arXiv subjects

Or Hadas

Publications and source records attributed to Or Hadas.

6 recordsLinked to original sources

arXiv2026

A storm-centered perspective of midlatitude near-inertial wave generation

Near-inertial waves (NIWs) generated by extratropical storms provide a major pathway for transferring atmospheric energy into the ocean, yet what determines the efficiency of this energy transfer at the level of individual storms remains poorly understood. Here, using a slab ocean model forced by ERA5 reanalysis and analyzed in storm-relative coordinates, we investigate NIW generation across three temporal scales: from the instantaneous structure of individual storms, through their life cycle, to variability across storms. Instantaneous storm structure reveals that the spatial layout of wind stress and storm propagation provides the required time evolution and rotation to excite a localized maximum in wind work on the right side of storm tracks, much like tropical cyclones do. Following storms through their life cycle further reveals that wind work peaks one day after cyclone genesis, about one day before storms reach their maximum intensity. This offset arises because storm translation slows during intensification, reducing the propagation-induced wind-stress tendency, while concurrent poleward propagation increases the Coriolis parameter and reduces the alignment between winds and near-inertial currents. Due to the temporal lag, regions of climatologically high NIW forcing are located upstream of the North Pacific storm-track maximum. Finally, examining variability across storms, we show that storm intensity, propagation speed, and latitude explain most of the variability in NIW generation, but not in extreme events. Focusing on extreme events, we find that the strongest NIW-generating storms feature colder cold sectors, which destabilize the marine boundary layer and enhance downward momentum transfer to the ocean surface.

physics.ao-ph↗

arXiv2026

Cloud radiative effects reinforce storm tracks

Midlatitude storm tracks play a central role in Earth's climate by transporting heat from low to high latitudes. Their strength is therefore set by the meridional temperature gradients, traditionally thought to be maintained solely by differential solar heating. This framework predicts a substantial seasonal reduction in storm activity, as meridional insolation gradients vanish during summer. Yet in the Southern Hemisphere, storm activity decreases by only 30% from its seasonal maximum. Here, we show that cloud radiative effects are essential for the seasonal maintenance of storm activity by reinforcing the temperature gradients that sustain it. Satellite observations reveal that sunlight reflected by midlatitude clouds in early summer creates a substantial meridional gradient in surface heating, despite the nearly uniform summer insolation. Idealized aquaplanet simulations then show that shortwave cloud radiative effects reinforce meridional sea-surface temperature gradients, thereby strengthening storm activity primarily during late summer and autumn, while longwave cloud effects partly offset this response. To interpret these results, we develop a simple theoretical model linking storms, clouds, and sea-surface temperature gradients. The model reproduces the simulated seasonal response and identifies two emergent cloud properties that control the feedback strength: the maximum attainable cloud albedo and the sensitivity of cloud cover to storm activity. Together, these findings indicate that cloud radiative feedbacks are key to maintaining the thermal gradients that sustain storm activity. More broadly, they reveal a strong coupling among storms, clouds, and the ocean spanning distinct spatial and temporal scales.

physics.ao-ph↗

arXiv2026

Modulation of the tropical meridional circulation by the Madden-Julian Oscillation

The Hadley circulation is Earth's dominant tropical overturning circulation, regulating atmospheric energy transport, tropical rainfall, and subtropical aridity. Although its variability has been extensively studied on seasonal to decadal climate-change timescales, its subseasonal behaviour remains poorly understood. Here we show that the Madden-Julian Oscillation (MJO), the leading mode of tropical intraseasonal variability, systematically modulates the Hadley circulation and influences global hydroclimate variability. Using reanalysis data and dynamical diagnostics, we identify a robust hemispherically asymmetric Hadley circulation anomaly associated with active MJO events, with amplitudes comparable to the climatological intraseasonal variability of the Hadley circulation. Breaking down the dynamical components reveals that the response is primarily maintained by latent heating associated with moist convection, while the overturning strength is driven by interhemispheric moisture gradients. Lead-lag analyses further show that the Hadley circulation lags the MJO by 4-15 days, indicating that MJO convection may drive overturning adjustments on subseasonal timescales. This coupled MJO-Hadley circulation state reveals a previously overlooked pathway linking tropical intraseasonal oscillations, meridional circulation and the hydrological cycle.

physics.ao-ph↗

arXiv2025

Predictability of Storms in an Idealized Climate Revealed by Machine Learning

The midlatitude climate and weather are shaped by storms, yet the factors governing their predictability remain insufficiently understood. Here, we use a Convolutional Neural Network (CNN) to predict and quantify uncertainty in the intensity growth and trajectory of over 200,000 storms simulated in a 200-year aquaplanet GCM. This idealized framework provides a controlled climate background for isolating factors that govern predictability. Results show that storm intensity is less predictable than trajectory. Strong baroclinicity accelerates storm intensification and reduces its predictability, consistent with theory. Crucially, enhanced jet meanders further degrade forecast skill, revealing a synoptic source of uncertainty. Using sensitivity maps from explainable AI, we find that the error growth rate is nearly doubled by the more meandering structure. These findings highlight the potential of machine learning for advancing understanding of predictability and its governing mechanisms.

physics.ao-ph↗

arXiv2025

Quantifying the Influence of Climate on Storm Activity Using Machine Learning

Extratropical storms shape midlatitude weather and vary due to the slowly evolving climate and the rapid changes in synoptic conditions. While the influence of each factor has been studied extensively, their relative importance remains unclear. Here, we quantify the climate's relative importance in mean storm activity and individual storm development using 84 years of ERA-5 data and convolutional neural networks. We find that the constructed model predicts over 90% of the variability in the mean storm activity. However, a similar model predicts about a third of the variability in individual storm properties, such as maximum intensity, showing their variability is dominated by synoptic conditions. Isolating the impact of present-day climate change on individual storms shows it contributes to about 0.1% for storm-intensity variability, whereas its contribution to storms' heat-anomaly variability is over three times greater, highlighting that focusing on variables directly tied to global warming offers a clearer attribution pathway.

physics.ao-ph↗

arXiv2025

A Lagrangian Perspective on the Growth of Midlatitude Storms

Extratropical storms dominate midlatitude climate and weather and are known to grow baroclinicaly and decay barotropicaly. Traditionally, quantitative climatic measures of storm growth have been mostly based on Eulerian measures, taking into account the mean state of the atmosphere and how those affect eddy growth, but they do not consider the Lagrangian growth of the storms themselves. Here, using ERA-5 reanalysis data and tracking all extratropical storms (cyclones and anticyclones) from 83 years of data, we examine the actual growth of the storms and compare it to the Eulerian characteristics of the mean state as the storms develop. In the limit of weak baroclinicity, we find that baroclinicity provides a good measure for storm maximum intensity. However, this monotonic relationship breaks for high baroclinicity levels. We show that although the actual growth rate of individual storms monotonically increases with baroclinicity, the reduction in maximum intensity at high baroclinicity is caused by a decrease in storm growth time. Based on the Lagrangian analysis, we suggest a nonlinear correction to the traditional linear connection between baroclinicity and storms' activity. Then, we show that a simplified model of storm growth, incorporating the baroclinicity effect on the vertical tilt of anomalies, reproduces the observed nonlinear relationship. Expanding the analysis to include the mean flow's barotropic properties highlights their marginal effect on storm growth rate, but the crucial impact on growth time. Our results emphasize the potential of Lagrangianly studying storm dynamics to advance understanding of the midlatitude climate.

physics.ao-ph↗