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Physics Physics Atmospheric and Oceanic Physics

physics.ao-ph Watch Atmospheric and Oceanic Physics

Wednesday 14 October · still arriving

Atmospheric and Oceanic Physics

Improved Precipitation Variability in the Community Atmosphere Model through Implementation of a Neural-Network Deep Convection Scheme

Xavier J. Levine, Griffin Mooers, Jack W. Atkinson et al. (+4 more)

Conventional deep convection schemes remain a primary source of uncertainty in global climate models, yet replacing them with data-driven alternatives while maintaining physical consistency and numerical stability is an open challenge. We present a hybrid configuration of the Community Atmosphere Model version 6 (CAM6), in which a fully connected deep neural network replaces the Zhang-McFarlane deep convection scheme (ZM), while radiation, turbulence, and other cloud processes are left unchanged. The network is trained on output from a storm-resolving global model - the global System for Atmospheric Modeling (gSAM) - using a coarse-graining approach to derive subgrid-scale convective fluxes and tendencies. This data-driven parameterization is ported to CAM6 and found to run stably for more than a decade while conserving energy and moisture. It produces a broadly realistic climatology without retuning the remaining parameterizations. Relative to the default Zhang-McFarlane scheme, the neural network parameterization substantially improves some long-standing model deficiencies. In particular, the biases in light precipitation and extreme precipitation are markedly reduced, and the diurnal cycle of tropical land precipitation shifts from a spurious midday peak over many regions to the observed late-afternoon peak. These results support targeted ML-based convection schemes as a viable path toward reducing systematic model errors in next-generation climate models.
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All authors Xavier J. Levine, Griffin Mooers, Jack W. Atkinson, Marat F. Khairoutdinov, Janni Yuval, Dominic Orchard, Paul A. O'Gorman

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

R&D activity in INAF on light instrumentation for continuous optical turbulence monitoring

Camilo Weinberger Elena Masciadri, Luca Carbonaro, Marlene de Sepibus et al. (+4 more)

Measurements of optical turbulence are essential in both ground-based astronomy and free-space optical communication systems contexts for multiple reasons including supporting and optimizing Adaptive Optics. Similarly, forecasting these parameters is highly valuable for planning observational campaigns; however, it requires at least two years of continuous data to generate a reliable training dataset. The most commonly used instruments to monitor continuously the optical turbulence include DIMM, MASS, SLODAR, RINGSS sensors. The Arcetri team is leading two operational forecast systems for two different Observatories: ALTA above LBT, and FATE above VLT. Both systems are in continuous evolution and obviously forecast performances depend also on available measurements. Our long-term goal is to develop our own instruments to obtain continuous measurements directly from local observations. In this work, we present an automated system designed for continuous seeing measurements using multiple sensors. This portable station integrates a Cyclope, a RINGSS unit, a scintillometer which operate together to provide comprehensive night-time atmospheric information. We will compare the performance of low-cost sensors, such as the Cyclope and the scintillometer, with that of the RINGSS turbulence profiler, which has been fully installed and programmed by our team in collaboration of LBT team. A dedicated insturment for night and day-time measurements is planned for near future. This system incorporates a fully automatic on-site system that monitors operational status, delivers real-time information on sensor conditions and measurements, and manages synchronization with the data storage unit. The system's real-time acquisition capability enables continuous turbulence analysis, supports R&D activities, facilitates local data generation, and allows on-site testing of forecasting methodologies.
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All authors Camilo Weinberger Elena Masciadri, Luca Carbonaro, Marlene de Sepibus, Fernando Pedichini, Ciro Del Vecchio, Christian Veillet, Sam Ragland

Atmospheric and Oceanic Physics

PrecipCorrDiff: A Deep Learning Framework for Generating 0.0625$^\circ$ High-Resolution IMD Gridded Precipitation Data

Bipin Kumar, Rajib Chattopadhyay, Harsh P. Bachhav et al. (+7 more)

High-resolution precipitation datasets are essential for weather forecasting, hydrological modeling, and climate risk assessment, yet observation-based gridded products often lack the spatial detail required for regional applications. Here we present PrecipCorrDiff, a diffusion-based downscaling framework built by modifying the original corrector diffusion based archetecture that incorporates a customized loss function and a multi-scale residual generation strategy to improve the precipitation especially the representation of extremes. PrecipCorrDiff generates 0.0625{\deg} resolution India Meteorological Department (IMD) precipitation fields from 0.25{\deg} gridded data while preserving precipitation statistics and reconstructing fine-scale spatial variability. Validation against independent IMD station observations, comparing the proposed framework with the SRGAN-based model and the original CorrDiff model, indicates that the proposed framework substantially improves the representation of spatial precipitation patterns, rainfall intensity distributions, and extreme rainfall events, while eliminating the systematic negative bias present in the previously developed SRGAN-based model. Across four climatologically distinct homogeneous Indian monsoon regions, PrecipCorrDiff reproduces observed precipitation characteristics with near-zero spatial bias, demonstrating the potential of diffusion-based downscaling for generating physically consistent, high-resolution precipitation datasets for hydrological, meteorological, and climate applications.
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All authors Bipin Kumar, Rajib Chattopadhyay, Harsh P. Bachhav, Jannu, Bhupendra Bahadur Singh, Ramesh Ragala, Ananda Kumar Das, Manish Modani, Thara Prabhakaran, Mrutyunjay Mohapatra

