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Physics Physics Atmospheric and Oceanic Physics

physics.ao-ph Watch Atmospheric and Oceanic Physics

Thursday 8 October

Atmospheric and Oceanic Physics

Evaluating LLMs for Translating Legacy Fortran Atmospheric Physics to GPU-Accelerated JAX

Iris Linck, John Dennis, John Schreck et al. (+2 more)

Legacy Fortran physics packages in atmospheric models such as CESM, WRF, and E3SM pose a barrier to GPU modernization, while manual porting requires accelerator-programming expertise. We present LAFT (LLM-Assisted Fortran Translation), an agentic framework for converting Fortran atmospheric physics parameterizations into GPU-accelerated JAX while preserving numerical agreement with the Fortran reference. LAFT coordinates source-code analysis, translation, repair, runtime validation, and performance profiling. It combines a deterministic front end for metadata extraction and bridge generation, structured translation prompts, numerical validation, and profiling-guided feedback to identify inefficient execution patterns. The bridge handles array-layout conversion separately from scientific computation. We evaluate LAFT using the Kessler warm-rain microphysics scheme and four LLMs: Claude, GPT, Gemini, and Qwen. All four validated translations reproduce the Fortran reference outputs to near floating-point precision for the evaluated case, but their GPU performance differs substantially with execution design. The Gemini-generated implementation achieves up to a 357-fold speedup over serial Fortran in cached execution at 1,000,000 atmospheric columns, excluding compilation and host-device transfers. The results demonstrate that numerical agreement alone does not ensure efficient GPU execution and motivate combining numerical validation with hardware-aware performance analysis. LAFT provides a practical workflow for modernizing legacy atmospheric physics code through LLM-assisted translation, deterministic interface generation, automated validation and repair, and reproducible profiling.
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All authors Iris Linck, John Dennis, John Schreck, Karen Stengel, David John Gagne II

Atmospheric and Oceanic Physics

legoESM: a modular, differentiable, multiscale, AI-ready Earth system model built with AI agents

Pierre Gentine, Dhruv Balwada, Aytaç Paçal et al. (+12 more)

Earth system models (ESMs) have grown tremendously in realism, yet key uncertainties persist in the climate response to greenhouse-gas forcing, particularly due to cloud radiative feedbacks. In addition, their software architecture was not designed for accelerator hardware or modern artificial intelligence (AI). Here we present legoESM, a composable, differentiable, multiscale ESM written in JAX. It builds on decades of community-developed parameterizations and numerical methods, recast in a unified framework by AI coding agents under a human-specified scientific contract and verified through benchmarking. Dynamical cores, physics schemes, grids, complexity levels and components are swappable like building blocks, and can use conventional physics or machine-learned emulators. A single code base spans metre-scale large-eddy simulation to global simulations and weather to climate. End-to-end differentiability enables gradient-based calibration, variational data assimilation and online training. legoESM modular architecture enables systematic evaluation of diverse model variants to explore structural uncertainty and test hypotheses. legoESM produces realistic simulations across scales, reduces land-surface temperature bias through gradient-based calibration, and scales efficiently on GPUs to kilometer-scale simulations. It offers an open, community infrastructure for hypothesis testing, research and teaching in Earth sciences and a template for multiscale physical systems.
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All authors Pierre Gentine, Dhruv Balwada, Aytaç Paçal, Linnia Hawkins, Alistair Adcroft, Hang Fan, Jianing Fang, Aya Lahlou, Kara D. Lamb, David M. Lawrence, Julien Le Sommer, Joseph Mouallem, Juan Nathaniel, Duncan Watson-Parris, Veronika Eyring

Atmospheric and Oceanic Physics

A Physics-Constrained Implicit Profile Network for Continuous Reconstruction of Tropical Cyclone Near-Surface Wind Profiles

Jian Ma, Yilin Yang, Robert Rogers et al. (+2 more)

Near the ocean surface, tropical cyclone winds change rapidly with height, but direct measurements are limited because aircraft dropsondes provide only sparse and irregular observations. Continuous wind profiles are important for understanding hurricane boundary-layer processes, improving storm-surge prediction, supporting offshore engineering, and assessing coastal hazards. In this study, we developed an artificial intelligence model that combines machine learning with physical principles to reconstruct continuous wind profiles from sparse observations. The model is designed to preserve the observed surface winds while generating realistic changes in wind speed and direction with height. Tests using more than two decades of NOAA hurricane observations show that the method accurately reproduces the vertical structure of tropical cyclone winds over a wide range of storm intensities. The framework can also extend satellite-derived surface wind measurements into three-dimensional near-surface wind fields, providing new opportunities for hurricane research, operational forecasting, and engineering applications.
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All authors Jian Ma, Yilin Yang, Robert Rogers, Jun A. Zhang, Jie Tang

Atmospheric and Oceanic Physics

A Graph Neural Network for Global Daily Fire Radiative Power Prediction at Medium-Range Lead Times

Li Zhang, Jun Wang, Isidora Jankov et al. (+6 more)

