Supercells are the heavyweight champions of local severe storms: deep, long-lived thunderstorms whose updrafts rotate persistently, feeding them the power to generate giant hail, destructive straight-line winds, and most tornadoes. Because surface-based supercells draw their fuel from the lowest few hundred meters of the atmosphere, scientists have suspected for years that the altered surface conditions beneath them—heat, roughness, and moisture supplied by an urban canopy—could meaningfully reshape their evolution. Case studies over the US Great Plains and long-term simulations over Moscow have previously hinted at storm intensification over cities, but those studies either examined single events or relied on heavily simplified urban representations. The new work, led by Francesco De Martin of the University of Bologna and the University of Oklahoma together with Christopher Rozoff, Andrea Zonato, Stefano Alessandrini, and Silvana Di Sabatino, takes a fundamentally more rigorous approach.

The team ran ensembles of idealized numerical simulations with the Weather Research and Forecasting model at one-kilometer horizontal grid spacing, pairing the model with state-of-the-art urban parameterizations: the Building Effect Parametrization and the Building Energy Model, which together represent the effects of roofs, streets, walls, canyon shadowing, radiation trapping, and anthropogenic heat on the atmosphere. The setup was deliberately clean. A flat domain covered in croplands hosted a single circular city, and a supercell was triggered upstream using a thermal bubble so that the storm, after two hours of development, would collide with the urban area near its peak intensity. The environment was tuned to a classic severe weather profile: roughly 2740 joules per kilogram of surface-based convective available potential energy, strong deep-layer shear of about 32 meters per second, and moderate storm-relative helicity in the lowest three kilometers.

What makes the study statistically powerful is its scale. Twenty-five numerical experiments were conducted, each with eleven ensemble members generated using a stochastic kinetic energy backscatter scheme that perturbs the flow in physically realistic ways. This allowed the researchers to separate genuine urban signals from the chaotic variability that always plagues convective simulations. The morphological parameters varied were city size, with diameters up to 30 kilometers; urban fraction, the proportion of the surface covered by built materials; building height, spanning 10 to 60 meters; and building density, ranging from dispersed to highly compact configurations. Parameter ranges were anchored in real morphological data from European and American cities frequently hit by severe storms, using the GLOBUS global building-height dataset, with Milan, Italy serving as the reference city.

The first major finding is a statistically significant weakening of the supercell as city size increases. Updraft helicity—a diagnostic combining vertical velocity and vorticity that tracks storm rotation and intensity—declined consistently in the ensemble means as the city grew wider, with the suppression most pronounced over and downstream of large cities with high urban fractions. In the most extreme configuration, a 30-kilometer-wide, fully built-up city, the suppression of the incoming storm’s updraft helicity swath was nearly complete. In smaller cities the effect was weaker and sometimes not statistically significant, with a few ensemble members even showing transient intensification, a reminder that storm-city interactions remain sensitive to the fine details of the flow.

The culprit behind the weakening is not the buildings themselves but the moisture budget of the city. Urban surfaces, lacking the vegetation of surrounding croplands, evapotranspire far less water, producing an urban dry island. In the simulations the dry island reduced near-surface specific humidity by up to about 2 grams per kilogram and relative humidity by roughly 13 percent, values consistent with observations from Moscow and Beijing. That drying carved out a wake of sharply reduced convective available potential energy extending up to 80 kilometers downwind, with CAPE deficits reaching 1000 joules per kilogram—about twice the reduction reported in a previous Dallas–Fort Worth case study. Because surface-based supercells feed on low-level air, this depleted reservoir of instability starved the storm. The authors note this is the first time an urban-induced CAPE reduction has been directly linked to an urban-induced weakening of severe storms, and the discrepancy with earlier idealized studies that found intensification appears to hinge on the fact that those studies omitted the dry island entirely.

But the city has a second face. The same urban surfaces also trap heat, generating an urban heat island of about 1.5 kelvin in the simulations. Through a quantitative decomposition of pressure perturbations, the researchers showed that this heat island thermodynamically induces a pressure minimum of up to about one hectopascal downstream of the city, driven overwhelmingly by buoyancy rather than by the mechanical blocking of airflow by buildings. That pressure minimum draws in converging surface winds, and along the convergence line, vertical motions intensify, locally reaching one meter per second at the 950-hectopascal level. In the largest and most fully urbanized experiment, this mechanism proved potent enough to do something remarkable: while the original supercell was dissipated by the dry island, new convective cells fired along the convergence corridor and, in 8 of the 11 ensemble members, organized into a brand-new right-moving supercell downwind of the city. Notably, this corridor also coincided with enhanced storm-relative helicity on its northern flank, providing the rotation the new storm needed.

