Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets
Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets
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DOI：
10.5194/essd-15-5105-2023
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发表时间：
2023-11
影响因子：
11.4
通讯作者：
S. Dhomse;M. Chipperfield
中科院分区：
文献类型：
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作者：
S. Dhomse;M. Chipperfield
抽象的。监测大气中温室气体(GHGs)的浓度对于提高我们对其气候影响的了解至关重要。然而，没有重要温室气体的长期概况数据集可以用来更好地洞察控制其在大气中变化的过程。在这项研究中，我们基于两台太阳掩星仪器：卤素掩星实验(HALOE)和大气化学实验-傅里叶变换光谱仪(ACE-FTS)的轮廓测量，对化学传输模型(CTM)的输出进行了修正。其目标是为两个重要的温室气体建立长期(1991-2021年)、无空隙的平流层剖面数据集，以下简称TCOM。为了估计需要应用于CTM配置文件的校正，我们使用极端梯度提升(XGBoost)回归模型。对于甲烷(TCOM-CH4)，我们利用1992年至2018年的HALOE和ACE卫星剖面测量数据来训练XGBoost模型，而2019年至2021年的剖面作为独立的评估数据集。由于没有更早年份的一氧化二氮(N2O)剖面测量，我们推导出XGBoost导出的校正项，仅使用2004-2018年的ACE-FTS剖面来构建TCOM-N2O剖面，并使用2019-2021年的剖面进行独立评估。总体而言，TCOM-CH4和TCOM-N2O剖面与现有的基于卫星测量的数据集显示出很好的一致性。我们发现，与评估剖面相比，在整个平流层，TCOM-CH4和TCOM-N2O的偏差通常分别小于10%和50%。每日纬向平均廓线数据集，涵盖海拔(15-60公里)和气压(300-0.1百帕)水平，可通过以下链接公开获得：TCOM-CH4的https://doi.org/10.5281/zenodo.7293740(Dhomse，2022a)和TCOM-N2O的https://doi.org/10.5281/zenodo.7386001(Dhomse，2022b)。
Abstract. Monitoring the atmospheric concentrations of greenhouse gases (GHGs) is crucial to improve our understanding of their climate impact. However, there are no long-term profile data sets of important GHGs that can be used to gain a better insight into the processes controlling their variations in the atmosphere. In this study, we apply corrections to chemical transport model (CTM) output based on profile measurements from two solar occultation instruments: the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS). The goal is to construct long-term (1991–2021), gap-free stratospheric profile data sets, hereafter referred to as TCOM, for two important GHGs. To estimate the corrections that need to be applied to the CTM profiles, we use the extreme gradient boosting (XGBoost) regression model. For methane (TCOM-CH4), we utilize both HALOE and ACE satellite profile measurements from 1992 to 2018 to train the XGBoost model, while profiles from 2019 to 2021 serve as an independent evaluation data set. As there are no nitrous oxide (N2O) profile measurements for earlier years, we derive XGBoost-derived correction terms to construct TCOM-N2O profiles using only ACE-FTS profiles from the 2004–2018 time period, with profiles from 2019–2021 used for the independent evaluation. Overall, both TCOM-CH4 and TCOM-N2O profiles exhibit excellent agreement with the available satellite-measurement-based data sets. We find that compared to evaluation profiles, biases in TCOM-CH4 and TCOM-N2O are generally less than 10 % and 50 %, respectively, throughout the stratosphere. The daily zonal mean profile data sets, covering altitude (15–60 km) and pressure (300–0.1 hPa) levels, are publicly available via the following links: https://doi.org/10.5281/zenodo.7293740 for TCOM-CH4 (Dhomse, 2022a) and https://doi.org/10.5281/zenodo.7386001 for TCOM-N2O (Dhomse, 2022b).