Skillful prediction of biomass-burning activity several days in advance is important for air-quality forecasting and aerosol prediction. Two operational constraints motivate this work. First, the GBBEPx satellite fire radiative power (FRP) product used to initialize NOAA's GEFS-Aerosols is available with about a 1.5-day latency, so each forecast cycle relies on the most recently available, but already outdated, fire observations. Second, these fire inputs are then held fixed throughout the subsequent 5-day operational forecast, or 7 days in the GSL experimental system, effectively assuming no evolution in fire activity. We develop a data-driven model that predicts global FRP one to seven days ahead from the most recent available observations. The model adapts a spatiotemporal graph neural network using reanalysis meteorology, land-cover and vegetation information, recent fire history, and GBBEPx FRP as the training target. It is trained on 2020-2022 data and evaluated for 2023-2024. The model reproduces the global seasonal cycle and substantially outperforms persistence. At 0.1$^\circ$ resolution, mean squared error is reduced by 32% at one-day lead and 43% at seven days in 2023, and by 24% and 40% in 2024. At 1$^\circ$ resolution, the critical success index ranges from 0.32 to 0.60. Detection skill declines only modestly with lead time, whereas intensity skill degrades more rapidly. Large fires are detected reliably, but their radiative power is systematically underestimated. These results demonstrate useful predictability of fire activity several days ahead and identify intensity calibration and small-fire placement as the main remaining challenges before predicted FRP can support operational aerosol forecasts.
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All authors Li Zhang, Jun Wang, Isidora Jankov, Yongxin Liu, Gonzalo A. Ferrada, Ravan Ahmadov, Ligia Bernardet, Haonan Chen, Shobha Kondragunta

Wednesday 7 October

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

Low-rank tensor structure of precipitation and its application to satellite-reference merging

Ryan Solgi, Rohan Shankar and Hugo A. Loaiciga

The intermittent and variable nature of precipitation makes its accurate estimation over extended domains difficult, yet its spatiotemporal structure suggests that a low-rank representation may be possible. This work represents daily precipitation over the contiguous United States (CONUS) as spatiotemporal tensors and applies CANDECOMP/PARAFAC factorization, showing that preserving the native spatial and temporal modes yields more accurate reconstruction than factorizing independent daily fields or unfolded space–time matrices. Building on this finding, this work presents TMerge, a tensor-based framework that integrates satellite precipitation with sparse reference observations through shared low-rank spatial and temporal factors. TMerge was applied to correct the IMERG Final Run product with climate prediction center reference observations over CONUS. During 2019-2022, TMerge increased correlation from 0.53 to 0.85 and reduced root-mean-square error and mean absolute error by 48.2% and 29.3%, respectively. TMerge consistently outperformed linear bias correction, quantile mapping, and neural networks across seasons, precipitation-intensity regimes, and regions. Improvements were spatially coherent and largest in coastal regions where IMERG errors were greatest. These results demonstrate that low-rank tensor structure parsimoniously approximates the dominant spatiotemporal variability of precipitation and provides a practical mechanism for improving satellite estimates under limited reference observations over extended domains.
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Atmospheric and Oceanic Physics · Cross-listed from cs.AI

SciExam for ENSO: Can AI Agents Build Climate Models?

Yinling Zhang, Langchen Liu, Dongbin Xiu et al. (+4 more)

Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a language-model reviewer, none of which can tell whether a new scientific model is valid. The AI Science Exam for El Nino-Southern Oscillation (SciExam for ENSO) is a benchmark in which agents build low-order stochastic models of ENSO, the dominant mode of interannual climate variability, from real observations. Within a six-hour budget, agents process the observations, write their own diagnostics, which are then frozen, and develop a model using only these diagnostics as feedback. Hidden graders then test whether the model reproduces ENSO's statistics, recovers unobserved variables, and forecasts held-out years, and score a published model in the same way. Across twelve agent systems, six produce models that score higher than the published model, mainly through better reconstruction and forecasting. The simplified forms of the stronger models are each compatible with one of the two competing explanations of ENSO's warm-cold asymmetry, an open debate that the task never mentions. Controlled runs of the top system under varied information suggest that its scores do not come from recalling the dated observational record and that the information it receives shapes how it builds its model. SciExam for ENSO can thus evaluate agent research where no answer is known, and the results suggest that agents can already build competitive models whose structures bear on questions that scientists still debate.
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All authors Yinling Zhang, Langchen Liu, Dongbin Xiu, Xueyan Zou, Xu Kuang, Mengdi Wang, Shilong Liu

Atmospheric and Oceanic Physics · Cross-listed from physics.flu-dyn

Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields

Peng Liu, Shaoxiang Qin, Theodore Potsis et al. (+3 more)

Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computational cost. Existing regressive data-driven models offers quick outputs, but they produce only deterministic point predictions that inherently fail to represent turbulent stochasticity. This paper adopts a novel generative framework of Conditional Flow Matching (CFM) that uses building geometry and mean flow as guidance to generate plausible three-dimensional instantaneous velocity and temperature fields for urban microclimate in seconds. To overcome the GPU memory bottleneck of pixel space 3D generation, the model operates in parallel on overlapping pixel space through a shared-noise initialization that preserves high spatial continuity of flow structure across the entire domain. Against reference LES data, the CFM surrogate can rapidly and accurately restore the first-order statistics with Normalized Root Mean Square Error (NRMSE) of 2.99% for wind and 1.77% for temperature, second-order turbulence metrics with NRMSE of 7.17% for wind and 8.84% for temperature, turbulent kinetic energy with NRMSE of 7%, probability density function and vertical profiles in representative locations. Wind engineering application of local gust prediction demonstrate that the speed and accuracy of CFM, supporting the use of generative AI for making turbulence-aware resilient urban design and climate adaptation more computationally feasible.
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All authors Peng Liu, Shaoxiang Qin, Theodore Potsis, Lili Ji, Dingyang Geng, Liangzhu Leon Wang

Atmospheric and Oceanic Physics

Three-dimensional Lagrangian ecosystems: carbon dynamics and potential for artificial fertilization

Stefano Campagnola, Stephanie Dutkiewicz, Michael J. Follows et al. (+1 more)

Transient supplies of nutrients to the surface ocean, both natural and artificial, stimulate blooms of phytoplankton and the formation of organic matter, driving air-sea gradients and uptake of CO$_2$. However, quantifying the associated carbon budget remains challenging, as it requires tracking the coupled biophysical evolution of water masses while they are transported, stretched and diluted. Here, we present an idealized three-dimensional model that describes biomass production and carbon dynamics within a Lagrangian patch in the ocean. The framework reproduces observed biogeochemical patterns from an artificial fertilization experiment and provides integrated metrics for the local carbon budget. Utilizing large ensembles of simulations, we examine the sensitivity of patch-scale primary production and carbon uptake to biochemical and physical factors. Our results show that patch dilution can enhance the ecosystem response, and how carbon uptake is sensitive to initial injected area, horizontal divergence and vertical diffusivity. In the context of renewed interest in ocean fertilization strategies for climate mitigation, our approach can thus provide quantitative tools to assess their efficacy and potential.
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All authors Stefano Campagnola, Stephanie Dutkiewicz, Michael J. Follows, Enrico Ser-Giacomi

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

Learning joint probabilistic weather forecasts from station observations alone

Chaeyeon Yi and Yun Am Seo

Assessing compound weather risks requires forecasts representing dependence between variables. CLARA (Calibrated Advection-Routing Attention) learns joint Gaussian predictive distributions of five surface variables from station observations alone, without numerical weather prediction or reanalysis; the approximately 28,000-parameter model supports CPU training and prediction. Across six multi-year folds on 96 stations, its lead-mean energy score is 4.9% lower than that of a learned comparator with matched temporal inputs (4.7% with a similar parameter count) and 11-65% lower than those of statistical baselines. Holding marginal variances fixed, removing learned correlations worsens joint negative log-likelihood by 1.0-2.8 nats per station. A covariance-scale estimator, proved consistent under stated assumptions, improves short-lead calibration but over-corrects at long leads. Synthetic interventions show an attention-bias coefficient alone does not measure forecast influence. Retrained in ten regions on six continents, CLARA outperforms persistence in all 60 multi-year region-lead comparisons and a similarly sized learned model in 57 of 60.
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Atmospheric and Oceanic Physics

Generative and deterministic deep learning models comparison for fine-scale precipitation retrievals from infrared brightness temperature

Matthieu Meignin, Cécile Mallet and Nicolas Viltard

Accurate precipitation estimation at fine spatial scales is critical for hydrology, agriculture, and climate studies. Infrared brightness temperatures from geostationary satellites offer excellent temporal coverage over continental-scale domains. However, because these measurements primarily characterize cloud-top properties rather than precipitation processes near the surface, their correlation with rainfall intensity remains limited, making quantitative precipitation estimation challenging. In this study, we conduct a systematic inter-comparison of state-of-the-art deep learning models for high-resolution precipitation retrieval from Meteosat Second Generation infrared brightness temperatures over metropolitan France. These models include deterministic U-Nets, transformer-based architectures, conditional GANs, and diffusion models. We construct a curated dataset spanning 2008–2023, combining Météo-France radar mosaics as reference with multi-channel infrared observations, and design preprocessing and sampling strategies to address the heavy-tailed, intermittent nature of rainfall. Our results show that deterministic models provide robust mean estimates and excel in pixel-wise accuracy, but systematically underestimate extreme precipitation. In contrast, generative models better capture the full precipitation distribution, including rare and heavy rainfall events, producing more realistic spatial structures at the cost of reduced pixel-wise fidelity. These results highlight a trade-off between pixel-wise accuracy and precipitation variability, showing that generative approaches are advantageous for extreme-event detection and probabilistic applications. This work establishes a reproducible framework for evaluating infrared- based precipitation retrieval methods and provides guidance for designing models that balance precision, variability, and extreme-event representation.
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Atmospheric and Oceanic Physics

Artificial intelligence pathways from weather to climate

Tom Beucler, J. David Neelin, Hui Su et al. (+10 more)

Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecasts at reduced cost. We review these advances and consider their extension to climate horizons, where the challenge shifts from initial-condition skill to producing reliable statistical responses under altered forcings. AI-powered climate prediction systems must produce credible forced responses to drivers (e.g., greenhouse gases, land-use change) typically outside the observed record. We propose two minimum requirements for AI in climate modeling: (i) external forcing agents must enter explicitly enough to support interventions in which they vary independently; and (ii) robustness must be stress-tested in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading AI autoregressive emulators and hybrid physics-AI models, we identify development and coupling challenges. Comparing the reported throughput of these models with that of GPU-ported dynamical models highlights how AI can reduce time-to-solution by advancing only the target variables at the required resolution and using longer time steps, rather than integrating a full high-frequency, multivariate state. Diverse AI downscaling strategies can partially substitute for explicit fine-scale resolution, paving the way toward inexpensive local hazard assessment across prediction horizons.
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All authors Tom Beucler, J. David Neelin, Hui Su, Shivanshi Asthana, Chris Bretherton, Will Chapman, Costa Christopoulos, Spencer K. Clark, Aditya Grover, Ignacio Lopez-Gomez, Tapio Schneider, Adam Subel, Oliver Watt-Meyer

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

EC-EarthFlow: Probabilistic emulation of daily transient global climate model simulations with flow matching

Kirien Whan, Nikolaj T. Mücke and Karin van der Wiel

We introduce EC-EarthFlow, a generative flow matching model that emulates simulations from the physical climate model EC-Earth3. The model is trained on transient simulations from EC-Earth3 (1950-2166, SSP2-4.5) to predict the day ahead temperature field from the previous days temperature as well as annual mean temperature. Predictions are made auto-regressively with rollout periods of between a month and an extended season. Using only this variable of interest, we are able to reproduce the daily variability, spatial patterns, annual cycle and long-term trend from EC-Earth3 at a substantially lower computational cost than the physical model. We demonstrate that EC-EarthFlow is stable for long inference periods, and that it can learn the physical relationships as simulated in EC-Earth3.
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Atmospheric and Oceanic Physics · Cross-listed from stat.AP

Statistical Oceanography of Profiling Floats and Surface Drifters

Mikael Kuusela, Sofia C. Olhede and Adam M. Sykulski

The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled in time and space by various instruments and systems. Statistical models, especially spatio-temporal ones, are useful for enabling inferences, forecasts and decisions from sparse oceanic observations. This article focuses on the statistical treatment of two types of in situ observations in the Global Ocean Observing System: from profiling floats and surface drifters, focusing on the Argo and Global Drifter Programs. We describe the spatio-temporal models that have been developed in recent years for these data, to give a picture of the statistical challenges faced in modern physical oceanography. We will discuss in detail two types of reference frames when modeling such data: Eulerian and Lagrangian, the former of which is more appropriate for profiling floats and the latter for surface drifters.
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Tuesday 6 October

Atmospheric and Oceanic Physics

Similar evapotranspiration responses mask contrasting modes of water access during drought in California

Yan Zhang, Veronica J. Berrocal, Joshua B. Fisher et al. (+3 more)

Atmospheric warming raises evaporative demand, but actual evapotranspiration (ETa) may track demand or decouple under water limitation. Here, we quantified demand-tracking ability from regression slopes relating deseasonalized ETa and reference evapotranspiration (ETo) anomalies across California during 2008-2024. Forests and croplands retained similar positive tracking under severe drought, whereas grasslands and shrublands showed weak or negative responses. Despite similar demand tracking, forest responses were consistent with subsurface-storage access, whereas cropland responses reflected managed water access and crop-system adjustment. Positive ETa-ETo coupling on active fields coexisted with a higher statewide crop-attributed fallow share of managed cropland than under no drought, showing that active-field estimates alone did not capture whole-crop-system adjustment. Late-period demand-tracking ability was lower at 54.9% of grid cells, with larger regional declines accompanying greater groundwater-storage losses. Our results indicate that similar ETa responses mask contrasting modes of water access, requiring joint consideration of hydrology and management when interpreting drought responses.
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All authors Yan Zhang, Veronica J. Berrocal, Joshua B. Fisher, Octavia Crompton, Alvar Escriva-Bou, Angela J. Rigden

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

Assessment of sky brightness in central Portugal: the Dark Sky destination "Aldeias do Xisto" case study

Domingos Barbosa, Dalmiro Maia, Bruno Morgado et al. (+5 more)

A combination of remote sensing techniques, open space data and field testing was developed to derive astronomical and meteorological parameters and assess Dark Sky brightness of the hitherto unstudied Schist Villages region in the mountainous Interior Center of Portugal. This study covered the period 2012-2017 and established the methodology for regular light pollution and sky brightness studies in the region using satellite imagery to reduce cost and time and increase accuracy of the final analysis. Long-term Artificial Light At Night (ALAN) data from VIIRS instrument aboard the Suomi-NPP satellite along with cloud cover inferred from MODUS/AQUA satellite data and orographic data, were analyzed in a Geographical Information Systems (GIS) environment. The derived ALAN values were consistent with a preliminary site survey selection, confirming the accuracy of the identified dark sky spots in the region. The analyzed region area spans 1624 Km$^2$ and the darkest site within it showed a sky brightness of 21.4 mag/arcsec$^2$. Ground-based SQM photometer observations of the Night Sky Brightness (NSB) at the zenith validated the satellite derived ALAN data. The devised field testing is itself a low cost solution that can be used by teams in developing countries to easily assess dark sky potential prior to the development of local observatories and astro-tourism activities. This study supported the initial certification of this region as a Dark Sky Starlight Tourist Destination since 2018, an important milestone for dark sky protection in central Portugal.
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All authors Domingos Barbosa, Dalmiro Maia, Bruno Morgado, Pedro Cruz, Bernardo Relvas, Valério Ribeiro, Allan Kardec de Almeida Jr., Timothée Vaillant

