Pith. sign in

REVIEW 3 major objections 6 minor 76 references

Wind shear and the role of eddy vapor transport in driving water convection on Jupiter

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that eddy transport of water vapor, not thermal or mechanical forcing, is the primary trigger of moist convection on Jupiter in EPIC GCM simulations.

desk verdict A model study with a genuinely new CAPE decomposition and a plausible moisture-front mechanism, but the diagnostic's 'chemical' term partly absorbs thermal effects on saturation humidity, so treat the mechanism as model-supported rather than proven. read the letter →

arxiv 2505.17213 v1 pith:MRLSX5EE submitted 2025-05-22 astro-ph.EP

Ramanakumar Sankar , Michael H Wong , Csaba Palotai , Shawn Brueshaber This is my paper · ORCID
classification astro-ph.EP
keywords JupiteratmospheremoistconvectionCAPEeddyvaportransportwindshearEPICGCMwaterbaroclinicinstability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that, in a global circulation model of Jupiter with parameterized convection, the trigger for moist water storms is not local heating or column squeezing but the horizontal eddy transport of water vapor at the cloud base. Simulated convection concentrates where this 'chemical' tendency raises convective available potential energy (CAPE) the most, typically along a moisture front between vapor-rich and vapor-poor air. The authors vary the deep vertical wind shear through a single slope parameter and find that stronger shear produces more baroclinic eddies, stronger meridional vapor gradients, and far more convective storms; with zero shear, almost no deep convection develops. If the result holds for the real planet, it offers one dynamical mechanism that could connect observed lightning belts, folded filamentary regions, and equatorial volatile enrichment.

What carries the argument

The central object is a three-way decomposition of the CAPE tendency, $d(\mathrm{CAPE})/dt \sim \int g/\tilde L [C_p(dT_{\mathrm{base}}/dt - dT/dt) + (d\Phi_{\mathrm{base}}/dt - d\Phi/dt) + L_v(dq_{\mathrm{base}}/dt - dq_{\mathrm{sat}}/dt)]\,dz$, labeled thermal, mechanical, and chemical. The decomposition assumes the updraft is non-entraining so the parcel's moist static energy equals its cloud-base value, and each tendency is evaluated from horizontal advection alone, $\partial/\partial t = -u\,\partial/\partial x - v\,\partial/\partial y$. This diagnostic is applied to simulations with the EPIC GCM using the Relaxed Arakawa-Schubert convective scheme and a cloud microphysics parameterization, with the deep zonal wind constructed by a shear slope $m$ in log-pressure space from cloud-tracked winds. The machinery identifies which physical process raises CAPE and where, and it is what allows the paper to attribute convection to vapor transport rather than to thermal or mechanical effects.

What would settle it

A variant of the CAPE-decomposition calculation that includes the full entrainment profile of the RAS updraft, or a cloud-resolving simulation with explicit updrafts, would settle it: if the chemical term no longer dominates at the 17-21°N and equatorial active latitudes, the moisture-front trigger is an artifact of the no-entrainment assumption. Observations could also falsify the mechanism: if high-resolution 5-micron water vapor mapping showed convective outbreaks at locations with no measurable meridional water-vapor gradient, or with vapor decreasing into the storm, eddy vapor transport would not be the universal trigger claimed.

Watch

Extended reading notes

Core claim

In the EPIC GCM, moist convection on Jupiter is driven primarily by the 'chemical' CAPE tendency: the advection of water vapor that raises the moist static energy at the cloud base and pushes CAPE above its trigger value. Decomposing the tendency of CAPE into thermal, mechanical, and chemical parts (Eq. 9), the chemical term exceeds the thermal and mechanical terms by at least an order of magnitude at active latitudes, and its peaks coincide with simulated convective towers. The paper identifies the physical picture as an eddy-driven moisture front: deep baroclinic eddies, amplified by vertical wind shear, mix vapor-rich air into drier regions, increasing CAPE ahead of each updraft, with precipitation recycling vapor below the cloud base to feed the next packet. The same mechanism explains the arc and chevron cloud patterns and explains why the shear-free case produces almost no convection.

Load-bearing premise

The key load-bearing premise is that the diagnostic breakdown can ignore entrainment and compute tendencies from horizontal advection only; if real updrafts entrain dry air or vertical motions carry the vapor, the chemical term would shrink relative to thermal and mechanical terms and the conclusion could flip.

