# Evidence of pure ammonia clouds in Jupiter's Northern Temperate domain from Juno/JIRAM infrared spectral data

F Biagiotti, D Grassi, G Liuzzi, G Villanueva, G Piccioni, T Guillot, L Fletcher, S Atreya, A Mura, F Oliva, et al.

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► To cite this version:

F Biagiotti, D Grassi, G Liuzzi, G Villanueva, G Piccioni, et al. Evidence of pure ammonia clouds in Jupiter's Northern Temperate domain from Juno/JIRAM infrared spectral data. Monthly Notices of the Royal Astronomical Society, 2025, 538 (3), pp.1535-1564. 10.1093/mnras/staf381. hal-05292882

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https://hal.science/hal-05292882v1

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# Evidence of pure ammonia clouds in Jupiter's Northern Temperate domain from Juno/JIRAM infrared spectral data

F. Biagiotti<sup>1,2*</sup> D. Grassi<sup>1</sup>,<sup>2</sup> G. Liuzzi,<sup>3</sup> G. Villanueva,<sup>4</sup> G. Piccioni,<sup>2</sup> T. Guillot,<sup>5</sup> L. N. Fletcher<sup>1,6</sup> S. K. Atreya,<sup>7</sup> A. Mura<sup>1,2</sup> F. Oliva,<sup>2</sup> F. Tosi<sup>1,2</sup> E. D'Aversa,<sup>2</sup> C. Castagnoli,<sup>2,8,9</sup> A. Adriani,<sup>2</sup> G. Sindoni,<sup>10</sup> C. Plainaki,<sup>10</sup> R. Sordini,<sup>2</sup> R. Noschese,<sup>2</sup> A. Cicchetti,<sup>2</sup> G. Eichstädt<sup>7</sup>,<sup>11</sup> S. Brueshaber,<sup>12</sup> C. J. Hansen,<sup>13</sup> G. Orton,<sup>14</sup> S. Levin,<sup>14</sup> C. Li<sup>7</sup> and S. Bolton<sup>15</sup>

Accepted 2025 February 27. Received 2025 February 17; in original form 2024 September 17

## ABSTRACT

In this work, we analyse data from the Jovian Infrared Auroral Mapper (JIRAM) imaging spectrometer on board the NASA mission Juno, to investigate the presence of spectrally identifiable ammonia clouds (SIACs). Focusing on the data from the first perijove passage, we found that a white vortex structure near 40°N provides the best candidate. Implementing atmospheric retrieval thanks to the planetary spectrum generator, we fit the JIRAM spectra (in the 2.5–3.1 µm range) inside and outside the vortex, varying the gaseous ammonia profiles, and the clouds and hazes properties and composition. We found that outside the vortex, the best fit is achieved using main clouds composed of tholins (approximation of an unknown contaminant material). Inside the vortex the best fit is achieved when main cloud decks are composed of pure ammonia ice, or ammonia-coated tholins. We therefore claim the detection of 38 SIACs, all detected over the white vortex structure. With respect to the external regions, the retrieved parameters distributions inside the vortex show: (i) higher altitude hazes and clouds, (ii) smaller haze's effective radii, and (iii) higher gaseous ammonia relative humidity values. Both the detection of pure ammonia ice clouds and the retrieved physical parameters are consistent with the vortex being the result of a moist convection storm that uplifted fresh ammonia from the deep troposphere which in turn either condensed or became a possible source of coating material for existing cloud particles. This work confirms the trend established by space and ground-based observations, for which ammonia clouds on Jupiter are rare and connected to strong convective episodes.

**Key words:** techniques: spectroscopic – planets and satellites: atmospheres – planets and satellites: individual: Jupiter.

## 1 INTRODUCTION

Ammonia ($NH_3$) is historically considered the main source of condensable species for Jupiter's main cloud layer (0.5–1 bar level). Equilibrium Chemistry Condensation Models (ECCMs) predict the presence of (Atreya et al. 1999):

(i) an ammonia ice cloud deck at the 0.5–0.7 bar level,

(ii) a deeper ammonia hydrosulfide cloud layer around 1.5–2 bar,

(iii) water ice at pressures larger than 5 bar.

The analysis of images and spectra of Jupiter taken from ground-based and space missions is needed to confirm or refine/modify these results. However, the characterization of aerosols in a planetary atmosphere is a well-established challenge for at least three main reasons: (i) aerosols (clouds and hazes) usually tend to flatten the

<footnote n="1"><sup>1</sup>Department of Physics, University of Rome 'La Sapienza', Piazzale Aldo Moro 2, I-00185 Rome, Italy</footnote>

<footnote n="2"><sup>2</sup>INAF - Istituto di Astrofisica e Planetologia Spaziali (INAF-IAPS), Via Fosso del Cavaliere 100, I-00133 Rome, Italy</footnote>

<footnote n="3"><sup>3</sup>School of Engineering, Università degli Studi della Basilicata, Via dell'Ateneo Lucano 10, I-85100 Potenza, PZ, Italy</footnote>

<footnote n="4"><sup>4</sup>NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</footnote>

<footnote n="5"><sup>5</sup>Observatoire de la Côte d'Azur, UniCA, Lagrange CNRS, F-06304 Nice, France</footnote>

<footnote n="6"><sup>6</sup>School of Physics and Astronomy, University of Leicester, University Rd, Leicester LE1 7RH, UK</footnote>

<footnote n="7"><sup>7</sup>Department of Climate and Space Sciences and Engineering, University of Michigan, 2455 Hayward St, Ann Arbor, MI 48109, USA</footnote>

<footnote n="8"><sup>8</sup>ISAC-CNR, via Piero Gobetti 101, I-40129 Bologna, Italy</footnote>

<footnote n="9"><sup>9</sup>University of Tor Vergata, Department of Physics, via della Ricerca Scientifica 1, I-00133 Rome, Italy</footnote>

<footnote n="10"><sup>10</sup>Agenzia Spaziale Italiana, Via del Politecnico, snc, I-00133 Roma, Italy</footnote>

<footnote n="11"><sup>11</sup>Independent Scholar, D-70184 Stuttgart, Germany</footnote>

<footnote n="12"><sup>12</sup>Department of Mechanical and Aerospace Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931, USA</footnote>

<footnote n="13"><sup>13</sup>Planetary Science Institute, 1700 E Fort Lowell Rd STE 106, Tucson AZ 85719, USA</footnote>

<footnote n="14"><sup>14</sup>Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109, USA</footnote>

<footnote n="15"><sup>15</sup>Southwest Research Institute, 6220 Culebra Rd, San Antonio, TX 78238, USA</footnote>

<footnote>*E-mail: francesco.biagiotti@uniroma1.it</footnote>
signals (Kreidberg et al. 2014; Sing et al. 2016) and, most of the time, do not present sharp and easily identifiable spectral features, (ii) the retrieval of aerosol properties is an extremely degenerate situation (Dahl et al. 2021), and (iii) to model the presence of aerosol, multiple scattering radiative transfer simulations are needed, and usually they require large computational capabilities. In the context of Jovian aerosols the NH₃ ice has the benefit of producing white clouds that should stand out with respect to the brown-orange backgrounds; moreover, ammonia ice should present several strong absorption features (Martonchik, Orton & Appleby 1984) in the near and mid-infrared and this led to several research works that aimed to identify them. Early ground-based observations (Orton, Appleby & Martonchik 1982), however, did not report the observation of absorption features in the far infrared, at 9.4 and 26 microns, and the works by Carlson, Lacis & Rossow (1993) and de Pater et al. (2001) pointed out that the ammonia condensation should happen at pressure levels lower than 600 mbar. Also Galileo-NIMS observations showed that clear spectral features connected to the presence of pure ammonia clouds [expected near 2 microns and 3 microns, Howett et al. (2007), two regions dominated by the gaseous ammonia absorption] are absent in most of the cases (Irwin et al. 2001), with the exception of particularly dynamic regions that cover less than 1 per cent of the globe (Baines, Carlson & Kamp 2002). These rare regions containing SIACs (spectrally identifiable ammonia clouds) were thought to be formed by fresh ammonia that lasts less than two days in the atmosphere, and that is transported from the deep troposphere to the cloud level by convective uplifting events. The SIACs found in the NIMS data present clear 2 micron anomalies which are correlated to a 2.74 micron anomaly, not expected if only the ammonia ice extinction is considered. This finding was supported also by the subsequent work of Wong et al. (2004) that detected the further signature in the mid-infrared near 10 microns thanks to an analysis of the CIRS data.

The following observations confirmed this trend. In particular, observations from the Infrared Space Observatory (ISO) (Brooke et al. 1998; Sromovsky & Fry 2010a) and *Cassini*-VIMS (Sromovsky & Fry 2010b) showed that a better fit of the 2.5–3.1 micron region is achieved when particles of both NH₄SH and NH₃ were included in the main cloud layer, with the predominant opacity provided by NH₄SH. Using observations from New Horizons/LEISA in the 1–2.5 micron range (Sromovsky & Fry 2018) also detected evidence for the presence of SIACs in small regions near the equator and with the same characteristics of the ones detected by *Galileo*. Thanks to the use of precise multiple scattering retrievals the authors found that these further SIACs are best fitted by an extended (between the 3 and 0.3 bar levels) optically thick NH₃ cloud and with an effective radius close to 3 microns.

In June of 2016 the NASA *Juno* mission arrived in the Jovian system (Bolton et al. 2017a), and began observing Jupiter's atmosphere and interior with its instrument package. *Juno* is the satellite that is currently orbiting the Jupiter system before the arrival of the ESA JUICE mission in the 2030s (Grasset et al. 2013; Fletcher et al. 2023). Thanks to the optimal spectral resolution between 2 and 5 microns of the Jovian Infrared Auroral Mapper (JIRAM) image spectrometer, it has been possible to investigate if the *Juno* data are in agreement with the previous missions and if they confirm the rarity of NH₃ ice clouds. The work of Sindoni et al. (2017) studied a particular region of the Southern Hemisphere of Jupiter, the SSTZ, in the solar-dominated region of the JIRAM range (2.5–3 µm). They found that the best fit is achieved when a hydrazine haze, a main cloud deck of NH₃ particles with a coating of tholins, and a deep cloud made of NH₄SH are invoked. Grassi et al. (2021) repeated the analysis

in a wider spectral range (2–3.2 µm) and for a large collection of spectra that covers all the Jovian globe in longitude and the [−30, 20] degrees range in latitude. In this case, most of the time reasonable fits are obtained using just a pure reflecting tropospheric haze and two cloud decks made of tholins from Imanaka et al. (2012). It should be stressed that neither of the two studies claim that true Titan-like tholins are actually present in the Jovian atmosphere but that they represent a very good approximation of the unknown amorphous material that should be present in Jupiter's clouds and should bear the N-H and C-N bonds (Grassi et al. 2024). The origin of this material is still unknown but should be connected to the chemical reactions between methane, gaseous ammonia, and the hydrocarbon products of photochemistry in the upper troposphere and the stratosphere (Atreya et al. 2005; Kalogerakis et al. 2008; Atreya 2010; Carlson et al. 2016). We discuss this further in Section 5.4. Surely, without invoking the presence of clouds composed of materials with optical properties similar to Titan's tholins in the 2–3.5 µm range, it is very difficult to fit the main reflection peak in the JIRAM data. This result is in agreement with the main conclusion of the Sromovsky & Fry (2010a) work, that highlight how using only main clouds composed only of pure ammonia ice it is very complicated to model accurately the ubiquitous 3 µm feature observed with ISO. It is also noteworthy that visible observations of the Jovian atmosphere did not report the detection of spectral features associated with ammonia clouds. As ammonia ice or ammonia hydrosulfide particles should produce bright white clouds, the red/orange colour of many features like the Great Red Spot (GRS) has long been a mystery. Therefore, the presence of an unknown chromophore material able to absorb UV and blue light was hypothesized (West, Strobel & Tomasko 1986). Carlson et al. (2016) measured the transmission spectrum and imaginary part of the refractive index for the resulting material of chemical reactions between photolyzed NH₃ and C₂H₂ in the 0.4–0.75 µm range. Their resulting reflectance radiative transfer simulations, where the measured chromophore was used as a coating for existing cloud particles, were consistent with *HST* and *Cassini*'s observations of the GRS. Subsequently, Sromovsky et al. (2017) were able to obtain excellent fits of *Cassini/VIMS* visible spectra for different belts and zones, demonstrating that the Carlson et al. (2016) material may be a nearly universal chromophore on Jupiter. Baines et al. (2019) was able to obtain excellent fits also of the VIMS spectra of the GRS, using the Carlson et al. (2016) chromophore as a thin haze on top of the main cloud decks ('Crème brûlée' model). However, due to the lack of laboratory measurements in the near-infrared range it was impossible to test if the Carlson et al. (2016) chromophore could also fit JIRAM spectra adequately.

To summarize, the general picture regarding the presence of ammonia ice clouds is still unclear even after decades of ground-based and space exploration. These clouds are expected by theory but only rarely observed. Moreover, the search for these elusive clouds pointed out that most of the time to model typical regions of Jupiter the presence of contaminants or coated particles is needed, meaning that they are characterized by clouds composed, at least in part, of unknown materials not predicted by equilibrium condensation chemistry.

In this work, we investigate the presence of SIACs in the *Juno/JIRAM* data set focusing on an intermediate latitude region in the Northern hemisphere. This is the first work that analyses quantitatively the spectra of these regions, which are easily accessible by *Juno* thanks to its polar unique orbit. These intermediate latitude regions are particularly interesting because are highly turbulent, causing the appearance of numerous chaotic cyclonic-like storms which take the name of folded filamentary regions (Fletcher et al.
2022). These cyclonic features appear as bright and white in the visible (Palotai et al. 2022; Guillot et al. 2023) and are clearly connected to convective events that evolve rapidly over time-scales of hours and days (see also Rogers et al. 2022), making them the most promising regions where to search for the presence of SIACs. In Section 2 we present the JIRAM observations and the relative observing conditions. In Section 3 we describe the methods used to perform the atmospheric retrievals, focusing on the chosen parametrization of gases and aerosols, the optical properties of the considered aerosols, and the used forward model. In Section 4 we will present the results of the retrieval analysis and in Section 5 we provide a discussion about them.

## 2 OBSERVATIONS

JIRAM is an imaging spectrometer built and designed by the joint effort of Leonardo, ASI, and the INAF-IAPS (Adriani et al. 2017). It is part of the payload of Juno, the second orbiter mission around Jupiter, and is the only instrument on board that can acquire spectra in the infrared spectral range. JIRAM relies on the solid heritage coming from the Italian-made visible and near-infrared imaging spectrometers produced in the past years such as the visible channel of the VIMS instrument aboard Cassini (Brown et al. 2004), VIRTIS-M aboard Rosetta (Coradini et al. 2007), and Venus Express (Piccioni et al. 2007), and VIR-Dawn (de Sanctis et al. 2011). The optical layout is based on a modified Schmidt telescope (equivalent aperture 44 mm) and thanks to a mirror system, the optical beam is divided between an imager channel and a spectrometer channel, where Hybrid Thinned HgCdTe photodiodes (CMOS) detectors are placed. The imager focal plane is divided into two equal areas defined by the superimposition of an L (centred at 3.455 µm with a 290 nm bandwidth) and an M filter (centred at 4.780 µm with a 480 nm bandwidth). The L-filter was designed to acquire high-resolution images of aurorae, as $H_3^+$ is expected to emit strongly at these wavelengths (Mura et al. 2018). The spectrometer channel is able to acquire co-located imaging spectroscopy in the 2.0–5.0 µm range. The sampling points are 336 and the sampling step is 9 nm. These characteristics have been accurately chosen to study a transition zone in Jupiter’s spectra. In fact, in all JIRAM spectra, we can observe two regions: (i) a solar region – from 2 to 3.2 µm – dominated by reflected sunlight, and (ii) a thermal region – from 4.2 to 5 µm – dominated by the radiation coming from the deeper troposphere (6–7 bar level).

