Earth and Environmental Sciences

NASA's Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite sensor provides high temporal and spatial resolution measurements critical for monitoring air quality. During the Synergistic TEMPO Air Quality Science (STAQS) component of the 2023 AGES+ campaign, extensive surface, airborne, and remote-sensing observations were collected over the New York City/Long Island Sound region, enabling comprehensive investigation of ozone and its precursors, including nitrogen dioxide (NO 2 ) and formaldehyde (HCHO). Evaluating TEMPO (version 3) NO 2 and HCHO column retrievals against Pandora and GEO-CAPE Airborne Simulator (GCAS) observations, based on the limited number of coincident flight days available, shows TEMPO can capture urban-suburban pollution gradients and exhibits biases comparable to previous satellite validation studies with strong NO 2 column correlations ( R ≈0.79–0.81), though sharp transitions between high and low emission regions remain challenging. The high-resolution (1.33 km × 1.33 km) WRF-Chem simulation reproduces the major spatiotemporal patterns of surface ozone and NO 2 ( R ≈0.56–0.73), supporting its use to fill observational gaps. Integrating TEMPO, WRF-Chem, in situ measurements, and ozone and wind lidar observations, we characterize the spatiotemporal ozone dynamics under different pollution regimes. High-pollution days involve early urban precursor accumulation and sea-breeze-driven coastal recirculation of pollutant-rich air. Moderate days exhibit localized enhancements driven by transport, such as downwind plume transport, while low-pollution days show efficient dispersion and limited ozone formation. This multi-platform framework highlights the importance of resolving fine-scale variability in coastal and transition zones and illustrates TEMPO's potential for improving ozone forecasting and mitigation in complex environments.

The East Sea (Sea of Japan) is a semi-enclosed marginal sea with an energetic mesoscale eddy field along the Tsushima Warm Current and its subpolar front. Unlike prior point reconstructions of Dokdo sea level, we use 0.125° gridded satellite altimetry (DUACS, 1993–2024) to map the spatial structure of the basin’s rise, its variability, and its extremes. The basin-mean rise is 4.1 ± 0.5 mm yr−1, heterogeneous (1.6–6.6 mm yr−1) yet significant across essentially the entire basin, and fastest along the East Korea Warm Current and in the Ulleung Basin at Dokdo. With the trend removed, the leading mode of variability is basin-coherent, concentrated over the southern eddy-energy maximum, and only weakly related to remote climate modes or local wind stress (R2 ≤ 0.08). Extreme high sea-level days, counted against a fixed 1993–2002 95th-percentile threshold, rose roughly sevenfold (4.9 ± 1.0 days yr−1), an increase driven by the rising mean rather than by a broadening of the daily distribution, though the multiplication factor is sensitive to the threshold definition (three- to twenty-eight-fold) and reflects open-water exceedance rather than coastal flooding frequency. The altimetry agrees with open-water tide gauges (mean r = 0.83) and an in situ Dokdo bottom-pressure record (r = 0.88), and a steric/non-steric decomposition shows that Dokdo’s coherence with the basin is dynamic rather than thermal. These results reframe Dokdo sea level as an expression of regional warm-current and eddy dynamics relevant to coastal-hazard exposure.

Abstract Extreme wave events in the North Atlantic have been linked to the North Atlantic Oscillation (NAO), though the connection varies spatially and the related synoptic-scale mechanisms remain unclear. We quantify how the winter NAO affects both the occurrence and magnitude of significant wave height extremes, defined here as seasonal winter maxima, in the Nordic seas and the North Sea. We show that this relationship exhibits pronounced spatial variability, with more and higher wave extremes in the Norwegian Sea during positive NAO, a weaker influence in the western North Sea, and reduced wave extremes in the southern Barents Sea. By linking extreme wave events to synoptic-scale processes, we demonstrate that this spatial variability arises from differences in cyclone characteristics and wave growth mechanisms. We find that most wave extremes occur in connection with cyclones. However, wave extremes in the Norwegian Sea and the western North Sea exhibit substantial differences in cyclone pathways, intensity, translation speed, and development. Extreme wave heights in the Norwegian Sea are associated with running fetch conditions, i.e., cyclones moving close to the group velocity of the waves. In contrast, in the western North Sea and southern Barents Sea, wave growth is fetch-limited due to land interaction and sea ice, with extreme waves primarily generated by northerly winds in the rear of cyclones. These differences in cyclone characteristics help reconcile the regionally varying relationship between extreme waves and the NAO, demonstrating that the influence of large-scale atmospheric patterns on wave extremes depends critically on regional wave growth characteristics.

This study assesses the impact of assimilating remote sensing observations of significant wave height (SWH) and 10 m wind speed (WSPD) into the analysis using a weakly coupled data assimilation (WCDA) framework to improve ocean surface monitoring offshore of the Contiguous US (CONUS). We assimilated SWH and WSPD data from 10 altimeters and six scatterometers into the Local Ensemble Transform Kalman Filter (LETKF) data assimilation (DA) scheme over 6 months, from 1 January to 1 July 2024, to update the SWH and WSPD variables of the 30-member Global Ensemble Forecast System wave component (GEFS-wave), which provided the background fields. We validated the SWH and WSPD analyses against assimilated remote sensing observations and found that assimilating these observations corrected the systematic background bias in WSPD values above 15 m/s to near zero, with uncertainty as low as 0.4 m/s. For SWH, the background overestimation averaged 30 cm for observed values between 6.2 and 6.4 m and was reduced to 6 cm in the analysis, with a 30 cm RMSE. These results were consistent with cross-validation experiments that assimilated data from six altimeters for the SWH analysis and three scatterometers for WSPD analysis updates, respectively, and were evaluated against independent remote sensing observations. We show that the diurnal cycle in the spatial coverage and temporal sampling of state-of-the-art altimeter and scatterometer constellations over 6 months in maritime CONUS identified regions where remote sensing data had the greatest impact on the analysis and where they were scarce and had limited impact when assimilated using a reduced 1-h window. A case study showed that assimilating altimeter data during a Pacific storm that developed on 1 February 2024 allowed the LETKF to adjust the SWH analysis spatially.

Renewable energy production is strongly influenced by climate variability and change, making the energy sector sensitive to fluctuations on decadal timescales. Decadal climate predictions, which aim to forecast climate variability over the next few years, therefore offer potential value for anticipating near-term changes in wind and solar resources and supporting climate-informed energy planning. However, the predictive skill of decadal forecasts for energy-relevant indicators remains poorly quantified, which is crucial to know the potential usability of any forecast product. This study evaluates the skill of decadal climate predictions over Europe for forecast years 1–3 using a multi-model ensemble from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Decadal Climate Prediction Project (DCPP). We assess three energy-relevant indicators: photovoltaic potential (PVpot), wind capacity factor (WCF), and a compound indicator describing the number of energy drought days (NED), defined as days with inefficient production from both wind and solar resources. The skill is evaluated against the ERA5 reanalysis, and the added value of the model initialization is estimated by comparing the decadal predictions against the non-initialized historical forcing simulations. PVpot exhibits the highest and most spatially homogeneous skill for annual, spring and summer aggregations, closely reflecting the high predictability of surface solar radiation. WCF shows low and spatially heterogeneous skill, consistent with the high intrinsic variability of wind. The compound NED indicator displays strong seasonal dependence: its predictability is largely controlled by solar conditions in high-radiation seasons and by wind in winter and autumn. Model initialization generally provides added value where historical simulations already show some skill, especially for PVpot, while its impact is lower for WCF. This work shows the specific seasons, regions and energy indicators for which decadal predictions can provide actionable climate information to support renewable energy applications.

A variety of chemical and dynamical processes in the troposphere and stratosphere affect tropical total column ozone (TCO), the net effect of which may cause changes in surface UV radiation and impact human and ecosystem health. We use dynamical linear modeling to estimate changes in tropical TCO and partial column ozone (PCO) in the troposphere and three stratospheric layers to assess agreement between satellite composites and chemistry-climate model simulations from two multi-model experiments (CCMI-1 and CCMI-2022). While both model experiments show tropical TCO increases over 2000–2021, multimodel-mean CCMI-2022 changes (+2.5 DU) agree slightly better with observations (+3.3 DU) than CCMI-1 (+1.6 DU). However, this overall agreement obscures multiple systematic differences in PCO changes between the models and observations across atmospheric layers. For example, since 2000 tropical tropospheric PCO increased significantly in CCMI-2022 (+1.5 DU) but not in CCMI-1 (+0.3 DU), largely explaining the difference in TCO changes. Also, despite nearly identical stratospheric PCO changes, CCMI-2022 changes are slightly more negative than CCMI-1 in the lower stratosphere (by ∼ 0.5 DU) and more positive in the middle/upper stratosphere. Crucially, substantial differences exist between observational PCO changes, particularly in the troposphere and middle/upper stratosphere, and these disagreements limit the ability to evaluate CCM fidelity for past changes. While early and late century trends are correlated across models, suggesting a potential emergent constraint on ozone, the spread in observational trends means they are unable to provide guidance on the credibility of model projections for a given emissions scenario.

