Vertical Structure and Color of Jovian Latitudinal Cloud Bands during the Juno Era

TL;DR

This study tests the Crème Brûlée cloud model for Jupiter's atmosphere using Juno spacecraft spectra, finding it generally effective but not universally applicable during dynamic weather events.

Contribution

It evaluates the validity of the Crème Brûlée cloud model against new spectral data from Juno, highlighting its strengths and limitations during different atmospheric conditions.

Findings

The model can reproduce Jupiter's visible spectrum with the Carlson chromophore.

Modifications to the chromophore's optical properties improve model fit.

The model is less accurate during dynamic weather events like the 2016-2017 SEB outbreak.

Abstract

The identity of the coloring agent(s) in Jupiter's atmosphere and the exact structure of Jupiter's uppermost cloud deck are yet to be conclusively understood. The Cr\`{e}me Br\^ul\'ee model of Jupiter's tropospheric clouds, originally proposed by Baines et al. (2014) and expanded upon by Sromovsky et al. (2017) and Baines et al. (2019), presumes that the chromophore measured by Carlson et al. (2016) is the singular coloring agent in Jupiter's troposphere. In this work, we test the validity of the Cr\`{e}me Br\^ul\'ee model of Jupiter's uppermost cloud deck using spectra measured during the Juno spacecraft's 5$^{\mathrm{th}}$ perijove pass in March 2017. These data were obtained as part of an international ground-based observing campaign in support of the Juno mission using the NMSU Acousto-optic Imaging Camera (NAIC) at the 3.5-m telescope at Apache Point Observatory in Sunspot, NM. We find that the Cr\`{e}me Br\^ul\'ee model cloud layering scheme can reproduce Jupiter's visible spectrum both with the Carlson et al. (2016) chromophore and with modifications to its imaginary index of refraction spectrum. While the Cr\`{e}me Br\^ul\'ee model provides reasonable results for regions of Jupiter's cloud bands such as the North Equatorial Belt and Equatorial Zone, we find that it is not a safe assumption for unique weather events, such as the 2016-2017 Southern Equatorial Belt outbreak that was captured by our measurements.