Earth and Environmental Sciences

ABSTRACT Persistent heavy rainfall (PHR) during the Meiyu period frequently leads to severe flooding in China. Since the early 2000s, such events have become increasingly common over the Yangtze‐Huaihe region, the northernmost sector of China's Meiyu monitoring region, where precipitation exhibits pronounced multiscale characteristics. However, a systematic understanding of how multiscale processes interact to sustain PHR remains lacking. Based on 43 PHR events over the Yangtze‐Huaihe region during the Meiyu period of 1961–2020, this study investigates their formation mechanisms from a multiscale perspective and classifies them into three types. Multiscale contribution decomposition reveals that coupling of the background (> 30 days), quasi‐biweekly (10–30 days), and synoptic‐scale (< 10 days) wind fields with the background moisture field accounts for 93.8% of total precipitation. The events are classified into three types: background‐persistent (13 events), synoptic‐transient (11 events), and multiscale hybrid (19 events). The background‐persistent type is dominated by background circulation with broad, stable moisture transport from the northwestern Pacific. The Meiyu frontal zone is the strongest and most persistent among the three types, which in turn intensifies the upper‐level subtropical jet and causes its eastward displacement. The enhanced jet strengthens the secondary circulation at the jet entrance, while the 10–30 days variability gains energy from the basic flow, strengthening the vertical circulation. The synoptic‐transient type is dominated by synoptic‐scale disturbances (> 54% contribution), with rainfall depending on wave trains propagating along the jet that induce low‐level northerly–southwesterly confluence, and the frontal zone shows large variability. The multiscale hybrid type reflects the combined influence of an intensified subtropical jet, a 10–30 days meridional wave train, and intensified synoptic‐scale wave trains, with a relatively weak frontal zone. Overall, this study provides a scientific basis for classification‐based monitoring and refined forecasting of PHR events over the Yangtze‐Huaihe region.

ABSTRACT Compound drought and heatwave event (CDHE) exerts profound impacts on high‐elevation areas, garnering increasing attention from the scientific community and relevant stakeholders. However, the physical mechanisms of CDHE involving land‐atmosphere interaction are not fully understood in high‐elevation areas. This study firstly analyzes the variations in CDHE frequency (CDHEF) over the Tibetan Plateau (TP) during the warm season (May–September) from 1980 to 2018. High‐frequency CDHE occurs in the southwestern TP, where a significant increasing trend in CDHEF—exceeding 0.5 days per year—has been identified. Due to the semi‐arid and semi‐humid characteristics in the southwestern TP, we mainly investigate how the land‐atmosphere interactions affect the interannual variations of CDHEF over this region. After removing the trend, a strong negative correlation (−0.34) is observed between soil moisture (SM) deficit and CDHEF. During years of persistent SM deficit, near‐surface conditions are characterised by positive geopotential height anomalies and negative specific humidity anomalies. Meanwhile, divergence in the lower atmosphere and convergence in the upper atmosphere are observed, accompanied by subsidence throughout the entire atmospheric column over the southwestern TP. These processes work together to create favourable conditions for CDHEs. Furthermore, land‐atmosphere interactions are amplified under relatively dry conditions. Reduced SM limits evapotranspiration—particularly ground evaporation and transpiration—which enhances sensible heating and elevates near‐surface air temperatures. Concurrently, the lifting condensation level (LCL) grows deeper than the planetary boundary layer (PBL) height, resulting in a positive LCL deficit that further suppresses rainfall formation. The SM‐evapotranspiration partitioning relationship reveals that the southwestern TP intends to shift from an energy‐limited to a moisture‐limited regime during dry years. Particularly, the persistent SM deficit is the potential forcing for the intensification of CDHE based on the analyses of antecedent SM‐latent heat flux relationship. These findings highlight a positive feedback mechanism in which land‐atmosphere interactions significantly amplify the CDHEF in southwestern TP.

Ocean alkalinity enhancement (OAE) is a promising marine carbon dioxide removal (mCDR) method that aims to reduce atmospheric CO 2 by increasing the ocean's storage capacity. While global ocean and Earth system models are necessary to track air-sea CO 2 equilibration and far-field alkalinity transport on extended timescales, OAE efficiency is strongly influenced by local oceanography and climate. Regional ocean modeling is a powerful tool for capturing the effects of these influences on near-field plume dispersion and CO 2 uptake variability. Here, we used a high-resolution, three-dimensional hydrodynamic and biogeochemical model (2 km) to evaluate the effects of deployment location and interannual climate variability on OAE efficiency in Bass Strait, southeast Australia. We simulated the addition of 113.21 Gmol of alkalinity (a theoretical uptake capacity of ~4.2 Mt CO 2 ) over one month, via four infrastructure-constrained pathways: a desalination outfall, a shipping lane, a ferry track, and a series of coastal outfalls. These additions were repeated across three Southern Annular Mode (SAM) phase end-member winters: 2017 (positive SAM–low winds), 2021 (neutral SAM–moderate winds), and 2023 (negative SAM–strong winds). CO 2 uptake efficiency variability (mol CO 2 /mol TA) is primarily influenced by delivery method and location (95.2%) rather than by interannual climate variability (3.9%). At the shelf-break, 70.67% ± 10.53% of added alkalinity is subducted below the mixed layer before equilibration with the atmosphere; this alkalinity is then exported from the Bass Strait region at depth. The subduction and loss of alkalinity from the region before equilibration reduces the realised CO 2 uptake and contributes to efficiencies (0.11–0.27) that are lower than those simulated by a global model in the same region (0.31). This mismatch, driven by regionally specific oceanographic processes, has implications for OAE deployment and equilibration timescales in other dynamic shelf environments. To resolve these scale-dependent limitations, we recommend integrated monitoring, reporting, and verification frameworks that combine observational networks, regional models, and global models (with regional model exports as input to global models). This approach is necessary to accurately quantify net carbon removal and to constrain the long-term fate of added alkalinity.

Abstract Atlantic Water (AW) is the main oceanic heat source in the Arctic and a key driver of sea ice retreat. Based on Multidisciplinary drifting Observatory for the Study of Arctic Climate microstructure data from the Eurasian Basin, we examined the spatial heterogeneity of AW upward heat flux. We found that diffusive convection (∼76% occurrence; heat flux 2.14 W m −2 ) dominates upward heat transport in the AW interface layer (∼130–180 m) in the inner Eurasian Basin, whereas turbulent mixing (∼80% occurrence; heat flux 5.01 W m −2 ) prevails in the outer region. Intrusion‐induced negative temperature gradients (∼81% occurrence in the entire Eurasian Basin) trap heat below the AW interface layer (∼180–240 m), suppressing upward heat transfer. Our results indicate that AW upward heat release mainly originates from the interface layer in the Eurasian Basin, whereas most AW heat remains trapped beneath this layer and is largely isolated from upper‐ocean processes, thereby influencing Arctic sea ice variability.

Abstract Inadequate nitrogen (N) management in maize production causes large losses of reactive nitrogen (Nr) via N₂O emissions, NH₃ volatilisation and NO₃⁻ leaching, reducing fertilizer efficiency and contributing to planetary-boundary transgression. This global meta-analysis (3,412 observations from 312 field studies in 43 countries) quantified the effects of four fertilization strategies (mineral, organic, enhanced-efficiency fertilizers (EEFs), and combined mineral-organic) on maize yield and the three major Nr loss pathways, while identifying key moderators (management, climate, soil). Effect sizes were calculated as log response ratios and analysed with hierarchical random-effects models and meta-regressions. Combined mineral-organic fertilization produced the largest yield gain (127% [87–175%]) relative to unfertilised controls. EEFs and organic fertilizers minimised N₂O (174–180%) and NO₃⁻ leaching (133–141%), while mineral fertilizers caused the highest losses across all pathways. Yield was primarily driven by N rate and soil organic carbon (SOC); N₂O by soil pH and temperature seasonality; NH₃ by N rate and coarse texture; and NO₃⁻ leaching by soil pH and texture. Integration of organic with mineral fertilizers or adoption of EEFs offers the best trade-off between productivity and environmental protection. Management should be site-specific, tailored to soil pH, texture, SOC and climate.

Abstract North American wildfires are growing in frequency, extent, and intensity in recent decades, threatening ecosystems, human health, and infrastructure. While long-term trends are driven by anthropogenic warming, internal modes of oceanic variability can influence wildfire weather through adjustment in large-scale circulation and surface energy fluxes. Here, we examine how decadal to multidecadal variability, including the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO), modulates the influence of the El Niño–Southern Oscillation (ENSO) on early summer (April–June) wildfire risk. El Niño is associated with increases in intensity of wildfire weather by up to 25% and earlier onset of favorable fire weather conditions by up to 10 days in western and central northern North America relative to the 1960–2020 climatology, whereas La Niña corresponds to broadly opposite patterns. Decadal to multidecadal variability, such as PDO and AMO, further modulates ENSO responses, with compound phase alignments associated with larger regional anomalies and shifts in fire weather onset. These findings clarify how natural climate variability shapes regional wildfire weather and seasonal timing, providing insight relevant to risk assessment and adaptation in a warming climate.

Abstract Dryness in soil and atmosphere individually imposes substantial stress on vegetation. However, their compound impact on ecosystems remains poorly understood. This work addresses how increasing compound land-atmosphere dryness modulates the relative roles of soil moisture (SM) and vapor pressure deficit (VPD) in regulating vegetation carbon (GPP:Gross Primary Productivity) and water (WUE:Water Use Efficiency) fluxes globally. We find that the likelihood of CLAD is highest over deciduous forests in dry regions, shrublands in humid regions, and evergreen forests in very-humid regions. Both GPP and WUE exhibit stronger negative anomalies in dry deciduous forests. In very-humid regions, negative GPP anomalies are more pronounced over shrubland-grasslands, and similarly for WUE in evergreen forests, highlighting biome-specific responses. We further observe a weakening of SM-VPD coupling with increasing dryness in wetter regions, predominantly over forests, whereas the opposite pattern emerges in non-forest ecosystems in dry regions. Using non-linear partial correlation analysis, we find that SM consistently exerts a stronger control on vegetation fluxes than VPD across all dryness-levels. However, the control of SM and VPD differs with increasing compound dryness. As compound dryness intensifies, the influence of SM on WUE amplifies 2.5-fold, whereas the impact of VPD on GPP rises 1.7-fold. Overall, GPP response to compound dryness is stronger in shallow-rooted ecosystems in humid regions, whereas the same for WUE is in deep-rooted trees in dry regions. The contrasts between SM and VPD impacts are more prominent over dry deciduous forests and less pronounced in grasslands in very humid regions. These findings demonstrate that the hierarchy between SM and VPD in controlling ecosystem carbon and water fluxes is not static but is intensity-dependent and biome-stratified. This work provides crucial benchmarks for improving compound stress representations in land-surface and terrestrial biosphere models, which could help to reduce uncertainty while assessing land-carbon sink due to increasing aridity.

We present a 42-year (1984–2025) Landsat consistent satellite vegetation trajectory for coastal wetlands in the Shell Beach area in the Breton Sound estuary, Louisiana. We applied the Controlled Interrupted Time Series (CITS) analysis to the satellite record to quantify the causal effect of the 2009 Mississippi River Gulf Outlet (MRGO) closure on the coastal wetland vegetation. The analysis used NDVI, kNDVI, and NDII across 88 vegetation transect plots located within five Coastal Reference and Monitoring Systems (CRMS) stations in the Shell Beach wetlands. Vegetation communities identified included Saline, Brackish, Freshwater, and Intermediate marsh. Sentinel-2 data from 2015 to 2025 were retained as an independent parallel record for NDRE analysis only. Quarterly median composites were decomposed using the Seasonal-Trend decomposition using LOESS (STL) to isolate de-seasonalized vegetation anomalies. The CITS design used segmented Ordinary Least Squares (OLS) regression with Newey–West HAC standard errors (lag = 3) at the study area. Northern Barataria Bay was used as an untreated regional control site to remove concurrent climate and sea level rise confounders. Whilst Hurricane Katrina and subsequent years (2005–2008) were excluded from the models, the single group ITS identified significant negative post-closure slope change across three indices. These were NDVI (β3 = −0.0034 yr−1, p = 0.000), NDII (β3 = −0.0032 yr−1), and kNDVI (β3 = −0.0016 yr−1). These values indicated continued site-level decline relative to the pre-closure trend. Community-stratified ITS analysis showed a distinct divergent pattern with Freshwater marshes demonstrating significant recovery, with NDVI β3 = +0.0190 yr−1, p = 0.000, whilst Saline, Brackish, and Intermediate communities continued to decline. CITS Difference-in-Differences (DiD) confirmed that site-level NDII and kNDVI declines were MRGO-specific. The DiD findings were that NDII = −0.00313 yr−1, p < 0.001; kNDVI = −0.00123 yr−1, p = 0.008. These findings isolated that physiological water stress and the non-linear biomass losses were a result of the MRGO-closure. The Freshwater DiD for NDVI (+0.02071 yr−1, p = 0.000) was the strongest evidence of MRGO-specific recovery. Barataria Freshwater declined, whilst the Shell Beach Freshwater recovered. The results demonstrated that multi-index decadal Landsat monitoring with seasonal decomposition and full inter-sensor harmonization is essential for restoration trajectory assessment in managed coastal wetlands.

Study region Iran. Study focus Drought dynamics vary markedly across climatic regimes, and the reliability of conventional drought indicators may decline in hydro-climatically heterogeneous regions. This study presents a climate-sensitive, multi-scale comparative assessment of drought behavior across four major climatic classes of Iran using five conventional drought indices (SPI, SPEI, MSDI, DPI, and RDI), two machine-learning models (SVM and ANF-PSO), and a Pattern Mining Engine (PME). Using 44 years of observations, the study examined how drought intensity, duration, frequency, and event structure vary with climatic class and temporal scale, and whether PME provides more stable behavior under contrasting hydro-climatic conditions. Model performance was evaluated using accuracy statistics, Taylor diagrams, correlation analysis, and uncertainty diagnostics. New Hydrological Insights for the Region Drought behavior and model performance were strongly climate-dependent. Indices incorporating evaporative demand, particularly SPEI and RDI, were more reliable in very dry and dry climates, whereas precipitation-based indices showed comparatively more stable behavior in humid conditions. No conventional index performed uniformly well across all climatic classes. Within this comparative framework, PME demonstrated a clear advantage by consistently capturing the dominant spatial and temporal structure of drought across all climatic classes and time scales, while maintaining lower sensitivity to climatic variability. Unlike conventional indices and baseline ML models, PME integrates multivariate hydro-meteorological interactions through pattern-based representation, enabling more robust identification of drought conditions under heterogeneous environments. In contrast, MSDI showed less stable behavior, especially in semidry and humid regions. These findings highlight the added value of PME as a climate-sensitive framework for improving drought characterization and support more reliable basin-scale monitoring, early warning, and water-resources planning.

Numerous attempts have been made to detect signs of ozone layer recovery over Antarctica, which has been expected since the beginning of the 21st century as a result of the reduction in concentrations of ozone-depleting substances in the Antarctic stratosphere, in accordance with the provisions of the 1987 Montreal Protocol and subsequent amendments aimed at protecting the ozone layer. Large year-to-year variability in the Antarctic ozone, driven by changes in atmospheric dynamics, has made it difficult to draw definitive conclusions about the rate of Antarctic ozone recovery. In this paper, we present an alternative approach to analyse ozone recovery by examining patterns in blue–red heatmaps of total ozone column (TOC) trends during the winter–spring period from 1980 to 2025. Three annual TOC time series (winter average, 15 September value, and spring minimum) were analysed to monitor the ozone hole development over the Syowa and Amundsen–Scott stations. Various sources of the daily TOC data were examined, including reanalysis data, ground-based measurements, and satellite observations. Regardless of the data source, we found that, for both stations, blue cells (negative trends) dominated in the areas of the heatmap where the TOC trends ended before 2000, while red cells (positive trends) appeared mostly afterwards. These results confirm the hypothesis of a trend reversal, i.e., a recovery beginning in the early 2000s, which was obscured in the original, noisy TOC time series.

Abstract Late Cenozoic global cooling is well documented, yet its impact on erosion rates in active mountain belts, particularly after the onset of Quaternary glaciation, remain contentious. This ambiguity partially stems from the potential methodological biases in erosion rate quantification, particularly in the Himalaya, where recent studies report no significant increase. To address this, we integrate optically stimulated luminescence and cosmogenic nuclide dating, trace‐elements geochemistry and apatite fission‐track dating of well‐preserved terrace sediments from the eastern Himalaya with thermal kinematic modeling, reconstructing hinterland erosion rates over time. Our results reveal a marked acceleration in erosion rates beginning ∼2 Ma, synchronous with global cooling and enhanced glacial cyclicity. This supports the paradigm that climate cooling drives heightened erosion in orogenic systems, countering hypotheses proposing climate induced stabilization or suppression of erosion. By bridging methodological gaps, our work offers a refined framework for assessing Quaternary climate erosion feedbacks in mountain belts.

Accurate retrieval of cloud liquid water content (LWC) and ice water content (IWC) vertical profiles remains limited by strong vertical variability and nonlinear dependencies among observed variables. Ground-based cloud radar reflectivity and microwave radiometer-derived thermodynamic profiles provide complementary constraints, but their joint use requires consistent time–height matching and bias-controlled predictors. This study develops a vertically structured machine-learning framework that explicitly represents profile-level dependencies by constructing vertical-structure-enhanced features to encode local gradients and contextual information, integrating multiple tree-based learners with heterogeneous configurations through a profile-aware stacking strategy, and introducing a profile-level refinement step to suppress layer-to-layer inconsistencies. The framework is evaluated using year-round Cloudnet observations from the Lindenberg site, where IWC RMSE decreases from 0.0152 g m−3 to 0.0092 g m−3 with R2 increasing from 0.412 to 0.784, and LWC RMSE decreases from 0.0786 g m−3 to 0.0591 g m−3 with R2 increasing from 0.303 to 0.606. Additional boundary-region evaluation shows that the improvement is particularly evident near radar-derived cloud boundaries, where cloud structure and hydrometeor content may vary rapidly with height. These results indicate that treating cloud retrieval as a vertically structured learning problem reduces inconsistencies inherent in pointwise models and establishes a data-driven baseline for incorporating vertical constraints into atmospheric profile retrieval.

Precise urban air quality management requires a high-resolution understanding of complex emission landscapes. This study proposes a multi-sectoral high-resolution emission refinement system for urban-scale modeling and evaluates its impact through comparative numerical experiments. By leveraging building-specific GIS data and road network information, the Clean Air Policy Support System (CAPSS), South Korea's national inventory, was refined to a 20-m resolution and chemically speciated for the Harvard chemistry mechanism to be used in CFD-Chem modeling. The high-resolution emission experiment (HE) successfully resolved localized concentration hotspots intrinsically linked to urban morphology, which were obscured in the 1-km grid-based conventional emission experiment (CE). Specifically, the HE demonstrated superior performance in reproducing the spatial heterogeneity of NO 2 concentration, showing strong agreement with road-adjacent monitoring stations. The difference ratio analysis between the two experiments revealed that NO 2 concentrations in the HE differed by up to 245.6% from the CE in dense urban corridors, effectively mitigating smoothing errors inherent in coarse-grid emissions. Furthermore, the HE proved pivotal in capturing non-linear photochemical reactions; refined roadside NO emissions in the HE actively triggered local ozone titration, resulting in a reduction in ozone concentrations of up to 50.3% along major road networks compared to the CE. While simulation fidelity for CO and PM 2.5 depended on regional background concentrations, the HE provided a more realistic representation of localized chemical destruction and production cycles. This study establishes a robust scientific foundation for precision air quality management, offering a key instrument for designating intensive management zones and establishing effective pedestrian exposure mitigation measures.

High-altitude rock slides frequently occur in the high-mountain canyon regions of the eastern Tibetan Plateau, posing significant disaster risks. The Baige landslide catastrophically failed in October 2018, blocking the Jinsha River and forming a major landslide-dammed lake. However, quantitative understanding of the spatiotemporal evolution and environmental control mechanisms remains insufficient, particularly regarding stage-dependent driving mechanisms. This study investigates the Baige landslide using mall Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), Seasonal-Trend decomposition based on Loess (STL) time-series decomposition, Principal Component Analysis–Independent Component Analysis (PCA-ICA) signal analysis, and slope-unit spatial statistics. Results indicate that: (1) deformation exhibited three stages separated by October 2018: slow pre-slide deformation, post-slide residual creep, and long-term sustained acceleration; (2) instability caused systematic restructuring of the deformation field, with valid pixels decreasing from 2766 to 560, deformation changing from slight positive line-of-sight (LOS) displacement to pronounced negative LOS displacement, and global standard deviation increasing from 21.40 mm to 40.55 mm, with stronger disturbances in the steep front zone; and (3) the driving mechanism shifted from short-term multi-factor control to a temperature-dominated long-term environmental control regime after failure, while gravity-driven creep and post-failure structural adjustment remained important background controls. Slope fragmentation and structural reorganization likely contributed to this transition.

Introduction The combination of enhanced rock weathering (ERW) with pyrogenic carbon capture and storage (PyCCS) has been proposed to harness synergistic effects on carbon dioxide removal (CDR). Synergies may arise from co-application of silicate rock powder and biochar, or from co-pyrolysis of rock powder and biomass to produce rock-enhanced (RE-)biochar. While co-pyrolysis with silicate rock powder is well documented not to affect the carbon yield nor the aromaticity of RE-biochar, the effect of co-pyrolysis and co-application on alkalinity production by ERW remains poorly constrained. Methods We quantified the daily and cumulative production of carbonate alkalinity (TA carb g −1 basanite) in a controlled weathering experiment conducted in columns, comparing 14 treatments consisting of basanite rock powder, biochar, co-applications or RE-biochars produced at contrasting highest treatment temperatures (HTT) of 450 °C and 750 °C. The experiment was run under two conditions: sandy, agricultural topsoil under ambient p CO 2 , and washed, quasi non-reactive quartz sand under elevated p CO 2 , the latter designed to better isolate leachate signals originating from the amendments alone. Column flushing with demineralized water prior to the experiment and subtraction of TA carb signals from the matrix material and biochar amendments enabled quantification of the net TA carb signal from ERW, here referred to as Net_TA carb . Complementary pseudo-lysimeter experiments (i.e., the same treatments set up in larger vegetated soil columns) were used to assess effects on plant growth. Results Cumulative Net_TA carb production was significantly higher from co-deployments than from pure basanite ( p < 0.05), with RE-biochars producing more alkalinity than co-applications under both p CO 2 regimes. Thermal treatment of pure basanite increased its mean specific surface area ( p > 0.05) but decreased its Net_TA carb production. The release of dissolved silica correlated with Net_TA carb production. In the pseudo-lysimeter experiments, no significant effect of any amendment on plant growth was observed. Discussion Carbon sinks from PyCCS and ERW have complementary sequestration curves. Silicate rock powder may therefore be co-deployed with biochar to hedge carbon losses from biochar mineralization while increasing alkalinity production and unlocking additional agronomic co-benefits.

Rising temperatures and increasingly frequent soil droughts are reshaping forest ecosystems and driving shifts in species composition, especially in transitional zones such as beech-oak ecotones. These shifts may alter canopy structure and microclimate, with potential feedbacks on tree physiological responses and carbon uptake under warming. Yet, their effects remain poorly understood. We investigated how species composition influences canopy size, microclimate, key foliar functional traits, and photosynthetic and respiratory thermal acclimation at the canopy top and bottom in mature European beech ( Fagus sylvatica L.) and downy oak ( Quercus pubescens Willd.) occurring in pure beech, mixed, and pure oak forest stands along a beech-oak ecotone in Switzerland. Canopy structure and microclimate differed markedly between pure and mixed forest stands, with beech exhibiting larger canopies (+34.5% of total leaf area) and stronger air cooling (reaching -3.2°C compared to -2.1 to -2.6°C) in the pure stand than in the mixture, whereas downy oak showed the opposite trend, with smaller canopies (41.5% lower total leaf area) in the pure stand than in the mixture, and similar canopy air cooling (-2.1°C across sites). These differences had little impact on photosynthetic and respiratory thermal acclimation, but led to species-specific adjustments in leaf traits in both species. Variation in respiration rates across sites and canopy levels was strongly associated with specific leaf area, suggesting that morphological adjustments play a greater role than physiological acclimation in regulating carbon exchange at this ecotone under current climate. Understanding these processes is critical as projected increases in temperature and soil drought intensity may further reduce microclimatic buffering and amplify physiological stress in beech, while potentially favoring more drought‑tolerant oak species. Further research should explore how tree-level structural adjustments and local environmental drivers modulate beech and oak foliar functional and physiological responses in mature forests to better predict their vulnerability to future climate.

Abstract. Understanding the susceptibility of warm clouds to aerosol loading, quantified by the aerosol–cloud interactions (ACI) index, is essential for assessing ACI and their climate impacts. Previous studies have demonstrated that this susceptibility is strongly modulated by environmental conditions. The South China Sea (SCS), influenced alternately by the southwest and northeast monsoons, provides a unique natural laboratory for examining ACI under contrasting thermodynamic and moisture conditions. Using long-term satellite observations and reanalysis data, we investigate ACI in non-raining warm liquid clouds over the SCS across three monsoon phases: the southwest monsoon wet (SWMW), northeast monsoon wet (NEMW), and northeast monsoon dry (NEMD) periods. The robust Twomey effect is observed across all periods. Shallow stratocumulus clouds show no significant differences in ACI across periods, whereas deeper cumulus clouds exhibit the strongest ACI during NEMD, with no clear separation between SWMW and NEMW. The enhanced ACI during NEMD is consistent with the relatively dry and stable lower-tropospheric environment (LTS), where stable conditions may enhance ACI through aerosol accumulation, while moist environments are likely to weaken it via enhanced condensational and coalescence growth. However, these differences likely reflect co-varying environmental conditions across monsoon periods rather than a single dominant controlling factor. Limitations of the aerosol index (AI) as a marine cloud condensation nuclei (CCN) proxy and satellite retrieval biases may affect these conclusions. These findings suggest that, within a monsoon-organized framework, the interplay among aerosols, humidity, and stability is associated with marine warm-cloud microphysics, providing observational constraints for climate model representation of ACI.

Abstract Landfall reorganizes the tropical cyclone eyewall boundary layer (BL) through elevated surface roughness and reduced enthalpy fluxes. Using a 100‐m resolution nested large‐eddy simulation of Typhoon Hato (2017), we compare eyewall BL structures before and during landfall. Kilometer‐scale rolls and streaks dominate variance and vertical fluxes in both stages, but become shallower and less vertically coherent during landfall, redistributing turbulent transport toward lower levels. Flux decomposition reveals that while tangential momentum flux weakens, low‐level radial momentum flux is enhanced by its large‐scale component. Simultaneously, increased stability near the mixed‐layer top strengthens coupling between vertical velocity and thermodynamic perturbations, indicating a wave‐like response superimposed on residual turbulence. Cross‐scale diagnostics show that the modulation of sub‐kilometer turbulence by kilometer‐scale coherent motions becomes vertically compressed and increasingly confined to the lower BL. These results highlight how landfall‐induced surface forcing modifies multi‐scale turbulent organization, enhancing our understanding of landfalling typhoon dynamics.

The Brazilian Cerrado, a global biodiversity hotspot, is under increasing pressure from agricultural expansion and native vegetation conversion, underscoring the need for efficient monitoring to support conservation and environmental policies. In heterogeneous landscapes, land use and land cover (LULC) mapping using supervised classification methods faces a major bottleneck: the need for extensive and high-quality training datasets. To address this challenge, we propose a semi-automated, clustering-based methodology for mapping secondary vegetation within previously deforested areas, reducing training-sample requirements and enabling scalable mapping through the clustering of satellite image time series. In the first stage, an unsupervised process integrates graphics processing unit (GPU)-accelerated Self-Organizing Maps and hierarchical clustering with Dynamic Time Warping to produce spectro-temporal clusters. In the second stage, specialists label and refine these clusters by visual interpretation, transferring expert knowledge from individual pixels to grouped spectro-temporal patterns. Applied to 692,000 km2 of previously deforested land in the Cerrado biome, the methodology produced a mapped secondary vegetation area of 81,209 km2 (11.74%). The design-based estimated area was 98,683 ± 10,071 km2, with an overall accuracy of 96.45 ± 1.52%, a user’s accuracy of 96.27 ± 2.40%, a producer’s accuracy of 79.22 ± 7.94%, and an F1-score of 86.90%. The initial cluster labeling accounted for 86.3% of the final secondary vegetation area and limited the interpretation task to approximately 3000 cluster-level decisions. Implemented in the TerraClass Cerrado 2024 cycle, the workflow reduced the secondary vegetation mapping phase from approximately two years to six months while maintaining the thematic accuracy required for large-scale operational monitoring.

The Northeast China Cold Vortex (NCCV), a typical synoptic-scale system in Northeast China, North China, and the Jianghuai region, frequently triggers thunderstorms, strong winds, and heavy precipitation, making it significant for meteorological monitoring and operational forecasting. However, the cloud microphysical properties of NCCV-associated cloud systems remain poorly characterized, as long-term cloud microphysical observations are limited. This study utilizes cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to analyze cloud-type frequencies and four key cloud microphysical properties under NCCV conditions: liquid effective radius (Re_liq), ice effective radius (Re_ice), liquid water path (LWP), and ice water path (IWP). Nearly identical cloud-type compositions are found for the two groups, NCCV and non-NCCV samples with similar cloud fractions on the regional scale, which are dominated by stratocumulus (Sc), altostratus (As), cumulus (Cu), and stratus (St), with Sc accounting for above 40% of total cloud occurrence. Yet microphysical properties differ markedly between these two groups. LWP shows the most contrast and it is evidently larger in NCCV than in non-NCCV cloud systems. As for the spatial structure of cloud microphysics in the NCCV domain, it is found that Sc, As, St, and nimbostratus (Ns) constitute the primary background, and Sc remains the dominant cloud type in almost all spatial sectors. LWP and IWP tend to have stronger spatial heterogeneity than Re_liq and Re_ice. LWP gets notably larger in the northern to northwestern sectors, whereas IWP shows much higher variations in both radial and azimuthal dimensions. These results reveal the statistical microphysical characteristics of cloud systems associated with NCCV from the perspective of satellite observations, providing a reference for a deeper understanding of their unique cloud and precipitation physical processes.

Accurate and transferable estimation of forest carbon is essential for operational forest management and national greenhouse gas reporting, yet it remains challenging because of variation in stand structure and site conditions. Unmanned aerial vehicle-based light detection and ranging (UAV-LiDAR) provides detailed structural information for modelling tree-level carbon, but model transferability across sites is often limited. In this study, we compared three modelling approaches—a linear mixed-effects model (LMM), a generalised additive model (GAM), and Random Forest (RF)—within a unified framework of multi-site, locally post hoc calibrated, and fully local model-fitting strategies. Using data from 20 radiata pine (Pinus radiata D. Don) plantation stands across New Zealand (35,201 trees), a leave-one-site-out (LOSO) framework was used to assess multi-site model transferability and support post hoc calibration, while local models were evaluated using repeated within-site train/test splits. We also evaluated how prediction accuracy changed with increasing local sample size and compared random tree selection with plot-based sampling. Multi-site models showed poor generalisation, with mean relative RMSE ranging from 35.9% to 56.9% and substantial site-level bias. Applying post hoc calibration to the multi-site model using a 50-tree sample reduced prediction error by 30 to 60% (mean relative RMSE 22.8–25.0%) and substantially reduced bias across sites. The fitting of fully local models with the same sample size yielded only modest further improvements (mean relative RMSE 21.9–23.1%). Gains in accuracy were minimal with increasing sample sizes above 50 trees for post hoc calibration and 175 trees for the fully local models, and differences in accuracy between sampling strategies were small. These results show that post hoc calibration of multi-site UAV-LiDAR models with a small local sample provides a practical and efficient approach for tree-level carbon estimation in plantation forests.

Study region The Yellow River Basin, China. Study focus Irrigation plays a critical role in sustaining agricultural development, particularly in water-scarce regions such as the Yellow River Basin (YRB). Yet, the response of irrigation water requirement (IWR) to drought events across different crop types and growth stages remained inadequately characterized. Here, we developed a daily-scale, multi-crop simulation framework that integrated the FAO Penman-Monteith method and the Standardized Precipitation Evapotranspiration Index (SPEI) to assess the spatiotemporal heterogeneity of IWR responses to drought events across four major crops (wheat, maize, rice, and soybeans) in the YRB during 1980–2018, which enabled systematic comparison of drought impacts across crop types and growth stages. New hydrological insights Drought significantly increased IWR for all crops, with basin-average increases of 27.70–31.36% during drought periods compared to non-drought periods. IWR increased consistently with drought severity, corroborated by significant linear correlations between SPEI and IWR anomaly, particularly in the Wei River Basin characterized by intensive irrigation and severe drought conditions. Specifically, rice exhibited the strongest sensitivity to drought across all growth stages, whereas the mid-season stage was identified as the most critical period for drought-induced IWR amplification for most crops. These findings moved beyond aggregate drought-impact assessments to provide an explicit understanding of irrigation sensitivity to drought, offering a scientific basis for targeted water allocation and drought adaptation strategies in water-stressed regions.

Abstract. Understanding the vertical distribution of aerosol and clouds i.s critical for climate modeling, weather forecasting, and air quality monitoring. Lidar observations are central to profiling atmospheric composition, yet signal attenuation in optically thick layers limits the effective retrieval of some important properties above those layers. More complex measurement approaches, using a combination of Lidar and cloud radar systems, can be taken to support more inclusive and accurate inference. In this study, we develop a deep learning framework to address this trade-off and gap in the cost of data acquisition by enabling full-column aerosol and cloud classification using only standard lidar inputs, achieving particularly high skill for aerosol typing while demonstrating robust, physically consistent classification of ice-cloud fields even under conditions of strong lidar signal attenuation, with liquid-cloud uncertainties primarily arising from closely related microphysical classes. The approach is based on a U-Net architecture trained to predict combined aerosol and cloud types from vertical profiles of backscatter and depolarization. Classification targets integrate established aerosol typing from PollyXT with cloud and precipitation categorization from Cloudnet, facilitating a unified scheme. The model achieves high precision, recall, and F1-scores above 95 %. By evaluating numerous complex case studies, we establish the model's ability to exploit information embedded in the lidar signal below attenuating layers, including structural and contextual features, to infer atmospheric conditions at higher altitudes, offering a robust AI-based enhancement to lidar-based atmospheric profiling and target classification. The application of AI in this context closes the gap between the need for vertical cloud maps and the sparse availability of Cloudnet.

Relative radiometric normalization (RRN) is fundamental to multi-temporal remote sensing analysis; however, conventional techniques often struggle with nonlinear distortions, outlier contamination, and heterogeneous land-cover conditions. To address these challenges, we propose a diffusion-based probabilistic framework that models radiometric inconsistency as a combination of deterministic residuals and stochastic perturbations. In this framework, the forward process injects structured noise and stochastic perturbations, while the reverse process restores radiometric consistency through a dual-objective variational formulation. At the core of this framework is a spatial–spectral attention residual network (SSARN), which integrates residual learning with dual attention mechanisms to capture cross-band dependencies and multi-scale spatial context. A preprocessing stage guided by the structural similarity index (SSIM) further enhances robustness by automatically selecting stable pseudo-invariant regions for model training. Comprehensive experiments on multi-temporal Sentinel-2 datasets demonstrate that the proposed method consistently outperforms existing approaches, achieving higher accuracy and enhanced spectral fidelity. Moreover, the framework ensures greater consistency of the normalized difference vegetation index (NDVI) and preserves fine-grained textural details, underscoring its potential as a scalable and resilient solution for large-scale RRN in remote sensing applications.