Editorial extensions

If this is right

  • If the chemical tendency dominates, Jupiter's convective storm locations should track horizontal water-vapor gradients at the cloud base rather than only cyclonic shear or static instability.
  • The 17-21°N lightning maximum, which sits in anticyclonic shear, becomes a natural consequence of a moisture front, providing a sharper test for Juno MWR observations.
  • Deep wind shear controls convection indirectly: stronger shear means stronger baroclinic eddies and steeper vapor gradients, so observed plume spacing and storm frequency can be inverted to constrain Jupiter's deep wind profile.
  • The absence of convection north of 24°N in the model is tied to weak meridional vapor gradients, implying that observed high-latitude lightning requires either persistent cyclonic vortices or inhomogeneous deep water distribution not captured here.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension is to run the same CAPE decomposition with entrainment included; if the chemical term still dominates, moisture-front triggering becomes a robust mechanism rather than a diagnostic artifact.
  • The same eddy-vapor-transport trigger could operate in other moist atmospheres, including Saturn's storms and humid exoplanet atmospheres, wherever a condensible species is horizontally inhomogeneous at the condensation level.
  • The paper's assumption of a globally constant shear slope could be relaxed: a latitudinally varying $m$ should shift modeled convection toward the observed belt and high-latitude lightning distribution, and plume spacing would then be a local measure of shear.
  • If the mechanism is right, anticipating where convection will erupt on Jupiter reduces to predicting where eddies steepen water-vapor gradients, which depends on baroclinic instability and on the deep water distribution observable with microwave and 5-micron spectroscopy.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. This manuscript uses the EPIC GCM with the Relaxed Arakawa-Schubert (RAS) convective parameterization and the Palotai-Dowling cloud microphysics to simulate water and ammonia convection on Jupiter under three deep wind-shear profiles (m = 0, 0.25, 0.4). The authors decompose the advective tendency of CAPE into thermal, mechanical, and chemical components (Eq. 9) and report that, at the latitudes of active convection, the chemical component exceeds the other two by at least an order of magnitude (Sec. 4.2, Fig. 8). The paper's central claim is that eddy transport of water vapor along meridional 'moisture fronts,' strengthened by baroclinic eddies whose intensity increases with wind shear, is the primary trigger and maintainer of deep convection on Jupiter, while thermal and mechanical effects are secondary. The model reproduces several observed qualitative features — belt/zone cloud contrasts, arc and chevron shapes near 17-21 deg N, a convective peak near 10 deg N — and fails in acknowledged ways: it generates sustained equatorial convection and almost no convection north of 24 deg N, contrary to Juno lightning observations (Sec. 5.2).

Significance. If the central mechanism holds, the paper makes a useful and timely interpretive contribution to the Juno era: it gives a concrete, physically argued explanation for the observed latitudinal concentration of Jovian convection and a falsifiable prediction that the zonal periodicity of convective plumes (e.g., the 2017 SEB plume trains of Fig. 14) can be used to constrain the deep wind shear. The CAPE decomposition of Eq. (9) is a clear, well-presented analytic frame for GCM interpretation, and the three terms are honestly computed outputs of the simulation rather than imposed inputs; the wind-shear parameter m is varied, not fitted. The authors are also commendably explicit about two model failures (no convection north of 24 deg N and spurious equatorial convection) and about the approximations in their diagnostic. I agree with the reader's assessment that the dominance of the chemical term is a model output and not circular, but I also find that the skeptics' concerns land: the 'chemical' label conflates cloud-base vapor advection with a saturation-humidity response to temperature, and the robustness of Fig. 8 to entrainment is unquantified.