As for June 2024, Juno performed a total of 62 peri-jove passages (called PJs hereafter). Due to Juno’s orbit and rotation, the spatial coverage of each instrument is different for each PJ. Regarding JIRAM, the PJ1 is the one with the best spatial coverage, as it was possible to map most of the Jovian disc as it is possible to see in Fig. 1(a). The spatial coverage achieved by JunoCam images during the PJ1 passage proved sufficient to produce a global mosaic of Jupiter,<fnref n="1" /> which was already used in Grassi et al. (2021). The composite mosaic was obtained using JunoCam’s images acquired with Blue (450–500 nm), Green (500–600 nm), and Red (600–800 nm) filters (Hansen et al. 2017). A portion of this mosaic serves as a reference background in the present analysis. To account for the different acquisition times of JIRAM and JunoCam over the areas of interest – separated by a few hours – and the drift of atmospheric features caused by zonal winds, a manual longitude shift was applied

to the JunoCam mosaic coordinates. This shift was determined by minimizing the positional differences between the darker features in JunoCam images (assumed, in most cases, to be regions with thinner upper clouds) and the brighter spots observed in JIRAM infrared (M-filter) images, acquired simultaneously with JIRAM spectral slits.

As we are interested in precisely mapping a possible folded filamentary region, we decided to use only the PJ1 data for this work. These data were acquired between 2016 August 25 and 26. The spectra have been corrected for the presence of spikes caused by encounters with energetic particles in the Jovian environment (or cosmic rays). We used a modified version of the algorithm presented in Whitaker & Hayes (2018) to correct the spectral regions below $\lambda < 4.5$ µm. Although the method is no longer effective above this limit – because of the frequent gaseous absorption features – our analysis remains nevertheless unaffected since in this work we are interested mainly in the solar reflected signal, in order to retrieve the aerosol properties.

To select a collection of spectra we decided to consider two different and independent spectral indicators, called respectively $\alpha$ and $\beta$. They are defined as:

$$ \alpha = \frac{\bar{S}_{[2.98-3.02]\,\mu\text{m}}}{\bar{S}_{[2.94-2.98]\,\mu\text{m}}}, \qquad (1) $$

$$ \beta = \frac{S_{4\,\mu\text{m}}}{S_{2.96\,\mu\text{m}}}, \qquad (2) $$

where the notation $S_\lambda$ indicates the radiance value in W µm⁻<fnref n="1" /> sr⁻<fnref n="1" /> m⁻² at a reference wavelength $\lambda$, while the notation $\bar{S}_{[\lambda_1, \lambda_2]}$ indicates the mean of the radiances of all the channels between $\lambda_1$ and $\lambda_2$. Both the indicators are expected to be sensitive to the presence of possible high-altitude ammonia ice clouds. The $\alpha$ indicator should be higher if the signal in the [2.94–2.98] µm range is lower, exactly at the position where the absorption feature of NH₃ ice is expected (see Fig. A3 in the Appendix). The $\beta$ indicator was selected starting from figs 2 and 10 in Sromovsky & Fry (2018). The 4 and 2.97 µm wavelengths are characterized by a similar transmission weighting function and therefore they should probe the same pressure level; however, as said for the $\alpha$ indicator, the ammonia clouds are expected to strongly absorb at 2.96 microns. This means that we should get the same value of this indicator almost everywhere on Jupiter, with the exception of regions with absorbing aerosols at 2.96 µm or anomalous clouds. In Fig. 1 we present the $\beta$ indicator for all the PJ1 spectra. We note that it is low (< 0.4) for almost all the zones and belts. However, it is possible to note that the indicator is slightly higher in correspondence of the zones, as expected for uplifting regions. The higher values are reached in four regions of interest (ROIs) in the Northern hemisphere, marked by red boxes. It is also possible to note that in general higher values are reached in the polar regions. However, in most circumstances this behaviour is caused mainly by the extreme observing conditions. In fact, in the polar regions, where an emission and/or incidence angle > 80° or < 10° is easily reached, the $\beta$ indicator cannot be easily compared to equatorial, sub-observer regions, given the enhanced role of multiple scattering. In this work we focused on the ROI #1 for different reasons: (i) in this region the JunoCam images show the occurrence of two vortices that appear similar (both in shape, size, and latitude location) to the ‘Nautilus’ convective storm presented in Guillot et al. (2024); (ii) this is the first work in literature in which the JIRAM data of these relatively high-latitude regions are analysed, and, moreover, we wanted to focus on a transition-case between the belts and zones structures and the subpolar regions. The vortex, see Fig. 1(b), has a ‘snail-like’ form, and a characteristic white colour. Both these qualitative characteristics are a hint that we are dealing with a strong

<footnote n="1">¹https://britastro.org/section_information/_jupiter-section-overview/results-from-juno-2016-2017-perijoves-1-10/junocam-at-perijove-1-map-of-the-planet</footnote>
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Spatial distribution of JIRAM spectra considered in this work and their characteristics. (a) $\beta$ indicator, equation (2), computed using all the JIRAM PJ1 collected and non-corrupted spectra. Five regions of interest (ROIs), where the values seem to form a pattern, are highlighted with boxes. (b) Zoom on the #1 ROI. (c) $\beta$ indicator distribution for the #1 ROI. (d) $\alpha$ indicator distribution for the #1 ROI. (e) Signal measured by JIRAM at 2.73 µm corrected for the cosine of the emission angle. For all the panels, the background is a global map created from visible images acquired by the JunoCam.</figcaption></figure>

convective event that may have brought fresh ammonia in the upper troposphere, i.e. a candidate folded filamentary region. In Fig. 2 it is possible to compare ROI #1 against a larger fraction of the Jovian disc, both captured by JunoCam. In Fig. A1 it is possible to observe it in context with the whole JunoCam mosaic.

In total, the number of selected spectra for the #1 ROI are 433. The rest of the JIRAM PJ1 data and the remaining ROIs will be the subject of future work. Figs 1(c) and (d) show the patterns of the $\beta$

and $\alpha$ indicators. Fig. 1(e) shows the signal at 2.73 µm (where the maximum of solar reflected radiation in the JIRAM spectral range is expected), roughly corrected for the emission angle. From the same figures it is also possible to note that the Sun illuminates the scene from the right side. This is possibly the cause of the shadow region that can be noted in the left of the snail-like vortex in Fig. 1(b). Lastly in Fig. A2 it is possible to observe the geometrical observing conditions for each of the spectra in the collection.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** The snail-like white vortex structure (the brighter feature) and its surroundings (box) captured by Juno Cam, in context with the rest of the Jovian Northern hemisphere.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 3.** An example of two JIRAM spectra in the 2.5–3.1 µm range. The black spectrum was collected outside the white vortex structure, while the grey one inside, as it is possible to observe in the subpanel. The spectra are normalized for visualization purposes. The location of the maximum of the reflection peak is indicated with dashed lines (black and grey), as the location of the expected absorption feature caused by pure ammonia ice (sky blue).</figcaption></figure>

Moving to spectral data, Fig. 3 shows two example spectra in the reflected solar radiation region, one inside and the other outside the vortex. We note that with respect to the spectrum outside the vortex, the inner spectrum exhibits both a shifted solar reflected peak and a weak absorption dip near 2.95 µm. The latter spectral distortion is expected in the presence of pure ammonia ice clouds and was largely discussed in the literature. On the other hand, the displaced reflection peak was not mentioned before in literature. Indeed, Baines et al. (2002) noted a deficiency in the 2.73 µm channel of NIMS for the candidate SIAC spectra but did not report a change in the spectral shape. In fact, NIMS took and transmitted spectrally sparse data, due to the severe cuts in data volume demanded by the failure of the primary antenna. Probably, this observation has become possible with the improved spectral resolution of JIRAM, and thanks to the good quality of the transmitted data. The spectral dichotomy presented in

Fig. 3 can be connected to the difference in colour, and so in single scattering albedo, observed also in the JunoCam visible images. In fact, at these wavelengths the gas opacities of methane and ammonia are rather low, making the contribution from the aerosol layers more significant. In order to perform a more quantitative assessment, a complete retrieval is required. This is the subject of the next section.

## 3 METHODS

To derive the physical properties of the aerosols and clouds inside and outside the snail-like vortex we performed atmospheric retrievals on the JIRAM data. Several reasons led us to focus only on the spectral region [2.4–3.1] µm: (i) to put this work in continuity with the ones of Grassi et al. (2021) and Sindoni et al. (2017), (ii) because we had problems in modelling the 2 µm and >3.1 µm regions, where the introduction of high-altitude hazes (which in turn requires a more complex atmospheric model) seems to be mandatory, (iii) because we want to focus on the previously non-observed shift in the reflection peak and the absorption feature of NH₃ ices, and (iv) enduring uncertainties on H₂-H₂ opacities around 2.1 µm. Moreover, we wanted to focus on the 2.5–3 µm range, because the JIRAM contribution functions here peak between the 0.4 and 1.2 bar level. Since we suspect that the change in colour and the shift of the 2.73 µm peak is caused by the presence of particular main cloud decks, it is important to perform retrievals from spectral regions that are sensing neither too high nor too deep in the troposphere but near the expected ammonia condensation level.

An atmospheric retrieval is any method (or a series of methods) that aims to derive physical properties, such as cloud particle radii or density, simulating a spectrum of an atmosphere and finding the best match with the corresponding experimental data. So, any retrieval algorithm has two fundamental components: a forward model and a scheme that improves our current guess on the basis of the observed spectral difference between it and the data. In this work, we adopted the NASA Planetary Spectrum Generator (PSG) (Villanueva et al. 2018) as the forward method, and a classic optimal estimation (OE) scheme (Rodgers 2000), as implemented in the *pyoptimalestimation* package (Maahn et al. 2020).

PSG is a powerful and hybrid radiative transfer suite used to model spectra of planets (Liuzzi et al. 2020), exoplanets (Haqq-Misra et al. 2022; Latouf et al. 2023; Villanueva et al. 2024), and also satellites and asteroids (Faggi et al. 2021; Villanueva et al. 2023a, b). As of the author's knowledge, this is the first work regarding Jupiter for which PSG has been adopted. PSG relies on a correlated-k tables approach (Goody et al. 1989) to treat gas opacity. The tables are generated using the latest release data from the HITRAN collaboration [HITRAN2020; Gordon et al. (2022) and references within]. Alternatively, it is also possible to use custom cross-section tables or the ones tabulated by the ExoTransmit programme (Kempton et al. 2017). PSG has also the notable advantage of dealing with multiple scattering very efficiently, a characteristic that is fundamental to analysing Jupiter and the ice giants planets in our solar system. The radiative transfer equation is solved using the PSGDORT<fnref n="2" /> algorithm, ultimately based on DISORT (Stamnes et al. 1988), without performing further approximation other than dealing with plane parallel layers. PSGDort adapts DISORT to operate with a variety of spectral grids (Villanueva et al. 2015), and generalizes the methods presented in Smith et al. (2009), that also include a correction for pseudo-spherical geometry as described by Dahlback

<footnote n="2">²https://github.com/nasapsg/psgdort</footnote>
& Stamnes (1991). The number of streams is a free parameter, such as the number of Legendre polynomials that must be considered to describe the scattering phase function. Consequently, PSG can read two kinds of look-up tables for the aerosol's optical properties computed at different effective radii. These must express the Mie extinction coefficient $Q_{ext}$, the single scattering albedos, and the phase function described using the Legendre decomposition coefficients or, alternatively, the asymmetry parameters of the corresponding Heyney-Greenstein approximated functions (Heney & Greenstein 1941). Once that PSG generates a synthetic spectrum, it is convolved with the JIRAM instrument response function (Adriani et al. 2017) and then inserted in the retrieval architecture. Regarding the second component of the scheme, the PYOE package implements the Bayesian methods presented in Rodgers (2000) in Python language. OE is one of the most used and efficient methods in earth and planetary sciences, where usually a huge amount of data is available. Essentially, it is trying to find an optimal solution $x_{op}$ for the free parameters array $x$, given an observation and prior information using an iterative process.

Calling:

(i) $y_{obs}$ the experimental data points of the spectrum and $S_y$ the relative covariance matrix, i.e. $S_y = \sigma_y^T \sigma_y$, where $\sigma_y$ are the spectral uncertainties. In our case, $y_{obs}$ refers to the JIRAM spectral points, while $\sigma_y$ to the experimental error. In our case this is a quadratic sum of signal variance from observations of deep sky (that we took as noise equivalent radiance, NER) and of photon shot noise. It is worth reminding that $S_y$ is diagonal only if systematic errors are absent.

(ii) $y = \mathcal{F}(x)$ the forward model that generates a theoretical spectrum given an array of free parameters $x$; in our case $y = \mathcal{F}(x)$ coincides with the PSG's output, given an atmospheric parametrization.

(iii) $K = \frac{\partial y}{\partial x}$ the Jacobian matrix.

(iv) $x_a$ the a priori parameters values and the relative covariance matrix $S_a$; these refer to the prior information and uncertainties that we have about the physical parameters that we are trying to retrieve, and can be extracted from literature or set following physical constraints. As for $S_y$, also $S_a$ can be approximated as diagonal using the expression $S_a = \sigma_a^T \sigma_a$. In this case $\sigma_a$ will represent the standard deviation of the a-priori guess, which may be related to the confidence level that we put on the a-priori guess. As a rule of thumb, the larger $\sigma_a$ is, and then the larger the parameter space will be explored.

OE aims to find the best-fitting solution iteratively:

$$x_{i+1} = x_a + (\eta S_a^{-1} + K_i^T S_y^{-1} K_i)^{-1} K_i^T S_y^{-1} \times [y - \mathcal{F}(x_i) + K_i(x_i - x_a)], \quad (3)$$

where $\eta$ is called regularization parameter (Carissimo, De Feis & Serio 2005; Turner & Löhnert 2014) and acts as a tradeoff between the a-priori values of the parameters to be retrieved and the observations in defining the updated solution. The iteration stops when convergence is reached, for example equation (5.29) of Rodgers (2000), or if the chi-squared ($\chi^2$) values are lower than a certain threshold. These are defined as:

$$\chi^2(x) = \sum_{i=1}^{N} \frac{(y_{obs}, i - \mathcal{F}_i(x))^2}{\sigma_y^2}, \quad (4)$$

where $N$ is the number of data points. The chi-squared is very useful statistical indicator, but does not account for the degrees of freedom of a fit. For this reason it is useful to introduce two other statistical indicators known as Bayesian Information Criterion (BIC)

and Akaike Information Criterion (AIC) (Liddle 2007; Patra et al. 2017), defined as:

$$\text{BIC} = \chi^2 + N_x \ln N, \quad (5)$$

$$\text{AIC} = \chi^2 + 2N_x, \quad (6)$$

where $N_x$ is the number of parameters of the model.

The model atmosphere that we considered encompasses the following items:

(i) a fixed pressure temperature profile extrapolated from Braude et al. (2020). The model atmosphere is a plane-parallel one composed of 98 layers. It covers the 8–0.1 bar range in pressures.

(ii) constant mixing ratio profiles for H$_2$, He, and CH$_4$. The values were assumed to be respectively 0.86, 0.13, and 0.00181 as measured by the Galileo entry probe (Niemann et al. 1996; von Zahn, Hunten & Lehmacher 1998). Regarding CH$_4$ we used the PSG internal tables [see Gordon et al. (2022) and references within for details]. These tables are computed at Earth isotopic ratios, which are different from Jupiter's ones. However, in the spectral region of our interest, the contribution from CH$_3$D and the other isotopologues is negligible, as demonstrated in Fig. A4. Regarding the CIAs (H$_2$-H$_2$, H$_2$-He, and H$_2$-CH$_4$) we used custom cross-section tables computed starting from the Abel & Frommhold (2013), Abel et al. (2012), and Borysow & Frommhold (1986) simulations. These, are given up to 200 K, so to obtain the absorption coefficients at lower temperatures we extrapolated them, as suggested as appropriate from new laboratory constraints (Vitali et al. 2025).

