Papers

A non-linear, non-Bayesian method called sequential bound estimation (SBE) derived 100% confidence intervals of location (CIL) for 219 explosions in the ocean from measurements of their time differences of arrivals among five widely-spaced time-unsynchronized receivers on the ocean bottom. The explosion's locations were measured with the global positioning system. A blind evaluation revealed all 219 explosions were within their CIL. The probability this could happen by chance is $4 \times 10^{-116}$. The explosions were detonated in shallow water on the eastern continental shelf of the U.S. over the so-called New England Mud Patch.

High-resolution oceanographic fields are critical for resolving mesoscale and sub-mesoscale coastal dynamics, yet their generation remains constrained by both computational cost and observational sparsity. We present OcDiffSR, a conditional denoising diffusion probabilistic model (DDPM) for oceanographic super-resolution that reconstructs high-resolution sea-surface fields from coarse-resolution reanalysis inputs. The model is trained on ten years (2011-2020) of paired low-resolution (GLORYS12V1, 1/12) and high-resolution (Mediterranean Sea Physics Reanalysis, Med MFC, 1/24) data, and evaluated on an independent test year (2009) over the Adriatic Sea. OcDiffSR employs a conditional U-Net augmented with multi-scale low-resolution encoders, cross-attention bottleneck layers, and sinusoidal seasonal embeddings via Feature-wise Linear Modulation (FiLM), enabling joint super-resolution of sea-surface temperature (SST), salinity (SSS), and horizontal velocity components with visually coherent circulation patterns. Benchmarked against bilinear interpolation and the state-of-the-art residual diffusion model CorrDiff, OcDiffSR achieves substantially lower reconstruction errors for scalar fields (RMSESST=0.477 C, RMSESSS=0.346 psu), near-unity Pearson correlation (PCC >= 0.999), and high structural similarity (SSIM >= 0.964). For dynamical vector fields, OcDiffSR outperforms both baselines in absolute error and spatial coherence, though moderate correlation (PCC = 0.64) reflects the intrinsic stochasticity of oceanic velocity fields. Daily and monthly evaluations confirm temporal robustness across all seasons. These results establish OcDiffSR as a reliable framework for high-fidelity oceanographic downscaling and reanalysis enhancement, producing fields that are visually consistent with known ocean dynamics.

Offshore wind farms operate within the marine atmospheric boundary layer, where ocean waves can modify air-sea momentum transfer, turbine inflow, and wake recovery. Wave-resolved simulations can capture phase-dependent wind–wave interactions, but their computational cost and implementation complexity limit their use in large atmospheric and wind-farm domains. This study describes applications and tests of the recently proposed moving surface drag (MOSD) model, a flat-bottom wall-stress formulation for horizontally resolved moving waves, to a sequence of increasingly complex wind–wave and offshore wind-energy applications. The model is first evaluated for turbulent flow over Stokes-like waves using laboratory measurements, where it reproduces mean velocity profiles and captures the qualitative structure of wave-induced motions, although their amplitude is underpredicted. MOSD is then applied to broadband wave spectra fields and offshore wind-farm boundary layers, showing good agreement with mean velocity profiles from wave-resolved simulations across different wave ages, grid resolutions, and turbine spacings. Finally, exploratory offshore wind-farm simulations using MOSD under conventionally neutral conditions demonstrate its application at wind-farm scale. Large-scale waves leave detectable signatures in near-surface velocity spectra, but these signatures decay with height and do not produce clear peaks in temporal spectra of aggregate turbine power. Overall, the results demonstrate that the MOSD model provides a tractable framework for incorporating resolved wave effects in wind-wave and offshore wind-farm simulations.

Surface Stokes drift depends strongly on the energy and directions of short waves, which are incompletely resolved by routine wave observations. We derive surface Stokes drift vectors from wave measurements collected by compact drifting buoys during three deployments in the North-East Atlantic and the Alboran Sea. The calculation uses vertical-acceleration spectra and first directional Fourier moments, which describe the mean wave direction and directional concentration at each frequency; it accounts for the Doppler shift caused by buoy motion relative to the water and adds a calibrated high-frequency tail above an intrinsic frequency of 0.7 Hz. Across 13,139 records, the median estimated speed is 0.081 m/s at a median wind speed of 6.8 m/s. Over the measured band of 0.04-1 Hz, accounting for wave directions reduces the magnitude by a median 39% relative to the unidirectional assumption. The median ratio of the parameterised tail magnitude above 0.7 Hz to the total estimated magnitude is 0.37. Comparisons with WAVEWATCH III and Copernicus Marine MFWAM show strong covariation and similar wind-dependent differences from the buoy-derived estimates. On the station-matched sample from the two Atlantic deployments, WAVEWATCH III directional spectra indicate that these differences within the compared band arise mainly from spectral levels rather than from net directional reduction. The observations provide constraints for model evaluation; the contribution of the unresolved short waves remains sensitive to the assumed spectral tail and its directional spreading.

