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Physics Physics Atmospheric and Oceanic Physics

physics.ao-ph Watch Atmospheric and Oceanic Physics

Thursday 3 September

Atmospheric and Oceanic Physics

Cold Extremes during Dansgaard-Oeschger Oscillations

Ignacio del Amo and Peter Ditlevsen

This paper studies the statistics of extreme cold air surface temperatures in a climate that experiences abrupt changes. We employ a CCSM4 simulation of Last Glacial Maximum conditions that exhibits rapid switching between stadial and interstadial states as data. Non-stationary linear Generalized Extreme Value (GEV) distributions are fitted to find the regions that show the most prominent changes and associate them with physical processes. The results are then compared with a non-linear model, which gives a more detailed picture of how the parameters change as a function of the AMOC strength. While different regions of the world show different extremal behaviours, many regions show an approximately linear relationship between the parameters of the GEV distributions and the strength of the AMOC within the stadial and interstadial states, with some non-linear oscillation or jump where the transition between them takes place. Physical processes such as the expansion and retreat of the sea ice and the relative changes in the strength of the currents are shown to impact the magnitude and variability of the extremes, with significant changes observed in the three parameters of the GEV. They also create teleconnections that are compared whenever possible with various proxies for the temperature of the air and the water. We show how mapping the parameters of the GEV distributions into the AMOC strength gives a way to compare results between different climate models and different climate states. Comparisons, however, need to pay heed to the dynamical characteristics of the state and the location to be meaningful.
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Atmospheric and Oceanic Physics

Radiative and Dynamical Controls on the Land-Ocean Warming Contrast in Climate Models

Paolo Giani, Arlene M. Fiore, Raffaele Ferrari et al. (+3 more)

Surface air over land warms substantially more than over the ocean under greenhouse forcing, a phenomenon known as the land-ocean warming contrast. Current explanations for this contrast are commonly expressed either in terms of energetic constraints, from top-of-atmosphere and surface energy balance, or dynamical constraints, from large-scale atmospheric dynamics. We show that these perspectives are complementary when viewed through the lens of atmospheric moist static energy (MSE) transport, and that connecting them yields new insight into the controls of the warming contrast and the spread in climate models. We use this framework to construct an interpretable emulator that reproduces the land-ocean warming response across 22 models from the latest Coupled Model Intercomparison Project (CMIP6). We find that the strength of the land-ocean warming contrast emerges from the interplay between a model-dependent radiative baseline and a robust dynamical restoring mechanism that favors greater warming over land. This interplay leads to two broad model regimes that align with climate sensitivity. In low-climate-sensitivity models, more stabilizing radiative feedbacks over the ocean directly favor greater land warming. In high-climate-sensitivity models, radiative feedbacks alone would instead favor greater ocean warming, but a strong MSE-transport feedback (approximately 0.2 PW/K) more than compensates for this tendency. The intermodel spread in the land-ocean warming contrast is closely related to the ratio of radiative feedbacks over land and ocean, highlighting a broader connection between climate sensitivity and the land-ocean warming contrast.
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All authors Paolo Giani, Arlene M. Fiore, Raffaele Ferrari, Paul A. O'Gorman, Vincent T. Cooper, Noelle E. Selin

Atmospheric and Oceanic Physics

Added value of dynamical downscaling in sub-seasonal tropical cyclone forecast

Taehyung Kim, Eunji Kim, Haerin Park et al. (+2 more)

Improving the sub-seasonal forecast of tropical cyclones (TCs) remains a significant challenge for climate models. This study evaluated the characteristics of tropical cyclones (TCs) in sub-seasonal forecasts using the Global Seasonal Forecast System 6 (GloSea6), an operational seasonal-to-sub-seasonal forecasting model operated by the Korea Meteorological Administration (KMA) during June-September (JJAS) from 1993 to 2016 over the western North Pacific (WNP). GloSea6 was found to underestimate TC frequency, tracks, particularly in mid-latitudes, lifetime, and intensity across all months, with the most significant errors occurring in August. To address these deficiencies, we examined whether applying dynamical downscaling to GloSea6 during August 2016, a period characterized by the lowest TC forecast skill in GloSea6, could improve sub-seasonal TC forecasts. The application of dynamical downscaling demonstrated added value by directly improving the simulation of TCs in terms of frequency, structure, intensity, and lifetime, although slight overestimations were observed. Furthermore, improved reproductions of the Indian monsoon and circumglobal teleconnection (CGT), both of which strongly influence the western North Pacific subtropical high (WNPSH), contributed to more accurate forecasts of WNPSH variability and, consequently, better predictions of TC activity in the mid-latitudes and East Asia. Therefore, dynamical downscaling can significantly advance sub-seasonal TC forecasts by not only directly improving TC characteristics but also indirectly enhancing the environmental fields (e.g., WNPSH, Indian monsoon, and CGT) that are critical to TC activity.
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All authors Taehyung Kim, Eunji Kim, Haerin Park, Dong-Hyun Cha, Johan Lee

Wednesday 2 September

Atmospheric and Oceanic Physics

Adjoint-based Inversion for the Diffusion Coefficient in Marine Lake Models

Alex Ho, François Blanchette, Noémi Petra et al. (+1 more)