major comments (3)
  1. [Sec. 2, Eq. (9); Sec. 4.2, Fig. 8; Sec. 5.1] The no-entrainment assumption that leads to Eq. (9) is load-bearing for the order-of-magnitude dominance claim in Sec. 4.2. Section 5.1 acknowledges that a realistic entraining parcel would have a decreasing moist static energy with height, but defends the assumption only with the qualitative statement that entrainment is 'fairly low' for deep convection. The manuscript never quantifies how the decomposition changes when the parcel is allowed to entrain, even though the model's RAS scheme already computes an entraining updraft profile (Eq. 11). Please recompute the three terms of Eq. (9) using the RAS entrainment profile, or with a one-dimensional plume model spanning entrainment rates from zero to the RAS value, and show that the chemical term remains larger than the thermal and mechanical terms by an order of magnitude at the latitudes of boxes (a) and (b) in Fig. 8. Absent this test, the stated 10x margin cannot be distinguished from a property of the diagnostic rather than a property of the simulation.
  2. [Sec. 2, Eq. (9); Sec. 4.2, Fig. 8; Sec. 5.3] The term labeled 'chemical' in Fig. 8 and Sec. 5.3 is L_v(dq_base/dt - dq_sat/dt), but its two pieces have different physical content. L_v dq_base/dt is the vapor tendency at the cloud base, whereas -L_v dq_sat/dt is, at each level, essentially -L_v(dq_sat/dT)(dT/dt + ...), a response of the ambient saturation humidity to temperature change, not an advection of water vapor. Sections 2.3 and 5.1 and the Fig. 8 caption attribute the whole term to 'advection of water vapor,' which is only justified if the first piece dominates. To support the central mechanistic claim, please plot L_v dq_base/dt and -L_v dq_sat/dt separately at the latitudes of boxes (a) and (b), and also separate the eddy contribution from the mean advection, since the mechanism stated in Sec. 5.3 is specifically eddy transport while the quantity plotted in Fig. 8 is the full horizontal advection. If the -L_v dq_sat/dt part is non-negligible, the conclusions should be reframed as 'moisture convergence plus saturation-humidity effects' rather than vapor transport alone.
  3. [Sec. 4.2, Eq. (12)] The diagnostic tendencies are computed from horizontal advection only, with the justification that 'the vertical velocities in our model are significantly smaller than the horizontal velocities and are therefore negligible for transport.' The relevant comparison is not between u and w but between u dX/dx + v dX/dy and w dX/dz. For water vapor above the condensation level and for temperature in the stably stratified troposphere, dX/dz is very large, so w dX/dz may be non-negligible even for small w; this matters directly for dq_sat/dt and hence for the 'chemical' term of Eq. (9). Please either recompute the Fig. 8 fields including the vertical advection term, or show offline that w dq/dz and w dT/dz are subdominant relative to the horizontal terms at 4 bar and in the convective layer above it. Without this check, the reported dominance of the chemical term could reflect the choice of what was omitted rather than the model's actual CAPE budget.
minor comments (6)
  1. [Abstract] The first sentence ('convection is strongly concentrated at specific locations on planet') is missing an article and should read '...at specific locations on the planet.'
  2. [Sec. 3.1] The text says the temperature profile is applied at 23 deg N ('which we found to produce the most stable configuration') and two sentences later says the Moses et al. (2005) profile is applied at 24 deg N; please clarify whether these are intentionally different latitudes and state which reference latitude is used for the thermal-wind integration.
  3. [Sec. 4.1] Minor typo: 'cloud densities were two low' should be 'too low.'
  4. [Sec. 4.2, Fig. 8] The ordinate of Fig. 8 is a logarithmic scale with a discontinuity at zero, which compresses the negative excursions of the thermal and mechanical terms and makes it difficult to verify the quantitative claims about cancellation (e.g., 'the other terms are sufficiently negative to counteract this increase' near box (c)). Including a linear-axis panel or stating the signed values averaged over the active latitudes would make the text's claim checkable.
  5. [Sec. 2, Eq. (2)] The factor L-tilde is described only as 'a term that scales the latent heat with virtual effects (Moorthi & Suarez 1999)'; a short definition at its first use would make the derivation of Eq. (9) self-contained and checkable.
  6. [Sec. 5.4, Fig. 11] Figure 11(b) shows that the 600 mb vapor ratio is not monotonic in m at all latitudes (e.g., near 45 deg S); the text in Sec. 5.4 states generally that 'the strength of the eddy transport increases with zonal wind shear.' A sentence qualifying this generalization to the latitudes where the mechanism operates would help the reader reconcile panel (b) with the claim.

Circularity Check

1 steps flagged · score 2.0 of 10

No significant circularity in the central CAPE-tendency claim; one minor circular self-consistency check in the cloud-top scaling.

  1. fitted input called prediction [Section 4.1 (Cloud formation); cf. Section 3.1, Eq. 10]
    "The equator and 20◦N are also where our methane-band scaling (Fig. 5) defines the highest cloud tops, providing a self-consistent justification for the scaling assumption."

    The cloud-top pressure p0 is set from the same Hubble CH4 reflectivity (Section 3.1) and enters the wind profile u(p) via Eq. 10. The model's cloud heights are therefore partly forced by this reflectivity-derived p0, so the coincidence of high modeled clouds at the reflectivity-defined latitudes is a consistency loop rather than an independent justification. The paper itself calls it 'self-consistent,' so it is not a prediction, but the validation uses the same data as the input.

full rationale

The paper's central claim—that the 'chemical' CAPE tendency (eddy water-vapor transport) dominates thermal and mechanical terms—is a diagnostic output, not an input. The three terms in Eq. 9 are evaluated from model state variables using the horizontal-advection tendency of Eq. 12; no parameter is fitted to produce the dominance, and the wind-shear slope m is varied (0, 0.25, 0.4) rather than tuned. The RAS closure (Section 3.2) does set the convective response equal to the total dCAPE/dt by construction, but the decomposition into contributing processes is a posteriori and could in principle have favored thermal or mechanical terms. The no-entrainment simplification in Eq. 9 is explicitly acknowledged in Section 5.1; the cited low-entrainment result (Sankar & Palotai 2022) is a self-citation but is accompanied by a physical rationale and does not by itself force the chemical-term magnitude. A separate correctness risk (not circularity) is that the 'chemical' term includes -L_v dq_sat/dt, which depends on temperature advection through q_sat(T); the paper does not separate this from true vapor advection, so the 'eddy vapor transport' interpretation may overstate the mechanism. The only true circular step is the cloud-top 'self-consistent justification' in Section 4.1, where the CH4-reflectivity input and the model cloud-height output are the same observable. This is minor and does not affect the central diagnostic conclusion.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The central claim rests on a small set of model choices rather than on many fitted constants. The main free parameters are the wind shear slope m and the pressure p_c defining the shear layer; the paper varies m but does not fit it to the result. The diagnostic leans on two simplifications stated in the text: no entrainment in the parcel (Eq. 9) and horizontal-only advection (Eq. 12). No new physical entities are introduced.

