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 (CO 2, CH 4, N 2 O, 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-CO 2 retrievals) ( )

COCO Objectives 1.Develop algorithms for retrieving CO 2 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 models wind fields consistent with the temperature and CO 2 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.

CO 2 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 CO 2 as a tracer in the forecast model, enabling a full 4D-Var CO 2 analysis. This will allow a transport model constraint on the CO 2 analysis that will probably reduce the horizontal scatter. CO 2 Data Assimilation at ECMWF Current status of CO 2 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. CO 2 Data Assimilation at ECMWF Example Result

First analysis of stratospheric CO 2 shows Brewer- Dobson type of circulation. Pole-equator difference in the right ballpark Variability is also much smaller than in troposphere. CO 2 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 CO 2 emissions, by: a)unifying the existing CO 2 networks in Europe b)Extending the network with regular aircraft soundings c)Using an innovative multiple tracers inverse atmospheric modelling approach, based on O 2 and 13CO 2 concentration (ocean-land interaction), 14CO 2 (fossil fuel contribution ), and CO measurements (validation as a cost-effective alternative for 14CO 2 ). AEROCARB Objectives

Hourly mean Atmospheric CO2 in Jul and Dec at different stations Comparison Obs (in red) / model

European biospheric monthly fluxes DEHM LDMZ REMO TM3

West Eur Cent Eur Est Eur Ext Est Eur 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

Original TEM Optimised TEM for key Sites 20 th Century Simulation of European sink Carbon Cycle Data Assimilation Systems Fluxes of CO 2 and H 2 0, Inventory data Weather data, Land management, N deposition Atmos CO 2, Satellite data LOCAL CONSTRAINTS HISTORICAL CONSTRAINTS SPATIAL CONSTRAINTS CAMELS Use of Data Constraints in CAMELS

Optimisation Algorithm Sensitivity to TEM parameters, State variables TEM parameters, State variables Surface CO 2 fluxes Offline TEM Atm Transport Model (ATM) Adjoint offline TEM and ATM Simulated fAPAR Satellite fAPAR Simulated CO 2 Concentrations Measured CO 2 Concentrations Climate, soils, Land-use drivers Cost Function CAMELS Offline Carbon Cycle Data Assimilation (after Wolfgang Knorr et al.)

Slide from Wolfgang Knorr

GEMS-GHG Deliverables 2006: Maps of column integral CO 2, 120km, 10-day mean 2007: 4d fields of CO 2, quality controlled against in situ measurements (120km,10-day mean) 2008: CO 2 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 CH 4, N 2 O, 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 -> CO 2 (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 CO 2 fluxes: 1.New inversion method (based on work within AEROCARB) to blend the satellite-based and ground based CO 2 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 CO 2 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 CO 2 ?) 2.Does the consortium have sufficient expertise in all of the relevant areas? Do we need additional groups for the forward modelling of non-CO 2 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 CO 2 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 CO 2 Annual CO 2 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-model uncertainty between-model uncertainty (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 CO 2, CH 4, N 2 O 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 CO 2 Network of upward looking passive instruments calibrated with aircraft measurements to quality control retrievals Satellite instrument to detect boundary layer CO 2 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 regionsAll 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 fluxesDaily fluxes ?

LMDZ transport model : ZOOM over EUROPE Nudged with ECMWF 192 x 146 and 19 vertical levels x 0.5 degree in zoom - 4 x 4 degree at the lowest

Mesure Inverse Transport : retro-plume approach Frederic Hourdin, J. P. Issartel 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 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 ~ parameters

Daily Fit to the data : November 98 Concentration (ppm) Monte Cimone Hungaria Mace-HeadPlateau-Rosa SchauinslandWesterland Posterior Prior Obs

Contribution from all components : Days (November 98) Mace-Head Pixels (Europe) Big regions Initial conditions Schauinsland Days (November 98) Posterior Concentration (ppm) Prior

November flux over Europe Total : GtC (non fossil) Distribution controlled by Prior correlation structure gc/m2/month

Posterior – Prior Fluxes 2 sites only (MHD + SCH) gc/m2/month Corel 0.5 Corel 0.9 Structure of spatial Prior correlations ? P according to Inter-annual inversion (Bio + Fos) P Uniform spatially

Error reduction 2 sites only (MHD + SCH) percentage Corel 0.5 Corel 0.9 Structure of spatial Prior correlations ? P according to Inter-annual inversion (Bio + Fos) P Uniform spatially