Atmospheric and Oceanic Physics

Generative modelling for multivariate spatio-temporal downscaling of monthly climate fields

Yu Huang, Shangshang Yang, Sebastian Bathiany et al. (+3 more)

Inferring climate fields at higher spatio-temporal resolutions from coarse representations is essential for accessing socioeconomic and environmental impacts. The downscaling of modelled climate fields faces a trade-off between data storage constraints, simulation efficiency, and resolution requirements. While many methods for spatial downscaling have been proposed, joint spatio-temporal downscaling has received comparable little attention. When deriving, for example, daily fields from climate datasets that are available only as monthly averages at coarser spatial resolution, it is highly challenging to faithfully reproduce extreme event statistics and spatiotemporal consistency. To address this task, we introduce a generative machine learning approach, using a diffusion model to jointly downscale monthly temperature and precipitation fields into temporally consistent daily fields at higher spatial resolution. We demonstrate that our approach can generate high-dimensional multivariate climate fields at daily temporal and 0.25{\deg} spatial resolution from monthly input fields at 1{\deg} spatial resolution, exhibiting coherent temporal dynamics and physically plausible spatial structures. In particular, by incorporating a conditional guidance technique, we show that a pre-trained diffusion model can be directly leveraged to efficiently generate daily high-resolution fields conditioned on monthly inputs, without requiring additional fine-tuning or retraining. Our probabilistic approach thus provides an efficient and generalizable framework for spatio-temporal climate downscaling, enabling flexible climate fields generation for climate impact assessments and digital Earth simulator constructions.
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All authors Yu Huang, Shangshang Yang, Sebastian Bathiany, Michael Aich, Philipp Hess, Niklas Boers

Atmospheric and Oceanic Physics

Unmasking Compensating Errors in Numerical Weather Models: A Process-Additive Neural Network Approach

Yuiko Ichikawa, Edward Groot and Jack Atkinson

This work introduces a Process Additive Neural Network (PANN) framework to disentangle compensating biases in atmospheric models, where errors in different physical processes can offset each other and obscure their origins. Using a single-column model, each PANN branch represents the tendency error associated with a separate physical process, and the summed branch outputs approximate the total error relative to a reference tendency. Once trained successfully, the branch outputs are expected to provide process-level error attributions and, when averaged, estimates of process-level bias. Initial experiments focus on cloud-related processes in OpenIFS SCM case studies, where cloud and convection tendencies are strongly coupled and compensation is difficult to diagnose using standard approaches. The aim is not to replace human interpretation of physical processes, but to extend it to regimes where model complexity, process interaction, and compensating errors make direct diagnosis difficult. By providing a physically structured form of machine-learning interpretability, PANN can help connect complex model behaviour with process-level explanations. This work supports both improved understanding of compensating errors and the development of more physically consistent weather and climate models.
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Atmospheric and Oceanic Physics

Physical Processes Driving Year-to-Year Surface Temperature Variability Changes in a Warming Climate

Rory Basinski-Ferris and Ian Eisenman

Under anthropogenic greenhouse forcing, climate models project a decrease in year-to-year temperature variability in the mid-to-high latitudes and an increase in the tropics and subtropics. Here, we examine year-to-year temperature variability in the CESM Large Ensemble in concert with an idealized stochastic moist energy balance model (SMEBM) with effective surface heat capacity and local feedback parameter specified based on CESM. We find that the zonal-mean CESM variability change pattern largely persists after filtering out the impact of El Niño. The SMEBM largely captures this change pattern. In the SMEBM, changes in the mid-to-high latitudes are driven by changes in the effective heat capacity due to sea ice retreat, combined with changes in anomalous moist static energy convergence modulated by the polar-amplified time-mean temperature change. The CESM local feedback parameter evolves to be less stabilizing in the low latitudes, which causes tropical and subtropical variability to increase in the SMEBM.
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Tuesday 13 October · still arriving

Atmospheric and Oceanic Physics

Collaborative Neural Networks significantly Improve Global Aerosol Retrievals From Multi-Angle Polarimeters

Guangliang Fu, Sha Lu, Meng Gao et al. (+2 more)

We present the RemoTAP-Hybrid algorithm for global aerosol products retrieved from Multi-Angle Polarimeters (MAP), and apply it to one-year aerosol retrievals from the SPEXone MAP instrument. For the first time, Multiple Collaborated Neural Networks (MCNN) are used to perform global aerosol MAP retrievals over land and ocean. The combined use of MCNN with a full physics inversion in RemoTAP-Hybrid, significantly improves accuracy and computational speed of SPEXone aerosol retrievals. We validate the retrieved Aerosol Optical Depth (AOD), Angstrom Exponent (AE), Single Scattering Albedo (SSA), Absorption AOD (AAOD), Fine mode AOD (FAOD), Coarse mode AOD (CAOD), and fine mode effective radius (reff_fine) with AERONET data. The validation suggests that aerosol retrievals can be further improved when extending the aerosol description to include a fine non-spherical mode. For AOD, the Root-Mean-Square Error (RMSE) is 0.051 (0.037) over land (ocean), while 78.3% (80.8%) of the retrievals are within the requirement formulated by the Global Climate Observing System (GCOS). The RMSE is 0.186 (0.207) for AE, 0.038 (0.044) for SSA, 0.023 (0.014) for AAOD, 0.047 (0.031) for FAOD, 0.043 (0.027) for CAOD, and 0.021 (0.026) for reff_fine over land (ocean). Compared to the current RemoTAP algorithm, RemoTAP-Hybrid shows improved agreement with AERONET for AOD, AE, and FAOD over land and ocean while the number of valid retrievals is increased by 4% (3%) over land (ocean) and the computational time is reduced by 43% (32%). Finally, we present the first global seasonal maps of aerosol optical and microphysical properties and chemical composition from SPEXone.
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All authors Guangliang Fu, Sha Lu, Meng Gao, Jeroen Rietjens, Otto Hasekamp

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

Simulations of the Evolving Ozone Layer: Implications for the Early Earth, Exoplanets, and the Search For Life

Akhil Kumar and Gregory J. Cooke

Over 4 billion years, Earth's atmosphere has been transformed from a composition devoid of molecular oxygen (O$_2$) to one where O$_2$ is now 21% of our modern atmosphere by volume. With high quantities of O$_2$, an ozone (O$_3$) layer can form and partially shield the surface from biologically harmful ultraviolet radiation (UV). The evolution of the O$_3$ layer has previously been simulated under the modern Sun by independent groups with separate models. Here, we present simulations of Earth's atmosphere from four open-source one-dimensional (1D) photochemical models and a 3D chemistry-climate model, focussing on O$_3$. We find that the O$_3$ layer thickness exhibits a clear deviation between the 1D models and the 3D model due to several factors, including treatments of O$_2$ photolysis, chemical networks, H$_2$O microphysics and 3D transport. We find that 1D models introduce a bias for unobserved climate states and show how these biases may propagate to calculations of biological UV damage and surface habitability. We show that when applied to specific exoplanet atmospheres, these discrepancies can become more pronounced, propagating through to observational predictions for the next generation of telescopes, such as HWO and LIFE, that will search for habitable and inhabited exoplanets.
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Atmospheric and Oceanic Physics · Cross-listed from astro-ph.IM

Establishing baseline model performances for optical turbulence forecasting

M. De Sepibus, E. Masciadri, C. Weinberger et al. (+4 more)

Accurate forecasting of optical turbulence (OT) is useful to optimise telescope scheduling and maximising observing efficiency, as the performance of adaptive optics systems strongly depends on turbulence conditions. While various numerical weather prediction and machine learning models are developed to predict these complex turbulence properties, properly evaluating their added value requires well-defined baseline reference models. This study establishes baseline performance thresholds for key astroclimatic parameters using two distinct models: one forecasting the parameter average over a specific timescale, and the other forecasting the temporal evolution over the same specific timescale. We apply these frameworks to extensive, multi-year datasets from the sites of the Very Large Telescope (VLT) and the Large Binocular Telescope (LBT) and discuss the possibility of extrapolating reference values of forecast performances for other sites based on their optical turbulence statistical characteristics. Our results show that baseline predictive accuracy varies according to the observing site, the specific parameter, the chosen timescale, and the forecasting approach. Therefore, an OT forecasting model's accuracy cannot be quantified in absolute terms, but relatively to the baseline.
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All authors M. De Sepibus, E. Masciadri, C. Weinberger, A. Otarola, S. Miesske, C. Veillet, S. Ragland

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

Some generalizations of the tornado model

S. N. Artekha

A possible mechanism of tornado generation is associated with the Schwarzschild criterion for convective instability. The supercooled cloud-air mixture near the axis starts to descend and, due to the super adiabatic temperature gradient, the overheated near-surface air mass rises. The complete system of equations for full model includes equations for the stream function, for the azimuthal velocity component, and for the change in density. The refined model considers rotating convection within the framework of ideal hydrodynamics. The simplest model uses a separate equation for the stream function and can be applied to the initial stage of vortex generation. Within the framework of the simplest model, an analytical solution is combined from 8 linear dependencies by height. Rotation increases due to the conservation of angular momentum. Within the framework of the refined model, some unidirectional differential rotation arises, but it depends on boundary conditions (can be local). The full model studies the tornado development as rotating convection from the very beginning to the quasi-stationary stage. Velocity components increase in a coordinated manner. Two cases are considered: a single whirlwind and a two-eyed tornado. Radial flows converge towards the central part of the tornado near the earth surface, and there is an outflow from the "wall" to the axis and to the periphery near the top of the tornado. The maximum vertical velocity component is achieved at a certain radial distance at a certain height. The maximum azimuthal velocity component is achieved in the tornado "wall". Tornado generation criteria are discussed.
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Atmospheric and Oceanic Physics · Cross-listed from physics.flu-dyn

Preferential concentration of cloud droplets: Coupled effects of phase change and gravitational settling