free parameters (7)
  • Wind shear slope m = 0, 0.25, 0.4 (0.5 excluded after numerical instability)
    Parameterizes the vertical zonal wind profile in log-pressure space (Eq. 10). Chosen to span plausible deep shear values; not fitted to observations. The paper's conclusions depend on comparing these cases.
  • Critical pressure p_c = 30 bar
    Pressure at which the wind shear goes to zero in Eq. 10. Chosen by hand; affects the depth of the shear layer.
  • Cloud-top pressure p0(latitude) = scaled from 1 bar to 200 mb from methane-band reflectivity
    Used to define the cloud-tracked wind reference level; a rough observational proxy, not a measured cloud top.
  • RAS relaxation timescale tau_relax = 1 hour
    Relaxation timescale for convective adjustment; adopted from Sankar & Palotai (2022); affects convective response.
  • Bottom heating and top cooling rates = 0.008 W/kg at bottom, 0.01 W/kg at top
    Tuned so net heat flux is 7-10 W/m^2, matching Jupiter's internal luminosity; affects thermal structure and convection.
  • Hyperviscosity coefficients = nu8, nu_div; nu6 for m=0.4
    Numerical damping coefficients chosen to remove high-frequency modes; sensitivity shown in Appendix A.
  • Initial perturbation amplitude = <5 m/s, 100 Gaussian vortices
    Added to break zonal symmetry; chosen to not alter the mean wind profile.
assumptions (6)
  • domain assumption Thermal wind balance relates the 3D temperature field to the zonal wind profile
    Section 3.1: The temperature profile is obtained by integrating the thermal wind equation from the wind field, assuming geostrophic balance. This determines the meridional temperature gradients that set the vapor gradients.
  • ad hoc to paper Convection triggers when CAPE exceeds a critical value, assumed to be zero on Jupiter
    Section 3.2: In the absence of measurements, the authors set CAPE_crit = 0, so any positive CAPE change triggers convection. This affects storm frequency.
  • ad hoc to paper The diagnostic parcel does not entrain, so hat(h) = h(z_base)
    Section 2, Eq. 9: Simplification used to derive the three-term CAPE tendency. Acknowledged as not how the model treats convection.
  • ad hoc to paper Tendencies are computed using only horizontal advection (Eq. 12)
    Section 4.2: Vertical velocities are neglected in the CAPE tendency diagnosis, which may miss vertical transport of moisture and temperature.
  • domain assumption The deep wind shear profile (Eq. 10) from Garcia-Melendo et al. (2005) with constant m globally
    Section 3.1: The paper parameterizes the unknown deep flow with a single global shear slope, noting the real shear likely varies with latitude and depth.
  • domain assumption Eady critical N^2 identifies baroclinic instability regions
    Section 4.4: The paper uses a 2-layer Eady stability criterion on N^2 to locate eddy sources, a standard but simplified diagnostic.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Wind shear and the role of eddy vapor transport in driving water convection on Jupiter." pith.science (2026). https://pith.science/paper/MRLSX5EE

@misc{pith2026250517213,
  author       = {Pith},
  title        = {Pith review of: Wind shear and the role of eddy vapor transport in driving water convection on Jupiter},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MRLSX5EE}},
  note         = {Machine review of arXiv:2505.17213}
}
read the original abstract

Recent observations of convection in the jovian atmosphere have demonstrated that convection is strongly concentrated at specific locations on planet. For instance, observations of lightning show that the cyclonic features (e.g,. belts and folded filamentary regions - FFRs) show increased convective activity compared to anti-cyclonic regions. Meanwhile, the distribution of ammonia and water vapor show a large enrichment near the equator, which is also suggestive of strong upwelling and convective activity. Marrying these different observations is challenging due to a lack of data concerning the characteristics of the deep jovian atmosphere, and a resulting inability to observe the true deep source of the various convective phenomena. To understand the nature of these convective events and \paperedit{the role of the } structure of the deep atmosphere \paperedit{in driving convective events}, we run simulations of cloud formation and convection using the Explicit Planetary hybrid-Isentropic Coordinate General Circulation Model (EPIC GCM). We vary the dynamics of the atmosphere by parameterizing the deep wind shear and studying the resulting effect on the strength, frequency and distribution of convective storms. We find that convection in our model is strongly tied to the local dynamics and the deep wind shear. We further decompose the generation of convective available potential energy (CAPE) into three components (thermal, mechanical, and moist/chemical), and find that the chemical mechanism is the strongest component, working to advect water vapor from moisture-rich regions to moisture-poor regions and to drive convection along a ``moisture front.''

Figures

Figures reproduced from arXiv: 2505.17213 by the authors.

