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Physics Physics Atmospheric and Oceanic Physics

physics.ao-ph Watch Atmospheric and Oceanic Physics

Thursday 1 October

Atmospheric and Oceanic Physics

Dependence of the precipitation intensity distribution on spatial and temporal resolution in observations

Akshay Rajeev, Maxim A. Masleyev and Angeline G. Pendergrass

Precipitation intensity varies with both spatial and temporal resolution, with broad implications across climate science, yet the nature of this dependence is poorly characterized. We investigate how precipitation intensity distributions change with spatial and temporal resolutions and introduce a framework that approximates these changes between resolutions. Using merged satellite-gauge data regridded to various grid sizes, we construct precipitation amount distributions for spatial resolutions ranging from a base resolution of 0.1$^\circ$ for IMERG and 1$^\circ$ for the FROGS database to a coarse 10$^\circ$. Similarly, we use gauge observations coarsened to temporal resolutions ranging from 1-minute to 24-hours to construct precipitation amount distributions. This approach allows us to remove physical differences in order to isolate the effect of resolution on precipitation. Results show that the precipitation intensity distribution varies across spatial and temporal resolutions even without changes in physical processes. We demonstrate that these resolution-dependent changes in the precipitation amount distribution can be effectively characterized by shifting the distribution in logarithmic space without changing its shape, called the shift mode. Our findings emphasize the need to account for the effects of scale on precipitation when comparing and interpreting differences in gridded precipitation from models or observations.
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Atmospheric and Oceanic Physics

AI Emulation of Stochastic Sudden Stratospheric Warming with Interpretable Latent Structure

C. Daniel Boscu, Daniel Hernandez, Fabio Alvarez Ventura et al. (+4 more)

Rare weather regime transitions pose a challenge for data-driven modeling due to class imbalance. In this study, we develop a probabilistic deep learning emulator for a prototypical system with regime transitions, the stochastic Holton–Mass model of stratospheric variability, and analyze the structure of its learned latent space. The Holton–Mass model exhibits two metastable regimes, a strong and a weak polar vortex, maintained by nonlinear wave–mean flow interactions, with weak stochastic forcing intermittently triggering rare transitions between these regimes that qualitatively represent SSW events. We employ a ResNet-inspired Conditional Variational Autoencoder with six-layer encoder and decoder layers and explicit current-state conditioning to model the distribution of the system's state at the next time step (one day). The emulator accurately reproduces short-term dynamics, steady-state probability distributions, regime persistence statistics, rare transition rates, the transition committor function, and the transition expected lead time of the physical model. Beyond emulation fidelity, we interrogate the learned latent representation to understand how the model internalizes the underlying metastable structure of the dynamics. Principal Component Analysis of the 32-dimensional latent space reveals a clear and unsupervised separation into four physically interpretable clusters corresponding to strong versus weak vortex regimes and stable versus transition-prone configurations. Such emergent regime separation in latent space is hard to identify for deep generative models applied to high-dimensional stochastic systems. Our results show that carefully designed probabilistic emulators can uncover physically meaningful manifolds governing extreme-event dynamics, potentially aiding the development of improved operational advanced warning systems.
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All authors C. Daniel Boscu, Daniel Hernandez, Fabio Alvarez Ventura, Justin Finkel, Ashesh Chattopadhyay, Pedram Hassanzadeh, Dorian S. Abbot

Atmospheric and Oceanic Physics

Varda-single-1.0: deterministic data-driven weather forecasting at 1 km resolution over Switzerland's complex topography

Alberto Pennino, Francesco Zanetta, Michele Cattaneo et al. (+28 more)

We present Varda-single-1.0, a medium-range data-driven weather prediction system built for the Alpine domain. It provides hourly deterministic regional forecasts on a mesh of 1 km resolution and global forecasts on a 31 km mesh. The system comprises two independently trained stretched-grid Graph Transformer models with encoder-processor-decoder architecture, developed in the Anemoi framework: a 6-hourly autoregressive forecaster and a temporal downscaler reconstructing hourly forecasts between the forecaster's steps. Its training curriculum includes pre-training on ERA5 reanalysis data, followed by training on a 20-year kilometre-scale regional reanalysis, and finally fine-tuning on operational kilometre-scale analyses. Verified over one year against operational analyses and surface station observations, Varda-single is competitive with or improves on MeteoSwiss' operational numerical weather prediction baselines for most headline scores and variables. It broadly matches the skill of the high-resolution 1 km ICON-CH1-EPS control at lead times up to +33 h and generally outperforms the 2 km ICON-CH2-EPS control at lead times up to +120 h. Despite competitive aggregate scores, Varda-single underestimates some local wind maxima and produces overly smooth convective precipitation fields, consistent with the smoothing associated with squared-error training. To gain insight into the model's behaviour, we investigate three case studies beyond the aggregated headline scores, and find particular weaknesses in Varda-single's representation of local winds over complex terrain. Varda-single represents an important step in the development of high-resolution ML forecasting over complex terrain, in complementing the operational regional numerical weather prediction models of MeteoSwiss with data-driven models and in providing a pretrained model for researchers and user-specific applications.
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All authors Alberto Pennino, Francesco Zanetta, Michele Cattaneo, Claire Merker, Radi Radev, Jonas Bhend, Louis Frey, Hugues de Laroussilhe, Ophélia Miralles, Carlos Osuna, Daniele Nerini, Andreas Pauling, Daniel Hupp, Ulrich Hamann, Mary McGlohon, Marti Bosch, Luca Lanzilao, Marco Arpagaus, Lukas Jansing, Daniel Leuenberger, Mark A. Liniger, Katrin Ehlert, Matthew Chantry, Håvard Homleid Haugen, Gert Mertes, Ana Prieto Nemesio, Mario Santa Cruz, Jasper Wijnands, Gabriel Moldovan, Harrison Cook, Oliver Fuhrer

