North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable locations in the world’s oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs do not need additional transfer learning to successfully predict the phasing of multidecadal variability in an observational dataset, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.

Our understanding of tree stem methane (CH4) emissions is evolving rapidly. Few studies have combined seasonal measurements of soil, water and tree stem CH4 emissions from forested wetlands, inhibiting our capacity to constrain the tree stem CH4 flux contribution to total wetland CH4 flux. Here we present annual data from a subtropical freshwater Melaleuca quinquenervia wetland forest, spanning an elevational topo-gradient (Lower, Transitional and Upper zones). Eight field-campaigns captured an annual hydrological flood-dry-flood cycle, measuring stem fluxes on 30 trees, from four stem heights, and up to 30 adjacent soil or water CH4 fluxes per campaign. Tree stem CH4 fluxes ranged several orders of magnitude between hydrological seasons and topo-gradient zones, spanning from small CH4 uptake to ~203 mmol m-2 d-1. Soil CH4 fluxes were similarly dynamic and shifted from maximal CH4 emission (saturated soil) to uptake (dry soil). In Lower and Transitional zones respectively, tree stem CH4 contribution to the net ecosystem flux was greatest during flooded conditions (49.9 and 70.2 %) but less important during dry periods (3.1 and 28.2 %). Minor tree stem emissions from the Upper elevation zone still offset the Upper zone CH4 soil sink capacity by ~51% during dry conditions. Water table height was the strongest driver of tree stem CH4 fluxes, however tree emissions peaked once the soil was inundated and did not increase with further water depth. This study highlights the importance of quantifying the wetland tree stem CH4 emissions pathway as an important and seasonally oscillating component of wetland CH4 budgets.

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well-being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and management may put this buffering capacity at risk. Here, we summarise the evidence that reductions in biodiversity can destabilise the functioning of ecosystems facing climate extremes. We then explore if impaired ecosystem functioning could, in turn, exacerbate climate extremes. We argue that only a comprehensive approach, incorporating both ecological and hydrometeorological perspectives, enables to understand and predict the entire feedback system between altered biodiversity and climate extremes. This ambition, however, requires a reformulation of current research priorities to emphasise the bidirectional effects that link ecology and atmospheric processes.

Precipitation forecasts, particularly at subseasonal-to-seasonal (S2S) time scale, are essential for informed and proactive water resources management. Although S2S precipitation forecasts have been evaluated, no systematic decomposition of the skill, Nash-Sutcliffe Efficiency (NSE) coefficient, has been analyzed towards understanding the forecast accuracy. We decompose the NSE of S2S precipitation forecast into its three components – correlation, conditional bias, and unconditional bias – by four seasons, three lead times (1–12-day, 1-22 day, and 1-32 day), and three models (ECMWF, CFS, NCEP) over the Conterminous United States (CONUS). Application of dry mask is critical as the NSE and correlation are lower across all seasons after masking areas with low precipitation values. Further, a west-to-east gradient in S2S forecast skill exists and forecast skill was better during the winter months and for areas closer to the coast. Overall, ECMWF’s model performance was stronger than both ECCC and NCEP CFS’s performance, mainly for the forecasts issued during fall and winter months. However, ECCC and NCEP CFS performed better for the forecast issued during the spring months, and also performed better in in-land areas. Post-processing using simple Model Output Statistics could reduce both unconditional and conditional bias to zero, thereby offering better skill for regimes with high correlation. Our decomposition results also show efforts should focus on improving model parametrization and initialization schemes for climate regimes with low correlation values.

