Spatial and temporal variations in infrared emissions of the upper atmosphere. 2. 15-μm carbon dioxide emission

2017 ◽  
Vol 57 (5) ◽  
pp. 597-601 ◽  
Author(s):  
A. I. Semenov ◽  
I. V. Medvedeva ◽  
V. I. Perminov ◽  
Yu. A. Zheleznov
Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Aaron Sidder

Infrared emissions from nitric oxide and carbon dioxide in Earth’s upper atmosphere, which are closely tied to incoming solar radiation, are drastically lower than in the previous solar cycle.


Atmosphere ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 231
Author(s):  
Zhenghan Lv ◽  
Yusheng Shi ◽  
Shuying Zang ◽  
Li Sun

Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China.


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