SENSITIVITY OF OBSERVATIONAL DATA TO TIME DEPENDANT INVERSION
Takashi Maki
Senior Coordinator for Chemical Transport ModelingAtmospheric Environment Division
Japan Meteorological Agency
CONTENTS1. Purposes2. Experimental Methods3. Results4. Summary and Future Plans
1-1. Background
We usually adopt CO2 observation dataset provided by CMDL (GLOBALVIEW) in
inversion
In GLOBALVIEW, CO2 data are smoothed, interpolated and extrapolated
We tried to use CO2 raw data to estimate CO2 flux more realistically in inversion
There is a possibility that we tend to underestimate the CO2 flux variability
1-2. Outline of our experiments
TransCom 3 Level 3 control run
Inversion 1
Inversion 2
Inversion 3
Inversion 2.5
As similar as possible
Preparation for raw data
Use raw data (WDCGG)
Use as many raw data as possible
2-1. Outline of Inversion 1
Model outline JMACDTM (2.5deg. 32levels) Offline, 6 hourly JMA-winds (1997) Diffusion (Cumulus, Turbulent and shallow convection)Inversion method Bayesian (Greens functions) approach Internal shape, land CASA Internal shape, ocean Flat Prior flux and error small error Prior data and error GLOBALVIEW Offset of CO2 345.99ppm
Difference between Inversion 1 and 2Aim: Reduce Constraints when there are no available observational data
Inversion 2 provide a base data in estimating inversion 2.5.
2-2. Outline of Inversion 2
Inversion 1 Inversion 2
Data Uncertainty
As in T3L2 Multiple by 10.0 when there are
no raw data
Difference between Inversion 2 and 2.5Aim: Replace GLOBALVIEW data with WDCGG raw data if available.
2-3. Outline of Inversion 2.5
Inversion 2 Inversion 2.5
Prior data As in T3L2 Use WDCGG monthly data if available
Data error As in Inversion 2
Raw data GV x 1Others GV x 10
Difference between Inversion 2.5 and 3Aim: Use as many sites as possible
We did not use site where the annual mean concentration is too high as zonal mean concentration (as in WDCGG No.27)
We select 106 sites
2-4. Outline of Inversion 3
Inversion 2.5 Inversion 3
Sites selection
As in T3L2 Select site where there are enough raw data (60%)
2-5. GLOBALVIEW and WDCGG
GLOBALVIEW WDCGG
Organization CMDL/NOAA JMA(WMO/GAW)
Data interval Weekly etc. Monthly etc.
Data management
Smoothed, interpolated and extrapolated
Reported data (if not available, calculated by WDCGG)
Media CD-ROM, Internet
CD-ROM, Internet
2-6. Data management by WDCGG
•Format check•Threshold value check (300ppm-500ppm)•Unnatural value check (same value etc.)
WDCGG contact each laboratory and correct data if possible. The data are updated every month (http://gaw.kishou.go.jp).
Suspicious data
In principle, data are edited and selected by the data submitter.
2-7. Outline of 106 Sites (Inv. 3)
Data from WDCGG 98 Sites@when there is no raw data, use data from GLOBALVIEW
Data from GLOBALVIEW 8 Sites(car030, car040, car050, car060, cri02, lef030, ljo04, opw00)
Rejected sites in WDCGGLack of data amount 9 sitesScale is unknown 4 sitesConc. are too high (low) 15 sites
0
100
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700
1988
1989
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Inv1Inv2Inv25Inv3
0
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1988
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Inv1Inv2Inv25Inv3
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1988
1989
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Inv1Inv2Inv25Inv3
3-1. Data amount and estimated error
Southern Hemisphere
Northern Hemisphere Tropical Region
25
26
27
28
29
30
31
1988 1990 1992 1994 1996 1998 2000
Year
Tota
l Unc
. (G
tC/y
)
Inv1Inv2Inv2.5Inv3
Total estimated error
3-2. Annual estimated fluxEstimated flux in global scale
- 3
- 2
- 1
0
1
2
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Year
Flux
(GfC
/y)
Cntlinv1Inv2Inv25Inv3
Inversion 3 shows larger fluctuation
3-3. Annual estimated flux Northern Hemisphere Tropical Region
Southern Hemisphere
N.H. Control and Inv.3 is similar
Inv.2 and Inv. 2.5 is same
T.R. Control and Inv.1 is same
Inv.2 and Inv. 2.5 is same
S.H. Inv.1 – Inv.3 are similar
-3.0
-2.5 -2.0
-1.5 -1.0
-0.5 0.0
0.5
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
GtC
/y
CntlInv1Inv2Inv25Inv3
-3.0
-2.0
-1.0
0.0
1.0
2.0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
GtC
/y
CntlInv1Inv2Inv25Inv3
-2.0
-1.0
0.0
1.0
2.0
3.0
4.0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
GtC
/y
CntlInv1Inv2Inv25Inv3
3-4. Inversion 1 and control run
Correlation coefficient for every month
In Tropical region, the coefficients tend to small.
Land01 0.953 Land07 0.993 Ocean01 0.886 Ocean07 0.914
Land02 0.905 Land08 0.971 Ocean02 0.892 Ocean08 0.925
Land03 0.783 Land09 0.917 Ocean03 0.926 Ocean09 0.879
Land04 0.724 Land10 0.990 Ocean04 0.949 Ocean10 0.948
Land05 0.778 Land11 0.979 Ocean05 0.993 Ocean11 0.895
Land06 0.788 Ocean06 0.728
3-5. Inversion 1 and Inversion 2
Standard deviation of monthly flux gap
Region Flux gap
Temp. Asia 0.080
Europe 0.071
Temp. N. America 0.061
Bor. Eurasia 0.058
N. Africa 0.057
Trop. Asia 0.047
Data amount reduced regions
Region Rate
Australia 31.3%
Temp. N. America 26.2%
Europe 24.3%
W. Trop. Pacific 23.4%
Temp. Asia 22.5%
N. Ocean 22.4%
Flux gap appears in the region where there are less raw data
@ There are no data in B. Eurasia, N. Africa and Tr. Asia
3-6. Inversion 1 and Inversion 2
Increase in mean monthly error gap
Increase in Error is small (in land region)
0.00320E. Trop. Pacific
0.00351North Pacific
0.00502Southern Ocean
0.00526South Pacific
0.00649Bor. Eurasia
0.00748Europe
0.00776Temp. N. America
0.00976Trop. Asia
0.01413Temp. Asia
Err gapRegion
3-7. Inversion 2 and Inversion 2.5
Standard deviation of monthly error gap
Australia 0.0051
Europe 0.0030
B. N. America 0.0012
B. Eurasia 0.0010
S. Indian Ocean 0.0010
Temp. N. America 0.0008
Difference appeared (small) in data amount reduced region!
Estimated errors tend to increase (small)
Data amount reduced regions
S. Indian Ocean 15.5%
B. N. America 8.0%
Europe 5.2%
Australia 3.1%
E. Trop. Pacific 1.2%
S. Pacific 0.8%
3-8. Result of Inversion 3
Estimated flux in 1997 - 1998
From inversion 3, CO2 flux increased in Tropical land regions at 1997 – 1998.
0.8
0.6
0.4
0.2
0.0
0.2
0.4
0.6
J - 97 A- 97 J - 97 O- 97 J - 98 A- 98 J - 98 O- 98
Flux
(G
tC/y
)
L01L02L03L04L05L06L07L08L09L10L11O01O02O03O04O05O06O07O08O09O10O11SUM/ 4
3-9. Result of inversion 1-3Average of estimated flux and data error
In inversion 2 and 2.5, errors increased.In inversion 3 flux error reduced.
Experiment Flux (GtC/y) Data (unit less)
Inversion 1 0.103 0.829
Inversion 2 0.107 0.885
Inversion 2.5 0.108 0.891
Inversion 3 0.103 1.006
4-1. Result from inversion 1
In global scale, inversion 1 is similar to control run.
Inversion 1 underestimate N.H flux and overestimate S.H flux.
In tropical region, total flux is similar to control run. But in each region, there is a difference between control run and inversion 1
There is a room to modify greens function
4-2. Result from inversion 2
The effect of increasing prior data errors are not so large.
Flux gap and error increase appears in data amount reduced region.
Estimated data error tend to increase from inversion 1.
Estimated flux error have a good correlation with data coverage.
4-3. Result from inversion 2.5
The result shows that we can combine GLOBALVIEW and WDCGG dataset without reduce accuracy.
The change occurs data amount reduced regions.
Estimated flux and data error tend to increase but not so large from inversion 2. This shows a option to use raw data.
4-4. Result from inversion 3
Data extension made possible to reduce total estimated flux error.
Estimated data error are larger than inversion 1, 2 and 2.5.
Data coverage and quality of data could affect this result.
4-5. Conclusions
Sensitivity of dataset to time dependant inversion is not so large when we select measurement appropriately.
There is one option to use raw data to time dependant inversion.
We need more data which are well selected to run time dependant inversion.
Please submit data to WDCGG!
4-6. Future Plan
Enhance data quality control activity @ Statistical analysis for observations is needed. Use internally varying winds @ NCEP reanalysis or JRA25 (planned)Use On-line model @ Based upon Kosa prediction model
Estimate carbon flux more precisely