Stratosphere-troposphere exchange (STE) plays a fundamental role in the global atmospheric budget of chemical constituents. The troposphere-to-stratosphere transport (TST), as a part of STE, can inject anthropogenic pollutants from the Earth’s surface into the stratosphere, changing its chemical composition and influencing radiative processes. On record, TST is a multi-scale process with various contributing mechanisms, often not fully qualified nor quantified. In the tropics, typhoons and the corresponding overshooting convection and updrafts have recently been highlighted as one of the TST mechanisms, contributing for instance to the moistening of the lower stratosphere. Expanding on this, our study proposes a novel mechanism for TST connected with the interaction of typhoons with orography, including modulation of typhoon updrafts and convection, orographic lifting, and orographic gravity waves. Combining a Lagrangian modeling tool with a high-resolution simulation of the landfall of typhoon Molave (2020) in the Philippines, our results show that the presence of orography enhances the transport of air from the planetary boundary layer to the upper troposphere–lower stratosphere (UTLS) region. The presented findings advance our understanding of tropical cyclones impacts on STE and may have significant implications for the long-range atmospheric transport of pollutants originating from tropics.

Abstract The Solotvyno salt mine in West Ukraine presents significant environmental and infrastructural risks due to uncontrolled salt dissolution following mine flooding, causing ground subsidence, sinkhole formation, and transboundary contamination of the Tisza River. This study employs an innovative multidisciplinary approach to characterize subsurface conditions and contamination pathways. The UX-1Neo autonomous underwater robotic platform conducted 14 dives in two flooded mine shafts, revealing intact main shaft structures but worrying blockages in horizontal passages, distinct haloclines at different depths (60 m in ventilation shaft 9, 140 m in shaft 10), and evidence of ongoing salt crystallization. Ground geophysical surveys such as electrical resistivity tomography, very low frequency radio-magnetotellurics and horizontal loop electromagnetics identified compromised protective clay layers (“pallag”) and potential air-filled voids, explaining accelerated dissolution and surface deformation. Hydrodynamic and contaminant transport modeling quantified salt fluxes (~ 2000 m 3 /day through “pallag” zones, ~ 13–15 m 3 /day from direct mine leakage) and confirmed dissolution rates of approximately 5.0–5.5 m 3 /day of rock salt, driven by both anthropogenic influences and natural gradients toward the Tisza River. NETPATH mixing models revealed variable contamination throughout the system, with the Black Moor area showing highest vulnerability (up to 27.4% mine water contribution) while the Tisza River maintains relatively good water quality despite measurable contamination (1.97% mine water). The suggested approach provides critical insights into risk assessment and remediation planning, representing a significant advancement in monitoring and managing complex environmental hazards associated with abandoned salt mines.

Foundation models have shown remarkable generalization ability for few-shot learning (FSL). However, their potential for remote sensing scene classification has not been fully explored. Existing methods mainly adapt a single vision–language model and seldom exploit the complementary strengths of different foundation models. Moreover, the inconsistency between visual and textual representations limits the effectiveness of cross-modal learning under limited supervision. To address these issues, we propose a framework for few-shot remote sensing scene classification leveraging collaboration of foundation models. Specifically, the proposed framework integrates a large language model, a text-to-image diffusion model, and a vision–language model to enrich class semantics, synthesize category-related training samples, and learn transferable visual-textual representations. Furthermore, a Deep Cross-modal Alignment (DCA) module is developed to improve feature consistency across modalities. The DCA module incorporates multi-scale visual features, lightweight adapters, and a contrastive learning objective to obtain more discriminative task-adaptive representations. Extensive experiments on 12 remote sensing scene classification datasets under various few-shot settings demonstrate that the proposed framework achieves competitive performance compared with existing prompt learning and efficient parameter tuning methods, with consistent improvements observed on average across the evaluated datasets. Recent multimodal large language models evaluated in the zero-shot setting are additionally reported as references, while the 2-shot results of our method illustrate the performance when limited labeled data are available. This comparison provides a broader view of the trade-offs between label availability, classification accuracy, and inference cost across the two paradigms.

Extreme weather hazards are increasing and stakeholders need rapid, transparent information during unfolding events. We present RHITA (Real-time Hazard Identification and Tracking Algorithm), an open-source framework and web tool for near real-time detection and tracking of weather-related hazards over Europe. RHITA identifies grid cells exceeding local quantile thresholds, groups them into spatial clusters, and links clusters through time to reconstruct three-dimensional events in longitude, latitude, and time. For each event, RHITA provides intensity, extent and duration metrics and estimates rarity through return periods derived from a long historical record. RHITA is operated with ECMWF open forecasts for daily monitoring and ERA5 reanalysis for a consistent historical archive from 1950 to 2024. We target four hazards: heatwaves, cold spells, heavy precipitation and strong winds. Key spatial and temporal parameters are optimized against EM-DAT disaster records (2000 to 2023). Applying RHITA to ERA5 yields a European climatology of hazard events and reveals robust increases in heatwave frequency, intensity and affected area, a decline in cold spell frequency, and more heterogeneous signals for heavy precipitation and strong winds at the continental scale. RHITA provides open access data and an interactive interface to support rapid hazard characterization, event contextualization and downstream risk analysis.