(iii) a variable mixing ratio profile for gaseous NH$_3$. Following Irwin et al. (1998), the profile is defined by two parameters: a deep volume mixing ratio (VMR) and a relative humidity (RH). Once the saturated vapor pressure profile for ammonia is computed from:

$$p_{\text{sat}}(T)[\text{bar}] = 10^{-3} \cdot 10^{A - \frac{B}{T} + C}, \quad (7)$$

where $A = 10.1883$, $B = 1633.785 \text{ K}^{-1}$, and $C = 0.12490182$ (Lange & Forker 1953), the expected mixing ratio can be computed as follows:

$$\text{mr}_{\text{sat}}(p, T) = \frac{p_{\text{sat}}(T)}{p} \frac{\text{RH}}{100}. \quad (8)$$

The considered mixing ratio profile is defined as:

$$\text{mr}_{\text{NH}_3}(p, \text{VMR}, \text{RH}) = \begin{cases} \text{VMR} & \text{if } \text{mr}_{\text{sat}}(p, T) > \text{VMR} \\ \text{mr}_{\text{sat}}(p, T) & \text{if } \text{mr}_{\text{sat}}(p, T) < \text{VMR} \end{cases} \quad (9)$$

As for CH$_4$, for gaseous ammonia we used PSG internal correlated-k tables.

(iv) a slab-like tropospheric pure-reflecting haze. The profile has four free parameters, to be adjusted during the fit procedure: the effective radius of the haze in microns ($r_h$), the central pressure level in bar ($p_h$), the width of the slab in bar ($w_h$), and the number density of the haze expressed in part cm$^{-3}$ ($N_h$). The so-defined profile assumes a constant number density in altitude per unit volume between $p_h + \frac{1}{2}w_h$ and $p_h - \frac{1}{2}w_h$. It is important underlining that a constant number density profile does not result in a constant volume mixing ratio profile. We considered the PSG Heyney-Greenstein option, and we computed the optical properties considering a refractive index of (1.4,0) (Sromovsky & Fry 2010b) and using a Mie-based code<fnref n="3" /> in Python that implements the methods from Bohren & Huffman (1983). A log-normal size distribution with a variance of 0.3 was considered.

<footnote n="3">³https://github.com/nasapsg/scattering</footnote>
(v) a main cloud layer with a Gaussian number density vertical profile. In order to replicate a profile similar to the computations of Atreya et al. (1999) and to the measures of the *Galileo* mission nephelometer (Ragent et al. 1998), we decided to use a fixed full width at half maximum (FWHM) of 0.04 bar. The profile has three free parameters, to be adjusted during the fit procedure: the effective radius of the cloud, in microns ($r_C$), the pressure level of the cloud deck ($p_C$), and the maximum number density, expressed always in part cm$^{-3}$ ($N_C$). The parameter $p_C$ can be considered as the mean (in pressure levels) of the Gaussian, whose maximum value (in number density) is constrained by $N_C$. We adopted again the PSG Heyney-Greenstein option. We considered different compositions for this cloud layer:

(a) Tholin particles with refractive indexes from Imanaka et al. (2012), following the previous works of Grassi et al. (2021) and Sindoni et al. (2017); it is worth stressing that Titan's tholins are used as an approximation of a real unknown contaminant material;

(b) Pure ammonia ice particles with refractive indexes from Hudson, Gerakines & Yarnall (2022);

(c) Imanaka's tholin cores coated with ammonia ice with a core-to-total-radius ratio ($\gamma = r_{core}/r_{total}$) of 50 per cent;

(d) Imanaka's tholin cores coated with ammonia ice with a core-to-total-radius ratio ($\gamma = r_{core}/r_{total}$) of 85 per cent.

For (1) and (2) we used the same Mie-based code cited above, while for (3) and (4) we used the PY-MIE python package,<fnref n="4" /> which is a python wrapper of the Fortran methods developed by Toon & Ackerman (1981). The PY-MIE computations were also validated against the BART code.<fnref n="5" /> A log-normal distribution with a variance of 0.3 was considered.

(vi) a deeper cloud layer with a Gaussian number density vertical profile. We considered a fixed FWHM of 0.04 bar and a fixed effective radius of 1 µm. The composition was chosen to be Imanaka et al. (2012) tholins. This profile has two free parameters: the pressure level in bar ($p_D$) and the number density in part cm$^{-3}$ ($N_D$).

In Fig. 4 is possible to visualize the chosen parametrization, while in Table 2 a summary of the parameter's names and meanings is given. In short, we considered four different models, as the four different compositions chosen to represent the main cloud deck. Each model atmosphere has a total of 11 free parameters. We resume the composition of the aerosol layers for each Model atmosphere in Table 1.

The choice of this parametrization was subjected to a series of different tests and a priori simulations. We will discuss them in the following subsections and, lastly, explain the retrieval strategy.

## 3.1 PSG validation

We verified that the PSG simulations are consistent with the ARS (Ignatiev, Grassi & Zasova 2005) radiative transfer code used to analyse Jupiter in the past (Grassi et al. 2020, 2021). To do that we focused on two different physical situations: (i) an atmosphere composed of a mixture of hydrogen, helium, methane, and ammonia at Jovian conditions [Braude et al. (2020) pressure-temperature profile], and (ii) an atmosphere composed only of a uniform haze (1.4+0.0001i) deck, again, at Jovian conditions. For the first case, the considered mixing ratio profiles are the ones presented above,

<figure><img src="image_6.png" /><figcaption>**Figure 4.** Gaseous and aerosols vertical profiles used in the retrieval stage of this work. The H₂, He, and CH₄ are held fixed. All the other profiles represents the first guess, and have been obtained using the parametrizations described in Section 3 and the values listed in Table 2. The considered pT profile is showed using a dashed line.</figcaption></figure>

with the exception of NH₃ for which a constant mixing ratio of 3×10⁻<fnref n="4" /> was used. The PSG-ARS comparisons are shown in Fig. 5. It is possible to observe that the agreement between the two codes is optimal both in the absorption and scattering regimes (in the 2.5–3.1 µm range). The differences at wavelengths >3.2 µm are connected to the different isotopologue ratio considered (as a reminder PSG uses correlated-k tables computed considering Earth isotopologue ratios). This ensures that PSG is able to model adequately the Jovian atmosphere, and in turn, can be adapted also to model the other gas (and ice) giants of our solar system.

## 3.2 The role of water and phosphine

We verified that the addition of water and phosphine resulted in changes to the spectral radiance that are smaller than (or in order of) the JIRAM NER (always in the 2.5–3.1 µm range). This behaviour was also noted by Grassi et al. (2021) using the ARS radiative transfer code (Ignatiev et al. 2005). We considered two typical profiles for PH₃ and H₂O starting from the ones of Grassi et al. (2020). It is possible to observe the chosen profiles and the resulting spectra in Fig. A5 in the Appendix.

## 3.3 The role of cloud parametrization

We investigated the role of the chosen cloud parametrization on the resulting retrievals. The ‘final’ cloud structure eventually adopted in our analysis (a haze + two thin clouds) and described extensively above was indeed selected after a preliminary assessment aimed to mitigate the high degeneracy of retrieval results when multiple scattering is involved. Focusing on a typical spectrum outside the vortex, we tested OE retrievals using five different cloud structures (see Fig. 6): (i) a single tholin Gaussian cloud, constrained to not become wider (FWHM) than 0.2 bars (red curves), (ii) a single Gaussian tholin cloud with no constraints on its width (orange curves), (iii) a single fixed-width (Atreya et al. (1999)-like) Gaussian tholin cloud and a pure reflecting haze (green curves), (iv) the ‘final’ cloud structure, but with the number density of the deepest cloud considered fixed (sky blue curves), and (v) the ‘final’ cloud structure (dark blue curves). In Fig. 6 it is possible to observe the best-fitting

<footnote n="4">⁴https://github.com/darothen/py-mie</footnote>

<footnote n="5">⁵https://www.ugr.es/aquiran/codigos.htm</footnote>
<table><caption>**Table 1.** Summary of the aerosol compositions used in the four different model atmospheres considered to fit JIRAM data.</caption><thead><tr><th>Model N.</th><th>Haze</th><th>Main Cloud</th><th>Deep cloud</th></tr></thead><tbody><tr><td>1</td><td>n = 1.4, k = 0</td><td>Tholins (Contaminant), Imanaka et al. (2012)</td><td>Tholins (Contaminant), Imanaka et al. (2012)</td></tr><tr><td>2</td><td>n = 1.4, k = 0</td><td>Pure ammonia ice, Hudson et al. (2022)</td><td>Tholins (Contaminant), Imanaka et al. (2012)</td></tr><tr><td>3</td><td>n = 1.4, k = 0</td><td>50 per cent ammonia-coated tholins</td><td>Tholins (Contaminant), Imanaka et al. (2012)</td></tr><tr><td>4</td><td>n = 1.4, k = 0</td><td>85 per cent ammonia-coated tholins</td><td>Tholins (Contaminant), Imanaka et al. (2012)</td></tr></tbody></table>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5.** Comparisons between two test spectra obtained using respectively ARS and PSG, and considering: *upper panel*, a mixture of H₂, He, CH₄, and NH₃ at Jovian conditions.; *lower panel*, a single a uniform haze (1.4+0.0001i) deck at Jovian conditions.</figcaption></figure>

OE retrievals of the test JIRAM spectrum and the best-fitting cloud profiles in each case. The haze + two clouds structure is indeed the one that provides the lowest chi-squared value, justifying its adoption for the rest of our analysis. Notably, this sensitivity test proved to be extremely informative for other aspects. First, each aerosol structure is consistent with the others, suggesting that the main source of reflectivity in the 2.5–3 µm range should lie between 0.6 and 1 bar, but requiring, at the same time, an extended layer of particles between 0.5 bar and the tropopause. Secondly, the (ii) and (iii) structures both provide a similar fit quality and reflect the degeneracy between the cloud extension and its number density. This means that is very difficult to distinguish between a single largely extended cloud (from 2 to 0.1 bar) and a haze layer combined with a thin denser cloud. This degeneracy is unbreakable from JIRAM data,

<figure><img src="image_9.png" /><figcaption>**Figure 6.** Comparison between different PSG/OE retrievals on a JIRAM test spectrum, obtained varying the aerosol parametrization. In the subpanels it is possible to observe also the best-fitting aerosol's profiles with the same colour scheme of the best-fitting models.</figcaption></figure>

so we decided to be guided by physics and theoretical predictions. It is physically possible that an extended layer of small particles may lay in the upper troposphere because of the photochemistry in the upper Jovian atmosphere, consistently with our 'final' structure. Moreover, Jupiter is expected to have very high values of the sedimentation parameter – i.e. compact and not-extended clouds – (Ackerman & Marley 2001) contrary to, for example, the extreme hot Jupiter planets (Robbins-Blanch et al. 2022). Compact clouds were also predicted by equilibrium condensation models (Atreya et al. 1999) and measured in the peculiar regions called hotspots by the *Galileo* nephelometer (Ragent et al. 1998). Lastly, it is worth discussing the role of the deeper cloud that is mandatory to obtain higher reflectivity and achieve better-fit quality in the [2.7–2.75] µm range. This physically means that the main cloud layer is not completely optically thick but that some radiation can pass through, to reach a deeper deck and be reflected and added in this way to the total observed radiation budget.

## 3.4 The choice of the main cloud's aerosol species

*Galileo* and ISO data suggested that a 3 µm absorber aerosol was needed to explain the observed reflectivity (Sromovsky & Fry 2010a). This absorption feature is strictly connected to chemical species bearing the N-H bond. In fact, referring to Fig. A3, it is possible to observe that species such as ammonia NH₃ (Hudson et al. 2022), ammonium hydrosulfide NH₄SH (Ferraro, Sill & Fink 1980), hydrazine (Clapp & Miller 1996), and Titan's tholins (Sagan & Khare 1979; Imanaka et al. 2012) all present imaginary refractive indexes that become monotonically larger as we approach 3 µm. However, also species like water (Warren 1984) present a similar behaviour. Nevertheless, at the 0.5–1 bar level only ammonia and contaminant materials similar to Titan's tholins can form and sustain in the
solid form. The former is formed thanks to moist convection and condensation, while the latter is the final product of photochemistry in the upper troposphere that may coagulate and accrete. In fact, ‘tholins’ should be intended as poorly characterized materials made of repeating chains of linked sub-units and complex combinations of functional groups – (typically nitriles and hydrocarbons) – that should bear both the N-H and C-N bonds (Sagan & Khare 1979). The latter one may play a role to explain the limb-darkening maps at 5 µm obtained from JIRAM data (Grassi et al. 2023, 2024). In principle, a tholin-like contaminant material can also act as a coating for the previously formed ammonia particles, masking their characteristics and optical features. Water ice particles can be present at these pressure levels due to convective storms (Guillot et al. 2020a).

For the reasons explained above, we only decided to focus on tholins, pure ammonia ice particles, and ammonia-coated tholin particles. The optical constants used to compute pure ammonia ice optical properties are the ones presented in Hudson et al. (2022). We decided to use this data set instead of the ones presented in Howett et al. (2007) or Martonchik et al. (1984) for two main reasons: (i) this data set is most recent and is derived from laboratory measurements that adopts the most advanced technology, and (ii) it presents explicitly optical constants for crystalline particles at 100 K, a temperature closer to the Jovian atmosphere with respect to the 80 K of Howett et al. (2007).

To compute the optical properties of the aerosol layers we always considered spherical particles. This is a very crude approximation as aerosols are expected to be in solid form and not spherical liquid droplets (Lodge, Wakeford & Leinhardt 2024), but justified by the quality of our data set, which cannot let us derive significant information about the shape. An example of the optical properties computed for a log-normal distribution of spherical particles with $r_{\text{eff}} = 3$ µm and variance set to 0.3 considering the selected aerosol species is given in Fig. 7. It is possible to observe that both tholins and pure ammonia present decreasing single scattering albedo and increasing asymmetry parameters going from 2.5 µm towards 2.95 µm. However, the curves for pure ammonia always include the classical absorption signature at 2.95 µm, marked by a dashed line. The optical properties of ammonia-coated tholins at $\gamma = 50$ per cent and $\gamma = 85$ per cent have intermediate characteristics between pure ammonia and ‘pure’ tholins, as expected. This is consistent with the idea that tholin-like material can mix, after precipitating, with pre-existing ammonia particles, masking their spectral features. A detailed view of the 2.60–2.90 µm region for the single scattering albedo profiles is presented in the middle panel of Fig. 7. We note that the different species present very different behaviours. In particular, the pure ammonia and $\gamma = 50$ per cent ammonia-coated tholins profiles seem to decay less rapidly toward longer wavelengths than the $\gamma = 85$ per cent ammonia-coated tholins and pure tholins ones.

## 3.5 Retrieval strategy

Regarding the retrieval stage, we ran PSG using the ‘Nadir’ observing mode and giving as inputs for each spectrum the relative viewing conditions (emission, incidence, and phase angles represented in Fig. A2). After a series of preliminary tests, we decided to use a value of 5 for the regularization parameter $\eta$. The selected number of streams was five, while the number of considered Legendre polynomial was 99 (the maximum provided by PSG). The a priori values and relative uncertainties are given in Table 2 for each parameter, together with the references that have been considered to select them. We decided to use logarithmic values for the parameters in order to avoid unphysical negative results during the parameter

<figure><img src="image_10.png" /><figcaption>**Figure 7.** Optical properties of the four different aerosol species considered in this work: tholins, pure ammonia ice, and ammonia-coated tholins with different core-to-radius parameter ($\gamma$). The properties are computed considering a log-normal distribution of particles with an effective radius of 3 µm and a variance of 0.3.</figcaption></figure>

space exploration. We used the previous works of Sindoni et al. (2017), Grassi et al. (2021), and Sromovsky & Fry (2018) to choose the a priori values for the aerosol parameters, and the recent MWR measurements to choose the one of the $\text{NH}_3$ deep VMR (Li et al. 2017). For the haze density, we decided to put a further constraint and not let it vary above 1000 part. cm$^{-3}$. This value makes the haze become optically thick at optical wavelengths (considering the chosen size distribution), and this is not consistent with the associated JunoCam images. The retrieval strategy follows the so-called Occam razor principle. We decided to first perform the retrievals for each spectrum in the data set considering Model 1, as this one is considered to be the simplest model that can fit the typical Jupiter’s spectra as seen by JIRAM. Then, for the spectra with which Model 1 retrieval did not converge, or converged with a high chi-squared value, we decided to perform Models 2, 3, and 4 retrievals, to verify if relaxing assumptions on cloud compositions can get lower chi-squared values.

