sensitivity of observational dataset to co 2 flux inversion takashi maki, kazumi kamide atmospheric...
Post on 22-Dec-2015
216 views
TRANSCRIPT
Sensitivity of observational dataset to CO2 flux inversion
Takashi Maki, Kazumi Kamide
Atmospheric Environment Division
Japan Meteorological Agency
Backgrounds
Current carbon cycle analysis, GLOBALVIEW (NOAA/CMDL) is the standard dataset.
In GLOBALVIEW, CO2 data are smoothed, interpolated and extrapolated.
In higher resolution analysis in time and space, we tend to need well selected raw data.
We tried to compare estimated flux by smoothed data and raw data.
MethodsUsing smoothed dataset (from WDCGG
analysis or GLOBALVIEW) and raw data (from WDCGG) with the same scenarios.
The scenarios consist of annual mean inversion and time dependent inversion.
We did not analyze absolute value of estimate flux and flux uncertainties, but analyze sensitivity of observational dataset.
WDCGG: World Data Centre for Greenhouse Gases (JMA)
http://gaw.kishou.go.jp
Our Model (JMA CDTM)
Off-line transport model based upon JMA operational Global spectral model (GSM9603).
Resolution 2.5 x 2.5 deg (Horizontal)
32 layers (Surface to 10hPa)
Advection Semi-Lagrangian (Horizontal)
Box-scheme (Vertical)
Diffusion Cumulus convection (Kuo)
Turbulent diffusion (Mellor-Yamada)
Shallow convection (Tiedtke)
Annual mean inversion casePeriods 1997 – 2001(1996 is spin-up)
Winds JMA operational analysis(1997-2001)
Data From WDCGG (selected) monthly data
Smoothed or raw data
Uncertainty Residual from smoothed data
Site Selected by inversion (see later page)
Prior flux As in TransCom 3 Level 1(Background)
Previous year (each region)
Inversion As in TransCom 3 Level 1
Terrestrial and Oceanic regions
From TransCom 3 HP(http://transcom.colostate.edu/TransCom_3/transcom_3.html)
Site Selection We adopt sites where the misfit of inversed data
and observational data is smaller than 2ppm in every year. This is the first selection.
Also we select sites where average misfit is smaller than 0.75ppm from 1997 to 2001.
We continue these selections until we reject no site.
From these selections, we can select sites where there are small effect of anthropogenic and local sources.
Finally, we choose the same 71 sites (from 91 sites) for annual mean scenarios.
Selected sites (Organization)
Organizations sites Organizations sites
NOAA/CMDL 41 CAMM 1
MRI 13 INM 1
CSIRO 6 ISAC 1
JMA 3 NIPR 1
NIES 2 SAWS 1
UBA 1
Thank you very much for submitting data to WDCGG!
Selected sites
We can make use of higher altitude (Tokyo – Sydney) data!
Our model can represent vertical profile of CO2 concentrations.
Selected sites Region N Constrain Region N Constrain
Bor. N. America 1 1.25 N. Pacific 15* 28.14 Temp. N. America 3 3.60 W. Pacific 11* 26.20 Trop. America 0 0.00 E. Pacific 3 5.75 South America 1 2.75 S. Pacific 4 9.34 Tropical Africa 1 3.82 Northern Ocean 4 7.06 S. Africa 0 0.00 N. Atlantic 4 6.52 Boreal Eurasia 0 0.00 Tropical Atlantic 2 5.76 Temp. Eurasia 3 2.75 S. Atlantic 1 4.31 S.E. Asia 1* 3.82 Southern Ocean 8** 45.67 Australasia 4* 2.75 Trop. Indian Ocean 1 2.13 Europe 3 3.82 S. Indian Ocean 1 6.85
Constrain is defined as sum of (1/uncertainty) in the region.* : contain aircraft data, **:contain Antarctic sites
Estimated flux from last year (Raw)
In 1998, Tropical land regions are remarkable source of CO2.
We analyze tropical land flux in our presentation.
Difference between smoothed and raw data (annual mean)
Largest standard deviation from 1997 – 2001.
Station Region Unit : ppm
1 Tae-ahn Peninsula Temp. Eurasia 0.272
2 Cape Ferguson Australasia 0.228
3 Pacific Ocean(5N) W. Pacific 0.213
4 Shemya Island N. Pacific 0.190
5 Pacific Ocean(20N) N. Pacific 0.180
6 Estevan Point Temp. N. America 0.173
7 Schauinsland Europe 0.171
8 Pacific Ocean(15N) N. Pacific 0.171
The difference appears relatively constrained regions!
Estimated flux shows some difference between smoothed and raw data (see later page).
Estimated flux uncertainties are completely same in all regions (Data uncertainties are same)!
Difference of estimated flux between smoothed and raw data
Flux uncertainty
Model Transport
Prior flux uncertainty
Data uncertainty
Estimated flux variability
Smoothed analysis tend to show larger inter-annual variability!