Atmospheric and Oceanic Physics

Multi-model ocean oxygen fields predicted by conditional diffusion models

Linus Vogt and Laure Zanna

Dissolved oxygen is important for the ocean's ecosystems and biogeochemical cycles. Yet, Earth System Models (ESMs) vary in their simulations of the present-day and future ocean oxygen inventory. To narrow down the uncertainty in estimates of ocean oxygen content, we train a conditional generative diffusion model on outputs of a multi-model ESM ensemble to learn the conditional distribution of upper-ocean oxygen given physical input variables such as temperature and salinity. This generative model has considerable skill in the Atlantic and Southern Oceans, and can generate realistic oxygen samples under conditions not seen in the training data. We validate this model using observational datasets, and use it to generate oxygen fields for models without oxygen data using only temperature and salinity as conditioning inputs. This physics-conditioned extrapolation suggests that model biases in the tropical Pacific Oxygen Minimum Zone may be smaller than currently assumed when considering a larger set of physical ocean states. Our approach provides a complementary way to represent probabilistic multi-model climate distributions.
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Atmospheric and Oceanic Physics

Satellite observations of nitrous oxide emissions from adipic acid production

Nicholas Balasus and Daniel H. Cusworth

The production of adipic acid generates nitrous oxide as a by-product, which, if emitted, very effectively warms the climate and depletes the stratospheric ozone layer. There exists cost-effective technology to mitigate these nitrous oxide emissions from adipic acid production, but the degree of implementation of this technology, most notably at sites in China, is uncertain. Using satellite observations from summer 2026, we quantify nitrous oxide emissions from three adipic acid production sites in China, finding emissions that total over half a million tonnes per year, a rate that implies limited emissions abatement. With respect to climate change, eliminating the annual emissions from just these three sites would be equivalent to eliminating the annual emissions from 49 coal-fired power plants or 43 million gas-powered vehicles in the United States.
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Monday 5 October

Atmospheric and Oceanic Physics

The interface of data assimilation and machine learning

Eviatar Bach

Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilated the model will quickly lose skill. DA is routinely performed (usually every 6 hours) at operational forecasting centres around the world. In this article we discuss the interface of machine learning (ML) and DA. This is still an emerging and quickly developing field, and this article tries to give an overview of some of the main topics and methods.
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Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Skillful Data-Driven Subseasonal Soil Moisture Forecasting: Prospects and Limits for Flash Drought Prediction

Noelia Otero, Atahan Özer, Miguel-Ángel Fernández-Torres et al. (+1 more)

Despite substantial progress in short-to-medium-range weather forecasting, predicting high-impact events such as flash droughts remains a key challenge for both early warning operations and physically-based subseasonal-to-seasonal (S2S) prediction systems. Here we demonstrate that, for S2S soil-moisture forecasting over Europe, forecast skill depends as much on how the prediction problem is formulated as on the forecasting model itself. Using a Vision Transformer-based architecture with dual-pathway temporal and spatial attention, we show that residual learning is essential to outperform persistence. This advantage is realized only when forecasting root-zone soil moisture in physical units rather than standardized anomalies, revealing that the target representation itself constrains predictability. A probabilistic extension via quantile-head fine-tuning further provides well-calibrated predictive distributions. Benchmarked against deep-learning and operational ECMWF S2S baselines over 2021-2022, our model achieves the highest deterministic and probabilistic skill at all lead times and reliably detects anomalously dry root-zone states (below the 20th percentile). Yet flash drought onset, defined by multi-pentad intensification criteria, remains a fundamental challenge shared across all current S2S systems. These findings advance data-driven S2S soil-moisture forecasting while highlighting the remaining challenge of predicting rapid drought development.
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All authors Noelia Otero, Atahan Özer, Miguel-Ángel Fernández-Torres, Jackie Ma

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

Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior

Eliot Walt, Miltiadis Kofinas, Nikolaj Mücke et al. (+2 more)

Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a fraction of the computational costs of classic physics-based models. However, most existing foundation models are deterministic, limiting the generation of large ensembles for accurate uncertainty quantification, extreme weather risk assessment, and long-range weather forecasting. Furthermore, these models incur a large, often prohibitive, computational overhead to train from scratch. To address these shortcomings, we turn a pretrained deterministic prior model, namely the Aurora foundation model, into a generative ensemble-prediction model. To that end, we introduce a novel generative method, Denoising Stochastic Interpolants, combined with a replay buffer for Stochastic Differential Equation (SDE) rollout, enabling probabilistic training of SDE trajectories. Our stochastic foundation model, Xaurora, is finetuned from the small Aurora version, yet it approaches the state-of-the-art on global ensemble metrics and is competitive with the large version of Aurora. Our method is parameter and sample efficient, and generates skilful 15-day forecasts in 13 minutes. Our results demonstrate that deterministic foundation models can be efficiently extended into even stronger stochastic models.
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All authors Eliot Walt, Miltiadis Kofinas, Nikolaj Mücke, Efstratios Gavves, Dim Coumou