Marine lakes offer a unique opportunity to study how physical processes, such as turbulent mixing, biomixing, and tidal exchanges, regulate the vertical transport of heat, oxygen, and nutrients in relatively isolated ecosystems. The effective diffusion coefficient characterizes the overall mixing and is critical in models of marine lakes, but it is difficult to measure directly. This paper addresses the problem of inferring the depth-dependent diffusion coefficient from synthetic and real measurements of vertical temperature and salinity profiles. To do so, an inverse problem governed by a marine lake model described by the screened-Poisson equation (similar to Helmholtz) is formulated. The inverse problem is formulated as a nonlinear least-squares optimization problem, where the cost functional quantifies the misfit between the observed and recovered profiles. A Tikhonov regularization term is added to improve the conditioning of the discretized problem, with a regularization parameter chosen using the L-curve method. We solve this problem using an adjoint-based inexact Gauss-Newton method. To leverage the complementary information in these profiles, the formulation optimizes the fit across both datasets simultaneously. The accuracy of the reconstructed diffusion coefficient and the method's robustness to noise are investigated through comprehensive synthetic studies. Additionally, we contrast this framework with physics-informed neural networks, highlighting the advantages and limitations of each. When applied to real marine lake data, the adjoint method reconstructed a smaller diffusion coefficient in regions of strong density stratification, in line with physical expectations. The results indicate that the proposed approach provides a foundation for accurate modeling of marine lake dynamics.
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All authors Alex Ho, François Blanchette, Noémi Petra, Michael N Dawson

Atmospheric and Oceanic Physics

Improving global precipitation forecasts with an AI weather model trained on satellite observations

Julian F. Schmitt, Bertrand Delorme, Robert C. King et al. (+4 more)

Precipitation forecasts shape decision-making across the global economy, particularly in sectors such as agriculture. However, unlike variables such as temperature, precipitation is highly intermittent and localized, making it difficult to forecast. While recent advances in AI weather prediction systems have enabled them to surpass physical models on globally averaged metrics, improvements in mean error rarely translate to actionable forecasts of severe flooding or dry crop fields. Furthermore, most of these models are trained and evaluated against a reanalysis data product, ERA5, which has well-known biases. Here we retrain AIFS, ECMWF's widely-used, open-source operational 0.25{\deg} probabilistic graph-transformer weather model, on satellite-based precipitation observations. Our model, Laxmi, improves global probabilistic accuracy by 19% and resolves systematic distributional biases in ERA5. Specifically, Laxmi reduces drizzle overprediction by 33% for amounts less than 3 mm per day. It also mitigates extreme rainfall underprediction, improving the global 95th percentile Brier skill score by 57%. Across a case study of 10 Indian tropical storms, Laxmi delivered the most accurate forecast of 150 mm event-total precipitation in 7 events, compared to 1 for AIFS and 2 for the leading physical model, IFS. Our results demonstrate that incorporating observation-based precipitation data directly into training can substantially improve forecasts.
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All authors Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain

Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP

Evolutionary pathways toward survival of a thick CO2- or SO2-rich atmosphere on the lava world TOI-561 b

Emma Postolec, Tim Lichtenberg, Johanna K. Teske et al. (+10 more)

Rocky planets evolve through the exchange of volatiles between their interiors and atmospheres, an interplay still poorly constrained by observations. Remarkably, highly irradiated ultrashort-period (USP) exoplanets may offer a window into this exchange – some retain low bulk densities compatible with volatile-rich envelopes surrounding rocky interiors, indicating possible secondary atmospheres. TOI-561 b is a prime example, with a bulk density of $4.3\pm0.4$ g cm$^{-3}$ and recent JWST observations favoring a thick volatile atmosphere overlying a dayside magma ocean. Here, we investigate the evolutionary pathways allowing TOI-561 b to retain a substantial atmosphere over gigayears using the PROTEUS coupled interior–atmosphere framework. We explore different core radius fractions, Bond albedos, atmospheric escape efficiencies, mantle redox states, and initial C–H–O–N–S volatile inventories, under in situ evolution and late inward migration. Over half of our simulations leave a bare interior too dense to match observations. Successful cases favor a volatile-rich origin ($\lesssim200$ Earth oceans of hydrogen, S/H $\le10$, and N/H $\le1$), an oxidized mantle ($f$O$_2 \gtrsim \mathrm{IW}+4$), a small iron core ($\le 0.40$ for the core radius fraction), and low escape efficiency ($\epsilon \lesssim 10^{-3}$) in the hydrodynamic escape regime. At present, TOI-561 b is consistent with a global magma ocean beneath a thick (surface pressure $\approx 10^{3}$–$10^{4}$ bar), high mean molecular weight atmosphere ($38$–$60$ g mol$^{-1}$). Two archetypes emerge, differentiated by bulk sulfur content: a CO$_2$-dominated and an SO$_2$-dominated atmosphere. Migration is viable but not required to reproduce the observations. Our study illustrates how interior–atmosphere coupling governs atmospheric retention on irradiated rocky planets.
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All authors Emma Postolec, Tim Lichtenberg, Johanna K. Teske, Harrison Nicholls, Mara Attia, Anjali Piette, Lisa Dang, Nicole L. Wallack, Mykhaylo Plotnykov, Alex McGinty, Samuel Boucher, Bo Peng, Diana Valencia

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Distilling deep optical flow stereo methods to retrieve dense three-dimensional wind fields

Thomas J. Vandal, Dong L. Wu, James L. Carr et al. (+5 more)