The average time required by PSG to generate a synthetic JIRAM spectrum with the a priori values given in Table 2 for a computer built equipped an i9 processor is 13 s. In turn, this implies that the average time required by a retrieval to reach convergence is in the order of minutes and not hours.

## 4 RESULTS

The atmospheric retrieval converged successfully for 419 spectra on a total of 433 when only Model 1 (main clouds composed of tholins) was considered. The number rises to 423 when all the Models are used. In Table 3 it is possible to observe the retrieved parameters
<table><caption>**Table 2.** Retrieval parameters. Their meaning, symbols, a priori values ($x_a$), and standard deviation of the a priori ($\sigma_a$) are given in the first columns. In the last columns the references used to set the values are listed, together with the median retrieved uncertainties.</caption><thead><tr><th>Parameter</th><th>Symbol</th><th>x<sub>a</sub></th><th>σ<sub>a</sub></th><th>Reference</th><th>σ<sub>ref</sub></th></tr></thead><tbody><tr><td>Logarithm (10) of the haze's effective radius</td><td>log r<sub>h</sub></td><td>-0.5 (0.316 µm)</td><td>0.5</td><td></td><td>0.13</td></tr><tr><td>Logarithm (10) of the main cloud's effective radius</td><td>log r<sub>c</sub></td><td>0.5 (3.16 µm)</td><td>0.5</td><td></td><td>0.09</td></tr><tr><td>Logarithm (10) of NH<sub>3</sub> deep VMR</td><td>log NH<sub>3VMR</sub></td><td>-3.48 (331 ppm)</td><td>0.1</td><td>Li et al. (2017)</td><td>0.095</td></tr><tr><td>Logarithm (10) of NH<sub>3</sub> deep relative humidity</td><td>log NH<sub>3RH</sub></td><td>1.7 (50 per cent)</td><td>0.20</td><td>Achterberg, Conrath & Gierasch (2006), Fletcher et al. (2016)</td><td>0.13</td></tr><tr><td>Logarithm (e) of the haze's pressure level</td><td>ln p<sub>h</sub></td><td>-1.2 (0.3 bar)</td><td>0.5</td><td>Simon-Miller, Banfield & Gierasch (2001), Sromovsky & Fry (2010a)</td><td>0.20</td></tr><tr><td>Logarithm (e) of the haze's width</td><td>ln w<sub>h</sub></td><td>-1.5 (0.22 bar)</td><td>0.4</td><td>Grassi et al. (2021)</td><td>0.35</td></tr><tr><td>Logarithm (10) of the haze's density</td><td>log N<sub>h</sub></td><td>2 (100 part. cm<sup>-3</sup>)</td><td>1</td><td>Sindoni et al. (2017), Grassi et al. (2021)</td><td>0.78</td></tr><tr><td>Logarithm (e) of the main cloud's pressure level</td><td>ln p<sub>c</sub></td><td>-0.1 (0.9 bar)</td><td>0.5</td><td>Sindoni et al. (2017), Grassi et al. (2021)</td><td>0.09</td></tr><tr><td>Logarithm (10) of the main cloud's density</td><td>log N<sub>c</sub></td><td>1.6 (40 part. cm<sup>-3</sup>)</td><td>1</td><td>Sindoni et al. (2017), Grassi et al. (2021)</td><td>0.20</td></tr><tr><td>Logarithm (e) of the deep cloud's pressure level</td><td>ln p<sub>D</sub></td><td>0.2 (1.22 bar)</td><td>0.5</td><td>Braude et al. (2020)</td><td>0.20</td></tr><tr><td>Logarithm (10) of the deep cloud's density</td><td>log N<sub>D</sub></td><td>2 (100 part. cm<sup>-3</sup>)</td><td>1</td><td>Sindoni et al. (2017), Grassi et al. (2021)</td><td>0.18</td></tr></tbody></table>

<table><caption>**Table 3.** Retrieved parameters considering respectively regions outside and inside the white vortex. The values are obtained through a weighted mean.</caption><thead><tr><th>Parameter</th><th>Unit</th><th>Outside the vortex</th><th>Inside the vortex</th></tr></thead><tbody><tr><td>r<sub>h</sub></td><td>µm</td><td>0.34 ± 0.02</td><td>0.24 ± 0.03</td></tr><tr><td>r<sub>c</sub></td><td>µm</td><td>3.02 ± 0.11</td><td>2.79 ± 0.20</td></tr><tr><td>NH<sub>3VMR</sub></td><td>ppm</td><td>328 ± 10</td><td>331 ± 27</td></tr><tr><td>NH<sub>3RH</sub></td><td>per cent</td><td>39 ± 2</td><td>53 ± 6</td></tr><tr><td>p<sub>h</sub></td><td>bar</td><td>0.32 ± 0.01</td><td>0.25 ± 0.02</td></tr><tr><td>w<sub>h</sub></td><td>bar</td><td>0.22 ± 0.01</td><td>0.23 ± 0.03</td></tr><tr><td>N<sub>h</sub></td><td>part. cm<sup>-3</sup></td><td>126 ± 35</td><td>128 ± 94</td></tr><tr><td>p<sub>c</sub></td><td>bar</td><td>0.60 ± 0.01</td><td>0.60 ± 0.01</td></tr><tr><td>N<sub>c</sub></td><td>part. cm<sup>-3</sup></td><td>9 ± 1</td><td>19 ± 3</td></tr><tr><td>p<sub>D</sub></td><td>bar</td><td>1.23 ± 0.04</td><td>1.33 ± 0.17</td></tr><tr><td>N<sub>D</sub></td><td>part. cm<sup>-3</sup></td><td>386 ± 23</td><td>268 ± 66</td></tr></tbody></table>

inside and outside the vortex obtained performing two separate weighted means. Aerosols properties have never been retrieved for regions at these latitudes, now easily accessible thanks to the Juno mission's orbit. Moreover, these retrieved parameters can be considered as the very first attempt to spectrally model a vortex in a folded filamentary region of Jupiter. All the free parameters are well-constrained by the fit, as it is possible to observe by comparing the retrieved uncertainty to the a-priori one, last column of Table 2. The parameters more difficult to constrain are the gaseous NH<sub>3</sub> deep mixing ratio and the haze densities. Indeed, this behaviour was largely expected. In fact, the deep mixing ratio cannot be well sounded at these wavelengths because of the wide contribution functions. The parametrized gaseous NH<sub>3</sub> profile used in this work must be intended as an approximation of the true mixing ratio profile which is very difficult to constrain using only near-infrared spectral observations. On the contrary, the haze density in the infrared is strongly correlated with the haze effective radius. In fact, the spectral effects of the haze are not as strong as in the visible, because the extinction cross-section of the haze peaks at wavelengths similar to the effective radii (≈ 0.1–0.4 µm), and decreases strongly towards longer wavelengths. So, the hazes act as a flat scatterer layer in the JIRAM spectral range. This in turn implies that there is an unbreakable correlation between the density and the effective radius. In fact, equally fitting models can be obtained with large density and small effective radius or vice versa, as both the parameters are fundamental to derive the haze extinction efficiency. We verified this behaviour by performing an independent analysis using a Monte Carlo Markov Chains approach (MCMC), as implemented by the `emcee` python package (Foreman-Mackey et al. 2013). For this test, a typical spectrum outside the vortex was considered, and the results are shown in Fig. A12. It is possible to observe that: (i) the posterior distributions of the parameters are consistent with the values derived with optimal estimations, (ii) the haze's density and effective radii distributions are clearly anticorrelated, since they follow a straight line.

Going back to sensitivity to other parameters, the pressure level and density of the deeper cloud were not constrained and remained stuck at the a-priori values when the main cloud deck became optically thick. Lastly, in Fig. A11 it is also possible to observe the Jacobians of the first iteration, that provide the sensitivity of the retrieval to the elements of state vector (i.e. free retrieval parameters) for a JIRAM test spectrum outside the vortex. From that figure we note that the spectral shape of the reflected peak is mostly sensitive to the main cloud deck, while the deeper one acts to increase the measured signal. So, in this spectral range we are not sensitive to the composition of the deepest cloud but only to its pressure level
<figure><img src="image_11.png" /><figcaption>**Figure 8.** Example of two best-fitting PSG/OE retrievals and corresponding statistical indicators. *Upper panel:* black JIRAM spectrum presented in Fig. 3. *Bottom panel:* grey JIRAM spectrum presented in Fig. 3. The best-fitting models follow the same colour convention presented in Fig. 7.</figcaption></figure>

and density. The choice of the tholin contaminants therefore is only conservative and driven by the findings of Grassi et al. (2021).

Fig. 8 presents the Model 1 (main clouds composed of tholins) best fit of the two example spectra introduced in Section 2. We note that Model 1 can model very well the spectra outside the snail-like white vortex, but not the one inside, where the retrieval is systematically poorer. This is easily observable in Fig. 9(a) that shows the resulting chi-square values of the retrievals performed using only Model 1. The poor fit quality is caused by the occurrence of shifted reflectance peaks and/or of spectral absorptions near 2.95 µm. Neither of these two features can be reproduced by clouds made of tholins. For this

reason, we also tested retrievals using Models 2, 3, and 4 on the spectra inside the vortex. The resulting chi-square map is shown in Fig. 9(b). There we note that inside the vortex the quality of the fit is greatly improved if pure ammonia ice or ammonia-coated tholin cloud decks are considered. In Fig. 10 it is possible to see which model provides the best fit for all the spectra in the set. From the same figure, we note that:

(i) most of the spectra inside the vortex are better reproduced considering Models 2, 3, and 4,

(ii) the spectra that do not display any shift in the reflectance peak but show the 2.95 µm absorption feature are, in most of the cases, best-reproduced by tholin particles coated with ammonia, with a core/total ratio of 85 per cent (upper right panel),

(iii) the spectra that display moderate shift in the reflectance peak and a 2.95 µm absorption are, in most of the cases, best-reproduced by tholin particles coated with ammonia, with a core/total ratio of 50 per cent (bottom left panel),

(iv) the spectra that present a strong shift in the reflectance peak and a 2.95 µm absorption are in most of the cases best-reproduced by pure ammonia ice particles (bottom right panel).

The choice of the best-fitting model was performed considering the difference between the BIC indicators from equation (5). We only considered the BIC as all the different models that we used to fit JIRAM data have the same number of free parameters. The chi-square and BIC values for the spectra modeled using all four models are given in Table 4. As the Bayes factor can be understood as proportional to $exp(\Delta\text{BIC}/2)$ (Patra et al. 2017), a BIC difference >10 indicates that the evidence favouring the lower BIC model against the other is quite strong (Cornish & Littenberg 2007). Looking at the values of $\Delta\text{BIC}$ presented in Table 4, we note that 25 spectra inside the vortex are best-reproduced by pure ammonia ice clouds (Model 2), while 15 spectra are best-reproduced by 50 per cent ammonia-coated tholins clouds (Model 3). For 38 of these spectra the Model 2 and 3 are favoured with respect to Model 1 by $\Delta\text{BIC}$ larger than 20–30 in most of the cases. This, in turn, implies that a total of 38 pixels covering spectrally identifiable ammonia clouds have been detected thanks to JIRAM in the PJ1 phase of the NASA Juno Mission. A total of nine spectra are instead best-reproduced by tholin particles coated with ammonia, with a core/total ratio of 85 per cent (Model 4), and so identified as peculiar spectra. In this case, however,

<figure><img src="image_12.png" /><figcaption>**Figure 9.** (a) $\chi^2/N$ maps obtained considering only Model 1 atmospheres during the retrieval stage. (b) $\chi^2/N$ maps obtained considering all the four Model atmospheres during the retrieval stage.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 10.** Upper panel: JIRAM coverage of the #1 ROI where the colour of each pixel represents the Model atmosphere that provide the lowest $\chi^2/N$ retrieval. Bottom panels: four examples of best-fitting retrievals for each Model atmosphere.</figcaption></figure>

not all the fits provide $\Delta$BIC larger than 20–30. The remaining 374 spectra of the data set are best-reproduced by tholin clouds (Model 1). Table A1 presents the retrieved parameters for the SIACs and peculiar spectra. The JIRAM spectra of the identified SIACs together with their best-fitting PSG synthetic spectra are represented in Figs A13, A14, and A15.

Once the best-fitting model has been identified for each spectrum in the data set, it is possible to derive maps of the retrieved physical parameters. These are presented in Figs 11, 12, A8, A9, and A10, and discussed in the following subsections.

## 4.1 NH$_3$ deep VMR and relative humidity

It is possible to observe the retrieved gaseous ammonia deep VMR in Fig. 11(a). The values vary from a minimum of 300 ± 70 ppm to a maximum of 342 ± 80 ppm. The mean uncertainty value is 74 ppm. Since in this wavelength interval we are not very sensitive to the deep atmosphere and we are studying cloudy regions, the values have to be taken with caution. Bearing this in mind, our results are in good agreement and are consistent at the 1σ-level with the results from MWR (Li et al. 2017) at the same planetocentric latitudes. A correlation with the snail-like vortex seems to be not evident, nor any longitude or latitude trend.

It is possible to observe the gaseous ammonia relative humidity values in Fig. 11(b). The values can vary from a minimum of 32

± 9 per cent to a maximum of 63 ± 13 per cent. The mean uncertainty value is 11 per cent. This is the first measure of gaseous ammonia relative humidity from Juno's data at these latitudes and it is in good agreement both with Cassini/CIRS results (Achterberg et al. 2006) and with ground-based TEXES measurements (Fletcher et al. 2016). Differently from the deep VMR, the relative humidity displays a longitudinal trend anticorrelated to the 2.73 µm signal. This may be connected to the effect of the incident solar radiation, which in turn may be linked to ammonia vaporization efficiency. Higher anomalous values are reported inside the vortex, suggesting a correlation between the presence of SIACs and higher relative humidity. It is worth to underline that the pixels that show the higher values in both Figs 11(a) and (b), are the same pixels for which the retrievals using Model 2 (pure ammonia ice) and Model 3 (50 per cent ammonia-coated tholins) converged with the lowest reduced chi-square values.

## 4.2 Haze and main cloud particles effective radii

Fig. 11(c) presents the distribution of the haze's effective radii. The values vary from a minimum of 0.21 ± 0.06 µm to a maximum of 0.42 ± 0.15 µm, in agreement with other remote sensing observations both in the infrared (Sromovsky & Fry 2010a) and in the optical (Simon-Miller et al. 2001), even if these were performed over regions of lower latitude. The mean uncertainty value is 0.1 µm. It is possible to observe a clear correlation with the vortex, in the proximity of which small (≈ 0.2 – 0.25 µm) effective radii are derived. Outside the vortex it is possible to observe two regions in the map: (i) a 'ring' around the vortex in which the radii are large (0.35–0.4 µm), and (ii) an outer 'halo' in which the radii are close to the median value (0.3 µm).