Urban environmental monitoring increasingly requires methods that translate Earth observation (EO) data into spatial units relevant to ecosystem-service assessment and planning. This study develops a landscape-unit framework for smart monitoring of urban cooling in two Polish Functional Urban Areas (FUAs): Zielona Góra and Gorzów Wielkopolski. Landsat-derived land surface temperature and surface urban heat island (SUHI) intensity were integrated with vegetation, water, imperviousness and NDVI indicators, together with the characteristics of directly adjacent landscape units. Spearman correlations, ordinary least squares and Spatial Error Models (SEMs) were used to diagnose the spatial structure of thermal conditions. The SEM variants removed residual spatial autocorrelation and showed the strongest full-sample fit, with RMSE values of approximately 0.85 SUHI units and MAE values of approximately 0.66; these statistics are used as model-fit diagnostics rather than as measures of out-of-sample predictive performance. The results were translated into a continuous weighted HeatCool Index and seven Jenks natural-break classes that represent the relative capacity of landscape units and their neighbourhoods to support cooling or reinforce warming. The classification is interpreted as an ecosystem-service screening product rather than as an independent validation of its component variables. Independently modelled annual and seasonal NO2 indicators were then aggregated to the same landscape units as a complementary pressure layer. After EDM correction, the NO2 model achieved R2 = 0.77, RMSE = 3.98 µg m−3 and MAE = 2.36 µg m−3 on the held-out validation subset. Across the HCI gradient, Classes 1–2 showed the lowest and least variable annual NO2 conditions, whereas Classes 6–7 exhibited the strongest winter deterioration and Class 7 contained the highest annual NO2 concentrations. Because NO2 was not used to construct the HCI, these gradients represent an independent comparison of co-occurring thermal and air-pollution pressures rather than evidence of a causal relationship. The proposed framework extends pixel-based thermal assessment by integrating landscape-unit classification, neighbourhood context and independently modelled air-pollution pressure within a common planning-oriented spatial framework.

Accurate estimation of clear-sky surface longwave downward radiation (LWDR) is fundamental to surface energy budget studies, yet widely used parameterizations and conventional lookup-table (LUT) searches often suffer from regional biases and limited transferability. This study develops a scene-mapped LUT framework that operates in brightness-temperature (BT) space and uses surface temperature (ST) and total column water vapor (TCWV) as physical descriptors to define scene-specific subsets and constrain spectral matching to locally consistent surface–atmosphere states. Two implementations are proposed within the scene-mapped framework: S-Poly (polynomial regression) and S-LUT (sub-LUT distance search). Validation against multi-network ground measurements using MODIS inputs yielded RMSEs of 26.36 and 26.89 W m −2 and biases of −0.86 and −2.75 W m −2 for S-LUT and S-Poly, respectively. The corresponding RMSEs were lower than those of the global-LUT benchmark (29.26 W m −2 ) and the evaluated general clear-sky parameterization benchmark (30.03 W m −2 ), representing reductions of 12.2% and 10.5% relative to the latter. Both methods generally maintained stable accuracy across diverse surface types and climatic regions. S-LUT was more consistent across the evaluated configurations and achieved an RMSE of 22.48 W m −2 under the mixed MODIS–ERA5 configuration. The flexible scene-mapping framework may also provide a useful methodological concept for adapting radiation retrieval to different sensors and exploring future extensions toward all-sky conditions.

Textual accounts establish essential context for port disruptions but can bias large language model synthesis before independent observations are examined. We present Port Agent, a remote-sensing agent that aligns Automatic Identification System (AIS) trajectories, Sentinel-2 vessel detections, and documents while retaining acquisition time, footprint, processing, and uncertainty. The evaluation follows the evidence chain from physical observation to agent synthesis across 12 views of 11 U.S. event-or-control groups, 24 quality-screened scenes, and 145,570,911 AIS positions. AIS first resolved distinct operational responses: cargo/tanker occupancy fell by 58.8% during the New York/New Jersey strike, 41.5% during the Baltimore channel restriction, and 44.1% around Hurricane Beryl at Houston, whereas the Seattle cyber case showed no comparable portwide contraction. Sentinel-2 then added spatially explicit evidence through a validated detector, georeferenced AIS–image correspondence, and observations during near-synchronous AIS gaps. Finally, a fixed-backend experiment tested how this evidence affected synthesis. Adding the aligned physical record improved concise review, and applying the evidence gate to that same record produced a further gain, particularly for misleading reports. The complete configuration achieved the highest scores under both report conditions. Relative to concise text-only review, it improved the common-outcome score by 19.24 and 22.16 points and reduced misleading-claim false acceptance from 100.0% to 33.3%. Supporting analyses found no detectable model-interface difference, little change in mean performance across temperatures, and no benefit from a longer prompt. Although the system comparison remains exploratory, the results support an auditable evidence chain in which AIS and satellite observations constrain text-led synthesis.