Zi-Mo Liao

Cloud droplets respond to turbulent motion with finite inertia and settle under gravity, while condensation and evaporation change their sizes. The nonuniform accumulation of cloud droplets in turbulent flows, known as preferential concentration, has attracted sustained interest because it affects droplet collision and coalescence rates. We develop an analytical theory that quantifies the coupled effects of droplet inertia, gravitational settling, and phase change on concentration changes. Applying this theory to simulations of two-dimensional homogeneous thermal convection reveals that phase change markedly enhances preferential concentration in this flow, primarily through spatial differences in settling speed. These differences arise because droplets experience different thermodynamic histories along their trajectories and thus acquire different sizes. Droplet transport further increases this settling contribution by rearranging droplets of different sizes. Fluid deformation partly offsets this enhancement by thinning concentration structures, reducing the preferential concentration measured at a fixed spatial scale.
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Atmospheric and Oceanic Physics

Linking Horizontal and Vertical Submesoscale Lagrangian Dynamics South of the Ibiza Channel

Sara Cloux, Ismael Hernandez-Carrasco, Alejandro Orfila et al. (+2 more)

Fronts, eddies, jets, and meanders populate the ocean surface and play a key role in organizing transport and mixing across a wide range of scales. From a Lagrangian perspective, frontal structures are conveniently detected as surface filaments that act as barriers to horizontal transport. However, the degree to which these structures maintain coherence below the surface and the relationship between horizontal and vertical motions in their vicinity remain poorly understood. Here we analyze two such filaments in the western Mediterranean Sea, south of the Ibiza Channel, using a high-resolution ocean simulation and Lagrangian diagnostics specifically designed to characterize submesoscale transport. Backward Finite-Size Lyapunov Exponents (FSLE) computed at successive depths reveal nearly vertical, curtain-like structures extending several tens of meters into the water column, with intensity decreasing with depth. Vertical-velocity sections across the filaments show coherent downward motion aligned with the filament cores, while Lagrangian trajectories indicate rapid horizontal convergence of fluid particles accompanied by sustained downward displacement. The barrier effect of Lagrangian Coherent Structures (LCSs) is commonly associated with persistent dynamical features of large-scale flows. Here, however, we demonstrate that FSLE filaments generated by rapidly fluctuating submesoscale dynamics can also act as effective transport barriers. The results demonstrate that the surface signatures of Lagrangian filaments identified through FSLE techniques correspond to three-dimensional transport structures that regulate both lateral aggregation and vertical connectivity in the upper ocean.
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All authors Sara Cloux, Ismael Hernandez-Carrasco, Alejandro Orfila, Emilio Hernandez-Garcia, Cristobal Lopez

Atmospheric and Oceanic Physics

Cumulus parameterization controls simulated rainfall more than microphysics, but not at heavy thresholds: a 3 * 2 factorial WRF study over the Western Himalaya

Satyam Kumar, Akshay Sunil and Devesh Sheoran

Rainfall forecasts over the Western Himalaya remain poor, and the relative contributions of the cumulus and microphysics parameterizations to that error are not well constrained.

Atmospheric and Oceanic Physics

Extreme events and statistics in an AI emulator of a global storm-resolving model

Anna Kwa, W. Andre Perkins, Troy Arcomano et al. (+7 more)

We present HiRO-ACEv2 (High Resolution Output Ai2 Climate Emulator), a two-stage framework for AI emulation of the 3~km resolution X-SHiELD global storm-resolving model (GSRM), and highlight its ability to generate large ensembles of data for studying the characteristics of tropical cyclones and extreme events in high-resolution simulations at an inference computational cost over 300 times lower than that of traditional numerical GSRMs. HiRO-ACEv2 expands upon the precipitation-only HiRO-ACEv1 model to generate coherent precipitation, 10~m winds, and sea level pressure outputs, enabling a broader range of events to be simulated at high-resolution. Fine-resolution predictions are generated by running an autoregressive emulator of coarsened X-SHiELD at 100 km, whose multivariate outputs are regionally downscaled to 3~km resolution via a separately trained diffusion model. Particular importance is placed on capturing the statistics of extreme events during the diffusion downscaling step. Our ability to match the tails of distributions and generate realistic-looking extreme events in both the `perfect prediction' case as well as in free-running emulator rollouts is improved via the use of multiple denoisers at inference time and several updates to the data sampling, noise sampling, and loss weighting during training. HiRO-ACEv2 closely matches the high-resolution reference distributions of precipitation and near-surface winds out to their 99.9999th percentile values, and broadly tracks the reference distribution of tropical cyclone intensities.
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All authors Anna Kwa, W. Andre Perkins, Troy Arcomano, Noah D. Brenowitz, Christopher S. Bretherton, Spencer K. Clark, Lucas M. Harris, Jeremy McGibbon, Michael S. Pritchard, Oliver Watt-Meyer

Atmospheric and Oceanic Physics

The hypervariational method: a general, automated technique based on innovations to tune the parameters of the Hybrid covariance matrix

