This study investigates the new particle formation (NPF) events at an urban location in the Eastern Mediterranean. Particle size distribution, particulate chemical composition, and gaseous pollutants were monitored in Rehovot, Israel (31°53″N 34°48″E) during two campaigns: from April 29 to 3 May 2021 (Campaign 1) and from May 3 to 11 May 2023 (Campaign 2), coinciding with an intensive bonfire burning festival. The organic aerosols (OA) source apportionment identified two major factors-Hydrocarbon-like OA and Biomass-burning OA-as well as two secondary factors-MO-OOA (more oxidized-oxygenated OA) and LO-OOA (low oxidized oxygenated OA). NPF events were frequently observed during the day (mostly well-defined nucleation events) and at night (burst of ultrafine mode particles without any discernible growth). A condensation sink value of (9.4 ± 4.0) × 10 s during Campaign 1 and (14.2 ± 6.0) × 10 s during Campaign 2 was obtained. The daytime events were associated with enhanced sulfuric acid proxy concentrations of (2-12) × 10 molecules cm, suggesting the role of gas-phase photochemistry in promoting NPF. A novel approach of hybrid positive matrix factorization analysis was used to deconvolve the chemical species responsible for the observed events. The results suggest the involvement of multiple components, including ammonium sulfate and MO-OOA, in the nucleation; Nitrate, HOA and LO-OOA participate in the subsequent particle growth for the daytime events. Nighttime events involve only semi-volatile species (LO-OOA, HOA and nitrate) along with ammonium sulfate.

This work aims to identify a mechanism of interaction between soil moisture (SM) state and the incidence of weakly forced synoptic scale MCS events during boreal summer by performing a sensitivity study using the Weather Research and Forecasting (WRF) model over the US Great Plains. A uniformly dry SM patch at a 5° × 5° scale is centered at the point of a documented MCS initiation to observe spatiotemporal changes of the simulated MCS events, totaling 97 cases between 2004 and 2017. A storm-centered composite analysis of SM at the location of simulated MCS events depicted SM heterogeneity [O(100) km] structured as significantly drier soils to the southwest (SW) transitioning to wetter soils northeast (NE) of the mean simulated initiation. Further analyses showed that this SM configuration influenced near-surface fluxes, which created a gradient of 2m-temperature and 2m-humidity, also aligned SW-to-NE, which affected the growth of the planetary boundary layer to trigger MCS initiations earlier in time (∼1-2 hr on average) compared to Control simulations. The implementation of the dry SM perturbation introduced drier-to-wetter SM gradients along the edges of the perturbed area, and MCS initiations were subsequently preferred on the drier side of those transition zones, with the most common orientation of simulated MCS events embedded within southwesterly flow. These results emphasize the importance of the low-level wind field alignment to organized SM gradients, which suggests that SM heterogeneity can drive MCS initiation related to near-surface atmospheric variable fluctuations as the main mechanism of interaction in weakly forced synoptic environments.

The spatial and seasonal variability in the composition of major PM (particles with aerodynamic diameters less than 2.5 μm) aerosol species in the United States were characterized using data from ground-based aerosol monitoring networks. The IMPROVE (Interagency Monitoring of Protected Visual Environments) network and the Chemical Speciation Network (CSN) operate in mostly rural/remote or urban/suburban sites, respectively. The networks have similar sampling schedules and analysis methods. Regional, monthly, and annual mean concentrations from 2019 to 2022 were calculated for ammonium sulfate (AS), ammonium nitrate (AN), particulate organic matter (POM), elemental carbon (EC), fine dust (FD), and sea salt (SS), as well as their relative contributions to reconstructed PM mass (RCFM). Organic aerosols were the largest contributor to RCFM across the United States (>40% annually, up to 80% monthly), with significant impacts from biomass smoke on POM and EC concentrations, contributions, and seasonality. AS concentrations and contributions were similar in urban and rural regions and contributed <20% annually to RCFM, considerably less than two decades ago. In general, urban concentrations were greater for AN, POM, and EC, suggesting additional urban sources. Some species, such as POM, FD, and AN, exhibited strong seasonal variability due to episodic source impacts or seasonal formation conditions. Evaluating the urban and rural monthly variability of major aerosol species is necessary for understanding the impacts of emission sources, regional transport, and atmospheric processes governing aerosol concentrations in the atmosphere.