ABSTRACT Based on the Global Precipitation Climatology Centre (GPCC) monthly precipitation data, the Extended Reconstructed Sea Surface Temperature (SST) version 5 (ERSSTv5) dataset derived from the National Oceanic and Atmospheric Administration (NOAA), and the Fifth‐generation European Centre for Medium‐Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) monthly dataset, the synergistic influences of the SST anomalies (SSTAs) in the North Atlantic (NA) and the Indian Ocean (IO) on summer precipitation in Central Asia (CA) are investigated. Results show that SST in both the NA and the IO positively correlates with summer precipitation in southern CA. The meridional movement of the subtropical westerly jet (SWJ) plays an important role in linking the SSTAs in the NA and the IO and summer precipitation in southern CA. The positive SSTAs in the NA can excite an eastward‐propagating wave train across Eurasia and cause an anomalous cyclone over CA, which is responsible for the southward movement of the SWJ. The positive SSTAs in the IO can induce an anomalous cyclone in the upper troposphere over the northwestern part of the South Asian summer monsoon region, according to the Gill‐type atmospheric response, and the SWJ over CA also moves southward. The southward movement of the SWJ over CA responds to the simultaneous SST warming in the NA and the IO. Furthermore, it can induce an anomalous cyclone in the middle troposphere over CA and strengthen the transport of water vapour from the Arabian Sea. Therefore, more summer precipitation can be observed in southern CA. In the process of synergistic influences of SSTAs in the NA and the IO on summer precipitation in CA, the former plays a more important role.

Abstract Saline irrigation represents a pivotal alternative strategy for enhancing crop production under freshwater scarcity. However, previous assessments have mainly quantified average yield responses, while how interacting environmental and management conditions shape region-specific irrigation-water salinity thresholds and associated spatial risks remain elusive across global major breadbaskets. This study combined a meta-analysis of 551 field observations with machine learning to quantify global wheat yield responses to saline irrigation, identify optimal region-specific thresholds, and evaluate spatial risks in four major wheat-producing areas: the North China Plain (NCP), Indo-Gangetic Plain (IGP), Middle East (ME), and Central California Valley (CCV). Meta-analysis revealed that saline irrigation led to significant average yield reductions ranging from 12.4% in the NCP to 31.4% in the CCV. Machine learning further showed that irrigation water salinity, growing-season temperature, and irrigation amount emerged as the dominant drivers, collectively explaining more than 54% of yield variation. An optimum irrigation amount of 500 mm was optimal for maximizing yield under saline conditions. Region-specific best practices included border irrigation at flowering and grain-filling stages for the NCP, drip irrigation at these stages for the IGP, and drip irrigation at the flowering stage for the ME and CCV. Spatial risk assessment revealed a mismatch between salinity tolerance thresholds and actual exposure. Under a conservative 5% yield-loss scenario, the IGP exhibited the highest model-derived salinity threshold of 4.14 dS m-1 but the largest risk area proportion due to elevated background salinity, whereas the ME faced the lowest risk, with a risk area of 7.7%, indicating high potential for saline water utilization. These findings highlight the limitations of universal water quality guidelines and demonstrate the necessity of localized management. By establishing a data-driven framework, this study provides actionable insights to safeguard global wheat supply, build climate resilience, and ensure agricultural sustainability in salt-affected environments.