Geostationary atmospheric motion vectors (AMVs) provide the dense horizontal wind vectors (u,v) and heights ingested into data assimilation systems. Traditional AMVs track features using window-based cross-correlation and estimate heights via infrared brightness temperatures paired with numerical weather prediction (NWP) background states, creating a circular dependency that yields inaccurate heights, high computational cost, and sparse retrievals. Stereo winds from GEO-GEO and GEO-LEO geometrically resolve heights from parallax shifts across different poses, eliminating NWP dependence and improving accuracy, but they remain computationally heavy with limited coverage. In this work, we replace window-based tracking in stereo matching with deep optical flow for efficient, improved retrieval. Fine-tuning balances a self-supervised geometric residual loss with supervised radiosonde reconstruction. To eliminate multi-satellite overlap requirements, we distill the stereo teacher into a single-satellite student model. Chi-square and height uncertainties from the teacher are emulated by the student for quality assurance. The student generates winds across full-disk GEO imagery globally. Validation compares stereo and student models against radiosondes, operational AMVs, ERA5 reanalysis, and EarthCARE cloud profiles. Results through triple collocation show that stereo winds improve performance beyond operational AMVs for water vapor bands (6.2, 6.9, and 7.3 {\mu}m), wit degradation in the long-wave infrared (11.2 {\mu}m) band.
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All authors Thomas J. Vandal, Dong L. Wu, James L. Carr, Derek J. Posselt, Elise Penn, Tristan Ballard, August Posch, Kate Duffy

Atmospheric and Oceanic Physics · Cross-listed from math-ph

Ruelle–Pollicott Theory for Metastable Systems: A Unified Framework for Tipping Transitions

Mickaël D. Chekroun and Valerio Lucarini

Tipping points—abrupt, potentially irreversible reorganizations of a system's statistical state—are commonly anticipated through critical slowing down: recovery slows, autocorrelation and variance rise, and spectra redden. This paradigm is powerful near simple equilibrium bifurcations but is not a general theory for stochastic, multistable, or metastable systems. We develop such a theory from the Ruelle–Pollicott (RP) spectrum of Kolmogorov generators of hypoelliptic Itô diffusions. Resolving the sensitivity of invariant statistics on RP spectral blocks shows that each contribution factorizes into a spectral denominator and a residue coupling the block to both the observable and perturbation direction. Small denominators permit large responses; residues produce them. Thus a closing RP gap is not sufficient for an early warning, while growing residues can generate the classical signature with no gap closure. An early-warning signal is therefore a property of a triple: RP block, observable, and perturbation direction. We demonstrate this on a stochastic non-normal system whose RP spectrum is exactly frozen, yet classical indicators become more alarming than during genuine gap closure. The RP decomposition attributes this to residue growth and yields an index $\mathcal{N}(f)$ satisfying $\mathcal{N}(f)\leq 1$ for reversible dynamics; hence $\mathcal{N}(f)>1$ certifies residue-driven amplification. For metastable systems, one killed problem yields two complementary spectral objects: the Doob $Q$-process isolates in-well recovery, while escape clocks and committor-weighted destination probabilities govern interwell transitions. In a one-dimensional fold, these scale as $(\epsilon_c-\epsilon)^{1/2}$ and $(\epsilon_c-\epsilon)^{3/2}$, separating bifurcation-induced from noise-induced tipping. Doob drift also connects to optimal Girsanov sampling.
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Atmospheric and Oceanic Physics

Did recent sea surface temperature warming reinforce the extreme East Asian summer monsoon precipitation in 2020?

Taeho Mun, Haerin Park, Dong-Hyun Cha et al. (+3 more)

We analyzed the possible effects of recent sea surface temperature (SST) warming on the extraordinary East Asian summer monsoon (EASM) precipitation in 2020 summer. The dynamic and thermodynamic impacts of SST are examined by conducting regional climate model experiments with observed SST and cold SST where the 22-year SST trend is removed. In the presence of warm SST, precipitation increases in low latitudes but decreases in the EASM region. This dipolar precipitation change pattern opposes the precipitation anomalies in 2020 summer, indicating that the extraordinary 2020 EASM precipitation is not likely driven by recent SST warming. The warm SST suppresses the western North Pacific subtropical high expansion and weakens the southwesterly from the South China Sea toward the EASM region. In terms of large-scale atmospheric circulations, SST-induced wind changes strengthen the local Walker circulation in the South China Sea and the Philippines and the local Hadley circulation across the EASM region. These support the reduced EASM rainfall in the control experiment compared to the cold SST experiment and imply that the precipitation reduction by dynamical effects could exceed the precipitation increase by thermodynamic effects in the EASM region under warm SST.
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All authors Taeho Mun, Haerin Park, Dong-Hyun Cha, Chang-Keun Song, Seung-Ki Min, Seok-Woo Son

Atmospheric and Oceanic Physics

Kilometer-Scale AI Downscaling of Atlantic Hurricanes with Generative Ensembles

Yingkai Sha, Talea L. Mayo, Ethan D. Gutmann et al. (+2 more)

This study presents an AI-based dynamical downscaling system for Tropical Cyclones (TCs). The system incorporates an AI-based limited-area model that downscales 3-hourly low-resolution boundary forcings into hourly high-resolution fields autoregressively, and a diffusion model that converts the outputs into ensembles of hazard-relevant variables. The system is trained on the regridded CONUS404 data with ERA5 forcings, and is evaluated on 20 TCs in 2020–2024. Verification shows stable downscaling performance across Atlantic hurricane seasons, with energy spectra closely matching the CONUS404 reference. The system is also verified to produce skillful TC-relevant weather extremes, largely improved over a deterministic AI baseline. The system performs well with forcing data from other models (GDAS/FNL) and can produce detailed eyewall, rainband, and landfall structures in TC case studies. The study provides a good example of how AI-based dynamical downscaling systems can be designed to resolve small-scale extreme weather events.
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All authors Yingkai Sha, Talea L. Mayo, Ethan D. Gutmann, Lulin Xue, Andrew Newman