Atmospheric and Oceanic Physics · Cross-listed from math.DS

From trends to optimized forecasts: Quantifying the skill and advancing the utility of dynamics-based early warnings for tipping events

Franco Du Plessis, Victoria Volodina, Chris A. Boulton et al. (+5 more)

In this paper we explore and extend the state of the art for understanding the skill and utility of early warnings of critical transitions (tipping points) in forced nonlinear systems. We highlight some of the challenges and opportunities of transforming estimates of a dynamics-based early warning system, based on trends in stability or resilience into forecasts with high skill. We highlight the importance of (a) quantifying consequences of possible actions in response to warnings that may be false negatives or positives (b) considering finite time horizon predictions to give verifiable predictions (c) assumptions necessary for valid extrapolations of trends. We evaluate approaches to improving the skill and utility of the forecast.
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All authors Franco Du Plessis, Victoria Volodina, Chris A. Boulton, Muhammed Fadera, Sneha Kachhara, Timothy M. Lenton, Paul D. L. Ritchie, Peter Ashwin

Atmospheric and Oceanic Physics

Explaining El Niño Forecasts with the Average Gradient Outer Product

Yuan Hui, Dorian S. Abbot and Robert J. Webber

An important and unresolved problem in the physical sciences is explaining the predictions made by neural networks. Several explainable artificial intelligence (XAI) methods have been proposed to address this problem, including gradient XAI, Integrated Gradients, and GradientSHAP. We evaluate the baseline XAI methods according to four scores: sensitivity (XAI patterns strongly affect predictions), attribution (XAI patterns reproduce the change in prediction relative to a baseline), robustness (XAI patterns remain stable for nearby inputs), and coherence (XAI patterns are spatially smooth). We also introduce a new method, average gradient outer product (AGOP) XAI, that uses global gradient information to identify an important direction for a specific input. We apply XAI to neural network predictions of the El Niño-Southern Oscillation (ENSO) based on data from the Zebiak-Cane model. AGOP XAI achieves the highest attribution, robustness, and coherence scores in the architecture and lead-time comparisons reported here. Its sensitivity is surpassed by gradient XAI, which is maximally sensitive by definition. Beyond diagnosing neural-network behavior, AGOP XAI can generate candidate hypotheses about physical mechanisms. The method highlights an equatorial thermocline-depth signal consistent with recharge oscillator physics, together with a southeastern-Pacific lobe that may be specific to the Zebiak-Cane model. Finally, we test the physical relevance of AGOP using optimized perturbations that move the Zebiak-Cane model along AGOP explanation coordinates. Such perturbations can suppress the selected extreme events or, from a near-neutral ensemble, generate strong El Niño or La Niña events 10 months later.
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Atmospheric and Oceanic Physics

Recent increase in near-perpendicular tropical cyclone landfall fraction projected to emerge under high-emissions warming

Cong Gao and Ning Lin

Tropical cyclone landfall angle strongly modulates storm-surge potential, but how it changes with climate remains unknown. From 1980 to 2024, the global fraction of near-perpendicular landfalls shows a weak, statistically insignificant increase, with no significant basin-scale trends. PepC-Global simulations driven by reanalyses reproduce observed landfall-angle and pre-landfall approach-angle distributions and broad geographic variations in the near-perpendicular fraction. When driven by output from 14 CMIP6 models, the simulations show a similarly weak and statistically insignificant increase during 1980-2024, suggesting a forced contribution not yet detectable above internal variability. Under high emissions, however, the global near-perpendicular fraction increases significantly through 2099, with the strongest and most spatially coherent changes along the eastern North Pacific, whereas no significant global trend emerges under intermediate emissions. The projected increase arises mainly from changes in near-perpendicular fractions at individual coastal locations rather than from a redistribution of landfalls. These results identify landfall angle as an overlooked dimension of changing tropical cyclone coastal risk, one whose evolution this century depends on the emissions pathway.
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Wednesday 30 September

Atmospheric and Oceanic Physics

OceanMind: A multi-agent AI system for ocean diagnosis

Fan Zhang, Weicong Cheng, Yuheng Chen et al. (+6 more)

Time-dependent, three-dimensional (3D) oceanic multi-variables define coherent states of the evolving ocean to facilitate ocean diagnosis and advance ocean science to better inform environmental and hazard management. However, extracting quantitative evidence from these variables requires substantial and complex analytical effort. We introduce OceanMind, a multi-agent AI system that directly couples large language models (LLMs) with comprehensive time-dependent 3D ocean states for swift and effective diagnosis. OceanMind organizes the analytical process into four coordinated complexity stages: Query Routing, Skill-based Planning, Tool Execution, and Evidence-based Summary Generation. Specialized agents interpret user requests, construct and execute multi-step computational workflows, and synthesize quantitative evidence. To ensure reliable workflow construction, 63 reusable ocean-specific analysis skills serve as procedural manuals that guide the LLM agent in selecting data, conducting diagnostics, and applying analytical tools. With reflection and replanning mechanisms that use execution feedback to repair invalid plans, OceanMind ensures reliable analysis workflows across diverse needs. On a benchmark of 240 computational-workflow queries spanning the four stages, OceanMind outperformed general ReAct agents with the same registered tool pool, achieving relative improvements of 41.2% in effectiveness and 21.5% in efficiency. Beyond the benchmark, OceanMind reproduced published oceanic diagnostics, validated hypotheses, and supported environmental decision-making over global oceans. Overall, OceanMind advances LLMs by integrating them with time-dependent 3D ocean analysis, enabling scientific interpretation and enhancing formulation of environmental policies based on quantitative ocean evidence.
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All authors Fan Zhang, Weicong Cheng, Yuheng Chen, Hiuseut Kung, Ying Zhang, Aixi Han, Quanjia Zhong, Can Yang, Jianping Gan

Atmospheric and Oceanic Physics

Weather Jiu-Jitsu: Exploring the Feasibility of Control Paradigms in Weather Foundation Models

