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Regional Inversion of continuous atmospheric CO 2 measurements A first attempt ! P. , P. , P. , P. , and P. Philippe Peylin , [ peylin@lsce.saclay.cea.fr] Peter Rayner , Philippe Bousquet , Philippe Ciais, Philippe Heinrich, F. hourdin. Outline. Measurements over Europes - PowerPoint PPT Presentation
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Regional Inversion
of continuous atmospheric CO2 measurements
A first attempt !
P. , P. , P. , P. , and P.
Philippe Peylin , [peylin@lsce.saclay.cea.fr]Peter Rayner ,Philippe Bousquet , Philippe Ciais,Philippe Heinrich,F. hourdin
Outline
• Measurements over Europes
• Requirements for regional inversions
• Time resolution in an inversion ?
• LMDz transport model :- direct approach- retro-plume approach
• European set up : primarily results
The European observing system :
AEROCARB database : http://www.aerocarb.cnrs-gif.fr/database.html
Flasks In-situAircraft Aircraft (project)
Aircraft measurements - Orleans, France
Free troposphere
CO2 concentrations for all flights
PBL
Continuous measurements At Mace Head
How to use such information ?
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 : - position in space and time properly represented
• Prior land fluxes should be improved : - Fossil fuel- Diurnal cycle of biospheric fluxes
• Inverse procedure need to be updated :- Spatial resolution of fluxes : pixel ?- Time resolution : identical for fluxes / obs ?
Monthly mean24 hr
Monthly meanFlask timing
Con
cent
ratio
ns (p
pm)
Diurnal rectification effect : Selection of model output according
to flask data timing (TM3)
[ ppm
][ p
pm ]
CO2 - fossils
Spatial position of Schauinsland station in mesoscale model REMO
REMO 30mREMO 130mObservations
DAYS(Chevillard, 2001)
Data selection
A 2GtC/yr Sink over Europe, consistent with all Observations (kaminsky et al.)
Spatial resolution of fluxes
Few large regions All pixels?
Compromise needed OR all pixel + correlations
• Aggregation error
• Estimation error
Time discretisation
• Estimation of flux X ={xi, i=1,n}with a temporal distribution xi
• Observations Y = {yi, i =1, m}with same errors R
• None Bayesien
Question : Should we average data at the time-resolution of the fluxes we solve for ?
- Annual flux : Monthly data ?- Monthly flux : Daily data ?- Satellite : assimilate individual shot ?
Little derivation :
mYYm
ii /
1
11
m
iix
with error mR /
m
i RYXH iiXJ
1
2
21
..)(
mRYXH
XJ/.
2
21 .)(
0)(
dXXdJ
HYX 1
~
mHdYXd
i .1~
1
i i
i ii
H
HYX
)().(~
22
i i
i
i HH
dYXd
)(
~2
2
Same weight for all data
Data weight proportional to Hi
Averaged dataY
All data{yi, i =1, m}
Hi = Transport o Flux-distribution {xi}
High values of Hi correspond to “ low mixing by transport ”
and / or“Peak of the flux time-distribution”
Error estimates
12
2 ~)( PXdXJd
mHR
mH
RPi i /.
~2
2
2
2
1
i iHRP 2
2
2 )(~
m
ii
m
ii HmH
1
22
1
)(/
We can show
21~~ PP
Uncertainties are always smaller with all individual data
month
Con
cent
ratio
n (p
pmv)
Temperate N. Amer. (3rd yr)
Annual response function sampled each monthTime pattern : total respiration (SiB2)
Pulse of 1GtC / year
5
10
15
20
25
Days
Con
cent
ratio
n (p
pmv)
Monthly response function sampled each dayTime pattern : flat
Pulse of 1GtC / month Western Europe (July pulse)
0
10
20
30
40
-10
Summary of time discretisation ?
• Need some caution when using data at higher time resolution than that of the fluxes
• Individual terms Hi need to be compared !
• Annual flux / Monthly data is not adapted
• Monthly flux / daily data ??
• Solution is probably : solve fluxes at the resolution of the data + time-correlation(equivalent to the “spatial aggregation problem”)
LMDz transport model• GCM from the LMD laboratory (Paris)
• Nudged with ECMWF
• Global with possible zoom- 0.5 x 0.5 degree in zoom- 4 x 4 degree at the lowest
• 19 vertical levels
• Backward mode possiblegrid zoomed over Europe
Inverse Transport : “retro-plume” approachFrederic Hourdin
Direct approach :
dtxdcJ
..
