Atmospheric and Oceanic Sciences

Abstract Accurate identification of cloud thermodynamic phases ‐warm water, supercooled water and ice—is essential for the global energy budget and hydrological cycle, and serves as a crucial prerequisite for retrieving cloud optical and microphysical parameters. Existing methods each have inherent limitations, and relying on a single approach often leads to ambiguous classifications. In this study, we develop a collaborative algorithm that integrates s hortwave infrared, w ater vapor a bsorption and p olarization measurements (hereafter referred to as SWaP) to retrieve cloud thermodynamic phases, relying solely on data from the Shortwave Infrared Polarization Multi‐Angle Imager onboard the Fengyun‐3G satellite. The SWaP algorithm is applied to a typhoon case, and the resulting spatial distribution of warm water, supercooled water, and ice clouds shows reasonable structure. The polarization signatures and brightness temperature characteristics of the retrieved cloud phases are highly consistent with theoretical expectations, demonstrating the reliability of the proposed algorithm.

Abstract Land surface temperature (LST) is a key variable governing land–atmosphere energy and water exchanges. Despite the importance of LST, satellite observations and reanalysis products often differ in how they define the effective LST depth and in the assumptions underlying their estimates, making comparisons and interpretation challenging. In this study, we present a detailed comparison of LST from GOES-16 (satellite) and MERRA-2 (reanalysis) across the contiguous United States for 2022 and 2023. The results reveal systematic diurnal and seasonal differences: GOES-16 tends to be warmer than MERRA-2 in the afternoon and at night, but cooler in the morning. The magnitude of these differences varies by season. At night, GOES-16 is warmest relative to MERRA-2 for forests; in the morning, it is coolest for croplands and grasslands; and in the afternoon, it is warmest for barren and shrublands. Within individual land cover types, variability in surface conditions—such as soil moisture and elevation—modulates the differences at night and in the morning, with GOES-16 LST being warmer at night and cooler in the morning for wetter soils and at higher elevations. Our analysis also indicates that Leaf Area Index plays a role during spring and autumn, likely due to the association of temperature with leaf emergence and senescence. These findings provide new insights into the mechanisms underlying LST differences between these datasets, and highlight the importance of accounting for surface condition variability when developing LST fusion and assimilation workflows.

Abstract This study describes idealized simulations that emulate the rapid weakening (RW) of Category-4 Hurricane Delta (2020) after encountering moderate vertical wind shear. One set of simulations exposes a small, Delta-like vortex to wind profiles with moderate shear concentrated in an upper layer, a middle layer, or a deep layer of the troposphere. RW occurs for each wind profile and is due to shear enhancing storm-relative inflow in the middle and upper troposphere. This inflow causes deep downdrafts to form on the upshear side of the eyewall and to the left of the vortex tilt. Backward trajectories reveal that the increasing shear causes a nearly five-fold increase in the number of air parcels radially transported from the surrounding middle troposphere into the inner core, resulting in cooling of the eyewall and the formation of downdrafts upshear. Both the downdrafts and ventilation signatures in the eyewall are evident 1 hour before the onset of RW and while the vortex tilt magnitude is small (< 5 km). Upper-level shear causes the vortex tilt to increase more gradually than the other shear profiles, but consistently causes an eyewall replacement cycle to occur shortly after the onset of RW. In a second set of simulations, increasing the size of the vortex exposed to moderate shear reduces the amount and duration of weakening. The larger vortices resist moderate shear more effectively than the smaller vortices by maintaining more coherent eyewall convection, driven by higher surface enthalpy fluxes in the inner core.

Abstract. Permafrost thaw affects the global carbon cycle and can significantly alter landscape morphology and associated processes of mass and energy transfer. An understudied aspect of thaw-affected permafrost landscapes is ubiquitous rivers connecting thermokarst lakes. These features of Arctic landscapes exhibit particularly high variability in water and energy transfer because, in lakes with larger water storage, excess water leads to comparatively small changes in water level and discharge, whereas in streams the channeled flow produces much larger fluctuations. Consequently, an equivalent absolute change in water level represents a much smaller relative change in lakes than in streams, resulting in a comparatively minor impact on overall heat content and energy transport. Such rivers thus provide an excellent field laboratory for analyzing how expected changes in meteorological forcing under climate change affect permafrost dynamics and carbon exchange within the land- and limnoscape. This paper presents a database from 2012 through 2022 for one such small stream connecting two thermokarst lakes. We instrumented two main stream cross sections with multiple subsurface thermistor chains to record temperature evolution from the land or water-land interface (≥ 5 cm depth) to soil depths of up to 5 m. The cross sections covered different topography and vegetation cover. One was located near the upper, and one in between the two thermokarst lakes. The main focus was set on the cross section midway between the two lakes (CS 9) due to the absence of a thermal imprint from the lake. Air, water, and ground temperatures, as well as physio-chemical river water and soil properties of the surrounding environment were measured continuously or during annual field campaigns and are provided as time-series or single tests. The data are organized in three main categories: atmosphere, water and ground, and are complemented by a GIS database including a digital surface model and an ortho-mosaic photo of the entire river valley to facilitate the search for measurements of interest. The database comes with a complete set of scripts to process any of the data, which are provided in CSV or other easily accessible standard file formats. Ultimately, the data can be used to develop models and validate numerical codes for improving the representation of permafrost processes in land surface and climate models where climate change induces significant changes in heat and mass transfer. All data and processing scripts are available through an online repository (https://doi.org/10.5281/zenodo.14619854, Pohl et al., 2025).

Abstract. Methane (CH4) is the second most important greenhouse gas, and accurate quantification of emissions is critical to improve the efficacy of climate mitigation policies. In this study, we thoroughly evaluated the performance of a commercially available in situ CH4 sensor (LGD-compact A CH4; Axetris AG, Kaegiswil, Switzerland) for quantifying anthropogenic CH4 emissions when deployed on an uncrewed aircraft system (UAS). Sensor stability was assessed through laboratory tests under controlled and varying temperature conditions. Under stable conditions, the sensor achieved a precision of 63 ppb at 2 Hz. Furthermore, the tests revealed the necessity of temperature control, and a water vapour correction was derived and applied to ensure accurate measurements. Additionally, the sensor was used to quantify whole-farm CH4 emissions, by spatially interpolating the measured mole fractions using a Gaussian weighting scheme. This yielded a mean mass emission rate of 4.1 ± 1.6 gCH4 s−1 averaged over four flights, which was comparable to the value of 4.2 ± 1.1 gCH4 s−1 obtained from simultaneous flights with the established AirCore technique. Finally, an uncertainty analysis based on the Ornstein-Uhlenbeck method was used to determine the influence of various sources of uncertainty. This analysis revealed that both wind-related uncertainties and background determination can significantly increase the overall uncertainty when not properly constrained. Furthermore, instrumental errors play a dominant role for smaller mass emission rates, while meteorological uncertainties remain significant even with an increased number of flights per day. Nevertheless, careful flight planning, e.g., ensuring extensive sampling outside of the plume and comprehensive wind monitoring, can reduce these uncertainties. Overall, our results demonstrate that a cost-effective sensor integrated onto a UAS can provide reliable CH4 mass emission rate estimates with uncertainties comparable to those of higher-cost UAS-based systems.

Abstract. Deep convective clouds substantially modify the balance of shortwave and longwave radiative energy at the top of the atmosphere. Although in the present-day these effects approximately balance out, projected changes in deep convective clouds could alter the future top-of-atmosphere energy balance. Past studies have found relationships between convection and anvil clouds, but our understanding of how convection typically controls the properties and evolution of anvil clouds that determine anvil radiative effects remains incomplete, limiting our ability to explain or justify projected changes in cloud optical properties. This manuscript presents a new method to track the lifecycle of deep convective clouds and their convective cores in three-dimensional space in km-scale global climate models. An analysis of how convective organisation, intensity and area relate to anvil properties in the ICOsahedral Non-hydrostatic (ICON) model is then presented. Approximately 1000 deep convective clouds are tracked over one simulation week in the tropical Amazon region. We find that while both updraft intensity and area correspond to larger anvils, the correlation between convective area and anvil size is stronger than that between anvil size and updraft intensity. Updraft intensity was associated with a 4-fold increase in anvil extent when convective cores were larger, compared to when they were in the bottom 50th size percentile. This result could not be explained by associated changes in peak convective mass flux or organisation. These results indicate how changes in the frequency or typical size of convective updrafts may link to changes in anvil development, extent and, ultimately, radiative effects.

Abstract. Accurate routine monitoring of the Total Ozone Column (TOC) is essential for understanding ozone temporal variability, assessing long-term trends and supporting satellite validation. In this work, we present TOC retrievals in both ultraviolet (UV, Huggins bands) and visible (VIS, Chappuis bands) spectral regions using direct sun Differential Optical Absorption Spectroscopy (DS-DOAS). We use the research-grade UV-VIS DOAS system Delta, recently designed and operated at the Laboratory of Atmospheric Physics in Thessaloniki, Greece. A dedicated retrieval algorithm was developed that includes the calibration of a measured reference spectrum using the Langley plot and the Minimum-Amount Langley-Extrapolation approaches. The analysis suggests that TOCs derived from the visible channel for the first time in Thessaloniki are highly consistent with those from the ultraviolet channel, with a median difference of −0.44 % and Pearson's correlation coefficient R= 0.97. The Delta TOC retrievals were compared with two collocated instruments, Brewer and Pandora, yielding very good agreement in both spectral regions (R>0.98), with median biases of −0.18 % and −0.63 % for the VIS and 0.08 % and −0.32 % for the UV channel compared to the Brewer and Pandora, respectively. The seasonal and diurnal variabilities of TOC were captured consistently from all three instruments, confirming the robustness of the retrievals. Enhanced aerosol loads, such as those observed during an extreme wildfire event, introduced significant deviations in the VIS TOC comparisons with the reference instruments, while the comparisons in the UV remained largely unaffected. The findings of this study confirm the capability of Delta to provide accurate and consistent TOC retrievals in both UV and VIS spectral bands. The successful exploitation of the Chappuis bands extends the applicability of DS-DOAS to conditions where UV sensitivity is limited, such as at high solar zenith angles or at high-latitude locations, thereby extending the continuity of ozone monitoring from ground-based systems and providing a complementary approach to traditional UV-based TOC retrievals.

Abstract Heat stress poses a growing threat to human health and socioeconomic activities across Africa, and climate change is expected to intensify this challenge. Stratospheric Aerosol Injection (SAI) has been proposed as a geoengineering strategy to mitigate temperature-related impacts, yet its effectiveness in reducing heat stress risks remains uncertain. This study evaluates the potential impacts of SAI on heat stress risk across Africa by bias-correcting and analysing climate simulations from CMIP6 and the ARISE-SAI experiment. Heat stress was quantified using a heat index that incorporates both temperature and relative humidity, while risk was defined as a function of hazard, exposure, and vulnerability, applying alternative normalization and aggregation methods. We assessed country-level heat stress risk, ranked nations accordingly, and examined shifts in rankings under the SSP2-4.5 scenario with and without SAI intervention. Results show that SSP2-4.5 increases the intensity and frequency of heat stress, amplifying hazards, population exposure, and overall risk. Although SAI reduces temperature and humidity, thereby offsetting some climate-driven increases, it does not fully restore safe heat stress conditions, particularly in coastal and Sudanese regions. While the efficiency of SAI in mitigating heat stress hazards over Africa is more than 85%, it is less than 26% for exposure and less than 38% for risk. Moreover, SAI fails to return about 65% of African countries to their baseline REF positions, thereby shifting the relative importance of vulnerability in determining national risk rankings. Effective management of heat stress in Africa therefore requires holistic frameworks that balance hazard, exposure, and vulnerability. SAI should be complemented by policies that strengthen socioeconomic resilience and adaptive capacity to ensure equitable risk reduction across the continent. Given the exploratory nature of the ARISE-SAI 1.5 simulations within CESM2-WACCM6, our findings highlight plausible scenarios and mechanisms but should be interpreted as qualitative indicators rather than definitive quantitative projections.

Abstract Stratospheric aerosol geoengineering (SAG) is proposed as a potential climate intervention strategy to counteract global warming. Previous studies have shown that year-round or summer/spring implementation of SAG in one hemisphere would shift the Inter-Tropical Convergence Zone (ITCZ) away from that hemisphere and cause a decrease in precipitation over the tropical summer monsoon regions in that hemisphere. In this paper, our objective is to enhance the tropical summer monsoon precipitation by implementing SAG only in the winter hemisphere. Using the Community Atmosphere Model version 5 (CAM5), we compare a simulation where aerosols are prescribed only over the winter hemispheres in the low and mid latitudes to a simulation where aerosols are prescribed uniformly around the globe year-round, both designed to offset a modest global warming of 1.9°C due to increased CO 2 levels. In the case where SAG is implemented only in the winter hemispheres, the summer ITCZ shifts poleward by 1.06° (0.92°) further into the northern (southern) hemisphere relative to the simulation with no SAG. Consequently, we see an increase in tropical summer monsoon precipitation in both hemispheres but a decrease in tropical annual mean precipitation because of larger tropical cooling. However, a larger decrease in evapotranspiration compared to precipitation leads to an increase in the annual mean surface water availability over tropical land when SAG is implemented over low and mid latitudes of winter hemispheres. Further, SAG implemented in the winter hemispheres would nudge the North Atlantic Oscillation (NAO) toward a positive phase and disrupt mid-latitude winter precipitation in the northern hemisphere. Our study highlights the potential trade-offs associated with SAG strategies for enhancing tropical summer monsoon precipitation which is a lifeline for over half the global population.

Abstract Quantifying the probability of rare climate extremes is essential for risk assessment, infrastructure design, and adaptation planning. Generalized extreme value (GEV) distributions provide a widely used statistical framework for estimating the return levels of low-probability, high-impact events, but such estimates are often derived from limited observational or simulated records. Using 13,000 years of simulations from an idealized, and thus computationally inexpensive, atmosphere-only climate model, we show that short time series (<30 years) generally lead to substantial underestimation of annual maximum daily precipitation and temperature return levels (through underestimation of the GEV shape parameter ξ). The minimum length required for unbiased estimation varies by variable and latitude, but generally lies between 30 and 50 years. Beyond this threshold, ensembles of multiple segments can be used to precisely and unbiasedly quantify uncertainty in rare-event statistics. Even when total data volume is large, ensembles composed of shorter segments retain substantial bias. Applying this framework to a 500-year GFDL-ESM4 simulation reveals similar behavior, with the shape parameter as the key predictor of return level uncertainty and bias. After biases have been removed, further reduction of uncertainty requires data records longer than 50 years. By linking theoretical bias behavior to physically interpretable climate features, this work provides practical guidance for designing and analyzing datasets used in climate hazard assessment. Long-duration ensemble members or observational segments are necessary for robust GEV-based estimates of low-probability, high-impact events.

Abstract The present study investigates the vertical structure of wave energy in the tropical Pacific Ocean using the output of a linear, continuously stratified ocean model driven by interannual wind forcing. We have applied an energy flux diagnosis which seamlessly connects the tropical and subtropical zones. During El Niño events, the downward energy flux is extremely strong in the equatorial and off-equatorial region of the eastern Pacific Ocean, which is associated with the vertical propagation of Kelvin and Rossby waves that are the important features for El Niño events. In the western basin, the downward transfer of wave energy penetrates deep into the ocean interior and exhibits a large vertical extent (from around 20 to 1200 m depths) in the Southwestern Tropical Pacific Ocean (SWTP, 15°S–5°S, 150°E–150°W). The authors have demonstrated that none of the local wind input, wave reflection or wave diffraction near the eastern boundary in the Southern Hemisphere contributes to the peak of downward energy flux in the SWTP. The significant downward energy flux in the SWTP originates from the wind input in the central equatorial Pacific Ocean (CEP, 5°S–5°N, 180°W–130°W). This energy input generates strong southwestward energy flux to the SWTP in austral summer during El Niño. The horizontally converged wave energy in the SWTP then transfers downward to the deeper layer below 1200 m depth forced by the local wind. The local wind forcing is closely linked to the equatorward migration of the South Pacific Convergence Zone during El Niño events, which causes Ekman suction with negative wind stress curl anomaly in the SWTP.

Abstract Anomalous heat fluxes across the air-sea interface are a dominant driver of upper ocean temperature variability in the Bering Sea. Atmospheric variability, specifically near-surface temperature, humidity, and wind, drive most of the ocean temperature response through modulation of the surface turbulent heat fluxes ( Q TH ). However, it remains unclear what atmospheric phenomena are responsible for the Q TH variability that regulates Bering Sea temperature. The objective of this work is to identify the timescales of air-sea coupling in the Bering Sea, which will improve understanding of the role of the atmosphere in regional ocean temperature trends and extremes. Using ERA5 fields, we show that anomalous surface sensible (Q SH ) and latent (Q LH ) heat fluxes over the Bering Sea are strongly coupled to anomalous large-scale meridional advection of heat and moisture by the atmosphere, and that the canonical view that storms drive regional surface turbulent flux variability is incomplete. Through a new application of bispectral analysis, we show evidence of cross-timescale coupling in Q SH and Q LH and their associated atmospheric circulation. Elevated bicoherence between the low-frequency annual cycle and a broad band of higher-frequencies indicates a pathway of interaction in which phenomena that vary on a specific timescale can manifest as variability occurring on a different timescale. The results of this analysis limit the candidate mechanisms that drive Bering Sea thermal variability through their modulation of surface turbulent heat exchange to those with a strong intraseasonal component and/or cross-frequency interactions.

Abstract Accurate streamflow estimation in urban catchments remains challenging under accelerating hydroclimatic variability and urbanisation. Conventional models such as Runoff-Routing-Burroughs (RORB), widely adopted in Australian practice, often struggle to represent the non-linear rainfall-runoff dynamics, governing urban flood responses. This study develops and evaluates a Deep-Learning-Neural-Network (DLNN) framework for streamflow estimation in the highly urbanised Gardiners Creek catchment, situated in southeastern Melbourne, using a dual-simulation-approach, integrating event-based and continuous-modelling. Historical rainfall and streamflow data (1989–2021) were utilised for calibration and validation against the RORB model, while future rainfall inputs were derived from dynamically downscaled ACCESSS1-0-CCAM projections under RCP4.5 and RCP8.5 scenarios from the Victorian-Climate-Projections dataset. Results demonstrate the DLNN’s improved overall predictive accuracy and adaptability (R²: 79.2–95.3%, NSE/KGE: 0.83–0.95, VE < 10%) relative to RORB (R²: 72.3–91.8%, NSE/KGE: 0.68–0.89, VE: −17.13% to − 13.20%). The DLNN-model effectively captured short-term flood peaks and long-term runoff variability, maintaining stability across diverse hydroclimatic regimes. Future projections indicate increased short-duration peak flows under RCP8.5, highlighting heightened flash-flood risks and limitations of current design frameworks. These findings establish DLNN as a robust, parsimonious, and climate-adaptive alternative for supporting flood prediction and resilient water resource management.

Study region Miyun Reservoir, Beijing, China. Study focus High-frequency monitoring of reservoirs is essential for managing extreme hydrological events but is often constrained by cloud contamination and satellite temporal gaps. This study integrates multi-source data from Sentinel-1, Sentinel-2, and the Surface Water and Ocean Topography (SWOT) satellite mission to monitor the Miyun Reservoir during the extreme "7.25" flood event of July 2025. To mitigate visibility limitations, we propose a Virtual Station (VS) strategy that establishes localized hypsometry relationships within morphologically stable inundation zones. This approach aims to reconstruct high-frequency hydrological dynamics by leveraging localized clear-sky windows rather than requiring whole-reservoir visibility. New hydrological insights for the region The VS strategy yielded a > 30% increase in valid temporal sampling of optical imagery relative to whole-reservoir approaches. Quantitative evaluation showed that the quadratic AVS-H model achieved superior accuracy (R² = 0.90, RMSE = 0.45 m), effectively filling a critical 21-day gap in SWOT overpasses. During the extreme rainfall event, the framework successfully reconstructed the hydrological trajectory, capturing a water level surge from 151.25 m to a peak of 155.14 m (a net rise of 3.89 m) and a cumulative storage increment of 5.84 × 108 m³ . Validated against official gauge records, these findings demonstrate that the proposed method robustly replicates rapid hydrological pulses, offering a scalable solution for continuous flood risk monitoring in cloud-prone and ungauged reservoirs.

Abstract. Wet deposition by snowfall refers to the scavenging of atmospheric dust by snow particles. Existing models only consider vertical scavenging under still-air conditions, neglecting turbulence-induced complex vertical and horizontal motions of snowfall particles in the actual atmosphere boundary layer, which leads to inaccurate estimation of wet deposition flux. Currently, precise quantitative analysis of dust collection mechanism during snow particle setting remains lacking under turbulence. We employ the Euler-Lagrange numerical method, simplifying snow particles as spherical particles, to simulate and analyze snow particles dynamic characteristics and dust collection in turbulent boundary layer. The study introduces a dimensionless parameter αd=Vt/κu*= 0.2 (Vt is the terminal settling velocity of snow particles, and κ= 0.4 is the von Kármán constant) to characterize the dynamic behavior of snow particles in turbulence. This parameter reflects the relative strength between gravitational setting and turbulent diffusion. Results show that when αd> 0.2, the vertical relative motion dominates (with stronger dominances as αd increases); when αd< 0.2, horizontal relative motion becomes dominant. This shift in dynamic characteristics significantly enhances total dust collection capacity of snow particles and causes the dominant collection mechanism from vertical to horizontal: for αd≥ 1, vertical collection for over 75 % of the total; when horizontal dominance, horizontal collection contributes over 50 %. The study demonstrates that neglecting horizontal collection underestimates wet deposition flux. Thus, we establish a quantitative wet deposition model, providing a theoretical basis for predicting atmospheric dust wet deposition and artificial dust removal.

Abstract. The unusual weather patterns and large anthropogenic emissions over the Indo-Gangetic Plain (IGP) make it a significant hotspot of greenhouse gases like carbon dioxide (CO2). Given the significance of the IGP and highly populated Delhi National Capital Region (Delhi-NCR), a GHG observatory was established at a suburban monitoring station in Sonipat, Haryana (28.95° N, 77.10° E; 228 m a.s.l.), about 45 km north of the Delhi state boundary. Using a laser-based cavity ring-down spectroscopy (CRDS) technique, we measured CO2 mole fraction from February 2023 to January 2025. An annual average CO2 mole fraction of 440.8 ± 19.7 parts per million (ppm) was recorded in 2024, which includes a strong seasonal variability, ranging from 422.6 ± 23.3 ppm during the monsoon (June–September) to 456.4 ± 30.8 ppm in post-monsoon (October–November). A strong CO2 diurnal amplitude of 29 ppm in May and 63 ppm in October was observed mainly due to seasonal changes in boundary layer mixing (faster in May than October) and biospheric activity (weaker in May than October). Further investigation of the drivers of strong seasonal and diurnal CO2 variability over IGP revealed a strong contrast to other global monitoring stations in the same latitude band. A strong correlation between CO2 and methane (CH4) indicated a co-located emission source, while the strong positive correlation between CO2 and carbon monoxide (CO) during post-monsoon emerges due to emissions from biomass burning. We demonstrated that the high temporal CO2 variability in the IGP region is driven by the complex interplay of local anthropogenic and biomass burning emissions, biospheric fluxes, and prevailing meteorology.

Accurate precipitation nowcasting is critical for many aspects of human life. A recurrent neural network (RNN) has demonstrated strong and relatively mature performance in machine learning approaches for precipitation nowcasting. However, their inherent recursive prediction structure leads to error accumulation, causing progressively blurred outputs and limiting practical applicability. To address this issue, we propose CSPP-RNN (Coupling-Similar-Precipitation-Processes RNN), a net that couples similar precipitation processes with a sequence-to-sequence RNN. For each prediction timestep, similar precipitation processes are retrieved, and their segments are then input into the encoder to obtain the corresponding hidden states. These hidden states replace the ones influenced by earlier predicted results in the recursive structure. Based on radar data from Beijing Daxing station, the comparison experiments of CSPP-RNN and ConvLSTM indicate that: (1) Over the 36–60 min lead time across the 0.1, 5.0, and 20.0 mm/h thresholds, the POD and CSI improved by 0.0334, 0.0170 on average, respectively, whereas the FAR degraded by 0.0586; (2) error accumulation was mitigated, retaining richer fine-scale structures in the predicted images; (3) the extra computational cost of coupling was controlled within an acceptable range. In conclusion, CSPP-RNN mitigates the error accumulation problem in RNN by coupling similar precipitation processes as part of the modification of the recursive prediction structure. This provides a potential new direction for optimizing the application of RNN in precipitation nowcasting.

Remote alpine lakes on the Xizang Plateau are important archives for tracing the long-range atmospheric transport (LRAT) of persistent organic pollutants, yet historical records of polybrominated diphenyl ethers (PBDEs) from this region remain scarce. The main objective of this study was to reconstruct the historical record of PBDEs in Yamzho Yumco sediments and to evaluate whether this record reflects source evolution, atmospheric transport, deposition, and post-emission environmental fractionation in a remote alpine receptor system. To achieve this objective, 17 PBDE congeners were determined in a 210Pb- and 137Cs-dated sediment core spanning 1930–2023. Σ17PBDE concentrations ranged from 5.80 to 263.13 pg/g dw, and depositional fluxes ranged from 2.67 to 121.04 pg/cm2/yr, both showing a marked increase after the 1970s and remaining elevated after 2000. Lower-brominated congeners, especially BDE-47, dominated the core, whereas nona- and deca-BDEs appeared mainly in recent sediments, indicating progressive source evolution in recent decades. Tri- to penta-BDEs remained the dominant homologue fraction throughout the record, while elevated post-2000 BDE-47/BDE-99 ratios point to congener-selective environmental fractionation during atmospheric transport and deposition. Together, these results suggest that Yamzho Yumco sediments preserve not only the history of regional PBDE input, but also the coupled imprint of source evolution, transport-related fractionation, and delayed environmental response in a remote high-altitude receptor system. This study highlights the value of Xizang Plateau Lake sediments for process-based interpretation of POP fate in mountain environments.

Abstract High‐resolution, repeat‐pass Sea Surface Height Anomaly (SSHA) observations from the Surface Water and Ocean Topography (SWOT) satellite are used to investigate Internal Solitary Waves (ISW) in the Andaman Sea over a one‐year period starting in July 2023. SWOT captured surface signatures of high‐amplitude ISW, with SSHA exceeding 20 cm. ISW amplitudes are modulated by spring tides and aided by weak stratification. Notably, this study uses extended Miles theory to quantitatively analyze oblique ISW interactions captured by SWOT. Two test cases demonstrate O‐type interaction with amplification factor in excellent agreement with theoretical estimates. An extreme ISW amplitude of 92.9 m, corresponding to an SSHA of 43 cm, was observed when spring tide, weak stratification, and oblique interaction co‐occurred. Computed phase speed (1.25–2.72 ms −1 ) is consistent with previous estimates and exhibiting decrease as the ISW traveled from the deeper Andaman Sea toward the coast of Thailand.

Under the continuous impact of global warming, the water cycle has undergone significant changes, causing a series of problems such as water shortage, frequent climate disasters and ecological environment deterioration. Therefore, understanding the evolution of regional historical and future drought and wet conditions is crucial for adapting and mitigating disasters. This paper discusses the evolution of drought and pluvial events in the Henan section of the Yellow River from 1970 to 2014, projects the future evolution of drought and wet conditions, and assesses the performance of various climate models from Coupled Model Intercomparison Project Phase 6 in simulating precipitation and temperature. Subsequently, future drought and wet conditions in the Henan section were projected for the 2015–2100 period across four SSP-RCP scenarios using Standardized Precipitation and Evapotranspiration Index (SPEI) and run theory. The results indicate that the Henan section of the Yellow River exhibited a significant drying trend during the historical period, with a rate of 0.15 per decade. Looking ahead, a wetting tendency is projected under the SSP1-2.6 scenario, with an increasing rate of 0.02 per decade, whereas the other three scenarios consistently show drying trends, with rates of −0.11, −0.15, and −0.23 per decade, respectively. Across all scenarios, drought and wetness variations exhibit pronounced periodicity, particularly at timescales of approximately 20–30 years, suggesting the persistence of multi-decadal hydroclimatic oscillations. Furthermore, drought and wetness events are projected to become more persistent and severe during the mid-to-late 21st century. Compared with the historical baseline, increasing radiative forcing is associated with an expansion in drought-affected areas, accompanied by reduced event frequency but longer duration and greater severity. In terms of risk, the SSP3-7.0 scenario presents the highest overall drought and wetness risk with the widest spatial extent, whereas the SSP2-4.5 scenario shows relatively lower risk levels and a more balanced spatial distribution.

Abstract. Aviation affects the Earth's energy balance through CO2 and non-CO2 emissions. Contrails mark one of the latter and can occur inside the cirrus clouds where they might affect the clouds' optical and microphysical characteristics as well as their climate impact. In this study, airborne lidar observations with the German research aircraft HALO during the ML-CIRRUS and CIRRUS-HL campaigns are used together with aircraft-location data to detect the occurrence of contrails that have formed within already existing cirrus clouds. Based on manual analysis, we developed (based on ML-CIRRUS) and verified (based on CIRRUS-HL) an automated two-step method for detecting embedded contrails in lidar measurements. In the first, threshold-based step, potential embedded contrail regions are identified by particle backscatter coefficients larger than 4 Mm−1 sr−1 and particle linear depolarization ratios smaller than 30 % or 43 % depending on the impact of pollution on the background cloud. The second step assesses the area of the identified objects in a lidar curtain for finding cases that could realistically be associated with an aircraft-related perturbation. Specifically, areas smaller than 10 pixels are dismissed as noisy data, while areas larger than 50 pixels are too homogeneous to be in line with the assumptions of the manual analysis that cloud regions that are perturbed by the passage of an aircraft occur in close vicinity to unperturbed cloud areas. The resulting contrail mask enables the detection and quantification of the occurrence rate of embedded contrails in airborne lidar measurements without the need for auxiliary air-traffic information.

Abstract. Line-shaped ice clouds known as contrails are produced by aircraft and play a notable role in aviation’s contribution to climate change. One promising and cost-effective approach to mitigating this impact is the operational avoidance of contrail formation. To enable the design and evaluation of such mitigation strategies, reliable automated detection of contrails using spaceborne geostationary sensors is essential. In this work, we present a contrail detection algorithm named COCOS (Contrail Confidence Score) for the Meteosat Second Generation (MSG) satellite. Contrail detection with MSG is challenging due to its moderate spatial resolution of 3 km at nadir. COCOS uses a combination of image processing techniques to identify line-shaped contrails. An adaptive thresholding technique as well as a new object separation method and advanced false alarm reduction procedures are implemented. Furthermore, instead of returning just a binary contrail mask as a result, COCOS returns a confidence score to indicate the degree of certainty of each contrail identification. COCOS is evaluated based on a human-labeled dataset. It comprises 140 images of 256 × 256 pixels from 2013–2024, about 60 % of which contain contrails according to human labelers, covering the entire MSG disk with a higher concentration over Europe and the North Atlantic flight corridor. COCOS outperforms the other known contrail detection algorithms in the literature for MSG. At similar recalls (the fraction of true positives correctly identified) it achieves precisions (the fraction of positive predictions that are correct) more than three times higher (0.65 for recall 0.25 and 0.3 for recall 0.5) than other MSG-based contrail detection algorithms, providing a significant improvement in contrail detection for MSG.

Vehicle emission inventories are highly sensitive to vehicle activity data, yet annual vehicle kilometers traveled (VKT) is still commonly represented using generalized default values whose representativeness at the city scale remains uncertain. In this study, large-scale vehicle inspection data from Haikou, China, were used to derive inspection-based VKT estimates and to quantify how activity assumptions affect urban vehicle emission inventories and policy evaluation. By holding vehicle population and emission factors constant across scenarios, we explicitly isolated the effect of activity representation on emission estimates. An inspection-based, age-sensitive VKT framework was further developed to capture within-fleet heterogeneity. The results showed that inspection-derived VKT accounted for only 36–75% of guideline-recommended values across major vehicle categories, with the largest discrepancies observed for diesel freight vehicles. As a result, the use of guideline-based VKT produced higher emission estimates by 34–39% for carbon monoxide (CO) and volatile organic compounds (VOCs) and by approximately 66–67% for nitrogen oxides (NOx) and particulate matter (PM). The influence of activity representation was also evident in policy assessment. In a case study of old diesel vehicle retirement, guideline-based VKT produced estimated emission reduction benefits that were more than 120% higher for most pollutants and nearly 200% higher for NOx than those derived from inspection-based VKT. These findings demonstrate that generalized activity assumptions can substantially affect both emission inventory estimates and policy-oriented assessments. Rather than merely refining a local mileage parameter, this study highlights a potential representativeness limitation of generalized activity assumptions when they are applied to city-specific emission inventories, particularly in medium-sized or geographically constrained urban systems. The inspection-based, age-sensitive approach proposed here provides a practical pathway for improving activity representation in data-rich urban environments, while its transferability should be evaluated according to local fleet structure and transport conditions.

Abstract. Atmospheric low molecular weight amines play important roles in aerosol physiochemical properties and climate. However, the compositions, sources, and secondary formation mechanisms of amines in offshore aerosols remain unclear. Here, an integrated observation of methylamine (MA), ethylamine (EA), dimethylamine (DMA), iso-propanamine (IPA), propanamine (PA), “trimethylamine + diethylamine” (TMDEA), and over 100 other chemical components was conducted in total suspended particles samples collected during a spring 2018 research cruise across the Yellow Sea and Bohai Sea, China. Concentrations of total amines exhibited a north-to-south gradient from the Bohai Sea to the South Yellow Sea, corresponding to the decreasing influence of terrestrial air masses. Source analyses of amines were performed using specific organic molecular tracers representing primary biogenic sources, higher plant waxes, marine/microbial sources, biogenic secondary organic aerosols, biomass burning, and fossil fuel combustion, and two major secondary formation pathways were inferred. MA, EA, and DMA were largely influenced by terrestrial biogenic and anthropogenic sources, with the majority (74.0 %, 52.6 %, and 65.7 %) formed via nitrate-associated secondary formation pathways. PA was mainly derived from combustion-related sources along with terrestrial and marine biogenic contributions. In contrast, the predominant TMDEA was mostly generated via sulfate-associated secondary formation pathways (61.8 %) and contributed by marine emissions, resulting in spatial pattern distinct from other major amines and the north-to-south increasing relative contributions of amines in aerosols. These results highlight the impact of terrestrial emissions on offshore aerosol chemistry and the importance of origins and multiphase chemistry of amines under varying ambient conditions.

Abstract Cities drive most global energy use and emissions of greenhouse gases and other pollutants, yet how these quantities scale with urban population remains debated. Scaling analysis captures this relationship through the exponent β, which defines whether a quantity increases sublinearly (β < 1), linearly (β = 1), or superlinearly (β > 1) with population. Here we conduct a meta-analysis of 27 scaling studies encompassing 362 scaling exponents across cities worldwide. Results show that energy consumption scales near-linearly to superlinearly: electricity, gas, and transportation fuel have median scaling exponents of 1.06 [25th percentile: 0.94, 75th percentile: 1.13], 1.18 [1.05, 1.34], and 1.22 [1.19, 1.27], respectively. These results suggest that gas use and congestion-related transportation fuel tend to intensify faster than population growth. In contrast, most pollutant emissions (PM2.5, PM10, NO2, SO2, and CO) scale sublinearly. CO2 emissions are sublinear globally and in China, with median scaling exponent of 0.86 [0.80, 1.03] and 0.63 [0.59, 0.99], respectively, although a few countries show superlinear scaling (e.g., U.S., Australia, and Canada), pointing to regional differences in technology and policy. Energy consumption and its associated emissions thus scale in opposite directions, a pattern we attribute to the spatial decoupling of production and consumption. Our analysis reveals that inconsistent city definitions, fragmented energy and emissions data, and limited analyses of temporal evolution introduce variability in reported scaling exponents and constrain the generalization of these findings. Standardized, long-term datasets and mechanistic models linking urban metabolism to emissions are needed to resolve these uncertainties and guide sustainable, low-carbon urban transitions.