JGR–Atmospheres

Understanding sources of oxygenated volatile organic compounds (OVOCs) in the atmosphere still has large uncertainties. In this study, an improved OVOC source apportionment model was developed by principal component analysis and multiple linear regression based on the online monitoring of nonmethane hydrocarbons (NMHCs) and OVOCs in a dense urban agglomeration in the winter. The modeled concentrations were in good agreement with the measured concentrations (R 2 = 0.56–0.97). The concentrations of major OVOCs, except for 2-methylacrolein, were greatly affected by anthropogenic sources (15.8%–76.8%) and secondary generation (0.0%–51.7%), while transport and natural sources contributed to 0.0%–26.8% and 0.0%–32.0%, respectively. The selection of isoprene as the natural tracer led to an underestimation of the OVOC species from primary emission and an overestimation from natural sources. In addition, photochemical reactions significantly reduced the simulation accuracy of the model for NMHCs in the afternoon, with the R 2 of 0.60 ± 0.23, which was lower than the overall value of 0.82 ± 0.11. However, the R 2 for OVOCs (0.83 ± 0.14) did not decrease significantly in the afternoon due to the compensation of secondary oxidation. Furthermore, the concentration gradient distribution of the species gradually changes from a normal distribution to an exponential normal distribution with a decrease in concentration, the accuracy of the model was influenced by the degree of matching between tracer and species concentration gradient as species concentration change. Developing models with additional tracers at different concentration levels may enhance the robustness of the OVOC source apportionment model without increasing its complexity.

Coastal marine heatwaves (MHWs) modulate coastal climate through ocean-land-atmosphere interactions, but little is known about how coastal MHWs interact with coastal cities and modify urban thermal environment. In this study, a representative urban coastal environment under MHWs is simplified to a mixed convection problem. Fourteen large-eddy simulations (LESs) are conducted to investigate how coastal cities interact with MHWs. We consider the simulations by simple urban roughness setup (Set A) as well as explicit urban roughness representation (Set B). Besides, different MHW intensities, synoptic wind speeds, surface fluxes of urban and sea patches are considered. Results suggest that increasing MHW intensity alters streamwise potential temperature gradient and vertical velocity direction. The magnitude of vertical velocity and urban heat island (UHI) intensity decrease with increasing synoptic wind speed. Changing urban or sea surface heat flux also leads to important differences in flow and temperature fields. Comparison between Set A and B reveals a significant increase of vertical velocity magnitude and UHI intensity. To further understand this phenomenon, a canopy layer UHI model is proposed to show the relationship between UHI intensity and urban canopy, thermal heterogeneity and mean advection. The effect of urban canopy is considered in terms of an additional vertical velocity scale that facilitates heat transport from the heated surface and therefore increases UHI intensity. The model can well explain the trend of the simulated results and implies that overlooking the effect of urban canopy underestimates canopy UHI in urban coastal environment.

The onset of the South China Sea summer monsoon (SCSSM) has profound impacts on meteorological conditions over East Asia. However, whether the interannual variability in monsoon onset date impacts ozone (O3) pollution remains unclear. Here, we investigate the relationship between early onset of SCSSM and late spring O3 in southern China. Our results show notable differences in surface O3 concentrations before and after SCSSM onset during early onset events in southern China. The enhanced O3 of 11.1 μg m−3 is supported by increased air temperature and solar radiation of 1.1 K and 30.9 W m−2 and reduced relative humidity of −5.7%. Both observation and model simulations confirm that O3-favorable meteorological conditions modulated by early SCSSM onset can be found in May. It increases the boundary layer height and biogenic emissions of volatile organic compounds, enhancing O3 by 10 μg m−3 over southern China. Chemical processes dominate such increases in O3 with enhanced chemical production of 0.27 Tg month−1. Descending motion in southern China vertically transports O3 to surface by 0.10 Tg month−1, whereas horizontal advection reduces O3 concentration by 0.12 Tg month−1. The meteorological responses to colder sea surface temperature (SST) in the central equatorial Pacific are pronounced, leading to higher O3 concentrations over the Yangtze River Delta, while warmer SST in the Philippine Sea contributes O3 over the Pearl River Delta and eastern China. This study highlights the importance of SCSSM onset with respect to O3 in southern China, with promising applications in management of air pollution and agriculture.