Prakriti Biswas, Kobi Abayomi and Upmanu Lall

Weather Jiu-Jitsu is a control paradigm for extreme climatological events, inspired by chaos theory. As a proposition, small, precise, targeted, and cost-inexpensive perturbations can redirect trajectories of a large dynamical system. This strategy has been demonstrated analytically in the Lorenz-63 system, where a naturally chaotic trajectory switching between two attractors can be confined to a single attractor, indefinitely, via arbitrarily small perturbations. This paper examines the feasibility of Microsoft's Aurora – a 1.3 billion parameter global atmospheric model – as a test bed for this strategy. This paper explores three questions: (1) Is Aurora a reliable enough simulation environment to serve as a meaningful testbed? (2) Are the perturbations required to redirect its trajectories small enough to be physically plausible? (3) Does Aurora's learned latent space (the parametric estimators on climatological attributes) yield any apparent, structured, and/or perhaps interpretable features that can convey a geo/atmospheric response to initial conditions? We find evidence consistent with all three: Aurora's modeled trajectories respond to perturbations beyond measurement drift, the perturbation magnitudes required are small relative to the model's own forecast uncertainty, and its latent representations exhibit directional structure that responds to Jiu-Jitsu-type interventions, even though that structure does not separate extreme from normal states outright. These results should be read as feasibility diagnostics rather than a demonstration of control: we do not implement or test an actual steering intervention on Aurora, and several of our findings, particularly around the model's latent-space geometry, are exploratory.
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Atmospheric and Oceanic Physics

Can we create a `race to the top' for weather forecasts to inform smallholder farmer decisions?

Colin Aitken, Michael K. Tippett, Pedram Hassanzadeh et al. (+9 more)

Artificial-intelligence weather prediction (AIWP) models have made it possible to produce high-quality tailored forecasts with limited computational resources. This advance has the potential to benefit hundreds of millions of farmers in low- and middle-income countries who lack access to forecasts of critical weather phenomena. However, it can be difficult for key stakeholders to evaluate forecast quality, risking a "race to the bottom" as cheap but low-quality forecasts crowd out forecasts that would benefit farmers. We propose a set of principles and protocols for evaluating agriculturally-relevant forecasts as a starting point for standards that would let forecasters credibly convey their forecasts' quality.
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All authors Colin Aitken, Michael K. Tippett, Pedram Hassanzadeh, Katherine Kowal, Rendani Mbuvha, John H. Marsham, Shruti Nath, Ousmane Ndiaye, Douglas J. Parker, Caroline M Wainwright, Michael Kremer, William R. Boos

Atmospheric and Oceanic Physics

Less is more: error-distance scaling relation for data-efficient kilometer-scale downscaling of extreme heat

Ahmed Marey, Henry Lu, Abhishek Gaur et al. (+6 more)

Extreme heat is where urban adaptation needs kilometer-scale data the most, but the simulations training a downscaler can cost more than they save, and how much is needed has not been identified. We measured it with CASPER, a U-Net with a structure-preserving loss downscaling 32 km reanalysis to 1 km temperature, humidity and wind, across 24 configurations of one to eight months. Held-out error grows linearly with climatological distance to the training data, RMSE = 0.83 + 2.95 d, explaining 90% of its variance against 7% for volume and predicting unseen months in advance. On held-out extreme summer weeks CASPER preserves the fine-scale structure and cross-variable physics that matched-budget baselines degrade, and matches station observations during documented heat waves to within 1.8 K. Transfer to a new region degrades geographically; 11 days of local simulation cuts Vancouver's held-out error from 3.8 to 1.3 K. Training periods should span the target climate: the same accuracy for four times less simulation, putting kilometer-scale downscaling of extreme heat within reach of groups without large computing facilities.
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All authors Ahmed Marey, Henry Lu, Abhishek Gaur, Sherif Goubran, Malek Aloui, Theodore Potsis, David Rolnick, Alex Hernandez-Garcia, Liangzhu Leon Wang

Atmospheric and Oceanic Physics

reGRAF: a global MPAS reforecast data set with convection-allowing refinements over the US and Europe

Thomas M Hamill, Akshay Subramaniam, Montgomery Flora et al. (+8 more)

NVIDIA and The Weather Company (TWC) have generated a data set of reforecasts from TWC's GRAF (Global high-Resolution Atmospheric Forecasting) model, a version of the National Center for Atmospheric Research (NCAR) Model for Predictions Across Scales (\href{https://ncar.ucar.edu/what-we-offer/models/model-prediction-across-scales-mpas}{\underline{MPAS}}). GRAF is global, but the configuration for this reforecast had a mesh refinement to \textasciitilde4 km over the US, Caribbean Basin, and Europe, and 15 km elsewhere. This model was designed to run much of the computation on graphical processing units (GPUs), with this development assisted by NVIDIA. The 1836 reforecast cases (\textasciitilde5 years) were generated from ECMWF reanalyses (ERA5) for selected initial condition dates spanning more than 20 years, 2004–2024. These dates of the chosen initial conditions were mostly selected based on high-impact weather in the contiguous US (CONUS) and Caribbean. Sampling in this way, the reforecast spanned a wider range of interesting, high-impact weather scenarios than had we performed five contiguous years of once-daily reforecasts. The reforecast still provides many samples in non-precipitating regions with more ordinary weather. GRAF reforecasts were mostly run to $+$27 h lead time, assuming a 3-h spin-up followed by a full diurnal cycle. Data were saved in zarr format on the native model vertical coordinate. Most fields were archived at 15-min intervals, though several precipitation variables were saved at 5-min cadence. Data are made publicly available to all through Amazon Web Services' Open-Data Initiative at https://registry.opendata.aws/graf-reforecast.
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All authors Thomas M Hamill, Akshay Subramaniam, Montgomery Flora, Raghu Raj Prasanna Kumar, Karthik Kashinath, Carl Ponder, Tao Ge, Sepideh Khajehie, John Wong, Brett Wilt, Peter Neilley

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

A library for differentiable signal processing and machine learning on the sphere

Thorsten Kurth, Max Rietmann, Mauro Bisson et al. (+6 more)

The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, including geophysics, planetary science, geodesy, atmospheric physics, quantum chemistry, cosmology, and virtual reality, among many others. As machine learning increasingly permeates these fields, the demand grows for robust tools that process and model functions on the sphere, while respecting the inherent topological and symmetry properties of the domain. We present torch-harmonics, a comprehensive library that offers efficient, differentiable implementations of advanced signal processing and machine learning (ML) methods for spherical data. These include the spherical harmonic transform (SHT), the spherical analogue of the Fourier transform, vector spherical harmonics, discrete-continuous and spectral convolutions, as well as both global and neighborhood spherical attention mechanisms. Beyond traditional representations, torch-harmonics provides the building blocks for state-of-the-art spherical ML architectures such as spherical transformers in order to enable scalable, rotationally-aware learning and inference in modern scientific and engineering applications.
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All authors Thorsten Kurth, Max Rietmann, Mauro Bisson, Andrea Paris, Alberto Carpentieri, Jean Kossaifi, Anima Anandkumar, Christian Hundt, Boris Bonev

Atmospheric and Oceanic Physics

Mathematical Exploration of Wave Motion due to Earth-Space Force (Ground Action) Consequences

Monday Sunday Adiaha and Oladiran Johnson Abimbola

This study mathematically explores wave motion induced by earth-space forces specifically ground actions. Developing and solving governing differential equations provides insights into intricate propagation dynamics caused by these forces. Numerical simulation modeled wave behavior and results were analyzed to understand ground action impacts. Initial wave generation and subsequent propagation were simulated using COMSOL Multiphysics which models wave dynamics in physical media. Partial differential equations describing wave motion alongside Fourier transform techniques were applied to analyze the frequency components. Mathematical derivations yielded wave equations while COMSOL's built-in statistical analysis tool analyzed results. Data visualization employed plotting tools within the software to create detailed graphs. The wave amplitude relative to distance km varied at 0.001 at 0 km and 0.001 at 10 km over 10 km. Amplitudes were recorded at intervals 0.05 0.1 0.15 0.2 0.25 1.05 1.1 1.15 1.2 and 1.25 s. The impact of varying amplitude and frequency on the behavior ranges from 1 s at 0.1 amplitude in Case 1 (0.2, 200) to 1 s at 0.2 amplitude in Case 3 (0.2, 300). Outputs indicate: (1) initial energy input significantly influences the propagation characteristics aiding seismic prediction and mitigation design; (2) propagation details underscore the medium properties' importance in shaping behavior vital for earthquake engineering and accurate ground-motion prediction; (3) sensitivity to amplitude and frequency suggests tailoring parameters can optimize wave-based geophysical surveys and environmental monitoring. This study serves as a policy guide for understanding wave dynamics to inform building codes and disaster preparedness strategies for mitigating seismic impacts.
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Atmospheric and Oceanic Physics

Butterfly Effect Confirmed in Global AI Weather Models: Evidence from Tropical Cyclone Forecasting

Jeremy Cheuk-Hin Leung, Daosheng Xu, Weiye Yu et al. (+9 more)

A paradox recently emerged in artificial intelligence (AI) weather prediction research. While some claim AI weather models cannot simulate atmospheric butterfly effect, this conflicts with AI models' limited predictability and advances in AI ensemble forecasting. This study demonstrates via counterexamples that the butterfly effect does exist in AI weather predictions. For Super Typhoon Khanun, AI predictions are constrained by a double-attractor system. Minor initial perturbations confined to two regions trigger state transitions between two local attractors, causing a 1006-km difference in the predicted storm position on Day 7. This behavior is consistent with numerical weather prediction models and observed in ~12% of tropical cyclones in the past 5 years. These findings verify AI's ability to capture atmospheric chaos and provide the physical basis for AI ensemble forecasting.
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All authors Jeremy Cheuk-Hin Leung, Daosheng Xu, Weiye Yu, Shaojing Zhang, Xiaodong Zeng, Gaozhen Nie, Jie Feng, Jingchen Pu, Yi Li, Kaijun Ren, Qingcun Zeng, Banglin Zhang

Atmospheric and Oceanic Physics

Long-term Variations of Cyclone-Influenced Exchange Flows in the Coastal Arctic in 2000-2025

Chunyan Li

Summertime synoptic weather regulates Arctic land-ocean exchange through meteorological tides during the ice-free period, when the predominantly easterly regime of the Beaufort High is punctuated by episodic Arctic cyclones, changing coastal winds to westerlies. We analyze wind-driven exchange flows in a northern Alaska lagoon using a 26-year reconstruction (2000-2025) validated by new in situ observations and FVCOM simulations. Phase-space EOF analysis of ERA5 data links deep cyclonic anomalies to 89% of Extreme Eastward Throughflow (EET) events (24 of 27). Coastal easterly occurrence frequencies show a consistent decrease (up to 38.3% at > 10 m/s) while low-to-moderate westerly occurrence frequencies increase by up to 30.9% but decrease by 40.6% at extreme westerly wind speeds (>10 m/s). Offshore ERA5 wind occurrence frequencies increased, with extreme offshore events (> 10 m/s) increasing by 82.4%. Local ASOS observations reveal a near-monotonic ~88% reduction in atmospheric forcing variability (East-West Wind Excursion variability drops from 2.98X10^4 to 0.35X10^4 km). The In-Out Flow Excursion variability declines steadily by 70% from 1.40X10^3 to <0.40X10^3 km, while the positive (outward) flow ratio variability contracts steadily from 7.4% to below 2.2%. Extreme Westward Throughflow (EWT) events were infrequent, but were concentrated in the most recent period, with five of six events occurring in 2022-2024 and one in 2013. These results indicate that the summertime coastal regime remains predominantly easterly, but exhibits progressively lower variability, while the wind distribution shifts toward more frequent low-to-moderate westerlies. This evolution of weather-forcing state may impact flushing and biogeochemical connectivity of Arctic lagoon ecosystems.
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Tuesday 29 September

