| Carbon dioxide is a major contributor to global warming and climate change. With the increasing use in fossil fuels and changes in land use and land cover, CO2 levels in the atmosphere were increased from 280 ppm before the industrial revolution to 397.91 ppm in 2014. Yangtze River Delta is one of the most economically dynamic regions in China, and has experienced a rapid urbanization development over the past several decades. It becomes a big challenge to control anthropogenic emissions effectively. Thus, accurate quantification of biospheric flux of CO2 is critical to simulate spatial and temporal variations of atmospheric CO2 concentrations. Such a study will provide theoretical support to our governments and decision-making departments to formulate effective measures for carbon dioxide emission reductions, and further understanding of interaction of climate change and carbon cycles.In this study, several key parameters in Vegetation Photosynthesis and Respiration model (VPRM) are optimized and verified with three-year tower flux observational data at Qianyanzhou (QYZ), a site of ChinaFlux network. The optimized parameters are applied to the online coupling model, WRF-VPRM to assess the impact of VPRM parameters on the simulations of CO2 flux and CO2 concentrations. A detailed study is presented to the case occurring during the period from July 26 to August 2,2010 over Yangtze River Delta region. Several major finding are obtained from this study:(1) Traditional method with Michael is-Menten equation is not suitable to determine the parameters of VPRM, whereas the method with parameters retrieved from the VPRM calculation equation provides much more reasonable results. Utilizing the optimized parameters, VPRM is able to capture diurnal and seasonal variations in NEE. The statistics calculation with one-year NEE simulation shows that, the mean bias is -0.86 umol/m2/s and correlation coefficient is 0.72. Overall, VPRM performs much better in growing season than the non-growing reason when the peak value of NEE is underestimated by 52%.(2) WRF can simulate the meteorological factors accurately. As compared to the surface observations, the mean bias of temperature at MLW, NUIST, and QYZ is 0.64℃,1.14℃, and 0.21℃, respectively. Their correlation coefficients are 0.82,0.85 and 0.92, respectively. Meanwhile, the correlation coefficients of downward shortwave radiation at these three sites are 0.94,0.90 and 0.91, respectively. This provides accurate input fields for further NEE calculations.(3) VPRM parameters have a great impact on the NEE simulations. NEE is underestimated under default parameters (casel) and parameters derived from QYZ site (case2) scheme in all three sites, while Hilton’s mean paramenters (case3) scheme overestimates NEE. There is a good agreement between simulated and observed NEE with user defined parameters (case4) scheme, and the mean bias at MLW, NUIST, QYZ is 0.08 mg/m2/s,-0.04 mg/m2/s and -0.13 mg/m2/s, respectively.(4) WRF-VPRM model is able capture the variation patterns of CO2 concentration, but not the magnitude over Yangtze River Delta. The mean bias varies from case to case. For example, at the MLW site, the mean biases for the cases 1-4 are 22.9 ppm,24.1 ppm,8.44 ppm, and 13.7 ppm, respectively. This indicates that VPRM parameters have important impact on CO2 concentrations, but the impact is very minor. In the future, more efforts should be put on refinement of anthropogenic emissions and optimization of boundary meteorology parameterization schemes in order to obtain more reasonable CO2 simulations. |
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