

EC

MW

F

Example Result

First analysis of stratospheric CO2 shows Brewer-Dobson type of circulation.

Pole-equator difference in the right ballpark

Variability is also much smaller than in troposphere.

CO2 assimilation – Stratosphere, May 2003

1. Demonstrate the feasibility of an integrated approach to estimate and monitor the net European carbon balance on monthly to decadal time scales, as a means to corroborate EU-wide controls of CO2 emissions, by:

a) unifying the existing CO2 networks in Europe

b) Extending the network with regular aircraft soundings

c) Using an innovative multiple tracers inverse atmospheric modelling approach, based on O2 and 13CO2 concentration (ocean-land interaction), 14CO2 (fossil fuel contribution ), and CO measurements (validation as a cost-effective alternative for 14CO2).

AEROCARB Objectives

Hourly mean

Atmospheric CO2 in Jul and Dec at different stationsComparison Obs (in red) / model

European biospheric monthly fluxes

DEHM LDMZ REMO TM3

-1.0

-0.5

0.5

1.0

WestEur

CentEur

EstEur

Ext EstEur

Annual optimized fluxes over Europe

GtC

-0.0

CAMELS

1. Best estimates and uncertainty bounds for the contemporary and historical land carbon sinks in Europe and elsewhere, isolating the effects of direct land-management.

2. A prototype carbon cycle data assimilation system (CCDAS) exploiting existing data sources (e.g. flux measurements, carbon inventory data, satellite products) and the latest terrestrial ecosystem models (TEMs), in order to produce operational estimates of “Kyoto sinks“.

CAMELS Objectives

OriginalTEM

OptimisedTEM for key

Sites

20th Century Simulation ofEuropean sink

Carbon CycleData AssimilationSystems

Fluxes of CO2 and H20,Inventory data

Weather data,Land management,

N deposition

Atmos CO2,Satellite data

LOCALCONSTRAINTS

HISTORICALCONSTRAINTS

SPATIALCONSTRAINTS

CAMELS

Use of Data Constraints in CAMELS

OptimisationAlgorithm

Sensitivity toTEM parameters,State variables

TEM parameters,State variables

SurfaceCO2 fluxesOffline

TEMAtm Transport Model (ATM)

Adjoint offline TEM and ATM

SimulatedfAPAR

SatellitefAPAR

Simulated CO2

Concentrations

Measured CO2

Concentrations

Climate, soils,Land-use drivers

CostFunction

CAMELSOffline Carbon Cycle Data Assimilation(after Wolfgang Knorr et al.)

Slide from Wolfgang Knorr

Slide from Wolfgang Knorr

Slide from Wolfgang Knorr

GEMS-GHG Deliverables

• 2006: Maps of column integral CO2,120km, 10-day mean

• 2007: 4d fields of CO2, quality controlled against in situ measurements (120km,10-day mean)

• 2008: CO2 sources and sinks and related process model parameters, (10-day mean, 120km) validated against bottom-up flux estimates (e.g. CARBOEUROPE)

• 2008: Maps of column integral CH4, N2O, CO, 120km, 10-day mean

GEMS-GHG Subproject Plan: 1. Retrieval of Concentration Fields

• Couple improved models of land carbon fluxes (developed in GEOLAND and CAMELS?) to IFS (Met Office, ECMWF)

• Retrievals of column integral GHGs (developed in COCO) from AIRS, SCHIAMACHY, IASI, MOPITT (LMD)

• 4d Var assimilation to retrieve GHG concentrations -> CO2(x,t) (12hr window), see paper by Chevalier and Engelen

(ECMWF)

GEMS-GHG Subproject Plan: 2. Estimation of Surface Fluxes

• Two approaches to estimate surface CO2 fluxes:

1. New inversion method (based on work within AEROCARB) to blend the satellite-based and ground based CO2 data to estimate surface fluxes, using IFS

transport (LCSE, ECMWF)2. Carbon Cycle Data Assimilation System (developed in CAMELS) to nudge

internal carbon model parameters based on atmospheric CO2 fields

(Met Office, MPI-BGC)

• Validation against aircraft and ground-based measurements (from CarboEurope) (MPI-BGC)

1. Are the subproject deliverables realistic given the time and resource constraints? (e.g. should we focus just on CO2?)

2. Does the consortium have sufficient expertise in all of the relevant areas? Do we need additional groups for the forward modelling of non-CO2 GHGs?

3. How will we deal with ocean-atmosphere fluxes? Links to MERSEA?

4. Can we find a way to unify the two approaches to modelling CO2

sources and sinks?

5. How should we subdivide GEMS GHG, and who will lead the writing of each part? (...keep your heads down!)

GEMS-GHG Outstanding Issues

Policy Motivation: Kyoto Sinks

Article 3.3 : “The net change in greenhouse gas emissions by sources

and removals by sinks resulting from direct human-induced land-use

change and forestry activities, …… measured as verifiable changes

… shall be used to meet the commitments.”