Urbanization has increased impervious surfaces in cities, raising surface temperatures and consequently enhancing sensible heat fluxes and longwave emission while reducing latent heat exchange. These surfaces influence runoff temperature, reduce infiltration and increase runoff volumes, thereby affecting urban thermal environments and human thermal comfort. Existing models tend to inadequately represent coupled thermal–hydrological processes. Data-driven approaches often omit fine-scale spatial heterogeneity, while physically based water temperature models are mainly designed for rivers and lakes and only partially address heat exchange for urban pavements. Although recent advances have improved this understanding, these findings are rarely incorporated into spatially distributed hydrodynamic frameworks resolving flow depth, velocity, and routing in urban environments. This study develops a spatially distributed and temporally resolved modeling framework for urban environments that explicitly resolves runoff dynamics and water temperature evolution. The framework extends a two-dimensional hydrodynamic model based on the full shallow-water equations by incorporating a water temperature module with heat transfer driven by prescribed surface temperatures. Model performance is evaluated using in-situ measurements from a controlled experiment on a concrete surface that lasted approximately 6 min, during which water temperatures rose by more than 7 °C before reaching steady state. Including time-dependent surface temperature forcing, in addition to meteorological forcing, substantially improves agreement between simulated and observed water temperatures, reducing the mean absolute error from 3.4 °C to 0.2 °C. The application to the Seestadt Aspern city quarter demonstrates the framework’s potential for spatially resolving runoff temperature dynamics of heavy rain events in a realistic urban setting.

ABSTRACT Meridional positions of the Asian Subtropical westerly jet (AWJ) in the upper troposphere and the South Asian high (SAH) in the lower stratosphere are closely linked to extreme weather and climate events in East Asia. Given the regional characteristics of these two circulation systems, this study separates each system into eastern and western centers—namely the East Asian jet (EAJ), the Central Asian jet (CAJ), the Tibetan High (TH) and the Iranian High (IH). We find that the north–south shifts of the EAJ and TH exert a more pronounced impact on precipitation and near‐surface temperature over East Asia than those of the CAJ and IH. Furthermore, the in‐phase configuration of meridional displacements of the EAJ and TH significantly modulates large‐scale three‐dimensional (3D) circulation over East Asia. In particular, when the EAJ and TH simultaneously shift northward, the Yangtze River Basin (YRB) and Tibetan Plateau (TP) region are controlled by an intense horizontal anticyclonic circulation and a common descending branch of two reversed meridional circulations, while the eastern TP is covered by the descending branch of a local zonal circulation. Consequently, there are widespread precipitation deficits and heat anomalies from the TP to the YRB. Diagnostic analysis identifies the central‐eastern equatorial Pacific SST cooling in the pre‐winter and the tropical Indian Ocean cooling in May–June as the main forcing factors of the simultaneous northward movement of the EAJ and TH during midsummer. These findings suggest that greater attention to the eastern centers of the AWJ and SAH (EAJ and TH) may benefit East Asian climate prediction.

ABSTRACT Tropical cyclones (TCs) are among the most damaging natural hazards affecting Australia, and climate change may alter their frequency, tracks, intensity, and associated exposure. Historical records provide a baseline for assessing changes. Here, we develop a climatology‐based statistical model of TC weather extent using Australian Bureau of Meteorology estimates of the radius of the outermost closed isobar, wind radii, and positional uncertainty. Explicitly incorporating positional uncertainty allows long and heterogeneous historical records to be analysed while accounting for changes in record confidence. The model is applied to International Best Track Archive for Climate Stewardship records of 1960/61–2023/24 to examine: (1) the variability in TC formation across the Southeast Indian–South Pacific basin, (2) Australia's differential exposure to TCs from different formation areas, and (3) decadal variability in TC activity. Results show that 95% of TCs producing TC weather events in Australia had their early lifecycle between 107.5°E and 166°E. TC weather events occur annually along northern Western Australia and every 2 years along north‐east Queensland. Event likelihood depends strongly on formation longitude and early lifecycle direction, reaching ~95% for formation longitudes between 135°E and 143°E and decreasing eastward and westward. TC‐frequency trends are spatially heterogeneous, with no robust basin‐wide change since 1960. Periodic reassessment as historical records lengthen and improve may refine estimates of TC exposure, while formation longitude and early lifecycle direction provide climatological indicators of the potential for developing TCs to affect Australia. Overall, the findings provide a long‐term climatological basis for Australian TC exposure assessment and risk management.

Abstract Observations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign are used to examine the processes controlling where cumulus clouds form over the Sierras de Córdoba (SDC) in Argentina. Although orographic lifting favors cloud initiation, the factors determining whether clouds develop over the ridge crest, east or west of the crest, or in multiple bands remain uncertain. Coordinated measurements from scanning radars, stereo cloud cameras, radiosondes, and GOES-16 imagery reveal how variations in thermally driven upslope flow, cross-barrier winds, and boundary-layer thermodynamics regulate cloud location. Crest-anchored clouds occur when deep thermally forced easterlies reach the ridge under weak opposing westerlies. East-of-crest and double-banded clouds develop when stronger westerlies aloft suppress upslope penetration, shifting convergence and cloud initiation to the lee slopes; small decreases in lifting-condensation-level height or boundary-layer stability can toggle these regimes between single and double bands. West-of-crest clouds arise when easterlies extend above crest height and mechanically lift air over the ridge. Across all cases, the superposition of mechanical (cross-barrier) and thermal (upslope) forcing—quantified using principal components derived from radar velocity profiles—systematically explains the observed cloud displacements. Positive or neutral superposition yields crest clouds, negative (i.e., destructive) superposition produces east-of-crest or double-banded convection, and reversed mechanical forcing favors west-of-crest clouds. These findings extend conceptual models of mountain convection by showing how small variations in forcing balance and boundary-layer thermodynamics control orographic cloud initiation.

