Inverse emission estimates for Europe using tall tower observations and the COMET inverse model Alex Vermeulen 1, Gerben Pieterse 1,2 1: ECN2: IMAU

8 th Transcom meeting Purdue Univ.; April 24, ECN and Cabauw observations

8 th Transcom meeting Purdue Univ.; April 24, Cabauw – CBW – The Netherlands (ECN) GasMethodOperationalPrecision CO 2 LICOR 7000Nov ppm Flask samplerCIONov RnANSTONov-0550 mBq.m -3 CH 4 GC-FID Nov-04 2 ppb COGC-FID1 ppb N2ON2OGC-ECD0.4 ppb SF 6 GC-ECD0.2 ppt Height: 200m AGL Base: -2m ASL Lon:04°56’ Lat:51°58’ Levels:20m, 60m, 120m, 200m LU:Grassland, crops

8 th Transcom meeting Purdue Univ.; April 24, CBW CO2 trend

8 th Transcom meeting Purdue Univ.; April 24, Flux and concentration vertical gradients Cabauw Casso-Torralba et al, 2007 (in prep)

8 th Transcom meeting Purdue Univ.; April 24, Measurements, Modelling, SRM’s and Inversion Independent verification of bottom up estimates: UNFCCC, process models It seems so easy: Subtract the influence of meteorology on the concentration, what remains is the influence of emissions BUT: the atmosphere is a very efficient mixer, most of the signal is lost in 1-2 days of travel SO: measure close to the sources at high temporal resolution, extend in the mixed layer to reduce the very local influence Current global network not sufficient Not many stations, but high frequency measurements and lots of noise in atmosphere and models Mathematically: Ill posed problem, we need constraints

8 th Transcom meeting Purdue Univ.; April 24, The COMET model Trajectory model, offline meteorology COMET model, 0.5 o windfields, Flextra trajectories ECMWF meteorology, MLH Crit. Richardson scheme CH 4 fluxes from METDAT (Berdowski et al, 1998): 3-hourly time-res. at 10’ horiz. resolution Mixed layer bulk concentration Hourly 144-hr backward trajectories CH 4 meas vs. model:  R 2 =0.84 during summer, bias=0 ppb, RMSE=115 ppb (6%)  Full year: R 2 =0.72 Vermeulen et al, ACPD, 8727, 2006

8 th Transcom meeting Purdue Univ.; April 24, Modeling framework at ECN Forward Calculations Spatial Aggregation Inverse Calculations dX k =C kl e l Trajectory data LPDM data Inventory data Flux model Concentration data Flux data? Recursive Spatial Aggregation LPDM model (FLEXPART 1 ) COMET model (FLEXTRA 1 ) (Co) Variance Optimization Concentrations Fluxes Synthetic data 1 Stohl, A. (1998): Computation, accuracy and applications of trajectories - a review and bibliography, Atm. Env., 32, Deposition fluxes Concentration enhancements SRM Calculations

8 th Transcom meeting Purdue Univ.; April 24, Diurnal variation get lost after 24 hours… Model: COMET; Background CO 2 levels from TM5 (Krol, pers comm).

8 th Transcom meeting Purdue Univ.; April 24, CBW HUN COMET model forward results: Mixed layer concentrations CO 2 for 2002

8 th Transcom meeting Purdue Univ.; April 24,

8 th Transcom meeting Purdue Univ.; April 24, The source aggregation scheme for SVD inversion Calculate Source-receptor relationship (SRM) per hour and per observation point at high resolution of 10’ (~10 km) or multiple of this Run transport model to determine maximum annual average SRM value ppm/(kg/(m 2.s)) or potential contribution (SRM*E) in ppm Aggregate neighbouring areas by joining until sum of area >= maximum contrib: SRM shrinks from 200*400 to ~200 rows=regions Rerun transport model to build SRM for aggregated regions Iteratively perform SVD and aggregate adjacent areas with high covariance in emissions (dipole) against observations Iteratively remove areas with resulting emission of high variance (e.g. >30%) from SRM Until stable number of regions or no regions left… Procedure retrieves the maximal spatial resolution that can be resolved from the combination of model and measurements. Modification for (partial) resolving emissions of source categories, temporal patterns or any combination of these is relatively easy

8 th Transcom meeting Purdue Univ.; April 24, METDAT prior emissions for 1998

8 th Transcom meeting Purdue Univ.; April 24, Source area aggregation based on SRM + =>

8 th Transcom meeting Purdue Univ.; April 24, Results: CBW station only Synthetic inversion: Forward modelled Concentration is input for inversion 200 km Cabauw measurements areas can be resolved Fluxes in kg(CH 4 ).km -2.s -1 Areas 24 is United Kingdom, 25 N-Germany, 1-8 are Netherlands Prior Posterior+s.d.

8 th Transcom meeting Purdue Univ.; April 24, Results for CBW single years: 2002 Prior Post 2002

8 th Transcom meeting Purdue Univ.; April 24, Results for CBW single years: 2003

8 th Transcom meeting Purdue Univ.; April 24, Inverse determined annual emissions for The Netherlands YearEmission kTon CH 4 /yr Prior (METDAT, 1998) Uncertainty: 20-30%

8 th Transcom meeting Purdue Univ.; April 24, Improvement forward COMET after emission update

8 th Transcom meeting Purdue Univ.; April 24, Inverse calculations for methane at multiple sites for 2002 Emission [mg m -2 min -1 ] Region index

8 th Transcom meeting Purdue Univ.; April 24, Conclusions & Outlook Emissions (of methane) can be constrained from atmospheric signal without a priori information on their size Atmospheric inversion of area of big emissions needs high resolution in space and time, 10’ and hourly or better: otherwise degradation of signal Source Aggregation+SVD form robust combination Systematic model errors still problematic: need to get the models right, minimise bias: background concentration, MLH CBW CH 4 concentration data constrains emissions of an area 400x400 km 2 Multiple years extend the constrained area Multiple stations extend the area as well (of course) Tall tower data very valuable provided continuous vertical gradients are measured at high frequency By 2007 and further 6 tall towers will provide 2006 data for CH 4, N 2 O, SF 6 and CO 2

8 th Transcom meeting Purdue Univ.; April 24, Acknowledgements Climate Changes spatial planning/Klimaat voor ruimte Research Program EU FP5: CHIOTTO, contract EVK National funding agencies: VROM Senter/NOVEM The Transcom continuous experiment: Maarten Krol (IMAU,WuR) for background CO 2 data from TM5 Sander Houweling (IMAU) for background CH 4 data from TM3 Pim van den Bulk Piet en Mike Jongejan Han Mols