Article 3.4 : “……each Party …… shall provide …… data to

establish its level of carbon stocks in 1990 and to enable an estimate

to be made of its changes in carbon stocks in subsequent years……”

Interannual Variability in Atmospheric CO2

Annual CO2 increase fluctuates by up to 1 ppmv/yr even though emissions increase smoothly

IPCC TAR (2001)

Inverse Model estimates of the carbon sink still have significant uncertainties, and are not strongly constrained by

ecophysiological understanding

within-modeluncertainty

between-modeluncertainty

(Gurney et al., Nature 2002)

Inventory of Models/Modules

• Radiative transfer (forward/inverse) models: LMD, ECMWF, NWP-SAF, Met Office

• Data assimilation system: IFS ECMWF

• Forward carbon models (ocean and land): MPI-BGC, LSCE, Met Office, Meteo-France

Institutes and Functionality

• All of modelling groups

• Validation:– Land: CARBOEUROPE– Ocean: MARCASSA

• Emission mapping ?

Gaps

• Emission mapping

• Data gathering/management activity

• Ocean modeling/assimilation

Cross-cutting Issues

• Land state (e.g. soil moisture, fPAR)

• Land surface emissivities

• Biomass burning

• Aerosols

• Ocean carbon cycle

Management Issues

• Definition phase for subproject to define interdependencies, required input etc.

• Reassess integrated project structure as we proceed (e.g. task force establishment)

Reanalysis Requirements

• Reprocess TOVS with variable CO2, CH4, N2O to improve physical state by avoiding aliasing.

• Future reanalysis with optimized forward carbon models (land+ocean)

Observational Requirements

• Need high-frequency aircraft campaign to calibrate satellite CO2

• Network of upward looking passive instruments calibrated with aircraft measurements to quality control retrievals

• Satellite instrument to detect boundary layer CO2 variations?

How to assimilate continental sites ?

• Transport models are to be improved : - Higher resolution in time and space

- Parameterization of PBL

Mesoscale models : boundary problems !Nested models : computing time !Global with zoom : LMDz model

• Data selection in models :

• Prior land fluxes should be improved : Fossil fuel / Diurnal cycle of biosphere

• Inverse procedure need to be updated :- Spatial resolution of fluxes ?- Time resolution : identical for fluxes / obs ?

Spatial resolution of fluxes

Few large regions All pixels?

• Aggregation error

• Estimation error

Compromise needed OR all pixels + correlations

Time discretisation

How to use synoptic data (daily data) ?

Daily fluxes + correlations probably the best solution

Monthly fluxes Daily fluxes?

LMDZ transport model : ZOOM over EUROPE

• Nudged with ECMWF

• 192 x 146 and 19 vertical levels- 0.5 x 0.5 degree in zoom- 4 x 4 degree at the lowest

Mesure

Inverse Transport : “retro-plume” approach

Frederic Hourdin, J. P. Issartel

dtxdcJ

..

J: measure = mean CO2 per kg of air ; C: concentration of CO2 (kg / kg air): density of air; : distribution of the measure; : spatial and temporal domain

S

ti dtdydxcxdcc *0

* ...

Initial conditions contribution

Flux contribution

C* Retro-tracer : Solution of adjoint “transport” equation

Simply run LMDZ backward in time

Injection at each site, each time t

Sensitivity from all pixels at all time < t

Example of retro-plumes

Day 1 Day 2

Day 4 Day 8

Schauinsland station in November 1998

Methodological experiment

• Data : 5 European sites Daily average values

• Period : Campaign type experiment : November 1998

• Regions : - Pixels for Western Europe - large regions elsewhere

• Time resolution : - Daily for pixels - Monthly else

• Priors : Flux: Bousquet et al. Error: Large for pixels

+ correlations

• Initial conditions : Special treatment

solve for

~ 65 000 parameters

Daily Fit to the data :

November 98 November 98

Co

nce

ntr

atio

n (

ppm

)

Monte Cimone Hungaria

Mace-Head Plateau-Rosa

Schauinsland Westerland

PosteriorPrior

Obs

Contribution from all components :

Days (November 98)

Mace-Head

Pixels (Europe)

Big regions

Initial conditions

Schauinsland

Days (November 98)

PosteriorC

on

cen

tra

tion

(pp

m)

Prior

November flux over Europe

Total : + 0.18 GtC (non fossil)

Distribution controlled by Prior correlation structure

-500 100 500-200

gc/m2/month

Posterior – Prior Fluxes 2 sites only (MHD + SCH)

-2 3 50.8gc/m2/month

Corel 0.5 Corel 0.9

“Structure” of spatial Prior correlations ?

P according to

Inter-annual inversion (Bio + Fos)

PUniform spatially

Error reduction

2 sites only (MHD + SCH)

0 50 8025percentage

Corel 0.5 Corel 0.9

“Structure” of spatial Prior correlations ?

P according to

Inter-annual inversion (Bio + Fos)

PUniform spatially