SearcharxivSearch

arXiv · 2504.06909

Climate impacts of forced equatorial superrotation in an idealized GCM

Abstract

While it is expected that the large-scale tropical circulation should undergo some changes in a warmer climate, it remains an open question whether its characteristic features, such as the Hadley cell, the intertropical convergence zone, or the weak surface easterlies, could take a completely different shape. As an example, it has been hypothesized that the Earth's atmosphere may have experienced equatorial superrotation -- i.e. westerly winds at the equator -- during its history. The possibility of equatorial superrotation has been studied in a range of planetary atmospheres, including Earth-like ones, with the objective of understanding the underlying dynamical processes. However, the broader impact that this dramatic circulation change would have on the climate system is practically unexplored. This is the question we address here. We perform idealized GCM simulations with an imposed equatorial torque to investigate how a forced superrotating atmosphere affects surface temperature and the water cycle. We show that these effects are quite large and directly related to the global circulation changes, which extend beyond the tropical atmosphere. Using tools including a forcing/feedback analysis and a moist energy balance model, we argue that the dominant mechanism is changes in atmospheric energy transport, driven in particular by the collapse of the meridional overturning circulation, and to a smaller extent by the appearance of an equatorial jet, and the concomitant redistribution of moisture in the tropics, leading to a much weaker relative humidity gradient which has strong radiative effects.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tim Marino, Michael P. Byrne, Corentin Herbert. 2025-04-09. Climate impacts of forced equatorial superrotation in an idealized GCM. https://doi.org/10.1175/jcli-d-25-0588.1

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