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Drought events have been linked with the enhancements of organic aerosols (OA), but the mechanisms have not been comprehensively understood. This study investigates the relationships between the monthly standardized precipitation-evapotranspiration index (SPEI) and surface OA in the contiguous United States (CONUS) during the summertime from 1998 to 2019. OA under severe drought conditions shows a significant increase in mass concentrations across most of the CONUS relative to non-drought periods with the Pacific Northwest (PNW) and Southeastern United States (SEUS) experiencing the highest average enhancement of 1.79 µg m-3 (112 %) and 0.92 µg m-3 (33 %), respectively. In the SEUS, a linear regression approach between OA and sulfate was used to estimate the isoprene epoxydiols derived secondary organic aerosol (IEPOX SOA), which is the primary driver of the OA enhancements under droughts due to the simultaneous increase of isoprene and sulfate. The rise of sulfate is mainly caused by the reduced wet deposition because of the up to 62 % lower precipitation amount. In the PNW, OA enhancements are closely linked to intensified wildfire emissions, which raise OA mass concentrations to be four to eight times higher relative to non-fire conditions. All ten Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) can capture the negative slopes between SPEI and OA in the PNW with CESM2-WACCM and GFDL-ESM4 performing the best and worst in predicting the OA enhancement under severe droughts. However, all models significantly underestimate the OA increase in the SEUS with Nor-ESM2-LM and MIRCO6 showing relatively better performance. This study reveals the key drivers of the elevated OA levels under droughts in the CONUS and underscores the deficiencies of current climate models in their predictive capacity for assessing the impact of future droughts on air quality. [ABSTRACT FROM AUTHOR]

Mechanisms for high offshore ozone (O3) events in the Houston area have not been systematically examined due to limited O3 measurements over water. In this study, we used the datasets collected by three boats deployed in Galveston Bay and the Gulf of Mexico during the Tracking Aerosol Convection Interactions ExpeRiment/Air Quality (TRACER-AQ) field campaign period (September 2021) in combination with the Weather Research and Forecasting (WRF) coupled Comprehensive Air quality Model with Extensions (CAMx) modeling system (WRF-CAMx) to investigate the reasons for high offshore O3. The model can capture the spatiotemporal variability of daytime (10:00-18:00) O3 for the three boats (R > 0.7) but tends to overestimate O3 by ~10 ppb on clean days and underestimate O3 by ~3 ppb during high-O3 events. The process analysis tool in CAMx identifies O3 chemistry as the major process leading to high O3 concentrations. The region-wide increase of long-lived VOCs through advection not only leads to more O3 production under a NOx-limited regime but also fosters VOC-limited O3 formation along western Galveston Bay and the Gulf coast under high-NOx conditions brought by the northeasterly winds from the Houston Ship Channel. Two case studies illustrate that high offshore O3 events can develop under both large- and meso-scale circulations, indicating both the regional and local emissions need to be stringently controlled. Wind conditions are demonstrated to be important meteorological factors in such events, so they must be well represented in photochemical models to forecast air quality over the urban coastal regions accurately. [ABSTRACT FROM AUTHOR]

As criteria pollutants from anthropogenic emissions have declined in the US in the last two decades, biomass burning (BB) emissions are becoming more important for urban air quality. Tracking the transported BB emissions and their impacts is challenging, especially in areas that are also burdened by anthropogenic sources like the Texas Gulf coast. During the Corpus Christi and San Antonio (CCSA) field campaign in Spring 2021, two long-range transport BB events (BB1 and BB2) were identified. The observed patterns of absorption Ångström Exponent (AAE), high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) BB tracer (f60), equivalent black carbon (eBC), acetonitrile and carbon monoxide (CO) during BB1 and BB2 indicated differences in the mixing of transported BB plumes with local anthropogenic sources. The combined information from HYSPLIT backward trajectory (BTs) and satellite observations revealed that BB1 had mixed influence of transported smoke plumes from fires in Central Mexico, the Yucatan peninsula, and the Central US, whereas BB2 was influenced majorly by fires in the Central US. The estimated transport time of smoke from the Mexican fires and the Central US fires to our study site were not too different (48–54 hours and 24–36 hours, respectively) and both events appeared to have undergone similar levels of atmospheric processing, as evident in the elemental ratios of bulk organic aerosol (OA). We observed a progression of f44 vs. f60 as a function of time elapsed during BB2. Positive matrix factorization (PMF) analysis of OA showed that BB1 had a mixture of organics from aged BB emission with an anthropogenic marine signal while the oxidized organic compounds from aged BB emissions dominated the aerosols during BB2. While aerosol measurements exhibited good agreement with respect to the BB designation, the CO and acetonitrile trends revealed more complicated source contributions. Our analysis from mobile and stationary measurements highlights that both CO and acetonitrile are likely impacted by local sources even during the BB events and specifically that acetonitrile cannot be used as a unique BB tracer for dilute BB plumes in an industrially influenced location. Finally, we provide evidence of the potential regional impacts of these transported BB events on urban O3 levels using measurements from the surface air quality monitoring network in Texas. [ABSTRACT FROM AUTHOR]

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, there remain large uncertainties in isoprene emissions under stressed conditions. Here we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the Southeast U.S. (SE US), a region of high isoprene emissions and prone to drought. OMI HCHO column density is found to be 5.3% (mild drought) - 19.8% (severe drought) higher than that in no-drought conditions. A global chemical transport model, GEOS-Chem, with the MEGAN2.1 emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.4-2.0 times of OMI HCHO, suggesting the need for a drought24 stress algorithm in the model. By minimizing the model-to-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γd_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6% (mild drought) - 20.7% (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γd_OMI the model predicts a non-uniform trend of increase in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite30 based drought stress factor performs better in capturing the regional scale drought-isoprene responses as indicated by the close-to-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosols (OA) simulations by 6.60% (mild drought) to 11.71% (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene suppressing effect on oxidants, with an <1 ppb increase in O3 in high-isoprene regions and a 1-3 ppbv decrease in O3 in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations 36 to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited. [ABSTRACT FROM AUTHOR]

Drought is a hydroclimatic extreme that causes perturbations to the terrestrial biosphere, and acts as a stressor on vegetation, affecting emissions patterns. During severe drought, isoprene emissions are reduced. In this paper, we focus on capturing this reduction signal by implementing a new percentile isoprene drought stress (yd) algorithm in NASA GISS ModelE based on the MEGAN3 (Model of Emissions of Gases and Aerosols from Nature Version 3) approach as a function of a photosynthetic parameter (Vc,max) and water stress (β) . Four global transient simulations from 2003–2013 are used to demonstrate the effect without yd (Default_ModelE) and with online yd (DroughtStress_ModelE). DroughtStress_ModelE is evaluated against the observed isoprene measurements at the Missouri Ozarks Ameriflux (MOFLUX) site during the 2012 severe drought where improvements in correlation coefficient indicate it is a suitable drought stress parameterization to capture the reduction signal during severe drought. The application of yd globally leads to a decadal average reduction of ~2.7 % which is equivalent to ~14.6 Tg yr-1 of isoprene. The changes have larger impacts in regions such as the Southeast U.S.. DroughtStress_ModelE is validated using satellite ΩHCHO column from the Ozone Monitoring Instrument (OMI) and surface O3 observations across regions of the U.S. to examine the effect of drought on atmospheric composition. It was found the inclusion of isoprene drought stress reduced the overestimation of ΩHCHO in Default_ModelE during the 2007 and 2011 southeastern U.S. droughts and lead to improvements in simulated O3 during drought periods. We conclude that isoprene drought stress should be tuned on a model-by-model basis, because the variables used in the parameterization responses are relative to the land surface model hydrology scheme (LSM) and the effects of yd application could be larger than seen here due to ModelE not having large biases of isoprene during severe drought. [ABSTRACT FROM AUTHOR]