Francesco Sardelli and Craig H. Bishop

The Hybrid forecast covariance model is an operationally successful way to build a background error covariance matrix. It is constructed as a weighted sum, with scalar weights, of the climatological covariance matrix and a localized ensemble covariance matrix, with a localization matrix typically depending on a localization length. For best performance, the Hybrid needs to be optimized by tuning those weights and, possibly, other parameters, such as the localization length. Here, a systematic and automated method for this tuning operation is introduced. This technique is referred to as hypervariational method, and it is formulated in terms of a cost function, built using a time series of innovations as an essential ingredient. This cost function, which depends on the parameters, is minimized when they have optimal values. Equations for its minimization, based on Newton's method, are derived. Besides the aforementioned, commonly used Hybrid, the hypervariational method is applicable to any known Hybrid (and to any set of parameters on which it depends analytically), including recently developed Hybrids, not necessarily constructed as weighted sums. Here, the hypervariational method is applied to tune the ensemble covariance matrix weight in the seminal-formulation Hybrid (which does not feature localization). This is done in a synthetic experimental setting where a theoretically-derived expression for the optimal value for that weight is known. Moreover, in that experimental setting, a further method, based on a linear fit procedure, to determine that optimal value is possible. Both the hypervariational method and that linear fit procedure are shown to produce an optimal weight value matching (up to a tolerance of $0.1$) the optimal value known from the theory. Furthermore, Newton's minimization in the hypervariational method is shown to converge immediately (at the first recursive step).
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Monday 12 October

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

Accounting for model error in ensemble variational data assimilation through learned generative dynamics

Marc Bocquet, Tobias S. Finn and Sibo Cheng

Many approaches have recently been proposed to combine data assimilation with machine learning for improved forecasts with geophysical models. Here, we focus on combining advanced sequential data assimilation schemes with a generative forecasting model which accounts for complex model errors. This generative model provides a stochastic approximation of the conditional distribution of the successor state given the current state. The proposed variational formulation is a novel one-step weak-constraint 4D-Var scheme in which a learned conditional stochastic forecast replaces the prescribed additive Gaussian model error representation. We derive and implement this formulation as an ensemble variational algorithm, the generative iterative ensemble Kalman filter (IEnKF-G), which generalises the iterative ensemble Kalman filter with additive Gaussian model error (IEnKF-Q). We extend the method to a smoother with an assimilation window spanning multiple time steps and propose a simplified variant. The IEnKF-G is evaluated on a stochastic variant of the Lorenz 1963 model to assess its robustness. It is then tested on a multi-scale variant of the one-dimensional Lorenz 1996 model, in which the resolved variables are affected by model error originating from the hidden small-scale variables. The algorithm is then compared to an ensemble Kalman filter that adaptively mitigates model and sampling errors, and to the IEnKF-Q. The IEnKF-G is generally more accurate in the tested configurations, with substantial gains under partial observations and significant model error. This demonstrates the potential of generative data-driven models when paired with classical data assimilation methods, particularly when model error is substantial.
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Atmospheric and Oceanic Physics

Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa

David Sagna, Redouane Lguensat and Samo Diatta

Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based architectures to downscale daily ERA5 fields (0.25{\deg}) to IMERG V07 satellite precipitation (0.1{\deg}) over West Africa for the June to October seasons of 2000 to 2024, using geopotential, temperature, humidity and wind at three pressure levels together with elevation and land fraction. Adding ERA5 precipitation as input slightly improves performance for this particular challenging region. We find that all the deterministic networks investigated for this work are too dry and lose almost all observed variance below a few hundred kilometers, which motivated our use of a GAN-based alternative. We also investigated permutation importance for the U-Net that ranks 500 hPa specific humidity first, consistent with the sensitivity of deep convection to mid-tropospheric moisture.
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Atmospheric and Oceanic Physics

Forecasting carbon-risk hotspots ahead of the extreme 2026 El Niño

Xiaomeng Du, Runda Jiang, Philippe Ciais et al. (+13 more)

A potentially super El Niño is forecasted in 2026, raising the need to anticipate land carbon-cycle risks before its impacts unfold. We develop a prospective early-warning framework that uses dynamic global vegetation models driven by an ensemble of ECMWF seasonal climate forecasts and machine-learning burned area predictions. Forecasts indicate a $0.23 \pm 0.21$ PgC reduction in global net biome productivity in the second half of 2026, with hotspots of sink weakening in eastern South America and elevated fire risk in Southeast Asia.
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All authors Xiaomeng Du, Runda Jiang, Philippe Ciais, Pierre Friedlingstein, Stephen Sitch, Mike O'Sullivan, Jefferson Goncalves de Souza, Tim Stripp, Piyu Ke, Yi Xi, Minxuan Sun, Xiaoran Wu, Mengjie Han, Shanshan Song, Qiang Zhang, Wei Li

Atmospheric and Oceanic Physics

Identifying Arctic states vulnerable to rapid ice loss with a convolutional neural network

Matthew Patterson, Ioana Colfescu, Nathan Kirk et al. (+1 more)

This work investigates the incidence of rapid ice-loss events in the Arctic, in which a substantial fraction of September sea-ice is lost in only a few years. Such events are seen to occur in climate model simulations forced with increasing greenhouse gas concentrations, but not as yet in the relatively short observational record. We train a convolutional neural network (CNN) using six large climate model ensembles to diagnose vulnerability to rapid ice-loss events. The trained model is shown to separate higher and lower risk states in out-of-sample climate models via a leave-one-out cross-validation. The model emphasises the role of persistently thin sea-ice, remaining sea-ice extent and sustained high upper ocean heat content as predictors of rapid ice-loss. The CNN is applied to observational data over the satelite-era showing a signal for rapid ice-loss prior to a drop around 2007 and an increasing risk from around 2015 to present. Such rapid-ice loss events would likely have dramatic consequences for Arctic ecosystems, coastal communities and global shipping.
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All authors Matthew Patterson, Ioana Colfescu, Nathan Kirk, Graeme MacGhilchrist

