Search Results (324)

Drought is a slowly evolving, multi-driver hydro-meteorological hazard whose accurate early prediction is a cornerstone of climate-smart agriculture and water-resource planning. Existing data-driven drought forecasting frameworks suffer from three persistent limitations: (i) most models concatenate heterogeneous climate variables into a single flat feature vector, implicitly assuming a single dominant driver such as precipitation, even though atmospheric moisture demand, radiation and wind-mediated evapotranspiration co-determine drought onset; (ii) wavelet preprocessing is typically applied to the full series, introducing future-information leakage that violates the operational causality requirement of forecasting; and (iii) most architectures predict a single horizon and provide no causal attribution explaining when, where and which climatic variables initiated the event. This study proposes WaveDroughtNet, a multi-modal, multi-horizon deep-learning framework that addresses these limitations through five integrated components: (a) a strictly causal Daubechies-4 wavelet decomposition computed in a rolling fashion; (b) six modality-specific encoders with stochastic modality dropout (p = 0.15); (c) cross-modal multi-head attention with four heads; (d) a four-layer temporal convolutional network (TCN) backbone with dilation factors yielding a 240-step receptive field; and (e) a post hoc DroughtOriginTracer that combines temporal attention, modal-attribution and inter-district propagation scans. The Standardised Precipitation Evapotranspiration Index (SPEI), used as the supervisory target, is computed following the canonical Vicente-Serrano formulation. water balance $D=P-\mathrm{P}\mathrm{E}\mathrm{T}$ (Hargreaves PET) at a 4-week (≈1-month) timescale, fitted with a three-parameter log-logistic distribution via L-moments, validated by Kolmogorov–Smirnov goodness-of-fit testing ($\alpha =0.05$) per district, and standardised through the inverse-normal cumulative distribution function. Trained on 18,304 weekly district records from NASA POWER reanalysis (2014–2025) covering all 32 districts of Tamil Nadu, India, WaveDroughtNet uses only 256,869 parameters and produces, in a single forward pass, four forecasts (1 week, 1 month, 3 months, 1 year). On the held-out 2024 test partition ($N=1728$), the model attains weighted $F1=0.9221$ and $R^2=0.8512$ at the 1-week horizon, and weighted $F1=0.8498$ and $R^2=0.6812$ at the 1-year horizon. Diebold–Mariano tests confirm that WaveDroughtNet significantly outperforms naive persistence, seasonal naive, LSTM, ConvLSTM and a vanilla Transformer at the 3-month and 1-year horizons (p < 0.001). The DroughtOriginTracer successfully back-projects 15 Coimbatore events to causal origins 29–41 weeks prior to onset. We explicitly acknowledge three limitations that constrain operational deployment in its current form—zero severe events in the 2024 test partition ($F{1}_{severe}$ = 0.000), static inter-district modelling, and absence of vegetation-index supervision—and propose concrete mitigation pathways in the Discussion. Full article

Tropospheric ozone (O₃) is a critical air pollutant that poses significant risks to human health and ecosystems. While previous studies have primarily focused on O₃ changes in Eastern China, limited attention has been given to Northwest China, where fragile but ecologically important systems may be vulnerable to O₃ pollution. The temporal evolution and driving mechanisms of surface O₃ in this region remain poorly understood. Using the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA5) datasets and simulations from the Community Atmosphere Model with Chemistry (CAM-Chem), we identified a significant increase in summer surface O₃ concentrations across Northwest China from 1980 to 2020, with the most pronounced rise occurring during 1993–2010. This period accounts for the majority of the long-term upward trend, despite relative declines before and after. The increase in O₃ during 1993–2010 is primarily attributed to rising surface temperatures, which reduce hydroperoxyl radical (HO₂) concentrations and enhance nitrogen dioxide (NO₂) production, leading to elevated nitrogen oxides (NO_x) levels and promoting O₃ formation. The warming trend is closely associated with a concurrent decrease in low cloud cover, which increases surface shortwave radiation and further contributes to surface warming. Further investigation reveals that warming sea surface temperature (SST) in the North Pacific influence atmospheric circulation through wave train processes, amplifying the regional geopotential height field. These circulation changes reinforce the reduction in low cloud cover and the associated increases in surface temperature and O₃ concentrations over Northwest China. The decadal variability of North Pacific SST may therefore serve as an important indicator of long-term surface ozone variability in this region. Full article

