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With its extreme axial tilt, Uranus' radiant energy budget (REB) and internal heat flux remain among the most intriguing mysteries in our solar system. By combining observations with modeling, we present the global REB over a complete orbital period (1946–2030), revealing significant seasonal variations. Despite these fluctuations, the global average emitted thermal power consistently exceeds absorbed solar power, indicating a net energy loss. Assuming no significant seasonal variation in emitted power, we estimate an internal heat flux of 0.078 ± 0.018 W/m2 by analyzing the energy budget over one orbital period. The combination of internal heat and radiant energies indicates substantial global and hemispheric imbalances, with excesses or deficits exceeding 85% of emitted power at the hemispheric scale. These findings are crucial for understanding Uranus' interior and atmosphere. A future flagship mission to Uranus would provide critical observations to address more unresolved questions of this enigmatic ice giant. 

Large-amplitude variations are commonly observed in the atmospheres of directly imaged exoplanets and brown dwarfs. VHS 1256B, the most variable known planet-mass object, exhibits a near-infrared flux change of nearly 40%, with red color and silicate features revealed in recent JWST spectra, challenging current theories. Using a general circulation model, we demonstrate that VHS 1256B’s atmosphere is dominated by planetary-scale dust storms persisting for tens of days, with large patchy clouds propagating with equatorial waves. This weather pattern, distinct from the banded structures seen on solar system giants, simultaneously explains the observed spectra and critical features in the rotational light curves, including the large amplitude, irregular evolution, and wavelength dependence, as well as the variability trends observed in near-infrared color-magnitude diagrams of dusty substellar atmospheres. 

Aerosols in Jupiter’s stratosphere form intriguing polar hoods, which have been investigated by ultraviolet cameras on Cassini and the Hubble Space Telescope. Transient, concentrated dark ovals of unknown origin have been noted within both the northern and southern polar hoods. However, a systematic comparative study of their properties, which could elucidate the physical processes active at the poles, has not yet been performed due to infrequent observations. Using 26 global maps of Jupiter taken by Hubble between 1994 and 2022, we detected transient ultraviolet-dark ovals with a 48% to 53% frequency of occurrence in the south. We found the southern dark oval to be 4 to 6 times more common than its northern counterpart. The southern feature is an anticyclonic vortex and remains within the auroral oval during most of its lifetime. The oval’s darkness is consistent with a 20 to 50 times increase in haze abundance or an overall upward shift in the stratospheric haze distribution. The anticyclonic vorticity of the dark oval is enhanced relative to its surroundings, which represents a deep extension of the higher-altitude vortices previously reported in the thermosphere and upper stratosphere. The haze enhancement is probably driven by magnetospheric momentum exchange, with enhanced aerosols producing the localized heating detected in previous infrared retrievals. 

Uncertainty Quantification (UQ) is vital for decision makers as it offers insights into the potential reliability of data and model, enabling more informed and risk-aware decision-making. Graphical models, capable of representing data with complex dependencies, are widely used across domains. Existing sampling-based UQ methods are unbiased but cannot guarantee convergence and are time-consuming on large-scale graphs. There are fast UQ methods for graphical models with closed-form solutions and convergence guarantee but with uncertainty underestimation. We propose LinUProp, a UQ method that utilizes a novel linear propagation of uncertainty to model uncertainty among related nodes additively instead of multiplicatively, to offer linear scalability, guaranteed convergence, and closed-form solutions without underestimating uncertainty. Theoretically, we decompose the expected prediction error of the graphical model and prove that the uncertainty computed by LinUProp is the generalized variance component of the decomposition. Experimentally, we demonstrate that LinUProp is consistent with the sampling-based method but with linear scalability and fast convergence. Moreover, LinUProp outperforms competitors in uncertainty-based active learning on four real-world graph datasets, achieving higher accuracy with a lower labeling budget. 

Abstract We present a new two-dimensional, bin-scheme microphysical model of cloud formation in the atmospheres of hot Jupiters that includes the effects of longitudinal gas and cloud transport. We predict cloud particle size distributions as a function of planetary longitude and atmospheric height for a grid of hot Jupiters with equilibrium temperatures ranging from 1000 to 2100 K. The predicted 2D cloud distributions vary significantly from models that do not consider horizontal cloud transport and we discuss the microphysical and transport timescales that give rise to the differences in 2D versus 1D models. We find that the horizontal advection of cloud particles increases the cloud formation efficiency for nearly all cloud species and homogenizes cloud distributions across the planets in our model grid. In 2D models, certain cloud species are able to be transported and survive on the daysides of hot Jupiters in cases where 1D models would not predict the existence of clouds. We demonstrate that the depletion of condensible gas species varies as a function of longitude and atmospheric height across the planet, which impacts the resultant gas-phase chemistry. Finally, we discuss various model sensitivities including the sensitivity of cloud properties to microphysical parameters, which we find to be substantially less than the sensitivity to the atmospheric thermal structure and horizontal and vertical transport of condensible material.