Earth and Environmental Sciences

Abstract In February 2022, Southern China experienced a severe compound wet and cold event (CWCE) that caused major socioeconomic impacts on transportation, power supply, and agriculture, ranking among China’s top ten natural disasters of the year. To what extent anthropogenic climate change has already affected such CWCEs remained unclear. This study employs a circulation analogue-based risk attribution framework with observation to quantify contribution of large-scale atmospheric circulation and the thermodynamic effect of climate change to the event, and further used simulations from Coupled Model Intercomparison Project (CMIP6) models under different external forcing to assess the anthropogenic influence. The main findings are as follows: atmospheric circulation anomalies were the dominant driver of this CWCE. The contribution of circulation patterns similar to those in February 2022 was estimated at 73% (95% confidence intervals, CIs: 71%, 76%). Thermodynamic effect of climate change reduced the probability of occurrence of this CWCE by 32% (95% CIs: 27%, 36%). Further analysis using CMIP6 models and a risk-based attribution approach confirms that anthropogenic forcing suppresses the occurrence of CWCE, reducing its probability by 21% (95% CIs: 5%, 33%).

Abstract This study investigates the characteristics and mechanisms of the tropical Pacific decadal variability (TPDV) in the Community Earth System Model version 2 (CESM2) on a 10-30-year timescale. The TPDV exhibits distinctive coupled atmosphere-ocean features: the warm phase is characterized by El Niño-like warm SST anomalies, a weakened zonal thermocline gradient, and weakened Pacific subtropical cells (STCs) and equatorial undercurrents, while the cold phase is largely symmetric to the warm phase, exhibiting opposite-signed patterns. Heat budget analysis indicates that interior STC-driven heat transports regulate phase transitions by removing or supplying warm water, primarily through interior STCs. Further analysis reveals that these transports are dominated by anomalous currents advecting the mean temperature field ( v'Ŧ ). Unlike ENSO, the TPDV is episodic and non- oscillatory, with more gradual phase transitions. These results highlight the complexity of decadal variability and suggest that nonlinear tropical dynamics and stochastic forcing could play crucial roles in shaping TPDV evolution.

Abstract. Sporadic explosive volcanic eruptions can inject large amounts of sulfur into the stratosphere, which forms volcanic sulfate aerosols with the potential to affect stratospheric ozone chemistry. Future volcanic eruptions have been represented in climate projection studies with varying degrees of realism despite their potential importance for polar ozone recovery. Climate projections typically use a constant volcanic forcing based on a historical average, which very likely underestimates the magnitude of future volcanic forcing and ignores the sporadic nature of volcanic eruptions. In this study, we use stochastic volcanic eruption scenarios and a plume-aerosol-chemistry-climate model (UKESM-VPLUME) to assess the effect of future volcanic sulfur injections on lower stratospheric ozone recovery over Antarctica and Southern Hemisphere mid-latitudes. We find that sporadic eruptions can delay Antarctic total column ozone recovery by up to five years, though this delay is relatively small when compared with the long-term ozone recovery timescale. Large-magnitude eruptions occurring before mid-century can, however, episodically cause more substantial delays in the recovery. Based on a composite analysis we show that the ozone response to volcanic sulfate aerosols over Antarctica and Southern Hemisphere mid-latitudes weakens over the 21st century due to declining chlorofluorocarbon concentrations. Overall, our findings underscore the need for fully interactive volcanic aerosol-chemistry coupling to assess the resilience of the Antarctic ozone layer in response to future volcanic eruptions and other stratospheric perturbation events. Our results also support previous calls for sustained monitoring of stratospheric composition and ozone-depleting processes to better anticipate and attribute changes in ozone recovery.

Abstract To enable the study of the climatology of convective and stratiform precipitation in tropical cyclones, thiswork develops a convective–stratiformprecipitation type classification model for the Global Precipitation Measurement satellite Microwave Imager (GMI) from the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset. The model uses the random forest classifier and takes as input the various brightness temperature observations from the GMI and calculated texture information. The optimal model setup is found through experimenting with the minimum number of samples in a leaf node and model calibration to prioritize classification accuracy. The final model is the sigmoid calibrated model with a minimum number of samples in a leaf node of four. Overall, the model does well at capturing the general precipitation structure of tropical cyclones. Model deficiencies are likely due to issues with nonuniformbeam filling. Finally, a dropout experiment is performed to investigate the most important predictors. The most important predictors are the 36.64- and 89.0-GHz brightness temperatures and texture information. While texture information are important, brightness temperature observations at observing frequencies of less than 89.0 GHz have a more significant impact towards model skill in classifying precipitation type from passive microwave observations.

The European Space Agency’s Sea State Climate Change Initiative adopted a physical retracking algorithm to reprocess two decades of satellite altimetry measurements and provide long time series of significant wave height in the global ocean. This paper describes the main characteristics of this algorithm, called WHALES, and analyzes the impact of algorithm choices on the estimates of significant wave height, particularly the statistically weighted analysis of residuals in the cost function. Moreover, the performance of WHALES is analyzed in terms of noise level and error statistics using buoy data as reference, with a particular focus on the coastal zone. We found that valid data records increased by 30% at 5 km from the coast when switching from the standard MLE retracker to WHALES. • The WHALES algorithm estimates wave height from radar altimetry signals. • The impact of algorithm choices on measurement physics is analyzed. • Validation shows noise reduction and improved data quality in coastal zones.

Abstract. Despite intensive research on midlatitude cyclones since the mid-twentieth century, open questions on their structure and development remain, like the question of their core temperature. Few studies have addressed what the proportion of cold-core and warm-core cyclones in midlatitudes is, but it is not clear yet if occluded cyclones are cold-core or warm-core cyclones and how different the processes leading to cold-core and warm-core cyclones are. To address these questions, a new cyclone phase space, denoted as ETC-CPS and adapted to extratropical cyclones, is developed by introducing three parameters: the core temperature, the thermal asymmetry and the baroclinic conversion rate. Differences with existing cyclone phase spaces are detailed by analyzing two consecutive storms in the North Atlantic, one ending with a warm seclusion and another with an occlusion. ETC-CPS is then applied to all midlatitude cyclones of the Northern Hemisphere (NH) tracked during winter and summer using ERA5 reanalysis. The results highlight that most of midlatitude NH cyclones are asymmetric warm-core cyclones. At the time of maximum intensity, the fraction of cyclones with a cold core temperature in the lower troposphere fluctuates around 10 %–15 % depending on the season while that of warm core cyclones is around 85 %–90 %. It indicates that, in addition to warm-seclusion cyclones, most occluded cyclones also have a warm core. Both cold-core and warm-core cyclones undergo a well-marked baroclinic growth phase before reaching their maximum intensity but their vertical structure differs during that phase. Warm-core cyclones exhibit a clear vertical westward tilt of the geopotential height anomaly contours as in the classical picture of a developing baroclinic unstable mode. In contrast, cold-core cyclones have a funnel-like vertical structure with the anomalous geopotential height field leaning more westward than eastward, which makes them also grow baroclinically but with a non-standard baroclinic structure. Differences between seasons are also noticeable. During winter, cold-core cyclones have much weaker intensity than warm-core cyclones and preferentially develop over continental regions whereas warm-core cyclones develop over the oceanic storm tracks. During summer, both types of cyclones preferentially develop over the oceanic storm tracks and have similar intensities.

Abstract. Spectrometer instruments have significantly contributed to monitoring atmospheric composition and climate change for decades. Among them, the Global Ozone Monitoring Experiment (GOME, 1995–2011) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, 2002–2012) were two well-known sensors whose missions overlapped by nearly a decade. Both instruments provided valuable data for atmospheric applications. However, to ensure data consistency and extend long-term time series, cross-calibration between the two instruments is important. The Fundamental Data Record for Atmospheric Composition (FDR4ATMOS) project, initiated by the European Space Agency (ESA), aims at harmonizing GOME and SCIAMACHY Level 1 data, i.e., irradiance and reflectance measurements. This paper presents, for the first time, a cross-calibration methodology for the top-of-atmosphere (TOA) reflectance of the spectrometers developed within the FDR4ATMOS project. Sub-pixel variability analysis, based on Polarization Monitoring Devices (PMD) data, enabled the evaluation of the homogeneity and similarity of the scene as observed by GOME and SCIAMACHY, thereby reducing the uncertainty in the cross-calibration process. Key challenges, including differing spatial resolutions, the absence of exact spatiotemporal overlap, and the need to preserve spectral structure, were addressed through targeted strategies. These included the selection of scenes with minimal acquisition time differences over Pseudo-Invariant Calibration Sites (PICS) characterized by stable meteorological and atmospheric conditions. A critical step consisted of spatially weighted averaging of SCIAMACHY pixels within each GOME footprint, followed by the computation of spectral channel–wise ratios across Bands 2B, 3, and 4, covering the ultraviolet, visible, and near-infrared (UV/VIS/NIR) spectral regions where many dominant trace gases are present. Furthermore, PMD data from both GOME and SCIAMACHY were analyzed to assess the spatial homogeneity of the pixels used in the cross-calibration and to evaluate its impact on cross-calibration performance. Observations under near-clear-sky conditions from 2003 were collocated over PICS and used to derive transfer functions (TFs). Polynomial TFs were fitted for Bands 2B and 3, while a constant TF was used for Band 4. The TFs showed dependence on viewing zenith angle (VZA), degradation, and wavelength. The uncertainty in the TFs increased with wavelength due to decreasing homogeneity of the cross-calibrated pixels, as indicated by the PMD data analysis, where pixel-to-pixel variability became larger. Using PMD measurements from cross-calibrated pixels as an indicator to filter out non-homogeneous pixels of the main spectral channels resulted in an uncertainty reduction of up to 70 % in the TFs. Overall, the presented cross-calibration approach and PMD-based analysis provide a pathway toward generating consistent and long-term spectrometer records. This work highlights the potential for extending the cross-calibration beyond traditional PICS and ideally suited scenes, increasing robustness across varied surfaces and atmospheric conditions.

• High temporal resolution greatly improves forest phenology accuracy. • Generated forest phenology dataset (1982–2020) for mid- to high-latitude. • Mitigated the discontinuities found in existing high-latitude phenology products. • Comparative analyses reveal strong sensitivity of phenology to temporal resolution. Phenological metrics derived from remote sensing data are highly sensitive to the temporal resolution of input time series. However, most existing large-scale and long-term phenology products are generated from multi-day composite datasets, which may obscure short-term vegetation dynamics. The Advanced Very High Resolution Radiometer (AVHRR) Long-Term Data Record (LTDR), characterized by its daily temporal resolution, provides an opportunity to improve the precision of phenology extraction. Nevertheless, the existing LTDR-derived phenology product (AVH09_phe) shows anomalous spatial patterns in high-latitude regions. Using AVHRR LTDR-based background field reconstruction NDVI (LTDR-BFR-NDVI), we systematically compared eight extraction strategies combining two temporal resolutions (daily and biweekly composites) and four algorithms: the first derivative (FD), second derivative (SD), and dynamic threshold methods (DT_0.2 and DT_0.5). Spatially representative field observations were used to assess their performance. The results showed that phenology derived from daily NDVI exhibits substantially stronger consistency and lower uncertainty compared to that from biweekly composites. For SOS, daily-based methods achieved higher correlations (R = 0.55–0.62) and lower errors (RMSE = 15.14–17.86 days), while biweekly-based methods showed weak correlations (R < 0.4) and larger uncertainties. For EOS, overall performance was lower, but daily-based methods still outperformed biweekly composites, with the second derivative method providing relatively balanced accuracy (R = 0.41, RMSE = 21.98 days, MAE = 13.92 days, bias = 1.14 days). The combination of daily NDVI and the second derivative method achieved the highest accuracy and was adopted to produce a new forest phenology product, LTDR-BFR-FP. Comparative analyses with other published satellite-derived phenology products revealed that LTDR-BFR-FP mitigates high-latitude artifacts in AVH09_phe. Furthermore, the comparison between daily and biweekly NDVI time series demonstrated that temporal resolution exerts a significantly influence both phenology accuracy and trends.

Abstract This study examines the subseasonal timescale climate variability in wintertime East Asia (EA) and its connection with North America (NA). Results reveal a seesaw-like oscillation in surface air temperature (SAT) anomalies between EA and NA: a rise of SAT over EA is followed by a drop of SAT over NA about one week later, and vice versa. It turns out that four well-known teleconnection patterns—the Scandinavian (SCA), Eurasian (EU), West Pacific (WP), and Pacific/North America (PNA) patterns—play crucial roles in this linkage. The SCA over Eurasia induces cold (warm) surges over EA; as the SCA propagates further into the subtropical western Pacific, it contributes to the formation of the WP through energy dispersion, or/and the merging of the SCA centers with the incipient WP centers. The WP further causes warm (cold) spells over NA. Day-to-day energy budget analysis indicates that the SCA is dominantly driven by the baroclinic energy conversion, while the WP is mainly driven by both the baroclinic and barotropic energy conversions. The transient eddy feedback forcing and the advection of available potential energy and kinetic energy by the zonal background flow also make substantial contributions to the growth of the WP. It is the advection of the potential available energy and kinetic energy by the background flow which plays a key role in the SCA-WP relationship. Also, the propagation of the EU over Eurasia followed by the formation of the PNA may cause a seesaw-like SAT oscillation between EA and NA, with similar dynamical mechanisms involved.

Abstract. We conduct a process-oriented analysis of the summertime diurnal cycle of precipitation (DCP) over China by comparing three widely used reanalyses (ERA5, JRA-55, and MERRA-2) with satellite observations. While all reanalyses capture the observed nocturnal precipitation peak related to elevated convection, they differ substantially in simulating the daytime rainfall timing. JRA-55 and MERRA-2 better capture the observed timing, whereas ERA5 exhibits a systematic 3 h phase advance. The superior performance of JRA-55 is attributed to its gradual development of deep convection, supported by sustained heating and convective eddy transport. In contrast, ERA5 develops deep convection too rapidly, resulting in premature peaks in heating and precipitation. MERRA-2 also produces early-peaking convective rainfall, but with notably weaker intensity, suggesting that its better diurnal cycle is achieved largely through the suppression of convective precipitation. Diurnal cloud structures further corroborate these differences. Whereas JRA-55 exhibits a slowly developing, upward-tilting cloud structure from morning to afternoon, ERA5 and MERRA-2 peak earlier and have a shorter duration. The role of large-scale forcing, quantified by CAPE and dynamic CAPE (dCAPE), is further tied to the performance of the convection schemes. Results show the peak timing of dCAPE lags that of CAPE and aligns more closely with the observed precipitation. While convective precipitation in ERA5 and MERRA-2 tracks CAPE more closely, in JRA-55 it aligns better with dCAPE, thereby yielding a more realistic DCP. This contrast highlights the critical influence of triggering choice on cumulus convection.

Underground coal mines are important global sources of methane, but emission estimates are uncertain. We show that emission estimates for individual mines from aircraft remote-sensing surveys in the United States agree within 40% with direct measurements used for national emission reporting (IPCC Tier 3 estimate). Such direct measurements are unavailable in most countries, which rely on estimated emission factors (EFs) applied to coal-production rates. We find that EFs from IPCC Tier 1 and the Model for Calculating Coal Mine Methane (MC2M) methods overestimate U.S. emissions 3-fold due to incorrect dependence on mine depth. An IPCC Tier 2 method using measured basin-specific mine gas content agrees with direct emission measurements but does not account for gob well emissions and requires gas content data that are generally unavailable. We show that aircraft remote sensing for a small sample of mines can successfully estimate basin-specific EFs for ventilation shafts and gob wells, enabling estimates of basin- and national-scale emissions. We discuss how the method can be applied with satellite remote sensing to quantify coal emissions worldwide.

• Landsat and XGBoost reconstructed 40-year DOCc and DOCs in Lake Taihu. • Persistent spatial heterogeneity and nearshore–offshore DOC gradients were resolved. • Lake-wide DOCs peaked in 2010 (16,906 t C) and declined at −138.51 t C/decade. • Meteorological factors dominated DOC variability over other driver groups. Dissolved organic carbon (DOC) plays a crucial role in lake carbon cycling, yet its long-term variations and key drivers remain poorly understood, particularly in large eutrophic lakes. Using 40 years of Landsat data and four machine learning (ML) models, we reconstructed DOC concentration (DOCc) and storage (DOCs) in Lake Taihu for the whole lake (T0) and six open-water zones: Gonghu Bay (T1), Meiliang Bay (T2), Zhushan Bay (T3), Central Lake (T4), Northwest Lake (T5), and Southwest Lake (T6). The XGBoost model achieved high accuracy (validation: R 2 = 0.73, RMSE = 0.51 mg/L, MAPE = 8.66%, RPD = 1.70), revealing spatiotemporal DOC variability (2.49–6.36 mg/L). DOCc peaked at 4.90 mg/L in 1996 and declined after 2000, with lower levels in several zones after 2010. Lake-wide DOCs showed interannual variability and a slight decrease, peaking at 16,906 t C in 2010 and showing a linear trend of −138.51 t C/decade. Regional differences highlight strong hydrodynamic flushing in T6 (−106.63 t C/decade), while localized increases in T1 and T5 may reflect nutrient-driven productivity and sedimentary carbon accumulation. Meteorological factors were the strongest direct controls on DOCc (−0.72) and DOCs (−0.98), exceeding human-activity proxies and other driver groups. These results highlight the potential of combining remote sensing with ML to provide a spatially explicit, long-term baseline for DOC monitoring and attribution in large optically complex lakes, thereby improving understanding of lake carbon dynamics under anthropogenic pressures and climate change

Abstract Deltas, the interface between riverine and coastal systems, host ∼500 million people and function as crucial ecological nexuses. The magnitude and frequency of extreme floods are projected to intensify as climate changes, yet the potential impacts on delta morphodynamics remain poorly understood. Using the pyDeltaRCM numerical model, we explore the impact of extreme flow frequency on delta morphology. Morphometric analyses demonstrate a non‐linear morphodynamic response across two orders of magnitude of extreme flow intermittencies, I . Under medium I , deltas are smallest with the fewest, narrowest channels. In contrast, at both low and high I , deltas grow the largest, with the widest and most numerous channels. Channel mobility appears unaffected and delta slope declines monotonically as I increases. We identify drivers of this non‐linear response, generating a novel conceptual model of delta adjustment to extreme flows. Our morphodynamic projections can help anticipate and manage geomorphic change on deltas undergoing flood‐frequency change.

Abstract It has long been a mystery why small Total Solar Irradiation changes have significant effects on Earth's climate. Solar cycle correlation studies abound but cannot conclusively point to a viable physical mechanism. Here, I show that geomagnetic storms have a profound terrestrial weather impact. Using 67 years of hourly Disturbance storm time (Dst) index and ERA5 atmosphere data over North America, I find geomagnetic storm impacts up to two orders of magnitude larger than the long‐term global mean surface temperature impact attributed to solar activity. Particle precipitation effects such as from cosmic rays, solar energetic particles, or magnetospheric electrons are least consistent with my results. In particular, the cosmic ray–cloudiness hypothesis is falsified by my results. A top‐down mechanism operating directly through the ionosphere or through stratospheric chemistry and the polar vortex appears to be more likely.

The Jinsha River dry-hot valley Accurately obtaining high-precision spatiotemporal precipitation information is crucial for hydrological modeling and soil erosion research in the dry-hot valley of southwest China. However, existing satellite precipitation products suffer from low spatial resolution and significant errors, which are not suitable for small-scale research. To address these gaps, this study constructed a downscaling-fusion framework based on random forest (RF) to improve the estimation accuracy of daily gridded precipitation in the dry-hot valley region. In the downscaling stage, the framework selected key influencing factors; in the fusion stage, it adopted a two-stage strategy of classification and regression, incorporating auxiliary variables and the spatial autocorrelation characteristics of satellite precipitation. Using the Jinsha River dry-hot valley as a case study, the results showed that: (1) The RF downscaling model more precisely characterized spatial distribution patterns of precipitation. (2) The two-stage fusion strategy outperformed single regression models in both precipitation event identification and quantitative estimation; however, its accuracy decreased with increasing elevation, and the underestimation of heavy precipitation was particularly pronounced in high-altitude areas. (3) Over the past two decades, no significant trend was observed in the total annual precipitation in the Jinsha River dry-hot valley, whereas the dry season became drier and precipitation concentration increased during the rainy season, leading to a rising risk of local heavy precipitation events. Overall, this study provides an effective scheme for constructing high-quality precipitation data in dry-hot valley regions. • An RF downscaling-fusion framework is developed to optimize precipitation estimates for the Jinsha River dry-hot valley. • The proposed framework demonstrates prominent capability in accurately capturing diverse precipitation conditions. • Vertical zonation characteristics of precipitation distribution were explicitly evaluated. • In the dry-hot valley, the dry season is becoming drier, and the risk of local heavy rainfall increases during the rainy season.

Abstract We report experimental evidence of the spontaneous formation of NO 3 − in suspended aqueous droplets containing NO 2 − , driven by hydroxyl radicals and NO 2 produced by the intrinsic properties of the air‐water interface. This oxidation of nitrite anions is observed in single droplets of micrometer size using optical tweezers, with the υ 1 (N–O) Raman mode of NO 3 − detected around 1,050 cm −1 as a function of time. The influence of pH, particle size, initial NO 2 − concentration, and different gaseous atmospheres (air and nitrogen) has been systematically studied. In sharp contrast, no bulk oxidation was observed under similar conditions. By using KSCN as an OH radical scavenger, we demonstrate the critical role of hydroxyl radicals generated at the air‐water interface in nitrate formation. These experiments suggest that a particle effect is indeed at play, leading to the spontaneous formation of NO 3 − through the interaction of NO 2 and OH radicals.

Abstract The Loess Plateau (LP) ecosystems are expected to play an important role in achieving the national carbon neutrality goals. However, significant uncertainty remains in assessing the spatiotemporal dynamics of carbon sequestration rates (CSR) in regional terrestrial ecosystems. Here, we integrated large amounts of field-plot data and models (forest carbon sequestration model and machine learning models) to systematically explore the CSR of terrestrial ecosystems over the LP from 1980 to 2060 under three climate scenarios. Results showed that an increase of 2.62 ± 0.43 Pg C for terrestrial ecosystems over the LP during 1980–2060, with 1.98 ± 0.31 Pg C and 0.64 ± 0.13 Pg C in vegetation and soil, respectively, representing an increase of 54.60 %. The CSR of forests, shrublands, grasslands, and croplands over the LP were estimated to be 36.63 ± 6.96, 0.09 ± 0.01, –7.74 ± 1.13, and 3.81 ± 0.66 Tg C yr–1, respectively. Between 1980 and 2060, the ecosystem CSR (ECSR) over the LP ranged from 9.62 to 64.45 Tg C yr–1, with an average of 32.78 ± 6.79 Tg C yr–1. The ECSR will be peaking during 2040–2050 before decreasing. Our findings provide comprehensive picture of the spatiotemporal dynamic trajectory of CSR over the LP and can be used to inform future policymaking to achieve regional carbon neutrality.

pollution event occurred and assessed its spatiotemporal characteristics and disparities at the grid scale. Our findings indicated an increase in global compound exposure, accompanied by significant regional disparities. From 2000 to 2023, 86% of Europeans experienced at least one compound event per year, 20 times the population more than in North America. More than 70% of countries with rising event days and population exposure were located in Asia and Africa. Total exposure increased from 64.5 to 91.3 million person-days, with the highest levels observed in high-income countries, where young adults, males, and urban populations were most affected. Our research provides a new perspective on understanding the inequities in compound exposure, particularly with respect to economic and regional disparities.

Abstract. Chloride depletion from sea salt aerosols (SSA) is frequently observed in polluted coastal regions, and despite they severely impact air quality and human health, the influencing mechanism of alkaline species in this phenomenon remains incompletely understood. Here, we conducted laboratory experiments to investigate the effect of alkaline species including NH3 and an organic amine (dimethylamine, DMA) on chloride depletion and the subsequent formation of organic chlorinated compounds. Results showed that alkaline species could weaken chloride depletion caused by acidic gases, mainly due to acid-base neutralization. Specifically, chloride depletion in the presence of NOx decreased from 20.1 % to 15.8 % when NH3 concentration increased from 100 to 300 ppb. Chloride depletion also decreased from 18.6 % to 13.5 % with DMA concentration increasing from 50 to 150 ppb. The weakening effect of DMA on chloride depletion is more pronounced than that of NH3, primarily DMA stronger alkalinity and nucleation ability. These alkaline species exhibit a stronger reduction of chloride depletion in the presence of SO2 than in the presence of NOx. The detection of organic chlorinated products, formed via active chlorine-induced oxidation, is consistent with the role of alkaline species in weakening chloride depletion, which subsequently results in the reduction of active chlorine. These findings suggest that alkaline species, more specifically organic amines, are significant factors influencing chloride depletion in the coastal atmosphere, further improving our understanding of this phenomenon.

Abstract Thunderstorms could cause the irregularities of electron density distributions in the ionosphere by exciting gravity waves and modifying ambient electric field ( E ‐field). By comparing the DPS‐4D ionosonde observation at 5‐min resolution at Fuke Station in Hainan, China with the lightning detection data, we studied the F ‐layer responses to a thunderstorm on 16 August 2016. The results show that the variation in the F ‐layer electron density corresponded, with ∼5‐min delay, to the time‐resolved lightning occurrence; the observed temporal delay likely reflects the E ‐field modifications associated with charge separation within thunderclouds. After the peak lightning activity, a weak spread‐ F appeared alongside sudden rises in plasma drift velocities. These features suggest that lightning disruptions affect ionospheric E ‐fields, drive E × B drifts, and cause irregularities in F ‐layer electron density via Rayleigh‐Taylor and E × B instabilities. It is the first high‐resolution ionosonde observation of thunderstorm‐induced F ‐layer disturbances at low latitudes, providing more insights into the troposphere‐ionosphere coupling.

Abstract The Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites (TRACERS) mission observes electron energy‐latitude dispersion at the equatorward edge of the magnetospheric cusp, and high‐cadence Analyzer for Cusp Electrons (ACE) measurements resolve the dispersed edge. The inverse velocity dispersion (low energy before high energy) encountered by TRACERS as it travels southward through the northern cusp rules out pure energy‐time dispersion from nearby injections or Alfvén wave‐driven acceleration. TRACERS observes electron dispersion at the equatorward edge of the northern cusp roughly half of the time for southward interplanetary magnetic field (IMF), and almost never for northward IMF. The TRACERS measurements therefore provide strong observational support for the hypothesis that the observed electron dispersion results from dayside magnetic reconnection and plasma convection, much like the ion dispersion that commonly extends across the cusp. Observations of multiple electron dispersions and electron steps suggest fine‐scale spatial and/or temporal variability in magnetic reconnection.

Abstract. The spatial resolution of hydrological modeling is a critical factor affecting flood simulation accuracy, especially in large watersheds characterized by complex watershed characteristics. However, its influence on the accuracy of hourly flood simulations at both watershed outlets and internal locations remains insufficiently understood, hindering rational spatial-resolution selection for large-scale flood forecasting. This study evaluates hourly flood simulations across five spatial resolutions (1, 3, 5, 10 km, and sub-watershed) at the watershed outlet and multiple internal stations in the Jialing River Basin, China (157 041 km2). An XGBoost-based model is employed to identify flood characteristics sensitive to spatial resolution and to quantify their nonlinear effects on simulation accuracy. Based on these relationships, spatial-resolution recommendations are derived for different flood-characteristic categories, and the effectiveness of spatial refinement under coarse rainfall inputs is examined. Results show that spatial refinement markedly improves simulation accuracy at internal locations but yields only marginal gains at the watershed outlet. Watershed area is identified as the dominant factor governing resolution sensitivity, while rainfall characteristics and underlying-surface properties exert strong nonlinear influences. Fine grids (1–3 km) are most effective under flood conditions with strong nonlinearity, but their advantages diminish rapidly as rainfall inputs become coarser, indicating that increased spatial resolution cannot compensate for insufficient rainfall information. Overall, these findings advance current understanding of spatial-resolution effects on hourly flood simulations and provide practical guidance for spatial-resolution selection in large-watershed modeling.

The middle reaches of the Heihe River Basin, an arid inland river system in northwest China, where groundwater plays a key role in sustaining oasis ecosystems under strong evaporative conditions. This study investigated groundwater hydrochemical characteristics and their linkages with vegetation dynamics using major ions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, NO₃⁻) and physicochemical parameters (pH, EC, TDS). Multivariate statistical methods, including K-means clustering and principal component analysis (PCA), were applied to identify hydrochemical types and controlling processes. Relationships among groundwater chemistry, normalized difference vegetation index (NDVI), and potential evapotranspiration (ET₀) were analyzed to explore ecohydrological interactions. Groundwater is weakly alkaline and dominated by Na⁺ and HCO₃⁻, mainly controlled by mineral dissolution, evaporation–concentration, and anthropogenic inputs. A clear salinity gradient was observed, with higher TDS in northern irrigated areas. Strong ecohydrological coupling is evident, as evaporation-driven salinity accumulation (positive ET₀–TDS relationship) constrains vegetation growth. NDVI shows negative correlations with TDS and major ions, indicating salinity stress. Notably, elevated HCO₃⁻ further suppresses vegetation, suggesting alkalization as an additional ecological constraint. These findings provide a scientific basis for salinity management and ecosystem protection in arid regions. • The dominant facies was Na–Ca–HCO₃, mainly controlled by rock weathering. • PCA extracted three components explaining 86.7% of variance, reflecting mineralization and anthropogenic influences. • Major ions were higher in northern and central oasis areas. • NDVI declined with increasing TDS and ions, indicating salinity-induced vegetation stress.

• We present a thermo-gravity modeling framework for a nearly-amagmatic ultraslow-spreading ridge at Southwest Indian Ridge 13–14°E. • Best-fit lithospheric thickness is 14–16 km with hydrothermal circulation confined to depths above 6–9 km. • Hydrothermal cooling contributes only 3–5 km to lithospheric thickening. • Spreading rate, rather than hydrothermal penetration, controls lithospheric thickness at nearly-amagmatic ultraslow-spreading ridges. The thermal dynamics of mid-ocean ridges (MORs) are driven by heating from asthenosphere upwelling and melt emplacement, and cooling via hydrothermal circulation. Nearly-amagmatic ultraslow-spreading ridges, like the Southwest Indian Ridge (SWIR) at 13–14°E, are expected to have the thickest and coldest axial lithosphere among global MORs,yet the extent to which hydrothermal circulation contributes to lithospheric thickening remains poorly constrained. Here we present a thermo-gravity modeling framework that integrates 2D hydrothermal convection simulations with gravity anomaly analysis, providing a quantitative constraint on lithospheric thickness in amagmatic settings. By systematically varying hydrothermal penetration depth (0–21 km) and permeability, we simulate a wide range of thermal structures, which are extended along spreading flowlines and tested against gravity anomalies. The best-fit lithospheric thickness beneath the SWIR 13–14°E is 14–16 km. It is based on the brittle–ductile transition (BDT) depth defined by the 650 °C isotherm, and consistent with global spreading-rate trends but much thinner than some seismic interpretations of >25 km. Within this framework, hydrothermal cooling accounts for no >5 km of lithospheric thickening, with circulation confined to depths above 6–9 km. These findings directly challenge the prevailing deep hydrothermal hypothesis: hydrothermal cooling is insufficient to generate an extremely thick lithosphere at a nearly-amagmatic ultraslow-spreading ridges. Instead, spreading rate emerges as the primary control. More broadly, our framework provides the first quantitative thermo-gravity constraint on hydrothermal cooling, reconciling seismicity, isostatic deviation, and gravity perspectives on lithosphere accretion, offering new insights into lithosphere accretion in magma-poor ridge environments.

The physical structure of lakes, particularly vertical temperature stratification, governs the dissolved oxygen distribution and underpins freshwater ecosystem health. Accurately predicting temperature profiles is therefore essential for understanding key ecological processes. Traditional mechanistic models provide physically consistent simulations but can be computationally intensive and sensitive to input assumptions, whereas purely data-driven models may offer high predictive accuracy, yet lack physical interpretability. In this work, we present a mixture-of-experts framework that integrates a physics-motivated neural network with an unconstrained deep neural network model to predict lake water temperatures across depths. The physics-motivated component enforces physically consistent vertical profiles via a learnable logistic formulation, while the data-driven expert captures empirical patterns from observations. A gating mechanism dynamically combines the two experts, balancing statistical flexibility and physical fidelity. Applied across lakes with diverse morphologies, the framework accurately reproduces the timing, magnitude, and vertical structure of thermal stratification. The model is further used to project future lake temperatures under climate change scenarios, which point to a warming trend in near-surface temperatures, providing a tool to assess potential shifts in stratification and their ecological consequences for freshwater systems.