Space and time play a crucial role in multi-hazard impact assessment. When two or more natural hazards occur simultaneously or in sequence at the same location, the physical integrity of assets and infrastructures can be compromised, and the resulting damage can be greater than that generated by individual hazards occurring in isolation. Despite widespread conceptual recognition of these interactions, the literature lacks quantitative, standardised methods for the systematic analysis of multi-hazard impacts. This study presents a quantitative methodology for evaluating multi-hazard physical damage to the built environment, translating qualitative impact dynamics into a transparent and reproducible analytical framework, implemented as modular Python code. The approach covers both concurrent and consecutive hazards by modelling: (i) the increased damage resulting from the combined impact of two or more concurrent hazards that overlap in space and time, and (ii) the effects of cumulative damage on asset vulnerability and the recovery dynamics in the case of consecutive hazards that overlap in space. Using the Python implementation, a model behaviour analysis is conducted to systematically explore how variations in inter-event time intervals, vulnerability interactions, and recovery trajectories affect cumulative physical damage. The methodology is applied to a real multi-hazard sequence in Puerto Rico, including the concurrent wind and flood impacts of Hurricane Maria and the consecutive seismic impacts of the 2019–2020 earthquake sequence. The reconstruction of past damage dynamics highlights that ignoring residual hurricane damage would significantly underestimate the subsequent earthquake losses, and that damage accumulation is path-dependent, strongly influenced by event timing and recovery processes. By providing a generalised, transparent, and reproducible quantitative structure, this study offers a tool for forensic analysis of past multi-hazard events, systematic exploration of damage drivers, and scenario-based assessment of alternative hazard and recovery conditions, supporting both post-disaster learning and planning-oriented applications.

Fine-scale ocean dynamics strongly influence coastal mixing, sediment transport, and vertical exchange, yet remain difficult to observe at the spatial scales at which they occur. Here, we use total surface current vectors measured by the airborne Ocean Surface Current Airborne Radar (OSCAR) to investigate sub-kilometer divergence, vorticity, shear rate, and vertical velocities in the tidally dominated Iroise Sea around Ushant Island. Observations acquired during ebb and flood tides in May 2022 reveal two distinct mechanisms generating intense fine-scale dynamics. North and west of Ushant Island, interactions between strong tidal currents and complex bathymetry produce alternating zones of divergence and convergence associated with flow acceleration and deceleration over bathymetric gradients. South of the island, a tidal jet flowing alongside comparatively calm waters generates strong lateral shear, producing divergence of up to O(20 f ) and vertical velocities approaching 0.2 m.s −1 . Comparison with the high-resolution MARS2D model shows that currents and bathymetry-driven divergence are reproduced reasonably well, whereas the shear-driven divergence associated with the southern tidal jet is underestimated by approximately an order of magnitude. Coarsening the OSCAR observations from 200 m to 1 km reduces divergence, vorticity, and shear rate from O(10 f ) to O(1 f ), demonstrating the strong sensitivity of derivative quantities to spatial resolution. These results highlight the importance of sub-kilometer observations for resolving fine-scale coastal dynamics, provide a unique observational benchmark for evaluating high-resolution numerical models, and illustrate the complementary roles of airborne, in situ and satellite measurements in characterizing coastal and submesoscale processes across a wide range of spatial and temporal scales.

Marine heatwaves (MHWs) and ocean acidity extremes (OAXs) are intensifying under anthropogenic climate change, and their compound co-occurrence can amplify ecological impacts. Yet the Agulhas Leakage, a key gateway transferring warm, saline Indian Ocean water into the Atlantic, remains understudied. Here we characterise MHWs over 1982–2024 and OAXs, and their compound events in this region over 1982–2021, comparing analyses of original and linearly detrended data. Under a fixed baseline, MHW cumulative intensity rose from approximately 29.95 to 109.51 °C-year in the original data and 49.4 to 66.7 °C-year after detrending between the first and fourth decades. OAX intensity increased from approximately 0.24 to 4.10 nmol kg - ¹-year in the original data and from 0.35 to 0.46 nmol kg - ¹-year after detrending. Compound events emerge only after the 2000s. Detrending reveals that most of this increase reflects the long-term warming and acidification trends, while residual variability remains in the dynamically active Agulhas Retroflexion, where anticyclonic eddies trap warm, high [H + ] Indian Ocean waters. A likelihood multiplication factor confirms statistically significant positive MHW–OAX dependence that persists after detrending, indicating statistically significant positive co-occurrence between MHW and OAX on interannual timescales, consistent with physical coupling through carbonate chemistry. Baseline choice critically shapes compound-extreme risk assessment.

