Verifying Fossil-Fuel Carbon Dioxide Emissions Forecasted by an Artificial Neural Network with the GEOS-Chem Model

In this study, the authors developed an en- semble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions （ff） in 2009. The authors built and trained 29 Elman neural net- works based on the monthly average grid emission data （1979-2008） from different geographical regions. A three-dimensional global chemical transport model, God- dard Earth Observing System （GEOS）-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from -1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, - 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and tem- poral distribution of the atmospheric C02 concentration and ft.

在这研究，作者开发了 Elman 神经网络的一个整体在 2009 预报石块燃料排出物(ff ) 的空间、时间的分发。基于月刊造并且训练 29 个 Elman 神经网络的作者从不同地理区域平均格子排放数据(19792008 ) 。戈达德一个三维的全球化学运输模型，观察系统(GEOS )-Chem, 的土被使用验证网络的有效性。结果证明网络捕获了年度增加趋势和 ff 的 interannual 变化很好。有原来、预言的 ff 的模拟之间的差别全球性从 1 ppmv 到 1 ppmv。同时，作者评估了在表面附近观察了并且模仿大气的公司 2 集中的纵贯的坡度。二个模仿的坡度看起来有一个类似的变化模式到观察，与稍微更高的背景公司 2 集中， 1 ppmv。结果显示 Elman 神经网络是为更好理解大气的公司 2 集中和 ff 的空间、时间的分发的一个有用工具。