sensitivity of observational data to time dependant inversion takashi maki senior coordinator for...

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.


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


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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1988

1989

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Inv1Inv2Inv25Inv3

0

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Inv1Inv2Inv25Inv3

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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


-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


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





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


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.


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

sensitivity of observational data to time dependant inversion takashi maki senior coordinator for...

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