Confidence intervals of location (CIL) of calling marine mammals, derived from time-differences-of-arrival (TDOA) between receivers, depend on errors of TDOAs, receiver location, clocks, sound speeds, and location method. When these errors are minuscule, simulations yield small errors of PAMGuard's 3D simplex localizer when click sounds of beaked and sperm whales originate in a 1000 x 1000 x 1000 $\mbox{m}^3$ region using five receivers having horizontal and vertical separations of 1000 m and 150 m respectively. Realistic uncertainties of sound speed up to $\pm 10$ m/s lead to errors up to $10^{14}$ m. With clocks maintained by atomic standards and common practice of correcting TDOA from synchronization measurements at the start and end of an experiment, errors of location are up to $10^{4}$ m. Errors up to $10^2$ and $10^3$ m are found when the receiver's locations are uncertain within 10 and 40 m respectively. Errors of PAMGuard's 3D hyperbolic localizer are almost independent of the above uncertainties, yielding errors of location up to about $10^4$ m even when simulated errors are minuscule. Causes of PAMGuard's 3D location errors are unknown. These algorithms are briefly compared to another method designed to yield a reliable CIL.

Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are widely used to assess the discrimination ability of forecasts for binary events, such as threshold exceedances or warnings of extreme events. In weather forecasting, forecasts are provided as spatial fields, yielding location-wise ROC and PR curves that are often aggregated to facilitate comparison. However, the effect of the aggregation strategy on performance assessment remains poorly understood. We investigate how different aggregation strategies for ROC and PR curves affect the assessment of discrimination ability. In particular, we identify conditions under which aggregation strategies satisfy two desirable properties for fair comparison: preservation of dominance between forecasts and preservation of concavity or achievability of the curves. We obtain sufficient conditions and propose two strategies satisfying them. They are compared with existing strategies from the literature, and we analyze their properties and highlight potential pitfalls that may lead to misleading interpretations. Based on these findings, we provide practical guidelines for the interpretation of aggregated ROC and PR curves. The proposed framework is illustrated with AI-based global weather forecasts, showing how different aggregation strategies can yield different rankings of competing forecasts.

The Madden-Julian oscillation (MJO) is a major source of subseasonal-to-seasonal (S2S) predictability. The MJO is commonly defined and tracked with indices such as the Real-time Multivariate MJO (RMM) index. Although the RMM provides a useful description of the MJO, its evolution can be noisy and difficult to predict. We define an MJO index using a data-driven approximation of the Koopman operator. The Koopman index captures similar tropical circulation and convection patterns to the RMM but evolves more smoothly and predictably. Skillful prediction extends to 46 days for the Koopman index compared to 11 days for the RMM under the same prediction framework. While this new approach does not recover the RMM as well as operational S2S models, which provide skillful forecasts up to 35 days, the Koopman index could complement existing MJO diagnostics in evaluating and developing extended-range forecast systems.

Cylindrical blast wave theory relates infrasound from hypersonic sources to energy deposition through a characteristic blast radius R_0, yet the normalization constant k in the scaling R_0 = k (E_0/p_0)^(1/2) differs by up to 40% across the formulations examined here. This uncertainty affects period-based source characterization for elevated atmospheric sources relevant to infrasound monitoring. Using infrasound from the OSIRIS-REx Sample Return Capsule reentry on 24 September 2023, observed periods at 39 stations spanning source altitudes of 44-62 km are inverted through a weak-shock propagation model to obtain period-inverted blast radii R_0,obs without prescribing a blast-wave normalization. Combining R_0,obs with aerodynamic drag energy yields a median k = 0.396, with a modified-bootstrap 95% interval of 0.378-0.474 when the reported period-measurement uncertainties are included. This result is consistent with the Sakurai (1965) and Jones et al. (1968)/Plooster (1970) normalizations, while Few (1969) lies outside the reference confidence interval. However, station-level k values exhibit significant along-trajectory structure (Spearman r_s = -0.84, p < 0.001), indicating that k_med is an ensemble-effective normalization rather than a station-invariant constant. The Mach-diameter approximation R_0 ~ M d_m overestimates R_0 by a factor of ~3.4 for this effectively non-ablating source. A period-based relation, R_0 = 23.99 tau - 0.73 (R^2 = 0.991, MAPE = 2.3%), provides a rapid blast-radius estimator whose residuals show no detectable altitude dependence. These results provide inverse calibration constraints and practical period-based source-characterization relations for OSIRIS-REx-like non-ablating hypersonic entries, while highlighting the need for multi-event validation before broader application.