Perhaps the most surprising result concerns skyscrapers. Contrary to the long-standing building-barrier hypothesis, which holds that tall structures mechanically force air upward and can even split storms around them, building height had little impact on supercell dynamics. In fact, taller buildings slightly reduced the urban heat island—deeper street canopies are shaded and retain moisture longer—and consequently weakened the downstream pressure minimum and convergence. Even with 60-meter-tall buildings, comparable to dense districts of New York or Chicago, the vertical velocities generated mechanically by the buildings were negligible compared with those produced by the heat island’s pressure signature. Building density fared little better, emerging as the least influential parameter in the entire study. Together, these findings suggest that in strongly synoptically forced environments, the thermodynamic personality of a city, not its architecture, dictates how it treats passing supercells.

The simulations also carry a hopeful message for urban design. An additional experiment in which every roof of the idealized city was covered with vegetation dramatically muted the city’s effect on the approaching storm, because green roofs restore evapotranspiration and shrink the dry island. For emergency managers and forecasters, the implications are equally practical: the same city that weakens one supercell can seed another downwind, meaning warnings cannot simply assume a storm will decay as it crosses a major metropolitan area. The results align with radar-based climatologies showing that storms approaching US Great Plains cities and Berlin are more frequently weakened than intensified downwind, and they may explain past case studies in which urban-triggered storms appeared to intensify—they were likely new convection born along the urban convergence line rather than a revived original storm.

The authors are careful to note the limits of their idealized framework. A single environmental profile was tested, aerosol effects were not represented, nocturnal events—when the heat island is strongest but storms tend to be elevated—remain unexplored, and the parameterizations, while sophisticated, do not explicitly resolve individual buildings. Large-eddy simulations at finer resolutions and studies across a wider range of atmospheric environments are logical next steps. Yet the core conceptual model stands: a city is simultaneously a moisture thief and a heat engine, capable of starving one supercell while igniting another. For the millions of people living where supercells and skylines intersect, that dual identity is now written into the physics.

Subject of Research: Urban land use effects on supercell thunderstorm intensity and initiation

Article Title: The dual effect of urban areas on supercell storms

Article References: De Martin, F., Rozoff, C., Zonato, A., Alessandrini, S., & Di Sabatino, S. (2026). The dual effect of urban areas on supercell storms. Weather and Climate Dynamics, 7(4), 2015-2042. https://doi.org/10.5194/wcd-7-2015-2026

Image Credits: AI Generated

DOI: 10.5194/wcd-7-2015-2026

Keywords: supercell, urban heat island, urban dry island, convective storms, numerical simulations, WRF model, city size, building height, convection initiation, CAPE, storm-relative helicity, urban meteorology

The scale separation in space is staggering. Reynolds numbers, which measure the ratio of inertial to viscous effects, routinely reach 10 billion or larger in astrophysical flows, meaning turbulence is essentially unavoidable. Unlike water or air, astrophysical plasmas are almost perfectly conducting, so their dynamics are governed by the MHD equations, coupling currents, magnetic fields and the Lorentz force. In our Galaxy, magnetic fields of around 5 microgauss reach rough equipartition with turbulent kinetic energy; in galaxy clusters, fields of 1 to 3 microgauss sit near a twentieth of equipartition. These fields are not decorative: they accelerate particles, fill the Universe with non-thermal radiation, and even enable the accretion of matter onto black holes through the magnetorotational instability, a process estimated to be the most potent energy source in the cosmos, exceeding thermonuclear burning in stars.

The mathematical backbone of the subject remains the Kolmogorov cascade picture. Energy injected at large scales passes, without dissipation, through an inertial range of scales until viscosity finally wins at the Kolmogorov scale. Dimensional analysis then yields the famous spectrum in which energy content scales as the wavenumber to the minus five-thirds power, with a dimensionless Kolmogorov constant that experiments and simulations place near 1.6. Beryesnyak emphasizes a rigorous tool called scaling convergence: because the underlying equations contain no preferred scale, small-scale statistics from simulations of different resolutions should collapse onto a single universal curve when properly normalized, a technique that drives statistical error virtually to zero and has even resolved the tiny intermittency correction of about 0.04 to the spectral slope.

MHD turbulence, however, refuses to behave like its hydrodynamic cousin. A large-scale magnetic field cannot be transformed away, and it remains dynamically important on every scale. Linearizing the MHD equations reveals four wave modes, of which the transverse Alfvén mode dominates the nonlinear cascade. Early theorists Iroshnikov and Kraichnan imagined weak, wave-like interactions, but later work showed that turbulence becomes stronger, not weaker, as it cascades. Energy transfer proceeds preferentially perpendicular to the field, producing extreme anisotropy. Goldreich and Sridhar proposed that this anisotropy is capped by critical balance, where the cascade time matches the wave period, yielding a perpendicular spectrum of minus five-thirds and the relation that the parallel wavenumber scales as the perpendicular wavenumber to the two-thirds power.

