The authors examine the distribution and varia- tion of carbon monoxide （CO） in the tropics from the sur- face to the lower stratosphere. By analyzing space-borne microwave limb sounder （MLS） measurements, measure- ments of pollution in the troposphere （MOPITT） and mod- em-era retrospective analysis for research and applications （MERRA） meteorological products, and atmospheric chemistry and climate model intercomparison project （ACCMIP） surface emission inventories, the influences of atmospheric dynamics and surface emissions are investi- gated. The results show that there are four centers of highly concentrated CO mixing ratio over tropical areas in differ- ent seasons： two in the Northern Hemisphere and another two in the Southern Hemisphere. All of these centers cor- respond to local deep convective systems and mon- soons/anticyclones. The authors suggest that both deep convections and anticyclones affect CO in the tropical tro- posphere and lower stratosphere--the former helping to transport CO from the lower to the middle troposphere （or even higher）, and the dynamical uplift and isolation effects of the latter helping to build up highly concentrated CO in the upper troposphere and lower stratosphere （UTLS）. Similarly, there are two annual surface emission peaks in- duced by biomass burning emissions： one from the North- ern Hemisphere and the other from the Southern Hemi- sphere. Both contribute to the highly concentrated CO mixing ratio and control the seasonal variabilities of CO in the UTLS, combining the effects of deep convections and monsoons. Results also show a relatively steady emission rate from anthropogenic sources, with a small increase mainly coming from Southeast Asia and lndia. These emis- sions can be transported to the UTLS over Tibet by the joint effort of surface horizontal winds, deep convections, and the Asian summer monsoon system.
LI QianSHI Hua-FengSHAO Ai-MeiBIAN Jian-ChunLü Da-Ren
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.
The mechanism of the CO2diurnal cycle is a basis for investigating the carbon budget and its impacts on climate and environment change.Regional diurnal variations in CO2concentration based on observations and modeling have been studied widely.However,few studies have focused on the pattern of the CO2diurnal cycle in China.In this study,a three-dimensional global chemical transport model,Goddard Earth Observing System(GEOS)-Chem,was applied to simulate the CO2concentration and its variation over China from 2004 to 2012.Meanwhile,we also analyzed the CO2concentration as observed by two eddy covariance flux observation towers,one located in Beijing(39°580N,116°220E)and one in Hefei(31°550N,117°100E),using LI-COR 7500A infrared gas analyzers.Observations showed the amplitude of the CO2diurnal cycle at Hefei to be larger than at Beijing,due to stronger ecological activities.GEOS-Chem successfully captured the main aspects of the diurnal cycle of the CO2concentration in the boundary layer observed at both Beijing and Hefei.However,some discrepancies between the model and observations did exist;specifically,the model tended to underestimate the amplitude of the CO2diurnal cycle.The data also showed that traffic emissions significantly enhanced the CO2concentration in the boundary layer.
Yinan WangDaren LQian LiMinzheng DuanFei HuShunxing Hu