The Madden-Julian Oscillation (MJO) is the leading intraseasonal driver of tropical weather, comparable to the interannual ”El Niño/La Niña”, yet remains largely unfamiliar outside the scientific community. The MJO is atmospherically like a ”La Niña” going around the earth, and metaphorically as ”La Ola”, Spanish for ”The Wave”, evoking a stadium wave. Thus a pedagogical nickname such as ”La Ola-MJO” could enhance forecast uptake, especially among climate-vulnerable communities.

The AMOC and the Subpolar Gyre are key components of the Earth's climate system, both potentially prone to destabilization and abrupt shifts under anthropogenic forcing.Given the strong influence of the AMOC and the SPG on global climate, detecting changes in their stability is of high importance, yet is hindered by the limited length of observational records.In the absence of long-term direct AMOC observations, several observation-based fingerprints of the AMOC have been proposed.However, existing fingerprints do not clearly distinguish between SPG and AMOC variability, instead reflecting an opaque mixture of dynamics from both systems.Here, we assess the performance of widely used AMOC fingerprints across CMIP6 models and compare them with statistically optimal fingerprints that we derive from sea surface temperature and salinity.We show that traditional AMOC fingerprints exhibit weak correlations with both AMOC and SPG strength.In contrast, our statistically optimal fingerprints, trained on pre-industrial control simulations, consistently outperform existing approaches across independent segments of piControl simulations and historical experiments.These fingerprints accurately reconstruct AMOC and SPG variability while minimizing overlap, enabling a clear separation between the two systems.Our results provide the first tailored observational fingerprints that robustly distinguish AMOC and SPG, demonstrating the potential of statistically optimized approaches to improve detection and attribution of changes in Atlantic circulation, including precursor signals of potential tipping behavior.Applying our approach to sea surface temperature observations, we find that our optimized fingerprints do not confirm the substantial AMOC weakening in the recent decades as suggested by traditional fingerprints, but also do not rule out a loss of stability.

Satellite ocean color algorithms translate water-leaving radiance into ecological information at spatial and temporal scales that cannot be achieved by field sampling alone. One important variable derived from water-leaving radiance is chlorophyll-a concentration ($\mathrm{CHL\text{-}a}$), a widely used indicator of phytoplankton biomass and physiology. Empirical retrieval algorithms are commonly used for $\mathrm{CHL\text{-}a}$ estimation, but their performance can vary across sensors and optically diverse waters. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission provides unprecedented spectral resolution, expanding the visible spectral information available for ocean color retrievals and raising a practical algorithm design question: how can this fine resolution spectrum be used to derive the next generation of interpretable $\mathrm{CHL\text{-}a}$ retrieval algorithms? We use symbolic regression to identify sparse equations that estimate $\log_{10}(\mathrm{CHL\text{-}a})$ from multi- and hyperspectral remote sensing reflectances. The analysis first tests the standard multispectral ocean color algorithm (OC3–OC6) inputs to ask whether symbolic regression recovers standard band ratio structure, then extends the search to hyperspectral PACE-like reflectances. The best expression discovered achieved a held out root mean square deviation of 0.253 in log$_{10}$ $\mathrm{CHL\text{-}a}$, compared with 0.307 for a fitted OC6 polynomial on the same split. Ablations showed that the predictive skill of hyperspectral models is almost 12% better than the best standard ocean color retrieval models in root mean squared deviation against held out data. When evaluated by environmental regime, differences were larger for high-chlorophyll samples, which often represent turbid water conditions.

This study analyses kinetic energy (KE) spectra, difference kinetic energy (DKE) spectra, and signatures of KE transfer across spatial scales in four state-of-the-art probabilistic machine learning weather prediction (MLWP) models: NeuralGCM-ENS, FourCastNet 3, AIFS-ENS, and GenCast. Results are compared with those from the physics-based numerical weather prediction model IFS-ENS. While NeuralGCM-ENS successfully reproduces the expected upscale transfer of KE, noise injection at its encoder stage underestimates mesoscale KE. Conversely, AIFS-ENS, GenCast, and FourCastNet 3 produce realistic KE spectral magnitudes but do not capture the expected upscale transfer of KE. In particular, AIFS-ENS and GenCast, which employ spatially uncorrelated stochastic perturbations, exhibit enhanced accumulation of KE at high wavenumbers. All examined models exhibit upscale error growth, reflected by the progressive shift of the DKE spectral peak toward larger wavelengths over time. However, the MLWP models struggle to reproduce the rapid initial growth of ensemble spread at small spatial scales associated with the butterfly effect. The results show that MLWP models can misrepresent the known scale transfer of kinetic energy despite producing skilful weather forecasts.

Understanding how particles such as microplastics and plankton spread, or accumulate in localized regions below the ocean surface is crucial both for the development of sea pollutant control strategies and for marine ecology. Yet the role of vertically inhomogeneous turbulence, due to the vertical structure of density stratification in the ocean, remains poorly understood. We study, by means of direct numerical simulations, the dynamics of weakly inertial, quasi-neutrally buoyant particles in an idealized two-layer fluid model aimed to represent the ocean convective mixed layer and the more stably stratified upper thermocline beneath. We find that particles eventually rise at the surface if their density matches the mean value of the fluid at a selected depth within the mixed layer. Interestingly, however, for all other values of density, they accumulate around their neutral-buoyancy depth within the thermocline, where stratification hinders vertical transport. Our analysis shows that the thickness of the accumulation layer results from the competition between buoyancy-driven confinement, internal-wave motions, and turbulent fluctuations penetrating from the mixed layer. Smaller particles, which respond more rapidly to flow fluctuations, form broader layers; layers also broaden as their equilibrium depth approaches the mixed layer. We then derive a reduced stochastic model that explains the dependence on particle inertia and reference depth of such spreading around the equilibrium position. These results show how the coupled action of stratification and decaying mixed-layer turbulence controls subsurface particle-layer formation, which may be relevant to ocean-biology oriented studies and to improve the sampling of plastic pollution in the sea.

Despite their high accuracy on point-wise metrics, machine learning weather forecasting models can exhibit different failure modes such as blurring, periodic irregularities, and other unphysical spatial artifacts. This has motivated a variety of metrics to detect known failure cases. Existing metrics fix a representation or transformation in advance, and that choice limits the artifacts they can detect. We propose to train a discriminator for separating reference data from the model's output, and using its output logit to obtain a divergence-like realism score. The discriminator learns whatever separates the model's fields from real weather, adapting to whichever failure mode that model exhibits. We compare our learned atmospheric critic to existing metrics using various synthetic corruptions applied to ERA5 reanalysis data. Our method successfully identifies the corruptions and ranks their severity, while existing metrics fail on at least one corruption. Additionally, we evaluate forecasts from real weather models, and find that the realism score degrades with longer lead times and the metric generally assigns higher realism to numerical models than to machine learning models.

Recently, radiative coolers have been widely explored for reducing cooling loads or lowering temperatures in buildings, and at urban scales as a heat-mitigation measure. However, the potential impacts of radiative cooler deployment on the chemical composition of the atmosphere remain largely unexplored. A defining feature of recently-designed radiative coolers is their high ultraviolet (UV) reflectance, which is required for sub-ambient cooling under strong sunlight. Yet, wide adoption of such UV-reflective radiative coolers could substantially increase the UV actinic flux in the atmosphere above. This, in turn, may affect tropospheric ozone concentrations, particularly in urban atmospheres with high NOx concentrations. Here, as a case study, we use a 0-dimensional photochemical box model, constrained by field measurements of meteorological conditions and chemical concentrations in the urban environment of Houston, Texas, to explore the potential impact of the widespread use of UV-reflective radiative coolers on ozone concentrations. Our calculations show that complete deployment of radiative coolers may increase tropospheric ozone levels by as much as 30% during specific meteorological conditions in Houston. Informed by the wavelength-dependent modelling results, we propose specific designs, namely pigmented radiative coolers with different UV reflectances, and UV-absorptive visible-reemitting fluorescent radiative coolers, that could minimize negative ozone formation while retaining appreciable cooling performance. Our results motivate further study on the effects of widespread deployment of radiative cooling designs like superwhite roof paints on air quality, and materials that simultaneously minimize adverse photochemical impact and maximize cooling performance.