Cloud radiative effects (CREs) and cloud-type mean CREs depend upon how clear-sky fluxes are computed over a large area: those of the immediate environment of clouds or the regional mean clear-sky fluxes. Five convectively active regions in the Tropics, two over land (Africa and Amazon) and three over ocean (eastern and western Pacific and Atlantic), are selected to understand the influence of immediate environment of clouds on CREs. Fluxes derived from 19 years of high-resolution CERES satellite data, categorized by cloud type, are utilized. The cloud types are classified based on the joint cloud top pressure and cloud optical depth distribution. For the entire tropical region, differences in cloud-type mean CRE with regional mean and immediate environment clear skies range from -7.8 to 10.7 Wm for shortwave (SW), 2.9 to 15.8 Wm for longwave (LW), and 6.1 to 17.9 Wm for net, respectively. The oceanic and Amazonia regions have negative (positive) SW (LW) CRE differences, typically 2-6 Wm in SW but 7-10 Wm in LW, whereas Africa has positive SW and LW CRE differences (typically 20-30 Wm, up to 40-50 Wm). The influence of immediate environment reduces the regionally averaged, that is, cloud-type mean CREs weighted by cloud fractions, SW cloud cooling, and LW cloud warming in four of the five regions except for Africa. For Africa, it increases the SW cloud cooling and greatly reduces the LW cloud warming, resulting in net cloud cooling as in other regions instead of warming. The implications of these findings for observational and modeling studies are discussed.

Particle aggregates blown along the surface of playas have been linked to the disruption of interparticle bonds, comminution, and dust production. This mechanism was investigated in a set of wind tunnel experiments with the purpose of examining the rate of comminution during transport, role of bed roughness, influence of humidity, system dynamics, and proportionate amount of dust production. The playa sediment selected for testing was obtained from Owens Lake in California, USA. Particle aggregates with diameters between 500 and 710 μm (0.5 ϕ-1.0 ϕ) were isolated by sieving. Small 20 g subsamples were then introduced into the wind tunnel via a drop tube, with some particles captured on a downwind array of sticky glass plates. Aggregate diameter was found to decrease linearly by 30-56 μm per meter of transport (  = 0.43) over a total distance of 8.3 m. Enhanced geometric roughness of the bed surface increased this rate, but not dust production and suspension. As compared to an equivalent mass of disaggregated parent material representing the dust emission potential, comminution produced only 0.5%-4% as much suspended PM. Weak longitudinal flow instabilities involving downwelling and upwelling were found to influence the vertically integrated mass transport rate. This affirms the high sensitivity of dust transport to the structure of the wind field and the need for a paradigm shift in wind tunnel simulations of dust emission to extend measurement of the factors governing dispersion beyond one dimension, and specifically, friction velocity.

Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud-to-ground (CG) lightning across the WUS during June-September from the spatial patterns of seven large-scale meteorological variables from reanalysis (1995-2022). Individually trained CNN models at each 1° × 1° grid cell ( = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.8) and perform best in parts of the interior Southwest where summertime CG lightning is most common. Further, interannual correlation between observed and predicted CG lightning days is high (median  = 0.87), demonstrating that locally trained CNNs realistically capture year-to-year variation in CG lightning activity across the WUS. We then use layer-wise relevance propagation (LRP) to investigate the relevance of predictor variables to successful CG lightning prediction in each grid cell. Using maximum LRP values, our results show that two thermodynamic variables-ratio of surface moist static energy to free-tropospheric saturation moist static energy, and the 700-500 hPa lapse rate-are the most relevant CG lightning predictors for 93%-96% of CNNs depending on the LRP variant used. As lightning is not directly simulated by global climate models, these CNNs could be used to parameterize CG lightning in climate models to assess changes in future CG lightning occurrence with projected climate change. Understanding changes in CG lightning risk and consequently lightning-caused wildfire risk across the WUS could inform fire management, planning, and disaster preparedness.

