Brown carbon (BrC), an efficiently light-absorbing carbonaceous aerosol, exerts significant impacts on the global energy budget and regional climate, attracting growing scientific attention. To advance understanding of the spatial variability of atmospheric BrC and its dominant formation pathways in the North China Plain (NCP), light absorption properties, chemical composition and formation process of the water-soluble BrC in 2023 winter were investigated by conducting simultaneous measurements at five sites across the NCP, namely, Beijing, Tianjin, Luancheng (rural site), Handan and Jinan. Our results showed that the average light absorption coefficient at 365 nm (abs365) in Luancheng was approximately 1.1–3.5 times higher than those in urban ones. However, mass absorption efficiency displayed a distinctly different spatial pattern, with the strongest light-absorptivity (1.40 ± 0.02 m2 g−1) recorded in Jinan. Notably, average abs365∼ 45 % from 2018 to 2023 compared to those previous observations. Furthermore, the light-absorptivity of water-soluble BrC was enhanced from clean to haze period at the most sampling sites along with the increasing N:C3
Land use and cover change (LUCC) can exacerbate cloud-mediated climate warming, yet long-term cloud responses to contrasting LUCC pathways remain poorly quantified. Here, we perform transient simulations based on the fully coupled Community Earth System Model (CESM) under the SSP1-2.6 scenario to quantify how long-term cloud trends respond to large-scale idealized afforestation (AF50, conversion of grassland or shrubland to forest), idealized bioenergy expansion (BE50, sugarcane expansion), and realistic afforestation (REAL). AF50 drives a pronounced vertical restructuring of cloud trends, steepening mid-level cloud decline and accelerating low-level cloud loss from −0.0153 ± 0.0030 % yr−1−0.0175 ± 0.0036 % yr−1, corresponding to 1.14 times the CTL rate globally and 1.52 times over land. Concurrently, it amplifies the global increase in high-level clouds by a factor of 1.5 and accelerates total cloud cover loss over land. Bioenergy expansion induces a similar but weaker vertical shift, whereas realistic afforestation slows the loss rate of low- and mid-level clouds and suppresses regional warming. Interpretable machine learning identifies changes in relative-humidity trends as the highest-ranked predictor of LUCC-induced changes in low-level cloud trends, with relative importance values of 20.4 %, 20.4 %, and 15.3 % under AF50, BE50, and REAL, respectively. The simulated low-level cloud response is consistent with surface darkening reducing albedo, increasing sensible heat flux, deepening the planetary boundary layer, enhancing dry-air entrainment, and raising the lifting condensation level relative to the boundary-layer top, thereby suppressing low-level cloud formation. The cloud response is also modulated by remote atmospheric adjustment. The accelerated depletion of low-level clouds strengthens positive shortwave cloud radiative forcing, while the concurrent increase in high-level clouds enhances longwave warming, together amplifying regional warming hotspots. These results demonstrate that larger-scale forest expansion does not necessarily provide greater climate cooling benefits; the sign and magnitude of the outcome depend not only on the latitude band of land conversion but, more critically, on the type of land conversion.
Substantial efforts and improvements in air quality across Europe lowered levels of air pollutants including particulate matter (PM) in the last decades. However, significant proportion of the European population still lives in areas exceeding WHO recommendations, especially in Northern Italy, Balkans, and Eastern Europe, including Serbia. Targeted PM mitigation strategies require extensive air quality monitoring and modelling including source apportionment (SA) studies. In the past, numerous SA studies were conducted for Belgrade city, capital of Serbia. Nevertheless, comparisons across the results are difficult, as they encompass different datasets of pollutants contained in PM fractions such as elements and/or ions and/or PAHs. Here, the aim is to offer a broader insight on PM101010101010
The amount of sunlight that reaches Earth's surface can be reduced by increasing cloud droplet number and decreasing droplet size (i.e., Twomey forcing), a central idea underpinning the Marine Cloud Brightening strategy. Cloud albedo depends nonlinearly on cloud droplet concentration, meaning the spatial extent of aerosol plumes could be an important constraint on the brightening potential. In this study, horizontal aerosol spreading is simulated using a Langevin particle model driven by a library of realistic, 2 d Large Eddy Simulations (LES) spanning the meteorological and aerosol phase space of northeast Pacific stratocumulus. The 2D reflectance fields from the LES are superimposed onto the 2D perturbed aerosol concentrations to calculate the 2D Twomey forcing response.The Day 1 and Day 2 LES regimes have distinct meteorological, aerosol, and turbulence characteristics associated with equatorward movement of the LES domain. Our results indicate that the Day 2 regime has substantially faster plume spreading than Day 1, with ensemble median differences exceeding 2 km h−1. Despite these differences, Twomey forcing is insensitive to the natural variability in spreading rate. This study suggests that Twomey forcing is resilient to variations in meteorology, aerosols, and turbulence, with its efficacy governed primarily by aerosol lifetime and assumptions surrounding cloud adjustments. Although the natural variation in spreading rate does not materially affect Twomey forcing, idealized plume spreading simulations suggest that current GCM assumptions of an infinitely fast spreading rate could lead to a 10 %–200 % overestimation of cooling.
Inverting point-source emissions from sparse atmospheric observations is difficult because emission rate, release height, wind speed, turbulence, and plume morphology can compensate for one another. We present NeuPlume, a neural-physical framework that formulates inversion as observation-guided generation and selection of complete three-dimensional concentration fields. A Lagrangian stochastic model builds a scenario-specific forward library, a conditional neural field compresses the simulated fields, and a latent diffusion model learns their conditional distribution. During inversion, diffusion posterior sampling generates candidate fields on a coarse-to-fine parameter grid, and NeuPlume returns the parameters and field that best match the observations. Across 30 synthetic cases, the selected fields achieve a mean spatial correlation of 0.942 and a mean emission-rate error of 13.6 %. Matched stress tests show that both observation geometry and transport-model fidelity govern inversion quality: trajectory-concentrated sampling weakens full-field reconstruction, while unrepresented transport alters parameter identification and, for effective plume rise, also degrades field reconstruction. Applied to four UAV methane transects above a coal-mine ventilation shaft, NeuPlume yields known-Ukt yr−1
Accurate spectral transformation across satellite sensors with similar but different spectral response functions (SRFs) is essential for applying the same retrieval algorithms. A novel physics-constrained transfer learning (TL) framework is developed for transferring satellite radiance observations across different sensors while preserving physical consistency. It integrates a core Spectral-Fidelity-Preserving (SFP) model based on extensive radiative transfer simulations, allowing broad adaptability for radiance transformation under diverse satellite observational conditions. Sensitivity experiments demonstrate the robustness of the TL framework relating to radiometric calibration uncertainties, particularly in infrared (IR) channels, and further highlight the critical role of SRF similarity between sensors. We further establish practical SRF-similarity thresholds – an allowable scaling-factor range and central-wavenumber-shift limits that are stricter for IR window channels – that support reliable radiance transfer (see Sect. 3.2 for details). Application to radiance observations from Fengyun-4A/B (FY-4A/B) geostationary (GEO) satellites explicitly indicates that the TL approach improves retrieval accuracy for key geophysical parameters such as cloud amount profile and quantitative precipitation estimation, when compared with results obtained without applying TL. Thus, the TL approach enhances cross-satellite data consistency and provides a practical tool for operational satellite data applications (e.g., transferring algorithms developed for FY-4A to FY-4B without operational interruption). It is worth noting that the FY-4A/FY-4B experiments presented here demonstrate retrieval-model transfer, validated against independent downstream satellite products. Beyond retrieval-model transfer, the framework can also support direct radiometric calibration transfer by propagating an absolute calibration reference at the radiance level between different spaceborne imagers.
The mesoscale organisation of marine stratocumulus clouds into closed and open cells strongly affects cloud albedo and thus their cooling effect on climate, yet the processes governing transitions between these regimes remain incompletely understood. The EarthCARE satellite provides collocated observations of cloud mesoscale structure from the Multi-Spectral Imager (MSI) together with vertically resolved cloud and precipitation measurements from the Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), enabling detailed characterization of stratocumulus cloud microphysics. We apply a convolutional neural network to MSI scenes to identify closed and open cells and relate these classifications to EarthCARE microphysical retrievals from the synergy of ATLID, CPR, and MSI. Open cells exhibit substantially lower droplet number concentrations (Nd), greater variability in liquid water path (LWP) and droplet sizes (re), and more frequent and heavier precipitation, although light drizzle is also common in closed cells. To investigate closed-to-open cell transitions, we combine EarthCARE overpasses with GOES/ABI geostationary imagery and ERA5-driven trajectories to track cloud scenes and determine transition timing. This combined approach allows us to reconstruct the temporal evolution of cloud properties around transitions. We find that LWP and rain amounts increase in closed cells up to 25 h before transitions, followed by decreasing Ndre, while cloud vertical structure remains largely unchanged. These findings support a precipitation-linked transition pathway, potentially triggered by enhanced boundary-layer moisture and amplified by aerosol scavenging–rain feedback. This new observational evidence advances our understanding of stratocumulus breakup with implications for the cooling effect of these clouds.
Aerosol iron (Fe) significantly impacts human health, atmospheric chemistry and marine biogeochemistry. The stable isotope ratio of Fe, typically reported as δ56Fe, has emerged as a promising method for source apportionment of total and soluble aerosol Fe. However, the δ56Feδ56Fe+0.14 ‰ ± 0.10 ‰ for the seven dust samples we examined, in good agreement with previous work. We found that different anthropogenic aerosols exhibit a wide range of Fe isotopic composition. Compared to desert dust, the average δ56Fe+0.26 ‰ ± 0.18 ‰, n=28), slightly lower for steelwork fly ash (−0.07 ‰ ± 0.41 ‰, n=18), and considerably lower for biofuel burning aerosol (−0.28 ‰ ± 0.39 ‰, n=11). In addition, the average δ56Fe+0.20 ‰ ± 0.12 ‰ for municipal incineration fly ash (n=2), +0.38 ‰ ± 0.13 ‰ for heavy oil bottom ash (n=1), and +0.08 ‰ ± 0.13 ‰ for certificated urban particulate matter sample (n=1). We suggest that not all the anthropogenic aerosol Fe is isotopically lighter than natural dust Fe, in contrast to what is conventionally assumed. Our findings also imply that Fe isotope-based source apportionment must account for the δ56Feδ56Fe
Nitrogen oxides (
Downdrafts play an essential role in the feedback between convective clouds and their surrounding environment, and they must be properly accounted for in cumulus parameterizations (CPs). The mechanisms for downdraft formation are often debated in past literature and inconsistently represented in CPs. To address this uncertainty, we investigate the ring of descent surrounding cloudy updrafts known as a subsiding shell, a leading contributor to downdraft mass flux. We analyze two LES of deep convection in the Amazonian dry and wet season, using composite soundings from the Green Ocean Amazon Campaign. The dry and wet season soundings differ in their middle tropospheric relative humidity (RH), which facilitates an assessment of the influence of RH on shell strength. Kinetic energy budgets along trajectories reveal that shells acquire their descent from evaporatively driven negative effective buoyancy along cloud edge and downward oriented dynamic pressure accelerations associated with the toroidal circulations of updraft thermals. Consistent with observations, shell downdrafts were strongest in the dry season simulation. Contrary to hypotheses which attributed this difference to greater evaporative cooling, we find that dry season shell downdrafts associated with deep convection were stronger because of larger dynamic pressure accelerations in the dry season. However, when investigating cumulus congestus clouds, negative effective buoyancy accelerations become increasingly important relative to pressure accelerations. The stronger accelerations in deep convective shells were attributed to stronger dry season updrafts, and consequently more intense toroidal circulations within thermals. Our results provide a foundation of understanding for future improvement of downdraft representation in CPs.
We estimate 2019 Brazilian fire carbon monoxide (CO) emissions using CO retrievals from multiple satellites in a new top-down inversion framework. The retrievals strongly constrain fire emissions and complement existing inventories. National-scale posterior emissions align well with the new Global Fire Emissions Database v5.1, supporting progress in fire emission models. Yet, posterior emissions in the Cerrado and Caatinga are systematically higher than inventories; we discuss potential drivers.
In 2019, Brazil experienced an intensive fire season, despite the absence of major climate anomalies to enhance fire activity. Existing carbon monoxide (CO) emission estimates from state-of-the-art fire emission inventories differ by a factor two for the period and region. We provide a top-down estimate on CO emissions from these fires using the new CarbonTracker Europe – Long-Window/Short-Window (CTE-LW/SW) inverse modelling framework driven by column-averaged dry air CO mole fractions (XCO) from the MOPITT and TROPOMI satellite instruments.Our analysis indicates that the 2019 fires in Brazil released approximately 47 TgCO. Although structural atmospheric-chemistry related uncertainties remain (±15 TgCO), the inversions converged strongly to a common posterior (46–48 TgCO) independent of the prior emission inventory (GFED5.1, GFAS v1.2) or assimilated dataset (TROPOMI, MOPITT).Posterior fire emissions closely resemble the newly released GFED5.1, supporting recent advances in bottom-up fire modelling. Nonetheless, at the biome level, our results reveal a systematic underestimation – by roughly a factor of two – in the Cerrado and Caatinga savannas relative to both fire emission priors. While more targeted uncertainty assessments are required, we speculate that this emission gap unlikely stems from inversion choices alone and may indicate an underestimation of fuel loads or emission factors.Overall, we demonstrate CTE-LW/SW effectively leverages XCO
This paper explores the long-term variability of climatic parameters over the Mediterranean region and their relationship with tropical belt expansion by investigating observational and reanalysis data from January 1980 to December 2022 through multiple approaches, including correlation, linear regression, and singular value decomposition (SVD) analysis. The lapse rate tropopause height exhibits upward trends of approximately 80.58 m per decade. Concurrently, the tropical belt shows a poleward expansion of approximately 0.14 and 0.27° per decade, estimated using the tropopause drop and tropopause gradient methods, respectively. The tropical edge latitudes (TELs) of both applied approaches show an evident seasonal variation. In addition, the two TEL definitions exhibit consistent correlation patterns with surface temperature (Ts), lower tropospheric temperature (Trp-1), upper tropospheric temperature (Trp-2), precipitation, and the Standardized Precipitation Evapotranspiration Index (SPEI). The results of the SVD analysis depict that although the leading mode explains the majority of the covariance between TELs and the examined climatic parameters, the second coupled mode shows more localized spatial structures, with TELs pronouncedly coupling with Ts and Trp-1 in the eastern Mediterranean. Furthermore, Ts and Trp-1 have significant increasing trends, which are abundant in the eastern Mediterranean. On the other hand, both precipitation and SPEI exhibit downward trends. Over the Mediterranean region, climatic parameters show an apparent spatial and temporal variation. The results confirm the linkage between climatic parameters' trends and variability with the TELs' variability patterns over the study area, especially in the eastern Mediterranean region.
The impact of mesoscale vertical atmospheric motion on the thermodynamic, microphysical, and convective transformations of air masses during marine cold air outbreaks (MCAOs) is largely unknown, partly due to scarcity of suitable observations in Arctic regions. To help close this gap, this study investigates the effects of mesoscale subsidence on the evolution of atmospheric boundary-layer (ABL) properties, cloud phase, and precipitation characteristics for a case study of a shallow MCAO observed in the Fram Strait during the HALO–(𝒜𝒞)3
The balance between sulfur and nitrogen (S-N) precursors influences secondary aerosol formation, yet its cross-phase links to particle composition and mixing states remain poorly constrained. Here, we integrated single-particle aerosol mass spectrometry (SPA-MS) with pollutant and meteorological observations during winter and summer emission control periods (ECPs) and corresponding normal periods (NPs) in Yangzhou, eastern China. By establishing gas-phase (gSNR), particle-phase (pSNR), and number-based (nSNR) sulfur-to-nitrogen ratio metrics, we combined causal inference with interpretable machine learning to analyze the underlying drivers. During ECPs, reduced NOx2∼ 57 % increase in pSNR across particle classes and a 27 % expansion in mean vacuum aerodynamic diameter (Dva) relative to NPs. Causal analysis revealed a stepwise propagation of S-N partitioning running sequentially from gas-phase precursors to single-particle chemistry and ultimately to the population mixing state. Particle-resolved chemistry demonstrated that pSNR enrichment was highest in particles containing both black carbon and organic carbon, but markedly lower in BC-free inorganic particles, indicating strong particle-type-dependent aging pathways. Relative humidity (RH) acted as a primary regulator of this cross-phase coupling: below 55 % RH, the SNR metrics decoupled, maintaining substantial chemical heterogeneity across particle classes (externally mixed state); above 85 % RH, aerosol liquid water uptake accelerated gas-particle exchange and multiphase processing, causing the SNR metrics to converge as aerosols evolve toward an internally mixed state. These findings suggest that future air-quality models would benefit from incorporating particle-type heterogeneity and humidity-dependent multiphase processes under shifting emission regimes.
Coastal urban environments exhibit strong vertical and horizontal heterogeneity in aerosol properties, complicating process-level understanding of aerosol–cloud interactions. This study analyzes tethered balloon system (TBS) measurements from 149 flights during summer over the greater Houston, Texas, region as part of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Tracking Aerosol Convection interactions ExpeRiment (TRACER) campaign. We characterized the vertical structure of aerosol number concentrations, size distributions, and inferred cloud condensation nuclei (CCN) concentrations. Air mass history was classified using back-trajectory analysis and k-means clustering into three clusters: (1) marine-influenced, (2) mixed marine and urban emissions, and (3) urban/anthropogenic and long-range transported aerosols. CCN concentrations are estimated from observed size distributions using κ-Köhler theory. The resulting profiles show pronounced vertical variability across clusters, strongly modulated by boundary-layer depth and coastal circulations, leading to substantial variability in the aerosol population available for cloud activation. The marine cluster showed the lowest concentrations, with CCN at 0.8 % supersaturation below 1000 cm−3, while urban and mixed clusters displayed higher concentrations and more complex layering. Profiles influenced by the mixed marine–urban cluster frequently exhibit decoupling between near-surface aerosol and elevated layers, including enhanced accumulation-mode number aloft, consistent with prior TBS-based compositional studies. A 6–7 September 2022 case study demonstrates that mesoscale transport can simultaneously transform the thermodynamic environment and the aerosol population, highlighting the importance of constraining boundary-layer dynamics and airmass origins before attributing cloud changes to aerosol effects in complex coastal environments.
The climate effects of black carbon (BC) remain highly uncertain, and one critical process that requires accurate representation in climate models is BC aging. This study implements a dynamic aging scheme, which accounts for both condensation and coagulation processes, into the regional climate and chemistry coupled model RegCM-Chem to evaluate the BC climate effects over East Asia in summer. Results indicate that in heavily polluted regions such as the North China Plain and the Sichuan Basin, the BC aging timescale is shorter than 10 h, promoting the formation and wet deposition of hydrophilic BC, which reduces the BC column burden over East Asia by an average of 0.12 mg m−2. Conversely, BC surface concentrations and optical depth exhibit an increase over eastern China due to the compensation of reduced dry deposition. The strengthened BC direct effects favor the development of the East Asian summer monsoon and enhance moisture convergence and cloud fraction in southern China. Additionally, accelerated aging also promotes increases in cloud droplet number concentrations and cloud optical depth. Under the dynamic aging scheme, the effective radiative forcing at the top of the atmosphere over East Asia due to BC–radiation interactions, BC–cloud interactions and BC–radiation–cloud interactions are +3.60, −0.58 and +0.90 W m−2, respectively. The climate effects of BC exhibit pronounced nonlinearity driven by adjustments in circulation and cloud. Overall, BC induces a much drier and warmer surface in northern China, whereas southern China experiences the opposite effect.
Stratospheric loss is a major sink for both carbonyl sulfide (COS) and methane (CH4), but their stratospheric lifetimes and sinks remain poorly constrained because high-resolution observations of their vertical distributions in the lower stratosphere are sparse. Here, we estimate the mean stratospheric lifetime and sink of COS and CH42O using two distinct methods applied to AirCore vertical profile measurements from three Northern Hemisphere summer campaigns. From these profiles, we derive a COS stratospheric lifetime of 69–90 years, corresponding to a sink of 30–41 GgS yr−1. For CH4,we find a stratospheric lifetime of 149–168 years, corresponding to a sink of 23–26 TgC yr−1. These values are in good agreement with previous estimates (39–76 years for COS, 152–160 years for CH4) and with estimates based on ACE-FTS observations (75–76 years for COS, 146–172 years for CH4). As has been noted previously, we also find a decline in the COS tropospheric burden between 2016 and 2020 in our AirCore samples, in contrast to the continued growth of CH42O. In addition, we found that tracer-tracer correlations vary among flights, and even within the same campaign, pointing to variability in lower-stratospheric composition. Although this variability may reflect differences in stratospheric transport, its origin remains unclear and requires further investigation. These results provide observational constraints on the stratospheric budgets of COS and CH4
Stratosphere-troposphere exchange (STE) plays a fundamental role in the global atmospheric budget of chemical constituents. The troposphere-to-stratosphere transport (TST), as a part of STE, can inject anthropogenic pollutants from the Earth’s surface into the stratosphere, changing its chemical composition and influencing radiative processes. On record, TST is a multi-scale process with various contributing mechanisms, often not fully qualified nor quantified. In the tropics, typhoons and the corresponding overshooting convection and updrafts have recently been highlighted as one of the TST mechanisms, contributing for instance to the moistening of the lower stratosphere.Expanding on this, our study proposes a novel mechanism for TST connected with the interaction of typhoons with orography, including modulation of typhoon updrafts and convection, orographic lifting, and orographic gravity waves. Combining a Lagrangian modeling tool with a high-resolution simulation of the landfall of typhoon Molave (2020) in the Philippines, our results show that the presence of orography enhances the transport of air from the planetary boundary layer to the upper troposphere–lower stratosphere (UTLS) region. The presented findings advance our understanding of tropical cyclones impacts on STE and may have significant implications for the long-range atmospheric transport of pollutants originating from tropics.
A variety of chemical and dynamical processes in the troposphere and stratosphere affect tropical total column ozone (TCO), the net effect of which may cause changes in surface UV radiation and impact human and ecosystem health. We use dynamical linear modeling to estimate changes in tropical TCO and partial column ozone (PCO) in the troposphere and three stratospheric layers to assess agreement between satellite composites and chemistry-climate model simulations from two multi-model experiments (CCMI-1 and CCMI-2022). While both model experiments show tropical TCO increases over 2000–2021, multimodel-mean CCMI-2022 changes (+2.5 DU) agree slightly better with observations (+3.3 DU) than CCMI-1 (+1.6 DU). However, this overall agreement obscures multiple systematic differences in PCO changes between the models and observations across atmospheric layers. For example, since 2000 tropical tropospheric PCO increased significantly in CCMI-2022 (+1.5 DU) but not in CCMI-1 (+0.3 DU), largely explaining the difference in TCO changes. Also, despite nearly identical stratospheric PCO changes, CCMI-2022 changes are slightly more negative than CCMI-1 in the lower stratosphere (by ∼ 0.5 DU) and more positive in the middle/upper stratosphere. Crucially, substantial differences exist between observational PCO changes, particularly in the troposphere and middle/upper stratosphere, and these disagreements limit the ability to evaluate CCM fidelity for past changes. While early and late century trends are correlated across models, suggesting a potential emergent constraint on ozone, the spread in observational trends means they are unable to provide guidance on the credibility of model projections for a given emissions scenario.
NASA's Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite sensor provides high temporal and spatial resolution measurements critical for monitoring air quality. During the Synergistic TEMPO Air Quality Science (STAQS) component of the 2023 AGES+2) and formaldehyde (HCHO). Evaluating TEMPO (version 3) NO22R≈0.79–0.81), though sharp transitions between high and low emission regions remain challenging. The high-resolution (1.33 km × 1.33 km) WRF-Chem simulation reproduces the major spatiotemporal patterns of surface ozone and NO2R≈0.56–0.73), supporting its use to fill observational gaps. Integrating TEMPO, WRF-Chem, in situ measurements, and ozone and wind lidar observations, we characterize the spatiotemporal ozone dynamics under different pollution regimes. High-pollution days involve early urban precursor accumulation and sea-breeze-driven coastal recirculation of pollutant-rich air. Moderate days exhibit localized enhancements driven by transport, such as downwind plume transport, while low-pollution days show efficient dispersion and limited ozone formation. This multi-platform framework highlights the importance of resolving fine-scale variability in coastal and transition zones and illustrates TEMPO's potential for improving ozone forecasting and mitigation in complex environments.