High-resolution direct numerical simulations have now put these ideas to demanding tests. Using the scaling convergence method on simulations up to 4096 cubed grid points, Beresnyak found that the perpendicular spectrum converges best near a slope of minus 1.7, consistent with the Kolmogorov picture rather than competing minus three-halves models. The Alfvénic Kolmogorov constant was measured as 3.3 with a total value near 4.2 when the passively advected slow mode is included. Remarkably, the residual energy, the difference between magnetic and kinetic energy, turns out to be a constant fraction, about 15 percent, of the total energy throughout the inertial range, with a corresponding Alfvén ratio of roughly 0.74, resolving earlier conceptual difficulties with theories that predicted scale-dependent behavior.

An elegant theoretical result connects the parallel spectrum to Lagrangian statistics. Because oppositely directed Alfvén wave packets propagate along magnetic field lines at a fixed speed, measuring fluctuations along the field is mathematically equivalent to following a fluid element in time. This argument yields a parallel spectrum proportional to the wavenumber to the minus second power, scaled by the inverse of the Alfvén speed, without ever invoking critical balance. Numerical measurements along the local magnetic field overwhelmingly confirm this minus-two law, matching observations from the solar wind, where spacecraft such as Helios 2 have recorded clean power-law spectra over decades of frequency.

Imbalanced turbulence, where waves traveling one direction dominate, presents a harder puzzle, and it is the norm in the solar wind and near astrophysical jets. Because critical balance cannot hold simultaneously for counter-propagating waves of unequal amplitude, several competing models were proposed. Simulations with systematically varied imbalance show that the Lithwick-Goldreich-Sridhar model captures the spectra at small imbalances, while the Beresnyak-Lazarian model, which relaxes locality for the dominant component, best matches the energy ratios and the diverging anisotropies of the two populations at strong imbalance.

Perhaps the most consequential result concerns the small-scale dynamo, the process by which turbulence amplifies weak magnetic fields. Once the kinematic, exponential phase ends, magnetic energy grows linearly in time as turbulence converts a fixed fraction of cascade power into magnetism. The measured efficiency constant is small, about 0.05, but the implications are enormous: in galaxy clusters, this keeps the ratio of magnetic to thermal energy constant at roughly 40 over the past 10 billion years, in agreement with Faraday rotation observations. The review also issues a caution to simulators. Because numerical Reynolds numbers are vastly smaller than astrophysical ones, starting a simulated young object such as a collapsing cloud with a vanishing field can artificially delay magnetization, and implicit large-eddy codes with zero initial field produce no field at all, in gross contradiction with nature, where the dynamo always jump-starts itself.

The frontier now extends to supersonic turbulence in molecular clouds, where Mach numbers near 10 produce densities varying by orders of magnitude and log-normal probability distributions sculpted by slow-mode shocks and sheared by Alfvénic motions, with direct consequences for star formation theory. It extends, too, to magnetic reconnection, where current sheets tear and spawn their own strong, critically balanced turbulence, producing reconnection rates of about 1.5 percent of the Alfvén speed that are independent of resistivity. From solar flares to black hole jets to the magnetization of the cosmic web, MHD turbulence has become the connective tissue of modern astrophysics, and the convergence of theory, simulation and spacecraft measurement suggests the field is finally converging on universal answers.

Subject of Research: Theory and numerical simulation of magnetohydrodynamic turbulence and its astrophysical applications

Article Title: MHD turbulence

Article References: Beresnyak, A. (2019). MHD turbulence. Living Reviews in Computational Astrophysics, 5(1), Article 2. https://doi.org/10.1007/s41115-019-0005-8

Image Credits: AI Generated

DOI: 10.1007/s41115-019-0005-8

Keywords: MHD turbulence, magnetohydrodynamics, astrophysical plasmas, Kolmogorov cascade, Alfvén waves, turbulent dynamo, solar wind, galaxy clusters, magnetic reconnection, interstellar medium, numerical simulations, star formation

The stakes are enormous. Since the landmark detection of GW170817 in 2017, gravitational-wave observatories have confirmed two likely binary neutron star mergers and at least two neutron star-black hole collisions. These events are cosmic alchemists: the neutron-rich matter they eject undergoes rapid neutron capture nucleosynthesis, forging many of the heavy elements in the periodic table, from iodine to gold to uranium. The radioactive decay of these freshly minted nuclei powers a kilonova, an optical and infrared glow that fades over days to weeks. Whether an outflow produces heavy elements or lighter ones hinges on a single crucial quantity, the electron fraction, which measures the balance of protons to neutrons in the fluid. And that balance is dictated almost entirely by how many electron neutrinos and antineutrinos the matter absorbs on its way out of the merger wreckage. Get the neutrino physics wrong, and predictions for the color, brightness, and duration of kilonovae collapse.

