Earth and Environmental Sciences

Abstract. Accurate diagnosis of ozone (O3) formation sensitivity (OFS) is essential for designing effective precursor-control strategies, yet long-term, observation-based, and interpretable national-scale assessments remain limited. Here, we combined OMI satellite observations of tropospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) with ground-based O3 measurements to derive region-specific FNR (HCHO / NO2) threshold ranges from the relationship between FNR and O3 exceedance probability. Based on these thresholds, we characterized the spatiotemporal evolution of OFS across China during the warm season (April–September) from 2005 to 2023 and coupled Random Forest (RF) with SHapley Additive exPlanations (SHAP) to quantify contributions from emission-related and meteorological factors. The results reveal a clear phase reversal in OFS over China. From 2005 to 2012, the national area fraction of nitrogen oxides (NOx)-limited regimes decreased by 7.9 %, while transitional and volatile organic compound (VOC)-limited regimes increased by 5.6 % and 2.3 %, respectively. After 2013, this pattern reversed, and by 2023 NOx-limited regimes expanded to 76.5 % of the polluted area, whereas VOC-limited regimes accounted for only 2.8 %. Regionally, after 2013, both Beijing–Tianjin–Hebei (BTH) and Fenwei Plain (FWP) exhibited transitions between transitional and NOx-limited regimes, while the Yangtze River Delta (YRD) showed a shift toward NOx-limited regimes. Sichuan Basin (SCB) remained predominantly transitional, and Pearl River Delta (PRD) also shifted toward NOx-limited regimes. SHAP analysis shows emission-related variables contributed 50.1 %–69.4 % of total importance, exceeding meteorological factors, which increased after 2013. Overall, OFS evolution is primarily associated with emission changes but increasingly modulated by meteorological conditions under cleaner conditions.

Abstract. Carbonyl oxide (CH2OO) is paramount in atmospheric oxidation chemistry, yet quantitative kinetics data for its bimolecular reactions are very limited and even unknown. Here we establish a computational framework to obtain quantitative kinetics from small to large reaction systems. For CH2OO + HCHO, we develop electronic structure methods to reach CCSDTQ/CBS accuracy for its activation enthalpies at 0 K. For CH2OO + aldehydes (RCHO; R= CH3–C5H11, CH2F, CHF2, CF3), we introduce two strategies that recover CCSDTQ/CBS-quality activation enthalpies at 0 K. A dual-level strategy has been used to calculate their kinetics. The calculated rate constants show excellent agreement with available experimental data for CH2OO + RCHO (R = CH3–C3H7), which validates the designed computational framework. We find that fluorination leads to exceptional rate enhancement, with reactions of CHF2CHO and CF3CHO exceeding 10−10 cm3 molecule−1 s−1 over 200–320 K, approaching the collision limit. We also find that fluorination-driven reactivity enhancement originates predominantly from lower-level electronic effects than that of post-CCSD(T). Incorporation of the kinetics into a global chemical transport model uncovers previously unrecognized atmospheric impacts, with CH2OO + HCHO reducing nighttime CH2OO and gas-phase sulfate concentrations by 25.3 % in Antarctica and 12.2 % over Canada, respectively. The present findings address a long-term challenge in how to obtain quantitative kinetics for large molecular systems, where post-CCSD(T) calculations are prohibitive and provide new insights into the chemical transformation of CH2OO and fluorinated aldehydes in the atmosphere.

Abstract. Tropospheric ozone pollution in South Asia is mainly blamed on anthropogenic emissions. However, based on ERA5 reanalysis data, this study highlights the contribution of stratospheric ozone intrusions into the Upper Troposphere and Lower Stratosphere (UTLS) associated with Sudden Stratospheric Warming (SSW) events in enhancing upper tropospheric ozone over the South Asian region. We report an enhancement in ozone in the UTLS by more than 80 % for 2018 and ∼ 30 % within ±6 d of the onset during SSW events concurrent with the westerly phase of Quasi-biennial oscillation (WQBO-SSW) compared to non-SSW years. The equatorward shift (south of 30° N) of the subtropical jet during WQBO-SSW causes lowering of the tropopause and more Rossby-wave breaking in the upper troposphere. This results in higher stratospheric ozone intrusions over the South Asian region. The ozone enhancement during WQBO-SSW events produces an instantaneous radiative forcing at the top of the atmosphere of 0.09±0.05 W m−2 due to UTLS ozone changes and 0.17±0.05 W m−2 from total-column ozone changes over South Asia.

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. 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 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.

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.

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.

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.

Abstract Land use and land cover change (LULCC) affects two climate-relevant landscape dimensions: land cover composition and its spatial configuration. While the climate impacts of changes in land cover composition are well documented, the role of changes in land cover configuration remains poorly understood. Here, we examine how configuration changes in cropland-forest landscapes affect the ecosystem´s climate-regulating functions by conducting a multi-scenario experiment. We use the Terrestrial System Modeling Platform (TSMP), a modeling framework to run coupled high-resolution regional climate models (RCMs) with a focus on terrestrial hydrology. We employ stylized landscapes as input for TSMP and combine machine learning with SHAP (SHapley Additive exPlanations) values, an explanatory AI technique, to disentangle direct and mediating contributions. Our results indicate that changes in land cover configuration influence topsoil and 2-meter air temperatures when composition is kept constant. Greater forest fragmentation is associated with overall warming, particularly over cropland during the growing season. This can mainly be attributed to reduced wind speed, lower latent heat flux, and higher sensible heat flux. These findings support the hypothesis that increasing forest fragmentation reduces the landscape’s capacity to mitigate local climate. However, we also discuss potential biases towards warming effects in contemporary RCMs. Our study demonstrates the potential of RCMs to address key land system science questions and promote interdisciplinary research at the intersection of climate change and landscape management. The type of experiments conducted may help to leverage both modeling and observational studies to advance our knowledge about the climate-relevance of landscape characteristics and arrive at scalable adaptation strategies.

Abstract Reforestation is a widely considered nature-based solution for climate change mitigation because of the large carbon sequestration capacity of forests. Yet, how the climate effects of reforestation depend on different future climate scenarios is not well understood. In this study, we use an Earth system model forced with contrasting land-use and a range of emissions scenarios to investigate the scenario dependence of biogeochemical and biogeophysical effects of reforestation. We find that the combined biogeochemical and biogeophysical effect of reforestation is a global cooling which decreases in high emission scenarios. This scenario dependence of total effects of reforestation is driven by the scenario dependence of biogeochemical effects. The biogeochemical effects on global mean surface air temperature increase from low to intermediate emission scenarios and decrease in high emission scenarios because of the non-monotonic nature of the radiative forcing due to reforestation with background emissions in future climate scenarios. The biogeophysical effects of reforestation cause a warming effect that counteracts the biogeochemical cooling effect globally. Further, the biogeophysical warming effects show weaker scenario dependence than biogeochemical cooling effects. We also show that estimating biogeochemical cooling effects of reforestation using model TCRE and diagnosed land carbon uptake leads to overestimation in high emission scenarios. Our results highlight the importance of accounting for the scenario dependence of effects of reforestation when developing climate mitigation strategies.

Abstract Tropical Cyclones (TCs) are intense storms that pose a persistent and considerable risk to coastal communities and infrastructure in the global tropics and subtropics, including the United States (US). With known limitations associated with observations and high-resolution earth system models, synthetic TC models that capture a wide spectrum of storm possibilities have been developed to robustly quantify TC risk. Here we examine the simulation of various TC features in the North Atlantic relevant for US coastal risk in three synthetic TC models forced with ERA5 reanalysis: MIT, CHAZ and RAFT. While there is a broad agreement among these models in terms of their representation of salient TC characteristics, certain differences do exist. To connect these modeling uncertainties with energy infrastructure resilience, we apply fragility curves that link simulated TC intensities to damage probabilities, demonstrating how uncertainty in storm states may translate into that in coastal impacts. Our study indicates that acknowledging and accounting for inter-model uncertainty leads to more reliable risk assessments, strengthening science-to-action pathways for managing risks associated with TCs.

• Evaluation of 18 bias correction methods across climatic, hydrological, and radiative variables. • Comparison of statistical, distribution-based, and machine learning approaches under diverse climatic conditions. • Multi-scale evaluation using complementary performance metrics and consistency with extremes and temporal cycles. • Analysis of calibration length, temporal sequencing, and iterative corrections. • Practical recommendations for context-specific bias correction applications.