On the contrary, the main cloud's effective radii distribution, shown in Fig. 11(d), does not show any clear correlation with the vortex nor with latitude or longitude. The median value is 3 ± 0.7 µm, in agreement with the values found by Sromovsky & Fry (2018) in the regions where SIACs were detected. However, these results are different from the ones from the studies that investigated the EZ, SEB, and NEB in the visible (Braude et al. 2020; Dahl et al. 2021), where smaller radii (≈ 1 µm) and wider, discrete cloud decks are found.

## 4.3 Haze pressure level and width

The haze pressure level and width maps are shown respectively in Figs 12(a) and A8. The haze's pressure levels vary from a minimum of 0.23 ± 0.07 bar to a maximum of 0.48 ± 0.05 bar. The mean uncertainty value is 0.06 bar. These results are in good agreement with other remote sensing observations both in the infrared (Sromovsky & Fry 2010a) and in the optical (Simon-Miller et al. 2001), over regions of lower latitude. Also, in this case, it is possible to observe a correlation with the vortex, where the hazes are located at lower pressures (i.e. higher altitudes). It is possible to observe a ring where the hazes seem to be lower and which appears to be in correspondence with the same region where larger effective radii are retrieved. Outside the ring, the behaviour is not clear. Particularly interesting is a region at approximately ≃ 42 N° and ≃ 143 W°, where the hazes seem to be considerably lower than in the region of the vortex and the nearby regions. In the same location, the JunoCam images reveal a relatively dark oval, Fig. 1(b), that infrared observations suggest as a zone of relative cloud clearance. However, we note from Fig. A2 that this region was observed by JIRAM at
<table><caption>**Table 4.** Statistical indicators χ² and BIC given for all the retrievals of the JIRAM spectra inside the white vortex, varying the model atmosphere.</caption><thead><tr><th rowspan="2">Longitude (°)</th><th rowspan="2">Latitude (°)</th><th colspan="2">Model 1 (Contaminant-Tholins)</th><th colspan="2">Model 2 (Pure Ammonia Ice)</th><th colspan="2">Model 3 (NH<sub>3</sub> coated tholins, γ = 0.50)</th><th colspan="2">Model 4 (NH<sub>3</sub> coated tholins, γ = 0.85)</th><th rowspan="2">Δ BIC (Model 1-Best Fit)</th></tr><tr><th>χ²/N</th><th>BIC</th><th>χ²/N</th><th>BIC</th><th>χ²/N</th><th>BIC</th><th>χ²/N</th><th>BIC</th></tr></thead><tbody><tr><td>136.67</td><td>38.98</td><td>2.57</td><td>226.84</td><td>1.53</td><td>153.87</td><td>1.44</td><td>147.46</td><td>2.0</td><td>173.14</td><td>79.38</td></tr><tr><td>135.69</td><td>39.43</td><td>2.75</td><td>239.12</td><td>1.97</td><td>184.39</td><td>1.89</td><td>178.83</td><td>2.0</td><td>184.39</td><td>60.29</td></tr><tr><td>135.93</td><td>39.48</td><td>2.76</td><td>240.04</td><td>—</td><td>—</td><td>1.74</td><td>168.25</td><td>2.0</td><td>179.11</td><td>71.79</td></tr><tr><td>136.18</td><td>39.53</td><td>2.73</td><td>237.8</td><td>1.98</td><td>185.55</td><td>1.98</td><td>185.55</td><td>5.0</td><td>408.84</td><td>52.25</td></tr><tr><td>138.11</td><td>39.91</td><td>2.17</td><td>198.45</td><td>1.31</td><td>138.72</td><td>1.12</td><td>125.05</td><td>1.0</td><td>138.72</td><td>73.4</td></tr><tr><td>134.86</td><td>39.57</td><td>3.02</td><td>258.25</td><td>—</td><td>—</td><td>1.94</td><td>182.22</td><td>—</td><td>—</td><td>76.03</td></tr><tr><td>136.56</td><td>39.92</td><td>2.83</td><td>245.05</td><td>—</td><td>—</td><td>1.56</td><td>156.03</td><td>2.0</td><td>181.84</td><td>89.02</td></tr><tr><td>137.78</td><td>40.16</td><td>4.2</td><td>340.5</td><td>2.01</td><td>187.14</td><td>1.79</td><td>172.1</td><td>2.0</td><td>175.66</td><td>168.4</td></tr><tr><td>138.75</td><td>40.34</td><td>1.56</td><td>155.62</td><td>0.98</td><td>115.11</td><td>1.01</td><td>117.47</td><td>7.0</td><td>539.48</td><td>86.8</td></tr><tr><td>134.82</td><td>40.22</td><td>—</td><td>—</td><td>2.34</td><td>210.62</td><td>2.25</td><td>204.03</td><td>3.0</td><td>264.38</td><td>45.08</td></tr><tr><td>135.06</td><td>40.27</td><td>4.09</td><td>333.07</td><td>—</td><td>—</td><td>2.55</td><td>224.99</td><td>3.0</td><td>267.03</td><td>108.08</td></tr><tr><td>136.53</td><td>40.57</td><td>4.17</td><td>338.45</td><td>—</td><td>—</td><td>2.21</td><td>201.25</td><td>—</td><td>—</td><td>137.2</td></tr><tr><td>136.78</td><td>40.62</td><td>2.91</td><td>250.58</td><td>—</td><td>—</td><td>2.43</td><td>216.82</td><td>—</td><td>—</td><td>33.76</td></tr><tr><td>137.51</td><td>40.76</td><td>2.6</td><td>228.58</td><td>—</td><td>—</td><td>2.55</td><td>225.36</td><td>—</td><td>—</td><td>3.22</td></tr><tr><td>138.58</td><td>39.34</td><td>1.12</td><td>124.95</td><td>1.34</td><td>140.61</td><td>0.98</td><td>115.09</td><td>1.0</td><td>109.84</td><td>15.11</td></tr><tr><td>135.35</td><td>39.67</td><td>3.05</td><td>260.17</td><td>—</td><td>—</td><td>2.05</td><td>190.49</td><td>2.0</td><td>185.99</td><td>74.18</td></tr><tr><td>135.59</td><td>39.72</td><td>3.63</td><td>300.77</td><td>—</td><td>—</td><td>—</td><td>—</td><td>2.0</td><td>197.52</td><td>103.25</td></tr><tr><td>135.84</td><td>39.77</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>—</td><td>2.0</td><td>218.58</td><td>-218.58</td></tr><tr><td>136.81</td><td>39.97</td><td>2.78</td><td>241.44</td><td>2.23</td><td>202.84</td><td>6.93</td><td>531.99</td><td>2.0</td><td>186.07</td><td>55.37</td></tr><tr><td>137.29</td><td>40.06</td><td>2.96</td><td>253.79</td><td>—</td><td>—</td><td>3.03</td><td>258.69</td><td>2.0</td><td>215.71</td><td>38.08</td></tr><tr><td>137.53</td><td>40.11</td><td>—</td><td>—</td><td>2.35</td><td>211.47</td><td>—</td><td>—</td><td>2.0</td><td>211.47</td><td>-211.47</td></tr><tr><td>138.99</td><td>40.39</td><td>1.19</td><td>130.3</td><td>1.24</td><td>133.7</td><td>1.21</td><td>131.65</td><td>1.0</td><td>126.89</td><td>3.41</td></tr><tr><td>133.1</td><td>39.84</td><td>—</td><td>—</td><td>6.16</td><td>478.11</td><td>6.16</td><td>478.11</td><td>6.0</td><td>478.11</td><td>-478.11</td></tr><tr><td>136.43</td><td>38.93</td><td>2.16</td><td>197.73</td><td>1.57</td><td>156.43</td><td>1.57</td><td>156.43</td><td>4.0</td><td>302.3</td><td>41.3</td></tr><tr><td>137.15</td><td>39.07</td><td>2.73</td><td>237.87</td><td>1.06</td><td>120.77</td><td>1.17</td><td>128.94</td><td>2.0</td><td>172.34</td><td>117.1</td></tr><tr><td>137.38</td><td>39.12</td><td>2.56</td><td>225.7</td><td>0.93</td><td>112.1</td><td>1.09</td><td>123.19</td><td>2.0</td><td>153.93</td><td>113.6</td></tr><tr><td>137.62</td><td>39.16</td><td>3.09</td><td>263.31</td><td>1.01</td><td>117.3</td><td>1.07</td><td>121.93</td><td>2.0</td><td>175.51</td><td>146.01</td></tr><tr><td>137.86</td><td>39.21</td><td>3.39</td><td>284.11</td><td>1.24</td><td>133.26</td><td>1.49</td><td>151.11</td><td>1.0</td><td>133.26</td><td>150.85</td></tr><tr><td>138.1</td><td>39.25</td><td>2.62</td><td>230.39</td><td>1.14</td><td>126.19</td><td>1.18</td><td>129.2</td><td>1.0</td><td>126.19</td><td>104.2</td></tr><tr><td>138.34</td><td>39.3</td><td>2.12</td><td>194.96</td><td>0.9</td><td>109.97</td><td>0.92</td><td>111.24</td><td>1.0</td><td>149.29</td><td>84.99</td></tr><tr><td>136.42</td><td>39.58</td><td>3.56</td><td>296.28</td><td>1.5</td><td>151.4</td><td>1.5</td><td>151.4</td><td>1.0</td><td>151.4</td><td>144.88</td></tr><tr><td>136.66</td><td>39.63</td><td>3.11</td><td>264.35</td><td>1.04</td><td>119.62</td><td>1.5</td><td>151.85</td><td>2.0</td><td>180.58</td><td>144.73</td></tr><tr><td>137.14</td><td>39.72</td><td>3.21</td><td>271.54</td><td>1.74</td><td>168.68</td><td>1.97</td><td>184.63</td><td>2.0</td><td>168.68</td><td>102.86</td></tr><tr><td>137.38</td><td>39.77</td><td>3.74</td><td>308.56</td><td>1.62</td><td>160.25</td><td>2.18</td><td>199.56</td><td>2.0</td><td>218.21</td><td>148.31</td></tr><tr><td>137.62</td><td>39.81</td><td>3.91</td><td>320.53</td><td>1.67</td><td>163.33</td><td>—</td><td>—</td><td>2.0</td><td>182.8</td><td>157.2</td></tr><tr><td>137.86</td><td>39.86</td><td>3.28</td><td>276.21</td><td>1.67</td><td>163.8</td><td>1.88</td><td>178.49</td><td>2.0</td><td>163.8</td><td>112.41</td></tr><tr><td>138.35</td><td>39.95</td><td>2.21</td><td>201.67</td><td>0.93</td><td>112.13</td><td>1.01</td><td>117.16</td><td>2.0</td><td>188.92</td><td>89.54</td></tr><tr><td>138.59</td><td>40.0</td><td>1.87</td><td>177.28</td><td>0.84</td><td>105.55</td><td>0.9</td><td>109.5</td><td>2.0</td><td>187.41</td><td>71.73</td></tr><tr><td>138.83</td><td>40.04</td><td>1.46</td><td>148.78</td><td>1.1</td><td>123.43</td><td>—</td><td>—</td><td>1.0</td><td>127.25</td><td>25.35</td></tr><tr><td>138.26</td><td>40.25</td><td>2.05</td><td>189.99</td><td>1.08</td><td>122.08</td><td>1.29</td><td>137.31</td><td>7.0</td><td>504.15</td><td>67.91</td></tr><tr><td>133.59</td><td>39.95</td><td>5.67</td><td>443.53</td><td>2.39</td><td>214.36</td><td>2.61</td><td>229.36</td><td>—</td><td>—</td><td>229.17</td></tr><tr><td>133.84</td><td>40.0</td><td>4.23</td><td>342.84</td><td>1.49</td><td>151.22</td><td>1.69</td><td>165.07</td><td>2.0</td><td>219.0</td><td>191.62</td></tr><tr><td>134.08</td><td>40.06</td><td>4.26</td><td>344.64</td><td>2.11</td><td>194.7</td><td>2.15</td><td>197.52</td><td>3.0</td><td>237.86</td><td>149.94</td></tr><tr><td>134.57</td><td>40.16</td><td>5.25</td><td>414.03</td><td>2.66</td><td>232.75</td><td>—</td><td>—</td><td>—</td><td>—</td><td>181.28</td></tr><tr><td>135.31</td><td>40.32</td><td>4.66</td><td>372.7</td><td>2.81</td><td>243.2</td><td>—</td><td>—</td><td>—</td><td>—</td><td>129.5</td></tr><tr><td>135.55</td><td>40.37</td><td>4.65</td><td>371.91</td><td>3.03</td><td>258.91</td><td>3.11</td><td>264.26</td><td>—</td><td>—</td><td>113.0</td></tr><tr><td>138.25</td><td>40.9</td><td>2.27</td><td>205.39</td><td>0.75</td><td>99.25</td><td>—</td><td>—</td><td>1.0</td><td>99.25</td><td>106.14</td></tr></tbody></table>
<figure><img src="image_14.png" /><figcaption>**Figure 11.** Retrieved parameters maps for: (a) gaseous ammonia deep volume mixing ratio, (b) gaseous ammonia relative humidity, (c) haze particles effective radius, and (d) main cloud particles effective radius. For all the panels, the background is a global map created from visible images acquired by the JunoCam.</figcaption></figure>

large incidence angle values, making it difficult to discern between a real downwelling or a geometry effect.

The widths of the haze's vertical distribution vary from a minimum of 0.21 ± 0.08 bar to a maximum of 0.26 ± 0.08 bar. The mean uncertainty value is 0.08 bar. These values combined with the pressure levels confirm the fact that an extended layer of small particles that covers the 0.5–0.08 bar levels is needed to fit the JIRAM reflected sunlight spectra. A clear correlation with the vortex is not as evident as for the pressure levels, though in correspondence with the vortex the hazes seem to be slightly more vertically extended than in the nearby surroundings. Interestingly, also in this case, the largest width's values are in correspondence with the dark oval where the hazes seem to be lower in altitude (i.e. at higher pressures).

## 4.4 Main cloud pressure levels

The main cloud's pressure level and deep cloud's pressure levels maps are shown respectively in Figs 12(c) and A9. The main cloud's pressure levels vary from a minimum of 0.54 ± 0.05 bar to a maximum of 1.12 ± 0.08 bar. The mean uncertainty value is 0.04 bar. The comparison with previous works that studied regions at lower latitudes is made difficult by the non-uniformity of the chosen aerosol parametrization. For example both Sromovsky & Fry (2010a, b) and Sromovsky & Fry (2018) used very wide single layers that

extend from a few bars to the haze level. These clouds are too wide for a comparison, since they encompass the ones that we considered in our models. More useful is the comparison with the results from the visible spectral range. The main cloud levels that we found are either close to the 0.6 bar level or near the 0.85 bar level, in good agreement with the ones retrieved by Simon-Miller et al. (2001), which did not report main clouds deeper than 1 bar level. The retrieved values show a clear correlation with the vortex, where higher clouds (0.6 bar) are detected. However, differently from the haze's levels case, here we do not observe a ring of lower clouds in the nearby region. We observe lower clouds (0.85 bar) only on the west of the vortex (39–42°N and 141–139°W). On the east regions that surround the vortex clouds near the 0.6–0.7 bar level are observed. This can be the cause of the shadow that was noted in the JunoCam images and discussed in Section 2. This may suggest that the vortex does not extend vertically at all longitudes. Interestingly, the main cloud's pressure levels retrieved in correspondence with the dark oval discussed above are much larger than the ones retrieved for the vortex and the nearby regions, suggesting again that this region may be connected to a possible downwelling event.

