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| 卫星遥感中国区域大气CO2柱浓度时空特征与不确定性分析 | |
| 其他题名 | Spatio-temporal Patterns and Uncertainty Analysis of Satellite Retrieved Atmospheric CO2 Columns in China |
| 别念 | |
| 出版年 | 2018 |
| 学位类型 | 博士 |
| 导师 | 雷莉萍 |
| 学位授予单位 | 中国科学院大学 |
| 中文摘要 | CO2是大气中最重要的温室气体，自工业革命以来大气CO2浓度逐年增高，极大影响着全球气候变化。卫星观测大气CO2柱浓度（XCO2）数据覆盖的地理范围广泛，为研究大气CO2含量变化提供了有力的数据支撑。然而，卫星遥感观测CO2存在以下两个主要问题：（1）卫星反演XCO2的精度受到地表状况、观测大气条件等影响，导致数据的不确定性情况复杂，因而全球范围内卫星观测XCO2的时空特征具有很大的不确定性，尤其是中国区域；（2）目前存在多种卫星反演算法得到的XCO2产品，却不存在一个周全的评价方法分析算法产品的表现。本研究针对这两个问题，在中国这个特殊区域，主要从以下两个方面展开：（1）搜集高分辨率格网排放数据（CHRED）调整模型的人为排放CO2通量数据，应用大气化学传输模型GEOS-Chem，特别是其中的高空间分辨率部分——GEOS-Chem亚洲区域嵌套模型，更精细地进行XCO2的模型模拟；（2）建立一个周全的评价方法，该评价方法充分考虑大气CO2本身的特性和时空特征，通过算法产品之间、模型模拟数据集与算法产品的相互比较，以此分析各个算法XCO2产品的表现。研究具体内容如下：获取了中国区域高时空分辨率CO2浓度数据集，并借助这套数据，对中国区域卫星反演XCO2数据进行空间不确定性评估。在此基础上，选择卫星反演数据异常区域，进一步借助获取的模型模拟CO2浓度数据集，对5种GOSAT反演算法（ACOS、NIES、OCFP、SRFP 和EMMA）的XCO2数据产品展开时空不确定性分析，通过算法产品之间、模型模拟数据集与算法产品的相互比较，评价了各个算法产品的区域整体表现，并获取了区域卫星反演XCO2的区域误差特征。此外，本研究针对影响反演精度的两个重要因子——气溶胶光学厚度和地表反照率，展开讨论，分析XCO2不确定性的时空特征与两个因子的关系及各算法的反演机理的内在联系。研究结果表明：（1）卫星反演XCO2在中国华北的高值合理地反映了CO2浓度与人为排放的正相关关系，而东北的低值则反映了CO2浓度与强植被吸收的负相关关系；在西北显示的高值很有可能是该荒漠地带的高气溶胶和高地表反照率导致卫星反演误差较大所造成的异常现象；（2）在典型研究区域，5种算法的卫星反演XCO2数据在东部人为排放量大的区域的一致性比西部高亮度地表的沙漠更好；（3）ACOS、NIES、OCFP、SRFP 和EMMA 5种算法产品中，在中国区域ACOS与SRFP表现最好；（4）在气溶胶光学厚度和地表反照率均处于较高水平时，卫星反演XCO2不确定性会随着二者之一或者二者的同时增加而变大。本研究的创新点主要在于：1、 更精细的模拟了中国区域大气CO2浓度的时空变化。使用经环保部基于点源排放统计的高分辨率数据集CHRED调整人为排放通量，在GEOS-Chem高分辨率亚洲嵌套模型作用下，本研究获取了更精细的模型模拟高时空分辨率CO2浓度数据集。2、 建立了一种卫星反演XCO2数据时空不确定性分析和评价方法。该方法选取地表特征典型区域，从以下三个方面综合评估卫星反演XCO2：（a）卫星反演XCO2与模型模拟XCO2的区域偏差特征及偏差的时空特征；（b）不同算法数据集XCO2的区域偏差特征及偏差的时空特征；（c）根据大气CO2浓度的季节循环规律及主要影响因子（如人为排放）等先验知识，评估各XCO2数据集时空变化特征的合理性。研究表明，特别是在缺少高精度地面站点观测数据的情况下，利用模型模拟及多种算法之间数据集的比较为评估卫星观测结果提供了有效解决方案。 |
| 英文摘要 | Atmospheric CO2 is the most important greenhouse gas in atmosphere. Atmospheric CO2 concentration has been increasing year by year since the Industrial Revolution began, which greatly affects global climatic change. Satellite-based column-averaged dry air mole fraction of CO2 (XCO2) provides atmospheric CO2 concentration data on a large geographic scale, which is greatly supportive for study in change of CO2 concentarion in atmosphere. However, the following two main problems exist in satellite-based XCO2: (1) the precision of satellite retrieved XCO2 is influenced by surface state, observed atmospheric condition et al., which leads to complicated uncertainty in XCO2 data. As a result, large uncertainty exists in spatio-temporal patterns of satellite-based XCO2 all over the globe, especially in China; (2) Mutiple XCO2 data products are released from different retrieval algorithms, but a general criterion in assessing their performance is still not available yet. Aiming at the above two, focusing on the special area, China, this study is carried out from the following two :(1) use atmospheric chemical transport model GEOS-Chem, particularly the high-resolution nested model in East Asia and introduce the Chinese High Resolution Emission Gridded Data (CHRED) to GEOS-Chem simulations of CO2 over the Chinese mainland as anthropogenic CO2 emissions data.In this way, XCO2 are simulated in a more finer scale than ever. (2) Build a general criterion for assessing the performance of individual XCO2 products from different retrieval algorithms, which take full consideration of CO2 characterisitcs and spatio-temporal patterns with intercomparison among model simulation XCO2 and different satellite-based XCO2 products. The detailed study is as bellow: Model simulations of CO2 at high spatio-temporal resolution and the finest scales ever are