OBSERVATIONAL DATA SCREENING TECHNIQUE USING TRANSPORT MODEL AND INVERSE MODEL IN ESTIMATING CO2 FLUX HISTORY Takashi MAKI,Kazumi KAMIDE and Yukitomo TSUTSUMI Japan Meteorological Agency TransCom 3 workshop in Paris, 15 th June 2005

Outline 1. We have developed a new data selection technique which can treat raw (not smoothed) observational data in inter-annual inversion. 2. We have determined proper threshold value to data selection in inversion. 3. We have performed several sensitivity tests to evaluate this technique.

Background We should use raw (not smoothed) observational data when we try to estimate CO2 flux. However, the representatives of each observational data (not site) is different and the observational network changes year by year. We should treat each observational data in accordance with their representatives. Monthly mean CO2 concentration reported to World Data Centre for Greenhouse Gases (WDCGG)

Problems in using raw observational data 1. The latest data are not reported 2. The lack of observational data 3. Affected by local sources?? 4. We have to treat observational data as their representative.

How to resolve these problems The latest data are not reported, the lack of observational data. Affected by local sources?? Creating dummy data (given large uncertainties and reject from analysis in actual) by smoothing, interpolating and extrapolating. Each observational data are selected by using inverse model. We have to treat observational data as their representative. Data uncertainty (representative) are determined by the difference between raw data and smoothed data (as in TC3L2).

Inversion setup *We did not use fossil fuel correction as Baker et al 2005.

Data selection by inversion Principle 1. The majority of raw observational data has a sub-continental scale representative. 2. CO2 fluxes are determined by the decision by majority of raw data. 3. Therefore, we can select raw observational data using transport model and inter-annual inversion (IAI). Merit of this method 1.We can treat raw data without developing a new data selection technique. 2.We could adopt this method to several kind of observational data (surface, upper air and column data). 3.We do not need huge computational resources (we need only a few iteration of IAI).

Data selection procedure Method 1. We use all raw observational data in IAI at first. 2. We reject observational data which the data mismatch is larger than the threshold value. 3. We use selected observational data in inversion with same condition (prior flux and prior flux uncertainty). 4. We repeat process 2 and 3 until we have no rejected data.

How do we determine the threshold value? 1. In smaller threshold value, we reject many observational data including back ground data. 2. In larger threshold value, we use many observational data including local representative data. We have determined the proper threshold value by checking data selection rate of Global Atmosphere Watch (GAW) global stations (back ground site).

Data selection rate in back ground sites To select 95% raw data in back ground sites, the threshold value (TV) should be larger than 2ppm (without Zugspitze). We choose this TV as the control case.

Data selection rate for all raw data About 82% of the all input data are selected in control case. Recently, the data selection rate tend to smaller?

Data availability* rate ( ) The site which continues observation longer period could contribute well in this analysis. *Availability rate = selected data / analysis period (12*years)

Data selection* rate ( ) Remote sites show better selection rate than continental sites. *Selection rate = selected data / input raw data

Iteration of inversion (TV=2ppm) Fluxes are remarkably changed from 1st to 2nd inversion.

Sensitivity tests We have performed several sensitivity tests 1. Threshold value (TV) sensitivity We have changed TV from 1.0 to 3.0ppm. We have changed TV from 1.0 to 3.0ppm. 2. Observational network sensitivity We have rejected specific site (Crozet, Izana, Mt. Waliguan) which have been shown by K. R. Gurney at last TransCom workshop from observational network. We have rejected specific site (Crozet, Izana, Mt. Waliguan) which have been shown by K. R. Gurney at last TransCom workshop from observational network.

In global scale, each result shows good agreement with each other except restrict selection (threshold value = 1ppm) case Baker et al., 2005 Sensitivity to the global CO2 flux

Sub-continental case (1) In Asian region (relatively less constrained region), Mt. Waliguan has a large role.

Sub-continental case (2) Relatively many number of observational data region (North America and Europe), loose selection (TV=2.5ppm and 3ppm) tend to enlarge inter-annual CO2 flux variability. Izana has some roles in Temperate America.

Sub-continental case (3) Less constrained region, loose selection tend to enlarge inter- annual CO2 flux variability. Crozet has some roles in South Africa.

Averaged flux uncertainty ( ) This work could reduce estimated flux uncertainty especially in formerly less-constrained regions. This selection method could contribute more homogeneous uncertainty in global carbon cycle than T3L2. Threshold value = 2ppm

Conclusions We have tried to use raw observational data in inter-anual inversion (IAI). We have developed data selection technique using IAI. In this selection method, we can use 80% of the raw observational data (from WDCGG) in IAI. In global scale, the difference in threshold value and observational network is small. In sub-continental scale, some specific site can effect less constrained region. Therefore, we need more observational data especially less constrained regions. We can reduce estimated uncertainty in using raw data. We can use raw observational data consistently in IAI!

Future plans We should have good model to data selection 1.Upgrade model and region spatial and temporal resolution. 2.From off-line model to on-line model? We should determine threshold value by statistical analysis 1.Calculate chi-square (selected data). 2.2ppm is too large (comparing with Baker et al) ?? We should know the reason which makes the difference between this work and T3L2. 1.Inversion set-up (prior flux uncertainties)? 2.Observational data network?

Acknowledgement and References Acknowledgement We thank to TransCom project for providing us the protocol, analysis, inversion codes and many products. We also thank to WDCGG data contributors. ( References Gurney, K.R., R.M. Law, A.S. Denning, P.J. Rayner, B. Pak, and the TransCom 3 L2 modelers, “Transcom 3 Inversion Intercomparison: Control results for the estimation of seasonal carbon sources and sinks”, Global Biogeochemical Cycles,18, GB1010, doi: /2003GB002111, Baker, D. F., R. M. Law, K. R. Gurney, P. Rayner, P. Peylin, A. S. Denning, et al., “TransCom 3 inversion intercomparison: Impact of transport model errors on the interannual variability of regional CO2 fluxes, ”, Global Biogeochemical Cycles, 2005, in review.

Appendix 1 : WDC* data policy From Minutes of GAW World Data Centre Managers Meeting, March WDCGG (JMA) proposal - Requirement to acknowledge data contributors with co-authorship is dis-incentive where large scale use of data made in modeling study. Therefore either acknowledge data centre, or have online acknowledgement board. *WDC: World Data Centre, AREP: Atmospheric Research and Environment Program, CAS: Commission for Atmospheric Sciences, EPAC: Environmental Pollution and Atmospheric Chemistry, PC:Precipitation Chemistry Acknowledgment of data originator is too variable. We forward option 2 of the JMA proposal to AREP and CAS-EPAC together with the recommendation from Albany (WDCPC) for further action. “The WDC managers recommended that the secretariat investigate means of ensuring that Journals require data originators and data centres be properly acknowledged in publications using their data”, with the request for advice and/or a standard acknowledgment/citation for use of WDC data.

Appendix 2 : TransCom continuous experiment (JMA experiment) We have submitted our result at Apr The futures of our model (JMACDTM) 1.The model calculate vertical mixing coefficient (cumulus convection, turbulent diffusion and shallow convection) making use of our GCM code. 2.The model does not use PBL height and cloud cover rate explicitly. 3.The model has artificial parameter (minimum diffusion coefficient) in near surface layers. Thanks to Wouter, Christain, Rachel and Lori for coordinating!!