Abstract. A better understanding of greenhouse gas surface sources and sinks is required in order to address the global challenge of climate change. Spaceborne remote estimations of greenhouse gas atmospheric concentrations can offer the global coverage that is necessary to improve the constraint on their fluxes, thus enabling a better monitoring of anthropogenic emissions. In this work, we introduce the Adaptable 4A Inversion (5AI) inverse scheme that aims to retrieve geophysical parameters from any remote sensing observation. The algorithm is based on Bayesian optimal estimation relying on the Operational version of the Automatized Atmospheric Absorption Atlas (4A/OP) radiative transfer forward model along with the Gestion et Étude des Informations Spectroscopiques Atmosphériques: Management and Study of Atmospheric Spectroscopic Information (GEISA) spectroscopic database. Here, the 5AI scheme is applied to retrieve the column-averaged dry-air mole fraction of carbon dioxide (X_CO2) from measurements performed by the Orbiting Carbon Observatory-2 (OCO-2) mission, and uses an empirically corrected absorption continuum in the O₂ A-band. For airmasses below 3.0, X_CO2 retrievals successfully capture the latitudinal variations of CO₂, as well as its seasonal cycle and long-term increasing trend. Comparison with ground-based observations from the Total Carbon Column Observing Network (TCCON) yields a difference of 1.33 ± 1.29 ppm, which is similar to the standard deviation of the Atmospheric CO₂ Observations from Space (ACOS) official products. We show that the systematic differences between 5AI and ACOS results can be fully removed by adding an average calculated – observed spectral residual correction to OCO-2 measurements, thus underlying the critical sensitivity of retrieval results to forward modelling. These comparisons show the reliability of 5AI as a Bayesian optimal estimation implementation that is easily adaptable to any instrument designed to retrieve column-averaged dry-air mole fractions of greenhouse gases.

中文翻译：

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适应性4A反演（5AI）：从OCO-2观测资料中描述和首次XCO ₂检索

摘要。为了应对全球气候变化的挑战，需要更好地了解温室气体地表源和汇。对温室气体大气浓度的星载远程估计可以提供全球覆盖范围，这一覆盖范围对于改善对通量的限制是必要的，从而可以更好地监控人为排放。在这项工作中，我们介绍了自适应4A反演（5AI）反演方案，旨在从任何遥感观测中检索地球物理参数。该算法基于贝叶斯最优估计，该估计依赖于自动大气吸收图集（4A / OP）辐射前移模型的运行版本以及Gestion etÉtudedes Informations SpectroscopiquesAtmosphériques：大气光谱信息（GEISA）光谱数据库的管理和研究。在这里，采用5AI方案检索二氧化碳的列平均干燥空气摩尔分数（X _{CO 2}）来自轨道碳观测站2（OCO-2）任务进行的测量，并在O ₂ A波段使用了经验校正的吸收连续体。对于低于3.0 airmasses，X _{CO 2}的检索成功捕获CO的纬度变化₂，以及它的季节循环和长期增加的趋势。与总碳柱观测网络（TCCON）的地面观测结果进行比较，得出的差异为1.33±1.29 ppm，与大气CO ₂的标准偏差相似太空观测（ACOS）官方产品。我们表明，通过在OCO-2测量结果中添加平均计算值-观察到的光谱残差校正值，可以完全消除5AI和ACOS结果之间的系统差异，从而成为检索结果对正向建模的关键敏感性。这些比较表明5AI作为贝叶斯最佳估计实现方式的可靠性，可以轻松地适用于任何设计来检索列平均温室气体干燥空气摩尔分数的仪器。