Climate models are essential to understand and project climate change, yet long-standing biases and uncertainties in their projections remain. This is largely associated with the representation of subgrid-scale processes, particularly clouds and convection. Deep learning can learn these subgrid-scale processes from computationally expensive storm-resolving models while retaining many features at a fraction of computational cost. Yet, climate simulations with embedded neural network parameterizations are still challenging and highly depend on the deep learning solution. This is likely associated with spurious non-physical correlations learned by the neural networks due to the complexity of the physical dynamical system. Here, we show that the combination of causality with deep learning helps removing spurious correlations and optimizing the neural network algorithm. To resolve this, we apply a causal discovery method to unveil causal drivers in the set of input predictors of atmospheric subgrid-scale processes of a superparameterized climate model in which deep convection is explicitly resolved. The resulting causally-informed neural networks are coupled to the climate model, hence, replacing the superparameterization and radiation scheme. We show that the climate simulations with causally-informed neural network parameterizations retain many convection-related properties and accurately generate the climate of the original high-resolution climate model, while retaining similar generalization capabilities to unseen climates compared to the non-causal approach. The combination of causal discovery and deep learning is a new and promising approach that leads to stable and more trustworthy climate simulations and paves the way toward more physically-based causal deep learning approaches also in other scientific disciplines.

Sea ice is a central part of the Arctic climate system, and its changes have a significant impact on the Earth's climate. Yet, its state, especially in summer, is not fully understood and correctly predicted in dynamical forecast systems. In this study, the seasonal prediction skill of Arctic sea ice is investigated in a high-resolution dynamical forecast system, the China Meteorological Administration Climate Prediction System (CMA-CPSv3). A 7-month-long retrospective forecast is made every other month from 2001 to 2021. Employing the anomaly correlation coefficient as the metric of the prediction skill, we show that CMA-CPSv3 can predict regional Arctic sea ice extent and sea ice thickness up to 7 lead months. The Bering Sea exhibits the highest prediction skill among the 14 Arctic subregions. CMA-CPSv3 outperforms the anomaly persistence forecast in the Bering Sea, Sea of Okhotsk, Laptev Sea, and East Siberian Sea. The sources of the sea ice prediction skill partly come from the good performance of upper ocean temperature in CMA-CPSv3. This holds true not only for winter sea ice in the Arctic marginal seas but also for summer sea ice in several Arctic central seas. Furthermore, CMA-CPSv3 exhibits a strong relationship between the variability of sea ice and surface heat fluxes. This underscores the importance of enhancing the representation of air-sea heat exchanges in dynamical forecast systems to improve the prediction skill of sea ice.

An unconventional, island-induced eyewall replacement (IER) occurred in Super Typhoon Mangkhut (2018) when it crossed Luzon Island. Upon landfall, its original compact eyewall broke down and dissipated rapidly. As Mangkhut exited Luzon and entered the South China Sea, a much larger new eyewall formed at a radius of 150–200 km from the storm center, three times larger than the original one. Unlike the eyewall replacement cycle in intense tropical cyclones, the breakdown of the original eyewall preceded the formation of the new eyewall (NEF) in Mangkhut. This evolution was reproduced reasonably well in a control experiment using the Weather Research and Forecasting Model. Two sensitivity experiments showed that the IER was triggered by Luzon Island, whose terrain is essential for not only the destruction of the original eyewall but also the NEF. In an axisymmetric framework, it is demonstrated for the first time that the NEF was preceded by the following processes: (a) an increase in the outward-directed agradient force in the boundary layer (BL) inflow region after landfall due to differential rates of weakening between the radial pressure gradient and the tangential wind, (b) creation of a BL deceleration zone, (c) localized reinforcement of BL inflow deceleration within the NEF region when Mangkhut re-entered the ocean, following an exisiting framework of an unbalanced dynamical pathway, and (d) strengthening of the BL convergence and uplift which initiated and sustained the deep convection of the new eyewall.

While heat waves are local extreme weather events, a slowly propagating planetary-scale Rossby wave pattern is statistically correlated to the occurrence of heat waves in the US. However, whether this correlation indicates that such planetary wave patterns physically cause the enhanced statistics of local heat waves is debatable. In this work, we hypothesize that the atmospheric climatological state controls the slowly propagating wave pattern, setting up a conducive large-scale environment for local US heat waves. We implement an idealized dry dynamic core model with an iterative approach to simulate the realistic North American summer climatological state. As the idealized atmospheric model can generate similar zonal wavenumber five planetary wave patterns propagating throughout North America, significantly more heat waves are generated, and the statistics of heat waves become consistent with those estimated in reanalysis products. The slowly propagating Rossby wave packets with a timescale of 20–30 days may serve as a new source of intraseasonal predictability in the midlatitudes.