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

Entropy-Stable and Well-Balanced Discontinuous Galerkin Methods for the Compressible Euler Equations in Vector-Invariant Form

Marco Artiano, Kieran Ricardo, Oswald Knoth et al. (+2 more)

We develop structure-preserving methods for the compressible Euler equations with gravity in vector-invariant form and potential temperature as a prognostic variable within the flux-differencing discontinuous Galerkin spectral element method (DGSEM) framework. By discretizing the nonconservative terms as symmetric and antisymmetric products, we derive two-point numerical fluxes that conserve both the thermodynamic entropy and the total energy. Moreover, we design an entropy-stable numerical flux that is well-balanced for both isothermal and isentropic background states. All properties are shown to carry over to the high-order DGSEM on general curvilinear meshes. Several numerical examples confirm the theoretical findings and show the robustness and accuracy of the scheme for use in modern dynamical cores for atmospheric flows.
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All authors Marco Artiano, Kieran Ricardo, Oswald Knoth, Peter Spichtinger, Hendrik Ranocha

Sunday 4 October

Atmospheric and Oceanic Physics

A Net Flux-Weighted Analytical Generalization of the Greenhouse Effect Beyond the Gray Approximation

Carmine Serio

Analytical greenhouse models traditionally rely on the gray approximation, simplifying radiative transfer but treating optical depth as an unconstrained parameter that yields unrealistic surface temperatures. We derive an exact, closed-form solution directly from Schwarzschild's equations for a non-gray atmosphere. While the gray approach is physically inadequate because it over-weights saturated bands that carry negligible net energy transport, we show that the non-gray formulation constrains column optical depth via the surface net radiative flux, naturally accounting for the atmospheric window without ad hoc corrections. The resulting effective optical depth penalizes saturated cores due to the negative spectral covariance between opacity and transmission. This shifts the computed surface equilibrium to an observationally consistent 288 K, yielding a purely radiative Transient Climate Response of +2.18 K under doubled \ce{CO2}, fully consistent with Intergovernmental Panel on Climate Change estimates. The framework eliminates any theoretical basis for greenhouse-effect saturation. While not intended to replace three-dimensional general circulation models, this work reconciles classical radiative transfer with modern climate estimates, demonstrating that proper flux-weighting aligns analytical theory with anthropogenic warming.
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Saturday 3 October

Atmospheric and Oceanic Physics

ClimateBench v2.0: Probabilistic Climate Model Benchmarking

Duncan Watson-Parris, Willa Tobin, Aytaç Paçal et al. (+32 more)

We present ClimateBench v2, a standardized protocol for evaluating climate models on diagnostics expected to be informative for their skill in projecting mid-century regional temperature and precipitation changes. The protocol is designed to evaluate any physics-based, data-driven, or hybrid climate model on equal footing using a common set of observational and out-of-distribution tests. We define three tiers of evaluation. Tier I establishes physical credibility through entry-ticket tests of energy conservation, coupled (co-)variability, and basic forced responses. Tier II scores models against post-2015 observations of surface temperature, precipitation, radiative fluxes, sea ice, and key modes of variability using fair CRPS as the primary probabilistic score, complemented by distributional and ensemble-consistency diagnostics. Tier III tests out-of-distribution generalization through paleoclimate simulations spanning the Last Interglacial, Last Glacial Maximum, and Mid-Holocene, and through perfect-model experiments in which data-driven models must predict the future climate of existing Earth system models from historical data alone. We reserve all observational data after 2015 for testing, and submissions must include multiple ensemble members to enable probabilistic evaluation. This reservation exploits a new opportunity provided by the decade of observations accumulated since the end of the CMIP6 historical experiment, which constitutes an out-of-sample record of forced climate change (and internal variability) for the current generation of models, and we quantify, in an idealized setting, the information it carries about mid-century warming. We provide the evaluation code, observational reference datasets, and perfect-model training data as an open benchmark to drive measurable progress in climate projection across all modeling approaches.
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All authors Duncan Watson-Parris, Willa Tobin, Aytaç Paçal, Manuel Schlund, V. Balaji, Kevin Bowman, Chris Bretherton, Peter M. Caldwell, Will Chapman, William D. Collins, Gregory S. Elsaesser, Pierre Gentine, Helene Hewitt, Stephan Hoyer, Ralph Keeling, Nikolay Koldunov, David M. Lawrence, Christian Lessig, Daniel J. Lunt, J. David Neelin, Mike Pritchard, Sarah Purkey, Gavin Schmidt, Tapio Schneider, Michael Schulz, Tiffany Shaw, Isla R. Simpson, Graeme Stephens, Aneesh C. Subramanian, Joao Teixeira, Jessica Tierney, Andrew I. L. Williams, Laure Zanna, Veronika Eyring, Rose Yu

