ICOS-D inverse modelling using the CarboScope regional inversion system
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1 ICOS-D inverse modelling using the CarboScope regional inversion system Christoph Gerbig, Panagiotis Kountouris, Christian Rödenbeck (MPI-BGC, Jena), Thomas Koch (DWD), Ute Karstens (ICOS-CP, Lund) ICOS-D Meeting, Offenbach, March
2 Overview The inversion system Synthe=c data inversion Real data inversion Uncertainty in atmospheric transport Summary
3 The Inversion system y = Kx +ε y K: transport operator y : observa=ons x : state vector to be op=mized (biosphereatmosphere exchange fluxes) Cost func=on: J = ( Kx y) T C 1 y ( Kx y) + ( x x prior ) T C 1 prior ( x x prior ) Observa=onal constraint Prior constraint
4 How does an atmospheric network see fluxes? Stochas(c Time Inverted Lagrangian Transport (STILT) Ensemble of par=cles released at measurement loca=ons Time reversed Par=cles driven by wind + turbulent process Footprint calcula=on NRT possible (ECMWF forecasts) => Footprints available through ICOS CP
5 VPRM prior - op=miza=on Vegeta=on Photosynthesis Respira=on Model (VPRM) [Pathmathevan et al., 2008] Ini=al op=miza=on of parameters against Eddy Cov. α, β, λ, and PAR0 SYNMAP land cover [Jung et al., 2006] NEE = GEE + R = α T + β ECMWF, NCEP, WRF or site measurements = λ Eddy Cov. data PAR Tscale (T ) Pscale ( LSWI, EVI ) Wscale ( LSWI ) EVI (1 + PAR PAR0 ) MODIS surface reflectance 8 day, 500 m
6 VPRM prior - op=miza=on Op=mizing 4 parameters for each of 6 vegeta=on types (30 parameters) Temporal data coverage ma\ers => data density weigh=ng Implementation of MODIS V06 NRT data for VPRM up to current week
7 Fossil fuel prior fluxes EDGAR v4.3 at 0.1º CO 2 and CH 4 (and CO) IPCC category and fuel type differen=a=on Time factors applied to create hourly temporal resolu=on Interannual varia=ons scaled according to BP energy sta=s=cs at na=onal level Extrapola=on to 1-2 years ader BP sta=s=cs => current year available
8 Jena regional inversion system TM3-STILT two step inversion Input : Atmospheric observa=ons, prior fluxes (biospheric, ocean, fossil fuel) TM3 global inversion 5 x 4 STILT regional inversion 0.25 x 0.25 State space: 0.5 resolu=on, 3hourly flux op=miza=on NRT not possible (global data for previous year avail. June) NRT possible (if ICOS data available) Rödenbeck et al., 2009
9 Atmospheric observa=ons: Inversion setup 16 atmospheric sta=ons (2007) (Con=nuous measurements and flask sample analysis) Day=me local (mountain: 23-04) CO 2 Model-data mismatch error in ppm (for weekly =me scales) S C M T UP S: Near shore C: Con=nental (surface) M: Mountain T: Tall tower UP: Urban polluted Prior error structure (derived from differences prior fluxes flux observa=ons): Diagonal: 2.3 μmoles/m 2 /s (daily fluxes, 0.5x0.5 lat-lon) error correla=ons: 30 days, 100 km => error infla=on needed to obtain consistency with global inversions 0.3 GtC/yr for annual and domain wide aggregated prior error B1 case: Error infla=on (scaling of covariance matrix) S1 case: Error infla=on by adding a bias term (constant in =me, respira=on shape)
10 Synthe=c data inversion Pseudo observa=ons generated using BIOME-BGC as truth
11 Pseudo data inversion EU-scale C budget Kountouris et al., 2016a ACPD Successfully retrieved fluxes at monthly and annual scales Case S1 (with bias component) results in lower posterior uncertain=es
12 Pseudo data inversion Country-scale C budget Successfully retrieved fluxes at monthly and na=onal scales Reduc=on in Uncertain=es (prior -> posterior) larger for countries with more observa=ons Kountouris et al., 2016a ACPD
