Longitudinal distribution of Io's dayside atmospheric density

The gist: The study uses 25 years of mid-infrared observations to determine two complete longitudinal distributions of Io’s equatorial SO2 column abundances, one representing perihelion and one representing aphelion, revealing significant hemispheric asymmetry and identifying frost distribution as a likely important factor alongside eclipses in driving this variability.

Longitudinal Variability Study

The research investigates the longitudinal variability of Io’s dayside atmosphere using TEXES high resolution mid-infrared spectrograph observations over 25 years to study the density of Io’s SO2-dominated atmosphere. The observations cover several SO2 absorption lines near 530 cm-1 and are used to retrieve the equatorial SO2 column density. By improving their modeling of longitudinal variability and using a seasonal model to apply a temporal normalization, they produce two complete longitudinal distributions of Io’s equatorial SO2 column abundances, one representing perihelion and one representing aphelion. Both distributions show the same hemispheric asymmetry that has been observed in previous studies, with the highest densities near 200◦W and the lowest densities near 305◦W. They find that the contrast between the maximum and minimum densities is a factor of ∼27 at aphelion and a factor of ∼16 at perihelion. Both longitudinal distributions are asymmetric, with a sharper drop-off on Io’s trailing hemisphere than on the leading.

Data Acquisition and Modeling

The study utilized 30 observing runs summarized in Table 1 to obtain the long-term dataset for TEXES observations. The observations were centered at 529.8 cm-1 (corrected for Io’s Doppler shift) and have a bandpass of ∼1.6 cm-1 across three cross-dispersed orders. The best-fit equatorial SO2 column density was retrieved for each independent observation using a χ2-minimization technique. The spectral model used a non-LTE model combined with a surface thermal emission model from G. J. Veeder et al. (1994) to generate synthetic top-of-atmosphere spectra. To account for longitudinal variability, the researchers performed an iterative process where they added the sub-observer longitude as a second variable when generating synthetic disk-integrated spectra. This process converged on a final longitudinal distribution using the updated forward model, which is now used to retrieve SO2 column densities for all 191 observations.

Seasonal and Temporal Normalization

The researchers employed a seasonal model to apply a temporal normalization to the longitudinal data, allowing them to combine observations from slightly different heliocentric distances into a single longitudinal distribution plot. They sub-selected data obtained near perihelion (heliocentric distance less than 5.04 AU) and near aphelion (greater than 5.04 AU) to study the two time periods. The two seasonal curves from the anti-Jovian and sub-Jovian hemispheres each produce different normalization factors, so for each data point, they use the central longitude to interpolate between the anti-Jovian curve and the sub-Jovian curve. This method produces two longitudinal distributions of Io’s equatorial SO2 column abundances, one representing perihelion and one representing aphelion.

Comparison with Other Drivers

The study compared their results to distributions expected if the spatial variability were primarily driven by volcanic activity, SO2-ice inhomogeneity or the effects of eclipses. Longitudinal distributions of volcanic activity from K. de Kleer et al. (2019) and M. Pettine et al. (2024) both show local minima in volcanic activity near 180◦W and do not correlate well with the atmospheric density distributions, suggesting that either volcanic activity does not drive Io’s atmospheric inhomogeneity, or that infrared volcanic thermal emission is not a good proxy for volcanic SO2 gas emission. In contrast, the SO2-ice distributions obtained from I. de Pater et al. (2020a), S. Dout´e et al. (2001), T. V. Johnson & C. Pilcher (1977) and A. S. McEwen et al. (1988) all show similarities with the atmospheric density distributions, with maxima on the anti-Jovian hemisphere, minima on the sub-Jovian hemisphere and sharper dropoffs on the trailing hemisphere than the leading. The effect of eclipses also produces significant hemispheric asymmetries (A. C. Walker et al. 2012; A.-C. Dott et al. 2025) but is insufficient to explain the longitudinal variability within the non-eclipsed anti-Jovian hemisphere. The researchers conclude that the longitudinal distribution of Io’s dayside atmospheric density is primarily driven by a combination of the eclipse effect and the inhomogenity of the SO2 surface ice, with volcanic activity playing a more minor role.

