Estimates of Carbon Transfer coefficients Using Probabilistic Inversion for Three Forest Ecosystems in East China Li Zhang 1, Yiqi Luo 2, Guirui Yu 1, Leiming Zhang 1 1 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Science and Natural Resources Research, Chinese Academy Sciences 2 Department of Botany and Microbiology, University of Oklahoma, Norman, USA Carbon transfer coefficients are the key parameters in carbon cycling models for quantifying the capacity of C leaving in each pool at a time step. They can be used to estimate the residence time of carbon, which determines the capacity of an ecosystem as a carbon source or sink. Here we will apply MCMC parameter estimation technique to inverse carbon transfer coefficients for eight pools against with measurements of carbon pools and carbon fluxes at three forest sites, and compare the estimates of parameters constrained by different sets of assimilation data. Introduction Discussions  Eddy covariance net ecosystem exchange of CO 2 (NEE) can provide with useful information for constraining carbon transfer rates between litter, microbes, slow SOM and passive SOM, but with little information for constraining carbon transfer rates from leaf, fine root and woody biomass to litter pool.  The effect of NEE data on parameters c4-c8 may result from its lower weight in the cost function compared with other observed data. Methods 8-pool modified TECOS model Table 1 Description of Carbon Transfer Coefficients ParameterDescription c1from pool “nonwoody biomass” to “metabolic litter” and “structure litter” c2from pool “fine root biomass” to “metabolic litter” and “structure litter” c3from pool “woody biomass” to “structure litter” c4from pool “metabolic litter” to “microbes” c5from pool “structure litter” to “microbes” and “slow SOM” c6from pool “microbes” to “slow SOM” and “passive SOM” c7from pool “slow SOM” to “microbes” and “passive SOM” c8from pool “passive SOM” to “microbes” Data sets The data sets used here are biomass (foliage, fine root, woody), litterfall, soil organic C, soil respiration, net ecosystem exchange of CO 2 (NEE) at CBS, QYZ and DHS sites. Parameter estimation Markov Chain Monte Carlo (MCMC) method was used to estimate the carbon transfer coefficients (Table 1). Three experiments were undertaken with different sets of assimilation data. OBS1 : Assimilating carbon pools, soil respiration and NEE (all data) OBS2 : Assimilating carbon pools and soil respiration OBS3: Assimilation NEE only Study sites CBS QYZ DHS Table 2 Site location and long-term climate variables CodeCBSQYZDHS Location42°24′N, 128°06′E 26°44′N, 115°04′E 23°10′N, 112°32′E Terrainflathillupland Elevation (m) Annual Precipitation (mm) Average temperature ( ℃ ) Canopy height (m) Age (yr) CBS: a broad-leaved and Korean pine mixed forest. QYZ: a young evergreen coniferous plantation. DHS: an evergreen conifer and broad-leaved mixed forest. Results Our results showed that the estimates of parameters c1, c2 and c3 will not be influenced by NEE data, but constrained by carbon pools data. Because there is no CO 2 release when carbon transfers from leaf, fine root and woody biomass to litter pool. On the contrary, carbon transfers between litter, microbes, slow SOM and passive SOM accompanied with CO2 release. Thus, the estimates of parameters c4, c5, c6, c7 and c8 changed when adding NEE to data sets. Fig. 1 Posterior Distribution of parameters with 3 different assimilation experiments in CBS site Fig. 2 Posterior Distribution of parameters with 3 different assimilation experiments in QYZ site Fig. 3 Posterior Distribution of parameters with 3 different assimilation experiments in DHS site Acknowledgements This work was supported by Chinese Academy of Sciences International Partnership Project "Human Activities and Ecosystem Changes" (No CXTD- Z2005-1). We thank all related staffs of ChinaFLUX and CERN for their contribution from observation to data processing.