Friday 2 October

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

KNOU-Flood: Advancing global streamflow forecasting with lead-aware probabilistic machine learning

Minjong Cheon

Global streamflow forecasting supports flood preparedness, yet data-driven models trained on reanalysis lose skill when driven by operational forecasts, increasingly so with lead time. AIFL addresses this shift with a two-stage transfer-learning recipe and a deterministic LSTM, but it feeds one forecast lead time into the whole input window and gives no uncertainty. We ask how far this open recipe can be extended without changing its recurrent core. KNOU-Flood keeps a single-layer LSTM and the two-stage training and changes the information around it: a longer input window (424 days), lead-aware forecast inputs, GraphCast as a second forecast source next to ECMWF HRES, and a latent-noise ensemble trained with the fair CRPS. We compare against a re-implementation of the AIFL recipe (its code is not public) under identical data, stations, splits and metrics. Our contributions are threefold. First, over three seeds and 2,099 test basins (2021-2023), KNOU-Flood reaches a median KGE' of 0.65 at LT1 and 0.48 at LT10 against 0.62 and 0.34, with significant gains at all ten lead times under two scoring protocols, also against a 1024-unit baseline and on held-out basins (one seed each). Second, identical weights give an LT10 KGE' of 0.18 or 0.34 depending on how forecasts are fed, so much of the lead-time decay reported for AIFL is an input-protocol effect; the probabilistic head contributes most to the gain, followed by the raw GraphCast channels, the longer window and mixed fine-tuning. Third, the ensembles reach lower CRPS than probabilistic versions of the baseline, are nearly calibrated with inference-time dropout, and detect 1.5- and 2-year floods better than all baselines; the benefit for rarer floods depends on the seed, and an exploratory tail-weighted variant improves 10-year and larger events. KNOU-Flood is a controlled extension of an open recipe, not a new architecture.
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Atmospheric and Oceanic Physics

A Multivariable Approach to the Regional Calibration of the Galvez Davison Index across Mexico Climate Regimes

Mauricio López-Reyes, Carlos Calvo-Sancho, Angel Meulenert et al. (+5 more)

The Gálvez Davison Index (GDI) is a thermodynamic diagnostic designed to summarize convective favorability in the tropical regions, yet its transferability across complex terrain and heterogeneous hydroclimates remains uncertain. Here we assess the climatological behavior and precipitation relevance of the GDI over Mexico using ERA5 hourly fields and CHIRPS daily precipitation for 1981 to 2024. We characterize the seasonal cycle and spatial contrasts of GDI and evaluate its association with rainfall using grid point and regional Pearson and Spearman correlations between daily maximum GDI and daily precipitation. To introduce an independent convective proxy, we also quantify the relationship between hourly GDI and ERA5 outgoing longwave radiation (OLR). Event level discrimination skill is assessed with receiver operating characteristic (ROC) curves and area under the curve (AUC) across regions and months. While GDI precipitation correlations are moderate and region dependent, GDI OLR coupling is consistently stronger, partly because OLR approaches a lower bound once deep convection develops, supporting its use as an intermediate proxy between thermodynamic favorability and rainfall. However, GDI values are systematically lower over elevated terrain because of the pressure level formulation of the index, which should be considered when interpreting the GDI over mountainous regions. ROC results indicate skill above random chance in nearly all region month combinations, with AUC frequently exceeding 0.75 during the core convective season. Building on these diagnostics, we propose a regionally calibrated GDI convective regime scale for May to October, adapted from the original GDI framework but recalibrated for Mexico using event conditioned distributions defined from OLR and precipitation and quantified with bootstrap uncertainty.
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All authors Mauricio López-Reyes, Carlos Calvo-Sancho, Angel Meulenert, José Francisco León-Cruz, Jose Manuel Gálvez, Javier Díaz Fernandez, Juan Jesús González-Alemán, María Luisa Martín Pérez

Atmospheric and Oceanic Physics

Stochastic thermodynamics and predictability of Ornstein-Uhlenbeck processes: an application to the Madden-Julian Oscillation

Roberta Benincasa, Danni Du, Gregory S. Duane et al. (+1 more)

Climate oscillations are intrinsically out-of-equilibrium phenomena, yet the connection between their predictability and nonequilibrium thermodynamic properties remains unexplored. We develop a coarse-grained stochastic thermodynamic framework for Ornstein-Uhlenbeck (OU) processes and apply this framework to climate data via linear inverse models (LIMs), linking predictability, entropy production, dynamical activity, and path action. Climate oscillations are often studied in a 2d phase space of climate indices, which are even under time-reversal, motivating our focus on 2d OU dynamics with even variables. We find that coordinate-invariant properties of 2d OU processes can be reduced to a minimal three-parameter description: two diffusivities and a phase-space rotation. We show that these quantities fundamentally characterize distinct aspects of the dynamics. We find that higher irreversibility and dynamical activity do not necessarily imply reduced predictability. Rather, it is the path action which is most closely connected to a commonly used measure of predictability, the anomaly correlation coefficient (ACC). As an illustrative example, we apply this framework to the Madden-Julian Oscillation (MJO) over the twentieth century. We find that the MJO has become simultaneously more predictable, as seen previously, while at the same time becoming more irreversible and more active. Predictability is driven predominantly by the reduced mean diffusion. Entropy production reflects the combined evolution of all three parameters. These results suggest that stochastic thermodynamics provides a powerful lens for studying nonequilibrium climate phenomena.
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All authors Roberta Benincasa, Danni Du, Gregory S. Duane, Jeffrey B. Weiss

