Quantifying the scattering effect of dust aerosols on satellite remote sensing retrievals of atmospheric carbon dioxide
Carbon dioxide(CO2)is an important greenhouse gas.Satellite remote sensing of atmospheric CO2 has the advantages of long-term and wide spatial range observation,which is crucial for verifying emission reduction strategies to cope with global warming.Aerosol scattering in the atmosphere is considered a major obstacle for remote sensing retrieval of CO2 with high accuracy.Previous studies have shown that over areas with high surface albedo,such as desert regions,satellite retrievals of atmospheric column-average dry-air mole fraction of CO2(XCO2)are systematically overestimated,and the bias can reach 50％of the allowable error to meet the practical application requirements.However,sufficient understandings and quantitative analysis of the systematic bias are still lacking.Focusing on this difficult problem,this thesis analyzes and quantifies the bias of XCO2 retrievals caused by the scattering effect of dust aerosol over desert regions using an accurate atmospheric radiative transfer model and a retrieval algorithm based on optimal estimation.This study starts from three important representative variables of aerosols,including aerosol optical depth(AOD),aerosol layer height(ALH),and single scattering albedo(SSA),to illustrate the physical mechanism of dust aerosol scattering effects on XCO2 remote sensing retrievals.From the perspective of spectral radiance generated from forward radiative transfer model,increasing AOD leads to a decrease in the spectrum continuum level(defined as radiance of channels where gas absorption can be neglected)in the case of high surface albedo through its extinction effect.Increasing ALH causes reduced relative absorption depth(defined as the ratio of radiance difference between continuum level and absorption channels to continuum level),which is closely related to the XCO2 retrievals.From the perspective of retrieval model,this thesis conducts separate retrieval experiments using the O2A band and the WCO2 band,respectively,and joint retrieval experiment using both bands.Results show that the underestimation of AOD or ALH of dust aerosols or the overestimation of SSA in satellite retrieval algorithms can be possible causes of the overestimation of XCO2 over deserts.Specifically,(1)in the case of not considering aerosol in the retrieval algorithm,XCO2 retrievals are overestimated by more than 1％when the actual AOD is larger than 1.0;(2)when AOD is underestimated by a value between 0.3 and 0.5,XCO2 retrievals are overestimated by 0.15％-1.28％;(3)when ALH is underestimated by more than 0.6 km,XCO2 retrievals are overestimated by more than 1％;(4)when SSA is overestimated,XCO2 retrievals are also overestimated but by no more than 0.15％.These simulation experiments reveal that accurate aerosol information is crucial to achieving accurate atmospheric XCO2 retrievals.Additionally,this thesis discusses the impact of potential"critical albedo"on retrievals and demonstrates that its effect is probably the cause of the bias in extracting useful aerosol information from CO2 monitoring satellites.This thesis proposes that this difficult problem can be addressed when observations from aerosol-observing instruments are included in actual retrievals to further constrain the aerosol information to improve the accuracy of XCO2 retrievals.
dust aerosolcarbon dioxide satelliteremote sensing retrieval algorithmscattering effectradiative transfer model
万方数据
维普