The fundamental difficulty is that neutrinos in a merger remnant live in two radically different regimes simultaneously. Deep inside the hot, dense remnant, neutrinos are trapped, scattering constantly off nucleons and reaching thermal equilibrium with the fluid, diffusing outward only slowly. Far away, in the tenuous ejecta streaming into space, they fly freely along geodesics of the curved spacetime, barely noticing the matter around them. In between lies a treacherous semi-transparent zone where neutrino-matter interactions profoundly alter the temperature and composition of the gas, yet the particles cannot be assumed to be in equilibrium with anything. A perfect simulation would solve the general relativistic Boltzmann equation, tracking the full six-dimensional distribution function of neutrinos coupled to Einstein’s equations and relativistic magnetohydrodynamics. No code on Earth can do that at acceptable cost, so every merger simulation to date relies on approximations whose errors are difficult to quantify.

Foucart’s review organizes the field’s approaches into three families. The simplest are leakage schemes, which do not transport neutrinos at all. Instead, they compute local emission rates for each grid cell, estimate an optical depth along the path of least resistance to the simulation boundary, and interpolate between free emission in transparent regions and slow diffusion in opaque ones. First developed in the 1990s and early 2000s, these schemes cost essentially nothing and capture the broad strokes of remnant cooling. But they cannot follow where emitted neutrinos go, so they miss the absorption of neutrinos in outflows, the very process that raises the electron fraction of hot ejecta from a neutron-rich value below 0.1 up to the 0.2 to 0.4 range that determines the nucleosynthesis outcome. More sophisticated variants now track trapped neutrino energy densities and propagate emission along rays to approximate reabsorption, but their accuracy is calibrated on symmetric test problems and degrades in the genuinely asymmetric geometry of a merger.

The workhorses of modern general relativistic merger simulations are moment schemes, which evolve angular moments of the neutrino distribution function, typically the energy density and energy flux, using equations formally similar to those of relativistic fluid dynamics. Because these equations can be cast in conservative form, they mesh naturally with the shock-capturing methods used for the fluid, and they automatically include emission, propagation, and reabsorption of neutrinos. The catch is closure: evolving only the lowest moments leaves the pressure tensor and higher moments unknown, forcing analysts to adopt approximate analytical prescriptions, most commonly the maximum-entropy closure of Minerbo, that interpolate between an isotropic diffusion limit and a free-streaming limit. In the optically thin regime this closure is simply wrong for realistic radiation fields, producing artificial collisions where crossing neutrino beams should pass through one another and inflating neutrino energy densities in the polar regions by factors of roughly two compared with more exact calculations.

Grey, energy-integrated moment schemes introduce a second, subtler problem. Neutrino cross-sections scale with the square of the neutrino energy, so estimating absorption and scattering opacities requires knowing the neutrino spectrum, which an energy-integrated scheme does not evolve. Assuming neutrinos sit in thermal equilibrium with the fluid can badly underestimate opacities in outflows, where escaping neutrinos carry energies of 10 to 20 megaelectronvolts while the gas has cooled to around one megaelectronvolt. Recent work addresses this by evolving the neutrino number density alongside the energy density, extracting an average energy from their ratio, and rescaling opacities accordingly, while also guaranteeing exact conservation of lepton number. Handling the diffusion of trapped neutrinos through dense regions poses yet another numerical trap: the dissipative terms in shock-capturing fluxes can overwhelm the physical diffusion rate, and the latest implementations switch to non-dissipative high-order fluxes precisely where the optical depth across a grid cell exceeds unity.

The newest entrant is Monte Carlo transport, which represents the neutrino distribution with packets, or superparticles, that propagate along null geodesics and interact with the fluid probabilistically. Because each packet carries a full energy and direction, Monte Carlo schemes sidestep the closure and spectral assumptions that plague moment methods, and comparisons show they agree with moment simulations on global quantities such as neutrino luminosities and ejecta masses to within roughly 10 to 20 percent. Their Achilles heel is the optically thick interior, where a single packet would undergo enormous numbers of interactions per time step. Practical codes borrow tricks from implicit Monte Carlo methods, transforming emission and absorption rates so that equilibration happens over a few time steps rather than within one, and treating scattering-dominated regions as a random walk advected with the fluid. These approximations achieve sub-percent accuracy in neutrino luminosities on test problems, but they remain poorly tested, and the sparse packet populations in low-density regions make it impossible to reconstruct the instantaneous neutrino distribution function, which is exactly what would be needed to model fast neutrino flavor oscillations.