A synthetic aperture radar (SAR) provides vital global observations of ocean waves. However, the quasi-linear inversion algorithm routinely used for Sentinel-1 Level-2 Ocean Swell Wave (OSW) products suffers from inherent nonlinear imaging limitations. These include severe distortions and the inability to resolve wind-sea components under a strong azimuth cut-off effect. To address these challenges, this paper proposes a novel prior-knowledge-integrated machine learning framework to reconstruct complete and accurate directional wave spectra from Sentinel-1A SAR wave mode data. First, an extreme gradient boosting model is trained to accurately estimate wind-sea heights, which are then used to construct a theoretical JONSWAP prior spectrum. Subsequently, a U-Net architecture seamlessly integrates this physical prior knowledge with the official OSW swell spectra baseline. Independent validation demonstrates that the proposed framework significantly increases the spectral similarity against ERA5 reanalysis compared to the standard OSW. Furthermore, the derived parameters of total significant wave height, mean wave period, and mean wave direction exhibit remarkable improvements, with root mean square errors of 0.4026 m, 0.4342 s and 20.42°, respectively. The enhancement of SAR inferred two-dimensional wave spectra is also examined and discussed by three typical case studies. It is indicated that integrating physical wave knowledge with machine learning robustly mitigates the non-linear limitations of SAR imaging, providing highly reliable directional wave spectra for global ocean monitoring and forecasting. Full article

Sea surface height anomaly (SSHA) observations from Surface Water and Ocean Topography (SWOT) provide a new opportunity for identifying crest lines of internal solitary waves (ISWs). However, L3 LR Unsmoothed SSHA is often affected by residual large-scale trends, rainfall contamination, and stripe noise, which limit segmentation performance. To address this issue, we propose an automatic segmentation workflow for SWOT SSHA. The workflow first applies Gaussian low-pass filtering for scale separation to extract high-frequency SSHA, then uses Otsu adaptive thresholding to segment ISW signals, and finally removes false targets using morphological geometric constraints. Validation based on 230 SWOT images from the northern South China Sea shows that, compared with the conventional method based on subtracting reanalysis fields, the proposed method increases the contrast-to-noise ratio (CNR) of high-frequency SSHA by 1.35 on average (Std = 0.99) and improves signal gain by 13.65 dB on average (Std = 7.71 dB). The method remains robust under complex conditions, including strong typhoons, severe stripe noise, weak shelf signals, and multi-wave interference. In some cases, quasi-synchronous optical imagery further confirms the authenticity of the extracted crest lines. Full article

The response of temperature extremes to recent warming at the local scale remains uncertain because changes in mean temperature may be accompanied by changes in the shape of the temperature distribution. While higher mean temperatures generally lead to more frequent heat waves and fewer cold events, variations in higher-order statistical moments can either amplify or moderate these effects. This study examines how the probability distribution of 2 m temperature has evolved during the last 80 years in Greece using the ERA-5 reanalysis dataset. The evolution of the first four statistical moments (mean, standard deviation, skewness and kurtosis) and of the 5th and 95th percentiles of daily mean temperature is calculated by splitting the time series into eight decades, with each decade representing a separate climatology. A clear increase in mean temperature is observed across Greece. However, trends in the higher-order moments are more complex: the standard deviation and skewness exhibit positive and negative trends that depend on the region and the season, while kurtosis trends are weaker with a few regional exceptions. These changes alter the response of temperature extremes to warming, resulting in non-uniform shifts of the 5th and 95th percentiles. In mountainous regions, extreme cold events during winter and autumn have decreased more strongly than expected from mean warming alone, while in marine regions extreme warm events during summer and autumn have increased beyond what would be expected by a shift in the mean. In other areas, changes in the distribution shape lead to weaker extremes than those predicted by mean warming alone. These results highlight the role that changes in temperature variability have in modulating the evolution of temperature extremes under climate warming. Full article

Atmospheric equatorial Kelvin waves—convective disturbances that manipulate tropical wind and rainfall patterns—can propagate eastward at speeds ranging from nearly stationary to 30 m/s, with variability determined by moist processes and advection by the background wind. Current studies on Kelvin waves lack a comprehensive climatology that explains how their structure and propagation speeds change in different background states. Thus, this work builds a variable regression model that uses ERA5 reanalysis data to reconstruct Kelvin waves during different background wind shear conditions and phases of the Madden–Julian Oscillation (MJO) and the El Niño–Southern Oscillation (ENSO) over the Pacific. Overall, Kelvin waves tend to speed up during background conditions that generate upper-tropospheric westerlies and slow down during upper-tropospheric easterlies. East Pacific Kelvin waves are faster than West Pacific Kelvin waves because of climatological westerly shear in the former and easterly shear in the latter. However, strong westerly shear over the East Pacific allows extratropical Rossby waves to impede on the Kelvin wave, while strong easterly shear over the West Pacific distorts classical Kelvin wave structure. The results provide references for weather prediction models to accurately resolve the interaction between Kelvin waves and background circulation. Full article