Abstract This study examines the global relationship between monthly mean near-surface temperature and monthly total precipitation using gridded climate data. Significant correlations (p < 0.01) are found across all regions, with positive correlations in approximately 74.5% of locations and negative correlations in the remainder. To move beyond correlation and assess directional predictability, we apply Granger causality analysis (GCA), impulse-response analysis (IRA), and cross-correlation (CC) to investigate causal and lead-lag relationships between the two variables. The results show significant Granger causality from temperature to precipitation across most regions, except for a few locations in the northern temperate zone. Conversely, precipitation does not Granger-cause temperature in many tropical and subtropical regions, while two-way Granger causality is more common in temperate zones. IRA indicates that a 1°C shock to temperature (1°C innovation in the temperature equation ) produces precipitation responses ranging from −8 to 10 mm, whereas a 1mm shock to precipitation ( 1mm innovation in the precipitation equation ) results in temperature changes of −0.3°C to 0.1°C. Precipitation responds more strongly and rapidly to temperature shocks than temperature responds to precipitation shocks. Overall, GCA, IRA, and CC consistently indicate that temperature generally leads precipitation in their Granger-causal relationship, referring to temporal precedence and incremental predictability within the bivariate system, except in a limited number of northern regions.

This study investigates a new methodology to estimate plant area index ( PAI ) and its spatial distribution from 3D point clouds acquired by a system of 3 LiDARs (2 nadir-viewing and one at 45°) mounted on the Phenomobile Unmanned Ground Vehicle designed for high-throughput phenotyping of herbaceous crops. It relies on the computation of the gap fraction at the voxel level using the trajectories of the laser beams to numerically invert the Beer-Lambert law (BL), enabling the simultaneous estimation of PAI and AIA (Average Inclination Angle, in°). The method is evaluated on wheat crops both in silico (i.e. simulations based on the AdelWheat functional structural crop model coupled with a point cloud simulator) and in actual phenotyping trials of a panel of 10 bread wheat genotypes grown in two study sites under different treatments of plant density, water stress and sowing dates. The in silico validation indicates that the numeric inversion of BL with the LiDAR system of the Phenomobile leads to a relative error of 9.8% (RMSE = 0.32, R 2 = 0.99) and 8.4% (RMSE =5.4°, R 2 = 0.95) in the estimation of PAI and AIA , respectively. The validation in actual phenotyping experiments against destructive measurements produced satisfactory results for canopy PAI (RMSE = 1.44, R 2 = 0.82), while requiring prior knowledge on AIA , e.g. either by (i) introducing a regularization term in the cost function used to invert BL, to avoid unexpected high AIA estimations, (ii) using a fixed, reference value of 60°. The analysis of the results indicates that the inclusion of a regularization term mitigates the impact of actual differences in leaf inclination among cultivars on PAI accuracy, while providing a relative description of the actual variability in the vertical profile of leaf inclination across the different cultivars.

Abstract This study integrates satellite and surface NO₂ measurements through the ensemble square root filter (EnSRF) and the GEOS‑Chem model to estimate anthropogenic NOₓ emissions for China, the contiguous U.S., and Europe over 2019–2020, and further examines how the COVID‑19 pandemic altered emission patterns and sectoral contributions. Our results show that in 2020, anthropogenic NOₓ emissions were 19.20 ± 5.87 Tg in China, 8.96 ± 2.90 Tg in the contiguous U.S., and 10.56 ± 2.95 Tg in Europe. Compared with 2019, emissions decreased by about 5.38% in China, 8.94% in the contiguous U.S., and 15.38% in Europe. In China, emission reductions came from transportation, power plants, and industry. In contrast, reductions in the contiguous U.S. and Europe were almost entirely from transportation. These differences reflect the fundamental gap in energy structure and industry models between developed and developing economies. During the first wave of the pandemic, emissions fell sharply compared to the same period in 2019. The decreases were 18.04% in China, 10.56% in the contiguous U.S., and 21.74% in Europe. Large spatial variations were observed within each region. This work provides observation-based evidence of NOₓ emission changes across major world regions during COVID 19. It highlights how emissions can behave differently under sudden public health crises. The findings are useful for checking bottom-up emission inventories, assessing extreme events’ impacts on air quality, and designing better emission reduction policies.