Key Findings on Drivers

The comparison with frost distribution shows that all four SO2 frost distributions show similarities with the longitudinal distribution of atmospheric SO2 obtained from their IRTF/TEXES data. All have the highest frost coverage on the anti-Jovian hemisphere, near to where the SO2 column density also peaks at ∼200◦W, and the lowest on the sub-Jovian hemisphere, where the SO2 column density has a minimum at ∼305◦W. They also all exhibit a sharper drop off in frost coverage on the trailing hemisphere (270◦W) than on the leading hemisphere (90◦W), which also matches the SO2 column density distribution. While simple vapor pressure equilibrium models suggest that only frost temperature controls column density, more complex models show that variations in fractional frost coverage alone can explain a factor of two difference in SO2 density between the anti-Jovian hemisphere and the sub-Jovian hemisphere.

Conclusion on Mechanisms

The final conclusion is that the longitudinal distribution of Io’s dayside atmospheric density is primarily driven by a combination of the eclipse effect and the inhomogenity of the SO2 surface ice. This finding is supported by the observation that eclipses produce significant hemispheric asymmetries but are insufficient to explain variability on the anti-Jovian hemisphere alone. The study also notes that they are not sensitive to the coupling between these two analyses when using longitudinal distributions derived from the seasonal model.

Data Availability and Acknowledgments

The reduced IRTF/TEXES data are available at the NASA/IPAC Infrared Science Archive. The authors were Visiting Astronomers at the Infrared Telescope Facility, which is operated by the University of Hawaii under contract 80HQTR19D0030 with the National Aeronautics and Space Administration. This work was funded by NASA Solar System Observations Grant 80NSSC21K1138. The authors performed the telescope observations, reduced the data, refined the spectral model and were responsible for writing and submitting the manuscript. The facilities used include IRTF(TEXES).

References

de Kleer, K., de Pater, I., Molter, E. M., et al. 2019, The Astronomical Journal, 158, 29.

de Pater, I., de Kleer, K., & Ad´amkovics, M. 2020a, The Planetary Science Journal, 1, 29.

de Pater, I., Goldstein, D., & Lellouch, E. 2023, in Io: A New View of Jupiter’s Moon (Springer), 233–290.

de Pater, I., Keane, J. T., de Kleer, K., & Davies, A. G. 2021, Annual Review of Earth and Planetary Sciences, 49, 643.

Dott, A.-C., Saur, J., Schlegel, S., et al. 2025, Journal of Geophysical Research: Planets, 130, e2024JE008869.

Dout´e, S., Schmitt, B., Lopes-Gautier, R., et al. 2001, Icarus, 149, 107.

Feaga, L. M., McGrath, M., & Feldman, P. D. 2009, Icarus, 218337.

Giles, R. S. 2026, Longitudinal distribution of Io’ s dayside atmospheric density [Dataset], Zenodo doi: 10.5281/zenodo.20273410.

Giles, R. S., Spencer, J. R., Tsang, C. C. C., et al.

1. The AI system can perform two complete longitudinal distributions of Io’s equatorial SO2 column abundances, one representing perihelion and one representing aphelion, which allows for a comparison of atmospheric density across different heliocentric distances.

2. The improved system can incorporate the updated seasonal model to apply a temporal normalization to the retrieved SO2 densities, allowing us to combine observations from slightly different heliocentric distances into a single longitudinal distribution plot.

3. The AI system can distinguish between potential drivers of longitudinal inhomogeneity by comparing the retrieved distributions against models of volcanic activity, SO2-ice inhomogeneity, and the effects of eclipses.

Since 2001, we have used the TEXES high resolution mid-infrared spectrograph at NASA's Infrared Telescope Facility to study the density of Io's SO 2-dominated atmosphere. The spectra cover several SO 2 absorption lines near 530 cm-1 and are used to retrieve the equatorial SO 2 column density. In addition to covering a long time period, our 191 independent observations also cover the full range of Io central longitudes, allowing us to study the longitudinal variability of Io's dayside atmosphere in more depth than has previously been possible. By improving our modeling of longitudinal variability and using a seasonal model to apply a temporal normalization to the retrieved SO 2 densities, we produce two complete longitudinal distributions of Io's equatorial SO 2 column abundances, one representing perihelion and one representing aphelion. Both show the same hemispheric asymmetry that has been observed in previous studies, with the highest densities near 200W and the lowest densities near 305W. We find that the contrast between the maximum and minimum densities is a factor of 27 at aphelion and a factor of 16 at perihelion. Both longitudinal distributions are asymmetric, with a sharper drop-off on Io's trailing hemisphere than on the leading. We compare these longitudinal distributions to the distributions expected if the spatial variability were primarily driven by volcanic activity, SO 2-ice inhomogeneity or the effects of eclipses. We conclude that the effect of eclipses is important, but insufficient to explain the shape of the observed longitudinal variability, and frost distribution is likely to also be important.