Figure 1
Figure 1. Shear profile for different values of m used in the model. where p0 is the location of the cloud top (diagnosed from Hubble CH4 I/F, as detailed below) and is variable with latitude, u(p0) is the cloud-tracked wind speed at p0 and pc = 30 bar, is the “critical” pressure in our model where the wind shear goes to 0. An illustration of this profile is shown in [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Input temperature (left) and static statibility parameter (right). The model’s vertical layers are shown with the blue lines. We apply this profile at a latitude of 23◦ N, which we found to produce the most stable configuration. The input temperature profile from (Moses et al. 2005) is applied at 24◦ N and we then use the thermal wind balance to obtain the 3D temperature field from the wind field. We tested differen… view at source ↗
Figure 4
Figure 4. The vertically integrated ammonia (left) and water (right) clouds in the model 70 days into the simulation after adding the perturbations. Note the increase in both the ammonia and water cloud density with the increase in wind shear [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figures from the paper (13 more)
Figure 5
Figure 5. Figure 5: Zonally and temporally averaged ammonia (left) and water (right) cloud density for each of the cases studied. The zonal wind profile is shown with the black lines, with each contour separated by 25 m/s. We see that clouds are forming thicker higher in the atmosphere at…
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: The ratio of water vapor mass mixing ratio be￾tween 600 mb and 4 bars (top) and 1 bar and 4 bar (bot￾tom), as a function of latitude. Here, the ratios are zonally and temporally averaged for each simulation. We see that the ratio peaks at specific latitudes, which show…
Figure 8
Figure 8. Figure 8: The contribution to the increase in CAPE from each of the processes described in Section 2. The total value is shown in the black line. We can see that across all the cases, the “chemical” effect (i.e., advection of water vapor) is the most prominent driver of CAPE inc…
Figure 9
Figure 9. Figure 9: (a) Integrated water cloud density (with the same colorbar as in [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]
Figure 10
Figure 10. Figure 10: Integrated water cloud density (left, same colorbar as in [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: (a) The average zonal wind profile at 4 bars. (b) The temporally and zonally averaged ratio of water vapor mass mixing ratio between 600 mb and 4 bar, which denotes the convection due to water (c) the eddy meridional water vapor mixing at 4 bars. (d) the critical N 2 …
Figure 13
Figure 13. Figure 13: shows our column-average ammonia and water mixing ratios, and corresponding vertically integrated cloud densities for the m = 0.4 case. The wind blows eastward (to the right) in these zonal profiles at 18◦N. We see that the ammonia vapor and cloud lag behind the conve…
Figure 14
Figure 14. Figure 14: A periodic spacing of convective plumes was visible in the South Equatorial Belt in 2017, most clearly seen in the infrared at 4 µm (upper panel) where reflectivity is greatest from cloud/haze features present at high altitude or containing large particle (or both). T…
Figure 15
Figure 15. Figure 15: Spectrogram showing the distribution of wavenumbers for different shear cases, averaged over the last 100 days at a latitude of 20◦N and 40◦S. Note the differ￾ence in the spectral distribution at the two locations, and for 20◦N, how the higher shear cases show both an…
Figure 16
Figure 16. Figure 16: The zonal mean cloud densities at 3 different timesteps from our model atmosphere. There is little change in the cloud densities between the three timesteps showing that the model has reached a quasi-steady state by day 50, but cloud densities do still continue to evo…
Figure 17
Figure 17. Figure 17: The difference between the temperature at days 50, 100 and 125 from the average temperature from the first 30 days of the simulation. The differences are zonally averaged. There is some evolution in the temperature field in the first 100 days, but the model achieves a…
Figure 18
Figure 18. Figure 18: Effect of the model hyperviscosity on the convected water vapor. We find that the most prominent effect is in the first 10-15 days, whereas beyond this adjustment phase, there is negligible effect of the hyperviscosity on the model atmosphere [PITH_FULL_IMAGE:figures…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

76 extracted references · 29 canonical work pages

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    " id="W5M0MpCehiHzreSzNTczkc9d

    thebibliography [1] 20pt to REFERENCES 6pt =0pt \@twocolumntrue 12pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key o...

    arXiv 2017

  4. [4]

    1989, Geochimica et Cosmochimica Acta, 53, 197, https://doi.org/10.1016/0016-7037(89)90286-X

    Anders, E., & Grevesse, N. 1989, Geochimica et Cosmochimica Acta, 53, 197, https://doi.org/10.1016/0016-7037(89)90286-X

    doi:10.1016/0016-7037(89)90286-x 1989

  5. [5]

    H., & Wu , C

    Arakawa , A., Jung , J. H., & Wu , C. M. 2011, Atmospheric Chemistry & Physics, 11, 3731, 10.5194/acp-11-3731-2011

    doi:10.5194/acp-11-3731-2011 2011

  6. [6]

    Arakawa , A., & Schubert , W. H. 1974, Journal of Atmospheric Sciences, 31, 674, 10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2

    doi:10.1175/1520-0469(1974)031 1974

  7. [7]

    J., & Scott , P

    Asplund , M., Grevesse , N., Sauval , A. J., & Scott , P. 2009, Annual Review of Astronomy and Astrophysics, 47, 481, 10.1146/annurev.astro.46.060407.145222

    arXiv 2009

  8. [8]

    H., Pollack , J

    Atkinson , D. H., Pollack , J. B., & Seiff , A. 1998, Journal of Geophysical Research, 103, 22911, 10.1029/98JE00060

    work page doi:10.1029/98je00060 1998

Show all 76 references
  1. [9]

