2010年—2020年全球陆地区域大气CO2时空变化特征分析
Spatio-temporal characteristics of CO2 in global land area from 2010 to 2020 based on GOSAT satellite data
- 2023年27卷第8期 页码：1782-1791
纸质出版日期： 2023-08-07
DOI： 10.11834/jrs.20232508
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摘要
本文基于2010年—2020年GOSAT卫星二氧化碳柱总量产品，从全球空间分布、纬向分布、区域分布、年际与季相变化特征等方面，分析了大气CO
2
的时空变化特征。结果表明，2010年—2020年全球陆地区域大气CO
2
浓度持续上升，年均值从387.42 ppm上升至410.32 ppm，年均增长率约为2.33 ppm/a，其中2016年全球陆地区域平均大气CO
2
浓度首次超过400 ppm，年增长量超过3 ppm，为近10年最高。季相变化方面，北半球春季最高、夏末秋初最低，南半球波动相位相反，波动幅度北半球远高于南半球，且纬度越高波动越大。纬向分布特征明显，从南向北总体呈现先升高后降低，受到云及数据质量控制等因素影响，在赤道附近，存在较为明显的下降，峰值出现在0°—10°N和30°N—40°N。区域分布差异较大，多年均值的最大值出现在南美热带，最小值出现在北美北部，差异将近30 ppm；年均增长率方面亚洲温带最高，亚洲北部最低，分别为2.36 ppm/a和2.27 ppm/a。
Abstract
Based on the total CO
2
column products of the GOSAT satellite from 2010 to 2020
this paper analyzes the spatio-temporal variation characteristics of atmospheric CO
2
concentration
including global spatial distribution
latitudinal distribution
regional distribution
interannual and seasonal variation characteristics. The comparison and verification results with TCCON ground-based observation data demonstrate that GOSAT XCO2 products have high observation accuracy and can be applied to large-scale analysis. The results show that the CO
2
concentration in the global land area continued to rise from 2010 to 2020
with an average annual value rising from 387.42 ppm to 410.32 ppm
and the average annual growth rate is about 2.33 ppm/a. Taking 2015 as the boundary
the average annual growth from 2010 to 2015 was 2.12 ppm
while the average annual growth from 2016 to 2020 was 2.46 ppm. In 2016
the average CO
2
concentration in the global land area exceeded 400 ppm for the first time
with an annual growth of over 3 ppm
the highest in the past decade. In terms of seasonal changes
the northern hemisphere has the highest in spring and the lowest in late summer and early autumn
while the southern hemisphere has opposite wave phases. The wave amplitude in the northern hemisphere is much higher than that in the southern hemisphere
and the higher the latitude
the greater the fluctuation. The latitudinal distribution characteristics are significant
showing an overall increase and then decrease from south to north. Influenced by factors such as cloud and data quality control
there is a significant decrease near the equator
with peaks appearing at 0°—10°N and 30°N—40°N. There are significant differences in regional distribution
with the highest annual mean occurring in the tropical South America and the lowest in the northern North America
with a difference of nearly 30 ppm. In terms of annual growth rate
the temperate zone of Asia has the highest
while the northern region of Asia has the lowest
with 2.36 ppm/a and 2.27 ppm/a respectively. In the past decade
the rise in CO
2
concentration in global terrestrial regions has been intensifying. At the same time
due to regional development differences
the spatial differences in CO
2
concentration are also intensifying globally. Based on the above results
we can obtain some insights. The concentration of CO
2
caused by industrial activities continues to rise
exacerbating global warming. The necessity and pressure for humans to control industrial emissions are increasing to ensure the sustainable development of humanity and the environment “Earth Home” that humans rely on for survival. Under the promotion of international frameworks and organizations such as the United Nations and the IPCC
the direction of human development and energy development has become a crucial theme
and the status and role of clean energy and alternative energy are particularly important. The global CO
2
monitoring satellite provides an effective and comprehensive monitoring data source for the above work
and there is huge space for the mining and utilization of this data information. The combination of satellite CO
2
observation and atmospheric transport models
as well as assimilation methods
can provide more effective tools for global monitoring and simulation.
关键词
GOSATCO2浓度全球陆地区域时空变化特征大气长时序遥感
Keywords
GOSAT satelliteCO2 concentrationglobal land areaspatio-temporal variation characteristicsatmospherelong time seriesremote sensing
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中国科学院空天信息创新研究院
中国科学院大学 电子电气与通信工程学院
中国科学院大气物理研究所
北京师范大学 遥感科学国家重点实验室
北京师范大学 地理科学学部 北京市陆表遥感数据产品工程技术研究中心
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