Climate change and increases in the frequency and severity of climate-driven wildfires, particularly in the western United States, has serious ramifications for enhanced downwind reactive nitrogen (Nr) emissions, deposition, and critical load exceedances. Here we present a multi-decadal (2002 - 2021), harmonized model-data-driven study using the George Mason University North American Chemical Reanalysis (NACR) system and simulations including both "with-fire" and "without-fire" conditions to quantify the change in trends of fire activity and source contributions to total Nr emissions and deposition over the U.S. Our results show that fire activity has increased substantially in the western U.S., especially in the west-northwest U.S. for wildfires, and that this increase is associated with positive annual near-surface temperature and vapor pressure deficit anomalies compared to the period average. Major results and implications of this work are increasing trends in the contribution of climate-driven wildfires to higher Nr emissions, deposition, and critical load exceedances of up to 20-40% due to fires in the western U.S. There are also smaller increases (<5 %) in Nr deposition trends for the eastern U.S., which are related to greater occurrence and reporting of agricultural and prescribed burns.

Climate-driven increases in wildfires are altering atmospheric deposition patterns across the United States, with implications for ecosystem acidification and critical load (CL) exceedances. Using the 2002–2021 North American Chemical Reanalysis (NACR), we quantify multi-decadal trends in fire-derived base cation (Ca 2+ , Mg 2+ , K⁺, Na⁺) deposition and evaluate their capacity to buffer concurrent increases in reactive nitrogen (Nr) and sulfur (S) deposition from fires. We find substantial fire-derived enhancements in base cation deposition, often exceeding 100% near major wildfire regions, yet these increases are spatially inconsistent with Nr and S deposition maxima. Our results suggest that regions exhibiting the greatest relative increases in base cation deposition do not regularly coincide with those experiencing the largest Nr increases, thus limiting the potential for widespread acid buffering. After 2017, base cation contributions from fires rise sharply (~ 30 to 80%), reflecting intensifying fire activity, but combined Nr + S deposition remains overwhelmingly dominant, with increases 100 times larger than base cations due to fires. Overall, our results show that although fire-derived base cations provide localized buffering, they do not offset the broader rise in acidifying Nr and S deposition driven by climate-driven wildfires, reinforcing concerns about increasing CL exceedances in vulnerable western U.S. ecosystems.

There is strong evidence that evaluating different parameterization schemes over diverse land surface forcings and surface-layer (SL) conditions will enhance our understanding of the physical processes required to improve the model parameterizations. Furthermore, shortcomings for representing SL heat, moisture, momentum, and turbulence using traditional parameterizations from Monin-Obukhov similarity theory (MOST) and the bulk Richardson approach are becoming well known within the scientific community. Overcoming the parameterizations' limitations requires evaluating the parameterizations across a range of land-cover types and meteorological conditions because the biosphere-atmosphere coupling is primarily linked to partitioning energy between sensible and latent heat fluxes. Recent studies over semiarid regions suggested that MOST better parameterized heat fluxes than the Richardson parameterizations, whereas the Richardson approach better parameterized kinematic and turbulence quantities. However, questions remain regarding whether the parameterizations' efficacy over drylands can be explained by physical parameters, such as the observed Bowen ratio (i.e., the ratio of the surface sensible heat flux to the surface latent heat flux). Addressing these questions allows one to more confidently use the parameterizations in land surface models. In this study, we used micrometeorological observations from two semiarid grassland sites, one in southeastern Arizona and a second in northwestern Texas, for a 3-yr period (1 January 2016-31 December 2018). We found that the heat flux, moisture flux, and turbulence parameterizations' efficacy do not vary with observed Bowen ratio. Furthermore, the MOST turbulence parameterizations sometimes performed better than the Richardson parameterizations, suggesting that caution is warranted particularly when applying the latter to semiarid regions.

