Airglow intensity-weighted temperature variations arise due to a variety of different factors. These papers focus on three factors: the increase of atmospheric CO2 concentration, solar cycle variation (F10.7 as a proxy), and geomagnetic activity (Ap index as a proxy).

Part 1 of this series of papers explores the quantitative results of simulations and analyzes how the three aforementioned factors influence airglow temperatures. Two airglow models (MACD-00 and OHCD-00) are used to simulate the O(1S) greenline, O2(0,1) atmospheric band, and OH(8,3) airglow temperature variations. According to the simulations, all three temperatures are highly correlated with the Ap index and O2(0,1) and O(1S) are moderately correlated with F10.7, with the OH temperature showing an anti-correlation. The O2(0,1) temperature has a larger response to F10.7 and Ap index variations compared to the O(1S) temperature; both temperatures have a positive correlation with the F10.7 and Ap indexes. On the other hand, the OH(8,3) temperature, which has the smallest response F10.7 and Ap index variations, is anticorrelated with the two indexes. The negative, small-magnitude trend for the OH(8,3) temperature may be due to the fact that NRMLSISE-00 that outputs kinetic temperature does not explicitly include the F10.7 effect below 110 km, and that there may be regions like in the OH airglow region that were under-sampled. The results of the simulations show that all three airglow temperatures decrease linearly over time in response to an increase in CO2 gas concentration. The results also demonstrate that geomagnetic activity can have a rather significant effect on airglow temperatures.

In Part 2 of this series of papers, the simulated data is compared to observational data from SABER (an instrument on NASA’s TIMED satellite). In the study, data associated with the geographic location 18° N, 290° E is used. Kinetic temperatures are selected from altitudes of 89 km, 95 km, and 97 km (corresponding to OH(8,3), O2, and O(1S) peak altitudes, respectively). From this data, annual average SABER temperatures are obtained. Linear regression performed for the annual average zonal mean SABER temperatures shows that the temperature has a high correlation with F10.7 (R=0.8972 to R=0.9309) and a moderate correlation with Ap (R=0.558 to R=0.6041). A time series for the O2 temperature overlaid with F10.7 and the Ap index and a trend analysis of the O2 temperature as a function of F10.7 and of the Ap index are shown below.

Finally, linear regression is performed with a 1-year shift in the Ap index and an unchanged time series of annual mean zonal mean SABER temperature; the correlation with the Ap index improves further in this case.

 

References: 

Huang, Tai-Yin and Vanyo, Michael. (2020). Trends in the Airglow Temperatures in the MLT Region—Part 1: Model Simulations, Atmosphere, 11, 468, doi.org/10.3390/atmos11050468.

Huang, T.-Y. and Vanyo, M. (2021). Trends in the Airglow Temperatures in the MLT Region – Part 2: SABER Observations and Comparisons to Model Simulations, Atmosphere. 2021; 12(2):167. https://doi.org/10.3390/atmos12020167.