Ocean surface currents (OSCs) are central to upper ocean dynamics and air–sea exchange, yet their retrieval from spaceborne synthetic aperture radar (SAR) is limited by wave-induced bias (WB). WB arises from the inherent motion of the scattering facets and from long-wave hydrodynamic and tilt modulations, and is therefore jointly controlled by sea state and radar viewing geometry. This study develops an observation geometry optimization framework. Five years of hourly ERA5 wind and wave reanalysis data over the Kuroshio are used as a representative ensemble of sea states to drive the KaDOP model, and an exhaustive grid search over line-of-sight (LOS) azimuth (0–360°) and incidence angle (20–60°) is performed to identify, for each location and season, the viewing geometry that minimizes the time-mean WB. These local optima are then summarized as mission-level metrics, including the minimum achievable WB, the coverage meeting prescribed WB thresholds, and the spatial coherence of the preferred LOS azimuth and incidence angle. Finally, the theoretical minima are compared with the fixed left-looking geometry of the Luojia-2 (LJ-2) satellite along a 213 km × 6 km observation corridor and with Gaofen-3 (GF-3) viewing geometries at four representative locations in the Kuroshio. Across these validation cases, the optimized geometry reduces mean absolute WB by about 20–60% for LJ-2 and 20–80% for GF-3, providing quantitative constraints for future SAR mission design targeting OSCs. Full article

Sandy shorelines respond to variability in boundary conditions over a wide range of time and spatial scales. While recent studies show that climate modes may affect shoreline evolution at interannual scales, such relationships remain unclear in the South Atlantic Ocean. Here, we investigate whether climate mode-driven variability in wave climate influences shoreline evolution using Ilha Comprida, a barrier island on the southeastern Brazilian coast, as a case study. Offshore wave conditions from the ERA5 reanalysis were analyzed over the last four decades and propagated to the nearshore using wave modeling. Shoreline change was quantified from satellite-derived shoreline positions, and relationships with interannual climate modes were evaluated using climate indices. Results show that the wave climate is bimodal and dominated by swell, with strong seasonality and no significant long-term trend in storminess. The El Niño–Southern Oscillation (ENSO) influences wave energy and extremes, with La Niña phases associated with higher wave power without a change in wave direction. No significant signal of the Southern Annular Mode (SAM) was found. At the coast, shoreline evolution is controlled by long-term sediment redistribution driven by alongshore transport gradients. ENSO-related shoreline signals are weak and spatially limited, occurring only in lower Empirical Orthogonal Function (EOF) modes of variability. These results suggest that, at Ilha Comprida, ENSO mainly modulates episodic wave-driven events rather than long-term shoreline patterns, emphasizing the need to distinguish between short-term energetic variability and longer-term morphodynamic response. This distinction is important for coastal management because even where climate modes do not produce persistent long-term shoreline trends due to site-specific aspects, they may still modulate event-scale risk, which can vary independently of the long-term average shoreline behavior. Full article

Harmonising heterogeneous met-ocean time series to a common temporal resolution is a prerequisite for integrated marine renewable energy assessments. Such datasets often differ in their sampling frequency, statistical distribution, and non-stationarity, complicating joint analysis. This study presents a practical multi-criteria framework for selecting temporal interpolation strategies for met-ocean datasets, explicitly balancing prediction accuracy and computational efficiency. Six environmental variables relevant to offshore renewable energy—wind speed, significant wave height, energy period, peak period, global horizontal irradiance, and upper-ocean thermal gradients—are analysed using ten-year reanalysis datasets for the Madeira Archipelago. Six commonly used deterministic time-domain interpolation methods are evaluated within a unified validation framework combining training–test splits, k-fold cross-validation, and Monte Carlo resampling. Their performances are quantified using the relative root mean square error and computational time, integrated through a composite performance score. The results show that makima interpolation provides the most consistent compromise between accuracy and efficiency for most variables in dense, regularly sampled met-ocean datasets, while spline-based approaches perform better for highly skewed solar irradiance. Preprocessing steps, such as detrending and distribution normalisation, yield only marginal improvements for dense, regularly sampled datasets, and method rankings remain stable under moderate changes in accuracy–speed weightings. Rather than proposing a universal interpolator, this work delivers a reproducible decision-support workflow for temporal resampling of multi-variable met-ocean datasets, supporting early-stage marine renewable energy assessments. Full article

A remote analysis of coastal sedimentation in northern KwaZulu-Natal (KZN), South Africa, describes how summer runoff and winter wave-action operate within a highly variable climate. Despite rising sea levels, the sediment flux can sustain beaches under certain conditions. Daily satellite red-band reflectivity and ocean–atmosphere reanalysis datasets were studied over the period of 2018–2025. Statistical results indicate that streamflow discharges are spread northward by oblique wave-driven currents. Sediment concentrations peak during late winter (>1 mg/L, May–October) when deep turbulent mixing (>40 m) mobilizes sand from the seabed. A case study from September 2021 revealed that ridging high-pressure/cut-off low weather patterns can simultaneously increase streamflow, wave energy, and wind power, creating a surf-zone sediment conveyor along the coast of northern KZN. Long-term climate diagnostics from 1981 to 2025 reveal upward trends in coastal runoff, vegetation, and turbidity (0.29 σ/yr) that point to an increasingly vigorous water cycle. The warming of the southeast Atlantic intensifies the sub-tropical upper-level westerlies and late winter storms over southeast Africa. These processes occur in 5–8 year cycles and drive shoreline advance and retreat, from accretion ~1 T/m and storm surge inundations up to 5.5 m. Using Digital Earth, it was noted that ~1/4 of beaches around Africa are gaining sediment while ~1/3 are eroding. Although remote information could not close the sediment budget, realistic estimates of long-shore transport in the surf-zone (>10⁴ kg/yr/m) and on the beach (>10³ kg/yr/m) were calculated. These provide an emerging explanation for the resilience of northern KZN beaches, as sea levels rise at a rate of 0.6 cm/yr. Full article

Older adults are particularly vulnerable to extreme heat, but evidence of the role of social factors in regional heat vulnerability remains limited. To assess the impacts of heat waves on cardiorespiratory hospitalizations and mortality, we developed a Climate Vulnerability Index by the Regional Health Department (RHD), including adults aged ≥ 60 years across 17 RHDs in São Paulo State, Brazil. Health data were obtained from national information systems, and heat wave exposure was derived from ERA5 reanalysis data, defined as periods of at least three consecutive days with daily mean temperature exceeding the seasonal climatological mean by ≥3 °C, for 2010–2019 and 2023–2024, excluding 2020–2022. Associations between heat waves and health outcomes were estimated using distributed lag non-linear models with lags of 0–15 days. Cumulative relative risks, along with sociodemographic, sanitation, and health system indicators, were integrated to construct the Index based on IPCC sensitivity and adaptive capacity domains. Heat waves were associated with increased risks of cardiorespiratory hospitalizations and mortality across all RHDs, with stronger effects observed for mortality and inland regions. Higher vulnerability was concentrated in RHDs characterized by larger older adult populations, greater heat-related risks, and weaker health system and sanitation indicators, whereas more developed regions showed lower vulnerability. Overall, the Index provides a practical tool to support territorial prioritization and targeted heat–health adaptation strategies in ageing populations. Full article

The acquisition of significant wave height (SWH) in coastal seas is significantly important to human activities. The Gaofen-3 (GF-3) satellites, comprising GF-3, GF-3B and GF-3C, are independently developed operational SAR of China, capable of providing high-precision, high-resolution, multi-polarization coastal ocean wave observations. In order to obtain SWH in coastal seas, the retrieval of SWH using Quad-Polarization Stripmap (QPS) mode data from GF-3 satellites based on the deep learning method is implemented in this study. Furthermore, to obtain more SWH data, the polarization ratio model was applied to the Fine Stripmap (FS) mode data and Ultra Fine Stripmap (UFS) mode data to extend the model application. Comparisons with ECMWF Reanalysis v5 (ERA5) wave heights show that the QPS mode SWH retrieval achieves a root mean square error (RMSE) of 0.33 m. For the FS mode, the RMSE is 0.44 m (vs. ERA5) and 0.52 m (vs. altimeter). For the UFS mode, the RMSE is 0.39 m (vs. ERA5). Evaluation results indicate the feasibility of the proposed method for coastal SWH retrieval. Full article

Wintertime extratropical cyclones frequently traverse the Kuroshio–Oyashio Extension frontal system. However, their net impacts on synoptic sea-surface temperature (SST) variability and mixed-layer structure remain uncertain in the presence of strong fronts and intrinsic ocean variability. Using reanalysis data, we classify extreme events into cyclone cold-sector and warm-sector types based on synoptic air–sea flux anomalies. With ensembles of single-column model experiments, we decompose the upper-ocean response into surface heat-flux forcing, wind-driven mechanical mixing, Ekman temperature advection, wave-breaking mixing, and freshwater effects. Cold-sector events amplify synoptic SST variability and deepen the mixed layer, whereas warm-sector events suppress SST variability and shoal the mixed layer. Surface heat flux is the primary driver of both responses. Ekman advection provides crucial modulation within the frontal zone. Wave-breaking mixing generally damps temperature perturbations. Freshwater forcing exerts a pronounced regional influence southeast of the subarctic front. The combined effects yield an asymmetric spatial fingerprint on SST variability and mixed-layer depth across the frontal system. Comparison between forced variability and total reanalysis variability indicates that within the frontal zone, atmospheric impacts can be redistributed or partly offset by intrinsic ocean processes, while outside the frontal zone, the behavior is closer to an externally forced response. Full article