The deep cloud's pressure levels vary from a minimum of 1.1 ± 0.2 bar to a maximum of 1.8 ± 0.4 bar. The mean uncertainty value is 0.24 bar. The maps in Fig. A9 consist only of pixels where the deep clouds can be sounded, i.e. where the main cloud did not become optically thick (see the next section). The median deep cloud level
<figure><img src="image_15.png" /><figcaption>**Figure 12.** Retrieved parameters maps for: (a) haze's pressure levels, (b) haze's density, (c) main cloud's pressure levels, and (d) main cloud's density. For all the panels, the background is a global map created from visible images acquired by the JunoCam.</figcaption></figure>

retrieved in our analysis is 1.23 ± 0.2 bar. This is consistent with the work of Braude et al. (2020) which found the peak in optical depth to be lower (in altitudes) than the 1 bar level in order to fit the MUSE/VLT visible spectra of the SEB, NEB, and NTBs.

## 4.5 Aerosol layers densities and optical depth

The haze and main cloud density maps are represented respectively in Figs 12(b) and (d). The deep cloud density map is shown instead in Fig. A10. The values in the maps are given in grams per liter (g l⁻¹) to facilitate the comparison between our results and previous and future works. The conversion from densities given in particles per unit of volume is possible once the radius of the particle are known and a fair assumption on density of aerosols is adopted. The retrieved haze's density values can vary from a minimum of (0.5 ± 1.0) × 10⁻⁸ g l⁻¹ (50 ± 40 part. cm⁻³) to a maximum of (4.3 ± 3.2) × 10⁻⁸ g l⁻¹ (750 ± 250 part. cm⁻³). The mean uncertainty value is 220 part. cm⁻³. Even if these values are less constrained by the retrievals, we obtained results that are consistent with the physics of the problem finding an extended deck of small particles that usually are far from becoming optically thick. It is not possible to observe a clear correlation neither with the vortex nor with latitude or longitude. The main cloud's density can vary from a minimum of (2.4 ± 2.0) × 10⁻⁷ g l⁻¹ (3 ± 2 part. cm⁻³) to a maximum of (6.4 ± 2.0) × 10⁻⁶ g l⁻¹ (79 ± 18 part. cm⁻³).

The mean uncertainty value is 3 part. cm⁻³. When the main cloud did not become optically thick it was possible also to retrieve the values of the deep cloud's density. These vary from from a minimum of (1.2 ± 2.5) × 10⁻⁷ g l⁻¹ (40 ± 30 part. cm⁻³) to a maximum of (3.6 ± 2.0) × 10⁻⁶ g l⁻¹ (1100 ± 900 part. cm⁻³). Both these values are consistent with previous observational (Braude et al. 2020) and theoretical (Zuchowski, Yamazaki & Read 2009) works. These values confirm that observations seem to be far from the predictions of Atreya et al. (1999), even for a folded filamentary region at high latitudes. However, it is worth keeping in mind that densities computed with ECCMs must be intended as upper limits, while the lifting condensation levels (cloud bases) are more robust.

The main cloud's densities are clearly correlated with the vortex, where higher values can be found. These values can easily make the cloud optically thick. Outside the vortex, the values are lower and mostly uniform. The only exception is again the dark oval where they become comparable with the vortex in terms of g l⁻¹ but not in part. cm⁻³, as in this region the retrieved effective radii are larger than the ones retrieved inside the vortex. This can be a consequence of the above discussed correlation, see Fig. A12. On the contrary, the deep cloud's densities inside the vortex are not constrained, given the high optical thickness of the main cloud. Outside the vortex deep clouds seem to be uniform, except in correspondence with the dark oval, where the lowest values are reached.
<figure><img src="image_16.png" /><figcaption>Figure 13. JIRAM coverage of the #1 ROI where the colour of each pixel represents the pressure level at which a value of 3 in the optical depth at 2.73 µm is reached.</figcaption></figure>

Using all the values presented in the previous subsections, it is possible to produce a map of the pressure level at which a value of 3 in optical depth is reached at 2.73 µm, the wavelength value where the gas opacity is lowest. This is shown in Fig. 13 and was obtained using the following relation:

$$ \tau_{2.73 \text{ µm}}(p_*) = \sum_{i=h, C, D} \int_{0}^{p_*} [N_i \cdot r_{\text{eff},i}^2 \cdot Q_{\text{ext},i} + \tau_{\text{gas}}] \cdot \Delta H, \quad (10) $$

where N must be expressed in part. cm⁻³, Qext is the estimate at 2.73 µm and is expressed as a non-dimensional number (i.e. independent of atmospheric properties), and ΔH represents the integration step that follows the considered pressure-temperature profile. τgas represents the optical depth per km and it is estimated from PSG cloud-free simulations. Fixing τ₂.₇₃ µm = 3 it is possible to solve equation (10) for p* and obtain the values presented in Fig. 13. The map shows that the atmosphere tends to become optically thick at the deep cloud level outside the vortex, and optically thick at the main cloud level inside the vortex. This is also a hint that the vortex feature is elevated and more dense than the background clouds. The depth (in pressure) to which it is possible to view into the vortex is only about half the depth it is possible to probe outside the vortex.

# 5 DISCUSSION

## 5.1 Explanation of the observed 2.73 µm shift

In Fig. 3 it is possible to observe how, for the spectra inside the vortex, the peak of maximum reflected sunlight seems to be shifted with respect to the spectra outside the vortex. The shape of this radiation peak is a very important diagnostic of the aerosol properties as it corresponds to a zone of high transmittance, i.e. low gas opacity (an atmospheric window). As a consequence, two aerosol layers different in altitude, vertical extension, and/or composition will produce different radiation peaks in shape and intensity. Differences in altitude and vertical extension should produce the effect of broadening the peak maintaining however the position of the maximum, and this was noted studying the GRS, whose clouds are thought to be higher than the surroundings (Carlson et al. 2016). There is no analytic relation that connects the Qext, single scattering albedo, and the HG asymmetry parameter to the resulting shape of the reflectance peak. However, referring to the zoom panel presented in Fig. 7 for a radius of 3 µm, it is possible to note that the single

scattering albedo spectra of tholins and pure ammonia are very different from each other. All the spectra tend to decline in this spectral region. However, the pure ammonia and γ = 50 per cent ammonia-coated tholins profiles decay with wavelength more slowly than γ = 85 per cent ammonia-coated tholins and pure tholins. In particular, the former starts decreasing for λ > 2.8 µm, while the latter already for λ > 2.74 µm. The same behaviour is observed for optical properties computed considering rₐff = 2 µm, Fig. A7, while it becomes negligible towards rₐff = 1 µm, Fig. A6.

As from the JunoCam images there is a clear difference in colour from the vortex (mostly white) and the outside (mostly greyish-brown), it is reasonable to suppose that this change is caused by the single-scattering albedo. This hypothesis is consistent with the retrieval results, as inside the vortex the majority of the spectra are best-reproduced by main clouds composed of either pure ammonia ice or γ = 50 per cent ammonia-coated tholins, as the single scattering albedo of these two species (computed for rₐff = 2 – 3 µm) can easily provide the shift of the reflection peak observed in JIRAM data.

## 5.2 Detection of ammonia clouds

We detected a total of 25 spectra that are best-reproduced by main clouds composed of pure ammonia ice particles and 15 spectra that are best-reproduced by main clouds composed of γ = 50 per cent ammonia-coated tholins. Based on a Bayesian model comparison framework thanks to the BIC indicator, it is possible to claim that, in total, we detected 38 pixels covering spectrally identifiable ammonia clouds (SIACs). All the detections are in the proximity of the white turbulent vortex observed in JunoCam images, that can be considered to be a folded filamentary region (FFR). This kind of vortices are formed commonly at latitudes > 35°, and due to their colour and thanks to the high-resolution JunoCam images, the presence of ammonia clouds was proposed and theorized (Fletcher et al. 2022; Guillot et al. 2024). With this work, spectroscopic evidence of the presence of ammonia clouds in high-latitude FFRs on Jupiter has been established.

We want to stress that this detection was driven by finding the best cloud model that can explain the reflectance peak and the 2.95 µm absorption feature. This was possible thanks to the use of a fast and efficient radiative transfer code such as PSG, which does not sacrifice accuracy in performing precise multiple scattering calculations. Trying different models it is fundamental to claim a strong detection and to explore efficiently the parameter space. In fact, multiple combinations of the physical properties and composition of the clouds can give similar shapes of the reflectance peak, but we found that only pure ammonia ice and heavily ammonia-coated tholin particles with effective radii between 2 and 3.5 µm can simultaneously provide a redshifted peak and an absorption dip at 2.95 µm, and so accurately fit the spectra inside the vortex. The spectra outside the vortex instead do not need ammonia clouds to be accurately reproduced, but pure tholins, which should be understood as an approximation (in the 2.5–3.1 µm range) for the real–unknown – material that composes the Jovian clouds. Some possibilities for tholins on Jupiter are discussed in Section 5.4.

It is worth underlining that if we only focused on the 2.95 µm region, which is embedded in the gaseous-ammonia high opacity range, it would have been more difficult to determine which cloud model best explains the spectra inside and the outside of the vortex. Lastly, we stress that this detection was also feasible thanks to the spectral and spatial resolution of JIRAM. It would have been a
lot more difficult observing different reflection peak shapes at a
resolution similar to NIMS (after the High Gain Antenna failure).

In the light of these results we propose studying both the absorption
features and the change in reflectance in low gaseous opacity regions
as an efficient method to retrieve aerosol and cloud's properties
of a planetary atmosphere. The 2.73 µm peak will be within the
range of the MAJIS spectrometer (Poulet et al. 2024) on board the
JUICE mission (Grasset et al. 2013). This instrument will have a
better and improved spectral resolution with respect to JIRAM, and
will also cover the 5.1–5.4 µm range where ammonia hydrosulfide
absorption features are expected. A similar 2.73 µm reflectance peak
caused by ammonia or contaminated clouds is present also on Saturn
(Sromovsky, Baines & Fry 2018; Coulter, Barnes & Fortney 2022)
and should be considered as an important proxy for the search of
ammonia ice clouds for future direct imaged Jupiter-like exoplanets
(Robinson, Stapelfeldt & Marley 2016). On Uranus and Neptune,
the same wavelength window can be used to infer the composition of
the ice giants' planets aerosols (Roman et al. 2023). The detection of
these SIACs in a turbulent region confirms the trend that ammonia
clouds are rare on Jupiter and connected to uplifting events, while on
the rest of the Jovian disc they are absent or at least masked through
a contaminant coating. This trend was established in the past 40 yr
thanks to NASA's *Galileo*, *Cassini*, and *New Horizon* missions, and
to ground-based observations. This detection is the first one made
thanks to the data from the Juno mission and places it in continuity
with previous missions and literature works.

**5.3 Interpretation of the vortex's nature**

The retrieved physical parameters presented in Section 4 and in Table 3 can be used to give an interpretation of the nature of the white snail-like vortex identified as a folded filamentary region. We propose that this white feature is the result of a moist convection storm similar to the ones described in Hueso & Sánchez-Lavega (2001, 2004) and Fletcher et al. (2017). These storms can bring fresh ammonia from the deep troposphere (2–8 bar) up to the upper troposphere (1–0.1 bar). Successively, this ammonia can either condense and form pure filamentary ice white clouds or become a coating material for the previously existing haze or main cloud's particles. The presence of these storms at mid and high latitudes is not unusual on Jupiter. Juno data showed the presence of lightning caused by liquid NH₃·H₂O between 1 and 2 bars (Becker et al. 2020), and that ammonia is depleted down to at least several bars at mid-latitudes (Li et al. 2017; Bolton et al. 2017b). Both the findings can be explained by frequent large convective storms capable of lifting water at the 1 bar level and inducing the formation of the so-called mushballs (Guillot et al. 2020a, b). Such an upwelling of ammonia (as well as water) clouds was observed in Saturn's Great Storm of 2010–2011 by Sromovsky, Baines & Fry (2013).

In this work, the convection storm hypothesis is supported by four
observations:

(i) We found differences of 0.08–0.15 bars in the haze pressure levels between the regions inside the vortex and the ones surrounding it. These differences are larger than the median uncertainties (0.05 bar) on the retrieved parameters and translate into differences of 5–10 km in altitude;

(ii) We found higher gaseous ammonia relative humidity values inside the vortex with respect to the outside. Inside the vortex, the values are systematically larger than 45 per cent and reach a maximum of (63 ± 13) per cent. Outside the vortex the values are systematically lower than 45 per cent, reaching a minimum of (32

± 12) per cent. This indicates that, above the condensation level,
inside the vortex, there is a higher concentration of (presumably
cold) gaseous ammonia than outside the vortex;

(iii) We found differences of 0.10–0.20 bars in the main cloud pressure levels between the regions inside the vortex and the ones surrounding it on the west (System III West Longitudes > 138°). These differences are larger than the median uncertainties (0.03) on the retrieved parameters and translate into differences of 5–10 km in altitude. Having observed this difference only with the region on the west of the vortex may indicate that the moist convection storm was not uniform in longitude but that is slightly inclined westward. In turn, these values can also provide an explanation for the putative shadow observed in the JunoCam images (Fig. 1).

(iv) We found smaller haze particles' effective radii in the regions inside the vortex. As we explained in Section 4, we found the haze particles' effective radius to be strongly correlated with the haze's density. However we found differences of ≈ 0.1–0.12 µm in the retrieved parameters between the regions inside the vortex and the ones surrounding it, and these values are slightly larger than the median retrieved uncertainties of 0.08–0.10 µm. This can be a hint of the microphysics of the haze formation, as higher hazes (and clouds) tend to be constituted by smaller and less complex particles.

The obliquity of the storm (point iii) is only a conjecture based on the retrieved parameters because the information about the deep (p > 1 bar) vertical structure of the clouds is not accessible, see Fig. 13. We found that inside the vortex, where pure ammonia ice or ammonia-coated tholins clouds are detected, values of 3 in the optical depth are reached at the main cloud level. This is consistent with the fact that the vortex, and the folded filamentary regions in general, are highly turbulent making the formation of optically thick clouds statistically favoured.

Outside the vortex the main clouds, composed of putative tholins, tend to be optically thin, and high values in optical depth are reached only at the deep cloud level, i.e. 1.1–1.4 bar level. This is consistent with the most recent findings from VLT/MUSE (Braude et al. 2020). The found cloud vertical structures in regions inside and outside the vortex are represented in Fig. 14, together with results from ECCMs calculations that assume a 3× solar NH₃ and H₂S. We make several observations: (i) the same value in optical depth is reached approximately at 0.6 bar inside the vortex, and at 1.3 bar outside, which translates into a difference of ≈20 km; (ii) the ECCMs can efficiently predict the pressure level at which the SIACs have been detected; (iii) the ECCMs cannot efficiently predict the found density values. Both the presence of contaminant clouds (represented by tholins) in regions outside the vortex and the found clouds' densities values [in agreement with previous works such as Braude et al. (2020)] indicate that the Atreya et al. (1999) models need to be expanded and re-evaluated in order to explain the results of atmospheric retrievals on Juno/JIRAM data.

**5.4 Possible origin of Titan-like tholin material on Jupiter**

Finally, Titan-like tholins as a coating material for the clouds seems
puzzling, since tholins contain complex C,N containing organic
compounds. Whereas Titan's atmosphere is dominated by nitrogen
and contains ample methane, N₂ has not been detected in Jupiter's
atmosphere, but it does contain other forms of nitrogen such as NH₃
and HCN, and methane is plentiful. This allows for the possibility
of forming N-C bearing coating material that may resemble Titan
tholins. The source could be in the stratosphere or upper troposphere.
Carlson et al. (2016) demonstrated from laboratory experiments that
<figure><img src="image_17.png" /><figcaption>**Figure 14.** Best-fitting aerosol profiles for two JIRAM spectra, respectively outside and inside the white vortex. ECCM results are also shown here. The calculation assumes 3× solar NH₃ and H₂S. Cloud densities are upper limits, the lifting condensation levels (cloud bases) are robust, however (Atreya et al. 1999). Grey dashed lines highlight the pressure levels at which $τ_{[2.73 µm]} = 3$ is reached.</figcaption></figure>

reactions between the photolysis products of ammonia and acetylene resulted in brownish chromophores resembling the colour of the Great Red Spot (GRS). Ammonia normally condenses at 0.5–0.7 bar, but in the GRS, it can be upwelled to the upper troposphere, where it can undergo photolysis. SIACs may also represent regions where ammonia is uplifted, allowing for its photolysis. The hypothesis that the Titan's tholins-like material 'detected' by JIRAM in the near-infrared (2–3.5 µm) correspond to the material that Sromovsky et al. (2017) found fitting the visual spectrum of clouds throughout the disc of Jupiter using Cassini/VIMS data. However, the spectral properties of the used NH₃-C₂H₂ material were only measured in the 0.4–0.7 µm range (Carlson et al. 2016). Therefore future laboratory measurements are needed to test this hypothesis.

