mesoscale inversions: from continental to local scales
DESCRIPTION
Mesoscale inversions: from continental to local scales. T. Lauvaux , C. Aulagnier, L. Rivier, P. Bousquet, P. Rayner, and others. Part 1 : Comparison of transport models from the global to the continental scales LMDz (3° by 2°) TM5 (1° by 1°) Chimère-MM5 (50km by 50km). - PowerPoint PPT PresentationTRANSCRIPT
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Mesoscale inversions: from continental to local scales
T. Lauvaux, C. Aulagnier, L. Rivier, P. Bousquet, P. Rayner, and others
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Part 1: Comparison of transport models from the global to the continental scales
LMDz (3° by 2°) TM5 (1° by 1°) Chimère-MM5 (50km by 50km)
Part 2: Potential of a high resolution inversion in the South West of France
Non-hydrostatic model Meso-NH (8km by 8km)
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CO2 Balance in Europe with a mesoscale model : CHIMERE
WHO:L. Rivier, C. Aulagnier, P. Rayner, M. Ramonet, P. Ciais, R. Vautard
WHAT for: What is the added value of increased resolution ? From 100km grids down to a few kms… Improved models for improved inversions at the regional scale ?
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Using of CHIMERE
MM5
CTM = CHIMERE
LMDZ
Biospheric Fluxes
+Inventaires (Oce-anic/Fossil Fluxes)
Surface Fluxes
TRANSCOM
/FOSEXP
Boundary Conditions
Meteo Forcing
// CO2 concentration
Fossil98
Taka02
SiB_hr
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Capacity of CHIMERE
• Model CHIMERE = French CTM developed by LMD/INERIS
Multi-species et multi-scale CTM (Horizontal Resolution from 100km to 1km)
Used for Ozone Daily Forecast in France (www.prevair.org)
• European Domain CONT3 used here =Resolution : 0.5 x 0.5 degrees (50Km)
20 vertical layers (1000 to 500hPa)
• Computation =10 minutes CPU for 5 days
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Sites of Comparison Mesure / Model
ORL
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Validation of BL Heightparameter
CHIMERE well capture of BL Height parameter, ORL
CHIMERE BL Height vs Mesures, for 68 points in Europe, night & day
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Schauinsland station
Bio signal
Fossil signal
CO2 signal
Well capture of seasonal cycle…
FOSEXP, hourly
… With CASA or ORCHIDEE, not with SIB which overestimate the summer 2003 (+) anomaly…
… While in the same time Fos98, which is a dynamic tracer, shows night overmixing, not EDGAR_hr
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Well capture of synoptic winter events…
... Driven by meteo
Cabauw200 station
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FOSEXP, hourly
Well capture of summer signal…
... With EDGAR/IER, not with Fos98, too highly variable
... Driven by vegetation…
Heidelberg station
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FOSEXP, hourly
Well capture of mean summer diurnal cycle for plain sites…
… With LMDZ-SIB-Fos98
… Or with CHIMERE-Orchidee-Edgar
Heidelberg station
Hungaria115 station
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Mars mean diurnal fluxes & CO2 cycle…
Orchidee begins to photo-synthetise too earlier, SIB & CASA OK.
Hungaria115 station
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Sept mean diurnal fluxes & CO2 cycle…
SiB & CASA stops to photo-synthetise too lastly, Orchidee OK.
Hungaria115 station
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Conclusions
• CHIMERE and TM5 forced with TRANSCOM tracers have a similar
behaviour and a better reactivity than LMDZ.
• CHIMERE well capture of BL Height « key » parameter
• CHIMERE forced with « highest spatio-temporal resolution tracers »
like EDGAR hourly /ORCHIDEE 0.35deg is able to capture satisfiyingly
CO2 seasonal cycle, synoptic signal, and mean diurnal cycle, in an improving way compared to global models (which seem to schow less difference between tracers, Cf. P. Peylin’s work …)
… So CHIMERE seems to be better adapted than global models for inversion at continentales scales.
