the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Apr 2025
Electron-Driven Variability of the Upper Atmospheric Nitric Oxide Column Density Over the Syowa Station in Antarctica
Abstract. In the polar middle and upper atmosphere, Nitric Oxide (NO) is produced in large amounts by both solar EUV and X-ray radiation and energetic particle precipitation, and its chemical loss is driven by photodissociation. As a result, polar atmospheric NO has a clear seasonal variability and a solar cycle dependency which have been measured by satellite-based instruments. On shorter timescales, NO response to magnetospheric electron precipitation has been shown to take place on a day-to-day basis. Despite recent studies using observations and simulations, it remains challenging to understand NO daily distribution in the mesosphere-lower thermosphere during geomagnetic storms, and to separate contributions of electron forcing and atmospheric chemistry and dynamics. This is due to the uncertainties existing in the available electron flux observations, differences in representation of NO chemistry in models, and differences between NO observations from satellite instruments. In this paper, we use mesospheric-lower thermospheric NO column density data measured with a millimeter-wave spectroscopic radiometer at the Syowa station in Antarctica. In the period 2012–2017, we study both the long-term and short-term variability of NO. Comparisons are made with results from the Whole Atmosphere Community Climate Model to understand the shortcomings of current electron forcing in models and how the representation of the NO variability can be improved in simulations. We find that, qualitatively, the simulated year-to-year variability of NO is in agreement with the observations. On the other hand, there is up to a factor of two underestimation of the NO column density in wintertime, and the model captures only 27 % of the measured magnitude in the day-to-day variability. The observed day-to-day variability has a good correlation with three different geomagnetic indices, indicating the importance of electron forcing in atmospheric NO production. Using electron flux measurements from the Arase satellite, we demonstrate their potential in atmospheric research. Our results call for improved representation of electron forcing in simulations to capture the observed day-to-day variability.
Competing interests: Yoshizumi Miyoshi is a member of the editorial board of Annales Geophysicae.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 06 Jun 2025)
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RC1: 'Comment on egusphere-2025-1691', Anonymous Referee #1, 16 May 2025
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General Comments
This study contributes to the fields of atmospheric and magnetospheric science by providing observational evidence that motivates improved proxies for EEP in global atmospheric models as well as obtaining more resolved spatial observations of energetic electron precipitation (EEP). This paper will likely be cited frequently to justify future observations and modeling studies involving energetic electron precipitation. This work compares radiometer measurements at Syowa Station from 2012-2017 with WACCM model simulations. The goal is to better understand the connections between NO concentrations in the upper atmosphere to geomagnetic activity and associated electron precipitation. The long-term, continuous, radiometer dataset and WACCM model (WACCM6 with meteorological reanalysis, ionospheric chemistry, ApEEP for medium-energy electrons, and Fang 2010 ionization) specifically highlights the role of medium energy electrons on both short and long-term NO variability in the mesosphere and upper thermosphere.
Strengths
This paper confirms that WACCM captures the observed year-to-year and seasonal variability of NO. The paper links day-to-day variability with geomagnetic indices and demonstrates the dominance of electron forcing over atmospheric dynamics in variability of NO during polar winter. (Section 3.4 on the polar vortex is useful for demonstrating that dynamical causes in day-to-day variability are likely not as significant as EEP.)
Another valuable conclusion is that WACCM underestimates NO column density in winter and does not adequately capture day-to-day variability. This most likely results from the statistically smoothed ApEEP proxy model for electron flux and demonstrates the need for proxies that include better representation of peaks in electron precipitation. Consequently, this study also motivates the need for more spatially and temporally resolved observations of energetic electron flux.
This paper also presents simulations driven by a series of events based on Arase measurements, providing an example of the role of future observations in improving estimates of electron flux driving the modeled atmospheric ionization.
Major Recommendations
The paper would benefit from additional discussion of the following topics:
1. The spatial extent and duration of EEP events. Describe what is known (and what is not known) about the spatial extent and durations of EEP (MEE) events. How does this spatial scale compare with timescales of zonal mixing from localized EEP events at the Syowa latitude? How much of the WACCM underestimate in day-to-day variability is consistent with zonal and latitudinal mixing of sporadic precipitation events? Will improving the day-to-day variability also improve the discrepancies in 31-day averages (I assume this is the implication, but it is never explicitly addressed)? The paper alludes to atmospheric dynamic mixing in lines 347-350…but a more detailed analysis and more discussion in the context of this paper’s conclusions would be useful (referencing, for example, discussions of MLT dependence in Verronen et al., 2020).
