Nonuniform Water Distribution in Jupiter's Mid Latitudes: Influence of Precipitation and Planetary Rotation

TL;DR

This work tackles the challenge of constraining Jupiter's atmospheric water abundance given its potential nonuniform distribution in the weather layer. It employs a high-resolution nonhydrostatic beta-plane simulation to explicitly resolve the water hydrological cycle, showing that falling precipitation depletes water vapor to depths of about $15$ bars beneath the lifting condensation level ($LCL$) and that the $7$-bar level exhibits latitudinal vapor variations up to a factor of ten. The authors argue that nonlinear large-scale eddies and waves drift air parcels along potential vorticity ($PV$) surfaces, sustaining the observed latitudinal moisture structure and linking it to the vertical stratification set by precipitation. They furthermore quantify diabatic and homogenization timescales, revealing that moisture and PV can behave quasi-conservatively under certain conditions, with broader implications for interpreting metallicity measurements on Jupiter and other fast-rotating giant planets.

Abstract

Knowing the composition of Jupiter's atmosphere is crucial for constraining Jupiter's bulk metallicity and formation history. Yet, constraining Jupiter's atmospheric water abundance is challenging due to its potential non-uniform distribution. Here, we explicitly resolve the water hydrological cycle in Jupiter's mid-latitudes using high-resolution simulations. Falling precipitation leads to a significant large-scale depletion of water vapor beneath the lifting condensation level. A non-uniform water vapor distribution emerges in the mid-latitude simulation with a changing Coriolis parameter across latitudes and spatially uniform cooling and heating. Water abundance at the 7-bar level varies by up to a factor of ten across latitudes, from sub-solar to super-solar values. We propose that nonlinear large-scale eddies and waves tend to drift air parcels across latitudes along constant potential vorticity (PV) surfaces, thereby sustaining latitudinal dependencies in water vapor and the interplay between water distribution and large-scale dynamics. Therefore, water distribution is influenced by the vertical structure of density stratification and changing Coriolis parameter across Jupiter's mid-latitudes, as quantified by PV. Additionally, the water hydrological cycle amplifies the specific energy of air parcels through the latent heat effect, thereby slowing down vertical mixing with a latent heat flux. The horizontal gradient of water is expected to be more pronounced with a super-solar water abundance. We suggest that similar interplays between precipitating condensates, planetary rotation, and distribution of condensable species generally exist in the weather layer of fast-rotating giant planets. The ongoing Juno mission and future Uranus mission may further reveal the non-uniform distribution of condensed species and their interplay with large-scale dynamics.

Figures (5)

• Figure 1: A snapshot of water vapor mass fraction (A and D), potential vorticity (B and E), and virtual potential temperature (C and F) at 7-bar level (A-C) and 9.5-bar level (D-F) on simulation Day 4,180. As a reference, the LCL of water vapor is at 5.5 bar, and the average 1-bar temperature is about 172 K (SI Appendix Fig. 1). Notably, the color bars of the same quantity have different color scales at different pressure levels. The white arrow in panel A represents a horizontal distance of 6,000 km, resolved by 40 grid points in our model. At the 9.5-bar level, the water vapor map is anti-correlated with the PV map but correlated with the virtual potential temperature map. The equivalent latitude $\phi$ is computed by the gradient of the planetary vorticity $\beta_{0}$ and the local Coriolis parameter $f = f_{0} + \beta_{0}y = 2\Omega\sin{\phi}$, where $f_{0}$ is the Coriolis parameter at $20^\circ$ N and $y$ is the meridional distance to the southern boundary. The equivalent longitude $\lambda$ is calculated by $\lambda = 180^\circ\arcsin{(x/\pi R_{p})}$ where $x$ is the horizontal distance and $R_{p}$ is Jupiter's radius.
• Figure 2: Horizontally and temporally averaged vertical profiles of the simulated water vapor mass fraction (red dashed line), water cloud density (green solid line), and water precipitation density (blue solid line) averaged from the $7_{\rm th}$ to $15_{\rm th}$ simulation year. The bottom abscissa measures the vapor mass fraction, while the top abscissa measures the condensate density. Simulation indicates that water vapor is depleted from the LCL at about 5.5 bar to the level where the water precipitation fully reevaporates. The mean precipitation flux is about $\sim\mathcal{O}(100\;\rm mm\;yr^{-1})$.
• Figure 3: Panels A-C respectively show the 2D distribution of temporally and zonally averaged water vapor mass fraction $q_{v}$, PV, and virtual potential temperature $\theta_{v}$ in the latitude-pressure cross-section from Day 4,170 to Day 4,300. We first calculate PV using the local density, buoyancy frequency, and the Coriolis parameter, and then take the average. In panel A, circled dots denote eastward prograde jets, while the circled crosses denote westward retrograde jets.
• Figure 4: Panel A shows the zonally and temporally averaged water vapor mass fraction (green solid isolines) and PV (the color-filled contour map) from Day 4,170 to Day 4,300. Panel B shows the horizontally and temporally averaged buoyancy frequency calculated from the vertical gradient of $\theta_{v}$. Panel B shows the stratification peaks at about 7.5 bar. We propose that nonlinear Rossby waves tend to drift air parcels along PV isolines, and turbulent mixing may occur to homogenize water vapor along the trajectory (black solid arrow), thereby sustaining a general correlation between PV and water vapor. On the other hand, diabatic mixing driven by precipitation mostly occurs in the vertical direction, as the falling precipitation responds to gravity (black dashed arrow). PV isolines in panel A are mostly determined by the absolute vorticity $f+\zeta$ and stratification $N^2$. $f$ changes by a factor of three from $\rm 20^\circ$ to $\rm 55^\circ$ in latitudes, and $N^2$ could change by more than a factor of ten from about $7\times 10^{-6}\;{\rm s^{-2}}$ to much less than $10^{-6}\;{\rm s^{-2}}$ in the vertical. In contrast, density $\rho$ changes from 0.52 $\rm kg\;m^{-3}$ to 0.75 $\rm kg\;m^{-3}$ (less than 50%), and virtual potential temperature $\theta_{v}$ changes from $\sim 170.5$ K to $\sim 169.5$ K ($\sim$1%).
• Figure 5: Scattering plots of water vapor mass fraction $q_{v}$, PV, and virtual potential temperature $\theta_{v}$ colored by the equivalent latitude. These dots are collected from a snapshot in the simulation from 6 to 10 bars on Day 4,180 (same day as the data in Fig. \ref{['fig:fig1-water']}). Panel A shows $q_{v}$ versus PV; panel B shows $q_{v}$ versus $\theta_{v}$; and panel C shows $\theta_{v}$ versus PV. Panels A and C show a general correlations between PV and other quasi-conserved tracers shift to general anti-correlations (at $q_{v} \sim 0.012 \;{\rm kg/kg}$ in panel A and $\theta_{v}\sim 170.1\;{\rm K}$ in panel C) because PV first increases in altitude and then decreases. Both the vertical variations of water vapor and virtual potential temperature are monotonic, while the vertical variation of PV is non-monotonic due to the stratification peak near the 7.5-bar level. All three panels show a separation of points characterized by three groups, mostly colored by cosmic cobalt, green, and yellow dots, which reflect the distribution of tracers within three staircases at 22$^\circ$-27$^\circ$ N, 27$^\circ$-42$^\circ$ N, and 42$^\circ$-55$^\circ$ N. The staircase separation and spreading of PV with a $q_{v}$ and $\theta_{v}$ indicates that staircase mixing is more efficient than global mixing. $q_{v}$ and PV distributions on constant $\theta_{v}$ maps are provided in SI Appendix Fig. 3.