Tuesday 1 September

Atmospheric and Oceanic Physics

Baseline Climatology of Tornadoes and Waterspouts in the Philippine Archipelago

Generich H. Capuli

Tornadoes are documented on every inhabited continent, yet the environmental conditions used to anticipate them were developed and validated overwhelmingly on mid-latitude records, leaving tropical regimes largely unexamined. Here, we collected 1,186 combined tornado and waterspout reports in the Philippines spanning the early 1900s to 2025 and paired each with an ERA5 proximity sounding to construct the first baseline climatology and environmental characterization of these events in the archipelago. Activity concentrates in three corridors, mostly flanked by mountain ranges, one in each major island group, and is sharply phased within a May-October window that closely tracks the annual cycle of thunderstorm frequency, peaking in August and lagging into October for waterspouts alongside the tropical cyclone season. These events cluster in the mid-to-late afternoon, consistent with a daytime heating regime rather than nocturnal convection. The environments are thermodynamically permissive but kinematically shallow, with convective available potential energy (CAPE) exceeding typical Great Plains values, while the 0-6 km bulk wind difference (BWD; DLS) remains weak at $\sim$10-14 kt, departing from the mid-latitude covariance in which instability and shear increase together. Although streamwise vorticity is modest in magnitude, nearly all discriminating variance resides in the lowest kilometer, where 68-79% of near-surface horizontal vorticity is streamwise, reaching $>$0.005 s$^{-1}$ and 96% streamwiseness in the most organized profiles, and the hodographs retain low-level curvature. Consequently, composite indices remain low even as tornadoes and waterspouts recur. Mid-latitude-calibrated parameters can therefore misdiagnose tropical-maritime regimes, and recalibration toward shallow-layer, orientation-aware kinematic discriminators may recover the diagnostic signal.
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Atmospheric and Oceanic Physics

A Sensor-Adaptive Incremental Learning Framework for Artifact Detection in Satellite Precipitation Data

Andres F. Monsalve, Hernan A. Moreno and Christian D. Kummerow

Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of cheaper, more compact satellites (SmallSats) capable of detecting rainfall proxies has led to a significant increase in private-sector initiatives for satellite launch and surface precipitation products. This rapid growth has yet to be matched by data validation efforts. Consequently, the need for a robust tool to detect anomalies in near-real-time data before it is disseminated to the public has become critical. In this paper, we present the development of an anomaly-detection system to identify artifacts in global satellite-based rainfall products. The developed framework leverages pre-trained computer vision models and incorporates scarce human-labeled data to detect specific anomalies. Our proposed anomaly detection strategy is tested on data from the Special Sensor Microwave Imager (SSMI) and the Special Sensor Microwave Imager/Sounder (SSMIS). Results demonstrate the efficacy of our approach at separating regular orbits from artifact-containing orbits for each satellite, with performance comparable to state-of-the-art in-place methods. Additionally, the framework offers explainability and the capacity for iterative refinement following false-positive or false-negative classifications.
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Atmospheric and Oceanic Physics

A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling

Filippo Dainelli, Amirpasha Mozaffari, Marina Castaño et al. (+5 more)

As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth pausing to consider what our design decisions imply for the science and for the computational resources we consume. A growing body of literature addresses the ethical and sustainable development of ML/AI, yet translating these principles into day-to-day research practice remains a challenge as most of best practices are dispersed across multiple studies and commentaries. Here, we distill these discussions into a practical checklist that ML/AI and Earth system science practitioners can use to assess and reduce the environmental footprint of their own applications, organised around the successive stages of the model development pipeline. We complement the checklist with a selection of metrics drawn from the literature for estimating the energy consumption and carbon footprint of a project. For each question, we point to concrete examples and actionable suggestions from recent literature, aiming to bridge the gap between aspirational principles and the decisions researchers face at every stage of the development cycle.
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All authors Filippo Dainelli, Amirpasha Mozaffari, Marina Castaño, Aina Gaya i Àvila, Lluís Palma Garcia, Alessio Melli, Oscar Dimdore Miles, Amanda Duarte

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting

Mohammad Kian Golkar, Luciano Alves de Oliveira and Mohammad Khanjani

High-resolution precipitation nowcasting is critical for reducing the impacts of severe weather but remains difficult because of rapid storm evolution. Deep learning models have shown great promise for this task, but their predictive skill often deteriorates over longer forecast horizons. This leads to increasingly blurry forecasts that fail to capture the complex, non-linear evolution of storm systems. In order to address these limitations, we introduce Spatio-Temporal U-DeepONet (GenONet), a novel architecture for long-range precipitation forecasting up to 3 hours, specifically designed to produce sharp and physically consistent results. GenONet's architecture pioneers the use of a Deep Operator Network (DeepONet) as a generator within a Generative Adversarial Network (GAN) framework for this task. The DeepONet learns the continuous-time dynamics of precipitation, ensuring stability over long forecast horizons. Adversial training against a spatio-temporal discriminator compels the model to produce sharp, coherent forecasts, while a physics-informed loss regularizer, derived from the Moisture Conservation Equation, improves physical plausibility in our ablation setting. Quantitative evaluations show that our model achieves consistently higher scores on most of the metrics, especially for highintensity events and at longer lead times. Qualitatively, GenONet produces structurally coherent forecasts that maintain their integrity, whereas baseline models degrade into indistinct patterns. Finally, an ablation study confirms the benefit of this physics-informed loss, highlighting the strength of combining operator learning with adversarial training.
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Monday 31 August