Indeed, the review emphasizes that algorithmic uncertainty is only half the story. Several physically important processes are absent from essentially all merger simulations. Neutrino-antineutrino pair annihilation above the remnant’s poles may deposit enough energy to help clear baryons from the jet funnel of short gamma-ray bursts, yet it is a nonlinear process coupling neutrino and antineutrino distributions that grey schemes can only treat at the order-of-magnitude level. Inelastic scattering, which thermalizes neutrinos as they diffuse out, is omitted entirely, likely affecting the energies of heavy-lepton neutrinos. And neutrino flavor oscillations, including the fast flavor instability that can transform flavors on nanosecond timescales wherever the net lepton flux changes sign between directions, could plausibly reshape the composition of outflows, but quantum kinetic equations are not yet coupled to any general relativistic merger code.

What emerges from a decade of increasingly sophisticated simulations is a coherent physical picture with quantified uncertainties. Neutrino emission peaks at a staggering 10^53 to 10^54 ergs per second in the first milliseconds, then settles to 10^52 to 10^53 ergs per second sustained for hundreds of milliseconds, with a long-lived neutron star remnant outshining its accretion disk. Hot ejecta from the collision interface and the disk corona are whipped up to electron fractions of 0.2 to 0.4 by neutrino absorption, while cold tidal tails remain neutron-rich below 0.05, guaranteeing a two-component kilonova, one red and infrared, one blue and optical, exactly the pattern observed after GW170817. Moment schemes, despite their known flaws, remain the best available source of quantitative predictions, while Monte Carlo methods are rapidly maturing into the field’s benchmark and error-estimation tool. The next frontier, the review concludes, lies in energy-dependent transport, better treatments of pair processes and inelastic scattering, and ultimately a self-consistent marriage of radiation transport with the magnetic turbulence that may launch the universe’s brightest explosions.

Subject of Research: Neutrino transport methods in general relativistic simulations of neutron star mergers

Article Title: Neutrino transport in general relativistic neutron star merger simulations

Article References: Neutrino transport in general relativistic neutron star merger simulations. (n.d.). https://doi.org/10.1007/s41115-023-00016-y

Image Credits: AI Generated

DOI: 10.1007/s41115-023-00016-y

Keywords: neutrino transport, neutron star mergers, general relativity, kilonova, r-process nucleosynthesis, gravitational waves, numerical simulations, Monte Carlo radiation transport, moment schemes, leakage schemes, accretion disks, gamma-ray bursts

A major review published in Living Reviews in Computational Astrophysics by Maarit J. Korpi-Lagg, Mordecai-Mark Mac Low, and Frederick A. Gent surveys the state of the art in numerical modeling of galactic dynamos, the mechanisms by which conducting, turbulent plasma amplifies magnetic fields. The authors conclude that the evidence now strongly supports a two-stage picture. Small-scale dynamos, which amplify magnetic fluctuations on turbulent eddy timescales, operate during the formation of the first galaxies, growing fields faster than the galaxies accrete gas. Large-scale dynamos, driven by the differential rotation of galactic disks, subsequently build the organized fields observed in low-redshift spiral galaxies, bringing the field to equipartition with turbulence and giving it substantial power at the largest scales.

The theoretical framework rests on magnetohydrodynamics, which treats the magnetized interstellar plasma as a conducting continuum. The evolution of the magnetic field is governed by the induction equation, coupling the field to the velocity field, while the dynamics of the gas follow the Navier-Stokes equation including rotation, gravity, pressure, and Lorentz forces. In the interstellar medium, turbulence is driven by multiple sources: supernova explosions and their clustering into superbubbles, stellar winds and ionizing radiation, gravitational instability of the gas disk, and, especially in young galaxies, the turbulent accretion of fresh gas. Ionizing radiation delivers roughly an order of magnitude more energy than supernovae, though supernovae contribute more momentum, and the resulting multiphase structure of the gas strongly affects how dynamos operate.

Dynamo theory traditionally separates the magnetic field into mean and fluctuating components. In the kinematic regime, when the field is still too weak to influence the flow, turbulent motions correlated through rotation and shear generate a turbulent electromotive force whose leading terms are the alpha effect, describing the inductive twisting of field lines by helical turbulence, and turbulent diffusion. A long-standing concern was that this alpha effect could suffer catastrophic quenching as magnetic helicity conservation suppresses the inductive action. Modern mean-field models address this by including helicity fluxes, such as those carried by galactic fountains and winds, which allow the large-scale dynamo to saturate at the observed equipartition strengths rather than stalling far below them.