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Toward a mesoscale flux inversion at high resolution in the South West of France
T.Lauvaux, C. Sarrat, F. Chevallier, P. Ciais, M. Uliasz, A. S. Denning, P. Rayner
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Observations+ errorsAircraftstowers
Sources and Sinksa priori+ errors
Forward Transport(meso-NH, Lafore et al., 98)
Retro transport(surface and boundaries)
Variationnal inversion(Chevallier et al., 2004)
Large scale [CO2]Boundary conditions (LMDZ)
Information on errorcoherence fromeddy-flux data
ParticleDispersionModel (LPDM, Uliasz, 94)
Inversion of sources and sinks of CO2
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CarboEurope Regional Experiment Network
Regional budget of CO2 in the South West of France from ground based observations and aircraft data
observation sites: Flux and CO2 concentration
Piper AztecFlux towerConcentration tower
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Mesoscale atmospheric modelling
Meso-NH coupled with ISBA-A-gs: dynamical fields corresponding to wind and turbulence
=> Prognostic parameters: u, v, w, Tp, TKE
=> Diagnostic parameters: u*, LMO, Boundary layer top, …
Resolution of 8km in a domain of about 700*700 km2 (South West of France)
=> Increased to 2km during the flight periods (two-way grid nesting)
Coupling with a vegetation scheme ISBA-Ag-s, parameterised with a 250m resolution vegetation cover map: Transport of atmospheric CO2 based on ISBA-A-gs fluxes (12 patches)
Transport and carbon fluxes from the 23rd to the 27th of May 2005
Surface scheme (Surfex) coupled on-line with hydrology and vegetation scheme
=> Momentum, heat, water, CO2
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Direct modelling: Aircraft data comparison
DimonaPiper Aztec
Sarrat et al., 2006, JGR
Good correlation ( < 3ppm ) 10ppm gradient between types Low decrease from West to East
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Lagrangian Particle Dispersion Model (Uliasz, 94)
Off-line coupling of mesoNH dynamical fields with LPDM: determination of diagnostic physical parameters
Particles backward in time from the receptors to the sources
Particle releasing frequency, number, particle lost (sedimentation,...), time dependant dynamics
Integration of instrumented tower data and aircraft data
4 vertical boundaries (N, S, E, W) with 2 vertical layers (BL, FT)
Surface grid (8km resolution)
Particle distribution from the 2 towers (Biscarosse and Marmande) released between 6:30am and 7:30 am the 27th of May 2005
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Influence function: surface and boundaries
Surface grid: 90*90 grid cells (8km resolution)
4 lateral boundaries: 2 vertical levels with 5 horizontal grid cells (LMDz resolution)
Low level = boundary layerHigh level = free troposphere
Free troposphere
Boundary layer
Particles backward = multiple surface contacts
=> One single boundary contribution per particle
State vector dimension at each hour = 90*90 + 4*5*2 (Surface) (boundaries)
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Meteorological context during the 27th of may
27th may - 6pm
27th may – 2am
27th may - 2pm
Early growth season for summer crops
Mainly influenced by the distant fluxes?
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Tower vs aircraft for surface flux influence
Flight 1 Flight 2
Marmande tower Marmande tower (normalised)
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Vertical profiles of aircraft particle clouds
Particles sheared by a main South Eastern wind closed to the ground and a western wind at higher altitudes (called Autan wind)
10 hours
20 hours
25 hours35 hours
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Error reduction on the 4-day inversion
2 towersBiscarosse (20m)Marmande (70m)
1 aircraft flight (transect Brodeaux-Toulouse)
2 towers and 3 flights
CERES domain
Error reduction > 30% for half of the domain
No spatial correlation on the prior flux error covariance
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Boundary contribution
Error reduction at the boundaries for the tower-only inversion around 5%=> Initial offset concentration or extra flux unknowns
Error reduction >90% on one or two grid cells at the boundaries with the flights
Uncertainty in the prior error covariance for the boundaries has no impact on the error reduction at the surface
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Footprint of a fictive tall tower
Biscarosse tower(20m high)
Fictive Biscarosse tower(300m high)
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Optimizing flight trajectory ?
12 virtual flights based on a long transect over the domain, with constant altitudes from 100m to 2500m high
=> Can we optimize future campaigns to get maximum of informations from the aircraft data?
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Time integration and space correlation
0
5
10
15
20
25
30
35
40
45
50
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
Hourly distribution of the particlesHourly distribution of the particles originating from the lateral boundaries
Limited time window for the inversion
1 12 24 36 hours
Ecosystems distance<50km distance>50km
Homogeneous 0.9 0.1 to 0.3
Heterogeneous 0.3 to 0.5 0.1
iobsel
jobsel
COCO
COCO
)(
)(
2mod2
2mod2
Correlation coefficient from the linear regression
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Conclusions and perspectives
Significant error reduction on the domain to start the real-data inversion
Uncertainties:
Transport error by using the variability from an ensemble of simulations (coupling files from a global model run with perturbed initial files)
Spatial correlation estimated from a long-term simulation of ISBA (5 weeks…) and the 11 flux towers of the campaign
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Thanks for your attention
Etna volcano’s eruption, Sicily