Probabilistic downscaling must represent kilometer-scale structure left unresolved by a deterministic regional prediction. We introduce an analog-initialized latent transport method that uses historical regional residuals as a meteorologically informed empirical source. A frozen graph neural network predicts the deterministic state from the Integrated Forecasting System (IFS). For each input, residuals from 20 training dates with similar IFS conditions initialize the ensemble. A conditional flow-matching model transports these fields in a carefully designed 65-factor, 100,000-node latent space, and a grid decoder reconstructs 81 atmospheric channels including precipitation at 2.5km spacing. On a season- and cycle-balanced 2025 test, ensemble CRPS improves over deterministic mean absolute error for all 81 variables. Matched analogs improve point and spatial errors over Gaussian initialization and reduce spectral error compared with covariance matching. Comparisons with released CorrDiff, a local CorrDiff-mini model and full grid EDM provide context on the 2023 validation sample. Total training of the downscaling model takes approximately 12h on one NVIDIA H200, and inference takes about 12s per 81-field member. The frozen networks also generate fields over France when supplied with local static data and a high-resolution IFS-derived analog bank. The method combines empirical initialization with whole-domain latent transport for computationally practical regional ensembles.

We present IRENE (Italian Radar Ensemble Nowcasting Experiment), a deep learning model for probabilistic short-range precipitation nowcasting over the Italian domain at \SI{1}{km} spatial and 5 min temporal resolution. IRENE adopts an encoder–forecaster architecture built on multi-scale Convolutional Gated Recurrent Units (ConvGRUs), trained on the national radar composite produced by the Italian Civil Protection Department (DPC). An importance-sampling scheme focuses training on precipitation-relevant events, while the almost-fair Continuous Ranked Probability Score (afCRPS) is adopted as the primary probabilistic loss function. Two additional training configurations are proposed: an adversarial (GAN) variant, IRENE-GAN, designed to improve the spatial sharpness of the generated forecasts, and a spectrally constrained variant, IRENE-GAN-RAPSD, in which the adversarial objective is complemented by an explicit penalty on the radially averaged power spectral density. The three configurations are evaluated against the stochastic extrapolation method STEPS and the pre-trained deep learning model DGMR. All IRENE configurations attain a lower Continuous Ranked Probability Score than both benchmarks at every lead time and rank histograms closer to uniformity, indicating better probabilistic skill and ensemble calibration. In terms of ensemble-mean mean absolute error the advantage is confined to the first 90 min, beyond which the strongly damped DGMR fields and, to a lesser extent, STEPS become competitive. Spectral analysis shows that the adversarial training removes the progressive loss of small-scale variance exhibited by IRENE, at the cost of an excess of fine-scale power at long lead times that the spectral penalty only partially controls.

Accurate multi-horizon wind direction forecasting is critical for turbine yaw control and grid security. Rapid directional shear (turning $\ge 90^\circ$) challenges models via non-Euclidean geometry on $S^1$, multiscale dynamics, and regime-dependent uncertainty. Conventional discrete models and foundation models suffer from mid-frequency phase lag and turning misalignments. We show that directional predictability decays at disparate rates across frequency subbands, rendering monolithic mechanisms suboptimal. We propose a predictability-guided paradigm: slow synoptic drift $\to$ deterministic regression; intermediate turning $\to$ continuous latent differential flows; unresolved turbulence $\to$ conditional residual diffusion; followed by causal recalibration. On a 10,000-sequence multi-year benchmark, our framework maintains calm-weather accuracy (Test MCE $38.48^\circ$) while reducing extreme-turning error (Case 1 MCE $60.69^\circ$ vs $70.42^\circ$ for zero-shot foundation models). The circular CRPS reaches $22.36^\circ$, with 93.88% coverage at nominal 95% (91.01% out-of-distribution). Density estimation further reveals near-antipodal bimodal structure under severe shear (13.39%–15.43% tail mass $\ge 135^\circ$), exposing a geometric bound where single-center calibration under-covers (81.56%), motivating multimodal circular manifold learning.

Near-inertial waves (NIWs) generated by extratropical storms provide a major pathway for transferring atmospheric energy into the ocean, yet what determines the efficiency of this energy transfer at the level of individual storms remains poorly understood. Here, using a slab ocean model forced by ERA5 reanalysis and analyzed in storm-relative coordinates, we investigate NIW generation across three temporal scales: from the instantaneous structure of individual storms, through their life cycle, to variability across storms. Instantaneous storm structure reveals that the spatial layout of wind stress and storm propagation provides the required time evolution and rotation to excite a localized maximum in wind work on the right side of storm tracks, much like tropical cyclones do. Following storms through their life cycle further reveals that wind work peaks one day after cyclone genesis, about one day before storms reach their maximum intensity. This offset arises because storm translation slows during intensification, reducing the propagation-induced wind-stress tendency, while concurrent poleward propagation increases the Coriolis parameter and reduces the alignment between winds and near-inertial currents. Due to the temporal lag, regions of climatologically high NIW forcing are located upstream of the North Pacific storm-track maximum. Finally, examining variability across storms, we show that storm intensity, propagation speed, and latitude explain most of the variability in NIW generation, but not in extreme events. Focusing on extreme events, we find that the strongest NIW-generating storms feature colder cold sectors, which destabilize the marine boundary layer and enhance downward momentum transfer to the ocean surface.

