The purpose of this paper is to present a simple way to approximate the dependence of the global mean air temperature at Earth＇s surface on atmospheric concentration of carbon dioxide. Current discussions include ene...The purpose of this paper is to present a simple way to approximate the dependence of the global mean air temperature at Earth＇s surface on atmospheric concentration of carbon dioxide. Current discussions include energy aspects of the greenhouse effect and the global warming. The starting point for the research are laws of thermodynamics, energy equilibrium and absorption and emission property of the atmosphere. According to the calculations, the natural and the anthropogenic changes effect the atmosphere. By the year 2100 the CO2 concentration will be doubled in comparison with the value of the pre-industrial ages. The doubled CO2 concentration will mean a temperature change about 1 ℃-1.5 ℃.展开更多
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-...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.展开更多
文摘The purpose of this paper is to present a simple way to approximate the dependence of the global mean air temperature at Earth＇s surface on atmospheric concentration of carbon dioxide. Current discussions include energy aspects of the greenhouse effect and the global warming. The starting point for the research are laws of thermodynamics, energy equilibrium and absorption and emission property of the atmosphere. According to the calculations, the natural and the anthropogenic changes effect the atmosphere. By the year 2100 the CO2 concentration will be doubled in comparison with the value of the pre-industrial ages. The doubled CO2 concentration will mean a temperature change about 1 ℃-1.5 ℃.
基金supported by the Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues of the Chinese Academy of Sciences (Grant No. XDA05040000)the National Natural Science Foundation of China (Grant Nos. 41005023 and 41275046)
文摘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.
