GEMS – GREENHOUSE GASES

1. Subproject objectives Parent Projects Subproject plan Outstanding issues GEMS – GREENHOUSE GASES Peter Cox (Met Office)Martin Heimann, Wolfgang Knorr (MPI-BGC)Tony Hollingsworth, Richard Engelen (ECMWF)Philippe Ciais, Philippe Peylin (LSCE)Alain Chedin (LMD)

2. GEMS-GHG Objectives 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. Validation of concentration fields using existing observational data. Inversion systems to infer carbon sources and sinks.

3. 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) GEMS-GHG Parent Projects

4. COCO Objectives • Develop algorithms for retrieving CO2 concentrations from satellite measurements with the instruments IASI, AIRS, AMSU and SCIAMACHY, comparing 3 possible methods: • Standalone approach: exploiting the synergy of passive thermal IR and microwave measurements of AIRS and AMSU satellite obs • 4D Var: using ECMWF model’s wind fields consistent with the temperature and CO2 fields. • Differential absorption: of solar radiation in the near-infrared region of the spectrum, using SCIAMACHY observations. • Assess the utility of this new data in estimating surface fluxes.

5. Current status of CO2 analysis at ECMWF • 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. CO2 Data Assimilation at ECMWF

6. Example Result CO2 Data Assimilation 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.

7. CO2 assimilation – Stratosphere, May 2003 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.

8. AEROCARB Objectives 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: unifying the existing CO2 networks in Europe Extending the network with regular aircraft soundings 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).

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

10. European biospheric monthly fluxes DEHM LDMZREMOTM3

11. Annual optimized fluxes over Europe 1.0 0.5 Cent Eur West Eur -0.0 Est Eur Ext Est Eur -0.5 -1.0 GtC

12. CAMELS CAMELS Objectives • Best estimates and uncertainty bounds for the contemporary and historical land carbon sinks in Europe and elsewhere, isolating the effects of direct land-management. • A prototype carbon cycle data assimilation system (CCDAS)exploiting existing data sources (e.g. flux measurements, carbon inventory data, satellite products) andthe latest terrestrial ecosystem models (TEMs), in order to produce operational estimates of “Kyoto sinks“.

13. CAMELS Use of Data Constraints in CAMELS LOCAL CONSTRAINTS HISTORICAL CONSTRAINTS SPATIAL CONSTRAINTS Weather data, Land management, N deposition Fluxes of CO2 and H20, Inventory data Atmos CO2, Satellite data Carbon Cycle Data Assimilation Systems 20th Century Simulation of European sink Optimised TEM for key Sites Original TEM

14. CAMELS Offline Carbon Cycle Data Assimilation(after Wolfgang Knorr et al.) Surface CO2 fluxes TEM parameters, State variables Offline TEM Atm Transport Model (ATM) Simulated fAPAR Simulated CO2 Concentrations Climate, soils, Land-use drivers Cost Function Optimisation Algorithm Satellite fAPAR Measured CO2 Concentrations Sensitivity to TEM parameters, State variables Adjoint offline TEM and ATM

15. Slide from Wolfgang Knorr

16. Slide from Wolfgang Knorr Slide from Wolfgang Knorr

17. 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 Deliverables

18. 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: 1. Retrieval of Concentration Fields

19. Two approaches to estimate surface CO2 fluxes: 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) 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) GEMS-GHG Subproject Plan: 2. Estimation of Surface Fluxes

20. GEMS-GHG Outstanding Issues Are the subproject deliverables realistic given the time and resource constraints? (e.g. should we focus just on CO2?) 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? How will we deal with ocean-atmosphere fluxes? Links to MERSEA? Can we find a way to unify the two approaches to modelling CO2 sources and sinks? How should we subdivide GEMS GHG, and who will lead the writing of each part? (...keep your heads down!)

21. 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 … shallbe 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……” Policy Motivation: Kyoto Sinks

22. Interannual Variability in Atmospheric CO2 IPCC TAR (2001) Annual CO2 increase fluctuates by up to 1 ppmv/yr even though emissions increase smoothly

23. 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)

24. 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 Inventory of Models/Modules

25. All of modelling groups Validation: Land: CARBOEUROPE Ocean: MARCASSA Emission mapping ? Institutes and Functionality

26. Emission mapping Data gathering/management activity Ocean modeling/assimilation Gaps

27. Land state (e.g. soil moisture, fPAR) Land surface emissivities Biomass burning Aerosols Ocean carbon cycle Cross-cutting Issues

28. Definition phase for subproject to define interdependencies, required input etc. Reassess integrated project structure as we proceed (e.g. task force establishment) Management Issues

29. Reprocess TOVS with variable CO2, CH4, N2O to improve physical state by avoiding aliasing. Future reanalysis with optimized forward carbon models (land+ocean) Reanalysis Requirements

30. 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? Observational Requirements

31. Data selection in models : • Prior land fluxes should be improved : Fossil fuel / Diurnal cycle of biosphere 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 • Inverse procedure need to be updated :- Spatial resolution of fluxes ? - Time resolution : identical for fluxes / obs ?

32. Time discretisation How to use synoptic data (daily data) ? ? Monthly fluxes Daily fluxes Daily fluxes + correlations probably the best solution Spatial resolution of fluxes ? Few large regions All pixels • Aggregation error • Estimation error Compromise needed OR all pixels + correlations

33. 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

34. 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 Inverse Transport : “retro-plume” approach Frederic Hourdin, J. P. Issartel Mesure J: measure = mean CO2 per kg of air ; C: concentration of CO2 (kg / kg air) r: density of air; m : distribution of the measure; W, t : spatial and temporal domain

35. Example of retro-plumes Schauinsland station in November 1998 Day 1 Day 2 Day 4 Day 8

36. 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

37. Obs Daily Fit to the data : Posterior Prior Hungaria Monte Cimone Mace-Head Plateau-Rosa Concentration (ppm) Schauinsland Westerland November 98 November 98

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

39. November flux over Europe Total : + 0.18 GtC (non fossil) Distribution controlled by Prior correlation structure gc/m2/month -200 100 -500 500

40. “Structure” of spatial Prior correlations ? Corel 0.5 Corel 0.9 P Uniform spatially P according to Inter-annual inversion (Bio + Fos) Posterior – Prior Fluxes 2 sites only (MHD + SCH) -2 0.8 3 5 gc/m2/month

41. “Structure” of spatial Prior correlations ? Corel 0.5 Corel 0.9 P Uniform spatially P according to Inter-annual inversion (Bio + Fos) Error reduction 2 sites only (MHD + SCH) 0 25 50 80 percentage