Atmospheric and Oceanic Physics

Methodological Changes to the Attention ResUNet Hourly Precipitation Postprocessor

Thomas M. Hamill

This note is a technical companion to a previously published preprint describing an Attention Residual U-Net that postprocesses deterministic forecasts from The Weather Company's Global and Regional Atmospheric Forecast (GRAF) model into probabilistic hourly precipitation forecasts. It documents what has changed in that method since publication. Feature-wise Linear Modulation conditioning on calendar season and forecast lead time is used to produce a single trained model for each season, replacing 192 separately trained per-month, per-lead checkpoints. Lead time is extended from 48 to 72 h. Two new input channels are used, per-pixel local solar hour and a static, monthly-varying precipitation climatology. During verification, the climatological reference against which the Brier Skill Score is computed now has an added diurnal dimension, on top of the monthly resolution it already had. Brier Skill Score and reliability are compared between the new vs. the previous training. Forecasts generated with the new training show a modest, consistent improvement of the current training over the original.
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Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP

Atmospheric dynamics and variability of periodically irradiated tidally locked planets

Deepayan Banik, Vasilii Pustovoit, Thaddeus D. Komacek et al. (+1 more)

Time-variable stellar heating may create new climate states, such as permanently hot or cold hemispheres, on asynchronous planets. Planets on eccentric orbits, circumbinary planets and planets on polar orbits around gravity-darkened stars all receive variable irradiation, yet variably irradiated planets (VIPs) have not been studied from a fundamental dynamical perspective. We present the first generalized study of the atmospheric response of variably irradiated tidally locked planets, using a 1.5-layer shallow-water model forced towards a time-dependent radiative equilibrium. We derive linearized analytic expressions for the mean, amplitude and phase lag of the response, which is that of a forced damped harmonic oscillator, and recover the established trends of tidally locked planets in the limit of steady forcing. When the instellation variation and wave timescales match and the damping is weak, a resonance occurs because waves propagate and interfere freely. The resonance produces a global standing wave mode and consequently a temporarily warmer nightside. Linear and nonlinear simulations reproduce these results and show that the hotspot oscillates longitudinally at the forcing frequency and reaches the antistellar point at resonance. The oscillation is eastward or westward of the substellar longitude depending on the rotation rate and the nonlinearity of the system. Synthetic phase curves carry signatures of this variability and invert in shape at resonance because of nightside warming. Among observed systems, we find 11 planets with at least 10% phase curve modulation from variable irradiation. Eccentric, pseudo-synchronous giants are the most promising targets, with HAT-P-2b having the largest predicted modulation.
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All authors Deepayan Banik, Vasilii Pustovoit, Thaddeus D. Komacek, Marta L. Bryan

Atmospheric and Oceanic Physics

Variations of atmospheric 12CO2 and 13CO2 from the year 1000 to 2024

W. A. van Wijngaarden and W. Happer

A simple model gives good agreement between observed emissions of CO2 into the atmosphere from the combustion of fossil fuels and observed increases of the atmospheric concentrations C of CO2 and decreases of the isotope delta value delta13C. If fossil fuel emissions were to cease the model predicts that: (1) the difference between the CO2 concentration C and the preindustrial value, Co = 280 ppm, would decay exponentially with time constant of 60 y; (2) the difference between the concentration-delta-value product C x delta13C and the preindustrial value Co x delta13Co = (280 ppm) x (-6.5%%), would decay exponentially with a time constant of 8 y. The decay times are different because of isotopic exchange, largely driven by photosynthesis and respiration on the land.
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Monday 28 September

Atmospheric and Oceanic Physics

Suêtes: An end-to-end differentiable non-hydrostatic limited-area dynamical core

Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo et al. (+2 more)

We present Suêtes, a fully differentiable, non-hydrostatic, limited-area atmospheric dynamical core implemented in JAX. Suêtes includes both a semi-implicit semi-Lagrangian scheme and a Split-Explicit Runge-Kutta scheme with acoustic substepping, enabling applications from coarse regional simulations to convection-permitting dynamics. Its terrain-following geometry, spatial discretization, time integration, initial conditions, lateral boundary treatment, and included physical parameterizations are all compatible with reverse-mode automatic differentiation. A single reverse-mode pass can therefore compute sensitivities of forecast diagnostics to selected initial and boundary fields, physical parameters, topography, and computational geometry. We provide a series of numerical experiments—including a tracer-source inverse problem, a sensitivity analysis of a three-dimensional squall-line, adversarial perturbation construction an ERA5-driven downslope hurricane force wind event, topography optimization, and terrain-following coordinate optimization—demonstrating the versatility of Suêtes as a framework for gradient-based diagnosis and model development at regional and convection-permitting scale.
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All authors Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo, Alejandro Di Luca, Alex Bihlo

Atmospheric and Oceanic Physics · Cross-listed from physics.geo-ph

Climate-informed cryospheric reanalysis via hierarchical Bayesian data assimilation

Kristoffer Aalstad, Esteban Alonso-González, Joel Fiddes et al. (+7 more)