Turbulence governs many atmospheric processes including mixing, transport, and energy transfer. Consequently, there is a strong need for the examination and validation of existing turbulence theories. The HOckey-Stick Transition (HOST) hypothesis was proposed to challenge traditional understanding of near-surface turbulence processes derived from Monin-Obukhov Similarity Theory (MOST). Within the MOST framework, the momentum flux entirely depends upon partial derivative U/partial derivative z (i.e., the change in mean wind speed (U) with height (z)), but this relationship is not as straightforward under HOST. Because HOST was developed using observations over relatively uniform, homogeneous terrain, questions arise regarding HOST's applicability within and above heterogeneous forest canopies where multi-level turbulence measurements are somewhat rare but are essential for developing a unified similarity scaling applicable over complex surfaces. To this end, we used one year (1 January 2016 through 31 December 2016) of turbulence measurements sampled at eight heights along a 60-m tower within and above a mixed deciduous forest at Chestnut Ridge in eastern Tennessee in the southeastern U.S. We examined the diurnal and seasonal variability of selected turbulence parameters (i.e., friction velocity (u*) and turbulence velocity scale (VTKE)) to detail the micrometeorological characteristics of the site during the study period. We then used these turbulence measurements to evaluate HOST by determining their relationship with U and to assess the dependencies of this relationship on time of day, season, wind direction, and atmospheric stability. We found that HOST is most applicable under very stable regimes, whereas the relationships between u* and U, and between VTKE and U, were more linear above the forest canopy than within the forest canopy and when the canopy was not foliated. Overall, this work builds upon previous studies that have described limitations in MOST and identifies scenarios when the HOST hypothesis may be more applicable than MOST for representing near-surface turbulence processes.

The presence of dense forest canopies significantly alters the near-field dynamical, physical, and chemical environment, with implications for atmospheric composition and air quality variables such as boundary layer ozone (O3). Observations show profound vertical gradients in O3 concentration beneath forest canopies; however, most chemical transport models (CTMs) used in the operational and research community, such as the Community Multiscale Air Quality (CMAQ) model, cannot account for such effects due to inadequate canopy representation and lack of sub-canopy processes. To address this knowledge gap, we implemented detailed forest canopy processes – including in-canopy photolysis attenuation and turbulence – into the CMAQv5.3.1 model, driven by the Global Forecast System and enhanced with high-resolution vegetation datasets. Simulations were conducted for August 2019 over the contiguous US. The canopy-aware model shows substantial improvement, with mean O3 bias reduced from +0.70 ppb (Base) to −0.10 ppb (Canopy), and fractional bias from +9.71 % to +6.37 %. Monthly mean O3 in the lowest model layer (∼ 0–40 m) decreased by up to 9 ppb in dense forests, especially in the East. Process analysis reveals a 75.2 % drop in first-layer O3 chemical production, with daily surface production declining from 673 to 167 ppb d−1, driven by suppressed photolysis and vertical mixing. This enhances NOx titration and reduces O3 formation under darker, stable conditions. The results highlight the critical role of canopy processes in atmospheric chemistry and demonstrate the importance of incorporating realistic vegetation-atmosphere interactions in CTMs to improve air quality forecasts and health-relevant exposure assessments.

Ammonia ( N H 3 ) concentration and flux measurements made in 2016 in a mixed deciduous forest at the western North Carolina Coweeta Hydrologic Laboratory are analyzed using a multi-layer, one-dimensional column model with detailed canopy physics and bi-directional exchange. Simulations for April 26-30 and July 19-30 are presented to assess the model's ability to represent measured in-canopy N H 3 profiles and probe the processes that control bi-directional exchange with the canopy and forest floor. During dry canopy conditions, model simulations are found to well reproduce measured in-canopy profiles for both the April and July periods, given appropriate model inputs. Results from the model, and the shape of in-canopy N H 3 profiles, are sensitive to vertical turbulent mixing, the values of the input soil/litter emission potential, and the assumed litter resistance. N H 3 fluxes simulated above the forest canopy are very small (-25 to -5 ng m-2 s-1 in April and < 1 ng m-2 s-1 in July) with primarily deposition to the canopy during the April time period, but with mixed deposition/emission during July. The model also suggests that net deposition or emission of N H 3 can be a function of location within the canopy, depending on the difference between the air concentration and the effective canopy compensation point. However, during periods when the canopy is wet from overnight dew and drying rapidly, the model does a poor job of replicating in-canopy profiles, typically underestimating N H 3 concentrations, since the model does not account for the release of N H 3 from evaporating dew. Although available data during the field campaign are not sufficient to rule out other potential hypotheses, given that the model reasonably reproduces in-canopy profiles during dry canopy periods, but fails during periods of rapid drying, the results are suggestive that dew is playing a major role in N H 3 concentration changes observed in July during the field study. Additional studies and measurements are needed to determine the processes and environmental controls that affect N H 3 absorption and release from dew and to evaluate the importance of this process for modeling deposition and re-emission on the regional scale. Further questions that arise from our findings are whether the variation of N H 3 deposition or emission with location in the canopy is important from an ecological perspective and how in-canopy dynamics might be represented in regional-scale air quality models. Traditional big-leaf approaches of modeling N H 3 bi-directional exchange cannot account for in-canopy variation such as that presented here, and so multi-layer approaches may need to be developed for more nuanced estimates of N H 3 deposition to forest ecosystems.

It is well known that parameterizations developed using observations from flat terrain have difficulty over complex terrain, which motivates a better understanding of turbulence exchanges occurring in these areas. In this work we addressed the question of how the vertical variability of turbulence features evolves over the lowest few hundred meters of the convective and nocturnal boundary layer above a forested ridge as a function of cloud cover and mean wind. We used one year of observations obtained from a WindCube V2.1 lidar installed in eastern Tennessee in the Southeast U.S. coupled with observations from a 60-m micrometeorological tower. The wind lidar has 20-m range gates spanning from 40 m to 300 m above ground. We used the lidar's high-frequency observations to derive turbulent kinetic energy (TKE), vertical velocity variance (62w), vertical velocity skewness (S), and kurtosis (K). We observed the largest decrease in the diurnal wind speed on clear, windy days. Under clear sky conditions, increasing TKE and 62w yielded positive S throughout the lower convective boundary layer. Under cloudy regimes, the distribution of TKE was height-independent and corresponded with smaller 62w and near-zero S. Our results provide insights into turbulence processes over forested complex terrain and support the refinement of turbulence parameterizations used in weather forecast models.

AbstractThe representation of vegetative sub‐canopy wind is critical in numerical weather prediction (NWP) models for the determination of the air‐surface exchange processes of heat, momentum, and trace gases. Because of the relationship between wind speed and fire behaviors, the influence of the canopy on near‐surface wind speed is critical for prognostic fire spread models used in regional NWP models. In practice, the wind speed at the midflame point of fires (midflame wind speed) is used to determine the rate of fire spread. However, the wind speeds from most in situ measurements and NWP models are taken at some reference height above the canopy and fire flames. Hence, this study develops a modular and computationally‐efficient one‐dimensional model set composed of a canopy wind model and a wind adjustment factor (WAF) model for NWP applications across scales. The model set uses prescribed foliage shape functions to represent the vertical vegetation profile and its impacts on the three‐dimensional structure of horizontal wind speeds. Results from the canopy wind model well agree with ground‐based observations with average mean absolute bias, root mean square error and determination coefficients around 0.18 m s−1, 0.40 m s−1and 0.90, respectively. The WAF model provides midflame wind speeds by estimating the WAF based on canopy, fire and flame characteristics. Various user‐definable options provide flexibility to adapt to variations in canopy characteristics and additional complexities associated with wildfires. The model set is expected to improve NWP models by providing an improved representation of the sub‐grid wind flows at any spatial scale.

Abstract Soil bulk electrical conductivity (BEC) was evaluated alongside soil volumetric water content (VWC) and soil temperature measurements using the HydraProbe (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP) with accuracy range of BEC ≤ 0.3 S m−1, and the time domain reflectometry (TDR)‐315L Probe (model TDR‐315L, Acclima, Inc.) (hereafter called AP) suitable for BEC up to 0.6 S m−1, at 23 stations of the U.S. Climate Reference Network. Previous evaluations revealed inconsistent performance of both sensors in some clay soils using manufacturer‐recommended calibrations in converting dielectric permittivity measurements to VWC. Here, we found that hourly values of BEC reached 0.6 S m−1 in high clay content soils and exceeded 2 S m−1 in high saline soils, and these high values of BEC were associated with poor performance and failures of both HP and AP sensors. Large values of BEC occurred in predominantly saturated soils where VWC values reached about 0.5 m3 m−3 for saline soils and about 0.7 m3 m−3 for clay soils, while low magnitudes of BEC were associated with low soil water content and seldomly saturated soils. Low hourly BEC values of less than 0.1 S m−1 were observed in wide variety of soil types, where sensor performance was typically excellent. The most influential factor on BEC was high soil water content conditions. Although dielectric permittivity measurements in estimating the soil water content were sensitive to BEC as some high clay content and high salinity soils increased BEC, the impact of large BEC on dielectric permittivity measurements was smaller in the well‐drained top soil layers than in deep soil layers that remained near saturation. Soil temperature had only a small impact on BEC. With high clay content and high salinity, the specific area of clay minerals was also associated with the magnitude of BEC.

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM2.5 concentrations near the source region but higher AOD and PM2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %-30 % near the source regions and 5 %-10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (> 35 mu gm(-3)) predictions. Higher plume heights generally produce larger downwind PM2.5 exceedance areas. The PM2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM2.5 concentration and predicted PM2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.

Abstract The U.S. Climate Reference Network (USCRN) has been engaged in ground‐based soil water and soil temperature observations since 2009. As a nationwide climate network, the network stations are distributed across vast complex terrains. Due to the expansive distribution of the network and the related variability in soil properties, obtaining site‐specific calibrations for sensors is a significant and costly endeavor. Presented here are three commercial‐grade electromagnetic sensors, with built‐in thermistors to measure both soil water and soil temperature, including the SoilVUE10 Time Domain Reflectometry (TDR) probe (hereafter called SP) (Campbell Scientific, Inc.), 50 MHz coaxial impedance dielectric sensor (model HydraProbe, Stevens Water Monitoring Systems, Inc.) (hereafter called HP), and the TDR‐315L Probe (model TDR‐315L, Acclima, Inc.) (hereafter called AP), which were evaluated in a relatively nonconductive loam soil in Oak Ridge, TN, from 2021 to 2022. The HP manufacturer‐supplied calibration equation for loam soils was used in this study. While volumetric water content data from HP and AP were 82–99% of respective gravimetric observations at 10 cm, data from SP were only 65–81% of respective gravimetric observations in the top 20‐cm soil horizon, where soil water showed relatively large spatial variability. The poor performance of the SP is likely due to poor contact between SP sensor electrodes and soil and the presence of soil voids caused by the installation method used, which itself may have caused soil disturbance.

Wildfires emit vast amounts of aerosols and trace gases into the atmosphere, exerting myriad effects on air quality, climate, and human health. Ensemble forecasting has been proposed to reduce the large uncertainties in the wildfire air pollution forecast. This study presents the development of a multi‐model ensemble (MME) wildfire air pollution forecast over North America. The ensemble members include regional models (GMU‐CMAQ, NACC‐CMAQ, and HYSPLIT), global models (GEFS‐Aerosols, GEOS5, and NAAPS), and global ensemble (ICAP‐MME). Performance of the ensemble forecast was evaluated with MAIAC and VIIRS‐SNPP retrieved aerosol optical depth (AOD) and AirNow surface PM 2.5 measurements during the 2020 Western United States “Gigafire” events (August–September 2020). Compared to individual models, the ensemble mean significantly reduced the biases and produced more consistent and reliable forecasts during extreme fire events. For AOD forecasts, the ensemble mean was able to improve model performance, such as increasing the correlation to 0.62 from 0.33 to 0.57 by individual models compared to VIIRS AOD. The ensemble mean also yields the best overall RANK (a composite indicator of four statistical metrics) when compared to VIIRS and MAIAC AOD. For the surface PM 2.5 forecast, the ensemble mean outperformed individual models with the strongest correlation (0.60 vs. 0.43–0.54 by individual models), lowest fractional bias (0.54 vs. 0.55–1.32), highest hit rate (87% vs. 40%–82%), and highest RANK (2.83 vs. 2.40–2.81). Finally, the ensemble shows the potential to provide a probability forecast of air quality exceedances. The exceedance probability forecast can be further applied to early warnings of extreme air pollution episodes during large wildfire events.

We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Surface-layer parameterizations for heat, mass, momentum, and turbulence exchange are a critical compo-nent of the land surface models (LSMs) used in weather prediction and climate models. Although formulations derived from Monin-Obukhov similarity theory (MOST) have long been used, bulk Richardson (Rib) parameterizations have re-cently been suggested as a MOST alternative but have been evaluated over a limited number of land-cover and climate types. Examining the parameterizations' applicability over other regions, particularly drylands that cover approximately 41% of terrestrial land surfaces, is a critical step toward implementing the parameterizations into LSMs. One year (1 January-31 December 2018) of eddy covariance measurements from a 10-m tower in southeastern Arizona and a 200-m tower in western Texas were used to determine how well the Rib parameterizations for friction velocity (u*), sensible heat flux (H), and turbulent kinetic energy (TKE) compare against MOST-derived parameterizations of these quantities. Independent of stability, wind speed regime, and season, the Rib u* and TKE parameterizations performed better than the MOST parameterizations, whereas MOST better represented H. Observations from the 200-m tower indicated that the parameterizations' performance degraded as a function of height above ground. Overall, the Rib parameterizations revealed promising results, confirming better performance than traditional MOST relationships for kinematic (i.e., u*) and turbulence (i.e., TKE) quantities, although caution is needed when applying the Rib H parameterizations to drylands. These findings represent an important milestone for the applica-bility of Rib parameterizations, given the large fraction of Earth's surface covered by drylands.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Vadose Zone Journal. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Evaluation of the SoilVUE10 Time Domain Reflectometry for soil water measurements in testbed field conditionsAuthorsTimothyWilsoniDJohnKochendorferHowardDiamondTildenMeyersMarkHallBrentFrenchLatoyaMylesiDRickSaylorSee all authors Timothy WilsoniDCorresponding Author• Submitting AuthorNOAAiDhttps://orcid.org/0000-0003-1785-5323view email addressThe email was not providedcopy email addressJohn KochendorferNOAA-ARLview email addressThe email was not providedcopy email addressHoward DiamondNOAA-ARLview email addressThe email was not providedcopy email addressTilden MeyersNOAA-ARLview email addressThe email was not providedcopy email addressMark HallNOAAview email addressThe email was not providedcopy email addressBrent FrenchNOAAview email addressThe email was not providedcopy email addressLatoya MylesiDNOAA-ARLiDhttps://orcid.org/0000-0001-6589-3004view email addressThe email was not providedcopy email addressRick SaylorNOAA-ARLview email addressThe email was not providedcopy email address

Dry deposition of aerosols from the atmosphere is an important but poorly understood and inadequately modeled process in atmospheric systems for climate and air quality. Comparisons of currently used aerosol dry deposition models to a compendia of published field measurement studies in various landscapes show very poor agreement over a wide range of particle sizes. In this study, we develop and test a new aerosol dry deposition model that is a modification of the current model in the Community Multiscale Air Quality (CMAQ) model. The new model agrees much better with measured dry deposition velocities across particle sizes. The key innovation is the addition of a second inertial impaction term for microscale obstacles such as leaf hairs, microscale ridges, and needleleaf edge effects. The most significant effect of the new model is to increase the mass dry deposition of the accumulation mode aerosols in CMAQ. Accumulation mode mass dry deposition velocities increase by almost an order of magnitude in forested areas with lesser increases for shorter vegetation. Peak PM2.5 concentrations are reduced in some forested areas by up to 40% in CMAQ simulations. Over the continuous United States, the new model reduced PM2.5 by an average of 16% for July 2018 at the Air Quality System monitoring sites. For summer 2018 simulations, bias and error of PM2.5 concentrations are significantly reduced, especially in forested areas.

Wildfire outbreaks can lead to extreme biomass burning (BB) emissions of both oxidized (e.g., nitrogen oxides; NOx=NO+NO2) and reduced form (e.g., ammonia; NH3) nitrogen (N) compounds. High N emissions are major concerns for air quality, atmospheric deposition, and consequential human and ecosystem health impacts. In this study, we use both satellite-based observations and modeling results to quantify the contribution of BB to the total emissions, and approximate the impact on total N deposition in the western U.S. Our results show that during the 2020 wildfire season of August - October, BB contributes significantly to the total emissions, with a satellite-derived fraction of NH3 to the total reactive N emissions (median ~ 40%) in the range of aircraft observations. During the peak of the western August Complex Fires in September, BB contributed to ~55% (for the contiguous U.S.) and ~83% (for the western U.S.) of the total NOx and NH3 emissions. Overall, there is good model performance of the George Mason University-Wildfire Forecasting System (GMU-WFS) used in this work. The extreme BB emissions lead to significant contributions to the total N deposition for different ecosystems in California, with ~ a 235% relative increase (from 8.7 to 29.1 kg ha-1 year-1) in average deposition rate to major vegetation types (mixed forests + grasslands/shrublands/savanna) compared to the GMU-WFS simulations without BB emissions. For mixed forest types only, the average N deposition rate increases (from 7.5 to 48.2 kg ha-1 year-1) are even larger at ~ 543%. Such large N deposition due to extreme BB emissions are much larger than low-end critical load thresholds for major vegetation types (e.g., 3 kg ha-1 year-1), and thus may result in adverse N deposition effects across larger areas of lichen communities found in California’s mixed conifer forests.

Wildfire outbreaks can lead to extreme biomass burning (BB) emissions of both oxidized (e.g., nitrogen oxides; NOx = NO+NO2) and reduced form (e.g., ammonia; NH3) nitrogen (N) compounds. High N emissions are major concerns for air quality, atmospheric deposition, and consequential human and ecosystem health impacts. In this study, we use both satellite-based observations and modeling results to quantify the contribution of BB to the total emissions, and approximate the impact on total N deposition in the western U.S. Our results show that during the 2020 wildfire season of August-October, BB contributes significantly to the total emissions, with a satellite-derived fraction of NH3 to the total reactive N emissions (median ~ 40%) in the range of aircraft observations. During the peak of the western August Complex Fires in September, BB contributed to ~55% (for the contiguous U.S.) and ~ 83% (for the western U.S.) of the monthly total NOx and NH3 emissions. Overall, there is good model performance of the George Mason University-Wildfire Forecasting System (GMU-WFS) used in this work. The extreme BB emissions lead to significant contributions to the total N deposition for different ecosystems in California, with an average August - October 2020 relative increase of ~78% (from 7.1 to 12.6 kg ha-1 year-1) in deposition rate to major vegetation types (mixed forests + grasslands/shrublands/savanna) compared to the GMU-WFS simulations without BB emissions. For mixed forest types only, the average N deposition rate increases (from 6.2 to 16.9 kg ha-1 year-1) are even larger at ~173%. Such large N deposition due to extreme BB emissions are much (~6-12 times) larger than low-end critical load thresholds for major vegetation types (e.g., forests at 1.5-3 kg ha-1 year-1), and thus may result in adverse N deposition effects across larger areas of lichen communities found in California's mixed conifer forests.

The latest operational National Air Quality Forecast Capability (NAQFC) has been advanced to use the Community Multiscale Air Quality (CMAQ) model (version 5.3.1) with the CB6r3 (Carbon Bond 6 revision 3) AERO7 (version 7 of the aerosol module) chemical mechanism and is driven by the Finite-Volume Cubed-Sphere (FV3) Global Forecast System, version 16 (GFSv16). This update has been accomplished via the development of the meteorological preprocessor, NOAA-EPA Atmosphere–Chemistry Coupler (NACC), adapted from the existing Meteorology–Chemistry Interface Processor (MCIP). Differing from the typically used Weather Research and Forecasting (WRF) CMAQ system in the air quality research community, the interpolation-based NACC can use various meteorological outputs to drive the CMAQ model (e.g., FV3-GFSv16), even though they are on different grids. In this study, we compare and evaluate GFSv16-CMAQ and WRFv4.0.3-CMAQ using observations over the contiguous United States (CONUS) in summer 2019 that have been verified with surface meteorological and AIRNow observations. During this period, the Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was performed, and we compare the two models with airborne measurements from the NASA DC-8 aircraft. The GFS-CMAQ and WRF-CMAQ systems show similar performance overall with some differences for certain events, species and regions. The GFSv16 meteorology tends to have a stronger diurnal variability in the planetary boundary layer height (higher during daytime and lower at night) than WRF over the US Pacific coast, and it also predicted lower nighttime 10 m winds. In summer 2019, the GFS-CMAQ system showed better surface ozone (O3) than WRF-CMAQ at night over the CONUS domain; however, the models' fine particulate matter (PM2.5) predictions showed mixed verification results: GFS-CMAQ yielded better mean biases but poorer correlations over the Pacific coast. These results indicate that using global GFSv16 meteorology with NACC to directly drive CMAQ via interpolation is feasible and yields reasonable results compared to the commonly used WRF approach.