全球XCO的时空变化分析
Zekun Gao1, Yutong Jiang1, Junyu He2
1Ocean College, Zhejiang University, Zhoushan, China.
The Science of the total environment
|June 9, 2023
概括
这项研究整合了卫星数据,以创建高分辨率的全球二氧化碳 (CO2) 测量. DINEOF-BME方法准确地捕捉到二氧化碳趋势和季节性变化,这对气候研究至关重要.
科学领域:
- 地球和环境科学 地球和环境科学
- 大气科学 大气科学
- 遥感 遥感 遥感 遥感
背景情况:
- 对二氧化碳柱度 (XCO2) 的精确,高覆盖度的时空数据对于科学研究至关重要.
- 现有的卫星数据 (GOSAT,OCO-2,OCO-3) 需要整合以进行全面的全球分析.
研究的目的:
- 使用卫星遥感数据生成一个精确的,长期的全球XCO2数据集.
- 评估从2010年到2020年全球XCO2的时空变化和趋势.
主要方法:
- 整合了来自GOSAT,OCO-2和OCO-3卫星的XCO2数据.
- 应用数据接实证直角函数 (DINEOF) 和基于混合的方法 (BME) 的数据融合和接框架.
- 使用与总碳柱观测网络 (TCCON) 数据进行交叉比较的验证.
主要成果:
- 创建了一个全球XCO2数据集,从2010-2020年每月覆盖率超过96%.
- 与TCCON数据相比,实现了高的插值精度 (R2=0.920).
- 确定全球XCO2的上升趋势为~23ppm,具有明显的季节性模式和半球差异.
结论:
- DINEOF-BME框架提供了准确和可概括的XCO2数据集成.
- 由此产生的长期XCO2数据集和发现的时空变化为气候变化研究提供了重要的支持.
- EOF和波形分析证实了XCO2度的主导变化模式和周期性模式.
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