Cheas 2006 Meeting Marek Uliasz: Estimation of regional fluxes of CO 2 …

Cheas 2006 MeetingMarek Uliasz: Estimation of regional fluxes of CO2 …Cheas 2006 MeetingMarek Uliasz: Estimation of regional fluxes of CO2 …

= LI-820 sampling from 75m above ground oncommunication towers.

= 40m Sylvania flux towerwith high-quality standardgases.

= 447m WLEF tower. LI-820, CMDLin situ and flaskmeasurements.

Problems of regional scale CO2 flux estimations by inversions:- limited domain - domain coverage by tower data

ASSUMPTION: SiB-RAMS is capable to realistically reproduce diurnal cycle and spatial distribution of CO2 (assimilation and respiration) fluxes. Therefore, observation data are used to correct those fluxes for errors in atmospheric transport.

ASSUMPTION: SiB-RAMS is capable to realistically reproduce diurnal cycle and spatial distribution of CO2 (assimilation and respiration) fluxes. Therefore, observation data are used to correct those fluxes for errors in atmospheric transport.

FCO2 (x, y, t) =βR(x,y)R(x,y,t) + βA(x,y)A(x,y,t))CO2 fluxCO2 flux

respiration & assimilation fluxes simulated by SiB-RAMS

respiration & assimilation fluxes simulated by SiB-RAMS

time independent corrections to beestimated from concentration data

for each inversion cycle

time independent corrections to beestimated from concentration data

for each inversion cycle

SiB-RAMSSiB-RAMS

LPDMLPDM

meteo fields CO2 fields and fluxes

influencefunctionsinfluencefunctions

inversiontechniquesinversiontechniques

BayesianBayesian MLEFMLEF

CO2 observations

corrected CO2 fluxes

typically run with several nested gridscovering a continental scale

run on any subdomain extracted from SiB-RAMS

corrected within each inversion cycle

MODELING FRAMEWORKMODELING FRAMEWORK

*

00 0 0

*00

0 0 0

* * * *

0 00 0 0 0 0 0

( )

ˆ ˆ( ) ( )

yx

yx

y x

x y

LLT

z

LL H

t

L LT H T H

W E S Nx x L y y L

C

C F dxdydt

C C dxdydz

uC C u C C dydzdt vC C vC C dxdzdt

=

=

= = = =

Φ =

+

+

+ + +

∫∫∫

∫∫∫

∫∫∫ ∫∫∫% %

surface fluxes

initial concentration

inflow fluxes

concentration sample

Representation of atmospheric concentration sample with the aid of influence function, C*, derived from a backward in time run of the LPDM.

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 0400

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 0500

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 0600

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 0700

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 0800

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 0900

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 1000

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 1100

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 1200

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

sunrisesunrise

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 1300

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 1500

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-500 -400 -300 -200 -100 0 100 200 300 400 500

X [km ]

-500

-400

-300

-200

-100

0

100

200

300

400

500

Y [km]

day 120 UTC 1600

1

5

10

50

100

500

1000

[10-10 x sm-3 ]

-300 -180 -60 60 180 300-300

-180

-60

60

180

300

-300 -180 -60 60 180 300-300

-180

-60

60

180

300

-300 -180 -60 60 180 300-300

-180

-60

60

180

300

-300 -180 -60 60 180 300-300

-180

-60

60

180

300

-300 -180 -60 60 180 300-300

-180

-60

60

180

300

0

0.001

0.005

0.01

0.05

0.1

0.2

0.5

-300 -180 -60 60 180 300-300

-180

-60

60

180

300

cycle 1 cycle 2 cycle 3

-300 -180 -60 60 180 300-300

-180

-60

60

180

300

cycle 4 cycle 5 cycle 6

cycle 7[ppm]

influence functions for 396mWLEF tower integrated over unit flux for7x10 day inversion cycles

C – observed concentrationk – index over observations (sampling times and towers)i – index over source grid cell (both respiration & assimilation fluxes)C*

R.A – influence function integrated with respiration & assimilation fluxesCIN – background concentration combining effect of the flow across lateral boundaries and initial concentration at the cycle start“beta’s” – corrections to be estimated

Implementation for a given inversion cycle Implementation for a given inversion cycle

Ck = βR,ii=1,n∑ CR,k,i

* + βA,ii=2n+1,2n∑ CA,k,i

* +CIN,k*

SiB-RAMS simulation: 75 days starting on April 25th, 2004 on two nested grids (10 km grid spacing on the finer grid)

INVERSION EXPERIMENTSINVERSION EXPERIMENTS



LPDM and influence function domain: 600x600km centered at WLEF tower




Concentration pseudo-data were generated for WLEF and the ring of towers from SiB-RAMS assimilation and respiration fluxes using correction values of 1





Model-data mismatch error was assumed to be higher for lower towers: 1 ppm for towers>100m, 1.5 ppm for towers > 50m, and 3 ppm for towers < 50m and very high values for short towers during nighttime





Model-data mismatch error was assumed to be higher for lower towers: 1 ppm for towers>100m, 1.5 ppm for towers > 50m, and 3 ppm for towers < 50m and very high values for short towers during nighttime

7 x 10 day inversion cycles were performed using Bayesian inversion technique with concentration pseudo data (initial corrections = 0.75 and their standard deviations = 0.1)

source area:20x20 kmNW of WLEF

10 day(cycle)average

24 houraverage


hourlyaverage


NEE uncertainty reduction [umol/m2/s]

μ mol m−2s−1μmol m−2s−1

cycle #1

NEE uncertainty reduction [umol/m2/s] cycle #2

NEE UNCERTAINTY: INITIAL, WLEF, RING

aggregation of source areas

C – observed concentrationk – index over observations (sampling times and towers)i – index over source grid cell (both respiration & assimilation fluxes)C*





* +CIN,k*

C – observed concentrationk – index over observations (sampling times and towers)i – index over source grid cell (both respiration & assimilation fluxes)l - index over time intervalsC*





* + β IN,ll=1,L∑ CIN,k,l

*

Pseudo-data experiments The ring of towers (Bayesian, MLEF) US continental scale (MLEF)

Real data experiments The ring of towers (Bayesian)

Pseudo-data experiments The ring of towers (Bayesian, MLEF) US continental scale (MLEF)

Real data experiments The ring of towers (Bayesian)

Inversion experiments:Inversion experiments:

new SiB-RAMS simulationsnew SiB-RAMS simulations

Influence functions to be integrated with user provided CO2 fluxes

Influence functions to be integrated with user provided CO2 fluxes

RUC-LPDM:RUC-LPDM:

Cheas 2006 Meeting Marek Uliasz: Estimation of regional fluxes of CO 2 …

Documents

Transcript of Cheas 2006 Meeting Marek Uliasz: Estimation of regional fluxes of CO 2 …