Atmospheric and Oceanic Physics

Reconciling the Lack of a Robust Anvil Cloud Amount Response to Warming

Zachary McGraw, Blaž Gasparini, Wouter Mol et al. (+1 more)

The stability-iris hypothesis proposes that tropical anvil cloud amount decreases with warming as anvils rise into environments of greater static stability. Here we show that there is no clear theoretical or simulated link between increasing static stability and anvil amount. We demonstrate analytically that identified sensitivities of static stability and pressure velocity to surface warming arise from decreasing air density aloft, which enters pressure-coordinate formulations but does not represent a process that dynamically constrains anvil formation. An ensemble of RCEMIP simulations verifies that the stability increase is robust yet arises from the air density reduction, and hence is not accompanied by a robust response in anvil amount, convectively driven water convergence into anvil levels, or the efficiency with which this convergence produces cloudiness. Overall, these findings dispel the expectation of a systematic reduction in anvil amount with warming arising from basic physical arguments.
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All authors Zachary McGraw, Blaž Gasparini, Wouter Mol, Aiko Voigt

Atmospheric and Oceanic Physics

How well is surface ocean carbon represented in observations and ocean models?

Viviana Acquaviva, Romina Wild, Alessandro Laio et al. (+3 more)

We introduce a general framework for quantifying the information content and representation quality of complex geophysical datasets based on the intrinsic dimension and differentiable information imbalance of data manifolds. We use it to derive and compare optimal representations of surface ocean carbon in the SOCAT database of observations and in global ocean biogeochemistry models (GOBMs) and to assess the robustness of the information we can extract from existing data. We find that within the most widely used feature set, the complexity of the data space of SOCAT observations is not fully captured by GOBMs, but the ranking and relative importance of variables learned through GOBMs are substantially correct. We observe that the learned representation of ocean carbon is less accurate in some regions, including the Southern Ocean, but doesn't appear to have evolved significantly over the last two decades. Finally, we show how the optimal representations can be used to improve the skill of distance-based machine learning models and demonstrate it for ocean carbon, and we propose two new metrics to compare models and observations that can be used to build more accurate weighted ensembles of estimates.
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All authors Viviana Acquaviva, Romina Wild, Alessandro Laio, Amanda R. Fay, Thea H. Heimdal, Galen A. McKinley

Atmospheric and Oceanic Physics

Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions

Rodrigo Almeida, Noelia Otero, Jost Arndt et al. (+3 more)

End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeline, including data assimilation, at a fraction of its cost. These systems are deterministic and issue no uncertainty. Here we render the Aardvark Weather model probabilistic by attaching one stochastic mechanism to each component: learned, input-dependent noise at the observation encoder, capturing aleatoric uncertainty inherited from the observing system, and Monte Carlo dropout in the processor, capturing epistemic uncertainty in the learned dynamics. The resulting nested ensemble attributes forecast spread to the two sources through a law-of-total-variance decomposition, cross-checked by withholding observation streams. Probabilistic finetuning significantly improves the mean forecast, by 4.2% on average across variables and lead times. The ensemble is calibrated against ERA5 through the medium range (spread-skill ratio 0.98), keeps station RMSE within 2.4% of the deterministic model while beating it in CRPS at every lead time, and trails the operational ECMWF ensemble. The encoder branch behaves as observation-driven uncertainty. Component-attributed uncertainty makes end-to-end forecasts more transparent, a step toward observation-driven digital twins of the atmosphere.
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All authors Rodrigo Almeida, Noelia Otero, Jost Arndt, Simon Baur, Wojciech Samek, Jackie Ma

Atmospheric and Oceanic Physics

Universal ordinary differential equations and the parameterization of warm-rain processes

Axel Seifert

The parameterization of warm-rain formation is a long-standing problem in cloud microphysics because the evolution of bulk cloud variables cannot be derived uniquely from the kinetic collection equation (KCE). Universal ordinary differential equations (UODEs)provide a scientific machine learning framework that combines neural networks with ordinary differential equations and can learn dynamical operators directly from time series. Here, the UODE framework is applied to warm-rain formation by training on trajectories generated with a super-droplet model that approximates the KCE. In contrast to most previous machine-learning approaches, only the prognostic state variables are used for training, without requiring autoconversion or accretion rates as targets. The learned closure accurately reproduces the KCE trajectories within the training domain and, although not explicitly constrained to do so, yields autoconversion and accretion rates that closely resemble those diagnosed from the KCE. Analyzing the learned operator provides new insight into the structure of the widely used Seifert and Beheng (2001) warm-rain parameterization. In particular, it suggests that the empirical suppression of accretion primarily compensates for an overestimation of autoconversion at small rain fractions. Motivated by these findings, a refined analytical formulation is proposed that improves agreement with the KCE while retaining the simplicity of the original parameterization. The results demonstrate that UODEs can serve not only as accurate surrogate models but also as a tool for understanding and improving analytical parameterizations of cloud microphysical processes.
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Atmospheric and Oceanic Physics

Ice-thickness based scaling of wave attenuation in sea ice: Application and assessment of wave spectra