13 Overview The inversion system Synthe=c data inversion Real data inversion Uncertainty in atmospheric transport Summary
14 Real data inversion 2007 Daily averaged flux es=mates in gc d-1 m-2 Small scale structure: vegeta=on coverage, radia=on, temperature (a priori) Larger scale correc=ons from atmospheric constrain Innova=on: posterior prior Kountouris et al., 2016b ACPD
15 Real data inversion 2007: Domain-wide C-budget Prior VPRM Prior BIOME-BGC VPRM, infla=on BIOME-BGC, infla=on B1, no mountain sta=ons B1, hyperbolic correla=on VPRM, annual bias R VPRM, annual bias NEE VPRM, monthly bias R Domain-wide sink ranges between GtC y -1 Kountouris et al., 2016b ACPD
16 Real data inversion 2007: Valida=on Extrac=ng posterior fluxes at Eddy Covariance Flux sites comparison to independent flux observa=ons (case B2, BIOME-BGC prior fluxes not dependent on flux observa=ons) Kountouris et al., 2016b ACPD
17 Overview The inversion system Synthe=c data inversion Real data inversion Uncertainty in atmospheric transport Summary
18 Impact of Uncertainty in Ver=cal Mixing Conceptual model Diurnal cycle CO 2 fluxes Diurnal cycle mixing height Subsidence Correct (true) MH Biased (model) MH
19 Time of the day [Kretschmer et al., 2013 & 2014]! height (km) height (km) <=&> height (km) CO2 [ppm] Average diurnal cycle CBW Sep. <JCK!I9(:978#> <:775I9%:57> <';!JCK> assimila=ng mixing heights (example: 9 Sep. 2009, 12:00) derived from IGRA data (radiosonde profiles) using KED (Kriging with external drid, Drid term WRF-MH) FGH!@A <'98=L(567?> Impact of Uncertainty in Ver=cal Mixing 9>
20 Using addi=onal data streams: PBLH DWD Ceilometer network 80 + ceilometers Nearby ICOS atmosphere sta=ons Co-located surface sta=ons Valida=on of ceilometer PBLH against radiosonde PBLH ECMWF PBLH (7 km res.) as transfer standard
21 Using addi=onal data streams: PBLH DWD Ceilometer network 80 + ceilometers Nearby ICOS atmosphere sta=ons Co-located surface sta=ons Valida=on of ceilometer PBLH against radiosonde PBLH ECMWF PBLH (7 km res.) as transfer standard
22 Using addi=onal data streams: PBLH Ceilometer vs. ECMWF First of 3 candidate PBLH chosen Only QC flag >1 15 s data -> hourly Rain excluded (weather data from co-located surface sta=on) Only 10:00-14:00 Up to 50% explained variance
23 Radiosonde vs. ECMWF Valida=on of transfer standard Using addi=onal data streams: PBLH around 50% explained variance
24 ECMWF vs. ECMWF Radiosonde and ceilometer loca=ons km apart Using addi=onal data streams: PBLH around 70% explained variance
25 Ceilometer vs. Radiosonde Using addi=onal data streams: PBLH 20-40% explained variance
26 Using addi=onal data streams: regular profiles Regular ver=cal profiles of GHGs from airliners
27 Summary Data driven approach from local to con=nental-scale to infer fluxes from the land biosphere Flux es=mates can be successfully retrieved down to country and monthly scales Spa=ally resolved flux es=mates poten=ally affected by assumed emissions => fossil CO2 prior at higher resolu=on for filtering up-to-date inversion products possible with near-real =me data U=lizing mixing heights from ceilometers seems possible Outlook: H2020 proposal VERIFY (Observa=on-based system for monitoring and verifica=on of greenhouse gases) Mul=ple emission inventories, Mul=ple NEE flux priors Valida=on against IAGOS profiles High-res. ffco 2 simula=ons for dense observa=ons within hotspot region
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