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.     

新一代温室气体观测卫星(GOSAT、OCO)传感器设置

为了深入了解国际上新一代CO2等温室气体的空间观测计划及进展情况,以日本和美国于2009年最新发射的温室气体测卫星呼吸号(GOSAT)、轨道碳观测者(OCO)为例,分析了观测计划的开展情况,对卫星上搭载的传感器设置做了分析,对新一代温室气体专用观测卫星与传统卫星上的传感器做了对比,得出了新一代的传感器在设置上的特点:波段设置在近红外太阳辐射以获得边界层的温室气体含量,具有高的光谱分辨率以保证观测精度,多种观测方式结合以获得最大的信息量。并提出了我国应对温室气体观测资料缺乏的对策,发展专用观测卫星或传感器,以提供我们自己的高精度的全球和区域温室气体观测数据。     

Column-averaged CO2 concentrations in the subarctic from GOSAT retrievals and NIES transport model simulations

The distribution of atmospheric carbon dioxide (CO₂) in the subarctic was investigated using the National Institute for Environmental Studies (NIES) three-dimensional transport model (TM) and retrievals from the Greenhouse gases Observing SATellite (GOSAT). Column-averaged dry air mole fractions of subarctic atmospheric CO₂ (XCO₂) from the NIES TM for four flux combinations were analyzed. Two flux datasets were optimized using only surface observations and two others were optimized using both surface and GOSAT Level 2 data. Two inverse modeling approaches using GOSAT data were compared. In the basic approach adopted in the GOSAT Level 4 product, the GOSAT observations are aggregated into monthly means over 5° × 5° grids. In the alternative method, the model–observation misfit is estimated for each observation separately. The XCO₂ values simulated with optimized fluxes were validated against Total Carbon Column Observing Network (TCCON) ground-based high-resolution Fourier Transform Spectrometer (FTS) measurements. Optimized fluxes were applied to study XCO₂ seasonal variability over the period 2009–2010 in the Arctic and subarctic regions. The impact on CO₂ levels of emissions from enhancement of biospheric respiration induced by the high temperature and strong wildfires occurring in the summer of 2010 was analyzed. Use of GOSAT data has a substantial impact on estimates of the level of CO₂ interanual variability.     

Model-Simulated Atmospheric Carbon Dioxide: Comparisons with Satellite Retrievals and Ground-Based Observations

Atmospheric CO2 concentrations from January 2010 to December 2010 were simulated using the GEOS-Chem（Goddard Earth Observing System-Chemistry） model and the results were compared to satellite Gases Observing Satellite（GOSAT） and ground-based the Total Carbon Column Observing Network（TCCON） data. It was found that CO2 concentrations based on GOSAT satellite retrievals were generally higher than those simulated by GEOS-Chem. The differences over the land area in January and April ranged from 1 to 2 ppm, and there were major differences in June and August. At high latitudes in the Northern Hemisphere in June, as well as south of the Sahara, the difference was greater than 5 ppm. In the high latitudes of the Northern Hemisphere the model results were higher than the GOSAT retrievals, while in South America the satellite data were higher. The trend of the difference in the high latitudes of the Northern Hemisphere and the Saharan region in August was opposite to June. Maximum correlation coefficients were found in April, reaching 0.72, but were smaller in June and August. In January, the correlation coefficient was only 0.36. The comparisons between GEOS-Chem data and TCCON observations showed better results than the comparison between GEOS and GOSAT. The correlation coefficients ranged between 0.42（Darwin） and 0.92（Izana）. Analysis of the results indicated that the inconsistency between satellite observations and model simulations depended on inversion errors caused by data inaccuracies of the model simulation＇s inputs, as well as the mismatch of satellite retrieval model input parameters.     

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.     

Spatial and Temporal Distributions of Atmospheric CO2 in East China Based on Data from Three Satellites

East China(23.6°–38.4°N, 113.6°–122.9°E) is the largest developed region in China. Based on CO₂ products retrieved from the Greenhouse Gases Observing Satellite(GOSAT), the spatial and temporal distributions of CO₂ mixing ratios in East China during 2014–17 are discussed, and the retrieved CO₂ from AIRS(Atmospheric Infrared Sounder) and OCO-2(Orbiting Carbon Observatory-2), as well as WLG(Waliguan) background station observations, are compared with those of GOSAT...     

基于GOSAT卫星的全球近地面甲烷浓度时空分布特征分析

甲烷(CH_4)作为自然界最常用的燃料,分布极广。同时,甲烷作为第二大温室气体,在温室效应的过程中有重要影响。近地面甲烷受多种因素影响在高度上分布不均,影响着人类的生存和生产生活。本文利用2009年6月到2012年5月GOSAT卫星上FTS传感器的L4B级数据,研究了近地面甲烷浓度在时间和空间上的分布特征,结果表明:在时间分布方面:(1)甲烷月平均浓度处于整体上升趋势,最大增量约为2.35×10~(-8)micromol/mol;(2)从每年9月到次年2月,甲烷月平均浓度以不同增速持续增加;(3)4月到9月,甲烷月平均浓度下降,期间出现下降幅度波动,下降幅度小于上升幅度;(4)2月到4月处于阶段性峰值;(5)甲烷季节浓度的大小关系为:春季冬季夏季秋季。在空间分布方面:(1)全球近地面甲烷白天的浓度与夜晚浓度分布不同,有集聚现象;(2)从整体上分析,甲烷主要分布在北半球,南半球甲烷稀少;(3)从洲际上分析,亚欧非大陆甲烷浓度大于美洲大陆。(4)从地区分析,甲烷浓度分布不均匀;(5)从海陆分析,陆地甲烷浓度明显大于海洋。     

Analyzing CO2 concentration changes and their influencing factors in Indonesia by OCO-2 and other multi-sensor remote-sensing data

We used OCO-2 products and considered three factors that potentially affect CO₂ concentration in Indonesia: sea surface temperature (SST), forest fires and vegetation. From 2014 to 2016, CO₂ concentration in Indonesia showed a trend of increase, which is consistent with the global increase reported by the Greenhouse Gases Observing Satellite (GOSAT) Project. As an archipelago country, the results indicate that SST has a direct effect on the CO₂ concentration in Indonesia. Their changing exhibits similar fluctuations; meanwhile, CO₂ concentration and SST also presented positive correlation. In 2015, the number of fire hotspots suddenly increased to 140,699, because of occurrence of the worst forest fire. Due to special geographic conditions, forest fires did not induce CO₂ concentration changes in Indonesia, but CO₂ concentration in the corresponding islands showed a trend of increase. CO₂ concentration increased in Kalimantan during the occurrence of forest fire in September–October 2014, and CO₂ concentration increased in Kalimantan and Sumatra during the occurrence of forest fire in September–October 2015. Vegetation indices were stable and presented no correlation with CO₂ concentration. This study demonstrated that OCO-2 is capable of monitoring CO₂ concentration at a regional scale; additionally, an effective method for using OCO-2 Level 2 products is proposed.