W. Erick Rogers, Jie Yu, Jean Rabault et al. (+2 more)

This study discusses recent advances in modeling waves in sea ice in the U.S. Navy's regional modeling system. It is applied in the marginal seas of the eastern Arctic Ocean, including the Barents Sea, Kara Sea, parts of the Greenland Sea, Norwegian Sea, and waters north of Svalbard. The focus is to assess the skills of two formulations of wave attenuation by sea ice used operationally in WAVEWATCH III. Both are derived from large field datasets, one from the Arctic and the other from the Antarctic. The new model (IC4M9) describes wave attenuation depending on the ice thickness in association with the dependence on wave frequency, while the earlier default scheme (IC4M6) omits the dependence on ice thickness. The modeling results are evaluated against the satellite wave observations from SWIM/CFOSAT and the buoy measurements from the Svalbard Marginal Ice Zone 2024 Campaign (SvalMIZ-24). The comparisons with SWIM data validate the wave model skill in regions of open water or with light ice coverage. When evaluated against the SvalMIZ-24 data, the statistical performance of IC4M9 is substantially better than that of IC4M6, showing the influence of ice thickness on waves in the MIZ. Moreover, diagnosing systematic errors in the predictions by IC4M9, we find that the ice thickness field provided by the sea ice model CICE to the wave model is biased high in the MIZ, thus penalizing the performance of IC4M9 while not affecting the model IC4M6, which depends on frequency only.
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All authors W. Erick Rogers, Jie Yu, Jean Rabault, Ana Carrasco, Malte Müller

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Diffusion-Based Refinement for Kilometer-Scale Probabilistic Precipitation Nowcasting

Dohyun Park, Changhoon Song, Tengyuan Chang et al. (+2 more)

Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty remains challenging. Here we introduce exPreCast-ENS, a conditional residual diffusion framework that transforms the deterministic 4 km radar nowcaster exPreCast into a 1 km probabilistic ensemble while correcting systematic forecast errors. Conditioning on both the forecast and preceding radar observations lets the ensemble-mean correct the baseline rather than perturb it, while members represent unresolved fine-scale variability. Over the Korean Peninsula, skill improves with ensemble size. In two high-impact events in 2023, a 30-member ensemble recovers 38-47% of heavy-rain pixels missed by exPreCast while retaining approximately 95% of its correct detections and alarming on under 1% of the pixels it correctly left clear. The method generates a 1-h forecast in 3.4 s on a single GPU and yields consistent improvements on the French regional MeteoNet radar dataset.
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All authors Dohyun Park, Changhoon Song, Tengyuan Chang, Yoo-Geun Ham, Youngjoon Hong

Sunday 30 August

Atmospheric and Oceanic Physics

Liutex Core Lines as a New Framework for Tropical Cyclone Vortex Analysis

Yifei Yu, Chenxi Ma and Chaoqun Liu

Liutex core lines are systematically applied to Hurricane Dorian to investigate the three-dimensional vortical organization of an intense tropical cyclone. Unlike conventional analysis based on wind speed or vorticity, Liutex core lines identify the local vortex axes and provide a compact geometrical representation of rotational structures. The results show that Dorian is not characterized by a single vortex core, but by a large population of vortices embedded within a coherent hurricane-scale circulation. Stronger and more vertically organized vortices are concentrated in the inner-core region, whereas numerous weaker, inclined, and curved vortical structures extend outward and collectively retain the spiral organization of the storm. The complete core line field therefore reveals a three-dimensional, multiscale vortical skeleton of the hurricane. These results suggest that a tropical cyclone can be viewed as an organized multiscale vortex system rather than a single monolithic vortex. Beyond visualization, Liutex core lines provide a potential framework for quantifying vortex geometry, spatial organization, and temporal evolution, offering a new approach for studying structural changes and vortex dynamics in tropical cyclones.
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Atmospheric and Oceanic Physics · Cross-listed from physics.flu-dyn

Splashing-regime transitions and secondary-droplet scaling in oblique drop impacts on a deep pool

Ying Xiong, Yang Zhang, Lingling Xie et al. (+1 more)

Oblique drop impact onto a deep liquid pool produces asymmetric crowns, directional jetting, and splashing transitions that cannot be characterized by the total impact inertia alone. We numerically investigate water drops impacting a quiescent deep pool over $41\leq We\leq1790$ and $10^\circ\leq\theta\leq90^\circ$. The simulations reproduce the principal features observed experimentally and identify five post-impact regimes in the $We$–$\theta$ plane: deposition, front splashing, side splashing, side-front splashing, and crown splashing. The deposition–front-splashing transition is described by the tangential-inertial parameter $K_s=We\cos\theta$, with $K_s^c\approx120$. This criterion follows from the competition between downstream crown-rim inertia and capillary retraction at the Taylor–Culick velocity. The transition from front to side-front splashing is instead governed primarily by normal impact inertia, with a critical normal Weber number $We_N^c\approx318$. Beyond these regime transitions, the secondary-droplet statistics reveal fragmentation behavior common to the different splashing regimes. The droplet-size distributions are positively skewed, and the median diameter follows $d_{s,\mathrm{med}}/D\sim We^{-3/5}$. Second-order velocity structure functions support a scale-dependent capillary–inertial description of rim and ligament breakup. Combined with mass conservation, this scaling gives $N_s\sim We^{9/5}$, providing a numerical explanation for the secondary-droplet-number scaling observed experimentally. Thus, directional impact inertia governs the macroscopic selection of splashing regimes, whereas the secondary-droplet populations across these regimes exhibit a common capillary–inertial fragmentation scaling.
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All authors Ying Xiong, Yang Zhang, Lingling Xie, Xiaolei Li

Atmospheric and Oceanic Physics

Python-Fortran Hybrid Programming to Fuse AI and Physical Models: Examples of AI-LDA in climate and weather models (Hf2pMDA_v1.0)

Xianrui Zhu, Zikuan Lin, Shaoqing Zhang et al. (+12 more)