Manganese (Mn) is a key cofactor in enzymes responsible for lignin decay (mainly Mn peroxidase), regulating the rate of litter degradation and carbon (C) turnover in temperate and boreal forest biomes.While soil Mn is mainly derived from bedrock, atmospheric Mn could also contribute to soil Mn cycling, especially within the surficial horizon, with implications for soil C cycling. However, quantification of the atmospheric Mn cycle, which comprises emissions from natural (desert dust, sea salts, volcanoes, primary biogenic particles, and wildfires) and anthropogenic sources (e.g. industrialization and land-use change due to agriculture) transport, and deposition into the terrestrial and marine ecosystem, remains uncertain. Here, we use compiled emission datasets for each identified source to model and quantify the atmospheric Mn cycle with observational constraints. We estimated global emissions of atmospheric Mn in aerosols (<10 µm in aerodynamic diameter) to be 1500 Gg Mn yr-1. Approximately 32% of the emissions come from anthropogenic sources. Deposition of the anthropogenic Mn shortened soil Mn “pseudo” turnover times in surficial soils about 1-m depth (ranging from 1,000 to over 10,000,000 years) by 1-2 orders of magnitude in industrialized regions. Such anthropogenic Mn inputs boosted the Mn-to-N ratio of the atmospheric deposition in non-desert dominated regions (between 5×10-5 and 0.02) across industrialized areas, but still lower than soil Mn-to-N ratio by 1-3 orders of magnitude. Correlation analysis revealed a negative relationship between Mn deposition and topsoil C density across temperate and (sub)tropical forests, illuminating the role of Mn deposition in these ecosystems.

We explore the sensitivity of Southern Ocean surface and deep ocean temperature and salinity biases in the FOCI coupled climate model to atmosphere-ocean coupling time step and to lateral diffusion in the ocean with the goal to reduce biases common to climate models. The reference simulation suffers from a warm bias at the sea surface which also extends down to the seafloor in the Southern Ocean and is accompanied by a too fresh surface, in particular along the Antarctic coast. Reducing the atmosphere-ocean coupling time step from 3 hours to 1 hour results in increased sea-ice production on the shelf and enhanced melting to the north which reduces the fresh bias of the shelf water while also strengthening the meridional density gradient favouring a stronger Antarctic Circumpolar Current (ACC). With the shorter coupling step we also find a stronger meridional overturning circulation with more upwelling and downwelling south and north of the ACC respectively, as well as a reduced warm bias at almost all depths. Tuning the lateral ocean mixing has only a small effect on the model biases, which contradicts previous studies using a similar model configuration. We note that the latitude of the surface westerly wind maximum has a northward bias in the reference simulation and that this bias is unchanged as the surface temperature and sea-ice biases are reduced in the coupled simulations. Hence, the surface wind biases over the Southern Hemisphere midlatitudes appear to be unrelated to biases in sea-surface conditions.

Radiation-topography interaction plays an important role in the surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of radiation-topography interaction over the TP. Land-atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for radiation-topography interaction overall improves E3SM’s performance in simulating surface climate. The winter cold bias in air temperature decreases from -4.48 K to -3.70 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP’s radiation-topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts.

This study analyzes fire-induced winds from a wind-driven fire (Thomas Fire) and a plume-dominated fire (Creek Fire). Two numerical experiments, one without the fire present and the other with the fire, were used. The fire-induced perturbations were then estimated by subtracting a variable value in the “No Fire Run” from the “Fire Run” (Fire - No Fire). For this study, spatial and temporal variability of winds, geopotential height, and convergence were analyzed. Furthermore, cloud water mixing ratio, precipitation, and fuel moisture were analyzed during the Creek Fire to assess fire-induced rainfall and its impact on fuel moisture. It was found that the wind-driven Thomas Fire created more widespread and generally stronger fire-induced winds than the plume-dominated Creek Fire. In addition, fire-induced wind speeds during the Creek Fire followed a diurnal cycle, while the Thomas Fire showed much less temporal variability. When analyzing geopotential height, the results were very similar to other idealized simulations. A localized low-pressure region was observed in front of the fire front, with a preceding high-pressure area. When analyzing precipitation, it was found that the fire increased precipitation accumulation in the area surrounding the active fire. This created an increase in fuel moisture which could have helped locally decelerate the fire spread. Further research into the processes behind fire-atmosphere interactions will lead to a better understanding of fire behavior and the extent to which these interactions can impact the fire environment. These studies will help assess the limitations of uncoupled operational models and improve fire modeling overall.

Emissions from flaring and venting (FV) in oil and gas (O&G) production are difficult to quantify due to their intermittent activities and lack of adequate monitoring and reporting. Given their potentially significant contribution to total emissions from O&G sector in the United States, we estimate emissions from FV using Visible Infrared Imaging Radiometer Suite satellite observations and state/local reported data on flared gas volume. These refined estimates are higher than those reported in the National Emission Inventory: by upto 15 times for fine particulate matter (PM2.5), two times for sulfur dioxides, and 22% higher for nitrogen oxides(NOx). Annual average contributions of FV to ozone (O3), NO2 and PM2.5 in the conterminous U.S. (CONUS) are less than 0.15%, but significant contributions of upto 60% are found in O&G fields with FV. FV contributions are higher in winter than in summer months for O3 and PM2.5; an inverse behavior is found for NO2. Nitrate aerosol contributions to PM2.5 are highest in the Denver basin whereas in the Permian and Bakken basins, sulfate and elemental carbon aerosols are the major contributors. Over four simulated months in 2016 for the entire CONUS, FV contributes 210 additional instances of exceedances to the daily maximum 8-hour average O3, and has negligible contributions to exceedance of NO2 and PM2.5 given the current form of the national ambient air quality standards. FV emissions are found to cause over $7.4 billion in health damages, 710 premature deaths and 73,000 asthma exacerbations among children annually.

The nitrogen isotopic composition of nitrogen oxide (NOx) is useful for estimating its sources and sinks. Several methods have been developed to convert atmospheric nitric oxide (NO) and/or nitrogen dioxide (NO2) to nitrites and/or nitrates for collection. However, the collection efficiency and blanks are poorly evaluated for many collection methods. Here, we present a method for collecting ambient NOx (NO and NO2 simultaneously) with over 90 % efficiency collection of NOx and low blank (approximately 0.5 µM) using a 3 wt% hydrogen peroxide (H2O2) and 0.5 M sodium hydride (NaOH) solution. The 1σ uncertainty of the nitrogen isotopic composition was {plus minus} 1.0 ‰. The advantages of this method include its portability, simplicity, and the ability to collect the required amount of sample to analyze the nitrogen isotopic composition of ambient NOx in a short period of time. Using this method, we observed the nitrogen isotopic compositions of NOx (δ15N(NOx)) at the Tsukuba and Yoyogi sites in Japan. The averaged value and standard deviation (1σ) in the Yoyogi site was (−2.7 {plus minus} 1.8) ‰ and in the Tsukuba site was (−1.7 {plus minus} 0.9) ‰ during the sampling period. The main NOx source appears to be the vehicle exhaust in the two sites. If the developed method is used by many researchers in the future, the database of δ15N(NOx) values will be enriched and a deeper understanding of NOx behavior will be gained.

Volcanic ash clouds are carefully monitored as they present a significant hazard to humans and aircraft. The primary tool for forecasting the transport of ash from a volcano is dispersion modelling. These models make a number of assumptions about the size, sphericity and density of the ash particles. Few studies have measured the density of ash particles or explored the impact that the assumption of ash density might have on the output of a dispersion model. In this paper, the raw apparent density of 23 samples taken from 15 volcanoes are measured with gas pycnometry, and a negative linear relationship is found between the density and the silica content. For the basaltic ash samples, densities were measured for different particle sizes, showing that the density is approximately constant for particles smaller than 100 μm. There is a deviation in density of up to 25% from the operational model currently used by the London Volcanic Ash Advisory Centre (VAAC); by inputting the measured density-size relationship into a numerical simulation, up to 18% difference in ash fallout time was found, with the VAAC model overestimating ash removal times.

Long-term carbon dioxide (CO2) flux measurements between the atmosphere and the ecosystem have been made since 1993 at a cool-temperate deciduous forest site (Takayama) in Japan influenced by Asian Monsoon, constituting the longest dataset among all the AsiaFlux sites. Interannual variations (IAVs) and trends of the annual carbon budget components and their environmental factors were examined. Annual net ecosystem production (NEP) (mean ± 1σ) during the period of eddy covariance measurement in 1999-2021 was 265 ± 86 gC m-2 yr-1, and its IAV was dependent more on gross primary production (GPP) than on ecosystem respiration. IAVs in annual NEP and GPP were correlated with the IAVs of the monthly mean NEP, GPP and leaf area index (LAI) from June to September, as well as with that of the length of the net carbon uptake period. Significant increasing and decreasing trends in the annual NEP and GPP were detected during 2004-2013 and 2013-2021, respectively; the increasing trends were mainly caused by the vegetation recovery from typhoon disturbances while the decreasing trends were partly influenced by recent extreme weather events. Significant positive correlations of the IAVs between the start and the end of the net carbon uptake period, and between the leaf expansion and leaf fall were found. These may be attributed to biological functions and interseasonal relationship of meteorological parameters associated with ENSO events that can also influence IAVs in annual NEP and GPP.

The seasonal deposition and sublimation of CO2 constitute a major element in the Martian volatile cycle. Here, we propose to use the shadow variations of the ice blocks at the foot of the steep scarps of the North Polar Layered Deposits (NPLDs) to infer the vertical evolution of the seasonal deposits at high polar latitudes. We conduct an experiment at a steep scarp centered at (85.0°N, 151.5°E). We show that the average thickness of the seasonal deposits due to snowfalls in Mars Year 31 is 0.97±0.13 m at Ls = 350.7° in late winter, which then gradually decreases in springtime. The large snow depth measured makes us wonder if snowfalls are more frequent and violent than previously thought. Meanwhile, we show that the average frost thickness due to direct condensation in Mars Year 31 reaches 0.64±0.18 m at Ls = 350.7° in late winter and quasi-linearly decreases towards the summer solstice. Combined, the total thickness of the seasonal cover in Mars Year 31 reaches 1.63±0.22 m at Ls = 350.7° in late winter, continuously decreases to 0.45±0.06 m at Ls = 42.8° in middle spring and 0.06±0.05 m at Ls = 69.6° in late spring. These estimates are up to 0.8 m lower than the existing MOLA results during the spring, which can be mainly attributed to MOLA-related biases. In terms of interannual variations, we observe that snow in the very early spring of Mars Year 36 can be 0.36±0.13 m deeper than that in Mars Year 31.

This paper studies a new concept of using GNSS signals coherently reflected over relatively smooth ocean and ice surfaces from very low elevation angles (below ~8°) and received by low Earth orbit (LEO) satellites to retrieve the tropospheric information. This approach can provide horizontal profiles of tropospheric zenith delay and total column water vapor (TCWV) with centimeter-level high precision and spatial resolutions of 10s of km by ~1km, depending on the elevation angle, with a sampling spacing of ~100m. This approach can potentially be applied to most sea ice and calm ocean areas and provide tropospheric sensing data, which can complement and augment existing observation systems. A few case studies are conducted in this paper using the Spire grazing-angle GNSS-R data. The retrieved TCWV is compared to ERA5 products and the Sentinal-3 OLCI measurements and shows promising performances. The errors associated with the GNSS-R tropospheric measurements are also discussed.

The ‘super-station’ approach has been adopted since 1980s as a pragmatic method of improving extreme‑value predictions by grouping short-length datasets from several measurement stations to become a larger dataset to reduce uncertainties due to random sampling variation. El Rafei et al. (2023, https://doi.org/10.1029/2023GL105286) analyzed reanalysis and randomly generated wind extremes datasets and claimed that this technique can introduce unexpected biases in typical situations. We demonstrate by Monte-Carlo simulation, assuming the same number of grouped stations and data lengths used, that applying the grouping technique to samples from homogeneous datasets does not lead to biased prediction of extremes. In addition, the grouping technique effectively reduces the uncertainty and sampling errors that result from short-length datasets from individual stations of consistent meteorology.

The vertical distribution of wildfire smoke aerosols is important in determining its environmental impacts but existing observations of smoke heights generally do not possess the temporal resolution required to fully resolve the diurnal behavior of wildfire smoke injection. We use Weather Surveillance Radar-1988 Doppler (WSR-88D) dual polarization data to estimate injection heights of Biomass Burning Debris (BBD) generated by fires. We detect BBD as a surrogate for smoke aerosols, which are often collocated with BBD near the fire but are not within the size range detectable by these radars. Injection heights of BBD are derived for 2-10 August 2019, using radar reflectivity (Z≥10 dBZ) and dual polarization correlation coefficients (0.2<C.C.<0.9) to study the Williams Flats Fire event. Results show the expected diurnal cycles with maximum injection heights present during the late afternoon period when the fire’s intensity and convective mixing are maximized. Radar and airborne lidar injection height comparisons reveal that this method is sensitive to outliers and generally overpredicts maximum heights by 40%, though mean and median heights are better captured (<20% mean error). Radar heights between the 75th and 90thpercentile seem to accurately represent the maximum, with the exception of heights estimated during the occurrence of pyro-cumulonimbus. Location specific mapping of radar and lidar injection heights reveal that they diverge further away from the fire due to BBD settling. Most importantly, radar-derived injection height estimates provide near continuous smoke height information, allowing for the study of diurnal variability of smoke injections.  

We present estimates of gravity wave momentum fluxes calculated from Project Loon superpressure balloon data collected between 2013 and 2021. In total, we analyzed more than 5000 days of data from balloon flights in the lower stratosphere, flights often over regions or during times of the year without any previous in-situ observations of gravity waves. Maps of mean momentum fluxes show significant regional variability; we analyze that variability using the statistics of the momentum flux probability distributions for six regions: the Southern Ocean, the Indian Ocean, and the tropical and extratropical Pacific and Atlantic Oceans. The probability distributions are all approximately log-normal, and using only their geometric means and geometric standard deviations we explain the sign and magnitude of regional mean and 99th percentile zonal momentum fluxes, and regional momentum flux intermittencies. We study the dependence of the zonal momentum flux on the background zonal wind and argue that the increase of the momentum flux with the wind speed over the Southern Ocean is likely due to a varying combination of both wave sources and filtering. Finally, we show that as the magnitude of the momentum flux increases, the fractional contributions by high-frequency waves increases, waves which need to be parameterized in large-scale models of the atmosphere. In particular, the near-universality of the log-normal momentum flux probability distribution, and the relation of its statistical moments to the mean momentum flux and intermittency, offer useful checks when evaluating parameterized or resolved gravity waves in models.

Aerosol Robotic Network (AERONET) measurement data is used in the validation of two prominent satellite aerosol property retrieval, Multiangle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS), then applied to examine the properties of aerosols and the direct influence on radiative forcing (RF) over two metropolitan cities, Cape Town (CPT) and Pretoria (PRT) in South Africa. The synoptic characteristics of aerosols over CPT for 2015-2019 indicate a general low aerosol optical depth (AOD) of an average of 0.08 ± 0.014 and are prevalently sea salt (SS) aerosols. In contrast, a high AOD value with an average of 0.23 ± 0.050 was observed over PRT between 2011-2019 and predominated by sulphate/nitrate aerosols. These two dominant aerosol types are found to be the primary motivator of the net cooling effect of RF due to aerosol in each location. While the average RF over CPT is -16.79 ± 5.61 during the study period, the value over PRT is estimated to be more than two times (-36.55 ± 10.54) of the former. The validation of MISR and MODIS satellite aerosol properties retrieval for the region demonstrated better accuracy over the land than in the maritime environment. Meanwhile, MODIS underestimated AOD by ≈ 32% but generally reported better precision across the board than the MISR instrument. Further investigation into the seasonal variation of aerosols over the two locations identified seasonality changes in the characteristics of aerosols mainly influenced by the transport of high-absorbing biomass-burning aerosols.

A new procedure to obtain a longitudinally varying and slowly evolving atmospheric background state for the analysis of Rossby waveguides is described and discussed. The procedure is a rolling zonalization scheme, redistributing Ertel potential vorticity in a moving window to separate waves from the background. Waveguides are subsequently diagnosed from the gradient of the logarithm of potential vorticity. The effectiveness of the wave-background separation, even in large-amplitude conditions, is illustrated with reanalysis data. Established climatological mean waveguide structures are recovered from the rolling-zonalized state in the limit of long-term aggregation. Two contrasting episodes of Rossby wave packet propagation demonstrate how the evolution of waveguides derived from rolling zonalization can correspond to the development of superposed wave packets. The ability of the procedure to work with snapshots of the atmosphere provides new opportunities for waveguide research.

The divergence method, a lightweight approach for estimating emission fluxes from satellite images, relies on a number of tacit assumptions. This paper explicitly outlines these assumptions by deriving the method from first principles. The assumptions are: the enhanced mass flux is dominated by advection, normal fluxes vanish at the top and bottom of the atmosphere, steady-state conditions apply, sources are multiplications of temporal and spatial functions, sinks are described as first-order reactions, and effective wind fields are made by weighing the fields with the enhanced concentration profiles. No such assumptions have to be assumed for the background field. The commonly used ‘topography correction term’ does not follow from this analysis and likely corrects data artifacts. The cross-sectional flux method follows naturally from the derived theory, and the methods are compared. Effects of discrete pixels and finite-difference operations are explored, leading to recommendations, primarily the recommendation to work with small regions only to minimize the influence of noise. Numerical examples featuring Gaussian plume and COSMO-GHG simulated plumes are provided. The Gaussian plume example suggests that the divergence method might underestimate emissions when assuming only advection in the presence of cross-wind diffusion. Conversely, the cross-sectional flux method remains unaffected, provided fluxes are integrated across the entire plume. The COSMO-GHG example reveals frequent violations of steady-state assumption, although the assumption remains valid proximal to the source (<20 km in this example). It is the hope that this paper provides a solid theoretical foundation for the divergence and cross-sectional flux methods.

A document by Aryav Bhesania. Click on the document to view its contents.

The arid region of Northwest China and Mongolia (NCM) receives most of the precipitation in the summer. The need for a better understanding of the synoptic-scale mechanism responsible for precipitation formation is accentuated by the recent wetting trend and its implications for future hydroclimate change. By conducting a hierarchical clustering analysis on an observationally-based daily precipitation dataset, we show that there are three distinct precipitation patterns over NCM, one of which is associated with strong precipitation over the western part of the region and another over the eastern part. The corresponding large-scale circulation anomalies indicate that these strong precipitation events are triggered by the upper-tropospheric disturbances in the form of transient Rossby wave packets. Furthermore, the wetting trend is linked to more frequent strong precipitation events over the eastern NCM, suggesting that it may have been induced remotely by atmospheric circulation perturbations.

Large reductions in anthropogenic emissions of particulate matter and its precursor emissions have occurred since the enactment of the Clean Air Act Amendments of 1990. The Interagency Monitoring of Protected Visual Environments (IMPROVE) network has measured PM2.5 gravimetric mass (mass of particles with aerodynamic diameters less than 2.5 µm, also referred to here as fine mass, “FM”) and speciated PM2.5 aerosol composition at remote sites since 1988. Measured species include inorganic anions such as sulfate, nitrate, and chloride, carbonaceous aerosols such as organic (OC) and elemental carbon (EC), and elemental concentrations used to derive fine dust (FD). Trend analyses of seasonal and annual mean mass concentrations were calculated from 2000 through 2021, a period that includes the largest reductions in emissions. On average, annual mean FM at remote sites in the continental United States has decreased at a rate of -1.8% yr-1. This reduction is largely due to annual mean trends in sulfate (-6.1% yr-1), nitrate (-2.7% yr-1), EC (-2.2% yr-1), FD (-1.3% yr-1), and OC (-0.9% yr-1), although the OC annual mean trend was insignificant. Seasonal and regional FM trends varied significantly, with strong reductions in the East in all seasons due to sulfate reductions, and flat and insignificant trends in summer and fall in the West due to the impacts of biomass burning emissions on OC trends. Evaluating regional and seasonal trends in aerosol composition helps identify sources that continue to adversely impact air quality and hinder progress in FM reductions due to successful regulatory activity.

Planetary boundary layer (PBL) modeling is a primary contributor to uncertainties in a numerical weather prediction model due to difficulties in modeling the turbulent transport of surface fluxes. The Weather Research and Forecasting model (WRF) has included many PBL schemes which may feature a non-local transport component driven by super-grid eddies or a one-and-half order turbulence closure model. In the present study, a turbulent kinetic energy (TKE)-based turbulence closure model is integrated into the non-local Asymmetric Convective Model version 2 (ACM2) PBL scheme and implemented in WRF. Non-local transport is modeled the same as ACM2 using the transilient matrix method. The new TKE-ACM2 PBL scheme is evaluated by comparing it with high spatiotemporal Doppler LiDAR observations in Hong Kong over 30 days each for summer and winter seasons to examine its capability in predicting the vertical structures of winds. Scatter plots of measured versus simulated instantaneous wind speeds show that TKE-ACM2 is able to reduce the root mean square error and mean bias and improve the index of agreement, especially at the urban observational site. The diurnal evolution of monthly averaged wind profiles suggests TKE-ACM2 can better match both the magnitudes and vertical gradients, revealing its superiority compared to ACM2 at stable atmospheric conditions. Other meteorological parameters including the potential temperature profiles, PBL heights, and surface wind speeds have also been investigated with references to various sources of observations.