沙尘气溶胶散射效应对大气二氧化碳卫星遥感反演误差的影响分析

二氧化碳(CO_(2))是一种重要的温室气体,利用卫星遥感可实现全球大气CO_(2)的持续大范围监测,对于制定减排策略应对全球气候变暖具有重要意义。大气中的气溶胶散射效应是制约卫星高精度CO_(2)反演的主要因素。现有研究表明,在地表反照率较高的地区,例如沙漠地区,卫星反演的CO_(2)大气柱平均干燥空气混合比(XCO_(2))比真实值普遍偏高,偏差程度可达到满足实际应用需求所允许误差的50%。然而目前学界对此还缺乏足够的理解和定量分析。围绕这一难点问题,本文应用精准的大气辐射传输模型和基于最优化估计的XCO_(2)全物理反演算法,分析和量化沙漠地区沙尘气溶胶散射效应所导致的XCO_(2)反演误差。本研究从气溶胶的3个重要特征变量出发,包括气溶胶光学厚度(AOD)、气溶胶层高(ALH)和单次散射反照率(SSA),解析沙尘气溶胶散射效应导致XCO_(2)卫星反演结果偏差的物理机制。研究结果表明卫星反演算法中低估沙尘气溶胶的AOD、低估ALH或高估SSA是沙漠地区XCO_(2)出现高估的可能原因。具体表现为:(1)在反演算法不考虑气溶胶的情况下,若实际AOD大于1.0,将导致XCO_(2)反演结果高估超过1%;(2)当AOD低估0.3至0.5时,XCO_(2)的反演结果将高估0.15%至1.28%;(3)当ALH低估超过0.6 km时,XCO_(2)将高估超过1%;(4)在高估单次散射反照率(SSA)的情况下,XCO_(2)反演结果偏高,但高估不超过0.15%。由本文的模拟实验可知,精准的气溶胶信息对于实现高精度的XCO_(2)反演极为关键。本文还探究了沙漠地区可能发生的“临界反照率”效应对反演结果的影响,并指出此效应可能是高亮地表中CO_(2)探测卫星无法同时量化气溶胶信息的根源。本文提出,为了解决这一难题,实际反演中需要综合气溶胶探测仪器的观测数据对气溶胶信息进行进一步约束以提高XCO_(2)的反演精度。本文结论可为未来卫星反演算法的改进提供参考依据。

Real-data assimilation experiment with a joint data assimilation system: assimilating carbon dioxide mole fraction measurements from the Greenhouse gases Observing Satellite

The performance of a joint data assimilation system（Tan-Tracker）,which is based on the PODEn4 Dvar assimilation method,in assimilating Greenhouse gases Observing SATellite（GOSAT） carbon dioxide（CO2） data,was evaluated.Atmospheric 3D CO2 concentrations and CO2 surface fluxes（CFs） from2010 were simulated using a global chemistry transport model（GEOS-Chem）.TheTan-Tracker system used the simulated CO2 concentrations and fluxes as a background field and assimilated the GOSAT column average dry-air mole fraction of CO2（X（CO2）） data to optimize CO2 concentrations and CFs in the same assimilation window.Monthly simulated X（CO2）（X（CO2）Sim）） and assimilated X（CO2）（X（CO2）,TT） data retrieved at different satellite scan positions were compared with GOSAT-observed X（CO2）（X（CO2）,obs）data.The average RMSE between the monthly X（CO2）,TT and X（CO2）,Obs data was significantly（30%） lower than the average RMSE between X（CO2）,Sim and X（CO2）,Obs）.Specifically,reductions in error were found for the positions of northern Africa（the Sahara）,the Indian peninsula,southern Africa,southern North America,and western Australia.The difference between the correlation coefficients of the X（CO2）,Sim）and X（CO2）,Obs and those of the X（CO2）Π）,TT and X（CO2）,Obs was only small.In general,the Tan-Tracker system performed very well after assimilating the GOSAT data.

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.

Analysis of XCO_2 retrieval sensitivity using simulated Chinese Carbon Satellite(TanSat) measurements

We present a study on the retrieval sensitivity of the column-averaged dry-air mole fraction of CO2(XCO2) for the Chinese carbon dioxide observation satellite(TanSat) with a full physical forward model and the optimal estimation technique. The forward model is based on the vector linearized discrete ordinate radiative transfer model(VLIDORT) and considers surface reflectance, gas absorption, and the scattering of air molecules, aerosol particles, and cloud particles. XCO2 retrieval errors from synthetic TanSat measurements show solar zenith angle(SZA), albedo dependence with values varying from 0.3 to 1 ppm for bright land surface in nadir mode and 2 to 8 ppm for dark surfaces like snow. The use of glint mode over dark oceans significantly improves the CO2 information retrieved. The aerosol type and profile are more important than the aerosol optical depth, and underestimation of aerosol plume height will introduce a bias of 1.5 ppm in XCO2. The systematic errors due to radiometric calibration are also estimated using a forward model simulation approach.

The TanSat mission： preliminary global observations

The Chinese global carbon dioxide monitoring satellite （TanSat） was launched successfully in December 2016 and has completed its on-orbit tests and calibration. TanSat aims to measure the atmospheric column-averaged dry air mole fractions of carbon dioxide （XCO2） with a precision of 4 ppm at the regional scale, and in addition, to derive global and regional CO2 fluxes. Progress towards these objectives is reviewed and the first scientific results from TanSat measurements are presented. TanSat on-orbit tests indicate that the Atmospheric Carbon dioxide GratingSpectrometer is in normal working status and is beginning to produce LIB products. The preliminary TanSat XCO2 products have been retrieved by an algorithm and compared to NASA Orbiting Carbon Observatory-2 （OCO-2） measurements during an over- lapping observation period. Furthermore, the XCO2 retrievals have been validated against eight groundsite measurement datasets from the Total Carbon Column Observing Network, for which the preliminary conclusion is that TanSat has met the precision design requirement, with an average bias of 2.11 ppm. The first scientific observations are presented, namely, the seasonal distributions of XCO2 over land on a global scale.

An advanced carbon dioxide retrieval algorithm for satellite measurements and its application to GOSAT observations

An advanced carbon dioxide retrieval algo- rithm for satellite observations has been developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences. The algorithm is tested using Greenhouse gases Observing SATellite （GOSAT） LIB data and validated using the Total Column Carbon Observing Network （TCCON） measurements. The retrieved XCO2 agrees well with TCCON measurements in a low bias of 0.15 ppmv and RMSE of 1.48 ppmv, and captured the seasonal vari- ation and increasing of XCO2 in Northern and Southern Hemisphere, respectively, as other measurements.