Another possibility for the formation of Titan-like tholins involves HCN. The detection of HCN on Jupiter (Tokunaga et al. 1981) provides an evidence of 'N' of NH₃ combining with 'C' of CH₄. Models show that HCN is formed by coupled photochemistry of acetylene (C₂H₂, from CH₄) and ammonia in Jupiter's upper troposphere (Kaye & Strobel 1983). HCN was also detected following the impact of comet Shoemaker-Levy 9 and then distributed planetwide over time (Moreno et al. 2003). HCN was likely the result of thermochemistry between CH₄ and NH₃. Once formed, HCN can undergo polymerization (Matthews 1997). Complex polymers synthesized from HCN potentially make up a significant portion of Titan-like tholins invoked in this paper. Yet, another potential candidate is in the form of nitrogen containing polycyclic aromatic hydrocarbon haze (PANH; Atreya 2010) and associated complex polymers from the upper atmosphere. PANHs require the insertion

of nitrogen into PAHs synthesized from benzene (Atreya et al. 2005). HCN may provide the nitrogen, either directly or through its dissociation products. Laboratory studies are needed to understand the feasibility of above processes.

## 6 SUMMARY

In this work, we investigated the presence of spectrally identifiable ammonia clouds in the Juno/JIRAM data set. We focused on the first peri-jove data, which provides the most accurate and complete spatial coverage of the Northern mid-latitude domain. With the aid of spectral indicators sensitive to the presence of high-altitude clouds and 2.95 µm absorptions, we identified different regions of interest. One of them, containing a white snail-like vortex structure, has been analysed. This vortex is located approximately at 40°N in latitude, and seems to be an example of a folded filamentary region. We decided to model the JIRAM spectra in correspondence with the regions outside and inside the vortex focusing on the 2.5–3.1 µm range and performing, for the first time in this region, multiple scattering atmospheric retrievals. We used the PSG to generate synthetic JIRAM-like spectra and an Optimal Estimation scheme to perform the retrievals. We used four different model atmospheres with a variable gaseous ammonia profile, a pure reflecting haze, and two clouds with a Gaussian density vertical profile. The four atmospheric structures differ from each other only by the composition of the main cloud which was set to vary between tholins, pure ammonia ice, and ammonia-coated tholins with core-to-radius ratios
of 50 per cent and 85 per cent. The main results of this work can be summarized as follows:

(i) We were able to successfully model JIRAM spectra in the solar-dominated range (2.5–3.1 µm) using PSG.

(ii) The JIRAM spectra outside the vortex are adequately reproduced by main clouds composed of tholins, which should represent an unknown contaminant material as proposed by Grassi et al. (2021). On the other hand, most of the JIRAM spectra inside the vortex are NOT well reproduced by tholin clouds. Instead, we obtained better quality fits (lower chi-squared, and Bayesian Information Criteria values) when main clouds composed either of pure ammonia ice clouds or ammonia-coated tholins with a low core-to-radius ratio of 50 per cent are invoked. For a total of 38 spectra, main clouds composed of pure ammonia ice are favoured with respect to clouds composed of tholins by ΔBIC larger than 20, which provide robust statistical evidence. For this reason, we claim the detection of 38 SIACs thanks to the data of JIRAM collected during the first perijove. This is the first spectral evidence of pure ammonia clouds provided by the Juno mission data.

(iii) We noted that the spectra inside the vortex are best-reproduced by pure ammonia ice main clouds because they present both a shifted 2.73 µm radiation peak with respect to the spectra outside and an absorption feature at 2.95 µm. The 2.73 µm peak represents a transparency window in the atmosphere of Jupiter, being the opacities of NH₃ and CH₄ relatively low between 2.6 and 2.8 µm. As cloud decks are present, the shape in wavelength of the reflection peak will be directly connected to the optical properties of the cloud's particles. We found that ammonia ice particles and ammonia-coated tholins with a low core-to-radius ratio of 50 per cent can both provide single scattering albedo values able to explain both the absorption features and the peak shifts observed by JIRAM.

(iv) For regions inside the vortex, the retrieved parameters show the presence of: (i) higher altitude hazes, (ii) smaller haze's effective radii, and (iii) higher gaseous ammonia relative humidity values. We also noted that the main clouds seem to be higher inside the vortex compared to the regions on the west of the vortex, while the main clouds retrieved for the regions on the east seem to lay at the same level. These patterns combined with the detection of pure ammonia ice clouds suggest that the vortex was the result of a moist convection storm that uplifted fresh ammonia from the deep troposphere. This in turn either condensed or became a coating for the already existing particles.

(v) We confirmed that spectrally identifiable ammonia clouds seem to be connected to large convection events, in our case a white vortex that represents an example of a folded filamentary region. In the other case, when typical Jovian regions are studied, SIACs seem to not be present, and if ammonia clouds are present their spectral signatures should be masked. This is consistent with the data collected in order by Galileo, Cassini, and New Horizons and by ground-based observatories. This may suggest that the classical equilibrium chemistry condensation models may need to be reviewed.

## ACKNOWLEDGEMENTS

We thank the anonymous referee for their careful reading and thoughtful comments that have greatly improved the paper. JIRAM is supported by the Italian Space Agency (ASI). This work is funded by the Addendum N. 2016-23-H.3-2023 to the ASI-INAF Agreement N. 2016-23-H.0. Part of the research activities described in this paper were carried out with contribution of the Next Generation

EU funds within the National Recovery and Resilience Plan (PNRR), Mission 4–Education and Research, Component 2–From Research to Business (M4C2), Investment Line 3.1–Strengthening and creation of Research Infrastructures, Project IR0000038 – ‘Earth Moon Mars (EMM)’. EMM is led by INAF in partnership with ASI and CNR. FB acknowledges the Ph.D. course in Astronomy, Astrophysics and Space Science of the University of Rome ‘Sapienza’, University of Rome ‘Tor Vergata’ and INAF, Italy. SKA acknowledges support from NASA through the Juno Project. Fletcher is supported by STFC Consolidated Grant reference ST/W00089/X1. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) license to the Author Accepted Manuscript version arising from this submission. TG acknowledges support from Centre National d'Etudes Spatiales (CNES). GS Orton is supported by funds from NASA distributed to the Jet Propulsion Laboratory, California Institute of Technology under a contract (80NM0018D0004). We thank N. Ignatiev (Space Research Institute of the Russian Academy of Sciences) for the permission to use his radiative transfer subroutines and his kind help in code development. JIRAM has been developed by Leonardo S.p.A. at the Officine Galileo-Campi Bisenzio site.

The JIRAM instrument was conceived and brought to reality by our late collaborator and institute Director Dr. Angioletta Coradini (1946–2011).

## DATA AVAILABILITY

The data underlying this article are available in NASA Planetary Data System at https://pds-atmospheres.nmsu.edu/data_and_service/s/atmospheres_data/JUNO/jiram.html