The small-scale, or fluctuation, dynamo operates differently. The interstellar medium is an extremely high magnetic Prandtl number fluid, with the ratio of magnetic to fluid resistivity estimated near ten billion billion, meaning magnetic fields dissipate on far smaller scales than the gas motions. Any flow whose magnetic Reynolds number exceeds a critical value of roughly 30 to 60 can exponentially amplify magnetic fluctuations on the turbulent eddy turnover time, far faster than the large-scale dynamo can act. Analytical theory predicts a characteristic Kazantsev spectrum for the growing field, and simulations confirm both this spectral shape and the rapid shift of magnetic power to larger scales as the dynamo saturates.

Perhaps the most striking recent result concerns seed fields. Kinetic simulations using hybrid and fully kinetic plasma models show that the Weibel instability can magnetize an initially unmagnetized turbulent plasma, and that small-scale dynamo action then amplifies those tiny fields to within a few percent of equipartition while increasing their characteristic length scale toward the turbulent driving scale. This process could operate behind the accretion shocks that form during the assembly of gas into galaxies, implying that galaxies may be born magnetized. The review’s authors argue that simulations of galaxy formation should therefore not begin with infinitesimal seed fields, since such a choice unphysically delays the onset of magnetohydrodynamic effects in galactic evolution.

At the kiloparsec scale, direct numerical experiments of supernova-driven, multiphase, rotating disks have now captured both dynamo modes simultaneously. In models resolved to parsec scales, the small-scale dynamo saturates within a few hundred megayears at only a few percent of equipartition with turbulence, a result that on its own conflicts with the observed strength of turbulent galactic fields. However, when differential rotation is included, the large-scale dynamo continues to grow after the small-scale dynamo saturates, and during the transition the turbulent field grows far more strongly than mean-field theory alone would predict, apparently through tangling of the emerging mean field. This tangling may explain why turbulent fields dominate the observed magnetic energy in real galaxies.

Global simulations of entire galaxies, including cosmological zoom-in models run with codes such as AREPO and RAMSES, reproduce the exponential growth of magnetic energy expected from the small-scale dynamo, confirmed both by growth rates and by the appearance of Kazantsev-like power spectra. In Milky Way-mass halos, magnetic pressure eventually rivals or exceeds the thermal pressure, while dwarf galaxies with shallower potentials saturate an order of magnitude lower. Synthetic observations of these models, including Faraday rotation maps and polarized synchrotron emission, now reproduce key features of the observed sky, particularly when sub-grid models of ionized regions around star clusters are included. The far-infrared radio correlation can likewise be reproduced by fully saturated small-scale dynamos coupled to cosmic ray transport.

Significant challenges remain. Numerical models still operate at magnetic Reynolds and Prandtl numbers many orders of magnitude below interstellar values, and only recently have local models reached resolutions where the small-scale dynamo growth rates converge. Measuring the turbulent transport coefficients that parameterize the large-scale dynamo, through methods such as the test-field technique, singular value decomposition, and the newer iterative removal of sources, reveals broadly consistent results but persistent discrepancies in turbulent resistivity. Whether helicity fluxes truly prevent catastrophic quenching awaits direct measurement in high Reynolds number experiments. Meanwhile, reconciling the ratio of mean to turbulent field strength between local models, which produce mean fields that are too strong, and global models, which often produce them too weak, will demand improved simulations, simulated observations, and ultimately global models that capture both dynamo modes in dwarf galaxies before scaling up to disks like our own.

Subject of Research: Numerical modeling of small-scale and large-scale dynamo mechanisms that generate and amplify magnetic fields in galaxies

Article Title: Computational approaches to modeling dynamos in galaxies

Article References: Korpi-Lagg, M. J., Mac Low, M.-M., & Gent, F. A. (2024). Computational approaches to modeling dynamos in galaxies. Living Reviews in Computational Astrophysics, 10(1), Article 3. https://doi.org/10.1007/s41115-024-00021-9

Image Credits: AI Generated

DOI: 10.1007/s41115-024-00021-9

Keywords: galactic dynamos, magnetic fields, magnetohydrodynamics, small-scale dynamo, large-scale dynamo, interstellar turbulence, supernova feedback, cosmic rays, Faraday rotation, numerical simulations, galaxy formation, magnetic helicity

The review, led by Hubert Klahr, Hans Baehr, Julio David Melon Fuksman, and Thomas Pfeil of the Max Planck Institute for Solar System Research, arrives at a moment when observational facilities such as the Atacama Large Millimeter/submillimeter Array are delivering breathtaking images of planet-forming disks. Rings, gaps, spiral arms, and hints of long-lived vortices appear routinely in the data, but interpreting these structures requires models that faithfully capture the underlying physics. The authors argue that the first step is deceptively simple: before adding magnetic fields, self-gravity, or dust feedback, a simulation must correctly reproduce the pure hydrodynamic behavior of an irradiated disk, and that behavior is far from trivial.

