Influences of CO2 increase, solar cycle variation, and geomagnetic activity on airglow from 1960 to 2015
Introduction
Airglow has been known to be very sensitive to the atmospheric environment, and can be used to infer atmospheric structure, dynamics, and chemistry in the Mesosphere and Lower Thermosphere (MLT) region (Khomich et al., 2008, Huang and Hickey, 2008, Huang and George, 2014). Routine measurements of airglow emissions have been used to deduce wave characteristics or momentum fluxes for short-term variation studies (Hecht et al., 1995, Espy et al., 2004). There are also numerous studies of finding solar cycle variation or linear trend in airglow intensity on a much longer time scale, using airglow observations or numerical simulations (Wiens and Weill, 1973, Scheer et al., 2005, Das et al., 2011, Grygalashvyly et al., 2014, Von Savigny, 2015, Gao et al., 2016, Huang, 2013, Huang, 2016).
The study by Roble and Dickinson (1989) demonstrated that an increase of anthropogenic gas emissions in the lower atmosphere could affect the number densities of species in the upper atmosphere. The number densities derived from satellite drag measurements showed a declining trend that seems to support this, see Huang and Kane (2013) for their reviews of previous works on density trends. Realizing that airglow is very sensitive to the atmospheric environment, research has been done to find the influence of anthropogenic gas emissions like CO2 on airglow (Shepherd et al., 1997, Huang, 2013, Huang, 2016).
Attempts have been made to deduce signature of solar cycle variation in the airglow measurements. For example, Das et al. (2011) found a 20% variation in O(1S) greenline based on the observations from 1979 to 1995. Wiens and Weill (1973) found that OH intensity follows a solar activity cycle and that the airglow intensity increase can be as large as ∼25% over a solar cycle. However, Scheer et al. (2005) did not find any correlation between OH airglow and solar cycle based on four-year observations. They did find a possible positive correlation between O2(0,1) atmospheric band and solar cycle. A recent study by Gao et al. (2016) reported that nightglow emissions, including OH emissions, were highly correlated with solar radiation based on 13-year worth of data.
As is well known, airglow intensity is a result of column integration of Volume Emission Rate (VER). Recent studies have also investigated if there are trends in the airglow VER peak heights. Using OH(3,1) emission observations from 2003 to 2011, Von Savigny (2015) found no obvious long-term trends or solar cycle variation in the OH emission altitude. Gao et al. (2016) also did not find any obvious correlation between OH nightglow peak altitude and solar radiation. However, a study by Sivakandan et al. (2016) using the same data set as Gao et al. but focusing on a region over the Indian section found a weak decreasing trend at a rate of ∼20 m/year.
A previous study by Huang (2016) numerically simulated airglow response to the increase of CO2 gas concentration and solar cycle variation for a period from 1980 to 1991. The airglow emissions under study were OH(8,3), O2(0,1) atmospheric band, and O(1S) greenline. The study found that airglow intensity variations and their VERs track the F10.7 solar cycle variation and display a small linear trend by the CO2 increase. The results also showed that solar cycle variation and CO2 increase do not seem to systematically change VER peak heights. The study suggested that a longer time period for the simulation study would help us better understand how airglow responds to these influences. Following up on the previous work, this study has included more solar cycles for a time period of 55 years from 1960 to 2015. In addition, the influence of geomagnetic activity (Ap index as a proxy) is included. We use the same models as in Huang (2016), which used an OH Chemistry Dynamics (OHCD) model (Huang and Hickey, 2007, Huang and Hickey, 2008) and a Multiple-Airglow Chemistry Dynamics (MACD) model (Huang and George, 2014, Huang, 2015) that employed MSISE-90 (Hedin, 1991) as a reference model.
The paper is organized as follows. Data sources (CO2, F10.7, and Ap index) and the models are described in section 2. Results are presented in section 3. Discussion is in section 4 and Conclusions are in section 5.
Section snippets
Data sources and models
The CO2 gas concentration (in ppm) are obtained from the NOAA website (http://www.esrl.noaa.gov/gmd/ccgg/trends/). The F10.7 index values in Solar Flux Unit (SFU) are taken from the NASA website (http://omniweb.gsfc.nasa.gov/form/dx1.html), and they are used as a proxy for the 11-year solar cycle variation. The Ap index (nT) values are taken from the World Data Center for Geomagnetism, Kyoto website (http://wdc.kugi.kyoto-u.ac.jp/kp/). These data are further averaged to produce yearly average
Results
Using the analytic functions from Table 1 in Huang (2016), the number density of gas species and temperature induced by the change of CO2 gas concentration can be obtained. These values should be considered as the global average values since they were derived from a global average model used in Roble and Dickinson. Fig. 1 shows the number density and temperature percent change (axis on left) induced by the CO2 gas concentration percent change (axis on right) at the OH airglow peak altitude
Discussion
The main features of this work on airglow intensities and peak VERs are the same as those presented in Huang (2016), i.e., their variations track the F10.7 variation. However, the results on VER peak heights differ. Huang (2016) showed that airglow VER peak heights of OH(8,3) airglow and O(1S) greenline might be affected by F10.7 solar cycle variation or CO2 increase, e.g., see Fig. 7, Fig. 8, Fig. 9 of Huang (2016). The height variation was only within 0.1-km range. It was noted that a 0.1-km
Conclusions
Airglow variations in response to the influences of CO2 increase, solar cycle variation (F10.7 as a proxy), and geomagnetic activity (Ap index as a proxy) were simulated for a period of 55 years from 1960 to 2015 for three scenarios. Two airglow chemistry dynamics models, OHCD-90 and MACD-90, were used in the simulation study. Airglow intensity, airglow peak VER, and airglow VER peak height of OH(8,3) airglow, O2(0,1) atmospheric band, and O(1S) greenline were under the investigation.
Our
Acknowledgements
T.-Y. Huang acknowledges previous support from the US NSF AGS-1202019 to The Pennsylvania State University.
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