Atmospheric and Oceanic Physics

Multivariate phase-space interpretation of ensemble forecasts: A comparison of physical and AI models

Martin Bonte and Stéphane Vannitsem

Ensemble forecasts from the ECMWF's Integrated Forecasting System (IFS) and the Artificial Intelligence Forecasting System (AIFS-ENS) are compared using a multi-dimensional evaluation framework. Within this framework, the uncertainty is interpreted as the linear subspace spanned by the ensemble members in phase space, from which various geometric properties are extracted. As revealed by the analysis, the error does not entirely lie in this subspace, but is reasonably well calibrated in all directions. Moreover, the framework allows to quantify that this subspace critically depends on the number of ensemble members, suggesting that the current setups only partially represent the uncertainty subspace. This phase-space interpretation furthermore allows a direct comparison of the error-representation patterns of AIFS-ENS and IFS by computing the principal angles between the subspaces: IFS and AIFS ensemble subspaces do not entirely overlap in phase space, even though they were shown to have similar abilities to represent the amplitude and the structure of the error.
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Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP

Interaction of eccentricity tide and convection

Jiachen Zheng and Xing Wei

Aim. We try to understand the interaction of eccentricity tide and convection in icy satellites. Method. We use the numerical calculation of rotating convection with eccentricity tide in a spherical shell. Results. We have the following results. (1) Energy scaling exponent about buoyancy force (or tidal force) decreases with increasing tidal force (or buoyancy force). (2) The combined tide-convection flow is not identical to superposition of two pure flows in respect of energy spectrum. (3) Tide controls large-scale of fluid motion but convection is important for small scale, and the mode-mode transition occurs at intermediate scales. (4) Tide suppresses heat transport efficiency. (5) Dissipation of kinetic energy scales as square of tidal force with a very weak dependence on buoyancy force, which has little impact on dissipation but suppresses the tidal influence on dissipation.
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Atmospheric and Oceanic Physics · Cross-listed from physics.flu-dyn

Planar intrusions from a narrow, jet-like source in a linearly stratified ambient

Hai Duc Vu, Murray Rudman and Anja Slim

We present two and three dimensional direct numerical simulations of planar intrusions injected from a continuously supplied, narrow source into a linearly stratified ambient at the level of neutral buoyancy. The evolution passes through three phases. A laminar leading vortex dipole first advances at constant speed with a varicose wave train behind it; the trailing jet then undergoes a sinuous instability whose initial growth rate and wavelength agree with inviscid unstratified planar-jet theory evaluated on the local jet; the flow finally relaxes into a buoyancy-controlled wedge whose area grows linearly in time. Three-dimensional simulations show that the transition remains organized by the planar instability, while three-dimensionality reshapes the near-source region into a slowly evolving, actively mixing zone that entrains an additional twenty to one hundred percent of the source flux depending on source width. Rescaled with this measured effective flux, the downstream intrusion collapses reasonably well onto the similarity solution for a wide-source intrusion.
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Sunday 11 October

Atmospheric and Oceanic Physics

Climate Sensitivity Derived from First-Principles Temperature Feedback Circuits

Ming Cai, Jie Sun, Zhe-Min Tan et al. (+3 more)

A long-standing challenge in climate science since the Charney Report in 1979 is to determine how much the Earth's climate warms in response to a unit external energy input at the top of the atmosphere (TOA). In this study, we show from first principles that temperature feedback within an atmosphere-surface column operates as a set of feedback circuits, each governed by an intrinsic gain less than unity, leading to geometric amplification of energy through successive feedback cycles. In particular, the surface-centered temperature feedback circuit determines the amplification of surface energy per unit TOA forcing. The surface energy amplification by non-temperature feedback is estimated from the amplification of solar energy absorption by downward longwave radiation at the surface in the climate mean state. Climate sensitivity then emerges as the product of these two amplifications scaled by the surface Planck feedback parameter. The climate sensitivity derived from the 1980-2000 climate mean state is 1.15 K (W m$^{-2}$)$^{-1}$. It yields a warming of 0.44 K from 1980-2000 to 2000-2020, consistent with the observed warming of 0.41 K, and equilibrium warmings of 3.02 and 5.83 K for CO$_2$ doubling and quadrupling, respectively, the former consistent with the Charney estimate near 3 degrees C. The framework provides a basis for predicting global surface temperature responses to other external forcing scenarios from the climate mean state.
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All authors Ming Cai, Jie Sun, Zhe-Min Tan, Xiaoming Hu, Guosheng Liu, Zhaohua Wu

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

Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models

Qi Chen, Peng Zhan, Hongxing Cui et al. (+2 more)

Deep-learning models increasingly rival numerical simulation in Earth-system forecasting, yet forecast error alone cannot determine whether their learned input-output dependencies are physically consistent. Here we introduce sensitivity fidelity as an additional criterion: alongside prediction error, model responses to input perturbations should be examined against a trusted reference or established physical knowledge. In an idealized Rayleigh-Bénard convection benchmark, four surrogate architectures achieved low forecast errors yet encoded markedly different sensitivities. Quantitative comparison with a matched adjoint distinguished the models far more sharply than forecast accuracy and showed that predictive skill and sensitivity fidelity need not coincide, while finite-perturbation experiments further linked these differences to the propagation of initial-condition uncertainty. We then applied this strategy to El Niño-Southern Oscillation prediction, where no adjoint reference exists and learned sensitivities showed event-wise robustness, validity under finite perturbations, and consistency with established precursors. These results identify gradient-based evaluation as a discriminating complement to forecast-error metrics, providing a practical test of whether data-driven Earth-system models learn physically credible dependencies.
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All authors Qi Chen, Peng Zhan, Hongxing Cui, Wenqing Cui, Ibrahim Hoteit

Saturday 10 October

Atmospheric and Oceanic Physics

Clarifying tipping terminology

Niklas Boers, Robert E. Kopp, Peter Ashwin et al. (+21 more)

The notion of tipping points and related terminology have so far had inconsistent and sometimes confusing definitions in both scientific and popular literature. While it is difficult for the scientific community to align popular usage of the term tipping point with a precise definition, technical usage of related terms would benefit from greater consistency and rigor. Toward this end, we offer a systematic set of terms to clarify formal communication around potential tipping dynamics, with focus on examples from Earth system dynamics.
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All authors Niklas Boers, Robert E. Kopp, Peter Ashwin, Sebastian Bathiany, Maya Ben-Yami, Reyk Börner, Jofre Carnicer, Henk Dijkstra, Thomas L. Frölicher, Michael Ghil, Elisabeth Gilmore, Gabriele C.Hegerl, Laura Jackson, Shipra Jain, David Lapola, Timothy M. Lenton, Valerio Lucarini, Teng Liu, Virna L. Meccia, Andreas Morr, Anastasia Romanou, Thomas F. Stocker, Shuting Yang, Cunde Xiao

Atmospheric and Oceanic Physics

Learning native eddy-resolving ocean dynamics outperforms coarse emulation and downscaling

Zilu Liu, Tao Zhang, Hailong Liu et al. (+8 more)

Eddy-resolving ocean simulations are essential for representing mesoscale dynamics that regulate ocean circulation, energy transport, and climate variability. However, they are costly to generate, store, and share. Artificial intelligence (AI) may help, but it remains unclear whether high-resolution ocean fields can be faithfully represented by evolving ocean states at coarse resolution followed by spatial downscaling, or whether dynamical evolution needs to be learned directly from native high-resolution simulations. We show that directly learning dynamical evolution on a 0.1$^\circ$ grid provides a more faithful representation of mesoscale variability than evolving ocean states on a 1$^\circ$ grid followed by downscaling reconstruction. We developed a full-depth 1$^\circ$ emulator and a native 0.1$^\circ$ sea surface temperature (SST) emulator, both of which remained stable over long-term free-running simulations without significant drift, providing reliable baselines for this comparison. Direct learning at 0.1$^\circ$ preserves mesoscale energy, spectral characteristics, and frontal gradients, whereas the coarse-resolution evolution followed by downscaling approach recovers large-scale patterns but fails to reproduce key mesoscale signatures. We show that the learned mesoscale statistics remain consistent across diverse hardware platforms, supporting the reproducibility and portability of AI-based ocean simulations. These results suggest that downscaling of low-resolution large-scale states alone is insufficient to represent ocean mesoscale variability, supporting the need to learn dynamical evolution directly in a high-resolution state space. These findings highlight the continued importance of high-resolution physics ocean models as a foundation for AI-based ocean simulation, providing dynamically resolved training data for physically faithful high-resolution emulators.
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All authors Zilu Liu, Tao Zhang, Hailong Liu, Kai Xu, Qinghui Lin, Maoxue Yu, Jiangfeng Yu, Jingwei Xie, Zhao Jing, Minghua Zhang, Lixin Wu

Friday 9 October

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

Unifying spherical convolutions through lifting

J. D. McEwen

We develop a unified description of convolution on the sphere by lifting to the rotation group. A number of definitions of spherical convolution have been proposed, differing in whether the kernel is axisymmetric or directional, whether the output is on the sphere or the rotation group, whether the signals carry spin, and in their computational cost. No unified treatment exists. Regarding the sphere as the homogeneous space $S^2 \cong \text{SO(3)}/\text{SO(2)}$, we lift a spin $s$ field to the rotation group, convolve on $\text{SO(3)}$, and project back to the sphere through the adjoint of the lift for a potentially different spin $s'$. We recover every rotation-equivariant convolution considered previously through constraints on the kernel, spin choices and inclusion or omission of the lift and projection. For maps between fields of fixed input and output spin, we prove that directional kernels, output on the sphere and equivariance cannot be achieved simultaneously. We make explicit that spin fields are sections of associated line bundles, linking the physics and bundle literatures, and express the spin rotation phase as the character of the twist of the section. We present harmonic-space algorithms for the exact and efficient computation of lifted spherical convolutions of band-limited fields by appealing to sampling theorems on the sphere and rotation group. All operators are implemented in the open-source s2conv code, and numerical experiments confirm the reductions and the equivariance of the operators to machine precision. Finally, we illustrate the operators on fields measured on the Earth and on the celestial sky.
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Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP

The Hydrological Cycle and Circulation of Tidally-Locked Planets with a Finite Moisture Inventory

Daniel A. Williams, Geoffrey K. Vallis, Stephen I. Thomson et al. (+1 more)

Tidally-locked planets express a range of climate regimes distinct from those found in our solar system. Their atmospheres are often dominated by planetary-wide overturning circulations and super- rotating jets. In this paper we explore the hydrological cycle of such planets and how it might depend on the total inventory of condensate. To this end we use a set of idealised GCM simulations to probe the response of the hydrological cycle to the surface moisture budget in their atmospheres. We find that for low moisture inventories the climate quickly develops a barren day side and a moist night side where the moisture is trapped, as might be expected. For higher moisture inventories the drying of the day side is significantly delayed by the development of a substellar lake just east of the substellar point, which is sustained by the presence of a local thunderstorm-like convective system that persists even after the lake disappears. The timescales for the lake to disappear are millennia, suggesting that even a weak transfer of moisture through surface processes could maintain a moist day side. We also find that adjusting the threshold at which the evaporative rate becomes constrained by the remaining surface liquid depth modifies the longevity of the lake and surrounding day side conditions. These findings have implications for understanding a diverse range of planetary climates and habitability, given the distribution of moisture and presence of a substellar lake.
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All authors Daniel A. Williams, Geoffrey K. Vallis, Stephen I. Thomson, William J. M. Seviour

Atmospheric and Oceanic Physics

Morphology-based retrieval of local wave parameters and phase shifts in satellite-observed atmospheric disturbances

Yuriy Klymenko, Alla Fedorenko, Evgen Kryuchkov et al. (+1 more)

Currently available methods for processing atmospheric wave disturbances are often based on integral wavelet transform techniques, which efficiently estimate spectral data but are fundamentally limited by the uncertainty principle. To address this limitation, this work proposes a novel morphological approach based on partitioning spatial disturbance profiles into separate, well-defined wave segments. By focusing exclusively on the local waveform morphology of a central half-wave and its two adjacent out-of-phase counterparts, and taking the modified Morlet wavelet as a primary example of a model wave packet, we successfully reconstruct the complete set of local wave parameters directly from the amplitudes and positions of these three dominant half-waves. Consequently, the theory demonstrates good agreement with observations, confirming the high accuracy of the proposed waveform morphology approach in reconstructing the localized structure of atmospheric disturbances. When applied to evaluating the phase shift between different wave field components, the method reveals that this shift is a weighted combination of the classical monochromatic phase difference and a morphological one determined by the internal symmetry of the underlying wave packets. Although the classical factor yields the dominant contribution, the symmetry-related factor provides a moderate correction to it. The applicability and accuracy of the approach are verified using observational data gathered over several orbits of the low-Earth-orbit satellite Dynamic Explorer 2 as it passed over Earth's polar caps. The obtained results confirm that the observed disturbances correspond to medium-scale acoustic-gravity waves propagating at a small angle to the horizontal plane, demonstrating the broader methodological potential of the proposed technique for analyzing a wide range of wave phenomena.
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All authors Yuriy Klymenko, Alla Fedorenko, Evgen Kryuchkov, Oleg Cheremnykh

Thursday 8 October

Atmospheric and Oceanic Physics

Parametric wave spectrum models for prediction of Stokes drift profiles

W. Erick Rogers, Yalin Fan, Luis Zamudio et al. (+1 more)

In this report, 15 variants of nine parametric models for non-directional ocean wave spectra are reviewed. They are compared against observation-based spectra created by taking spectra measured by moored buoys, binning by wind speed, and averaging. Surface Stokes drift (SSD) and Stokes drift profiles are computed using the model and observation-based spectra, and these are compared. This 'parametric wave model' strategy for estimating Stokes drift is contrasted with other methods for estimating Stokes drift, such as using spectra from a dynamical wave model. Advantages and shortcomings are reviewed. One practical application for Stokes drift profiles is in ocean circulation modeling. Here, the information is used to compute wave-enhanced mixing ('Langmuir turbulence'). We present examples of such cases, in the context of a global ocean model, tested in two modes (coupled and uncoupled) and with two types of forcing (Stokes drift approximated using wind speed and Stokes drift computed using a full ocean wave model). A more comprehensive review of the uncoupled tests with the full ocean wave model can be found in a separate report, Fan et al. (2026). We evaluate simple Stokes drift models that use wind speed and/or wind stress but do not require a dynamical wave model. We find some are sufficiently accurate for routine prediction of SSD. A subset of these models can also predict Stokes drift profiles (via parametric spectra), but none are accurate in this role. The SSD values computed from a very large drifting buoy dataset are compared with the total drift calculated using positional change of the same buoys. The SSD is roughly 25% of the total drift, consistent with expectations based on 'rules of thumb'. However, the scatter is large, indicating that such rules have predictive skill only in the mean sense.
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All authors W. Erick Rogers, Yalin Fan, Luis Zamudio, Elizabeth M. Douglass