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.     

Inference of surface concentrations of nitrogen dioxide (NO2) in Colombia from tropospheric columns of the ozone measurement instrument (OMI)

For the first time, maps of surface concentration of nitrogen dioxide (NO₂) are presented for the Colombian territory. NO₂ surface concentrations for the year 2007 are inferred based on two sources of tropospheric NO₂ column data: (1) a simulation using a three-dimensional global model (GEOS-Chem) and (2) measurements made by the ozone monitoring instrument (OMI) onboard the NASA Aura satellite. Results show monthly averages between 0.1 and 6 ppbv. We compare these inferred values to corrected ground measurements of NO₂. We find correlation coefficients of up to 0.91 between the inferred data and the corrected observational data. A significant source of NO₂ is biomass burning, which can be diagnosed by data of fire radiative power (FRP) from the Monitoring of Atmospheric Composition and Climate (MACC) reanalysis. We find a close relationship between high values of inferred NO₂ surface concentrations and biomass burning for a large area which encompasses the departments of Caquetá, Meta, Guaviare, Vichada, and Putumayo.     

不同陆地生态系统碳通量对GEOS-Chem模型模拟全球CO2浓度的影响

大气中CO₂含量的增加速率已经超过了自然界所能吸收的速度,并逐步影响到全球气候变暖。利用模型模拟分析已经成为一个重要的工具用以深入对碳循环的理解。本文使用2008～2010年的生物模型SiB3（Simple Biosphere version 3）与优化后的CT2016（Carbon Tracker 2016）陆地生态系统碳通量驱动GEOS-Chem大气化学传输模型模拟全球CO₂浓度。通过分析模拟CO₂浓度的空间分布与季节变化,加深对全球碳源汇分布特点的理解,探究陆地生态系统碳通量不确定性对模拟结果的影响,进而认识陆地生态系统碳通量反演精度提升的重要性。SiB3与优化后的CT2016陆地生态系统碳通量都具有明显的季节变化,但在欧洲地区碳源汇的表现相反,其全球总量与空间分布也存在极大的不确定性。模拟CO₂浓度结果表明:在人为活动较少地区,陆地生态系统碳通量对近地面CO₂浓度空间分布起主导作用,尤其在南半球和欧洲地区模拟浓度有明显差异,且两种模拟结果的季节差异依赖于陆地生态系统碳通量的季节变化。将模拟结果与9个观测站点资料进行对比,以期选用合适的陆地生态系统碳通量来提升GEOS-Chem模拟CO₂浓度的精度。实验结果表明:两种模拟结果均能较好的模拟CO₂浓度的季节变化及其峰谷值,但CT2016模拟的CO₂浓度在多数站点处更接近观测资料,模拟准确性更高。     

Simulated Spatial Distribution and Seasonal Variation of Atmospheric Methane over China: Contributions from Key Sources简

We used the global atmospheric chemical transport model,GEOS-Chem,to simulate the spatial distribution and seasonal variation of surface-layer methane （CH4） in 2004,and quantify the impacts of individual domestic sources and foreign transport on CH4 concentrations over China.Simulated surface-layer CH4 concentrations over China exhibit maximum concentrations in summer and minimum concentrations in spring.The annual mean CH4 concentrations range from 1800 ppb over western China to 2300 ppb over the more populated eastern China.Foreign emissions were found to have large impacts on CH4 concentrations over China,contributing to about 85％ of the CH4 concentrations over western China and about 80％ of those over eastern China.The tagged simulation results showed that coal mining,livestock,and waste are the dominant domestic contributors to CH4 concentrations over China,accounting for 36％,18％,and 16％,respectively,of the annual and national mean increase in CH4 concentration from all domestic emissions.Emissions from rice cultivation were found to make the largest contributions to CH4 concentrations over China in the summer,which is the key factor that leads to the maximum seasonal mean CH4 concentrations in summer.     

基于模式分析一次沙尘暴过程中沙尘表面非均相化学过程对中国地区污染物浓度的影响

利用戈达德对地观测系统（GEOS）提供的再分析气象场GEOS-5驱动的GEOS-Chem模式,模拟中国地区2009年4月22~29日沙尘暴期间沙尘气溶胶表面非均相化学过程对我国污染物的影响。模拟结果表明,沙尘暴期间,全国平均沙尘硝酸盐和沙尘硫酸盐浓度分别为0.2 μg m-3和0.4 μg m-3,占总硝酸盐（非沙尘硝酸盐与沙尘硝酸盐之和）和总硫酸盐（非沙尘硫酸盐与沙尘硫酸盐之和）的24%和10%。我国西部地区沙尘硝酸盐占比（ > 80%）要大于其他地区,而西部地区的沙尘硫酸盐占比则要小于下游地区。考虑非均相化学反应后,沙尘暴期间,全国平均的二氧化硫（SO₂）、硝酸（HNO₃）、臭氧（O₃）、非沙尘硫酸盐、总硫酸盐、非沙尘硝酸盐、总硝酸盐、NH₃、总铵盐浓度变化量分别为-7%、-15%、-2%、-8%、3%、-2%、14%、21%、-5%。     

A comparison of atmospheric CO2 concentration GOSAT-based observations and model simulations