AI provides an unprecedented opportunity for advancing physics numerical modeling including data assimilation, which is a highly efficient and critically-important tool for advancing our understanding on Earth system and its applications. At the same time, deep incorporation of AI and physical modeling can make great driving to advance AI by injecting it rich physics from long time physics-based modeling development. However, since such physics models are conventionally coded in Fortran and AI algorithms usually are conveniently designed in Python, difficulties exist to directly incorporate AI algorithms into physics models, vice versa. Here, based on the F2PY protocol, we have developed a procedure that implements an infrastructure which conveniently conducts Hf2pMDA to form a program entity so that AI algorithms and physical models can invoke mutually. As examples, within Hf2pMDA, a climate coupled data assimilation (CDA) system is naturally upgraded to a strongly CDA (SCDA) system, and a 1 km high-resolution weather DA system is conveniently implemented within a multi-layer downscaling model that has multiscale DA in different nesting layers. In the climate SCDA system, a coupled general circulation model (CGCM) and a multiscale filtering algorithm is integrated by a Python main controller (PMC) that calls Fortran CGCM components and Weakly-CDA modules as well as a data-trained SCDA algorithm by latent space autoencoder in Python. In the high-resolution weather DA system, the downscaled model consisting of traditional Fortran DA modules in all mother domains and Python AE DA algorithm in the central child domain is integrated by a PMC that organizes these components. With convenient realization of deep incorporation of any AI algorithm and physics model, the Hf2pMDA has a great potential to make progress on both AI and scientific modeling.
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All authors Xianrui Zhu, Zikuan Lin, Shaoqing Zhang, Zebin Lu, Songhua Wu, Xiangyun Hou, Zhisheng Xiao, Zhicheng Ren, Jiangyu Li, Jing Xu, Yang Gao, Rixu Hao, Xiaolin Yu, Mingkui Li, Guangliang Liu

Saturday 29 August

Atmospheric and Oceanic Physics

Is the Atmosphere Really Such a Good Blanket?

Slavoljub Mijovic

The incorrect application of the current methodology for treating and averaging Earth's surface temperatures has contributed to contradictions and misinterpretations in different scientific disciplines, particularly in climate change and global warming research. The methodology previously proposed by the author addresses these shortcomings and provides a physically based framework for further analysis. This paper demonstrates that the contribution attributed to the atmosphere in determining the warming of the Earth's surface may be substantially overestimated, with the estimated effect differing by approximately 10 degrees Celsius from the conventional interpretation. The analysis is based on the ERA5 reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), using hourly skin temperature data for 1950-2025 on a global grid of approximately 1.04 million cells representing the Earth's surface. The proposed method enables an absolute assessment of global warming, confirms the long-term warming trend, and provides a comparative analysis with results obtained using the conventional methodology.
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Friday 28 August

Atmospheric and Oceanic Physics · Cross-listed from astro-ph.HE

A Terrestrial Gamma-ray Flash with an Asymmetric Footprint Observed at the Pierre Auger Observatory

The Pierre Auger Collaboration, John Ortberg, David M. Smith et al. (+1 more)

The Pierre Auger Observatory, a $3000km^2$ detector array that sits 1400m above sea level in Argentina, has the ability to map the entire footprint of Terrestrial Gamma-ray Flashes (TGFs), which are short, intense bursts of gamma rays associated with lightning. This is in contrast to the vast majority of TGF detections that only occur in a single detector in space. In this paper we leverage this capability to perform two important analyses on an event from 2007, chosen for its large footprint and high signal quality. First, we triangulate the source of the TGF to $2.3\pm0.5$km height above ground level using the arrival time of the gamma rays themselves at the detector array, independently of any information regarding the lightning channel. With the source position established, we can analyze the spatial distribution of the TGF on the ground with respect to that source. Not only do we find that one side of the TGF footprint shows an order of magnitude higher flux than the other, but we also find an asymmetric azimuthal structure consisting of two clear peaks in intensity. These peaks are offset by about $120^o$ from each other. This azimuthal structure is not possible under the traditional uniform electric-field model of a TGF. Using Geant4 simulations of collimated beams of the relativistic runaway electron-avalanche spectrum, we attempt to reconstruct the source geometry of the TGF and find that it likely had significant components in the $\theta > 75^o$ range, where $\theta$ is the off-nadir angle.
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All authors The Pierre Auger Collaboration, John Ortberg, David M. Smith, Joseph Dwyer

Thursday 27 August

Atmospheric and Oceanic Physics

Application of the latent twins approach for clear sky retrieval from IASI observations

Michele Martinazzo, Cristina Sgattoni, Marco Menarini et al. (+3 more)

In recent years, data-driven approaches emerged as alternatives to traditional physics-based retrievals, taking advantage of machine learning techniques such as learnable pseudoinverse, random forests, or deep learning architectures. Classical data-driven models generalize poorly to out-of-sample regimes, as they optimize over finite datasets without incorporating underlying physical laws. This often requires large models and extensive data to achieve reliability. Physics-Informed Neural Networks address this by embedding physical constraints into the learning process, enabling improved extrapolation. However, they requires substantial computational cost due to the need to solve governing equations at each training step. In this work, we introduce a novel deep learning architecture, based on latent twin approach, that balances model complexity, dataset size, and training cost, while providing a quantitative measure of data quality. This architecture is applied to IASI spectra, with the goal to assess the robustness of this method for retrieving atmospheric profiles, including temperature, water vapor, ozone, surface emissivity, and surface temperature, in real-world clear-sky conditions. The algorithm is first applied on synthetic radiances derived from the NWP SAF database using the fast radiative transfer code sigma-IASI/F2N. After validating the architecture on synthetic data, the algorithm is applied to IASI Level 1C observations, along with their corresponding Level 2 products which serve as reference to evaluate the reconstruction accuracy of the autoencoder-based retrieval. The retrieval performances are discussed along with possible strategies to provide an error analysis for the reconstructed thermodynamical profiles.
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All authors Michele Martinazzo, Cristina Sgattoni, Marco Menarini, Chiara Zugarini, Tiziano Maestri, Luca Sgheri