Annual estimated flux
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1997 1998 1999 2000 2001
Year
GtC
/y
L03_RawL05_RawL06_RawL08_RawL09_RawL03_SmoothedL05_SmoothedL06_SmoothedL08_SmoothedL09_Smoothed
Standard deviation of estimated flux
from 1997 to 2001.
Smoothed analysis tend to show larger inter-annual variability!
1997-2001, Unit: GtC/y Smoothed Raw
L03: Tropical America 0.355 0.301
L05: Tropical Africa 0.247 0.181
L06: South Africa 0.240 0.173
L08: Temperate Eurasia 0.079 0.064
L09: Southeast Asia 0.320 0.312
Average of all regions 0.133 0.122
Estimated flux difference in each region
Averaging from 1997 – 2001. Unit is GtC/y.Red regions are larger than (average + 1 sigma) of all regions.
Smoothed-Raw St. Dev. Smoothed-Raw St. Dev.
L01 0.005 0.010 O01 0.004 0.019
L02 -0.055 0.018 O02 0.001 0.006
L03 0.027 0.067 O03 -0.003 0.015
L04 0.105 0.027 O04 -0.053 0.021
L05 -0.049 0.074 O05 -0.008 0.003
L06 -0.086 0.077 O06 -0.006 0.002
L07 -0.081 0.028 O07 0.011 0.008
L08 0.104 0.029 O08 0.018 0.010
L09 -0.034 0.009 O09 0.002 0.012
L10 -0.003 0.014 O10 -0.013 0.015
L11 0.091 0.031 O11 -0.015 0.008
Average Correlation coefficient 0.978
Summary of annual mean case
We can select site using inverse method.
Sensitivity of dataset is not so large. Average correlation coefficients is about 0.98.
Smoothed data analysis shows larger inter-annual variability than raw data analysis.
Difference between estimated flux by smoothed and raw data appears in less constrained regions and local source dominant regions.
Difference of each site does not always affect the estimated flux in the region.
Time dependent casePeriods 1990 – 2000 (1988, 1989 is spin-up)Winds JMA operational analysis (1997)Data From GLOBALVIEW 2002
(Smoothed)→ As in TransCom 3 Level 3From WDCGG monthly data (Raw)
Uncertainty As in TransCom 3 Level 3Site As in TransCom 3 Level 3 (76 Sites)Flux As in TransCom 3 Level 3Inversion As in TransCom 3 Level 3
Used sites (as in T3 L3) Region N Constrain Region N Constrain
Bor. N. America 2 1.87 N. Pacific 14* 20.41 Temp. N. America 7 5.75 W. Pacific 9* 16.93 Trop. America 0 0.00 E. Pacific 2 3.46 South America 0 0.00 S. Pacific 1 1.89 Tropical Africa 0 0.00 Northern Ocean 7 6.33 S. Africa 0 0.00 N. Atlantic 4 3.99 Boreal Eurasia 0 0.00 Tropical Atlantic 2 3.27 Temp. Eurasia 5 2.97 S. Atlantic 0 0.00S.E. Asia 1* 2.04 Southern Ocean 8** 22.86 Australasia 2 3.74 Trop. Indian Ocean 1 1.27 Europe 6 4.53 S. Indian Ocean 5 13.28
Constrain is defined as sum of (1/uncertainty) in the region.* : contain aircraft data, **: contain Antarctic sites
Difference between smoothed and raw data (monthly mean)
Largest standard deviation sites from 1990 – 2001.
Station Region Unit: ppm
1 Gosan Temp. Eurasia 2.68
2 Schauinsland Europe 2.60
3 Grifton Temp. N. America 2.24
4 Estevan Point Temp. N. America 1.06
5 Plateau Rosa Europe 0.90
6 Baltic Sea Europe 0.89
7 Ulaan Uul Temp. Eurasia 0.80
8 Cold Bay Bor. N. America 0.79
Large difference appears almost in land regions.
Estimated flux variability from climate
Climate : Average from 1990 to 2001 monthly flux.
Raw analysis data tend to show larger seasonal variability.
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
J ul-97 Nov-97 Mar-98 J ul-98
L03_SmoothedL05_SmoothedL06_SmoothedL08_SmoothedL09_SmoothedL03_RawL05_RawL06_RawL08_RawL09_Raw
GtC
/Month
Standard deviation of estimated flux from each climate (1990-2000)
Raw data analysis tend to show higher seasonal variability!
1990-2000, Unit: GtC Smoothed Raw
L03: Tropical America 0.056 0.058
L05: Tropical Africa 0.085 0.097
L06: South Africa 0.058 0.061
L08: Temperate Asia 0.103 0.122
L09: Southeast Asia 0.078 0.094
Total flux 0.204 0.250
Standard deviation between fluxes
Less constrained or local source dominant regions tend to differ!Red regions are larger than (average + 1 sigma) of all regions.
Smoothed-Raw Flux unc. Smoothed-Raw Flux unc.
L01 0.022 0.333 O01 0.024 0.309
L02 0.045 0.467 O02 0.017 0.204
L03 0.025 0.711 O03 0.011 0.206
L04 0.013 0.627 O04 0.029 0.401
L05 0.048 0.675 O05 0.007 0.126
L06 0.024 0.724 O06 0.006 0.202
L07 0.054 0.485 O07 0.005 0.202
L08 0.077 0.516 O08 0.005 0.256
L09 0.051 0.384 O09 0.012 0.225
L10 0.012 0.172 O10 0.023 0.309
L11 0.076 0.397 O11 0.012 0.214
Average Correlation coefficient 0.950
Summary of time dependent caseSensitivity of dataset appeared. Average
correlation coefficients is about 0.95.
Smoothed data analysis shows smaller seasonal variability than raw data analysis in time dependent inversion.
Difference between estimated flux by smoothed and raw data appears in less constrained or local source dominant regions.
Summary of our presentationSensitivity of dataset (smoothed or raw) is not so large in
annual mean inversion. The sensitivity become larger in time-dependent inversion.
→ If we tried to estimate higher resolution in time and space, the importance of data quality become clear.
Difference between estimated flux by smoothed and raw data appears not only local source dominant regions but also less constrained regions .
→ We have to put new observational sites in less constrained region and use good model in order to avoid making less constrained region as source dump.
We have an option to select site using inversion.
Our future plans
Using inter-annual analyzed wind in the time dependent inversion.
Using higher resolution version (If possible, On-line version) of CDTM.
Using more regions than now.
Joint experiment with FRSGC using Earth Simulator (From this year)!
We hope this experiment could contribute TransCom 3 (4?)!
We need more computational resources.
References (Data)GLOBALVIEW-CO2, Cooperative Atmospheric Data Integration Pr
oject - Carbon Dioxide, CD-ROM, NOAA CMDL, Boulder, Colorado, 2002.
WMO WDCGG Data Summary. GAW Data Vol. IV - Greenhouse Gases and Other Atmospheric Gases. WDCGG No. 27. World Meteorological Organization, Global Atmosphere Watch, World Centre for Greenhouse Gases (WDCGG), Japan Meteorological Agency, Tokyo 2003.
Conway, T. J., P. P. Tans, L. S. Waterman, K. W. Thoning, D. R. Kitzis, K. A. Masarie, and N. Zhang, Evidence for interannual variability of the carbon cycle from the National Oceanic and Atmospheric Administration/Climate Monitoring and Diagnostics Laboratory global air sampling network, J. Geophys. Res., 99, 22831-22855, 1994.
References (Data)
Matsueda, H., H. Inoue, Y. Sawa, Y. Tsutsumi, and M. Ishii, Carbon monoxide in the upper troposphere over the western Pacific between 1993 and 1996, J. Geophys. Res., 103, 19093-19110, 1998.
Rayner, P. J., I. G. Enting, R. J. Francey and R. Langenfelds, Reconstructing the recent carbon cycle from atmospheric CO2,d13C and O2/N2 observations, Tellus, 51B, 213-232, 1999.
Watanabe, F., O. Uchino, Y. Joo, M. Aono, K. Higashijima, Y. Hirano, K. Tsuboi, and K. Suda, 2000: Interannual variation of growth rate of atmospheric carbon dioxide concentration observed at the JMA's three monitoring stations: Large increase in concentration of atmospheric carbon dioxide in 1998. J. Meteor. Soc. Japan, 78, 673-682.
References (Inversion)
Enting, I. 2002. Inverse Problems in Atmospheric Constituent Transport. Cambridge University Press, Cambridge, U. K.
Gurney, K., Law, R., Rayner, P., and A.S. Denning, "TransCom 3 Experimental Protocol," Department of Atmospheric Science, Colorado State University, USA, Paper No. 707, 2000.
Tarantola, A. (1987), The least-squares (12-norm) criterion, in Inverse Problem Theory: Methods for Data Fitting and Parameter Estimation, chap. 4, pp. 187– 287, Elsevier Sci., New York.
Thank you very much for providing level 1 – 3 inversion codes!!
Appendix: WDCGG Data policyWMO WDCGG Data
The WDCGG acknowledges the support of the organizations and individual researchers that provide their measurement data for greenhouse and other related gases. Such data contributors should receive fair credit for their work. When you use and publish data and information in the WDCGG's publications or on this web site, please make reference properly to the contributors and data source and inform both WDCGG and the data contributors. If your work substantially depends on the data, it is recommended to contact the data contributors at an early stage to discuss co-authorship and other necessary arrangements. The following is an example of the citation when you need to cite data from the WDCGG website as a reference:
Tsutsumi, Y, M. Yoshida, S. Iwano, O. Yamamoto, M. Kamada, H. Morishita, Atmospheric CO2 monthly mean oncentration, Ryori, WMO WDCGG , JMA, Tokyo, 21 May 2003.