SearcharxivSearch

arXiv · 2509.17217

An idealized general circulation model for the atmospheric circulation on the ice giants

Abstract

Uranus and Neptune are the least explored planets in the Solar System. A key question regarding the two planets is the similarity of their observed flows despite the great differences in their obliquity and internal heating. To answer this fundamental question and understand the ice giants atmospheric circulation, we developed a new general circulation model (GCM). This tool will also be key to facilitating the success of future missions to the ice giants, for which atmospheric flows will be a measurable quantity. Past GCMs for the ice giants have struggled to reproduce the observed winds on Uranus and Neptune. Using our idealized GCM, we systematically explored how the zonal wind and meridional circulation respond to different model and physical parameters; our main focus was on the depth of the domain. We show that in cases where the bottom layer of the model is deep enough, the simulated flow is independent of the meridional structure of the forcing temperature, indicating that dynamical processes, and not the imposed thermal forcing, are the dominant drivers of the circulation and the thermal structure. A momentum balance analysis further shows that meridional and vertical eddy momentum flux convergence are both central to maintaining the circulation. These results provide a physical explanation for the similarity of the flow on Uranus and Neptune although their solar and internal forcing are significantly different. The modeling framework developed in this study can serve as a foundation for the development of more comprehensive GCMs of the ice giants and help guide the interpretation of future mission data.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ilai Guendelman, Yoahi Kaspi. 2025-09-21. An idealized general circulation model for the atmospheric circulation on the ice giants. https://doi.org/10.1051/0004-6361%2F202554015

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph