Mastodawn

Title: Breaching the Barrier: Transition Pathways of Coral Larval Connectivity Across the Eastern Pacific

arXiv:2603.12111v2 Announce Type: replace
Abstract: Genetic analyses indicate minimal gene flow across the so-called Eastern Pacific Barrier (EPB) in larvae of the reef-building coral \emph{Porites lobata}. Notably, Clipperton Atoll, situated on the eastern side of the EPB, is the only site that exhibits detectable genetic connectivi [...]

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Link: https://arxiv.org/abs/2603.12111

Breaching the Barrier: Transition Pathways of Coral Larval Connectivity Across the Eastern Pacific

Genetic analyses indicate minimal gene flow across the so-called Eastern Pacific Barrier (EPB) in larvae of the reef-building coral \emph{Porites lobata}. Notably, Clipperton Atoll, situated on the eastern side of the EPB, is the only site that exhibits detectable genetic connectivity with the Line Islands, which lie to the west of the EPB. To elucidate the relationship between this genetic signal and large-scale Pacific Ocean circulation, we analyze historical trajectories of surface-drifting buoys from the Global Drifter Program (GDP). We first discretize the GDP drifter trajectories into a Markov chain representation and subsequently apply transition path theory (TPT) in combination with Bayesian inference. The TPT analysis identifies reactive trajectories -- pathways that connect the Line Islands to Clipperton Atoll with minimal detours -- whose travel times do not exceed 5 months, which is taken as an upper bound for the larval survival time of \emph{P. lobata}. Consistently, the posterior distribution of transport from Pacific islands west of the EPB to Clipperton Atoll attains a local maximum in the Line Islands at a travel time of approximately 2.5 months. Our probabilistic characterization of the Lagrangian dynamics therefore supports a scenario of weak, but non-negligible, permeability of the EPB, in agreement with the genetic evidence, and it motivates a refined dynamical definition of the EPB based on the remaining duration of reactive trajectories. Furthermore, our results indicate that the connectivity between the Line Islands and Clipperton Atoll is governed primarily by the seasonal modulation of the North Equatorial Countercurrent, rather than by the phase of the El Niño--Southern Oscillation (ENSO). Finally, Clipperton Atoll's role as a terminal sink for trajectories is relevant to the planned mining operations.

arXiv.org

AOSbot 13h ago

Title: Key role of the Madden-Julian Oscillation on tropical and subtropical humid heat and heatwaves

arXiv:2509.17526v2 Announce Type: replace
Abstract: Humid heat stress and heatwaves pose significant risks for living organisms, from humans and wildlife to insects. These threats have wide-ranging health, ecological, and socio-economic impacts that are expected to worsen with climate change. How large-scale climate modes drive the week- [...]

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Link: https://arxiv.org/abs/2509.17526

Key role of the Madden-Julian Oscillation on tropical and subtropical humid heat and heatwaves

Humid heat stress and heatwaves pose significant risks for living organisms, from humans and wildlife to insects. These threats have wide-ranging health, ecological, and socio-economic impacts that are expected to worsen with climate change. How large-scale climate modes drive the week-to-month variability of humid heat remains poorly understood at the global scale. This limitation hinders the development of accurate forecasts necessary for risk-management measures, notably in the heavily populated and ecologically fragile regions of the tropics and subtropics. With forecast lead times up to several weeks, the Madden-Julian Oscillation (MJO), a global-scale intraseasonal tropical atmospheric disturbance circumnavigating earth in around 30-60 days, provides considerable predictability for weather conditions, and meteorological and oceanic extremes. Here we show that the MJO, and the associated boreal summer intraseasonal oscillation (BSISO), have a significant influence on humid heat and heatwaves over much of the tropics and subtropics across all seasons, both over terrestrial and marine regions. Humid heatwave likelihood can double or halve, depending on the MJO phase, in large areas of the Earth. The MJO/BSISO's influence on wet-bulb temperature is primarily via specific humidity rather than dry-bulb temperature anomalies. In the subtropics and other regions where we typically do not find a strong signal of the convection, we find that intraseasonal anomalies of specific humidity and dry-bulb temperature are influenced by horizontal advection in the planetary boundary layer. Particularly in the subtropics where advection of the climatological moisture and temperature gradient by MJO-related anomalous winds is an important term.

arXiv.org

AOSbot 13h ago

Title: AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability

arXiv:2506.14022v3 Announce Type: replace
Abstract: Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We explore the use of an interpretable AI-informed model analog forecasting approac [...]

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Link: https://arxiv.org/abs/2506.14022

AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability

Subseasonal-to-seasonal forecasting is crucial for public health, disaster preparedness, and agriculture, and yet it remains a particularly challenging timescale to predict. We explore the use of an interpretable AI-informed model analog forecasting approach, previously employed on longer timescales, to improve S2S predictions. Using an artificial neural network, we learn a mask of weights to optimize analog selection and showcase its versatility across three varied prediction tasks: 1) classification of Week 3-4 Southern California summer temperatures; 2) regional regression of Month 1 midwestern U.S. summer temperatures; and 3) classification of Month 1-2 North Atlantic wintertime upper atmospheric winds. The AI-informed analogs outperform traditional analog forecasting approaches, as well as climatology and persistence baselines, for deterministic and probabilistic skill metrics on both climate model and reanalysis data. We find the analog ensembles built using the AI-informed approach also produce better predictions of temperature extremes and improve representation of forecast uncertainty. Finally, by using an interpretable-AI framework, we analyze the learned masks of weights to better understand S2S sources of predictability.

arXiv.org

AOSbot 13h ago

Title: High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations

arXiv:2604.02850v1 Announce Type: new
Abstract: The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensio [...]

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Link: https://arxiv.org/abs/2604.02850

High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations

The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data. Our approach employs a conditional denoising diffusion probabilistic model (DDPM) trained on sea surface height and temperature observations with up to 99.9 percent sparsity, without reliance on a background dynamical model. By incorporating continuous depth embeddings, the model learns a unified vertical representation of the ocean states and generalizes to previously unseen depths. Applied to the Gulf of Mexico, the framework accurately reconstructs subsurface temperature, salinity, and velocity fields across multiple depths. Evaluations using statistical metrics, spectral analysis, and heat transport diagnostics demonstrate recovery of both large-scale circulation and multiscale variability. These results establish generative diffusion models as a scalable approach for probabilistic ocean reconstruction in data-limited regimes, with implications for climate monitoring and forecasting.

arXiv.org

AOSbot 13h ago

Title: MAG-Net: Physics-Aware Multi-Modal Fusion of Geostationary Satellite and Radar for Severe Convective Precipitation Nowcasting

arXiv:2604.02818v1 Announce Type: new
Abstract: Radar-based convective precipitation nowcasting suffers from rapid performance degradation beyond 30 minutes due to missing thermodynamic variables. Existing deep learning models also face blurring effects, training instability, and limited interpretability. T [...]

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Link: https://arxiv.org/abs/2604.02818

MAG-Net: Physics-Aware Multi-Modal Fusion of Geostationary Satellite and Radar for Severe Convective Precipitation Nowcasting

Radar-based convective precipitation nowcasting suffers from rapid performance degradation beyond 30 minutes due to missing thermodynamic variables. Existing deep learning models also face blurring effects, training instability, and limited interpretability. To address this, we propose MAG-Net, a Physics-Aware Multi-modal Attention-guided Generator Network. It integrates radar dynamics with selected geostationary satellite channels (IR 10.8, WV 7.1, BTD) to incorporate thermodynamic and microphysical precursors. MAG-Net features a Dual-Stream Encoder for heterogeneous modalities and a Symmetric Dual-Head Decoder optimizing reflectivity regression and event probability via an uncertainty-weighted multi-task strategy. Furthermore, an inference-time Gradient-Preserving Fusion (GPF) strategy combines probabilistic constraints with regression details for better high-frequency texture retention. Experiments on a large-scale dataset (2018-2023) over southeastern China show MAG-Net outperforms deterministic (e.g., CPrecNet) and generative (e.g., DGMR) baselines. Specifically, it improves CSI40 by 0.083 (0.172 to 0.255) over CPrecNet, enhancing intense convective echo detection. Finally, Integrated Gradients (IG) analysis reveals the model's reliance on satellite inputs increases with forecast lead time and convective intensity, confirming that satellite data captures critical precursors for severe weather prediction.

arXiv.org

AOSbot 13h ago

Title: On the White-Noise Limit of the Colored Linear Inverse Model

arXiv:2604.02519v1 Announce Type: new
Abstract: A recent paper by Lien et al. (2025) introduces the "colored linear inverse model" (colored LIM), in which stochastic forcing is modeled using Ornstein-Uhlenbeck colored noise rather than idealized white noise. In that work, it is shown that the derivative-based identification formulas used to estimate model parameters do n [...]

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Link: https://arxiv.org/abs/2604.02519

On the White-Noise Limit of the Colored Linear Inverse Model

A recent paper by Lien et al. (2025) introduces the "colored linear inverse model" (colored LIM), in which stochastic forcing is modeled using Ornstein-Uhlenbeck colored noise rather than idealized white noise. In that work, it is shown that the derivative-based identification formulas used to estimate model parameters do not admit a regular white-noise limit due to the loss of differentiability of the lag-correlation function at zero lag. Here we revisit the white-noise limit from the perspective of the underlying stochastic differential equations. Treating the colored LIM as an augmented Ornstein-Uhlenbeck system, we show that as the correlation time tau -> 0 the colored-noise-driven system reduces to the classical LIM, and the corresponding stationary covariance satisfies the standard fluctuation-dissipation relation. Re-examining the same linear system used by Lien et al. (2025), we illustrate this convergence numerically. These results highlight a distinction between the singular behavior of derivative-based identification formulas and the regular limiting behavior of the underlying stochastic model. Taken together with recent results showing convergence of estimated parameters in the white-noise limit, they provide a consistent interpretation in which the colored LIM recovers the classical LIM at the level of stochastic dynamics even though certain estimation procedures become ill-defined in that limit.

arXiv.org

AOSbot 3d ago

Title: On Using Medium-Range Ensemble Forecasts for Storm Transposition of Synoptic-Scale Systems in Probable Maximum Precipitation Estimation

arXiv:2602.19233v2 Announce Type: replace
Abstract: Most methods for estimating probable maximum precipitation (PMP) -- the greatest depth of precipitation that is physically possible over a given area and duration -- rely on storm transposition (ST), the process of transporting a storm, either hi [...]

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Link: https://arxiv.org/abs/2602.19233

On Using Medium-Range Ensemble Forecasts for Storm Transposition of Synoptic-Scale Systems in Probable Maximum Precipitation Estimation

Most methods for estimating probable maximum precipitation (PMP) -- the greatest depth of precipitation that is physically possible over a given area and duration -- rely on storm transposition (ST), the process of transporting a storm, either historically observed or simulated, from its original location to a target basin. Existing ST approaches, whether classical or physically based, involve assumptions and manipulations that can introduce inconsistencies, leaving the physical validity of the transposed storm uncertain. In this study, the internal variability leveraging (IVL) approach is used to transpose an atmospheric river cluster that affected the U.S. West Coast during 20-29 October 2021. Steering the storm toward the target basin and determining its transposition region are achieved by considering an ensemble of plausible storm evolutions and trajectories obtained from archived ECMWF medium-range forecasts. The Willamette River and Nass River watersheds, located approximately 6 deg N, 2 deg W and 16 deg N, 8 deg W, respectively, from the area most affected by the observed precipitation, were selected as target basins. For each basin, the IVL realization yielding the largest 24-h basin-average precipitation depth was identified, and the initial and boundary condition shifting method was subsequently applied to further enhance its impact, producing 24-h precipitation depths of 119 mm for the Willamette and 98 mm for the Nass.

arXiv.org

AOSbot 3d ago

Title: Possible, Yes; Ignorant, Perhaps: A Scorecard for Possibilistic Forecasts

arXiv:2604.02187v1 Announce Type: cross
Abstract: Probabilistic forecasts must sum to unity and cannot express ``I don't know.'' Possibility theory relaxes this constraint: a subnormal distribution explicitly measures how much of the plausibility budget remains unassigned, ignorance signal that probability cannot represent. This paper develops a verification [...]

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Link: https://arxiv.org/abs/2604.02187

Possible, Yes; Ignorant, Perhaps: A Scorecard for Possibilistic Forecasts

Probabilistic forecasts must sum to unity and cannot express ``I don't know.'' Possibility theory relaxes this constraint: a subnormal distribution explicitly measures how much of the plausibility budget remains unassigned, ignorance signal that probability cannot represent. This paper develops a verification framework for such forecasts, centred on a five-number scorecard that separately diagnoses whether the forecast pointed at the right outcome (depth-of-truth), how sharply (diffuseness, support margin), how confidently (ignorance), and how dominantly (conditional necessity). A possibility-to-probability conversion preserves ignorance for familiar frequency-based scoring; categorical threshold scores (POD, FAR, CSI, etc.) connect to operational practice. Together, these three complementary facets -- possibilistic, probabilistic, and categorical -- expose failure modes invisible to any single metric. Storm Prediction Center convective outlook categories serve as the running example throughout; a synthetic reforecast demonstrates diagnostic visualisations and scorecard interpretation. Ignorance is better expressed than repressed.

arXiv.org

AOSbot 3d ago

Title: A proposal for the safety and controllability requirements that SRM systems should meet

arXiv:2604.02283v1 Announce Type: new
Abstract: Solar Radiation Modification (SRM) may be the only way to limit global warming in the coming decades, leading to increased interest in the subject and to the expansion of related research & development (R&D) activity. Defining the safety and controllability requirements that any SRM system should [...]

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Link: https://arxiv.org/abs/2604.02283

A proposal for the safety and controllability requirements that SRM systems should meet

Solar Radiation Modification (SRM) may be the only way to limit global warming in the coming decades, leading to increased interest in the subject and to the expansion of related research & development (R&D) activity. Defining the safety and controllability requirements that any SRM system should meet is crucial for directing R&D activities and enabling governments to make informed decisions on the development and possible implementation of such systems. We present an initial proposal for this set of requirements, which also guides Stardust's R&D, as a basis for further discussion and consideration. While we focus on SRM systems based on Stratospheric Aerosol Injection (SAI), the proposed principles may be applicable more broadly.

arXiv.org

AOSbot 3d ago

Title: Assessing the ability of a stretched-grid deep-learning weather prediction model to capture physical balances

arXiv:2604.01454v1 Announce Type: new
Abstract: Weather forecasting has traditionally relied on Numerical Weather Prediction (NWP) models, which simulate weather by solving the governing fluid equations. Recently, the emergence of Deep Learning Weather Prediction (DLWP) models has opened a new era in weather forecasting, o [...]

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Link: https://arxiv.org/abs/2604.01454

Assessing the ability of a stretched-grid deep-learning weather prediction model to capture physical balances

Weather forecasting has traditionally relied on Numerical Weather Prediction (NWP) models, which simulate weather by solving the governing fluid equations. Recently, the emergence of Deep Learning Weather Prediction (DLWP) models has opened a new era in weather forecasting, offering a data-driven alternative to classical NWP approaches. Regional DLWP models such as the stretched-grid model Bris developed by Met Norway, have demonstrated performance on par with, or even slightly better than regional NWP models across a range of standard forecast metrics. By overcoming the coarse horizontal resolution that constrained earlier global data-driven models, the operational use of regional DLWP systems now appears increasingly promising. Nevertheless, the performance of such models during extreme events is generally inferior to that of regional NWP models, and comprehensive evaluations of their ability to generate physically realistic forecasts are still lacking. Here, we present a study comparing the physical consistency of the deterministic version of Bris with the control run of the operational MetCoOp Ensemble Prediction System (MEPS) in forecasting the severe extratropical cyclone Poly, which hit the Netherlands on 5 July 2023. We examine whether Bris accurately represents deviations from key atmospheric balances and whether it reproduces expected dynamics of the storm. We show that, despite its relatively good performance in terms of RMSE, Bris struggles to capture important mesoscale features of the event and that it significantly disrupts several atmospheric balances. This unrealistic disruption is mainly linked to the fine-scale noise evidenced in its output fields, which leads to incorrect and unrealistic spatial gradients. These results raise critical questions for improving AI-based models to better represent extreme events and how to ensure physical consistency in their predictions.

arXiv.org

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