J: measure = mean CO2 per kg of airC: concentration of CO2 (kg / kg air): density of air : distribution of the measure : spatial and temporal domain
C is govern by :01.
zck
zcdragV
tc
With - surface flux : =
- C = Ci at t0
zck
Inverse approach : Rewrite measure using the advection/diffusion equation like in lagrange multipliers
dtxdzck
zcdragV
tcdtxdcJ 1.... *c
c*: distribution to be determined
• integration by parts
• c* that satisfy
• 0 =
zck
zcdragV
tc *
** 1.
zck
*
Using :
We obtain :
Sti dtdydxcxdccJ *
0* ...
Contribution fromInitial conditions
Flux contribution
Retro-plume approach
• Simply run transport backward in time (need to save all mass flux in a forward run)
• C* is the sensitivity to both surface fluxes / initial conditions in ppmv / kgC
Direct mode / Inverse mode
Source variable Sample c* according to distribution
Emission according To selected data
Selection of data J
Example of retro-plumes
Day 1 Day 2
Day 4 Day 8
Schauinsland station in November 1998
Mace Head retro-plume Day 4
longitude
Day 4
European Inversion,
using continuous data,
with high temporal/spatial
flux resolution,
for a short period (campaign)
Only a first attempt !
methodological experiment
• Data : 2 sites Mace Head / Schauinsland Daily average values• Period : Campaign type experiment
one month : November 1998• Regions :
- Pixels for Western Europe- Rest of the world with large regions (18)
• Time resolution of fluxes :- Daily for pixels- monthly for the other large regions
• Priors Pixels Large regions
Flux: Bousquet et al. Bousquet et al. Error: 100 % of mean 1GtC + correlations (0.8)
• Special treatment for initial conditions
Map of regions + 2 sites
Mace Head Schauinsland
November November
Treatment of initial conditions
• Add additional unknowns corresponding to initial conditions
• Reduce the size of initial condition problem by projecting on main directions in the data space using SVD.
Cinit = H x Pprior (60 x 50000) (50000)
(SVD decomposition)
U . W . VT x Pprior
H’ x P’ (60 x 60) (60)
Solve for P’ with :- prior value from global simulation (using bousquet et al. fluxes)- error corresponding to 3 ppm
Fit to the data
Model components : MHD
Europe Pixels
Other big regions
Initial conditions
days
posteriorprior
ppm
v
-4
8
-4
8
-4
8
Model components : SCH
Initial conditions
days
posteriorprior
ppm
v
-4
8
-4
8
-4
8
Europe Pixels
Other big regions
Summary
• Regional CO2 flux estimates require complete and permanent monitoring of CO2
• Transport models have to be improved over the continents !
• Selection of the data (time and space) is crucial
• Inverse scheme : - High spatial resolution with correlations - Temporal resolution of fluxes adapted to the resolution of the data
• Initial conditions seem to be important for campaign based inversions (10 day !)
• => Need for other constraints : O2/N2, C14, C13, O18, Radon, …
AEROCARB project: European Inversion
Model REMO-D DEHM HANK LMDZ TM3Horiz.Resolution
0.5 x 0.5° 150 x 150km(N. hemis)50 x 50 km(Europe)
270 x 270km90 x 90 km
3.75° x 2.5° 3.5° x 3.5°
Vert.Resolutionlowest(nb layers)
60 m(20)
80 m(24)
100m(24)
150 m(19)
150 m(19)
Domain Euro+sib N. Hemis N. Hemis Globe GlobeWind forcing 6h ECMWF at
B.C.30h restore alldomain
Full nudgingECMWF (12h)
Full nudgingU,V, T, P (6h)NCEP
Nudging U,V(6h)ECMWF
6h ECMWF
- 5 models- European domain for regional models- Boundary from global model (TM3)- Use continuous data over Euro-Siberia (12 sites)- Account for “diurnal rectifier” / selection (during aircraft profiles)• Monthly inversion first
• Daily inversion in a second step
Days
Con
cent
ratio
n (p
pmv)
Monthly response function sampled each dayTime pattern : flat
Pulse of 1GtC / month Western Europe (January pulse)
month
Con
cent
ratio
n (p
pmv)
Annual response function sampled each monthTime pattern : total respiration (SiB2)
Pulse of 1GtC / year Boreal N. Amer. (3rd yr)
Conclusions Regional CO2 flux estimates require a complete and permanent monitoring of “regional”
air using atmospheric data (flask & continuous sites, towers, airplane, …)---> Monitoring of air is essential to estimate European carbon budget.
Currently data limited inversions are becoming model limited inversion as we intend to assimilate continental measurements----> Transport models have to be improved over the continents.
There is only one carbon cycle. There is no reason to assimilate only atmospheric data.----> A global carbon assimilation system should be developed
Addressing these issues should allow to provide Kyoto relevant estimates of European carbon budget within the next decade.
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