The adverse impacts of extreme heat on human health are greater when concurrent daytime and nighttime heatwaves (CDNHWs) occur than when daytime or nighttime heatwaves occur individually, due to reduced recovery time from heat exposure. This study projects future changes in CDNHWs across East Asia under RCP2.6, RCP8.5, SSP1-2.6, and SSP5-8.5 scenarios. Daily maximum and minimum temperatures are derived from 3-hourly temperatures at 25-km resolution produced by 12 GCM-RCM chains participating in CORDEX-East Asia Phase 2. During the historical period (1981-2005), the East Asian mean CDNHW occurrence period and rate from April to September are 10.9 days and 0.9 percent, respectively. By 2071-2100, they increase to 3 weeks and 3.7 percent under RCP2.6, 2 months and 20.5 percent under RCP8.5, 2 months and 15.6 percent under SSP1-2.6, and 3 months and 45.7 percent under SSP5-8.5. CDNHW intensity and spatial extent also increase substantially. The proportion of CDNHWs lasting more than 10 days increases from 0.2 percent historically to 1.2 percent, 7.2 percent, 6.1 percent, and 17.3 percent under RCP2.6, RCP8.5, SSP1-2.6, and SSP5-8.5, respectively. Particularly large increases in CDNHW occurrence and intensity are projected over Indochina, East and West China, and India. Under continued high greenhouse gas emissions, East Asia is expected to experience unprecedented heat stress as CDNHWs become substantially more frequent and intense by the end of the 21st century.

How global tropical cyclone frequency will respond to climate warming remains uncertain, with projections spanning both increases and decreases. Here we drive an environment-dependent probabilistic tropical cyclone model with 14 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under intermediate- and high-emissions scenarios. Initially weak global changes evolve into a clearer late-century decline, driven by a strengthening Southern Hemisphere decline and a diminishing Northern Hemisphere increase, and stronger under higher emissions. Counterfactual decomposition shows that this stronger decline is not explained by individual environmental predictors, whose contributions largely offset, but by negative nonlinear interactions among absolute vorticity, vertical wind shear, relative humidity and potential intensity. Across 28 model-scenario projections the frequency trend scales linearly with global warming; conditioning on 2 degrees C narrows the 95% confidence interval from [-20.6%, +9.9%] to [-8.2%, -2.1%]. These results reconcile divergent projections through a common warming-dependent decline.

We propose a fully automated, structural error-aware, interpretable climate model parameter estimation method that leverages Perturbed Parameter Ensembles (PPEs). It is based on history matching and aligns with an increasingly-used iterative simulation-emulation-calibration methodology. The method is motivated by the negative impacts of structural error and emulator and observational uncertainties on climate model parameter estimation efforts, as well as the problems associated with sparsely-sampled PPEs. To address these challenges, the method explicitly builds simpler emulators that avoid overfitting, detect structural error, avoids compensating for structural error through inflated mismatch tolerances, and sequentially excludes structurally inconsistent variables for parameter estimation. The method decomposes the high-dimensional calibration problem into linked low-dimensional subproblems, and integrates their constraints to reconstruct the jointly plausible region of the full parameter space. The method is applied to a 100-member PPE with 34 perturbed parameters generated by a version of CAM6 with machine learning-based warm rain microphysics parameterization. Through iterative application, the method greatly reduces the ensemble spread and improves the matching between simulated and observed zonal climatologies. The method also finds ensemble members that outperform the default CAM6 configuration in root mean square error across multiple diagnostics. Controlled experiments demonstrate that overly-conservative emulator uncertainty could lead to neglect of informative observations, and tolerance of the structural error, in the context of this method, biases the estimated parameters toward compensating for structural error. Our work also emphasizes the value of interpretability for diagnosing structural error and informing parameter estimation in PPE-based calibration.

Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, and parts of regional infrastructure systems. We examine vineyard wildfire resilience during the electrically initiated 2019 Kincade Fire in Sonoma County, California, using an open, event-anchored geospatial framework spanning 4,581 vineyard fields (8,813.2 ha), wildland vegetation, surveyed structures, roads, overhead smoke, and post-fire greenness. Sentinel-2, OpenET, gridMET, soils, terrain, NOAA smoke polygons, an ignition-date OpenStreetMap network, and three-dimensional data inventories were analyzed at native decision scales. Vineyard pixels showed substantially lower descriptive dNBR than wildland pixels inside the perimeter (means 0.130 and 0.337). This contrast did not identify a universal vineyard firebreak effect: a segment-clustered boundary model gave a small negative contrast at 100 m (tau = -0.0166) but changed across bandwidths, failed slope continuity, disappeared in a 100 m donut, and produced a wrong-signed placebo. A 250 m spatial GAM reversed the unconditional pattern: after conditioning on location, terrain, and water use, vineyard fraction was positively associated with dNBR, while residual Moran's I remained 0.519. Beyond spectral impact, all mapped vineyards intersected overhead smoke on at least one day (mean 7.78 potential smoke-days per field), 34.2% of road-network nodes were dead ends, and inside-perimeter vineyards showed a larger greenness deficit through 2021 (recovery ratios 0.815 inside and 0.854 outside). Lower immediate spectral impact therefore did not imply complete resilience. The study provides a reproducible urban-rural informatics template separating descriptive contrasts, conditional associations, exposure indicators, and recovery evidence for decisions in working landscapes.

This paper uses the ERA5 weather dataset to assess the impact of the wind on an equatorial space elevator. An analytical formulation establishes that the effect of wind on tether inclination and payload capacity can be captured by a single scalar: RMS wind velocity, averaged over air pressure. This formulation clarifies what constitutes excessive wind for a particular elevator and provides a direct link between wind data and elevator design parameters. Analysis of equatorial ERA5 data shows that equatorial wind loading varies strongly with geography with lower values over continents and higher values over oceans, with the majority contribution coming from the lowest 20 km of atmosphere. The data indicate that realistic equatorial winds pose a significant constraint on thin-ribbon elevator designs if align to face the wind. Combining wind and lightning statistics highlights several promising equatorial regions for anchor sites. Finally, the implications of wind loading for elevator design are examined, and several mitigation strategies are discussed to improve survivability and operational payload capacity under realistic wind conditions.

Droplet-laden cloud dynamics emerge from coupled phase transitions and turbulent mixing. Combining experiments and mean-field theory, we establish scaling laws for steam-injected clouds in unsaturated air seeded with condensation nuclei. Depending on whether the cloud is confined to the initial momentum-dominated zone of the jet or extends into the buoyancy-dominated plume, two asymptotic regimes arise. In the momentum regime, cloud dimensions scale with the injector radius. In the buoyancy regime, size follows a \(\dot{m}^{2/5}\) scaling, where \(\dot{m}\) is the injected steam mass flow rate. Assuming inter-droplet air remains humidity-saturated, our mean-field analysis predicts cloud persistence governed by enthalpy and humidity mixing. These findings bridge microphysical processes and macroscale dynamics, offering a quantitative framework for atmospheric cloud evolution.

We present PepC-Global, a global version of the Princeton environment-dependent probabilistic tropical cyclone (PepC) framework that uniquely implements basin-wise tuning within a single unified tropical cyclone climatology model. PepC-Global represents tropical cyclone climatology as a coupled stochastic process linking genesis, track, and intensity conditioned on large-scale environmental predictors. Each of the genesis, track, and intensity modules outperforms widely used linear models in out-of-sample tests. The intensity module also incorporates an environment-dependent over-land decay model, offering greater sensitivity to climate change signals than conventional fixed decay rate approaches. Systematic evaluation against observations demonstrates that PepC-Global closely reproduces genesis basin-wise frequency, seasonal cycles, interannual variability, and spatial distributions. The model also accurately captures basin-wise track patterns, along-coastline landfall frequency, and intensity statistics including lifetime maximum intensity and landfall intensity distributions. PepC-Global provides a versatile tool for probabilistic tropical cyclone hazard and risk assessment and a practical framework for investigating changes in tropical cyclone activity across future climate scenarios.