Atmospheric and Oceanic Physics

A Moisture-Vorticity Theory for the Boreal Summer Quasi-Biweekly Oscillation

Shubhrangshu Biswas, Jai Sukhatme and Bishakhdatta Gayen

We develop a moisture vorticity theory for the boreal summer quasi-biweekly oscillation (QBWO) in the tropics and analyze it with the aid of GPT-5.5. The model is formulated on a weak-temperature-gradient slow manifold, in which moisture anomalies diagnose the divergent circulation while the rotational circulation evolves through Rossby wave dynamics and vortex stretching. The key coupling is controlled by eddy advection of the background moisture gradient, with the projection $(\mathbf{k}\cdot\nabla\bar q)$ modifying propagation and the cross-gradient factor $(\mathbf{k}\times\nabla\bar q)_z$ controlling moisture extraction and growth. In the weak-coupling regime, which is relevant for the real-world situation, the QBWO is a Doppler-shifted Rossby mode whose growth or decay is determined by phase-coherent moisture extraction and subsequent vortex stretching; moisture damping weakens this feedback but does not introduce a sharp threshold in the minimal linear model. In terms of subtropical geographical locations, this renders the QBWO unstable over the Bay of Bengal with an intraseasonal growth rate and co-located vorticity and moisture anomalies, and it decays over relatively drier regions such as Central and West Africa with a quadrature relation between vorticity and moisture anomalies. The equations also admit a strong-coupling regime where the system approaches a moist-vortex limit. The theory interprets the QBWO as a Rossby-moisture-vorticity instability whose regional behavior depends on background moisture-gradient geometry, coupling strength, damping, and mean-flow advection.
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Atmospheric and Oceanic Physics · Cross-listed from math-ph

Coupled stochastic variational principles for multiscale surface gravity waves – Part I: theoretical framework

Etienne Mémin and Arnaud Debussche

This paper develops a comprehensive stochastic variational framework for surface gravity waves. Starting from Luke's variational principle for irrotational, incompressible free-surface flow, we introduce a decomposition of the velocity potential into a large-scale deterministic component and a regularized stochastic noise term representing unresolved scales. A path-wise variational principle yields the stochastic counterparts of the Laplace, Bernoulli, and kinematic boundary conditions. To close the system, a second variational principle in expectation is formulated, providing evolution equations for the noise correlation functions. The resulting coupled system preserves the Hamiltonian structure of the Zakharov–Craig–Sulem formulation. We further analyze explicit solutions via WKB approximation and show that the noise correlation functions satisfy a Hamilton-Jacobi equation with ray-tracing dynamics. This framework provides a rigorous foundation for reduced stochastic models of ocean waves and for the kinetic theory developed in the companion paper.
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Atmospheric and Oceanic Physics

Bridging short- and medium-range weather forecasting with machine learning

Timothy A. Smith, Mariah Pope, Sergey Frolov et al. (+4 more)

The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separation is practical, we argue that combining short- and medium-range weather into a single prediction system would provide the public with a useful distillation of global weather and its impacts. To this end, we present Nested-EAGLE (Experimental Artificial intelligence Global and Limited-area Ensemble): a 0.25{\deg} global weather model with a 6 km refinement over the Contiguous United States (CONUS). The model achieves significantly lower mean-squared error in near-surface and low-level quantities over CONUS compared to NOAA's Global Forecast System and High-Resolution Rapid Refresh (HRRR), while remaining competitive throughout the rest of the global atmosphere. We show that the skill gains for near-surface fields stem from incorporating high-resolution regional analysis data into training through the nesting process. Forecasts of precipitation amounts are less skillful than those from HRRR, owing to deterministic training. However, we show that Nested-EAGLE provides the most accurate forecasts of storm locations at longer leads, despite blurred extrema. Our results motivate future work to extend the skill gains beyond CONUS and improve precipitation representation.
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All authors Timothy A. Smith, Mariah Pope, Sergey Frolov, Brett Basarab, Daniel Abdi, Paul Madden, Isidora Jankov

Atmospheric and Oceanic Physics

Detectability of Forced ENSO Changes under Global Warming: Insights from the Recharge Oscillator

Sooman Han, Jérôme Vialard, Alexey V. Fedorov et al. (+1 more)

We use perfect-model, large-ensemble nonlinear Recharge Oscillator (RO) simulations to quantify ENSO internal variability and the detectability of forced changes in ENSO characteristics. Fixed-parameter simulations show that linear trends in ENSO standard deviation, period, and skewness as large as those observed since 1955 can arise from internal variability in 10% of simulations. RO parameters estimated from single realizations exhibit spurious drifts even without forcing, underscoring the need for ensembles. Using 100-member ensembles, comparable to the largest climate-model ensembles, we identify detectable parameter trends. For ENSO amplitude, the detectability thresholds for forced changes in underlying processes are 15% per century for stochastic forcing, 25% per century for basin adjustment, and 50% per century for the Bjerknes feedback. For ENSO period, the detectability threshold for forced changes in the underlying recharge–discharge processes and delayed oceanic feedback is 15% per century. These results provide a testbed for interpreting RO-diagnosed ENSO changes in climate-model ensembles.
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All authors Sooman Han, Jérôme Vialard, Alexey V. Fedorov, Soong-Ki Kim