    L., Wong , M

    Bjoraker , G. L., Wong , M. H., de Pater , I., Hewagama , T., & \'A d \'a mkovics , M. 2022, Remote Sensing, 14, 4567, 10.3390/rs14184567

    2022 doi

  2. [10]

    2024, , 687, A274, 10.1051/0004-6361/202245220

    Boissinot , A., Spiga , A., Guerlet , S., Cabanes , S., & Bardet , D. 2024, , 687, A274, 10.1051/0004-6361/202245220

    2024 doi

  3. [11]

    2018, , 558, 87, 10.1038/s41586-018-0156-5

    Brown , S., Janssen , M., Adumitroaie , V., et al. 2018, , 558, 87, 10.1038/s41586-018-0156-5

    2018 doi

  4. [12]

    R., Zhang, Z., Rogers, J

    Brueshaber, S. R., Zhang, Z., Rogers, J. H., et al. 2025, , 116465, https://doi.org/10.1016/j.icarus.2025.116465

    2025

  5. [13]

    S., et al

    Cao , H., Bloxham , J., Park , R. S., et al. 2023, , 959, 78, 10.3847/1538-4357/ad0cbb

    2023 doi

  6. [14]

    M., & Moeckel , C

    de Pater , I., Molter , E. M., & Moeckel , C. M. 2023, Remote Sensing, 15, 1313, 10.3390/rs15051313

    2023 doi

  7. [16]

    2019 b , The Astronomical Journal, 158, 139, 10.3847/1538-3881/ab3643

    ---. 2019 b , The Astronomical Journal, 158, 139, 10.3847/1538-3881/ab3643

    2019 doi

  8. [17]

    E., Fischer , A

    Dowling , T. E., Fischer , A. S., Gierasch , P. J., et al. 1998, , 132, 221, 10.1006/icar.1998.5917

    1998

  9. [18]

    E., & Gierasch , P

    Dowling , T. E., & Gierasch , P. J. 1989, in Bulletin of the American Astronomical Society, Vol. 21, 946

    1989

  10. [19]

    E., Bradley , M

    Dowling , T. E., Bradley , M. E., Col \'o n , E., et al. 2006, , 182, 259, 10.1016/j.icarus.2006.01.003

    2006 doi

  11. [20]

    2021, arXiv e-prints, arXiv:2110.07255

    Duer , K., Gavriel , N., Galanti , E., et al. 2021, arXiv e-prints, arXiv:2110.07255. 2110.07255

    2021 arXiv

  12. [21]

    A., Del Genio , A

    Dyudina , U. A., Del Genio , A. D., Ingersoll , A. P., et al. 2004, , 172, 24, 10.1016/j.icarus.2004.07.014

    2004 doi

  13. [22]

    M., Kunde , V

    Flasar , F. M., Kunde , V. G., Achterberg , R. K., et al. 2004, , 427, 132, 10.1038/nature02142

    2004 doi

  14. [23]

    N., Orton , G

    Fletcher , L. N., Orton , G. S., Rogers , J. H., et al. 2017, , 286, 94, 10.1016/j.icarus.2017.01.001

    2017 doi

  15. [24]

    N., Orton , G

    Fletcher , L. N., Orton , G. S., Greathouse , T. K., et al. 2020, Journal of Geophysical Research (Planets), 125, e06399, 10.1029/2020JE006399

    2020 doi

  16. [25]

    N., Oyafuso , F

    Fletcher , L. N., Oyafuso , F. A., Allison , M., et al. 2021, Journal of Geophysical Research (Planets), 126, e06858, 10.1029/2021JE006858

    2021 doi

  17. [26]

    Garc \' a-Melendo , E., S \'a nchez-Lavega , A., & Dowling , T. E. 2005, , 176, 272, 10.1016/j.icarus.2005.02.012

    2005 doi

  18. [27]

    2024, , 5, 101, 10.3847/PSJ/ad0ed3

    Ge , H., Li , C., Zhang , X., & Moeckel , C. 2024, , 5, 101, 10.3847/PSJ/ad0ed3

    2024 doi

  19. [28]

    J., Conrath , B

    Gierasch , P. J., Conrath , B. J., & Magalh \ a es , J. A. 1986, , 67, 456, 10.1016/0019-1035(86)90125-9

    1986 doi

  20. [29]

    J., Ingersoll , A

    Gierasch , P. J., Ingersoll , A. P., Banfield , D., et al. 2000, , 403, 628, 10.1038/35001017

    2000 doi

  21. [30]

    S., Greathouse , T

    Giles , R. S., Greathouse , T. K., Bonfond , B., et al. 2020, Journal of Geophysical Research (Planets), 125, e06659, 10.1029/2020JE006659

    2020 doi

  22. [31]

    Grevesse, N., & Sauval, A. J. 2001, Solar Abundances, ed. P. Murdin (Boca Raton, FL: CRC Press / Taylor & Francis Group), 4088--4091, 10.1201/9781003220435

    2001 doi

  23. [32]

    1995, Science, 269, 1697, 10.1126/science.7569896

    Guillot , T. 1995, Science, 269, 1697, 10.1126/science.7569896

    1995 doi

  24. [33]