The threat posed by the increasing concentration of carbon dioxide (CO) in the atmosphere motivates a detailed and precise estimation of CO emissions and removals over the globe. This study refines the spatial resolution of the CAMS/LSCE inversion system, achieving a global resolution of 0.7° latitude and 1.4° longitude, or three times as many grid boxes as the current operational setup. In a 2-year inversion assimilating the midday clear-sky retrievals of the column-averaged dry air mole fraction of carbon dioxide (XCO) from NASA's second Orbiting Carbon Observatory (OCO-2), the elevated resolution demonstrates an improvement in the representation of atmospheric CO, particularly at the synoptic timescale, as validated against independent surface measurements. Vertical profiles of the CO concentration differ slightly above 22 km between resolutions compared to AirCore profiles, and highlight differences in the vertical distribution of CO between resolutions. However, this disparity is not evident for XCO, as evaluated against independent reference ground-based observations. Global and regional estimates of natural fluxes for 2015-2016 are similar between the two resolutions, but with North America exhibiting a higher natural sink at high resolution for 2016. Overall, both inversions seem to yield reasonable estimates of global and regional natural carbon fluxes. The increase in calculation time is less than the increase in the number of operations and in the volume of input data, revealing greater efficiency of the code executed on a graphics processing unit. This allows us to make this higher resolution the new standard for the CAMS/LSCE system.

A fundamental characteristic of extreme precipitation events (EPEs) is their horizontal scale. This horizontal scale can influence the intensity of an EPE through its effect on the timescale of an EPE as well as its effect on the strength of convective feedbacks. Thus, to have confidence in future projections of extreme precipitation, the horizontal scales of EPEs in global climate models (GCMs) should be evaluated. Analyzing daily output from 27 models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6), including 13 models participating in the High Resolution Model Intercomparison Project (HighResMIP), we computed the horizontal scales of EPEs and extreme ascent for annual maximum EPEs during 1981-2000. We found that the horizontal scales of both EPEs and the associated ascending motion are resolution-dependent: for a factor of seven increase in horizontal resolution, the horizontal scale decreases by a factor of approximately two to five, with higher sensitivity in the tropics than in the midlatitudes. Further analysis in the southern hemisphere midlatitudes reveals that this resolution dependence results from precipitation during the simulated EPEs that is almost entirely resolved rather than parameterized. However, the EPEs are not simply grid box storms, and analysis of the horizontal scales of geopotential anomalies suggests that the planetary-scale dynamics in GCMs is not resolution-dependent. Thus, the dominance of resolved precipitation during EPEs is more likely due to convection on the model grid or formation of strong, poorly resolved fronts, and additional work is needed to explore these possibilities and find a remedy for this resolution dependence.

This study evaluates convective cell properties and their relationships with convective and stratiform rainfall within a season-long convection-permitting weather research and forecasting simulation over central Argentina using radar, satellite, and radiosonde measurements from the RELAMPAGO-CACTI field campaign. The simulation slightly underestimates radar-estimated rainfall over the ∼3.5-month evaluation period but underestimates stratiform rainfall by 46% and overestimates convective rainfall by 43%. As convective available potential energy (CAPE) increases, the convective rainfall overestimation decreases, but the stratiform rainfall underestimation increases such that the contribution of convective to total rainfall remains constantly high biased by ∼26%. Overestimated convective rainfall arises from the simulation generating 2.6 times more precipitating convective cells (14,299) than observed by radar (5,662) despite similar observed and simulated cell growth processes, with relatively wide cells contributing mostly to excessive convective rainfall. Relatively shallow cells, typically reaching heights of 4-7 km, contribute most to the cell number bias. This cell number bias increases as CAPE decreases, potentially because cells and their updrafts become narrower and more under-resolved as CAPE decreases. The gross overproduction of precipitating shallow cells leads to overly efficient precipitation and inadequate detrainment of ice aloft, thereby diminishing the formation of robust stratiform rainfall regions. Decreasing model horizontal grid spacing from 3 to 1 or 0.333 km for low (<300 J kg) and high CAPE (>1,000 J kg) cases results in minimal change to cell number, depth, and convective-to-stratiform partitioning biases. This suggests that improving prediction of these convective properties depends on factors beyond solely increasing model resolution.

In September 2020, the Western United States experienced anomalously severe wildfires that resulted in carbon monoxide (CO) emissions almost three times the 2001-2019 average. In this study, we investigate the influence of wildfires on atmospheric carbon monoxide (CO) variability through a comparative analysis of observations from the Measurements of Pollution in the Troposphere (MOPITT), the Infrared Atmospheric Sounding Interferometer (IASI), and the Tropospheric Monitoring Instrument (TROPOMI). Our focus is on the North American domain, aiming to understand the differences among these products. In general, all instruments show excellent agreement under typical atmospheric CO conditions. However, notable discrepancies were observed in the CO data from the three sensors, particularly in regions with elevated CO total column (TC) values. IASI and TROPOMI consistently showed higher CO values over the western U.S. compared to MOPITT. During the fire episodes, we found that the IASI retrievals suggested higher CO abundances near the surface than the MOPITT thermal infrared retrievals that are probably the result of the differences in the covariance matrices used in IASI and MOPITT retrievals. We also found that the high IASI and TROPOMI CO observations over the western U.S. coincided with high values of the TROPOMI aerosol index (AI), suggesting the presence of absorbing aerosols. The analysis exhibited better agreement between TROPOMI and MOPITT CO TC when the AI values were low. Our results suggest that appropriate quality filtering should be employed when analyzing pollution events with these data. In particular, utilizing the AI for quality filtering may be useful when analyzing extreme pollution events with these satellite products.

Cloud condensation and hydrometeor evaporation fractionate stable isotopes of water, enriching liquid with heavy isotopes; whereupon updrafts, downdrafts, and rain vertically redistribute water and its isotopes in the lower troposphere. These vertical water fluxes through the marine boundary layer affect low cloud climate feedback and, combined with isotope fractionation, are hypothesized to explain the depletion of tropical precipitation at higher precipitation rates known as the "amount effect." Here, an efficient and numerically stable quasi-analytical model simulates the evaporation of raindrops and enrichment of their isotope composition. It is applied to a drop size distribution and subcloud environment representative of Atlantic trade cumulus clouds. Idealized physics experiments artificially zero out selected processes to discern the separate effects on the isotope ratio of raindrops, of exchange with the environment, evaporation, and kinetic molecular diffusion. A parameterization of size-dependent molecular and eddy diffusion is formulated that enriches raindrops much more strongly (+5‰ for deuterated water [HDO] and +3.5‰ for O) than equilibrium evaporation as they become smaller than 1 mm. The effect on evaporated vapor is also assessed. Rain evaporation enriches subcloud vapor by +12‰ per mm rain (for HDO), explaining observations of enriched vapor in cold pools sourced by evaporatively cooled downdrafts. Drops smaller than 0.5 mm evaporate completely before falling 700 m in typical subtropical marine boundary layer conditions. The early and complete evaporation of these smaller drops in the rain size distribution enriches the vapor produced by rain evaporation.

Nighttime oxidation of monoterpenes (MT) via the nitrate radical (NO) and ozone (O) contributes to the formation of secondary organic aerosol (SOA). This study uses observations in Atlanta, Georgia from 2011-2022 to quantify trends in nighttime production of NO (PNO) and O concentrations and compare to model outputs from the EPA's Air QUAlity TimE Series Project (EQUATES). We present urban-suburban gradients in nighttime NO and O concentrations and quantify their fractional importance (F) for MT oxidation. Both observations and EQUATES show a decline in PNO, with modeled PNO declining faster than observations. Despite decreasing PNO, we find that NO continues to dominate nocturnal boundary layer (NBL) MT oxidation (F = 60%) in 2017, 2021, and 2022, which is consistent with EQUATES (F = 80%) from 2013-2019. This contrasts an anticipated decline in F based on prior observations in the nighttime residual layer, where O is the dominant oxidant. Using two case studies of heatwaves in summer 2022, we show that extreme heat events can increase NO concentrations and F, leading to short MT lifetimes (<1 h) and high gas-phase organic nitrate production. Regardless of the presence of heatwaves, our findings suggest sustained organic nitrate aerosol formation in the urban SE US under declining NO emissions, and highlight the need for improved representation of extreme heat events in chemistry-transport models and additional observations along urban to rural gradients.

Upward lightning (UL) has become a major threat to the growing number of wind turbines producing renewable electricity. It can be much more destructive than downward lightning due to the large charge transfer involved in the discharge process. Ground-truth lightning current measurements indicate that less than 50% of UL could be detected by lightning location systems (LLS). UL is expected to be the dominant lightning type during the cold season. However, current standards for assessing the risk of lightning at wind turbines mainly consider summer lightning, which is derived from LLS. This study assesses the risk of LLS-detectable and LLS-undetectable UL at wind turbines using direct UL measurements at instrumented towers. These are linked to meteorological data using random forests. The meteorological drivers for the absence/occurrence of UL are found from these models. In a second step, the results of the tower-trained models are extended to a larger study area (central and northern Germany). The tower-trained models for LLS-detectable lightning are independently verified at wind turbine sites in this area and found to reliably diagnose this type of UL. Risk maps based on cold season case study events show that high probabilities in the study area coincide with actual UL flashes. This lends credibility to the application of the model to all UL types, increasing both risk and affected areas.

Upward lightning is rarer than downward lightning and requires tall (100+ m) structures to initiate. It may be either self-initiated or triggered by other lightning discharges. While conventional lightning location systems (LLSs) detect most of the upward lightning flashes superimposed by pulses or return strokes, they miss a specific flash type that consists only of a continuous current. Globally, only few specially instrumented towers can record this flash type. The proliferation of wind turbines in combination with damages from upward lightning necessitates an improved understanding under which conditions self-initiated upward lightning and the continuous-current-only subtype occur. This study uses a random forest machine learning model to find the larger-scale meteorological conditions favoring the occurrence of the different phenomena. It combines ground truth lightning current measurements at the specially instrumented tower at Gaisberg mountain in Austria with variables from larger-scale meteorological reanalysis data (ERA5). These variables reliably explain whether upward lightning is self-initiated or triggered by other lightning discharges. The most important variable is the height of the -10°C isotherm above the tall structure: the closer it is, the higher is the probability of self-initiated upward lightning. For the different flash types, this study finds a relationship to the larger-scale electrification conditions and the LLS-detected lightning situation in the vicinity. Lower amounts of supercooled liquid water, solid, and liquid differently sized particles and no LLS-detected lightning events nearby favor the continuous-current-only subtype compared to the other subtypes, which preferentially occur with LLS-detected lightning events within 3 km from the Gaisberg Tower.

With the 2011 promulgation of the Mercury and Air Toxics Standards by the U.S. Environmental Protection Agency, and the successful negotiation by the United Nations Environment Program of the Minamata Convention, emissions of mercury (Hg) have declined in the United States. While the declines in atmospheric Hg concentrations in North America are encouraging, linking the declines to changing domestic and global source portfolios remains challenging. To address these research gaps, the U.S. Geological Survey initiated the first national-scale effort to establish a baseline of total gaseous mercury stable isotope values at 31 sites distributed across the United States. Results indicated that unique Hg sources, such as Hg evasion from an elemental Hg contaminated site or free tropospheric intrusions in high altitude sites, were distinguishable from background atmospheric values. Minor gradients were observed across the nation, with regions of heavy industrial activity demonstrating lower , but no consistent changes in other isotopes such as and were observed. Furthermore, was impacted by foliar uptake and senescence but trends varied between forested regions in the northeastern and midwestern United States. These data demonstrate regional emission sources and other environmental variables can impact total gaseous Hg (TGM) isotope values, highlighting the need to characterize atmospheric Hg isotopes over larger geographical areas to evaluate changes related to national and international Hg regulations.

Emissions of methane (CH) in the Permian basin (USA) have been derived for 2019 and 2020 from satellite observations of the Tropospheric Monitoring Instrument (TROPOMI) using the divergence method, in combination with a data driven method to estimate the background column densities. The resulting CH emission data, which have been verified using model data with known emissions, have a spatial resolution of approximately 10 km. The CH emissions show moderate spatial correlation with the locations of oil and gas production and drilling activities in the Permian basin, as well as with emissions of nitrogen oxides (NO). Analysis of the emission maps and time series indicates that a significant fraction of methane emissions in the Permian basin is from frequent widespread emissions sources, rather than from a few infrequent very large unplanned releases, which is important considering possible CH emission mitigation strategies. In addition to providing spatially resolved emissions, the divergence method also provides the total emissions of the Permian basin and its main sub-basins. The total CH emission of the Permian is estimated as 3.0 ± 0.7 Tg yr for 2019, which agrees with other independent estimates based on TROPOMI data. For the Delaware sub-basin, it is estimated as 1.4 ± 0.3 Tg yr for 2019, and for the Midland sub-basin 1.2 ± 0.3 Tg yr. In 2020 the emissions are 9% lower compared to 2019 in the entire Permian basin, and respectively 19% and 27% for the Delaware and Midland sub-basins.

Variations in atmosphere total column-mean CO (XCO) collected by the National Aeronautics and Space Administration's Orbiting Carbon Observatory-2 satellite can be used to constrain surface carbon fluxes if the influence of atmospheric transport and observation errors on the data is known and accounted for. Due to sparse validation data, the portions of fine-scale variability in XCO driven by fluxes, transport, or retrieval errors remain uncertain, particularly over the ocean. To better understand these drivers, we characterize variability in OCO-2 Level 2 version 10 XCO from the seasonal scale, synoptic-scale (order of days, thousands of kilometers), and mesoscale (within-day, hundreds of kilometers) for 10 biomes over North America and adjacent ocean basins. Seasonal and synoptic variations in XCO reflect real geophysical drivers (transport and fluxes), following large-scale atmospheric circulation and the north-south distribution of biosphere carbon uptake. In contrast, geostatistical analysis of mesoscale and finer variability shows that real signals are obscured by systematic biases across the domain. Spatial correlations in along-track XCO are much shorter and spatially coherent variability is much larger in magnitude than can be attributed to fluxes or transport. We characterize random and coherent along-track XCO variability in addition to quantifying uncertainty in XCO aggregates across typical lengths used in inverse modeling. Even over the ocean, correlated errors decrease the independence and increase uncertainty in XCO. We discuss the utility of computing geostatistical parameters and demonstrate their importance for XCO science applications spanning from data reprocessing and algorithm development to error estimation and carbon flux inference.

Uncertainties of regional precipitation projections are substantial, and users of such projections face the so-called practitioners dilemma: a plethora of projections with different models from different ensembles of different types and generations are available. But the consistency of these projections has not been systematically assessed, such that no clear guidance about the use of these ensembles exists. Therefore, we systematically compare, separately for each season, projections of mean precipitation and extremes of daily precipitation over Europe across a wide range of climate model ensembles. We specifically consider the global climate model ensembles CMIP3, CMIP5, Coupled Model Intercomparison Project Phase 6 (CMIP6), and HighresMIP as well as the regional climate model ensembles ENSEMBLES and EURO-CORDEX. All considered ensembles agree in their large-scale patterns of changes for both mean and extreme daily precipitation, but at the regional scale, substantial discrepancies and inconsistencies are evident. Within and across ensemble spread is strongest in summer, in particular for the drying trend over the Mediterranean and Eastern Europe. CMIP5 and CMIP6 are broadly consistent. The regional climate model (RCM) ensembles modify the signals of the driving global climate models indicating potential added value. The high resolution of the RCM and HighresMIP ensembles seems to be key over the Alps for summer precipitation. Our study provides important information for users of climate projections as it helps to establish continuity across generations and types of climate models, and aids the design of new climate impact studies.

Profiles of stratospheric aerosol size distributions have been measured using balloon-borne optical particle counters, from Laramie, Wyoming (41°N) since 1971. In 2019, this measurement record transitioned to the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, Colorado (40°N). The new LASP Optical Particle Counter (LOPC), the fourth generation of instruments used for this record, is smaller and lighter (2 kg) than prior instruments, measures aerosols with diameters ≥0.3-30 μm in up to 450 size bins, with a flow rate of 20 L min. The improved size resolution enables the complete measurement of size distributions, and calculation of aerosol extinction without fitting distribution shapes. The higher flow provides the sensitivity required to measure super-micron particles in the stratosphere. The LOPC has been validated against prior Wyoming OPCs, through joint flights, laboratory comparisons, and statistical comparisons with the Wyoming record. The agreement between instruments is generally within the measurement uncertainty of ±10%-20% in sizing and ±10% in concentration, and within ±40% for calculated aerosol moments. The record is being continued with balloon soundings every 2 months from Colorado, coordinated with measurements of aerosol extinction from the SAGE III instrument on the International Space Station. Comparisons of aerosol extinction from the remote and platforms have shown good agreement in the stratosphere, particularly for wavelengths <755 nm and altitudes <25 km. For extinction wavelengths ≥1,021 nm and altitudes above 25 km SAGE III/International Space Station extinction has a low bias relative to the measurements, yet still within the ±40% uncertainty.

Clouds and blowing snow (BLSN) occur frequently over Antarctica, where it is critical to understand their feedbacks to surface and atmospheric boundary layer processes. Dome C, an elevated East Antarctic station, dominated by lengthy periods of surface longwave (LW) radiative cooling, is selected to reveal cloud and BLSN impacts within a largely stable environment. The sky condition is classified as clear, cloudy, or BLSN, using 3 years of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite data. Co-located and contemporaneous in situ observations are used to investigate the relationship of sky condition with surface and atmospheric boundary layer thermal structure, focusing on seasonal variability. Results show that increased downwelling LW radiation from clouds abate surface radiative cooling losses, contributing to warming during all seasons. An increase of 3°C in the mean surface air temperature is observed during spring, whereas, a more dramatic rise (around 10°C), due to accompanying large-scale subsidence, is observed during fall and winter in association with clouds. For all seasons, the wind speed and wind speed shear are strongest during BLSN events, and the surface-based inversion is weakened by cooling which peaks in a shallow above-surface turbulent layer. The stronger background stability during fall and winter seasons, restricts turbulence and BLSN depths generally to the lowest tens of meters. The Earth's cryosphere is among the most rapidly evolving yet least well-observed regions, and knowledge of clouds and BLSN interactions with the typical stable atmospheric boundary layer can help further understand energy and moisture exchanges.

Wildfire emissions are a key contributor of carbonaceous aerosols and trace gases to the atmosphere. Induced by buoyant lifting, smoke plumes can be injected into the free troposphere and lower stratosphere, which by consequence significantly affects the magnitude and distance of their influences on air quality and radiation budget. However, the vertical allocation of emissions when smoke escapes the planetary boundary layer (PBL) and the mechanism modulating it remain unclear. We present an inverse modeling framework to estimate the wildfire emissions, with their temporal and vertical evolution being constrained by assimilating aerosol extinction profiles observed from the airborne Differential Absorption Lidar-High Spectral Resolution Lidar during the Fire Influence on Regional to Global Environments and Air Quality field campaign. Three fire events in the western U.S., which exhibit free-tropospheric injections are examined. The constrained smoke emissions indicate considerably larger fractions of smoke injected above the PBL ( , 80%-94%) versus the column total, compared to those estimated by the WRF-Chem model using the default plume rise option (12%-52%). The updated emission profiles yield improvements for the simulated vertical structures of the downwind transported smoke, but limited refinement of regional smoke aerosol optical depth distributions due to the spatiotemporal coverage of flight observations. These results highlight the significance of improving vertical allocation of fire emissions on advancing the modeling and forecasting of the environmental impacts of smoke.