Propagating easterly waves (EW) are analyzed here, within the dynamical environment of the Caribbean Low-Level Jet (CLLJ) using radiosondes from the Organization of Tropical East Pacific Convection (OTREC) field campaign, ERA5 reanalysis, and lightning from the World Wide Lightning Location Network (WWLLN) over $5^{\circ }$–${20}^{\circ }$ N, ${60}^{\circ }$–${100}^{\circ }$ W during 21 August–30 September 2019. Radiosondes resolve the vertical structure of the waves at San Andrés (Colombia), Limón and Santa Cruz–Guanacaste (Costa Rica), while ERA5 provides spatial–temporal continuity and vertically integrated diagnostics—namely, the vertically integrated moisture flux divergence (VIMFD) and the vertically integrated geopotential flux divergence (VIGFD). Lightning from WWLLN and precipitation from ERA5 and the Integrated Multi-satellite Retrievals for the Global Precipitation Measurement mission (GPM IMERG) offer independent convective proxies to track disturbances. Mean profiles from radiosondes and ERA5 show strong agreement at Limón and Guanacaste and some differences at San Andrés, yet all datasets capture coherent, phase-locked anomalies in zonal wind, meridional wind, temperature, humidity, vertical velocity and vorticity used to diagnose EW–CLLJ interactions. VIMFD, VIGFD, lightning and precipitation exhibit westward-propagating cores that align with the above anomalies, indicating that organized convection is coupled to the disturbances, whereas the mean state preconditions the environment to enable wave-induced upward motion. A robust vertical adjustment of the CLLJ is documented: the core shifts from near 925 hPa over the Caribbean Sea to about 700 hPa over the Eastern Tropical Pacific ($\Delta p\sim 150$ hPa). This feature is reproduced by a 30-year ERA5 climatology, consistent with jet-exit forcing and enhanced boundary-layer coupling over land. Conditions favorable for barotropic instability using the Rayleigh–Kuo criterion, were present over most of the period. A qualitative barotropic conversion proxy, computed from the eddy momentum covariance $⟨u^{\prime }{v}^{\prime }⟩$, shows positive values in the lower troposphere at Guanacaste and in the layer 850–700 hPa at San Andrés, suggesting mean-to-eddy momentum transfer, whereas the signal at Limón is weaker. Together, these results provide a physically consistent view of EW–CLLJ interactions across the IAS; therefore, a schematic of those mechanisms is proposed here. The results highlight the need for high-resolution modeling and full energy-budget analyses. Full article

Satellite-derived shorelines (SDSs) are increasingly used to monitor beach morphology worldwide, yet their application remains poorly validated in microtidal environments strongly influenced by atmospheric forcing. In this study, the performance of CoastSat and CoastSat.slope using nine years of in situ beach profiles from six sandy beaches in Buenos Aires (Argentina) was evaluated. The analysis compares alternative sea level forcings—including global tidal predictions (FES2022), a regional barotropic model with meteorological forcing (MSAS), and wave setup from reanalysis products—and evaluates the effect of using locally trained classifiers on shoreline detection. The results show that locally trained classifiers markedly reduced RMSE values, from 9–21 m with the default classifier to 7–12 m with the locally trained one, while the MSAS model consistently outperforms FES2022 for sea level corrections across all sites. CoastSat.slope provided effective slope estimates for tidal corrections but tended to overestimate values relative to field data. Sensitivity tests confirmed that overestimation has a smaller impact on water level correction than underestimation, explaining why validation metrics improved when using CS.slope-derived slopes. These findings translate into actionable guidelines: (i) prioritize regional sea level models when nontidal variability is large; (ii) apply wave setup corrections cautiously in microtidal coasts; and (iii) use locally trained classifiers in heterogeneous or urbanized beaches. Overall, this study demonstrates that with appropriate parameterization, CoastSat is a reliable tool for shoreline monitoring in atmospherically forced, microtidal coasts, and its methodological insights are transferable to other low-energy, data-scarce regions worldwide. Full article