Satellite observations of atmospheric CO2 are able to truly capture the variation of global and regional CO2 concentration.The model simulations based on atmospheric transport models can also assess variations of atmospheric CO2 concentrations in a continuous space and time,which is one of approaches for qualitatively and quantitatively studying the atmospheric transport mechanism and spatio-temporal variation of atmospheric CO2 in a global scale.Satellite observations and model simulations of CO2 offer us two different approaches to understand the atmospheric CO2.However,the difference between them has not been comprehensively compared and assessed for revealing the global and regional features of atmospheric CO2.In this study,we compared and assessed the spatio-temporal variation of atmospheric CO2 using two datasets of the column-averaged dry air mole fractions of atmospheric CO2(XCO2)in a year from June 2009 to May 2010,respectively from GOSAT retrievals(V02.xx)and from Goddard Earth Observing System-Chemistry(GEOS-Chem),which is a global 3-D chemistry transport model.In addition to the global comparison,we further compared and analyzed the difference of CO2 between the China land region and the United States(US)land region from two datasets,and demonstrated the reasonability and uncertainty of satellite observations and model simulations.The results show that the XCO2 retrieved from GOSAT is globally lower than GEOS-Chem model simulation by 2 ppm on average,which is close to the validation conclusion for GOSAT by ground measures.This difference of XCO2 between the two datasets,however,changes with the different regions.In China land region,the difference is large,from 0.6 to 5.6 ppm,whereas it is 1.6 to 3.7 ppm in the global land region and 1.4 to 2.7 ppm in the US land region.The goodness of fit test between the two datasets is 0.81 in the US land region,which is higher than that in the global land region(0.67)and China land region(0.68).The analysis results further indicate that the inconsistency of CO2concentration between satellite observations and model simulations in China is larger than that in the US and the globe.This inconsistency is related to the GOSAT retrieval error of CO2 caused by the interference among input parameters of satellite retrieval algorithm,and the uncertainty of driving parameters in GEOS-Chem model.     

Specific patterns of XCO2 observed by GOSAT during 2009–2016 and assessed with model simulations over China

Spatiotemporal patterns of column-averaged dry air mole fraction of CO2(XCO2)have not been well characterized on a regional scale due to limitations in data availability and precision.This paper addresses these issues by examining such patterns in China using the long-term mapping XCO2 dataset(2009-2016)derived from the Greenhouse gases Observing SATellite(GOSAT).XCO2 simulations are also constructed using the high-resolution nested-grid GEOS-Chem model.The following results are found:Firstly,the correlation coefficient between the anthropogenic emissions and XCO2 spatial distribution is nearly zero in summer but up to 0.32 in autumn.Secondly,on average,XCO2 increases by 2.08 ppm every year from2010 to 2015,with a sharp increase of 2.6 ppm in 2013.Lastly,in the analysis of three typical regions,the GOSAT XCO2 time series is inbetter agreement with the GEOS-Chem simulation of XCO2 in the Taklimakan Desert region(the least difference with bias 0.65±0.78 ppm),compared with the northern urban agglomerationregion(-1.3±1.2 ppm)and the northeastern forest region(-1.4±1.4 ppm).The results are likely attributable to uncertainty in both the satellite-retrieved XCO2 data and the model simulation data.     

Impacts of Seasonal Fossil and Ocean Emissions on the Seasonal Cycle of Atmospheric CO2

The seasonal cycle of atmospheric CO2 at surface observation stations in the northern hemisphere is driven primarily by net ecosystem production (NEP) fluxes from terrestrial ecosystems. In addition to NEP from terrestrial ecosystems, surface fluxes from fossil fuel combustion and ocean exchange also contribute to the seasonal cycle of atmospheric CO2. Here the authors use the Goddard Earth Observing System-Chemistry (GEOS-Chem) model (version 8-02-01), with modifications, to assess the impact of these fluxes on the seasonal cycle of atmospheric CO2 in 2005. Modifications include monthly fossil and ocean emission inventories. CO2 simulations with monthly varying and annual emission inventories were carried out separately. The sources and sinks of monthly averaged net surface flux are different from those of annual emission inventories for every month. Results indicate that changes in monthly averaged net surface flux have a greater impact on the average concentration of atmospheric CO2 in the northern hemisphere than on the average concentration for latitudes 30-90°S in July. The concentration values differ little between both emission inventories over the latitudinal range from the equator to 30°S in January and July. The accumulated impacts of the monthly averaged fossil and ocean emissions contribute to an increase of the total global monthly average of CO2 from May to December.An apparent discrepancy for global average CO2 concentration between model results and observation was because the observation stations were not sufficiently representative. More accurate values for monthly varying net surface flux will be necessary in future to run the CO2 simulation.     

Comparisons of a Chemical Transport Model with a Four-Year (April to September) Analysis of Fine- and Coarse-Mode Aerosol Optical Depth Retrievals Over the Canadian Arctic

We compared April to September retrievals of total, fine-mode (sub-micron), and coarse-mode (super-micron) aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET) with simulations from a global three-dimensional chemical transport model, the Goddard Earth Observing System (GEOS-Chem), across five Arctic stations and a four-year sampling period. It was determined that the AOD histograms of both the retrievals and the simulations were better represented by a lognormal distribution and that the successful simulation of this empirical feature as well as its consequences (including a better model versus retrieval coefficient of determination in log-log AOD space) represented a general indicator of model evaluation success. Seasonal (monthly averaged) AOD retrievals were sensitive to the way in which the averaging was performed; this was ascribed to the presence of highly variable fine-mode smoke in the western Arctic. The retrieved and modelled station-by-station fine-mode AOD averages showed a peak in April/May that decreased over the summer, while the model underestimated the fine-mode AOD by an average of about 0.004 (～6%). Both the retrievals and simulations showed seasonal coarse-mode AOD variations with a peak in April/May that was attributed to Asian and/or Saharan dust. The model's success in capturing such weak seasonal events helps to confirm the relevance of the separation of the fine and coarse modes and the general validity of model estimates in the Arctic.