The cryosphere regulates global cycles of water, energy, and carbon, affecting societies and ecosystems. By assimilating observations into models, we can infer the past trajectory of state variables, generating a cryospheric reanalysis. Existing cryospheric reanalyses typically treat each water year independently, with no pooling of information across years. With sparse and noisy observations, such 'climate-assumed' reanalyses perform poorly, reverting to an assumed yet uncalibrated background climatology. Conversely, Bayesian calibration with static parameters completely pools information across water years, inferring climatological parameters without capturing inter-annual variability. Here, we propose a new hierarchical Bayesian approach using partial pooling, which we coin 'climate-informed' cryospheric reanalysis. It jointly infers state trajectories, annual parameters, and local climatological hyperparameters using a nested hybrid particle smoothing workflow. We test the approach through three experiments by assimilating in situ snow water equivalent, satellite-based fractional snow-covered area, and glacier-wide mass balance data into a temperature index model at eight study areas, from the Swiss Alps to the High Arctic. Hierarchical reanalysis with partial pooling outperforms annually independent cryospheric reanalysis (no pooling) and static parameter calibration (complete pooling) for all experiments during calibration and for all but one during validation. Overall, it yields a 51% improvement in continuous ranked probability score relative to the prior, markedly exceeding that of annual reanalysis (38%) and static calibration (19%). Compared to a particle Markov chain Monte Carlo benchmark, our workflow reduces computational cost by several orders of magnitude through recycling and expectation maximization, with comparable or improved skill.
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All authors Kristoffer Aalstad, Esteban Alonso-González, Joel Fiddes, Brian Groenke, Gregoire Guillet, Regine Hock, Bartłomiej Luks, Norbert Pirk, Sebastian Westermann, Yeliz A. Y{\i}lmaz

Atmospheric and Oceanic Physics

A neural network-based Universal Thermal Climate Index for reliable global thermal-stress classification across extreme weather

Bikem Pastine, Milan Klöwer, Tianning Tang et al. (+2 more)

Extreme temperatures are the leading cause of climate-related mortality world-wide. Climate-health research and operational weather forecasting require accurate estimates of human thermal stress. The Universal Thermal Climate Index (UTCI) is among the most sophisticated and widely used feels-like temperature metrics. However, its ubiquitous polynomial approximation does not generalize well to extreme weather conditions. Here, we introduce Neural-UTCI, a neural network that calculates UTCI with substantially higher accuracy across global conditions at a lower computational cost for operational use. Neural-UTCI reduces the polynomial approximation RMSE from 2.78{\deg}C to 0.36 {\deg}C, an 87% improvement, and lowers thermal stress misclassification rates from 5.3% to 1.7%, with consistent performance across resampling experiments. These differences affect thermal exposure metrics. For example, during the 2003 European heatwave summer in Rome, Italy, the number of very strong heat stress days increases from 15 to 35 days when using Neural-UTCI compared to operational products like ERA5-HEAT. Simultaneously, Neural-UTCI reliably classifies extreme cold stress conditions, allowing continuous global application. By improving UTCI accuracy, Neural-UTCI can strengthen climate-health risk assessments and public weather warning systems, especially as global warming increases the incidence of extreme events.
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All authors Bikem Pastine, Milan Klöwer, Tianning Tang, Sarah Wilson Kemsley, Louise Slater

Atmospheric and Oceanic Physics

Explainable Deep Learning for Probabilistic Nowcasting of Radar Reflectivity in Tornadic Storms

Nathan Erickson, Amy McGovern and Aaron Hill

Tornadoes pose substantial risk to human life and property in the United States, causing more than 50 fatalities and \$100 million of property damage on average annually. When tornadoes are likely, weather radar provides critical information for forecasters by providing information on storm morphology, storm motion, and intensity trends. Additional tools such as satellite and numerical weather prediction model runs can provide useful short-term information for understanding changes in storm characteristics. This work demonstrates a U-Net deep-learning system for nowcasting the evolution of radar reflectivity following tornadogenesis, which can provide value to forecasters by synthesizing large amounts of input data (e.g., radar imagery, near-storm environment data) and generating predictions of radar reflectivity from its inputs. Inputs to the model are radar imagery from the Multi-Radar Multi-Sensor (MRMS) dataset and near-storm environment data from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model. The U-Net is trained on a dataset of tornadic storms to produce 30 minutes of probabilistic predictions of radar reflectivity following tornadogenesis, with probabilistic predictions obtained by predicting parameters of the SinhArcSinh, or SHASH, distribution. The model produces physically realistic predictions of radar evolution, achieves comparable skill to next-hour forecasts from the HRRR, demonstrates reasonable probabilistic calibration and is accompanied by a variety of explainability methods to improve understanding by end users. Additionally, predictions from the model can be obtained much more quickly than those from a numerical weather prediction model. With further development, this model could be extended to nowcast radar reflectivity evolution in an operational setting.
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Atmospheric and Oceanic Physics

A Reduced-Order Coupled Oscillator Model for the Emergence of Episodic Convection

Sooman Han, Soong-Ki Kim, Bowen Fan et al. (+3 more)

Extreme precipitation is a societal concern, and understanding tropical moist convection is increasingly important in a warming climate. Climate simulations show that, under hothouse Earth, convection can transition from quasi-steady precipitation to an episodic regime characterized by intense rainfall bursts separated by dry intervals, although the mechanisms governing this transition remain debated. Here we develop a reduced-order framework that represents tropical convection through column-integrated moisture (q) and thermal stratification (Delta), rather than bulk measures of convective instability. Analysis of cloud-resolving model (CRM) simulations shows that these variables evolve together: during dry periods, surface fluxes recharge moisture, while radiative and dynamical processes erode stratification generated by previous convection. We couple this evolution to the nonlinear moisture dependence of tropical precipitation (P), whereby rainfall increases sharply above a critical moisture threshold. A third prognostic variable, C, represents convective persistence, allowing convection to continue below its onset threshold and thereby producing hysteresis. With parameters constrained by CRM output, the model reproduces the evolution of q, Delta, and P in both regimes, representing quasi-steady convection as stable, noise-driven oscillations and episodic convection as an unstable, self-sustained limit cycle. Linear stability analysis shows that the transition depends on moisture-recharge and stratification-adjustment timescales, thermal-moisture sensitivity, and convective persistence. Because radiative and dynamical processes enter through their effects on thermal stratification, the framework is not tied to a specific mechanism, providing a unified dynamical link between moisture-threshold precipitation behavior and the emergence of episodic convection under warming.
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All authors Sooman Han, Soong-Ki Kim, Bowen Fan, Jérôme Vialard, Alexey V. Fedorov, Juan M. Lora