Disks around young stars are born as byproducts of star formation, a buffer for the excess angular momentum of a collapsing molecular cloud core. During the first tens of thousands of years, the star acquires most of its mass through the disk, a phase in which the disk’s own self-gravity supplies the main torque driving accretion. But after this main accretion phase, enough material remains to build planetary systems over the following ten million years, until winds and photoevaporation strip the gas away. Crucially, for planet formation, an overly vigorous accretion flow toward the star can wash away nascent planets, so understanding when and how disks settle into a calmer state matters enormously.

One of the review’s central themes is baroclinicity, the misalignment between surfaces of constant pressure and constant density in a disk. Because stellar irradiation heats the disk surface while viscous dissipation may warm the midplane, disks develop both radial and vertical temperature gradients. In such a configuration, pressure and density contours are inclined with respect to one another, a situation familiar from Earth’s atmosphere and oceans that also drives instabilities in disks. These effects were long missed in simulations because they are too weak to emerge at low resolution or with overly dissipative numerical schemes. Only recently, with increased computing power, have researchers been able to confirm the analytic predictions of thermal baroclinic instability theory with full numerical experiments.

The review catalogues the family of instabilities that radiation hydrodynamic simulations must capture. The vertical shear instability, or VSI, arises from the vertical gradient of rotation in a baroclinic disk and operates best when thermal relaxation is extremely fast, since rapid cooling prevents the stable vertical stratification from suppressing the unstable shear. The convective overstability, in contrast, is strongest when the thermal relaxation time is comparable to the orbital period; it grows from epicyclic oscillations of gas parcels displaced radially in a weakly convective environment. The Goldreich-Schubert-Fricke instability, a close cousin of the VSI inherited from the theory of rotating stars, operates alongside the convective overstability, and recent work has shown that any disk unstable to one is unstable to the other, because both depend equally on the disk’s baroclinicity. A third mechanism, the subcritical baroclinic instability, generates long-lived anticyclonic vortices from radial entropy gradients combined with thermal relaxation, though it lacks a linear growth-rate prediction and is therefore harder to use as a code benchmark.

To help researchers validate their codes, the authors lay out a systematic testing procedure. The method begins by constructing an equilibrium disk model, typically through a so-called 1+1-dimensional calculation that solves for vertical hydrostatic balance at each radius while enforcing energy conservation with flux-limited diffusion and physically motivated dust opacities. This equilibrium serves both as an initial condition for multidimensional simulations and as the basis for perturbation theory: analytic linear analysis yields predicted growth rates for unstable modes. A code that reproduces those growth rates in the linear regime can then be trusted to explore the nonlinear turbulence that follows. If a code fails to match the predicted growth, the review notes, this signals insufficient resolution or a numerically dissipative scheme rather than a failure of the underlying theory.

The practical details of such tests are demanding. In local axisymmetric simulations carved from a global disk model, the authors demonstrate that resolving roughly 256 cells per pressure scale height is required to reproduce growth rates down to ten thousandths of the orbital frequency. Numerical schemes matter as well: high-order reconstruction methods combined with accurate Riemann solvers recover the predicted linear growth, whereas more diffusive approximate solvers can suppress instability entirely. Even the handling of cooling, implemented through thermal relaxation of the pressure toward an equilibrium value, must be treated carefully, with operator splitting modified so that relaxation times shorter than the dynamical step remain stable. These benchmarks, the authors argue, should become standard practice for any group embarking on radiation hydrodynamic disk simulations.

Beyond linear tests, the review surveys how different radiative transfer approximations shape the outcomes of full three-dimensional simulations. Flux-limited diffusion, the workhorse of earlier decades, is computationally efficient and accurate in optically thick regions but smears out shadows and introduces unphysical diffusion where radiation streams freely. The M1 two-moment method, implemented in codes such as PLUTO, preserves the direction of radiative fluxes and captures shadowing, but it artificially merges crossing beams of light, which can overestimate midplane temperatures by more than 40 percent in single-group calculations. A newer half-moment scheme reduces that error to a few percent, while discrete ordinates and Monte Carlo methods offer the greatest accuracy at the highest cost. The review’s message is that no single method suits every problem, and the choice must weigh computational expense against the physics one needs to capture, whether self-shadowing, scattering, or frequency-dependent heating.

The consequences of getting radiation transport right extend deep into planet formation theory. Global simulations of the vertical shear instability show that the resulting turbulence generates stresses whose strength depends on the square of the radial temperature gradient and on the local cooling time, challenging the classical assumption that turbulent viscosity scales simply with gas pressure. VSI turbulence can spawn long-lived anticyclonic vortices, provided simulations span the full azimuthal extent of the disk, and these storm systems are expected to act as efficient traps for pebbles and dust, potentially accelerating planetesimal formation. In irradiated disks, radiation hydrodynamical studies reveal that the instability can be localized to the surface layers when dust depletion lengthens midplane cooling times, producing a quiescent midplane beneath a vigorously turbulent atmosphere, a stratification with direct consequences for where dust can settle and planets can grow.