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## APPENDIX A: SUPPLEMENTARY MATERIALS
<table><caption>Table A1. Retrieved parameters for the JIRAM spectra best-fitted by Models 2, 3, and 4 in regions inside the white vortex.</caption><thead><tr><th>Longitude (°)</th><th>Latitude (°)</th><th>Model</th><th>r<sub>B</sub> (μm)</th><th>r<sub>C</sub> (μm)</th><th>NH<sub>3</sub>V<sub>M</sub>R (ppm)</th><th>NH<sub>3</sub>R<sub>H</sub> (per cent)</th><th>p<sub>H</sub> (bar)</th><th>w<sub>H</sub> (bar)</th><th>N<sub>H</sub> (part. cm<sup>-3</sup>)</th><th>p<sub>C</sub> (bar)</th><th>N<sub>C</sub> (part. cm<sup>-3</sup>)</th><th>p<sub>D</sub> (bar)</th><th>N<sub>D</sub> (part. cm<sup>-3</sup>)</th></tr></thead><tbody><tr><td>135.95</td><td>38.83</td><td>2</td><td>0.32 ± 0.1</td><td>2.6 ± 0.45</td><td>329 ± 74</td><td>39.17 ± 10.64</td><td>0.32 ± 0.06</td><td>0.22 ± 0.08</td><td>160 ± 299</td><td>0.62 ± 0.03</td><td>19 ± 10</td><td>–</td><td>–</td></tr><tr><td>136.19</td><td>38.88</td><td>3</td><td>0.3 ± 0.09</td><td>3.39 ± 0.29</td><td>328 ± 75</td><td>45.93 ± 12.86</td><td>0.29 ± 0.06</td><td>0.22 ± 0.08</td><td>209 ± 377</td><td>0.63 ± 0.03</td><td>26 ± 10</td><td>–</td><td>–</td></tr><tr><td>136.43</td><td>38.93</td><td>2</td><td>0.25 ± 0.06</td><td>3.23 ± 0.74</td><td>330 ± 75</td><td>51.47 ± 14.48</td><td>0.27 ± 0.05</td><td>0.22 ± 0.08</td><td>470 ± 854</td><td>0.62 ± 0.03</td><td>13 ± 6</td><td>1.33 ± 0.55</td><td>190 ± 123</td></tr><tr><td>136.67</td><td>38.98</td><td>2</td><td>0.24 ± 0.07</td><td>3.34 ± 0.76</td><td>331 ± 75</td><td>54.15 ± 15.2</td><td>0.27 ± 0.05</td><td>0.22 ± 0.08</td><td>581 ± 1088</td><td>0.6 ± 0.03</td><td>12 ± 5</td><td>1.41 ± 0.55</td><td>231 ± 225</td></tr><tr><td>137.15</td><td>39.07</td><td>2</td><td>0.24 ± 0.07</td><td>3.45 ± 0.72</td><td>330 ± 75</td><td>52.84 ± 14.66</td><td>0.26 ± 0.05</td><td>0.22 ± 0.08</td><td>533 ± 1014</td><td>0.8 ± 0.03</td><td>12 ± 5</td><td>1.33 ± 0.54</td><td>231 ± 190</td></tr><tr><td>137.38</td><td>39.12</td><td>2</td><td>0.24 ± 0.07</td><td>3.72 ± 0.83</td><td>330 ± 75</td><td>53.78 ± 15.26</td><td>0.26 ± 0.05</td><td>0.22 ± 0.08</td><td>565 ± 1059</td><td>0.6 ± 0.03</td><td>9 ± 4</td><td>1.36 ± 0.56</td><td>178 ± 159</td></tr><tr><td>137.62</td><td>39.16</td><td>2</td><td>0.24 ± 0.06</td><td>3.76 ± 0.85</td><td>330 ± 75</td><td>50.77 ± 14.72</td><td>0.26 ± 0.05</td><td>0.22 ± 0.08</td><td>498 ± 896</td><td>0.2 ± 0.03</td><td>8 ± 3</td><td>1.28 ± 0.54</td><td>143 ± 68</td></tr><tr><td>137.86</td><td>39.21</td><td>2</td><td>0.27 ± 0.08</td><td>3.37 ± 0.79</td><td>331 ± 75</td><td>48.49 ± 14.11</td><td>0.27 ± 0.05</td><td>0.22 ± 0.08</td><td>277 ± 514</td><td>0.61 ± 0.03</td><td>9 ± 4</td><td>1.29 ± 0.44</td><td>215 ± 82</td></tr><tr><td>138.1</td><td>39.25</td><td>4</td><td>0.31 ± 0.1</td><td>3.37 ± 0.34</td><td>325 ± 73</td><td>42.22 ± 12.99</td><td>0.3 ± 0.06</td><td>0.23 ± 0.09</td><td>130 ± 242</td><td>0.63 ± 0.04</td><td>12 ± 4</td><td>1.4 ± 0.37</td><td>291 ± 220</td></tr><tr><td>134.97</td><td>39.28</td><td>3</td><td>0.31 ± 0.11</td><td>2.25 ± 0.07</td><td>330 ± 75</td><td>41.22 ± 12.01</td><td>0.29 ± 0.06</td><td>0.23 ± 0.09</td><td>113 ± 223</td><td>0.64 ± 0.03</td><td>52 ± 11</td><td>–</td><td>–</td></tr><tr><td>135.21</td><td>39.33</td><td>3</td><td>0.31 ± 0.1</td><td>2.22 ± 0.09</td><td>327 ± 74</td><td>38.24 ± 11.11</td><td>0.28 ± 0.06</td><td>0.23 ± 0.09</td><td>113 ± 220</td><td>0.65 ± 0.03</td><td>61 ± 12</td><td>–</td><td>–</td></tr><tr><td>135.45</td><td>39.38</td><td>3</td><td>0.37 ± 0.13</td><td>2.84 ± 0.63</td><td>320 ± 73</td><td>33.8 ± 90</td><td>0.34 ± 0.06</td><td>0.23 ± 0.09</td><td>94 ± 175</td><td>0.67 ± 0.03</td><td>25 ± 12</td><td>–</td><td>–</td></tr><tr><td>135.69</td><td>39.43</td><td>2</td><td>0.33 ± 0.1</td><td>3.88 ± 1.06</td><td>324 ± 74</td><td>39.11 ± 11.07</td><td>0.3 ± 0.06</td><td>0.22 ± 0.08</td><td>127 ± 230</td><td>0.62 ± 0.03</td><td>8 ± 4</td><td>1.21 ± 0.36</td><td>309 ± 209</td></tr><tr><td>135.93</td><td>39.48</td><td>2</td><td>0.28 ± 0.08</td><td>3.65 ± 0.92</td><td>327 ± 74</td><td>45.14 ± 12.76</td><td>0.27 ± 0.05</td><td>0.23 ± 0.09</td><td>225 ± 401</td><td>0.61 ± 0.03</td><td>10 ± 4</td><td>1.37 ± 0.5</td><td>251 ± 232</td></tr><tr><td>136.18</td><td>39.53</td><td>2</td><td>0.23 ± 0.07</td><td>2.67 ± 0.36</td><td>332 ± 75</td><td>56.05 ± 14.95</td><td>0.25 ± 0.04</td><td>0.22 ± 0.08</td><td>666 ± 1320</td><td>0.57 ± 0.03</td><td>18 ± 7</td><td>–</td><td>–</td></tr><tr><td>136.42</td><td>39.58</td><td>2</td><td>0.24 ± 0.07</td><td>2.71 ± 0.4</td><td>332 ± 75</td><td>51.63 ± 14.69</td><td>0.24 ± 0.05</td><td>0.23 ± 0.08</td><td>389 ± 779</td><td>0.56 ± 0.03</td><td>18 ± 7</td><td>–</td><td>–</td></tr><tr><td>136.66</td><td>39.63</td><td>2</td><td>0.24 ± 0.07</td><td>2.94 ± 0.49</td><td>331 ± 75</td><td>49.46 ± 14.18</td><td>0.24 ± 0.05</td><td>0.23 ± 0.08</td><td>380 ± 750</td><td>0.55 ± 0.03</td><td>15 ± 6</td><td>–</td><td>–</td></tr><tr><td>137.14</td><td>39.72</td><td>2</td><td>0.23 ± 0.07</td><td>2.68 ± 0.35</td><td>332 ± 75</td><td>52.17 ± 14.0</td><td>0.25 ± 0.05</td><td>0.23 ± 0.08</td><td>499 ± 977</td><td>0.57 ± 0.03</td><td>18 ± 6</td><td>–</td><td>–</td></tr><tr><td>137.38</td><td>39.77</td><td>3</td><td>0.26 ± 0.08</td><td>3.38 ± 0.31</td><td>330 ± 75</td><td>48.95 ± 14.56</td><td>0.26 ± 0.05</td><td>0.23 ± 0.09</td><td>298 ± 586</td><td>0.61 ± 0.04</td><td>25 ± 12</td><td>–</td><td>–</td></tr><tr><td>137.62</td><td>39.81</td><td>2</td><td>0.26 ± 0.07</td><td>2.89 ± 0.57</td><td>330 ± 75</td><td>48.63 ± 13.85</td><td>0.27 ± 0.05</td><td>0.23 ± 0.08</td><td>288 ± 540</td><td>0.61 ± 0.03</td><td>14 ± 7</td><td>1.29 ± 0.5</td><td>185 ± 79</td></tr><tr><td>137.86</td><td>39.86</td><td>2</td><td>0.3 ± 0.09</td><td>3.28 ± 1.07</td><td>329 ± 75</td><td>46.28 ± 14.3</td><td>0.29 ± 0.05</td><td>0.22 ± 0.09</td><td>196 ± 359</td><td>0.62 ± 0.04</td><td>8 ± 6</td><td>1.33 ± 0.57</td><td>135 ± 84</td></tr><tr><td>138.11</td><td>39.91</td><td>2</td><td>0.36 ± 0.11</td><td>2.94 ± 0.82</td><td>328 ± 73</td><td>40.22 ± 12.15</td><td>0.32 ± 0.06</td><td>0.23 ± 0.09</td><td>99 ± 176</td><td>0.63 ± 0.04</td><td>9 ± 6</td><td>1.43 ± 0.36</td><td>391 ± 265</td></tr><tr><td>133.16</td><td>39.2</td><td>4</td><td>0.29 ± 0.09</td><td>2.16 ± 0.09</td><td>329 ± 72</td><td>39.87 ± 12.22</td><td>0.28 ± 0.06</td><td>0.23 ± 0.09</td><td>136 ± 264</td><td>0.65 ± 0.03</td><td>39 ± 10</td><td>–</td><td>–</td></tr><tr><td>133.89</td><td>39.36</td><td>4</td><td>0.29 ± 0.1</td><td>2.28 ± 0.09</td><td>317 ± 71</td><td>35.4 ± 10.32</td><td>0.28 ± 0.06</td><td>0.23 ± 0.09</td><td>112 ± 221</td><td>0.66 ± 0.03</td><td>46 ± 9</td><td>–</td><td>–</td></tr><tr><td>134.13</td><td>39.42</td><td>4</td><td>0.25 ± 0.09</td><td>2.23 ± 0.06</td><td>317 ± 71</td><td>33.97 ± 9.99</td><td>0.25 ± 0.07</td><td>0.25 ± 0.09</td><td>123 ± 256</td><td>0.65 ± 0.03</td><td>52 ± 10</td><td>–</td><td>–</td></tr><tr><td>134.62</td><td>39.52</td><td>4</td><td>0.26 ± 0.09</td><td>2.13 ± 0.05</td><td>313 ± 70</td><td>32.28 ± 9.56</td><td>0.26 ± 0.07</td><td>0.24 ± 0.09</td><td>117 ± 247</td><td>0.65 ± 0.03</td><td>79 ± 17</td><td>–</td><td>–</td></tr><tr><td>135.11</td><td>39.62</td><td>3</td><td>0.29 ± 0.09</td><td>2.19 ± 0.09</td><td>324 ± 73</td><td>38.47 ± 11.58</td><td>0.27 ± 0.06</td><td>0.23 ± 0.09</td><td>133 ± 258</td><td>0.64 ± 0.03</td><td>48 ± 11</td><td>–</td><td>–</td></tr><tr><td>135.35</td><td>39.67</td><td>4</td><td>0.24 ± 0.07</td><td>3.34 ± 0.17</td><td>320 ± 73</td><td>44.52 ± 12.99</td><td>0.25 ± 0.05</td><td>0.23 ± 0.08</td><td>373 ± 734</td><td>0.61 ± 0.03</td><td>26 ± 6</td><td>–</td><td>–</td></tr><tr><td>135.72</td><td>39.72</td><td>4</td><td>0.23 ± 0.08</td><td>3.4 ± 0.2</td><td>323 ± 73</td><td>43.32 ± 13.08</td><td>0.23 ± 0.07</td><td>0.26 ± 0.08</td><td>225 ± 449</td><td>0.58 ± 0.03</td><td>15 ± 3</td><td>–</td><td>–</td></tr><tr><td>135.84</td><td>39.77</td><td>4</td><td>0.29 ± 0.08</td><td>2.4 ± 0.34</td><td>325 ± 74</td><td>40.34 ± 11.16</td><td>0.28 ± 0.05</td><td>0.23 ± 0.09</td><td>174 ± 324</td><td>0.62 ± 0.03</td><td>23 ± 8</td><td>–</td><td>–</td></tr><tr><td>136.08</td><td>39.82</td><td>3</td><td>0.26 ± 0.08</td><td>2.45 ± 0.71</td><td>327 ± 74</td><td>42.37 ± 12.73</td><td>0.26 ± 0.05</td><td>0.23 ± 0.09</td><td>224 ± 432</td><td>0.61 ± 0.03</td><td>28 ± 10</td><td>–</td><td>–</td></tr><tr><td>136.56</td><td>39.92</td><td>2</td><td>0.26 ± 0.08</td><td>2.6 ± 0.42</td><td>329 ± 75</td><td>44.99 ± 12.41</td><td>0.26 ± 0.05</td><td>0.23 ± 0.09</td><td>246 ± 470</td><td>0.61 ± 0.03</td><td>18 ± 9</td><td>–</td><td>–</td></tr><tr><td>136.81</td><td>39.97</td><td>3</td><td>0.28 ± 0.09</td><td>3.5 ± 0.43</td><td>329 ± 75</td><td>46.27 ± 14.08</td><td>0.27 ± 0.05</td><td>0.23 ± 0.09</td><td>196 ± 377</td><td>0.62 ± 0.03</td><td>17 ± 10</td><td>–</td><td>–</td></tr><tr><td>137.29</td><td>40.06</td><td>3</td><td>0.3 ± 0.1</td><td>2.32 ± 0.21</td><td>330 ± 75</td><td>42.46 ± 13.25</td><td>0.29 ± 0.05</td><td>0.23 ± 0.09</td><td>133 ± 256</td><td>0.64 ± 0.03</td><td>29 ± 6</td><td>–</td><td>–</td></tr><tr><td>137.53</td><td>40.11</td><td>4</td><td>0.34 ± 0.12</td><td>3.26 ± 0.28</td><td>324 ± 73</td><td>38.59 ± 11.66</td><td>0.3 ± 0.07</td><td>0.23 ± 0.09</td><td>83 ± 158</td><td>0.63 ± 0.04</td><td>15 ± 5</td><td>–</td><td>–</td></tr><tr><td>131.13</td><td>39.39</td><td>4</td><td>0.31 ± 0.1</td><td>2.56 ± 0.29</td><td>327 ± 74</td><td>40.59 ± 10.32</td><td>0.28 ± 0.05</td><td>0.23 ± 0.09</td><td>148 ± 274</td><td>0.6 ± 0.03</td><td>21 ± 7</td><td>–</td><td>–</td></tr><tr><td>131.37</td><td>39.45</td><td>2</td><td>0.24 ± 0.07</td><td>2.88 ± 0.39</td><td>329 ± 75</td><td>57.78 ± 14.62</td><td>0.26 ± 0.05</td><td>0.23 ± 0.08</td><td>534 ± 1001</td><td>0.59 ± 0.03</td><td>20 ± 6</td><td>–</td><td>–</td></tr><tr><td>131.87</td><td>39.56</td><td>2</td><td>0.23 ± 0.07</td><td>3.15 ± 0.55</td><td>330 ± 75</td><td>57.59 ± 15.1</td><td>0.26 ± 0.05</td><td>0.21 ± 0.08</td><td>694 ± 1317</td><td>0.6 ± 0.03</td><td>15 ± 5</td><td>–</td><td>–</td></tr><tr><td>132.11</td><td>39.62</td><td>2</td><td>0.24 ± 0.07</td><td>2.63 ± 0.38</td><td>331 ± 75</td><td>56.19 ± 14.44</td><td>0.26 ± 0.05</td><td>0.23 ± 0.08</td><td>556 ± 1040</td><td>0.62 ± 0.03</td><td>27 ± 10</td><td>–</td><td>–</td></tr><tr><td>132.36</td><td>39.68</td><td>2</td><td>0.24 ± 0.07</td><td>2.64 ± 0.4</td><td>331 ± 75</td><td>51.98 ± 14.39</td><td>0.24 ± 0.05</td><td>0.23 ± 0.08</td><td>458 ± 892</td><td>0.58 ± 0.03</td><td>23 ± 9</td><td>–</td><td>–</td></tr><tr><td>132.61</td><td>39.73</td><td>3</td><td>0.24 ± 0.06</td><td>3.87 ± 0.4</td><td>332 ± 75</td><td>51.61 ± 13.8</td><td>0.25 ± 0.04</td><td>0.21 ± 0.08</td><td>618 ± 1116</td><td>0.59 ± 0.03</td><td>16 ± 3</td><td>–</td><td>–</td></tr><tr><td>132.85</td><td>39.79</td><td>3</td><td>0.22 ± 0.07</td><td>3.44 ± 0.2</td><td>338 ± 75</td><td>56.68 ± 14.68</td><td>0.23 ± 0.05</td><td>0.22 ± 0.08</td><td>761 ± 1473</td><td>0.58 ± 0.03</td><td>24 ± 6</td><td>–</td><td>–</td></tr><tr><td>133.1</td><td>39.84</td><td>2</td><td>0.23 ± 0.07</td><td>2.4 ± 0.25</td><td>341 ± 76</td><td>62.27 ± 15.64</td><td>0.24 ± 0.05</td><td>0.22 ± 0.08</td><td>607 ± 1195</td><td>0.56 ± 0.03</td><td>27 ± 9</td><td>–</td><td>–</td></tr><tr><td>133.59</td><td>39.95</td><td>2</td><td>0.23 ± 0.07</td><td>3.2 ± 0.15</td><td>340 ± 75</td><td>57.48 ± 14.38</td><td>0.24 ± 0.05</td><td>0.22 ± 0.08</td><td>493 ± 930</td><td>0.58 ± 0.03</td><td>38 ± 9</td><td>–</td><td>–</td></tr><tr><td>134.06</td><td>40.06</td><td>3</td><td>0.22 ± 0.07</td><td>3.47 ± 0.21</td><td>335 ± 75</td><td>57.17 ± 14.87</td><td>0.23 ± 0.05</td><td>0.22 ± 0.08</td><td>582 ± 1146</td><td>0.57 ± 0.03</td><td>24 ± 7</td><td>–</td><td>–</td></tr><tr><td>134.57</td><td>40.16</td><td>3</td><td>0.25 ± 0.07</td><td>2.39 ± 0.36</td><td>334 ± 74</td><td>48.44 ± 15.32</td><td>0.25 ± 0.05</td><td>0.23 ± 0.08</td><td>352 ± 669</td><td>0.6 ± 0.03</td><td>23 ± 4</td><td>–</td><td>–</td></tr><tr><td>134.82</td><td>40.22</td><td>3</td><td>0.32 ± 0.1</td><td>3.27 ± 0.21</td><td>323 ± 73</td><td>41.14 ± 9.98</td><td>0.27 ± 0.06</td><td>0.24 ± 0.09</td><td>70 ± 133</td><td>0.66 ± 0.03</td><td>58 ± 18</td><td>–</td><td>–</td></tr><tr><td>135.55</td><td>40.37</td><td>2</td><td>0.27 ± 0.08</td><td>3.52 ± 0.7</td><td>329 ± 75</td><td>46.97 ± 13.45</td><td>0.27 ± 0.05</td><td>0.23 ± 0.09</td><td>209 ± 398</td><td>0.6 ± 0.03</td><td>7 ± 3</td><td>1.33 ± 0.48</td><td>179 ± 94</td></tr></tbody></table>
<figure><img src="image_18.png" /><figcaption>**Figure A1.** First perijove, PJ1, global map created from visible images acquired by the JunoCam. The snail-like white vortex object of this work and its surroundings are highlighted with a box.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Figure A2.** Geometry of the JIRAM observations presented in this work. In the first three panel in a clockwise order the JIRAM coverage of the #1 ROI is shown, and the colour of each pixel represents (a) the emission, (b) incidence, and (c) phase angles of the observations. The last panel, (d), show the relation between the above mentioned angles.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>**Figure A3.** Refractive indexes of the candidate 3-µm absorbers that are thought to be present in the Jovian clouds.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>**Figure A4.** *Upper panel:* comparison between two synthetic PSG spectra obtained considering a mixture of H₂, He, CH₄, and NH₃ at Jovian conditions. The brown spectrum was generated using the correct Jovian isotopologues ratios, while the green considering the terrestrial ones. *Lower panel:* differences between the two synthetic spectra compared to the JIRAM NER.</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>**Figure A5.** *Upper panel:* comparison between two synthetic PSG spectra obtained considering the a-priori Model 1 atmosphere, see Table 2 and Fig. 4, and adding (green) or not (blue) the effect of phosphine and water. The sub-panel shows the considered profiles for PH₃ and H₂O. *Lower panel:* differences between the two synthetic spectra compared to the JIRAM NER.</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>**Figure A6.** Optical properties of the four different aerosol species considered in this work: tholins, pure ammonia ice, and ammonia-coated tholins with different core-to-radius parameter (γ). The properties are computed considering a log-normal distribution of particles with an effective radius of 1 µm and a variance of 0.3.</figcaption></figure>
<figure><img src="image_24.png" /><figcaption>**Figure A7.** Optical properties of the four different aerosol species considered in this work: tholins, pure ammonia ice, and ammonia-coated tholins with different core-to-radius parameter ($\gamma$). The properties are computed considering a log-normal distribution of particles with an effective radius of 2 µm and a variance of 0.3.</figcaption></figure>

<figure><img src="image_25.png" /><figcaption>**Figure A8.** JIRAM coverage of the #1 ROI where the colour of each pixel represents the corresponding retrieved haze's width value.</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>**Figure A9.** JIRAM coverage of the #1 ROI where the colour of each pixel represents the corresponding retrieved deep cloud's pressure level value (if the main cloud did not become optically thick).</figcaption></figure>

<figure><img src="image_27.png" /><figcaption>**Figure A10.** JIRAM coverage of the #1 ROI where the colour of each pixel represents the corresponding retrieved deep cloud's density value (if the main cloud did not become optically thick).</figcaption></figure>
<figure><img src="image_28.png" /><figcaption>**Figure A11.** Spectral variations corresponding to a perturbation of 0.1·σ<sub>a</sub> in the retrieval parameters. These curves also represent the Jacobians after the first OE iteration.</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>**Figure A12.** Posterior distribution of the retrieval parameters after a MCMC fit of a JIRAM test spectrum outside the vortex. The red box highlight the existing correlation between the haze's particles effective radius and density.</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>**Figure A13.** JIRAM spectra best-fitted by Model 2 atmospheres, i.e. considering pure ammonia ice main clouds. The colour legend is the same of Fig. 8.</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>**Figure A14.** JIRAM spectra best-fitted by Model 2 atmospheres, i.e. considering pure ammonia ice main clouds. The colour legend is the same of Fig. 8.</figcaption></figure>
<figure><img src="image_32.png" /><figcaption>**Figure A15.** JIRAM spectra best-fitted by Model 3 atmospheres, i.e. considering $\gamma = 0.5$ ammonia-coated tholins. The colour legend is the same of Fig. 8.</figcaption></figure>

This paper has been typeset from a *TEX/LATEX* file prepared by the author.