Extreme precipitation events associated with atmospheric rivers (ARs) trigger floods, landslides, and avalanches that threaten lives and livelihoods in Southeast Alaska. Six rural and indigenous communities (Hoonah, Klukwan, Skagway, Yakutat, Craig, and Kasaan) identified specific needs regarding these hazards and joined the Southeast Alaska Coastlines and People (CoPe) Kutí Hub to address the shared challenge of understanding and predicting these events. This study presents a climatology (1980–2019) of synoptic, mesoscale, and local meteorological characteristics of ARs and heavy precipitation across this region. High-amplitude upper-level patterns across the northeastern Pacific Ocean favor ARs reaching Southeast Alaska, where moisture is orographically lifted, resulting in heavy precipitation. In the six communities, ARs occur 8–15 days per month, yet only 6 AR days per year account for up to 68%–91% of precipitation extremes. Furthermore, 80%–96% of days with extreme precipitation have >75th percentile integrated water vapor transport (IVT), demonstrating the strong relationship between IVT and extreme precipitation. This study also highlights the relationship between IVT direction and complex coastal topography in determining precipitation extremes. For example, in Klukwan and Skagway, 80%–90% of extreme AR days have south-southwesterly or south-southeasterly IVT. Coastal communities like Yakutat experience higher IVT and precipitation overall, and although southeasterly IVT is more common, extreme precipitation events are most common with southwesterly IVT. Collaboration with the National Weather Service in Juneau, Alaska will lead to improved situational awareness, forecasts, and Impact Decision Support Services to communities, saving lives and property in a region vulnerable to the impacts of climate change.

Sea surface temperature (SST) gradients are a primary driver of low-level wind convergence in the east Pacific Inter-Tropical Convergence Zone (ITCZ) through their hydrostatic relationship to the surface pressure gradient force (PGF). However, the surface PGF may not always align with SST gradients due to variations in boundary layer temperature gradients with height, that is, the boundary layer contribution to the surface PGF. In this study, we investigate the observed northern hemisphere position of the east Pacific ITCZ using a slab boundary layer model (SBLM) driven by different approximations of the boundary layer virtual temperature field. SBLM simulations using the entire boundary layer virtual temperature profile produce a realistic northern hemisphere ITCZ. However, SST-only simulations produce excessive equatorial divergence and southern hemisphere convergence, resulting in a latitudinally confined double ITCZ-like structure. Observed virtual temperature gradients highlight the importance of northward temperature gradients strengthening with height from the equator to 15°S below the trade wind inversion (TWI). Our interpretation is that the equatorial cold tongue induces relatively weak high surface pressure and double ITCZ-like convergence because the resulting layer of cold air is shallow. Concurrently, relatively strong high surface pressure spreads out in the southern hemisphere due to interactions between stratocumulus clouds and the ocean surface. Together, the equatorial cold tongue and the TWI/stratocumulus clouds enable a more northern hemisphere dominant ITCZ. Thus, we provide evidence of a dynamical link between the equatorial cold tongue, low clouds, and double ITCZs, which continue to be problematic in Earth system models.

The impacts on atmospheric ozone (O3) due to wildfires are difficult to characterize due to the many factors that affect O3's formation rate and the episodic nature of fire events. This study uses a very large set of air quality data (518,987 6-hr data points) collected in Western Canada from 2001 to 2019 to determine the prevalence and severity of fire-driven increases to measured O3 values. Wildfire events are identified using the automated Trajectory-Fire Interception Method (TFIM), looking for interceptions between HYSPLIT back-trajectories and wildfire hotspots. As with other studies, which have used more restricted sets of measurements, the results from this large-scale, data-driven approach indicate increases in the O3 mixing ratio with wildfire impact, on average ∼2 ppbv across all wildfire time periods. To understand the factors which lead to the largest increases, and to better compare to other studies looking at individual fire events, wildfire events are classified using their distance from the air quality measurement location, time of measurement, and corresponding PM2.5 value. Increases to O3 are largest during the daytime, when fires occur close to the air quality measurement, and with corresponding measurements of PM2.5 > 25 μg/m3. When an upper-limit correction for the bias in UV photometric detection of ozone with MnCl2 scrubbers is applied, the analysis still yields a persistent increase in O3 during wildfires except for the highest PM2.5 levels. However, a more accurate correction to the potential bias is needed to fully understand the magnitude of the impact of wildfires on O3.

Global satellite studies show a maximum in deep convection and lightning downstream of the Andes in subtropical South America. The Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign was designed to investigate the physical processes that contribute to the rapid development of deep convection and mesoscale convective systems (MCSs) in Argentina. A lightning mapping array (LMA) was deployed to Argentina as part of RELAMPAGO to collect lightning observations from extreme storms in the region. This study combines lightning data from the LMA and the Geostationary Lightning Mapper onboard GOES-16 with 1-km gridded radar data to examine the electrical characteristics of a variety of convective storms throughout their life cycle observed during RELAMPAGO. Results from the full campaign show 48% of flashes are associated with deep convection that occurs along the eastern edge of the Sierras de Córdoba (SDC) overnight. These flashes are 65 km2 smaller on average compared to stratiform flashes, which occur most frequently 50–100 km east of the SDC in the early morning hours, consistent with the upscale growth of MCSs off the terrain. Analysis of the 13–14 December MCS shows that sharp increases in flash rates correspond to deep and wide convective cores that have high graupel and hail mass, 35-dBZ volume, and ice water path. This work validates previous satellite studies of lightning in the region, but also provides higher spatial and temporal resolution information across the convective life cycle that has not been available in previous studies.

As a result of their important role in weather and the global hydrological cycle, understanding atmospheric rivers' (ARs) connection to synoptic-scale climate patterns and atmospheric dynamics has become increasingly important. In addition to case studies of two extreme AR events, we produce a December climatology of the three-dimensional structure of water vapor and O3 (ozone) distributions associated with ARs in the northeastern Pacific from 2004 to 2014 using MERRA-2 reanalysis products. Results show that positive O3 anomalies reside in dry intrusions of stratospheric air due to stratosphere-to-troposphere transport (STT) behind the intense water vapor transport of the AR. In composites, we find increased excesses of O3 concentration, as well as in the total O3 flux within the dry intrusions, with increased AR strength. We find that STT O3 flux associated with ARs over the NE Pacific accounts for up to 13% of total Northern Hemisphere STT O3 flux in December, and extrapolation indicates that AR-associated dry intrusions may account for as much as 32% of total NH STT O3 flux. This study quantifies STT of O3 in connection with ARs for the first time and improves estimates of tropospheric ozone concentration due to STT in the identification of this correlation. In light of predictions that ARs will become more intense and/or frequent with climate change, quantifying AR-related STT O3 flux is especially valuable for future radiative forcing calculations.

Recent climatic trends have increased forest fire activity in Canada. This study aimed to evaluate how forest fire conditions might evolve across the Canadian boreal forests in the future and to inform discussions about the impact of climate change on fire danger and severity. I generated surfaces of daily climate conditions using daily observational data from meteorological stations across Canada from 1976 to 2014. Simulated daily values of the same climatic variables were obtained from four earth system models of the Coupled Model Intercomparison Project Phase 5 project (CMIP5) for the historical period 1976–2005. Daily climate values for 2006–2100, forced by three climate change scenarios, Representative Concentration Pathway (RCP) 2.6, RCP 4.5, and RCP 8.5, were also simulated by the models. The simulated data were bias-corrected and delta-downscaled to project future trends in fire activity to assess the spatiotemporal variations of the potential impacts of climate change on forest fires. My results suggest fire danger and severity would increase in many Canadian boreal forests under RCP 8.5. The changes in fire conditions under RCP 2.6 were the least noteworthy; RCP 4.5 was associated with medium-level changes.

The maximum annual freeze depth (MFD) is a primary indicator of the thermal state of frozen ground, affecting ecosystems, hydrological processes, vegetation growth, infrastructure, and human activities in cold regions. It is thus important to quantify the past, present, and future spatial and temporal variability of MFD at the hemispheric scale. We develop a data-driven MFD simulation method within a machine learning framework, integrating MFD observations from meteorological stations and several environmental predictors, to analyze past and future scenarios in the Northern Hemisphere (NH). Based on ERA5 reanalysis estimates and historical to future CMIP6 scenarios, the NH MFD averaged 133 cm (ERA5) and 131 cm (CMIP6) during 1981–2010, and will vary 81–112 cm during 2015–2100 depending on the emission scenario. During 1950–2013, MFD decreased by 0.37 cm/a (ERA5) versus 0.22 cm/a (CMIP6), and is projected to decrease 0.16–0.69 cm/a by 2100. During 1981–2010, MFD decreased by an average of 19.1% (ERA5) and 13.9% (CMIP6), with a net change of −17 cm (ERA5) and −13 cm (CMIP6). Depending on the emission scenario, MFD will decrease 11% (−12 cm) to 42% (−19 cm) between 2015 and 2099 relative to the 1981–2010. Warming, increased moisture, warmer cold seasons, warmer warm seasons, shallower snow depths, and increased vegetation cover all lead to a reduction in MFD. The results from this novel machine learning approach provide useful insights regarding the fate of future frozen ground changes.

A new method is proposed for deriving extremely low frequency (ELF) wave arrival azimuths using the wide range of signal amplitudes, contrary to previously applied high amplitude impulses only. The method is applied to observations from our new magnetic sensor in the Hylaty station with an 18 bit dynamic range and a 3 kHz sampling frequency. We analyzed a day of 15 January 2022, to test the procedure against the ability to extract ELF signals generated during the Hunga Tonga volcano eruption. With complementary filtering of power line 50 Hz signatures, precise azimuth information can be extracted for waves from a multitude of thunderstorms on Earth varying during the day at different azimuths. A phenomenon of successive regular variation—decay or activation—of thunderstorms activity with varying azimuth is observed, possibly due to passing over the solar (day/night) terminator, and signatures of azimuth direction change during this passage can be noted. We also show that the erupting Hunga Tonga volcano associated impulses dispersed due to a long propagation path are clearly revealed in the azimuth distribution with analysis using parameters fitted to measure slowly varying signals, but not for fast varying impulses. We show that the Hunga Tonga related signals arrive from the azimuth ≈10° smaller than the geographic great circle path. The discrepancy is believed to be due to propagation through the polar region and in the vicinity of the solar terminator.

The Madden–Julian Oscillation (MJO) displays an evident seasonality in its spatiotemporal scale selection. In boreal winter, the MJO is best behaved at wavenumber 2 and oscillates on a narrow time scale centering on a ∼55-day period. In contrast, a wavenumber-1 selection and a broad oscillation frequency centering on a ∼33-day period are observed in boreal summer. Using the space-time cross-spectral analysis between convection and moisture budget, we reveal that column processes determine seasonal variations in the MJO spatial organization, while the MJO is strongly damped by the horizontal moisture advection mainly due to its meridional component (Vadv). A timescale decomposition analysis suggests that the damping effect of Vadv results primarily from high-frequency synoptic-scale disturbances, and the Vadv component related to the seasonal-mean moisture generally supports the MJO growth at large wavenumbers while inhibits the growth of small wavenumbers, which is the most evident in boreal summer. The advection of seasonal-mean moisture by anomalous zonal winds supports the MJO growth at all wavenumbers in boreal winter, but in boreal summer the growth becomes weak and even turns negative for small wavenumbers. Furthermore, different MJO timescale selections in winter versus summer are rooted in the Vadv, as both the column processes and zonal moisture advection mainly support the propagation of high-frequency modes. Observational results related to the horizontal seasonal-mean moisture advection are reasonably validated in a dynamical moisture model. These findings advance our understanding of the MJO seasonality and offer alternative diagnoses for evaluating the MJO simulation fidelity in contemporary climate models.

Polar Lows (PLs) are intense maritime meso-scale cyclones that form over cold ocean regions in high latitudes. They affect the weather and climate of Antarctica and the surrounding ocean by producing storms with near or above gale-force winds and heavy precipitation. This study investigated PLs in the Southern Ocean (SO) between 60 and 85°S from 2000 to 2020, using a tracking algorithm applied to the ERA5, CCMP, and NCEP/NCAR data sets. The results showed that PLs are most frequent in winter (JJA) and least frequent in summer (DJF), with a maximum in 2018 and a minimum in 2014. The results also show that PLs are concentrated over the Antarctic Circumpolar Current (ACC) region, along the Ross Sea, the Amundsen Sea, and the Bellingshausen Sea from April to October. These regions have strong thermal gradients and orographic forcing that enhance cyclogenesis. Our research reveals that there were 1,073 occurrences of PLs during the study period, with an average 51 cases per year. The study also explored the influence of sea surface temperature anomalies, sea surface currents, and MCAOs Index on PL genesis. The Amundsen-Bellingshausen Seas Low (ABSL) and Pacific Ocean currents have a considerable effect on PLs developing in the SO. This research provides valuable information on PLs in the SO, which are poorly observed and understood. However, more research is required to understand how these phenomena are evolving in the present, and how they will change in the future.

Investigating lightning is of key significance in understanding the lightning mechanism and mitigating lightning hazards. We reported an experiment of investigating lightning through three-dimensional (3D) thunder locating using a Distributed Acoustic Sensing (DAS) array in Hefei, China. In this experiment, we recast a 7.7 km long urban telecom optical fiber cable as 3,850 sensors using the DAS technique. From dense DAS recording, we manually identified 101 thunder events during six positive cloud-to-ground (CG) lightning flashes within 20 min. The DAS recorded thunder signals are dominated by direct acoustic waves rather than air-ground coupled surface waves. The thunder events were then located using the arrival times of thunder signals. The locations and amplitudes of thunder events are generally consistent with those from the conventional lightning detection data set and broadband magnetic field. There is likely a correlation between the maximum strength of thunder events and the highest peak current for individual CG flashes. Moreover, the comparison with weather radar observations indicates that lightning usually originated from areas of high reflectivity (e.g., ≥50dBz) with diffusely distributed (from ground surface to ∼5 km altitude) thunder events and extended in a narrow altitude range of 3–5 km to areas with low radar reflectivity.

The wetland loss rate in Iran is faster than the global average. Comprehending the shrinkage rate in Iranian wetlands and identifying the underlying drivers of these changes is essential for safeguarding their ecosystems' health and services. This study proposes a novel gray-box modeling framework to quantify the effects of climate change and anthropogenic activities on the wetlands, by combining process-based and machine learning models. The developed model is utilized to project the Anzali coastal wetland shrinkage by simulating the complex interaction between the meteorological, hydrological, anthropogenic and sea water level characteristics, and the changes in wetland water surface area. Our framework aggregates Soil and Water Assessment Tool model, the 12 General Circulation Models of the Coupled Model Intercomparison Project Phase 6, Landsat imagery, and the Long Short-Term Memory model to project the shrinkage of the wetland till 2100. A comprehensive range of climate and Land Use/Cover change scenarios are analyzed. The results show that wetland will seasonally desiccate in 2058, mainly due to increasing air temperature, reduction in precipitation and inflow, excessive sediment loading to the wetland, and decline in the Caspian Sea level. For optimistic scenarios, where no changes in the Caspian Sea level is considered, the wetland will gradually diminish and become a seasonal waterbody by 2100. The outcomes of this study highlight that the Anzali wetland desiccation has profound implications for the regional-scale ecological balance, ecosystem health and function, public health, and local economy. Robust environmental interventions and sustainable development strategies are urgently needed to mitigate the detrimental impacts of climate and anthropogenic drivers on the wetland.

Recent studies have suggested that microbial aerosolization in wildfire smoke is an understudied source of microbes to the atmosphere. Wildfire smoke can travel thousands of kilometers from its source with the potential to facilitate the transport of microbes, including microbes that can have far-reaching impacts on human or ecosystem health. However, the relevance of longer-range detection of microbes in smoke plumes remains undetermined, as previous studies have mainly focused on analyses of bioaerosols collected adjacent to or directly above wildfires. Therefore, we investigated whether wildfire smoke estimated to originate >30 km from different wildfire sources would contain detectable levels of bacterial and fungal DNA at ground level, hypothesizing that smoke-impacted air would harbor greater amounts and a distinct composition of microbes as compared to ambient air. We used cultivation-independent approaches to analyze 150 filters collected over time from three sampling locations in the western United States, of which 34 filters were determined to capture wildfire smoke events. Contrary to our hypothesis, smoke-impacted samples harbored lower amounts of microbial DNA. Likewise, there was a limited signal in the composition of the microbial assemblages detected in smoke-affected samples as compared to ambient air, but we did find that changes in humidity were associated with temporal variation in the composition of the bacterial and fungal bioaerosols. With our study design, we were unable to detect a robust and distinct microbial signal in ground-level smoke originating from distant wildfires.