Atmospheric and Oceanic Physics

MW-Nowcast: Six-hour ensemble nowcasting of extreme precipitation

Ning Wang, Zuliang Fang, Weixin Jin et al. (+19 more)

Extending reliable nowcasting of extreme precipitation could provide critical additional time for warnings and emergency response during high-impact events such as flash floods. Radar-based generative machine-learning models have enabled skilful hyperlocal precipitation nowcasting, but accurate prediction of intense precipitation remains confined to the first few hours. Because storm-scale structure is predictable for longer than individual cells, a natural strategy is to predict that structure while generatively modelling only the uncertain local growth, decay, reorganisation and initiation of storms. Here we present Microsoft Weather Nowcast (MW-Nowcast), a six-hour ensemble radar nowcasting model that jointly learns a deterministic predictor to capture organised precipitation structure shared across ensemble members, and a generator to produce diverse local residuals around this shared prediction. Across independent test data from the United States, Europe and China, MW-Nowcast achieves higher detection skill than leading methods for heavy and extreme precipitation throughout the 6 h horizon. For the most intense rainfall, MW-Nowcast doubles the available warning time across all three regions, delivering 6 h forecasts with skill previously limited to 3 h for the leading generative baseline. A cost-loss decision analysis shows that MW-Nowcast retains substantial value for a broad range of applications even at 4-6 h, where alternative methods offer little benefit. These additional hours can give forecasters and emergency managers the time to warn and act before extreme rainfall strikes, helping to protect lives and property.
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All authors Ning Wang, Zuliang Fang, Weixin Jin, Zhongjian Lv, Shuang Qin, Pengcheng Zhao, Siqi Xiang, Jiang Bian, Haoyi Xiong, Nan Guan, Bin Zhang, Liangjie Zhang, Denvy Deng, Qi Zhang, Matt Corey, Jitu Keshri, Sridhar Iyer, Hongyu Sun, Kit Thambiratnam, Jonathan Weyn, Richard E. Turner, Haiyu Dong

Atmospheric and Oceanic Physics

Low latency global carbon budget reveals strong land sink recovery in 2025

Philippe Ciais, Piyu Ke, Xiangjun Tian et al. (+32 more)

The atmospheric CO2 growth rate fell sharply in 2025, from a record 3.76 $\pm$ 0.09 ppm yr-1 in 2024 to 2.06 $\pm$ 0.09 ppm yr-1 (NOAA marine boundary layer observations), below the 2015-2022 mean of 2.47 ppm yr-1, even as fossil CO2 emissions rose by 0.7% to 10.38 GtC yr-1. Here we present a low-latency global and regional carbon budget for 2025, combining three dynamic global vegetation models (DGVMs) and ocean model emulators with four atmospheric inversions constrained by OCO-2 satellite retrievals. The global net land sink reached 2.36 $\pm$ 0.16 GtC yr-1 in 2025 (DGVMs: 2.04 $\pm$ 0.24; inversions: 2.68 $\pm$ 0.20 GtC yr-1), strengthening by 2.81 $\pm$ 0.31 GtC yr-1 from 2024 and exceeding the 2015-2022 mean by 0.71 $\pm$ 0.13 GtC yr-1. Ocean uptake (3.11 $\pm$ 0.36 GtC yr-1) remained similar to 2024, making the land sink rebound the dominant driver of the slowdown in CO2 growth. Tropical lands shifted from net sources in 2024 to net sinks in 2025, with enhanced uptake across much of Africa and northern Eurasia, and land flux anomalies covaried with GRACE terrestrial water storage. Where the sink had weakened substantially in 2023-2024, about 80% of the area showed some recovery, with overall recovery of 87.3% (DGVMs) to 99.5% (inversions). Recovery exceeded 100% in the tropics but remained incomplete in the northern extratropics, indicating a strong but spatially uneven rebound of the land carbon sink.
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All authors Philippe Ciais, Piyu Ke, Xiangjun Tian, Stephen Sitch, Wei Li, Xiaomeng Du, Xiaofan Gui, Ben Poulter, Thomas Colligan, Auke M. van der Woude, Anne-Wil van den Berg, Wouter Peters, Zhu Liu, Zhu Deng, Zhe Jin, Yilong Wang, Junjie Liu, Sudhanshu Pandey, Chris O'Dell, Jiang Bian, John Miller, Xin Lan, Jefferson Goncalves De Souza, Michael O'Sullivan, Pierre Friedlingstein, Youngryel Ryu, Helin Zhang, Julien Alléon, Yi Xi, Daniel S. Goll, Lei Zhu, Guido R. van der Werf, Yitong Yao, Shilong Piao, Frédéric Chevallier

Sunday 27 September

Atmospheric and Oceanic Physics

BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables

Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan et al. (+2 more)

Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systematic biases in physics-based models. Physical models, while widely used, rely on approximations and parameterizations that limit their accuracy under complex ocean-atmosphere conditions. To enhance ocean wave forecasting, we propose BridgeCast, within a physics-AI hybrid framework for bias correction. BridgeCast is a probabilistic model based on conditional flow matching (CFM) that learns to transform physical model forecasts into reanalysis-like fields. It treats physical forecasts as corrupted observations and employs a continuous-time generative process to bridge their distribution toward that of reanalysis data. BridgeCast is parameterized by a Transformer-based architecture that enables spatiotemporal modeling, incorporation of exogenous atmospheric variables, and flexible inference via both ordinary and stochastic differential equation formulations. Extensive experiments on real-world datasets demonstrate that BridgeCast consistently outperforms state-of-the-art baselines across regions and forecast lead times.
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All authors Siyu Gan, Dongsheng Luo, Kunxiaojia Yuan, Dongjin Song, Jingchao Ni