    J., et al

    Guillot , T., Li , C., Bolton , S. J., et al. 2020, Journal of Geophysical Research (Planets), 125, e06404, 10.1029/2020JE006404

    2020 doi

  25. [34]

    Holton, J. R. 2004, An introduction to dynamic meteorology , 4th edn., ed. R. Dmowska & J. R. Holton, International Geophysics Series (Burlington, MA: Elsevier Academic Press,), 535. http://books.google.com/books?id=fhW5oDv3EPsC

    2004

  26. [35]

    2001, , 151, 257, 10.1006/icar.2000.6606

    Hueso , R., & S \'a nchez-Lavega , A. 2001, , 151, 257, 10.1006/icar.2000.6606

    2001

  27. [36]

    2022, , 380, 114994, 10.1016/j.icarus.2022.114994

    Hueso , R., I \ n urrigarro , P., S \'a nchez-Lavega , A., et al. 2022, , 380, 114994, 10.1016/j.icarus.2022.114994

    2022

  28. [37]

    2020, , 336, 113475, 10.1016/j.icarus.2019.113475

    I \ n urrigarro , P., Hueso , R., Legarreta , J., et al. 2020, , 336, 113475, 10.1016/j.icarus.2019.113475

    2020

  29. [38]

    H., Kolma s ov \'a , I., et al

    Imai , M., Wong , M. H., Kolma s ov \'a , I., et al. 2020, Geophysical Research Letters, 47, e88397, 10.1029/2020GL088397

    2020 doi

  30. [39]

    P., Gierasch , P

    Ingersoll , A. P., Gierasch , P. J., Banfield , D., Vasavada , A. R., & Galileo Imaging Team . 2000, , 403, 630, 10.1038/35001021

    2000 doi

  31. [40]

    2018, Nature Astronomy, 2, 544, 10.1038/s41550-018-0442-z

    Kolma s ov \'a , I., Imai , M., Santol \' k , O., et al. 2018, Nature Astronomy, 2, 544, 10.1038/s41550-018-0442-z

    2018 doi

  32. [41]

    2017, , 598, A98, 10.1051/0004-6361/201629140

    Leconte , J., Selsis , F., Hersant , F., & Guillot , T. 2017, , 598, A98, 10.1051/0004-6361/201629140

    2017 doi

  33. [42]

    2019, , 240, 37, 10.3847/1538-4365/aafdaa

    Li , C., & Chen , X. 2019, , 240, 37, 10.3847/1538-4365/aafdaa

    2019 doi

  34. [43]

    Li , C., & Ingersoll , A. P. 2015, Nature Geoscience, 8, 398, 10.1038/ngeo2405

    2015 doi

  35. [44]

    2017, , 44, 5317, 10.1002/2017GL073159

    Li , C., Ingersoll , A., Janssen , M., et al. 2017, , 44, 5317, 10.1002/2017GL073159

    2017 doi

  36. [45]

    2020, Nature Astronomy, 4, 609, 10.1038/s41550-020-1009-3

    Li , C., Ingersoll , A., Bolton , S., et al. 2020, Nature Astronomy, 4, 609, 10.1038/s41550-020-1009-3

    2020 doi

  37. [46]

    2024, Icarus, 414, 116028, 10.1016/j.icarus.2024.116028

    Li , C., Allison , M., Atreya , S., et al. 2024, Icarus, 414, 116028, 10.1016/j.icarus.2024.116028

    2024

  38. [47]

    A., et al

    Li , L., Jiang , X., West , R. A., et al. 2018, Nature Communications, 9, 3709, 10.1038/s41467-018-06107-2

    2018 doi

  39. [48]

    Lian , Y., & Showman , A. P. 2010, , 207, 373, 10.1016/j.icarus.2009.10.006

    2010 doi

  40. [49]

    D., Ingersoll , A

    Little , B., Anger , C. D., Ingersoll , A. P., et al. 1999, Icarus, 142, 306, 10.1006/icar.1999.6195

    1999

  41. [50]

    2010, in Astrophysics and Space Science Proceedings, Vol

    Lodders , K. 2010, in Astrophysics and Space Science Proceedings, Vol. 16, Principles and Perspectives in Cosmochemistry, ed. A. Goswami & B. E. Reddy , 379, 10.1007/978-3-642-10352-0_8

    2010 doi

  42. [51]

    2021, Space Science Reviews, 217, 44, 10.1007/s11214-021-00825-8

    Lodders , K. 2021, Space Science Reviews, 217, 44, 10.1007/s11214-021-00825-8

    2021 doi

  43. [52]

    R., Macgorman , D

    Mansell , E. R., Macgorman , D. R., Ziegler , C. L., & Straka , J. M. 2005, Journal of Geophysical Research (Atmospheres), 110, D12101, 10.1029/2004JD005287

    2005 doi

  44. [53]

    2023, , 4, 25, 10.3847/PSJ/acaf6b

    Moeckel , C., de Pater , I., & DeBoer , D. 2023, , 4, 25, 10.3847/PSJ/acaf6b

    2023 doi

  45. [54]