Extreme salinity events in Florida Bay are a central concern of the Comprehensive Everglades Restoration Program (CERP). A large-scale seagrass die-off in the late 1980s, driven by hypersaline conditions following reduced freshwater delivery, made reliable salinity prediction essential to restoration planning. The Bay Assessment Model (BAM) is a hydrologic model that simulates salinity across 54 idealized basins representing Florida Bay. It is mass-conservative apart from where required for model stability. Interbasin fluxes are computed from hydraulic gradients across shoals, and each basin receives direct rainfall and evapotranspiration forcing. Along the shoreward boundary BAM is driven by observed water levels from the Everglades Depth Estimation Network (EDEN), or by output from upstream restoration planning models. Along the marine margins, tidal boundary conditions from NOAA subordinate station harmonic constituents are superimposed on a regional mean sea level anomaly. BAM is implemented entirely in Python and is open source. Over the extended 1999-2026 record the domain-wide mean water level bias is -0.024 m with a mean RMSE of 0.092 m, and the domain-wide mean salinity bias is 0.00 ppt with a mean RMSE of 6.21 ppt. Model bias varies systematically with hydrologic regime, increasing in magnitude from drought to wet conditions and reversing sign at one central bay basin, constraining the interpretation of anomaly-based metrics. Applying uniform offsets at the Everglades shoreward boundary yields salinity responses of 2-4 ppt in the northeastern bay, attenuating toward the marine margins; a fuller treatment of restoration and sea level scenarios is reported separately. BAM provides a transparent, efficient, and community-accessible tool for evaluating the salinity consequences of Everglades restoration actions in Florida Bay.

For a model of a thin spherical layer of an incompressible rotating fluid subjected to the action of Coriolis and buoyancy forces, both analytical solutions in the form of stationary thermal spots moving along the equator and self-similar solutions are found. The influence of the parameters determining the shape of the spots on their propagation speed and direction is studied. It is found that the speed and direction of motion of the spots depend not only on the sign of their thermal contrast with the background flow, but also on the ratio of the semi-axes determining their shape. In particular, if the ratio of the equatorial semi-axis to the meridional one $b/a$ is equal to $\pi/2$, the spots are at rest. If the spots are elongated along the equatorial axis to such an extent that $b/a>\pi/2$, then “cold” spots move westward and “hot” spots move eastward. Under the condition $0.747\leq b/a<\pi/2$, the situation is reversed: “hot” spots move westward and “cold” spots move eastward. Self-similar solutions in the model are realized strictly in the form of circular thermal spots, and their radius varies according to a power law with a scaling exponent $k$, which is determined by the behavior of the cross-frontal buoyancy gradient on the spot contour. We note that the regime $k=1/4$ is realized under the assumption of a constant buoyancy gradient, while the regime $k=1/6$ corresponds to the conservation of total buoyancy.

Computing matrix inverses (or, e.g., left inverses) using known algorithms is in general computationally prohibitive in high dimension, such as in an operational weather forecasting context. In this article, a swift, scalable algorithm to calculate the left inverse (through singular values decompositions techniques) of a square root of a horizontally localized ensemble covariance matrix is introduced. In this algorithm, calculations involving model variables located in different grid vertical columns can be performed separately. This is the aspect that makes this algorithm scalable by allowing for parallel computing. The algorithm is developed in the case when the number of ensemble members equals the number of model variables in each grid vertical column (this number is of order $10^3$ for current high-resolution models). The algorithm is tested in a synthetic experiment on a three-dimensional grid, and it is found to have very good accuracy. Additionally, a version of the algorithm outputting the left-multiplication of a generic vector by that above-mentioned left inverse is also introduced. For operational applications, if executed on a single processor, that version of the algorithm is already $9$ orders of magnitude faster than a traditional algorithm, and, with multiple, parallel processors, it scales until the number of processors reaches the order of $10^6$. In addition, an application of the algorithm is pointed out: performing the transform from model variables to an alternative set of variables, defined herein as standardized variables, being climatologically uncorrelated and each having unit climatological error variance. Furthermore, it is pointed out that the standardized variables allow constructing a new Hybrid covariance model, which is positive definite and climatologically unbiased, and it features a geographically dependent climatological error covariance matrix.

We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computational bottlenecks, we devise an orthogonal 4D-parallelization scheme that introduces a unique domain-tensor-parallelism strategy and a novel uncertainty parallel method, enabling us to fully leverage GPU capacity and efficiently scale model training. For a 2.4-billion-parameter model, we achieve a peak performance of 3.96 EFLOP/s on 20,480 NVIDIA GH200 GPUs on the JUPITER supercomputer. We train BEAST as a 700-million-parameter model with 96 random weight samples on 384 nodes on 40 years of data for nearly one million gradient updates. This model achieves predictive skill scores competitive with state-of-the-art probabilistic atmospheric AI models and numerical models, and can predict extreme events with exceptional skill, while generating large ensembles 3 to 4 times faster than the current-best AI model. Our contribution unlocks the potential of high-fidelity uncertainty quantification in atmospheric AI models, heralding a new era for AI-based models in climate and Earth system sciences.