Coastal regions are susceptible to multiple complex dynamic and chemical mechanisms and emission sources that lead to frequently observed large tropospheric ozone variations. These large ozone variations occur on a meso-scale which have proven to be arduous to simulate using chemical transport models (CTMs). We present a clustering analysis of multidimensional measurements from ozone Light Detection And Ranging (LiDAR) in conjunction with both an offline GEOSChem CTM simulation and the online GEOS-Chem simulation GEOS-CF, to investigate the vertical and temporal variability of coastal ozone during three recent air quality campaigns: 2017 Ozone Water-Land Environmental Transition Study (OWLETS) 1, 2018 OWLETS 2, and 2018 Long Island Sound Tropospheric Ozone Study (LISTOS). We developed and tested a clustering method that resulted in 5 ozone profile curtain clusters. The established 5 clusters all varied significantly in ozone magnitude vertically and temporally which allowed us to characterize the coastal ozone behavior. The lidar clusters provided a simplified way to evaluate the two CTMs for their performance of diverse coastal ozone cases. The two models have fair-to good relationships with the lidar observations (R = 0.66 to 0.69) in the low-level altitude range (0 to 2000 m), with low and unsystematic bias for GEOS-Chem and high systemically positive bias for GEOS-CF. In the mid-level altitude range (2000 to 4000 m), both models have difficulty simulating the vertical extent and variability of ozone concentrations in all 5 clusters, with a weak relationship with the lidar observations (R = 0.12 and 0.22, respectively). GEOS-Chem revealed a high systematic negative bias and GEOS-CF an overall low unsystematic bias range. Using ozone vertical and diurnal distribution from lidar measurements, this work provides new insights on model's proficiency in complex coastal regions. [ABSTRACT FROM AUTHOR]

The detection and attribution of high background ozone (O3) events in the southwestern U.S. is challenging but relevant to the effective implementation of the lowered National Ambient Air Quality Standard (NAAQS; 70 ppbv). Here we leverage intensive field measurements from the Fires, Asian, and Stratospheric TransportLas Vegas Ozone Study (FAST-LVOS) in MayJune 2017, alongside high-resolution simulations with two global models (GFDL-AM4 and GEOS-Chem), to pinpoint the sources of O3 during high-O3 events. We show stratospheric influence on four out of the ten events with daily maximum 8-hour average (MDA8) surface O3 above 65 ppbv in the greater Las Vegas region. While O3 produced from regional anthropogenic emissions dominates pollution in the Las Vegas Valley, stratospheric intrusions can mix with regional pollution to push surface O3 above 70 ppbv. GFDL-AM4 captures the key characteristics of deep stratospheric intrusions consistent with ozonesondes, lidar profiles, and co-located measurements of O3, CO, and water vapor at Angel Peak, whereas GEOS-Chem has difficulty simulating the observed features and underestimates observed O3 by ~ 20 ppbv at the surface. The two models also differ substantially during a wildfire event, with GEOS-Chem estimating ~ 15 ppbv greater O3, in better agreement with lidar observations. At the surface, the two models bracket the observed MDA8 O3 values during the wildfire event. Both models capture the large-scale transport of Asian pollution, but neither resolves some fine-scale pollution plumes, as evidenced from aerosol backscatter, aircraft, and satellite measurements. U.S. background O3 estimates from the two models differ by 5 ppbv on average and up to 15 ppbv episodically. Our multi-model approach tied closely to observational analysis yields process insights, suggesting that elevated background O3 may pose challenges to achieving a potentially lower NAAQS level (e.g., 65 ppbv) in the southwestern U.S. [ABSTRACT FROM AUTHOR]

Occurrences of devastating wildfires have been on the rise in the United States for the past decades. While the environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between fires and the environmental controls are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model integrating multiple machine learning algorithms to predict gridded monthly wildfire burned area during 2002-2015 over the South Central United States and identify the relative importance of the environmental drivers on the burned area for both the winter-spring and summer fire seasons of that region. The developed model is able to alleviate the issue of unevenly-distributed burned area data and achieve a cross-validation (CV) R2 value of 0.42 and 0.40 for the two fire seasons. For the total burned area over the study domain, the model can explain 50 % and 79 % of interannual total burned area for the winter-spring and summer fire season, respectively. The prediction model ranks relative humidity (RH) anomalies and preceding months' drought severity as the top two most important predictors on the gridded burned area for both fire seasons. Sensitivity experiments with the model show that the effect of climate change represented by a group of climate-anomaly variables contributes the most to the burned area for both fire seasons. Antecedent fuel amount and conditions are found to outweigh weather effects for the burned area in the winter-spring fire season, while the current-month fire weather is more important for the summer fire season likely due to the controlling effect of weather on fuel moisture in this season. This developed model allows us to predict gridded burned area and to access specific fire management strategies for different fire mechanisms in the two seasons. [ABSTRACT FROM AUTHOR]

China has pledged reduction of carbon dioxide emissions per unit GDP by 60–65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, disagreement among available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of control measures. In this study, we demonstrate an approach based on a long time series of surface CO2 observations to evaluate regional CO2 emissions rates in northern China estimated by three anthropogenic CO2 inventories – two of which are subsets from global inventories, and one of which is China-specific. Comparison of CO2 observations to CO2 predicted from accounting for global background concentration and atmospheric mixing of emissions suggests potential biases in the inventories. The period analyzed focuses on the key commitment period for the Paris accords (2005) and the Beijing Olympics (2008). Model-observation mismatch in concentration units is translated to mass units and is displayed against the original inventories in the measurement influence region, largely corresponding to northern China. Owing to limitations from having a single site, addressing the significant uncertainty stemming from transport error and error in spatial allocation of the emissions remains a challenge. Our analysis uses observations to support and justify increased use and development of China-specific inventories in tracking China's progress as a whole towards reducing emissions. Here we are restricted to a single measurement site; effectively evaluating and constraining inventories at relevant spatial scales requires multiple stations of high-temporal resolution observations. At this stage and with observational data limitations, we emphasize that this work is intended to be a comparison of a subset of anthropogenic CO2 emissions rates from inventories that were readily available at the time this research began. For this study's analysis time period, there was not enough spatially distinct observational data to conduct an optimization of the inventories. Rather, our analysis provides an important quantification of model-observation mismatch. In the northern China evaluation region, emission rates from the China-specific inventory produce the lowest model-observation mismatch at all timescales from daily to annual. Additionally, we note that averaged over the study time period, the unscaled China-specific inventory has substantially larger annual emissions for China as a whole (20 % higher) and the northern China evaluation region (30 %) than the unscaled global inventories. Our results lend support the rates and geographic distribution in the China-specific inventory. However, exploring this discrepancy for China as a whole requires a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and nationally. This study provides a baseline analysis for a small but import region within China, as well a guide for determining optimal locations for future ground-based measurement sites. [ABSTRACT FROM AUTHOR]

Air quality models have not been able to reproduce the magnitude of the observed concentrations of fine particulate matter (PM2.5) during wintertime Chinese haze events. The discrepancy has been at least partly attributed to low biases in modeled sulfate production rates due to the lack of heterogeneous sulfate production on aerosols in the models. In this study, we explicitly implement four heterogeneous sulfate formation mechanisms into a regional chemical transport model, in addition to gas-phase and in-cloud sulfate production. We compare the model results with observations of sulfate concentrations and oxygen isotopes (Δ17O(SO42-)) in the winter of 2014ndash;2015, the latter of which is highly sensitive to the relative importance of different sulfate production mechanisms. Model results suggest that heterogeneous sulfate production on aerosols accounts for about 20% of sulfate production in clean and polluted conditions, partially reducing the modeled low bias in sulfate concentrations. Model sensitivity studies in comparison with the Δ17O(SO42-) observations suggest that heterogeneous sulfate formation is dominated by transition metal ion catalyzed oxidation of SO2 [ABSTRACT FROM AUTHOR]

Chemical mechanisms responsible for rapid sulfate production, an important driver of winter haze formation in northern China, remain unclear. Here, we propose a potentially important heterogeneous hydroxymethanesulfonate (HMS) chemical mechanism. Through analyzing field measurements with aerosol mass spectrometry, we show evidence for a possible significant existence in haze aerosols of organosulfur primarily as HMS, misidentified as sulfate in previous observations. We estimate that HMS can account for up to about one-third of the sulfate concentrations unexplained by current air quality models. In addition, HMS in the presence of hydroxyl radicals can trigger rapid sulfate production in aerosol water. Heterogeneous production of HMS by SO2 and formaldehyde is favored under northern China winter haze conditions due to high aerosol water content, moderately acidic pH values, high gaseous precursor levels, and low temperature. These analyses identify an unappreciated importance of formaldehyde in secondary aerosol formation and calls for more research on sources and on the chemistry of formaldehyde in northern China winter. [ABSTRACT FROM AUTHOR]

Several recent studies have suggested that 21st-century climate change will significantly worsen the meteorological conditions leading to very high concentrations of fine particulate matter (PM2.5) in Beijing in winter (Beijing haze). We find that 81 % of the variance in observed monthly PM2.5 during 2010-2017 winters can be explained by a single meteorological mode, the first principal component (PC1) of the 850 hPa meridional wind velocity (V850) and relative humidity (RH). V850 and RH drive stagnation and chemical production of PM2.5, respectively, and thus have a clear causal link to Beijing haze. PC1 explains more of the variance in PM2.5 than either V850 or RH. Using additional meteorological variables does not explain more of the variance in PM2.5. Therefore PC1 can serve as a proxy for Beijing haze in the interpretation of long-term climate records and in future climate projections. Previous studies suggested that shrinking Arctic sea ice would worsen winter haze conditions in eastern China, but we show with the PC1 proxy that Beijing haze is correlated with a dipole structure in the Arctic sea ice rather than with the total amount of sea ice. Beijing haze is also correlated with dipole patterns in Pacific sea surface temperatures (SSTs). We find that these dipole patterns of Arctic sea ice and Pacific SSTs shift and change sign on interdecadal scales, so that they cannot be used reliably as future predictors for the haze. Future 21st-century trends of the PC1 haze proxy computed from the CMIP5 ensemble of climate models are overall very small and variable in sign. We thus find no evidence for a significant effect of climate change on Beijing haze. [ABSTRACT FROM AUTHOR]

China has pledged reduction of carbon dioxide emissions per unit GDP by 60-65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, disagreement among available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of regional control measures. In this study, we evaluate three anthropogenic CO2 inventories by tracking the fidelity of predicted concentrations of CO2 in the atmosphere to observations, focusing on the key commitment period for the Paris accords (2005) and the Beijing Olympics (2008). One inventory is China-specific and two are spatial subsets of global inventories. The inventories differ in spatial resolution, basis in national or subnational statistics, and reliance on global or China-specific emission factors. We use a unique set of historical atmospheric observations from 2005-2009 to evaluate the three CO2 emissions inventories within China's heavily industrialized and populated Northern region accounting for ~33-41 % of national emissions. Each anthropogenic inventory is combined with estimates of biogenic CO2 within a high-resolution atmospheric transport framework to model the time series of CO2 observations. Model-observation mismatch in concentration units is translated to mass units and used to optimize the original inventories in the measurement influence region, largely corresponding to Northern China. Except for the peak growing season, where assessment of anthropogenic emissions is entangled with the strong vegetation signal, we find the China-specific inventory based on subnational data and domestic field-studies agrees significantly better with observations than the global inventories at all timescales. On average, over the study time period, the China-specific inventory has substantially larger (20 %) emissions for all China than the global inventories. Our analysis uses observations to support and justify increased development of China-specific inventories in tracking China's progress towards reducing emissions. Here we are restricted to a single measurement site; effectively optimizing inventories at relevant spatial scales requires multiple high temporal resolution observations. We emphasize the need for a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and as a whole. [ABSTRACT FROM AUTHOR]

pH is an important property of aerosol particles but is difficult to measure directly. Several studies have estimated the pH values for fine particles in North China winter haze using thermodynamic models (i.e., E-AIM and ISORROPIA) and ambient measurements. The reported pH values differ widely, ranging from close to 0 (highly acidic) to as high as 7 (neutral). In order to understand the reason for this discrepancy, we calculated pH values using these models with different assumptions with regard to model inputs and particle phase states. We find that the large discrepancy is due primarily to differences in the model assumptions adopted in previous studies. Calculations using only aerosol phase composition as inputs (i.e., reverse mode) are sensitive to the measurement errors of ionic species and inferred pH values exhibit a bimodal distribution with peaks between −2 and 2 and between 7 and 10. Calculations using total (gas plus aerosol phase) measurements as inputs (i.e., forward mode) are affected much less by the measurement errors, and results are thus superior to those obtained from the reverse mode calculations. Forward mode calculations in this and previous studies collectively indicate a moderately acidic condition (pH from about 4 to about 5) for fine particles in North China winter haze, indicating further that ammonia plays an important role in determining this property. The differences in pH predicted by the forward mode E-AIM and ISORROPIA calculations may be attributed mainly to differences in estimates of activity coefficients for hydrogen ions. The phase state assumed, which can be either stable (solid plus liquid) or metastable (only liquid), does not significantly impact pH predictions of ISORROPIA. [ABSTRACT FROM AUTHOR]

Drought is a recurring extreme of the climate system with well-documented impacts on agriculture and water resources. The strong perturbation of drought to the land biosphere and atmospheric water cycle will affect atmospheric composition, the nature and extent of which are not well understood. Here we present observational evidence that surface ozone and PM2.5 in the US are significantly correlated with drought severity, with 3.5 ppbv (8 %) and 1.6 μg m-3 (17 %) increases respectively under severe drought. These enhancements show little sensitivity to the decreasing trend of US anthropogenic emissions, indicating natural processes as the primary cause. Elevated ozone and PM2.5 are attributed to the combined effects of drought on natural emissions (wildfires, biogenic VOCs and dust), deposition, and chemistry. Most climate-chemistry models are not able to reproduce the observed responses of ozone and PM2.5 to drought severity, suggesting a lack of mechanistic understanding of drought effects on atmospheric composition. The model deficiencies are partly attributed to the lack of drought-induced changes in land-atmosphere exchanges of reactive gases and particles and misrepresentation of cloud changes under drought conditions. By applying the observed relationships between drought and air pollutants to climate model projected drought occurrences, we estimate an increase of 1-6 % for ground-level O3 and 1-16 % for PM2.5 in the US by 2100 compared to the 2000s due to increasing drought alone. Drought thus poses another aspect of climate change penalty on air quality not recognized before. Improvements in the models are imperative to facilitate better prediction of air quality challenges due to changing hydroclimate and atmospheric composition feedback to climate. [ABSTRACT FROM AUTHOR]

The Bermuda High (BH) quasi-permanent pressure system is the key large-scale circulation pattern influencing summertime weather over the eastern and southern US. Here we developed a multiple linear regression (MLR) model to characterize the effect of the BH on year-to-year changes of monthly-mean maximum daily 8-hour average (MDA8) ozone in the Houston-Galveston-Brazoria (HGB) metropolitan region during June, July and August (JJA). The BH indicators include the longitude of the BH western edge (BH-Lon), and the BH intensity index (BHI) defined as the pressure gradient along its western edge. Both BH-Lon and BHI are selected by MLR as significant predictors (p < 0.05) of the interannual (1990-2015) variability of the HGB-mean ozone throughout JJA, while local-scale meridional wind speed is selected as an additional predictor for August only. Local-scale temperature and zonal wind speed are not identified as important factors for any summer month. The best-fit MLR model can explain 61%-72% of the interannual variability of the HGB-mean summertime ozone over 1990-2015 and shows good performance in cross-validation (R2 higher than 0.48). The BH-Lon is the most important factor, which alone explains 38%-48% of such variability. The location and strength of the Bermuda High appears to control whether or not low-ozone maritime air from the Gulf of Mexico can enter southeastern Texas and affect air quality. This mechanism also applies to other coastal urban regions along the Gulf Coast (e.g. New Orleans, LA; Mobile, AL; and Pensacola, FL), suggesting that the BH circulation pattern can affect surface ozone variability through a large portion of the Gulf Coast. [ABSTRACT FROM AUTHOR]

The Bermuda High (BH) quasi-permanent pressure system is the key large-scale circulation pattern influencing summertime weather over the eastern and southern US. Here we developed a multiple linear regression (MLR) model to characterize the effect of the BH on year-to-year changes of monthly-mean maximum daily 8-hour average (MDA8) ozone in the Houston-Galveston-Brazoria (HGB) metropolitan region during June, July and August (JJA). The BH indicators include the longitude of the BH western edge (BH-Lon), and the BH intensity index (BHI) defined as the pressure gradient along its western edge. Both BH-Lon and BHI are selected by MLR as significant predictors (p < 0.05) of the interannual (1990-2015) variability of the HGB-mean ozone throughout JJA, while local-scale meridional wind speed is selected as an additional predictor for August only. Local-scale temperature and zonal wind speed are not identified as important factors for any summer month. The best-fit MLR model can explain 61%-72% of the interannual variability of the HGB-mean summertime ozone over 1990-2015 and shows good performance in cross-validation (R² higher than 0.48). The BH-Lon is the most important factor, which alone explains 38%-48% of such variability. The location and strength of the Bermuda High appears to control whether or not low-ozone maritime air from the Gulf of Mexico can enter southeastern Texas and affect air quality. This mechanism also applies to other coastal urban regions along the Gulf Coast (e.g. New Orleans, LA; Mobile, AL; and Pensacola, FL), suggesting that the BH circulation pattern can affect surface ozone variability through a large portion of the Gulf Coast. [ABSTRACT FROM AUTHOR]