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

DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants

Erik Wikingsson, Martin Andrae, Tomas Landelius et al. (+1 more)

Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guidance, they offer a promising route to high-dimensional non-Gaussian data assimilation (DA), the problem of combining forecasts with observations to estimate latent system states. Existing filters, however, condition on a fixed history and assimilate only the most recent observation, leaving them unable to revise past states when new observations arrive. Estimates then stay tethered to a history that later observations may contradict, and errors accumulate over the assimilation run. To this end, we introduce **DAWIS**, a unified DA method covering filtering, fixed-lag smoothing, and block smoothing within a single framework. DAWIS replaces the single flow time of a state-level prior with a multitask stochastic interpolant over a window of consecutive states, assigning a separate flow time to each. An assimilation cycle inverts the window to a vector of per-state turning points and regenerates it under observation guidance, with the turning points controlling how strongly each state is held fixed, revised, or generated from scratch. The same construction can also absorb the forecast into the assimilation cycle, removing the need for a separate forecasting model. Experiments on challenging nonlinear systems show that DAWIS improves on both filtering and smoothing baselines under sparse, noisy, and nonlinear observations. The code for DAWIS is available at https://github.com/Erik-Wikingsson/DAWIS
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All authors Erik Wikingsson, Martin Andrae, Tomas Landelius, Fredrik Lindsten

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

SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs

Maria Marchenko, Martin Andrae, Fredrik Lindsten et al. (+1 more)

Existing machine learning weather forecasting models typically generate forecasts through autoregressive rollouts at a fixed temporal resolution. While highly efficient for long-range prediction, this formulation can suffer from severe error accumulation when used with shorter time steps and does not explicitly encode the locality and temporal continuity of atmospheric dynamics. To address these limitations, we introduce **SDECast**, a Neural Stochastic Differential Equation (SDE) framework for continuous-time probabilistic weather forecasting. SDECast extends SDE Matching to learn stochastic dynamics directly in physical space, without requiring repeated SDE simulation during training. On a simulated geophysical flow, we show that SDECast recovers meaningful drift dynamics and faithfully reproduces the underlying continuous-time behavior. We then demonstrate its scalability to global weather forecasting at hourly resolution, where SDECast produces skillful probabilistic forecasts for lead times of up to five days.
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All authors Maria Marchenko, Martin Andrae, Fredrik Lindsten, Christian A. Naesseth

Atmospheric and Oceanic Physics

S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales

Chenyu Dong and Gianmarco Mengaldo

The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture, energy, and water management. Yet, it is widely known as the `predictability desert'. Recent AI weather models excel up to two weeks ahead but deteriorate beyond, largely because they are trained to predict fine-scale details that are neither predictable nor essential at S2S timescales. We argue that a more physically grounded objective is to forecast only the slowly varying components that remain predictable. Computer vision reached the same conclusion with the Joint-Embedding Predictive Architecture (JEPA), which predicts in latent space, discarding unpredictable details. In this work, we introduce S2S-JEPA, which brings the JEPA paradigm to S2S forecasting. It is tailored to this task through design elements from state-of-the-art AI weather models. S2S-JEPA achieves comparable skill to the gold-standard ECMWF physics-based ensemble and surpasses it on multiple metrics at weeks 5 to 6.
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Thursday 1 October

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

Strategic Governance of AI Models in Earth Science

Makoto Kelp, Amirhossein Arzani, Patricia Castellanos et al. (+6 more)

AI foundation models pretrained on weather and climate data are increasingly fine-tuned to Earth science tasks well beyond weather forecasting. Their development and adoption are outpacing the scientific community's ability to evaluate them. These models are judged almost entirely by benchmark skill metrics, which measure how closely a forecast reproduces a reference product but not whether a model represents the physical processes governing the system it predicts. Forecast skill and physical reliability are therefore distinct properties. The distinction is most consequential under the nonstationary conditions of a changing climate for which these models were never trained. We identify five priorities for the physical evaluation of AI models in Earth science from task-specific emulators to foundation models, spanning training data, fine-tuning, behavioral testing, mechanistic interpretability, and output validation. We recommend three activities for the coming decade: 1) open AI-ready evaluation datasets, 2) a shared reporting standard for physics-based evaluation, and 3) a dedicated research program on the safety of these models.
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All authors Makoto Kelp, Amirhossein Arzani, Patricia Castellanos, Paul Griffiths, Ivan Higuera-Mendieta, Manuel Perez-Carrasco, Viral Shah, Patrick Obin Sturm, James Weber