Atmospheric and Oceanic Physics · Cross-listed from stat.AP

Early warning of road icing from antecedent meteorological conditions without consistent critical slowing down

Chaeyeon Yi and Yun Am Seo

Critical slowing down (CSD), the slowing of recovery from perturbations as a system approaches a critical transition, is widely used for early warning, but whether such signals appear in environmental transitions subject to rapidly changing external forcing has rarely been tested with observations. Here we show, using 2,427 icing-onset events in 1-min observations from 121 South Korean road weather stations, that such signals do not consistently precede road icing. Icing began from different prior surface states, but conditions at onset varied continuously with weak separation among pathways. Cold dose, deposition dose and freeze-thaw accumulation at onset exceeded those at non-icing control times, with cold-dose discrimination highest at decay timescales of 30 min or less. However, the increases in variance and lag-1 autocorrelation expected under CSD did not distinguish icing from control times; the same procedure recovered these increases in synthetic series with a declining recovery rate, and the null result was replicated for 3,091 events at 127 independent stations. Predictive information was nevertheless present: adding recent changes and cumulative antecedent conditions to current conditions improved discrimination, and surrounding meteorological fields increased the area under the ROC curve by 0.030-0.032 for forecast windows of 1 to 12 h and reduced false alarms by 62-67% at 3 and 6 h. These results suggest that road icing is more consistent with recent meteorological forcing moving the road surface into icing-favourable conditions than with a progressive decline in internal stability, and they indicate where early warning should look when a transition is externally forced.
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Atmospheric and Oceanic Physics

Baroclinic Annular Mode Modulates Global Atmospheric Rivers and Extreme Precipitation

Yuan-Bing Zhao, Lei Wang and Yi Ming

The Baroclinic Annular Mode (BAM) strongly influences extratropical circulation, yet its role in the global hydrological cycle remains unclear. Using ERA5 reanalysis and a global atmospheric river (AR) database, we show that BAM systematically reorganizes AR occurrence and extreme precipitation in both hemispheres. During high-BAM states, AR frequency increases along the flanks of major storm tracks and decreases near their climatological cores, redistributing preferred AR corridors. Relative AR-frequency changes locally exceed 50%, with the most zonally coherent patterns over the Southern Ocean. Extreme precipitation varies correspondingly, with 99th-percentile precipitation occurrence increasing by more than 50% across broad poleward storm-track regions. A large fraction of these extremes coincide with ARs, and AR-associated extreme precipitation shows similar BAM-related modulations. Low-BAM states show broadly opposite anomalies. These results identify BAM as a key mode linking large-scale circulation to global moisture transport and hydrological extremes.
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Saturday 26 September

Atmospheric and Oceanic Physics

Global Evaluation of AI and NWP Precipitation Forecasts During Atmospheric River Events

Marina Vicens-Miquel, Taylor Mandelbaum, Amy McGovern et al. (+2 more)

Atmospheric rivers (ARs) produce many of the world's most extreme precipitation events and hydrometeorological hazards. Although artificial intelligence weather prediction (AIWP) models have demonstrated skill comparable to or exceeding numerical weather prediction (NWP) systems for large-scale atmospheric variables, their ability to forecast AR-related precipitation remains insufficiently characterized globally. Here, we evaluate 24-hour precipitation forecasts from the Global Forecast System (GFS), Global Ensemble Forecast System (GEFS), GraphCast, and Artificial Intelligence Forecasting System (AIFS) from Day 1 through Day 10 globally and across North America, Europe, and Australia and New Zealand. Using the Extreme Weather Bench framework, forecasts are evaluated against Integrated Multi-satellitE Retrievals for GPM (IMERG) observations using measures of precipitation magnitude, spatial structure, and localization. GraphCast and AIFS exhibit greater spatial skill than GFS and GEFS, particularly for heavy precipitation and at longer lead times, and better preserve the spatial organization of AR-related precipitation through Day 10. However, this improved spatial skill does not translate into accurate precipitation magnitudes. AIWP models tend to overpredict moderate-to-heavy accumulations while underpredicting the heaviest precipitation at longer lead times, whereas NWP systems develop pronounced dry biases. These results reveal distinct strengths and limitations of AIWP for high-impact precipitation forecasting and provide a reproducible benchmark.
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All authors Marina Vicens-Miquel, Taylor Mandelbaum, Amy McGovern, Aaron J. Hill, Daniel Rothenberg

Atmospheric and Oceanic Physics

Generalizable Neural Downscaling of Earth System Model Wind Fields via Continuous Dynamics Modeling

Chenxi Yu, Jianan Wei, Hanlin Kong et al. (+3 more)

Accurate high-resolution wind field simulations are critical for resolving fine-scale atmospheric dynamics, yet the simulation of wind fields in Earth System Models (ESMs) remains limited by coarse spatial resolution and systematic biases. To address this, data-driven down scaling techniques have been widely used to enhance coarse-resolution ESM outputs. However, existing methods are typically tied to fixed discretizations, limiting generalization across models with different native resolutions. Here we formulate global near-surface wind downscaling as an operator-learning problem on continuous atmospheric state fields and develop a downscaling neural operator that maps coarse-scale fields to fine-scale counterparts across heterogeneous discretizations. The operator learning-based neural downscaling framework outperforms dominant baselines, recovers fine-scale physical structures, and generalizes to previously unseen ESMs and future climate scenarios without retraining, while preserving long-term wind projection trends. These findings establish a generalizable paradigm for high-resolution climate downscaling across diverse simulation outputs and future scenarios.
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All authors Chenxi Yu, Jianan Wei, Hanlin Kong, Hao Sun, Bian He, Wenguan Wang