obtained in China. This data set is then used to assess spatial uncertainty in satellite retrieved XCO2 in China. Additionally, this study chooses typical regions and pays attention to analyze spatio-temporal uncertainty in satellite retrieved XCO2 using XCO2 data sets from five GOSAT retrieval algorithms (ACOS, NIES, OCFP, SRFP and EMMA) as well as GEOS-Chem simulation XCO2. After intercomparisons among model simulation XCO2 and different satellite-based XCO2 products, the general performance of each individual satellite XCO2 products as well as the regional error characteristics is finally acquired. Furthermore, this study discusses the most likely attribution affecting factors on retrieval precision, including aerosol and surface albedo, and reveals how regional uncertainty in satellite retrieved XCO2 relates to them as well as the internal functioning mechanism of each retrieval algorithm.The study results indicate that: (1) satellite retrieved XCO2 in China reasonably reflect positive correlation of XCO2 with anthropogenic emissions by showing maximums in the north and negative correlation with strong absorption from vegetation by showing minimums in the northeast. The maximum region in the northwest is likely an illusion induced by high XCO2 retrieval error attributed to the combined effect of aerosol and albedo in deserts. (2) XCO2 retrievals from five algorithms demonstrate better agreement in eastern regions with strong anthropogenic emissions than those in western grids of desert with high brightness surface. (3) ACOS and SRFP are found to perform better than the other three algorithms (NIES, OCFP and EMMA). (4) The uncertainty in satellite retrieved XCO2 is likely to increase with AOD or albedo when both AOD and albedo are high.This study makes the following two major achievements:1. Spatio-temporal patterns of atmospheric CO2 concentration are simulated in finer scales than ever. CHRED, which is generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China, is introduced to GEOS-Chem nested simulations of CO2 over China. As a result, this study obtains a finer CO2 concentration simulation data set of high spatio-temporal resolution. 2. A general criterion about how to analyze and assess spatio-temporal uncertainty in satellite retrieved XCO2 are brought up. In study area with typical characteristics of land cover, this method designs to comprehensively assess satellite retrieved XCO2 from the following three aspects: (a) regional and spatio-temporal bias patterns between satellite retrieved XCO2 and model simulation XCO2; (b) regional and spatio-temporal bias patterns among XCO2 data sets retrieved by different algorithms; (c) the reasonability assessment about displayed spatio-temporal patterns of different XCO2 data sets, based on prior knowledge of atmospheric CO2 such as seasonal cycle and main influencing factors (anthropogenic emissions). This study has proved that using inter-comparisons between multiple XCO2 data sets, generated from satellite retrieval algorithms or model simulation, is effective in assess satellite retrieved XCO2 especially when high precision ground based measurements are not available. |
| 中文关键词 | CO2观测卫星 ; GEOS-Chem ; 时空特征 ; 不确定性分析 ; CO2柱浓度 |
| 英文关键词 | Greenhouse Gases Observing Satellite GEOS-Chem XCO2 Spatio-temporal patterns Regional uncertainty |
| 语种 | 中文 |
| 国家 | 中国 |
| 来源学科分类 | 地图学与地理信息系统 |
| 来源机构 | 中国科学院遥感与数字地球研究所 |
| 资源类型 | 学位论文 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/288203 |
| 推荐引用方式 GB/T 7714 | 别念. 卫星遥感中国区域大气CO2柱浓度时空特征与不确定性分析[D]. 中国科学院大学,2018. |
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