    Moorthi , S., & Suarez , M. J. 1992, Monthly Weather Review, 120, 978, 10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2

    1992 doi

  46. [55]

    Moorthi, S., & Suarez, M. J. 1999, NOAA technical report NWS/NCEP. https://repository.library.noaa.gov/view/noaa/11400

    1999

  47. [56]

    I., Fouchet , T., Bezard , B., et al

    Moses , J. I., Fouchet , T., Bezard , B., et al. 2005, Journal of Geophysical Research (Planets), 110, E08001, 10.1029/2005JE002411

    2005 doi

  48. [57]

    B., Atreya , S

    Niemann , H. B., Atreya , S. K., Carignan , G. R., et al. 1998, Journal of Geophysical Research, 103, 22831, 10.1029/98JE01050

    1998 doi

  49. [58]

    S., Momary , T., Ingersoll , A

    Orton , G. S., Momary , T., Ingersoll , A. P., et al. 2017, Geophysical Research Letters, 44, 4607, 10.1002/2017GL073019

    2017 doi

  50. [59]

    2022, Remote Sensing, 15, 219, 10.3390/rs15010219

    Palotai , C., Brueshaber , S., Sankar , R., & Sayanagi , K. 2022, Remote Sensing, 15, 219, 10.3390/rs15010219

    2022 doi

  51. [60]

    Palotai , C., & Dowling , T. E. 2008, , 194, 303, 10.1016/j.icarus.2007.10.025

    2008 doi

  52. [61]

    M., Moore, J

    Rochette, S. M., Moore, J. T., & Market, P. S. 1999, Natl. Wea. Dig, 23, 20

    1999

  53. [62]

    2021, , 368, 114589, 10.1016/j.icarus.2021.114589

    Sankar , R., Klare , C., & Palotai , C. 2021, , 368, 114589, 10.1016/j.icarus.2021.114589

    2021

  54. [63]

    2022, , 380, 114973, 10.1016/j.icarus.2022.114973

    Sankar , R., & Palotai , C. 2022, , 380, 114973, 10.1016/j.icarus.2022.114973

    2022

  55. [64]

    Showman , A. P. 2007, Journal of Atmospheric Sciences, 64, 3132, 10.1175/JAS4007.1

    2007 doi

  56. [65]

    A., Wong , M

    Simon , A. A., Wong , M. H., & Orton , G. S. 2015, The Astrophysical Journal, 812, 55, 10.1088/0004-637X/812/1/55

    2015 doi

  57. [66]

    A., Soderblom , L

    Smith , B. A., Soderblom , L. A., Johnson , T. V., et al. 1979, Science, 204, 951, 10.1126/science.204.4396.951

    1979 doi

  58. [67]

    Sugiyama , K., Nakajima , K., Odaka , M., Kuramoto , K., & Hayashi , Y. Y. 2014, , 229, 71, 10.1016/j.icarus.2013.10.016

    2014 doi

  59. [68]

    H., Pater , I

    Tollefson , J., Wong , M. H., Pater , I. d., et al. 2017, , 296, 163, 10.1016/j.icarus.2017.06.007

    2017 doi

  60. [69]

    Vallis , G. K. 2017, Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation , 10.1017/9781107588417

    2017 doi

  61. [70]

    R., & Showman , A

    Vasavada , A. R., & Showman , A. P. 2005, Reports on Progress in Physics, 68, 1935, 10.1088/0034-4885/68/8/R06

    2005 doi

  62. [71]

    2017, Dataset: Wide Field Coverage for Juno (WFCJ), MAST archive at Space Telescope Science Institute, 10.17909/T94T1H

    Wong, M. 2017, Dataset: Wide Field Coverage for Juno (WFCJ), MAST archive at Space Telescope Science Institute, 10.17909/T94T1H

    2017 doi

  63. [72]

    H., Atreya , S

    Wong , M. H., Atreya , S. K., Kuhn , W. R., Romani , P. N., & Mihalka , K. M. 2015, , 245, 273, 10.1016/j.icarus.2014.09.042

    2015 doi

  64. [73]

    H., Bjoraker , G

    Wong , M. H., Bjoraker , G. L., Goullaud , C., et al. 2023, Remote Sensing, 15, 702, 10.3390/rs15030702

    2023 doi

  65. [74]

    H., Mahaffy, P

    Wong, M. H., Mahaffy, P. R., Atreya, S. K., Niemann, H. B., & Owen, T. C. 2004, Icarus, 171, 153

    2004

  66. [75]

    H., Simon , A

    Wong , M. H., Simon , A. A., Tollefson , J. W., et al. 2020, , 247, 58, 10.3847/1538-4365/ab775f

    2020 doi

  67. [76]

    Young , R. M. B., Read , P. L., & Wang , Y. 2019 a , , 326, 225, 10.1016/j.icarus.2018.12.005

    2019 doi

  68. [77]

    2019 b , , 326, 253, 10.1016/j.icarus.2018.12.002

    ---. 2019 b , , 326, 253, 10.1016/j.icarus.2018.12.002

    2019 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.