2. Compare the ApEEP statistical proxy used in WACCM to other datasets of EEP. It would be useful to briefly discuss known weaknesses of ApEEP, for example as summarized in Nesse Tyssøy et al. (2021) "HEPPA III intercomparison experiment on electron precipitation impacts: 1. Estimated ionization rates during a geomagnetic active period in April 2010." https://doi-org.unh.idm.oclc.org/10.1029/2021JA029128
The ApEEP model only takes into account the 0 degree MEPED telescope from the POES satellites and is known to underestimate electron flux. There have been efforts to include data from both 0 and 90 degree telescopes to produce electron precipitation maps that would be good to reference, such as Pettit et al. (2021), "A new MEPED‐based precipitating electron data set", https://doi-org.unh.idm.oclc.org/10.1029/2021JA029667
How might using other indices (Ap, Dst, AE) improve model results? (It’s my understanding that there is also van de Kamp et al. Dst proxy similar to ApEEP.) What is the value of including higher energy electrons in WACCM, such as electron precipitation from EMIC waves that can reach lower altitudes? See Capannolo et al. (2023), "Electron precipitation observed by ELFIN using proton precipitation as a proxy for electromagnetic ion cyclotron (EMIC) waves" https://doi-org.unh.idm.oclc.org/10.1029/2023GL103519. And Capannolo et al. (2019) "Direct observation of subrelativistic electron precipitation potentially driven by EMIC waves". https://doi-org.unh.idm.oclc.org/10.1029/2019GL084202.
3. Strengthen Discussion and Conclusions sections. Provide more detail and insights into what is needed for future studies as a result of this study. For example, how can results shown in Figure 7 be used to improve electron precipitation estimates used to drive WACCM (currently based on Ap)? Are there examples of the “stochastic approach” recommended in lines 368-370? More discussion of how this study motivates next steps would be compelling, such as whether conclusions are consistent with recommendations in Sinnhuber et al. (2021) as well as articles such as Pettit et al. (2023), "Investigation of the drivers and atmospheric impacts of energetic electron precipitation. Frontiers in Astronomy and Space Sciences" https://doi.org/10.3389/fspas.2023.1162564. Adding a few additional sentences in the Conclusion to place the list of specific outcomes in context with other studies and promote future work would greatly enhance the impact of the paper.
Minor Recommendations
Line 40 “Ground-based radiometers provide a regional view on [of] NO variability.” Recommend explaining how localized measurement from a radiometer can be viewed as regional. Line 76 states, “The horizontal size of the observe area is estimated to be ~2 km at an altitude of 100 km”. I assume the regional aspect comes from the continuous measurements as winds transport enhanced NO over the site?
Line 97-99. Simplify (or split) the sentence. For example: “This analysis uses WACCM data co-located with Syowa Station to compare daily-averaged NO column density. Global model data are also used to locate the polar vortex.”
Lines 107-110. Is there a way to be more quantitative about how the 0-10 pitch angle observations from Arase map to the bounce loss cone at the top of the atmosphere? (I’m surprised it gives such good results and isn’t a huge overestimate with the mirrored particles).
Lines 111-112. How might the BERI (Boulder Electron Radiation to Ionization) model affect ionization rates? (If it might be significant, recommend adding a reference to let readers know this ionization scheme is also available).
Lines 135-136. Does the 27% of “observed magnetic variability” refer to the slope? If yes, why is the slope used instead of the coefficient of determination (R2 = 0.42) to compare the variability between model and observations?
Lines 137-138. Why is there a lower bound to NO column densities in WACCM? (Also suggest finding a better word than “saturate”)
Lines 194-196. Simplify sentence. For example, “However, here we use geomagnetic indices to relate EPP events with geomagnetic disturbances.”
Lines 194-197. Recommend briefly describing the difference in Ap, Dst, and AE indices and why one might expect EEP to behave differently with each. This is done for Dst, but more explanation would be useful for what each index means with respect to magnetospheric disturbances that lead to electron precipitation.
Lines 265 – 274. Are the Arase events used for the electron forcing of WACCM all at similar L-shells as Syowa Station (as in Figure 8?) How many hours MLT does Arase travel through during the 12-hour averaging period? Could the radiometer be detecting peaks of local MEE events that both Arase and ApEEP smooth out because of zonal mixing?
Lines 350-351. What does this sentence mean? Doesn’t the choice of proxy for EEP determine the magnitude of forcing?
Lines 366-368 Clarify this sentence.
Figures and Tables
Figures 3 and 4. Recommend adding legends to the contour colors (the annotated labels are too small to read).
Table 1. Recommend re-labeling “Peak NO” to “Peak NO difference” or “Peak ∆NO” in column labels. Even though this is explained in the caption, it would be easy for the reader to mistake the NO column as representing densities during the peak instead of differences.
Figure 9. Recommend changing the vertical axis scale to make the daily variability easier to see. Is there a need to include values less than zero? Or could those be omitted (and noted in caption) to help with the visual comparison?
Citation: https://doi.org/10.5194/egusphere-2025-1691-RC1
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