基于隐马尔可夫模型分析不同天气模态下东亚地区近地面CO2浓度变化特征

项目名称： 基于隐马尔可夫模型分析不同天气模态下东亚地区近地面CO2浓度变化特征

项目编号： No.41505132

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 王一楠

作者单位： 中国科学院大气物理研究所

项目金额： 21万元

中文摘要： 天气尺度大气CO2浓度变化对全球和区域碳源、汇的定量反演及碳卫星遥感的代表性误差有显著影响。太阳辐射同时强迫植物光合作用和大气热对流过程，导致两者在多时空尺度上存在相关性，了解天气尺度大气运动和地表碳源、汇的耦合机制对准确评估大气中CO2浓度变化具有重要意义。此外，诸如锋面、气旋等天气尺度系统发生时，常有云生成，而云通过削减太阳辐射会影响植被光合作用强度，引起天气尺度CO2浓度变化，进而可能造成碳卫星反演结果的时空代表性误差。因此，本项目利用CO2浓度地基观测数据和气象场再分析资料，应用隐马尔可夫数学模型并结合三维大气化学传输模式GEOS-Chem，开展针对不同天气条件下东亚地区近地面CO2浓度变化特征的研究，定量分析影响天气尺度CO2浓度变化的驱动因子，探讨东亚区域天气尺度大气运动和地表碳源、汇之间的耦合机制，进而为充分合理利用碳卫星数据和减小东亚区域碳源汇反演的不确定性提供研究基础。

中文关键词： 隐马尔可夫模型；GEOS-Chem；天气模态；近地面CO2浓度；碳卫星反演

英文摘要： Synoptic variations of atmospheric CO2 have a significant influence on global and regional inversions for carbon sources and sinks, which also likely causing representation errors of carbon satellite retrievals. Solar radiation drives both photosynthesis and thermal convection, resulting in a covariance between the biospheric flux and atmospheric transport on many spatial and time scales. In order to better evaluate the synoptic variations of atmospheric CO2, we need to understand the mechanisms of the coupling between the surface carbon flux and atmospheric transport. The cloud generated by frontal and cyclone process affects the strength of biospheric photosynthesis through reducing solar radiation and causes synoptic variations of CO2 concentration, which may introduce representativeness errors of satellite retrievals. Thus, this project utilizes near surface CO2 observations and meteorological reanalysis data to investigate the synoptic variations of near surface CO2 concentration in East Asia by combining a Hidden Markov Model and a three-dimensional global chemical transport model-GEOS-Chem. Further quantitative analysis of driving factors that controlling synoptic variations of CO2 could promote better knowledge of the atmospheric carbon dioxide transport mechanisms. These results will provide valuable information for taking full advantages of carbon satellite data and decrease the uncertainty of regional inversions of carbon fluxes in East Asia.

英文关键词： Hidden Markov Model;GEOS-Chem;Synoptic Pattern;Near Surface CO2 Concentration;Carbon Satellite Retrieval