The review closes with a forward-looking agenda. The authors recommend abandoning fixed-temperature disk models in favor of self-consistent thermal evolution, since every realistic disk structure is subject to thermal baroclinic instabilities that fixed-temperature setups either suppress artificially or misrepresent. Future work must couple evolving dust populations to the opacity and cooling calculations, treat the separate temperatures of gas, dust, and radiation, and develop well-balanced or low-Mach-number schemes that resolve subsonic fluctuations in a supersonically rotating medium. The payoff is substantial: radiation hydrodynamic simulations calibrated against linear theory will underpin the interpretation of molecular line kinematics and scattered-light images from current and next-generation telescopes, transforming stunning pictures of planet-forming disks into quantitative tests of how worlds are born.

Subject of Research: Numerical radiation hydrodynamics methods and stability tests for simulating protoplanetary disks around young stars

Article Title: Numerical radiation hydrodynamics for circumstellar disks

Article References: Klahr, H., Baehr, H., Melon Fuksman, J. D., & Pfeil, T. (2026). Numerical radiation hydrodynamics for circumstellar disks. Living Reviews in Computational Astrophysics, 12(1), Article 3. https://doi.org/10.1007/s41115-026-00026-6

Image Credits: AI Generated

DOI: 10.1007/s41115-026-00026-6

Keywords: protoplanetary disks, radiation hydrodynamics, planet formation, vertical shear instability, convective overstability, baroclinic instability, radiative transfer, turbulence, numerical simulations, stellar irradiation, computational astrophysics, disk stability

Traditionally, ocean turbulence has been understood mainly in coastal zones or near the surface, where wind and currents create visible churn. However, this research delves into the abyssal reaches of the ocean, exploring turbulence at scales of meters or less, millions of times smaller than the ocean itself. By deploying advanced numerical simulations combined with in situ measurements, researchers were able to characterize how these minute motions affect mixing processes that regulate temperature, salinity, and chemical distributions.

The study highlights that small-scale turbulence isn’t just a local oceanographic curiosity but a dynamically significant contributor to large-scale ocean circulation patterns. These turbulent eddies, though ephemeral and spatially limited, facilitate vertical mixing that transports heat downward and nutrients upward, fostering biological activity even in the remote interior. Crucially, the work demonstrates that this mixing mediates how heat absorbed at the surface penetrates ocean depths, impacting the ocean’s capacity to store and release thermal energy over climate-relevant timescales.

Moreover, the coupling between small-scale turbulence and larger ocean currents appears to modulate the ocean-atmosphere feedback loops integral to climate variability. The enhanced mixing enabled by turbulence can alter the stratification of the water column, influencing how the ocean communicates heat back to the atmosphere. This dynamic informs not only short-term weather patterns but also long-term trends such as ocean warming and carbon cycling.

One of the key technical advances underpinning the study is the integration of high-resolution computational fluid dynamics models with observational data from autonomous underwater vehicles equipped with finely calibrated turbulence sensors. This synergy allowed the team to quantify turbulence intensity, vertical fluxes, and the resultant impact on tracer distributions with unprecedented detail.

Importantly, the findings urge a reconsideration of how climate models incorporate ocean mixing processes. Current global climate models often parameterize small-scale turbulence in simplified ways due to computational constraints, potentially underestimating the ocean’s role in modulating surface temperatures and carbon sequestration. By providing a mechanistic framework, this study offers pathways to refine these parameterizations, potentially improving climate predictions and policy-relevant projections.

The implications extend beyond theoretical climate science. Understanding the nuanced role of small-scale turbulence could inform the design of climate interventions or geoengineering efforts aimed at enhancing ocean carbon uptake. It also sharpens our picture of how climate change might feedback into ocean dynamics, possibly altering turbulence patterns themselves and creating complex feedbacks.

Ultimately, this pioneering research illuminates the hidden microcosm of ocean turbulence as a vital cog in the Earth’s climate machinery. As climate challenges intensify, unraveling such subtle yet powerful processes will be critical for forecasting and mitigating the impacts on our planet’s future.

Subject of Research: Oceanic small-scale turbulence and its influence on climate processes.

Article Title: Climatic reach of small-scale turbulence in the ocean interior.

Article References:
Cimoli, L., Mashayek, A., Naveira Garabato, A.C. et al. Nat Commun 17, 5212 (2026). https://doi.org/10.1038/s41467-026-73809-3

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41467-026-73809-3