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Abstract Prior investigations have attempted to characterize the longitudinal variability of the column number density ratio of atomic oxygen to molecular nitrogen (O/N₂) in the context of non‐migrating tides. The retrieval of thermosphericO/N₂from far ultra‐violet (FUV) emissions assumes production is due to photoelectron impact excitation on O and N₂. Consequently, efforts to characterize the tidal variability inO/N₂have been limited by ionospheric contamination from O⁺ + e radiative recombination at afternoon local times (LT) around the equatorial ionization anomaly. The retrieval ofO/N₂from FUV observations by the Ionospheric Connection Explorer (ICON) provides an opportunity to address this limitation. In this work, we derive modifiedO/N₂datasets to delineate the response of thermospheric composition to non‐migrating tides as a function of LT in the absence of ionospheric contamination. We assess estimates of the ionospheric contribution to 135.6 nm emission intensities based on either Global Ionospheric Specification (GIS) electron density, International Reference Ionosphere (IRI) model output, or observations from the Extreme Ultra‐Violet imager (EUV) onboard ICON during March and September equinox conditions in 2020. Our approach accounts for any biases between the ionospheric and airglow datasets. We found that the ICON‐FUV data set, corrected for ionospheric contamination based on GIS, uncovered a previously obscured diurnal eastward wavenumber 2 tide in a longitudinal wavenumber 3 pattern at March equinox in 2020. This finding demonstrates not only the necessity of correcting for ionospheric contamination of the FUV signals but also the utility of using GIS for the correction. 

Abstract Most ionospheric models cannot sufficiently reproduce the observed electron density profiles in the E‐region ionosphere, since they usually underestimate electron densities and do not match the profile shape. Mitigation of these issues is often addressed by increasing the solar soft X‐ray flux which is ineffective for resolving data‐model discrepancies. We show that low‐resolution cross sections and solar spectral irradiances fail to preserve structure within the data, which considerably impacts radiative processes in the E‐region, and are largely responsible for the discrepancies between observations and simulations. To resolve data‐model inconsistencies, we utilize new high‐resolution (0.001 nm) atomic oxygen (O) and molecular nitrogen (N₂) cross sections and solar spectral irradiances, which contain autoionization and narrow rotational lines that allow solar photons to reach lower altitudes and increase the photoelectron flux. This work improves upon Meier et al. (2007,https://doi.org/10.1029/2006gl028484) by additionally incorporating high‐resolution N₂photoionization and photoabsorption cross sections in model calculations. Model results with the new inputs show increased O⁺production rates of over 500%, larger than those of Meier et al. (2007,https://doi.org/10.1029/2006gl028484) and total ion production rates of over 125%, while production rates decrease by ∼15% in the E‐region in comparison to the results obtained using the cross section compilation from Conway (1988,https://apps.dtic.mil/sti/pdfs/ADA193866.pdf). Low‐resolution molecular oxygen (O₂) cross sections from the Conway compilation are utilized for all input cases and indicate that is a dominant contributor to the total ion production rate in the E‐region. Specifically, the photoionization contributed from longer wavelengths is a main contributor at ∼120 km. 

Understanding global patterns of genetic diversity (GD) is essential to describe, monitor, and preserve the processes giving rise to life on Earth. To date, efforts to map macrogenetic patterns have been restricted to vertebrate groups that comprise a small fraction of Earth’s biodiversity. Here, we construct the first global map of predicted insect genetic diversity. We calculate the global distribution of GD mean (GDM) and evenness (GDE) of insect assemblages, identify the global environmental correlates of insect GD, and make predictions for undersampled regions. Based on the largest and most species-rich single-locus genetic dataset assembled to date, we find that both GD metrics follow a bimodal latitudinal gradient, where GDM and GDE correlate with contemporary climate variation. Our models explain 1/4 and 1/3 of the observed variation in GDM and GDE in insects, respectively, making an important step towards describing global biodiversity patterns in the most diverse animal taxon.