cheas 2006 meeting marek uliasz: estimation of regional fluxes of co 2 …
DESCRIPTION
Cheas 2006 Meeting Marek Uliasz: Estimation of regional fluxes of CO 2 …. = 40m Sylvania flux tower with high-quality standard gases. = LI-820 sampling from 75m above ground on communication towers. = 447m WLEF tower. LI-820, CMDL in situ and flask measurements. - PowerPoint PPT PresentationTRANSCRIPT
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Cheas 2006 MeetingMarek Uliasz: Estimation of regional fluxes of CO2 …Cheas 2006 MeetingMarek Uliasz: Estimation of regional fluxes of CO2 …
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= 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
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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.
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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
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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
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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
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*
00 0 0
*00
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* * * *
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( )
ˆ ˆ( ) ( )
yx
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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.
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cycle 7[ppm]
influence functions for 396mWLEF tower integrated over unit flux for7x10 day inversion cycles
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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*
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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
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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
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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
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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
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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
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)
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source area:20x20 kmNW of WLEF
10 day(cycle)average
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24 houraverage
source area:20x20 kmNW of WLEF
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hourlyaverage
source area:20x20 kmNW of WLEF
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NEE uncertainty reduction [umol/m2/s]
μ mol m−2s−1μmol m−2s−1
cycle #1
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NEE uncertainty reduction [umol/m2/s] cycle #2
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NEE uncertainty reduction [umol/m2/s] cycle #3
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NEE uncertainty reduction [umol/m2/s] cycle #4
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NEE uncertainty reduction [umol/m2/s] cycle #5
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NEE uncertainty reduction [umol/m2/s] cycle #6
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NEE uncertainty reduction [umol/m2/s] cycle #7
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NEE UNCERTAINTY: INITIAL, WLEF, RING
aggregation of source areas
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NEE UNCERTAINTY: INITIAL, WLEF, RING
aggregation of source areas
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NEE UNCERTAINTY: INITIAL, WLEF, RING
aggregation of source areas
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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*
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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*
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
* + β IN,ll=1,L∑ CIN,k,l
*
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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
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Influence functions to be integrated with user provided CO2 fluxes
Influence functions to be integrated with user provided CO2 fluxes
RUC-LPDM:RUC-LPDM: