A key challenge in semiarid savannas is determining whether ambient isoprene directly traces local biogenic emissions or represents an integrated signal shaped by chemistry and atmospheric dynamics. Here we use hourly measurements of isoprene, methyl vinyl ketone, and methacrolein from August to December 2024 at a savanna site near the Okavango Delta, Botswana, spanning dry to early wet seasons. During the early-wet seasons, daytime isoprene increased with vegetation growth and followed the expected daytime pattern of biogenic emission, suggesting a stronger local contribution. During the dry seasons, isoprene often increased at night, with patterns consistent with stronger atmospheric processing and shallow nighttime boundary-layer influence. Back-trajectory analyses suggested a preferred upwind sector, consistent with possible recent transport. These results suggest that seasonal state affects both isoprene abundance and its representativeness as a proxy for local biogenic emissions in semiarid savannas.

Climate warming induced wildfires are rapidly increasing at high latitudes, yet their climate impacts remain poorly understood. These deeply smoldering fires may release long-stored carbon and thus perturbate the global carbon cycle and further emit light-absorbing carbonaceous particles enhancing snow and ice melt after deposition. We newly investigate the carbon isotopic and light-absorbing characteristics of carbonaceous particles produced in laboratory combustion experiments on Arctic-boreal peats and compare these with biomass from boreal forest and savanna environments. We provide the first observational evidence that boreal and especially Arctic peat smoldering may release millennial-aged carbon into the atmosphere, which upsets radiocarbon-based source attribution, separating fossil-fuel-derived sources from modern biomass. Moreover, above- and below-ground material combust differently, and hence the fraction of modern carbon (F14C), i.e., the average age, of the original biomass and the produced carbonaceous particles may differ. Furthermore, we show that peat smoldering produces significant amounts of Brown Carbon, which absorbs light at a similar magnitude to Black Carbon in these samples. Our results indicate that the increasing number of Arctic-boreal peat fires may exacerbate Arctic warming more than previously estimated.

Wildfires emit large quantities of brown carbon, a class of light-absorbing organic aerosols whose climate effects remain poorly constrained. Brown carbon displays a broad spectrum of absorptivity, ranging from weakly absorbing chromophores in the near-ultraviolet to strongly absorbing species that extend into the visible spectrum-yet its optical properties, global distribution and radiative influence remain largely uncertain. Here we assess the global prevalence and optical characteristics of dark brown carbon through an integrated analysis of aircraft measurements, ground-based observations and satellite retrievals. We show that this strongly absorbing aerosol is widespread in wildfire plumes around the world. Measured dark brown carbon mass absorption efficiencies range from 0.5 to 1.5 m(2) g(-1) at 500 nm, with absorption frequently comparable to-or even exceeding-that of black carbon. When these observationally constrained optical properties are incorporated into a global aerosol-climate model, we estimate a direct radiative effect of +0.097 W m(-2) (spanning +0.050 to +0.276 W m(-2)) attributable to wildfire-derived brown carbon, with the upper bound surpassing black carbon's contribution and extending into mid- and high-latitude regions, including the Arctic. These results position dark brown carbon as a critical yet underrecognized contributor to wildfire radiative forcing, highlighting the need to account for its substantial warming influence in climate assessments.

Abstract. This study addresses the challenge of integrating ship-based lidar measurements with numerical weather prediction models to improve offshore wind characterisation. Accurate wind measurements are vital for the development of offshore wind energy, yet traditionally used fixed devices, such as meteorological masts and platform- or buoy-based lidars, are expensive and scarce. Ship-based lidar systems offer a flexible, cost-effective alternative by collecting wind data over large areas; however, the non-stationarity of ships results in low data density at any specific location. To overcome this challenge, we propose a novel calibration methodology to assimilate ship-mounted lidar observations into the ERA5 reanalysis by statistically adjusting its wind speed outputs. Inspired by observational nudging, which influences model state variables over time to match observational data, our approach applies a weighted correction directly to the model’s wind speed output, preserving the model’s underlying physics while ensuring computational efficiency and flexibility. The calibration parameters, including calibration strength, temporal window, and spatial radius of influence, were optimised to maximise the impact and accuracy of the calibration process. The comparison between ERA5 before and after the calibration demonstrates that the methodology effectively reduces the systematic underestimation of wind speeds, particularly in coastal regions where ERA5 struggles with complex flow dynamics. The methodology has been validated against independent measurements from a fixed Doppler lidar system deployed on an island in the northern Baltic Sea, demonstrating the calibration’s effectiveness in reducing bias and error spread at this location as well. However, it highlights that the calibration effect is strongly dependent on the distance between the ship and the lidar station, with a bias reduction of 0.2 m s-1 when the ship is within 60 km, compared to 0.05 m s-1 when considering data within 90 km, as a consequence of the intermittent influence of the ship-based lidar data.

Secondary organic aerosol (SOA) formed from wildfire/biomass-burning emissions (BB) represents a significant fraction of global SOA production. However, there are large uncertainties in representing BB-SOA in climate models. We studied the evolution of organic aerosols (OA) from burning three biomass samples─savannah grass, savannah wood, and boreal forest surface─under different combustion conditions and during daytime (photochemical oxidation) and nighttime (dark oxidation) aging in an atmospheric chamber. OA dominated the BB emissions by contributing ∼82-99% of the total PM1 mass. Atmospheric aging by both oxidation processes produced comparable amounts of net OA mass. We show, with PMF analysis, that this is connected to the more efficient loss of primary OA, which compensates for the more efficient gas-phase oxidation during daytime aging compared with nighttime. The observed moderate OA mass enhancement (0.75-1.3 times) agrees well with field observations, thereby addressing the discrepancy between laboratory and field studies. For both aging scenarios, total OA emission factors after aging are similar to those of primary OA, providing new insights into the evolution of BB emissions. Our results suggest a simplified treatment of OA in climate models in remote areas with low NOx concentrations.

The Asian Summer Monsoon (ASM) lofts air from polluted boundary layer regions to the upper-troposphere and lower-stratosphere. High concentrations of aerosols have been observed in ASM outflow, which influence climate by interacting with radiation and clouds and affecting atmospheric composition. Prior observations indicate that small particles form in ASM convective outflow. Laboratory and modelling studies hypothesise the importance of lofted ammonia in this process and growth of the particles to sizes where they can interact with radiation and clouds. Here we combine in-situ observation in ASM convective outflow with trajectory and Earth-system modelling to show the likely role of pollution in enhancing ASM outflow new particle formation. We show that the particles grow to sizes where they can interact with clouds and radiation, and are transported over large areas within the troposphere and stratosphere.

Some HALO Photonics Doppler lidars measure the atmospheric volume depolarization ratio at a wavelength of 1565 nm. We inspected 4 years of data from 6 instruments operated in different environments across Finland with the aim of retrieving the atmospheric aerosol depolarization ratio. The long-term performance of these instruments was examined by investigating the stability of the noise floor and the amount of polarizer bleed-through. We further developed the method for correcting the background noise and constructed an algorithm distinguishing aerosol from hydrometeors. We observed that the 4-year averaged aerosol depolarization ratio varies from 0.07 at sub-arctic Sodankylä to 0.13 in the boreal forest in Hyytiälä. At all locations, the aerosol depolarization ratio peaks during spring and early summer, which we attribute to pollen. Overall, our analysis supports the long-term usage of HALO Doppler lidar depolarization ratio measurements, especially the detection of aerosols that may pose a safety risk for aviation.

Understanding new particle formation (NPF) and the fate of nanoparticles is crucial because of their close links to air quality, cloud formation, and climate. These effects vary spatially and temporally owing to diverse aerosol sources and their relatively short atmospheric lifetime. Here, we present a comprehensive analysis of long-term trends in NPF-associated nucleation-mode particles and cloud condensation nuclei (CCN) concentrations across diverse observation environments using quality-controlled particle number size distribution (PNSD) and CCN data from 37 sites, primarily from Global Atmosphere Watch (GAW) stations. We identify declining decadal trends in both NPF occurrences and nucleated particle concentrations across most site types, with the strongest declines in urban areas. We observe simultaneous reductions in both CCN concentrations and nucleation-mode particles, suggesting that newly formed particles are a potential source of CCN. This, in turn, suggests that cloud microphysical properties and radiative effects can be indirectly influenced through aerosol-cloud interactions that modify cloud droplet formation. These findings indicate that decreasing anthropogenic emissions could influence the climate forcing potential of aerosol-cloud interactions, with important implications for future climate projections.

Land surface models (LSMs) are fundamental for simulating coupled carbon, water and energy exchanges in Earth system models, yet their performance remains poorly constrained across Africa’s diverse hydroclimatic gradients. We evaluate the Joint UK Land Environment Simulator (JULES) using eddy covariance observations from 16 flux towers spanning major African ecosystem types. Model skill is assessed for gross primary productivity (GPP), ecosystem respiration (Reco), and evapotranspiration (ET), and we examine how model bias and error vary along gradients of mean annual temperature, aridity index, and precipitation anomalies. JULES reproduced broad seasonal dynamics of carbon and water fluxes across several ecosystems but showed systematic limitations in simulating Reco and interannual variability of GPP. Model performance differed among ecosystem types, with wetlands exhibiting large model-observation mismatches and cropland sites showing GPP overestimation linked to excessive simulated leaf area index. To isolate environmental controls on performance, we applied linear mixed-effects modelling to partition site-level variability from climate-driven effects. Aridity and precipitation anomalies emerged as dominant predictors of model bias and error, with wetter and anomalously humid conditions associated with larger deviations from observations, indicating limitations in representing soil hydrology and vegetation water stress processes. By integrating an expanded African flux tower network, this study provides a comprehensive regional evaluation of JULES. The results identify hydroclimatic regimes where model development should be prioritised and emphasise the importance of strengthening benchmarking across underrepresented African ecosystems to improve model transferability and support reliable climate and land management decision support under environmental change. Plain Language Summary : Land surface models are computer programs that help scientists understand how land ecosystems exchange water and carbon with the atmosphere. They are widely used to study climate change and support land and water management. However, most testing of these models has been carried out in regions with extensive data, and their performance across Africa’s varied ecosystems is less well understood. In this study, we evaluated the Joint UK Land Environment Simulator using measurements from 16 eddy-covariance flux towers across Africa. These towers directly measure how much carbon ecosystems absorb through photosynthesis and release through respiration, and how much water returns to the atmosphere through evapotranspiration. We compared model simulations with these observations across a range of climates, from dry to wet regions and from cooler to warmer sites. JULES successfully reproduced seasonal patterns of carbon and water exchange at many locations. However, it struggled to accurately represent ecosystem respiration and year-to-year variation in photosynthesis. Using a statistical approach that accounts for differences between sites, we found that dryness and unusual rainfall conditions were the main causes of model errors. These results highlight the need to improve how this model represents soil water processes and vegetation responses to water availability.

Particle linear depolarization ratio is a widely used parameter in lidar research to distinguish different aerosol types and the thermodynamic phase of water. It is most frequently measured at ultraviolet and visible wavelengths (355 and 532 nm), yet multi-wavelength observations suggest that this parameter can vary substantially with wavelength. In this work, we assessed particle linear depolarization ratios at 1565 nm using Halo Photonics StreamLine Doppler lidars. We examined the depolarization ratio through three case studies featuring extremely fresh and aged smoke, and volcanic ash aerosol particles in the troposphere. Both fresh and aged smoke aerosol particles induced low values. Specifically, aerosol layers dominated by extremely fresh smoke showed a depolarization ratio of 0.017 ± 0.004, whereas aged long-range transported smoke particles exhibited marginally higher values. Volcanic aerosol layers induced high depolarization ratios with layer mean values of 0.45 ± 0.01. For the extremely fresh smoke case, we further estimated the smoke mass concentration using the lidar observations at 1565 nm and found good agreement with the in situ observations. These results demonstrate that Halo Doppler lidars operating at 1565 nm wavelength are capable of distinguishing several key aerosol types, enabling a comprehensive characterization of atmospheric conditions by simultaneously observing aerosol properties and wind dynamics.

Abstract. South Africa is a global hotspot for anthropogenic atmospheric NO2, where emissions from the industrialised Mpumalanga Highveld influence air quality across the southern African region. In the atmosphere, NO2 is a key player in oxidative chemistry and contributes to particulate nitrate formation and the biogeochemical nitrogen cycle through deposition. At the same time, various ecosystem processes act as sources and sinks of NO and NO2. The net result of these factors implies differences in the ecosystem scale flux of NO2. Here we perform the first-ever high-resolution NO2 measurements with a quantum cascade laser (QCL) instrument in a grazed African savannah landscape from 2015 to 2020. Micrometeorological eddy covariance measurements were used to quantify the NO2 flux and explore temporal trends at diurnal, monthly, seasonal and interannual scales. Our findings highlight the variability of NO2 flux within this system, with notable interannual change observed at both monthly and hourly scales. Seasonal differences in NO2 flux and deposition velocity were strongly linked to the rainfall season, with negligible differences between dry season months. Diurnal flux trends peaked during daylight hours, with consistently low NO2 flux during nighttime. These findings contribute to our understanding of near-surface atmospheric NO2 dynamics in arid landscapes and, for the first time, can be used to estimate ecosystem-scale compensation point of NO2.

Abstract. Aerosol particles larger than roughly 50–100 nm in diameter are climatically important because they can act as cloud condensation nuclei (CCN), making their global number concentrations essential for understanding aerosol–cloud interactions. However, observationally constrained, long-term global datasets of particle number concentrations in this size range remain scarce. In this investigation, we present a global dataset of ground-level particle number concentrations for the period 2003–2024, produced by combining in situ observations with a machine-learning approach. The dataset includes two variables: the number concentrations for particles larger than 100 nm (N100) and larger than 50 nm (N50), provided at 0.75° × 0.75° spatial resolution and daily temporal resolution. To generate this dataset, we trained an eXtreme Gradient Boosting (XGB) model using measurements from 62 in situ stations as targets and reanalysis variables as predictors, enabling a data-driven representation of particle number concentrations at the global scale. We evaluated the dataset against independent observations from 12 additional stations. At 2/3 of these stations, the dataset shows good performance, capturing the median concentrations within a factor of 1.5 from the observations. Furthermore, we describe the main characteristics of the dataset in terms of global spatial patterns, temporal variability, and seasonal cycles, and demonstrate its ability to capture long-term trends in particle number concentrations, including both increasing and decreasing tendencies reported in the literature. This work provides the first observation-constrained, machine-learning-based global dataset of N50 and N100 at daily resolution over two decades, bridging the gap between sparse measurements and computationally expensive process-based models. The dataset, publicly available at https://doi.org/10.5281/zenodo.20202080, offers a valuable resource for evaluating model simulations, improving CCN-related parameterizations, and supporting weather and climate studies without the need for explicit knowledge of the aerosol particle microphysics.

Wildfire smoke strongly affects air quality, human health, climate, and the Earth system. During atmospheric aging, wildfire aerosol particles undergo complex chemical and microphysical transformations that modify their optical properties, radiative effects, and cloud-forming ability. Of particular interest are organic surface coatings, which can enhance light absorption through lensing effects and increase particle hygroscopicity.Here, we present single-particle mass spectrometry measurements from a boreal forest wildfire smoke experiment, resolving the coexistence of hydrophilic compounds and hydrophobic polycyclic aromatic hydrocarbons (brown carbon) within individual particles. We show that glyoxal and methylglyoxal are directly emitted during combustion, contributing to the initial hygroscopicity of freshly emitted particles. During photochemical aging, rapid oxalate formation is observed, accompanied by a moderate increase in hygroscopicity, while PAH signals decrease on a slower timescale. The decay rates of individual PAHs are similar but show a clear dependence on relative humidity, indicating that PAH degradation is controlled by viscosity-dependent radical diffusion into the particles. In contrast, highly oxidized products form on much shorter timescales, suggesting that these reactions are largely confined to the particle surface. At elevated relative humidity, surface oxidation continues, whereas it rapidly ceases under dry conditions. These observations highlight the central role of relative humidity in controlling the microphysical properties, optical effects, and cloud activation potential of aged wildfire smoke.

Abstract. Biomass burning (BB) emits large amounts of pollutants in the particle and gas phases, with significant implications for air quality, human health and climate. Here, we investigate the emission of organic vapors from controlled burns of relatively understudied biomass fuels: woody plants and grasses from African savannah and European boreal forest surface using a high-resolution proton transfer reaction-mass spectrometer. To understand the effect of different oxidation regimes, organic vapors were aged in a 29 m3 Teflon chamber, where photochemical and dark aging were simulated. The average total primary emission factors (EFs) for organic vapors varied considerably with fuel type, ranging from 69 to 161 g kg-1. Photochemical aging led to substantial depletion of furanics, phenolics and oxygenated aromatics, accompanied by enhancements of carbonyl B compounds and O-containing compounds C<6 across experiments. In contrast, dark aging under low-NOx conditions produced minimal compositional changes. Hierarchical clustering of relative composition showed clear regime dependence, with regime-associated differences accounting for 73 % of the variance in group-level composition. Toluene and furan showed a strong negative correlation with secondary oxygenated volatile organic compounds (OVOCs), including anhydrides and small acids, consistent with their role as precursors. After 0.5 equivalent day of photochemical aging, organic vapors shifted to higher O/C (>0.70) and an increased fraction of CxHyOz (z≥3). These results highlight the integral role of OH·-driven photo-oxidation in governing the atmospheric evolution and composition of BB organic vapors and underscore the need for secondary organic aerosols (SOA) models to include non-traditional precursors.

Atmospheric particulate matter (PM) contains a significant fraction of carbonaceous materials, which include organic- (OC) and elemental carbon (EC). These species significantly influence the global radiation balance and have negative health impacts. Although South Africa is an important emitter of carbonaceous aerosols, very little OC and EC studies have been published in the peer reviewed public domain. Therefore, the aim of this paper was augmenting the sparse OC and EC data for South Africa through a detailed assessment of an extensive dataset collected daily for 24 h over a period of 14 months at the regional background site, Welgegund. In total. 587 datasets were collected, which is the most comprehensive OC and EC dataset collected for this region. Seasonal OC and EC concentration patterns indicated significant contributions from open biomass burning in the months with highest fire frequencies, as well as contributions from household combustion during winter. In addition to these sources, the important influence of the industrial hub in the South African interior on OC and EC levels were also indicated. Meteorological conditions also contributed to increased OC and EC during the colder months. Since fire occurrences and population density decreased from east to west, OC and EC data associated with air masses mainly passing over eastern and western defined regions were compared. Although the differences were not as large as expected, statistical differences could be confirmed with air masses passing over the eastern region corresponding to higher OC and EC concentrations. Contextualization of OC and EC concentrations revealed OC and EC levels determined in this study were similar or slightly elevated compared to concentrations reported for other background sites. Furthermore, EC correlated well with equivalent black carbon (eBC), indicating that EC at this site can be used as a proxy for eBC at regional background sites.

We investigate the dynamics of the atmospheric Boundary Layer (BL) over the Atlantic Ocean, with a focus on the region surrounding Cabo Verde during the Joint Aeolus Tropical Atlantic Campaign (JATAC) and the ASKOS experiment, using a combination of ground-based PollyXT and Doppler lidars, satellite lidar data from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), radiosondes, and the model outputs of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The comparison of CALIPSO lidar results with ECMWF/IFS reanalysis for 2012–2022, revealed good agreement for BL top over open ocean regions but weaker relation over dust-affected areas of the African continent. In these regions, daytime CALIPSO retrievals typically indicate lower BL tops than ECMWF, while at night CALIPSO often detects aerosols within the residual layer, leading to higher estimates than the model. Observations in Cabo Verde highlight distinctive Marine Atmospheric Boundary Layer (MABL) characteristics, such as limited diurnal evolution, but also show the potential for BL heights to reach up to 1 km, driven by factors like strong winds that increase mechanical turbulence. Additionally, the technical and physical challenges in estimating the BL height using different datasets and methods are discussed, examining cases with different thermodynamical conditions and aerosol load that directly affect the dynamics of the BL. The findings underline the strengths and limitations of different observational and modeling approaches, and emphasizes on the importance of considering local meteorology and aerosol conditions when interpreting BL height.

The accurate representation of microphysical properties of atmospheric aerosol particles – such as the number, mass, and cloud condensation nuclei (CCN) concentration – is key to constraining climate forcing estimations and improving weather and air quality forecasts. Lidars capable of vertically resolving aerosol optical properties have been increasingly utilized to study aerosol–cloud interactions, allowing for estimations of cloud-relevant microphysical properties. Recently, lidars have been employed to identify and monitor pollen particles in the atmosphere, an understudied aerosol particle with health and possibly climate implications. Lidar remote sensing of pollen is an emerging research field, and in this study, we present for the first time retrievals of particle number, mass, CCN, giant CCN (GCCN), and ultragiant CCN (UGCCN) concentration estimations of birch pollen derived from polarization lidar observations and specifically from a PollyXT lidar and a Vaisala CL61 ceilometer at 532 and 910 nm, respectively. A pivotal role in these estimations is played by the conversion factors necessary to convert the optical measurements into microphysical properties. This set of conversion parameters for birch pollen is derived from in situ observations of major birch pollen events at Vehmasmäki station in eastern Finland. The results show that under well-mixed conditions, surface measurements from in situ instrumentation can be correlated with lidar observations at higher altitudes to estimate the conversion factors. Better linear agreement to the in situ observations was found at the longer wavelength of 910 nm, which is attributed to a combination of lower overlap and higher sensitivity to bigger particles compared to observations at 532 nm. Then, the conversion factors are applied to ground-based lidar observations and compared against in situ measurements of aerosol and pollen particles. In turn, this demonstrates the potential of ground-based lidars such as a ceilometer network with the polarization capacity to document large-scale birch pollen outbursts in detail and thus to provide valuable information for climate, cloud, and air quality modeling efforts, elucidating the role of pollen within the atmospheric system.